A method, apparatus, and system for analyzing flight qualities of an aircraft

Abstract

The application provides a method for analyzing flight quality of an aircraft, which comprises receiving flight data and hierarchically managing the received flight data; collecting parameter decoding rule configurations of various types of aircraft and hierarchically managing the parameter decoding rule configurations to construct a parameter decoding library applicable to various types of aircraft; performing parameter matching decoding of the flight data according to parameter decoding rules in the same hierarchy to generate engineering value data; constructing key points and intervals, setting threshold values corresponding to the key points and the intervals and writing judgment analysis rules; reading segment engineering value data corresponding to the key points and the intervals in the engineering value data, comparing parameter engineering value data with preset threshold values of corresponding parameters, and then detecting event content in the segment engineering value data based on a comparison result; and based on the generated engineering value data, analyzing and evaluating flight quality of the aircraft according to the detected event content according to a flight quality monitoring manual.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, and in particular to a method, device and system for analyzing flight quality of an aircraft. BACKGROUND

[0002] With multiple types of aircrafts being put into operation, the amount of data generated by aircraft flight continues to increase. In the face of these massive and continuously increasing flight data, the industry can only passively and temporarily store them, and cannot effectively process them, let alone accurately analyze the flight information contained in the flight data to analyze the flight quality of the aircraft.

[0003] Flight data usually includes real-time data and post-flight data, wherein the QAR data obtained after flight has great significance for analyzing the handling characteristics, safety and economy of aircraft flight. Moreover, in order to fully apply flight data, flight data from flight recorders usually needs to be decoded first. However, flight data of different types of aircrafts are usually different in recording structure and rule configuration, which leads to the complexity of flight data decoding work for multiple types of aircrafts.

[0004] To solve these problems, the present application provides a method, device and system for analyzing flight quality of an aircraft, which can not only efficiently realize flight data decoding of various types of aircrafts, but also analyze flight quality based on information contained in flight data. SUMMARY

[0005] Therefore, in order to overcome the problem that the existing method cannot efficiently realize flight data decoding of multiple types of aircrafts, and cannot effectively mine and analyze a large amount of flight data, thereby reducing the efficiency of utilizing flight data, and thus cannot accurately analyze the flight quality of an aircraft, the present application provides a method, device and system for analyzing flight quality of an aircraft.

[0006] The present application solves the above technical problems by the following technical solutions:

[0007] Specifically, according to a first aspect of the present application, a method for analyzing flight quality of an aircraft is provided, characterized in that the method comprises the following steps:

[0008] Flight data from a flight recorder of an aircraft is received by using a wireless communication network or FTP, and the received flight data is managed in layers according to the classification mode of aircraft type, aircraft number, flight data type and flight data version;

[0009] The parameter decoding rule configurations from various types of aircrafts of the airlines are collected, and the collected parameter decoding rule configurations are managed in layers according to the classification of the aircraft types, aircraft numbers, flight data types and flight data versions, so as to construct a parameter decoding library applicable to various types of aircrafts.

[0010] The parameters in the parameter decoding rule and the parameters in the flight data in the same layer are matched, and the matched parameters are decoded, so as to generate corresponding engineering value data.

[0011] Key points and intervals are constructed according to the general flight phases and key events in the flight process of the aircraft, threshold values corresponding to the key points and intervals are set according to the occurrence conditions of the key events, and corresponding judgment analysis rules are written.

[0012] The segment engineering value data corresponding to the key points and intervals in the engineering value data is read, the engineering value data of the parameters in the segment engineering value data is compared with the preset threshold values of the corresponding parameters according to the judgment analysis rules, and then the event content in the segment engineering value data is detected based on the comparison results by using the distributed analysis engine; and

[0013] Based on the generated engineering value data, the flight quality of the aircraft is analyzed and evaluated according to the detected event content according to the provisions of the flight quality monitoring manual.

[0014] The method can quickly obtain parameter decoding rule configurations applicable to various types of flight data based on the hierarchical matching method through hierarchical management of the flight data and the parameter decoding rule configurations, thereby efficiently and accurately realizing parameter decoding and obtaining correct engineering value data. Moreover, the method can detect specific event content in the flight process by constructing key points, intervals and threshold values and comparing the engineering value data in the intervals with the threshold values, so as to judge the actual flight state and further perform subsequent flight quality evaluation. Further, suggestions such as flight technology improvement, aircraft maintenance and adjustment of flight frequency are proposed according to the flight quality of the aircraft, which has great economic and safety significance.

[0015] According to an embodiment of the present application, the step of managing the flight data in layers includes reading aircraft type, aircraft number, flight data type and flight data version data corresponding to the received flight data according to the attributes of the flight data, and managing the flight data in layers.

[0016] Different types of aircraft and different flight recorders follow different data recording rules. By reading the corresponding aircraft model, aircraft number and flight data type, the flight data can be classified, so that the parameter decoding rule configuration suitable for the current flight data can be quickly obtained. Moreover, the version of the data recorded by the flight recorder may be upgraded, and the recording structure and rules after the upgrade may also change. This method classifies the flight data version, which can also facilitate matching the parameter decoding version at the corresponding time stage, thereby ensuring the accuracy of the decoded data.

[0017] According to another embodiment of the present application, the engineering value data includes original parameter engineering value data, and the method further comprises directly obtaining the original parameter engineering value data by matching parameter decoding of the flight data.

[0018] According to another embodiment of the present application, the engineering value data further includes synthetic parameter engineering value data, and the method further comprises forming the synthetic parameter engineering value data by directly splicing the character type original parameter engineering value data and / or performing mathematical operations on the numerical value type original parameter engineering value data.

[0019] According to another embodiment of the present application, the engineering value data further includes derived parameter engineering value data, and the method further comprises calculating the original parameter engineering value data and / or the synthetic parameter engineering value data according to the user input instruction and the operation logic to form the derived parameter engineering value data, and displaying it through the interactive display device, wherein the user input includes the output maximum value, the output minimum value, and the operation logic includes the comparison of the size.

[0020] The method can solve the problem of partial parameters being split or scattered by synthesizing the original parameter engineering value data, thereby ensuring the accuracy of the obtained engineering value data. Moreover, the method can further combine the data of multiple sensors of a certain component of the aircraft into parameters on the ground or in the air by synthesizing the original parameter engineering value data, so that the user can more clearly understand the information included in the obtained engineering value data. In addition, the method can also determine some special states of the aircraft by combining several original parameter engineering value data, thereby improving the granularity of flight quality analysis.

[0021] According to another embodiment of the present application, the method further comprises the following steps: identifying the flight phase corresponding to each flight data according to the original parameter engineering value data, the synthesized parameter engineering value data and the derived parameter engineering value data, and classifying the engineering value data according to the flight phase, wherein the flight phase includes the initial phase, the sliding-out phase, the maximum sliding speed phase, the entering runway phase, the take-off phase, the leaving ground phase, the maximum climb airspeed phase, the climb vertex phase, the cruising phase, the descending vertex phase, the approach phase, the ground contact airspeed point and the stopping point.

[0022] According to another embodiment of the present application, the method further comprises the following steps: setting the threshold values corresponding to the key points and the intervals and writing the corresponding judgment analysis rules according to the decoded parameter names, the positions and states of the operating components and the occurrence conditions of the key events, so as to form a detection analysis library including the parameter judgment analysis, the instruction detection analysis and the event detection analysis.

[0023] The method can extract the key state data of the aircraft in the flight process by constructing the detection analysis library for parameter judgment and instruction detection analysis, and can realize more comprehensive and flexible analysis of the aircraft in combination with the event detection analysis.

[0024] According to another embodiment of the present application, the key events include the large descent event, and the method further comprises the following steps: creating a specific interval through the key points and the intervals, detecting the following indexes of the engineering value data of each type of aircraft in the specific interval: the maximum value, the minimum value, the average value, the change rate and the height at the maximum descent rate, calculating the trend range and the distribution of the aforementioned indexes in sequence by using the python language, and calculating the correlation between the trend range and the distribution of each index and the actually occurred large descent event by using the Pearson correlation method, so as to screen out the important reasons triggering the large descent event.

[0025] The method can also obtain the main factors of the event occurrence according to the engineering value data analysis of the factors related to the event based on the constructed detection analysis library through the decoded engineering value data, so as to facilitate subsequent key event prediction.

[0026] According to another embodiment of the present application, the key events include the unstable approach event, and the method further comprises the following steps: reading the engineering value data of the features related to the unstable approach event with high correlation and forming a data set, calculating the probability of the engineering value data of the features falling into different preset numerical ranges, and establishing a prediction model of the unstable approach event by using the linear regression method according to the calculated probability, so as to perform subsequent unstable approach event prediction, wherein the features include the flight attitude, the pilot operation, the position of the control stick or the position of the pedal. The method can predict whether the subsequent event can occur based on the features capable of representing the occurred event and the distribution of the engineering value data of the features.

[0027] According to another embodiment of the present application, the method further comprises the steps of: according to the engineering value data and the event detection analysis result, counting the total number of events, today's flight events, the total number of flights, airline events, airport event rankings and non-event flights; and according to the counted events, establishing an event tree applicable to multiple types of aircraft, marking the true and false events by artificial judgment, displaying the list of counted events, the event tree, the marking of true and false events through an interactive display device, and allowing a user to click on an event in the event list to view the specific content of the event and edit notes on the viewed event content.

[0028] According to another embodiment of the present application, the types of key points include time points, flight altitude points, flight speed points and flight state switching points, and the types of intervals include time intervals, flight altitude intervals, speed intervals and state intervals.

[0029] According to another embodiment of the present application, when the flight data is received through a wireless communication network, a breakpoint resume mode is adopted; and when the flight data is received through FTP, a unified FTP site configuration mode is adopted.

[0030] According to a second aspect of the present application, a device for analyzing the flight quality of an aircraft is provided, which comprises:

[0031] a data receiving module configured to receive flight data from a flight recorder and to manage the received flight data in a hierarchical manner according to the classification of aircraft type, aircraft number, flight data type and flight data version;

[0032] a decoding library module configured to collect parameter decoding rule configurations of various types of aircraft and to manage the collected parameter decoding rule configurations in a hierarchical manner according to the classification of aircraft type, aircraft number, flight data type and flight data version, so as to form a parameter decoding library applicable to multiple types of aircraft;

[0033] a matching decoding module configured to be in communication connection with the data receiving module and the decoding library module, to match the parameters in the flight data with the parameter decoding rule configurations in the same hierarchical level, to subsequently decode the parameters in the flight data according to the parameter decoding rule in the same hierarchical level, and to generate corresponding engineering value data;

[0034] a judgment analysis rule module configured to construct key points and intervals according to the general flight phases and key events in the flight process of the aircraft, to set threshold values corresponding to the key points and intervals according to the occurrence conditions of the key events, and to write corresponding judgment analysis rules;

[0035] a data judgment analysis module configured to read the segment engineering value data corresponding to the key points and intervals in the engineering value data generated by the matching decoding module, compare the parameter engineering value data of the segment engineering value data with the preset threshold value of the corresponding parameter according to the written judgment analysis rule, and then detect the event content in the segment engineering value data based on the comparison result using the distributed analysis engine; and

[0036] a flight quality evaluation module configured to be communicatively connected with the matching decoding module and the data judgment analysis module, so as to be capable of receiving the generated engineering value data and the detected event content, and further capable of analyzing and evaluating the flight quality of the aircraft based on the generated engineering value data according to the detected event content according to the flight quality monitoring manual.

[0037] According to a third aspect of the present application, a computer readable storage medium is also provided, which stores computer execution instructions for implementing the foregoing method when the computer execution instructions are executed by a processor.

[0038] According to a fourth aspect of the present application, a system for analyzing the flight quality of an aircraft is also provided, which includes a processor configured to obtain flight data in a flight recorder and receive user input, and execute the foregoing method.

[0039] On the basis of common knowledge in the art, the above-mentioned preferred conditions can be combined in any manner, i.e., to obtain each preferred example of the present application.

[0040] The method for analyzing the flight quality of an aircraft according to the foregoing embodiments of the present application has the following beneficial technical effects and advantages:

[0041] The method can quickly obtain the parameter decoding rule configuration corresponding to the flight data by performing hierarchical management on the received flight data and establishing the parameter decoding library of the plurality of types of aircraft after hierarchical management, thereby efficiently and accurately obtaining the engineering value data. Moreover, the method can facilitate the user to understand the actual flight state of the aircraft and obtain the desired aircraft state data by splicing and synthesizing the obtained original parameter engineering value data and exporting the same. In addition, the method can detect the actual event content in the flight data by the constructed key points and intervals and the written judgment analysis rule, thereby performing subsequent flight quality evaluation. The device for analyzing the flight quality of an aircraft according to the foregoing embodiments of the present application and the beneficial technical effects and advantages thereof are the same as those of the foregoing method. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 FIG. 1 is a flowchart of a method for analyzing the flight quality of an aircraft according to a preferred embodiment of the present application.

[0043] Figure 2 An exemplary schematic diagram for parameter coding rule configuration according to a preferred embodiment of the present application.

[0044] Figure 3 An exemplary schematic diagram for coded engineering value data according to a preferred embodiment of the present application.

[0045] Figure 4A An exemplary schematic diagram for constructing key points according to a preferred embodiment of the present application.

[0046] Figure 4B An exemplary schematic diagram for constructing key points according to a preferred embodiment of the present application.

[0047] Figure 4C An exemplary schematic diagram for constructing intervals according to a preferred embodiment of the present application.

[0048] Figure 4D An exemplary schematic diagram for threshold values corresponding to key points and intervals according to a preferred embodiment of the present application.

[0049] Figure 4E An exemplary schematic diagram for reading fragment engineering value data according to a preferred embodiment of the present application.

[0050] Figure 4F An exemplary schematic diagram for detecting event content according to a preferred embodiment of the present application.

[0051] Figure 5 Another exemplary schematic diagram for detecting event content according to a preferred embodiment of the present application. And

[0052] Figure 6 An exemplary schematic diagram for writing judgment analysis rule according to a preferred embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the objects, technical solutions and advantages of the present application clearer, the following will be combined with the accompanying drawings for brief, complete and clear description of the technical solutions in the embodiments of the present application. It should be understood that all other embodiments obtained by those of ordinary skill in the art based on the embodiments described in the present application without creative labor will belong to the scope of protection of the present application.

[0054] In the following detailed description, the sequential terms such as "first", "subsequently", "again" and the like are used with reference to the order of steps described in the accompanying drawings. The specific steps of the embodiments of the present application can be placed in various different orders, and the sequential terms are used for the purpose of example and are not limiting.

[0055] Figure 1As shown, the method for analyzing flight quality of an aircraft in the present embodiment includes the following steps:

[0056] Step one, receiving data and hierarchical management, receiving flight data from the flight recorder of an aircraft (airplane) through a wireless communication network or FTP, and performing hierarchical management on the received flight data according to the classification manner of aircraft type, aircraft number, flight data type and flight data version;

[0057] Step two, constructing a parameter decoding library applicable to multiple aircraft types, collecting parameter decoding rule configurations from various aircraft types of an airline, and performing hierarchical management on the collected parameter decoding rule configurations according to the classification manner of aircraft type, aircraft number, flight data type and flight data version, so as to construct a parameter decoding library applicable to multiple aircraft types;

[0058] Step three, matching hierarchical levels and parameter decoding, matching the hierarchical levels of parameter decoding rule configurations and flight data, matching parameters in flight data according to parameter decoding rules of the same hierarchical level and decoding the matched parameters, so as to generate corresponding engineering value data;

[0059] Step four, constructing key points, intervals and writing judgment analysis rules, constructing key points and intervals according to general flight stages and key events in the flight process of an aircraft, setting threshold values corresponding to the key points and intervals according to the occurrence conditions of key events and writing corresponding judgment analysis rules;

[0060] Step five, detecting event content, reading segment engineering value data corresponding to the key points and intervals in the engineering value data, comparing parameter engineering value data of the segment engineering value data with preset threshold values of corresponding parameters according to the judgment analysis rules, and then detecting event content in the segment engineering value data based on the comparison results using a distributed analysis engine; and

[0061] Step six, analyzing flight quality, based on the generated engineering value data, according to the detected event content, analyzing the flight quality of an aircraft according to a flight quality monitoring manual.

[0062] In step one, a data receiving channel is constructed through a network or a file transfer protocol (FTP) to receive flight data from the flight recorder of an aircraft. When flight data is received through a network, network file transmission can be performed in a breakpoint resume mode, thereby receiving flight data. When flight data is received through an FTP, file transmission is performed through a unified FTP site, thereby receiving and managing flight data.

[0063] Moreover, in step one, the aircraft number refers to the aircraft registration number, such as B-3321. The flight data type refers to the flight data from the flight data recorder (FDR), the wireless quick access recorder (WQAR), and the quick access recorder (QAR) according to the source device of the data recording. The data version refers to the version of the flight data recorded by the flight data recorder, which can be upgraded. After the upgrade, the recording structure and rules change, so it is necessary to match the version of the recorded version data on the aircraft at the corresponding time stage according to the aircraft type, aircraft number, time, and other information, so as to match the parameter decoding version and ensure the accuracy of the decoded data.

[0064] In step one, after receiving the flight data, the aircraft type, aircraft number, flight data type, and flight data version are identified according to the file name of the read data, i.e. the attributes of the received flight data. To meet the management needs of various types of aircraft, the received flight data is classified according to the classification method of the aircraft type, aircraft number, flight data type, and flight data version, thereby realizing the hierarchical management of flight data.

[0065] Different types of aircraft have different specifications for recording flight data by the aircraft recorder, which results in significant differences in decoding rules configuration. For example, flight data is based on ARINC717 specification and consists of Word, SubFrame, and Frame, wherein each Frame consists of 4 SubFrames, each SubFrame can contain 64, 128, 256, 512, or 1024 Word structures, and 1 Word consists of 12 bits. One SubFrame is equivalent to one second of recorded data, and the Word frequency number varies depending on the flight equipment. Moreover, each SubFrame has a synchronization word, and the synchronization words of different types of aircraft are different, such as TELEDYNE: 247, 5b8, a57, db8; HAMILTON: e24, 1da, e25, 1db.

[0066] Therefore, different types of aircraft have different aircraft protocols and parameter recording specifications, and their decoding rules are completely different, which makes different types of aircraft and different flight data recorders on the same type of aircraft result in different parameter decoding rule configurations for decoding flight data.

[0067] In step two, as Figure 2As shown, the parameter decoding rule configuration of the flight data based on the ARINC717 specification can be defined in a graphical manner and stored in the database according to the structure configuration of the flight data record. In the parameter decoding rule configuration, the parameter name, parameter type, ATA (Air Transport Association), parameter full name record frequency, parameter word, high bit, subframe, bit length, parameter type, and analysis configuration can be defined. The structure configuration refers to the configuration of the parameter decoding map structure, mainly including the structure of the data record frame, synchronization word, word length, record order, and the number of subframes contained in each frame.

[0068] Specifically, for the character value data, the Most Significant Bit (MSB) and bit length content can be configured. For the continuous value data, the bit length, coefficient, decoding range maximum value, decoding range minimum value, data type, unit, total length, and precision content can be configured. For the linear continuous value, the bit length, coefficient, decoding range maximum value, decoding range minimum value, data type, unit, total length, and precision content can be configured. For the discrete value, the original value and display value content can be configured. For the BCD value, the system, value, decoding range maximum value, and decoding range minimum value content can be configured. For the double continuous value, the bit length, coefficient, maximum value, minimum value, data type, unit, and calculation formula content can be configured.

[0069] Moreover, in step two, different parameter decoding rule configurations of multiple aircraft models can form a decoding library, and the received flight data can be managed in layers according to the classification of the aircraft model, aircraft number, flight data type, and flight data version. After management, the flight data of different categories can automatically match and automatically execute the content of the decoding library.

[0070] In step three, the specific process of decoding flight data using the decoding library includes first matching the aircraft model, aircraft number, flight data type, and flight data version of the flight data to find the corresponding parameter decoding rule configuration, and then using the parameter decoding rule configuration of the same level to match and decode different parameters in the flight data to obtain the engineering value data of the decoded parameters in the flight data. The aforementioned decoding library supports the.dat data packet processed by the ground station, supports 64WPS, 128WPS, 256WPS, 512WPS, and 1024WPS source data, and can realize decoding of multiple aircraft models.

[0071] Furthermore, due to factors such as the frequency and accuracy of onboard data recording, some parameters may be recorded separately or in a scattered manner, requiring concatenation during decoding. Moreover, some parameters directly characterize sensor states, such as landing gear compression or uncompressed parameters; for ease of human understanding, these parameters need to be synthesized. For example, multiple landing gear compression sensor data can be used to construct parameters indicating whether the aircraft is on the ground or in the air for easier comprehension. Sometimes, several parameters are needed to determine specific aircraft states. For instance, calculating the distance between the aircraft and the runway threshold requires a comprehensive assessment of the aircraft's altitude, speed, and touchdown status. Therefore, after decoding, in addition to the raw parameter engineering values ​​directly generated during the decoding process, composite parameter engineering values, derived parameter engineering values, and flight phase data are also generated after subsequent processing.

[0072] As can be seen from the above, the original data file content can be automatically matched and decoded. After decoding, according to the constructed rules, different types of synthetic parameters and derived parameters, including continuous and discrete types, are formed. The original parameters are the parameters represented by engineering values ​​obtained after direct decoding, including aircraft flight status, pilot operation input information, aircraft control component status, aircraft system status, and external environmental information.

[0073] like Figure 3 According to Figure 2 After configuring the decoding rules, the original parameter, the left inner wheel speed (LEFT INBOARD WHEEL SPEED-BCU1), is directly generated during the decoding process. Based on this parameter's basic information, the left inner wheel speed is a BNR parameter with a conversion factor of 0.25, a unit of knots, and a recording frequency of 1 / 2 Hz. The parameter recording information is as follows: Figure 2 and 3 As shown, the original parameter values ​​are obtained from bits 1-12 of the binary data record in 197 words of the first and third subframes, and then converted from binary to decimal to obtain... Figure 3 The parameter format is BNR format. After multiplying the parameter by the conversion factor of 0.25, the wheel speed value of the left inner wheel is generated, and the unit is knots.

[0074] The synthetic parameter is a parameter obtained by directly splicing a character type original parameter and performing mathematical operation on a numerical value type parameter. For example, a date and time string data is constructed according to a year, month and day, and a takeoff and landing airport of an airplane is obtained according to a starting point and a terminal point longitude and latitude of an airplane flight segment. Exemplarily, a synthetic parameter date (DATE) is combined and spliced by three parameters of a year (CURRENT DATE YEAR), a month (CURRENT DATE MONTH) and a day (CURRENT DATE DAY), and after decoding the original parameters, a unified date parameter in the form of YYYY-MM-DD is formed according to the combination order of year-month-day.

[0075] The derived parameter is a parameter obtained by comprehensive calculation based on a script mode according to the original parameter engineering value data and the synthetic parameter engineering value data. Generally, the parameter that needs to be processed and calculated for display according to business needs is the derived parameter, and therefore whether it is a derived parameter depends more on the needs.

[0076] As can be seen from the above, the derived parameter engineering value data can be formed by calculating the original parameter engineering value data and / or the synthetic parameter engineering value data according to the user input instruction and the operation logic. In addition, the derived data can be displayed through an interactive display device, wherein the user input includes an output maximum value and an output minimum value, and the operation logic includes a comparison of sizes. For example, a derived parameter vertical speed maximum value parameter (VRTG_MAX) is obtained by taking the maximum value of 8 values of the original parameter vertical acceleration (CNORACE, 8Hz) within one second, to obtain the vertical speed maximum value parameter (VRTG_MAX) at the moment.

[0077] However, it should be noted that although in actual implementation, the user input is generally more for taking the maximum or minimum value, there are also user inputs such as removing abnormal values, time counting, calculating distance and running to the head distance.

[0078] In step three, the flight phase corresponding to each flight data can also be identified according to the original parameter engineering value data, the synthetic parameter engineering value data and the derived parameter engineering value data, and the engineering value data is classified according to the flight phase, wherein the flight phase includes an initial stage, a sliding out stage, a maximum sliding speed stage, an entering runway stage, a takeoff stage, a leaving ground stage, a maximum climb airspeed stage, a climb vertex stage, a cruising stage, a descending vertex stage, an approach stage, a ground contact airspeed point and a stopping point. The key events include a large ground contact overload, a large wheel lifting speed, an unstable takeoff taxiway direction, a large turning taxiway speed, and a need to determine the key states of the airplane sliding out, turning, taking off, leaving the ground and landing in monitoring.

[0079] In step four, the method further comprises constructing key points, intervals, etc., setting threshold values corresponding to the key points and intervals according to the decoded parameter names, positions and states of operation components, and occurrence conditions of key events, and writing corresponding judgment analysis rules (judgment analysis scripts) to form parameter judgment analysis, instruction detection analysis, and event detection analysis libraries. Subsequently, the parameter content, instruction content, and event content can be detected by using a big data distributed engine to form the final judgment analysis results including parameter judgment analysis results, instruction detection results, and event detection results, etc., to realize preliminary analysis of data.

[0080] In step four, the key point is only an absolute point, but to know the information contained in a segment of flight data, at least an interval between two key points is needed to calculate the flight data in the form of an interval. Generally, the key points include time points, flight altitude points, flight speed points, and flight state switching points in the flight data. Among them, the flight state switching points are mainly points of flight state switching. Flight state switching refers to, for example, conversion of airplane air-ground state, conversion of climbing state and cruising state, conversion of cruising state and descending state, conversion of approach and landing state, etc. The intervals correspondingly include time intervals, flight altitude intervals, speed intervals, and state intervals in the flight data.

[0081] The main purpose of steps four and five is to be used for event detection analysis, but from the whole airplane aspect, it has greater value in the process of parameter judgment and instruction judgment, and the key state data of the airplane that can be extracted can be used for subsequent more comprehensive and flexible analysis.

[0082] Exemplarily, for the late landing gear retracting event, it is needed to monitor whether the airplane ground clearance at the time of selecting the retracting landing gear in the climbing phase exceeds the threshold value. At this time, the rule writing of event detection can be completed by creating the point of "landing gear retracting time", the interval of "takeoff to climbing end", the height threshold value of "airplane ground clearance at the landing gear retracting time", and the comparison of the height threshold value and the measured value.

[0083] For the late landing gear retracting event, as shown in Figures 4A to 4F , first, the takeoff start point and the climbing end point are created, and the interval of "takeoff to climbing end" is created. Then, as shown in Figure 4D , the threshold point of "landing gear selection retracting time" is set in the created interval of "takeoff to climbing end", that is, when LDG_SEL_UP = 1 is true for the first time. As shown in Figure 4E , the height of the measured value extracting the landing gear retracting time is created. Finally, as shown in Figure 4F , in the event editing, the measured value is directly referenced for comparison with the threshold value, that is, the detection of the late landing gear retracting event is completed.

[0084] In addition, event detection analysis can also be completed by writing a judgment analysis script. Exemplarily, for the key event of autopilot on early event, it is necessary to monitor whether the height at the time of autopilot on during the climb is lower than 600 feet. As shown in Figure 5 , the event type is selected as "data definition" in event creation. In Figure 5 the event definition, a script is selected, and the script content as shown in Figure 6 is input, so that event detection is performed according to the logic in the script, and the event content is detected.

[0085] The judgment analysis script can read the script content in combination with point, interval, and segment engineering value data (measured value), so as to realize access to point, interval, measured value name, function name, and the like. Meanwhile, in the script, keywords defined by code can be defined, such as data types such as int and float, if and else judgment types, for loop types, and the like. Meanwhile, in the script, access to the defined function can be directly performed, and input of a variable in the function can also obtain a variable result from the function.

[0086] In step six, based on the generated engineering value data, according to the detected event content, the flight quality analysis of the aircraft is analyzed according to the flight quality monitoring manual, so that different analysis result contents are displayed. The analysis result display can include the statistics and display of event total, flight total, event-free flight, flight event trend statistics, airport event ranking, today's flight event, and airline event. The event total refers to the number of valid events triggered in the system and the like. The flight total refers to the flight information obtained through the screening condition (through data decoding, one piece of flight information can be obtained), which is actually a flight statistic.

[0087] Specifically, according to the engineering value data and the event detection analysis result, the event total, today's flight event, flight total, airline event, airport event ranking, and event-free flight are counted. And according to the counted events, an event tree suitable for multiple types of aircraft is established, the true and false of the events are marked in a manual marking manner, the list of counted events, the event tree, the marking of true and false events are displayed through the interactive display device, and the specific content of an event in the event list is viewed by clicking the event, and the viewed event content is edited and noted. The analysis result display can display the query of the event list, the event tree, the editing note, the true and false event, and the event viewing.

[0088] In analyzing the large event of the descent rate, the point and interval can be used to screen the specific interval, and the maximum value, minimum value, average value, change rate, maximum descent rate height and other indexes in the interval are detected, the trend range and distribution of the indexes are further analyzed by using python, and the trend range and distribution of the indexes are calculated by using the Pearson correlation method. The correlation of the actual occurrence of the large event of the descent rate is used to screen the important reason for triggering the large event of the descent rate, so as to explore the specific reason for triggering the event.

[0089] For the quality analysis result, for the key event concerned, such as the unstable approach related event, the feature set with high correlation can be extracted to form a data set, the triggering probability of the feature in different numerical ranges is studied, and a prediction model is established. At this time, step six also includes reading the engineering value data of the features with high correlation with the unstable approach event and forming a data set, calculating the probability of the engineering value data of the features falling into different preset numerical ranges, and establishing a prediction model of the unstable approach event according to the calculated probability by using the linear regression method, so as to perform subsequent prediction of the unstable approach event, wherein the features include flight attitude during approach, pilot operation, joystick position or pedal position.

[0090] The analysis result display can display economic analysis results, such as aircraft take-off fuel consumption, take-off fuel consumption rate, climb fuel consumption, climb fuel consumption rate, descent fuel consumption, approach fuel consumption, air fuel consumption, and landing fuel amount. In economic analysis, the fuel consumption in a period of time can be calculated by integrating the fuel flow parameter, the fuel consumption in a period of time can be obtained by calculating the average value of the fuel flow parameter, and the fuel consumption in a period of time can be calculated according to different filtering conditions, such as different flight stages (slip out, take-off, climb, cruise, descent, approach, etc.), air, and wheel stop. The fuel consumption and fuel consumption of the corresponding fuel consumption are calculated, and the differences in fuel consumption of different flight stages, different aircraft models or different seasons are compared and analyzed, so as to analyze the flight quality of the aircraft.

[0091] The application also provides a device for analyzing the flight quality of an aircraft, which comprises a data receiving module, a decoding library module, a matching decoding module, a judgment analysis rule module, a data judgment analysis module and a flight quality evaluation module. The data receiving module is configured to receive flight data from a flight recorder, and to manage the received flight data in a hierarchical manner according to the classification of aircraft models, aircraft numbers, flight data types and flight data versions.

[0092] The decoding library module is configured to collect parameter decoding rule configurations of various aircraft models, and manage the collected parameter decoding rule configurations in a hierarchical manner according to the classification of aircraft models, aircraft numbers, flight data types and flight data versions, to form a parameter decoding library applicable to multiple aircraft models. The matching decoding module is configured to be communicatively connected with the data receiving module and the decoding library module, match parameters in the flight data and the parameter decoding rule configurations in the same hierarchy, then match the parameters in the flight data according to the parameter decoding rule in the same hierarchy, and decode the matched parameters to generate corresponding engineering value data.

[0093] The judgment analysis module is configured to construct key points and intervals according to general flight stages and key events in the flight process of the aircraft, set threshold values corresponding to the key points and intervals according to occurrence conditions of the key events, and compile corresponding judgment analysis rules. The data judgment analysis module is configured to read segment engineering value data corresponding to the key points and intervals in the engineering value data, compare parameter engineering value data of the segment engineering value data with preset threshold values of corresponding parameters according to the judgment analysis rules, and then detect event content in the segment engineering value data based on the comparison results by using the distributed analysis engine. The flight quality evaluation module is configured to be communicatively connected with the matching decoding module and the data judgment analysis module, and is capable of reading engineering value data generated based on the generated engineering value data and the detected event content, thereby being capable of analyzing and evaluating flight quality according to the flight quality monitoring manual based on the generated engineering value data and the detected event content.

[0094] In addition, the device can further include a data storage module configured to store raw data, analysis result data, judgment analysis rule script data and the like by using a distributed storage system. The device can further include a data analysis scheduling module to schedule processes such as data parsing (decoding engine), data judgment analysis, event detection analysis and the like. In addition, the device can further include a data mining analysis module configured by using a secondary development language such as python, and performs data analysis such as quality analysis / economic analysis based on decoded engineering value data and engineering value data after mining analysis configuration.

[0095] The present application also provides a computer readable storage medium having computer execution instructions stored therein, when the computer execution instructions are executed by a processor, the foregoing method is implemented.

[0096] In addition, the present application also provides a system for analyzing flight quality of an aircraft, the system including a processor configured to obtain flight data in a flight recorder and receive user input and execute the foregoing method.

[0097] The beneficial technical effects of the above preferred embodiments of the present application include not only the ability to realize the decoding of flight data of various types of aircraft, but also the ability to realize batch import, decoding, viewing and analysis mining, as well as structure configuration, parameter definition, event detection and other functions of massive flight data, to meet the increasingly large data analysis mining management requirements. In addition, flight quality analysis can also be performed using the decoded flight data, which facilitates subsequent daily operation and management of the aircraft.

[0098] Although the specific embodiments of the present application are described above, those skilled in the art should understand that these are only illustrative, and the protection scope of the present application is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present application, and such changes and modifications all fall within the protection scope of the present application.

Claims

1. A method for analyzing flight qualities of an aircraft, characterized in that, The method includes the following steps: Flight data from the aircraft's flight recorder is received using a wireless communication network or FTP, and the received flight data is managed hierarchically according to the classification method of aircraft type, aircraft number, flight data type, and flight data version. We collect parameter decoding rule configurations from various aircraft models of airlines and manage them hierarchically according to the classification methods of aircraft model, aircraft number, flight data type and flight data version, so as to build a parameter decoding library applicable to multiple aircraft models. The parameters at the same level are matched with the parameters in the flight data, and the matched parameters are decoded to generate the corresponding engineering value data. Key points and intervals are constructed based on the general flight phases and key events during the flight process of the aircraft. Thresholds corresponding to the key points and intervals are set according to the occurrence conditions of the key events, and corresponding judgment and analysis rules are written. The process involves reading segment engineering value data corresponding to the key points and intervals from the engineering value data, comparing the parameter engineering value data of the segment engineering value data with preset thresholds for the corresponding parameters according to the judgment and analysis rules, and then using a distributed analysis engine to detect the event content in the segment engineering value data based on the comparison results; and Based on the generated engineering value data, and according to the detected event content, the flight quality of the aircraft is analyzed and evaluated in accordance with the provisions of the flight quality monitoring manual.

2. The method of claim 1, wherein, The steps for hierarchical management of received flight data include: Based on the attributes of the received flight data, the corresponding aircraft type, aircraft number, flight data type, and flight data version data are read and managed hierarchically.

3. The method of claim 2, wherein, The engineering value data includes the original parameter engineering value data, and the method further includes the following steps: The original parameter engineering value data is obtained directly by decoding the matching parameters of the flight data.

4. The method of claim 3, wherein, The engineering value data also includes synthetic parameter engineering value data, and the method further includes the following steps: The synthesized parameter engineering value data is formed by directly concatenating character-type raw parameter engineering value data and / or performing mathematical operations on numerical-type raw parameter engineering value data.

5. The method of claim 4, wherein, The engineering value data also includes exported parameter engineering value data, and the method further includes the following steps: The original parameter engineering value data and / or the synthesized parameter engineering value data are calculated according to the user input instructions and calculation logic to form the derived parameter engineering value data, and are displayed through an interactive display device. The user input includes output maximum value and output minimum value, and the calculation logic includes comparison of size.

6. The method of claim 5, wherein, The method further includes the following steps: Based on the original parameter engineering value data, the synthesized parameter engineering value data, and the derived parameter engineering value data, the flight stage corresponding to each flight data is identified, and the engineering value data is classified according to the flight stage. The flight stage includes the initial stage, taxiing stage, maximum taxiing speed stage, runway entry stage, takeoff stage, takeoff stage, maximum climb airspeed stage, climb peak stage, cruise stage, descent peak stage, approach stage, touchdown airspeed point, and stopping point.

7. The method of claim 5, wherein, The method further comprises the following steps: According to the decoded parameter name, the position and state of the operating component, and the occurrence condition of the key event, a threshold corresponding to the key point and the interval is set, and a corresponding judgment analysis rule is written, thereby forming a detection analysis library including parameter judgment analysis, instruction detection analysis, and event detection analysis.

8. The method of claim 5, wherein, The key event includes a large drop event, and the method further comprises the following steps: A specific interval is created through the key point and the interval, and the following indexes of the engineering value data of each type of aircraft in the specific interval are detected: maximum value, minimum value, average value, change rate, and height at the maximum drop rate; the trend range and distribution of the indexes are calculated in sequence using Python language, and the trend range and distribution of each index are calculated for correlation with the actually occurring large drop event using the Pearson correlation method, so as to screen out important reasons for triggering the large drop event.

9. The method of claim 5, wherein, The key event includes an unstable approach event, and the method further comprises the following steps: The engineering value data of features with high correlation with the unstable approach event are read and composed into a data set, the probability of the engineering value data of the features falling into different preset numerical ranges is calculated, a prediction model of the unstable approach event is established using a linear regression method according to the calculated probability, thereby performing subsequent prediction of the unstable approach event, wherein the features include flight attitude, pilot operation, joystick position, or pedal position.

10. The method of claim 7, wherein, The method further comprises the following steps: According to the engineering value data and the results of the event detection analysis, the total number of events, today's flight events, the total number of flights, airline events, airport event rankings, and event-free flights are counted; and According to the counted events, an event tree suitable for multiple types of aircraft is established, the true and false events are labeled using a manual judgment labeling method, the list of counted events, the event tree, the labeling of true and false events are displayed through the interactive display device, and the specific content of an event in the event list is viewed and edited by clicking the event content.

11. The method according to any one of claims 1 to 10, characterized in that, The types of the key points include time points, flight altitude points, flight speed points, and flight state switching points, and the types of the intervals include time intervals, flight altitude intervals, speed intervals, and state intervals.

12. The method according to any one of claims 1 to 10, characterized in that, When the flight data is received using a wireless communication network, a breakpoint continuation mode is adopted; when the flight data is received using FTP, a unified FTP site configuration mode is adopted.

13. An apparatus for analyzing flight qualities of an aircraft, characterized by, The device comprises: a data receiving module configured to receive flight data from a flight recorder and to manage the received flight data in a hierarchical manner according to categories of aircraft type, aircraft number, flight data type, and flight data version; a decoding library module configured to collect parameter decoding rule configurations of various types of aircraft and to manage the collected parameter decoding rule configurations in a hierarchical manner according to categories of aircraft type, aircraft number, flight data type, and flight data version, so as to form a parameter decoding library suitable for multiple types of aircraft; and a parameter decoding library module configured to collect parameter decoding rule configurations of various types of aircraft and to manage the collected parameter decoding rule configurations in a hierarchical manner according to categories of aircraft type, aircraft number, flight data type, and flight data version, so as to form a parameter decoding library suitable for multiple types of aircraft. a matching decoding module, which is configured to be communicatively connected with the data receiving module and the decoding library module, match parameters in the flight data and the parameter decoding rule configuration of the same level, and then decode the matched parameters to generate corresponding engineering value data; a judgment analysis rule module, which is configured to construct key points and intervals according to general flight stages and key events in the flight process of the aircraft, set threshold values corresponding to the key points and intervals according to occurrence conditions of the key events, and compile corresponding judgment analysis rules; a data judgment analysis module, which is configured to read segment engineering value data corresponding to the key points and intervals in the engineering value data, compare parameter engineering value data of the segment engineering value data with preset threshold values of corresponding parameters according to the judgment analysis rules, and then detect event content in the segment engineering value data based on a comparison result by using a distributed analysis engine; and a flight quality evaluation module, which is configured to be communicatively connected with the matching decoding module and the data judgment analysis module, read the generated engineering value data and the detected event content, and thus analyze and evaluate flight quality according to the flight quality monitoring manual based on the generated engineering value data and the detected event content.

14. A computer-readable medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed on a device, cause the device to perform the method according to any one of claims 1 to 12.

15. A system for analyzing flight qualities of an aircraft, characterized in that, The system comprises a processor configured to obtain flight data in a flight recorder and receive user input, and perform the method according to any one of claims 1 to 12.

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