Flight log analysis method, device, storage medium and product

Abstract

This application discloses a flight log analysis method, device, storage medium, and product, relating to the field of data processing technology. The flight log analysis method includes: upon receiving a flight log analysis command, extracting flight data and time synchronization data from each flight log in a UAV formation; modifying the time axis of each UAV based on the flight log analysis command, the flight data, and the time synchronization data to obtain an aligned relative time axis; and analyzing each flight log. Since the time synchronization data records the information transmission and reception timestamps on each UAV's local time axis, this application can more accurately align the time axis using time synchronization data and flight data, thereby enabling more accurate flight log analysis. Therefore, this application can improve the accuracy of UAV time axis alignment, thus providing more accurate UAV flight log data.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to flight log analysis methods, devices, storage media, and products. Background Technology

[0002] With the development of drone swarm flight technology, multiple drones often coordinate to perform tasks through swarm control algorithms. However, the flight log files of each drone are generated based on the relative timestamp after the startup of its own flight control system. When performing flight log analysis of multiple drone swarms, it is necessary to align the times of the flight logs of each drone in the swarm.

[0003] Currently, manual estimation is commonly used to time-align the flight logs of drone formations. However, manual estimation can only roughly align the flight logs with low accuracy, making it impossible to accurately align the flight logs of drone formations. This results in low accuracy in analyzing the flight logs of multiple drone formations. Summary of the Invention

[0004] The main objective of this application is to provide a flight log analysis method, device, storage medium, and product, which aims to solve the technical problem of low accuracy in flight log analysis of multi-UAV formations.

[0005] To achieve the above objectives, this application proposes a flight log analysis method, which includes:

[0006] When a flight log analysis command is received, the flight logs of each drone in the drone formation are retrieved.

[0007] Extract flight data and time synchronization data from each of the flight logs, wherein the time synchronization data includes the information sending time point and the information receiving time point of each UAV on its local timeline;

[0008] Based on the flight log analysis instructions, the flight data, and the time synchronization data, the timelines of each UAV are modified to obtain the aligned relative timelines;

[0009] The flight logs are analyzed based on the flight log analysis instructions, the relative time axis, and the flight data.

[0010] In one embodiment, the drone includes a lead drone and multiple wingmen. The step of modifying the timeline of each drone based on the flight log analysis command, the flight data, and the time synchronization data to obtain an aligned relative timeline includes:

[0011] Based on the time synchronization data, the time axis offset between the lead aircraft and each of the wingmen is determined.

[0012] Based on the data type of each flight data in the flight log of the lead aircraft, the flight events of the lead aircraft during flight are determined;

[0013] Based on the flight log analysis instructions, the flight events, and the timeline offset, the timelines of the lead aircraft and each of the wingmen are modified to obtain an aligned relative timeline.

[0014] In one embodiment, the time synchronization data includes multiple time synchronization data between the lead aircraft and each of the wingmen within a preset time period. The step of determining the time axis offset between the lead aircraft and each of the wingmen based on the time synchronization data includes:

[0015] Based on the difference between the information reception time and the information transmission time in each of the time synchronization data, multiple initial offsets are obtained;

[0016] Delete the initial offsets that are higher than a preset abnormal offset threshold, and delete multiple normal offsets.

[0017] Based on the multiple normal offsets corresponding to the lead aircraft and each of the wingmen, the average value of each normal offset is calculated to obtain the time axis offset corresponding to the lead aircraft and each of the wingmen.

[0018] In one embodiment, the step of modifying the timelines of the lead aircraft and each of the wingmen based on the flight log analysis command, the flight events, and the timeline offset to obtain an aligned relative timeline further includes:

[0019] Based on the instruction type of the flight log analysis command, a baseline flight event is determined from the flight events;

[0020] Based on the time axis offset, the time axis of each of the wingmen is modified to obtain a unified time axis for the lead aircraft and each of the wingmen;

[0021] Based on the time point of the baseline flight event, the unified timeline is modified to obtain the relative timeline.

[0022] In one embodiment, the instruction type includes flight analysis instructions and formation analysis instructions, the flight events include takeoff events and formation mode switching events, and the step of determining a baseline flight event from the flight events based on the instruction type of the flight log analysis instructions includes:

[0023] If the instruction type is the flight analysis instruction, then the baseline flight event is determined to be the takeoff event;

[0024] If the instruction type is the formation analysis instruction, and the flight event is determined to be the formation mode switching event.

[0025] In one embodiment, after the step of modifying the timeline of each UAV based on the flight log analysis command, each flight data, and each time synchronization data to obtain the aligned relative timeline, the method further includes:

[0026] Calculate the time interval between each data point in the flight data;

[0027] If the time interval is higher than a preset time interval threshold, then the corresponding target flight data and the data missing time point of the target flight data are determined;

[0028] Obtain the first target data at the moment before the data missing time point and the second target data at the moment after the data missing time point from the target flight data;

[0029] Based on the first target data, the second target data, and the missing time point, calculate the interpolated data of the target flight data at the missing time point;

[0030] The interpolated data is added to the missing time point corresponding to the target flight data to obtain the interpolated target flight data.

[0031] In one embodiment, the step of analyzing each of the flight logs based on the flight log analysis command, the relative time axis, and the flight data includes:

[0032] Based on the flight log analysis instructions, the flight position data, flight speed data, and flight control input data are extracted from each of the flight data, wherein the flight control input data includes the desired position data and desired speed data of each of the UAVs;

[0033] Based on the flight position data, the flight speed data, the desired position data, and the desired speed data, the control error for each of the UAVs is calculated.

[0034] Based on the flight data, the flight position difference of each UAV is calculated, and based on the flight data, the flight speed difference of each UAV is calculated.

[0035] Based on the difference in flight position and the difference in flight speed, the formation error of each UAV is determined.

[0036] In addition, to achieve the above objectives, this application also proposes a flight log analysis device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the flight log analysis method as described above.

[0037] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the flight log analysis method described above.

[0038] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the flight log analysis method described above.

[0039] One or more technical solutions proposed in this application have at least the following technical effects:

[0040] When this application receives a flight log analysis command, it acquires the flight logs of each UAV in the UAV formation, extracts flight data and time synchronization data from each flight log, wherein the time synchronization data includes the information transmission time and information reception time of each UAV on its local timeline. Based on the flight log analysis command, the flight data, and the time synchronization data, the timeline of each UAV is modified to obtain an aligned relative timeline. Based on the flight log analysis command, the relative timeline, and the flight data, the flight logs are analyzed.

[0041] Current manual estimation methods can only roughly align flight logs with low precision, failing to accurately align the flight logs of drone formations and resulting in low accuracy in multi-drone flight log analysis. This application extracts flight data and time synchronization data from the flight logs to create a timeline for each drone. Since time synchronization data records the sending and receiving timestamps of information on each drone's local timeline, this application allows for more precise timeline alignment using both time synchronization and flight data, leading to more accurate flight log analysis. Therefore, this application improves the accuracy of drone timeline alignment, thereby enhancing the accuracy of drone flight log analysis. Attached Figure Description

[0042] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating an embodiment of the flight log analysis method of this application.

[0045] Figure 2 This is a flowchart illustrating Embodiment 2 of the flight log analysis method of this application;

[0046] Figure 3 This is a schematic diagram of the device structure of the hardware operating environment involved in the flight log analysis method in this application embodiment;

[0047] Figure 4 This is a schematic diagram illustrating the data acquisition consent process involved in the flight log analysis method in this application embodiment.

[0048] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0049] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0050] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0051] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or flight log analysis device capable of performing the above functions. The following description uses a flight log analysis device as an example to illustrate this embodiment and the subsequent embodiments.

[0052] Based on this, embodiments of this application provide a flight log analysis method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the flight log analysis method of this application.

[0053] In this embodiment, the flight log analysis method includes steps S10 to S40:

[0054] Step S10: When a flight log analysis command is received, the flight logs of each UAV in the UAV formation are obtained.

[0055] It should be noted that the flight log analysis command refers to the command signal triggered by the user or automated testing system to initiate comprehensive analysis of flight logs of multiple UAV formations. This can be a manual button click, a script call, or an analysis request automatically generated by the testing platform. UAV formation refers to a multi-UAV system consisting of a lead UAV and at least one wingman in a cooperative flight mission, achieving coordinated movement through formation control algorithms. Each UAV's flight log refers to the raw data file recorded by its flight control system during flight, typically in ULog format (.ulg), containing sensor data, flight status, control inputs, mission information, and communication messages, with its timestamp based on the relative time after the system's startup. In this embodiment, the flight control system can be PX4 (Paparazzi for eXperimentation, experimental UAV flight control system).

[0056] Understandably, after multi-drone formation testing, developers currently need to manually collect and analyze the flight log files of each drone. However, the lack of a unified automated entry point and systematic processing flow leads to low log acquisition efficiency and a high risk of omissions. Therefore, this step automatically triggers and acquires the flight log files of all drones in the formation by receiving flight log analysis commands, completing the data preparation phase of the analysis process. This achieves centralized and automated log data collection, avoiding omissions and errors caused by manual intervention, and improving the completeness and repeatability of subsequent analyses.

[0057] Step S20: Extract flight data and time synchronization data from each of the flight logs. The time synchronization data includes the information sending time and information receiving time of each UAV on its local timeline.

[0058] It should be noted that flight data refers to various information related to the flight status recorded during the drone's flight, including attitude angles, speed, actuator control commands, and mission information. Time synchronization data refers to communication messages related to the time synchronization mechanism in the log. In this embodiment, it is the TIMESYNC (Time Synchronization) message in the MAVLink (Micro Air Vehicle Link) protocol, used to help estimate the clock deviation between different drones.

[0059] The information transmission time point refers to the local timestamp used by a drone when sending a TIMESYNC message, representing the current time as perceived by that device. The information reception time point refers to the local timestamp of its own flight control system recorded by another drone when it receives a TIMESYNC message, used for subsequent calculations of the clock deviation between the two devices.

[0060] Understandably, current methods cannot effectively solve the problem of inconsistent time bases in multi-drone logs. This embodiment analyzes the flight logs of each UAV to extract flight data for flight behavior analysis and time synchronization data for time alignment. The time synchronization data includes the information transmission and reception times of each UAV on its local timeline, thus providing a data foundation for accurate estimation of clock deviations. This enables the extraction of key time markers for time alignment from the original logs, improving the data availability and accuracy of multi-drone collaborative analysis.

[0061] Specifically, when the lead machine sends a synchronization request in its local time and the wingman receives the request in its local time, the time difference between the two includes communication delay and clock skew. Therefore, this embodiment constructs a data foundation for cross-device time mapping by extracting bidirectional timestamps with time correlation in a structured manner. This makes subsequent time alignment no longer a rough estimate based on fuzzy events, but a precise calculation based on communication protocols, thereby significantly improving the accuracy and reliability of multi-machine log alignment.

[0062] Step S30: Based on the flight log analysis command, each flight data and each time synchronization data, modify the time axis of each UAV to obtain the aligned relative time axis;

[0063] It should be noted that modifying the timelines of the various drones refers to transforming the relative timestamps, originally based on the startup time of each drone's local system, into a unified reference time base through mathematical transformations (such as adding or subtracting time offsets). The aligned relative timelines use a selected moment as the zero point, and all drone log data are re-timestamped based on this reference.

[0064] Understandably, existing technologies, lacking a unified time reference, cannot accurately compare the flight status of multiple drones at the same physical moment, severely restricting the quantitative analysis of formation coordination performance. This embodiment comprehensively estimates the clock deviation between drones based on the context of flight log analysis commands, key events in the extracted flight data, and bidirectional timestamps in the time synchronization data. It then corrects the original timeline accordingly, ultimately mapping the log data of all drones onto a unified relative timeline. This achieves precise alignment of multi-drone flight data in the time dimension, providing a reliable time reference for subsequent cross-drone collaborative analysis.

[0065] Specifically, while relying solely on flight events as time anchors is intuitive, its accuracy is limited. Alignment errors can occur due to human judgment or sensor response delays, while relying on a single TIMESYNC message is susceptible to communication latency fluctuations. This step analyzes time pairs from multiple TIMESYNC messages, using statistical methods to estimate clock deviations closer to the true value. Then, by combining key events identified in the flight data as a unified time zero point, the original timestamps of each wingman are corrected, completing the timeline alignment. This significantly improves the accuracy and robustness of time alignment, enhancing the accuracy of subsequent analysis.

[0066] In one feasible implementation, the drone includes a lead drone and multiple wingmen. The specific implementation of modifying the timelines of each drone based on the flight log analysis instructions, the flight data, and the time synchronization data to obtain aligned relative timelines can also be:

[0067] Based on the time synchronization data, the time axis offset between the lead aircraft and each of the wingmen is determined. Based on the data type of each flight data in the lead aircraft's flight log, the flight events of the lead aircraft during flight are determined. Based on the flight log analysis instructions, the flight events, and the time axis offset, the time axes of the lead aircraft and each of the wingmen are modified to obtain the aligned relative time axes.

[0068] It should be noted that "lead drone" and "wingmen" refer to the main drone (lead drone) that plays a leading role in the drone formation and the subordinate drones (wingmen) that follow it to perform cooperative flight missions. The lead drone is usually responsible for path planning and status broadcasting, while the wingmen adjust their own flight status based on the information from the lead drone. The time axis offset refers to the clock deviation of the wingmen relative to the lead drone, that is, the difference between the timestamps recorded by the two at the same physical moment, in microseconds or milliseconds. In this embodiment, this offset is calculated by analyzing the sending and receiving times in the time synchronization message.

[0069] The data type of flight data refers to the information category represented by different topics in the flight log, such as aircraft status, position setpoint, mission status, etc. The semantics of different data types can be used to identify specific flight phases or events. Flight events refer to key moments in the flight process that have clear physical meaning and can be identified by data, such as takeoff, entering formation mode, and the start of formation change.

[0070] It is understandable that while relying solely on flight events for alignment has clear physical significance, factors such as sensor response delays and differences in log recording start times can lead to alignment errors. While relying solely on time synchronization messages can estimate clock skew, it lacks semantic relevance to the actual flight process, potentially resulting in time synchronization but mismatched flight events. This implementation first calculates the timeline offset, then identifies key flight events on the lead aircraft, and finally sets the global time zero point to the lead aircraft's local time. For the lead aircraft's own logs, time remapping is performed directly using its own time. For each wingman, clock skew is first compensated for, and then the event time is subtracted.

[0071] This embodiment uses the above steps to re-timestamp all UAV data with the lead aircraft's takeoff time, ensuring both the physical accuracy of time synchronization and the consistency between the alignment result and the actual flight logic. This allows for the accurate calculation of indicators such as wingman response delay and formation configuration error in subsequent operations.

[0072] In one feasible implementation, the time synchronization data includes multiple time synchronization data between the lead aircraft and each of the wingmen within a preset time period. A further implementation of determining the corresponding time axis offset between the lead aircraft and each of the wingmen based on the time synchronization data may be:

[0073] Based on the difference between the information reception time and the information transmission time in each of the time synchronization data, multiple initial offsets are obtained. The initial offsets that are higher than the preset offset abnormal threshold are deleted, and multiple normal offsets are obtained. Based on the multiple normal offsets corresponding to the lead aircraft and each of the wingmen, the average value of each normal offset is calculated to obtain the time axis offset corresponding to the lead aircraft and each of the wingmen.

[0074] It should be noted that the multiple time synchronization data within the preset time period refer to multiple sets of time synchronization messages exchanged periodically or event-triggered between the lead and wingmen during drone formation flight. These messages are continuously generated within a certain time window, forming a time synchronization sample set that can be used for statistical analysis. The difference between the information reception time and the information transmission time is the difference between the local timestamp of the wingmen receiving the time synchronization message and the local timestamp of the lead drone when it sends the message. This difference includes communication delay and clock deviation between the two drones.

[0075] The initial offset represents the unprocessed estimate of clock discrepancy measured in a single time synchronization interaction, and its value is affected by non-ideal factors such as network latency and message processing delay. The offset anomaly threshold is a pre-defined numerical range boundary used to determine whether the initial offset of a measurement significantly deviates from the normal distribution; if it exceeds this threshold, it is considered anomalous data. The normal offset refers to the initial offset that falls within a reasonable range after outlier removal, representing a more reliable clock bias observation. The time axis offset is the final clock bias value used for time axis correction, obtained by averaging multiple normal offsets, serving as an estimate of the wingman's systematic time offset relative to the lead aircraft.

[0076] It is understandable that a single time synchronization measurement is susceptible to interference from random factors such as communication jitter and message queuing delays, and direct use will lead to a decrease in time alignment accuracy. This implementation method acquires multiple sets of time synchronization data between the lead aircraft and each wingman within a preset time period, calculates the initial offset of each communication, removes outliers exceeding a preset abnormal threshold, retains multiple normal offsets, and takes their average as the final time axis offset. This effectively suppresses the influence of random noise, improves the stability and accuracy of clock deviation estimation, and achieves more robust multi-aircraft time synchronization.

[0077] Specifically, in real flight environments, the communication links between UAVs are subject to uncertainty. This can cause the transmission delay of a single time synchronization message to fluctuate drastically due to wireless interference, routing changes, or flight control task scheduling, resulting in a significant deviation of the initial offset from the true clock deviation. Directly using this value for time axis correction would introduce significant errors. Therefore, this implementation collects multiple time synchronization data points to form a set of initial offset samples. Then, a reasonable offset anomaly threshold is set to identify and remove extreme values ​​that significantly deviate from the central trend, retaining only the normal offsets that reflect systematic clock deviations. Subsequently, the average value of these normal values ​​is calculated, allowing random noise to cancel each other out in multiple measurements, thus approximating the true clock deviation. Through these steps, this implementation effectively improves the reliability of the time axis offset, enabling subsequent high-precision time alignment.

[0078] In one feasible implementation, the specific implementation of modifying the timelines of the lead aircraft and each of the wingmen based on the flight log analysis instructions, the flight events, and the timeline offset to obtain the aligned relative timelines can also be:

[0079] Based on the instruction type of the flight log analysis command, a baseline flight event is determined from the flight events. Based on the time axis offset, the time axis of each of the wingmen is modified to obtain a unified time axis for the lead aircraft and each of the wingmen. Based on the time point of the baseline flight event, the unified time axis is modified to obtain the relative time axis.

[0080] It should be noted that the instruction type refers to the metadata or parameters carried by the flight log analysis instruction, which indicates the specific mode or objective of this analysis task. This could be takeoff alignment mode, formation formation alignment mode, or mission start alignment mode, etc. Different instruction types determine which flight event should be selected as the global time reference. The reference flight event refers to a key time point selected from multiple identifiable flight events based on the current analysis task requirements, serving as the reference origin for the entire formation time alignment.

[0081] Understandably, current time alignment often uses a fixed zero point, lacking flexibility and failing to meet the diverse time reference requirements of different analysis scenarios. This implementation method dynamically selects the benchmark flight event that best matches the analysis objective by parsing the instruction type of the flight log analysis command. First, it uses time axis offset to align the time axes of each wingman to the lead aircraft's time system, forming a unified time axis. Then, it redefines the zero point of time with the occurrence time of the benchmark flight event as the origin, ultimately generating a relative time axis with clear engineering semantics. This achieves configurability and scenario adaptability of the time alignment strategy, improving the readability and practicality of the analysis results.

[0082] Specifically, this implementation breaks down time alignment into two stages. The first stage is physical alignment, which corrects the original timestamps of each wingman based on the calculated timeline offset, aligning them with the lead aircraft's time to form a unified timeline and resolving the issue of misalignment among multiple aircraft. The second stage is semantic alignment, which identifies the corresponding baseline flight event from the lead aircraft's flight data based on the command type of the flight log analysis instructions, and further transforms the timestamps of all UAVs into timestamps under a relative timeline. Through these steps, this implementation ensures that regardless of when each UAV starts or when the log begins, all data is mapped to a relative time coordinate system based on the baseline flight time. This makes the time reference not only accurate but also intuitive and interpretable, significantly improving the efficiency and accuracy of data analysis.

[0083] In one feasible implementation, the instruction types include flight analysis instructions and formation analysis instructions, the flight events include takeoff events and formation mode switching events, and the specific implementation of determining the baseline flight event from the flight events based on the instruction types of the flight log analysis instructions can also be:

[0084] If the instruction type is the flight analysis instruction, then the baseline flight event is determined to be the takeoff event; if the instruction type is the formation analysis instruction, then the flight event is determined to be the formation mode switching event.

[0085] It should be noted that the flight analysis command indicates that the analysis focuses on the basic flight performance of a single aircraft or the formation as a whole, such as takeoff, cruise, and landing, including power response and attitude stability. The formation analysis command indicates that the analysis focuses on multi-aircraft cooperative behavior, such as formation maintenance, following accuracy, and control synchronization, which are formation-specific indicators.

[0086] Understandably, this implementation dynamically selects the baseline flight event based on the specific type of the flight log analysis command. When the command is a flight analysis command, the takeoff event is used as the time zero point, which facilitates the evaluation of the dynamic response during takeoff. When the command is a formation analysis command, the formation mode switching event is used as the time zero point, which facilitates focusing on the coordinated behavior after formation control is initiated. This achieves precise matching between the time baseline and the analysis target, improving the relevance and interpretability of the analysis results.

[0087] In one embodiment, after obtaining the aligned relative time axis, the sending time of the lead aircraft's control command is identified based on the relative time axis and the flight control input data of each UAV. The response delay of each wingman to the control command is calculated in combination with the change time of the wingman's flight status data. When the response delay exceeds a preset delay threshold, a corresponding control synchronization alarm message is generated.

[0088] Understandably, traditional methods for analyzing the logs of multi-drone formations often focus on static errors or post-event trajectory comparisons, making it difficult to quantify the end-to-end transmission and response performance of control commands. This step utilizes an aligned relative time axis to accurately identify the sending time of the lead drone's control commands and the starting time of the wingman's state changes, calculates the time difference as the response delay, and sets a threshold for anomaly detection, thereby enabling the accurate calculation of abnormal delay events in flight events.

[0089] Specifically, in an unaligned time system, there is an unknown discrepancy between the log timestamps of the lead aircraft and the wingman. When a delay occurs in a command, it is impossible to determine whether the delay is a real delay or an illusion caused by time misalignment. However, since this embodiment maps all UAV logs to the same relative time axis, it is possible to accurately detect commands with delays, thereby accurately calculating abnormal delay events in flight events.

[0090] In one embodiment, after obtaining the aligned relative time axis, a two-way communication delay sequence during formation flight is constructed based on the relative time axis and the communication status data of each UAV. According to the statistical characteristics of the communication delay sequence, the offset compensation parameters of the time axis of each wingman are dynamically adjusted, the aligned relative time axis is regenerated, and the formation error is recalculated based on the updated relative time axis.

[0091] Understandably, current time alignment methods are typically based on static or single-time estimated clock deviations, which are insufficient to address the impact of dynamic changes in communication links during flight, leading to decreased alignment accuracy in later stages. This implementation method utilizes communication status data recorded in logs to construct a two-way communication delay sequence between the lead aircraft and wingmen, analyzes its statistical characteristics to identify high-latency or unstable intervals, and dynamically corrects the time axis offset compensation parameters accordingly. This enables iterative optimization of the time alignment results, thereby maintaining high-precision multi-aircraft data synchronization even in complex electromagnetic environments and significantly improving the reliability of formation error calculation.

[0092] In one embodiment, a two-way communication delay sequence during formation flight is constructed based on the relative time axis and the communication status data of each UAV. The offset compensation parameters of the time axis of each wingman are dynamically adjusted according to the statistical characteristics of the communication delay sequence to regenerate the aligned relative time axis. The formation error is then recalculated based on the updated relative time axis. Alternatively, the specific implementation may include:

[0093] Based on the relative time axis and the communication status data of each UAV, multiple sets of time synchronization message interaction records between the lead aircraft and each wingman during the flight are extracted. Each set of interaction records includes a first local timestamp when the lead aircraft sends a request, a second local timestamp when the wingman receives the request, a third local timestamp when the wingman sends a response, and a fourth local timestamp when the lead aircraft receives the response.

[0094] Based on the four timestamps, the round-trip delay for each interaction is calculated, and interaction records with round-trip delays less than a preset minimum threshold are extracted as a valid sample set. Based on the minimum difference between the second local timestamp and the first local timestamp in the valid sample set, the initial time axis offset between the lead aircraft and the wingman is determined.

[0095] Within the sliding window, if the standard deviation of multiple consecutive round-trip delays exceeds the preset stability threshold, the current time axis offset compensation parameter is frozen, and the initial time axis offset is maintained until communication returns to stability.

[0096] The formation error is recalculated based on the updated relative time axis, and the changes in formation error before and after the adjustment are compared. If the error is significantly reduced, a message indicating successful time alignment optimization is output, and the current offset compensation parameters are retained for subsequent analysis tasks.

[0097] Understandably, in multiple time synchronization interactions, communication latency fluctuates due to queueing, scheduling, and wireless interference. However, the minimum latency is closest to the physical propagation time. Therefore, this implementation calculates the minimum latency closest to the physical propagation time by calculating the minimum difference between timestamps. Furthermore, since frequent offset updates can introduce erroneous corrections when communication fluctuates drastically, this implementation freezes the current time axis offset compensation parameter when the standard deviation of multiple consecutive round-trip delays exceeds a preset stability threshold, thus achieving alignment stability control in dynamic environments.

[0098] Step S40: Analyze each of the flight logs based on the flight log analysis command, the relative time axis, and the flight data.

[0099] It is understood that this embodiment performs targeted comprehensive analysis based on the analysis target specified by the flight log analysis command, combined with the aligned relative timeline and the flight data of each UAV. Since this embodiment first modifies the timeline of each UAV based on the flight data and the time synchronization data to obtain the aligned relative timeline, and then analyzes the flight logs based on the relative timeline, it can avoid the impact of timeline misalignment on flight log analysis, thereby improving the accuracy of flight log analysis.

[0100] In summary, upon receiving a flight log analysis command, this embodiment acquires the flight logs of each drone in the drone formation, extracts flight data and time synchronization data from each flight log, wherein the time synchronization data includes the information transmission time and information reception time of each drone on its local timeline. Based on the flight log analysis command, the flight data, and the time synchronization data, the timelines of each drone are modified to obtain aligned relative timelines. Based on the flight log analysis command, the relative timelines, and the flight data, the flight logs are analyzed.

[0101] Current manual estimation methods can only roughly align flight logs with low precision, failing to accurately align the flight logs of drone formations and resulting in low accuracy in multi-drone flight log analysis. This embodiment extracts flight data and time synchronization data from the flight logs to create a timeline for each drone. Since time synchronization data records the sending and receiving timestamps of information on each drone's local timeline, this embodiment achieves more precise timeline alignment by combining time synchronization data and flight data, leading to more accurate flight log analysis. Therefore, this embodiment improves the accuracy of drone timeline alignment, thereby enhancing the accuracy of drone flight log analysis.

[0102] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 After step S30 of the flight log analysis method, steps A31 to S35 are also included:

[0103] Step A31: Calculate the time interval between each data point in the flight data;

[0104] It should be noted that the time interval between data points in flight data refers to the time difference between two adjacent valid data records of the same type of flight data in the time series. For example, if the position data of a certain UAV has two records at t=1.000s and t=1.020s, the time interval is 20ms. This interval reflects the sampling frequency of the data.

[0105] It is understandable that different drones or different sensors / control modules of the same drone may have different data sampling frequencies, and the sampling may be uneven due to system load. This can cause data from multiple drones to not be accurately aligned to the same moment on a unified time axis, affecting the accuracy of collaborative analysis. Therefore, this embodiment quantifies the temporal density and regularity of the data stream by calculating the time interval between adjacent data points in the flight data, providing a basis for subsequent determination of whether time interpolation processing is needed.

[0106] Step A32: If the time interval is higher than a preset time interval threshold, then determine the corresponding target flight data and the data missing time point of the target flight data;

[0107] It should be noted that the preset time interval threshold is a pre-defined time interval sampling threshold used to determine whether the current sampling time interval is too large and whether missing data supplementation is necessary. Target flight data refers to flight data that is determined to have excessively large sampling intervals, requiring subsequent time interpolation to supplement missing information. Missing data time points refer to the set of critical moments in the time series of target flight data where observations are lacking between two known data points due to the sampling interval exceeding the threshold.

[0108] It is understandable that this embodiment compares the calculated time interval with a preset threshold. When a certain interval is found to be too large, the corresponding data stream is determined to be the target flight data, and the missing data interval in its time series is located, thereby clarifying the object and location that needs to be interpolated and repaired.

[0109] Specifically, if all data is interpolated without setting judgment conditions, it will not only increase unnecessary computational overhead, but may also introduce spurious fluctuations due to over-interpolation. Therefore, this embodiment ensures that the interpolation operation focuses on areas where information gaps truly exist, avoiding redundant processing of high-frequency stable data. Furthermore, this embodiment enhances the system's flexibility and adaptability by differentiating thresholds for different data types.

[0110] Step A33: Obtain the first target data at the moment before the data missing time point and the second target data at the moment after the data missing time point from the target flight data;

[0111] It should be noted that the first target data at the previous moment refers to the nearest recorded valid data point before the data loss point, containing complete status information and its corresponding timestamp at that moment. The second target data at the next moment refers to the nearest recorded valid data point after the data loss point, also containing status information and a timestamp.

[0112] It is understandable that this embodiment obtains contextual information for interpolation calculation by locating the nearest valid data point before and after the time point of missing data. This provides a basic support for the subsequent generation of intermediate data that conforms to the flight state change pattern, realizes the data traceability and physical consistency of the interpolation process, and improves the credibility of the supplementary data.

[0113] Specifically, when performing time interpolation, randomly selecting or choosing reference points with excessively large spans can cause the interpolation results to deviate from the actual flight trajectory. Therefore, this embodiment selects the two nearest valid data points as the interpolation basis. Since the movement of the UAV is continuous and its state changes approximately linearly over a short period, using the two nearest valid data points as the interpolation basis reduces the impact of sampling gaps caused by system latency, task scheduling jitter, and other factors on the analysis. It also avoids misjudgments caused by interpolating across periods of intense maneuvering. Therefore, this embodiment provides high-fidelity input conditions for subsequent interpolation algorithms by accurately acquiring the real data on both sides of the missing point, ensuring the rationality of the completed data in terms of physical trends, thereby guaranteeing the accuracy and reliability of state comparison in multi-UAV collaborative analysis.

[0114] Step A34: Based on the first target data, the second target data, and the missing time point, calculate the interpolated data of the target flight data at the missing time point;

[0115] It should be noted that interpolated data refers to target flight data values ​​estimated using mathematical methods at missing time points, used to fill gaps in the original data so that the time series has continuous and verifiable values ​​at that point.

[0116] It should also be noted that the interpolation calculation formula in this embodiment can be:

[0117] x =x1 + (x2 - x1)×(t -t1) / (t2 - t1)

[0118] Where x is the calculated difference, x1 is the data at the previous time, x2 is the data at the next time, t is the missing time point, t1 is the time point at the previous time, and t2 is the time point at the next time.

[0119] It is understood that this embodiment uses mathematical interpolation to calculate the estimated value of the moment based on the acquired adjacent data and the specific location of the missing time point, thereby achieving high-fidelity reconstruction of the sparse data stream, ensuring the integrity and continuity of flight data on a unified time axis, and providing a reliable data foundation for subsequent high-precision collaborative analysis.

[0120] Specifically, most flight state variables of UAVs (such as position, speed, and attitude angular rate) have the characteristic of approximately linear change in a short period of time. Therefore, this embodiment calculates the difference that needs to be supplemented by using data before and after the data missing point. This is not only computationally efficient, but also has high physical rationality within a small time window. This allows the data gaps caused by differences in sampling frequency or system jitter to be reasonably filled. The flight data of all UAVs can be aligned and matched at any time on a unified relative time axis, thereby supporting the accurate calculation of key indicators such as relative error and response delay, and significantly improving the completeness and accuracy of multi-aircraft log analysis.

[0121] Step A35: Add the interpolated data to the missing time point corresponding to the target flight data to obtain the interpolated target flight data.

[0122] It is understood that in this embodiment, the calculated interpolated data is inserted into the corresponding missing time points of the target flight data in chronological order to form a complete and continuous data sequence, thereby achieving seamless repair of sparse data and ensuring high-density alignment of flight logs on a unified time axis, which enables accurate calculation of subsequent multi-aircraft collaborative indicators.

[0123] In one feasible implementation, the specific implementation of analyzing each of the flight logs based on the flight log analysis command, the relative time axis, and the flight data can also be:

[0124] Based on the flight log analysis instructions, flight position data, flight speed data, and flight control input data are extracted from each of the flight data. The flight control input data includes the desired position data and desired speed data of each UAV. Based on the flight position data, the flight speed data, the desired position data, and the desired speed data, the control error for each UAV is calculated. Based on each of the flight data, the flight position difference for each UAV is calculated. Based on each of the flight data, the flight speed difference for each UAV is calculated. Based on the flight position difference and the flight speed difference, the formation error for each UAV is determined.

[0125] It should be noted that flight position data refers to the actual position information of each UAV in the local coordinate system, used to describe the real-time coordinates of the aircraft in three-dimensional space. Flight speed data refers to the actual velocity components of each UAV in three directions, usually used to reflect its motion state. Flight control input data refers to the control commands received or generated by the flight control system, including desired target values, such as desired position and desired speed, representing the state that the controller wants the aircraft to achieve.

[0126] Control error refers to the deviation between the actual state and the desired state of the UAV, such as the difference between the actual position and the desired position, and is used to evaluate the tracking accuracy of the controller. Flight position difference refers to the difference in the actual position vectors of the wingman and the lead aircraft at the same moment, reflecting the maintenance of the formation configuration. Flight speed difference refers to the difference in the speed vectors of the wingman and the lead aircraft at the same moment, reflecting the synchronization of the formation's movement. Formation error is an index calculated by combining position difference and speed difference, used to quantify the overall cooperative performance of the formation.

[0127] Understandably, this implementation method is based on flight log analysis instructions to extract key data such as the actual position, speed and desired target of each UAV. It calculates the control error, position difference and speed difference at each moment on a unified relative time axis, and further integrates the position and speed differences to determine the formation error. This allows for multi-dimensional collaborative performance evaluation, improving the measurability and optimizability of the formation control algorithm.

[0128] Specifically, this implementation first extracts desired position / velocity data and actual flight data, allowing the system to calculate the control error of a single aircraft at each time point. At the multi-aircraft level, using aligned relative time axes, the system synchronously extracts the actual positions of the lead and wingmen to calculate the flight position difference, representing the spatial offset of the wingman relative to the lead. Simultaneously, it calculates the flight speed difference to reflect whether the two aircraft's movement rhythms are synchronized; a sustained speed difference may lead to formation stretching or compression. Finally, the position and speed differences are merged into a formation error, forming a comprehensive index that considers both spatial configuration stability and dynamic synchronization.

[0129] In summary, this embodiment calculates the time interval between each data point in the flight data. If the time interval is higher than a preset time interval threshold, the corresponding target flight data and the missing data time point of the target flight data are determined. The first target data at the moment before the missing data time point and the second target data at the moment after the missing data time point are obtained from the target flight data. Based on the first target data, the second target data, and the missing time point, the interpolated data of the target flight data at the missing time point is calculated. The interpolated data is added to the missing time point corresponding to the target flight data to obtain the interpolated target flight data.

[0130] This embodiment identifies sparsely sampled regions by detecting the time interval between data points, locates the missing data time points that need to be filled, and obtains the real data before and after them as the basis for interpolation calculation. The interpolation algorithm is used to calculate the estimated value of the missing time point, and finally injects it into the original data stream to realize the time series reconstruction of flight data. This significantly improves the integrity and continuity of multi-aircraft logs on a unified time axis, thereby improving the accuracy of subsequent flight log analysis.

[0131] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the flight log analysis method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0132] This application provides a flight log analysis device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the flight log analysis method in Embodiment 1 above.

[0133] The following is for reference. Figure 3 The diagram illustrates a structural schematic of a flight log analysis device suitable for implementing embodiments of this application. The flight log analysis device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, tablets, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The flight log analysis device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0134] like Figure 3As shown, the flight log analysis device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the flight log analysis device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the flight log analysis device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a flight log analysis device with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0135] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0136] The flight log analysis device provided in this application, employing the flight log analysis method described in the above embodiments, can solve the technical problem of low accuracy in flight log analysis of multi-UAV formations. Compared with the prior art, the beneficial effects of the flight log analysis device provided in this application are the same as those of the flight log analysis method provided in the above embodiments, and other technical features of this flight log analysis device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0137] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0138] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0139] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the flight log analysis method in the above embodiments.

[0140] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0141] The aforementioned computer-readable storage medium may be included in the flight log analysis device; or it may exist independently and not be assembled into the flight log analysis device.

[0142] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the flight log analysis device, cause the flight log analysis device to perform the aforementioned flight log analysis method.

[0143] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0144] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0145] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0146] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described flight log analysis method, which can solve the technical problem of low accuracy in flight log analysis of multi-UAV formations. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the flight log analysis method provided in the above embodiments, and will not be repeated here.

[0147] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the flight log analysis method described above.

[0148] The computer program product provided in this application can solve the technical problem of low accuracy in flight log analysis of multi-UAV formations. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the flight log analysis method provided in the above embodiments, and will not be repeated here.

[0149] All user-related data involved in this application was obtained with the user's permission or consent, as per [reference]. Figure 4 In other words, when this application is applied to a specific product or technology, user permission is required to acquire and process the relevant data, and the processing of the relevant data must comply with the relevant laws, regulations and regulatory standards of the relevant countries and regions.

[0150] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A flight log analysis method, characterized in that, The method includes: When a flight log analysis command is received, the flight logs of each drone in the drone formation are obtained, wherein the drones include the lead drone and multiple wingmen; Extract flight data and time synchronization data from each of the flight logs, wherein the time synchronization data includes the information sending time point and the information receiving time point of each UAV on its local timeline; Based on the flight log analysis instructions, the flight data, and the time synchronization data, the timelines of each UAV are modified to obtain the aligned relative timelines; The step of modifying the timeline of each UAV based on the flight log analysis command, each flight data, and each time synchronization data to obtain the aligned relative timeline includes: Based on the time synchronization data, the time axis offset between the lead aircraft and each of the wingmen is determined. Based on the data type of each flight data in the flight log of the lead aircraft, the flight events of the lead aircraft during flight are determined; Based on the flight log analysis instructions, the flight events, and the timeline offset, the timelines of the lead aircraft and each of the wingmen are modified to obtain an aligned relative timeline; Based on the flight log analysis instructions, the relative time axis, and the flight data, each of the flight logs is analyzed. The step of obtaining the aligned relative time axis further includes: Based on the relative time axis and the flight control input data of each UAV, the sending time of the control command of the lead aircraft is identified, and combined with the change time of the flight data of the wingmen, the response delay of each wingman to the control command is calculated. When the response delay exceeds a preset delay threshold, a corresponding control synchronization alarm message is generated.

2. The method as described in claim 1, characterized in that, The time synchronization data includes multiple time synchronization data between the lead aircraft and each of the wingmen within a preset time period. The step of determining the corresponding time axis offset between the lead aircraft and each of the wingmen based on the time synchronization data includes: Based on the difference between the information reception time and the information transmission time in each of the time synchronization data, multiple initial offsets are obtained; Delete the initial offsets that are higher than the preset abnormal offset threshold to obtain multiple normal offsets; Based on the multiple normal offsets corresponding to the lead aircraft and each of the wingmen, the average value of each normal offset is calculated to obtain the time axis offset corresponding to the lead aircraft and each of the wingmen.

3. The method as described in claim 1, characterized in that, The step of modifying the timelines of the lead aircraft and each of the wingmen based on the flight log analysis instructions, the flight events, and the timeline offset to obtain the aligned relative timelines further includes: Based on the instruction type of the flight log analysis command, a baseline flight event is determined from the flight events; Based on the time axis offset, the time axis of each of the wingmen is modified to obtain a unified time axis for the lead aircraft and each of the wingmen; Based on the time point of the baseline flight event, the unified timeline is modified to obtain the relative timeline.

4. The method as described in claim 3, characterized in that, The command types include flight analysis commands and formation analysis commands, the flight events include takeoff events and formation mode switching events, and the step of determining the baseline flight event from the flight events based on the command types of the flight log analysis commands includes: If the instruction type is the flight analysis instruction, then the baseline flight event is determined to be the takeoff event; If the instruction type is the formation analysis instruction, and the flight event is determined to be the formation mode switching event.

5. The method as described in claim 1, characterized in that, After the step of modifying the time axis of each UAV based on the flight log analysis command, each flight data, and each time synchronization data to obtain the aligned relative time axis, the method further includes: Calculate the time interval between each data point in the flight data; If the time interval is higher than a preset time interval threshold, then the corresponding target flight data and the data missing time point of the target flight data are determined; Obtain the first target data at the moment before the data missing time point and the second target data at the moment after the data missing time point from the target flight data; Based on the first target data, the second target data, and the missing time point, calculate the interpolated data of the target flight data at the missing time point; The interpolated data is added to the missing time point corresponding to the target flight data to obtain the interpolated target flight data.

6. The method as described in claim 1, characterized in that, The steps for analyzing each flight log based on the flight log analysis command, the relative time axis, and the flight data include: Based on the flight log analysis instructions, the flight position data, flight speed data, and flight control input data are extracted from each of the flight data, wherein the flight control input data includes the desired position data and desired speed data of each of the UAVs; Based on the flight position data, the flight speed data, the desired position data, and the desired speed data, the control error for each of the UAVs is calculated. Based on the flight data, the flight position difference of each UAV is calculated, and based on the flight data, the flight speed difference of each UAV is calculated. Based on the difference in flight position and the difference in flight speed, the formation error of each UAV is determined.

7. A flight log analysis device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the flight log analysis method as described in any one of claims 1 to 6.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the flight log analysis method as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the flight log analysis method as described in any one of claims 1 to 6.

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