Airport equipment global trusted positioning drift determination method and system

Abstract

The application discloses an airport equipment global trusted positioning drift judgment method and system, and provides a systematic solution, which is no longer simply dependent on positioning data at a single moment, but comprehensively judges in combination with historical trajectories and trust levels, and can adapt to complex environmental changes of the airport by introducing a multi-state machine model, so that trajectory abnormalities caused by positioning drift are effectively reduced, and the continuity and stability of positioning output are improved.

Description

Technical Field

[0001] This invention relates to the field of equipment positioning, and in particular to a method and system for determining reliable positioning drift of airport equipment across the entire area. Background Technology

[0002] The efficient scheduling and safety management of airport ground equipment heavily rely on its real-time location information. While widely used satellite positioning terminals perform well in open areas, they are susceptible to multipath effects and attenuation in obstructed areas such as hangars, maintenance sheds, and under jet bridges. This leads to significant location drift, often reaching 30-50 meters, and in severe cases exceeding 100 meters, or even temporary loss of lock-on. This can cause misjudgments, such as equipment appearing to be parked on the tarmac when it is actually in the hangar, seriously impacting scheduling efficiency and safety monitoring.

[0003] In summary, a reliable global positioning drift determination method and system for airport equipment is needed to address the shortcomings of existing technologies. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for determining reliable global positioning drift of airport equipment, aiming to solve the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for determining the global reliable positioning drift of airport equipment, comprising the following steps:

[0006] Step S1: Obtain the credibility level by acquiring the positioning data of the airport ground equipment in real time and calculating the credibility level of the positioning point at each moment based on the signal quality parameters.

[0007] Step S2: Define the state set and state transition rules of the multi-state machine. The multi-state machine model contains at least four states: open area high confidence mode, occluded area evaluation mode, suspected drift pending judgment mode and signal loss area maintenance mode, and configure the entry conditions and output strategies for each state.

[0008] Step S3: Execute the trajectory processing strategy. Based on the confidence level and the historical motion trajectory of the device, drive the multi-state machine to switch between different states and execute the corresponding trajectory processing strategy in each state.

[0009] Step S4: Collect low-confidence location data. When a low-confidence location point is detected, open a look-ahead time window or quantity window, wait for and collect subsequent location data.

[0010] Step S5: Update the output trajectory. Based on the spatial clustering and physical motion logic consistency of the positioning data within the window, comprehensively determine whether the low-confidence positioning point is a valid moving point or a random drifting point, and update the device's output trajectory accordingly.

[0011] Optionally, step S1 is implemented in the following manner:

[0012] Step S11: Data acquisition and real-time calculation. Receive raw NMEA data or binary data frames output by the satellite positioning module in real time, parse the horizontal precision factor value from the data frames, and parse the velocity information of the current point.

[0013] Step S12: Determine the confidence level. Based on the horizontal precision factor value and the preset threshold rules, classify the current location point to obtain the confidence level of the current location point.

[0014] Step S13: Execute the action by attaching the calculated confidence level as an attribute to the current location data packet for subsequent state machine logic calls.

[0015] Optionally, the four states in step S2 are implemented in the following ways:

[0016] High-reliability mode for open areas: This mode is activated when N high-reliability positioning points are acquired consecutively, and the real-time coordinate trajectory is directly output.

[0017] Occlusion area assessment mode: This mode is activated when a medium-confidence location point appears and the device approaches a known occlusion area, and the trajectory is evaluated in conjunction with the motion model.

[0018] Suspected drift pending judgment mode: Entered when a low-confidence location point is detected, without immediately making a final judgment on the current jump point;

[0019] Signal Loss Area Holding Mode: Entered when the positioning signal is lost, holding and outputting the last reliable position.

[0020] Optionally, the suspected drift determination mode is implemented in the following ways:

[0021] When a significant spatiotemporal jump is detected at a location point, it is marked as a reference point to be determined. An analysis window is opened, and the adoption or discarding of the reference point to be determined is paused. The subsequent M location points are then obtained in advance to form a data set.

[0022] A comprehensive judgment is made based on the spatial clustering and physical-logical consistency of the data set:

[0023] If all subsequent points point to the same reasonable area and the HDOP value improves, then the jump point is determined to be a valid move, and the jump point is adopted and converted.

[0024] If subsequent points are scattered and HDOP values ​​continue to deteriorate, the transition point is determined to be a random drift, and the transition point and intermediate abnormal points are discarded.

[0025] Optionally, step S4 is implemented in the following manner:

[0026] Step S41: Condition triggering. When the system is in the occlusion area evaluation mode or the signal loss area holding mode, if a low-confidence positioning point is detected with a horizontal accuracy factor greater than the set threshold or a speed change exceeding the physical limit, the point is marked as a reference point to be judged and the look-ahead observation window is opened to wait for the collection of subsequent positioning data.

[0027] Step S42: Open the look-ahead window, mark the detected low-confidence positioning points as reference points to be determined, and open the look-ahead observation window to wait for the collection of subsequent positioning data;

[0028] Step S43: Wait and collect data. During the observation window, continuously receive and cache subsequent positioning data points and collect future data for comprehensive analysis of the properties of the reference point to be determined.

[0029] Optionally, step S5 is implemented in the following manner:

[0030] Step S51: Spatial clustering and physical logic analysis, extract subsequent positioning data within the forward observation window, calculate its spatial distribution, velocity and acceleration relative to the reference point to be determined, and compare the obtained motion parameters with the physical motion constraints of the airport ground equipment;

[0031] Step S52: Combine judgment and trajectory update. If the subsequent positioning points are spatially concentrated and meet the physical motion constraints, they are judged as valid moving points and included in the trajectory, and the state machine is updated. Otherwise, they are judged as random drifting points, removed, and the reference point to be judged is reset to repeat the judgment process.

[0032] Optionally, the comprehensive determination in step S52 is implemented in the following manner:

[0033] If the subsequent positioning points are spatially concentrated relative to the low-confidence positioning points, and the calculated velocity and acceleration conform to the physical motion constraints of the airport ground equipment, then the low-confidence positioning point is determined to be a valid moving point and is included in the trajectory output.

[0034] If the spatial distribution of subsequent positioning points relative to the low-confidence positioning points is discrete and cannot form a movement trend that conforms to physical logic, then the low-confidence positioning point is determined to be a random drift point and is removed.

[0035] Optionally, the method further includes introducing a low-cost acceleration module and a vibration detection module for collaborative assistance, wherein the collaborative assistance specifically includes:

[0036] When the acceleration module detects that the device has changed from motion to a stationary state, and the vibration detection module does not capture vibration feedback within a set time, the device is determined to be in an absolutely stationary state.

[0037] In the state of absolute stillness, the device's position output is locked, and any position drift points received during this period are discarded.

[0038] An airport equipment global reliable positioning drift determination system, employing the aforementioned airport equipment global reliable positioning drift determination method, includes a data acquisition and reliability assessment module, a multi-state machine management module, a look-ahead window and data caching module, a comprehensive determination and trajectory update module, and an auxiliary sensor collaboration module;

[0039] The data acquisition and credibility assessment module is responsible for receiving raw data from the satellite positioning module in real time, parsing out key parameters, calculating and determining the credibility level of each positioning point, and attaching the credibility level as a tag to the positioning data packet.

[0040] The multi-state machine management module is used to drive the state machine to switch between different modes based on the credibility level transmitted from the data acquisition and credibility assessment module and the historical trajectory of the device, and to call the corresponding trajectory processing strategy for the current state.

[0041] The look-ahead window and data caching module is used to handle suspicious location points. When the system detects a low-confidence location point and enters the suspected drift pending judgment mode, this module will be triggered to open a look-ahead window in terms of time or quantity. During this period, the final judgment on the suspicious point is suspended, and subsequent location data is continuously received and cached.

[0042] The comprehensive judgment and trajectory update module is used to comprehensively determine whether the low-confidence positioning point is a valid moving point or a random drifting point based on the spatial clustering and physical motion logic consistency of the positioning data within the window, and update the output trajectory of the device accordingly.

[0043] The auxiliary sensor coordination module determines that the device is in an absolutely stationary state when the acceleration module detects that the device has changed from motion to a stationary state and the vibration detection module has not captured vibration feedback within a set time. In the absolutely stationary state, the device's position output is locked and any positioning drift points received during this period are discarded.

[0044] The beneficial effects of this invention are:

[0045] 1. This invention provides a systematic solution that no longer relies solely on location data at a single moment, but instead combines historical trajectory and reliability level for comprehensive judgment. By introducing a multi-state machine model, it can adapt to the complex environmental changes at airports, effectively reduce trajectory anomalies caused by location drift, and improve the continuity and stability of location output.

[0046] 2. This invention achieves real-time cleaning and grading of raw positioning data. By analyzing the horizontal accuracy factor HDOP and velocity information, low-quality data can be quickly identified, providing accurate decision-making basis for subsequent state machine switching and avoiding the direct use of obviously erroneous data for trajectory calculation;

[0047] 3. This invention solves the problem of difficulty in determining sudden changes in trajectory. By opening the analysis window and observing subsequent points, spatial clustering is used to distinguish between real rapid movement and random drift, effectively preventing trajectory changes caused by single-point signal interference, while preserving the possibility of real movement.

[0048] 4. This invention introduces a time-for-space strategy. When a low-confidence point is detected, the result is not output immediately, but rather the system waits and collects subsequent data. This proactive approach allows the system to have more information for decision-making, significantly improving the accuracy of the judgment. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of a method flow of the present invention.

[0050] Figure 2 This is a schematic diagram of step S1 of the present invention.

[0051] Figure 3 This is a schematic diagram of step S4 of the present invention.

[0052] Figure 4 This is a schematic diagram of step S5 of the present invention.

[0053] Figure 5 This is a schematic diagram of a system structure according to the present invention.

[0054] Figure 6 This is a schematic diagram of a state transition according to the present invention. Detailed Implementation

[0055] To more clearly illustrate the technical solutions in the embodiments of the invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] like Figures 1 to 4 As shown, a reliable global positioning drift determination method for airport equipment includes the following:

[0057] Step S1: Obtain the credibility level by acquiring the positioning data of the airport ground equipment in real time and calculating the credibility level of the positioning point at each moment based on the signal quality parameters.

[0058] Implemented in the following ways:

[0059] Data acquisition and real-time calculation: receive raw NMEA data or binary data frames output by the satellite positioning module in real time, parse the horizontal precision factor value HDOP from the data frame. HDOP is a key indicator for measuring the impact of satellite geometric distribution on positioning accuracy, and parse the velocity information of the current point.

[0060] The credibility level is determined by classifying the current location point according to the horizontal precision factor value and the preset threshold rules to obtain the credibility level of the current location point.

[0061] The action is executed, and the calculated confidence level is attached as an attribute to the current location data packet for subsequent state machine logic calls.

[0062] Step S2: Define the state set and state transition rules of the multi-state machine. The multi-state machine model contains at least four states: open area high confidence mode, occluded area evaluation mode, suspected drift pending judgment mode and signal loss area maintenance mode, and configure the entry conditions and output strategies for each state.

[0063] Implemented in the following ways:

[0064] State A: High-reliability mode for open areas

[0065] Entry requirements: Acquire N consecutive L1-level high-reliability positioning points. N is a configurable parameter, with a reference range of 3-10 points at a 1Hz sampling frequency.

[0066] Decision and output: When the system is in the optimal positioning state, it directly reports real-time coordinates, and the trajectory is continuous and smooth.

[0067] State B: Occlusion Area Assessment Mode

[0068] Entry conditions: An L2 level location appears, and the historical trajectory indicates that the device is approaching or entering a known obstruction geofence, such as a hangar entrance.

[0069] Decision and output: If the spatial clustering of the point set is good and there is no abnormal jump in the displacement of adjacent points greater than 20m / s, the positioning reliability is considered average, but the path can still be output normally.

[0070] If a significant spatiotemporal jump is detected, such as a speed exceeding 20 m / s, then state C is triggered.

[0071] Status C: Suspected drift pending judgment mode

[0072] Entry condition: An L3 low-confidence point is detected in state B or state D.

[0073] Decision and output: The system does not immediately make a judgment on the suspicious point, but sets it as a "reference point to be judged" and opens an observation window to wait for the subsequent M positioning points, where M is a configurable parameter.

[0074] Determining it as a valid movement: If the displacement and HDOP value of the subsequent M points calculated based on the reference point can both be determined to be L1 / L2 level, then the jump is determined to be a valid movement. If the equipment moves out of the obstruction area, the jump point and subsequent points are adopted, and the status A or B is returned according to the HDOP value.

[0075] Random drift is identified as follows: If an L3 level point is detected again within the observation window, the previous "reference point to be determined" is identified as a drift point and removed. The system will update the last transition point as the new "reference point to be determined" and repeat this determination process.

[0076] State D: Signal Loss Area Holding Mode

[0077] Entry condition: The positioning signal continues to lose lock, entering the L4 level state.

[0078] Decision and output: Maintain the last reliable historical location point, at least at level L2, and clearly mark the signal loss.

[0079] When the signal is recaptured, a non-causal window judgment mechanism similar to that in state C is adopted to cross-validate the new point sequence with the expected position of the region. By updating the trajectory in the back, misjudgment caused by single-point jumps is avoided.

[0080] Step S3: Execute the trajectory processing strategy. Based on the confidence level and the historical motion trajectory of the device, drive the multi-state machine to switch between different states and execute the corresponding trajectory processing strategy in each state.

[0081] Step S4: Collect low-confidence location data. When a low-confidence location point is detected, open a look-ahead time window or quantity window, wait for and collect subsequent location data.

[0082] Implemented in the following ways:

[0083] This step is mainly triggered when the system enters state C, a suspected drift pending judgment mode. Its core purpose is to postpone the judgment and wait for more evidence.

[0084] Triggering condition: When the system is in State B occlusion area evaluation mode or State D signal loss area maintenance mode, an L3 low confidence location point is detected.

[0085] This L3 level point typically exhibits the following characteristics: HDOP > 2, or the calculated velocity mutation exceeds the physical limit, such as > 20 m / s.

[0086] Open the look-ahead window: The system does not immediately make a final decision on whether to adopt or discard the L3 point, but marks the L3 point as a reference point to be determined.

[0087] Simultaneously, a forward-looking observation window is opened. This window can be a time window, such as waiting for data in the next few seconds; or a quantity window, such as waiting for the next M location points, where M is a configurable parameter.

[0088] Waiting and collecting data: During the observation window, the system continuously receives and caches subsequent positioning data points. The purpose of this stage is to collect enough future data to conduct a comprehensive analysis of the nature of the reference point to be determined, which is a manifestation of a non-causal system.

[0089] Step S5: Update the output trajectory. Based on the spatial clustering and physical motion logic consistency of the positioning data within the window, comprehensively determine whether the low-confidence positioning point is a valid moving point or a random drifting point, and update the output trajectory of the device accordingly.

[0090] Implemented in the following ways:

[0091] Spatial clustering and physical logic analysis: The system extracts subsequent positioning data collected within the forward observation window, calculates the spatial distribution of subsequent positioning points relative to the reference point to be determined, and calculates their velocity and acceleration by combining the HDOP value obtained at the same time. The calculated motion parameters are then compared with the physical motion constraints of the airport ground equipment.

[0092] Based on the comprehensive judgment and trajectory update, and according to the analysis results, the system executes one of the following two judgment branches:

[0093] Branch 1: Determined as a valid movement point

[0094] Judgment criteria: If the spatial distribution of subsequent positioning points is concentrated relative to low-confidence positioning points, and the calculated velocity and acceleration conform to the physical motion constraints of airport ground equipment.

[0095] Action to be performed: Determine that the low confidence positioning point is a valid movement point. If the device normally moves out of the obstruction area, include it in the trajectory output, and return the state machine to state A (open area high confidence mode) or state B (obstruction area evaluation mode) according to the HDOP value.

[0096] Branch 2: Determined as a random drift point

[0097] Judgment criteria: If the spatial distribution of subsequent positioning points is discrete relative to low-confidence positioning points and cannot form a movement trend that conforms to physical logic, for example, if an L3 level point is detected again within the observation window.

[0098] Action performed: Determine the low-confidence location point as a random drift point and remove it. The system will update the last jump point as the new reference point to be determined and repeat this determination process.

[0099] like Figure 5 As shown, an airport equipment global reliable positioning drift determination system adopts the airport equipment global reliable positioning drift determination method, including a data acquisition and reliability assessment module, a multi-state machine management module, a look-ahead window and data caching module, a comprehensive determination and trajectory update module, and an auxiliary sensor collaboration module;

[0100] The data acquisition and credibility assessment module is responsible for receiving raw data from the satellite positioning module in real time, parsing out key parameters, calculating and determining the credibility level of each positioning point, and attaching the credibility level as a tag to the positioning data packet.

[0101] The multi-state machine management module is used to drive the state machine to switch between different modes based on the credibility level transmitted from the data acquisition and credibility assessment module and the historical trajectory of the device, and to call the corresponding trajectory processing strategy for the current state.

[0102] The look-ahead window and data caching module is used to handle suspicious location points. When the system detects a low-confidence location point and enters the suspected drift pending judgment mode, this module will be triggered to open a look-ahead window in terms of time or quantity. During this period, the final judgment on the suspicious point is suspended, and subsequent location data is continuously received and cached.

[0103] The comprehensive judgment and trajectory update module is used to comprehensively determine whether the low-confidence positioning point is a valid moving point or a random drifting point based on the spatial clustering and physical motion logic consistency of the positioning data within the window, and update the output trajectory of the device accordingly.

[0104] The auxiliary sensor coordination module determines that the device is in an absolutely stationary state when the acceleration module detects that the device has changed from motion to a stationary state and the vibration detection module has not captured vibration feedback within a set time. In the absolutely stationary state, the device's position output is locked and any positioning drift points received during this period are discarded.

[0105] Specifically, it includes the following:

[0106] The system consists of a positioning terminal (including a satellite positioning module, communication module, and MCU) and a cloud / local monitoring platform. The terminal is responsible for raw positioning data acquisition, local real-time analysis, and scheduled online reporting; the platform is responsible for big data analysis, state machine algorithm optimization, and trajectory backtracking. The core processing flow is as follows:

[0107] Multi-level data credibility assessment, with the terminal calculating the credibility level (L1~L4) of the location point in real time:

[0108] L1 High Confidence: HDOP≤1, speed change is continuous and less than the threshold, such as 30km / h.

[0109] In L2, the HDOP is between 1 and 2, with slight speed jumps that can be explained by the motion model.

[0110] L3 Low Trust: HDOP>2, or velocity mutation exceeds the threshold (set to 20m / s for ground equipment application scenarios), which may be a drift point.

[0111] L4 Loss of Lock: No valid satellite positioning data available; only used as a reference for state transition.

[0112] Intelligent trajectory management based on multi-state machines and non-causal decision-making

[0113] The core of this invention lies in introducing a multi-state decision machine with forward-looking, non-causal judgment capabilities to manage the trajectory output of the device under different positioning qualities. This state machine includes at least the following states, and its transition logic is as follows: Figure 6 As shown, each state can maintain itself as long as the condition for its continuation is met.

[0114] Specific coordination and recovery mechanisms between different states

[0115] State A: Open Area High-Confidence Mode

[0116] Entry condition: Continuously acquire N (N is an integer greater than 1, the parameter is configurable, at a positioning point sampling frequency of 1 Hz, the reference range is 3-10) L1 level high-confidence positioning points.

[0117] Decision and output: When the system is in the optimal positioning state, it directly reports real-time coordinates, and the trajectory is continuous and smooth.

[0118] State B Occlusion Area Assessment Mode:

[0119] Entry conditions: An L2 level location appears, and the historical trajectory indicates that the device is approaching or entering a known obstructed geofence, such as a hangar entrance.

[0120] Decision and Output: If a point within the set only has a high HDOP value, but adjacent positioning points do not exhibit a drift greater than 20m, then its spatial clustering is considered good. In this case, the positioning reliability is judged to be average, but close to the actual path, and the path can be output normally. If a significant spatiotemporal jump occurs within the set, such as the calculated speed from point Pt to Pt+1 exceeding 20m / s, which airport ground equipment typically cannot achieve, then state C is triggered.

[0121] State C: Suspected drift pending judgment mode

[0122] Entry condition: An L3 level point is detected in state B or D.

[0123] Decision and Output: The system enters the "Non-causal" analysis window and no longer makes immediate judgments on individual suspicious points. The system does not immediately discard or adopt the L3 level points received when entering the state, but instead sets them as "reference points to be judged" and observes the distribution of the subsequent M points (M is an integer greater than 1, and the parameter configuration strategy is the same as N): If the displacement of the subsequent points based on the reference point and combined with the HDOP value can be judged as L1 / L2 level positioning points, then the jump is judged as a valid movement (e.g., the equipment quickly moves out of the obstruction area), the jump point and subsequent points are adopted, and the state transition operation is performed. The state returns to A or B depending on the situation: If the HDOP values ​​of all reference points to be judged and subsequent points are <1 before the state transition, then state A is returned; otherwise, state B is returned.

[0124] If an L3 level point is detected again before the state transition, all points received after entering state C, except for the last point, are determined to be drift points and discarded. The last transition point is then updated to the "reference point to be determined," and this determination process is repeated. Until there are sufficient subsequent points to support the determination to return to state A or B, no reliable data is reported.

[0125] State D signal lost lock region hold mode:

[0126] Entry condition: The L4 level positioning signal is continuously lost.

[0127] Decision-making and output: Maintain historical reliable positioning, at least at Level 2, displaying the last reliable location and clearly indicating signal loss. When the positioning signal is recaptured after loss of lock, the system cross-validates the new point sequence with the expected location of "region preservation". Validation also uses non-causal window judgment (e.g., multiple consecutive new points pointing to the same reasonable area), and avoids misjudgments caused by single-point jumps by updating the trajectory later.

[0128] Based on relevant application scenarios and requirements, this invention can introduce a mechanism that can postpone judgment and wait for more evidence, using non-causal forward-looking decision-making to resolve inherent contradictions.

[0129] Conventional filtering or thresholding methods belong to causal systems, which make an immediate and final decision to adopt or discard the current point based solely on current and historical data. When faced with a drift point that appears similar to the first valid point after exiting the obstruction zone, this can lead to a dilemma of mistakenly discarding the valid point or mistakenly keeping the drift point.

[0130] The multi-state mechanism introduced in this invention forms a non-causal system. Instead of immediately making a final decision on L3-level positioning points, it sets them as "judgment benchmarks" and proactively observes the data set relationships within a future finite window (the subsequent M points). By analyzing the spatial clustering and logical consistency of the data within this window, the system can distinguish between valid movements and random drifts, thereby significantly reducing the false rejection rate while maintaining high filtering accuracy. This is the core principle behind achieving the crucial effect of "not mistakenly affecting normal points."

[0131] The four states are not simply parallel, but an organic whole. Their state transitions and decision-making logic are a concrete manifestation of the integration of multi-dimensional information. The state machine dynamically schedules and weights the participation of "physical constraints" and "business rules" based on the real-time input "signal quality" data, and introduces "non-causal prospective analysis" at key decision points, thereby realizing the adaptive and intelligent integration of multi-source information in the time series.

[0132] Through the above principles and mechanisms, the system is no longer merely a passive filter for raw location data, but becomes an active, reliable trajectory generator:

[0133] When the signal is good (state A), it outputs a precise coordinate sequence.

[0134] When the signal is interfered with (state B / C), it outputs a "path segment" or "high confidence region" verified by higher-order logic.

[0135] When a signal is lost (state D), it outputs a clear "last known area" identifier.

[0136] This intelligent switching of output format ensures that the system ultimately delivers a continuous, reasonable, and business-readable trajectory, rather than discontinuous, contradictory, or scattered points that require manual interpretation. This directly achieves the core objective of improving scheduling efficiency and security.

[0137] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions or improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for determining reliable global positioning drift of airport equipment, characterized in that, Includes the following steps: Step S1: Obtain the credibility level by acquiring the positioning data of the airport ground equipment in real time and calculating the credibility level of the positioning point at each moment based on the signal quality parameters. Step S2: Define the state set and state transition rules of the multi-state machine. The multi-state machine model contains at least four states: open area high confidence mode, occluded area evaluation mode, suspected drift pending judgment mode and signal loss area maintenance mode, and configure the entry conditions and output strategies for each state. Step S3: Execute the trajectory processing strategy. Based on the confidence level and the historical motion trajectory of the device, drive the multi-state machine to switch between different states and execute the corresponding trajectory processing strategy in each state. Step S4: Collect low-confidence location data. When a low-confidence location point is detected, open a look-ahead time window or quantity window, wait for and collect subsequent location data. Step S5: Update the output trajectory. Based on the spatial clustering and physical motion logic consistency of the positioning data within the window, comprehensively determine whether the low-confidence positioning point is a valid moving point or a random drifting point, and update the device's output trajectory accordingly.

2. The airport equipment global reliable positioning drift determination method according to claim 1, characterized in that, Step S1 is implemented in the following manner: Step S11: Data acquisition and real-time calculation. Receive raw NMEA data or binary data frames output by the satellite positioning module in real time, parse the horizontal precision factor value from the data frames, and parse the velocity information of the current point. Step S12: Determine the confidence level. Based on the horizontal precision factor value and the preset threshold rules, classify the current location point to obtain the confidence level of the current location point. Step S13: Execute the action by attaching the calculated confidence level as an attribute to the current location data packet for subsequent state machine logic calls.

3. The airport equipment global reliable positioning drift determination method according to claim 1, characterized in that, The four states in step S2 are implemented in the following manner: High-reliability mode for open areas: This mode is activated when N high-reliability positioning points are acquired consecutively, and the real-time coordinate trajectory is directly output. Occlusion area assessment mode: This mode is activated when a medium-confidence location point appears and the device approaches a known occlusion area, and the trajectory is evaluated in conjunction with the motion model. Suspected drift pending judgment mode: Entered when a low-confidence location point is detected, without immediately making a final judgment on the current jump point; Signal Loss Area Holding Mode: Entered when the positioning signal is lost, holding and outputting the last reliable position.

4. The airport equipment global reliable positioning drift determination method according to claim 1, characterized in that, The suspected drift pending determination mode is implemented in the following ways: When a significant spatiotemporal jump is detected at a location point, it is marked as a reference point to be determined. An analysis window is opened, and the adoption or discarding of the reference point to be determined is paused. The subsequent M location points are then obtained in advance to form a data set. A comprehensive judgment is made based on the spatial clustering and physical-logical consistency of the data set: If all subsequent points point to the same reasonable area and the HDOP value improves, then the jump point is determined to be a valid move, and the jump point is adopted and converted. If subsequent points are scattered and HDOP values ​​continue to deteriorate, the transition point is determined to be a random drift, and the transition point and intermediate abnormal points are discarded.

5. The airport equipment global reliable positioning drift determination method according to claim 1, characterized in that, Step S4 is implemented in the following manner: Step S41: Condition triggering. When the system is in the occlusion area evaluation mode or the signal loss area holding mode, if a low-confidence positioning point is detected with a horizontal accuracy factor greater than the set threshold or a speed change exceeding the physical limit, the point is marked as a reference point to be judged and the look-ahead observation window is opened to wait for the collection of subsequent positioning data. Step S42: Open the look-ahead window, mark the detected low-confidence positioning points as reference points to be determined, and open the look-ahead observation window to wait for the collection of subsequent positioning data; Step S43: Wait and collect data. During the observation window, continuously receive and cache subsequent positioning data points and collect future data for comprehensive analysis of the properties of the reference point to be determined.

6. The airport equipment global reliable positioning drift determination method according to claim 1, characterized in that, Step S5 is implemented in the following manner: Step S51: Spatial clustering and physical logic analysis, extract subsequent positioning data within the forward observation window, calculate its spatial distribution, velocity and acceleration relative to the reference point to be determined, and compare the obtained motion parameters with the physical motion constraints of the airport ground equipment; Step S52: Combine judgment and trajectory update. If the subsequent positioning points are spatially concentrated and meet the physical motion constraints, they are judged as valid moving points and included in the trajectory, and the state machine is updated. Otherwise, they are judged as random drifting points, removed, and the reference point to be judged is reset to repeat the judgment process.

7. The airport equipment global reliable positioning drift determination method according to claim 6, characterized in that, The comprehensive determination in step S52 is implemented in the following manner: If the subsequent positioning points are spatially concentrated relative to the low-confidence positioning points, and the calculated velocity and acceleration conform to the physical motion constraints of the airport ground equipment, then the low-confidence positioning point is determined to be a valid moving point and is included in the trajectory output. If the spatial distribution of subsequent positioning points relative to the low-confidence positioning points is discrete and cannot form a movement trend that conforms to physical logic, then the low-confidence positioning point is determined to be a random drift point and is removed.

8. The airport equipment global reliable positioning drift determination method according to claim 1, characterized in that, The method further includes introducing a low-cost acceleration module and a vibration detection module for collaborative assistance, the collaborative assistance specifically including: When the acceleration module detects that the device has changed from motion to a stationary state, and the vibration detection module does not capture vibration feedback within a set time, the device is determined to be in an absolutely stationary state. In the state of absolute stillness, the device's position output is locked, and any position drift points received during this period are discarded.

9. A global reliable positioning drift determination system for airport equipment, employing the global reliable positioning drift determination method for airport equipment as described in any one of claims 1-8, characterized in that, It includes a data acquisition and credibility assessment module, a multi-state machine management module, a look-ahead window and data caching module, a comprehensive judgment and trajectory update module, and an auxiliary sensor collaboration module; The data acquisition and credibility assessment module is responsible for receiving raw data from the satellite positioning module in real time, parsing out key parameters, calculating and determining the credibility level of each positioning point, and attaching the credibility level as a tag to the positioning data packet. The multi-state machine management module is used to drive the state machine to switch between different modes based on the credibility level transmitted from the data acquisition and credibility assessment module and the historical trajectory of the device, and to call the corresponding trajectory processing strategy for the current state. The look-ahead window and data caching module is used to handle suspicious location points. When the system detects a low-confidence location point and enters the suspected drift pending judgment mode, this module will be triggered to open a look-ahead window in terms of time or quantity. During this period, the final judgment on the suspicious point is suspended, and subsequent location data is continuously received and cached. The comprehensive judgment and trajectory update module is used to comprehensively determine whether the low-confidence positioning point is a valid moving point or a random drifting point based on the spatial clustering and physical motion logic consistency of the positioning data within the window, and update the output trajectory of the device accordingly. The auxiliary sensor coordination module determines that the device is in an absolutely stationary state when the acceleration module detects that the device has changed from motion to a stationary state and the vibration detection module has not captured vibration feedback within a set time. In the absolutely stationary state, the device's position output is locked and any positioning drift points received during this period are discarded.

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