Method for establishing the association between ACARS message data and QAR data

Abstract

This disclosure presents a method for establishing the association between ACARS message data and QAR data, comprising the following steps: acquiring multiple ACARS message data, identifying and extracting ACARS flight segment records along with the extracted ACARS flight segment information fields to form an ACARS flight segment record table; acquiring multiple post-flight QAR raw data files and decoding and parsing them to extract the aircraft tail number data and timestamp data contained therein, thereby forming a QAR file data table; performing a matching operation on the ACARS flight segment record table and the QAR file data table to establish a mapping relationship between the two records that meet predetermined matching conditions; and summarizing all mapping relationships to form a fused dataset. This method can automatically, accurately, and efficiently achieve intelligent association between ACARS message data and QAR data represented by flight segments.

Description

Technical Field

[0001] This disclosure relates to data processing techniques in the aviation field, and in particular to a method for establishing a correlation between ACARS message data and QAR data. Background Technology

[0002] Aircraft condition monitoring is a core component for ensuring civil aviation flight safety and improving operational efficiency. Currently, domestic and international airlines generally use a combination of Aircraft Communication Addressing and Reporting Systems (ACARS) and Onboard Quick Access Recorders (QARs) for aircraft and engine condition monitoring and fault diagnosis.

[0003] As an airborne data recording device, the QAR system can continuously record the operating parameters of thousands of aircraft and engine systems at a frequency of once per second. It features high data accuracy, continuous recording, and comprehensive parameter coverage, making it a valuable data source for aircraft performance analysis, trend monitoring, and fault diagnosis. However, QAR data is essentially post-flight data, usually copied by ground personnel or downloaded wirelessly after the aircraft lands, lacking the real-time advantage of the ACARS system.

[0004] In practical operations, a common and critical need for both primary manufacturers' operational support systems and airline engineering systems is to generate complete flight segments from real-time received ACARS messages and accurately associate them with the QAR data files generated post-flight. Currently, this association process largely relies on manual operation and experience-based judgment. However, because ACARS messages themselves may contain errors or be lost, ground systems cannot automatically and accurately associate an ACARS segment with its corresponding QAR data file. Existing technologies lack an efficient, automated, and fault-tolerant association method.

[0005] The closest existing technology is the ACARS and QAR data association method commonly used by airlines, which relies on manual comparison. This method mainly depends on engineers manually verifying basic information such as flight number, date, and departure time to complete the association.

[0006] This method has the following obvious shortcomings: 1. Inefficiency: Manual comparison is time-consuming and labor-intensive, and cannot cope with the massive amounts of data generated by a large fleet, becoming a bottleneck in data processing.

[0007] 2. Poor accuracy: It relies entirely on human judgment, which is prone to errors due to fatigue or negligence, and it cannot effectively handle abnormal situations such as incorrect message time or missing messages.

[0008] 3. Poor fault tolerance: When ACARS messages are delayed, timestamps are inaccurate, or OOOI events are lost, manual methods are difficult to perform effective fuzzy matching and fault tolerance, which can easily lead to association failure and the formation of "data silos".

[0009] 4. Insufficient scalability: This method is a non-standardized operating procedure, which is difficult to integrate into an automated status monitoring system and cannot provide intelligent, fast, convenient and accurate support for high-level data fusion analysis and applications.

[0010] Therefore, there is an urgent need to provide a new method that can automatically, accurately, and efficiently achieve intelligent association between ACARS message data and QAR data represented by flight segments, in order to overcome the above-mentioned defects and give full play to the fusion value of the two types of data. Summary of the Invention

[0011] One objective of this disclosure is to propose a method for establishing the association between ACARS message data and QAR data, in order to overcome the aforementioned deficiency in the existing technology of lacking a data processing method that can fully utilize the value of both ACARS data and QAR data.

[0012] This disclosure provides a method for establishing the association between ACARS message data and QAR data, including the following steps: Multiple ACARS message data are acquired. A message parser is used to identify and extract ACARS segment records containing ACARS segment information fields from each ACARS message data. The ACARS segment records, together with the extracted ACARS segment information fields, are stored to form an ACARS segment record table. The ACARS segment information fields include aircraft tail number, flight date, and segment time window fields. Multiple post-flight QAR raw data files are acquired and decoded and parsed to extract the aircraft tail number data and timestamp data contained in the QAR raw data files, thereby forming a QAR file data table. The QAR file data table contains multiple QAR file records. Each QAR file record includes a QAR raw data file and its corresponding QAR file path, aircraft tail number data, and timestamp data. Perform a matching operation on the ACARS segment record table and the QAR file data table to establish a mapping relationship between ACARS segment records and QAR file records that meet predetermined matching conditions, and store the mapping relationship. All the aforementioned mappings are aggregated to form a fused dataset of ACARS segment records and QAR file records.

[0013] According to some embodiments of this disclosure, the flight segment time window field includes the door closing time, takeoff time, and landing time.

[0014] According to some embodiments of this disclosure, the predetermined matching conditions include a first matching condition related to the aircraft tail number and the aircraft tail number data, and a second matching condition related to the flight date, the flight segment time window field, and the timestamp data.

[0015] According to some embodiments of this disclosure, the steps for performing a matching operation on the ACARS segment record table and the QAR file data table include: The search yielded a pair of ACARS segment records and QAR file records that matched the aircraft tail number. At least one ACARS segment time point is calculated based on the flight date of the ACARS message data in the pair of ACARS segment records and QAR file records and at least one of the segment time window fields. A fault-tolerant time threshold is then extended with the ACARS segment time point as the origin to form at least one ACARS segment time window. Verify whether there is timestamp data in the QAR file record of the pair of ACARS segment records and QAR file records that falls within the time window of at least one ACARS segment. If so, establish a mapping relationship between the pair of ACARS segment records and QAR file records.

[0016] According to some embodiments of this disclosure, the step of performing a matching operation on the ACARS segment record table and the QAR file data table involves traversing each ACARS segment record and each QAR file record contained therein.

[0017] According to some embodiments of this disclosure, the method further includes the following steps: Mark all ACARS segment records not involved in the aforementioned mapping relationship as segments to be verified.

[0018] According to some embodiments of this disclosure, the method further includes the following steps: Mark all QAR file records not involved in the aforementioned mapping relationship as isolated QAR files.

[0019] According to some embodiments of this disclosure, the method further includes the following steps: A visualization report is generated based on the fused dataset. The visualization report displays the segment number of the paired ACARS segment records and the file name of the corresponding QAR file record contained in all the mapping relationships.

[0020] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of the present disclosure.

[0021] The positive and progressive effects of this disclosure are as follows: The method for establishing the association between ACARS message data and QAR data according to this disclosure can achieve automatic, accurate, and fault-tolerant matching of ACARS real-time flight segments and QAR post-flight data files, providing a deeply integrated data foundation for aircraft status monitoring, fault diagnosis, and data analysis. Attached Figure Description

[0022] Figure 1 The flowchart of a method for establishing the association between ACARS message data and QAR data according to a preferred embodiment of the present disclosure is illustrated schematically. Detailed Implementation

[0023] The preferred embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings. The following description is exemplary and not intended to limit the scope of this disclosure. Any other similar situations also fall within the protection scope of this disclosure.

[0024] In the following detailed description, directional terms such as "left," "right," "up," "down," "front," "back," etc., are used with reference to the directions described in the accompanying drawings. Components of embodiments of this disclosure may be positioned in a variety of different orientations, and the directional terms are for illustrative purposes and not for limitation.

[0025] refer to Figure 1 As shown, a method for establishing the association between ACARS message data and QAR data according to a preferred embodiment of this disclosure includes the following steps: Multiple ACARS message data are acquired. A message parser is used to identify and extract ACARS segment records containing ACARS segment information fields from each ACARS message data. The ACARS segment records, together with the extracted ACARS segment information fields, are stored to form an ACARS segment record table. The ACARS segment information fields include the aircraft tail number (Registration or A / C Reg), flight date (LegDate), and segment time window field. Multiple post-flight QAR raw data files are acquired and decoded and parsed to extract the aircraft tail number data and timestamp data contained in the QAR raw data files, thereby forming a QAR file data table. The QAR file data table contains multiple QAR file records. Each QAR file record includes a QAR raw data file and its corresponding QAR file path, aircraft tail number data, and timestamp data. Perform a matching operation on the ACARS segment record table and the QAR file data table to establish a mapping relationship between ACARS segment records and QAR file records that meet predetermined matching conditions, and store the mapping relationship. All the aforementioned mappings are aggregated to form a fused dataset of ACARS segment records and QAR file records.

[0026] The flight segment time window field includes the door closing time (OutTime), takeoff time (OffTime), and landing time (OnTime).

[0027] In the step of performing the matching operation, the predetermined matching conditions include a first matching condition related to the aircraft tail number and the aircraft tail number data, and a second matching condition related to the flight date, the flight segment time window field, and the timestamp data.

[0028] More specifically, the steps for performing a matching operation on the ACARS segment record table and the QAR file data table include: The search yielded a pair of ACARS segment records and QAR file records that matched the aircraft tail number. At least one ACARS segment time point is calculated based on the flight date of the ACARS message data in the pair of ACARS segment records and QAR file records and at least one of the segment time window fields. A fault-tolerant time threshold is then extended with the ACARS segment time point as the origin to form at least one ACARS segment time window. Verify whether there is timestamp data in the QAR file record of the pair of ACARS segment records and QAR file records that falls within the time window of at least one ACARS segment. If so, establish a mapping relationship between the pair of ACARS segment records and QAR file records.

[0029] The timestamp data extracted from the QAR raw data file may include key time identifiers such as flight date (FlightDate) and departure UTC time (DepartureTimeUtc).

[0030] In other words, the first matching condition mentioned above can be aircraft identifier matching, that is, the tail number of the ACARS segment that meets the first matching condition must be consistent with the tail number recorded in the QAR file. The first matching condition mentioned above can be time window matching condition.

[0031] More specifically, for example, a flexible time window (e.g., ±25 minutes) is constructed based on the LegDate of the ACARS flight segment (which can also be replaced by the door closing time (OutTime), takeoff time (OffTime), landing time (OnTime), etc.). The time in the QAR file (preferably using FlightDate, or DepartureTimeUtc if empty) must fall within this time window. That is, the time window matching condition is set as: (ACARS_LegDate - ΔT) <= QAR_Time <= (ACARS_LegDate + ΔT), where ΔT is a configurable fault tolerance time threshold (e.g., 25 minutes).

[0032] According to a preferred embodiment, to further improve fault tolerance, the time window matching condition can be further extended to: (ACARS_LegDate - 25 minutes) less than or equal to QAR_Time AND (QAR_Time less than or equal to ACARS_LegDate + 25 minutes OR QAR_Time less than or equal to ACARS_OutTime + 25 minutes OR QAR_Time less than or equal to ACARS_OffTime + 25 minutes OR QAR_Time less than or equal to ACARS_OnTime).

[0033] The process of performing the matching operation on the ACARS segment record table and the QAR file data table can be automatically executed, for example, by a matching algorithm program written in programming languages ​​such as Java or Python. The matching algorithm logic is roughly as follows: 1. Retrieve one unprocessed QAR file record Q from the QAR file data table; 2. Based on the tail number of aircraft Q, filter out all ACARS segment records A with the same tail number from the ACARS segment record table; 3. For each ACARS segment record R in CARS segment record list A, perform the following operations: (3.1) Determine the alignment time T of Q: T = Q.FlightDate. If Q.FlightDate is empty, then T = Q.DepartureTimeUtc.

[0034] (3.2) Verify whether T satisfies the following matching rules (i.e., whether it satisfies the following time window matching conditions): Condition A: (R.LegDate - ΔT) <= T <= (R.LegDate + ΔT) / / Main rule, ΔT is a configurable fault tolerance threshold (e.g., 25 minutes) Extended matching rules, set to enhance the robustness of the matching algorithm, could be, for example, verifying whether T satisfies one of the following conditions: B, C, or D. Condition B: T<= (R.OutTime + ΔT) OR Condition C: T <= (R.OffTime + ΔT) OR Condition D: T <= R.OnTime 4. If T satisfies the above matching rules, then it is determined that the ACARS segment record R and QAR file record Q meet the predetermined matching conditions. Therefore, a mapping relationship is established between the two and the mapping relationship is stored.

[0035] It is understood that in the above method, the step of performing a matching operation on the ACARS segment record table and the QAR file data table, or the matching algorithm performed, traverses every ACARS segment record and every QAR file record contained therein, that is, traverses every record in the ACARS segment record table and the QAR file data table.

[0036] According to a further preferred embodiment, the method further includes the following steps: Mark all ACARS segment records not involved in the aforementioned mapping relationship as segments to be verified. Optionally, the following steps may also be included: Mark all QAR file records not involved in the aforementioned mapping relationship as isolated QAR files.

[0037] Therefore, the fused dataset of ACARS segment records and QAR file records formed by traversing each record in the ACARS segment record table and the QAR file data table records all successfully associated QAR files and their corresponding ACARS segments, as well as ACARS segments that failed to match QAR files and isolated QAR files without corresponding ACARS segments.

[0038] More preferably, the method further includes the following steps: A visualization report is generated based on the fused dataset. The visualization report displays the segment number of each pair of ACARS segment records and the filename of the corresponding QAR file record contained in all the mapping relationships. The preferred embodiment of this disclosure, as detailed above, provides a method for establishing the association between ACARS message data and QAR data. This method enables automatic, accurate, and fault-tolerant matching of ACARS real-time flight segments and QAR post-flight data files, providing a deeply integrated data foundation for aircraft status monitoring, fault diagnosis, and data analysis. Furthermore, this method can be executed automatically and efficiently, improving work efficiency. Through two layers of matching rules and time window matching rules (matching conditions), it effectively addresses issues such as ACARS message delays, loss, timestamp errors, and incomplete time information in QAR files, significantly improving the success rate and accuracy of establishing the association between ACARS message data and QAR data.

[0039] Furthermore, the fused dataset and / or visualization report generated by the method described above can be retrieved and displayed intuitively on the ACARS real-time monitoring platform, providing a richer monitoring dimension for ACARS real-time monitoring. Simultaneously, this also provides a solid data foundation for subsequent fused big data analysis based on "ACARS real-time data + QAR high-precision historical data" (such as trend prediction and fault early warning).

[0040] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure 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 this disclosure, and all such changes and modifications shall fall within the scope of protection of this disclosure.

Claims

1. A method for establishing the association between ACARS message data and QAR data, comprising the following steps: Multiple ACARS message data are acquired. A message parser is used to identify and extract ACARS segment records containing ACARS segment information fields from each ACARS message data. The ACARS segment records, together with the extracted ACARS segment information fields, are stored to form an ACARS segment record table. The ACARS segment information fields include aircraft tail number, flight date, and segment time window fields. Multiple post-flight QAR raw data files are acquired and decoded and parsed to extract the aircraft tail number data and timestamp data contained in the QAR raw data files, thereby forming a QAR file data table. The QAR file data table contains multiple QAR file records. Each QAR file record includes a QAR raw data file and its corresponding QAR file path, aircraft tail number data, and timestamp data. Perform a matching operation on the ACARS segment record table and the QAR file data table to establish a mapping relationship between ACARS segment records and QAR file records that meet predetermined matching conditions, and store the mapping relationship. All the aforementioned mappings are aggregated to form a fused dataset of ACARS segment records and QAR file records.

2. The method for establishing the association between ACARS message data and QAR data as described in claim 1, characterized in that, The flight segment time window field includes the cabin door closing time, takeoff time, and landing time.

3. The method for establishing the association between ACARS message data and QAR data as described in claim 2, characterized in that, The predetermined matching conditions include a first matching condition related to the aircraft tail number and the aircraft tail number data, and a second matching condition related to the flight date, the flight segment time window field, and the timestamp data.

4. The method for establishing the association between ACARS message data and QAR data as described in claim 3, characterized in that, The steps for performing a matching operation on the ACARS segment record table and the QAR file data table include: The search yielded a pair of ACARS segment records and QAR file records that matched the aircraft tail number. At least one ACARS segment time point is calculated based on the flight date of the ACARS message data in the pair of ACARS segment records and QAR file records and at least one of the segment time window fields. A fault-tolerant time threshold is then extended with the ACARS segment time point as the origin to form at least one ACARS segment time window. Verify whether there is timestamp data in the QAR file record of the pair of ACARS segment records and QAR file records that falls within the time window of at least one ACARS segment. If so, establish a mapping relationship between the pair of ACARS segment records and QAR file records.

5. The method for establishing the association between ACARS message data and QAR data as described in any one of claims 1-4, characterized in that, The steps for performing a matching operation on the ACARS segment record table and the QAR file data table involve traversing each ACARS segment record and each QAR file record contained therein.

6. The method for establishing the association between ACARS message data and QAR data as described in claim 5, characterized in that, The method further includes the following steps: Mark all ACARS segment records not involved in the aforementioned mapping relationship as segments to be verified.

7. The method for establishing the association between ACARS message data and QAR data as described in claim 5, characterized in that, The method further includes the following steps: Mark all QAR file records not involved in the aforementioned mapping relationship as isolated QAR files.

8. The method for establishing the association between ACARS message data and QAR data as described in claim 5, characterized in that, The method further includes the following steps: A visualization report is generated based on the fused dataset. The visualization report displays the segment number of the paired ACARS segment records and the file name of the corresponding QAR file record contained in all the mapping relationships.

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