Intelligent aviation data analysis terminal based on meteorological data encryption sharing

Abstract

The invention belongs to the field of aeronautical data analysis, and particularly discloses an intelligent aeronautical data analysis terminal based on meteorological data encryption sharing, which is characterized in that three-dimensional point segmentation is performed on a navigation area of a target aircraft, so that spatial distribution of meteorological data is considered, and a time factor is combined; according to the method, the corresponding risk level of the meteorological data to which the pre-arrival navigation position of the target aircraft in each time limit point within the pre-arrival time duration belongs is identified, so that meteorological risks possibly encountered by the target aircraft at different time points can be evaluated more accurately and more comprehensively, a coping basis is provided for real-time early warning, and the flight safety is further improved; according to the method, the navigation route weather forecast of the target aircraft is constructed, and the navigation optimization direction of the target aircraft is determined in a customized manner, so that the aircraft can make a response more quickly and take appropriate countermeasures 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, with the rapid development of the aviation industry and the increasingly complex and changeable meteorological conditions, the requirements for flight safety are also constantly increasing. Traditional aviation data analysis methods mainly rely on static meteorological observation data and empirical judgments. It is difficult to comprehensively and accurately assess the meteorological risks that the target aircraft may encounter at different time points, and it is also impossible to provide real-time and effective navigation optimization suggestions for aircraft. Therefore, there is an urgent need for a technical solution that can acquire and process meteorological data in real time and accurately to meet the needs of intelligent analysis of meteorological data in flight missions.

[0003] There are several limitations in the existing technology, specifically: 1. The existing technology often only assesses meteorological risks based on 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 in 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: It is 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: It is used to warn the target aircraft of the corresponding risk level of the meteorological data at the expected flight position at each time point within the expected flight time.

[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 air temperature, air pressure, rainfall, wind speed and direction, and cloud and fog amount.

[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 of 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 the 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 belonging to the predicted flight position of the target aircraft 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 limit 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 navigation area of ​​the target aircraft, comparing them with the preset conventional meteorological data of each radial and latitudinal direction, and identifying the conventional meteorological data to which the coordinates of each departure point of the target aircraft on each elevation line belong.

[0018] The meteorological data belonging to the predicted flight position of the target aircraft 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 predicted flight position of the target aircraft at each time limit within the predicted flight duration.

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

[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, 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 whose difference values ​​exceed the preset safety difference values ​​are selected from other elements other than the key elements corresponding to each risk level, and recorded as the corresponding marking elements of the expected flight position of the target aircraft at each time limit point within the expected flight duration.

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

[0025] Preferably, the personalized suggestion generation module includes the following contents: obtaining the difference values ​​of each abnormal element and each marked element corresponding to the expected flight position of the target aircraft at each time limit point within the expected flight duration, taking each time limit point as the horizontal coordinate and the difference value of each abnormal element and each marked 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 the elements exceeding the benchmark number are concentrated at a certain time limit 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 navigation anomaly element of the target aircraft is not a time-concentrated anomaly, it is identified whether the navigation anomaly element of the target aircraft is a regional-concentrated anomaly. When the navigation anomaly element of the target aircraft is a regional-concentrated anomaly, the navigation optimization direction of the target aircraft is further identified.

[0027] Preferably, the content of setting the navigation optimization direction of the target aircraft as the navigation duration includes: obtaining the historical conventional navigation energy consumption Q1 and the navigation duration T1 of the alternative path from the recorder of the target aircraft, and extracting the expected energy consumption Q2 and the 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 shape of the element in the scatter diagram 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 expected navigation position of several consecutive time limit points to which the element belongs is further obtained; if the target aircraft's expected navigation position of several consecutive time limit points to which the element belongs is near the landing location, 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) The present invention divides the navigation area of ​​the target aircraft into three-dimensional points, which not only takes into account the spatial distribution of meteorological data, but also combines the time factor to identify the corresponding risk level of the meteorological data of the predicted navigation position of the target aircraft 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 flight route of the target aircraft and determines the navigation optimization direction of the target aircraft in a customized manner, such as optimizing the travel speed according to the temporal concentration of abnormal elements, and optimizing the route 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 accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

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

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work 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 are such as high-altitude detection data and radar data.

[0040] The meteorological data is obtained in real time by running 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 stereo 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 encryption domain.

[0045] The homomorphic operation processor performs necessary data processing in the encryption domain, and 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 random number cases of r, the two ciphertexts C1=(R1, S1) and C2=(R2, S2) can be subjected to the following operations: 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, which is 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 calculations on encrypted meteorological data without decryption, thereby 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 the 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 weather 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.

[0057] In a preferred embodiment, the identification of the corresponding risk level of the meteorological data belonging to the predicted flight position of the target aircraft at each time limit 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 limit 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 limit 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 navigation area of ​​the target aircraft, comparing them with the preset conventional meteorological data of each radial and latitudinal direction, and identifying the conventional meteorological data to which the coordinates of each departure point of the target aircraft on each elevation line belong.

[0061] The meteorological data belonging to the predicted flight position of the target aircraft 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 predicted flight position of the target aircraft 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 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 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 pre-arrival navigation position of the target aircraft at each time limit, comparing with each other to screen out the maximum risk level, as the corresponding risk level of the meteorological data belonging to the pre-arrival navigation position of the target aircraft at each time limit.

[0063] In a further preferred implementation, the risk levels include 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 the flight plan, and the emergency level refers to extremely dangerous weather conditions for which immediate countermeasures must be taken.

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

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

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

[0069] The early warning module is used to warn the target aircraft of the corresponding risk level of the meteorological data of the predicted navigation position at each time point within the predicted flight time.

[0070] The present invention performs three-dimensional point segmentation on the navigation area of ​​the target aircraft, which not only takes into account the spatial distribution of meteorological data, but also combines the time factor to identify the corresponding risk level of meteorological data of the predicted navigation position of the target aircraft at each time limit point within the predicted flight duration, thereby being able to more accurately and comprehensively evaluate the meteorological risks that the target aircraft may encounter at different time points, providing a response basis for real-time warning, and thus improving flight safety.

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

[0072] In a preferred embodiment, the personalized suggestion generation module includes: obtaining the difference values ​​of each abnormal element and each marking element corresponding to the expected flight position of the target aircraft at each time limit point within the expected flight time, taking 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 navigation abnormality performance, identifying the element scatter point form of the navigation abnormality performance, if the element scatter point form is that the elements exceeding the benchmark number are concentrated at a certain time limit point, then it is judged that the navigation abnormality element of the target aircraft is a time-concentrated abnormality, and the navigation optimization direction of the target aircraft is set to the flight time, which is specifically achieved by comparing the energy consumption and the time limit through route changes or travel speed, wherein the navigation optimization direction includes airport landing conditions, path conditions, and flight time.

[0073] When the navigation anomaly element of the target aircraft is not a time-concentrated anomaly, it is identified whether the navigation anomaly element of the target aircraft is a regional-concentrated anomaly. When the navigation anomaly element of the target aircraft is a regional-concentrated anomaly, the navigation optimization direction of the target aircraft is further identified.

[0074] In a further preferred embodiment, the navigation optimization direction of the target aircraft is set to the navigation duration, including: 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 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 expected navigation position of several consecutive time limit points to which the element belongs is further obtained; if the target aircraft's expected navigation position of 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, 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 the 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 flight route of the target aircraft and customizes the navigation optimization direction of the target aircraft, such as optimizing the travel speed according to the temporal concentration of abnormal elements, and optimizing the route 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 facing 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 specific embodiments described 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 shall all fall within the protection scope 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, and the corresponding types of meteorological data such as high-altitude detection data and radar data; Homomorphic operation processing module: used to perform data analysis and calculation on the ciphertext domain and perform stereo point segmentation on the navigation area of ​​the target aircraft; 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; Early warning module: used to warn the target aircraft of the corresponding risk level of the meteorological data of the expected flight position at each time point within the expected flight time; Personalized suggestion generation module: used to generate customized aviation optimization suggestions based on the weather forecast of the target aircraft's flight route.

2. According to claim 1, the intelligent aviation data analysis terminal based on meteorological data encryption sharing is characterized in that: The high-altitude detection data include temperature, air pressure, rainfall, wind speed and direction, and cloud and fog volume; 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 corresponding route information of the navigation area of ​​the target aircraft, including longitude and latitude, route altitude, and construct 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 altitude 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 the 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 corresponding risk level of the meteorological data of the predicted flight position of the target aircraft at each time point within the predicted flight time includes: 1) The time series level analysis process is as follows: obtaining the predicted flight time 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 limit 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 time. 2) The spatial sequence level analysis process is as follows: obtaining the radial and latitudinal coordinates of each departure point coordinate on each elevation line corresponding to the navigation area of ​​the target aircraft, comparing them with the preset conventional meteorological data of each radial and latitudinal direction, and identifying the conventional meteorological data to which the coordinates of each departure point of the target aircraft on each elevation line belong; The meteorological data belonging to the predicted flight position of the target aircraft 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 predicted flight position of the target aircraft 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 as follows: the meteorological data belonging to the target aircraft's expected flight position at each time limit point are compared with the conventional meteorological data belonging to the coordinates of each departure point of the target aircraft on each elevation line, and the difference values ​​of each element between the meteorological data belonging to the target aircraft's expected flight position at each time limit point and the conventional meteorological data belonging to the corresponding flight position are obtained, and the difference values ​​are compared with the corresponding element difference value ranges of each risk level to obtain the risk level of each element in the meteorological data belonging to the target aircraft's expected flight position at each time limit point, and the maximum risk level is screened out by mutual comparison as the corresponding risk level of the meteorological data belonging to the target aircraft's expected flight position at each time limit point.

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

8. The intelligent aviation data analysis terminal based on meteorological data encryption and sharing according to claim 7 is characterized in that: The personalized suggestion generation module includes: Obtain the difference values ​​of each abnormal element and each marked element corresponding to the expected flight position of the target aircraft at each time limit point within the expected flight duration, take each time limit point as the horizontal coordinate and the difference value of each abnormal element and each marked element as the vertical coordinate, construct a scatter plot of the target aircraft's flight abnormality performance, identify the element scatter point form of the flight abnormality performance, if the element scatter point form is that the elements exceeding the benchmark number are concentrated at a certain time limit 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, where the flight optimization direction includes airport landing conditions, path conditions, and flight duration; When the navigation anomaly element of the target aircraft is not a time-concentrated anomaly, it is identified whether the navigation anomaly element of the target aircraft is a regional-concentrated anomaly. When the navigation anomaly element of the target aircraft is a regional-concentrated anomaly, the navigation optimization direction of the target aircraft is further identified.

9. The intelligent aviation data analysis terminal based on meteorological data encryption and sharing according to claim 8 is characterized in that: The content of the target aircraft's flight optimization direction set as the flight duration includes: Obtain the historical conventional navigation energy consumption Q1 and navigation time T1 of the alternative route from the recorder of the target aircraft, and extract the expected energy consumption Q2 and navigation time T2 of the target aircraft on the navigation route, and compare and analyze the optimization rate of navigation route 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.

10. The intelligent aviation data analysis terminal based on meteorological data encryption and sharing according to claim 8, 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 shape in the scatter diagram of the target aircraft's flight anomaly performance is that the difference values ​​of a certain element at several consecutive time limit points are all within the specified range, then the target aircraft's flight anomaly element is judged to be a regional concentrated anomaly, and the target aircraft's predicted flight position at several consecutive time limit points to which the element belongs is further obtained. If the target aircraft's predicted flight position at several consecutive time limit points to which the element belongs is near the landing location, 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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