Meteorological support system for low-altitude unmanned aerial vehicle

Abstract

The invention discloses a low-altitude unmanned aerial vehicle meteorological support system, relates to the technical field of meteorological monitoring, and constructs a management and control instruction set for a plurality of unmanned aerial vehicles deployed in an unmanned aerial vehicle platform so as to control the unmanned aerial vehicles to perform low-altitude operation, and obtains flight parameters of the unmanned aerial vehicles during low-altitude operation in real time so as to supervise the real-time flight state of the unmanned aerial vehicles. And determining whether to participate in flight control, shooting meteorological cloud pictures in different time sections and position areas through the unmanned aerial vehicles working at low altitude, performing superposition processing in local terminals of the unmanned aerial vehicles to obtain meteorological cloud picture sets of the monitored position areas, and constructing a data interaction net to perform bidirectional data interaction with each unmanned aerial vehicle. The meteorological cloud atlas set stored in each unmanned aerial vehicle is imported into the data interaction net, all the meteorological cloud atlas sets are analyzed, the live meteorological conditions of different position areas are obtained, and finally safeguard measures are taken for the position areas where the live meteorological conditions do not reach the standard.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological monitoring, and specifically to a low-altitude unmanned aerial vehicle (UAV) meteorological support system. Background Art

[0002] Existing meteorological monitoring and support systems usually rely on technologies such as ground meteorological stations and satellite remote sensing. To a certain extent, these technologies can provide meteorological data over a large range, but there are limitations in meteorological monitoring in specific areas, especially in the low-altitude domain.

[0003] Low-altitude meteorological data is crucial for fields such as agriculture, aviation, and urban planning. However, current monitoring methods often cannot provide sufficiently accurate and real-time low-altitude meteorological information. In addition, when existing meteorological support systems detect areas where meteorological conditions do not meet the standards, they lack the ability to respond quickly and intervene precisely, making it difficult to effectively support specific areas. Summary of the Invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a low-altitude UAV meteorological support system.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A low-altitude UAV meteorological support system includes a monitoring center, which is communicatively connected to a UAV control module, a meteorological monitoring module, a data interaction module, a data analysis module, and a meteorological support module;

[0006] The UAV control module is used to construct a control instruction set for a number of UAVs deployed inside the UAV platform, thereby controlling the UAVs to perform low-altitude operations, and to obtain in real time the flight parameters of the UAVs during low-altitude operations, supervise the flight status of the UAVs through the flight parameters, and decide whether to participate in flight control;

[0007] The meteorological monitoring module is used to take meteorological cloud images at different time periods and location areas through UAVs performing low-altitude operations, and to superimpose and process them into a meteorological cloud image set of the monitored location area in each local terminal of the UAV;

[0008] The data interaction module is used to construct a data interaction network, perform two-way data interaction with each UAV through the data interaction network, and then import the meteorological cloud image set stored in each UAV into the data interaction network;

[0009] The data analysis module is used to analyze all the meteorological cloud image sets in the data interaction network, and then obtain the actual meteorological conditions in different location areas;

[0010] The meteorological support module is used to execute support measures for location areas where the actual meteorological conditions do not meet the standards.

[0011] Further, a control instruction set is constructed for a number of unmanned aerial vehicles (UAVs) deployed within a UAV platform, and the process of controlling the UAVs to perform low-altitude operations includes:

[0012] Deploy a number of UAVs within the UAV platform and number them as i, where i = 1, 2, 3, ……, n, and n is a natural number greater than 0. Set the first operation period, the second operation period, and the third operation period;

[0013] Within the first operation period, construct control instructions for the UAVs. The control instructions include positioning and cruising instructions, hovering and obstacle avoidance instructions, and data collection instructions. After integration, they are used as the initial control instruction set;

[0014] Within the second operation period, input the initial control instruction set into each UAV. When inputting the initial control instruction set, synchronously monitor the input environment. When pre-undefined abnormal information is detected, mark the locations of all abnormal information and traverse them in sequence. Whenever an abnormal information at a location is traversed, correct it until all abnormal information at all locations has been corrected, and then start the third operation period;

[0015] Within the third operation period, the UAVs perform a low-altitude operation test according to the input control instruction set, and determine whether the UAVs reach the respective preset completion standard when performing positioning and cruising, hovering and obstacle avoidance, and data collection according to the control instruction set;

[0016] If so, the control instruction set is constructed successfully, and control the UAVs to perform low-altitude operations;

[0017] If not, modify the control instructions that do not meet the completion standard.

[0018] Further, the process of obtaining the flight parameters of the UAVs during low-altitude operations in real time and supervising the flight status of the UAVs through the flight parameters includes:

[0019] The flight parameters include positioning and cruising parameters, hovering and obstacle avoidance parameters, and data collection parameters;

[0020] The positioning and cruising parameters include GPS signal strength, cruising speed, cruising altitude, and heading;

[0021] The hovering and obstacle avoidance parameters include hovering altitude, vertical speed, horizontal speed, obstacle avoidance trajectory, hovering stability duration, and obstacle avoidance response time;

[0022] The data collection parameters include sensor parameters, shooting speed, shooting frames, collection mode, lens shaking angle, and lens imaging parameters;

[0023] Set up parameter comparison tables for positioning and cruising parameters, hovering and obstacle avoidance parameters, and data acquisition parameters respectively. If any of the positioning and cruising parameters, hovering and obstacle avoidance parameters, and data acquisition parameters do not conform to the corresponding parameter comparison tables, the result of supervising the drone is that the current flight status of the drone is an abnormal state; otherwise, the result of supervision is that the current flight status of the drone is a normal state.

[0024] Further, the process of determining whether to participate in flight control includes:

[0025] When the flight status of the drone is normal, no operation is performed;

[0026] When the flight status of the drone is abnormal, it is decided to participate in flight control for the corresponding drone.

[0027] Further, the process of using drones for low-altitude operations to capture meteorological cloud images in different time periods and location areas includes:

[0028] Each drone is responsible for low-altitude operations in a location area. Set the start time and end time of the low-altitude operation corresponding to the drone numbered i, and record them as Start[i] and End[i] respectively. Then obtain the total operation duration of the drone numbered i, denoted as T[i];

[0029] T[i] = |End[i] - Start[i]|;

[0030] Set the imaging control duration and denote it as t;

[0031] Based on the total operation duration of each drone and the imaging control duration, obtain the shooting frequency corresponding to each drone. Denote the shooting frequency when the drone numbered i performs low-altitude operations as N[i], then N[i] = T[i] / t;

[0032] According to the shooting frequency corresponding to each drone, divide the total operation duration of each drone into its corresponding N[i] time periods. Each drone takes corresponding meteorological cloud images within its N[i] time periods, and thus obtains several meteorological cloud images in several time periods of the location area where the drone is located.

[0033] Further, the process of superimposing and processing in each drone's local terminal to form a meteorological cloud atlas for the monitored location area includes:

[0034] Each drone is equipped with a local terminal for superimposing and processing all the meteorological cloud images obtained by the drone. The content of the superimposing and processing is: in the order of time from the earliest to the latest, perform image correction, feature analysis, and interval determination on each meteorological cloud image in turn;

[0035] Convert the meteorological cloud image into a standard image to be operated through image correction;

[0036] Obtain a number of feature point clouds corresponding to the standard image to be operated through feature analysis, and each feature point cloud is associated with corresponding point cloud reference information and point cloud dispersion;

[0037] Set a point cloud dimension cluster for interval determination. The point cloud dimension cluster includes a high-dimensional interval cluster, a medium-dimensional interval cluster, and a low-dimensional interval cluster, and different point cloud dimension clusters are associated with corresponding discrete intervals;

[0038] Denote the discrete intervals of the high-dimensional interval cluster, the medium-dimensional interval cluster, and the low-dimensional interval cluster as Ω1, Ω2, and Ω3 respectively, and denote the point cloud dispersion of the feature point cloud as τ;

[0039] Cluster all feature point clouds with τ∈Ω1 into high-dimensional meteorological cloud images;

[0040] Cluster all feature point clouds with τ∈Ω2 into medium-dimensional meteorological cloud images;

[0041] Cluster all feature point clouds with τ∈Ω3 into low-dimensional meteorological cloud images;

[0042] Overlay the high-dimensional meteorological cloud images, medium-dimensional meteorological cloud images, and low-dimensional meteorological cloud images in the same position area to obtain a meteorological cloud image set in the corresponding position area, and repeat the above operations to obtain the corresponding meteorological cloud image set for each position area.

[0043] Furthermore, the process of performing two-way data interaction with each unmanned aerial vehicle (UAV) through a data interaction network and then importing the meteorological cloud image set stored in each UAV into the data interaction network includes:

[0044] Configure the network environment of the data interaction network, create a number of interaction ports corresponding to the data interaction network, and determine whether the network environment is in a normal state;

[0045] If so, assign an interaction port to each UAV through the data interaction network, and enter an interaction file into the interaction port. Test whether the two-way data interaction between the data interaction network and the UAV is successfully established through the interaction file;

[0046] If so, import the meteorological cloud image set stored in each UAV into the data interaction network;

[0047] If not, locate the data interaction direction where the two-way data interaction is abnormal, retrieve the data information causing the current data interaction direction to be abnormal, and input it into a preset historical database to match the processing steps for solving the abnormality;

[0048] Otherwise, perform data cleaning on the data interaction network, filter abnormal data or suspected abnormal data in the data interaction network into a preset data isolation area, count the abnormal probability of the suspected abnormal data, and continue to divide the suspected abnormal data into normal data and abnormal data according to the abnormal probability, eliminate the abnormal data, and transmit the normal data back to the data interaction network.

[0049] Further, the process of parsing all meteorological cloud atlases in the data interaction network to obtain the actual meteorological conditions in different location areas includes:

[0050] The data parsing module accesses the data interaction network to obtain the meteorological cloud atlases of all location areas in the data interaction network. The data parsing module consists of a preprocessing unit and a parsing unit. The preprocessing unit checks whether the integrity of the meteorological cloud atlas corresponding to each location area meets the standard, checks whether the image format of the meteorological cloud atlas conforms to the preset image format, and removes invalid data and noise data in the meteorological cloud atlas;

[0051] Enter the meteorological cloud atlas processed by the preprocessing unit into the parsing unit. The parsing unit performs data parsing, feature extraction, and actual meteorological analysis on the meteorological cloud atlas of each location area to obtain the actual meteorological conditions of different location areas;

[0052] Sort out the analysis results of the actual meteorological analysis, and then generate an actual meteorological condition report corresponding to each location area.

[0053] Further, the process of the meteorological guarantee module implementing guarantee measures for location areas where the actual meteorological conditions do not meet the standard includes:

[0054] The meteorological guarantee module is provided with a warning database and a measure database;

[0055] The warning database is used to input the actual meteorological condition report corresponding to each location area. After the actual meteorological condition report is entered into the warning database, analyze and give early warnings to the actual meteorological condition report synchronously to obtain the risk coefficient, warning level, and warning type of the location area corresponding to the actual meteorological condition report;

[0056] Record the risk coefficient as γ, set the maximum limit value of the risk coefficient corresponding to when the meteorological conditions meet the standard, and record it as Max-D. When γ < Max-D, it means that the meteorological conditions in the corresponding location area are up to standard. When γ ≥ Max-D, it means that the meteorological conditions in the corresponding location area do not meet the standard;

[0057] Input the warning level and warning type obtained from the warning database for all location areas where the meteorological conditions do not meet the standard into the measure database;

[0058] The measure database is used to match and execute the corresponding safeguard measures for a location area according to the warning level and warning type of the location area. Different safeguard measures corresponding to different warning levels and warning types are stored in the measure database.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows: By means of the low-altitude operation of the unmanned aerial vehicle (UAV), meteorological cloud maps of a specific time period and location area can be obtained, improving the accuracy and real-time performance of low-altitude meteorological monitoring. The UAV control module can obtain the flight parameters of the UAV in real time and monitor its flight state, ensuring the stable acquisition and transmission of meteorological data. The data interaction network constructed by the data interaction module realizes two-way data interaction with the UAV, enabling the meteorological cloud map set to be quickly imported into the data interaction network for subsequent processing and analysis. The data analysis module can accurately analyze the meteorological cloud map set to obtain the actual meteorological conditions of different location areas. Based on this, the meteorological safeguard module executes safeguard measures for areas where the actual meteorological conditions do not meet the standards, effectively improving the ability to respond to sudden meteorological disasters and realizing rapid-response meteorological safeguard measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is the schematic diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0061] As Figure 1 shown, a low-altitude UAV meteorological safeguard system includes a monitoring center, and the monitoring center is communicatively connected to a UAV control module, a meteorological monitoring module, a data interaction module, a data analysis module, and a meteorological safeguard module;

[0062] The UAV control module is used to construct a control instruction set for a number of UAVs deployed inside the UAV platform, thereby controlling the UAV to perform low-altitude operations, and obtaining the flight parameters of the UAV during low-altitude operations in real time, supervising the actual flight state of the UAV through the flight parameters, and deciding whether to participate in flight control;

[0063] The meteorological monitoring module is used to take meteorological cloud maps of different time periods and location areas through the UAV performing low-altitude operations, and superimpose and process them into a meteorological cloud map set of the monitored location area in each local terminal of the UAV;

[0064] The data interaction module is used to construct a data interaction network, perform two-way data interaction with each UAV through the data interaction network, and thereby import the meteorological cloud map set stored in each UAV into the data interaction network;

[0065] The data analysis module is used to analyze all the meteorological cloud map sets in the data interaction network, and thereby obtain the actual meteorological conditions of different location areas;

[0066] The meteorological support module is used to execute support measures for location areas where the actual meteorological conditions do not meet the standards.

[0067] It should be further noted that in the specific implementation process, the UAV control module constructs a control instruction set for several UAVs deployed inside the UAV platform, and the process of controlling the UAVs to perform low-altitude operations includes:

[0068] Several UAVs are deployed inside the UAV platform, numbered, and the numbers are denoted as i, where i = 1, 2, 3,..., n, and n is a natural number greater than 0. Set the first operation period, the second operation period, and the third operation period corresponding to the UAV control module;

[0069] Among them, within the first operation period, control instructions are constructed for the UAVs. The control instructions include positioning and cruising instructions, hovering and obstacle avoidance instructions, and data collection instructions, and after integration, they are used as the initial control instruction set;

[0070] The UAVs fly within the location areas where meteorological monitoring is required by executing the positioning and cruising instructions; the UAVs maintain their positions unchanged or avoid the obstacles encountered within the location areas where monitoring is required by executing the hovering and obstacle avoidance instructions; the UAVs perform several shooting actions within the location areas where monitoring is required at the preset shooting frame rate, shooting time interval, and shooting parameters by executing the data collection instructions;

[0071] Within the second operation period, the initial control instruction set is input into each UAV deployed inside the UAV platform. When inputting the initial control instruction set, the input environment is synchronously monitored. When undefined alien information is detected in the input environment, the locations of all the alien information in the input environment are marked, and all the alien information at the locations is traversed in sequence;

[0072] Whenever the alien information at a location is traversed, the alien information is corrected. After all the alien information at all locations is corrected, the third operation period is started;

[0073] Within the third operation period, the UAVs perform a low-altitude operation test according to the input control instruction set, and judge whether the UAVs reach the corresponding preset completion standard when performing positioning and cruising, hovering and obstacle avoidance, and data collection according to the control instruction set;

[0074] If so, the control instruction set for the UAVs to perform low-altitude operations is completed, and the control instruction set of the UAVs is executed to control the UAVs to perform low-altitude operations; if not, the control instructions that do not meet the completion standard are modified until the modified control instructions can reach the corresponding completion standard after being executed by the UAVs;

[0075] Among them, the positioning cruise, hovering obstacle avoidance, and data collection carried out by each drone in its respective position area all belong to the content of low-altitude operations.

[0076] It should be further noted that in the specific implementation process, the flight parameters of the drone during low-altitude operations are obtained in real time, and the flight status of the drone is supervised through the flight parameters, and the process of deciding whether to participate in flight control includes:

[0077] The flight parameters of the drone during low-altitude operations include positioning cruise parameters, hovering obstacle avoidance parameters, and data collection parameters. Denote the flight parameters corresponding to the drone numbered i as Fly[i], then Fly[i] = {A[i], B[i], C[i]};

[0078] Among them, A[i] represents the positioning cruise parameters corresponding to the drone numbered i, B[i] represents the hovering obstacle avoidance parameters corresponding to the drone numbered i, and C[i] represents the data collection parameters corresponding to the drone numbered i;

[0079] The positioning cruise parameters include GPS signal strength, cruise speed, cruise altitude, and heading;

[0080] The hovering obstacle avoidance parameters include hovering altitude, vertical speed, horizontal speed, obstacle avoidance trajectory, hovering stability duration, and obstacle avoidance response time;

[0081] The data collection parameters include sensor parameters, shooting speed, shooting frames, collection mode, lens shake angle, and lens imaging parameters;

[0082] Set the parameter comparison tables corresponding to the positioning cruise parameters, hovering obstacle avoidance parameters, and data collection parameters respectively. If any of the positioning cruise parameters, hovering obstacle avoidance parameters, and data collection parameters does not conform to the corresponding parameter comparison table, the result of supervising the drone is: the flight status of the current drone is an abnormal state; otherwise, the result of supervising the drone is: the flight status of the current drone is a normal state;

[0083] When the flight status of the drone is in a normal state, no operation is performed;

[0084] When the flight status of the drone is in an abnormal state, it is decided to participate in the flight control of the corresponding drone.

[0085] It should be further noted that in the specific implementation process, the process by which the meteorological monitoring module captures meteorological cloud images in different time periods and position areas through the drones for low-altitude operations and superimposes and processes them into a meteorological cloud atlas of the monitored position area in each drone local terminal includes:

[0086] After all the drones deployed within the drone platform are started, each drone is responsible for performing low-altitude operations in a location area, starting from the serial number i = 1 and ending at the serial number i = n. Each time, the value of i is incremented by one. Set the start time and end time of the low-altitude operation corresponding to the drone with the serial number i, and record them as Start[i] and End[i] respectively;

[0087] Furthermore, obtain the total operation duration corresponding to the drone with the serial number i, and record it as T[i];

[0088] T[i] = |End[i] - Start[i]|;

[0089] Set the imaging control duration, and record it as t;

[0090] According to the total operation duration of each drone and the imaging control duration, obtain the shooting frequency corresponding to each drone. Record the shooting frequency when the drone with the serial number i performs low-altitude operations as N[i], then N[i] = T[i] / t;

[0091] According to the shooting frequency corresponding to each drone, divide the total operation duration corresponding to each drone into N[i] respective time intervals. Each drone takes corresponding numbers of meteorological cloud images within its N[i] time intervals, and thus obtains several meteorological cloud images under several time intervals in the location area where the drone is located;

[0092] A corresponding local terminal is set in each drone. The local terminal is used to perform superposition processing on all the meteorological cloud images obtained by the drone. The content of the superposition processing is: in the order of time from the earliest to the latest, perform image correction, feature analysis, and interval determination on each meteorological cloud image in turn;

[0093] Convert the meteorological cloud image into a standard image to be operated on through image correction;

[0094] Image correction includes removing image noise points, image distortion points, image bad points, and image blurry areas in the meteorological cloud image;

[0095] Obtain several feature point clouds corresponding to the standard image to be operated on through feature analysis. Each feature point cloud is associated with and marked with corresponding point cloud reference information and point cloud dispersion;

[0096] The point cloud reference information includes point cloud type, point cloud size, point cloud vector information, point cloud origin coordinates, and the position coordinates of each point cloud data included in the feature point cloud relative to the point cloud origin coordinates. Among them, record the point cloud origin coordinates as P 0=(0, 0), the position coordinates of each point cloud data relative to the point cloud origin coordinates are denoted as P = (x, y), where x is the horizontal distance between the point cloud data and the point cloud origin coordinates, y is the vertical distance between the point cloud data and the point cloud origin coordinates, and both x and y are real numbers greater than 0;

[0097] Set the point cloud dimension clusters for interval determination. The point cloud dimension clusters include high-dimensional interval clusters, medium-dimensional interval clusters, and low-dimensional interval clusters. Different point cloud dimension clusters are associated with corresponding discrete intervals. Among them, the discrete intervals of the high-dimensional interval cluster, medium-dimensional interval cluster, and low-dimensional interval cluster are denoted as Ω1, Ω2, and Ω3 respectively;

[0098] Ω1, Ω2, and Ω3 are specifically as follows:

[0099] Ω1 = [0.8, 1);

[0100] Ω2 = [0.5, 0.8);

[0101] Ω3 = (0, 0.5);

[0102] The value range of the point cloud dispersion degree corresponding to the characteristic point cloud is (0, 1);

[0103] Denote the point cloud dispersion degree corresponding to the characteristic point cloud as τ;

[0104] Cluster all the characteristic point clouds with τ ∈ Ω1 into high-dimensional meteorological cloud maps;

[0105] Cluster all the characteristic point clouds with τ ∈ Ω2 into medium-dimensional meteorological cloud maps;

[0106] Cluster all the characteristic point clouds with τ ∈ Ω3 into low-dimensional meteorological cloud maps;

[0107] Overlay the high-dimensional meteorological cloud maps, medium-dimensional meteorological cloud maps, and low-dimensional meteorological cloud maps in the same position area to obtain the meteorological cloud map set in the corresponding position area. Repeat the above operations to obtain the meteorological cloud map set corresponding to each position area.

[0108] It should be further noted that in the specific implementation process, the data interaction module constructs a data interaction network, and conducts two-way data interaction with each drone through the data interaction network. Then, the process of importing the meteorological cloud map set stored in each drone into the data interaction network includes:

[0109] The data interaction module constructs a data interaction network, configures the network environment of the data interaction network, creates several interaction ports corresponding to the data interaction network, each interaction port is associated with a corresponding port code, and judges whether the network environment is in a normal state;

[0110] If yes, allocate an interaction port to each drone through the data interaction network, enter the interaction file into the interaction port, and test whether the two-way data interaction between the data interaction network and the drone is successfully established through the interaction file;

[0111] If yes, the meteorological cloud atlas stored in each drone is imported into the data interaction network;

[0112] If not, locate the data interaction direction in which the two-way data interaction is abnormal, retrieve the data information that causes the current data interaction direction to be abnormal, input the retrieved data information into a preset historical database, and then match the processing steps and procedures for solving the abnormality from the historical database, and after executing the processing steps and procedures, restore the two-way data interaction between the drone and the data interaction network;

[0113] If not, the data interaction network is cleaned. Through data cleaning, abnormal data or suspected abnormal data in the data interaction network is screened into the preset data isolation area, and the abnormal probability of suspected abnormal data is counted in the data isolation area. According to the abnormal probability, the suspected abnormal data is further divided into normal data and abnormal data, the abnormal data is eliminated, and the normal data is transmitted back to the data interaction network.

[0114] The abnormal probability of the suspected abnormal data is recorded as Py, and the abnormal determination threshold is set, and the abnormal determination threshold is recorded as μ, wherein Py and μ are both real numbers greater than 0 and less than 1, and the suspected abnormal data are divided according to the numerical relationship between Py and μ;

[0115] When Py ≥ μ, the corresponding suspected abnormal data are classified as abnormal data;

[0116] When Py<μ, the corresponding suspected abnormal data is classified as normal data.

[0117] It should be further explained that, in the specific implementation process, the data analysis module analyzes all meteorological cloud atlases in the data interaction network, and then obtains the actual meteorological conditions of different location areas, including:

[0118] The data analysis module is connected to the data interaction network to obtain the meteorological cloud atlas corresponding to all the location areas in the data interaction network. The data analysis module is composed of a preprocessing unit and a parsing unit. The preprocessing unit checks whether the integrity of the meteorological cloud atlas corresponding to each location area meets the standard, and also checks whether the image format of the meteorological cloud atlas meets the preset image format, and removes invalid data and noise data in the meteorological cloud atlas.

[0119] Input the meteorological cloud atlas after being processed by the preprocessing unit into the analysis unit. The analysis unit performs data analysis, feature extraction, and real-time meteorological analysis on the meteorological cloud atlas of each location area, and then obtains the real-time meteorological conditions of each location area;

[0120] The real-time meteorological conditions are used to characterize the weather conditions, precipitation probability, and wind conditions of the corresponding location area; the weather conditions include sunny, cloudy, overcast, rainy, snowy, and foggy days, etc., and the wind conditions include the intensity and direction of the wind;

[0121] Data analysis includes parsing metadata, image decoding, and image registration;

[0122] The content of parsing metadata is: extract metadata such as the timestamp, location, and sensor information corresponding to the meteorological cloud atlas;

[0123] The content of image decoding is: decode the image format of the meteorological cloud atlas, and then convert the current image format into a compilable format;

[0124] The content of image registration is: mark the meteorological cloud atlases with different timestamps and shooting angles from the meteorological cloud atlas, establish a Cartesian coordinate system, and map the meteorological cloud atlases under different timestamps and shooting angles to the Cartesian coordinate system;

[0125] Feature extraction includes cloud type identification and cloud height estimation;

[0126] Locate different types of clouds through cloud type identification. Different types of clouds include cumulus clouds, stratus clouds, and cumulonimbus clouds, etc.; extract cloud atlas features from the meteorological cloud atlas, and estimate the cloud height through the cloud atlas features to obtain the height of the cloud layer in each location area;

[0127] The content of real-time meteorological analysis is: use the relevant data corresponding to the meteorological cloud atlas after feature extraction as the real-time analysis data, take the real-time analysis data as the analysis object, first calculate the cloud amount of different location areas, and judge the weather conditions of the corresponding location areas. The weather conditions include sunny, cloudy, overcast, rainy, snowy, and foggy days, etc.; then analyze the possibility and precipitation probability of precipitation in the current location area according to the cloud atlas features; finally, perform wind field inference to obtain the direction and intensity of the wind in the current location area;

[0128] Organize the analysis results of the real-time meteorological analysis, and then generate a real-time meteorological condition report corresponding to each location area, and use the information of the person in charge of each location area as the distinguishing identification information of the real-time meteorological condition report under the corresponding location area.

[0129] It should be further noted that in the specific implementation process, the process of the meteorological guarantee module implementing guarantee measures for the location areas where the real-time meteorological conditions do not meet the standards includes:

[0130] The meteorological support module is provided with a warning database and a measure database;

[0131] The warning database is used to input the actual meteorological condition reports corresponding to each location area. After the actual meteorological condition reports are entered into the warning database, the actual meteorological condition reports are analyzed and warned synchronously, and then the risk coefficient, warning level and warning type of the location area corresponding to the actual meteorological condition reports are obtained;

[0132] The risk coefficient is denoted as γ, and the highest limit value of the risk coefficient corresponding to the qualified meteorological conditions is set and denoted as Max-D. When γ < Max-D, it means that the meteorological conditions of the corresponding location area are qualified. When γ ≥ Max-D, it means that the meteorological conditions of the corresponding location area are unqualified;

[0133] The warning levels and warning types obtained from the warning database for all location areas with unqualified meteorological conditions are input into the measure database;

[0134] The measure database is used to match and obtain the corresponding support measures for the location area according to the warning level and warning type of the location area and execute them. The support measures corresponding to different warning levels and warning types are stored in the measure database;

[0135] The support measures include emergency response measures, safety protection measures and meteorological intervention measures.

[0136] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A low-altitude UAV weather support system, including a monitoring center, characterized in that: The monitoring center is communicatively connected to a drone control module, a meteorological monitoring module, a data interaction module, a data analysis module, and a meteorological support module; The drone control module is used to build a control instruction set for several drones deployed in the drone platform, thereby controlling the drones to perform low-altitude operations, and obtaining the flight parameters of the drones in real time when performing low-altitude operations, monitoring the actual flight status of the drones through the flight parameters, and deciding whether to participate in flight control; The meteorological monitoring module is used to take meteorological cloud images in different time periods and location areas by using a low-altitude drone, and to overlay and process the images into a meteorological cloud atlas of the monitored location area in each drone local terminal; The data interaction module is used to build a data interaction network, perform two-way data interaction with each drone through the data interaction network, and then import the meteorological cloud atlas stored in each drone into the data interaction network; The data analysis module is used to analyze all meteorological cloud atlases in the data interaction network, and then obtain the actual meteorological conditions of different location areas; The weather guarantee module is used to execute guarantee measures for the location area where the actual weather conditions do not meet the standards.

2. A low-altitude UAV weather support system according to claim 1, characterized in that: The process of building a control instruction set for several drones deployed in the drone platform and then controlling the drones to perform low-altitude operations includes: Deploy a number of drones in the drone platform and number them as i, i=1, 2, 3, ..., n, where n is a natural number greater than 0, and set a first operation period, a second operation period, and a third operation period; During the first operation period, control instructions are constructed for the drone. The control instructions include positioning and cruising instructions, hovering and obstacle avoidance instructions, and data collection instructions, which are integrated as the initial control instruction set. In the second operation period, the initial control instruction set is recorded into each drone. When recording the initial control instruction set, the recording environment is monitored synchronously. When pre-defined alienation information is detected, the location of all alienation information is marked and traversed in sequence. Whenever the alienation information at a location is traversed, correction is made until the alienation information at all locations is corrected, and then the third operation period is started; During the third operation period, the UAV performs a low-altitude operation test according to the recorded control instruction set, and determines whether the UAV meets the corresponding preset completion standards when performing positioning cruise, hovering obstacle avoidance and data collection according to the control instruction set; If so, the control instruction set is constructed, and the drone is controlled to perform low-altitude operations; If not, modify the control instructions that do not meet the completion criteria.

3. A low-altitude UAV weather support system according to claim 2, characterized in that: The process of obtaining the flight parameters of the drone in real time when it is performing low-altitude operations and monitoring the real-time flight status of the drone through the flight parameters includes: Flight parameters include positioning and cruising parameters, hovering and obstacle avoidance parameters, and data collection parameters; Positioning cruise parameters include GPS signal strength, cruise speed, cruise altitude and heading; Hover obstacle avoidance parameters include hovering height, vertical speed, horizontal speed, obstacle avoidance trajectory, hovering stability time, and obstacle avoidance response time; Data collection parameters include sensor parameters, shooting speed, shooting frame rate, acquisition mode, lens shake angle, and lens imaging parameters; Set the parameter comparison tables of positioning cruise parameters, hovering obstacle avoidance parameters and data collection parameters. If any of the positioning cruise parameters, hovering obstacle avoidance parameters and data collection parameters does not conform to the corresponding parameter comparison table, the result of supervising the UAV is: the current actual flight status of the UAV is abnormal; otherwise, the result of supervision is: the current actual flight status of the UAV is normal.

4. A low-altitude UAV weather support system according to claim 3, characterized in that: The process of deciding whether to engage in flight control includes: When the drone's live flight status is normal, no operation is performed; When the actual flight status of the UAV is abnormal, it is decided to participate in the flight control of the corresponding UAV.

5. A low-altitude UAV weather support system according to claim 4, characterized in that: The process of using low-altitude drones to take weather cloud images in different time periods and location areas includes: Each drone is responsible for low-altitude operation in a location area. Set the start time and end time of the low-altitude operation corresponding to the drone numbered i, and record them as Start[i] and End[i] respectively, and then obtain the total operation time of the drone numbered i, recorded as T[i]; T[i]=|End[i]-Start[i]|; Set the imaging control duration and record it as t; According to the total operation time and imaging control time of each drone, the shooting frequency of each drone is obtained. The shooting frequency of the drone numbered i when performing low-altitude operations is recorded as N[i], then N[i] = T[i] / t; According to the shooting frequency of each drone, the total operation time of each drone is divided into their corresponding N[i] time segments. Each drone takes a corresponding number of meteorological cloud pictures within its own N[i] time segments, and then obtains several meteorological cloud pictures in several time segments of the drone's location area.

6. A low-altitude UAV weather support system according to claim 5, characterized in that: The process of superimposing the meteorological cloud atlas of the monitored location area in each drone local terminal includes: Each UAV is equipped with a local terminal for superimposing all the meteorological cloud images obtained by the UAV. The superimposition processing includes: image correction, feature analysis and interval determination are performed on each meteorological cloud image in chronological order; Convert the meteorological cloud image into a standard operational image through image correction; Through feature analysis, several feature point clouds corresponding to the standard operation map are obtained, and each feature point cloud is associated with corresponding point cloud reference information and point cloud discreteness; Set the point cloud dimension cluster for interval judgment. The point cloud dimension cluster includes high-dimensional interval cluster, medium-dimensional interval cluster and low-dimensional interval cluster. Different point cloud dimension clusters are associated with corresponding discrete intervals. The discrete intervals of the high-dimensional interval cluster, the medium-dimensional interval cluster, and the low-dimensional interval cluster are denoted as Ω1, Ω2, and Ω3, respectively, and the point cloud discreteness of the feature point cloud is denoted as τ; Cluster all feature point clouds of τ∈Ω1 into high-dimensional meteorological cloud images; Cluster all feature point clouds of τ∈Ω2 into a medium-dimensional meteorological cloud map; Cluster all feature point clouds of τ∈Ω3 into low-dimensional meteorological cloud images; The high-dimensional meteorological cloud map, medium-dimensional meteorological cloud map and low-dimensional meteorological cloud map in the same location area are superimposed to obtain the meteorological cloud atlas in the corresponding location area. The above operation is repeated to obtain the corresponding meteorological cloud atlas in each location area.

7. A low-altitude UAV weather support system according to claim 6, characterized in that: The process of building a data interaction network, performing two-way data interaction with each drone through the data interaction network, and then importing the meteorological cloud atlas stored in each drone into the data interaction network includes: Configure the network environment of the data interaction network, create several interaction ports corresponding to the data interaction network, and determine whether the network environment is in a normal state; If yes, allocate an interaction port to each drone through the data interaction network, enter the interaction file into the interaction port, and test whether the two-way data interaction between the data interaction network and the drone is successfully established through the interaction file; If yes, the meteorological cloud atlas stored in each drone is imported into the data interaction network; If not, locate the data interaction direction in which the two-way data interaction is abnormal, retrieve the data information that causes the current data interaction direction to be abnormal, and input it into the preset historical database to match the processing steps for solving the abnormality; If not, the data interaction network is cleaned, and the abnormal data or suspected abnormal data in the data interaction network is screened into the preset data isolation area, and the abnormal probability of the suspected abnormal data is counted. The suspected abnormal data is further divided into normal data and abnormal data according to the abnormal probability, the abnormal data is eliminated, and the normal data is transmitted back to the data interaction network.

8. A low-altitude UAV weather support system according to claim 7, characterized in that: The process of parsing all meteorological cloud atlases in the data exchange network and obtaining the actual meteorological conditions in different locations includes: The data analysis module is connected to the data interaction network to obtain the meteorological cloud atlas of all the location areas in the data interaction network. The data analysis module is composed of a preprocessing unit and a parsing unit. The preprocessing unit checks whether the integrity of the meteorological cloud atlas corresponding to each location area meets the standard, checks whether the image format of the meteorological cloud atlas meets the preset image format, and removes invalid data and noise data in the meteorological cloud atlas; The meteorological cloud atlas processed by the preprocessing unit is input into the parsing unit, and the parsing unit performs data parsing, feature extraction and real-time meteorological analysis on the meteorological cloud atlas of each location area to obtain the real-time meteorological conditions of different location areas; The analysis results of the real-time meteorological analysis are collated to generate a real-time meteorological condition report corresponding to each location area.

9. A low-altitude UAV weather support system according to claim 8, characterized in that: The process of the meteorological guarantee module implementing guarantee measures for the location area where the actual meteorological conditions do not meet the standards includes: The meteorological support module is equipped with a warning database and a measures database; The early warning database is used to input the real-time meteorological condition report corresponding to each location area. After the real-time meteorological condition report is input into the early warning database, the real-time meteorological condition report is simultaneously analyzed and warned to obtain the risk coefficient, warning level and warning type of the location area corresponding to the real-time meteorological condition report; The risk factor is recorded as γ, and the maximum limit value of the corresponding risk factor when the meteorological conditions meet the standards is set and recorded as Max-D. When γ<Max-D, it means that the meteorological conditions in the corresponding location area are up to standard, and when γ≥Max-D, it means that the meteorological conditions in the corresponding location area are not up to standard; Input the warning level and warning type obtained from the warning database into the measures database for all locations where the meteorological conditions do not meet the standards; The measures database is used to match and execute the safeguard measures for the corresponding location area according to the warning level and warning type of the location area. The safeguard measures corresponding to different warning levels and warning types are stored in the measures database.

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