A Low-Altitude Meteorological Early Warning Method and System Based on the Internet of Things

Abstract

The present invention discloses a low-altitude weather warning method and system based on the Internet of Things, which relates to the technical field of low-altitude flight traffic control. The low-altitude weather warning method collects meteorological data in real time, including temperature, humidity, wind speed, air pressure, etc., by deploying a variety of Internet of Things sensor devices in the low-altitude flight area; uploads the collected meteorological data to the cloud server by using a data transmission network; combines a meteorological prediction model and historical big data to conduct real-time analysis and prediction of the low-altitude weather conditions; and when detecting abnormal or potentially dangerous meteorological conditions, sends warning messages to relevant users through a variety of communication methods. The present invention improves the accuracy and timeliness of low-altitude weather warning and effectively guarantees the safety of low-altitude flight air traffic.

Description

Technical Field

[0001] The present invention relates to the technical field of low-altitude flight traffic control, and particularly to a low-altitude meteorological early warning method and system based on the Internet of Things. Background Art

[0002] With the wide application of low-altitude aircraft such as drones, the low-altitude flight area has been gradually opened. In order to ensure the safety of low-altitude airspace flight, especially to avoid flight accidents caused by weather factors, therefore, through the Internet of Things technology, the environmental changes in the low-altitude area are sensed in real time, the flight state is monitored, data is transmitted and processed, to help low-altitude aircraft effectively respond to meteorological changes. At present, the low-altitude meteorological early warning system based on the Internet of Things is difficult to respond in time and give graded early warnings according to the interference suffered by meteorological changes at different times, and it is difficult to plan the optimal flight path and thus cannot avoid the safety hazards brought by meteorological impacts.

[0003] In summary, how to respond in time and give graded early warnings to the safety hazards brought by meteorological changes, so as to plan the optimal flight path to avoid flight risks is an urgent problem to be solved and optimized for the low-altitude meteorological early warning system based on the Internet of Things. Summary of the Invention

[0004] The present invention provides a low-altitude meteorological early warning method and system based on the Internet of Things, which solves the technical problem of how to respond in time and give graded early warnings to the safety hazards brought by meteorological changes, so as to plan the optimal flight path to avoid flight risks.

[0005] To solve the above technical problem, the present invention provides a low-altitude meteorological early warning method and system based on the Internet of Things, and the specific technical solutions are as follows:

[0006] In a first aspect, a low-altitude meteorological early warning method based on the Internet of Things includes:

[0007] S100, collecting a variety of historical meteorological data to obtain a meteorological data set; according to the meteorological data set, obtaining a variety of environmental parameters corresponding to each meteorological data item, and combining them into an environmental parameter set; obtaining the low-altitude flight space coordinates of the current time node to obtain flight coordinate data;

[0008] S200, constructing and training the meteorological data set and the environmental parameter set to obtain a meteorological prediction model;

[0009] S300, based on the meteorological prediction model, obtaining the environmental parameters of the low-altitude flight meteorological data of the current time node in real time, inputting the environmental parameters of the meteorological data of multiple time nodes into the meteorological prediction model to output the meteorological data prediction result representing the next time node;

[0010] The S400 determines the meteorological change trend according to the meteorological data prediction result of the meteorological prediction model; sets multiple warning thresholds for change trends, responds to the preset warning thresholds according to different meteorological change trends, and outputs warning information.

[0011] Plans multiple low-altitude flight strategies according to the meteorological change situation; the low-altitude flight strategy obtains flight path coordinate nodes according to the warning information; by adding the original coordinate nodes to the open list and continuously expanding the neighbor nodes, the path cost and estimated value are updated in real time until the target node is found or the open list is empty, that is, the safe coordinate nodes are obtained.

[0012] As a further optimization scheme of the present invention, multiple low-altitude flight strategies corresponding to multiple meteorological changes are planned according to various meteorological change situations; based on the low-altitude flight strategy, according to the warning information of multiple meteorological change trends, the low-altitude flight strategy is timely adjusted.

[0013] As a further optimization scheme of the present invention, the meteorological prediction model includes:

[0014] Constructs a data set to generate structure data from the meteorological data set and the environmental parameter set, encodes the structure data into sequence data, and trains to obtain the meteorological prediction model.

[0015] Inputs the sequence data into the meteorological prediction model; the meteorological prediction model includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, transmits the intermediate representation data of multiple hidden layers to the output layer, and the output layer outputs the meteorological data recognition results corresponding to multiple environmental parameters.

[0016] Inputs at least two environmental parameter data items of the current time node into the meteorological prediction model, and the output layer of the meteorological prediction model outputs the current meteorological data prediction result.

[0017] As a further optimization scheme of the present invention, determining the meteorological change trend according to the meteorological data prediction result of the meteorological prediction model includes:

[0018] According to the meteorological data prediction result, obtains the meteorological data and environmental parameters of multiple time nodes; based on the environmental parameters of the meteorological data at different time nodes, through Obtains the change rates of multiple environmental parameters at different time nodes;

[0019] In the formula, H represents the change rate between adjacent time nodes, and respectively represent at least one environmental parameter variable value A of the environmental parameter at time node t i and t i+1 and t i+1 represents ti The next time node of the time node, represents the time difference between the t-th and the (t + 1)-th adjacent time nodes;

[0020] Obtain the change rates between at least three different adjacent time nodes to obtain a control group of change rates; compare at least two of the control groups of change rates to obtain a comparison result; based on the existence of multiple different comparison results for different control groups of change rates, judge the meteorological change trend.

[0021] As a further optimized solution of the present invention, set multiple change trend warning thresholds, and according to different meteorological change trends, respond to the preset warning thresholds and output warning information, including:

[0022] Based on the multiple different comparison results existing in different control groups of change rates, set multiple change trend warning mechanisms; the multiple change trend warning mechanisms set a change trend warning threshold range [G i , G j , when the change rates of various environmental parameters at different time nodes of meteorological data show obvious changes within the warning threshold range, output the first meteorological warning information;

[0023] The first meteorological warning information includes an upward trend warning information and a downward trend warning information; when the comparison result of the control group of change rates of different environmental parameters is in an obvious upward trend, and at this time the change rate is gradually increasing, output the upward trend warning information; when the comparison result of the control group of change rates of different environmental parameters is in an obvious downward trend, and at this time the change rate is gradually decreasing, output the downward trend warning information.

[0024] As a further optimized solution of the present invention, the warning information further includes:

[0025] Based on the meteorological data set and the environmental parameter set, obtain the environmental parameter characteristics of different meteorological data; the environmental parameters include air pressure, temperature, humidity, wind speed and direction, precipitation, visibility, cloud height, and thunderstorm activity; obtain the characteristic properties of the environmental parameters existing in different meteorological changes through multiple sensors;

[0026] The first meteorological warning information monitors the rapid changes of environmental parameters to judge whether an extreme meteorological event may occur; first, set a warning threshold range, which is usually based on the meteorological data changes and the obvious fluctuations of environmental parameters in the past period of time;

[0027] Based on the first meteorological warning information, summarize the impact results of the rising or falling of environmental parameters that change significantly within the warning threshold range to obtain the final meteorological change trend information, and output the second meteorological warning information; the impact results of the rising or falling of the environmental parameters represent different meteorological changes caused by various environmental parameters with different change trends, and thus have different impacts on low-altitude flight.

[0028] The second meteorological warning information includes meteorological change improvement warning information and meteorological change deterioration warning information; when the change trends of various environmental parameters at the same time node conform to the environmental parameter characteristics of meteorological change improvement, output the meteorological change improvement warning information; when the change trends of various environmental parameters at the same time node conform to the environmental parameter characteristics of meteorological change deterioration, output the meteorological change deterioration warning information.

[0029] Compare the predicted meteorological change trend result with the actually occurring meteorological change to obtain the comparison result; and according to the comparison result, through To calibrate the predicted meteorological change trend result, where MSE represents the mean square error calibration algorithm, N represents the type of meteorological data, Represents the predicted value; Represents the true value.

[0030] As a further optimization scheme of the present invention, according to various meteorological change situations, plan low-altitude flight strategies corresponding to various meteorological changes, including:

[0031] Based on the flight coordinate data, obtain the low-altitude flight coordinate position of the current node; according to the meteorological data, set various low-altitude flight strategies.

[0032] The low-altitude flight strategy includes that under normal meteorological conditions, the low-altitude flight flies smoothly along the preset path; when abnormal meteorology occurs, based on the environmental parameters of the abnormal meteorological data, through To obtain the estimated values of the coordinate nodes involved in the abnormal meteorological flight path.

[0033] In the formula, f(x) represents the estimated value of the planned path coordinate node, g(x) represents the distance cost from the original coordinate node to the current coordinate node, h(x) represents the distance cost from the current coordinate node to the target coordinate node, and x represents a coordinate node in the path planning.

[0034] As a further optimization scheme of the present invention, add the original coordinate node to the open list, set g(x)=0; h(s) is the heuristic estimated value of the original coordinate node, where s represents the original coordinate node.

[0035] Select the node with the minimum distance from the open list, which is the current coordinate node S; expand each neighbor node S' of S to obtain g(S') and h(S'), where g(S') represents the updated distance cost from the original coordinate node to the current coordinate node, and h(S') is the estimated value of the real-time updated distance coordinate node;

[0036] When the neighbor node S' has not been added to the open list, add it to the open list; when the neighbor node S' is already in the list and the obtained path distance cost is smaller, update the path cost value in real time; move the current coordinate node S to the closed list to avoid repeated expansion;

[0037] Repeat the operation until the target coordinate node is found or the open list is empty.

[0038] As a further optimization scheme of the present invention, based on the low-altitude flight strategy, according to the early warning information of various meteorological change trends, timely adjust the low-altitude flight strategy, including:

[0039] Based on the low-altitude flight strategy, through To obtain the low-altitude flight yaw scheduling coordinates; the low-altitude flight yaw scheduling coordinates are the target coordinates for adjusting the low-altitude flight strategy;

[0040] In the formula, C(path) is the low-altitude flight path coordinate point after yaw scheduling, x i 、y i 、z i respectively represent the current low-altitude flight control parameters, and the control parameters include the coordinate position, flight direction and flight angle of the current low-altitude flight, T i represents the flight time, cost represents the flight risk or energy consumption cost based on meteorological changes, and n represents the number of low-altitude flight control parameters;

[0041] Based on the low-altitude flight yaw scheduling coordinates, obtain multiple low-altitude flight yaw scheduling coordinates to obtain the adjusted low-altitude flight route, through To gradually optimize the flight strategy, thereby obtaining the optimal flight path strategy; according to the optimal flight path strategy, avoid the interference of meteorological changes; in the formula, V(t) represents the optimal strategy cost at time t, represents the flight cost from time to t.

[0042] In the second aspect, the system is provided with an electronic device including a memory, a processor, and a program of a low-altitude meteorological early warning method based on the Internet of Things stored on the memory and executable on the processor. When the program of the low-altitude meteorological early warning method based on the Internet of Things is executed by the processor, it realizes the steps of a low-altitude meteorological early warning method based on the Internet of Things. The system includes:

[0043] Data acquisition module: It is used to collect a variety of historical meteorological data to obtain a meteorological data set; according to the meteorological data set, to obtain a variety of environmental parameters corresponding to each meteorological data item and compile them into an environmental parameter set; obtain the low-altitude flight space coordinates of the current time node to obtain flight coordinate data;

[0044] Model training module: It is used to construct and train the meteorological data set and the environmental parameter set to obtain a meteorological prediction model; based on the meteorological prediction model, obtain each environmental parameter in the low-altitude flight meteorological data of the current time node in real time, and input the meteorological data environmental parameters of multiple time nodes into the meteorological prediction model to output the meteorological data prediction result representing the next time node;

[0045] Early warning response module: It is used to judge the meteorological change trend according to the meteorological data prediction result of the meteorological prediction model; set a variety of change trend early warning thresholds, and according to different meteorological change trends, respond to the preset early warning thresholds and output early warning information.

[0046] The present invention has at least the following beneficial effects: The present invention collects a variety of historical meteorological data and constructs a meteorological data set; by collecting a variety of historical meteorological data, the diversity and comprehensiveness of the data source are ensured. These data include various meteorological factors such as air pressure, temperature, humidity, wind speed and direction, precipitation, visibility, cloud height, and thunderstorm activity, providing rich information for subsequent modeling; the construction of the meteorological data set provides high-quality data input for subsequent steps, can provide accurate historical data support for the prediction model, and reduce errors; by integrating a variety of meteorological data items and environmental parameters, the impact of meteorological conditions on low-altitude flight can be comprehensively evaluated, and the accuracy of the prediction result can be improved.

[0047] By constructing a meteorological prediction model; based on the training of the historical meteorological data set and the environmental parameter set, a special meteorological prediction model can be constructed according to the actual flight environment and requirements to accurately predict the meteorological changes in the low-altitude flight environment; by associating meteorological data and environmental parameters, the model can not only predict common weather changes, but also consider the special impact of the flight environment on meteorology (such as terrain, flight trajectory, and flight time), improving the prediction accuracy; by training the model with advanced algorithms (such as machine learning or deep learning), complex patterns in historical data can be automatically captured, enhancing the prediction ability of the model.

[0048] Then, it obtains real-time low-altitude flight meteorological data and conducts predictions. Based on the trained meteorological prediction model, it can obtain real-time low-altitude flight meteorological data at the current time node and dynamically predict future meteorological conditions, thus providing immediate decision-making support. By inputting data from multiple time nodes into the model, it can capture the trend of meteorological changes rather than making a prediction at a single time point, providing more comprehensive trend information. As real-time meteorological data is input, the model can continuously adjust the prediction during the flight to ensure the timeliness and accuracy of meteorological data and avoid errors that may be caused by static predictions.

[0049] Judge the meteorological change trend according to the meteorological prediction result and output early warning information. By judging the meteorological change trend based on the meteorological prediction result, it can early warn of potential severe weather or meteorological changes (such as strong winds, heavy rains, and rain-snow weather), reducing the flight safety risk. According to different meteorological change trends and set early warning thresholds, multi-level early warning responses can be achieved, thus providing targeted countermeasures. For example, a higher-level response is taken for sudden meteorological changes, while a lower-level response is taken for minor changes. The analysis of meteorological change trends and the output of early warning information provide scientific decision-making support for flight crew or dispatch centers, helping them adjust flight paths, control flight altitudes, or take other necessary safety measures. By accurately grasping the meteorological change trend, real-time safety warnings can be made to prevent potential impacts of meteorology on low-altitude flight and significantly improve flight safety.

[0050] Through the coordinated cooperation among various steps, it jointly provides effective support for meteorological prediction and decision-making in low-altitude flight. From data collection, model training to real-time prediction and early warning output, the whole process constructs a closed-loop meteorological prediction system, providing more accurate and real-time meteorological information for flight missions. By identifying meteorological change trends in advance and issuing early warnings, flight safety is enhanced. Real-time meteorological data and dynamic predictions can provide real-time and scientific decision-making basis for flight crew, reducing risks during flight. Brief Description of the Drawings

[0051] Figure 1 It is a schematic flowchart of a low-altitude meteorological early warning method based on the Internet of Things provided by an embodiment of the present invention;

[0052] Figure 2 It is a schematic diagram of a low-altitude meteorological early warning system based on the Internet of Things provided by an embodiment of the present invention. Detailed Embodiments

[0053] The present application will be further described in detail below with reference to the accompanying drawings. It is necessary to point out here that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the protection scope of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0054] A low-altitude weather warning method and system based on the Internet of Things provided in this embodiment are specifically implemented as follows:

[0055] As Figure 1 shown, a low-altitude weather warning method based on the Internet of Things includes the following steps:

[0056] S100, collect various historical weather data to obtain a weather data set; according to the weather data set, obtain various environmental parameters corresponding to each weather data item and compile them into an environmental parameter set; obtain the low-altitude flight space coordinates of the current time node to obtain flight coordinate data;

[0057] S200, construct and train the weather data set and the environmental parameter set to obtain a weather prediction model;

[0058] S300, based on the weather prediction model, obtain the environmental parameters of the low-altitude flight weather data of the current time node in real time, and input the environmental parameters of the weather data of multiple time nodes into the weather prediction model to output the weather data prediction result representing the next time node;

[0059] S400, judge the weather change trend according to the weather data prediction result of the weather prediction model; set various change trend warning thresholds, and respond to the preset warning thresholds according to different weather change trends and output warning information.

[0060] In the implementation of the present invention, various sources of historical weather data are collected based on step S100, including basic meteorological elements such as air pressure, temperature, humidity, wind speed and direction, precipitation, visibility, cloud height, and thunderstorm activity. The data may come from meteorological stations, satellite remote sensing, unmanned aerial vehicle monitoring, or various relevant environmental parameter sensors (lidar sensors for monitoring wind speed and direction, temperature sensors, humidity sensors, and air pressure sensors), etc.; for example, in clear weather, the typical characteristics of each environmental parameter can be summarized as:

[0061] Air pressure: Usually high and stable.

[0062] Temperature: High during the day, low at night, with a large temperature difference between day and night.

[0063] Humidity: Low.

[0064] Wind speed: Low, with a stable wind direction.

[0065] Precipitation: Almost zero.

[0066] Visibility: Very good, usually at the maximum value.

[0067] Cloud height: Cloudless or only thin high-altitude clouds.

[0068] Thunderstorm activity: Absent or extremely rare.

[0069] In rainy days, meteorological parameters usually show the following characteristics:

[0070] Atmospheric pressure: Low, usually in a low-pressure area or a frontal system.

[0071] Temperature: During rainfall, the temperature changes little, but may decrease, especially behind a cold front.

[0072] Humidity: Humidity rises significantly, approaching 100%.

[0073] Wind speed: Wind speed is high, and may increase sharply during heavy rainstorms.

[0074] Wind direction: Wind direction changes greatly, usually affected by a frontal surface or a low-pressure area.

[0075] Precipitation: Precipitation is large, possibly ranging from light rain to heavy rainstorms.

[0076] Visibility: Visibility decreases, and the stronger the precipitation, the worse the visibility.

[0077] Cloud height: Clouds are low and dense, with low-level clouds and cumulonimbus clouds being common.

[0078] Thunderstorm activity: Thunderstorms are common in summer, especially during heavy precipitation or rainstorms; they occur occasionally or not at all in winter.

[0079] In snowy days, meteorological parameters usually show as follows:

[0080] Atmospheric pressure: Low, usually under the influence of a low-pressure area or a frontal system.

[0081] Temperature: The temperature is low, usually below 0°C, and the lower the temperature, the denser the snowfall.

[0082] Humidity: Humidity is relatively high, but slightly lower than in rainy days because the water vapor content in the air is less at low temperatures.

[0083] Wind speed: Wind speed may be high, especially in blizzards or cold snaps.

[0084] Wind direction: Wind direction often changes, affected by low-pressure systems and frontal surfaces, and changes violently especially during storms.

[0085] Precipitation: Precipitation is usually small, mainly snow, and the snowfall is affected by temperature, humidity, and wind speed.

[0086] Visibility: Visibility is poor, especially in heavy snow, blizzard, or snowstorm weather, where visibility may drop significantly.

[0087] Cloud height: Clouds are low and dense, and the cloud base is usually low.

[0088] Thunderstorm activity: Thunderstorm activity is less, but it may occasionally occur in special weather (such as winter thunderstorms).

[0089] In haze weather, meteorological parameters usually show as follows:

[0090] Atmospheric pressure: Atmospheric pressure is high. Usually under the control of high pressure, the air does not flow, resulting in the inability of pollutants to disperse.

[0091] Temperature: Temperature changes are small and usually low. Especially in winter, the inversion effect may cause the temperature of the lower atmosphere to be low.

[0092] Humidity: Humidity is high. The moisture combines with pollutants to form haze particles, further affecting visibility.

[0093] Wind speed: Wind speed is low, and the air mobility is poor, resulting in the accumulation of pollutants in the near-surface layer and difficulty in dispersing.

[0094] Wind direction: Wind direction is stable, the wind force is weak, the air flow is not smooth, and the haze is easy to maintain.

[0095] Precipitation: Precipitation is small or there is no precipitation. When humidity is high, haze precipitation may occur, but it cannot effectively remove pollutants.

[0096] Visibility: Visibility is poor. Haze makes the visibility less than 1 km, and it can drop to a few hundred meters in severe cases.

[0097] Cloud height: Clouds are low and dense, usually stratus clouds, and the cloud base is close to the ground. Haze and low clouds jointly affect visibility.

[0098] Thunderstorm activity: Thunderstorm activity is extremely rare. Haze weather is usually accompanied by stable atmospheric conditions, lacking the conditions for thunderstorm formation.

[0099] According to the collected meteorological data (clear weather, rainy days, snowy days, haze days, and daytime periods), obtain the associated environmental parameters and preprocess and normalize the relevant environmental parameters involved in the relevant meteorological data to obtain data with a unified dimension. Then, use the data with a unified dimension to form an environmental parameter set through deep learning model rules or multi-task learning algorithms or induction, as the background input for meteorological prediction.

[0100] According to the task requirements at the current time node, collect relevant spatial coordinate data of low-altitude flight, such as the coordinates of the drone flight path or the three-dimensional spatial range of the flight area. This part of the data is used in combination with meteorological data to locate the low-altitude flight meteorological environment.

[0101] S200 inputs the collected meteorological data and environmental parameter set into the data model training process. This process includes data cleaning, normalization processing, and feature extraction to ensure the validity and consistency of the model input data; uses machine learning or deep learning algorithms (such as regression analysis, time series prediction, or convolutional neural network) to build a meteorological prediction model. By learning from historical meteorological data and environmental parameters, the model can extract the laws of weather changes.

[0102] S300 uses the real-time meteorological data and environmental parameters at the current time node (such as current temperature and humidity, wind speed, etc.), combined with the coordinate data of low-altitude flight, as the model input; inputs the meteorological data of multiple time nodes into the prediction model, and gradually calculates the meteorological prediction results of the next time node, such as the wind speed or rainfall in the next 10 minutes, etc. This recursive prediction method can provide real-time updated meteorological information for flight path planning.

[0103] S400 compares the changes in meteorological parameters at different time nodes based on the meteorological data prediction results output by the model to identify meteorological trends, such as a sudden drop in temperature, an increase in wind speed, or a fluctuation in air pressure, etc.; sets warning thresholds for different meteorological change trends, such as triggering corresponding warnings when the wind speed exceeds a certain critical value or the visibility is lower than a certain standard; when the predicted meteorological data reaches the set warning threshold, the system automatically issues a warning message (such as an alarm sound, a graphical prompt, or a text reminder), reminding relevant personnel to pay attention and providing coping suggestions.

[0104] The above steps cooperate with each other. Through multi-dimensional data input and machine learning models, the accuracy of meteorological prediction is improved, especially for the prediction of local weather changes in the low-altitude flight environment; based on real-time data for prediction and output of future meteorological trends, providing real-time meteorological guarantee for low-altitude aircraft such as drones; through the analysis and warning function of meteorological change trends, it can effectively avoid the impact of bad weather on low-altitude flight tasks and improve flight safety; using a variety of historical meteorological data and environmental parameters, the system can adapt to different terrains and climate conditions and be widely applied to various low-altitude flight tasks, such as express delivery drones, agricultural spraying, or post-disaster rescue; the warning mechanism helps pilots or drone operators make quick decisions, such as adjusting the flight path or aborting the task, which helps to save resources and avoid risks.

[0105] In another preferred embodiment of the present invention, according to various meteorological change conditions, low-altitude flight strategies corresponding to various meteorological changes are planned; based on the low-altitude flight strategies, according to the early warning information of various meteorological change trends, the low-altitude flight strategies are adjusted in a timely manner.

[0106] In the implementation of the present invention, first, according to historical meteorological data, real-time meteorological data, and environmental parameters, various meteorological change conditions that may affect low-altitude flight are identified. Common meteorological changes include increased wind speed, rainfall and snowfall, haze, pressure changes, etc.; corresponding low-altitude flight strategies are formulated for different meteorological change conditions. For example:

[0107] When the wind speed is relatively high, the flight strategy may be to reduce the flight altitude, select a sheltered area, or adjust the flight path;

[0108] During thunderstorm weather, the flight strategy may be to suspend the flight and continue the mission after the weather stabilizes;

[0109] In case of low visibility or heavy fog, it may be necessary to enhance the automatic navigation ability of the aircraft, slow down the flight speed, and maintain the altitude.

[0110] According to the types of meteorological changes and the requirements of the flight mission, multiple alternative flight strategies are planned, and the strategy library is updated and optimized to ensure flexible response to sudden meteorological changes.

[0111] Based on the low-altitude flight strategies, the flight strategies are adjusted according to the early warning information of the meteorological change trends. According to the meteorological prediction model, the early warning information of the meteorological change trends is received in real time. This information usually includes possible wind speed changes, precipitation, temperature changes, etc., to predict the meteorological changes in the next few minutes to hours in advance; the received early warning information is classified to determine which meteorological changes may have a direct impact on the flight mission. According to the severity of the impact, priorities are set. For example, thunderstorm weather and extreme wind speed changes may be set as high-priority early warnings, while temperature changes may belong to low-priority.

[0112] Based on the real-time meteorological change trends and early warning information, the system automatically or manually adjusts the flight strategies. If the meteorological change trends show that the wind speed is about to exceed the safe range, the system will immediately adjust the altitude or path of the aircraft, or suspend the flight until the meteorological conditions return to safety. The pilot or operator will also receive adjustment suggestions and reminders to ensure the safe conduct of the flight mission.

[0113] The collaborative cooperation among various steps is achieved by receiving real-time weather data from meteorological data sources, combining the geographical location of the flight mission and the current meteorological conditions, obtaining a trend prediction of meteorological changes, and analyzing the received meteorological data to identify meteorological changes that may affect low-altitude flight. According to different meteorological changes, the system will automatically plan corresponding flight strategies to ensure the smooth completion of the flight mission under different meteorological conditions; during the flight, if meteorological changes occur, the system will adjust the flight strategy in a timely manner according to the warning information. For example, if the meteorological change warning shows an increase in wind speed, the system will automatically adjust the flight altitude or route according to the planned wind speed strategy; continuously feedback the implementation effect of meteorological changes and flight strategies. If an unexpected situation occurs during the actual flight (such as the warning fails to accurately predict weather changes), the system will adjust the strategy according to the actual data feedback and optimize the strategy library to improve the ability to handle similar situations.

[0114] In a preferred embodiment of the present invention, the meteorological prediction model in step S200 includes:

[0115] S201, constructing a dataset generation structure data from the meteorological dataset and the environmental parameter set, encoding the structure data into sequence data, and training to obtain the meteorological prediction model;

[0116] S202, inputting the sequence data into the meteorological prediction model; the meteorological prediction model includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer, and an output layer, transmitting the intermediate representation data of multiple hidden layers to the output layer, and the output layer outputs the meteorological data recognition results corresponding to various environmental parameters;

[0117] S203, inputting at least two environmental parameter data items of the current time node into the meteorological prediction model, and the output layer of the meteorological prediction model outputs the current meteorological data prediction results.

[0118] In the implementation of the present invention, in step S201, the collected meteorological data and relevant environmental parameters are integrated to generate a dataset. The meteorological data may include, but is not limited to, air pressure, temperature, humidity, wind speed and direction, precipitation, visibility, cloud height, and thunderstorm activity, and the environmental parameter set may include other factors such as geographical location and time. These data are usually organized in a table or other structured format.

[0119] The sorted meteorological data and environmental parameter set are converted into a structured dataset. Usually, this structured dataset will contain multiple feature columns, each column representing a data type or environmental parameter, and the rows represent different time nodes or different observation samples.

[0120] Encode the constructed structured data for input into the deep learning model. Since meteorological data and environmental parameters usually have time series characteristics, the serialization process may be for handling time dependencies, i.e., the correlation between data before and after on the time axis. This step is transformed into an input format suitable for the deep learning model through certain encoding methods (e.g., numerical normalization, time series windowing, etc.).

[0121] By constructing the dataset and structured data, meteorological data and environmental parameters from different sources can be organically combined, enabling subsequent model training to handle rich and useful features; encoding into sequence data helps capture dependencies in the time series and improve the prediction accuracy of the model for future meteorological changes.

[0122] The sequence data encoded in step S201 in step S202 is input into the meteorological prediction model. This model may be a neural network (such as an LSTM or GRU network), containing multiple hidden layers to learn the temporal characteristics of the input data.

[0123] Meteorological prediction model structure:

[0124] Input layer: Receive the input data from step S201;

[0125] First, second, and third hidden layers: These hidden layers make the model able to learn complex patterns and time dependencies in the data by weighted combining the input data and performing non-linear transformations;

[0126] Output layer: Transmit the intermediate representation data of the multi-layer hidden layers to the output layer and output the meteorological prediction result;

[0127] Transmission of intermediate representation to the output layer: The outputs of each hidden layer will serve as intermediate representations and be passed layer by layer to the final output layer. The model will comprehensively consider the learning information of multiple hidden layers to make the final prediction decision.

[0128] Multiple hidden layers can give the model stronger learning ability to capture complex relationships in the data, especially the non-linear relationships between meteorological data and environmental parameters; through the deep network architecture, the model can better infer future meteorological changes in the face of different meteorological and environmental parameters.

[0129] Based on the meteorological prediction model constructed in step S202, at least two environmental parameter data items at the current time node are input into the meteorological prediction model as real-time data in step S203. This step is carried out after training for actual meteorological prediction.

[0130] Output of meteorological prediction results: Based on the environmental parameter data input at the current time node, the model calculates the corresponding meteorological prediction results through the trained hidden layer. This result may be an estimate of meteorological data such as temperature and precipitation at a future time point; by inputting the current environmental parameters, the model can achieve real-time meteorological prediction to help decision-makers quickly respond to meteorological changes; the trained meteorological prediction model can generate relatively accurate prediction results based on real-time input data. Especially in complex meteorological environments, the advantages of deep learning models can be better demonstrated.

[0131] In a preferred embodiment of the present invention, in step S400, based on the meteorological data prediction results of the meteorological prediction model to judge the meteorological change trend, it includes:

[0132] S401, based on the meteorological data prediction results, to obtain meteorological data and environmental parameters at multiple time nodes; based on the environmental parameters of meteorological data at different time nodes, through Obtain the change rates of various environmental parameters at different time nodes;

[0133] In the formula, H represents the change rate between adjacent time nodes, and respectively represent at least one environmental parameter variable value A of the environmental parameter at time node t i and t i+1 , and t i+1 represents the next time node after the t i time node, represents the time difference between the t and t + 1 adjacent time nodes;

[0134] S402, obtain at least three change rates between different adjacent time nodes to obtain a change rate control group; compare at least two of the change rate control groups to obtain a comparison result; based on the existence of multiple different comparison results for different change rate control groups, judge the meteorological change trend.

[0135] In the implementation of the present invention, in step S401, obtaining meteorological data and environmental parameters at multiple time nodes:

[0136] In step S202, the meteorological prediction model has generated meteorological data prediction results for each future time node. At this time, the system extracts meteorological data (such as temperature, humidity, wind speed, etc.) and environmental parameters (such as air pressure, geographical location, etc.) at multiple time nodes from the prediction results.

[0137] Based on the environmental parameters of meteorological data at different time nodes, associate the time nodes with the corresponding meteorological data and environmental parameters to construct meteorological forecasts and environmental data for each time node. This data can be continuous (e.g., time series of hours, days, weeks) or irregular (e.g., real-time data collected at specific time intervals).

[0138] For the meteorological data and their environmental parameters between different time nodes, calculate the rate of change between adjacent time nodes. The rate of change represents the speed of change of meteorological data or environmental parameters, usually the relative change of a certain value. For example, the rate of change of temperature can represent the rate of increase or decrease of temperature between two time points. The rate of change helps to quantify the change trend of meteorology and the environment; obtaining the rate of change can effectively quantify the dynamic changes of meteorological parameters and environmental data and reveal the change laws of meteorology;

[0139] Through the rate of change, the rate of change of meteorological parameters can be directly observed, which helps to better understand the urgency of meteorological changes, especially when predicting sudden meteorological events; obtaining the meteorological data and rates of change of multiple time nodes can lay a foundation for subsequent trend analysis and comparison.

[0140] In step S402, for the data of multiple time nodes, calculate the rate of change between each pair of adjacent time nodes. For example, assume there are time nodes t 1 、t 2 、t 3 etc., the rate of change between t 1 and t 2 can be calculated, and the rate of change between t 2 and t 3 ; ensure that there are at least three calculation results of the rate of change between different time nodes to ensure an accurate judgment of the change trend.

[0141] Combine the rates of change between different time nodes into a "rate of change control group", that is, compare the rates of change of each pair of adjacent time nodes. Through the control group, the change trends and patterns of meteorological parameters between different time periods can be clearly observed. The rate of change control group of multiple time nodes will provide rich information for judging the meteorological change trend.

[0142] The rate of change control group between multiple time nodes helps to compare meteorological changes from the perspective of multiple time periods, so as to more comprehensively understand the change trend of meteorology; through the control group method, the periodic and non-periodic fluctuations in meteorological changes can be effectively captured, providing a basis for subsequent prediction or alarm mechanisms.

[0143] Compare at least two rate of change control groups and analyze the differences between them. For example, compare from t 1 to t 2and t 2 to t 3 The change rate of meteorological parameters between them is observed to see if their changes are consistent, if there are large fluctuations, or if there are certain periodic or regular changes.

[0144] When comparing multiple change rate control groups, different trends in the change rate of meteorological data can be seen. For example, if the change rates at different time nodes are all positive and gradually increasing, it may indicate that meteorological parameters such as temperature are continuously rising. On the contrary, if the change rate shows fluctuations or decreases in multiple control groups, it may indicate that the meteorological changes tend to be stable or decreasing.

[0145] Comparing the change rate control groups at different time nodes helps to reveal the long-term or short-term trends of meteorological changes, helps predict future meteorological trends. When the change of one group of change rate control groups is abnormal (for example, the change rate in a certain time period is significantly different from other time periods), it represents meteorological abnormal phenomena such as climate mutations and extreme weather, and early warnings can be given in advance.

[0146] By synthesizing the comparison results of multiple change rate control groups, the accuracy of meteorological change trends can be enhanced, and scientific decisions can be made for the next step of prediction; finally, the trend of meteorological changes is judged according to the obtained comparison results. For example, if the change rate trends at multiple time nodes show consistent growth or decline, the model may judge that the meteorological change is a continuous upward or downward trend. If there are significant fluctuations in the change rate, it may indicate that the meteorological change tends to be unstable or there are sharp changes; judging the trend of meteorological changes not only needs to pay attention to the rate of change, but also consider factors such as the direction of change (such as temperature rising or falling), amplitude and periodicity. By synthesizing the comparison results of multiple change rate control groups, the system can give a more accurate prediction of meteorological trends, helping decision-makers formulate relevant countermeasures.

[0147] By judging the trend of meteorological changes, the changes in meteorological conditions can be predicted in advance, so as to take countermeasures in advance, which is particularly important in extreme weather warnings and disaster prevention; accurate judgment of the trend of meteorological changes can help relevant departments and personnel allocate resources reasonably, providing a scientific basis for industries such as agriculture, transportation, and aviation; synthesizing the judgments of multiple change rate control groups can improve the accuracy of meteorological change prediction, avoid the deviation of a single time node or a single change rate, and provide a more stable and reliable judgment of meteorological changes.

[0148] In a preferred embodiment of the present invention, in step S400, a variety of change trend warning thresholds are set, and according to different meteorological change trends, preset warning thresholds are responded to, and warning information is output, including:

[0149] Step S403, based on a variety of different comparison results of different change rate control groups, set a variety of change trend warning mechanisms; the variety of change trend warning mechanisms set a change trend warning threshold range [G i , G j . When the change rates of various environmental parameters at different time nodes of meteorological data show obvious changes within the warning threshold range, output the first meteorological warning information;

[0150] Step S404, the first meteorological warning information includes an upward trend warning information and a downward trend warning information; when the comparison result of the change rate control group of different environmental parameters is in an obvious upward trend, at this time the change rate is gradually increasing, so as to output the upward trend warning information; when the comparison result of the change rate control group of different environmental parameters is in an obvious downward trend, at this time the change rate is gradually decreasing, so as to output the downward trend warning information.

[0151] In the implementation of the present invention, in step S403, multiple change trend warning mechanisms are generated according to the comparison of the change rates of environmental parameters at different time nodes and the results of the control group. Different change rates correspond to different warning levels or different warning processing methods; by setting the change trend warning threshold range [Gi, Gj], that is, setting a change rate interval, when the change rates of various environmental parameters in meteorological data change significantly within this interval, a warning is triggered.

[0152] The warning threshold [Gi, Gj] is a dynamically adjustable range for flexible adjustment according to actual situations. For example, when some meteorological parameters change greatly within a specific time period, the threshold may be tighter and the warnings will be more frequent; by setting specific change trend thresholds, customized warnings can be carried out for different environmental parameter change trends, rather than just for a single change, making the warnings more accurate; the threshold range [Gi, Gj] can be dynamically adjusted according to real-time data and historical trends, avoiding false alarms or missed alarms that may be caused by fixed thresholds, improving the flexibility of the system; it can comprehensively judge meteorological changes based on the change rates of different environmental parameters, ensuring multi-angle and multi-level environmental change monitoring.

[0153] In step S404, when the comparison result of the change rate control group of environmental parameters shows that the change trend is significant (such as an upward trend or a downward trend) and obvious changes occur, the corresponding first meteorological warning information is output through the set warning mechanism; when the change rate of one or more environmental parameters shows a continuous upward trend, it indicates that the weather or meteorological environment may become more intense or unstable, and the system outputs the warning information of the upward trend; when the change rate shows that the environmental parameter continuously decreases, it means that the meteorological environment may become more stable, so as to output the warning information of the downward trend.

[0154] When there are obvious meteorological changes (whether it is an upward or downward trend), the system can quickly issue a warning in the initial stage, providing an opportunity for early prevention; by analyzing the change rate, it can accurately judge the future meteorological trend (such as the upcoming extreme weather or sudden drop in temperature), helping relevant departments to make preparations; and it can output different types of warning information according to different trends of change (upward or downward trend), so that relevant departments and the public can take different countermeasures according to the specific meteorological change trend.

[0155] In the process from step S403 to step S404, step S403 mainly sets a change trend warning mechanism according to historical and current meteorological data, and determines the change rate threshold [Gi, Gj], which lays a foundation for the subsequent warning mechanism; after setting the threshold range and warning mechanism in step S403, step S404 determines whether to trigger a warning according to the actual data and outputs the corresponding warning information. In other words, S404 depends on the warning mechanism setting of S403; step S403 sets different threshold ranges for different change rates, and step S404 generates specific warning information based on these set warning mechanisms, enabling the system to make a precise response to meteorological changes. Through this feedback mechanism, the system can continuously optimize the warning decision according to the actual data; the joint work of step S403 and S404 makes the warning mechanism flexible and can adapt to environmental changes in real time, providing meteorological warnings with high accuracy and reliability.

[0156] In a preferred embodiment of the present invention, step S403 further includes:

[0157] Step S4031, based on the meteorological data set and the environmental parameter set, to obtain the environmental parameter characteristics of different meteorological data; the environmental parameters include air pressure, temperature, humidity, wind speed and direction, precipitation, visibility, cloud height, and thunderstorm activity; the characteristic properties of the environmental parameters existing in different meteorological changes are obtained through multiple sensors;

[0158] Step S4032, the first meteorological warning information judges whether an extreme meteorological event may occur by monitoring the rapid change of environmental parameters; first, a warning threshold range is set, which is usually based on the change of meteorological data and the obvious fluctuation of environmental parameters in the past period of time;

[0159] Step S4033, based on the first meteorological warning information, summarize the influence results of the rising or falling of the environmental parameters that change significantly within the warning threshold range to obtain the final meteorological change trend information, and output the second meteorological warning information; the influence results of the rising or falling of the environmental parameters represent different meteorological changes generated by the environmental parameters of different change trends, and thus have different impacts on low-altitude flight;

[0160] Step S4034, the second meteorological warning information includes meteorological change improvement warning information and meteorological change deterioration warning information; when the change trends of various environmental parameters at the same time node conform to the environmental parameter characteristics of meteorological change improvement, the meteorological change improvement warning information is output; when the change trends of various environmental parameters at the same time node conform to the environmental parameter characteristics of meteorological change deterioration, the meteorological change deterioration warning information is output;

[0161] Step S4035, compare the predicted meteorological change trend result with the actually occurred meteorological change to obtain a comparison result; and according to the comparison result, calibrate the predicted meteorological change trend result through where MSE represents the mean square error calibration algorithm, N represents the type of meteorological data, represents the predicted value; represents the true value.

[0162] In the implementation of the present invention, in step S4031, based on the meteorological data set and the environmental parameter set, the environmental parameters required for different meteorological changes are obtained through step S100; and the characteristics of the environmental parameters will reflect the changes in different meteorological conditions and have different impacts on the traffic control of low-altitude flight; it can provide accurate basic data for subsequent meteorological prediction and warning. By extracting and analyzing the characteristics of the environmental parameters, a clear meteorological change background can be provided for the subsequent warning system.

[0163] Step S4032 determines whether there is a risk of extreme meteorological events by monitoring the rapid changes of environmental parameters. Based on step S403, a warning threshold range is set, usually based on past meteorological data and the fluctuations of environmental parameters; thus, possible extreme weather events such as strong winds, heavy rains, thunderstorms, etc. can be detected in time, and the warning program can be started in time, which helps to take preventive measures in advance.

[0164] In step S4033, after the first meteorological warning information is issued in step S403, the impacts of obvious changes (increase or decrease) of environmental parameters are summarized, the meteorological change trend is analyzed, and the second meteorological warning information is output. This step is to judge the specific trend of meteorological change (such as tending to deteriorate or improve) according to the data change after the first meteorological warning information; it can further refine the warning information, enhance the accuracy and response speed of meteorological warning, provide more accurate information for decision-makers, and help in the formulation of emergency measures.

[0165] Step S4034 determines whether the weather is improving or deteriorating based on the changing trend of environmental parameters. If the changing trend of environmental parameters conforms to the characteristics of improving or deteriorating weather changes, it outputs improving or deteriorating meteorological warning information respectively; to ensure that the meteorological warning information is more detailed and accurate, different responses can be made for specific meteorological changes. This not only helps to prevent extreme weather but also reduces unnecessary overreactions.

[0166] Step S4035 compares the predicted meteorological change trend with the actually occurring meteorological change to obtain a comparison result, and calibrates the prediction model according to the comparison result; by comparing the prediction with the actual situation, the accuracy of the prediction model can be corrected, making future meteorological warnings more accurate. This feedback mechanism continuously optimizes the warning system and improves its adaptability and reliability.

[0167] Through multiple warnings and gradual calibration, the system can provide more accurate meteorological warning information, reducing false alarms and missed reports; early warnings and multiple predictions enable decision-makers to take response measures in a timely manner, reducing the impact of extreme meteorological events on fields such as flight and safety; by refining the warning levels and comparing actual data, meteorological changes under different environmental conditions can be flexibly and real-timely responded to, enabling the entire system to adapt to different meteorological change scenarios.

[0168] In a preferred embodiment of the present invention, for planning low-altitude flight strategies corresponding to various meteorological changes based on the above-mentioned various meteorological change situations, it further includes:

[0169] Based on the flight coordinate data, obtain the low-altitude flight coordinate position of the current node; set various low-altitude flight strategies according to the meteorological data;

[0170] The low-altitude flight strategy includes that under normal weather, the low-altitude flight flies smoothly along a preset path; when abnormal weather occurs, based on the environmental parameters of the abnormal weather data, by to obtain the estimated values of the coordinate nodes involved in the abnormal weather flight path;

[0171] In the formula, f(x) represents the estimated value of the planned path coordinate node, g(x) represents the distance cost from the original coordinate node to the current coordinate node, h(x) represents the distance cost from the current coordinate node to the target coordinate node, and x represents a coordinate node in path planning.

[0172] In the implementation of the present invention, first extract the spatial position where the aircraft is currently located from the flight coordinate data. These coordinate data are from the aircraft's navigation system (such as GPS) or low-altitude flight monitoring system; accurately positioning the position of the aircraft in real time lays the foundation for the next flight path planning, ensuring that subsequent strategy adjustments are based on the real aircraft state.

[0173] Based on the current meteorological data, the system differentiates between normal and abnormal meteorological conditions and sets corresponding low-altitude flight strategies: under normal meteorological conditions, it flies smoothly along the preset path; under abnormal meteorological conditions, it dynamically adjusts the flight path using the environmental parameters of the abnormal meteorological data (such as wind speed, visibility, etc.); it provides a flexible flight control mechanism so that the aircraft can operate smoothly under different meteorological conditions and reduce the flight risks caused by meteorological changes.

[0174] Under abnormal meteorological conditions, path planning is based on the distance cost from the original coordinate node to the current coordinate node; it represents the consumption of the already flown path, which is usually related to meteorological conditions (wind speed, wind direction) and flight time.

[0175] The distance cost from the current coordinate node to the target coordinate node; it represents the predicted cost for the aircraft to fly from the current position to the final target position, estimated based on the current meteorological environment.

[0176] The system will calculate the path cost of the aircraft at different nodes in real time to select the path with the minimum cost as the flight plan; thus allowing the system to dynamically plan the path under abnormal meteorological conditions; by comprehensively evaluating the environmental impact factors, it selects the optimal path to reduce flight risks; it balances flight distance, time, and fuel consumption to achieve an economical and efficient flight; the aircraft flies strictly according to the preset path without frequently adjusting the path nodes; it simplifies flight operations, reduces complexity, and improves flight efficiency; based on the current meteorological changes, it adjusts the path plan to increase the flexibility of the flight strategy; it provides a reliable path adjustment plan in bad weather to improve the safety and adaptability of the aircraft; by obtaining the flight coordinate position and meteorological data in real time, the system can quickly respond to environmental changes; it sets multiple flight strategies that can adapt to normal and abnormal meteorological conditions to enhance the aircraft's ability to handle complex environments; by comprehensively considering the cost of path planning), it minimizes the threat of meteorology to low-altitude flight to the greatest extent; the dynamic path planning model ensures flight efficiency and stability by balancing path cost and flight safety.

[0177] In a preferred embodiment of the present invention, the original coordinate node is added to the open list, and g(x) = 0 is set; h(s) is the heuristic estimated value of the original coordinate node, where s represents the original coordinate node;

[0178] Select the node with the minimum distance from the open list, which is the current coordinate node S; expand each neighbor node S' of S to obtain g(S') and h(S'), where g(S') represents the updated distance cost from the original coordinate node to the current coordinate node, and h(S') is the estimated value of the real-time updated distance coordinate node;

[0179] If the neighbor node S’ has not been added to the open list, add it to the open list; if the neighbor node S’ is already in the list and the obtained path distance cost is smaller, update the path cost value in real time; move the current coordinate node S to the closed list to avoid repeated expansion;

[0180] Repeat the operation until the target coordinate node is found or the open list is empty.

[0181] In the implementation of the present invention, the original coordinate node is added to the open list. The open list (OpenList): This is a set of candidate nodes, initially containing the starting node. The starting value of each node represents the actual path cost from the starting point to that node, and the estimated cost from that node to the target node is usually estimated by a heuristic function (such as Euclidean distance or Manhattan distance).

[0182] Initially, the node of the starting point is set to 0 because the actual cost from the starting point to the starting point is g(x)=0, and h(s) is the heuristic estimated value of the original starting point; through this step, the system provides a basis for subsequent path expansion. Set g(x) and h(s) for each node, and obtain g(S’) and h(S’) through coordinate node expansion, which can help the algorithm determine which path target node is better and prepare for selecting the optimal path; select the node with the smallest distance from the open list as the current coordinate node. In the open list, select the node with the smallest current f(x)=g(x)+h(x) value as the current node, that is, the node most likely to move towards the target. This node will be used for further path expansion.

[0183] Selecting the node with the smallest distance enables the algorithm to always expand in the direction of the target, thereby improving the efficiency and accuracy of path planning and avoiding unnecessary expansion; expand each neighbor node of the current coordinate node to obtain the corresponding expanded nodes of the neighbor nodes, and update the estimated value of the distance from the coordinate node in real time. Process all neighbor nodes of the current node to obtain the actual cost from the current node to the neighbor node and the estimated cost from the neighbor node to the target node; it is obtained by adding the g(x) of the current node to the edge weight (or movement cost) from the current node to the neighbor node; when the heuristic estimated value of the neighbor node to the target node.

[0184] Update the expanded neighbor nodes of the neighbor nodes and obtain the path cost value in real time, so that the path cost of each node is accurately evaluated, thereby providing a basis for selecting the next expanded node; if the neighbor node is not in the open list, add it to the open list and prepare to expand it further; ensure that all possible paths are considered and all potential shortest path candidate nodes can be accessed in a timely manner; if the neighbor node is already in the list and the obtained path distance cost is smaller, update the path cost value in real time;

[0185] When a neighbor node already exists in the open list and the path through the current node is shorter (i.e., the new distance cost value is smaller), update the path distance to reflect the shorter path; this update process ensures the optimality of the path. By continuously optimizing the path cost of the expanded nodes, ensure that the final path cost is minimized; move the current coordinate node to the closed list to avoid re-expanding the current node. After the current node is processed, it is added to the closed list (ClosedList). The closed list contains all the nodes that have been expanded to avoid re-expanding them.

[0186] By moving the processed nodes to the closed list, avoid repeated calculations and improve the algorithm efficiency. In addition, it prevents dead loops in path planning and ensures that nodes are processed effectively; repeat the operation until the target coordinate node is found or the open list is empty; select the optimal node from the open list for expansion until the target node is found or the open list is empty; means that the path planning is completed and the shortest path can be obtained by backtracking the path from the target node; means that no path is found, indicating that the target is unreachable; by continuously repeating the expansion of nodes and updating the path cost, the algorithm ensures the final finding of the best path. If the open list is empty, it means that the path is unreachable, which helps to avoid the generation of incorrect or invalid paths.

[0187] In a preferred embodiment of the present invention, based on the above low-altitude flight strategy, according to the warning information of various meteorological change trends, timely adjust the low-altitude flight strategy, and further include:

[0188] Based on the low-altitude flight strategy, through to obtain the low-altitude flight yaw scheduling coordinates; the low-altitude flight yaw scheduling coordinates are the target coordinates for adjusting the low-altitude flight strategy;

[0189] In the formula, C(path) is the low-altitude flight path coordinate point after yaw scheduling, x i 、y i 、z i respectively represent the current adjustment parameters of the low-altitude flight. The adjustment parameters include the current coordinate position, flight direction and flight angle of the low-altitude flight, T i represents the flight time, cost represents the flight risk or energy consumption cost based on meteorological changes, and n represents the number of low-altitude flight adjustment parameters;

[0190] Based on the low-altitude flight yaw scheduling coordinates, obtain multiple low-altitude flight yaw scheduling coordinates to obtain the adjusted low-altitude flight route, through to gradually optimize the flight strategy, thereby obtaining the optimal flight path strategy; according to the optimal flight path strategy to avoid the interference of meteorological changes; in the formula, V(t) represents the optimal strategy cost at time t, represents from time Flight cost to t;

[0191] In the implementation of the present invention, according to the low-altitude flight strategy, the yaw scheduling coordinates are first calculated by obtaining the regulation parameters of low-altitude flight. The regulation parameters of low-altitude flight include the current coordinate position (x i , y i , z i ), flight direction, flight angle and flight time (T i ); when these parameters jointly define the flight path and the dynamic state of flight; x i , y i and z i represent the three-dimensional coordinates of the current position of the aircraft, determining the heading and inclination of the aircraft; t i represents time, that is, the state of the aircraft at a given time point.

[0192] Through the above regulation parameters, the yaw scheduling coordinates of low-altitude flight can be obtained, that is, the updated flight path or position, and these coordinate points are the target coordinates that the aircraft needs to reach next; through accurate regulation parameters, dynamic and real-time path adjustment can be provided for low-altitude aircraft, so that the aircraft can flexibly respond to real-time changing environmental conditions;

[0193] Based on the obtained yaw scheduling coordinates of low-altitude flight, the system gradually optimizes the low-altitude flight path through the calculation of multiple scheduling coordinate points. Each newly calculated flight path is based on the result of optimizing the previous path, gradually reducing energy consumption, flight time and flight risk; the goal of optimization is to avoid adverse weather conditions, reduce flight risks, or optimize flight energy efficiency by adjusting path coordinates. The optimization process is usually based on a cost function, where the cost function may include factors such as risk assessment of the flight path, energy consumption, time cost, etc.; thus, the optimization strategy can effectively reduce flight risks, especially in low-altitude flight, which is affected by factors such as weather changes, obstacles, and the performance of the aircraft itself. Gradually optimizing the path can help the aircraft avoid danger and improve flight safety and efficiency.

[0194] Finally, the system will calculate an optimal flight path that can minimize the flight cost while ensuring flight safety. It usually involves factors such as the energy consumption and flight time of the flight path; at each moment t, the system will adjust the flight path according to real-time data such as flight status and meteorological changes to optimize the flight cost. Through this process, the optimization of the flight path can be updated in real time to adapt to the changes in the flight environment; through the optimization of this optimal path, the aircraft can not only effectively avoid the risks brought by meteorological changes, but also maximize the flight energy efficiency, reduce energy consumption, and extend the flight time. At the same time, since the path optimization process is adjusted in real time, the system can flexibly respond to various uncertain factors that occur during the flight to ensure the stability and safety of the flight.

[0195] A key objective of the optimal flight path strategy is to avoid the interference of meteorological changes. The optimization of the flight path will combine real-time meteorological data (such as wind speed, air pressure, temperature, etc.) to adjust the flight route. Through this adjustment, the aircraft can avoid encountering severe weather areas and reduce the risks during the flight; since meteorological factors are one of the key factors affecting low-altitude flight, being able to effectively avoid meteorological interference can greatly improve flight safety and avoid accident risks brought by meteorological anomalies, such as storms, lightning, strong airflows, etc.

[0196] During the entire flight process, the calculation of the cost function and the flight cost is the basis of the gradual optimization process. These costs reflect the impacts in aspects such as the cost, energy consumption, and time during the flight. By optimizing these cost functions, the flight path can be adjusted to the optimal solution to ensure that the aircraft completes the flight mission at the lowest cost (including energy, time, and risk); minimizing the flight cost can not only improve flight efficiency and reduce energy consumption, but also provide optimal decisions at each moment of the flight to ensure that the aircraft can operate stably in a complex environment and avoid unnecessary delays and additional costs.

[0197] As Figure 2 shown, an Internet of Things-based low-altitude meteorological early warning system includes:

[0198] Data acquisition module: It is used to collect a variety of historical meteorological data to obtain a meteorological data set; according to the meteorological data set, to obtain a variety of environmental parameters corresponding to each meteorological data item and compile them into an environmental parameter set; obtain the low-altitude flight space coordinates of the current time node to obtain flight coordinate data;

[0199] Model training module: It is used to construct and train the meteorological data set and the set of environmental parameters to obtain a meteorological prediction model; based on the meteorological prediction model, the environmental parameters in the low-altitude flight meteorological data at the current time node are obtained in real time, and the meteorological data environmental parameters of multiple time nodes are input into the meteorological prediction model to output the prediction result of the meteorological data representing the next time node;

[0200] Early warning response module: It is used to judge the meteorological change trend according to the meteorological data prediction result of the meteorological prediction model; set a variety of change trend early warning thresholds, and respond to the preset early warning thresholds according to different meteorological change trends, and output early warning information.

[0201] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device can be a well-known general-purpose intelligent device. Therefore, the object of the present invention can also be achieved only by providing a program product containing program codes for implementing the method or system. It should also be noted that in the device and method of the present invention, obviously, each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present invention. And the steps of performing the above series of processes can naturally be executed in chronological order according to the description, but it is not necessary to execute them in chronological order. Some steps can be executed in parallel or independently of each other.

Claims

1. A low-altitude meteorological early warning method based on the Internet of Things, characterized in that: The following methods are included: S100, collecting a variety of historical meteorological data to obtain a meteorological data set; obtaining a variety of environmental parameters corresponding to each meteorological data item according to the meteorological data set, and assembling the environmental parameter set; obtaining the low-altitude flight space coordinates of the current time node to obtain flight coordinate data; S200, constructing and training the meteorological data set and the environmental parameter set to obtain a meteorological prediction model; S300, based on the meteorological prediction model, real-time acquisition of low-altitude flight meteorological data environment parameters at the current time node, inputting the meteorological data environment parameters at multiple time nodes into the meteorological prediction model to output a meteorological data prediction result representing the next time node; S400, judging the weather change trend according to the weather data prediction result of the weather prediction model; Set a variety of change trend warning thresholds, respond to the preset warning thresholds according to different meteorological change trends, and output warning information; The method of determining the meteorological change trend based on the meteorological data prediction results of the meteorological prediction model includes: According to the meteorological data prediction results, meteorological data and environmental parameters of multiple time nodes are obtained; based on the environmental parameters of meteorological data at different time nodes, Obtain the change rates of various environmental parameters at different time nodes; In the formula, H represents the change rate between adjacent time nodes, and Respectively represent the time node t i and t i+1 At least one environmental parameter variable value A, and t i+1 Indicates t i The time node after the time node, Represents the time difference between the tth and t+1th adjacent time nodes; Obtaining the change rates between at least three different adjacent time nodes to obtain a change rate control group; comparing at least two of the change rate control groups to obtain a comparison result; judging the meteorological change trend according to the presence of a plurality of different comparison results of different change rate control groups; Among them, multiple change trend warning thresholds are set, and according to different meteorological change trends, the preset warning thresholds are responded to and warning information is output, including: Based on the various comparison results of different change rate control groups, a variety of change trend warning mechanisms are set; the various change trend warning mechanisms set a change trend warning threshold range [Gi, Gj], and the warning threshold [Gi, Gj] is a dynamically adjustable range for dynamic adjustment according to real-time data and historical trends. When the change rate of various environmental parameters at different time nodes of meteorological data changes significantly within the warning threshold range, the first meteorological warning information is output; The first meteorological warning information includes an upward trend warning information and a downward trend warning information; when the comparison result of the control group of the change rate of different environmental parameters is in an obvious upward trend, the change rate is gradually increasing, so as to output the upward trend warning information; when the comparison result of the control group of the change rate of different environmental parameters is in an obvious downward trend, the change rate is gradually decreasing, so as to output the downward trend warning information; A variety of low-altitude flight strategies are planned according to meteorological changes; the low-altitude flight strategy obtains flight path coordinate nodes according to warning information; by adding the original coordinate node to the open list and continuously expanding the neighboring nodes, the path cost and estimated value are updated in real time until the target node is found or the open list is empty, that is, the safe coordinate node is obtained.

2. The low-altitude meteorological early warning method based on the Internet of Things according to claim 1 is characterized in that: According to various meteorological changes, low-altitude flight strategies corresponding to various meteorological changes are planned; based on the low-altitude flight strategies, according to the early warning information of various meteorological change trends, the low-altitude flight strategies are adjusted in time.

3. The low-altitude meteorological early warning method based on the Internet of Things according to claim 1 is characterized in that: The meteorological prediction model comprises: The meteorological data set and the environmental parameter set are used to construct a data set to generate structured data, and the structured data is encoded into sequence data to train the meteorological prediction model; The sequence data is input into the weather forecast model; the weather forecast model comprises an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, the intermediate representation data of multiple hidden layers are transmitted to the output layer, and the output layer outputs the weather data recognition results corresponding to the multiple environmental parameters; At least two environmental parameter data items at the current time node are input into the meteorological prediction model, and the output layer output of the meteorological prediction model represents the current meteorological data prediction result.

4. The low-altitude meteorological early warning method based on the Internet of Things according to claim 1 is characterized in that: The warning information also includes: Based on the meteorological data set and the environmental parameter set, the environmental parameter characteristics of different meteorological data are obtained; based on the first meteorological warning information, the impact results of the increase or decrease of the environmental parameters that have changed significantly within the warning threshold range are summarized to obtain the final meteorological change trend information, so as to output the second meteorological warning information; The second meteorological warning information includes warning information for improved meteorological changes and warning information for worse meteorological changes; when the change trend of each environmental parameter at the same time node meets the environmental parameter characteristics of improved meteorological changes, the warning information for improved meteorological changes is output; when the change trend of each environmental parameter at the same time node meets the environmental parameter characteristics of worse meteorological changes, the warning information for worse meteorological changes is output; Compare the predicted meteorological change trend results with the actual meteorological changes to obtain the comparison results; and according to the comparison results, The meteorological change trend results predicted by calibration are: MSE represents the mean square error calibration algorithm, N represents the type of meteorological data, represents the predicted value; Represents the true value.

5. The low-altitude meteorological early warning method based on the Internet of Things according to claim 4 is characterized in that: According to various weather changes, we plan low-altitude flight strategies corresponding to various weather changes, including: Based on the flight coordinate data, obtain the low-altitude flight coordinate position of the current node; according to the meteorological data, set a variety of low-altitude flight strategies; The low-altitude flight strategy includes: in normal weather conditions, the low-altitude flight is smoothly flown along a preset path; when abnormal weather conditions occur, the environmental parameters based on the abnormal weather data are To obtain the estimated values ​​of the coordinate nodes involved in the abnormal weather flight path; In the formula, f(x) represents the estimated value of the planned path coordinate node, g(x) represents the distance cost from the original coordinate node to the current coordinate node, h(x) represents the distance cost from the current coordinate node to the target coordinate node, and x represents a coordinate node in the path planning.

6. The low-altitude meteorological early warning method based on the Internet of Things according to claim 5 is characterized in that: Add the original coordinate node to the open list and set g(x)=0; h(s) is the heuristic estimate of the original coordinate node, where s represents the original coordinate node; Select the node with the smallest distance from the open list, which is the current coordinate node S; expand each neighbor node S' of S to obtain g(S') and h(S'), where g(S') represents the update distance cost from the original coordinate node to the current coordinate node, and h(S') is the estimated value of the real-time update distance coordinate node; If the neighbor node S' has not been added to the open list, add it to the open list; if the neighbor node S' is already in the list, the obtained path distance cost is smaller, and the path cost value is updated in real time; move the current coordinate node S to the closed list to avoid repeated expansion; Repeat the operation until the target coordinate node is found or the open list is empty.

7. The low-altitude meteorological early warning method based on the Internet of Things according to claim 6 is characterized in that: Based on the low-altitude flight strategy, the low-altitude flight strategy is adjusted in a timely manner according to the early warning information of various meteorological change trends, including: Based on the low-altitude flight strategy, To obtain the low-altitude flight yaw scheduling coordinates; the low-altitude flight yaw scheduling coordinates are the target coordinates for regulating the low-altitude flight strategy; Where C(path) is the coordinate point of the low-altitude flight path after yaw scheduling, x i ,y i and z i They represent the control parameters of the current low-altitude flight, which include the coordinate position, flight direction and flight angle of the current low-altitude flight, T i represents the flight time, cost represents the flight risk or energy consumption cost based on weather changes, and n represents the number of low-altitude flight control parameters; Based on the low-altitude flight yaw scheduling coordinates, a plurality of the low-altitude flight yaw scheduling coordinates are obtained to obtain a low-altitude flight route after regulation, The flight strategy is gradually optimized to obtain the optimal flight path strategy; the interference of weather changes is avoided according to the optimal flight path strategy; where V(t) represents the optimal strategy cost at time t, Indicates from time The flight cost to t.

8. A low-altitude meteorological early warning system based on the Internet of Things, characterized in that: The system is provided with an electronic device including a memory, a processor, and a low-altitude meteorological early warning method program based on the Internet of Things stored in the memory and executable on the processor. When the low-altitude meteorological early warning method program based on the Internet of Things is executed by the processor, the steps of the low-altitude meteorological early warning method based on the Internet of Things are implemented as described in any one of claims 1 to 7. The system includes: Data collection module: It is used to collect a variety of historical meteorological data to obtain a meteorological data set; according to the meteorological data set, to obtain a variety of environmental parameters corresponding to each meteorological data item, and to compile an environmental parameter set; to obtain the low-altitude flight space coordinates of the current time node to obtain flight coordinate data; Model training module: it is used to construct and train the meteorological data set and the environmental parameter set to obtain a meteorological prediction model; based on the meteorological prediction model, various environmental parameters in the low-altitude flight meteorological data at the current time node are obtained in real time, and the meteorological data environmental parameters of multiple time nodes are input into the meteorological prediction model to output the meteorological data prediction result representing the next time node; Early warning response module: It is used to judge the meteorological change trend according to the meteorological data prediction results of the meteorological forecast model; set multiple change trend early warning thresholds, respond to the preset early warning thresholds according to different meteorological change trends, and output early warning information.

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