A method and system for automatically collecting flight support node data based on a Bluetooth beacon device

Abstract

The application discloses a kind of based on bluetooth beacon equipment automatic collection flight guarantee node data method and system, method includes: in the each guarantee node area of flight ground guarantee process, respectively install bluetooth beacon equipment, each bluetooth beacon equipment is associated with the corresponding guarantee node area by identity coding;For guarantee personnel configuration terminal equipment, terminal equipment is configured to when entering or leaving the signal coverage boundary of any bluetooth beacon equipment, automatically obtain the identity coding of this bluetooth beacon equipment and generate trigger data upload to background system;Background system is in real time to interface flight dynamic information system, according to the trigger data received determination corresponding guarantee node area, and associate guarantee node area current matching flight information and guarantee task information.The application realizes the full automation collection and real-time matching of flight guarantee node time data by the dynamic interaction of bluetooth beacon equipment and terminal equipment, significantly improves data accuracy and guarantee efficiency.

Description

Technical Field

[0001] This invention relates to the field of flight support technology, and in particular to a method and system for automatically collecting flight support node data based on Bluetooth beacon devices. Background Technology

[0002] Currently, the collection of flight support node time data mainly relies on manual recording or semi-automated systems (such as RFID, QR code scanning, etc.), which has the following problems: High dependence on manual labor: Support personnel need to manually input or scan devices, which is prone to data delays or input errors; Insufficient real-time performance: Although existing automation technologies can collect node times, they cannot match flight information in real time, resulting in data application delays; Low positioning accuracy: Traditional methods are difficult to accurately associate support nodes with specific apron areas, affecting the accuracy of data analysis; Lack of dynamic adaptability: Existing systems cannot adjust support tasks in real time according to dynamic changes such as flight delays, diversions, and cancellations, resulting in resource waste and low efficiency. Summary of the Invention

[0003] To address the technical problems existing in the background art, this invention proposes a method and system for automatically collecting flight support node data based on Bluetooth beacon devices. Through dynamic interaction between Bluetooth beacon devices and terminal devices, combined with differentiated deployment strategies, multi-beacon arbitration mechanisms, and flight dynamic adaptive matching technology, fully automated collection and real-time matching of flight support node time data are achieved, significantly improving data accuracy and support efficiency. This method can be widely applied to airports, airlines, and ground support units, promoting the intelligent upgrade of flight support processes.

[0004] In a first aspect, the present invention proposes a method for automatically collecting flight support node data based on a Bluetooth beacon device, the method comprising:

[0005] S1. Install Bluetooth beacon devices at each support node area of ​​the flight ground support process. Each Bluetooth beacon device is associated with the corresponding support node area through an identity code.

[0006] S2. To ensure that personnel are equipped with terminal devices that support Bluetooth communication, the terminal devices are configured to automatically obtain the identification code of the Bluetooth beacon device and generate trigger data when entering or leaving the signal coverage boundary of any of the Bluetooth beacon devices.

[0007] S3. Upload the trigger data to the backend system;

[0008] S4. The background system connects to the flight dynamic information system in real time, determines the corresponding support node area based on the received trigger data, and associates the currently matched flight information and support task information of the support node area.

[0009] Preferably, the trigger data further includes at least a trigger timestamp, terminal holder identifier, current support task identifier, signal strength value, and terminal device air pressure data.

[0010] Preferably, when a terminal device simultaneously receives beacon signals from multiple Bluetooth beacon devices, the backend system employs a multi-beacon arbitration mechanism to determine the true corresponding protection node area of ​​the terminal device; the multi-beacon arbitration mechanism includes:

[0011] Calculate the signal strength weights for each beacon;

[0012] The duration of time a terminal device remains within the coverage area of ​​each beacon;

[0013] Based on the terminal device's historical movement path and current task type, calculate the probability that the terminal device will go to the location of the Bluetooth beacon device that has received the beacon signal.

[0014] By combining the signal strength weight, dwell time, and probability value, a weighted scoring method is used to determine the actual protection node area corresponding to the terminal device.

[0015] Preferably, the backend system uses a weighted scoring formula to determine the actual protection node area corresponding to the terminal device; the weighted scoring formula is:

[0016]

[0017] in, Let be the overall score of the i-th beacon; The signal strength weight of the i-th Bluetooth beacon device; Let be the duration of stay within the coverage area of ​​the i-th beacon; The longest dwell time across all beacon coverage areas; The probability of the path to the target location; Let i be the i-th Bluetooth beacon device, Task be the current task type, and History be the historical path; These are the weighting coefficients.

[0018] Preferably, the flight information and support task information currently matched in the associated support node area mentioned in S4 specifically include adaptive matching:

[0019] The backend system dynamically adjusts the execution plans and / or resource binding relationships of support tasks associated with the support node area based on real-time flight dynamic information obtained from the flight dynamic information system. This solves the problem of existing technologies being unable to adjust support tasks in real time according to dynamic changes such as flight delays, diversions, and cancellations, leading to resource waste and inefficiency.

[0020] Preferably, the flight dynamic information includes at least one of flight delays, flight diversions, flight cancellations, gate changes, flight advances, and temporary extra flights.

[0021] Preferably, the backend system associates the identity code and trigger timestamp in the triggered data with the current corresponding protection plan time, calculates the deviation between the actual trigger time and the current protection plan time, and triggers a graded warning when the deviation exceeds a threshold.

[0022] Preferably, the backend system also generates a support efficiency analysis report based on the received trigger data, which includes at least one of the following indicators: node coverage rate, timeliness rate, accuracy rate, and flight dynamic adaptability rate.

[0023] Wherein, node coverage rate = number of protected node areas collected / total number of protected node areas to be collected × 100%;

[0024] Timeliness rate = (Number of guaranteed support nodes completed on time according to the dynamically adjusted planned time / Total number of guaranteed support nodes) × 100%;

[0025] Accuracy rate = (Percentage of personnel and tasks correctly matched according to the support plan) × 100%

[0026] Flight dynamic adaptability rate = Number of times the system successfully responded to flight changes and dynamically adjusted related support tasks / Total number of flight dynamic changes × 100%.

[0027] Preferably, step S1 further includes: developing a differentiated Bluetooth beacon device deployment plan based on the airport type; this step is used to address the poor adaptability of existing technologies to complex environments, where traditional positioning technologies are prone to problems such as signal interference and positioning errors in complex environments such as multi-story buildings, underground passages, and mega-hub airports.

[0028] The Bluetooth beacon device deployment scheme includes:

[0029] For single-level airports, a horizontally distributed Bluetooth beacon equipment deployment scheme is adopted to ensure seamless coverage of Bluetooth beacon signals to each support node area;

[0030] For multi-story airports, the transmission power of the Bluetooth beacon devices on different floors is set differently, and a barometer is configured in the terminal device to assist in positioning.

[0031] For hub airports, a zoned and hierarchical deployment scheme for Bluetooth beacon equipment is adopted, with dense deployment in areas with a high density of support nodes and sparse deployment in areas with a low density of support nodes.

[0032] For underground areas, increase the transmission power of the Bluetooth beacon devices and increase the deployment density of the Bluetooth beacon devices.

[0033] In a second aspect, a system for automatically collecting flight support node time data based on a Bluetooth beacon device to implement the method described in the first aspect includes:

[0034] Bluetooth beacon devices deployed in each support node area are associated with the corresponding support node area through an identity code;

[0035] The terminal device configured for support personnel is configured to automatically acquire the identification code of the Bluetooth beacon device and generate trigger data when entering or leaving the signal coverage boundary of any of the Bluetooth beacon devices;

[0036] The back-end processing platform is used to connect with the flight dynamic information system in real time, determine the corresponding support node area based on the received trigger data, and associate the currently matched flight information and support task information of the support node area.

[0037] This invention achieves fully automated collection and real-time matching of flight support task node time data through dynamic interaction between Bluetooth beacon devices and terminal devices, significantly improving data accuracy and support efficiency. Attached Figure Description

[0038] Figure 1 This is a flowchart of a method for automatically collecting flight support node data based on Bluetooth beacon devices, as proposed in this invention.

[0039] Figure 2 This is a schematic diagram illustrating the deployment scheme of Bluetooth beacon devices in a multi-story airport according to the present invention;

[0040] Figure 3 This is a schematic diagram of the multi-beacon arbitration mechanism in the signal overlap region of the present invention;

[0041] Figure 4 The flowchart below shows the adaptive matching process performed by the background system of this invention after receiving trigger data. Detailed Implementation

[0042] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings and embodiments. The embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operating procedures. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them, and the scope of protection of the present invention is not limited to the following embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0043] In a specific implementation method, refer to Figure 1This invention proposes a method for automatically collecting flight support node data based on Bluetooth beacon devices, the method comprising:

[0044] S1. Install low-power Bluetooth beacon devices at each support node area in the flight ground support process. Each Bluetooth beacon device is associated with the corresponding support node area through an identity code.

[0045] The service area includes at least one of the following: ground service pick-up and drop-off points, jet bridge docking points, passenger boarding bridge docking points, shuttle bus arrival points, baggage loading and unloading areas, in-flight catering points, aircraft refueling points, aircraft towing vehicle points, multi-story waiting areas, underground parking lots, underground passages, stairwells, and elevators.

[0046] It should be noted that the deployment scheme for Bluetooth beacon devices varies depending on the type of airport. For example:

[0047] 1. Single-level airport.

[0048] A horizontally uniform distribution scheme is adopted, with the spacing between Bluetooth beacon devices set to 20-30 meters to ensure seamless coverage of all support nodes. In open apron areas, the beacon spacing can be appropriately increased to 30 meters; in dense jet bridge areas, the beacon spacing is reduced to 20 meters. The beacon transmission power is uniformly set to standard power (coverage radius 12-15 meters), and the signal strength trigger threshold is set to -70dBm.

[0049] 2. Multi-story airport

[0050] like Figure 2 As shown, a vertical signal isolation scheme is adopted, using differentiated transmission power control and barometer-assisted positioning on the terminal equipment (accuracy ±1 meter) to ensure that signals from different floors do not interfere with each other. The specific implementation method is as follows:

[0051] a. Set the transmission power of the upper beacon (such as the three-layer starting layer F3) to be reduced by 20-30% (coverage radius 8-10 meters), and set the signal strength trigger threshold to -80dBm.

[0052] b. Reduce the transmission power of the middle layer beacon (such as the second-layer relay layer F2) by 10-20% (coverage radius 10-12 meters), and set the signal strength trigger threshold to -75 dBm.

[0053] c. Set the transmission power of the lower-level beacon (such as the first-level arrival layer F1) to maintain the standard power (coverage radius 12-15 meters), and set the signal strength trigger threshold to -68dBm.

[0054] d. The terminal device integrates a barometer sensor to collect air pressure data in real time and calculates the floor height based on the air pressure difference (accuracy ±1 meter, approximately ±0.3 floors) to help determine the current floor. For example, Figure 2The barometer sensor collected the air pressure values ​​of the three-layer departure layer F3 as 1010 hPa, the two-layer departure layer F2 as 1013 hPa, and the one-layer departure layer F1 as 1016 hPa.

[0055] The back-end system uses three indicators—beacon identification code, air pressure data, and signal strength—to accurately determine the floor and location of the personnel providing support. The accuracy rate of the comprehensive positioning judgment is ≥98%.

[0056] 3. Mega-hub airport

[0057] A zoned and tiered deployment scheme is adopted, with dense deployment in key protection areas where the distribution density of protection nodes is high, and sparse deployment in transitional areas where the distribution density of protection nodes is low. The specific implementation method is as follows:

[0058] a. Main protection areas (airbridge, baggage loading and unloading area, refueling area, catering area): Set beacon spacing of 10-15 meters, transmission power of standard power, signal strength trigger threshold of -70dBm to ensure accurate positioning.

[0059] b. Transition areas (passages, rest areas, parking lots): Set beacon spacing of 30-40 meters, appropriately increase transmission power (coverage radius 20-25 meters), signal strength trigger threshold -80dBm, and ensure coverage continuity.

[0060] c. Key nodes (security checkpoints, boarding gates, baggage carousels): Add redundant beacons to ensure 100% coverage and high reliability.

[0061] 4. Underground area

[0062] A signal enhancement scheme is adopted, increasing beacon transmission power and deployment density to overcome signal attenuation and multipath effects in underground environments. The specific implementation method is as follows:

[0063] a. Increase the beacon transmission power to 1.5-2 times the standard power (coverage radius 15-20 meters), and set the signal strength trigger threshold to -75 to -80 dBm.

[0064] b. The spacing between beacons is reduced to 8-12 meters, and beacons are added at key locations such as corners of underground passages, elevator entrances, and stairwells.

[0065] c. Install dedicated Bluetooth beacon devices in each parking zone of the underground parking lot to ensure accurate positioning of vehicles and personnel.

[0066] Mega-hub airports can be determined based on their proportion of the total throughput of all airports nationwide. When a mega-hub airport includes multi-story buildings or underground areas, the Bluetooth beacon equipment deployment schemes for multi-story airports and underground areas described above can be followed.

[0067] Each Bluetooth beacon device is assigned a unique identification code at the factory or after deployment (format: airport code-area code-device number, e.g., PEK-A03-001). In the backend system, the identification code of each Bluetooth beacon device is bound to a specific location in a support node area (including information such as gate number, floor, and area type), so as to achieve full coverage of beacon signals in each support node area.

[0068] S2. To ensure that personnel are equipped with terminal devices that support Bluetooth communication, the terminal devices are configured to automatically obtain the identification code of the Bluetooth beacon device and generate trigger data when entering or leaving the signal coverage boundary of any of the Bluetooth beacon devices.

[0069] Support personnel include ground staff, cleaners, refueling personnel, catering staff, baggage handlers, and other service personnel providing flight support. Terminal devices are typically smartphones or dedicated handheld terminals, integrating Bluetooth modules, barometer sensors, and GPS modules. When support personnel using the terminal device enter or leave the signal coverage area of ​​any Bluetooth beacon device, the terminal device automatically obtains the identification code of that Bluetooth beacon device and generates trigger data containing the current trigger timestamp, the terminal holder's identifier (support personnel ID), and the current support task identifier (task ID).

[0070] Specifically, the signal coverage boundary of the Bluetooth beacon device is preset to a trigger range of 5-15 meters by adjusting the transmission power or the signal strength from -50 to -85 dBm. Within the signal coverage area, the transmission power of the Bluetooth beacon device is dynamically adjusted according to the deployment environment to ensure that the signal strength meets the triggering requirements.

[0071] In practical deployments, due to overlapping coverage areas of Bluetooth beacon devices, terminal devices may detect multiple beacon signals simultaneously, making it impossible to accurately determine the true location of support personnel and leading to data collection errors. To address this issue, the backend system of this invention employs a multi-beacon arbitration mechanism to determine the true support node area corresponding to the terminal device. The multi-beacon arbitration mechanism includes signal strength weighting, dwell time determination, and historical path prediction. Details are as follows:

[0072] 1. Signal strength weighting determination

[0073] When a terminal device detects signals from multiple Bluetooth beacon devices simultaneously, it collects the RSSI (Received Signal Strength Indicator) values ​​of each beacon in real time, and the background system calculates the weighted value of the signal strength of each Bluetooth beacon device.

[0074] The weighting formula is:

[0075]

[0076] in The signal strength weight of the i-th Bluetooth beacon device; Let be the signal strength of the i-th Bluetooth beacon device; The strongest signal strength among all beacon signals received by the terminal device; This represents the weakest signal strength among all beacon signals received by the terminal device.

[0077] For example, if the terminal device simultaneously detects three beacon signals: beacon A (RSSI=-60dBm), beacon B (RSSI=-70dBm), and beacon C (RSSI=-80dBm), then:

[0078]

[0079] Calculations show that beacon A has the highest signal strength weight, and the background system initially determines it to be the most likely target location.

[0080] 2. Determination of stay duration

[0081] Based on the duration of the terminal device's stay within the coverage area of ​​each beacon, a duration threshold (e.g., 3-5 seconds) is set to determine valid triggers, filtering out false triggers caused by brief crossovers. Specifically, the implementation process is as follows:

[0082] The terminal device continuously monitors the signal strength of each beacon and records the timestamps of entering and leaving the coverage area of ​​each beacon. After the data is uploaded, the backend system calculates the duration of stay within the coverage area of ​​each beacon. At the same time, a time threshold is preset. (For example, 3-5 seconds), only beacons whose dwell time exceeds the threshold are considered to be validly triggered.

[0083] For example, if a security personnel quickly walks from the passage (beacon A) to the baggage handling area (beacon B), stays within the coverage area of ​​beacon A for 2 seconds, and stays within the coverage area of ​​beacon B for 8 seconds, then beacon A is determined to be a brief crossing (invalid trigger), and beacon B is determined to be a valid trigger.

[0084] 3. Historical Path Prediction

[0085] By combining the historical movement paths of support personnel (path models built from historical trigger data) and the current task type (e.g., baggage handlers typically do not appear in the refueling area), the backend system predicts the most likely target location. Specifically, the implementation process is as follows:

[0086] Based on historical trigger data, the backend system establishes typical movement path models for various support personnel. For example, the typical path for baggage handling personnel is: rest area → baggage handling area → baggage carousel → rest area.

[0087] Based on the current task type (such as baggage loading and unloading task), combined with the current location (such as beacon A at the passage) and historical path model, predict the next most likely support node area to be reached.

[0088] The backend system uses Bayesian probability formulas to calculate the path probability of support personnel reaching the locations of each candidate Bluetooth beacon device:

[0089]

[0090] in, The candidate Bluetooth beacon devices are: Task, which is the current task type, and History, which is the historical path.

[0091] in, Let be the target posterior probability, representing the probability that, given the current task type and historical path, support personnel will go to the support node area corresponding to the i-th Bluetooth beacon device. Let represent the probability of task completion, which indicates the probability that the current task type can be completed in the area corresponding to the protection node of the i-th Bluetooth beacon device. For example, the probability of task completion is high in the baggage loading and unloading area. is the historical prior probability, representing the prior probability of personnel traveling to the protection node area corresponding to the i-th Bluetooth beacon device based on historical path movement data, used to reflect the impact of historical paths on path selection; The marginal probability represents the overall probability of completing the current task (independent of the specific beacon location). In the above formula, it serves as a normalization constant to ensure that the sum of the probabilities of all paths is 1.

[0092] For example, if a baggage handler is currently near beacon A in the passageway and simultaneously detects signals from beacon B in the baggage handling area and beacon C in the refueling area, according to the historical path model, the probability of the baggage handler going to the baggage handling area is 90%, while the probability of going to the refueling area is only 5%. Therefore, beacon B is determined to be the most likely target location.

[0093] Considering the above three factors, a weighted scoring mechanism is used to determine a unique and valid trigger beacon, and the beacon's identification code is used as the final trigger data. The scoring formula is as follows:

[0094]

[0095] in, Let be the overall score of the i-th beacon. For signal strength weights, The dwell time detected by the terminal device within the coverage area of ​​the i-th beacon. The longest dwell time detected by the terminal device across all beacon coverage areas. Let be the path probability to the target location. Weighting coefficients (e.g.) ).

[0096] For example, such as Figure 3 As shown, the current terminal device simultaneously detects and receives beacon signals from beacons A, B, and C. The data is as follows:

[0097]

[0098] The backend system uses a multi-beacon arbitration mechanism to determine the trigger data uploaded by the terminal device:

[0099]

[0100] Based on the scoring results, beacon B is determined to be the only valid trigger beacon, and the area of ​​the protection node where Bluetooth beacon device B is located is the target location that the terminal device needs to go to.

[0101] In other embodiments, signal strength weights, dwell time, and path probability can also be represented as scores. For example:

[0102] For signal strength weighting, you can set -60dBm=50 points, -70dBm=40 points, -80dBm=30 points, etc. The higher the RSS1 value, the higher the score.

[0103] For dwell time, you can set a dwell time > 5 seconds as 30 minutes, a dwell time of 2-5 seconds as 20 minutes, and a dwell time < 2 seconds as 10 minutes. The longer the dwell time, the more likely it is to be the actual target location.

[0104] For path probabilities, the probability value can be set to equal the corresponding score, such as 80%=80 points, 50%=50 points, 20%=20 points, etc.

[0105] When personnel using the terminal device enter or leave the signal coverage boundary of any Bluetooth beacon device, the terminal device automatically obtains the identification code of that Bluetooth beacon device and generates trigger data, which is then uploaded to the backend system. The backend system, based on a multi-beacon arbitration mechanism, selects the beacon with the highest score from the trigger data as the final valid trigger beacon.

[0106] Specifically, the trigger data must contain at least the following fields:

[0107] a. Beacon identification code (e.g., PEK-A03-001);

[0108] b. Trigger timestamp (accurate to the second, such as 2024-01-15 16:40:23);

[0109] c. Terminal holder identifier (security personnel ID, such as EMP-10086).

[0110] d. Current support task identifier (task ID, such as TASK-CA3243-BAGGAGE);

[0111] e. Signal strength value (RSSI value, such as -68dBm);

[0112] f. Terminal equipment air pressure data (e.g., 101.3 kPa, used for floor determination).

[0113] g. GPS coordinates of the terminal device (e.g., 40.08 degrees North latitude, 116.58 degrees East longitude, for auxiliary positioning);

[0114] h, Trigger type (enter or leave).

[0115] S3. Upload the trigger data to the backend system.

[0116] The trigger data is packaged into structured data (such as JSON format) and uploaded to the backend system in real time via airport WiFi and mobile networks (4G / 5G). If there is no network connection, the terminal device temporarily stores the trigger data (the number of trigger data entries that can be temporarily stored can be set, such as 1000 entries), and automatically re-uploads the data after the network is restored to ensure that no data is lost.

[0117] The upload protocol uses HTTPS encrypted transmission to ensure data security. The backend system adopts a distributed architecture and supports high-concurrency data reception (supporting 10,000+ terminal devices online simultaneously, processing 50,000+ trigger data entries per second).

[0118] S4. The back-end system connects to the flight dynamic information system in real time, determines the corresponding support node area based on the received trigger data, and associates the currently matched flight information and support task information of the support node area.

[0119] like Figure 4 As shown, after receiving the trigger data, the backend system parses the fields in the trigger data and extracts key information such as beacon identification code, trigger timestamp, support personnel ID, and task ID. Then, it performs the following operations:

[0120] 1. Associate support node areas. By querying the association between beacon identification codes and support task areas, the specific support task area that triggered the event can be determined (such as the baggage handling area of ​​gate A03).

[0121] If the trigger data includes air pressure data, then the floor level (e.g., the third floor, the departure floor) is further determined.

[0122] 2. Real-time connection to flight status information systems. The backend system connects in real-time to the following flight status information systems via API interface or database connection:

[0123] (1) AODB system: used to provide information such as flight schedule, gate position, status (scheduled, delayed, canceled, etc.), and number of passengers;

[0124] (2) Wireless apron system: used to provide information such as flight scheduling, diversion decision, crew information, and special support needs.

[0125] The backend system synchronizes the latest flight status information from the above system every 30 seconds (configurable), including:

[0126] Flight Delay: Delay duration, reason for delay, estimated departure time;

[0127] Flight diversion: Diverted airport, reason for diversion, diverted parking position;

[0128] Flight cancellation: Reason for cancellation, cancellation time;

[0129] Camera location change: original camera location, new camera location, reason for change, and time of change;

[0130] Flights advanced: duration of advance, reason for advance;

[0131] Temporary overtime: Overtime flight number, gate, and departure time.

[0132] 3. Adaptive matching of flights and support tasks. Based on the support task ID in the trigger data, query the flight information associated with the task (e.g., flight number CA3243), and combine it with the latest flight status information to perform adaptive task adjustments.

[0133] In a specific embodiment, the background system receives flight delay information: when it detects that flight CA3243 is delayed by 30 minutes (e.g., the original scheduled departure time is 17:10, and the delayed departure time is 17:40), the background system performs the following operations:

[0134] a. Automatically postpone the planned time of all support tasks for the flight: For example, the baggage handling task originally scheduled for 16:40 will be adjusted to 17:10, and the refueling task originally scheduled for 17:00 will be adjusted to 17:30.

[0135] b. Recalculate resource scheduling priority: Delayed flights are given higher priority to ensure that the support tasks for delayed flights are executed first.

[0136] c. Push task adjustment notification to relevant support personnel: Push the message "Your flight CA3243 is delayed by 30 minutes, and the baggage loading and unloading task will be adjusted to 17:10" to the terminal device.

[0137] d. In a preferred embodiment, the back-end system is also equipped with a visual interface: at this time, the status of flight CA3243 can be marked as yellow (delayed) on the operation control command screen, and the planned time of the support mission can be updated.

[0138] In a specific embodiment, the backend system receives flight diversion messages: when it detects that flight CA3243 has diverted (original gate A03, diverted to gate B12), the backend system performs the following operations:

[0139] a. Automatically identify the alternate landing position B12 and query the beacon equipment identification code associated with the support node area corresponding to position B12.

[0140] b. Transfer the support tasks from the original gate A03 to the alternate gate B12: For example, update the baggage handling tasks from beacon PEK-A03-005 in the support node area corresponding to gate A03 to beacon PEK-B12-003 in the support node area corresponding to gate B12.

[0141] c. Update the task list of support personnel: reassign the support personnel responsible for station A03 to station B12.

[0142] d. Notify relevant support personnel to proceed to the new gate: Push the message "Flight CA3243 under your responsibility has been diverted to gate B12. Please proceed to gate B12 to perform baggage handling tasks." to the terminal equipment.

[0143] e. Release the resources of the original gate A03 and allocate gate A03 to other flights.

[0144] In a specific embodiment, the backend system receives a flight cancellation message: when flight CA3243 is detected to be cancelled, the backend system performs the following operations:

[0145] a. Release the support resources occupied by the flight: including support personnel, support equipment (such as baggage trolleys and refueling trucks), and gate A03.

[0146] b. Stop processing related task data: After receiving the trigger data for the support tasks related to flight CA3243, the background system treats it as invalid data.

[0147] c. Reassign the support personnel for this flight to other flights: for example, reassign the support personnel originally responsible for baggage handling on CA3243 to flight MU5137.

[0148] d. Notify relevant support personnel of mission cancellation: Push a message to the terminal device: "Your flight CA3243 has been cancelled, and you have been reassigned to flight MU5137."

[0149] In a specific embodiment, the backend system receives a message about a change in gate position: when it detects a change in gate position for flight CA3243 (from gate A03 to gate A05), the backend system performs the following operations:

[0150] a. Automatically update the beacon device identification code associated with the support task: update the baggage handling task from beacon PEK-A03-005 in the support node area corresponding to gate A03 to beacon PEK-A05-002 in the support node area corresponding to gate A05.

[0151] b. Ensure that subsequent trigger data can be correctly associated with the changed gate position: When the support personnel trigger the system at beacon PEK-A05-002 in the support node area corresponding to gate position A05, the system can correctly identify it as the baggage loading and unloading task for flight CA3243.

[0152] c. Notify relevant support personnel of the gate change: Push the message "The gate for flight CA3243 you are responsible for has been changed to A05. Please proceed to gate A05 to perform your duties" to the terminal device.

[0153] 4. Link flight and support mission information. Link trigger data, support node area, flight information (updated according to dynamic information), and support mission information to generate a complete support mission data record.

[0154] For example: On January 15, 2024, at 16:40:23, support staff member Zhang San (EMP-10086) performed baggage handling for flight CA3243 (delayed by 30 minutes, rescheduled time 17:10) at the baggage handling area (beacon PEK-A03-005) of gate A03. The actual trigger time was 16:40:23, which was 30 minutes earlier than the rescheduled time of 17:10.

[0155] 5. Data Storage. The backend system stores the received trigger data in a structured manner into a database (supporting MySQL, PostgreSQL, MongoDB, etc.) for querying and analysis by dimensions such as flight, task, gate, and time.

[0156] In a preferred embodiment, the backend system associates the identity code and trigger timestamp in the triggered data with the current corresponding protection plan time (i.e., the plan time has been adjusted in real time according to flight dynamic information), calculates the deviation between the actual trigger time and the currently adjusted protection plan time, and triggers a graded warning when the deviation exceeds the threshold.

[0157] For example, the baggage handling for flight CA3243 was originally scheduled for 16:40, but due to a 30-minute delay, the rescheduled time was 17:10. The actual start time for support personnel was 17:22, a deviation of 12 minutes (later than the scheduled time).

[0158] Based on a preset deviation threshold, for example, the deviation threshold is set as follows:

[0159] Deviation of 5-10 minutes: The backend system triggers a yellow warning, indicating "Timeout is imminent";

[0160] Deviation of 10-15 minutes: The background system triggers an orange alert, prompting "Timeout has occurred, progress needs to be accelerated";

[0161] If the deviation exceeds 15 minutes: the backend system triggers a red alert, indicating "serious timeout, urgent handling required".

[0162] In this example, the deviation was 12 minutes, triggering an orange alert. The alert information can be displayed on the visual interface and pushed to the operations control center and relevant management personnel (such as the ground support department manager) in real time, showing "The baggage handling task for flight CA3243 has exceeded the time limit by 12 minutes, please pay attention".

[0163] In a preferred embodiment, the visualization interface is used to display the real-time progress status of the flight ground support process based on trigger data. For example, the visualization interface uses a timeline or Gantt chart to display the support task progress of each flight based on the deviation between the actual completion time and the planned time, enabling commanders to have a clear overview of the overall support progress.

[0164] For example, progress status is indicated by different colors to indicate whether it is completed on time, about to time out, or has already timed out:

[0165] Green indicates that the task was completed on time (the actual trigger time deviates from the planned time by less than 5 minutes).

[0166] Yellow indicates that timeout is imminent (deviation 5-10 minutes);

[0167] Orange indicates that the timeout has occurred (with a deviation of 10-15 minutes).

[0168] Displayed in red: indicates a serious timeout (deviation exceeding 15 minutes).

[0169] In a preferred embodiment, the backend system also performs statistical analysis based on the received trigger timestamp to generate a guarantee efficiency analysis report that includes at least one of the following indicators.

[0170] a) Node coverage

[0171] Node coverage rate = Number of protected node areas collected / Total number of protected node areas to be collected × 100%.

[0172] For example, if a flight should collect data from 8 support nodes (airport arrival, jet bridge, baggage handling, refueling, catering, cleaning, boarding, and pushback), but only 7 support nodes are actually collected (the cleaning node is missing), then the node coverage rate = 7 / 8 × 100% = 87.5%.

[0173] b. Timeliness

[0174] Timeliness rate = Number of guaranteed node areas completed on time according to the dynamically adjusted planned time / Total number of guaranteed node areas × 100%.

[0175] For example, if 6 out of 8 support points for a flight are completed on time (with a deviation of less than 5 minutes), and 2 support points are overdue, then the on-time rate = 6 / 8 × 100% = 75%.

[0176] c. Accuracy

[0177] Accuracy rate = the percentage of personnel and tasks that correctly match the support plan × 100%.

[0178] For example, if the personnel at 7 out of 8 support points for a flight are consistent with the support plan (e.g., baggage handling is done by designated baggage handlers), and the personnel at 1 support point are inconsistent with the support plan (e.g., baggage handling is done by other temporarily assigned personnel), then the accuracy rate = 7 / 8 × 100% = 87.5%.

[0179] d. Flight dynamic adaptability rate

[0180] Flight dynamic adaptability rate = Number of times the system successfully responded to flight changes and dynamically adjusted related support tasks / Total number of flight dynamic changes × 100%.

[0181] For example, if there are 100 flight dynamic changes on a certain day (including delays, diversions, cancellations, gate changes, etc.), and the system successfully responds and adjusts the task 95 times (5 times it fails to respond in time due to system delays or data errors), then the flight dynamic adaptability rate = 95 / 100 × 100% = 95%.

[0182] These indicators enable airport operations control departments to comprehensively analyze trigger data, fully assess flight support efficiency, identify weaknesses, and formulate improvement measures. The back-end system, through deviation warnings, efficiency analysis, and visualization, provides strong data support for real-time scheduling and process optimization in the airport operations control command center, offering efficient and reliable technical means to improve the refined and intelligent management of flight ground support.

[0183] In a specific implementation, the present invention also provides a system for automatically collecting flight support node time data based on a Bluetooth beacon device to implement the above method, comprising:

[0184] Bluetooth beacon devices are deployed in various support node areas. The Bluetooth beacon devices are associated with the corresponding support node areas through identity codes, and support dynamic adjustment of transmission power (adjustable from 5 to 15 meters) and dynamic configuration of signal strength trigger threshold (adjustable from -50 to -85dBm).

[0185] The terminal equipment configured for support personnel integrates a Bluetooth module, a barometer sensor, and a GPS module. It is configured to automatically obtain the identification code of any Bluetooth beacon device and generate trigger data when entering or leaving the signal coverage boundary of any Bluetooth beacon device (determined according to the multi-beacon arbitration mechanism).

[0186] The back-end processing platform adopts a distributed architecture and supports high-concurrency data reception (10,000+ terminal devices online simultaneously, processing 50,000+ trigger data per second). It is used to receive trigger data, connect in real time to flight dynamic information systems such as AODB, A-CDM, and CDM, and adaptively match the current flight and support task information corresponding to the support node area based on the trigger data and flight dynamic information, and perform functions such as task adjustment, resource scheduling, and early warning push.

[0187] The present invention has the following technical effects:

[0188] 1. Differentiated deployment strategy enhances environmental adaptability: Differentiated beacon deployment solutions (including optimization of parameters such as beacon spacing, transmission power, trigger threshold, and deployment density) are provided for different types of airports, such as single-story, multi-story, super-large hubs, and underground areas. This effectively solves problems such as signal interference, floor confusion, and signal attenuation in complex environments, and improves the positioning accuracy from 75-80% of traditional methods to 95-99.5%, significantly improving the system's environmental adaptability and deployment flexibility.

[0189] 2. Multi-beacon arbitration mechanism improves positioning accuracy: Through the comprehensive scoring of signal strength weighting, dwell time determination and historical path prediction, the positioning conflict and false triggering problems in signal overlap areas are effectively solved, and the positioning accuracy in signal overlap areas is increased from 75-80% of the traditional method to more than 95-97%, which significantly improves the accuracy and reliability of data collection.

[0190] 3. Improved Efficiency Through Dynamic Flight Matching: By connecting in real time with flight dynamic systems such as AODB, A-CDM, and wireless apron (with a 30-second synchronization cycle), automatic response (task adjustment within 1-2 minutes) and adaptive task adjustment are achieved for scenarios such as flight delays, diversions, cancellations, and gate changes. This avoids the delays (10-20 minutes for traditional methods) and errors (20-30% error rate for traditional methods) caused by manual intervention, improving flight support efficiency by 20-40% (depending on different scenarios and airport types), with a flight dynamic adaptation rate of 95-98%.

[0191] 4. Fully automated data acquisition eliminates reliance on manual labor: By using the signal coverage boundary triggering mechanism of Bluetooth beacon devices, the complex flight ground support task process is discretized into a series of automatically recordable time point events, realizing automatic and accurate capture of each node in the flight ground support process (node ​​coverage rate of 98-99.5%), eliminating reliance on manual operation (saving more than 80% of manual recording costs), reducing data acquisition latency from the traditional 5-10 minutes to real-time (upload within 1 second after triggering), and achieving high acquisition accuracy.

[0192] 5. Real-time data supports precise decision-making: Triggered data upload in real time (within 1 second), the backend system can immediately and accurately associate the time point with specific support task nodes, gate positions, flights and tasks, and adjust in real time according to flight dynamic information (30-second synchronization cycle), realizing real-time data availability. This provides strong data support for real-time scheduling and process optimization in the airport operations control center, and supports functions such as visualization (green / yellow / orange / red status indicators), graded early warning (yellow / orange / red warning), and efficiency analysis (node ​​coverage, timeliness, accuracy, flight dynamic adaptability, etc.).

[0193] 6. Low cost and easy to promote: Compared with complex continuous positioning systems (such as UWB, indoor GPS, etc., which are costly and complicated to deploy), this invention uses simple Bluetooth beacon devices (the cost of a single device is 50-100 yuan, and the deployment is simple), with low hardware costs (the total cost is only 30-50% of that of traditional systems), and simple deployment and maintenance (no need for complex wiring and base station construction). It is particularly suitable for large-scale promotion in various airports, and the investment return cycle is short (usually the cost can be recovered in 1-2 years).

[0194] This specification provides method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one of many possible execution orders and does not represent the only possible execution order. In actual system or server product execution, the method can be executed in the order shown in the embodiments or drawings, or in parallel (e.g., in a parallel processor or multi-threaded processing environment), or the execution order of steps without timing constraints can be adjusted.

[0195] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for automatically collecting flight support node data based on Bluetooth beacon devices, characterized in that the method... The method comprises the following steps: S1, installing a Bluetooth beacon device at each support node area of the flight ground support process, and each Bluetooth beacon device is associated with a corresponding support node area through an identity code; S2, configuring a terminal device supporting Bluetooth communication for support personnel, the terminal device is configured to automatically obtain the identity code of the Bluetooth beacon device and generate trigger data when entering or leaving the signal coverage boundary of any Bluetooth beacon device; S3, uploading the trigger data to a background system; S4, the background system is connected to a flight dynamic information system in real time, and determines the corresponding support node area according to the received trigger data, and dynamically adjusts the execution plan and / or resource binding relationship of the support task associated with the support node area according to the real-time flight dynamic information obtained from the flight dynamic information system; When the terminal device simultaneously receives the beacon signals of multiple Bluetooth beacon devices, the background system uses a multi-beacon arbitration mechanism to determine the real corresponding support node area of the terminal device; The multi-beacon arbitration mechanism comprises: calculating the signal strength weight of each beacon; calculating the residence time of the terminal device in the coverage range of each beacon; based on the historical movement path of the terminal device and the current task type, calculating the probability of the terminal device going to the position where the Bluetooth beacon device receiving the beacon signal is located; comprehensive signal strength weight, residence time, probability value, determine the real corresponding support node area of the terminal device through the weighted scoring formula; the weighted scoring formula is: wherein, is the comprehensive score of the i-th beacon; is the signal strength weight of the i-th Bluetooth beacon device; is the stay duration within the coverage of the i-th beacon; is the longest stay duration within the coverage of all beacons; is the weight coefficient; is the path probability to the target location, calculated using the Bayes' theorem, which is as follows: wherein, is a candidate Bluetooth beacon device, Task is a current task type, and History is a historical path; is a target posterior probability, representing a probability of the safety personnel going to a safety node area corresponding to the i-th Bluetooth beacon device given the current task type and the historical path; is a task completion likelihood probability, representing a probability of being able to complete the current task type in the safety node area corresponding to the i-th Bluetooth beacon device; is a historical prior probability, representing a prior probability of the safety personnel going to the safety node area corresponding to the i-th Bluetooth beacon device based on historical path movement data; is a marginal probability, representing an overall probability of completing the current task.

2. The method of claim 1, wherein, The trigger data at least further includes a trigger timestamp, a terminal holder identifier, a current support task identifier, a signal strength value, and terminal device air pressure data.

3. The method of claim 1, wherein, The flight dynamic information includes at least one of flight delay, flight diversion, flight cancellation, flight change, flight advance, and temporary overtime.

4. The method of claim 1, wherein, The background system associates the current corresponding support plan time according to the identity code and trigger timestamp in the trigger data, calculates the deviation between the actual trigger time and the current support plan time, and triggers a hierarchical early warning when the deviation exceeds a threshold.

5. The method of claim 1, wherein, The background system also generates a support efficiency analysis report containing at least one of the following indicators: node coverage, timeliness, accuracy, and flight dynamic adaptability rate; Node coverage = number of collected support node areas / total number of support node areas that should be collected × 100%; Timeliness = number of support node areas completed on time according to the plan time after dynamic adjustment / total number of support node areas × 100%; Accuracy = proportion of support personnel and support tasks correctly corresponding to the support plan × 100%; Flight dynamic adaptability rate = number of times the system successfully responds to flight changes and dynamically adjusts the associated support task / total number of flight dynamic changes × 100%.

6. The method of claim 1, wherein, S1 also includes: formulating a differentiated Bluetooth beacon device deployment scheme according to the type of the airport; the Bluetooth beacon device deployment scheme comprises: For single-story airports, a horizontally distributed Bluetooth beacon device deployment scheme is adopted to seamlessly cover the Bluetooth beacon signal for each support node area; For multi-floor airport, the transmission power of the Bluetooth beacon device on different floors is set differently, and an air pressure gauge assisted positioning is configured in the terminal device; For hub airport, a zoning and grading Bluetooth beacon device deployment scheme is adopted, dense deployment is adopted in the area with large node area distribution density, and sparse deployment is adopted in the area with small node area distribution density; For underground area, the transmission power of the Bluetooth beacon device is increased and the deployment density of the Bluetooth beacon device is increased.

7. A system for automatically collecting flight support node time data based on a Bluetooth beacon device for implementing the method of any one of claims 1-6, characterized in that, It comprises: Bluetooth beacon devices deployed in each node area, the Bluetooth beacon device is associated with the corresponding node area through identity coding; The terminal device configured for the support personnel, the terminal device is configured to automatically obtain the identity code of the Bluetooth beacon device and generate trigger data when entering or leaving the signal coverage boundary of any Bluetooth beacon device; A background processing platform is used to real-time interface with a flight dynamic information system, determine the corresponding node area according to the received trigger data, and dynamically adjust the execution plan and / or resource binding relationship of the support task associated with the node area according to the real-time flight dynamic information obtained from the flight dynamic information system; The flight dynamic information includes at least one of flight delay, flight diversion, flight cancellation, flight change, flight advance, and temporary overtime.

Images