A method and apparatus for analyzing lightning density in high-rise building areas

Abstract

This invention relates to the field of lightning detection technology, specifically disclosing a method and apparatus for analyzing lightning density in areas with tall buildings. The method includes: acquiring historical lightning location data for the area with tall buildings; analyzing the distribution characteristics of ground flashes in the historical lightning location data to obtain an expression for the observed lightning density distribution; simplifying the observed lightning density distribution expression to obtain a convolution expression for the observed lightning density; and using the Richardson-Lucy algorithm to iteratively calculate the convolution expression for the observed lightning density to obtain the optimal estimate of the true lightning density. This invention provides a refined analysis of the density distribution of lightning location records in areas with tall buildings and iteratively calculates the convolution expression for the observed lightning density. This solves the problem that, due to lightning location errors, directly applying the small-grid method can lead to a false increase in lightning density values ​​around tall buildings, while the lightning density value at the center is much smaller than the true value.

Description

Technical Field

[0001] This invention relates to the field of lightning detection technology, and specifically to a method and apparatus for analyzing lightning density in areas with tall buildings. Background Technology

[0002] With the development of lightning detection technology, large-area lightning location systems composed of multiple lightning detection substations can monitor lightning activity in real time and over a wide area, detecting parameters such as the time, location, peak lightning current, and polarity of cloud-to-ground discharges (ground flashes). Currently, ground flash location systems have become the most important lightning monitoring technology, with a detection efficiency exceeding 90%, but a location error of several hundred meters still exists. Currently, with the increasing number and height of tall buildings in cities, researchers are paying more attention to statistically analyzing the lightning distribution characteristics in urban areas with tall buildings using lightning location detection data.

[0003] The grid method is currently the most commonly used method for analyzing the two-dimensional geospatial distribution characteristics of lightning. It involves dividing the area to be analyzed into equally spaced grids, assigning lightning to a specific grid based on its geographic location parameters, and then further analyzing the lightning data within each grid. To analyze the differences in lightning distribution in detail, the grid spacing may even need to be as small as 100 meters. This is already smaller than the average positioning error of most current operational lightning location systems. Therefore, directly applying the grid method could easily lead to misclassification of lightning grid assignments.

[0004] In areas with tall buildings, most lightning strikes occur on top of these buildings, while the areas near them receive very few strikes due to the shielding effect of the buildings. Therefore, the horizontal gradient of lightning distribution in these areas is actually quite large. However, this characteristic is often difficult to analyze using the grid method directly (even with a grid spacing of 100 meters or less). For example, in a flat area with a very tall building in the center, all lightning strikes in the area occur on top of that building. However, due to the location error in LLS (Lightning Loss Detection and Ranging), the location points will inevitably be scattered around the building. This causes a false increase in lightning density values ​​at surrounding grid points, while the lightning density at the grid containing the very tall building will appear to decrease falsely.

[0005] Sima Wenxia et al. (2012) and Shi Xiangbo et al. (2015) attempted to improve the grid method. Specifically, for a finely divided grid (grid spacing less than 1 km), the statistical analysis of lightning parameters (e.g., lightning density) was not limited to that grid, but rather applied to all lightning data within a large, artificially defined circular area centered on that grid (generally with a radius of several kilometers). It should be noted that this method is essentially a smoothing method and still cannot yield refined information on the differences in lightning distribution in areas with tall buildings.

[0006] To address the statistical error in lightning density gridding in high-building areas caused by random errors in lightning location results, the inventors developed an improved analysis method. Using this method, they optimized the ground lightning return stroke density in high-building areas of Guangzhou with a grid spacing of 100 meters and compared the analysis results before and after optimization.

[0007] Based on this technical background, the present invention proposes a method and apparatus for analyzing lightning density in areas with tall buildings. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a method and apparatus for analyzing lightning density in areas with tall buildings. This method performs refined analysis of the density distribution of lightning location records in areas with tall buildings. It uses the Richardson-Lucy algorithm to iteratively calculate the convolution expression of the observed lightning density, solving the problem that the direct application of the small-grid method due to lightning location errors can lead to a false increase in the lightning density value around tall buildings, while the lightning density value at the center of tall buildings is much smaller than the true value. This achieves the optimal estimation of the true lightning density, which is more consistent with the actual situation.

[0009] To achieve the above objectives, a first aspect of the present invention provides a method for analyzing lightning density in areas with tall buildings, comprising:

[0010] Obtain historical data on lightning location in areas with tall buildings;

[0011] The distribution characteristics of ground flashes were analyzed using the historical lightning location data to obtain an expression for the observed lightning density distribution.

[0012] The expression for the observed lightning density distribution is simplified to obtain the convolution expression for the observed lightning density;

[0013] The Richardson-Lucy algorithm is used to iteratively calculate the convolution expression of the observed lightning density to obtain the optimal estimate of the true lightning density.

[0014] A second aspect of the present invention provides a lightning density analysis device for high-rise building areas, comprising:

[0015] The acquisition module is used to acquire historical lightning location data for areas with tall buildings.

[0016] The feature analysis module is used to perform ground lightning distribution feature analysis on the historical lightning location data to obtain the expression for the observed lightning density distribution.

[0017] A simplification module is used to simplify the observed lightning density distribution expression to obtain a convolution expression for the observed lightning density;

[0018] The iterative module is used to iteratively calculate the convolution expression of the observed lightning density using the Richardson-Lucy algorithm to obtain the optimal estimate of the true lightning density.

[0019] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0020] Memory, which stores executable instructions;

[0021] A processor that executes the executable instructions in the memory to implement the lightning density analysis method for tall building areas as described in the first aspect.

[0022] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the lightning density analysis method for tall building areas described in the first aspect.

[0023] The beneficial effects of this invention include:

[0024] The proposed lightning density analysis method for tall building areas provides a refined analysis of the density distribution of lightning location records in these areas. It employs the Richardson-Lucy algorithm to iteratively calculate the convolution expression of the observed lightning density. This solves the problem that, due to lightning location errors, directly applying the small-grid method can lead to a false increase in lightning density values ​​around tall buildings, while the lightning density value at the center of the tall building is much smaller than the true value. This method achieves the optimal estimation of the true lightning density, which is more consistent with reality.

[0025] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0026] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings.

[0027] Figure 1 This is a flowchart illustrating the lightning density analysis method for tall building areas proposed in this invention.

[0028] Figure 2 This is a schematic diagram of the direct statistical results of the lightning return stroke frequency distribution in a high-building area, as a specific embodiment of the lightning density analysis method for high-building areas proposed in this invention.

[0029] Figure 3 This is a schematic diagram illustrating the optimized lightning return stroke frequency distribution analysis results in a specific embodiment of the lightning density analysis method for tall building areas proposed in this invention.

[0030] Figure 4 This is a schematic diagram showing the difference before and after optimization in a specific implementation of the lightning density analysis method for tall building areas proposed in this invention. Detailed Implementation

[0031] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0032] This invention provides a method for analyzing lightning density in areas with tall buildings, such as... Figure 1 As shown, it includes:

[0033] Obtain historical data on lightning location in areas with tall buildings;

[0034] By analyzing the distribution characteristics of ground lightning based on historical lightning location data, an expression for the observed lightning density distribution was obtained.

[0035] The expression for the observed lightning density distribution is simplified to obtain the convolution expression for the observed lightning density;

[0036] The Richardson-Lucy algorithm is used to iteratively calculate the convolution expression of the observed lightning density to obtain the optimal estimate of the true lightning density.

[0037] In this invention, a refined analysis of the lightning location record density distribution in areas with tall buildings is performed. The Richardson-Lucy algorithm is used to iteratively calculate the convolution expression of the observed lightning density. This solves the problem that, due to the influence of lightning location errors, directly applying the small grid method can lead to a false increase in the lightning density value around tall buildings, while the lightning density value at the center of tall buildings is much smaller than the true value. This achieves the optimal estimation of the true lightning density, which is more consistent with the actual situation.

[0038] According to the present invention, the analysis of ground flash distribution characteristics based on historical lightning location data includes:

[0039] Set the grid analysis size for historical lightning location data;

[0040] Remove positive polarity cloud-to-cloud lightning records from historical lightning location data;

[0041] Retain records of negative polarity cloud-based lightning in historical lightning location data;

[0042] All positioning records undergo eastward system bias correction.

[0043] According to the present invention, the condition for the expression to represent the observed lightning density distribution is:

[0044] The statistical time frame of historical lightning location data meets the length required for analyzing the distribution characteristics of ground lightning:

[0045] The lightning location system has stable detection performance.

[0046] According to the present invention, the expression for the observed lightning density distribution is:

[0047] g(x,y)=H·f(x,y)+n(x,y);

[0048] Where g(x,y) is the lightning density distribution observed by the lightning location system, f(x,y) is the actual lightning density distribution, H is the operator that causes "blurring" or "distortion", and n(x,y) is noise;

[0049] The fuzzy or distorted operator is generated by the distortion of the actual lightning density distribution due to the positioning error of the lightning positioning system.

[0050] According to the present invention, the condition for simplifying the expression for the observed lightning density distribution is:

[0051] Throughout the entire area of ​​tall buildings, the random error distribution of each lightning location result follows the same distribution function.

[0052] Preferably, the convolution expression for the observed lightning density is:

[0053]

[0054] Where p(x,y) is the distribution function. It is a convolution operator.

[0055] According to the present invention, the expression for the distribution function is:

[0056]

[0057] Where σ is the variance, and x and y are two-dimensional normally distributed variables.

[0058] The present invention will be described in more detail below through embodiments.

[0059] Example 1:

[0060] This embodiment proposes a method for analyzing lightning density in areas with tall buildings, including:

[0061] 1) Lightning location data explanation:

[0062] The Guangdong-Hong Kong-Macao Lightning Location System was jointly constructed by the meteorological departments of Guangdong Province, Hong Kong, and Macao starting in 2005. By 2007, six detection substations (using IMPACT probes) had been built. In 2012, 11 new detection substations were added. The newly constructed stations adopted Vaisala's LS-700X series probes, which can simultaneously detect ground lightning and cloud lightning (theoretically, the detection efficiency for cloud lightning is about 40-50%), thereby improving the detection capability of lightning activity in the Pearl River Delta region. In 2018, the Guangdong-Hong Kong-Macao Lightning Location System completely replaced the original IMPACT probes with LS-700X series probes and added two new substations. The Guangdong-Hong Kong-Macao Lightning Location System uses a time difference-direction integrated positioning method, and the detection parameters include GPS time, latitude and longitude, intensity, cloud lightning / ground lightning identification, and polarity of lightning discharges.

[0063] 2) Lightning density optimization:

[0064] Assuming that the lightning location system has stable detection performance over a sufficiently long statistical period, and the true distribution of lightning density in a certain area is f(x,y), and the operator that causes the true lightning density distribution to be "blurred" or "distorted" due to the location error of the lightning location system is H, and the lightning density distribution observed by the lightning location system is g(x,y), and n(x,y) is noise, then we have:

[0065] g(x,y)=H·f(x,y)+n(x,y)……………………(1);

[0066] Consider the following simplified case: assuming that the random error distribution of each lightning location result follows the same distribution function p(x,y) throughout the entire tall building area; then equation (1) becomes:

[0067]

[0068] In the formula It is a convolution operator;

[0069] According to the evaluation results of Chen et al. (2020), after systematic bias correction, the arithmetic mean (median) of the positioning errors of the first return stroke, subsequent return stroke, and upward lightning return stroke of the Guangzhou Tower are approximately 563 (192) m, 185 (89) m, and 225 (106) m, respectively. For simplification, in this embodiment, it is assumed that... Where σ = 100m, that is, it is assumed that the positioning error of all lightning positioning records in the area of ​​tall buildings follows a two-dimensional normal distribution with a standard deviation of 100m;

[0070] The essence of the lightning density optimization method is to obtain the optimal estimate of the true lightning density f(x,y) based on the lightning density g(x,y) obtained from lightning location data; as can be seen from equation (2), this is very similar to the deconvolution problem of blurred image restoration, and existing mature image restoration algorithms can be referenced; since the degradation function p(x,y) has been set, the Richardson-Lucy algorithm can be directly used to iteratively calculate f(x,y);

[0071] 3) Detailed analysis of lightning density in high-rise building areas of Guangzhou:

[0072] The area with the highest density of tall buildings in Guangzhou is mainly located along the north-south central axis of Guangzhou, from the Canton Tower to CITIC Plaza; the distribution of major tall buildings in this area is as follows: Figure 2 ( Figure 2 The map shows the distribution of bounce frequency of tall buildings in Guangzhou from 2014 to 2018. Letters A to E represent five tall buildings with a height of over 350m: Guangzhou Tower (600m, completed in 2009), Guangzhou Chow Tai Fook Financial Centre (530m, completed in 2014), Guangzhou International Finance Centre (440m, completed in 2009), CITIC Plaza (390m, completed in 1996), and Guang Sheng International Building (360m, completed in 2011). Solid triangles, hollow triangles, and crosses represent tall buildings with heights in different ranges, such as 300-349m, 250-299m, and 200-249m, respectively.

[0073] This study analyzed the distribution characteristics of ground flashes in the Guangzhou high-rise building area using historical lightning location data from the Guangdong-Hong Kong-Macao Greater Bay Area over the past five years (2014-2018), with a grid size of 0.001° × 0.001°. Firstly, positive polarity cloud-to-ground flash records were removed from the data. Secondly, positive polarity ground flash location records with an intensity of 0-10 kA were also removed, as they were likely obtained due to the lightning location system misidentifying cloud-to-ground flashes as positive ground flashes. Thirdly, negative polarity ground flashes with a grounding point height ≥ 200 m were easily misidentified as cloud-to-ground flashes, so negative polarity cloud-to-ground flash records were retained in the location data. Finally, a systematic bias correction of 0.001° eastward was applied to all location records within the area.

[0074] Figure 2Direct statistical results of the lightning return stroke frequency distribution in the Guangzhou high-rise building area from 2014 to 2018 are given. The maximum grid value of the return stroke density center near Guangzhou Tower (A) is 264. According to the statistical results of Lü Weitao et al. (2020), the number of lightning strikes observed at Guangzhou Tower by TOLOG (Guangzhou High-rise Building Lightning Observation Station) alone reached 172 during 2014-2018, and the actual number should be more (the number of upward lightning strikes without return strokes is yet to be counted). According to the statistics of Chen Luwen et al. (2020), the number of ground lightning return strokes within a 200m radius of the lightning return stroke density center of Guangzhou Tower from 2014 to 2018 was 655, while the number of return strokes within a radius of 200-500m was 143. It can be inferred that the actual number of ground lightning return strokes occurring at Guangzhou Tower during 2014-2018 should be more than 600. The maximum value obtained by directly using the grid method is only 264, which is much smaller than the actual number.

[0075] Figure 3 The optimized analysis of lightning return stroke frequency distribution in the Guangzhou high-rise building area shows that the maximum grid point value at the center of return stroke density near the Guangzhou Tower is 558. It can be seen that the optimized maximum value is more consistent with the actual situation. Looking at the entire area, after optimization... Figure 2 The high lightning return density centers of several tall buildings in the study were not only preserved, but also further enhanced and highlighted; for example, the lightning return density centers of the two closely spaced (less than 300 meters) tall buildings, East Tower (B) and West Tower (C), were more clearly separated; the lightning return density centers of Guang Sheng International Building (E) and the Yuexiu Financial Building and other buildings to its east were also more clearly separated.

[0076] Figure 4 A comparison of the differences before and after optimization is presented. It can be seen that after optimization, the lightning return stroke density center of Guangzhou Tower is significantly enhanced, while the lightning distribution in the outer area of ​​Guangzhou Tower is significantly reduced. It must be pointed out that this optimization method follows the principle of overall conservation of lightning count, meaning that the total number of return strokes within the entire high-rise building area remains unchanged before and after optimization. In other words, the return strokes originally distributed around the periphery of Guangzhou Tower are now more concentrated at the center of the lightning density around Guangzhou Tower after optimization. Wu Shanshan et al., using TOLOG optical observation data from 2009 to 2014, found that the attraction of Guangzhou Tower to down-to-ground lightning in the surrounding area resulted in no ground lightning being observed within a radius of about 1 km. Therefore, after optimization, the change in lightning distribution around high-rise buildings showing a "central enhancement and peripheral weakening" characteristic is more consistent with the actual situation.

[0077] 4) Conclusion and Discussion:

[0078] This embodiment performs an optimization analysis on the return stroke density of ground flashes in high-rise building areas of Guangzhou with a grid spacing of 100 meters, and compares the analysis results before and after optimization. The results show that:

[0079] 1) Due to the influence of lightning location error, directly applying the small grid method can lead to a false increase in the lightning density value in the area around tall buildings, while the lightning density value at the center of tall buildings is much smaller than the true value.

[0080] 2) After applying the optimization method based on the principle of fuzzy image restoration, the lightning distribution map of tall buildings obtained by the small grid method shows the change characteristics of "enhanced in the center and weakened around the edges", which is more in line with the actual situation.

[0081] The number of lightning strikes on tall buildings, comparisons of lightning strike counts for buildings of different heights, and refined characteristics of ground flash distribution in areas of tall buildings (or groups of tall buildings) are issues of great concern to lightning researchers and have significant application value in lightning protection engineering practice. Clearly, compared to methods such as installing lightning counters, using lightning location data offers unparalleled convenience. However, overcoming the location error of current operational lightning location systems and obtaining more refined analysis results is a challenge we need to address. The method proposed in this embodiment solves the problem that, due to lightning location errors, directly applying the small-grid method can lead to a false increase in lightning density values ​​around tall buildings, while the lightning density value at the center of the tall building is much smaller than the true value. This method achieves the optimal estimation of the true lightning density, which is more consistent with reality.

[0082] Example 2:

[0083] This embodiment provides a method for analyzing lightning density in areas with tall buildings, such as... Figure 1 As shown, it includes:

[0084] Obtain historical data on lightning location in areas with tall buildings;

[0085] By analyzing the distribution characteristics of ground lightning based on historical lightning location data, an expression for the observed lightning density distribution was obtained.

[0086] The expression for the observed lightning density distribution is simplified to obtain the convolution expression for the observed lightning density;

[0087] The Richardson-Lucy algorithm is used to iteratively calculate the convolution expression of the observed lightning density to obtain the optimal estimate of the true lightning density.

[0088] In this embodiment, the analysis of ground flash distribution characteristics based on historical lightning location data includes:

[0089] Set the grid analysis size for historical lightning location data;

[0090] Remove positive polarity cloud-to-cloud lightning records from historical lightning location data;

[0091] Retain records of negative polarity cloud-based lightning in historical lightning location data;

[0092] In this embodiment, the condition for the expression to represent the observed lightning density distribution is:

[0093] The statistical time frame of historical lightning location data meets the length required for analyzing the distribution characteristics of ground lightning:

[0094] The lightning location system has stable detection performance;

[0095] In this embodiment, the expression for the observed lightning density distribution is:

[0096] g(x,y)=H·f(x,y)+n(x,y);

[0097] Where g(x,y) is the lightning density distribution observed by the lightning location system, f(x,y) is the actual lightning density distribution, H is the operator that causes "blurring" or "distortion", and n(x,y) is noise;

[0098] The fuzzy or distorted operator is generated due to the distortion of the actual lightning density distribution caused by the positioning error of the lightning positioning system;

[0099] In this embodiment, the condition for simplifying the expression for the observed lightning density distribution is:

[0100] Throughout the entire area of ​​tall buildings, the random error distribution of each lightning location result follows the same distribution function;

[0101] In this embodiment, the convolution expression for observing lightning density is:

[0102]

[0103] Where p(x,y) is the distribution function. It is a convolution operator;

[0104] In this embodiment, the expression for the distribution function is:

[0105]

[0106] Where σ is the variance, and x and y are two-dimensional normally distributed variables.

[0107] Example 3:

[0108] This embodiment provides a lightning density analysis device for tall building areas, including:

[0109] The acquisition module is used to acquire historical lightning location data for areas with tall buildings.

[0110] The feature analysis module is used to analyze the distribution characteristics of ground lightning based on historical lightning location data, and to obtain the expression for the observed lightning density distribution.

[0111] The simplification module is used to simplify the expression for the observed lightning density distribution to obtain the convolution expression for the observed lightning density;

[0112] The iterative module is used to iteratively calculate the convolution expression of the observed lightning density using the Richardson-Lucy algorithm to obtain the optimal estimate of the true lightning density.

[0113] In this embodiment, the analysis of ground flash distribution characteristics based on historical lightning location data includes:

[0114] Set the grid analysis size for historical lightning location data;

[0115] Remove positive polarity cloud-to-cloud lightning records from historical lightning location data;

[0116] Preserve negative polarity cloud lightning records from historical lightning location data.

[0117] All positioning records undergo eastward systematic bias correction;

[0118] In this embodiment, the condition for the expression to represent the observed lightning density distribution is:

[0119] The statistical time frame of historical lightning location data meets the length required for analyzing the distribution characteristics of ground lightning:

[0120] The lightning location system has stable detection performance;

[0121] In this embodiment, the expression for the observed lightning density distribution is:

[0122] g(x,y)=H·f(x,y)+n(x,y);

[0123] Where g(x,y) is the lightning density distribution observed by the lightning location system, f(x,y) is the actual lightning density distribution, H is the operator that causes "blurring" or "distortion", and n(x,y) is noise;

[0124] The fuzzy or distorted operator is generated due to the distortion of the actual lightning density distribution caused by the positioning error of the lightning positioning system;

[0125] In this embodiment, the condition for simplifying the expression for the observed lightning density distribution is:

[0126] Throughout the entire area of ​​tall buildings, the random error distribution of each lightning location result follows the same distribution function;

[0127] In this embodiment, the convolution expression for observing lightning density is:

[0128]

[0129] Where p(x,y) is the distribution function. It is a convolution operator;

[0130] In this embodiment, the expression for the distribution function is:

[0131]

[0132] Where σ is the mean squared error, and x and y are two-dimensional normally distributed variables.

[0133] Example 4:

[0134] This invention provides an electronic device including a memory and a processor, comprising:

[0135] Memory, which stores executable instructions;

[0136] The processor runs executable instructions in memory to implement a method for analyzing lightning density in tall building areas.

[0137] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0138] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the invention, the processor is used to execute computer-readable instructions stored in the memory.

[0139] Those skilled in the art should understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this invention.

[0140] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0141] Example 5:

[0142] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for analyzing lightning density in areas with tall buildings.

[0143] A computer-readable storage medium according to embodiments of the present invention stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present invention are performed.

[0144] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0145] The lightning density analysis method for tall building areas proposed in the embodiments of the present invention performs a refined analysis of the density distribution of lightning location records in tall building areas. It uses the Richardson-Lucy algorithm to iteratively calculate the convolution expression of the observed lightning density, which solves the problem that the direct application of the small grid method due to the influence of lightning location errors can lead to a false increase in the lightning density value in the area around the tall building, while the lightning density value at the center of the tall building is much smaller than the true value. This achieves the optimal estimation of the true lightning density, which is more in line with the actual situation.

[0146] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for analyzing lightning density in areas with tall buildings, characterized in that, include: Obtain historical data on lightning location in areas with tall buildings; The distribution characteristics of ground flashes were analyzed using the historical lightning location data to obtain an expression for the observed lightning density distribution. The expression for the observed lightning density distribution is simplified to obtain the convolution expression for the observed lightning density; The Richardson-Lucy algorithm is used to iteratively calculate the convolution expression of the observed lightning density to obtain the optimal estimate of the true lightning density.

2. The method according to claim 1, characterized in that, The analysis of the distribution characteristics of ground flashes based on the historical lightning location data includes: Set the grid analysis size for the lightning location history data; Remove positive polarity cloud-to-cloud lightning records from the aforementioned historical lightning location data; Retain the negative polarity cloud flash records from the aforementioned lightning location history data; All positioning records undergo eastward system bias correction.

3. The method according to claim 1, characterized in that, The condition for the expression for the observed lightning density distribution to hold is: The statistical timeframe of the historical lightning location data meets the length required for the analysis of ground lightning distribution characteristics. The lightning location system has stable detection performance.

4. The method according to claim 3, characterized in that, The expression for the observed lightning density distribution is: ； in, The lightning density distribution observed by the lightning location system. This represents the actual lightning density distribution. For operators that cause "fuzziness" or "distortion", For noise; The fuzzy or distorted operator is generated by the distortion of the actual lightning density distribution due to the positioning error of the lightning positioning system.

5. The method according to claim 4, characterized in that, The condition for simplifying the expression for the observed lightning density distribution is: Throughout the entire area of ​​tall buildings, the random error distribution of each lightning location result follows the same distribution function.

6. The method according to claim 4, characterized in that, The convolution expression for the observed lightning density is: ； in, The distribution function, It is a convolution operator.

7. The method according to claim 6, characterized in that, The expression for the distribution function is: ； in, Let x be the standard deviation, and y be two-dimensional normally distributed variables.

8. A lightning density analysis device for high-rise building areas, characterized in that, include: The acquisition module is used to acquire historical lightning location data for areas with tall buildings. The feature analysis module is used to perform ground lightning distribution feature analysis on the historical lightning location data to obtain the expression for the observed lightning density distribution. A simplification module is used to simplify the observed lightning density distribution expression to obtain a convolution expression for the observed lightning density; The iterative module is used to iteratively calculate the convolution expression of the observed lightning density using the Richardson-Lucy algorithm to obtain the optimal estimate of the true lightning density.

9. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the lightning density analysis method for tall building areas according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the lightning density analysis method for tall building areas as described in any one of claims 1-7.

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