Method, system and equipment for measuring height of D layer of large-area ionized layer based on lightning electromagnetic pulse

Abstract

The invention belongs to the field of ionosphere detection, artificial intelligence and deep learning, and particularly relates to a lightning electromagnetic pulse-based large-area ionosphere D layer height measurement method, system and equipment. The problems that a traditional detection method is high in cost, limited in coverage range and prone to being interfered by natural disasters and strong electromagnetic pulses are solved. The method comprises the following steps: acquiring lightning event information based on an electromagnetic pulse detection station; calculating a ground wave and sky wave arrival time difference based on a pre-trained deep learning model, and calculating to obtain the ionosphere D layer height of a single position; performing gridding processing on the obtained ionosphere D layer height data at different positions according to certain time and spatial resolution, performing interpolation null value filling on the gridded data, and performing smoothing processing; and outputting a calculation result of the D layer height of the large-area ionized layer. According to the invention, wide-area low-cost ionosphere detection is realized by adopting the natural lightning radiation source, and the measurement precision is improved by optimizing the model for different lightning types.

Description

Background Art

[0002] The ionosphere is an important part of the Earth's atmosphere, located between the neutral atmosphere and the magnetosphere, with a height range of approximately 50 km to 1000 km. The D layer of the ionosphere is at its bottom (50 - 90 km), with a relatively low electron density. It can reflect low-frequency and very low-frequency radio waves, and can also partially reflect medium-frequency waves, thus playing an important role in radio wave propagation. Due to the relatively low electron density of the D layer, its detection is relatively difficult.

[0003] Traditional detection methods include sounding rockets, VHF radar technology, fixed-frequency VLF signal emission technology, etc. However, these methods are costly and have limited coverage. In addition, the D layer is vulnerable to interference factors such as natural disasters and strong electromagnetic pulses. Studying the changes in the height of the D layer helps to identify these disturbance factors.

[0004] Based on this, the present invention proposes a method, system and device for measuring the height of the D layer of the large-area ionosphere based on lightning electromagnetic pulses. Summary of the Invention

[0005] To solve the above problems in the prior art, that is, the D layer of the ionosphere is difficult to detect due to its low electron density, traditional detection methods are costly, have limited coverage, and are vulnerable to natural disasters and strong electromagnetic pulse interference, the present invention provides a method, system and device for measuring the height of the D layer of the large-area ionosphere based on lightning electromagnetic pulses.

[0006] In the first aspect of the present invention, a method for measuring the height of the D layer of the large-area ionosphere based on lightning electromagnetic pulses is provided. The method includes the following steps:

[0007] Step S10: Use an electromagnetic pulse detection station to convert the collected analog electromagnetic pulse signal into a digital signal, obtain lightning electromagnetic pulse waveform data and the arrival time of the lightning electromagnetic pulse, and perform positioning calculations to obtain lightning event information; the lightning event information includes at least time, longitude and latitude, height, lightning type, and information of the electromagnetic pulse detection stations participating in the positioning.

[0008] Step S20: Input the lightning electromagnetic pulse waveform data as input data into a pre-trained deep learning model, and output the ground wave peak index value and the sky wave peak index value for calculating the time difference between the arrival times of the ground wave and the sky wave.

[0009] Step S30: Based on the arrival time difference, station information, and lightning event information, select a corresponding calculation method according to the lightning type, and calculate the height of the D layer of the ionosphere at a single location.

[0010] Step S40: Jump to step S10 and loop through steps S10 - S30. Process the ionospheric D-layer height data at different positions obtained with a certain time and spatial resolution into a grid, and after obtaining the two-dimensional ionospheric D-layer height data, jump to step S50;

[0011] Step S50: Interpolate and fill in the null values for the gridded data and perform smoothing processing;

[0012] Step S60: Output the calculation result of the ionospheric D-layer height in a large area.

[0013] Furthermore, perform positioning calculation, and the method is as follows:

[0014] The electromagnetic pulse detection station sends the converted digital signal to the data processing center through the data transmission network. The data processing center is used to perform real-time positioning calculation and output lightning event information.

[0015] Furthermore, for the deep learning model, its pre-training method includes:

[0016] Step S21: Establish the ground wave and sky wave data sets in the lightning electromagnetic pulse waveform data as input data, and mark the peak index values of the ground wave and sky wave as label data;

[0017] Step S22: Input the input data into the pre-constructed deep learning model, train the deep learning model, output the corresponding peak index value, and compare it with the label data to obtain a comparison result;

[0018] Among them, the deep learning model is constructed based on an input layer, a hidden layer, and an output layer. The input layer is used to transmit the ground wave and sky wave data of the lightning electromagnetic pulse to be mapped. The hidden layer is used to extract and fuse the features of the input data. The output layer outputs the ground wave peak index value and sky wave peak index value of the lightning;

[0019] Step S23: Adjust the network parameters of the deep learning model based on the comparison result and jump to step S22 for iterative training until convergence to obtain a trained deep learning model.

[0020] Furthermore, the hidden layer includes a one-dimensional convolutional layer, a first batch normalization layer, a first activation layer, a max pooling layer, multiple residual blocks, a global average pooling layer, a fully connected layer, and a second activation layer stacked in sequence.

[0021] Furthermore, each of the residual blocks includes a first residual block and a second residual block;

[0022] Both the first residual block and the second residual block include a first branch and a second branch;

[0023] The first branch includes a first convolutional layer, a second batch normalization layer, a third activation layer, a second convolutional layer, a third batch normalization layer, and a fourth activation layer stacked in sequence;

[0024] The input of the second branch is the input of the first convolutional layer. The output of the second branch is added element-wise to the output of the third batch normalization layer in the first branch, and the result after addition is input to the fourth activation layer;

[0025] The output of the fourth activation layer in the first residual block is connected to the first convolutional layer in the second residual block; the output of the fourth activation layer in the second residual block is connected to the first convolutional layer in the first residual block of the next residual block;

[0026] Among them, the parameters of the corresponding convolutional layers in the first residual block and the second residual block are different, and no third convolutional layer is provided in the second branch of the first residual block.

[0027] Furthermore, the data dimension of the output layer is 1×3, corresponding to the peak index values of the ground wave, the first-hop sky wave, and the second-hop sky wave respectively.

[0028] Furthermore, when the lightning type is cloud-to-ground lightning, the height of the D layer of the ionosphere at a single location is calculated as follows:

[0029] A coordinate system with the center of the earth as the origin is established. Combining the lightning and receiving station coordinates, the great circle distance, and the ionosphere height, the precise height of the D layer of the ionosphere and the coordinates of the reflection point are obtained through the initial solution and non-linear numerical solution.

[0030] Furthermore, when the lightning type is pocket cloud lightning, the height of the D layer of the ionosphere at a single location is calculated as follows:

[0031] Based on the time difference of arrival of the ground wave, the first-hop sky wave, and the second-hop sky wave and the lightning height, the initial ionosphere height is calculated, and the precise solution of the height of the D layer of the ionosphere is obtained through the correction of the earth's curvature and the altitude of the receiving station.

[0032] In the second aspect of the present invention, a large-area ionosphere D layer height measurement system based on lightning electromagnetic pulses is proposed. Based on a large-area ionosphere D layer height measurement method based on lightning electromagnetic pulses, the system includes:

[0033] A signal preprocessing module configured to convert the collected analog electromagnetic pulse signal into a digital signal using an electromagnetic pulse detection station and perform positioning calculations to obtain lightning event information; the lightning event information includes at least time, longitude and latitude, height, lightning type, and information on the electromagnetic pulse detection stations participating in the positioning;

[0034] A time difference calculation module configured to input electromagnetic pulse data as input data into a pre-trained deep learning model, and output a ground wave peak index value and a sky wave peak index value for calculating the arrival time difference between the ground wave and the sky wave;

[0035] An altitude calculation module configured to calculate the height of the D layer of the ionosphere at a single location based on the arrival time difference, station information, and lightning event information, and select a corresponding calculation method in combination with the lightning type;

[0036] A gridded data generation module configured to jump to the signal preprocessing module, and loop through the signal preprocessing module - altitude calculation module to perform gridding processing on the obtained D layer height data of the ionosphere at different locations according to a certain time and space resolution. After obtaining the two-dimensional D layer height data of the ionosphere, it jumps to the two-dimensional data processing module;

[0037] A two-dimensional data processing module configured to interpolate and fill in null values for the gridded data and perform smoothing processing;

[0038] An output module configured to output the calculation result of the D layer height of the large-area ionosphere.

[0039] In a third aspect of the present invention, an electronic device is proposed, including:

[0040] At least one processor; and

[0041] A memory communicatively connected to at least one of the processors; wherein,

[0042] The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement a method for measuring the height of the D layer of the large-area ionosphere based on lightning electromagnetic pulses.

[0043] Advantages of the present invention:

[0044] (1) Wide-area low-cost detection: By using lightning as a natural radiation source to replace traditional artificial signal transmission or satellite detection methods, the height measurement of the D layer of the ionosphere in a large area (covering the lightning activity area) is realized, significantly reducing the equipment deployment and maintenance costs.

[0045] (2) Real-time performance and anti-interference ability: The signal processing model based on deep learning directly extracts features from the time-domain waveform of lightning electromagnetic pulses, automatically identifies the ground wave and sky wave peak indices, and combines real-time data transmission and positioning calculation to quickly obtain the propagation delay time difference, avoiding the problem of being sensitive to noise in traditional artificial threshold determination and adapting to a strong electromagnetic interference environment.

[0046] (3) Optimization of measurement accuracy: In view of the differences in the electromagnetic wave propagation characteristics between cloud-to-ground lightning (ground reflection path) and pocket cloud lightning (vertical path within the cloud), ionospheric height calculation models are designed respectively (such as spherical reflection geometry correction and vertical path simplified calculation) to reduce the errors caused by different lightning types and improve the inversion accuracy. Description of the Drawings

[0047] Other features, objects, and advantages of the present application will become more apparent by reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0048] Figure 1 is a schematic flowchart of a method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses according to the present invention;

[0049] Figure 2 is a schematic diagram of the data transmission link and system between the electromagnetic pulse detection station and the data processing center for a method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses according to the present invention;

[0050] Figure 3 is a schematic diagram of the structure of a deep learning model in a method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses according to the present invention;

[0051] Figure 4 is a schematic diagram of the structure of a residual block in a method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses according to the present invention;

[0052] Figure 5 is a calculation model established for calculating the height of the D layer of the ionosphere by using cloud-to-ground lightning electromagnetic pulse signals in a method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses according to the present invention;

[0053] Figure 6 is a schematic diagram of the calculation result of the height of the D layer of the ionosphere in a method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses according to the present invention. Detailed Embodiments

[0054] The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention and are not intended to limit the invention. Additionally, it should be noted that for the sake of description, only parts related to the relevant invention are shown in the drawings.

[0055] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and embodiments.

[0056] The present invention provides a method for measuring the height of the ionospheric D layer in a large area based on lightning electromagnetic pulses, and the method comprises the following steps:

[0057] Step S10: Using an electromagnetic pulse detection station to convert the collected analog electromagnetic pulse signal into a digital signal, and performing positioning calculation to obtain lightning event information; the lightning event information includes at least time, longitude and latitude, height, lightning type, and information of the magnetic pulse detection stations participating in the positioning;

[0058] Step S20: Inputting the electromagnetic pulse data as input data into a pre-trained deep learning model, and outputting the ground wave peak index value and the sky wave peak index value for calculating the arrival time difference between the ground wave and the sky wave;

[0059] Step S30: Based on the arrival time difference, station information, and lightning event information, selecting a corresponding calculation method according to the lightning type, and calculating the height of the ionospheric D layer at a single position;

[0060] Step S40: Jumping to step S10, and cyclically executing steps S10 - S30 for the ionospheric D layer height data at different positions according to the preset time and space resolutions until all the height data are gridified into two-dimensional data, and then jumping to step S50;

[0061] Step S50: Interpolating and filling the null values for the gridified data, and performing smoothing processing;

[0062] Step S60: Outputting the calculation result of the height of the ionospheric D layer in a large area.

[0063] For a clearer description of the method for measuring the height of the ionospheric D layer in a large area based on lightning electromagnetic pulses in the present invention, the following combines Figure 1 The following details each step in the embodiment of the present invention, and each step is described in detail as follows:

[0064] Step S10: Using an electromagnetic pulse detection station to convert the collected analog electromagnetic pulse signal into a digital signal, and performing positioning calculation to obtain lightning event information; the lightning event information includes at least time, longitude and latitude, height, lightning type, and information of the electromagnetic pulse detection stations participating in the positioning;

[0065] As Figure 2 shown, it is a schematic diagram of the data transmission link between the electromagnetic pulse detection station and the data processing center of the present invention. The electromagnetic pulse detection station sends the converted digital signal to the data processing center through the data transmission network, and the data processing center is used for real-time positioning calculation and outputting lightning event information.

[0066] Among them, the electromagnetic pulse detection station information at least includes the station's longitude and latitude, altitude, lightning electromagnetic pulse waveform data, and the arrival time of the lightning electromagnetic pulse.

[0067] The electromagnetic pulse detection station in this embodiment includes a signal acquisition module, a signal processing module, a data transmission module, and a power supply module. The device receives electromagnetic field signals in the frequency band of 3 kHz to 400 kHz. After the signals pass through the antenna, filtering and amplification, and analog / digital conversion circuits, digital signals are generated. The antenna is composed of a magnetic loop antenna, a flat capacitor antenna, or a whip antenna, and simultaneously senses magnetic field and electric field signals.

[0068] Step S20: Input the electromagnetic pulse data as input data into the pre-trained deep learning model, and output the ground wave peak index value and the sky wave peak index value, which are used to calculate the time difference between the arrival times of the ground wave and the sky wave.

[0069] In this embodiment, the pre-training method of the deep learning model includes:

[0070] Step S21: Establish a ground wave and sky wave data set in the lightning electromagnetic pulse waveform data as input data, and mark the peak index values of the ground wave and the sky wave as label data.

[0071] Among them, the marking method uses manual marking.

[0072] Step S22: Input the input data into the pre-constructed deep learning model, train the deep learning model, output the corresponding peak index value, and compare it with the label data to obtain a comparison result.

[0073] Among them, the deep learning model is a model that mutually maps the lightning electromagnetic pulse, the lightning ground wave, and the time difference between the arrival times of the sky wave. It is constructed based on an input layer, a hidden layer, and an output layer. The input layer is used to transmit the ground wave and sky wave data of the lightning electromagnetic pulse to be mapped. The hidden layer is used to extract and fuse the features of the input data. The output layer outputs the lightning ground wave peak index value and the sky wave peak index value.

[0074] More specifically, the hidden layer includes a one-dimensional convolutional layer, a first batch normalization layer, a first activation layer, a max pooling layer, multiple residual blocks, a global average pooling layer, a fully connected layer, and a second activation layer stacked in sequence.

[0075] Among them, in this embodiment, the number of channels of the one-dimensional convolutional layer is set to 16, the convolutional kernel size is 7, and the stride is 2; the pooling size of the max pooling layer is 3, the stride is 2, and the number of nodes of the fully connected layer is 3.

[0076] Each of the residual blocks includes a first residual block and a second residual block.

[0077] Both the first residual block and the second residual block include a first branch and a second branch;

[0078] The first branch includes a first convolutional layer, a second batch normalization layer, a third activation layer, a second convolutional layer, a third batch normalization layer, and a fourth activation layer stacked in sequence;

[0079] The input of the second branch is the input of the first convolutional layer. The output of the second branch is added element-wise to the output of the third batch normalization layer in the first branch, and the sum is input to the fourth activation layer;

[0080] The output of the fourth activation layer in the first residual block is connected to the first convolutional layer in the second residual block; the output of the fourth activation layer in the second residual block is connected to the first convolutional layer in the first residual block of the next residual block;

[0081] Among them, the parameters of the corresponding convolutional layers in the first residual block and the second residual block are different, and the third convolutional layer is not provided in the second branch of the first residual block.

[0082] Among them, the number of channels of the first convolutional layer, the second convolutional layer, and the third convolutional layer is the same as the number of channels of the residual block.

[0083] In this embodiment, the parameters of the corresponding convolutional layers in different residual blocks are different. Specifically:

[0084] The convolutional kernel sizes of the first convolutional layer and the second convolutional layer in the first residual block are 3, the stride is 2, and the convolutional kernel size of the third convolutional layer is 1 and the stride is 2; the convolutional kernel sizes of the remaining residual blocks except the first residual block are 3 and the stride is 1.

[0085] In this embodiment, the second activation layer uses the Sigmoid activation function, and the first activation layer and the third activation layer use the ReLU activation function.

[0086] The data dimension of the output layer is 1×3, corresponding to the peak index values of the ground wave and the first-hop sky wave and the second-hop sky wave respectively.

[0087] As Figure 3 shown, a model structure and parameters for mutually mapping lightning electromagnetic pulses and the arrival time difference between lightning ground waves and sky waves are established based on the deep learning method of the present invention. The data dimension of the model input layer is 1×1000. The number of filters in the convolutional layer is set to 16, the kernel size is set to 17, and the stride is set to 2.

[0088] Step S23, adjust the network parameters of the deep learning model based on the comparison takeover, and jump to step S22 for iterative training until convergence to obtain a trained deep learning model.

[0089] Step S30: Based on the time difference of arrival, station information, and lightning event information, select a corresponding calculation method according to the lightning type, and calculate the height of the D layer of the ionosphere at a single location.

[0090] Among them, the lightning types include cloud-to-ground lightning and miniature cloud lightning.

[0091] When the lightning type is cloud-to-ground lightning, the calculation method for the height of the D layer of the ionosphere at a single location is as follows:

[0092] Establish a coordinate system with the center of the earth as the origin. Combining the coordinates of the lightning and the receiving station, the great circle distance, and the ionosphere height, obtain the precise height of the D layer of the ionosphere and the coordinates of the reflection point through an initial solution and non-linear numerical solution. The specific steps are as follows:

[0093] Step A1: Establish a calculation model. With the center of the earth O as the origin, the coordinates of lightning A are (x A , y A ), the coordinates of receiving station B are (x B , y B ), the great circle distance between lightning A and receiving station B is d, the ionosphere height is H, and the coordinates of the reflection point S of the lightning electromagnetic pulse signal on the D layer of the ionosphere are (x S , y S ). The following expression as shown in Equation (1) can be obtained:

[0094] Equation (1):

[0095]

[0096] Among them, R is the average radius of the earth, h A , h B are the altitudes of the lightning and the receiving station respectively.

[0097] Step A2: Calculate the initial solution. Calculate the initial value H0 of the height of the D layer of the ionosphere and the slope k0 of the signal radiated to the ionosphere according to Equation (2):

[0098] Equation (2):

[0099]

[0100] Among them, t is the time difference between the peak of the ground wave and the peak of the first-hop sky wave, t sw , t gw1 are the peak data index values of the ground wave and the first-hop sky wave respectively, f s is the signal sampling rate, and c is the speed of light in vacuum.

[0101] Step A3: Calculate the precise solution. The calculation expression is as shown in Equation (3):

[0102] Equation (3):

[0103]

[0104] By using a non - linear numerical solution method for solving, the height H of the D - layer of the ionosphere, the slope k of the signal radiated to the ionosphere, and the coordinates (x S , y S ) of the reflection point S can be obtained.

[0105] When the lightning type is pocket cloud flash, the height of the D - layer of the ionosphere at a single position is calculated as follows:

[0106] Based on the time difference of arrival of the ground wave, the first - hop sky wave and the second - hop sky wave and the lightning height, calculate the initial ionosphere height, and obtain the accurate solution of the height of the D - layer of the ionosphere through the correction of the earth's curvature and the altitude of the receiving station. The specific steps are as follows:

[0107] Step B1, calculate the initial solution: Calculate the initial value H0 of the height of the D - layer of the ionosphere according to Equation (4):

[0108] Equation (4):

[0109]

[0110] Where h A is the lightning height, t sw , t gw1 , t gw2 are the peak data index values of the ground wave, the first - hop sky wave, and the second - hop sky wave respectively, f s is the signal sampling rate, c is the speed of light in vacuum, and d is the great - circle distance between lightning A and receiving station B.

[0111] Step B2: Calculate the accurate solution: The calculation expression of the accurate solution of the height H of the D - layer of the ionosphere is shown in Equation (5):

[0112] Equation (5):

[0113]

[0114] Where h B is the altitude of the receiving station, and R is the average radius of the earth.

[0115] As Figure 5 shown, this is a calculation model established by the present invention for calculating the height of the D - layer of the ionosphere using the electromagnetic pulse signal of cloud - to - ground lightning. With the center O of the earth as the origin, the coordinates of lightning A are (x A , y A ), and the coordinates of receiving station B are (x B , y B), the great circle distance between lightning A and receiving station B is d, the ionosphere height is H, and the coordinates of the reflection point S of the lightning electromagnetic pulse signal in the ionosphere layer D are (x S ,y S ).

[0116] Step S40, jump to step S10, loop through step S10-step S30 for the ionospheric D layer height data at different positions according to preset time and spatial resolution, until all height data are gridded into two-dimensional data, and then jump to step S50;

[0117] In one embodiment of the present invention, the gridding processing of the ionospheric D layer height data is performed by filling data according to a 2.5°×2.5° grid, and the selected data time period is set to 1 hour. When there are multiple data in the grid, the average value of all the data in the grid is taken.

[0118] Step S50, interpolating the gridded data to fill in the blanks and performing smoothing;

[0119] In one embodiment of the present invention, the numerical interpolation is to interpolate empty data grid points in the grid using a linear interpolation method, and the numerical smoothing is to perform mean processing on the data in a window of size 3 using a moving average method.

[0120] Step S60, outputting the calculation result of the large-area ionosphere D layer height.

[0121] like Figure 6 As shown in the figure, the distribution map of the ionosphere D layer height over China in a certain period of time obtained by a large-area ionosphere D layer height measurement method based on lightning electromagnetic pulses of the present invention. The height range is 50-100km, and the height is represented by different colors. The hatched line in the figure represents the terminator.

[0122] Although the various steps in the above embodiment are described in the above-mentioned order, those skilled in the art can understand that in order to achieve the effect of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple changes are within the scope of protection of the present invention.

[0123] The second embodiment of the present invention proposes a large-area ionospheric D-layer height measurement system based on lightning electromagnetic pulses, which is based on a large-area ionospheric D-layer height measurement method based on lightning electromagnetic pulses of the first embodiment. The system includes:

[0124] A signal preprocessing module, configured to convert the acquired analog electromagnetic pulse signal into a digital signal by using an electromagnetic pulse detection station, and perform positioning calculation to obtain lightning event information; the lightning event information at least includes time, longitude and latitude, altitude, lightning type, and information of the electromagnetic pulse detection stations participating in the positioning;

[0125] A time difference calculation module, configured to input the electromagnetic pulse data as input data into a pre-trained deep learning model, and output the ground wave peak index value and the sky wave peak index value for calculating the time difference between the arrival times of the ground wave and the sky wave;

[0126] An altitude calculation module, configured to calculate the height of the D layer of the ionosphere at a single location by selecting a corresponding calculation method in combination with the lightning type based on the arrival time difference, station information, and lightning event information;

[0127] A grid data generation module, configured to jump to the signal preprocessing module, and loop through the signal preprocessing module - altitude calculation module to perform grid processing on the height data of the D layer of the ionosphere at different positions obtained according to a certain time and space resolution. After obtaining the two-dimensional height data of the D layer of the ionosphere, it jumps to the two-dimensional data processing module;

[0128] A two-dimensional data processing module, configured to perform interpolation to fill in null values and perform smoothing processing on the grid data;

[0129] An output module, configured to output the calculation result of the height of the D layer of the ionosphere in a large area.

[0130] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and related explanations of the above-described system can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0131] It should be noted that the above-described large-area ionosphere D-layer height measurement system based on lightning electromagnetic pulses is only illustrated by the above division of each functional module. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further split into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing each module or step, and are not regarded as an improper limitation of the present invention.

[0132] An electronic device according to the third embodiment of the present invention includes:

[0133] At least one processor; and

[0134] A memory communicatively connected to at least one of the processors; wherein,

[0135] The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses.

[0136] A computer-readable storage medium according to a fourth embodiment of the present invention, the computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses.

[0137] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes and related descriptions of the above-mentioned storage device and processing device can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0138] Those skilled in the art should be able to realize that the modules and method steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. The programs corresponding to the software modules and method steps can be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the art. For the sake of clearly illustrating the interchangeability of electronic hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in the form of electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0139] The terms "first", "second", etc. are used to distinguish similar objects, rather than to describe or represent a specific order or sequence.

[0140] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, so that a process, method, article, or device / equipment including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to these processes, methods, articles, or devices / equipment.

[0141] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses, characterized in that, The method includes the following steps: Step S10: Use an electromagnetic pulse detection station to convert the collected analog electromagnetic pulse signal into a digital signal, obtain lightning electromagnetic pulse waveform data and the arrival time of the lightning electromagnetic pulse, and perform positioning calculations to obtain lightning event information; the lightning event information includes at least time, longitude and latitude, altitude, lightning type, and information of the electromagnetic pulse detection stations participating in the positioning; Step S20: Input the lightning electromagnetic pulse waveform data as input data into a pre-trained deep learning model, and output the ground wave peak index value and the sky wave peak index value for calculating the time difference between the arrival times of the ground wave and the sky wave; Step S30: Based on the arrival time difference, station information, and lightning event information, select a corresponding calculation method according to the lightning type, and calculate the height of the D layer of the ionosphere at a single location; Step S40: Jump to Step S10, and loop through Steps S10 - S30. Grid the height data of the D layer of the ionosphere at different locations obtained with a certain time and space resolution. After obtaining the two-dimensional height data of the D layer of the ionosphere, jump to Step S50; Step S50: Interpolate the gridded data to fill in the null values and perform smoothing processing; Step S60: Output the calculation result of the height of the D layer of the ionosphere in a large area.

2. The method for measuring the height of the D layer of the large-area ionosphere based on lightning electromagnetic pulse according to claim 1, wherein The positioning calculation is performed as follows: The electromagnetic pulse detection station sends the converted digital signal to the data processing center through a data transmission network. The data processing center is used to perform real-time positioning calculations and output lightning event information.

3. A method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses according to claim 1, characterized in that, For the pre-training method of the deep learning model: Step S21: Establish a ground wave and sky wave data set in the lightning electromagnetic pulse waveform data as input data, and mark the peak index values of the ground wave and sky wave as label data; Step S22: Input the input data into a pre-constructed deep learning model, train the deep learning model, output the corresponding peak index value, and compare it with the label data to obtain a comparison result; Among them, the deep learning model is constructed based on an input layer, a hidden layer, and an output layer. The input layer is used to transmit the ground wave and sky wave data of the lightning electromagnetic pulse to be mapped. The hidden layer is used to extract and fuse features of the input data. The output layer outputs the ground wave peak index value and the sky wave peak index value of the lightning; Step S23: Adjust the network parameters of the deep learning model based on the comparison result, and jump to Step S22 for iterative training until convergence to obtain a trained deep learning model.

4. A method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses according to claim 3, characterized in that, The hidden layer includes a one-dimensional convolutional layer, a first batch normalization layer, a first activation layer, a max pooling layer, multiple residual blocks, a global average pooling layer, a fully connected layer, and a second activation layer stacked in sequence.

5. A method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses according to claim 4, characterized in that, Each of the residual blocks includes a first residual block and a second residual block; The first residual block and the second residual block both include a first branch and a second branch; The first branch includes a first convolutional layer, a second batch normalization layer, a third activation layer, a second convolutional layer, a third batch normalization layer, and a fourth activation layer stacked in sequence; The input of the second branch is the input of the first convolutional layer. The output of the second branch is added element-wise to the output of the third batch normalization layer in the first branch, and the sum is input to the fourth activation layer. The output of the fourth activation layer in the first residual block is connected to the first convolutional layer in the second residual block. The output of the fourth activation layer in the second residual block is connected to the first convolutional layer in the first residual block of the next residual block. Among them, the parameters of the corresponding convolutional layers in the first residual block and the second residual block are different, and the third convolutional layer is not provided in the second branch of the first residual block.

6. The method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulse according to claim 4, characterized in that, The data dimension of the output layer is 1×3, corresponding to the peak index values of the ground wave, the first-hop sky wave, and the second-hop sky wave respectively.

7. A method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses according to claim 1, characterized in that When the lightning type is cloud-to-ground lightning, the height of the D layer of the ionosphere at a single location is calculated as follows: A coordinate system with the center of the earth as the origin is established. Combining the lightning and receiving station coordinates, the great circle distance, and the ionosphere height, the precise height of the D layer of the ionosphere and the reflection point coordinates are obtained through the initial solution and non-linear numerical solution.

8. A method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses according to claim 1, characterized in that When the lightning type is pocket cloud lightning, the height of the D layer of the ionosphere at a single location is calculated as follows: Based on the arrival time difference between the ground wave, the first-hop sky wave, and the second-hop sky wave, and the lightning height, the initial ionosphere height is calculated, and the precise solution of the height of the D layer of the ionosphere is obtained through the earth curvature correction and the receiving station altitude.

9. A large-area ionospheric D-layer height measurement system based on lightning electromagnetic pulses, based on the method for measuring the height of the large-area ionospheric D-layer based on lightning electromagnetic pulses according to any one of claims 1-8, characterized in that, The system includes: A signal preprocessing module configured to use an electromagnetic pulse detection station to convert the collected analog electromagnetic pulse signal into a digital signal and perform positioning calculations to obtain lightning event information; the lightning event information includes at least time, longitude and latitude, height, lightning type, and the information of the electromagnetic pulse detection stations participating in the positioning. A time difference calculation module configured to input the electromagnetic pulse data as input data into a pre-trained deep learning model, and output the ground wave peak index value and the sky wave peak index value for calculating the arrival time difference between the ground wave and the sky wave. A height calculation module configured to calculate the height of the D layer of the ionosphere at a single location based on the arrival time difference, the station information, and the lightning event information, and select the corresponding calculation method according to the lightning type. A grid data generation module configured to jump to the signal preprocessing module and loop through the signal preprocessing module - height calculation module to perform grid processing on the height data of the D layer of the ionosphere at different positions obtained according to a certain time and space resolution. After obtaining the two-dimensional height data of the D layer of the ionosphere, it jumps to the two-dimensional data processing module. A two-dimensional data processing module configured to interpolate and fill in the null values of the grid data and perform smoothing processing. An output module configured to output the calculation result of the height of the D layer of the ionosphere in a large area.

10. An electronic device, characterized in that, Includes: At least one processor; And A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement a method for measuring the height of the D layer of the ionosphere in a large area based on lightning electromagnetic pulses according to any one of claims 1-8.

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