CN114707305A - Ground-lightning activity analysis method and system - Google Patents
Ground-lightning activity analysis method and system Download PDFInfo
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Abstract
The invention relates to the technical field of disaster prediction, and particularly discloses a method and a system for analyzing ground lightning activities, wherein the method comprises the steps of selecting a target area, acquiring historical ground lightning data of the target area, and dividing the historical ground lightning data into a model generation library and a model detection library; carrying out grid division on the target area, and counting terrain factors in each grid; establishing a ground frequency flash frequency prediction model and a current intensity prediction model based on the model generation library and the terrain factors; historical terrestrial flash data in the model detection library are extracted, and the terrestrial flash frequency prediction model and the current intensity prediction model are detected based on the historical terrestrial flash data in the model detection library. The technical scheme of the invention obtains the change conditions of ground strobe frequency, current intensity and altitude; the distribution characteristics of the ground flash frequency and the current intensity in each terrain are mastered; establishing a ground flash frequency and current intensity prediction model based on ground surface elements; the prediction capability of the ground flash activity is greatly improved.
Description
Technical Field
The invention relates to the technical field of disaster prediction, in particular to a method and a system for analyzing ground lightning activities.
Background
The ground flashing activity is greatly influenced by factors such as regions, terrains and the like of underlying surfaces, has strong local and regional characteristics, and is influenced by factors such as ground surface roughness and ground surface energy flux under different ground surface environments, and the ground flashing frequency and the current intensity have great difference. At present, many researches mainly aim at the relationship between the frequency of earth flashes and the density and the altitude to carry out corresponding analysis. Ju-peng et al found that the variation of lightning activity with altitude based on global lightning data on satellites showed "peak valley features". Li Yongfu and the like research lightning current parameters in western Chongqing, and the result shows that the negative earth flash density is negatively related to the altitude increase. The relation between the frequency of lightning and the altitude of a Chongqing area is counted by plum blossom et al, and the frequency of the lightning is considered to be reduced along with the rise of the altitude. Liuhai soldiers and the like find that the average current intensity of lightning in Jiangxi province and the altitude of the lightning show positive correlation. The analysis of the relationship between the lightning density and altitude in hong Kong area by Lei bud et al revealed that the lightning density increased with increasing altitude. Zhao Sheng Hao, etc. find that the slope direction is negatively related to the ground flash density.
However, these studies have conducted little research into the prediction of the frequency of the ground flashover, the relationship between the current intensity and a plurality of surface factors, and the like. Especially in the lightning disaster defense work, the lightning disaster risk of the project location needs to be evaluated, and the lightning risk analysis requirement of a user unit within the appointed future N years can not be met based on the existing ground lightning data in the current method.
Disclosure of Invention
The present invention is directed to a method and a system for analyzing a lightning activity, so as to solve the problems of the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
a method of lightning activity analysis, the method comprising:
selecting a target area, acquiring historical terrestrial flash data of the target area, and dividing the historical terrestrial flash data into a model generation library and a model detection library;
carrying out grid division on the target area, and counting terrain factors in each grid; the terrain factors comprise an altitude H, a slope SL, a slope AS, a section curvature SE, a plane curvature SU, a slope variability SOS, a slope variability SOA, a terrain relief RDLS, a surface cutting depth CD, a surface roughness RO and an elevation variation coefficient EV;
establishing a ground flash frequency prediction model and a current intensity prediction model based on the model generation library and the terrain factor;
historical terrestrial flash data in the model detection library are extracted, and the terrestrial flash frequency prediction model and the current intensity prediction model are detected based on the historical terrestrial flash data in the model detection library.
As a further scheme of the invention: the step of establishing a ground flash frequency prediction model based on the model generation library and the terrain factor comprises the following steps:
reading historical terrestrial flash data in the model generation library, extracting terrestrial flash frequencies corresponding to different years, and calculating annual average terrestrial flash frequency based on the terrestrial flash frequencies corresponding to different years; wherein the ground flash frequency is the ground flash frequency of a unit area;
respectively calculating Pearson correlation coefficients between the annual average ground flash frequency and various topographic factors;
comparing the Pearson correlation coefficient with a preset correlation threshold, and marking a corresponding terrain factor when the Pearson correlation coefficient reaches the preset correlation threshold;
and establishing a multiple linear regression model of the annual average earth flash frequency based on the marked terrain factors.
As a further scheme of the invention: the step of establishing a current intensity prediction model based on the model generation library and the terrain factor comprises:
reading historical earth flash data in the model generation library, extracting current intensities corresponding to different years, and calculating an annual average value of the current intensities based on the current intensities corresponding to the different years; wherein the current intensity is a current intensity per unit area;
respectively calculating Pearson correlation coefficients between the annual average current intensity value and various topographic factors;
comparing the Pearson correlation coefficient with a preset correlation threshold, and marking a corresponding terrain factor when the Pearson correlation coefficient reaches the preset correlation threshold;
and establishing a multiple linear regression model of the annual average current intensity based on the marked terrain factors.
As a further scheme of the invention: the step of extracting historical terrestrial lightning data in the model detection library, and detecting the terrestrial lightning frequency prediction model and the current intensity prediction model based on the historical terrestrial lightning data in the model detection library comprises the following steps:
randomly extracting historical lightning data in the model detection library;
actually acquiring a terrain factor, and determining prediction data according to the terrain factor;
comparing the historical data with the predicted data, and calculating the accuracy of the model according to the comparison result.
As a further scheme of the invention: the ground flash frequency prediction model is as follows:
Y＝3.666-0.002H-46.873EV+7.80×10-11SU+8.05×10-5RO+6.73×10-11SE+0.108CD-0.174RDLS
wherein the average annual snapback frequency per unit area Y, the altitude H, the elevation variation coefficient EV, the plane curvature SU, the surface roughness RO, the section curvature SE, the surface cutting depth CD and the topographic relief RDLS.
As a further scheme of the invention: the current intensity prediction model is as follows:
I＝28.252+0.026H+372.709EV+0.195AS-0.453SOS-0.002RO-2.289CD+3.347RDLS
the system comprises a power supply, a power supply control module and a power supply control module, wherein the absolute value of annual average current intensity I, the altitude H, the elevation variation coefficient EV, the slope AS, the slope variability SOS, the surface roughness RO, the surface cutting depth CD and the topographic relief RDLS are arranged in the power supply control module.
The technical scheme of the invention also provides a system for analyzing the lightning activity, which comprises:
the historical data acquisition module is used for selecting a target area, acquiring historical land flash data of the target area, and dividing the historical land flash data into a model generation library and a model detection library;
a terrain factor acquisition module, which is used for carrying out grid division on the target area and counting the terrain factors in each grid; the terrain factors comprise an altitude H, a slope SL, a slope AS, a section curvature SE, a plane curvature SU, a slope variability SOS, a slope variability SOA, a terrain relief RDLS, a surface cutting depth CD, a surface roughness RO and an elevation variation coefficient EV;
the model generation module is used for establishing a ground flash frequency prediction model and a current intensity prediction model based on the model generation library and the terrain factors;
and the model detection module is used for extracting historical terrestrial flash data in the model detection library and detecting the terrestrial flash frequency prediction model and the current intensity prediction model based on the historical terrestrial flash data in the model detection library.
As a further scheme of the invention: the model generation module includes:
the first average value calculating unit is used for reading historical ground flash data in the model generating library, extracting ground flash frequencies corresponding to different years, and calculating annual average ground flash frequency based on the ground flash frequencies corresponding to the different years; wherein the ground flash frequency is the ground flash frequency of a unit area;
the first correlation analysis unit is used for respectively calculating Pearson correlation coefficients between the annual average ground flash frequency and various terrain factors;
the first comparison marking unit is used for comparing the Pearson correlation coefficient with a preset correlation threshold value, and marking a corresponding terrain factor when the Pearson correlation coefficient reaches the preset correlation threshold value;
the first execution unit is used for establishing a multiple linear regression model of the annual average ground flash frequency based on the marked terrain factors.
As a further scheme of the invention: the model generation module includes:
the second mean value calculating unit is used for reading historical terrestrial flash data in the model generation library, extracting current intensities corresponding to different years, and calculating a current intensity annual mean value based on the current intensities corresponding to the different years; wherein the current intensity is the current intensity of a unit area;
the second correlation analysis unit is used for respectively calculating Pearson correlation coefficients between the annual average current intensity value and each topographic factor;
the second comparison marking unit is used for comparing the Pearson correlation coefficient with a preset correlation threshold value and marking a corresponding terrain factor when the Pearson correlation coefficient reaches the preset correlation threshold value;
and the second execution unit is used for establishing a multiple linear regression model of the annual average current intensity based on the marked terrain factor.
As a further scheme of the invention: the model detection module includes:
the data extraction unit is used for randomly extracting historical flash data in the model detection library;
the prediction calculation unit is used for actually acquiring a terrain factor and determining prediction data according to the terrain factor;
and the accuracy calculation unit is used for comparing the historical lightning data with the prediction data and calculating the accuracy of the model according to a comparison result.
Compared with the prior art, the invention has the beneficial effects that: the technical scheme of the invention obtains the change conditions of ground strobe frequency, current intensity and altitude; the distribution characteristics of the ground flash frequency and the current intensity in each terrain are mastered; establishing a ground flash frequency and current intensity prediction model based on ground surface elements; the prediction capability of the ground flash activity is greatly improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
FIG. 1 is a flow chart diagram of a method of analyzing lightning activity.
FIG. 2 is a sub-flow block diagram of a method for analyzing lightning activity.
Fig. 3 is a block diagram of the composition structure of the ground flash activity analysis system.
FIG. 4 is a block diagram of the structure of a model detection module in the ground-based lightning activity analysis system.
Fig. 5 is a graph of annual average return stroke frequency of unit area in the sand growing region in 2009-2018 as a function of altitude.
FIG. 6 is a distribution diagram of the average current intensity of the snapback in the sand area of 2009-2018.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
Fig. 1 is a flow chart of a lightning activity analysis method, and in an embodiment of the present invention, the method includes:
step S100: selecting a target area, acquiring historical terrestrial flash data of the target area, and dividing the historical terrestrial flash data into a model generation library and a model detection library;
step S200: carrying out grid division on the target area, and counting terrain factors in each grid; the terrain factors comprise an altitude H, a slope SL, a slope AS, a section curvature SE, a plane curvature SU, a slope variability SOS, a slope variability SOA, a terrain relief RDLS, a surface cutting depth CD, a surface roughness RO and an elevation variation coefficient EV;
step S300: establishing a ground frequency flash frequency prediction model and a current intensity prediction model based on the model generation library and the terrain factors;
step S400: historical terrestrial flash data in the model detection library are extracted, and the terrestrial flash frequency prediction model and the current intensity prediction model are detected based on the historical terrestrial flash data in the model detection library.
In one example of the technical solution of the present invention:
the first step is as follows: in the process of pre-processing the ground-lightning data, i.e. obtaining historical ground-lightning data of the target area in step S100, some processing needs to be performed on the data, as follows:
when the data are statistically flashed, 2 stations are removed from positioning, the lightning current intensity falls to-500 kA-500 kA, and the lightning current data of-5 kA-5 kA are deleted, wherein the absolute value of the gradient interval of the lightning current wave front is less than 500 kA/mu s and more than 0 kA/mu s.
The second step is that: extracting topographic elements;
grids are divided in a certain area by 3km multiplied by 3km, 11 surface factors such as longitude and latitude, altitude H, gradient SL and the like corresponding to lightning data in each grid are counted and stored in an excel table.
Specifically, the elevation height H is extracted from 2009-old sand area ground lightning positioning data and DEM digital elevation data, 10 parameters such AS the slope SL, the slope AS, the section curvature SE, the plane curvature SU, the slope variability SOS, the slope variability SOA, the terrain relief RDLS, the ground surface cutting depth CD, the ground surface roughness RO, the elevation variation coefficient EV are calculated based on the DEM data, and then the relation between the 11 factors and the ground lightning parameters (the ground lightning frequency and the lightning current intensity) can be analyzed based on the terrain factors to establish a ground lightning parameter forecasting model.
The third step: ground strobe frequency distribution characteristics;
as shown in fig. 5, the annual average unit area lightning frequency of each altitude interval in the 2009-2018 sand growing region is counted at an altitude 50m interval. The distribution of the annual average unit area lightning frequency of every 50m of altitude in the invention is described, and the specific conclusion is as follows: before the altitude of 200m, the ground flashback frequency is increased sharply from 0.58 times Km-2. a-1 to 1.78 times Km-2. a-1. The ground bounce frequency exhibits fluctuating changes from 200m to 600m in altitude. The altitude rises from 600m to 1550m, and the ground flashback frequency is reduced from 1.28 times Km-2. a-1 to 0.07 times Km-2. a-1. Statistical analysis shows that the ground bounce frequency shows a decreasing trend as the altitude increases.
Fourthly, current intensity distribution characteristics are obtained; the fourth step and the third step are not in sequence;
as shown in fig. 6, a ground flashback average current intensity variation curve per 50m altitude is plotted, which describes the distribution characteristics of the ground flashback average current intensity at every 50m altitude in the sand growing region in 2009-2018 in the invention. The specific conclusions are as follows: in the altitude interval of 700m and below, the average intensity of the preflash is relatively flat and varies between 46 and 51 kA. At 700-1450 m, the average intensity of the terrestrial flash increases in a fluctuating manner from 50.73kA at 700m to 83.94 kA. Above 1450m, the average amperage of the lightning decreases with increasing altitude. The results show that the average current intensity of the terrestrial lightning increases with the altitude.
In an example of the technical solution of the present invention, the step of establishing the ground flash frequency prediction model based on the model generation library and the terrain factor includes:
reading historical terrestrial flash data in the model generation library, extracting terrestrial flash frequencies corresponding to different years, and calculating annual average terrestrial flash frequency based on the terrestrial flash frequencies corresponding to different years; the number of the ground flashovers is the number of the ground flashovers in a unit area;
respectively calculating Pearson correlation coefficients between the annual average ground flash frequency and various topographic factors;
comparing the Pearson correlation coefficient with a preset correlation threshold, and marking a corresponding terrain factor when the Pearson correlation coefficient reaches the preset correlation threshold;
and establishing a multiple linear regression model of the annual average earth flash frequency based on the marked terrain factors.
According to the statistics of the altitude every 50m, SPSS software is used for calculating the Pearson correlation coefficient of the 2009-old 2018 annual average lightning frequency per unit area of the sand area and 11 terrain factors, and the correlation coefficient is subjected to significance test, as can be seen from Table 1, the correlation coefficients of Y, EV and SE are respectively 0.550 and 0.434, which are in a medium positive correlation, and are in a negative correlation with H, SU, RO, CD and RDLS, and the significance levels are all less than 0.05, so that the significance test is passed.
TABLE 12009-year-old-sand-region annual-average-area lightning frequency and topographic factor correlation analysis in 2018-year-old-sand-region
Note: "+" indicates a positive correlation, "-" indicates a negative correlation, and "×" indicates a non-significant correlation.
Through the analysis, a multiple linear regression model of the annual average flashback frequency per unit area is established on the basis of the terrain factors with significant correlation:
Y＝3.666-0.002H-46.873EV+7.80×10-11SU+8.05×10-5RO+6.73×10-11SE+0.108CD-0.174RDLS；
wherein the average annual snapback frequency per unit area Y, the altitude H, the elevation variation coefficient EV, the plane curvature SU, the surface roughness RO, the section curvature SE, the surface cutting depth CD and the topographic relief RDLS.
The multiple correlation coefficient of the regression model is 0.944, which reaches 0.05 significant level and is a high positive correlation.
Further, the step of establishing a current intensity prediction model based on the model generation library and the terrain factor comprises:
reading historical terrestrial flash data in the model generation library, extracting current intensities corresponding to different years, and calculating a current intensity annual average value based on the current intensities corresponding to the different years; wherein the current intensity is the current intensity of a unit area;
respectively calculating Pearson correlation coefficients between the annual average current intensity value and various topographic factors;
comparing the Pearson correlation coefficient with a preset correlation threshold, and marking a corresponding terrain factor when the Pearson correlation coefficient reaches the preset correlation threshold;
and establishing a multiple linear regression model of the annual average current intensity based on the marked terrain factors.
For the 2009 and 2018 long sand region ground flashback data, the annual mean value of the current intensity is counted according to the altitude of every 50m, and the Pearson correlation coefficient of the annual mean value of the ground flashback current intensity and the interval average altitude is shown in the table 2.
TABLE 22009 and 2018 correlation analysis of annual average current intensity and topographic factor in sand growing region
Note: "+" indicates a positive correlation, "-" indicates a negative correlation, and "×" indicates a non-significant correlation.
Through the analysis, a multiple linear regression model of the annual average current intensity per unit area is established based on the terrain factors with significant correlation:
I＝28.252+0.026H+372.709EV+0.195AS-0.453SOS-0.002RO-2.289CD+3.347RDLS；
the system comprises a power supply, a power supply control module and a power supply control module, wherein the absolute value of annual average current intensity I, the altitude H, the elevation variation coefficient EV, the slope AS, the slope variability SOS, the surface roughness RO, the surface cutting depth CD and the topographic relief RDLS are arranged in the power supply control module.
The multiple correlation coefficient of the regression model is 0.936, reaches 0.05 significant level, and is highly positive correlation.
Fig. 2 is a sub-flow block diagram of the method for analyzing the lightning activity, wherein the step of extracting historical lightning data in the model detection library, and the step of detecting the lightning frequency prediction model and the current intensity prediction model based on the historical lightning data in the model detection library comprises:
randomly extracting historical lightning data in the model detection library;
actually acquiring a terrain factor, and determining prediction data according to the terrain factor;
comparing the historical data with the predicted data, and calculating the accuracy of the model according to the comparison result.
In one example of the technical scheme, the lightning data of 2019 years are substituted into a model for inspection, distribution graphs of annual and uniform flash-back frequency and average current intensity of a unit area are drawn according to 3km multiplied by 3km grid points and are respectively compared with live data. And determining an accuracy according to the comparison result, thereby realizing the detection function of the model.
It is worth mentioning that in practical applications, the accuracy of the two models mentioned in the above examples is around 90%.
Example 2
Fig. 3 is a block diagram of a structure of a system for analyzing a lightning activity, in an embodiment of the present invention, the system 10 includes:
the historical data acquisition module 11 is used for selecting a target area, acquiring historical terrestrial flash data of the target area, and dividing the historical terrestrial flash data into a model generation library and a model detection library;
a terrain factor obtaining module 12, configured to perform mesh division on the target area, and count terrain factors in each mesh; the terrain factors comprise an altitude H, a slope SL, a slope AS, a section curvature SE, a plane curvature SU, a slope variability SOS, a slope variability SOA, a terrain relief RDLS, a surface cutting depth CD, a surface roughness RO and an elevation variation coefficient EV;
the model generation module 13 is used for establishing a ground flash frequency prediction model and a current intensity prediction model based on the model generation library and the terrain factors;
and the model detection module 14 is configured to extract historical terrestrial flash data in the model detection library, and detect the terrestrial flash frequency prediction model and the current intensity prediction model based on the historical terrestrial flash data in the model detection library.
Further, the model generation module 13 includes:
the first mean value calculating unit is used for reading historical terrestrial flash data in the model generating library, extracting terrestrial flash frequencies corresponding to different years, and calculating annual average terrestrial flash frequencies based on the terrestrial flash frequencies corresponding to the different years; wherein the ground flash frequency is the ground flash frequency of a unit area;
a first correlation analysis unit, for respectively calculating Pearson correlation coefficients between the annual average earth flash frequency and each terrain factor;
the first comparison marking unit is used for comparing the Pearson correlation coefficient with a preset correlation threshold value, and marking a corresponding terrain factor when the Pearson correlation coefficient reaches the preset correlation threshold value;
the first execution unit is used for establishing a multiple linear regression model of the annual average ground flash frequency based on the marked terrain factors.
Specifically, the model generation module 13 includes:
the second mean value calculating unit is used for reading historical terrestrial flash data in the model generation library, extracting current intensities corresponding to different years, and calculating a current intensity annual mean value based on the current intensities corresponding to the different years; wherein the current intensity is the current intensity of a unit area;
the second correlation analysis unit is used for respectively calculating Pearson correlation coefficients between the annual average current intensity value and each topographic factor;
the second comparison marking unit is used for comparing the Pearson correlation coefficient with a preset correlation threshold value, and marking a corresponding terrain factor when the Pearson correlation coefficient reaches the preset correlation threshold value;
and the second execution unit is used for establishing a multiple linear regression model of the annual average current intensity based on the marked terrain factor.
Fig. 4 is a block diagram illustrating a structure of a model detection module in the ground-based lightning activity analysis system, wherein the model detection module 14 includes:
the data extraction unit is used for randomly extracting historical flash data in the model detection library;
the prediction calculation unit is used for actually acquiring a terrain factor and determining prediction data according to the terrain factor;
and the accuracy calculation unit is used for comparing the historical flash data with the prediction data and calculating the accuracy of the model according to the comparison result.
The functions that can be realized by the method for analyzing the ground flash activity are all completed by a computer device, the computer device comprises one or more processors and one or more memories, and at least one program code is stored in the one or more memories and is loaded and executed by the one or more processors to realize the functions of the method for analyzing the ground flash activity.
The processor fetches instructions and analyzes the instructions from the memory one by one, then completes corresponding operations according to the instruction requirements, generates a series of control commands, enables all parts of the computer to automatically, continuously and coordinately act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
Illustratively, a computer program can be partitioned into one or more modules, which are stored in memory and executed by a processor to implement the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device.
Those skilled in the art will appreciate that the above description of the service device is merely exemplary and not limiting of the terminal device, and may include more or less components than those described, or combine certain components, or different components, such as may include input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal equipment and connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the terminal device by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory mainly comprises a storage program area and a storage data area, wherein the storage program area can store an operating system, application programs (such as an information acquisition template display function, a product information publishing function and the like) required by at least one function and the like; the storage data area may store data created according to the use of the berth-state display system (e.g., product information acquisition templates corresponding to different product types, product information that needs to be issued by different product providers, etc.), and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The terminal device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the modules/units in the system according to the above embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the functions of the embodiments of the system. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes performed by the present invention or directly or indirectly applied to other related technical fields are also included in the scope of the present invention.
Claims (10)
1. A lightning activity analysis method, characterized in that the method comprises:
selecting a target area, acquiring historical terrestrial flash data of the target area, and dividing the historical terrestrial flash data into a model generation library and a model detection library;
carrying out grid division on the target area, and counting terrain factors in each grid; the terrain factors comprise an altitude H, a slope SL, a slope AS, a section curvature SE, a plane curvature SU, a slope variability SOS, a slope variability SOA, a terrain relief RDLS, a surface cutting depth CD, a surface roughness RO and an elevation variation coefficient EV;
establishing a ground frequency flash frequency prediction model and a current intensity prediction model based on the model generation library and the terrain factors;
historical terrestrial flash data in the model detection library are extracted, and the terrestrial flash frequency prediction model and the current intensity prediction model are detected based on the historical terrestrial flash data in the model detection library.
2. The method for analyzing the activity of the terrestrial lightning according to claim 1, wherein the step of establishing a terrestrial lightning frequency prediction model based on the model generation library and the terrain factor comprises:
reading historical terrestrial flash data in the model generation library, extracting terrestrial flash frequencies corresponding to different years, and calculating annual average terrestrial flash frequency based on the terrestrial flash frequencies corresponding to different years; wherein the ground flash frequency is the ground flash frequency of a unit area;
respectively calculating Pearson correlation coefficients between the annual average ground flash frequency and various topographic factors;
comparing the Pearson correlation coefficient with a preset correlation threshold, and marking a corresponding terrain factor when the Pearson correlation coefficient reaches the preset correlation threshold;
and establishing a multiple linear regression model of the annual average earth flash frequency based on the marked terrain factors.
3. The method for analyzing lightning activity according to claim 1, wherein the step of establishing a current intensity prediction model based on the model generation library and the terrain factor comprises:
reading historical terrestrial flash data in the model generation library, extracting current intensities corresponding to different years, and calculating a current intensity annual average value based on the current intensities corresponding to the different years; wherein the current intensity is the current intensity of a unit area;
respectively calculating Pearson correlation coefficients between the annual average current intensity value and various topographic factors;
comparing the Pearson correlation coefficient with a preset correlation threshold, and marking a corresponding terrain factor when the Pearson correlation coefficient reaches the preset correlation threshold;
and establishing a multiple linear regression model of the annual average current intensity based on the marked terrain factors.
4. The method for analyzing the earth-lightning activity according to claim 2 or 3, wherein the step of extracting historical earth-lightning data in the model detection library, and the step of detecting the earth-lightning frequency prediction model and the current intensity prediction model based on the historical earth-lightning data in the model detection library comprises the steps of:
randomly extracting historical lightning data in the model detection library;
actually acquiring a terrain factor, and determining prediction data according to the terrain factor;
comparing the historical data with the predicted data, and calculating the accuracy of the model according to the comparison result.
5. The method for analyzing the ground-flash activity according to claim 2, wherein the ground-flash frequency prediction model is:
Y＝3.666-0.002H-46.873EV+7.80×10-11SU+8.05×10-5RO+6.73×10-11SE+0.108CD-0.174RDLS
wherein the average annual snapback frequency per unit area Y, the altitude H, the elevation variation coefficient EV, the plane curvature SU, the surface roughness RO, the section curvature SE, the surface cutting depth CD and the topographic relief RDLS.
6. The lightning activity analysis method according to claim 3, wherein the current intensity prediction model is:
I＝28.252+0.026H+372.709EV+0.195AS-0.453SOS-0.002RO-2.289CD+3.347RDLS
the system comprises a power supply, a power supply control module and a power supply control module, wherein the absolute value I of annual average current intensity, the altitude H, an elevation variation coefficient EV, a slope direction AS, a slope rate SOS, surface roughness RO, surface cutting depth CD and topographic relief RDLS are adopted.
7. A lightning activity analysis system, the system comprising:
the historical data acquisition module is used for selecting a target area, acquiring historical land flash data of the target area, and dividing the historical land flash data into a model generation library and a model detection library;
the terrain factor acquisition module is used for carrying out grid division on the target area and counting terrain factors in each grid; the terrain factors comprise an altitude H, a slope SL, a slope AS, a section curvature SE, a plane curvature SU, a slope variability SOS, a slope variability SOA, a terrain relief RDLS, a surface cutting depth CD, a surface roughness RO and an elevation variation coefficient EV;
the model generation module is used for establishing a ground flash frequency prediction model and a current intensity prediction model based on the model generation library and the terrain factors;
and the model detection module is used for extracting historical terrestrial flash data in the model detection library and detecting the terrestrial flash frequency prediction model and the current intensity prediction model based on the historical terrestrial flash data in the model detection library.
8. The system according to claim 7, wherein the model generation module comprises:
the first mean value calculating unit is used for reading historical terrestrial flash data in the model generating library, extracting terrestrial flash frequencies corresponding to different years, and calculating annual average terrestrial flash frequencies based on the terrestrial flash frequencies corresponding to the different years; wherein the ground flash frequency is the ground flash frequency of a unit area;
the first correlation analysis unit is used for respectively calculating Pearson correlation coefficients between the annual average ground flash frequency and various terrain factors;
the first comparison marking unit is used for comparing the Pearson correlation coefficient with a preset correlation threshold value, and marking a corresponding terrain factor when the Pearson correlation coefficient reaches the preset correlation threshold value;
the first execution unit is used for establishing a multiple linear regression model of the annual average ground flash frequency based on the marked terrain factors.
9. The system according to claim 7, wherein the model generation module comprises:
the second mean value calculating unit is used for reading historical terrestrial flash data in the model generation library, extracting current intensities corresponding to different years, and calculating a current intensity annual mean value based on the current intensities corresponding to the different years; wherein the current intensity is a current intensity per unit area;
the second correlation analysis unit is used for respectively calculating Pearson correlation coefficients between the annual average current intensity value and each topographic factor;
the second comparison marking unit is used for comparing the Pearson correlation coefficient with a preset correlation threshold value, and marking a corresponding terrain factor when the Pearson correlation coefficient reaches the preset correlation threshold value;
and the second execution unit is used for establishing a multiple linear regression model of the annual average current intensity based on the marked terrain factor.
10. The system according to claim 8 or 9, wherein the model detection module comprises:
the data extraction unit is used for randomly extracting historical flash data in the model detection library;
the prediction calculation unit is used for actually acquiring a terrain factor and determining prediction data according to the terrain factor;
and the accuracy calculation unit is used for comparing the historical flash data with the prediction data and calculating the accuracy of the model according to the comparison result.
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