An intelligent aviation data analysis terminal based on encrypted sharing of meteorological data

Abstract

The present invention belongs to the field of aviation data analysis, and specifically discloses an intelligent aviation data analysis terminal based on encrypted sharing of meteorological data, comprising: by performing three-dimensional point segmentation on the navigation area of ​​the target aircraft, not only the spatial distribution of the meteorological data is considered, but also the time factor is combined to identify the corresponding risk level of the meteorological data of the target aircraft's predicted navigation position at each time limit within the predicted flight duration, so as to more accurately and comprehensively assess the meteorological risks that the target aircraft may encounter at different time points, provide a response basis for real-time warning, and thus improve flight safety; by constructing a weather forecast for the navigation route of the target aircraft, the navigation optimization direction of the target aircraft is customized to be determined, so that the aircraft can respond more quickly and take appropriate response measures when facing sudden weather conditions, thereby avoiding known severe weather areas and reducing the risk of encountering dangerous weather.

Description

Technical Field

[0001] The invention belongs to the field of aviation data analysis and relates to an intelligent aviation data analysis terminal based on encrypted sharing of meteorological data. Background Art

[0002] In the field of aviation data analysis, the rapid development of the aviation industry and the increasingly complex and volatile weather conditions are driving increasing demands for flight safety. Traditional aviation data analysis methods rely primarily on static meteorological observations and empirical judgments, making it difficult to comprehensively and accurately assess the meteorological risks that a target aircraft may encounter at different points in time, nor to provide real-time, effective flight optimization recommendations. Therefore, a technical solution is urgently needed to accurately acquire and process meteorological data in real time to meet the demand for intelligent analysis of meteorological data during flight missions.

[0003] There are several limitations in existing technologies, specifically: 1. Existing technologies often assess meteorological risks based on only a single meteorological data source or limited meteorological observation points, resulting in incomplete consideration of the spatial distribution of meteorological data.

[0004] 2. Existing technologies often use general risk assessment methods and standards, and lack customized solutions for different aircraft types, route characteristics and flight conditions. Summary of the Invention

[0005] In view of this, in order to solve the problems raised in the above background technology, an intelligent aviation data analysis terminal based on encrypted sharing of meteorological data is proposed.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an intelligent aviation data analysis terminal based on encrypted sharing of meteorological data, including: a data receiving module: used to obtain the navigation area of ​​the target aircraft, receive and manage the encrypted meteorological data of the target aircraft within the navigation area, and the corresponding types of meteorological data such as high-altitude detection data and radar data.

[0007] Homomorphic operation processing module: used to perform data analysis and calculation on the ciphertext domain and perform three-dimensional point segmentation on the navigation area of ​​the target aircraft.

[0008] Meteorological analysis module: used to identify the corresponding risk level of meteorological data of the target aircraft's predicted flight position at each time point within the predicted flight duration, and to construct a weather forecast for the target aircraft's flight route.

[0009] Early warning module: used to issue early warnings on the corresponding risk levels of meteorological data of the target aircraft's predicted flight position at each time point within the predicted flight duration.

[0010] Personalized suggestion generation module: used to generate customized aviation optimization suggestions based on the weather forecast of the target aircraft's flight route.

[0011] Preferably, the high-altitude detection data includes temperature, air pressure, rainfall, wind speed and direction, and cloud and fog volume.

[0012] The radar data includes reflectivity factor and velocity field.

[0013] Preferably, the three-dimensional point segmentation of the navigation area of ​​the target aircraft includes: obtaining the corresponding route path information of the navigation area of ​​the target aircraft, including longitude and latitude, route altitude, and constructing a three-dimensional coordinate system with the corresponding connecting line between the starting point and the end point on the route path as the X-axis, the horizontal and vertical line of the X-axis as the Y-axis, and the path height as the Z-axis.

[0014] The altitude of each route change is extracted from the route path information as the coordinate height of each elevation line corresponding to the Z-axis coordinate, and the coordinate points with fixed intervals on the horizontal planes corresponding to the X-axis and Y-axis are extracted and recorded as each departure point, so as to obtain the coordinates of each departure point on each elevation line corresponding to the navigation area of ​​the target aircraft.

[0015] Preferably, the identification of the corresponding risk level of the meteorological data of the target aircraft's predicted flight position at each time point within the predicted flight duration includes: 1) the time series level analysis process is: obtaining the predicted flight duration of the target aircraft in the navigation area, and then extracting the meteorological data of the coordinates of each departure point on each elevation line at each time point within the predicted flight duration.

[0016] The predicted flight position of the target aircraft is extracted in real time, and the meteorological data of the predicted flight position of the target aircraft at each time point within the predicted flight time is obtained.

[0017] 2) The spatial sequence level analysis process is as follows: obtaining the radial and latitudinal coordinates of each departure point on each elevation line corresponding to the target aircraft's navigation area, comparing them with the preset conventional meteorological data for each radial and latitudinal direction, and identifying the conventional meteorological data to which the coordinates of each departure point on each elevation line of the target aircraft belong.

[0018] The meteorological data belonging to the target aircraft's predicted flight position at each time limit within the predicted flight duration are compared with the conventional meteorological data belonging to the coordinates of each departure point of the target aircraft on each elevation line to identify the corresponding risk level of the meteorological data belonging to the target aircraft's predicted flight position at each time limit within the predicted flight duration.

[0019] Preferably, the risk level identification method is: comparing the meteorological data belonging to the target aircraft's pre-arrival navigation position at each time limit point with the conventional meteorological data belonging to the coordinates of each departure point of the target aircraft on each elevation line, obtaining the difference values ​​of each element between the meteorological data belonging to the target aircraft's pre-arrival navigation position at each time limit point and the conventional meteorological data belonging to the corresponding navigation position, comparing it with the corresponding element difference value range of each risk level, obtaining the risk level of each element in the meteorological data belonging to the target aircraft's pre-arrival navigation position at each time limit point, and comparing with each other to screen out the maximum risk level as the corresponding risk level of the meteorological data belonging to the target aircraft's pre-arrival navigation position at each time limit point.

[0020] Preferably, the risk level includes information level, attention level, warning level, and emergency level.

[0021] Preferably, the construction of the target aircraft's flight route weather forecast includes: obtaining key elements corresponding to preset risk levels, comparing them with the risk levels corresponding to the meteorological data of the target aircraft's predicted flight position at each time point within the predicted flight duration, and obtaining key elements corresponding to the target aircraft's predicted flight position at each time point within the predicted flight duration.

[0022] Retrieve the difference values ​​of the key elements corresponding to the target aircraft's predicted flight position at each time limit within the predicted flight duration, compare them with the preset safety difference values ​​of the corresponding key elements, and screen out the key elements with difference values ​​greater than the preset safety difference values, and record them as the abnormal elements corresponding to the target aircraft's predicted flight position at each time limit within the predicted flight duration.

[0023] At the same time, elements other than the key elements corresponding to each risk level whose difference values ​​exceed the preset safety difference values ​​are screened out and recorded as the corresponding marking elements of the target aircraft's predicted navigation position at each time limit point within the predicted flight duration.

[0024] The abnormal elements and marking elements corresponding to the predicted navigation position of the target aircraft at each time point within the predicted flight duration are constructed into a weather forecast for the navigation route of the target aircraft.

[0025] Preferably, the personalized suggestion generation module includes the following contents: obtaining the difference values ​​between each abnormal element and each marking element corresponding to the target aircraft's predicted flight position at each time point within the predicted flight duration, taking each time point as the horizontal coordinate and the difference value between each abnormal element and each marking element as the vertical coordinate, constructing a scatter plot of the target aircraft's flight abnormality performance, identifying the element scatter morphology of the flight abnormality performance, if the element scatter morphology is that more than a benchmark number of elements are concentrated at a certain time point, then it is judged that the flight abnormality element of the target aircraft is a time-concentrated abnormality, and the flight optimization direction of the target aircraft is set to the flight duration, wherein the flight optimization direction includes airport landing conditions, path conditions, and flight duration.

[0026] When the target aircraft's navigation anomaly element is not a time-concentrated anomaly, identify whether the target aircraft's navigation anomaly element is a region-concentrated anomaly. When the target aircraft's navigation anomaly element is a region-concentrated anomaly, further identify the target aircraft's navigation optimization direction.

[0027] Preferably, the navigation optimization direction of the target aircraft is set to the navigation duration, which includes: obtaining the historical conventional navigation energy consumption Q1 and navigation duration T1 of the alternative path from the recorder of the target aircraft, and extracting the expected energy consumption Q2 and navigation duration T2 of the target aircraft on the navigation route, and comparing and analyzing the navigation route change optimization rate. e is a natural constant.

[0028] The navigation path change optimization rate is compared with the preset change optimization reference rate. If the navigation path change optimization rate exceeds the preset change optimization reference rate, the route path of the target aircraft is changed; otherwise, the travel speed of the target aircraft is changed.

[0029] Preferably, the content of identifying whether the navigation anomaly element of the target aircraft is a regional concentrated anomaly includes: if the scatter point form of the element in the scatter plot of the navigation anomaly performance of the target aircraft is that the difference values ​​of a certain element at several consecutive time limit points are all within a specified range, then the navigation anomaly element of the target aircraft is judged to be a regional concentrated anomaly, and the target aircraft's pre-arrival navigation position at several consecutive time limit points to which the element belongs is further obtained; if the target aircraft's pre-arrival navigation position at several consecutive time limit points to which the element belongs is near the landing site, then the navigation optimization direction of the target aircraft is set to the airport landing condition.

[0030] If the target aircraft's estimated navigation position for several consecutive time points to which the element belongs is on the middle navigation route, the navigation optimization direction of the target aircraft is set as the path condition.

[0031] Compared with the prior art, the present invention has the following beneficial effects: (1) By performing three-dimensional point segmentation on the navigation area of ​​the target aircraft, the present invention not only considers the spatial distribution of meteorological data, but also combines the time factor to identify the corresponding risk level of meteorological data of the target aircraft's predicted navigation position at each time point within the predicted flight duration, thereby being able to more accurately and comprehensively assess the meteorological risks that the target aircraft may encounter at different time points, providing a response basis for real-time warning, and thereby improving flight safety.

[0032] (2) The present invention constructs a weather forecast for the target aircraft's flight route and determines the target aircraft's flight optimization direction in a customized manner, such as optimizing the travel speed according to the temporal concentration of abnormal elements and optimizing the flight path or landing conditions according to the regional concentration of abnormal elements. This enables the aircraft to respond more quickly and take appropriate countermeasures when faced with sudden weather conditions, thereby avoiding known severe weather areas and reducing the risk of encountering dangerous weather.

[0033] (3) The present invention uses homomorphic encryption technology to perform operations on encrypted meteorological data without decryption, thereby protecting data privacy while achieving effective sharing. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 This is a schematic diagram of the system module connection of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] See also Figure 1 As shown, the present invention provides an intelligent aviation data analysis terminal based on encrypted sharing of meteorological data, including: a data receiving module, a meteorological processing module, an early warning module, and a personalized suggestion generation module.

[0038] The data receiving module is connected to the meteorological processing module, the meteorological processing module is connected to the early warning module, and the early warning module is connected to the personalized suggestion generating module.

[0039] The data receiving module is used to obtain the navigation area of ​​the target aircraft, receive and manage the encrypted meteorological data of the target aircraft in the navigation area, and the corresponding types of meteorological data include high-altitude detection data and radar data.

[0040] The meteorological data is obtained in real time by operating satellite cloud images and radar.

[0041] In a preferred embodiment, the high-altitude detection data includes air temperature, air pressure, rainfall, wind speed and direction, and cloud and fog amount.

[0042] The radar data includes reflectivity factor and velocity field.

[0043] The homomorphic operation processing module is used to perform data analysis and calculation on the ciphertext domain and perform three-dimensional point segmentation on the navigation area of ​​the target aircraft.

[0044] The data analysis and calculation performed on the ciphertext domain are specifically as follows: after the device receives the encrypted meteorological data through the communication module, it verifies the integrity and source reliability of the data to ensure that the data has not been tampered with, and then determines the required data processing and analysis methods based on user input or preset task types, and finally uses a homomorphic operation processor to perform necessary data processing in the encrypted domain.

[0045] The homomorphic operation processor performs necessary data processing in the encryption domain. The specific steps are as follows: a1. Key generation: select a suitable elliptic curve E, defined on a finite field Fp, select a base point G on the curve, and generate a private key sk and a corresponding public key pk=sk*G.

[0046] a2. Encryption: Map the plaintext message m to a point M on the elliptic curve, select a random number r, and calculate the ciphertext C = (r*G,M+r*pk).

[0047] a3. Homomorphic operations: Based on the two ciphertexts C1 = (R1, S1) and C2 = (R2, S2) constructed based on the two random number cases of r, the following operations can be performed: addition C1 + C2 = (R1 + R2, S1 + S2) and scalar multiplication k*C1 = (k*R1, k*S1), where k represents a scalar and k is an arbitrary integer used to scale or transform the ciphertext while maintaining the additive homomorphism between the ciphertexts.

[0048] a4. Decryption: The two key components R and S generated during the encryption process together form the ciphertext C = (R, S), use the private key sk to calculate M = S-sk*R, and then map M back to the plaintext message m.

[0049] Based on the above algorithm, the present invention implements a secure sharing model through the following process: b1. The data provider uses a public key to encrypt the original meteorological data to generate a data set in ciphertext form.

[0050] b2. The encrypted data is transmitted to the terminal device through a secure channel.

[0051] b3. The terminal device receives the encrypted data and performs the required calculations and analysis on the ciphertext domain.

[0052] b4. Use the private key to decrypt the calculation results only when the final results need to be displayed.

[0053] The present invention uses homomorphic encryption technology to perform operations on encrypted meteorological data without decryption, protecting data privacy while achieving effective sharing.

[0054] In a preferred embodiment, the three-dimensional point segmentation of the navigation area of ​​the target aircraft includes: obtaining the corresponding route path information of the navigation area of ​​the target aircraft, including longitude and latitude, route altitude, and constructing a three-dimensional coordinate system with the corresponding connecting line between the starting point and the end point on the route path as the X-axis, the horizontal and vertical line of the X-axis as the Y-axis, and the path height as the Z-axis.

[0055] The altitude of each route change is extracted from the route path information as the coordinate height of each elevation line corresponding to the Z-axis coordinate, and the coordinate points with fixed intervals on the horizontal planes corresponding to the X-axis and Y-axis are extracted and recorded as each departure point, so as to obtain the coordinates of each departure point on each elevation line corresponding to the navigation area of ​​the target aircraft.

[0056] The weather processing module is used to identify the corresponding risk level of the meteorological data of the target aircraft's predicted navigation position at each time point within the predicted flight duration, and to construct a weather forecast for the target aircraft's navigation route.

[0057] In a preferred embodiment, the identification of the corresponding risk level of the meteorological data of the target aircraft's predicted navigation position at each time point within the predicted flight duration includes: 1) the time series level analysis process is: obtaining the predicted flight duration of the target aircraft in the navigation area, and then extracting the meteorological data of each departure point coordinate on each elevation line at each time point within the predicted flight duration from the encrypted meteorological data of the target aircraft in the navigation area.

[0058] The estimated flight time of the target aircraft in the navigation area refers to the estimated flight time of the target aircraft in the navigation area at a normal speed.

[0059] The predicted flight position of the target aircraft is extracted in real time, and the meteorological data of the predicted flight position of the target aircraft at each time point within the predicted flight time is obtained.

[0060] 2) The spatial sequence level analysis process is as follows: obtaining the radial and latitudinal coordinates of each departure point on each elevation line corresponding to the target aircraft's navigation area, comparing them with the preset conventional meteorological data for each radial and latitudinal direction, and identifying the conventional meteorological data to which the coordinates of each departure point on each elevation line of the target aircraft belong.

[0061] The meteorological data belonging to the target aircraft's predicted flight position at each time limit within the predicted flight duration are compared with the conventional meteorological data belonging to the coordinates of each departure point of the target aircraft on each elevation line to identify the corresponding risk level of the meteorological data belonging to the target aircraft's predicted flight position at each time limit within the predicted flight duration.

[0062] In a further preferred embodiment, the risk level identification method is: comparing the meteorological data belonging to the target aircraft's pre-arrival navigation position at each time limit point with the conventional meteorological data belonging to the coordinates of each departure point of the target aircraft on each elevation line, obtaining the difference values ​​of each element between the meteorological data belonging to the target aircraft's pre-arrival navigation position at each time limit point and the conventional meteorological data belonging to the corresponding navigation position, comparing it with the corresponding element difference value range of each risk level, obtaining the risk level of each element in the meteorological data belonging to the target aircraft's pre-arrival navigation position at each time limit point, and comparing with each other to screen out the maximum risk level as the corresponding risk level of the meteorological data belonging to the target aircraft's pre-arrival navigation position at each time limit point.

[0063] In a further preferred embodiment, the risk level includes information level, attention level, warning level, and emergency level.

[0064] The information level refers to weather conditions that have little impact on flight, the caution level refers to weather conditions that may affect flight and require close attention, the warning level refers to weather conditions that seriously affect flight safety and require changes to flight plans, and the emergency level refers to extremely dangerous weather conditions that require immediate response measures.

[0065] In a further preferred embodiment, the construction of the weather forecast for the flight route of the target aircraft includes: obtaining key elements corresponding to each preset risk level, comparing them with the corresponding risk levels of the meteorological data of the target aircraft's predicted flight position at each time limit within the predicted flight duration, and obtaining key elements corresponding to the target aircraft's predicted flight position at each time limit within the predicted flight duration.

[0066] From the difference values ​​of each element between the meteorological data belonging to the target aircraft's predicted flight position at each time limit point and the regular meteorological data belonging to the corresponding flight position, retrieve the difference values ​​of the key elements corresponding to the target aircraft's predicted flight position at each time limit point within the predicted flight duration, compare them with the preset safety difference values ​​of the corresponding key elements, and screen out the key elements with difference values ​​greater than the preset safety difference values, and record them as the abnormal elements corresponding to the target aircraft's predicted flight position at each time limit point within the predicted flight duration.

[0067] At the same time, elements other than the key elements corresponding to each risk level whose difference values ​​exceed the preset safety difference values ​​are screened out and recorded as the corresponding marking elements of the target aircraft's predicted navigation position at each time limit point within the predicted flight duration.

[0068] The abnormal elements and marking elements corresponding to the predicted navigation position of the target aircraft at each time point within the predicted flight duration are constructed into a weather forecast for the navigation route of the target aircraft.

[0069] The early warning module is used to issue early warnings for the corresponding risk levels of meteorological data corresponding to the target aircraft's predicted flight position at each time point within the predicted flight duration.

[0070] The present invention performs three-dimensional point segmentation on the navigation area of ​​the target aircraft, not only taking into account the spatial distribution of meteorological data, but also combining the time factor to identify the corresponding risk level of meteorological data at the target aircraft's predicted navigation position at each time limit within the predicted flight duration. This can more accurately and comprehensively assess the meteorological risks that the target aircraft may encounter at different time points, provide a response basis for real-time warning, and thus improve flight safety.

[0071] The personalized suggestion generation module is used to generate customized aviation optimization suggestions based on the weather forecast of the target aircraft's flight route.

[0072] In a preferred embodiment, the personalized suggestion generation module includes the following contents: obtaining the difference values ​​of each abnormal element and each marking element corresponding to the target aircraft's predicted flight position at each time limit point within the predicted flight duration, using each time limit point as the horizontal coordinate and the difference value of each abnormal element and each marking element as the vertical coordinate, constructing a scatter plot of the target aircraft's flight abnormality performance, identifying the element scatter point form of the flight abnormality performance, if the element scatter point form is that more than a benchmark number of elements are concentrated at a certain time limit point, then the flight abnormality element of the target aircraft is judged to be a time-concentrated abnormality, and the flight optimization direction of the target aircraft is set to the flight duration, specifically by comparing energy consumption and time limit to achieve route changes or travel speed, wherein the flight optimization direction includes airport landing conditions, path conditions, and flight duration.

[0073] When the target aircraft's navigation anomaly element is not a time-concentrated anomaly, identify whether the target aircraft's navigation anomaly element is a region-concentrated anomaly. When the target aircraft's navigation anomaly element is a region-concentrated anomaly, further identify the target aircraft's navigation optimization direction.

[0074] In a further preferred embodiment, the navigation optimization direction of the target aircraft is set to the navigation duration, which includes: obtaining the historical conventional navigation energy consumption Q1 and navigation duration T1 of the alternative path from the recorder of the target aircraft, and extracting the expected energy consumption Q2 and navigation duration T2 of the target aircraft on the navigation route, and comparing and analyzing the navigation path change optimization rate. e is a natural constant.

[0075] The navigation path change optimization rate is compared with the preset change optimization reference rate. If the navigation path change optimization rate exceeds the preset change optimization reference rate, the route path of the target aircraft is changed; otherwise, the travel speed of the target aircraft is changed.

[0076] In a further preferred embodiment, the content of identifying whether the navigation anomaly element of the target aircraft is a regional concentrated anomaly includes: if the scatter point shape of the element in the scatter plot of the navigation anomaly of the target aircraft is that the difference values ​​of a certain element at several consecutive time points are all within a specified range, then the navigation anomaly element of the target aircraft is judged to be a regional concentrated anomaly, and the target aircraft's pre-arrival navigation position of the several consecutive time points to which the element belongs is further obtained; if the target aircraft's pre-arrival navigation position of the several consecutive time points to which the element belongs is near the landing site, then the navigation optimization direction of the target aircraft is set to the airport landing condition, which is specifically achieved by changing the landing site.

[0077] If the target aircraft of several consecutive time points to which the element belongs is expected to reach its navigation position on the middle navigation route, the navigation optimization direction of the target aircraft is set as the path condition, which is specifically achieved by changing the alternative path.

[0078] The present invention constructs a weather forecast for the target aircraft's flight route and customizes the target aircraft's flight optimization direction, such as optimizing the travel speed based on the temporal concentration of abnormal elements, and optimizing the flight path or landing conditions based on the regional concentration of abnormal elements. This allows the aircraft to respond more quickly and take appropriate countermeasures when faced with sudden weather conditions, thereby avoiding known severe weather areas and reducing the risk of encountering dangerous weather.

[0079] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. An intelligent aviation data analysis terminal based on encrypted sharing of meteorological data, characterized in that: include: Data receiving module: used to obtain the navigation area of ​​the target aircraft, receive and manage the encrypted meteorological data of the target aircraft in the navigation area. The corresponding types of meteorological data include high-altitude detection data and radar data; Homomorphic operation processing module: used to perform data analysis and calculations on the ciphertext domain and perform three-dimensional point segmentation on the navigation area of ​​the target aircraft; Meteorological analysis module: used to identify the risk level of meteorological data corresponding to the target aircraft's predicted flight position at each time point within the predicted flight duration, and to construct a weather forecast for the target aircraft's flight route; Early warning module: used to issue early warnings based on the risk level of meteorological data corresponding to the target aircraft's predicted flight position at each time point within the predicted flight duration; Personalized suggestion generation module: used to generate customized aviation optimization suggestions based on the weather forecast of the target aircraft's flight route; The personalized suggestion generation module includes: Obtain the difference between each abnormal element and each marked element corresponding to the target aircraft's predicted flight position at each time limit point within the predicted flight duration. Construct a scatter plot of the target aircraft's flight anomaly performance, with each time limit point as the horizontal axis and the difference between each abnormal element and each marked element as the vertical axis. Identify the element scatter pattern of the flight anomaly performance. If the element scatter pattern shows that more than a baseline number of elements are concentrated at a certain time limit point, determine that the target aircraft's flight anomaly element is a time-concentrated anomaly, and set the target aircraft's flight optimization direction to flight duration. The flight optimization direction includes airport landing conditions, path conditions, and flight duration. When the target aircraft's navigation anomaly element is not a time-concentrated anomaly, identify whether the target aircraft's navigation anomaly element is a region-concentrated anomaly. When the target aircraft's navigation anomaly element is a region-concentrated anomaly, further identify the target aircraft's navigation optimization direction.

2. The intelligent aviation data analysis terminal based on meteorological data encryption and sharing according to claim 1, characterized in that: The high-altitude detection data includes temperature, air pressure, rainfall, wind speed and direction, and cloud and fog amount; The radar data includes reflectivity factor and velocity field.

3. The intelligent aviation data analysis terminal based on meteorological data encryption and sharing according to claim 1 is characterized in that: The three-dimensional point segmentation of the navigation area of ​​the target aircraft includes: Obtain the target aircraft's flight path information, including longitude and latitude, and altitude. Construct a three-dimensional coordinate system using the line connecting the starting and ending points on the flight path as the X-axis, the line perpendicular to the X-axis as the Y-axis, and the altitude of the flight path as the Z-axis. The altitude of each route change is extracted from the route path information as the coordinate height of each elevation line corresponding to the Z-axis coordinate, and the coordinate points with fixed intervals on the horizontal planes corresponding to the X-axis and Y-axis are extracted and recorded as each departure point, so as to obtain the coordinates of each departure point on each elevation line corresponding to the navigation area of ​​the target aircraft.

4. The intelligent aviation data analysis terminal based on meteorological data encryption and sharing according to claim 3 is characterized in that: The identification of the target aircraft's predicted flight position at each time point within the predicted flight duration and the corresponding risk level of the meteorological data include: 1) The analysis process at the time series level is as follows: obtain the predicted flight time of the target aircraft within the navigation area, and then extract the meteorological data of each departure point coordinate on each elevation line at each time point within the predicted flight time; Extract the target aircraft's predicted flight position in real time and obtain the target aircraft's predicted flight position at each time point within the predicted flight duration. 2) The spatial sequence level analysis process is as follows: obtain the longitude and latitude coordinates of each departure point on each elevation line corresponding to the target aircraft's navigation area, compare them with the preset conventional meteorological data for each longitude and latitude, and identify the conventional meteorological data to which the coordinates of each departure point on each elevation line belong; The meteorological data belonging to the target aircraft's predicted flight position at each time limit within the predicted flight duration are compared with the conventional meteorological data belonging to the coordinates of each departure point of the target aircraft on each elevation line to identify the corresponding risk level of the meteorological data belonging to the target aircraft's predicted flight position at each time limit within the predicted flight duration.

5. The intelligent aviation data analysis terminal based on meteorological data encryption and sharing according to claim 4 is characterized in that: The risk level identification method is: comparing the meteorological data belonging to the target aircraft's pre-arrival navigation position at each time limit point with the conventional meteorological data belonging to the coordinates of each departure point of the target aircraft on each elevation line, obtaining the difference values ​​of each element between the meteorological data belonging to the target aircraft's pre-arrival navigation position at each time limit point and the conventional meteorological data belonging to the corresponding navigation position, comparing it with the corresponding element difference value range of each risk level, obtaining the risk level of each element in the meteorological data belonging to the target aircraft's pre-arrival navigation position at each time limit point, and comparing them with each other to screen out the maximum risk level as the corresponding risk level of the meteorological data belonging to the target aircraft's pre-arrival navigation position at each time limit point.

6. The intelligent aviation data analysis terminal based on meteorological data encryption and sharing according to claim 1, characterized in that: The risk levels include information level, attention level, warning level, and emergency level.

7. The intelligent aviation data analysis terminal based on meteorological data encryption and sharing according to claim 1, characterized in that: The construction of the target aircraft's flight route weather forecast includes: Obtaining each key element corresponding to each preset risk level, comparing it with the corresponding risk level of the meteorological data of the target aircraft's predicted flight position at each time limit within the predicted flight duration, and obtaining each key element corresponding to the target aircraft's predicted flight position at each time limit within the predicted flight duration; Retrieve the difference values ​​of the key elements corresponding to the target aircraft's predicted flight position at each time limit within the predicted flight duration, compare them with the preset safety difference values ​​of the corresponding key elements, and screen out the key elements with difference values ​​greater than the preset safety difference values. Record them as the abnormal elements corresponding to the target aircraft's predicted flight position at each time limit within the predicted flight duration; At the same time, elements other than the key elements corresponding to each risk level whose difference values ​​exceed the preset safety difference values ​​are selected and recorded as the corresponding marking elements of the target aircraft's predicted flight position at each time limit within the predicted flight duration; The abnormal elements and marking elements corresponding to the predicted navigation position of the target aircraft at each time point within the predicted flight duration are constructed into a weather forecast for the navigation route of the target aircraft.

8. The intelligent aviation data analysis terminal based on meteorological data encryption and sharing according to claim 1, characterized in that: The content of the target aircraft's flight optimization direction set as flight duration includes: Obtain historical conventional flight energy consumption for alternative routes from the target aircraft's recorder and flight duration , and extract the expected energy consumption of the target aircraft on the navigation route and flight duration , compare and analyze the optimization rate of navigation path change , e is a natural constant; The navigation path change optimization rate is compared with the preset change optimization reference rate. If the navigation path change optimization rate exceeds the preset change optimization reference rate, the route path of the target aircraft is changed; otherwise, the travel speed of the target aircraft is changed.

9. The intelligent aviation data analysis terminal based on meteorological data encryption and sharing according to claim 1, characterized in that: The content of identifying whether the navigation anomaly element of the target aircraft is a regional centralized anomaly includes: If the element scatter point pattern in the target aircraft's flight anomaly performance scatter plot shows that the difference values ​​of a certain element at several consecutive time limit points are all within a specified range, then the target aircraft's flight anomaly element is determined to be a regional concentrated anomaly. Further, the target aircraft's predicted flight positions at the several consecutive time limit points to which the element belongs are obtained. If the target aircraft's predicted flight positions at the several consecutive time limit points to which the element belongs are near the landing site, then the target aircraft's flight optimization direction is set to the airport landing condition. If the target aircraft's estimated navigation position for several consecutive time points to which the element belongs is on the middle navigation route, the navigation optimization direction of the target aircraft is set as the path condition.

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