Meteorological observation-based low-altitude aircraft icing early warning method and system

Abstract

The invention provides a low-altitude aircraft icing early warning method and system based on meteorological observation. The method comprises the following steps: acquiring historical meteorological data, historical flight data, aircraft data and icing conditions of a low-altitude aircraft in the same period; respectively establishing corresponding neural network models according to the division of the ice accretion components, and respectively training to obtain corresponding target neural network models; acquiring various data acquired by the low-altitude aircraft in real time, respectively inputting the data into the corresponding target neural network models, and outputting corresponding icing thickness and icing type; according to an output result of the target neural network model, judging whether the preset icing component reaches an early warning standard or not; if yes, first early warning is carried out; if not, weighting processing is carried out according to the icing thickness and the icing type of each icing component, and an evaluation value is obtained; judging whether the evaluation value is greater than a threshold; and if yes, second early warning is carried out, specifically, the flight risk can be effectively reduced by evaluating the icing condition of the aircraft.

Description

Technical Field

[0001] The present invention belongs to the technical field of low-altitude aircraft icing warning, and in particular relates to a low-altitude aircraft icing warning method and system based on meteorological observation. Background Art

[0002] Low-altitude aircraft refer to all types of aircraft flying at a relatively low altitude from the ground. Low-altitude aircraft generally fly in airspace below 1,000 meters. There is an icing risk in the cloud layer range of 0-3,000 meters, especially at an altitude of 1,500 to 3,000 meters, which is a common high-incidence area for low-altitude aircraft such as helicopters.

[0003] In order to reduce the risk of aircraft icing when manned low-altitude aircraft are performing flight missions, predicted meteorological data is usually obtained and the flight route is planned based on the predicted meteorological data. However, this method is more dependent on the accuracy of weather forecasts. Understandably, the more accurate the predicted meteorological data and the more reasonable the planned flight route, the lower the probability of aircraft icing. In other words, if the weather changes suddenly and the pilot still flies according to the established route, or takes countermeasures too late, there will be greater safety hazards. Summary of the invention

[0004] Based on this, an embodiment of the present invention provides a low-altitude aircraft icing warning method and system based on meteorological observation, which aims to evaluate the icing condition of the aircraft in real time according to meteorological data to reduce flight risks.

[0005] A first aspect of an embodiment of the present invention provides a low-altitude aircraft icing warning method based on meteorological observation, the method comprising: Obtain historical meteorological data, historical flight data, aircraft data and ice accumulation conditions of the low-altitude aircraft in the same period, wherein the historical meteorological data includes temperature, humidity, wind speed, wind direction, air pressure, precipitation type and relative position of clouds, the historical flight data includes flight altitude, flight speed and flight time, the aircraft data includes material and surface characteristics, and the ice accumulation conditions include ice accumulation components, ice thickness and ice accumulation type; According to the division of ice accumulation components, corresponding neural network models are established respectively, and training is performed respectively to obtain corresponding target neural network models. Specifically, historical meteorological data, historical flight data and aircraft data are used as inputs of the neural network model, and ice accumulation thickness and ice accumulation type at preset time intervals are used as outputs of the neural network model. Acquire meteorological data, flight data and aircraft data collected by low-altitude aircraft in real time, input them into the target neural network model corresponding to each icing component, and output the corresponding ice thickness and ice type; According to the output results of the target neural network model, it is determined whether the preset ice accumulation components meet the warning standard; If it is determined that the preset ice accumulation component reaches the warning standard, a first warning is issued; If it is determined that the preset ice accumulation component does not meet the warning standard, weighted processing is performed according to the ice accumulation thickness and ice accumulation type of each ice accumulation component to obtain an evaluation value; Determine whether the evaluation value is greater than a threshold; If it is determined that the evaluation value is greater than the threshold, a second warning is issued.

[0006] Furthermore, the method further comprises: An initial planned path is obtained, and the initial planned path is adjusted according to the warning situation.

[0007] Furthermore, the step of obtaining the initial planned path and adjusting the initial planned path according to the warning situation includes: Obtain a three-dimensional satellite cloud image of the current low-altitude aircraft's flight position, and determine the target area in the three-dimensional satellite cloud image that is conducive to alleviating icing; When the warning situation is the first warning, a first flight path for the low-altitude aircraft to enter the first target area is planned according to a first target area closest to the current flight position of the low-altitude aircraft, and the initial planned path is adjusted according to the first flight path; When the warning situation is the second warning, the weather forecast data is corrected according to the three-dimensional satellite cloud map, and the target three-dimensional satellite cloud map is generated according to the corrected weather forecast data, and a second flight path with the least deviation from the initial planned path of the low-altitude aircraft is planned, and the initial planned path is adjusted according to the second flight path.

[0008] Furthermore, the step of generating a target three-dimensional satellite cloud map according to the revised meteorological forecast data and planning a second flight path of the low-altitude aircraft with the least deviation from the initial planned path includes: Mapping the initial planned path to the target three-dimensional satellite cloud map, and removing the line segments corresponding to the flight paths at the preset intervals from the initial planned path to form gaps; Obtaining target meteorological forecast data in a preset spatial range corresponding to the flight path at the preset time interval, inputting the target meteorological forecast data into a target neural network model corresponding to each icing component, and outputting the corresponding predicted icing thickness and predicted icing type; Determine the target planning route based on the predicted ice thickness and predicted ice type; The line segments corresponding to the target planned route are merged into the gap to obtain the second flight path.

[0009] Furthermore, the step of determining the target planning route according to the predicted ice accumulation thickness and the predicted ice accumulation type includes: According to the importance of the ice accretion component, and the predicted ice accretion thickness and predicted ice accretion type corresponding to the ice accretion component, determine a second target area within the preset spatial range where the predicted ice accretion thickness and the predicted ice accretion type meet the requirements; In the second target area, the shortest flight route of the low-altitude aircraft is determined as the target planning route.

[0010] Furthermore, the neural network model is composed of an input layer, a hidden layer and an output layer, wherein the input layer comprises a plurality of input nodes, the number of the input nodes being the same as the number of the input data types, and being used to receive different types of input data; The hidden layer consists of five sublayers. The first hidden sublayer has 64 nodes. The LeakyReLU function is used to extract features of the input data to find key features related to the output results. The second and third hidden sublayers each have 128 nodes. The Sigmoid function is used to further mine the features of the output data of the first hidden sublayer and capture the intrinsic connections between the nodes. The fourth and fifth hidden sublayers each contain 64 nodes. The ReLU activation function is used to perform secondary processing on the data features output by the third hidden sublayer. The output layer is provided with a number of output nodes, the number of output nodes corresponds to the number of output data types, and the output layer adopts a linear activation function to output the final prediction result.

[0011] Furthermore, in the step of performing weighted processing according to the ice thickness and ice type of each ice accretion component to obtain an evaluation value, the expression for weighted processing is:

[0012] Among them, P represents the evaluation value, represents the weight coefficient of the kth ice accretion component, It represents the score value of the kth ice accretion component determined according to the ice accretion thickness and ice accretion type.

[0013] A second aspect of an embodiment of the present invention provides a low-altitude aircraft icing warning system based on meteorological observation, which is used to implement the low-altitude aircraft icing warning method based on meteorological observation described in the first aspect, and the system includes: The first acquisition module is used to acquire historical meteorological data, historical flight data, aircraft data and ice accumulation conditions of the low-altitude aircraft in the same period, wherein the historical meteorological data includes temperature, humidity, wind speed, wind direction, air pressure, precipitation type and relative position of clouds, the historical flight data includes flight altitude, flight speed and flight time, the aircraft data includes material and surface characteristics, and the ice accumulation conditions include ice accumulation components, ice accumulation thickness and ice accumulation type; A model training module is used to establish corresponding neural network models according to the division of ice-accumulated parts, and train them respectively to obtain corresponding target neural network models. Specifically, historical meteorological data, historical flight data and aircraft data are used as inputs of the neural network model, and ice thickness and ice type at preset time intervals are used as outputs of the neural network model. The second acquisition module is used to acquire the meteorological data, flight data and aircraft data collected by the low-altitude aircraft in real time, input them into the target neural network model corresponding to each ice accumulation component, and output the corresponding ice accumulation thickness and ice accumulation type; A first judgment module is used to judge whether the preset ice accumulation component reaches the warning standard according to the output result of the target neural network model; A first warning module, configured to issue a first warning if it is determined that a preset ice accumulation component has reached a warning standard; A weighted processing module, for performing weighted processing according to the ice thickness and ice type of each ice accretion component to obtain an evaluation value if it is determined that the preset ice accretion component does not meet the warning standard; A second judgment module, used to judge whether the evaluation value is greater than a threshold; The second warning module is used to issue a second warning if it is determined that the evaluation value is greater than a threshold.

[0014] A third aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the low-altitude aircraft icing warning method based on meteorological observation provided in the first aspect.

[0015] A fourth aspect of an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the low-altitude aircraft icing warning method based on meteorological observation provided in the first aspect is implemented.

[0016] A low-altitude aircraft icing warning method and system based on meteorological observation is provided in an embodiment of the present invention. The method obtains historical meteorological data, historical flight data, aircraft data and ice accumulation conditions of the low-altitude aircraft in the same period; establishes corresponding neural network models according to the division of ice accumulation components, and trains them respectively to obtain corresponding target neural network models; obtains meteorological data, flight data and aircraft data collected by the low-altitude aircraft in real time, inputs them into the target neural network models corresponding to each ice accumulation component, and outputs the corresponding ice accumulation thickness and ice accumulation type; judges whether the preset ice accumulation component meets the warning standard according to the output result of the target neural network model; if so, a first warning is issued; if not, weighted processing is performed according to the ice accumulation thickness and ice accumulation type of each ice accumulation component to obtain an evaluation value; judges whether the evaluation value is greater than a threshold value; if it is judged that the evaluation value is greater than the threshold value, a second warning is issued. Specifically, by evaluating the icing condition of the aircraft, the flight risk can be effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a method for early warning of icing of low-altitude aircraft based on meteorological observation provided in Embodiment 1 of the present invention; Figure 2 A structural block diagram of a low-altitude aircraft icing warning system based on meteorological observation provided in Embodiment 2 of the present invention; Figure 3 This is a structural block diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0018] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0019] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0021] Embodiment 1 According to an embodiment of the present invention, an embodiment of a low-altitude aircraft icing warning method based on meteorological observation is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0022] In this embodiment 1, a low-altitude aircraft icing warning method based on meteorological observation is provided, which can be used in electronic equipment, such as computers. Figure 1 , Figure 1 A flowchart of an implementation of a low-altitude aircraft icing warning method based on meteorological observation provided in Embodiment 1 of the present invention is shown, which specifically includes steps S01 to S08.

[0023] Step S01, obtaining historical meteorological data, historical flight data, aircraft data and ice accumulation conditions of the low-altitude aircraft in the same period.

[0024] Specifically, historical meteorological data include temperature, humidity, wind speed, wind direction, air pressure, precipitation type and relative position of clouds; historical flight data include flight altitude, flight speed and flight time; aircraft data include material and surface characteristics; icing conditions include iced components, ice thickness and ice type. It should be noted that the above data have varying degrees of impact on the icing of low-altitude aircraft.

[0025] Among them, for temperature, ice on the aircraft surface usually occurs in the temperature range of 0℃ to -20℃, while strong ice mostly occurs at -2℃ to -10℃; for humidity and air pressure, when the air humidity in the cloud reaches a temperature dew point difference between 0℃ and 5℃, ice is more likely to occur; for wind speed and wind direction, it can be combined with the flight speed of the aircraft. When the relative speed is large, the collision frequency and energy between the aircraft and supercooled water droplets increase, and supercooled water droplets are more likely to adhere to the surface of the aircraft and freeze quickly; for precipitation type, in freezing rain and drizzle weather, when the outside temperature is below 0℃, supercooled raindrops are easy to freeze once they come into contact with the aircraft. In a sleet environment, the coexistence of snowflakes and supercooled water droplets will also increase the possibility of aircraft icing; in terms of the relative position of the clouds, when flying over cumulonimbus clouds or cumulonimbus clouds, strong icing usually occurs on the aircraft surface; while when flying over stratus clouds or stratocumulus clouds, weak or moderate icing usually occurs; in terms of flight altitude, altitudes between 1,500 and 3,000 meters are common areas with high incidence of icing; in terms of material and surface characteristics, if the aircraft surface is made of metal material with good thermal conductivity, it is easier to quickly reduce the surface temperature in a low temperature environment, causing water vapor to condense and freeze. If the surface is rough and uneven, it will increase the probability of supercooled water droplets attaching and make it easier to freeze.

[0026] In addition, the ice accumulation effect varies according to the function and location of the ice-accumulating parts. For example, rotating parts such as helicopter rotors are more prone to ice formation than fixed parts due to heat exchange with the air and complex airflow during rotation. Protruding parts such as engine air intakes, pitot tubes, windshields, antennas, etc. are also more likely to freeze because they are more likely to come into contact with supercooled water droplets.

[0027] The above data are collected by various sensors carried by low-altitude aircraft. For example, ice thickness and relative position of clouds can be obtained by installing visual sensors such as cameras on the aircraft to take images of the aircraft surface. The ice accumulation area in the image is analyzed and processed through image recognition technology.

[0028] In some other embodiments of the present invention, ice accumulation observation data from high mountain weather stations and meteorological data such as wind, humidity, temperature, and pressure may also be used for reference.

[0029] Step S02: establishing corresponding neural network models respectively according to the division of ice accumulation components, and training them respectively to obtain corresponding target neural network models.

[0030] The historical meteorological data, historical flight data and aircraft data are used as inputs of the neural network model, and the ice thickness and ice type at preset intervals are used as outputs of the neural network model. Before the data is input into the neural network model, data preprocessing can be performed, including missing value processing (interpolation), outlier removal (based on physical constraints), feature engineering (Z-score normalization) and standardization (Z-score normalization).

[0031] In this embodiment, the neural network model is composed of an input layer, a hidden layer and an output layer. The input layer includes a number of input nodes, the number of which is the same as the number of input data types, and is used to receive different types of input data; The hidden layer consists of five sub-layers. The first hidden sub-layer has 64 nodes. The LeakyReLU function is used to extract the features of the input data and find the key features related to the output results. The second and third hidden sub-layers each have 128 nodes. The Sigmoid function is used to further explore the features of the output data of the first hidden sub-layer and capture the internal connections between the nodes. The fourth and fifth hidden sub-layers each contain 64 nodes. With the help of the ReLU activation function, the data features of the output of the third hidden sub-layer are processed twice. The output layer is equipped with several output nodes, the number of which corresponds to the number of output data types. The output layer uses a linear activation function to output the final prediction result.

[0032] Specifically, 80% of the randomly shuffled data set samples are used as training set samples, and the other 20% are used as test set samples. The neuron formulas of the hidden layer and the output layer can be expressed as: First hidden sublayer:

[0033] Second hidden sublayer:

[0034] The third hidden sublayer:

[0035] Fourth hidden sublayer:

[0036] Fifth hidden sublayer:

[0037] Output layer:

[0038] Among them, x j and iThey represent the jth node of the input layer and the ith node of the output layer respectively. Indicates the connection The weights of the jth node in the layer and the ith node in the output layer, Indicates The bias term of the i-th node in the layer, for example, is represented as the i-th neuron in the first hidden sublayer, is represented as the i-th neuron in the second hidden sublayer, is represented as the i-th neuron in the third hidden sublayer, is represented as the i-th neuron in the fourth hidden sublayer, Represented as the i-th neuron in the fifth hidden sublayer. Similarly, is represented as the jth neuron in the first hidden sublayer, is represented as the jth neuron in the second hidden sublayer, is represented as the jth neuron in the third hidden sublayer, is represented as the jth neuron in the fourth hidden sublayer, It represents the jth neuron in the fifth hidden sublayer. LeakyReLU, Sigmoid and ReLU are activation functions.

[0039] Step S03, obtaining in real time the meteorological data, flight data and aircraft data collected by the low-altitude aircraft, respectively inputting them into the target neural network model corresponding to each ice accumulation component, and outputting the corresponding ice accumulation thickness and ice accumulation type.

[0040] Step S04, judging whether the preset ice accumulation component reaches the warning standard according to the output result of the target neural network model, if so, executing step S05, if not, executing step S06.

[0041] It is understandable that the preset ice accumulation components may include key aerodynamic components, power system related components and sensor components. For example, ice accumulation on power system components such as engine air intakes and propellers will affect the normal operation of the engine, leading to problems such as power reduction and increased vibration. Once signs of ice accumulation are found in these parts, even if only a small amount of ice accumulation is found, timely warnings should be given.

[0042] Step S05: issuing a first warning.

[0043] Among them, pilots can be reminded through sound alarms and scrolling subtitles that icing may occur and they should take safety precautions.

[0044] In step S06, weighted processing is performed according to the ice thickness and ice type of each ice accretion component to obtain an evaluation value.

[0045] Specifically, the expression of weighted processing is:

[0046] Among them, P represents the evaluation value, represents the weight coefficient of the kth ice accretion component, It represents the score value of the kth ice accretion component determined according to the ice accretion thickness and ice accretion type. It should be noted that the more critical the ice accretion component is, the greater the weight coefficient is. In addition, a mapping relationship between the ice accretion thickness and ice accretion type of each ice accretion component and the score value is established to determine the score value according to the mapping relationship.

[0047] Step S07, determining whether the evaluation value is greater than a threshold, if so, executing step S08.

[0048] Step S08: a second warning is issued.

[0049] The pilot can also be reminded of the possibility of icing by means of sound alarms and scrolling subtitles, and be advised to take safety precautions. In some other embodiments of the present invention, after the warning is issued, the user can be more intelligently helped to plan the flight path to further reduce the flight risk. Specifically, the initial planned path is obtained, and the initial planned path is adjusted according to the warning situation, wherein the initial planned path is the result of comprehensive considerations such as meteorological data, flight distance, and terrain. It should be noted that a three-dimensional satellite cloud map of the current low-altitude aircraft flight position is obtained, and the target area in the three-dimensional satellite cloud map that is conducive to alleviating icing is determined, wherein the three-dimensional satellite cloud map is realized based on volume rendering and other technologies. The current Cloud Fantasy system already has this function. Since the three-dimensional satellite cloud map is rendered based on meteorological data, the meteorological data around the current low-altitude aircraft flight position can be obtained, and then the meteorological data can be input into the target neural network, so that the target area in the three-dimensional satellite cloud map that is conducive to alleviating icing can be determined; When the warning situation is the first warning, it means that the weather changes sharply and the situation is urgent, and it is necessary to avoid danger quickly. Then, according to the first target area closest to the current flight position of the low-altitude aircraft, the first flight path of the low-altitude aircraft into the first target area is planned, and the initial planned path is adjusted according to the first flight path; When the warning situation is the second warning, it means that there is a potential risk and the path can be adaptively adjusted. The meteorological forecast data is corrected according to the three-dimensional satellite cloud map, and the target three-dimensional satellite cloud map is generated according to the corrected meteorological forecast data. The second flight path of the low-altitude aircraft is planned to minimize the deviation from the initial planned path, and the initial planned path is adjusted according to the second flight path. It can be understood that a real-time three-dimensional satellite cloud map is obtained, that is, the meteorological data has a higher accuracy. The previously predicted meteorological data is corrected by the meteorological data with higher accuracy, so as to obtain a corrected and more accurate target three-dimensional satellite cloud map.

[0050] Specifically, the initial planned path is mapped to the target three-dimensional satellite cloud map, and the line segments corresponding to the flight paths at intervals of preset time are removed from the initial planned path to form gaps; Obtain target meteorological forecast data in a preset spatial range corresponding to the flight path at the preset interval time, input the target meteorological forecast data into the target neural network model corresponding to each ice accumulation component, and output the corresponding predicted ice accumulation thickness and predicted ice accumulation type. It can be understood that the preset spatial range refers to a rectangular, spherical, and other spatial ranges obtained by expanding the line segment corresponding to the flight path at the preset interval time as a reference, and the target meteorological forecast data is obtained based on the target three-dimensional satellite cloud image; According to the predicted ice accretion thickness and the predicted ice accretion type, a target planning route is determined, wherein, according to the importance of the ice accretion components and the predicted ice accretion thickness and the predicted ice accretion type corresponding to the ice accretion components, a second target area in a preset spatial range where the predicted ice accretion thickness and the predicted ice accretion type meet the requirements is determined. For example, if there are seven ice accretion components a, b, c, d, e, f, and g, wherein a, b, and c are key ice accretion components and d, e, f, and g are common ice accretion components, according to the importance levels of a, b, and c, position points that do not meet the requirements are eliminated in turn within the preset spatial range. Not meeting the requirements can be understood as the ice accretion type not meeting the requirements, the ice accretion thickness exceeding the standard, etc. Within the preset spatial range after eliminating the position points that do not meet the requirements, position points of the four common ice accretion components d, e, f, and g that all meet the requirements are determined, and the position points are merged to form a second target area; In the second target area, the shortest flight route of the low-altitude aircraft is determined as the target planning route; The line segments corresponding to the target planned route are merged to the gap to obtain the second flight path.

[0051] In summary, the low-altitude aircraft icing warning method based on meteorological observation in the above-mentioned embodiment of the present invention obtains historical meteorological data, historical flight data, aircraft data and ice accumulation conditions of the low-altitude aircraft in the same period; establishes corresponding neural network models according to the division of ice-accumulated components, and trains them respectively to obtain corresponding target neural network models; obtains meteorological data, flight data and aircraft data collected by the low-altitude aircraft in real time, inputs them into the target neural network models corresponding to each ice-accumulated component, and outputs the corresponding ice thickness and ice type; judges whether the preset ice-accumulated component meets the warning standard according to the output result of the target neural network model; if so, a first warning is issued; if not, weighted processing is performed according to the ice thickness and ice type of each ice-accumulated component to obtain an evaluation value; judges whether the evaluation value is greater than a threshold value; if it is judged that the evaluation value is greater than the threshold value, a second warning is issued. Specifically, by evaluating the icing condition of the aircraft, the flight risk can be effectively reduced.

[0052] Embodiment 2 See also Figure 2 , Figure 2 This is a structural block diagram of a low-altitude aircraft icing warning system based on meteorological observation provided by Embodiment 2 of the present invention. The low-altitude aircraft icing warning system based on meteorological observation 200 is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0053] Specifically, the low-altitude aircraft icing warning system 200 based on meteorological observation includes: a first acquisition module 21, a model training module 22, a second acquisition module 23, a first judgment module 24, a first warning module 25, a weighted processing module 26, a second judgment module 27 and a second warning module 28, wherein: The first acquisition module 21 is used to acquire historical meteorological data, historical flight data, aircraft data and ice accumulation conditions of the low-altitude aircraft in the same period, wherein the historical meteorological data includes temperature, humidity, wind speed, wind direction, air pressure, precipitation type and relative position of clouds, the historical flight data includes flight altitude, flight speed and flight time, the aircraft data includes material and surface characteristics, and the ice accumulation conditions include ice accumulation components, ice accumulation thickness and ice accumulation type; The model training module 22 is used to establish corresponding neural network models according to the division of ice accumulation components, and train them respectively to obtain corresponding target neural network models. Specifically, historical meteorological data, historical flight data and aircraft data are used as inputs of the neural network model, and ice accumulation thickness and ice accumulation type at preset time intervals are used as outputs of the neural network model. The neural network model is composed of an input layer, a hidden layer and an output layer. The input layer includes a plurality of input nodes, and the number of input nodes is the same as the number of input data types, and is used to receive different types of input data. The hidden layer consists of five sublayers. The first hidden sublayer has 64 nodes. The LeakyReLU function is used to extract the features of the input data to find the key features related to the output results. The second and third hidden sublayers each have 128 nodes. The Sigmoid function is used to further explore the features of the output data of the first hidden sublayer and capture the internal connections between the nodes. The fourth and fifth hidden sublayers each contain 64 nodes. The ReLU activation function is used to perform secondary processing on the data features output by the third hidden sublayer. The output layer is provided with a number of output nodes, the number of output nodes corresponds to the number of output data types, and the output layer adopts a linear activation function to output the final prediction result; The second acquisition module 23 is used to acquire the meteorological data, flight data and aircraft data collected by the low-altitude aircraft in real time, input them into the target neural network model corresponding to each ice accumulation component, and output the corresponding ice accumulation thickness and ice accumulation type; A first judgment module 24 is used to judge whether the preset ice accumulation component reaches the warning standard according to the output result of the target neural network model; A first warning module 25, configured to issue a first warning if it is determined that the preset ice accumulation component has reached the warning standard; The weighted processing module 26 is used to perform weighted processing according to the ice thickness and ice type of each ice accretion component to obtain an evaluation value if it is determined that the preset ice accretion component does not meet the warning standard. The expression of the weighted processing is:

[0054] Among them, P represents the evaluation value, represents the weight coefficient of the kth ice accretion component, represents the score value of the kth ice accretion component determined according to the ice accretion thickness and ice accretion type; A second judgment module 27, used to judge whether the evaluation value is greater than a threshold; The second warning module 28 is configured to issue a second warning if it is determined that the evaluation value is greater than a threshold value.

[0055] Furthermore, in some optional embodiments of the present invention, the low-altitude aircraft icing warning system 200 based on meteorological observation further includes: The adjustment module is used to obtain an initial planned path and adjust the initial planned path according to the warning situation.

[0056] Further, in some optional embodiments of the present invention, the adjustment module includes: An acquisition unit is used to acquire a three-dimensional satellite cloud image of the current flight position of the low-altitude aircraft and determine a target area in the three-dimensional satellite cloud image that is conducive to alleviating icing; A first adjustment unit is used for, when the warning situation is the first warning, planning a first flight path for the low-altitude aircraft to enter the first target area according to a first target area closest to the current flight position of the low-altitude aircraft, and adjusting the initial planned path according to the first flight path; The second adjustment unit is used to correct the meteorological forecast data according to the three-dimensional satellite cloud map when the warning situation is the second warning, and generate a target three-dimensional satellite cloud map according to the corrected meteorological forecast data, plan a second flight path in which the low-altitude aircraft deviates from the initial planned path to the minimum, and adjust the initial planned path according to the second flight path.

[0057] Further, in some optional embodiments of the present invention, the second adjustment unit includes: A mapping subunit, used for mapping the initial planned path to the target three-dimensional satellite cloud map, and removing the line segments corresponding to the flight paths at the preset intervals from the initial planned path to form gaps; an acquisition subunit, configured to acquire target meteorological forecast data in a preset spatial range corresponding to the flight path at the preset time interval, input the target meteorological forecast data into a target neural network model corresponding to each ice accretion component, and output a corresponding predicted ice accretion thickness and predicted ice accretion type; A determination subunit is used to determine a target planning route according to the predicted ice accretion thickness and the predicted ice accretion type, and specifically, to determine a second target area within the preset spatial range where the predicted ice accretion thickness and the predicted ice accretion type meet the requirements according to the importance of the ice accretion component and the predicted ice accretion thickness and the predicted ice accretion type corresponding to the ice accretion component; In the second target area, determining the shortest flight route of the low-altitude aircraft as the target planning route; The merging subunit is used to merge the line segments corresponding to the target planned route into the gap to obtain the second flight path.

[0058] Embodiment 3 Another aspect of the present invention provides an electronic device, see Figure 3, shown is an electronic device in Embodiment 3 of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, the low-altitude aircraft icing warning method based on meteorological observation as described above is implemented.

[0059] In some embodiments, the processor 10 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 20, such as executing access restriction programs.

[0060] The memory 20 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 20 may be an internal storage unit of an electronic device, such as a hard disk of the electronic device. In other embodiments, the memory 20 may also be an external storage device of an electronic device, such as a plug-in hard disk equipped on the electronic device, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. Further, the memory 20 may also include both an internal storage unit and an external storage device of the electronic device. The memory 20 may be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or is to be output.

[0061] It should be pointed out that Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than those shown in the figure, or combine certain components, or arrange the components differently.

[0062] The embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned low-altitude aircraft icing warning method based on meteorological observation.

[0063] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0064] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0065] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0066] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0067] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.

Claims

1. A low-altitude aircraft icing early warning method based on meteorological observation, characterized in that: The method comprises: Obtain historical meteorological data, historical flight data, aircraft data and ice accumulation conditions of the low-altitude aircraft in the same period, wherein the historical meteorological data includes temperature, humidity, wind speed, wind direction, air pressure, precipitation type and relative position of clouds, the historical flight data includes flight altitude, flight speed and flight time, the aircraft data includes material and surface characteristics, and the ice accumulation conditions include ice accumulation components, ice thickness and ice accumulation type; According to the division of ice accumulation components, corresponding neural network models are established respectively, and training is performed respectively to obtain corresponding target neural network models. Specifically, historical meteorological data, historical flight data and aircraft data are used as inputs of the neural network model, and ice accumulation thickness and ice accumulation type at preset time intervals are used as outputs of the neural network model. Acquire meteorological data, flight data and aircraft data collected by low-altitude aircraft in real time, input them into the target neural network model corresponding to each icing component, and output the corresponding ice thickness and ice type; According to the output results of the target neural network model, it is determined whether the preset ice accumulation components meet the warning standard; If it is determined that the preset ice accumulation component reaches the warning standard, a first warning is issued; If it is determined that the preset ice accumulation component does not meet the warning standard, weighted processing is performed according to the ice accumulation thickness and ice accumulation type of each ice accumulation component to obtain an evaluation value; Determine whether the evaluation value is greater than a threshold; If it is determined that the evaluation value is greater than the threshold, a second warning is issued.

2. The low-altitude aircraft icing early warning method based on meteorological observation according to claim 1 is characterized in that: The method further comprises: An initial planned path is obtained, and the initial planned path is adjusted according to the warning situation.

3. The low-altitude aircraft icing early warning method based on meteorological observation according to claim 2 is characterized in that: The step of obtaining the initial planned path and adjusting the initial planned path according to the warning situation includes: Obtain a three-dimensional satellite cloud map of the current low-altitude aircraft's flight position, and determine the target area in the three-dimensional satellite cloud map that is conducive to alleviating icing; When the warning situation is the first warning, a first flight path for the low-altitude aircraft to enter the first target area is planned according to a first target area closest to the current flight position of the low-altitude aircraft, and the initial planned path is adjusted according to the first flight path; When the warning situation is the second warning, the weather forecast data is corrected according to the three-dimensional satellite cloud map, and the target three-dimensional satellite cloud map is generated according to the corrected weather forecast data, and a second flight path with the least deviation from the initial planned path of the low-altitude aircraft is planned, and the initial planned path is adjusted according to the second flight path.

4. The low-altitude aircraft icing early warning method based on meteorological observation according to claim 3 is characterized in that: The step of generating a target three-dimensional satellite cloud image according to the revised meteorological forecast data and planning a second flight path where the low-altitude aircraft deviates from the initial planned path to the minimum extent comprises: Mapping the initial planned path to the target three-dimensional satellite cloud map, and removing the line segments corresponding to the flight paths at the preset intervals from the initial planned path to form gaps; Obtaining target meteorological forecast data in a preset spatial range corresponding to the flight path at the preset time interval, inputting the target meteorological forecast data into a target neural network model corresponding to each icing component, and outputting the corresponding predicted icing thickness and predicted icing type; Determine the target planning route based on the predicted ice thickness and predicted ice type; The line segments corresponding to the target planned route are merged into the gap to obtain the second flight path.

5. The low-altitude aircraft icing early warning method based on meteorological observation according to claim 4 is characterized in that: The step of determining the target planning route according to the predicted ice accumulation thickness and the predicted ice accumulation type comprises: According to the importance of the ice accretion component, and the predicted ice accretion thickness and predicted ice accretion type corresponding to the ice accretion component, determine a second target area within the preset spatial range where the predicted ice accretion thickness and the predicted ice accretion type meet the requirements; In the second target area, the shortest flight route of the low-altitude aircraft is determined as the target planning route.

6. The low-altitude aircraft icing early warning method based on meteorological observation according to claim 5 is characterized in that: The neural network model is composed of an input layer, a hidden layer and an output layer. The input layer includes a number of input nodes, the number of which is the same as the number of input data types, and is used to receive different types of input data; The hidden layer consists of five sublayers. The first hidden sublayer has 64 nodes. The LeakyReLU function is used to extract the features of the input data to find the key features related to the output results. The second and third hidden sublayers each have 128 nodes. The Sigmoid function is used to further explore the features of the output data of the first hidden sublayer and capture the internal connections between the nodes. The fourth and fifth hidden sublayers each contain 64 nodes. The ReLU activation function is used to perform secondary processing on the data features output by the third hidden sublayer. The output layer is provided with a number of output nodes, the number of output nodes corresponds to the number of output data types, and the output layer adopts a linear activation function to output the final prediction result.

7. The low-altitude aircraft icing early warning method based on meteorological observation according to claim 6 is characterized in that: In the step of performing weighted processing according to the ice thickness and ice type of each ice accretion component to obtain an evaluation value, the expression for weighted processing is: Among them, P represents the evaluation value, represents the weight coefficient of the kth ice accretion component, It represents the score value of the kth ice accretion component determined according to the ice accretion thickness and ice accretion type.

8. A low-altitude aircraft icing warning system based on meteorological observation, characterized in that: Used to implement the low-altitude aircraft icing warning method based on meteorological observation as described in any one of claims 1 to 7, the system comprises: The first acquisition module is used to acquire historical meteorological data, historical flight data, aircraft data and ice accumulation conditions of the low-altitude aircraft in the same period, wherein the historical meteorological data includes temperature, humidity, wind speed, wind direction, air pressure, precipitation type and relative position of clouds, the historical flight data includes flight altitude, flight speed and flight time, the aircraft data includes material and surface characteristics, and the ice accumulation conditions include ice accumulation components, ice accumulation thickness and ice accumulation type; A model training module is used to establish corresponding neural network models according to the division of ice-accumulated parts, and train them respectively to obtain corresponding target neural network models. Specifically, historical meteorological data, historical flight data and aircraft data are used as inputs of the neural network model, and ice thickness and ice type at preset time intervals are used as outputs of the neural network model. The second acquisition module is used to acquire the meteorological data, flight data and aircraft data collected by the low-altitude aircraft in real time, input them into the target neural network model corresponding to each ice accumulation component, and output the corresponding ice accumulation thickness and ice accumulation type; A first judgment module is used to judge whether the preset ice accumulation component reaches the warning standard according to the output result of the target neural network model; A first warning module, configured to issue a first warning if it is determined that a preset ice accumulation component has reached a warning standard; A weighted processing module, for performing weighted processing according to the ice thickness and ice type of each ice accretion component to obtain an evaluation value if it is determined that the preset ice accretion component does not meet the warning standard; A second judgment module, used to judge whether the evaluation value is greater than a threshold; The second warning module is used to issue a second warning if it is determined that the evaluation value is greater than a threshold.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the low-altitude aircraft icing warning method based on meteorological observation as described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the low-altitude aircraft icing warning method based on meteorological observation as described in any one of claims 1 to 7 is implemented.

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