Flood control and drainage control method and equipment for airport and medium

Abstract

The embodiment of the invention discloses a flood control and drainage control method and device for an airport and a medium, and relates to the technical field of drainage, and the method comprises the steps: collecting real-time water level data, real-time flight dynamic data and weather bureau short-term and imminent forecast data of a target airport in real time, the real-time water level data comprises first real-time water level data of a runway area and second real-time water level data of at least one airport key area; according to the real-time water level data and the weather bureau short-term and imminent forecast data, the ponding risk of the target airport is predicted, corresponding ponding risk prediction information is determined, and the ponding risk prediction information comprises the predicted ponding depth, the ponding prediction position and the ponding prediction moment; and based on the real-time flight dynamic data, determining a drainage and ponding depth threshold value, and generating a drainage control instruction according to the ponding risk prediction information and the drainage and ponding depth threshold value to control a pump station to drain water.

Description

Technical Field

[0001] This specification relates to the field of drainage technology, and in particular to a method, equipment and medium for flood control and drainage in airports. Background Technology

[0002] As major transportation hubs, airports face more stringent safety requirements for flood control and drainage systems than urban public areas. Traditional drainage schemes primarily rely on manual inspections and fixed threshold drainage control. These traditional methods suffer from insufficient monitoring coverage and rigid scheduling mechanisms. First, point-based water level sensors cannot capture real-time water accumulation dynamics in critical areas such as runways and aprons, and reliance on manual inspections results in response lags, making it difficult to address the risk of sudden flooding across large areas of the airport. Furthermore, existing pumping station controls employ fixed water level threshold triggering mechanisms; however, airport drainage processes do not consider the temporal and spatial constraints of flight takeoff and landing windows, making drainage operations prone to conflict with aircraft activities, potentially leading to flight delays and runway safety risks. Therefore, traditional drainage methods ignore the unique characteristics of airports, using fixed water level threshold triggering and indiscriminate drainage within designated areas, posing a safety risk to aircraft operations. Summary of the Invention

[0003] This specification provides one or more embodiments of a flood control and drainage method, equipment, and medium for airports, which addresses the following technical problem: traditional drainage methods ignore the special characteristics of airports, use fixed water level thresholds for triggering, and drain water indiscriminately within the area, which poses a safety risk to aircraft operations.

[0004] One or more embodiments of this specification employ the following technical solutions:

[0005] This specification provides one or more embodiments of a flood control and drainage method for airports. The method includes: real-time acquisition of real-time water level data, real-time flight dynamic data, and short-term meteorological forecast data of a target airport, wherein the real-time water level data includes first real-time water level data of the runway area and second real-time water level data of at least one key area of ​​the airport; predicting the water accumulation risk of the target airport based on the real-time water level data and the short-term meteorological forecast data, and determining corresponding water accumulation risk prediction information, wherein the water accumulation risk prediction information includes predicted water accumulation depth, predicted water accumulation location, and predicted water accumulation time; determining a drainage water accumulation depth threshold based on the real-time flight dynamic data, and generating a drainage control command based on the water accumulation risk prediction information and the drainage water accumulation depth threshold to control the pumping station to drain water.

[0006] This specification provides one or more embodiments of a flood control and drainage control device for airports, comprising:

[0007] At least one processor; and,

[0008] A memory communicatively connected to the at least one processor; wherein,

[0009] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the above-described method.

[0010] This specification provides one or more embodiments of a non-volatile computer storage medium storing computer-executable instructions configured to perform the above-described method.

[0011] The at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects: Through the technical solutions in the embodiments of this specification, the airport drainage system has achieved a leap from passive response to active defense through deep fusion of multi-source data and intelligent adaptation to flight dynamics. Traditional methods rely on isolated water level monitoring and fixed threshold control. In the runway area, the water level false alarm rate is high due to interference from the metal pavement, which often leads to drainage malfunctions that interfere with flight takeoffs and landings. This solution, however, uses millimeter-wave radar penetration monitoring combined with AI video to pinpoint water accumulation areas, accurately capturing the dynamics of runway shoulder confluence and apron surface water accumulation, avoiding detection blind spots. In addition, conventional methods often disrupt the spatiotemporal correlation between meteorological, hydrological, and flight data, leading to potential conflicts between drainage operations and aircraft activities during heavy rain. This solution uses short-term forecasts to drive the generation of water accumulation risk prediction information and couples flight window dynamics to calculate water depth thresholds. When heavy rainfall causes flight delays, the system automatically compresses the safe operation window based on the wake dissipation time and runway preparation time, and simultaneously increases the power of the pumping station to respond to the water diffusion rate in minutes. This not only eliminates the risk of water mist obscuring the pilot's vision, but also effectively improves the energy efficiency of the equipment. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0013] Figure 1 A schematic flowchart illustrating a flood control and drainage method for airports provided in this specification.

[0014] Figure 2 This is a schematic diagram of a flood control and drainage control device for an airport, provided as an embodiment of this specification. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0016] This specification provides a flood control and drainage control method for airports. It should be noted that the executing entity in this specification can be a server or any device with data processing capabilities. Figure 1 A schematic flowchart illustrating a flood control and drainage method for airports, provided as an embodiment of this specification, is shown below. Figure 1 As shown, the main steps include the following:

[0017] Step S101: Real-time water level data, real-time flight dynamic data, and short-term forecast data from the meteorological bureau are collected from the target airport.

[0018] The real-time water level data includes first real-time water level data for the runway area and second real-time water level data for at least one key area of ​​the airport.

[0019] Before collecting real-time water level data of the target airport, the method further includes: acquiring airport area distribution data of the target airport to determine multiple airport areas other than the airport runway area based on the airport area distribution data; acquiring regional pedestrian flow heat data and vehicle flow trajectory data of each airport area within a historical time period, and determining pedestrian and vehicle traffic indicators for each airport area through the regional pedestrian flow heat data and vehicle flow trajectory data; acquiring at least one runway adjacent area adjacent to the runway area based on the airport area distribution data, and acquiring regional elevation data for each runway adjacent area and the corresponding runway area; filtering target runway adjacent areas among the runway adjacent areas based on the regional elevation data, and determining the target airport area based on the pedestrian and vehicle traffic indicators, so as to determine the key areas of the airport based on the target runway adjacent areas and the target airport area.

[0020] By using the area's pedestrian heat map data and vehicle trajectory data, the pedestrian and vehicle traffic indicators for each airport area are determined. Specifically, this includes: determining the first cumulative duration during which the pedestrian density exceeds the preset density threshold in each historical time period based on the area's pedestrian heat map data and preset density threshold; calculating the frequency of vehicle trajectory intersections in each historical time period based on the vehicle trajectory data for each airport area; determining the pedestrian aggregation indicator in each historical time period by using the ratio of the first cumulative duration to the historical time period; and determining the pedestrian and vehicle traffic indicators for each airport area within the historical event period based on the pedestrian density indicator and the frequency of vehicle trajectory intersections.

[0021] In airport flood control and drainage control systems, traditional methods rely on manual experience to delineate fixed monitoring points. As the core area for aircraft takeoff and landing, the runway's 300-meter radius (including connecting taxiways and runways) must meet civil aviation safety standards for zero water accumulation. However, water flow is affected by runway slope and confluence paths, meaning that monitoring only the runway itself cannot provide early warning of upstream backflow risks. Furthermore, water accumulation in densely populated areas such as aprons and cargo areas can easily lead to ground crew accidents, and frequent vehicle traffic can damage water level sensors. Therefore, it is necessary to dynamically select high-activity areas for priority coverage. Deploying airport drainage equipment is costly, and evenly distributing monitoring points would result in a significant waste of resources in low-risk areas.

[0022] In one embodiment of this specification, digitized airport area distribution data is retrieved from the airport Geographic Information System (GIS), extracting all functional zones except runways, such as aprons, cargo areas, and maintenance areas. Each zone is stored using vector boundary coordinates. The terminal security system database is accessed to obtain time-segmented infrared heatmap data for each zone. Slices are taken at 15-minute intervals from 06:00 to 24:00 daily, and the percentage of pixels exceeding civil aviation safety standards (person / ㎡) in each slice is calculated. The duration is accumulated to obtain a high-density duration. Next, GPS tracks of special vehicles, such as refueling trucks and baggage carts, are extracted from the airside vehicle management system. Using the area polygon as the calculation unit, the number of spatiotemporal intersections of different vehicle tracks within a 10-meter radius is detected. After removing duplicate tracks, the daily average intersection frequency is generated.

[0023] Dividing the duration of high-density traffic by the total daily operating time yields the passenger flow aggregation index. The frequency of intersection between the passenger flow density index and vehicle flow trajectory for each airport area determines the pedestrian and vehicle traffic indicators for that airport area within the historical event period. The average passenger flow density index and the average frequency of vehicle flow trajectory intersection are calculated over multiple historical event periods. Using preset density index thresholds and intersection frequency thresholds as criteria, areas where the average passenger flow density index exceeds the density index threshold or the average vehicle flow trajectory intersection frequency exceeds the intersection frequency threshold are designated as Category I airport critical areas. It should be noted that the two types of areas may overlap; the Category I airport critical areas are the areas after final deduplication.

[0024] The digital elevation model (DEM) of the runway and adjacent areas is retrieved from the airport survey database, with a resolution of at least 0.5 meters. Adjacent areas sharing a boundary with the runway area and exhibiting a slope greater than 3% are extracted as potential catchment areas, resulting in the second type of airport critical areas. It should be noted that the height of these potential catchment areas should be higher than the runway area sharing the boundary; if water accumulates in the potential catchment area, the slope created by the height difference may cause the water to spread towards the runway area. The first and second types of airport critical areas are merged and deduplicated using topology, outputting a list of coordinates for the airport critical areas.

[0025] In one embodiment of this specification, differentiated water level monitoring devices are deployed in key airport areas and runway areas. For the runway area, millimeter-wave radar water level gauge arrays are installed at a specific distance from the edge of the runway shoulder (meeting civil aviation electromagnetic compatibility safety distances). The array density is calculated according to civil aviation equipment deployment specifications based on the runway length, ensuring that at least two radars are deployed on each runway segment for cross-checking. The radar installation height and depression angle are dynamically adjusted based on the pavement cross slope data provided by the airport, ensuring that the radar beam centerline is perpendicular to the water surface.

[0026] For densely populated and vehicular areas within critical zones, i.e., Category I airport critical zones such as cargo corridors and aircraft maintenance areas, pressure-resistant ultrasonic water level sensors are installed at area entrances and intersections of vehicle traffic trajectories. The sensors' protection level must meet vehicle crush resistance standards and be connected to the airport's IoT platform via a LoRa wireless network. Simultaneously, high-definition video surveillance PTZ cameras are deployed at high points in these areas, ensuring unobstructed coverage of all pre-set water accumulation monitoring points. For critical areas near the runway, i.e., Category II airport critical zones such as runway-end connecting taxiways, multi-parameter hydrological sensor arrays are installed in manholes downstream of the water catchment path. These arrays include ultrasonic flow meters, siltation thickness detectors, and waterproof pressure transmitters. The sensor arrays are connected to the nearest airport navigation light well power supply system via explosion-proof cables. After all monitoring devices are installed, the central server performs end-to-end verification, simulating water level changes under different rainfall intensities to verify the time synchronization and spatial coverage integrity of radar-video-hydrological sensor data. For areas where response delays exceed system tolerances or spatial coverage is insufficient, a reinstallation command is triggered.

[0027] In one embodiment of this specification, after the water level data acquisition protocol is initiated, the millimeter-wave radar water level gauge transmits frequency-modulated continuous waves at a specific frequency. The runway surface water depth is calculated using the echo delay difference, generating a first real-time water level data stream. The video monitoring terminal calls the embedded AI water accumulation recognition model to perform semantic segmentation on the real-time video stream frame by frame, extracting the water surface contour and converting pixel depths into actual water depth values ​​based on camera calibration parameters. This data is then mapped to the airport coordinate system using the PTZ camera azimuth data to form a second real-time water level data stream. The pipeline hydrological sensor group packages flow, siltation, and pressure data using a timestamp synchronization method, filters it through an edge computing gateway, and uploads it to the decision system. All three types of data streams are tagged with the geocoding of the source device and the acquisition timestamp. They are uniformly stored and quality-verified through the airport's spatiotemporal big data platform. Any data anomalies, such as radar signal interference from aircraft takeoffs and landings or a sudden drop in video frame rate, trigger an alarm and initiate redundant equipment switching to ensure continuous and reliable water level data acquisition.

[0028] The above technical solution involves the traditional method of deploying a specified number of water level gauges around the runway, but this method cannot detect sudden confluence in upstream areas where no gauges are deployed. The embodiment in this specification uses elevation-driven water flow prediction to enable monitoring points to automatically cover the water source, effectively improving the advance warning of runway backflow accidents, while reducing redundant sensor deployment.

[0029] Step S102: Based on real-time water level data and short-term forecast data from the meteorological bureau, predict the water accumulation risk of the target airport and determine the corresponding water accumulation risk prediction information.

[0030] The flood risk prediction information includes the predicted flood depth, predicted flood location, and predicted flood time.

[0031] Based on the real-time water level data and the meteorological bureau's short-term forecast data, the risk of water accumulation at the target airport is predicted, and the corresponding water accumulation risk prediction information is determined. Specifically, this includes: inputting the first real-time water level data into a pre-constructed runway hydrological model to output the predicted runway water accumulation depth and water diffusion direction; overlaying the second real-time water level data and the meteorological bureau's short-term forecast data, and generating water accumulation depth distribution data corresponding to the key water accumulation area through a surface water accumulation simulation algorithm; and fusing the predicted runway water accumulation depth, the water diffusion direction, and the water accumulation depth distribution data to determine the water accumulation risk prediction information, wherein the water accumulation risk prediction information includes water accumulation area boundary information and predicted depth data for each water accumulation area at different prediction times.

[0032] In one embodiment of this specification, after receiving real-time water level data and short-term meteorological forecasts, a multi-level prediction engine is activated. First, runway area risks are addressed by inputting the first real-time water level data (including timestamps and geocoding) collected by millimeter-wave radar into a pre-set runway hydrological model. This model is constructed based on measured data from the airport construction phase, including pavement material permeability coefficients, transverse and longitudinal slopes, and calibration parameters from historical water accumulation events. The runway hydrological model is calibrated based on test data from the Civil Aviation Airport Pavement Drainage Test Specification (MH / T 5042). The model performs three-dimensional unsteady flow calculations to simulate the flow process of rainwater in the area from the runway shoulder to the centerline, outputting a prediction matrix for future water accumulation depth and a diffusion direction vector field at specific time intervals. The diffusion direction is indicated by angle values ​​representing the deflection relative to the runway centerline. Simultaneously addressing risks in key areas, the system integrates real-time water level data from AI video recognition, including gridded water depth values ​​for areas such as the apron and taxiways, as well as short-term forecast data from the meteorological bureau (0-6 hour rainfall intensity spatiotemporal matrix). Dynamic simulations are then performed using a surface-area water accumulation simulation algorithm. It should be noted that the algorithm parameters are derived from the pipe diameter, slope, and catchment area in the airport's as-built drawings. Based on the airport's underground pipe network topology database and the surface runoff characteristics of the digital elevation model, the infiltration-runoff balance is iteratively calculated within the GIS platform at the raster unit level. This generates heat maps of water accumulation depth distribution in key areas at multiple future time points, with the heat map resolution aligned with the video surveillance grid.

[0033] After completing the regional predictions, spatial-temporal fusion is performed. The depth matrix and direction vector output from the runway hydrological model are spatially overlaid with the heatmap generated by the surface domain algorithm using the airport's unified coordinate system. For runway-adjacent areas (within a specific distance from the runway boundary), a weighted fusion strategy is adopted, with the weight of the runway prediction result decreasing as distance increases; for non-adjacent areas, the surface domain algorithm result is directly used. During the fusion process, two types of data conflicts are resolved simultaneously: when the depth difference between radar data and video recognition data in the overlapping area exceeds the system tolerance, a third-party verification mechanism based on pipeline flowmeter readings is triggered; when there is an angle between the short-term forecast of heavy rainfall and the current direction of water accumulation diffusion, vector synthesis correction is initiated. Finally, a structured water accumulation risk prediction information table is generated. Each record contains the water accumulation area boundary represented by a polygon vertex coordinate sequence, the water accumulation depth data for that area at each node of the prediction time axis, and the probability distribution of the diffusion rate. All output information is linked to the grid cells of the airport electronic map through spatiotemporal coding for use by the decision-making level.

[0034] It should be noted that quality monitoring is embedded throughout the entire risk prediction information generation process. After each calculation, the deviation between the previous predicted value and the actual sensor recorded value is automatically compared. When the average deviation of a specific number of consecutive calculations exceeds a preset threshold, a self-calibration process for model parameters is triggered. The calibration process calls upon the historical rainstorm event dataset stored in the digital twin platform. By comparing the differences between the actual waterlogging diffusion pattern and the simulation results, the permeability coefficient of the hydrological model and the runoff coefficient of the surface domain algorithm are optimized in reverse, ensuring that the prediction accuracy continuously improves as the system operates. The final output waterlogging risk prediction information table is transmitted to the drainage control terminal via the airport's intranet with encryption, and the data confidence level is marked according to the dynamic calculation based on the input data quality and the model correction status.

[0035] Traditional methods rely on manual inspection of runway end water accumulation, resulting in a long average time from detection to response. The embodiments in this manual, through the coupling of millimeter-wave radar and runway hydrological models, accurately predict the diffusion path and speed of water accumulation towards the runway center, enabling earlier intervention. By integrating video AI and meteorological raster data, key area coverage is achieved, and the prediction error of apron water depth is controlled within industry-acceptable thresholds. An automatic overlay channel for short-term forecasts and water level data is established, enabling automatic rolling output of forecast maps within the forecast time period. This allows for the early closure of low-lying taxiways during sudden severe convective weather, ensuring the golden time for flight adjustments.

[0036] Step S103: Based on real-time flight dynamic data, determine the floodwater depth threshold, and generate drainage control instructions based on flood risk prediction information and the floodwater depth threshold to control the pumping station to drain water.

[0037] In airport flood control and drainage control systems, the dynamic setting of water depth thresholds is a core technical aspect in resolving the conflict between aviation safety and drainage efficiency. Traditional fixed threshold modes present challenges in controlling safety risks; for example, runway water depth exceeding the safety limit for aircraft takeoff and landing friction coefficients can lead to hydroplaning accidents. However, fixed thresholds cannot respond to real-time changes in flight takeoff and landing windows. For instance, drainage operations are prohibited within 20 minutes of flight landing; using conventional thresholds in this situation would delay critical response opportunities. Furthermore, during heavy rain, pumping stations operate at full capacity, consuming enormous amounts of energy, but fixed thresholds cannot dynamically adjust drainage intensity based on flight intervals, resulting in ineffective energy consumption. Therefore, traditional fixed threshold triggering methods cannot adapt to the strong correlation between drainage actions and flight events in airport drainage scenarios.

[0038] Based on the real-time flight dynamic data, the flood drainage depth threshold is determined, specifically including: analyzing the takeoff and landing time windows in the real-time flight dynamic data to determine the safe operation time window corresponding to each runway area; obtaining the most recent prediction timestamp of the flood risk prediction information, and setting the dynamic runway drainage threshold corresponding to each runway area based on the relationship between the most recent prediction timestamp and the safe operation time window. Analyzing the takeoff and landing time windows in the real-time flight dynamic data to determine the safe operation time window corresponding to each runway area specifically includes: determining the current actual landing time of the flight and the planned takeoff time of the next flight for each runway area; determining the start time of the safe operation time window based on the current actual landing time of the flight and the pre-acquired wake dissipation duration; and determining the end time of the safe operation time window based on the planned takeoff time of the next flight and the pre-acquired runway preparation time.

[0039] In one embodiment of this specification, flight dynamic data streams are first acquired in real time from the Air Traffic Control Collaborative Decision Making (A-CDM) system. The flight event sequence corresponding to each runway is analyzed to extract the actual landing time (accurate to the second-level timestamp) of the currently completed flight and the planned takeoff time of the next flight. Based on the aircraft type-environment parameter mapping table pre-stored in the Civil Aviation Airport Operation Specification Database, the wake dissipation standard duration of the landing flight (such as the extended time for heavy aircraft under crosswind conditions) is matched. The actual landing time is superimposed with the wake dissipation duration to generate the start timestamp of the safety operation time window. Simultaneously, the runway preparation operation standard duration, including pavement inspection, foreign object removal, and other processes, is invoked. The end timestamp of the safety operation window is calculated backward from the planned takeoff time, thereby constructing a dedicated safety operation window for each runway segment. It should be noted that there are multiple safety operation windows each day, and the number of safety operation windows is related to the number of flight events. In addition to the safe operation time window, the standard duration of preparation work before the scheduled flight departure time is marked as a specific preparation interval. For example, the start of a specific preparation interval is the end timestamp of the safe operation time window, and the end is the departure time of the next flight. At the same time, the standard duration of wake dissipation after the scheduled flight landing time is marked as the landing buffer interval. For example, the start of the landing buffer interval is the actual landing time of the current flight, and the end is the start timestamp of the safe operation time window.

[0040] Based on the relationship between the most recent forecast timestamp and the safe operation time window, the corresponding drainage water depth threshold for each runway area is dynamically set. Specifically, this includes: determining the current drainage period based on the relationship between the most recent forecast timestamp and the safe operation time window, wherein the current drainage period includes the pre-takeoff warning period, the safe operation period, and the post-landing buffer period; dynamically setting the corresponding drainage water depth threshold based on the current drainage period; wherein, during the pre-takeoff warning period, a first water depth threshold is determined based on the pre-acquired runway friction coefficient safety threshold and a preset water depth-friction coefficient conversion relationship; during the safe operation period, a second water depth threshold is determined based on the maximum drainage flow rate of the pumping station and the drainable area per unit time; during the post-landing buffer period, a third water depth threshold is determined based on the second water depth threshold and a preset meteorological attenuation coefficient, wherein the meteorological attenuation coefficient is related to the short-term forecast data from the meteorological bureau.

[0041] In one embodiment of this specification, the latest output prediction timestamp, i.e., the most recent valid prediction time point, is received and matched temporally with the safe operation window. When the most recent prediction timestamp falls within a specific preparation interval before the scheduled flight departure time, it is marked as the pre-departure alert period; if the prediction timestamp is within the safe operation window, it is marked as the safe operation period; if the prediction timestamp is within the buffer zone after the actual flight landing time, it is marked as the post-landing buffer period. After this division, thresholds are dynamically calculated based on aviation safety constraints for different time periods.

[0042] During the pre-flight alert period, the friction coefficient safety threshold from the airport runway characteristics database is retrieved. It should be noted that this friction coefficient safety threshold is certified by the Civil Aviation Administration of China (CAAC) and, combined with the water depth-friction coefficient conversion curve calibrated by the runway materials laboratory, maps the friction coefficient threshold to a water depth threshold. Specifically, by querying the conversion parameter table corresponding to the runway pavement material (e.g., the difference coefficient between asphalt and concrete pavements), a linear interpolation algorithm is used to output the first water depth threshold. This process strictly adheres to the safe lower limit requirement for runway friction coefficient during aircraft takeoff and landing, ensuring that the water depth does not pose a risk of water skidding on aircraft.

[0043] Once the safe operation period begins, the system switches to a flood drainage efficiency optimization mode. The maximum drainage flow rate parameters of each pump are retrieved from the pump station performance database. Simultaneously, based on the pipeline topology model, the effective drainage area per unit time within the current drainage zone is calculated. This area is determined by the pipe diameter, slope, and manhole location distribution. Using the drainage capacity balance equation, the maximum drainage flow rate is divided by the drainage area to obtain the theoretical limit depth value. This value is then multiplied by a preset safety margin coefficient, which can be calibrated through regression analysis of historical drainage data. Finally, a second water accumulation depth threshold is generated. This threshold represents the maximum safe drainage capacity achievable during flight intervals.

[0044] For the post-landing buffer period, a meteorological response-based attenuation strategy is adopted. First, the second water depth threshold calculated during the safe operating period is locked as a baseline value. Then, the short-term forecast data stream from the meteorological bureau is accessed in real time to analyze the future short-term rainfall intensity trend. Based on the rainfall intensity level, a corresponding scaling factor is matched in a predefined attenuation coefficient lookup table; heavy rainfall corresponds to a high attenuation value, and light rainfall corresponds to a low attenuation value. The scaling factor is multiplied by the second water depth threshold to generate a third water depth threshold. This design prevents sudden water accumulation immediately after flight landing and avoids frequent equipment start-ups and shutdowns due to sudden threshold changes.

[0045] It should be noted that all the above threshold calculation results are bound to specific runway numbers and valid time periods, and distributed to the drainage decision engine through the airport's spatiotemporal data platform. The entire process calculation is re-executed at specific intervals, and real-time correction is immediately triggered when flight schedule changes or short-term forecast updates are detected to ensure that the thresholds are synchronized with the latest operational status.

[0046] Through the above technical solutions, the traditional fixed threshold mode still triggers drainage according to conventional standards during flight takeoff and landing, and adopts a friction coefficient-constrained threshold during the pre-takeoff warning period. The water depth-friction coefficient conversion model directly quantifies aviation safety requirements into drainage control boundaries. This effectively reduces the incidence of runway closure accidents and solves the drainage safety problem during the wake disturbance period after heavy aircraft landing. Conventional solutions maintain conservative drainage intensity during periods without flights, resulting in long-term inefficient operation of pumping stations. The embodiments in this specification enable a pumping station capacity-driven threshold during safe operating periods, calculating the drainage efficiency per unit area in real time based on the pipeline topology, allowing the threshold to dynamically match the current equipment's maximum performance. Furthermore, static thresholds require manual adjustment during periods of intensified rainfall, delaying critical decision-making windows. The embodiments in this specification innovatively introduce a meteorological attenuation threshold (third threshold) during the post-landing buffer period, automatically narrowing the threshold range based on the short-term forecast rainfall intensity gradient. When a strong convective cloud cluster is detected approaching rapidly, the system tightens the threshold in advance to trigger preventative drainage, gaining crucial response time for mobile pump truck dispatch. Changes in the safety window due to flight delays are the main cause of traditional system failures. This specification establishes a real-time closed loop for flight dynamics and threshold calculation. When the air traffic control system pushes changes to flight schedules, the safe operating window is immediately recalculated and the three types of thresholds are adjusted simultaneously to avoid conflicts between drainage control and flight scheduling. This upgrades airport drainage from a passive response to an active adaptation, releasing maximum drainage efficiency while ensuring absolute aviation safety through a three-dimensional threshold decision chain based on flight times, equipment capabilities, and weather changes.

[0047] Based on the waterlogging risk prediction information and the drainage waterlogging depth threshold, a drainage control instruction is generated. Specifically, this includes: determining multi-dimensional information to trigger drainage based on the drainage waterlogging depth threshold and the waterlogging risk prediction information, wherein the multi-dimensional information to trigger drainage includes a trigger drainage timestamp, a trigger drainage area, and corresponding predicted waterlogging depth data; determining the trigger drainage period based on the trigger drainage timestamp, and determining the corresponding pump station control power through the trigger drainage period, the trigger drainage area, and the corresponding predicted waterlogging depth data, so as to generate drainage control indicators for the corresponding safe operation period.

[0048] In one embodiment of this specification, a real-time output data packet is received from the waterlogging risk prediction module. This data packet includes a set of waterlogging area boundary coordinates encoded by a geographic grid, the predicted waterlogging depth value of each grid cell within the prediction time series, and the corresponding prediction timestamp. Simultaneously, the drainage waterlogging depth threshold corresponding to each runway area is acquired. It should be noted that the drainage waterlogging depth threshold here corresponds to a threshold under multiple prediction timestamps. When comparing the predicted waterlogging depth value of each area with the corresponding drainage waterlogging depth threshold, it is necessary to ensure that the timestamps match. Through real-time comparison of the predicted waterlogging depth value of the corresponding area with the corresponding drainage waterlogging depth threshold, all grid cells whose predicted waterlogging depth exceeds the threshold are identified. Adjacent over-limit grids are aggregated to form a continuous triggering area, generating a multi-dimensional information entity for triggering drainage. This entity contains three elements: the triggering drainage timestamp (i.e., the first predicted over-limit moment), the triggering drainage area, and the predicted waterlogging depth data, which may include the depth extreme value and growth rate of the over-limit grid.

[0049] Subsequently, the system triggers a drainage timestamp and performs a time-series matching with the real-time flight dynamic data stream. Based on predefined rules for dividing the pre-takeoff alert period, safe operation period, and post-landing buffer period, it determines the drainage time period label corresponding to the triggered drainage timestamp. This label, along with the spatial attributes of the triggered drainage area (area type code, distance from the runway centerline), is input into the drainage strategy decision tree.

[0050] When the operation is within a safe operating period and the area type is the runway adjacent zone, the available pump list is retrieved from the pump station performance database, filtering out equipment currently in normal condition. Based on the pipeline topology model, the weighted paths from the trigger area to each pump station are calculated. The weighted path calculation method here is pipe length × reciprocal of pipe diameter + elevation difference. The three pumps with the lowest weights are selected to form the optimal cluster. Simultaneously, the power level is determined based on the predicted water depth growth rate. If the predicted water depth growth rate falls within the preset low-speed growth zone, 75% of the rated power is activated; if it falls within the medium-speed growth zone, 90% of the rated power is activated; and if it falls within the high-speed growth zone, 100% of the rated power is activated. Following this method, a JSON instruction set containing pump ID, power value, and duration is generated.

[0051] When in the pre-flight alert period or in areas with high pedestrian and vehicle density, the friction coefficient protection protocol is activated, and the runway friction coefficient sensor data stream is accessed in real time. If the friction coefficient is lower than the safety threshold, a shutdown command for drainage equipment within 500 meters is immediately generated. The pavement drying equipment is simultaneously activated, and the priority of the area is reduced. For non-runway areas, the mobile drainage equipment scheduling algorithm is invoked to send the preparatory coordinates to the nearest available pump truck to the trigger area, and the standby power (30% of the rated power) is set to maintain pipeline unobstructed flow.

[0052] During the post-landing buffer period, meteorological attenuation power control is implemented. The gradient of future rainfall intensity changes is analyzed from short-term forecast data. When the gradient exceeds the preset threshold, the power of the pumping station is forcibly limited to ≤50%. When the gradient is gentle, the power is maintained at 70% and the rate of change of water depth is monitored.

[0053] The final drainage control command set is distributed through the airport's industrial IoT platform, employing a dual-channel verification mechanism. The main channel transmits the data to the pump station PLC controller via a fiber optic network, while the backup channel sends it to mobile device terminals via a 5G private network. All commands are accompanied by spatiotemporal validity tags. When flight schedule changes or abnormal drops in water depth are detected, the central server immediately sends a command cancellation signal.

[0054] The above technical solution overturns the traditional crude mode of fixed power response triggered by water level in drainage systems, achieving a deep integration of drainage control with airport operation scenarios. Traditional methods, ignoring the time constraints of flight activities, often perform high-intensity drainage during takeoff and landing, causing runway water mist to interfere with pilots' vision and affecting the calibration of precision navigation equipment due to pump station vibration. The embodiments in this specification, through dynamic matching of drainage trigger timestamps and flight schedules, strictly constrain drainage operations within a safe operating window and adjust power in real-time according to predicted water depth data, completely eliminating flight delays caused by drainage operations in actual tests at Beijing Capital International Airport. Meanwhile, conventional systems use a homogenized control strategy for non-runway areas such as aprons and cargo areas, resulting in delayed responses in high-value service areas. The embodiments in this specification, however, intelligently allocate pump station resources based on the type labels of the drainage trigger areas, such as runway adjacent areas and cargo channels. When AI video identifies that the water depth in the cargo loading and unloading area is approaching the safety threshold, it automatically upgrades the pump station in that area to a higher priority level and triggers light alarms, reducing the ground handling accident rate. Crucially, traditional static commands lack the ability to respond to sudden weather changes, and short-term heavy rainfall often leads to the overload and paralysis of drainage systems. The embodiments in this specification use the diffusion rate vector in the water accumulation risk prediction information and the rainfall intensity gradient in the short-term forecast for double verification. This automatically generates a pre-boost command for pumping station power before the rainstorm intensifies, thus achieving the effect of securing an emergency response window during the flood season and avoiding economic losses caused by runway closure.

[0055] The technical solutions implemented in this specification, through deep fusion of multi-source data and intelligent adaptation to flight dynamics, achieve a leap from passive response to proactive defense in airport flood drainage. Traditional methods rely on isolated water level monitoring and fixed threshold control, leading to high false alarm rates in runway areas due to interference from metallic pavement, often causing drainage malfunctions that disrupt flight takeoffs and landings. This solution, however, combines millimeter-wave radar penetration monitoring with AI video-based water accumulation area labeling to accurately capture the dynamics of runway shoulder confluence and apron surface water accumulation, avoiding detection blind spots. Furthermore, conventional methods, by severing the spatiotemporal correlation between meteorological, hydrological, and flight data, often result in conflicts between drainage operations and aircraft activities during heavy rain. This solution uses short-term forecasts to drive the generation of water accumulation risk prediction information and couples it with flight window dynamics to calculate water depth thresholds. When heavy rainfall causes flight delays, the system automatically compresses the safe operation window based on the wake dissipation time and runway preparation time, and simultaneously increases the power of the pumping station to respond to the water diffusion rate in minutes. This not only eliminates the risk of water mist obscuring the pilot's vision, but also effectively improves the energy efficiency of the equipment.

[0056] This specification also provides an embodiment of a flood control and drainage control device for airports, such as... Figure 2 As shown, the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method.

[0057] This specification also provides a non-volatile computer storage medium storing computer-executable instructions configured to execute the above-described method.

[0058] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0059] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0060] The devices, media, and methods provided in the embodiments of this specification are one-to-one correspondences. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0061] Those skilled in the art will understand that embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0066] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0067] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0068] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, the phrase "comprising a…" … ” The definition of a specific element does not preclude the presence of other identical elements in the process, method, product, or apparatus that includes the element.

[0069] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A flood control and drainage method for airports, characterized in that, The method includes: Real-time water level data, real-time flight dynamic data, and short-term forecast data from the meteorological bureau are collected from the target airport. The real-time water level data includes first real-time water level data for the runway area and second real-time water level data for at least one key area of ​​the airport. Based on the real-time water level data and the short-term forecast data from the meteorological bureau, the risk of water accumulation at the target airport is predicted, and the corresponding water accumulation risk prediction information is determined. The water accumulation risk prediction information includes the predicted water accumulation depth, the predicted water accumulation location, and the predicted water accumulation time. Based on the real-time flight dynamic data, a floodwater depth threshold is determined, and a drainage control command is generated according to the flood risk prediction information and the floodwater depth threshold to control the pumping station to drain water.

2. The flood control and drainage method for airports according to claim 1, characterized in that, Before collecting real-time water level data of the target airport, the method further includes: Obtain airport area distribution data of the target airport, and based on the airport area distribution data, determine multiple airport areas other than the airport runway area; Obtain regional pedestrian flow heat map data and vehicle flow trajectory data for each airport area within a historical time period, and determine the pedestrian and vehicle traffic indicators for each airport area using the regional pedestrian flow heat map data and the vehicle flow trajectory data; Based on the airport area distribution data, at least one runway adjacent area adjacent to the runway area is obtained, so as to obtain the regional elevation data of each runway adjacent area and the corresponding runway area. Based on the elevation region, a target runway adjacent region is selected from the runway adjacent region, and a target airport region is determined according to the pedestrian and vehicle traffic indicators, so as to determine the key airport region based on the target runway adjacent region and the target airport region.

3. The flood control and drainage method for airports according to claim 2, characterized in that, By using the regional pedestrian flow heat map data and the vehicle flow trajectory data, the pedestrian and vehicle traffic indicators for each airport area are determined, specifically including: Based on the regional pedestrian flow thermal data and the preset density threshold, determine the first cumulative duration during which the pedestrian flow density is greater than the preset density threshold in each historical time period; Based on the traffic flow trajectory data of each airport area, the frequency of traffic flow trajectory intersections within each historical time period is calculated. The ratio of the first cumulative duration to the historical time period is used to determine the population gathering index within each historical time period. Based on the pedestrian density index and the frequency of intersection of vehicle trajectories, the pedestrian and vehicle traffic index for each airport area within the historical event period is determined.

4. The flood control and drainage method for airports according to claim 1, characterized in that, Based on the real-time water level data and the short-term forecast data from the meteorological bureau, the risk of water accumulation at the target airport is predicted, and the corresponding water accumulation risk prediction information is determined, specifically including: The first real-time water level data is input into the pre-built runway hydrological model to output the predicted runway water depth and the direction of water spread. The second real-time water level data and the short-term forecast data from the meteorological bureau are superimposed, and the water depth distribution data corresponding to the key water accumulation area is generated by the area water accumulation simulation algorithm. By integrating the predicted runway water depth, the water diffusion direction, and the water depth distribution data, the water accumulation risk prediction information is determined. The water accumulation risk prediction information includes water accumulation area boundary information and predicted depth data for each water accumulation area at different prediction times.

5. A flood control and drainage method for airports according to claim 1, characterized in that, Based on the real-time flight dynamic data, the threshold for drainage water depth is determined, specifically including: Analyze the takeoff and landing time windows in the real-time flight dynamic data to determine the safe operation time window corresponding to each runway area; Obtain the most recent prediction timestamp of the waterlogging risk prediction information, and set the dynamic runway drainage threshold for each runway area based on the relationship between the most recent prediction timestamp and the safe operation time window.

6. A flood control and drainage method for airports according to claim 5, characterized in that, The takeoff and landing time windows in the real-time flight dynamic data are analyzed to determine the safe operation time window for each runway area, specifically including: Determine the actual landing time of the current flight and the planned departure time of the next flight for each runway area; Based on the actual landing time of the current flight and the pre-acquired wake dissipation duration, the start time of the safe operation time window is determined; The end time of the safe operation window is determined based on the planned departure time of the next flight and the pre-acquired runway preparation time.

7. A flood control and drainage method for airports according to claim 5, characterized in that, Based on the relationship between the most recent predicted timestamp and the safe operation time window, the floodwater depth threshold for each runway area is dynamically set, specifically including: Based on the relationship between the most recent predicted timestamp and the safe operation time window, the current drainage period is determined, wherein the current drainage period includes the pre-flight warning period, the safe operation period, and the post-landing buffer period; Based on the current drainage period, the corresponding floodwater depth threshold is dynamically set; During the pre-takeoff warning period, a first water depth threshold is determined based on a pre-acquired runway friction coefficient safety threshold and a preset water depth-friction coefficient conversion relationship. During the safe operation period, the second water accumulation depth threshold is determined based on the pump station's maximum drainage flow rate and the area that can be drained per unit time. During the post-landing buffer period, a third water depth threshold is determined based on the second water depth threshold and a preset meteorological attenuation coefficient, wherein the meteorological attenuation coefficient is related to the short-term forecast data from the meteorological bureau.

8. A flood control and drainage method for airports according to claim 7, characterized in that, Based on the waterlogging risk prediction information and the drainage waterlogging depth threshold, a drainage control instruction is generated, specifically including: Based on the flood drainage depth threshold and the flood risk prediction information, multi-dimensional information for triggering flood drainage is determined, wherein the multi-dimensional information for triggering flood drainage includes the triggering flood drainage timestamp, the triggering flood drainage area, and the corresponding predicted flood depth data; Based on the triggered drainage timestamp, the triggered drainage period is determined. Then, by using the triggered drainage period, the triggered drainage area, and the corresponding predicted water depth data, the corresponding pump station control power is determined to generate the corresponding drainage control indicators for the safe operation period.

9. A flood control and drainage control device for airports, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are configured to perform the method as described in any one of claims 1-8.

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