CN116008671A - Lightning positioning method based on time difference and clustering - Google Patents

Abstract

The invention discloses a lightning positioning method based on time difference and clustering, which comprises the following steps: four-station combined positioning is carried out on lightning strike-back data based on the arrival time difference principle, initial lightning positioning data are obtained, an initial lightning positioning data set is formed, a k-means clustering algorithm is adopted to carry out clustering analysis on the initial lightning positioning data set to obtain k clustering cluster sets, and the cluster center with the largest lightning positioning data point in the k clustering cluster sets is selected to be output and used as a final lightning positioning result. And removing the locating outliers to obtain a final accurate locating value. The invention can effectively improve the lightning positioning precision and reduce the influence of factors such as data coarse difference, station network layout and the like on the lightning positioning effect.

Description

A lightning location method based on time difference and clustering

Technical Field

The invention belongs to the technical field of meteorological lightning location, and in particular relates to a lightning location method based on time difference and clustering.

Background Art

Lightning is an atmospheric discharge phenomenon, often accompanied by severe convective weather processes such as hail and rainstorms. Lightning has physical effects such as strong current, electromagnetic radiation, and scorching heat, so it often causes lightning disasters, including huge damage to buildings, power equipment, information and communication equipment, oil tank storage and transportation, etc. The monitoring, early warning and accurate positioning of lightning are of great significance to reducing lightning disasters.

The lightning location system uses the electromagnetic field characteristics of lightning return radiation to remotely measure the time, location, intensity and polarity of its occurrence, and has been applied to meteorology, electricity, aerospace and other fields. Positioning accuracy is a key technical indicator for evaluating lightning location systems. The Time Difference Of Arrival (TDOA) positioning method has become the mainstream lightning location method due to its high positioning accuracy. This method determines the positioning hyperbola based on the distance difference between each lightning detection station and the lightning radiation source, and determines the relative position of the lightning radiation source signal relative to each detection station by solving the hyperbola equation group.

The TDOA positioning method is based on time difference positioning. Since the lightning electromagnetic field will be interfered by factors such as terrain and earth conductivity during the propagation process, the original lightning data used for positioning will be affected by various error factors and contain gross errors, making it difficult for the TDOA algorithm positioning curve to intersect at one point. Therefore, this method has problems such as inaccurate positioning and poor anti-interference.

Summary of the invention

The purpose of the present invention is to address the above problems and propose a lightning location method based on time difference and clustering to improve the lightning location accuracy and further reduce the influence of factors such as data errors and station network layout on the lightning location effect.

The clustering algorithm can extract the required information from a large amount of fuzzy, noisy or random actual data. The TDOA four-station positioning method is used to combine and locate the lightning return stroke data to obtain the preliminary lightning positioning result. Then, the k-means clustering algorithm is used to classify the preliminary positioning results and eliminate outliers to obtain the final lightning positioning result, thereby improving the positioning accuracy and anti-interference ability.

To achieve the above object, the present invention adopts the following technical solutions:

In a first aspect, a lightning location method based on time difference and clustering is provided, comprising:

Step S1, obtaining data information of a lightning to be located, wherein the lightning data information includes lightning return stroke data of at least four lightning detection stations receiving the lightning signal to be located; the lightning return stroke data includes site location information of the lightning detection station and lightning arrival time;

Step S2, combining the lightning data information to obtain multiple groups of lightning data, wherein each group of lightning data includes lightning return stroke data of lightning signals to be located received by four lightning detection stations;

Step S3: for each set of lightning data, the lightning radiation source is located respectively using the four-station time difference positioning method to obtain initial lightning positioning data; all the initial lightning positioning data constitute a lightning positioning data set;

Step S4, using a k-means clustering algorithm to perform cluster analysis on all lightning location data in the lightning location data set to obtain k cluster sets;

Step S5: Select the cluster center n _p , 1≤p≤k, which contains the most lightning location data points in the output k cluster sets as the final lightning location result.

In some embodiments, in step S1, the site location information is the site latitude, longitude and height information, and further includes converting the site latitude, longitude and height information into coordinates x, y, and z positioned in a spatial rectangular coordinate system;

Among them, the longitude L, latitude B, and altitude H of the lightning detection station, e is the first eccentricity of the ellipsoid, and N is the radius of curvature of the ellipse.

组闪电数据，其中n为闪电探测站的总个数。In some embodiments, step S2 includes: traversing all lightning detection stations, combining lightning data information of every four stations, and obtaining

A set of lightning data, where n is the total number of lightning detection stations.

In some embodiments, in step S3, the lightning radiation source is located respectively using the four-station time difference positioning method to obtain the initial lightning positioning data, including:

According to the time difference of arrival TDOA positioning principle, the distance difference equation is:

Wherein, the location of lightning occurrence is (x, y, z), the occurrence time is t, the coordinate position of the i-th lightning detection station is (x _i , y _i , z _i ), and the arrival time is t _i ; i = 0 represents the primary station, and i = 1, 2, 3…n represents the secondary station; the distance from the lightning to the primary station (x ₀ , y ₀ , z ₀ ) is r ₀ , the distance to the i-th secondary station is _ri , _Δri is the distance difference between _ri and r ₀ , and c is the propagation rate of the electromagnetic wave signal;

The number of detection stations is 4, i.e., i = 0, 1, 2, 3; formula (1) changes to:

ri2-r02＝di-d0 (2)

Where d _i =(xx _i ) ² +(yy _i ) ² +(zz _i ) ² ,d ₀ =(xx ₀ ) ² +(yy ₀ ) ² +(zz ₀ ) ² ;

By moving terms, squaring, and simplifying equation (2), we can obtain:

In formula (3), i = 1, 2, 3, which is a nonlinear equation system about (x, y, z, t). Taking r ₀ as a known quantity, we get the matrix expression:

AX＝B (4)

Right now:

When the lightning detection stations are not deployed on the same plane, the rank of the coefficient matrix A is equal to 3, and we get:

X = (A ^T A) ^-1 A ^T B (6)

Substituting X into equation (1) we can get the equation:

ar ₀ ² +br ₀ +c＝0 (7)

After solving the quadratic equation (7) to obtain the numerical solution of r ₀ , substitute r ₀ into equation (6) to obtain the coordinates of the lightning radiation source in the spatial rectangular coordinate system.

When Δ＝b ² -4ac＞0, r ₀ has two solutions r ₀₁ and r ₀₂ , that is, the hyperboloid has two intersection points; r ₀ indicates that the distance must be a positive number, and when r ₀₁ and r ₀₂ are one positive and one negative, the positive root is selected as the positioning solution; when r ₀₁ and r ₀₂ are both positive numbers, the positioning ambiguity is eliminated by adding azimuth auxiliary information;

When Δ＝b ² -4ac＝0, r ₀ has a unique solution and there is no positioning ambiguity problem.

In some embodiments, step S4, using a k-means clustering algorithm to perform cluster analysis on all lightning location data in the lightning location data set, includes:

S41. For the first time, randomly select k objects from the lightning location dataset X to form the first training subset T ₁ (T ₂ ∈X), where k < n, and use the K-means algorithm to build a local model containing k clusters to obtain k preliminary cluster centers;

S42. For the second time, based on the first time, randomly select C ₂ objects other than T ₁ from X to form the second training subset T ₂ (T ₂ ∈XT ₁ ); use the K-means algorithm to add the objects in T ₂ to k clusters, and update the cluster center of each cluster; and so on, repeat the iteration steps S41 to S42 until the preset iteration stop condition is reached, and obtain the final set of k clusters S = {S ₁ , S ₂ , S ₃ , … , S _k }.

Further, in some embodiments, step S4 includes:

In the lightning location dataset X, k lightning location data sample points are randomly selected as the initial cluster centers, denoted as n ⁽⁰⁾ = (n ₁ ⁽⁰⁾ , …, n _l ⁽⁰⁾ , …, n _k ⁽⁰⁾ ); where k < n, n is the total number of lightning detection stations; in the m-dimensional vector lightning location dataset X, sample point x _i ∈ X, x _i = (x _1i , x _2i , …, x _mi ) ^T ;

For a fixed cluster center n ^(t) = ( _n1 ^(t) , ..., _nl ^(t) , ..., _nk ^(t) ), where _nl ^(t) is the cluster center of cluster _Sl , the lightning location data sample points are clustered according to the distance between the sample points and the cluster center:

Calculate the distance from each sample point to the cluster center, and assign each lightning location data sample point to the cluster with the smallest distance based on the calculated distance, and obtain a set of k clusters S = {S ₁ , S ₂ , S ₃ , …, S _k }, generating a preliminary clustering result C ^(t) ;

Calculate the sample mean u _i of each cluster for the clustering result C ^(t) :

Where _xi is the lightning location data sample point in category l, s ⁽ⁱ⁾ is the category to which _xi belongs, and z is the total number of sample points in each cluster; the mean is taken as the new cluster center u ^(t+1) = ( _u1 ^(t+1) ,…, _ul ^(t+1) ,…, _uk ^(t+1) );

The sum of the distances between the lightning location data sample points and the center of the class to which they belong is defined as the final loss function W(C):

为第l个类簇的聚类中心；

中I(C(i)＝l)表示取值为0或1的指示函数；函数W(C)表示相同类簇中样本点的相似程度；k均值聚类转换为一个优化问题的求解：in

is the cluster center of the lth cluster;

Where I(C(i)=l) represents an indicator function with a value of 0 or 1; the function W(C) represents the similarity of sample points in the same cluster; k-means clustering is converted into a solution to an optimization problem:

If the iteration converges or meets the iteration stopping condition, that is, the loss function W(C) reaches the minimum, the final clustering result C ^* = C ^(t) is output; otherwise, the iteration continues, the number of iterations t = t+1, and the loss function is recalculated.

In some embodiments, step S5 further includes: converting the lightning location result from the coordinates x, y, and z located in the spatial rectangular coordinate system into longitude, latitude, and height information in the spatial geodetic coordinate system; the solution formula for longitude L, latitude B, and height H in the WGS-84 ellipsoid model is:

Where a and b are the major and minor semi-axes of the ellipsoid respectively, a=6378.137km, b=6356.752km; e is the first eccentricity of the ellipsoid, and N is the radius of curvature of the ellipse.

In a second aspect, the present invention provides a lightning location device based on time difference and clustering, including a processor and a storage medium;

The storage medium is used to store instructions;

The processor is configured to operate according to the instructions to execute the steps of the method according to the first aspect.

In a third aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in the first aspect.

The present invention adopts the TDOA four-station positioning method to combine and locate the lightning return stroke data to obtain the lightning positioning result, and then uses the k-means clustering algorithm to classify the positioning results and remove outliers to obtain the final positioning result, solving the problems of inaccurate positioning and poor anti-interference in traditional methods. Finally, the test verifies that this method has a good positioning effect.

Compared with the prior art, the present invention has the following beneficial effects:

The present invention provides a lightning location method based on time difference and clustering. The TDOA location method is based on time difference location. Since the lightning electromagnetic field will be interfered by factors such as terrain and earth conductivity during propagation, the original lightning data used for positioning will be affected by various error factors and contain gross errors, so that the TDOA algorithm positioning curve often cannot intersect at one point. Therefore, this method has problems such as inaccurate positioning and poor anti-interference. The clustering algorithm can extract the required information from a large amount of fuzzy, noisy or random actual data. Based on this, a lightning location method based on a clustering algorithm is proposed. The TDOA four-station positioning method is used to combine and locate the lightning return stroke data to obtain the lightning location result, and then the k-means clustering algorithm is used to classify the obtained positioning results and eliminate outliers to obtain the final positioning results, thereby improving the positioning accuracy and anti-interference ability.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a TDOA positioning principle according to an embodiment of the present invention.

FIG. 2 is a schematic diagram of a lightning location system station layout according to an embodiment of the present invention.

FIG3 is a flow chart of a lightning location method according to an embodiment of the present invention.

DETAILED DESCRIPTION

The present invention is further described below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

In the description of the present invention, "several" means more than one, "many" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

In the description of the present invention, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

Example 1

A lightning location method based on time difference and clustering, comprising:

Step S1, obtaining data information of a lightning to be located, wherein the lightning data information includes lightning return stroke data of at least four lightning detection stations receiving the lightning signal to be located; the lightning return stroke data includes site location information of the lightning detection station and lightning arrival time;

Step S2, combining the lightning data information to obtain multiple groups of lightning data, wherein each group of lightning data includes lightning return stroke data of lightning signals to be located received by four lightning detection stations;

Step S3: for each set of lightning data, the lightning radiation source is located respectively using the four-station time difference positioning method to obtain initial lightning positioning data; all the initial lightning positioning data constitute a lightning positioning data set;

Step S4, using a k-means clustering algorithm to perform cluster analysis on all lightning location data in the lightning location data set to obtain k cluster sets;

Step S5: Select the cluster center _np containing the most lightning location data points in the output k cluster sets, 1≤p≤k, as the final lightning location result.

Step S4, using the k-means clustering algorithm to perform cluster analysis on all lightning location data in the lightning location data set, including:

S41. For the first time, randomly select k objects from the lightning location dataset X to form the first training subset T ₁ (T ₂ ∈X), where k < n, and use the K-means algorithm to build a local model containing k clusters to obtain k preliminary cluster centers;

S42. For the second time, based on the first time, randomly select C ₂ objects other than T ₁ from X to form the second training subset T ₂ (T ₂ ∈XT ₁ ); use the K-means algorithm to add the objects in T ₂ to k clusters, and update the cluster center of each cluster; and so on, repeat the iterative steps S41 to S42 until the preset iteration stop condition is reached, and obtain the final set of k clusters S = {S ₁ ,S ₂ ,S ₃ ,…,S _k }.

In some embodiments, as shown in FIG1 , a TDOA positioning principle diagram is provided. TDOA is a wireless positioning method that requires three or more lightning detection stations with known position coordinates for positioning. In FIG1 , there are three lightning detection stations A, B, and C, and S is the location where lightning occurs. Assume that the distances from the lightning radiation source S to the detection stations A, B, and C are r ₁ , r _{2 ,} and r ₃ , respectively. In actual situations, the distances are unknown. Lightning detection stations A and B can measure the time when the electromagnetic wave signal emitted by the lightning return radiation source S reaches each detection station. There is a time difference between the two stations. Multiplying the time difference by the propagation speed of the lightning signal can determine the distance difference between the two stations r ₂₁ = r ₂ -r ₁ , forming a hyperbola L ₂ with the detection stations A and B as the focus and the distance difference r ₂₁ as the major axis. The location where lightning occurs is at a certain point on this hyperbola. Detection station C and lightning detection station B can also form another positioning hyperbola L ₁ , and the intersection S of the two hyperbolas is the location of the lightning radiation source.

As shown in Figure 2, it is a schematic diagram of the station layout of the lightning location system. Assume that the location of lightning is S (x, y, z), the occurrence time is t, the coordinate position of the i-th lightning detection station is (x _i , y _i , z _i ), and the arrival time is t _i . When i = 0, it represents the primary station, and when i = 1, 2, 3…n, it represents the secondary station.

In some specific embodiments, as shown in FIG3 , FIG3 is a lightning location flow chart, including:

1) The lightning detection station receives the lightning return stroke data X of the same lightning signal;

种组合；2) All lightning detection stations are combined in groups of four, and the maximum number of

Combination of

3) Using the four-station time difference positioning method to locate the lightning radiation sources respectively, the lightning location data set X = {p ₁ , p ₂ , p ₃ ,…, p _m } can be obtained;

4) Use the k-means clustering algorithm to perform cluster analysis on all positioning data. Data points without noise or with less noise can be clustered into one category due to good aggregation, and data points with more noise can be clustered into another category or several categories, for a total of k categories: s ₁ , s ₂ , s ₃ ..., s _k ;

5) Select the cluster center n _p , 1≤p≤k, which contains the most lightning location data points in the output k cluster sets {s ₁ ,s ₂ ,s ₃ ,…,s _k } as the final lightning location result, and exclude the interference location points in the location result;

6) Analyze the positioning performance of the algorithm based on the actual positioning results.

According to the time difference of arrival TDOA positioning principle, the distance difference equation is:

Wherein, the location of lightning occurrence is (x, y, z), the occurrence time is t, the coordinate position of the i-th lightning detection station is (x _i , y _i , z _i ), and the arrival time is t _i ; i = 0 represents the primary station, and i = 1, 2, 3…n represents the secondary station; the distance from the lightning to the primary station (x ₀ , y ₀ , z ₀ ) is r ₀ , the distance to the i-th secondary station is _ri , _Δri is the distance difference between _ri and r ₀ , and c is the propagation rate of the electromagnetic wave signal;

If the number of detection stations is 4, that is, i = 0, 1, 2, 3, equation (1) changes to:

ri2-r02＝di-d0 (2)

Where d _i =(xx _i ) ² +(yy _i ) ² +(zz _i ) ² ,d ₀ =(xx ₀ ) ² +(yy ₀ ) ² +(zz ₀ ) ² ;

By moving terms, squaring, and simplifying equation (2), we can obtain:

In formula (3), i = 1, 2, 3, which is a nonlinear equation system about (x, y, z, t). Taking r ₀ as a known quantity, we get the matrix expression:

AX＝B (4)

When the lightning detection stations are not deployed on the same plane, the rank of the coefficient matrix A is equal to 3, and we get:

X = (A ^T A) ^-1 A ^T B (6)

Substituting X into equation (1) we can get the equation:

ar ₀ ² +br ₀ +c＝0 (7)

After solving the quadratic equation (7) to obtain the numerical solution of r ₀ , substitute r ₀ into equation (6) to obtain the coordinates of the lightning radiation source in the spatial rectangular coordinate system.

Furthermore, the method further includes: converting the coordinates x, y, and z of the lightning radiation source located in the spatial rectangular coordinate system into longitude and latitude information in the spatial geodetic coordinate system.

The lightning return stroke data recorded by the lightning location system is the longitude, latitude, and altitude information (spatial geodetic coordinate system) of each lightning detection station, and the spatial geodetic coordinate system needs to be converted into a spatial rectangular coordinate system.

Since the real shape of the earth's surface is not a perfect regular sphere, the WGS-84 ellipsoid model is used as a reference model for lightning location. The solution formula for longitude L, latitude B, and height H in the WGS-84 ellipsoid model is:

In the formula, a and b are the major and minor semi-axes of the ellipsoid, a = 6378.137 km, b = 6356.752 km. e is the first eccentricity of the ellipsoid, and N is the radius of curvature of the ellipse.

The solution formula for the coordinates x, y, and z located in the spatial rectangular coordinate system under the same reference is:

In summary, after the lightning location system records the latitude and longitude information of the station that receives the lightning return data and the arrival time, it performs coordinate conversion through equation (9), and calculates the analytical solution of the distance _r0 through equation (7). Substituting _r0 into equation (6) can obtain the coordinates of the lightning radiation source in the spatial rectangular coordinate system, and finally obtains the latitude and longitude position of the lightning through equation (8).

In some embodiments, equation (7) may have an ambiguous solution:

When Δ＝b ² -4ac＞0, r ₀ has two solutions r ₀₁ and r ₀₂ , that is, the hyperboloid has two intersection points; r ₀ indicates that the distance must be a positive number, and when r ₀₁ and r ₀₂ are one positive and one negative, the positive root is selected as the positioning solution; when r ₀₁ and r ₀₂ are both positive numbers, the positioning ambiguity is eliminated by adding azimuth auxiliary information;

When Δ＝b ² -4ac＝0, r ₀ has a unique solution and there is no positioning ambiguity problem;

When Δ＝b ² -4ac＜0, there is no solution for r ₀ and the lightning radiation source cannot be located.

In some embodiments, step 4) comprises the following steps:

First, the centroid of lightning location samples is initialized. In the m-dimensional vector lightning location dataset X, the sample element x _i ∈ X, x _i = (x _1i , x _2i , …, x _mi ) ^T . In the first iteration, k (k < n) lightning location data sample points are randomly selected from the set X as the initialized cluster center points, denoted as n ⁽⁰⁾ = (n ₁ ⁽⁰⁾ , …, n _l ⁽⁰⁾ , …, n _k ⁽⁰⁾ ).

For a fixed cluster center n ^(t) = ( _n1 ^(t) , ..., _nl ^(t) , ..., _nk ^(t) ), where _nl ^(t) is the center of cluster _Sl , the lightning location data sample points are clustered according to the distance between the sample point and the initial cluster center. The Minn distance between the sample point x _i and the cluster center n _j is:

The Euclidean distance is used to calculate the distance from each lightning location data sample point to the cluster center in the lightning data set X. In formula (10), p = 2 can be expressed as the Euclidean distance:

According to formula (11), the distance from each sample point to the cluster center is calculated. Based on the calculated distance, each lightning location data sample point is assigned to the cluster with the smallest distance to it. A set of k clusters S = {S ₁ , S ₂ , S ₃ , … , S _k } can be obtained, and a preliminary clustering result C ^(t) is generated.

Calculate the sample mean of each cluster for the clustering result C ^(t) :

Where _xi is a lightning data sample point in category l, s ⁽ⁱ⁾ is the category to which _xi belongs, and z is the total number of sample points in each cluster. The mean is taken as the new cluster center point u ^(t+1) = ( _u1 ^(t+1) , ..., _u1 ^(t+1) , ..., _uk ^(t+1) ). The sum of the distances between the lightning sample point and the center of the class to which it belongs is defined as the final loss function:

为第l个类的质心(即聚类中心点)。

中I(C(i)＝l)表示取值为0或1的指示函数。函数W(C)表示相同类簇中样本点的相似程度。k均值聚类转换为一个优化问题的求解：in

is the centroid of the lth class (i.e., the cluster center).

Where I(C(i)=l) represents an indicator function with a value of 0 or 1. Function W(C) represents the similarity of sample points in the same cluster. K-means clustering is converted into a solution to an optimization problem:

If the iteration converges or meets the iteration stopping condition, that is, the error square sum W(C) in equation (13) reaches the minimum, the final clustering result C ^* = C ^(t) is output; otherwise, the iteration continues, the number of iterations t = t+1 and returns to equation (14) to recalculate.

The present invention provides a lightning location method based on time difference and clustering. There are many methods and ways to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be considered as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Example 2

In a second aspect, this embodiment provides a lightning location device based on time difference and clustering, including a processor and a storage medium;

The storage medium is used to store instructions;

The processor is used to operate according to the instructions to execute the steps of the method according to embodiment 1.

Example 3

In a third aspect, this embodiment provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in Example 1 are implemented.

Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims (10)

1. A lightning location method based on time difference and clustering, characterized by comprising: Step S1, obtaining data information of a lightning to be located, wherein the lightning data information includes lightning return stroke data of at least four lightning detection stations receiving the lightning signal to be located; the lightning return stroke data includes site location information of the lightning detection station and lightning arrival time; Step S2, combining the lightning data information to obtain multiple groups of lightning data, wherein each group of lightning data includes lightning return stroke data of lightning signals to be located received by four lightning detection stations; Step S3: for each set of lightning data, the lightning radiation source is located respectively using the four-station time difference positioning method to obtain initial lightning positioning data; all the initial lightning positioning data constitute a lightning positioning data set; Step S4, using a k-means clustering algorithm to perform cluster analysis on all lightning location data in the lightning location data set to obtain k cluster sets; Step S5: Select the cluster center containing the most lightning location data points in the output k cluster sets as the final lightning location result. 2. The method according to claim 1, characterized in that in step S1, the site location information is the site latitude and longitude height information, and further comprises converting the site latitude and longitude height information into coordinates x, y, and z positioned in a spatial rectangular coordinate system;

Among them, the longitude L, latitude B, and altitude H of the lightning detection station, e is the first eccentricity of the ellipsoid, and N is the radius of curvature of the ellipse. 3. The method according to claim 1, characterized in that step S2 comprises: traversing all lightning detection stations, combining lightning data information of every four stations, and obtaining

A set of lightning data, where n is the total number of lightning detection stations. 4. The method according to claim 1, characterized in that, in step S3, the lightning radiation source is located respectively using the four-station time difference positioning method to obtain the initial lightning positioning data, comprising: According to the time difference of arrival TDOA positioning principle, the distance difference equation is:

Wherein, the location of lightning occurrence is (x, y, z), the occurrence time is t, the coordinate position of the i-th lightning detection station is (x _i , y _i , z _i ), and the arrival time is t _i ; i = 0 represents the primary station, and i = 1, 2, 3…n represents the secondary station; the distance from the lightning to the primary station (x ₀ , y ₀ , z ₀ ) is r ₀ , the distance to the i-th secondary station is _ri , _Δri is the distance difference between _ri and r ₀ , and c is the propagation rate of the electromagnetic wave signal; The number of detection stations is 4, i.e., i = 0, 1, 2, 3; formula (1) changes to: _ri ² -r ₀ ² = d _i -d ₀ (2) Where d _i =(xx _i ) ² +(yy _i ) ² +(zz _i ) ² ,d ₀ =(xx ₀ ) ² +(yy ₀ ) ² +(zz ₀ ) ² ; By moving terms, squaring, and simplifying equation (2), we can obtain:

In formula (3), i = 1, 2, 3, which is a nonlinear equation system about (x, y, z, t). Taking r ₀ as a known quantity, we get the matrix expression: AX＝B (4) Right now:

When the lightning detection stations are not deployed on the same plane, the rank of the coefficient matrix A is equal to 3, and we get: ^{X＝(ATA)-1ATB ( 6} ) Substituting X into equation (1) we can get the equation: ar ₀ ² +br ₀ +c＝0 (7) After solving the quadratic equation (7) to obtain the numerical solution of r ₀ , substitute r ₀ into equation (6) to obtain the coordinates of the lightning radiation source in the spatial rectangular coordinate system. 5. The method according to claim 4, characterized in that When Δ＝b ² -4ac＞0, r ₀ has two solutions r ₀₁ and r ₀₂ , that is, the hyperboloid has two intersection points; r ₀ indicates that the distance must be a positive number, and when r ₀₁ and r ₀₂ are one positive and one negative, the positive root is selected as the positioning solution; when r ₀₁ and r ₀₂ are both positive numbers, the positioning ambiguity is eliminated by adding azimuth auxiliary information; When Δ＝b ² -4ac＝0, r ₀ has a unique solution and there is no positioning ambiguity problem. 6. The method according to claim 1, characterized in that step S4, using a k-means clustering algorithm to perform cluster analysis on all lightning location data in the lightning location data set, comprises: S41. For the first time, k objects are randomly selected from the lightning location dataset X to form the first training subset T ₁ (T ₁ ∈X), where k < n, and a local model containing k clusters is constructed using the K-means algorithm to obtain k preliminary clustering centers; S42. For the second time, based on the first time, randomly select C ₂ objects other than T ₁ from X to form the second training subset T ₂ (T ₂ ∈XT ₁ ); use the K-means algorithm to add the objects in T ₂ to k clusters, and update the cluster center of each cluster; and so on, repeat the iterative steps S41 to S42 until the preset iteration stop condition is reached, and obtain the final set of k clusters S = {S ₁ ,S ₂ ,S ₃ ,…,S _k }. 7. The method according to claim 6, characterized in that step S4 comprises: In the lightning location data set X, k lightning location data sample points are randomly selected as the initial cluster centers, denoted as n ⁽⁰⁾ = (n ₁ ⁽⁰⁾ ,…,n _l ⁽⁰⁾ ,…,n _k ⁽⁰⁾ ); where k < n, n is the total number of lightning detection stations; in the m-dimensional vector lightning location data set X, sample point x _i ∈ X, x _i = (x _1i ,x _2i ,…,x _mi ) ^T ; For a fixed cluster center n ^(t) = ( _n1 ^(t) , ..., _nl ^(t) , ..., _nk ^(t) ), where _nl ^(t) is the cluster center of cluster _Sl , the lightning location data sample points are clustered according to the distance between the sample points and the cluster center: Calculate the distance from each sample point to the cluster center, and assign each lightning location data sample point to the cluster with the smallest distance based on the calculated distance, and obtain a set of k clusters S = {S ₁ , S ₂ , S ₃ , …, S _k }, generating a preliminary clustering result C ^(t) ; Calculate the sample mean u _l of each cluster for the clustering result C ^(t) :

Where _xi is the lightning location data sample point in category l, s ⁽ⁱ⁾ is the category to which _xi belongs, and z is the total number of sample points in each cluster; the mean is taken as the new cluster center u ^(t+1) = ( _u1 ^(t+1) ,…, _ul ^(t+1) ,…, _uk ^(t+1) ); The sum of the distances between the lightning location data sample points and the center of the class to which they belong is defined as the final loss function W(C):

in

is the cluster center of the lth cluster;

Where I(C(i)=l) represents an indicator function with a value of 0 or 1; the function W(C) represents the similarity of sample points in the same cluster; k-means clustering is converted into a solution to an optimization problem:

If the iteration converges or meets the iteration stopping condition, that is, the loss function W(C) reaches the minimum, the final clustering result C ^* = C ^(t) is output; otherwise, the iteration continues, the number of iterations t = t+1, and the loss function is recalculated. 8. The method according to claim 1, characterized in that step S5 further comprises: converting the lightning location result from the coordinates x, y, z located in the spatial rectangular coordinate system into longitude, latitude and height information in the spatial geodetic coordinate system; the solution formula for longitude L, latitude B and height H in the WGS-84 ellipsoid model is:

Where a and b are the major and minor semi-axes of the ellipsoid respectively, a=6378.137km, b=6356.752km; e is the first eccentricity of the ellipsoid, and N is the radius of curvature of the ellipse. 9. A lightning location device based on time difference and clustering, characterized by comprising a processor and a storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 8. 10. A storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method according to any one of claims 1 to 8 when executed by a processor.

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