A thunder and lightning detection method, system, device and medium for dynamic networking of unmanned aerial vehicles

Abstract

The application is suitable for the technical field of lightning detection, and particularly relates to a lightning detection method, system, device and medium of unmanned vehicle dynamic networking. The lightning occurrence probability is determined by acquiring the convective effective potential, lifting index, cloud top temperature and lightning frequency of each lightning area; when the lightning occurrence probability of the lightning area exceeds the preset lightning occurrence probability threshold, the area is taken as a target lightning area, and the central coordinates and the estimated radius thereof are determined; according to the central coordinates, the estimated radius, the detection radius and the detection angle of each unmanned vehicle, the initial position coordinates of each unmanned vehicle are determined, and the electric field intensity detection value is collected, so as to determine the lightning positioning result; lightning detection is realized by using the multi-layer unmanned vehicle adaptive formation mode, the high-probability lightning area and the surrounding buffer area can be accurately detected, the three-dimensional spatial position and the electric field intensity distribution of lightning can be accurately reflected, and the lightning positioning precision and the detection accuracy are improved.

Description

Technical Field

[0001] This invention relates to the field of lightning detection, and in particular to a method, system, equipment and medium for lightning detection using dynamic networking of unmanned aerial vehicles (UAVs). Background Technology

[0002] Existing lightning detection methods mostly employ ground-based methods based on the theory of lightning electromagnetic radiation transmission, utilizing ground-based lightning locators and other equipment deployed at ground base stations. These ground-based lightning locators often employ very low frequency (VLF) / low frequency (LHF) three-dimensional lightning locators. However, while these ground-based VLF / LHF three-dimensional lightning locators are highly efficient at detecting ground flashes, they are less efficient at capturing cloud flash data, resulting in poor overall positioning accuracy. Furthermore, ground-based lightning locators are susceptible to the influence of the underlying surface, making deployment difficult in some areas. In mountainous regions, jungles, and other complex terrains, not only is large-scale deployment of lightning detection sites challenging, but the terrain's blocking and reflection effects on electromagnetic radiation propagation also severely interfere with signal quality, reducing detection efficiency. In addition, due to the significant time-varying characteristics of lightning itself, the discharge intensity, spectral characteristics, and spatial distribution of radiation sources all dynamically change over time, leading to low detection accuracy. Summary of the Invention

[0003] This invention provides a method, system, device, and medium for lightning detection using dynamic networking of unmanned aerial vehicles (UAVs), to solve the problems of poor positioning accuracy and low detection accuracy in existing ground-based lightning detection methods.

[0004] In a first aspect, the present invention provides a lightning detection method for dynamic networking of unmanned aerial vehicles (UAVs), the lightning detection method for dynamic networking of UAVs comprising: Step 100: Obtain the convective effective potential energy, lifting index, cloud top temperature, and lightning frequency of the Q pre-divided lightning regions at time t-1, where Q is a positive integer greater than 1; Step 200: Based on the convective effective potential energy, the lifting index, the cloud top temperature, and the lightning frequency at time t-1, and combining the mapping relationship between convective effective potential energy, the lifting index, the cloud top temperature, the lightning frequency, and the probability of lightning occurrence, determine the probability of lightning occurrence in the Q lightning regions at time t-1. Step 300: At time t-1, select A target lightning regions from the Q lightning regions whose lightning occurrence probability exceeds a preset lightning occurrence probability threshold, and determine the center coordinates and estimated radius of the A target lightning regions at time t-1, where A is a positive integer and A is less than Q; Step 400: For the a-th target lightning region, based on the center coordinates at time t-1, the estimated radius, the height difference between the preset N-layer drone formation, and the detection radius and detection angle of each drone in the preset n-layer drone formation, determine the initial position coordinates of each drone at time t, and send the initial position coordinates of each drone to the corresponding drone, so that each drone at time t flies to the corresponding position according to the received initial position coordinates to collect the electric field strength detection value of the a-th target lightning region, n=1, 2, ..., N, where N is a positive integer greater than 1, a=1, 2, ..., A; Step 500: Obtain the electric field strength detection value of the a-th target lightning region sent by each UAV in the UAV formation of layer N at time t, and determine the lightning location result of the a-th target lightning region based on each electric field strength detection value.

[0005] Secondly, embodiments of the present invention provide a lightning detection system for dynamic networking of unmanned aerial vehicles (UAVs). The lightning detection system for dynamic networking of UAVs includes a ground server and an N-layer UAV formation. The nth layer of the UAV formation contains at least two UAVs. Each UAV communicates with the ground server. The ground server and each UAV cooperate to implement the lightning detection method for dynamic networking of UAVs as described in the first aspect. n = 1, 2, ..., N, where N is a positive integer greater than 1.

[0006] Thirdly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the lightning detection method for dynamic networking of unmanned aerial vehicles as described in the first aspect.

[0007] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the lightning detection method for dynamic networking of unmanned aerial vehicles as described in the first aspect.

[0008] The aforementioned method and system for lightning detection using dynamic networking of unmanned aerial vehicles (UAVs) determines the probability of lightning occurrence in each lightning region by acquiring historical data on convective effective potential energy, lift index, cloud top temperature, and lightning frequency. When the probability of lightning occurrence in a lightning region exceeds a preset threshold, the region is identified as a target lightning region, and its center coordinates and estimated radius are determined. Subsequently, based on the center coordinates, estimated radius, preset height differences between different UAV formations, and preset detection radii and angles of each UAV within each formation, the initial position coordinates of each UAV are determined. Each UAV receives its initial position coordinates and flies to the corresponding location to collect the electric field strength detection value of the target lightning region. Finally, the collected electric field strength detection value is used to determine the lightning location result of the target lightning region. Compared to existing technologies, this invention determines the target lightning area requiring UAV network detection by predicting the probability of lightning occurrence. It uses a multi-layer UAV formation detection method to collect the electric field intensity of the target lightning area and calculates the initial position of each UAV in each layer of the UAV formation. This ensures that the detection coverage of the multi-layer UAV formation can completely cover the high-probability lightning area and the surrounding buffer zone, accurately reflecting the three-dimensional spatial location and electric field intensity distribution of lightning, thus improving the accuracy of lightning detection. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of an application environment for a lightning detection method using dynamic networking of unmanned aerial vehicles (UAVs) according to an embodiment of the present invention. Figure 2 This is a flowchart of a lightning detection method for dynamic networking of unmanned aerial vehicles in one embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] The lightning detection method for dynamic networking of unmanned aerial vehicles (UAVs) provided in this embodiment of the invention can be applied to, for example... Figure 1 The application environment is shown. Specifically, this UAV dynamic networking lightning detection method is applied in a UAV dynamic networking lightning detection system, which includes, as shown in the example... Figure 1 The diagram shows a ground server and N×M drones. The ground server and each drone communicate via a network to achieve real-time updates of dynamic drone networking for lightning detection. The ground server refers to the program that provides local services to each drone. The ground server can be installed on, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices.

[0013] In one embodiment, such as Figure 2 As shown, this embodiment provides a lightning detection method for dynamic networking of unmanned aerial vehicles (UAVs), which is applied to... Figure 1 Taking the ground server as an example, the lightning detection method for dynamic networking of UAVs includes: Step 100: Obtain the convective effective potential energy, lifting index, cloud top temperature, and lightning frequency of the Q pre-divided lightning regions at time t-1, where Q is a positive integer greater than 1; Among these, the lightning zone refers to the designated geographical area where lightning detection is required; convective effective potential energy (CPE) refers to the energy generated by positive buoyancy when an air parcel is above the free convection height and below the equilibrium height. It is used to characterize the degree of atmospheric instability. The higher the CPE value, the more unstable the atmosphere, the more vigorous the convection, and the higher the probability of lightning occurrence; the lift index refers to the temperature difference between the air parcel temperature and the surrounding environment temperature when the air parcel is lifted from the ground to a height of 500 hPa. A negative lift index indicates atmospheric instability, and the smaller the lift index, the higher the degree of atmospheric instability and the greater the probability of lightning occurrence; cloud top temperature refers to the atmospheric temperature value at the top of a convective cloud. The lower the cloud top temperature, the higher the development of the convective cloud, the richer the ice crystal content within the cloud, and the stronger the lightning activity; lightning frequency refers to the number of lightning occurrences observed in the lightning zone per unit time. The higher the lightning frequency, the more active the lightning activity, and it is one of the important indicators for judging the probability of lightning occurrence.

[0014] For example, methods for dividing lightning zones include: setting up ground-based lightning locators, such as very low frequency / low frequency three-dimensional lightning locators, in multiple ground base stations deployed on the ground, and dividing the detection area into several lightning zones based on the geographical coordinates of each locator and its effective detection radius.

[0015] Methods for delineating lightning zones also include: deploying an atmospheric electric field meter network in key protection areas (such as substations, wind farms, and launch sites), or establishing a graded and encrypted atmospheric electric field monitoring network based on the lightning zone delineation needs of provincial, municipal, and county-level administrative regions. This network monitors the ground electric field intensity and its rate of change in real time. When the electric field intensity monitored by a certain station exceeds a preset threshold or the rate of change of the electric field increases sharply, the location of the thundercloud charge center is deduced by combining the electric field gradient distribution of adjacent stations, and a potential lightning strike hazard zone with a radius (such as 5-10 km) around the station is delineated, thereby achieving the delineation of lightning zones.

[0016] The method for determining convective available potential energy includes: obtaining atmospheric temperature, pressure, and humidity vertical profiles through weather balloons released from upper-air meteorological stations to obtain data on the distribution of temperature, pressure, and dew point temperature at different altitudes; calculating the temperature changes along dry and wet adiabatic processes during the uplift of an air parcel from the ground based on these vertical profiles; and estimating the virtual temperature (i.e., imaginary temperature) based on the actual air temperature and water vapor content. The free convection height and equilibrium height are then determined using the virtual temperature. Finally, between the free convection height and equilibrium height, the virtual temperature is used to replace the actual temperature, and the product of the difference between the virtual temperature of the air parcel and the ambient virtual temperature and the gravitational acceleration is integrated along the altitude to obtain the convective available potential energy, expressed in J / kg.

[0017] The method for determining the lift index includes: obtaining the ambient temperature at an altitude of 500 hPa using a weather balloon, and obtaining the air parcel temperature by dry adiabatic lifting of the ground air parcel to 500 hPa. The lift index value is obtained by subtracting the two values, with the unit being °C.

[0018] Methods for determining cloud top temperature include: deploying ground-based millimeter-wave cloud radar at ground base stations; the ground-based millimeter-wave cloud radar emits electromagnetic wave pulses towards the zenith and receives the backscattered electromagnetic waves by cloud droplet particles to determine the cloud base and cloud top positions; subsequently, a weather balloon carrying a radiosonde is launched into the air to directly measure the temperature data at the cloud top position, in °C.

[0019] Methods for determining lightning frequency include: using very low frequency / low frequency three-dimensional lightning locators deployed in ground base stations to capture electromagnetic radiation signals generated by lightning discharges in real time; using positioning algorithms such as time difference of arrival to calculate the occurrence time, location, and type (cloud lightning / ground lightning) of each lightning event; and then counting the number of lightning strikes per unit time within the defined lightning area to obtain the lightning frequency, which is expressed as strikes / min.

[0020] By acquiring convective effective potential energy (CEP), uplift index (HPI), cloud top temperature (YST), and lightning frequency (FF) values ​​for each predefined lightning zone using ground-based base stations, the probability of lightning occurrence is significantly increased when the CEP value exceeds a preset threshold. Similarly, when the HPI value is less than or equal to a preset threshold, the weather is classified as severe convective weather, indicating favorable conditions for lightning. Likewise, when the YST value is less than or equal to a preset threshold, the weather is classified as severe convective weather, indicating favorable conditions for lightning. Finally, when the FFF value exceeds a preset threshold, the probability of lightning occurrence is significantly increased.

[0021] Step 200: Based on the convective effective potential energy, the lifting index, the cloud top temperature, and the lightning frequency at time t-1, and combining the mapping relationship between convective effective potential energy, the lifting index, the cloud top temperature, the lightning frequency, and the probability of lightning occurrence, determine the probability of lightning occurrence in the Q lightning regions at time t-1. The probability of lightning occurrence refers to the probability value of a lightning event occurring in each pre-defined lightning zone within a preset time window. It is usually a real number between 0 and 1. The higher the value, the greater the possibility of lightning occurring.

[0022] Because atmospheric physical processes are continuous and predictable, lightning activity develops gradually from the accumulation of atmospheric instability to the occurrence of lightning. Therefore, the probability of lightning occurrence at time t can be accurately predicted by using the historical lightning occurrence rate at time t-1. For example, the continuous change in convective effective potential energy over a short period reflects the accumulation trend of atmospheric unstable energy; the evolution of the lifting index towards a negative value reflects the process of atmospheric stratification instability being disrupted; the continuous decrease in cloud top temperature reflects the change in the vertical development height of convective clouds; and the persistence of lightning frequency makes current lightning activity an important basis for predicting the probability of future lightning occurrence.

[0023] For example, the pre-divided first v The probability of lightning occurrence in a lightning-prone area P v It can be expressed by the formula as follows P v = α × CAPE v + β × LI v + γ × T v + δ × X v ,in v This represents the pre-defined v-th lightning zone. v =1, 2, ...,Q , Q It is a positive integer greater than 1; P v This represents the probability of lightning occurring in the v-th lightning region; α This represents the weight of the convective effective potential energy contribution, used to characterize the intensity of the influence of atmospheric instability on the probability of lightning occurrence; CAPE v This represents the convective effective potential energy of the v-th lightning region; β This indicates the weighting of the lifting index, used to characterize the intensity of the influence of atmospheric stratification stability on the probability of lightning occurrence. LI v This represents the uplift index of the v-th lightning region; γ The cloud top temperature contribution weight is used to characterize the intensity of the influence of the development height of convective clouds on the probability of lightning occurrence. T v This represents the cloud top temperature of the v-th lightning region; δ This represents the contribution weight of lightning frequency, used to characterize the strength of the influence of the current lightning activity intensity on the probability of lightning occurrence; X v This represents the lightning frequency in the v-th lightning region.

[0024] Among them, the convective effective potential energy of the vth lightning region CAPE v Raising the index LI v Cloud top temperature T v and lightning frequency X v All parameters are normalized dimensionless parameters to eliminate the influence of dimensional differences on the contribution weight coefficients. The normalization formula is as follows: x’ =2×arctg( x )÷π, where x This represents the raw data, such as the actual collected convective effective potential energy (unit: J / kg), lift index (unit: ℃), cloud top temperature (unit: ℃), and lightning frequency (unit: times / min) values ​​for the v-th lightning region. x’ This represents dimensionless parameters after normalizing the original data, such as the convective effective potential energy, uplift index, cloud top temperature, and lightning frequency of the v-th lightning region, all of which are unitless.

[0025] Step 300: At time t-1, select A target lightning regions from the Q lightning regions whose lightning occurrence probability exceeds a preset lightning occurrence probability threshold, and determine the center coordinates and estimated radius of the A target lightning regions at time t-1, where A is a positive integer and A is less than Q; The lightning occurrence probability threshold is a pre-set critical value used to determine whether to initiate a UAV lightning detection mission. This threshold can be set according to warning level requirements, regional lightning climate characteristics, or user-defined parameters, and its value ranges from 0 to 1. The center coordinates refer to the spatial coordinates of the center location within the target lightning area where lightning activity is most likely to occur or has the highest intensity. They are typically expressed in latitude and longitude coordinates (longitude and latitude), and may include altitude information if necessary. The estimated radius refers to the radius of the spatial range that lightning activity may affect, centered on the center coordinates of the target lightning area. The center coordinates and estimated radius of A target lightning areas are determined based on Q pre-divided lightning areas.

[0026] For example, lightning location data over a period of time is obtained based on a ground-based lightning location network. The lightning points are then spatially gridded and statistically analyzed. The area with the highest lightning frequency is selected, and the density center of the lightning point set is calculated using a weighted average method. When the lightning data is sparse, the centroid of strong radar echoes is used for correction, thereby determining the center coordinates of the target lightning area. Based on the lightning location data, the minimum circumcircle radius or standard deviation ellipse radius of the lightning point set is calculated. The minimum circumcircle radius or standard deviation ellipse radius is multiplied by a safety factor (ranging from 1 to 1.5) to obtain the estimated radius of the target lightning area.

[0027] Step 400: For the a-th target lightning region, based on the center coordinates at time t-1, the estimated radius, the height difference between the preset N-layer drone formation, and the detection radius and detection angle of each drone in the preset n-layer drone formation, determine the initial position coordinates of each drone at time t, and send the initial position coordinates of each drone to the corresponding drone, so that each drone at time t flies to the corresponding position according to the received initial position coordinates to collect the electric field strength detection value of the a-th target lightning region, n=1, 2, ..., N, where N is a positive integer greater than 1, a=1, 2, ..., A; Among them, the height difference refers to the vertical distance between two adjacent layers of UAV formations in the vertical direction. This height difference is used to determine the flight altitude of each layer of UAV formations to ensure that the multi-layer formations form a reasonable distribution in vertical space. The typical value is 100-500m. The detection radius refers to the effective detection distance of the electric field instrument carried by each UAV in the preset nth layer of formation in the horizontal direction. This radius determines the coverage area that a single UAV can effectively collect electric field strength data. The detection angle refers to the effective detection angle range of the electric field instrument carried by each UAV in the preset nth layer of formation in the horizontal direction. The initial position coordinates refer to the spatial coordinates of the initial deployment position assigned to each UAV, which is used to guide the UAV to fly from the take-off point to the predetermined detection position. The electric field strength detection value refers to the real-time electric field strength value of the target lightning area collected by the electric field instrument carried by each UAV at the detection point (i.e., at the initial position coordinates of each UAV). The electric field strength detection value reflects the magnitude and direction of the atmospheric electric field at the detection point and is the core parameter for lightning location and intensity analysis.

[0028] In A target lightning regions, a drone squadron is assigned to each target lightning region for lightning detection. In the pre-defined n-th layer drone squadron, the larger the detection radius of the M drones, the wider the effective detection coverage of a single drone, and the fewer drones required for the squadron; conversely, the smaller the detection radius, the more drones are needed to effectively cover the same area. Similarly, the larger the detection angle of the M drones in the pre-defined n-th layer drone squadron, the wider the effective detection coverage angle of a single drone, but this may reduce detection accuracy; the smaller the detection angle, the narrower the effective detection coverage angle of a single drone, but the stronger the detection directionality.

[0029] Step 500: Obtain the electric field strength detection value of the a-th target lightning region sent by each UAV in the UAV formation of layer N at time t, and determine the lightning location result of the a-th target lightning region based on each electric field strength detection value.

[0030] Among them, the lightning location result refers to the comprehensive information such as the location and intensity of lightning in the target lightning area obtained by data fusion and inversion calculation based on the electric field strength detection values ​​collected by multi-layer UAV formation.

[0031] For example, based on the attenuation relationship between the electric field strength detection values ​​collected by the UAV formation and the detection distance, as well as the changes in electric field strength, the location of the lightning source can be inverted using the electric field strength detection values ​​from multiple measuring points, thereby determining the location of lightning occurrence within the target lightning area. The ground server can generate a real-time map of lightning activity based on the electric field strength detection values ​​transmitted by the UAVs. The method for generating this lightning activity map refers to the existing patent application text with publication number CN119936501A. By integrating electric field strength data with geographic coordinate labels onto the map, the location, intensity, and distribution range of lightning activity are presented in an intuitive and visual way. Operators can monitor the lightning dynamics within the detection area in real time and promptly identify potential danger zones.

[0032] Based on the electric field strength detection values ​​collected by the drone formation, the lightning intensity can be determined. A first electric field strength threshold and a second electric field strength threshold are preset, where the first threshold is less than the second. When the collected electric field strength detection value is lower than the first threshold, the lightning intensity is determined to be Level 1 (weak), meaning the electric field is weak, there is no obvious lightning activity, no warning is needed, and routine monitoring continues. When the collected electric field strength detection value is greater than or equal to the first threshold and less than the second threshold, the lightning intensity is determined to be Level 2 (moderate), meaning the electric field is enhanced, lightning activity is active, a yellow lightning warning is issued, and detection equipment is deployed. When the collected electric field strength detection value is greater than the second threshold, the lightning type is determined to be Level 3 (high), meaning the electric field is extremely strong, lightning activity is extremely intense, and it may be accompanied by strong ground flashes, thunderstorms, and strong winds, a red lightning warning is issued, and protective measures are taken.

[0033] In addition, to achieve precise identification of lightning types, a three-dimensional magnetic field sensor (such as an orthogonal loop magnetic field antenna) needs to be integrated into the drone formation. The drones will send the collected electric field intensity data and magnetic field direction data to the ground server. The ground server will determine the lightning type by analyzing the polarity of the electric field waveform, the direction of the magnetic field, and the characteristics of the electric field change rate.

[0034] For example, the polarity of lightning can be determined based on the polarity of the electric field waveform. When the electric field waveform exhibits a negative polarity pulse, the bottom of the thundercloud is a negatively charged region, inducing positive charges on the ground, and the electric field direction is perpendicular to the sky; in this case, the lightning polarity is negative. When the electric field waveform exhibits a positive polarity pulse, the bottom of the thundercloud is a positively charged region, inducing negative charges on the ground, and the electric field direction is perpendicular to the ground; in this case, the lightning polarity is positive. The type of lightning can also be determined based on the direction of the magnetic field and the characteristics of the electric field waveform. When the magnetic field direction is a stable horizontal direction, the electric field waveform has a steep rising edge (microsecond rise time) and is accompanied by a return stroke sequence, the lightning type is ground lightning. When the magnetic field direction is horizontal or inclined, the electric field waveform rises relatively gently, the pulse width is wide, and it does not contain a typical ground lightning return stroke structure, the lightning type is cloud lightning. By combining the polarity and type of lightning, the lightning type can be determined. Positive polarity combined with ground lightning indicates positive cloud-to-ground lightning, negative polarity combined with ground lightning indicates negative cloud-to-ground lightning, positive polarity combined with cloud lightning indicates positive cloud-to-ground lightning, and negative polarity combined with cloud lightning indicates negative cloud-to-ground lightning.

[0035] The lightning detection method for dynamic networking of UAVs in this embodiment determines the probability of lightning occurrence in the target lightning region by acquiring the convective effective potential energy, lift index, cloud top temperature, and lightning frequency. When the probability of lightning occurrence exceeds a preset lightning occurrence probability threshold, the center coordinates and estimated radius of the target lightning region are further determined. Subsequently, based on the center coordinates, estimated radius, preset height differences between each layer of UAV formation, and preset detection radii and detection angles of each UAV in each layer of UAV formation, the initial position coordinates of each UAV are determined. Each UAV receives its corresponding initial position coordinates and flies to the corresponding position to collect the electric field strength detection value of the target lightning region. Finally, the collected electric field strength detection value is used to determine the lightning location result of the target lightning region. Compared to existing technologies, this invention pre-divides various lightning zones, calculates the probability of lightning occurrence in each zone, determines the target lightning zones requiring UAV network detection, and simultaneously uses a multi-layer UAV formation detection method to collect the electric field intensity of the target lightning zones. By calculating the initial position of each UAV in each layer of the UAV formation, it ensures that the detection coverage of the multi-layer UAV formation can completely cover high-probability lightning zones and surrounding buffer zones, accurately reflecting the three-dimensional spatial location and electric field intensity distribution of lightning, thus improving the accuracy of lightning detection to a certain extent.

[0036] In one embodiment, after step 400 and before step 500, the method further includes: Step 601: Determine the optimal flight altitude of each UAV at time t+1 based on the electric field intensity detection values ​​at time t, the initial position coordinates of each UAV, and the preset ideal electric field intensity standard value of the a-th target lightning region. Among them, the ideal electric field strength standard value refers to the ideal electric field strength value of the core area of ​​the target lightning area, that is, the theoretical or reference value of the electric field strength that should be reached when lightning activity occurs at a specific spatial location (such as a certain height layer) in the target lightning area, which is preset according to historical lightning data; the optimal flight altitude refers to the final flight altitude determined after guiding each UAV to adjust its position in the vertical direction, so that the electric field strength detection value collected by the UAV at time t+1 approaches the ideal electric field strength standard value.

[0037] Step 602: Based on the electric field intensity detection values ​​at time t, determine the variance of the nth electric field intensity detection value of the ath target lightning region at time t; Among them, the variance of the electric field strength detection value refers to the statistical measure of the degree of dispersion among the electric field strength detection values ​​collected by multiple drones in the same layer of the drone formation at the same time, and is used to characterize the uniformity of the electric field strength distribution within the detection range of the formation.

[0038] Step 603: Based on the variance of the nth electric field strength detection value at time t and the preset horizontal spacing adjustment value of two horizontally adjacent drones in the nth layer of the drone formation, determine the optimal horizontal spacing between two horizontally adjacent drones in the nth layer of the drone formation at time t+1. The horizontal spacing adjustment value refers to the step size parameter used to adjust the horizontal distance between two horizontally adjacent drones in the nth layer drone formation. Its magnitude is determined based on the deviation between the variance of the current electric field strength detection value and the preset threshold, and is used to control the magnitude of the spacing adjustment. The optimal horizontal spacing refers to the horizontal distance between two horizontally adjacent drones when the variance of the electric field strength detection value of the nth layer drone formation falls within the preset reasonable threshold range under the current detection conditions. At this time, the detection coverage and resolution of the drone formation reach a balanced state.

[0039] Step 604: Based on the initial position coordinates of each UAV at time t and the optimal horizontal distance between two horizontally adjacent UAVs in the UAV formation at the nth layer at time t+1, determine the optimal horizontal position coordinates of each UAV at time t+1. The optimal horizontal position coordinates refer to the target position coordinates of each UAV in the nth layer UAV formation on the horizontal plane after rearranging according to the optimal horizontal spacing. These coordinates make the horizontal distance between horizontally adjacent UAVs in the UAV formation equal to the optimal horizontal spacing, thereby achieving the optimal balance between detection coverage and spatial resolution.

[0040] Step 605: Determine the optimal position coordinates of each UAV at time t+1 based on the optimal flight altitude and optimal horizontal position coordinates of each UAV at time t+1.

[0041] Among them, the optimal position coordinates refer to the target position coordinates of the UAV in three-dimensional space obtained by combining the optimal flight altitude and the optimal horizontal position coordinates. This coordinates are used to guide the UAV to fly to the position at time t+1 to collect lightning data, thereby achieving the optimal spatial layout of the UAV formation in the vertical and horizontal directions.

[0042] This embodiment of the UAV dynamic networking lightning detection method determines the optimal flight altitude of each UAV in the future by using the electric field strength detection value of the target lightning region at the current moment, the initial position coordinates of each UAV, and the preset ideal electric field strength standard value collected by the UAV formation. It then determines the optimal horizontal spacing between two horizontally adjacent UAVs in each layer of the UAV formation in the future by using the electric field strength detection values ​​and the preset horizontal spacing adjustment values ​​between two horizontally adjacent UAVs in each layer of the UAV formation. Subsequently, based on the optimal horizontal spacing and the initial position coordinates of each UAV, it determines the optimal horizontal position coordinates of each UAV in the future. Finally, based on the optimal flight altitude and the optimal horizontal coordinates, it determines the optimal position coordinates of each UAV in the future. Compared to existing technologies, this invention selects the target lightning region within a pre-determined lightning region, collects the electric field strength detection value of the target lightning region using a UAV formation, and dynamically adjusts the flight positions of each UAV. When the charge center of the target lightning region shifts or the electric field strength changes abruptly, the UAV formation can automatically adjust its formation, always maintaining effective detection of the core area of ​​lightning activity, maximizing the overall lightning detection coverage, and achieving a dynamic optimal balance between detection accuracy and coverage.

[0043] In one embodiment, step 400 includes: Step 401: Based on the detection radius of each UAV preset at time t-1, determine the initial horizontal distance between two horizontally adjacent UAVs in the UAV formation at the nth layer at time t. The initial horizontal spacing refers to the horizontal distance between adjacent drones that is predetermined based on the detection radius of each drone before the drone formation performs a lightning detection mission. This distance is used to determine the initial layout of the drone formation.

[0044] For example, the initial horizontal spacing between two horizontally adjacent drones in the nth layer drone formation at time t. d n ( t This can be expressed by the formula: d n ( t )≤ a × r ( t -1), where n This represents the nth layer of drone formation. n =1, 2, ...,N , N It is a positive integer greater than 1; d n ( t () represents the initial horizontal distance between two horizontally adjacent drones in the nth layer of the drone formation at time t; a The spacing coefficient represents the proportional relationship between the initial horizontal spacing between horizontally adjacent UAVs and the detection radius. a It is a real number greater than 2; r ( t -1) represents the preset detection radius of each UAV at time t-1.

[0045] Step 402: Divide the product of the estimated radius at time t-1 and 2π by the initial horizontal distance between two horizontally adjacent drones in the drone formation at time t, and round up to determine the number of drones in the drone formation at time t. The number of drones refers to the total number of drones that need to be configured in the nth layer drone formation to cover the detection range of the target lightning area.

[0046] For example, the number of drones in the nth layer drone formation at time t. M n ( t This can be expressed by the formula: M n ( t )=roundup[2π R ( t -1)÷ d n ( t )],in, n This represents the nth layer of drone formation. n =1, 2, ..., N , N It is a positive integer greater than 1; M n ( t () represents the total number of drones in the nth layer drone formation at time t. M n ( t ) is a positive integer greater than 2; R ( t -1) represents the estimated radius of the target lightning area at time t-1; d n ( t ) represents the initial horizontal distance between two horizontally adjacent UAVs in the nth layer UAV formation at time t; roundup(·) represents the round-up function.

[0047] Step 403: Based on the estimated radius at time t-1, the detection angle, and the preset height difference between two vertically adjacent drone formations in the N-layer drone formation, determine the flight radius of the nth layer drone formation at time t. The flight radius refers to the radius of the circular path formed by the flight trajectory of the nth layer of UAV formation on the horizontal plane at time t, which is used to guide the initial deployment position of each UAV in the horizontal direction of the UAV formation at that layer.

[0048] For example, the flight radius of the nth layer of the UAV formation at time t. R n ( t This can be expressed by the formula: R n ( t )= R ( t -1)+( n -1)×Δ h ×tan θ ,in n This represents the nth layer of drone formation. n =1, 2, ..., N , N It is a positive integer greater than 2; R n ( t () represents the flight radius of the nth layer of UAV formation at time t; R ( t -1) represents the estimated radius of the target lightning area at time t-1; Δ h This represents the height difference between two vertically adjacent drone formations in a preset N-layer drone formation; θ This indicates the detection angle of each drone in a pre-defined N-layer drone formation.

[0049] Step 404: Based on the center coordinates at time t-1, the number of drones in the drone formation at time t, the flight radius of the drone formation at time t, and the height difference between two vertically adjacent drone formations in the drone formation at time N, determine the initial position coordinates of each drone in the drone formation at time t.

[0050] For example, the initial position coordinates of each drone in the nth layer of the drone formation at time t ( x n,m (t), y n,m (t), z n,m (t) can be expressed by the formula: , in, nThis represents the nth layer of drone formation. n =1, 2, ..., N , N It is a positive integer greater than 2; m This represents the m-th drone in the n-th layer drone formation, where m is a positive integer greater than 2; x n,m (t) represents the horizontal coordinate of the m-th drone in the n-th layer drone formation at time t; y n,m (t) represents the horizontal ordinate of the m-th drone in the n-th layer drone formation at time t; z n,m (t) represents the flight altitude of the m-th drone in the n-th layer drone formation at time t; x 0(t-1) represents the horizontal abscissa of the center coordinate of the target lightning region at time t-1; y 0(t-1) represents the horizontal ordinate of the center coordinate of the target lightning region at time t-1; z 0(t-1) represents the initial height of the center coordinates of the target lightning region at time t-1; R n ( t () represents the flight radius of the nth layer of UAV formation at time t; M n ( t ) represents the total number of drones in the nth layer drone formation at time t; Δ h This represents the height difference between two vertically adjacent drone formations in a preset N-layer drone formation.

[0051] Step 405: Send the initial position coordinates of each UAV to the corresponding UAV, so that at time t, each UAV flies to the corresponding position according to the received initial position coordinates to collect the electric field intensity detection value of the a-th target lightning region.

[0052] This embodiment of the UAV dynamic networking lightning detection method determines the initial horizontal distance between two horizontally adjacent UAVs in the current UAV formation by using the preset detection radius of each UAV. Using the initial horizontal distance and the estimated radius of the target lightning region at a historical time, the number of UAVs in each layer of the UAV formation at the current time is determined. Then, based on the estimated radius, the preset detection angle of each UAV, and the preset height difference between two adjacent UAV layers, the flight radius of each layer of the UAV formation is determined. Finally, using the center coordinates of the target lightning region at a historical time, the number of UAVs, the flight radius, and the height difference, the initial position coordinates of each UAV in the N-layer UAV formation at the current time are determined. Compared to existing technologies, this invention utilizes the pre-determined centroid coordinates and estimated radius of the target lightning region, as well as preset physical parameters such as the detection radius and detection angle of each UAV, to accurately calculate the initial position coordinates of each UAV. This achieves three-dimensional lightning detection and dynamic resource optimization, enabling flexible deployment of UAV formations in lightning regions of different sizes and shapes, and is suitable for various complex lightning detection scenarios.

[0053] In one embodiment, step 200 further includes: In the mapping relationship, the probability of lightning occurrence is positively correlated with the convective effective potential energy and the lightning frequency, and the probability of lightning occurrence is negatively correlated with the uplift index and the cloud top temperature.

[0054] Positive correlation means that the two variables change in the same direction, that is, when the value of one variable increases, the value of the other variable also increases; conversely, when the value of one variable decreases, the value of the other variable also decreases. Negative correlation means that the two variables change in opposite directions, that is, when the value of one variable increases, the value of the other variable decreases; conversely, when the value of one variable decreases, the value of the other variable increases.

[0055] For example, the higher the value of convective effective potential energy or lightning frequency, the higher the probability of lightning occurrence; conversely, the lower the value of convective effective potential energy or lightning frequency, the lower the probability of lightning occurrence. Similarly, the higher the value of uplift index or cloud top temperature, the lower the probability of lightning occurrence; conversely, the lower the value of uplift index or cloud top temperature, the higher the probability of lightning occurrence.

[0056] This embodiment of the UAV dynamic networking lightning detection method clarifies the mapping relationship between convective effective potential energy, uplift index, cloud top temperature, lightning frequency, and lightning occurrence probability, quantitatively calculating the lightning occurrence probability in the target lightning area. The lightning occurrence probability is positively correlated with convective effective potential energy and lightning frequency, and negatively correlated with uplift index and cloud top temperature. Compared to existing technologies, this invention transforms qualitative meteorological judgments into quantitative lightning occurrence probability values, providing a scientific decision-making basis for UAV formation deployment, achieving lightning detection and optimal resource allocation, and effectively improving lightning detection efficiency and resource utilization.

[0057] In one embodiment, step 601 includes: Step 6011: Sum the electric field strength detection values ​​collected by each UAV in the UAV formation at time t and take the average value to determine the nth average electric field strength detection value of the ath target lightning region at time t; The average electric field strength detection value refers to the arithmetic mean of the nth electric field strength detection values ​​of the ath target lightning region collected by each drone in the nth layer drone formation at time t.

[0058] For example, the average electric field strength detected at time t for the nth target lightning region. E a,n ( t This can be expressed by the formula: , in, a This represents the target lightning area, a =1, 2, ..., A , A It is a positive integer greater than 2; m Let m represent the m-th drone in the lightning region of the a-th target, where m = 1, 2, ..., M, and M is a positive integer greater than 2; n This represents the nth layer of drone formation; E a,n ( t ) represents the average electric field strength detected in the nth lightning region of the a-th target at time t, which is the average electric field strength detected in the a-th target lightning region collected by M drones in the nth layer of the drone formation; E a,m,n ( t ) represents the electric field strength detection value of the lightning region of the target a collected by the m-th drone in the n-th layer drone formation at time t.

[0059] Step 6012: Take the absolute value of the difference between the nth average value of the electric field strength detection at time t and the standard value of the ideal electric field strength, square it, and sum it to determine the electric field strength detection error between the N layers of the UAV formation at time t; Among them, the electric field strength detection error refers to the sum of squares of the deviations between the average value of each electric field strength detection at time t and the preset ideal electric field strength standard value, which is used to quantitatively assess the overall deviation between the current detection data and the ideal state.

[0060] For example, the electric field strength detection error among N-layer drone formations at time t. J ( t This can be expressed by the formula: , in, n This represents the nth layer of drone formation. n =1, 2, ..., N , N It is a positive integer greater than 2; J ( t The ) represents the electric field strength detection error between the N-layer UAV formation at time t; E a,n ( t The expression represents the average electric field strength detected at time t for the nth lightning region of the a-th target, which is the average electric field strength detected by each drone in the nth layer of the drone formation for the a-th target lightning region. a =1, 2, ..., A , A It is a positive integer greater than 2; E 0 indicates the preset ideal electric field strength standard value for the target lightning area.

[0061] Step 6013: Based on the initial position coordinates of each UAV at time t, the electric field strength detection error of the UAV formation, the average value of the nth electric field strength detection, and the preset learning rate, determine the optimal flight altitude of the nth layer of the UAV formation at time t+1. The learning rate is a preset parameter used to control the step size of the drone's flight altitude adjustment. It is used to determine the magnitude of altitude adjustment in each iteration of optimization, balancing the optimization convergence speed and adjustment stability.

[0062] For example, the optimal flight altitude of the nth layer of UAV formation at time t+1. h n ( t +1) can be expressed by the formula: h n ( t +1)= h n( t )- b ×[ E a,n ( t )- E 0]×[ h n ( t )÷ E a,n ( t )],in n This represents the nth layer of drone formation. n =1, 2, ..., N , N It is a positive integer greater than 2; h n ( t +1) represents the optimal flight altitude of the nth layer of UAV formation at time t+1; h n ( t () represents the initial flight altitude of the nth layer of UAV formation at time t, that is, the vertical height in the initial position coordinates; b This represents the preset learning rate, used to control the step size for adjusting the flight altitude of the drone formation; E a,n ( t The expression represents the average electric field strength detected at time t for the nth lightning region of the a-th target, which is the average electric field strength detected by each drone in the nth layer of the drone formation for the a-th target lightning region. a =1, 2, ..., A , A It is a positive integer greater than 2; E 0 indicates the preset ideal electric field strength standard value for the target lightning area.

[0063] Step 602 includes: Based on the electric field intensity detection values ​​at time t and the average value of the nth electric field intensity detection, determine the variance of the nth electric field intensity detection value of the ath target lightning region at time t; For example, the variance Var[ of the nth electric field intensity detection value of the a-th target lightning region at time t] E a,n ( t This can be expressed by the formula: , in, m Var[ represents the m-th drone in the n-th layer drone formation, where m = 1, 2, ..., M, and M is a positive integer greater than 2; E a,n ( t[)] represents the variance of the nth electric field intensity detection value of the a-th target lightning region at time t. a =1, 2, ..., A , A It is a positive integer greater than 2; E a,m,n ( t () represents the electric field strength detection value of the lightning region of the target a collected by the m-th drone in the n-th layer drone formation at time t; E a,n ( t ) represents the average value of the electric field strength detected in the nth lightning region of the a-th target at time t, which is the average value of the electric field strength detected in the a-th target lightning region collected by each drone in the nth layer of the drone formation.

[0064] Step 603 includes: Step 6031: Preset a first electric field strength variance threshold and a second electric field strength variance threshold, wherein the first electric field strength variance threshold is greater than the second electric field strength variance threshold; The first electric field strength variance threshold is an upper limit threshold used to determine whether the electric field strength distribution is excessively uneven; the second electric field strength variance threshold is a lower limit threshold used to determine whether the electric field strength distribution is excessively uniform.

[0065] Step 6032: When the variance of the nth electric field strength detection value at time t is greater than the first electric field strength variance threshold, reduce the horizontal distance between two horizontally adjacent UAVs in the nth layer of the UAV formation according to the horizontal spacing adjustment value, and determine the variance of the nth electric field strength detection value of the ath target lightning area at time t+1, and return to step 602. Step 6033: When the variance of the nth electric field strength detection value at time t is less than the preset second electric field strength variance threshold, increase the horizontal distance between two horizontally adjacent UAVs in the nth layer of the UAV formation according to the horizontal distance adjustment value, and determine the variance of the nth electric field strength detection value of the ath target lightning area at time t+1, and return to step 602. Step 6034: When the variance of the nth electric field strength detection value at time t is greater than or equal to the second electric field strength variance threshold and less than or equal to the first electric field strength variance threshold, the horizontal distance between two horizontally adjacent drones in the nth layer of the drone formation at time t is determined to be the optimal horizontal distance between two horizontally adjacent drones in the nth layer of the drone formation at time t+1.

[0066] For example, assuming the preset first electric field strength variance threshold is 0.5, the second electric field strength variance threshold is 0.1, and the horizontal spacing adjustment value of two horizontally adjacent drones in the nth layer drone formation is 500m.

[0067] The variance Var of the electric field intensity detection value of the target lightning area at time t E a ( t When the value is greater than 0.5, it indicates that the lightning detection signal intensity distribution is uneven and there is a detection blind zone. It is necessary to reduce the distance between two horizontally adjacent drones in each layer of the drone formation. The adjustment range is 50m. After the first adjustment is completed, the electric field intensity detection value of the target lightning area is re-acquired and its variance is recalculated to complete the threshold judgment.

[0068] The variance Var of the electric field intensity detection value of the target lightning area at time t E a ( t When the value is less than 0.1, it indicates that the distribution of lightning detection signal intensity is too uniform and the distance between two horizontally adjacent drones in each layer of the drone formation is too small, resulting in excessive energy consumption. It is necessary to increase the distance between two horizontally adjacent drones in each layer of the drone formation by 50m. After the first adjustment is completed, the electric field intensity detection value of the target lightning area is re-acquired and its variance is recalculated to complete the threshold judgment.

[0069] The variance Var of the electric field intensity detection value of the target lightning area at time t E a ( t When the value is greater than or equal to 0.1 and less than or equal to 0.5, it indicates that the horizontal spacing between two adjacent drones in each layer of the drone formation is reasonable. While ensuring detection accuracy, it can reduce drone energy consumption. Therefore, no spacing adjustment is made, and the horizontal spacing between two adjacent drones in the nth layer of the drone formation at time t is taken as the optimal horizontal spacing between two adjacent drones in the nth layer of the drone formation at time t+1.

[0070] This embodiment of the UAV dynamic networking lightning detection method calculates the average electric field strength of each layer of the UAV formation by analyzing the electric field strength detection values ​​collected by each UAV in the formation. It then determines the optimal flight altitude of each UAV using its initial flight altitude, a preset ideal electric field strength standard value for the target lightning region, and a learning rate. The variance of the electric field strength detection values ​​for the target lightning region is determined using the electric field strength detection values ​​and the average electric field strength detection value. Finally, the variance of the electric field strength detection values ​​is compared with preset first and second electric field strength variance thresholds to adjust the horizontal spacing between two horizontally adjacent UAVs in the formation, thereby determining the optimal horizontal spacing between them. Compared to existing technologies, this invention dynamically adjusts the flight altitude and optimal horizontal spacing of each UAV, ensuring that the UAV formation always flies at an altitude level where the electric field strength is closest to the ideal standard value. This adapts to the vertical and horizontal changes in lightning clouds, effectively improving resource utilization and maximizing detection efficiency with a limited number of UAVs.

[0071] In one embodiment, such as Figure 1 As shown, a lightning detection system based on dynamic networking of unmanned aerial vehicles (UAVs) is provided. The system includes a ground server and an N-layer UAV formation. Each layer of the UAV formation contains at least two UAVs. Each UAV communicates with the ground server. The ground server and the UAVs work together to implement the lightning detection method based on dynamic networking of UAVs as described in the above embodiment. n = 1, 2, ..., N, where N is a positive integer greater than 1. For example... Figure 2 Steps 100 to 500 shown are omitted here to avoid repetition.

[0072] In one embodiment, each of the drones includes a body and a processor built into the body, the processor comprising: The first data acquisition module is used to acquire the signal-to-noise ratio of the communication link between each UAV and the ground server and the electric field strength detection value of the target lightning area at time t+1. Signal-to-noise ratio (SNR) refers to the ratio of effective signal power to noise power in the communication link between each UAV and the ground server, and is used to characterize the quality of the communication link. A higher SNR indicates better communication quality and more reliable data transmission; a lower SNR indicates worse communication quality and the possibility of data transmission interference or interruption.

[0073] The first data transmission module is used to transmit the electric field strength detection value to the ground server when the signal-to-noise ratio of the communication link is greater than or equal to a preset signal-to-noise ratio threshold, using a data compression ratio of 1:1. The preset signal-to-noise ratio threshold refers to a pre-set critical value for the signal-to-noise ratio used to determine whether the quality of the communication link meets the requirements for uncompressed data transmission.

[0074] The second data transmission module is used to transmit the electric field strength detection value to the ground server using a data compression ratio of P to 1 when the signal-to-noise ratio of the communication link is less than a preset signal-to-noise ratio threshold, where P is any real number greater than 1. For example, when the signal-to-noise ratio (SNR) of the communication link between the drone and the ground server is greater than or equal to the preset SNR threshold, it indicates that the communication quality is good and lossless transmission can be used, i.e., the data compression ratio is 1:1; when the SNR is lower than the preset SNR threshold, it indicates that the communication quality has deteriorated and compressed transmission or the transmission strategy needs to be adjusted to ensure that the data arrives reliably, i.e., the data compression ratio needs to be increased.

[0075] The ground server includes: The relay drone determination module is used to select N drones with the highest signal strength from among the various drones as relay drones; Among them, a relay drone is a drone selected from multiple drones participating in electric field strength detection to aggregate and forward data. It receives electric field strength detection values ​​collected by other ordinary drones, summarizes them with its own collected values, and then transmits them to the ground server. Simultaneously, the remaining drones transmit their electric field strength detection values ​​at time t+1 to the nearest relay drone.

[0076] The ground data acquisition module is used to acquire the electric field strength detection values ​​received and collected by N relay UAVs at time t+1. When the number of data transmission failures of the N relay UAVs is less than or equal to a preset data transmission failure threshold, data is retransmitted according to a preset data transmission interval. When the number of data transmission failures of the N relay UAVs is greater than the data transmission failure threshold, the data is temporarily stored in the local storage module of the corresponding relay UAV. When the signal-to-noise ratio of the communication link of the relay UAV is greater than or equal to the signal-to-noise ratio threshold, data is retransmitted again.

[0077] This embodiment of the UAV dynamic networking lightning detection system uses a method where each UAV determines its communication quality by comparing the signal-to-noise ratio (SNR) of its communication link with the ground server to a preset SNR threshold. When the communication quality is good, a 1:1 lossless direct transmission method is used; when the communication quality is poor, compressed transmission is used. The ground server selects the N UAVs with the highest signal strength as relay UAVs. The remaining UAVs transmit the collected electric field strength detection values ​​to the nearest relay UAV. The relay UAV decides whether to retransmit or temporarily store the data based on the number of transmission failures, and then retransmit the data once the SNR recovers. Compared to existing technologies, this invention employs an adaptive data transmission strategy and the selection of relay UAVs. It dynamically selects the data transmission method based on the real-time communication SNR and utilizes relay UAVs to improve data transmission efficiency, effectively increasing the data transmission success rate, expanding the communication coverage, and improving communication efficiency and resource utilization to a certain extent.

[0078] In one embodiment, the processor further includes: The second data acquisition module is used to acquire the electric field strength detection value of the target lightning region at time t. The data processing unit is used to take the absolute value of the difference between the electric field strength detection value at time t and the electric field strength detection value at time t+1 to obtain the change in the electric field strength detection value of the target lightning area at time t+1. Among them, the change in the average value of electric field strength detection refers to the degree of change in the electric field strength of the target lightning area per unit time, which reflects the development trend and intensity of lightning activity.

[0079] The third data transmission module is used to transmit the change in the electric field strength detection value at time t+1 to the ground server when the change in the electric field strength detection value at time t+1 is greater than or equal to a preset threshold for the change in electric field strength. When the change in the electric field strength detection value at time t+1 is less than the preset threshold for the change in electric field strength, the electric field strength detection values ​​at time t+1 are transmitted to the ground server.

[0080] The first threshold for electric field strength change refers to a pre-set critical value used to determine whether the change in electric field strength has reached the standard of significant change. When the change in the average value of the detected electric field strength is greater than or equal to this threshold, it indicates that the electric field strength has changed significantly, and lightning activity has fluctuated significantly or developed rapidly. This change needs to be transmitted as key data to the ground server so that the detection strategy can be adjusted in a timely manner or an early warning can be issued.

[0081] In this embodiment of the UAV dynamic networking lightning detection system, each UAV calculates the difference between the current electric field strength detection value and the electric field strength detection value collected at a future time, taking the absolute value to determine the change in the electric field strength detection value. When the change in the electric field strength detection value is greater than or equal to a preset rated electric field strength change threshold, only the change in the electric field strength detection value is transmitted to the ground server; when the change in the electric field strength detection value is less than the preset rated electric field strength change threshold, only the electric field strength detection value at a future time is transmitted to the ground server. Compared with the prior art, this invention adaptively determines the data transmission content, reduces data transmission time and data transmission volume, and achieves optimal allocation of communication resources.

[0082] In one embodiment, such as Figure 3 As shown, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the lightning detection method for dynamic networking of unmanned aerial vehicles (UAVs) in the above embodiments, for example... Figure 2 Steps 100 to 500 shown are omitted here to avoid repetition.

[0083] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the lightning detection method for dynamic networking of unmanned aerial vehicles (UAVs) in the above embodiment, for example... Figure 2 Steps 100 to 500 shown are omitted here to avoid repetition.

[0084] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0085] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0086] While specific embodiments of the invention have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the invention. The scope of this invention is defined by the appended claims.

Claims

1. A lightning detection method using dynamic networking of unmanned aerial vehicles (UAVs), characterized in that, The lightning detection method for dynamic networking of unmanned aerial vehicles includes: Step 100: Obtain the convective effective potential energy, lifting index, cloud top temperature, and lightning frequency of the Q pre-divided lightning regions at time t-1, where Q is a positive integer greater than 1; Step 200: Based on the convective effective potential energy, the lifting index, the cloud top temperature, and the lightning frequency at time t-1, and combining the mapping relationship between convective effective potential energy, the lifting index, the cloud top temperature, the lightning frequency, and the probability of lightning occurrence, determine the probability of lightning occurrence in the Q lightning regions at time t-1. Step 300: At time t-1, select A target lightning regions from the Q lightning regions whose lightning occurrence probability exceeds a preset lightning occurrence probability threshold, and determine the center coordinates and estimated radius of the A target lightning regions at time t-1, where A is a positive integer and A is less than Q; Step 400: For the a-th target lightning region, based on the center coordinates at time t-1, the estimated radius, the height difference between the preset N-layer drone formation, and the detection radius and detection angle of each drone in the preset n-layer drone formation, determine the initial position coordinates of each drone at time t, and send the initial position coordinates of each drone to the corresponding drone, so that each drone at time t flies to the corresponding position according to the received initial position coordinates to collect the electric field strength detection value of the a-th target lightning region, n=1, 2, ..., N, where N is a positive integer greater than 1, a=1, 2, ..., A; Step 500: Obtain the electric field strength detection value of the a-th target lightning region sent by each UAV in the UAV formation of layer N at time t, and determine the lightning location result of the a-th target lightning region based on each electric field strength detection value.

2. The lightning detection method for dynamic networking of unmanned aerial vehicles according to claim 1, characterized in that, After step 400 and before step 500, the following also applies: Step 601: Determine the optimal flight altitude of each UAV at time t+1 based on the electric field intensity detection values ​​at time t, the initial position coordinates of each UAV, and the preset ideal electric field intensity standard value of the a-th target lightning region. Step 602: Based on the electric field intensity detection values ​​at time t, determine the variance of the nth electric field intensity detection value of the ath target lightning region at time t; Step 603: Based on the variance of the nth electric field strength detection value at time t and the preset horizontal spacing adjustment value of two horizontally adjacent drones in the nth layer of the drone formation, determine the optimal horizontal spacing between two horizontally adjacent drones in the nth layer of the drone formation at time t+1. Step 604: Based on the initial position coordinates of each UAV at time t and the optimal horizontal distance between two horizontally adjacent UAVs in the UAV formation at the nth layer at time t+1, determine the optimal horizontal position coordinates of each UAV at time t+1. Step 605: Determine the optimal position coordinates of each UAV at time t+1 based on the optimal flight altitude and optimal horizontal position coordinates of each UAV at time t+1.

3. The lightning detection method for dynamic networking of unmanned aerial vehicles according to claim 1, characterized in that, Step 400 includes: Step 401: Based on the detection radius of each UAV preset at time t-1, determine the initial horizontal distance between two horizontally adjacent UAVs in the UAV formation at the nth layer at time t. Step 402: Divide the product of the estimated radius at time t-1 and 2π by the initial horizontal distance between two horizontally adjacent drones in the drone formation at time t, and round up to determine the number of drones in the drone formation at time t. Step 403: Based on the estimated radius at time t-1, the detection angle, and the preset height difference between two vertically adjacent drone formations in the N-layer drone formation, determine the flight radius of the nth layer drone formation at time t. Step 404: Based on the center coordinates at time t-1, the number of drones in the drone formation at time t, the flight radius of the drone formation at time t, and the height difference between two vertically adjacent drone formations in the drone formation at time N, determine the initial position coordinates of each drone in the drone formation at time t. Step 405: Send the initial position coordinates of each UAV to the corresponding UAV, so that at time t, each UAV flies to the corresponding position according to the received initial position coordinates to collect the electric field intensity detection value of the a-th target lightning region.

4. The lightning detection method for dynamic networking of unmanned aerial vehicles according to claim 1, characterized in that, Step 200 also includes: In the mapping relationship, the probability of lightning occurrence is positively correlated with the convective effective potential energy and the lightning frequency, and the probability of lightning occurrence is negatively correlated with the uplift index and the cloud top temperature.

5. The lightning detection method for dynamic networking of unmanned aerial vehicles according to claim 2, characterized in that, Step 601 includes: Step 6011: Sum the electric field strength detection values ​​collected by each UAV in the UAV formation at time t and take the average value to determine the nth average electric field strength detection value of the ath target lightning region at time t; Step 6012: Take the absolute value of the difference between the nth average value of the electric field strength detection at time t and the standard value of the ideal electric field strength, square it, and sum it to determine the electric field strength detection error between the N layers of the UAV formation at time t; Step 6013: Based on the initial position coordinates of each UAV at time t, the electric field strength detection error of the UAV formation, the average value of the nth electric field strength detection, and the preset learning rate, determine the optimal flight altitude of the nth layer of the UAV formation at time t+1. Step 602 includes: Based on the electric field intensity detection values ​​at time t and the average value of the nth electric field intensity detection, determine the variance of the nth electric field intensity detection value of the ath target lightning region at time t; Step 603 includes: Step 6031: Preset a first electric field strength variance threshold and a second electric field strength variance threshold, wherein the first electric field strength variance threshold is greater than the second electric field strength variance threshold; Step 6032: When the variance of the nth electric field strength detection value at time t is greater than the first electric field strength variance threshold, reduce the horizontal distance between two horizontally adjacent UAVs in the nth layer of the UAV formation according to the horizontal spacing adjustment value, and determine the variance of the nth electric field strength detection value of the ath target lightning area at time t+1, and return to step 602. Step 6033: When the variance of the nth electric field strength detection value at time t is less than the preset second electric field strength variance threshold, increase the horizontal distance between two horizontally adjacent UAVs in the nth layer of the UAV formation according to the horizontal distance adjustment value, and determine the variance of the nth electric field strength detection value of the ath target lightning area at time t+1, and return to step 602. Step 6034: When the variance of the nth electric field strength detection value at time t is greater than or equal to the second electric field strength variance threshold and less than or equal to the first electric field strength variance threshold, the horizontal distance between two horizontally adjacent drones in the nth layer of the drone formation at time t is determined to be the optimal horizontal distance between two horizontally adjacent drones in the nth layer of the drone formation at time t+1.

6. A lightning detection system with dynamic networking of unmanned aerial vehicles (UAVs), characterized in that, The lightning detection system of the UAV dynamic networking includes a ground server and an N-layer UAV formation. The nth layer of the UAV formation contains at least two UAVs. Each UAV communicates with the ground server. The ground server and each UAV work together to implement the lightning detection method of the UAV dynamic networking as described in any one of claims 1 to 5, where n = 1, 2, ..., N, and N is a positive integer greater than 1.

7. The lightning detection system for dynamic networking of unmanned aerial vehicles according to claim 6, characterized in that, Each of the aforementioned drones includes a body and a processor internally located within the body, the processor comprising: The first data acquisition module is used to acquire the signal-to-noise ratio of the communication link between each UAV and the ground server and the electric field strength detection value of the target lightning area at time t+1. The first data transmission module is used to transmit the electric field strength detection value to the ground server when the signal-to-noise ratio of the communication link is greater than or equal to a preset signal-to-noise ratio threshold, using a data compression ratio of 1:

1. The second data transmission module is used to transmit the electric field strength detection value to the ground server using a data compression ratio of P to 1 when the signal-to-noise ratio of the communication link is less than a preset signal-to-noise ratio threshold, where P is any real number greater than 1. The ground server includes: The relay drone determination module is used to select N drones with the highest signal strength from among the various drones as relay drones; The ground data acquisition module is used to acquire the electric field strength detection values ​​received and collected by N relay UAVs at time t+1. When the number of data transmission failures of the N relay UAVs is less than or equal to a preset data transmission failure threshold, data is retransmitted according to a preset data transmission interval. When the number of data transmission failures of the N relay UAVs is greater than the data transmission failure threshold, the data is temporarily stored in the local storage module of the corresponding relay UAV. When the signal-to-noise ratio of the communication link of the relay UAV is greater than or equal to the signal-to-noise ratio threshold, data is retransmitted again.

8. The lightning detection system for dynamic networking of unmanned aerial vehicles according to claim 7, characterized in that, The processor also includes: The second data acquisition module is used to acquire the electric field strength detection value of the target lightning region at time t. The data processing unit is used to take the absolute value of the difference between the electric field strength detection value at time t and the electric field strength detection value at time t+1 to obtain the change in the electric field strength detection value of the target lightning area at time t+1. The third data transmission module is used to transmit the change in the electric field strength detection value at time t+1 to the ground server when the change in the electric field strength detection value at time t+1 is greater than or equal to a preset threshold for the change in electric field strength. When the change in the electric field strength detection value at time t+1 is less than the preset threshold for the change in electric field strength, the electric field strength detection values ​​at time t+1 are transmitted to the ground server.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the lightning detection method for dynamic networking of unmanned aerial vehicles as described in any one of claims 1 to 5.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the lightning detection method for dynamic networking of unmanned aerial vehicles as described in any one of claims 1 to 5.

Images