Lightning detection station layout method and system based on intelligent optimization algorithm

Abstract

The present application relates to lightning monitoring and strategy optimization technical field, especially a kind of lightning detection station layout method and system based on intelligent optimization algorithm.The site coordinates are used as the initial population satisfying the site position constraint of solution generation, and the individual corresponding to solution is 3N-dimensional vector, corresponding to the three-dimensional coordinates of N sites;Then population iteration is carried out through population mutation, crossing.This application can maintain the higher diversity of individual in the process of individual mutation, and randomly selects multiple parent individuals for mutation.Although the convergence speed is slow in high-dimensional, multi-peak experimental environment, it has stronger global search ability and the advantage of avoiding local convergence.The present application solves the problem that lightning positioning site distribution cannot consider terrain and cannot guarantee detection efficiency and accuracy in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lightning monitoring and strategy optimization, and particularly relates to a lightning detection station layout method and system based on an intelligent optimization algorithm. BACKGROUND

[0002] Lightning, as one of the "ten most serious natural disasters" announced by the United Nations, is a kind of instantaneous discharge phenomenon with high voltage, large current and strong electromagnetic characteristics. The lightning positioning system plays a crucial role in early warning, space flight, power supply and natural disaster prevention in many fields, and its core goal is to monitor and accurately locate lightning activities in real time. The system is composed of multiple lightning detection stations distributed in different geographical locations. Each station calculates the occurrence time, location and height of lightning by receiving VLF or LF signals generated by lightning radiation, and estimates the intensity, polarity and other key physical characteristics. The positioning accuracy of the system is the core indicator of its performance, and the key factor affecting the positioning accuracy is the spatial layout design of the detection stations. In the plains or open areas, the lightning signal propagation path is relatively stable, and the stations are easy to arrange to achieve balanced coverage. However, in complex terrain areas such as mountains and hills, the terrain difference is significant, and factors such as mountain shielding, terrain diffraction, signal reflection and attenuation will seriously interfere with the reception quality of lightning signals, causing detection blind areas, positioning ambiguity, and even completely ineffective areas, greatly limiting the actual effectiveness of the positioning system. Therefore, considering the actual terrain factors to optimize the layout of the detection stations is the key to improving the application effect of the lightning positioning system in complex environments.

[0003] The existing station layout strategy of the lightning positioning system is mostly based on the assumption of plane distance or uniform distribution, and lacks the ability to deeply couple and dynamically adapt to real terrain conditions. In particular, in mountainous areas, if the stations are too dense, although the local coverage rate can be improved, it will bring data redundancy and resource waste. If the distribution is too sparse, it is easy to form a monitoring blind area, especially in low-lying areas or blocked areas where signal propagation is easily blocked, the detection efficiency and accuracy are significantly reduced. Therefore, how to scientifically optimize the spatial distribution of lightning detection stations while considering the terrain undulation, station accessibility and signal propagation effectiveness has become a key challenge in the deployment of the current lightning positioning system. SUMMARY

[0004] In order to overcome the defects in the prior art that the distribution of lightning positioning stations cannot consider the terrain and cannot guarantee the detection efficiency and accuracy, the present application proposes a lightning detection station layout method based on an intelligent optimization algorithm, aiming to improve the coverage efficiency and detection accuracy of the station distribution.

[0005] The lightning detection station layout method based on an intelligent optimization algorithm proposed by the present application comprises the following steps:

[0006] S1, set the number of stations and station location constraints;

[0007] S2, generate an initial population satisfying the station location constraints with station coordinates as the solution, the individual corresponding to the solution being a 3N-dimensional vector corresponding to the three-dimensional coordinates of N stations;

[0008] S3, mutate the population individuals: first randomly select three parent individuals in the population, construct a difference vector of two parent individuals plus a topographic enhancement mutation coefficient weighted considering the topography of the station, and then operate with the remaining parent individuals to generate a mutated individual; the topographic enhancement mutation coefficient is adjusted on the basis of the set mutation factor combined with the penalty factor considering the topography;

[0009] S4, cross the dimension values before and after the mutation of the individual to obtain a crossed individual;

[0010] S5, for each individual in the population, calculate the fitness function, and for the case where the fitness value of the crossed individual is less than the fitness value of the corresponding original individual, replace the individual with the corresponding crossed individual to realize population iteration;

[0011] Repeat steps S3-S5 until the population converges, select the individual with the smallest fitness from the population as the final decision vector, and analyze the three-dimensional coordinates of each station.

[0012] Preferably, the penalty factor of each dimension in the individual is the penalty factor of the corresponding station, which is expressed by the formula:

[0013] ;

[0014] Wherein, R i is the penalty factor of the i-th station; H i and Φ i are the ground elevation value and slope value of the i-th station; H max and Φ max are the maximum elevation value and slope value in the monitoring target area; α and β are both weight coefficients.

[0015] Preferably, the mutation of the population individual X G in S3 is as follows:

[0016] ;

[0017] Wherein, is the mutation result of dimension d in individual X G , , and are the values of dimension d in the three parent individuals randomly selected when the individual X G is mutated; the dimension d in the individual belongs to the coordinates of the i-th station, is the terrain-enhanced variation coefficient of the i-th site, and δ is a set weight; is the local gradient correction term of the i-th site, and the gradient of the elevation sequence composed of the elevation values of the i-th site in each generation of optimal individuals is adopted.

[0018] Preferably:

[0019] ;

[0020] wherein, is a set basic variation factor, is the i-th site corresponding to the penalty factor R G of the individual X i .

[0021] Preferably, the fitness function of the individual X is calculated by the following formula:

[0022] ;

[0023] wherein, N is the number of sites, is the evaluation index of the i-th site, and R i is the penalty factor of the i-th site; and λ is a preset penalty weight coefficient.

[0024] Preferably, in step S4, the G-th individual X G in the population is set as x d,G , and the crossover individual corresponding to the d-th dimension of the i-th site is recorded as U G [ u d,G ];

[0025] ;

[0026] ;

[0027] wherein, x d,G is the value of the d-th dimension in the individual X G ; and are the values of the d-th dimension in the variation individual and the crossover individual of the individual X G , respectively; r(d) represents assigning a random floating-point number to the d-th dimension, and r(d) ∈ (0, 1); the d-th dimension belongs to the i-th site; is the crossover probability of the i-th site in the individual X G ; is a set basic probability; is the slope value of the i-th site in the individual X G ; maxTo monitor the maximum slope value within the target area; is a random integer that takes a value in the interval [1, 3N].

[0028] Preferably, the lightning detection station layout method based on intelligent optimization algorithm is characterized in that one station is designated as the main station and the rest as secondary stations; the station location constraints include: horizontal coordinate constraints of the station, vertical coordinate constraints of the station, and distance constraints of the secondary stations relative to the main station.

[0029] Preferably, the horizontal coordinate constraints and vertical coordinate constraints of the site are both composed of corresponding maximum and minimum thresholds; the method for initializing the population in step S2 is as follows: set the number of individuals, and sample the dimension value of each individual one by one; dimension value x d The sampling method is as follows: using x d The product of the corresponding minimum threshold, the corresponding range, and the random number r∈(0,1.0) is used as a candidate value. It is then determined whether the candidate value satisfies the site location constraint. If yes, the candidate value is used as x. d If the initial value is not found, then update the random number r and recalculate the candidate value; x d The corresponding range is the difference between the maximum threshold and the minimum threshold.

[0030] Preferably, during the population iteration process, when the optimization objective range of the most recent N0 generation population is less than the set stagnation threshold, the individuals with the highest fitness in the population, i.e., c%, are restarted; the restart method is either initialization or individual perturbation according to the set rules; the optimization objective of the population is the minimum fitness function of the individuals in the population.

[0031] The present invention proposes a lightning detection station layout system based on an intelligent optimization algorithm, comprising a memory and a processor. The memory stores a computer program, and the processor is connected to the memory. The processor is used to execute the computer program to realize the lightning detection station layout method based on the intelligent optimization algorithm.

[0032] The advantages of this invention are:

[0033] (1) The lightning detection station layout method based on intelligent optimization algorithm proposed in this invention is essentially a three-dimensional wide-area very low frequency lightning detection station optimization layout method that takes into account actual terrain. This method introduces a site selection mechanism that adapts to terrain constraints, combines digital elevation model data for terrain occlusion analysis, and designs a dynamic parameter adjustment mechanism to effectively adapt to terrain interference conditions while maintaining search capability. This invention can automatically adjust the station spacing according to terrain features, and preferentially deploy lightning detection stations in locations with advantages in field of view and propagation path, which can effectively improve the effective coverage and comprehensive positioning accuracy of the detection network.

[0034] (2) The application can maintain high diversity of individuals by randomly selecting multiple parent individuals for mutation in the individual variation process. Compared with the existing operation which depends on optimal individual mutation, the new individual generated by mutation will quickly approach the current optimal individual, and it is easy to fall into a local optimal situation. Although the convergence speed is slow in the high-dimensional and multi-peak experimental environment, the application has stronger global search ability and the advantage of avoiding local convergence.

[0035] (3) The application focuses on elevation data and slope data, improves the applicability and effectiveness in complex mountainous terrain, and provides key technical support for building a more efficient and accurate lightning monitoring network in mountainous areas.

[0036] (4) The application introduces a stagnation detection and partial restart mechanism in the optimization process to improve the global optimization ability and avoid falling into a local optimum under complex terrain constraints.

[0037] (5) The optimization target constructed by the application considers positioning error and site terrain, realizes the collaborative optimization of site selection, number configuration and propagation path evaluation, and improves the consistency of signal reachability and positioning accuracy from the source. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 A lightning detection station layout method flowchart based on an intelligent optimization algorithm is proposed for the application;

[0039] Figure 2 An iteration curve diagram of the method of the application;

[0040] Figure 3 A positioning accuracy distribution diagram of the solution obtained by the method of the application;

[0041] Figure 4 An iteration curve diagram of the traditional difference algorithm;

[0042] Figure 5 A positioning accuracy distribution diagram of the solution obtained by the traditional difference algorithm;

[0043] Figure 6 A positioning accuracy distribution diagram of the solution obtained by the Y-shaped distribution method. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0045] REFERENCE Figure 1The embodiment provides a lightning detection station layout method based on an intelligent optimization algorithm.

[0046] S1, a number of stations is set, one of the stations is a main station, and the rest of the stations are vice stations; station position constraints are set, including horizontal coordinate constraints, vertical coordinate constraints and distance constraints; the distance constraints are used for limiting the relative distance between any vice station and the main station;

[0047] In specific implementation, the horizontal coordinate constraints are as follows: |x i -x0|≤M1, and |y i -y0|≤M1; 1≤i≤N.

[0048] The vertical coordinate constraints are as follows: M2≤z i ≤M3.

[0049] The distance constraints are as follows: |x j -x T |≤d max , |y j -y T |≤d max , and |z j -z T |≤h max ; 1≤j≤N-1.

[0050] Wherein, (x0, y0) is the horizontal coordinate of the center point of a monitoring target area, M1 is a set horizontal threshold value, M2 and M3 are respectively a lower limit and an upper limit of the vertical coordinate of the station; (x i ,y i ,z i ) is the coordinate of the i-th station, (x j ,y j ,z j ) is the coordinate of the j-th vice station, and (x T ,y T ,z T ) is the coordinate of the main station; N is the total number of stations, N-1 is the total number of vice stations; d max is a set horizontal distance threshold value of the station, and h max is a set vertical distance threshold value of the station.

[0051] S2, an initial population meeting the station position constraints is generated by taking the station coordinates as a solution, and an individual in the population is expressed by using a D=3N-dimensional decision vector, and is recorded as X=[x1,x2,…,x d ,…,x D ] T , 1≤d≤D.

[0052] That is, the individual in the population is a 3N-dimensional column vector formed by splicing the three-dimensional coordinates of each station.

[0053] In this step, the value of each dimension x d The initialization decision vector X is obtained in a sampling manner, x d The sampling manner is: taking x d The corresponding minimum threshold plus the product of the corresponding range and a random number r ∈ (0, 1.0) as the candidate value, and judging whether the candidate value meets the site position constraint. If yes, the candidate value is taken as the initial value of x d ; otherwise, update the random number r and recalculate the candidate value.

[0054] The value of dimension d of individual i in the population The initialization assignment process of x

[0055] (15);

[0056] Wherein, M1 is the minimum threshold of the corresponding site dimension, ∈[x0-M1;y0-M1;M2]; M2 is the maximum threshold of the corresponding site dimension, ∈[x0+M1;y0+M1;M3]; Indicates that the value of the corresponding site dimension meets the corresponding site position constraint.

[0057] S3, mutate the population individuals; first, randomly select three parent individuals in the population, construct two parent individuals plus the difference vector considering the terrain factor weighting, and operate with another randomly selected parent individual to generate a child mutation individual, referred to as a mutation individual;

[0058] The formula of this step is:

[0059] (16);

[0060] (17);

[0061] Wherein, is the mutation result of dimension d in individual X G , that is, the value of dimension d in the mutation individual V G of X G ; , and are the values of dimension d in the three parent individuals randomly selected when individual X G is mutated; specifically, when individual X G is mutated, individual X s1,G , X s2,G , X s3,GAs a parent individual , and X s1,G X s2,G X s3,G The value on dimension d;

[0062] For individual X G The local gradient correction term corresponding to dimension d, This indicates a small movement along an upward slope, which helps prevent the mutated individual from falling into low-lying, heavily obstructed areas. Specifically, The method for obtaining the elevation sequence is as follows: First, identify the optimal individual in each generation of the population, i.e., the individual with the smallest fitness function; then, according to the population iteration order, read the elevation sequence of the stations corresponding to dimension d in each optimal individual, and calculate the gradient of this elevation sequence as... In practice, the gradient of the high-order sequence can be obtained using the finite difference approximation method.

[0063] Dimension d belongs to the i-th station, meaning that dimension d in an individual belongs to a coordinate value in the three-dimensional coordinates of the i-th station; Let be the terrain enhancement variation coefficient for the i-th station; The baseline variation factor was set to 0.6 in this example; δ, α, and β were weighting coefficients of 0.1, 0.3, and 0.2, respectively. and Individual X G The ground elevation and slope values ​​of the i-th station; H max and Φ max These represent the maximum elevation and maximum slope values ​​within the monitored target area, respectively.

[0064] It is worth noting that, , For individual X G The penalty factor corresponds to the i-th station. Thus, let the elevation ratio be the ratio of the ground elevation value of the station to the maximum elevation value in the monitoring target area, and the slope ratio be the ratio of the slope value of the station to the maximum slope value in the monitoring target area. The station penalty factor is the weighted sum of the elevation ratio and the slope ratio. In this way, the station factor and slope are considered in the decision optimization, which is conducive to improving the adaptability of the station solution to the terrain.

[0065] S4. Cross-reference the dimensional values ​​before and after individual variation;

[0066] Let individual X G =[x 1,G ,x 2,G ,…,x d,G ,…,xD,G ];

[0067] X G Variation operation on individual V G =[ v 1,G , v 2,G ,…, v d,G ,…, v D,G ];

[0068] Individual X G and V G Crossover operation on individual U G =[ u 1,G , u 2,G ,…, u d,G ,…, u D,G ];

[0069] (18);

[0070] (19);

[0071] wherein r(d) represents a random floating point number assigned for dimension d, and r(d) is randomly taken in interval (0, 1); the dimension d belongs to the i th site; is a crossover probability; is a set basic probability, and specifically can be taken as 0.8; is a slope value of the i th site in individual X G , and Φ max is a maximum slope value in a monitoring target region;

[0072] is a random integer taken in interval [1, D], and ensures that at least one dimension of the crossover individual U G comes from the variation individual V G .

[0073] The introduction of the crossover probability can increase the terrain adaptability; and the value is dynamically updated through the steepness of the local terrain. When the terrain tends to be flat, the value is larger, the updating component of the test individual is larger, and the population evolution speed is faster; when the terrain is steep and rugged, the value is smaller, the updating component of the test individual is smaller, the population evolution speed is slower, more current individuals are reserved, and jumping into unfeasible or worse terrain caused by too much updating is avoided.

[0074] S5, combine the cross individual iteration population; calculate each individual X in the population G and the fitness function of the cross individual U G , if the cross individual fitness function is smaller, the individual is replaced by the cross individual; traverse the population to complete population iteration; let the Gth individual in the population after iteration be marked as X' G , then:

[0075] ;

[0076] is the fitness function, and the calculation formula is as follows:

[0077] ;

[0078] ;

[0079] Wherein, N is the number of sites, R i is the penalty factor of the ith site; λ is a preset penalty weight coefficient, and in the embodiment, the value is 0.2; H i and Φ i are the ground elevation value and the slope value of the ith site respectively; H max and Φ max are the maximum elevation value and the slope value in the monitoring target area respectively; α and β are both weight coefficients, and can be specifically taken on the region (0, 1), and in the subsequent embodiment, α = 0.3 and β = 0.2.

[0080] is the evaluation index of the ith site, and can be specifically adopted to include Cramer-Rao Lower Bound (CRLB), Confidence Ellipse (CE), Circular Error Probability (CEP), Geometric Dilution Precision (GDOP) and the like. In the subsequent embodiment, the GDOP index is specifically adopted.

[0081] In specific implementation, the individual layout lightning positioning station can be generated according to the individual layout lightning positioning station in the simulation environment, and then the target monitoring area is divided into a 1Km*1Km grid, and lightning is randomly generated in each grid to count the evaluation index of each site in the individual corresponding solution .

[0082] S6, judge whether the population iteration number reaches the set maximum number, and the maximum number can be specifically set to 300 times;

[0083] Yes, then select the individual with the minimum fitness function in the population after iteration as the site solution, i.e. extract the three-dimensional coordinates of each site from the site solution;

[0084] No, then execute step S7;

[0085] S7, taking the minimum value of the fitness function of the individual in the population as the optimization target, judging whether the optimization target of the population is stagnant; the stagnation judgment condition of the optimization target of the population is that the optimization target range value of the last N0 generation population is less than the set stagnation threshold; N0 can be set to 50;

[0086] Yes, restart the c% individuals with the maximum fitness in the population, and then return to step S3; in the embodiment, c is set to 30; the individual restart mode is: initializing the individual, or adding disturbance according to the set rule, for example, resetting the value of any dimension in the individual to be restarted to the mean value of the corresponding dimension of a plurality of non-restarted individuals selected randomly;

[0087] No, return to step S3;

[0088] It is worth noting that before returning to step S3, the population completes iteration, i.e. the Gth individual in the individual is updated to X' G X' G .

[0089] The above lightning detection station layout method based on intelligent optimization algorithm is described below in combination with specific embodiments.

[0090] In this embodiment, a certain actual terrain is selected for modeling to form a spatial pattern with large height difference, strong fluctuation, broken terrain and diverse microclimate; such strong fluctuation and complex topography have a significant impact on electromagnetic signal propagation: mountain shielding and terrain diffraction can change the effective propagation path and line-of-sight conditions, resulting in system deviation of TDOA (time difference of arrival positioning technology) and multi-solution ambiguity; at the same time, if the site is arranged in different geomorphic units such as mountain ridges, slope surfaces, valley bottoms and basin edges, the geometric visibility, coverage sector and GDOP difference are obvious. The elevation data of the space shuttle radar terrain mapping is selected as the terrain source in this embodiment, and the boundary is cut in ArcGIS. The coordinate reference is kept as WGS84, the resolution is 1", the NoData value is set and kept as 32767, and basic mapping inspection (such as color band stretching, shadow rendering contrast) is performed to verify the rationality of the elevation fluctuation. After preprocessing, the result is exported in GeoTIFF format; in the MATLAB environment, the spatial reference information of the GeoTIFF format file is read, the coordinate system, pixel size, spatial range and ArcGIS export information are checked to be consistent; at the same time, the NoData pixels at the boundary are identified to ensure that the subsequent analysis is not affected by the null value. 32767, and basic mapping inspection (such as color band stretching, shadow rendering contrast) is performed to verify the rationality of the elevation fluctuation. After preprocessing, the result is exported in GeoTIFF format; in the MATLAB environment, the spatial reference information of the GeoTIFF format file is read, the coordinate system, pixel size, spatial range and ArcGIS export information are checked to be consistent; at the same time, the NoData pixels at the boundary are identified to ensure that the subsequent analysis is not affected by the null value.

[0091] In this embodiment, the number of stations is set to 5, and the monitoring target area is a square with a side length of 300 Km, i.e. when the two-dimensional coordinates of the center of the monitoring target area are set to zero, the horizontal coordinates of each station are constrained to -150 Km≤x i ≤150 Km, -150 Km≤y i ≤150 Km; the vertical coordinate is constrained to 0.5 Km≤z i ≤5 Km; and the distance constraint is that the distance between the sub-station and the main station is less than 30 Km to avoid the formation of a local optimal solution due to the close distance between the stations. Considering that lightning in the troposphere often occurs at a height range of about 5-20 Km, the target layer to be positioned is fixed at a height range of 10 Km in this embodiment, and the GDOP index of the station is calculated.

[0092] In this embodiment, the lightning detection station layout method based on the intelligent optimization algorithm (referred to as the method of the present application) proposed by the present application, the traditional differential algorithm and the representative Y-shaped station layout method of lightning positioning are used for station layout. The GDOP is used as the evaluation index in the three methods.

[0093] In the method of the present application and the traditional differential algorithm, the population size is 150, and the individual fitness function of the traditional differential algorithm is the average GDOP of all stations in the individual.

[0094] In this embodiment, the iteration curves of the method of the present application and the traditional differential algorithm are shown in Figure 2 , Figure 4 .

[0095] As can be seen from Figure 2 , the curve of the method of the present application converges after about 110 iterations, the iteration curve presents a monotonically decreasing trend of gradient, and finally tends to be stable, indicating that the algorithm is converging to the global optimal or near-global optimal solution. The iteration curve converges rapidly at the beginning, gradually slows down in the middle period, and finally slowly tends to the optimal solution, indicating that the optimization algorithm can improve the calculation efficiency while ensuring the global search ability, avoid premature convergence to obtain a local optimal solution, and ensure the quality of the final solution.

[0096] As can be seen from 4, the traditional differential algorithm shows obvious limitations in the convergence process: although it has a faster decline in the early stage, it then appears 2-3 times of platform period, and stagnates for a long time with small fluctuations, indicating that the traditional differential algorithm is prone to fall into a local optimal solution in the early stage, the population diversity is insufficient, and it lacks the ability to jump out of the local; and in the later stage, due to the fixed step size and crossover parameters, the adaptability is poor, the anisotropy of complex terrain lacks adjustment ability, and the search radius is limited, resulting in only slow exploration, and finally approaching the optimal solution after about 190 iterations, with low convergence efficiency.

[0097] Comparing Figure 2 ,Figure 4 It can be known that the application improves the traditional difference algorithm to introduce an adaptive difference mutation and crossover mechanism based on a terrain factor, and combines a restart strategy and a penalty factor to maintain population diversity, so that it can avoid excessive contraction in the early stage and retain more exploration, and can also break through local optimum through a directional difference vector in the middle and late stages; the method converges obviously faster, and improves the lightning station distribution efficiency.

[0098] In the embodiment, the station distribution results of the three algorithms are shown in Table 1 and Figure 3 、 Figure 5 、 Figure 6

[0099] Table 1 Average positioning GDOP error (unit: Km) of the monitoring target area under three station distribution forms

[0100] ;

[0101] It can be known from Figure 3 that the station distribution of the method is relatively average, the positioning error in the monitoring target area is less than 3.0 Km, generally less than 0.43 Km, and the average error is 0.50963 Km.

[0102] It can be known from Figure 5 that the station distribution of the traditional difference algorithm is relatively regular, but the high-precision monitoring areas of each station are highly overlapped, there are many large error (error more than 10 Km) areas, and the average error in the monitoring target area is 1.5687 Km.

[0103] It can be known from Figure 6 that the station distribution of the Y-shaped station distribution method is relatively concentrated, the error of part of the areas in the monitoring target area is greater than 4.0 Km, and the error of a very small part reaches 8 Km, and the average error in the monitoring target area is 0.89328 Km.

[0104] The overall effect of the Y-shaped station distribution method is better than that of the traditional difference algorithm, but worse than that of the method.

[0105] In the results in Table 1, the method has a significant improvement in positioning accuracy compared with the traditional difference algorithm: the average positioning error is reduced from 1.5687 km to 0.50963 km, with a relative improvement of about 67.5%, which fully verifies the effectiveness of the improved strategy in improving the optimization effect and robustness.

[0106] ​Of course, the present application is not limited to the details of the above-described exemplary embodiments but comprises the same or similar structures which can be realized in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all respects as illustrative and not restrictive, the scope of the present application being defined by the appended claims rather than the above description, and it is intended that all changes which come within the meaning and range of equivalency of the claims are embraced therein. Any reference signs in the claims should not be construed as limiting the claims to the figures in which the reference signs are used.

[0107] Furthermore, it should be understood that although the description is made on the embodiments, not every embodiment contains only one independent technical solution, and the description of the specification is only for the sake of clarity, and the skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be combined appropriately to form other embodiments which can be understood by the skilled in the art.

[0108] The technologies, shapes, and structural parts not described in detail in the present application are well-known technologies.

Claims

1. A method for lightning detection station layout based on intelligent optimization algorithm, characterized in that, The method comprises the following steps: S1, setting the number of stations and station position constraints; S2, generating an initial population satisfying the station position constraints with station coordinates as the solution, the solution corresponding to an individual being a 3N-dimensional vector corresponding to the three-dimensional coordinates of N stations; S3, mutating the population individual: first, randomly selecting three parent individuals in the population, constructing a difference vector of two parent individuals plus a topographic enhancement mutation coefficient weighted considering the topography, and then operating with the remaining parent individuals to generate a mutated individual; The topographic enhancement mutation coefficient is adjusted on the basis of the set mutation factor combined with the consideration of the topographic penalty factor, and is positively correlated with the penalty factor, which is positively correlated with the topographic slope and elevation; S4, crossing the dimension values before and after the individual mutation to obtain a crossed individual; S5, for each individual in the population, calculate the fitness function, and for the case where the fitness value of the crossed individual is less than the fitness value of the corresponding original individual, replace the individual with the corresponding crossed individual to realize population iteration; Steps S3-S5 are repeated until the population converges, and the individual with the smallest fitness is selected from the population as the final decision vector, and the three-dimensional coordinates of each station are analyzed.

2. The intelligent optimization algorithm based lightning detection station layout method of claim 1, wherein, The penalty factor of each dimension in the individual is the penalty factor of the corresponding station, and the formula is: wherein R i is a penalty factor for the i-th station; H i and Φ i are the ground elevation value and the slope value of the i-th station, respectively; H max and Φ max are the maximum elevation value and the slope value in the monitoring target area, respectively; and α and β are weight coefficients.

3. The intelligent optimization algorithm based lightning detection station layout method of claim 1, wherein, S3 in the population individual X G The way of variation is: wherein, is the individual X G is the variation result of the dimension d in the individual X , and is the individual X G is the value of the dimension d in the three parent individuals randomly selected in the variation; the dimension d in the individual belongs to the coordinate of the i-th site, is the terrain enhancement variation coefficient of the i-th site, and δ is a set weight; is the local gradient correction term of the i-th site, and the gradient of the elevation sequence formed by the elevation value of the i-th site in the optimal individual of each generation is adopted.

4. The lightning detection station layout method based on the intelligent optimization algorithm of claim 3, characterized in that: wherein, is a set base variation factor, is an individual X G is a penalty factor R corresponding to the i-th station in the set i .

5. The intelligent optimization algorithm based lightning detection station layout method of claim 2, wherein, Fitness function of an individual The formula is: wherein N is the number of stations, is an evaluation index of the i-th station, R i is a penalty factor of the i-th station; and λ is a preset penalty weight coefficient.

6. The intelligent optimization algorithm based lightning detection station layout method of claim 5, wherein, In step S4 the Gth individual X in the population is allowed to mate with the individual U G [ x d,G |1≤d≤3N] the corresponding crossed individual is noted U G [ u d,G |1≤d≤3N]; wherein, x d,G is X G the value of the dimension d in the mutated individual and the crossed individual; r(d) represents assigning a random floating point number to the dimension d, r(d) ∈ (0, 1); the dimension d belongs to the i-th station; and is X G the value of the dimension d in the mutated individual and the crossed individual; r(d) represents assigning a random floating point number to the dimension d, r(d) ∈ (0, 1); the dimension d belongs to the i-th station; is the individual X G the crossover probability of the i-th station; is the set base probability; is the individual X G the slope value of the i-th station, Φ max is the maximum slope value in the monitoring target area; is a random integer taking a value on the interval [1, 3N].

7. The intelligent optimization algorithm based lightning detection station layout method of claim 1, wherein, The lightning detection station layout method based on the intelligent optimization algorithm is characterized in that one of the stations is a main station and the remaining stations are vice stations; the station position constraints include the horizontal coordinate constraint of the station, the vertical coordinate constraint of the station, and the distance constraint of the vice station relative to the main station.

8. The intelligent optimization algorithm based lightning detection station layout method of claim 7, wherein, The horizontal coordinate constraint of the station and the vertical coordinate constraint of the station are both composed of a corresponding maximum threshold and a minimum threshold; the manner of initializing the population in step S2 is: setting the number of individuals, and sampling the dimension value of each individual one by one; the sampling manner of x d is: taking the product of the corresponding minimum threshold and a random number r∈(0, 1.0) and the corresponding range as a candidate value, judging whether the candidate value satisfies the station position constraint, yes, then taking the candidate value as the initial value of x d ; no, then updating the random number r and recalculating the candidate value; the corresponding range of x d is the difference between the corresponding maximum threshold and the minimum threshold. d ​ 9. The intelligent optimization algorithm based lightning detection station layout method of claim 1, wherein, During the population iteration process, when the optimization target range value of the last N0 generation population is less than the set stagnation threshold, the individual with the maximum fitness c% in the population is restarted; the restart mode is initialization processing or individual disturbance processing according to the set rules; the optimization target of the population is the minimum value of the fitness function of the individual in the population.

10. A lightning detection station layout system based on intelligent optimization algorithm, characterized in that, The method comprises a memory and a processor, the memory stores a computer program, the processor is connected to the memory, and the processor is used to execute the computer program to realize the lightning detection station layout method based on the intelligent optimization algorithm of any one of claims 1-9.

Images