KR102508762B1 - An Air-route weather forecast and update method, apparatus and system based on ADS-B data - Google Patents

Abstract

According to an embodiment of the present invention, in the method of predicting and updating route weather based on ADS-B data, receiving ADS-B data including route weather data collected by at least one aircraft from at least one aircraft step; extracting the route meteorological data included in the ADS-B data; constructing a route weather prediction engine by learning the extracted route weather data; and predicting in advance dangerous meteorological phenomena that may occur on the route of the predicted aircraft by using the constructed route weather prediction engine; can include

Description

An Air-route weather forecast and update method, apparatus and system based on ADS-B data}

The present specification provides a method, device, and system for learning aircraft collected weather data received through an ADS-B (Automatic Dependent Surveillance-Broadcast) system through artificial intelligence techniques and predicting dangerous weather phenomena on aircraft routes in advance based on this learning. Suggest.

Recently, machine learning that can be applied to various fields has been developed. Such machine learning is a field of artificial intelligence, and is divided into supervised learning, unsupervised learning, and reinforcement learning according to its type, and includes artificial neural networks, boosting ( Boosting), decision trees, support vector machines, random forests, and the like, various algorithms are being developed.

An artificial neural network includes an input layer, a hidden layer, and an output layer, and is divided into a shallow neural network and a deep neural network according to the method of stacking the hidden layer. In addition, convolutional neural networks ('ConvNet'), which are more advanced artificial neural networks in deep neural networks, recurrent neural networks, deep belief networks, and deep Q-networks etc. are being developed, and the machine learning method of these artificial neural networks (including deep neural networks) is commonly referred to as deep learning. In other words, deep learning refers to machine learning that attempts a high level of abstraction through a combination of several nonlinear transformation techniques, as it learns data by utilizing an information input/output layer similar to neurons in the brain.

Most of the aviation accidents that occur during takeoff and landing are caused by sudden turbulence. Turbulence refers to a phenomenon in which the wind changes irregularly and is difficult to predict, and representative turbulent currents that are dangerous during take-off and landing include wind shear and micro burst. An aircraft entering the runway when wind shear occurs initially pushes the aircraft up due to an abnormal increase in lift caused by the headwind, so the pilot lowers the thrust for landing. It suddenly loses the lift required to maintain it and falls.

The risk of aviation accidents can be greatly reduced even if pilots are notified a little in advance about dangerous weather conditions that can have a fatal effect on safety.

Related industries develop and sell various equipment to detect such dangerous weather situations, and representatively, LLWAS (Low Level Wind Shear Alert System), LIDAR (Light Detection and Ranging), TDWR (Terminal Doppler Weather Radar), etc. this is being used

However, these aerial meteorological observation equipment only provides a function of detection, not prediction, and is very expensive, so it is a cost burden in terms of airport operation. Therefore, there is a limitation in that it is difficult to increase the reliability/accuracy of a weather prediction system to effectively respond to sudden turbulence only with the current detection technology. Furthermore, since all of the above-mentioned equipment is installed on the ground, and the meteorological conditions of the "ground" are observed and the meteorological phenomena in the sky are detected based on this, the prediction of such sudden phenomena in the sky has been treated as virtually impossible.

Recently, the National Institute of Meteorological Science has developed a wind shear prediction model using two or more weather models using an ensemble technique and is conducting a pilot service for the Aviation Meteorological Administration. level is impossible.

According to an embodiment of the present invention, in the method for predicting route weather based on aircraft observed weather data collected by the Automatic Dependent Surveillance-Broadcast (ADS-B) system, the route collected by at least one aircraft from at least one aircraft Receiving ADS-B data including weather data; extracting the route meteorological data included in the ADS-B data; constructing a route weather prediction engine by learning the extracted route weather data; and predicting in advance dangerous meteorological phenomena that may occur on the route of the predicted aircraft by using the constructed route weather prediction engine; can include

According to an embodiment of the present invention, weather data on a “route” on which the safety of an actual aircraft must be secured is transmitted through the ADS-B system, and dangerous weather that may occur on the “route” based on the meteorological data thus transmitted. Since the phenomenon is predicted, there is an effect that reliability and accuracy can be guaranteed higher than conventional weather forecasting equipment/systems that predict dangerous weather phenomena on the “airway” based on weather data on the “ground”.

In addition, according to an embodiment of the present invention, the route weather prediction result is verified using PIREP, AIREP, and/or weather information collection data of subsequent aircraft flying on the same route, and the aviation weather prediction engine is operated according to the verification result. Since it is continuously updated, there is an effect that the reliability and accuracy of the aviation weather prediction engine are maintained high above a certain level.

In addition, the effects of the present invention are not limited to the above effects, and will be described in detail below with reference to each drawing and embodiment.

1 is a diagram of a route weather prediction system according to an embodiment of the present invention.
2 is a state diagram of a route weather prediction method according to an embodiment of the present invention.
3 is a flowchart of a method for predicting aviation weather according to an embodiment of the present invention.
4 is a flowchart of a method for predicting dangerous weather phenomena according to an embodiment of the present invention.
5 is a diagram of a route weather prediction system according to an embodiment of the present invention.
6 is a flowchart illustrating a method for receiving ADS-B data according to an embodiment of the present invention.
7 is a diagram illustrating ADS-B data and route meteorological data extracted from the ADS-B data according to an embodiment of the present invention.
8 is a flowchart illustrating a verification method of a route weather prediction engine according to an embodiment of the present invention.

Since the technology to be described below can have various changes and various embodiments, specific embodiments will be illustrated in the drawings and described in detail. However, this is not intended to limit the technology described below to specific embodiments, and it should be understood to include all modifications, equivalents, or substitutes included in the spirit and scope of the technology described below.

Terms such as first, second, A, B, etc. may be used to describe various elements, but the elements are not limited by the above terms, and are merely used to distinguish one element from another. used only as For example, without departing from the scope of the technology described below, a first element may be referred to as a second element, and similarly, the second element may be referred to as a first element. The terms and/or include any combination of a plurality of related recited items or any of a plurality of related recited items.

In the terms used in this specification, singular expressions should be understood to include plural expressions unless clearly interpreted differently in context, and terms such as “comprising” refer to the described features, numbers, steps, operations, and components. , parts or combinations thereof, but it should be understood that it does not exclude the possibility of the presence or addition of one or more other features or numbers, step-action components, parts or combinations thereof.

Prior to a detailed description of the drawings, it is to be clarified that the classification of components in the present specification is merely a classification for each main function in charge of each component. That is, two or more components to be described below may be combined into one component, or one component may be divided into two or more for each more subdivided function. In addition, each component to be described below may additionally perform some or all of the functions of other components in addition to its main function, and some of the main functions of each component may be performed by other components. Of course, it may be dedicated and performed by .

In addition, in performing a method or method of operation, each process constituting the method may occur in a different order from the specified order unless a specific order is clearly described in context. That is, each process may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.

The route weather prediction system proposed in this specification is basically based on aircraft collected weather data collected through the ADS-B system.

Here, ADS-B (Automatic Dependent Surveillance-Broadcast) corresponds to a GPS satellite (Global Positioning System), an aircraft and ground control monitoring system. More specifically, the GPS satellite transmits the aircraft's location information to the aircraft, which periodically and automatically sends various flight information such as its location, altitude, flight speed and/or weather conditions along the route to ground control stations or other aircraft. A transmission/broadcasting system is collectively referred to as ADS-B. At this time, the flight information periodically transmitted / broadcasted by the aircraft may be transmitted in the form (or format) of ADS-B data (or message / information) defined in the ADS-B standard.

An object of the present invention is to build a route weather prediction engine capable of predicting dangerous weather phenomena (eg, wind shear and / or microburst, etc.) that may occur on the route in advance by learning such ADS-B data. .

First, the route weather prediction system, which is the basis for constructing the route weather prediction engine, is examined.

1 is a diagram of a route weather prediction system according to an embodiment of the present invention.

Referring to FIG. 1 , the route weather prediction system of the present invention may be largely composed of an ADS-B data transmission system 100 and an aviation weather prediction service 110.

The ADS-B data transmission system 100 refers to a network configured to receive ADS-B data including route meteorological data collected by the aircraft 101 from the aircraft 101 . The ADS-B data transmission system 100 may largely include an aircraft 101 and/or an aircraft collection weather information receiving device 102 .

The aircraft 101 is a body that flies on a route and can transmit/broadcast ADS-B data to the outside. In particular, in the ADS-B data transmission system 100 of the present invention, the aircraft 101 broadcasts (periodically) ADS-B data including its own flight information (in particular, weather information/data on the route) ( broadcast).

Furthermore, the aircraft 101 may additionally transmit aviation engine-side meteorological information received from an Aircraft Meteorological Data Relay (AMDAR) system in order to improve reliability of collected information, expand a prediction range, and review accuracy. AMDAR is a cooperative project between the government and private companies recommended by the World Meteorological Organization (WMO), and corresponds to real-time aviation agency-side meteorological data that provides weather data/data/information observed and collected by aircraft to the control system. AMDAR is used for accurate weather forecasting in particular. In the present invention, such AMDAR can be included in ADS-B data as weather information/data on a route and used to predict dangerous weather phenomena along the route. AMDAR includes various meteorological information/data, such as the airspeed measured through the pitot tube of the aircraft 101, the air pressure measured through the static pressure tube, the airspeed and ground speed calculated through GPS, and the temperature measured through the temperature sensor. can

In this specification, an embodiment in which AMDAR is transmitted through ADS-B data is described, but is not limited thereto, and AMDAR can be used as route weather data for route weather prediction independently of ADS-B data, and this In this case, AMDAR may be transmitted to the aviation weather prediction service 110 through various communication systems other than the ADS-B data transmission system.

The aircraft collection weather information receiving device 102 may serve to receive/collect ADS-B data transmitted/broadcast from the aircraft 101 and deliver it to the aviation weather prediction service 110 .

The aviation weather forecasting service 110 receives/collects ADS-B data to predict/monitor dangers (in particular, dangerous weather phenomena) that may occur on the route of the aircraft 101 in flight, and a network configured to prepare for them. means net. The aviation weather prediction service 110 may largely include a collection server 111 and a processing server 112 .

The collection server 111 may correspond to a device/server that serves to collect and store ADS-B data transmitted from the ADS-B data transmission system 100 . The processing server 112 loads and learns the stored ADS-B data to build a route weather prediction engine, and uses the built route weather prediction engine to predict dangerous weather phenomena that may occur on the route of the predicted aircraft in advance. It may correspond to a predicting device/server. The collection server 111 and the processing server 112 may be integrated and implemented as one device/server or implemented as a plurality of independent devices/servers according to embodiments.

Embodiments described in this specification may be performed based on such a route weather prediction system, and specific embodiments of the route weather prediction method will be described in detail with reference to each drawing below.

2 is a state diagram of a route weather prediction method according to an embodiment of the present invention.

Referring to FIG. 2, the route weather prediction method according to an embodiment of the present invention can be largely divided into three states: learning (201), prediction (202), and verification (203), and the states are cycled sequentially. do. For example, the route weather prediction method may be performed by sequentially cycling the states in the form of learning (201) → prediction (202) → verification (203) → learning (201) → ..., which is particularly It can be performed by using the route weather prediction engine by the processing server described above.

For example, the route weather forecasting system (particularly, the processing server) can 'learn (201)' the ADS-B data to build a route weather prediction engine, and use the built route weather prediction engine to predict the target aircraft. The weather condition on the route can be 'predicted (202)', the predicted result is 'verified (203)' using actual weather information, and the verified result is 'learned (201)' again to operate the route weather prediction engine. can be updated

As such, the route weather forecasting method of the present invention is configured with a structure that not only builds a route weather prediction engine by learning the ADS-B data received from at least one aircraft, but also continuously verifies the built route weather prediction engine, It has the effect of being able to build/maintain a route weather prediction engine with very high reliability and accuracy. A more specific route weather prediction engine construction method will be described in detail below with reference to FIGS. 3 and 4 .

3 is a flowchart of a method for predicting aviation weather according to an embodiment of the present invention.

In the case of this flowchart, at least one step may be excluded or newly added according to the embodiment.

Referring to FIG. 3 , the priority route weather prediction system may receive ADS-B data from at least one aircraft (S301). More specifically, the aircraft collection weather information receiving device of the route weather prediction system may receive ADS-B data including route weather data collected by at least one aircraft. At this time, ADS-B data may be broadcast/transmitted (periodically) from at least one aircraft.

Next, the route weather prediction system (particularly, the processing and/or collection server) may extract route weather data of at least one aircraft included in the received ADS-B data (S302). For ADS-B data, the data format defined in the ADS-B standard is followed, and each data format includes aircraft identification information, time, ICAO, CallSign, latitude, longitude, altitude, ground speed (aircraft relative speed relative to the ground), True It may contain various flight information such as Air Speed (aircraft relative speed), True track angle, Heading, Mach Number, Roll, FlagRoll, Vertical Speed Rate, and/or AMDAR. The processing server uses the flight information included in the ADS-B data (for example, by substituting the factors included in the flight information into a predetermined equation), and the weather on the route of the aircraft that transmitted the ADS-B data. data can be extracted.

For example, the processing server may calculate the wind direction and speed along the route using the vector difference between the relative speed of the aircraft relative to the ground and the relative speed of the aircraft based on the air.

Aircraft can directly monitor air temperature through air temperature sensors mounted on the aircraft, but ADS-B data transmitted to the ground does not include such air temperature information. Accordingly, the processing server may calculate the air temperature by substituting the True Air Speed and Mach Number included in the ADS-B data into Equation 2.

Here, T is the temperature, γ is the ratio of specific heats, Rd is the Gas Constant of dry air, and Vt is the True Air Speed.

The intensity of turbulence (i.e., the vertical acceleration value of the aircraft), which is a phenomenon in which the aircraft shakes up and down, is obtained by substituting the vertical velocity of the aircraft included in the ADS-B data into Equation 3 (i.e., dividing the vertical velocity of the aircraft by time (Δt)). ) can be calculated by

In addition, the processing server may extract weather data on the route by substituting various flight data included in the ADS-B data into a predefined equation.

Next, the route weather prediction system (particularly, the processing server) may build a route weather prediction engine by learning the extracted route weather data (S303). More specifically, the route weather prediction system can predict and establish specific weather conditions for dangerous weather phenomena to occur by filtering past weather conditions in which hazardous weather phenomena occurred based on extracted route weather data and learning them. there is. For example, if the first temperature, second wind speed, third wind direction, fourth vertical acceleration, etc. are calculated/extracted as meteorological conditions by analyzing ADS-B data transmitted by aircraft at the time of wind shear, en route weather prediction The system can predict and establish the weather condition as the weather condition for wind shear to occur. Furthermore, the route weather forecasting system may build a route weather prediction engine using/based on the predicted and established weather conditions. Here, the route weather prediction engine receives the route meteorological conditions of the prediction target aircraft as input data, and predicts the possibility of occurrence of dangerous meteorological phenomena on the route of the prediction target aircraft based on the degree of similarity between the input data and the established weather conditions. It can mean an engine (or algorithm/system/protocol/program/application) that outputs as a result. Such a route weather prediction engine may be performed/driven by a processing server in a route weather prediction system.

When the route weather prediction system (particularly, the processing server) learns past weather conditions, it may learn based on various machine learning, machine learning, or deep learning. For example, enroute weather forecasting systems use artificial neural networks (ANNs), deep neural networks (DNNs), convolution neural networks (CNNs), recurrent neural networks (RNNs), and/or extreme gradient boosting (XGBoost) input layers/data. As a result, it is possible to learn by inputting the past meteorological conditions in which dangerous meteorological phenomena occurred.

In particular, in the case of learning based on ANN, the route weather prediction system learns past weather conditions, sets and fine-tunes weights for each weather condition/factor (for example, the learned result, By assigning a higher weight to factors judged to be higher), a route weather prediction engine can be constructed. And/or, in the case of learning based on DNN/CNN, the route weather forecasting system learns past weather conditions in which dangerous weather phenomena occurred and extracts certain features/patterns from them, resulting in more accurate dangerous weather phenomenon occurrence conditions. can be predicted and established. And/or, in the case of learning based on RNN, the route weather prediction system uses a circular structure to combine past learning (eg, weather conditions established in the past) and current learning (eg, newly established weather conditions through verification). Established weather conditions) can be connected to continuously build/update the en route weather prediction engine.

Finally, the route weather forecasting system (particularly, the processing server) may predict dangerous weather phenomena that may occur on the route of the predicted aircraft in advance using the constructed route weather prediction engine (S304). A more detailed description of this will be described later in detail with reference to FIG. 4 .

4 is a flowchart of a method for predicting dangerous weather phenomena according to an embodiment of the present invention.

In particular, this flowchart illustrates a specific embodiment of step S304 of FIG. 3 .

Referring to FIG. 4 , the priority route weather forecasting system may receive ADS-B data from a forecast target aircraft (S401).

Next, as in step S302 of FIG. 3, the route weather prediction system may extract various route weather conditions of the predicted target aircraft from the received ADS-B data (S402).

Next, the route weather prediction system may input the extracted route weather conditions to the route weather prediction engine as input data (S403). In this case, the enroute weather prediction engine may predict whether or not a dangerous meteorological phenomenon will occur by mutually comparing the current meteorological conditions on the route of the predicted target aircraft with the previously established meteorological conditions. The route weather prediction engine may predict that the occurrence of dangerous weather phenomena is highly likely as the similarity between the current weather conditions and the established weather conditions is high.

If the probability of occurrence of dangerous weather phenomena is predicted by the route weather prediction engine to be higher than a predetermined level (eg, similarity of 80% or more), the route weather prediction system provides an alert (alert) notifying the risk of occurrence of dangerous weather phenomena. ) can be provided to the user. In this case, the user can notify the pilot of the predicted target aircraft that a dangerous weather phenomenon may occur and guide/instruct the pilot to prepare for it, and as a result, aviation accidents due to deteriorating weather can be prevented in advance.

And/or, the route weather forecasting system directly transmits the prediction result to the predicted target aircraft (in real time or periodically), or when the risk of dangerous weather phenomena is predicted, a warning message is sent to directly predict the target aircraft. can also be sent to

5 is a diagram of a route weather prediction system according to an embodiment of the present invention.

In particular, this drawing corresponds to an embodiment in which the system diagram of FIG. 1 is more specific for each configuration. That is, FIG. 5 is only one embodiment in which FIG. 1 is embodied, and the route weather prediction system diagram of the present invention is not limited to FIG. 5 .

Referring to FIG. 5 , the aircraft collection weather information receiving device 102 may include an ADS-B data collection unit 102-1, a processor 102-2, and/or a communication unit 102-3.

The ADS-B data collector 102-1 may periodically/non-periodically receive ADS-B data (or message/information) transmitted from at least one aircraft. The ADS-B data collecting unit 102-1 may transmit the collected ADS-B data to the processor 102-2.

The processor 102-2 may process and collect ADS-B data received from the ADS-B data collector 102-1. Furthermore, the processor 102-2 may transmit the received ADS-B data to the communication unit 102-3, and the communication unit 102-3 may transmit the received ADS-B data using at least one wired or wireless communication protocol. It can be transmitted to the aviation weather prediction service 110. For example, the communication unit 102-3 may include at least one (preferably, two) Long-Term Evolution (LTE) virtual private network (VPN) routers, and may include Transmission Control Protocol (TCP/IP) routers. ADS-B data received through Internet Protocol) communication may be transmitted to the aviation weather forecasting service 110. For smooth and reliable communication between the aircraft collection weather information receiving device 102 and the aviation weather prediction service 110, a dedicated line (billing line) between the aircraft collection weather information reception device 102 and the Aviation Meteorological Agency may be provided. there is.

The collection server 111 and the processing server 112 commonly include communication units 111-1 and 112-1, processors 111-2 and 112-2, and/or storage units 111-3 and 112-3. Hereinafter, common functions of each configuration for each server will be first described, and then different functions for each server will be separately described.

Like the communication unit 102-2 of the ADS-B data collection device 102, the communication units 111-1 and 112-1 can perform communication and transmit/receive data using at least one wired or wireless communication protocol.

The processors 111-2 and 112-2 communicate with and control at least one other element included in the servers 111 and 112, thereby performing the embodiments proposed in this specification. In particular, the processors 111-2 and 112-2 may perform calculations for at least one application or program for executing a method according to embodiments of the present invention. For example, the processors 111-2 and 112-2 may be a Central Processing Unit (CPU), Micro Processor Unit (MPU), Micro Controller Unit (MCU), Application Processor (AP), Application Processor (AP), or the present invention. It may be configured to include at least one processor of any type well known in the art. Accordingly, the processors 111-2 and 112-2 may be described as replacing the devices/servers 102, 111, and 112 that are the subject of the embodiment.

The storage units 111-3 and 112-3 may store various digital data, and in this specification, the storage units 111-3 and 112-3 may store ADS-B data in particular. The storage units 111-3 and 112-3 provide not only hardware storage space such as flash memory, hard disk drive (HDD), and solid state drive (SSD), but also virtual storage space such as cloud and database. It may mean a software-type storage space such as a service platform/application to be used. In particular, in the latter case, the storage unit 111-3 of the collection server 111 may correspond to influxDB (eg, kafka), and the storage unit 112-3 of the processing server 112 may correspond to PostgreSQL, respectively. .

Although not shown in this flowchart, in addition to this, the processing server 112 may further include an input/output unit (not shown). The input/output unit may include at least one sensor to sense various inputs of a user, and may include at least one output unit to output various information/data. For example, the input/output unit may include a gravity sensor, a geomagnetic sensor, a motion sensor, a gyroscope sensor, an acceleration sensor, an infrared sensor, an inclination sensor, a brightness sensor, an altitude sensor, an olfactory sensor, a temperature sensor, and a depth sensor. , A pressure sensor, a bending sensor, an audio sensor, a video/camera sensor, a GPS (Global Positioning System) sensor, a touch sensor, and a grip sensor, and the like. Also, the input/output unit may output various information including at least one of various output means such as a display and/or a speaker. In particular, the input/output unit of the processing server 112 may sense various control inputs of the user and provide various route weather conditions/prediction information to the user according to the sensed control inputs.

6 is a flowchart illustrating a method for receiving ADS-B data according to an embodiment of the present invention.

As illustrated in this figure, the route weather prediction system (particularly, the processing server) can improve the reliability of the route weather prediction system by storing and processing only validated ADS-B data.

Referring to FIG. 6, the route weather prediction system stores ADS-B data in the order of 'receiving ADS-B data (S601) → decoding ADS-B data (S602) → validating (S603) → storing (S604)' and can be processed. In this case, validation may be performed using a Cyclic Redundancy Check (CRC) calculation predefined in the ADS-B standard.

That is, summarizing the embodiments of FIGS. 5 and 6, the device for receiving aircraft collected weather information may transmit ADS-B data received from the aircraft to the collection server, and the collection server may store the received ADS-B data. Thereafter, a processing server requiring ADS-B data may receive and process the corresponding data through the storage unit of the collection server. More specifically, the processing server may receive and decode specific ADS-B data from the aggregation server depending on the running service. Furthermore, the processing server verifies the validity of the decoded ADS-B data through a CRC calculation and verification process according to the ADS-B standard, and then selectively stores and processes only the validated ADS-B data.

7 is a diagram illustrating ADS-B data and route meteorological data extracted from the ADS-B data according to an embodiment of the present invention.

Referring to FIG. 7, as described above in FIG. 3, the route weather prediction system receives ADS-B data including flight information, and uses the flight information included in the received ADS-B data to generate various route weather data. can be extracted. This figure corresponds to an embodiment in which vertical rate, wind direction, wind speed, and temperature are derived from ADS-B data. As described above, the route meteorological data illustrated in this figure can be extracted by substituting the ADS-B data into Equations 1 to 3.

The collection server may collect and store ADS-B data in the form of raw data, and the processing server separately processes (eg, decodes and/or validates) the ADS-B data collected and stored in the form of raw data. Verification, etc.), then route meteorological data can be extracted. The route meteorological data extracted in this way is not only used to build a route weather prediction engine, but also used as input data of the built route weather prediction engine.

8 is a flowchart illustrating a verification method of a route weather prediction engine according to an embodiment of the present invention.

In particular, this flowchart corresponds to an embodiment in which the process of cycling to the learning state 201 through the verification state in FIG. 2 is more specified. Accordingly, when the operation of this flowchart is completed, the prediction state of FIG. 2 may be performed based on the newly updated route weather prediction engine.

Referring to FIG. 8 , in order to verify the route weather prediction engine, the route weather prediction system may first receive PIREP and/or AIREP (S801). Here, PIREP is an abbreviation of 'Pilot (Weather) Report', which is data generated and transmitted by the pilot of an aircraft to directly report the weather conditions along the route, and AIREP is an abbreviation of 'Air (Weather) Report', which is This is data generated and transmitted to report the meteorological conditions directly sensed/recognized/detected by the aircraft. These PIREPs and/or AIREPs may be encoded and delivered to the Meteorological Administration, Aviation Meteorological Administration, Air Traffic Service Department, and the like. Since PIREP and/or AIREP includes direct reporting/sensing information about meteorological conditions on an actual route, accuracy and reliability are very high. Therefore, the route weather forecasting system of the present invention can receive these PIREPs and/or AIREPs and use them to verify the accuracy of the route weather prediction engine. To this end, the route weather prediction system may separately/additionally include a receiving device (or receiver) for receiving PIREP and/or AIREP.

Next, the route weather prediction system may verify the prediction result of the route weather prediction engine using the received PIREP and/or AIREP (S802). More specifically, the route weather prediction system compares the result predicted by the route weather prediction engine for the forecast target aircraft with PIREP and / or AIREP transmitted by the forecast target aircraft to determine whether they match each other, thereby obtaining the predicted result can be verified Furthermore, the route weather prediction system may quantify the degree of agreement (ie, the verification result) and calculate it as 'prediction accuracy/reliability'. For example, if the en route weather forecasting engine predicted dangerous weather phenomena but the dangerous weather phenomenon did not occur as a result of PIREP and/or AIREP analysis, the dangerous weather phenomenon was not predicted but the result of PIREP and/or AIREP analysis When a dangerous weather phenomenon actually occurs, etc., the route weather prediction system may set the prediction accuracy of the current route weather prediction engine to be lower than the prediction accuracy calculated as the result of the previous evaluation (eg, route weather prediction in the past). If the predictive accuracy you rated the engine was 80, you could rate it down by 5 to 75). In the opposite case, the route weather prediction system may evaluate the prediction accuracy of the route weather prediction engine higher than the previously evaluated prediction accuracy. The route weather prediction system may evaluate forecast accuracy in real time, or may evaluate forecast accuracy by comprehensively analyzing forecast results for a certain period of time. For example, the route weather prediction system may compare prediction results predicted from April 1 to April 15 with PIREP and/or AIREP collected during the corresponding period to calculate the degree of agreement as prediction accuracy. The period of verifying (or evaluating the accuracy of) the route weather prediction engine may be set to a specific date period by the user.

Next, as a result of verification, the route weather prediction system may determine whether the prediction accuracy of the route weather prediction engine is equal to or less than a preset level (S803). If the prediction accuracy is equal to or less than the predetermined level, the route weather prediction system may update the route weather prediction engine (S804).

More specifically, the route weather prediction system may extract route weather data from ADS-B data when the forecast result of the route weather prediction engine is different from the weather conditions included in PIREP and/or AIREP. A method of extracting route meteorological data is as described above with reference to FIG. 3 . Next, the route weather forecasting system may learn the extracted route weather data to fine-tune the weather conditions previously established in the route weather prediction engine. For example, the temperature condition previously established in the route weather prediction engine is -20.56°C, but as a result of learning route weather data extracted from ADS-B data in which the route weather prediction system was incorrectly predicted, If the average temperature condition is -22.88°C, the temperature condition of the route weather prediction engine may be adjusted to -22.88°C or -21.72°C, which is the average value of -20.56°C and -22.88°C.

Through this flow chart, the route weather forecasting system has the advantage of always maintaining the forecast accuracy and reliability above a certain level by constantly verifying and updating the route weather prediction engine.

An embodiment according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of hardware implementation, one embodiment of the present invention provides one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), FPGAs ( field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, etc.

In addition, in the case of implementation by firmware or software, an embodiment of the present invention is implemented in the form of a module, procedure, function, etc. that performs the functions or operations described above, and is stored on a recording medium readable through various computer means. can be recorded. Here, the recording medium may include program commands, data files, data structures, etc. alone or in combination. Program instructions recorded on the recording medium may be those specially designed and configured for the present invention, or those known and usable to those skilled in computer software. For example, recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs (Compact Disk Read Only Memory) and DVDs (Digital Video Disks), floptical It includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, such as a floptical disk, and ROM, RAM, flash memory, and the like. Examples of program instructions may include high-level language codes that can be executed by a computer using an interpreter or the like as well as machine language codes generated by a compiler. These hardware devices may be configured to act as one or more software modules to perform the operations of the present invention, and vice versa.

In addition, an apparatus or terminal according to the present invention may be driven by a command that causes one or more processors to perform the functions and processes described above. For example, such instructions may include interpreted instructions, such as script instructions such as JavaScript or ECMAScript instructions, or executable code or other instructions stored on a computer readable medium. Furthermore, the device according to the present invention may be implemented in a distributed manner over a network, such as a server farm, or may be implemented in a single computer device.

In addition, a computer program (also known as a program, software, software application, script or code) loaded into a device according to the present invention and executing the method according to the present invention includes a compiled or interpreted language or a priori or procedural language. It can be written in any form of programming language, and can be deployed in any form, including stand-alone programs, modules, components, subroutines, or other units suitable for use in a computer environment. A computer program does not necessarily correspond to a file in a file system. A program may be in a single file provided to the requested program, or in multiple interacting files (e.g., one or more modules, subprograms, or files that store portions of code), or parts of files that hold other programs or data. (eg, one or more scripts stored within a markup language document). A computer program may be deployed to be executed on a single computer or multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

For convenience of description, each drawing has been divided and described, but it is also possible to design to implement a new embodiment by merging the embodiments described in each drawing. In addition, the present invention is not limited to the configuration and method of the described embodiments as described above, but the above-described embodiments are configured by selectively combining all or part of each embodiment so that various modifications can be made. It could be.

In addition, although preferred embodiments have been shown and described above, this specification is not limited to the specific embodiments described above, and those skilled in the art in the art to which the specification pertains without departing from the subject matter claimed in the claims Of course, various modifications are possible by the person, and these modifications should not be individually understood from the technical spirit or perspective of the present specification.

100: ADS-B data transmission scheme
110: Aviation weather forecasting service
101: aircraft
102: aircraft collection meteorological information receiving device
111: collection server
112: processing server

Claims (7)

In the route weather prediction and update system based on ADS-B data,
an aircraft-collected weather information receiving device for receiving ADS-B data including route weather data collected by at least one aircraft from at least one aircraft;
a collection server that collects the ADS-B data from the aircraft collection weather information receiving device; and
a processing server that processes the ADS-B data collected by the collection server; Including,
The processing server,
extracting the route meteorological data included in the ADS-B data;
Based on the extracted route meteorological data, past meteorological conditions in which dangerous meteorological phenomena occurred were learned, and a route weather prediction engine in which meteorological conditions for the occurrence of the dangerous meteorological phenomena were established was established,
The ADS-B data is received from the predicted target aircraft using the constructed route weather prediction engine, and the route weather condition of the predicted target aircraft is extracted from the received ADS-B data to determine the route of the predicted target aircraft. Predict dangerous meteorological phenomena that may occur in advance, receive at least one of PIREP (Pilot Report) and AIREP (Air Report) about the meteorological conditions on the route of the prediction target aircraft from the prediction target aircraft, and the PIREP and the AIREP A prediction result of the enroute weather prediction engine for the prediction target aircraft is verified using at least one of the following, and the enroute weather prediction engine is updated when the prediction accuracy of the enroute weather prediction engine is below a preset level as a result of the verification. do,
The route weather prediction engine,
Receiving the weather condition of the route of the predicted aircraft as input data, and based on the degree of similarity between the input data and the established meteorological conditions, outputting the possibility of occurrence of dangerous weather phenomena on the route of the predicted aircraft as a predicted result Engine, route weather prediction and update system based on ADS-B data. According to claim 1,
Learning the past meteorological conditions in which the dangerous meteorological phenomenon occurred,
Past weather when the dangerous weather phenomenon occurred based on at least one of Artificial Neural Network (ANN), Deep Neural Network (DNN), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Extreme Gradient Boosting (XGBoost) A route weather prediction and update system based on ADS-B data that learns conditions. According to claim 1,
An ADS-B data-based route weather forecasting and updating system for transmitting the prediction result to the prediction target aircraft. According to claim 1,
When the possibility of occurrence of the dangerous weather phenomenon is predicted by the route weather prediction engine to be higher than a predetermined level,
A route weather prediction and update system based on ADS-B data that provides an alert indicating the risk of occurrence of the dangerous weather phenomenon. According to claim 1,
Updating the route weather prediction engine,
Extracting route weather data from ADS-B data when the prediction result of the route weather prediction engine is different from the weather condition included in at least one of the PIREP and AIREP;
A route weather prediction and update system based on ADS-B data, which learns the extracted route weather data and finely adjusts the weather conditions previously established in the route weather prediction engine. According to claim 1,
Dangerous weather phenomena on the route include wind shear and microburst, ADS-B data-based route weather prediction and update system. According to claim 1,
The route weather data collected by the at least one aircraft includes an Aircraft Meteorological Data Relay (AMDAR), ADS-B data-based route weather prediction and update system.

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