An aircraft flight state recognition method and system based on ADS-B

Abstract

This invention discloses an aircraft flight status identification method and system based on ADS-B, comprising: acquiring ADS-B data of the aircraft in real time and caching the ADS-B data in a high-speed cache database; extracting the latest ADS-B data points of the aircraft from the high-speed cache database at set time intervals, and storing multiple consecutively extracted latest ADS-B data points into a fixed-length queue of a preset length to form a temporal ADS-B data sequence; calculating estimated parameters for status determination by combining the aircraft's flight dynamic data with preset geospatial information; and identifying the current flight stage of the aircraft through a state machine model based on the temporal ADS-B data sequence and the estimated parameters, wherein the current flight stage includes one of taxiing, takeoff, climb, cruise, descent, approach, and landing. The method and system of this application improve the accuracy, continuity, and real-time performance of flight status identification.

Description

Technical Field

[0001] This invention relates to the field of aviation surveillance technology, and in particular to an aircraft flight status identification method and system based on ADS-B. Background Technology

[0002] ADS-B is a widely used real-time surveillance technology in the civil aviation industry. It continuously monitors air traffic conditions by broadcasting parameters such as aircraft position, altitude, speed, and vertical rate. However, current ADS-B data analysis primarily focuses on real-time position monitoring, flight path reconstruction, flight performance calculation, and airspace occupancy monitoring. The following issues remain regarding flight status identification:

[0003] Insufficient real-time and continuous data processing: Most methods rely on single-point or limited historical data for instantaneous judgments, failing to adequately consider the potential for brief loss or fluctuations in ADS-B signals. Due to the limited coverage of ground receiving stations (typically with a radius of approximately 400 kilometers), data reception blind spots or intermittent data exist in remote mountainous areas and vast sea areas, making judgments based on instantaneous data prone to failure and unable to reliably track the continuous changes in flight status.

[0004] The accuracy and adaptability of state recognition are limited: Existing technologies mostly rely on fixed thresholds in a single dimension for state classification, such as simply using a fixed altitude value as the boundary between climb and cruise. However, in actual flight, there are overlapping and gradual transition areas between different stages, such as from takeoff to stable climb, and from descent approach to landing. These transition processes are affected by many factors such as airport elevation, aircraft performance, and air traffic control instructions. Using fixed thresholds makes it difficult to accurately capture the timing of state transitions, resulting in crude recognition results and a high misjudgment rate.

[0005] Lack of multi-source information fusion and domain knowledge utilization: Traditional methods typically process ADS-B data streams in isolation, failing to effectively integrate prior knowledge such as flight plans, airport geographic information, and aircraft performance models. For example, they fail to dynamically adjust the logical starting and ending points of state judgments by combining the altitudes of the departure and destination airports, and they also fail to utilize the standard performance envelopes of different aircraft types to assist in judging the rationality of climb, descent, and other phases, resulting in weak "intelligence" and scenario adaptability of the recognition logic. Summary of the Invention

[0006] To address the technical problems existing in the background art, this invention proposes an aircraft flight status identification method and system based on ADS-B.

[0007] This invention proposes an aircraft flight status identification method based on ADS-B, comprising the following steps:

[0008] S1. Acquire the aircraft's ADS-B data in real time and cache the ADS-B data to a high-speed cache database;

[0009] S2. Extract the latest ADS-B data points of the aircraft from the cache database at set time intervals, and store the multiple latest ADS-B data points extracted in succession into a fixed-length queue of a preset length to form a time-series ADS-B data sequence.

[0010] S3. Combine the aircraft's flight dynamic data with preset geospatial information to calculate the estimated parameters used for state determination;

[0011] S4. Based on the time-series ADS-B data sequence and the estimated parameters, identify the current flight phase of the aircraft through a state machine model. The current flight phase includes one of taxiing, takeoff, climb, cruise, descent, approach, and landing.

[0012] S5. Output and store flight status information including the current flight phase, the duration of the phase, and the start time of the phase.

[0013] Preferably, the preset geospatial information includes at least an airport geographic information database; step S3 specifically includes:

[0014] Obtain the flight dynamic data of the aircraft, which includes at least the planned departure airport code and the planned arrival airport code;

[0015] Based on the planned departure airport code, the altitude of the departure airport is obtained by querying the airport geographic information database.

[0016] Based on the planned airport code, the altitude of the airport is obtained by querying the airport geographic information database.

[0017] Based on the altitude of the takeoff airport and the preset takeoff climb rate model, the estimated duration of the takeoff phase is calculated;

[0018] Based on the altitude of the landing airport and the preset approach descent rate model, the estimated duration of the approach phase is calculated.

[0019] The estimated duration of the takeoff phase and the estimated duration of the approach phase constitute the estimated parameters used for state determination.

[0020] Preferably, the application process of the preset takeoff climb rate model includes:

[0021] Based on the aircraft type, the corresponding standard climb performance data table is consulted. Combining the aircraft's actual takeoff weight and real-time atmospheric data, the average climb rate under the current conditions is determined by interpolation calculation.

[0022] The estimated duration of the takeoff phase is calculated based on the average rate of climb and the altitude difference between the takeoff airport and the first preset target altitude.

[0023] Preferably, the application process of the preset approach descent rate model includes:

[0024] The baseline descent rate is determined based on the aircraft type and the approach procedure type of the planned landing airport;

[0025] The baseline descent rate is corrected by combining real-time atmospheric data to obtain an applicable descent rate;

[0026] Based on the applicable descent rate and the altitude difference between the second preset target altitude and the landing airport altitude, the estimated duration of the approach phase is calculated.

[0027] Preferably, step S4 specifically includes:

[0028] Define a set of states, which includes taxiing, takeoff, climb, cruise, descent, approach, and landing states;

[0029] Based on the relationship between the geographical location of the latest data point in the time-series ADS-B data sequence and the preset airport area range, the initial state of the aircraft is determined to be either taxiing or cruising.

[0030] If, during taxiing, the timing ADS-B data sequence shows that the aircraft altitude is continuously greater than zero and the vertical speed is greater than a first set threshold, the state is triggered to switch to takeoff state.

[0031] In takeoff mode, if the time-series ADS-B data sequence shows that the aircraft altitude is continuously higher than the first altitude threshold and lower than the second altitude threshold, the state is triggered to switch to climb mode.

[0032] During climb, if the time-series ADS-B data sequence shows that the aircraft's altitude change rate approaches zero and the altitude value remains above the third altitude threshold, the state is switched to cruise mode.

[0033] During cruise, if the time-series ADS-B data sequence shows that the aircraft altitude is continuously decreasing and the vertical rate is less than zero, the state is triggered to switch to descent mode.

[0034] During descent, if the time-series ADS-B data sequence shows that the aircraft's altitude is consistently below the second altitude threshold and its geographical location is close to the planned landing airport, a state switch to approach state is triggered.

[0035] During approach, if the time-series ADS-B data sequence shows that the aircraft altitude is continuously close to the altitude of the landing airport and the ground speed is lower than the second preset threshold, the state is triggered to switch to landing state.

[0036] If the condition for triggering a state transition is not met, the state machine model maintains the previous state of the aircraft unchanged.

[0037] Preferably, step S5 specifically includes:

[0038] When the state machine model detects a change in flight phase, it will trigger the system time at the time of this change determination and record it as the start time of the new phase.

[0039] During the current flight phase, the system continuously acquires the current time, and calculates and updates the duration of the phase in real time by subtracting the current time from the phase start time.

[0040] The current flight phase, the phase duration calculated in real time, and the phase start time are encapsulated into a structured state data object;

[0041] The state data object is written to a non-volatile memory for persistent storage, and a real-time data interface is provided to external systems to output the state data object.

[0042] Preferably, the length of the fixed-length queue in step S2 is 6.

[0043] This invention proposes an aircraft flight status identification system based on ADS-B, comprising:

[0044] The data caching module is used to acquire the aircraft's ADS-B data in real time and cache the ADS-B data to a high-speed cache database;

[0045] The time-series data processing module is used to extract the latest ADS-B data points of the aircraft from the cache database at set time intervals, and store multiple consecutively extracted latest ADS-B data points into a fixed-length queue of a preset length to form a time-series ADS-B data sequence.

[0046] The status determination module is used to calculate the estimated parameters for status determination by combining the aircraft's flight dynamic data with preset geospatial information.

[0047] The flight status identification module is used to identify the current flight phase of the aircraft based on the time-series ADS-B data sequence and the estimated parameters, using a state machine model.

[0048] The status output module is used to output and store flight status information including the current flight phase, the duration of the phase, and the start time of the phase.

[0049] Preferably, in the time-series data processing module, the length of the fixed-length queue is 6.

[0050] The present invention proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the aircraft flight status identification method based on ADS-B as described above.

[0051] This invention presents an ADS-B-based aircraft flight status identification method and system. By constructing a time-series data queue, fusing multi-source prior information, and applying a domain-knowledge-based state machine model, it achieves high-precision and robust real-time identification of the entire aircraft flight process. This overcomes the problems of inaccurate identification and poor real-time performance caused by discontinuous ADS-B data reception, ambiguous state transition boundaries, and reliance on a single data source. It improves the accuracy and continuity of flight status identification, providing a reliable and timely data foundation for airlines' refined operational management and in-depth awareness of air traffic conditions. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating the process of an aircraft flight status identification method based on ADS-B proposed in this invention.

[0053] Figure 2 This is a schematic diagram of the system architecture of an aircraft flight status identification system based on ADS-B proposed in this invention. Detailed Implementation

[0054] Reference Figure 1 and Figure 2 The present invention proposes an aircraft flight status identification method based on ADS-B, comprising the following steps:

[0055] S1. Acquire ADS-B data of the aircraft in real time and cache the ADS-B data to a high-speed cache database.

[0056] S2. Extract the latest ADS-B data points of the aircraft from the cache database at set time intervals, and store the multiple latest ADS-B data points extracted in succession into a fixed-length queue of a preset length to form a time-series ADS-B data sequence.

[0057] Specifically, the length of the fixed-length queue in step S2 is 6.

[0058] It should be noted that the time interval is set to 5 seconds.

[0059] S3. Combine the aircraft's flight dynamics data with preset geospatial information to calculate the estimated parameters used for state determination.

[0060] In this embodiment, the preset geospatial information includes at least an airport geographic information database; step S3 specifically includes: acquiring aircraft flight dynamic data, which includes at least the planned departure airport code and the planned arrival airport code; querying and obtaining the departure airport altitude from the airport geographic information database based on the planned departure airport code; querying and obtaining the arrival airport altitude from the airport geographic information database based on the planned arrival airport code; calculating the estimated duration of the takeoff phase based on the takeoff airport altitude and a preset takeoff climb rate model; calculating the estimated duration of the approach phase based on the arrival airport altitude and a preset approach descent rate model; wherein, the estimated duration of the takeoff phase and the estimated duration of the approach phase constitute the estimated parameters used for state determination.

[0061] In this embodiment, the application process of the preset takeoff climb rate model includes:

[0062] Based on the aircraft type, the corresponding standard climb performance data table is consulted. Combined with the aircraft's actual takeoff weight and real-time atmospheric data, the average climb rate under the current conditions is determined by interpolation calculation.

[0063] The estimated duration of the takeoff phase is calculated based on the average rate of climb and the altitude difference between the takeoff airport and the first preset target altitude.

[0064] In this embodiment, the application process of the preset approach descent rate model includes:

[0065] The baseline descent rate is determined based on the aircraft type and the approach procedure type of the planned airport.

[0066] The baseline descent rate is corrected by combining real-time atmospheric data to obtain the applicable descent rate;

[0067] The estimated duration of the approach phase is calculated based on the applicable descent rate and the altitude difference between the second preset target altitude and the landing airport.

[0068] Specifically, first, based on the aircraft's ICAO address or flight number, its flight dynamic data is queried to obtain key information: the planned departure airport code and the planned arrival airport code.

[0069] Next, the system queries the airport geographic information database. This database stores at least the ICAO / IATA codes for airports worldwide, the latitude and longitude of the airport's reference point, and the airport's altitude. Using the airport code, the altitudes of both the departure and arrival airports can be quickly obtained.

[0070] Takeoff Phase Prediction: The takeoff climb rate model is invoked. This model contains standard climb performance data tables for different aircraft types under varying takeoff weights and atmospheric conditions. Based on the aircraft type, estimated actual takeoff weight, and real-time atmospheric data such as temperature and air pressure, the system uses interpolation to look up an average climb rate (e.g., 1500 feet / minute) in the data table. Then, the altitude difference between the first preset target altitude (e.g., 120 meters above ground or airport elevation + 300 meters) and the takeoff airport altitude is calculated. Finally, using the formula: Estimated Duration = Altitude Difference / Average Climb Rate, the estimated time from takeoff to reaching the initial climb point is calculated.

[0071] Approach Phase Prediction: The approach descent rate model is invoked. This model considers the impact of aircraft type and approach procedure type on the descent profile. First, a baseline descent rate is determined, and then corrected for by real-time wind speed and temperature to obtain an applicable descent rate. The altitude difference between the second preset target altitude (e.g., 300 meters above the airport elevation) and the landing airport altitude is calculated to estimate the estimated time from the start of stable approach to touchdown.

[0072] The estimated durations of the takeoff and approach phases calculated above are used together as estimated parameters for state determination, providing important timing reference boundaries for the subsequent state machine.

[0073] S4. Based on the time-series ADS-B data sequence and estimated parameters, identify the current flight phase of the aircraft through a state machine model. The current flight phase includes one of the following: taxiing, takeoff, climb, cruise, descent, approach, and landing.

[0074] In this embodiment, step S4 specifically includes:

[0075] Define a set of states, which includes taxiing, takeoff, climb, cruise, descent, approach, and landing states;

[0076] Based on the relationship between the geographical location of the latest data point in the time-series ADS-B data sequence and the preset airport area range, the initial state of the aircraft is determined to be either taxiing or cruising.

[0077] If, during taxiing, the timing ADS-B data sequence shows that the aircraft altitude is continuously greater than zero and the vertical speed is greater than a first set threshold, the state is triggered to switch to takeoff state.

[0078] In takeoff mode, if the time-series ADS-B data sequence shows that the aircraft altitude is continuously higher than the first altitude threshold and lower than the second altitude threshold, the state is triggered to switch to climb mode.

[0079] During climb, if the time-series ADS-B data sequence shows that the aircraft's altitude change rate approaches zero and the altitude value remains above the third altitude threshold, the state is switched to cruise mode.

[0080] During cruise, if the time-series ADS-B data sequence shows that the aircraft altitude is continuously decreasing and the vertical rate is less than zero, the state is triggered to switch to descent mode.

[0081] During descent, if the time-series ADS-B data sequence shows that the aircraft's altitude is consistently below the second altitude threshold and its geographical location is close to the planned landing airport, a state switch to approach state is triggered.

[0082] During approach, if the time-series ADS-B data sequence shows that the aircraft altitude is continuously close to the altitude of the landing airport and the ground speed is lower than the second set threshold, the state is triggered to switch to landing state.

[0083] If the condition for triggering a state transition is not met, the state machine model maintains the aircraft's previous state unchanged.

[0084] S5. Output and store flight status information including the current flight phase, the duration of the phase, and the start time of the phase.

[0085] In this embodiment, step S5 specifically includes:

[0086] When the state machine model detects a transition in the flight phase, it will trigger the system time at the time of this transition determination and record it as the start time of the new phase.

[0087] During the current flight phase, continuously acquire the system's current time, calculate the difference between the current time and the phase start time, and update the duration of the phase in real time;

[0088] The current flight phase, the phase duration calculated in real time, and the phase start time are encapsulated into a structured state data object;

[0089] The state data object is written to non-volatile memory for persistent storage, and at the same time, a real-time data interface is provided to external systems to output the state data object.

[0090] Specifically, whenever the state machine recognizes a phase transition (such as switching from climb to cruise):

[0091] Record time: Record the system time when the state transition determination takes effect (or the timestamp of the data point message that triggered the transition) as the phase start time.

[0092] Calculate duration: Starting from the stage start time, the system obtains the current time every second and subtracts from it to calculate and update the duration of the current stage in real time.

[0093] Reference Figure 1 and Figure 2 The present invention proposes an aircraft flight status identification system based on ADS-B, comprising:

[0094] The data caching module is used to acquire aircraft ADS-B data in real time and cache the ADS-B data to a high-speed cache database;

[0095] The time-series data processing module is used to extract the latest ADS-B data points of the aircraft from the cache database at set time intervals, and store multiple consecutively extracted latest ADS-B data points into a fixed-length queue of a preset length to form a time-series ADS-B data sequence.

[0096] In this embodiment, the length of the fixed-length queue in the time-series data processing module is 6.

[0097] The status determination module is used to combine the aircraft's flight dynamic data with preset geospatial information to calculate the estimated parameters for status determination.

[0098] The flight status identification module is used to identify the current flight phase of an aircraft based on a time-series ADS-B data sequence and estimated parameters, using a state machine model.

[0099] The status output module is used to output and store flight status information including the current flight phase, the duration of the phase, and the start time of the phase.

[0100] The present invention is based on a distributed data processing system. This system mainly includes:

[0101] Data access layer: responsible for receiving raw message data streams broadcast from ADS-B ground receiving stations distributed throughout the country.

[0102] Real-time computing layer: As the core processing unit, it is equipped with the recognition system described in this invention, which includes modules such as data caching, time-series processing, state determination, state recognition and output.

[0103] Data storage layer: Used for persistent storage of raw ADS-B data, intermediate calculation results, and final structured flight status information. The cache uses a Redis cluster, while persistent storage can use a relational database (such as MySQL) or a time-series database.

[0104] Application Service Layer: Provides API interfaces to push or query structured flight status information in real time to upstream business systems such as flight operation control, fuel management, and carbon emission monitoring.

[0105] The data access layer continuously receives ADS-B messages organized in a specific format, such as:

[0106] (e.g., "MSG,3,1,1,AAAAAA,1,2025-10-28,12:14:19.000,..."). After performing basic checks on each message (such as field integrity and ICAO address validity), the system immediately writes it to the Redis cache database. To improve write efficiency and scalability, a consistent hashing algorithm is used to shard the data. The shard key is calculated from the aircraft's 24-bit ICAO address (hex_ident), ensuring that data from the same aircraft is routed to the same Redis shard, facilitating subsequent aggregation. This process corresponds to the "data caching module" in the system.

[0107] The system maintains a separate processing thread or coroutine for each tracked aircraft (identified by its ICAO address). This processing unit reads the latest ADS-B data point for the aircraft from its corresponding Redis shard every 5 seconds (the set time interval).

[0108] The system instantiates a fixed-length queue, BoundList, for each aircraft, with a preset length of 6 (meaning it stores data from the most recent 30 seconds). Each time a new data point is read, it is inserted at the tail of the queue. If the queue is full, the oldest historical data point at the head of the queue is automatically removed. In this way, the system constantly maintains a time-series ADS-B data sequence containing multiple consecutive time points to reflect the recent movement trends of the aircraft. This process corresponds to the "time-series data processing module" in the system.

[0109] This application provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the technical solution shown in the above method embodiments. The implementation principle and beneficial effects are similar and will not be repeated here.

[0110] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for identifying aircraft flight status based on ADS-B, characterized in that, Includes the following steps: S1. Acquire the aircraft's ADS-B data in real time and cache the ADS-B data to a high-speed cache database; S2. Extract the latest ADS-B data points of the aircraft from the cache database at set time intervals, and store the multiple latest ADS-B data points extracted in succession into a fixed-length queue of a preset length to form a time-series ADS-B data sequence. S3. Combining the aircraft's flight dynamic data with preset geospatial information, calculate the estimated parameters used for state determination. The preset geospatial information includes at least an airport geographic information database. S4. Based on the time-series ADS-B data sequence and the estimated parameters, identify the current flight phase of the aircraft through a state machine model. The current flight phase includes one of taxiing, takeoff, climb, cruise, descent, approach, and landing. S5. Output and store flight status information including the current flight phase, the duration of the phase, and the start time of the phase; Specifically, step S3 includes: Obtain the flight dynamic data of the aircraft, which includes at least the planned departure airport code and the planned arrival airport code; Based on the planned departure airport code, the altitude of the departure airport is obtained by querying the airport geographic information database. Based on the planned airport code, the altitude of the airport is obtained by querying the airport geographic information database. Based on the altitude of the takeoff airport and the preset takeoff climb rate model, the estimated duration of the takeoff phase is calculated; Based on the altitude of the landing airport and the preset approach descent rate model, the estimated duration of the approach phase is calculated. The estimated duration of the takeoff phase and the estimated duration of the approach phase constitute the estimated parameters used for state determination. The application process of the preset takeoff climb rate model includes: Based on the aircraft type, the corresponding standard climb performance data table is consulted. Combining the aircraft's actual takeoff weight and real-time atmospheric data, the average climb rate under the current conditions is determined by interpolation calculation. Based on the average rate of climb and the altitude difference between the takeoff airport and the first preset target altitude, the estimated duration of the takeoff phase is calculated. The application process of the preset approach descent rate model includes: The baseline descent rate is determined based on the aircraft type and the approach procedure type of the planned landing airport; The baseline descent rate is corrected by combining real-time atmospheric data to obtain an applicable descent rate; Based on the applicable descent rate and the altitude difference between the second preset target altitude and the landing airport altitude, the estimated duration of the approach phase is calculated.

2. The aircraft flight status identification method based on ADS-B according to claim 1, characterized in that, Step S4 specifically includes: Define a set of states, which includes taxiing, takeoff, climb, cruise, descent, approach, and landing states; Based on the relationship between the geographical location of the latest data point in the time-series ADS-B data sequence and the preset airport area range, the initial state of the aircraft is determined to be either taxiing or cruising. If, during taxiing, the timing ADS-B data sequence shows that the aircraft altitude is continuously greater than zero and the vertical speed is greater than a first set threshold, the state is triggered to switch to takeoff state. In takeoff mode, if the time-series ADS-B data sequence shows that the aircraft altitude is continuously higher than the first altitude threshold and lower than the second altitude threshold, the state is triggered to switch to climb mode. During climb, if the time-series ADS-B data sequence shows that the aircraft's altitude change rate approaches zero and the altitude value remains above the third altitude threshold, the state is switched to cruise mode. During cruise, if the time-series ADS-B data sequence shows that the aircraft altitude is continuously decreasing and the vertical rate is less than zero, the state is triggered to switch to descent mode. During descent, if the time-series ADS-B data sequence shows that the aircraft's altitude is consistently below the second altitude threshold and its geographical location is close to the planned landing airport, a state switch to approach state is triggered. During approach, if the time-series ADS-B data sequence shows that the aircraft altitude is continuously close to the altitude of the landing airport and the ground speed is lower than the second preset threshold, the state is triggered to switch to landing state. If the condition for triggering a state transition is not met, the state machine model maintains the previous state of the aircraft unchanged.

3. The aircraft flight status identification method based on ADS-B according to claim 1, characterized in that, Step S5 specifically includes: When the state machine model detects a change in flight phase, it will trigger the system time at the time of this change determination and record it as the start time of the new phase. During the current flight phase, the system continuously acquires the current time, and calculates and updates the duration of the phase in real time by subtracting the current time from the phase start time. The current flight phase, the phase duration calculated in real time, and the phase start time are encapsulated into a structured state data object; The state data object is written to a non-volatile memory for persistent storage, and a real-time data interface is provided to external systems to output the state data object.

4. The aircraft flight status identification method based on ADS-B according to claim 1, characterized in that, The length of the fixed-length queue mentioned in step S2 is 6.

5. An aircraft flight status identification system based on ADS-B, characterized in that, include: The data caching module is used to acquire the aircraft's ADS-B data in real time and cache the ADS-B data to a high-speed cache database; The time-series data processing module is used to extract the latest ADS-B data points of the aircraft from the cache database at set time intervals, and store multiple consecutively extracted latest ADS-B data points into a fixed-length queue of a preset length to form a time-series ADS-B data sequence. The status determination module is used to combine the aircraft's flight dynamic data with preset geospatial information to calculate the estimated parameters for status determination. The preset geospatial information includes at least an airport geographic information database. The flight status identification module is used to identify the current flight phase of the aircraft based on the time-series ADS-B data sequence and the estimated parameters, using a state machine model. The status output module is used to output and store flight status information including the current flight phase, the duration of the phase, and the start time of the phase; The status determination module specifically includes: Obtain the flight dynamic data of the aircraft, which includes at least the planned departure airport code and the planned arrival airport code; Based on the planned departure airport code, the altitude of the departure airport is obtained by querying the airport geographic information database. Based on the planned airport code, the altitude of the airport is obtained by querying the airport geographic information database. Based on the altitude of the takeoff airport and the preset takeoff climb rate model, the estimated duration of the takeoff phase is calculated; Based on the altitude of the landing airport and the preset approach descent rate model, the estimated duration of the approach phase is calculated. The estimated duration of the takeoff phase and the estimated duration of the approach phase constitute the estimated parameters used for state determination. The application process of the preset takeoff climb rate model includes: Based on the aircraft type, the corresponding standard climb performance data table is consulted. Combining the aircraft's actual takeoff weight and real-time atmospheric data, the average climb rate under the current conditions is determined by interpolation calculation. Based on the average rate of climb and the altitude difference between the takeoff airport and the first preset target altitude, the estimated duration of the takeoff phase is calculated. The application process of the preset approach descent rate model includes: The baseline descent rate is determined based on the aircraft type and the approach procedure type of the planned landing airport; The baseline descent rate is corrected by combining real-time atmospheric data to obtain an applicable descent rate; Based on the applicable descent rate and the altitude difference between the second preset target altitude and the landing airport altitude, the estimated duration of the approach phase is calculated.

6. The aircraft flight status identification system based on ADS-B according to claim 5, characterized in that, In the time-series data processing module, the length of the fixed-length queue is 6.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 4.

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