Intelligent monitoring and active anti-corrosion method and system for residual de-icing and anti-icing fluid on airport pavement

Abstract

The invention discloses an intelligent monitoring and active anti-corrosion method and system for residual de-icing and anti-icing fluid on airport pavements, and relates to the technical field of airport pavement maintenance, and the method comprises the steps: obtaining first monitoring data and second monitoring data of a plurality of airport pavements in a target airport; judging whether the feature data of the corrosive medium of the airport pavement is abnormal data, if the judgment result is yes, calculating a dynamic risk value of the airport pavement according to the first monitoring data and the second monitoring data, comparing the dynamic risk value with a preset threshold value, and determining a risk level of the airport pavement according to a comparison result; determining a pavement maintenance decision of each airport pavement in the target airport according to the risk assessment level; according to the method, rapid identification and response of deicing liquid residues in a high-risk area can be realized, the residence time of high-concentration corrosive liquid is greatly shortened, and the chemical erosion process of a concrete surface layer is effectively inhibited.

Description

Technical Field

[0001] This application relates to the field of airport pavement maintenance technology, and in particular to a method and system for intelligent monitoring and active corrosion prevention of residual anti-icing fluid on airport pavements. Background Technology

[0002] Airports use large quantities of alcohol-based and salt-based de-icing fluids during winter. Residual fluids seep into the joints and pores of airport pavements, causing concrete spalling, steel corrosion, or asphalt aging. Current methods for detecting residual de-icing fluid on airport pavements rely on manual inspections and periodic flushing, which suffer from slow response times, inaccurate location, and resource waste.

[0003] In related technologies, for example, CN222332574U discloses a portable de-icing fluid recovery device. However, such devices mainly focus on collecting and physically recovering waste fluid through mechanical structures, essentially acting as passive tools. Lacking the ability to actively sense and monitor the distribution of residual fluid on the pavement surface in real time, these devices cannot determine the specific residual concentration and distribution of de-icing fluid in critical areas such as pavement joints and low-lying areas. Therefore, in actual operations, they often rely on manual experience for blind covering or blanket-style operations, which is not only labor-intensive but also unable to achieve precise removal in high-corrosion-risk areas, making it difficult to guide corrosion prevention and maintenance from the source through monitoring data. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for intelligent monitoring and active corrosion prevention of residual anti-icing fluid on airport pavements, which can realize rapid identification and response to residual anti-icing fluid in high-risk areas, significantly shorten the residence time of high-concentration corrosive liquids, and effectively inhibit the chemical erosion process of concrete surface.

[0005] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for intelligent monitoring and active corrosion prevention of residual anti-icing fluid on airport pavements, the method comprising: Acquire first monitoring data of multiple airport pavements at the target airport and second monitoring data of the target airport. The first monitoring data includes temperature data, humidity data, and characteristic data of corrosive media on the airport pavements. The second monitoring data includes multidimensional meteorological information and flight data of the target airport. The characteristic data of the corrosive media includes electrical conductivity and spectral characteristics. Determine whether the characteristic data of the corrosive medium of the airport pavement is abnormal. If the determination result is yes, calculate the dynamic risk value of the airport pavement based on the first monitoring data and the second monitoring data, compare the dynamic risk value with a preset threshold, and determine the risk level of the airport pavement based on the comparison result. Based on the risk assessment level, pavement maintenance decisions are made for each airport pavement in the target airport, and the pavement maintenance decisions correspond to the risk assessment level. The step of calculating the dynamic risk value of the airport pavement based on the first monitoring data and the second monitoring data includes: The characteristic data of the corrosive medium are processed to obtain the pavement concentration gradient of the corrosive medium; Dynamic risk values ​​are calculated based on the airport pavement temperature and humidity data, the multidimensional meteorological information, the flight data, and the pavement concentration gradient.

[0006] Secondly, this application provides an intelligent monitoring and active corrosion prevention system for residual anti-icing fluid on airport pavements, the system comprising: The sensing layer is used to acquire first monitoring data of multiple airport pavements in the target airport and second monitoring data of the target airport. The first monitoring data includes temperature data, humidity data, and characteristic data of corrosive media of the airport pavements. The second monitoring data includes multi-dimensional meteorological information and flight data of the target airport. The characteristic data of the corrosive media includes electrical conductivity and spectral characteristics. In addition, determine whether the characteristic data of the corrosive medium on the airport pavement is abnormal; An edge layer is used to calculate the dynamic risk value of the airport pavement based on the first monitoring data and the second monitoring data when the characteristic data of the corrosive medium on the airport pavement is judged to be abnormal. The dynamic risk value is compared with a preset threshold, and the risk level of the airport pavement is determined based on the comparison result. The control center is used to determine pavement maintenance decisions for each airport pavement in the target airport based on the risk assessment level, and the pavement maintenance decisions correspond to the risk assessment level. The step of calculating the dynamic risk value of the airport pavement based on the first monitoring data and the second monitoring data includes: The characteristic data of the corrosive medium are processed to obtain the pavement concentration gradient of the corrosive medium; Dynamic risk values ​​are calculated based on the airport pavement temperature and humidity data, the multidimensional meteorological information, the flight data, and the pavement concentration gradient.

[0007] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the intelligent monitoring and active corrosion prevention method for residual anti-icing fluid on airport pavement as described above.

[0008] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the intelligent monitoring and active corrosion prevention method for residual anti-icing fluid on airport pavement as described above.

[0009] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the intelligent monitoring and active corrosion prevention method for residual anti-icing fluid on airport pavement as described above.

[0010] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method and system for intelligent monitoring and proactive corrosion prevention of residual anti-icing fluid on airport pavements. First, the dynamic risk value of each airport pavement is calculated based on various monitoring data, enabling rapid identification and response to residual anti-icing fluid in high-risk areas. This significantly shortens the residence time of high-concentration corrosive liquids and effectively inhibits the chemical erosion process of concrete surfaces. Then, a risk assessment is conducted on each airport pavement based on this dynamic risk value. Finally, maintenance decisions are made based on the assessment results. An automated monitoring network replaces high-frequency manual inspections, and cleaning resources are dynamically adapted. The flushing intensity is intelligently adjusted based on the real-time risk level to avoid resource waste caused by excessive water usage. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating an intelligent monitoring and active corrosion prevention method for residual anti-icing fluid on airport pavement in one embodiment of this application. Figure 2 This is a schematic diagram of the structure of a sensor node in one embodiment of this application; Figure 3 This is a schematic diagram of the structure of an intelligent monitoring and active corrosion prevention system for residual anti-icing fluid on airport pavement according to one embodiment of this application; Figure 4 This is a schematic diagram of the corrosion heat map generation process in one embodiment of this application.

[0013] Reference numerals: 10-Sensor housing, 11-Miniature near-infrared spectral probe, 12-Temperature and humidity sensor, 13-Waterproof cable, 14-Waterproof vent, 15-High-strength glass window, 16-Piezoelectric ceramic vibrator, 17-Conductivity electrode, 18-Sealant, 19-Top end face; 20-Subgrade soil; 30-Concrete pavement. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] First Embodiment Please see Figure 1 This application provides a method for intelligent monitoring and active corrosion prevention of residual de-icing fluid on airport pavements, comprising the following steps S101 to S103. Wherein: Step S101: Acquire first monitoring data of multiple airport pavements at the target airport and second monitoring data of the target airport. The first monitoring data includes temperature data, humidity data, and characteristic data of the corrosive medium of the airport pavement. The second monitoring data includes multidimensional meteorological information and flight data of the target airport. The characteristic data of the corrosive medium includes conductivity and spectral characteristics. It should be noted that the corrosive medium in this embodiment is an alcohol-based or salt-based de-icing fluid, such as ethylene glycol, propylene glycol, and acetate-based de-icing fluids.

[0017] Step S102: Determine whether the characteristic data of the corrosive medium of the airport pavement is abnormal. If the determination result is yes, calculate the dynamic risk value of the airport pavement based on the first monitoring data and the second monitoring data, compare the dynamic risk value with a preset threshold, and determine the risk level of the airport pavement based on the comparison result.

[0018] In step S102 of this application embodiment, determining whether the characteristic data of the corrosive medium of the airport pavement is abnormal data includes: determining whether the characteristic data of the corrosive medium of the airport pavement meets preset abnormality judgment conditions; if the judgment result is yes, marking the characteristic data of the corrosive medium of the airport pavement as abnormal data; the abnormality judgment conditions are concentration threshold judgment conditions, component fingerprint judgment conditions, or critical risk judgment conditions, wherein: The concentration threshold judgment condition is that the conductivity of the corrosive medium is greater than a first preset value; as an optional implementation of this application, the first preset value is 500 μS / cm. When the collected conductivity E>500 μS / cm (corresponding to about 0.5% of the anti-icing fluid concentration), it is determined that there is significant chemical residue on the pavement.

[0019] The component fingerprint determination condition is that the matching degree between the spectral characteristics of the corrosive medium and the preset corrosive medium fingerprint is greater than a second preset value; as an optional embodiment of this application, the second preset value is 85%. When the matching degree between the spectral characteristics of the de-icing fluid and the preset de-icing fluid fingerprint is >85%, the liquid property is confirmed to be de-icing fluid.

[0020] The critical risk judgment condition is that the difference between the temperature data of the airport pavement and the freezing point of the residual liquid is less than a third preset value. As an optional implementation of this application, the third preset value is 3%. When the difference between the pavement temperature and the freezing point of the liquid is <3℃, it is determined that there is a high risk of icing. The freezing point of the liquid can be obtained by identifying the liquid chemical components through spectral features S, and then calling the "concentration-freezing point characteristic curve" corresponding to the component.

[0021] It should be noted that when any of the above three anomaly judgment conditions are met, the characteristic data of the corrosive medium is judged as abnormal data. Abnormal data will be marked with high priority and uploaded immediately for risk assessment calculation; while normal data will be regarded as background data and will only be compressed and stored locally.

[0022] In step S102 of this application embodiment, calculating the dynamic risk value of the airport pavement based on the first monitoring data and the second monitoring data includes the following steps S201 to S203. Wherein: Step S201: Process the characteristic data of the corrosive medium to obtain the surface concentration gradient of the corrosive medium, including steps S301 to S302. Wherein: Step S301: Invert the conductivity and spectral characteristics of the corrosive medium to obtain the concentration of the corrosive medium; Step S302: Calculate the pavement concentration gradient of the corrosive medium based on the concentration of the corrosive medium and the geographical location information of the airport pavement.

[0023] Step S202: Calculate the dynamic risk value based on the airport pavement temperature and humidity data, the multi-dimensional meteorological information, the flight data, and the pavement concentration gradient; wherein, the formula for calculating the dynamic risk value is: ; In the formula, Indicates dynamic risk value; Represents flight data. This represents the operational urgency coefficient calculated based on flight data; This represents the pavement concentration gradient; This represents the surface concentration gradient of the corrosive medium. Represents multidimensional meteorological information. This represents the trend factor calculated based on multidimensional meteorological information; This represents the corresponding weighting coefficient; Indicates based on pavement concentration gradient The physical corrosion rate is calculated based on temperature T and humidity H.

[0024] It should be noted that in the above calculation formula, the pavement concentration gradient... The concentration of de-icing fluid was obtained by inversion from the conductivity and spectral characteristics measured by sensors; the temperature data T and humidity data H of the airport pavement were used to perform Arrhenius correction on the corrosion rate and determine the wetting time (TOW). This represents the trend factor calculated from macroscopic meteorological data M fused by the cloud platform based on the control center; the multidimensional meteorological information... In addition to atmospheric temperature and humidity, which are part of the airport environment, the data also includes precipitation type, precipitation intensity, and wind speed. This embodiment provides multi-dimensional meteorological information. Originating from the airport meteorological observation system, it is used to assess the macroscopic impact trend of environmental background on pavement condition. Based on flight data The operational urgency coefficient is calculated and determined by the following method: real-time acquisition of airport flight schedules. For areas with frequent aircraft taxiing or parking in the next hour (such as runway entrances and busy parking stands), a higher weighting coefficient is set to reflect the high sensitivity of the area to stress corrosion and the high timeliness requirements of maintenance operations. For idle areas without flight schedules, a baseline coefficient (K=1.0) is set.

[0025] In step S102 of this application embodiment, comparing the dynamic risk value with a preset threshold and determining the risk level of the airport pavement based on the comparison result includes the following steps S401 to S402. Wherein: Step S401: Determine a preset threshold, wherein the preset threshold includes a first threshold and a second threshold; wherein the first threshold is less than the second threshold; Step S402: Compare the dynamic risk value with the first threshold A and the second threshold B. When the dynamic risk value is less than the first threshold A, determine that the airport pavement corresponding to the dynamic risk value is at a low risk level (R1). When the dynamic risk value is greater than or equal to the first threshold A and less than the second threshold B, determine that the airport pavement corresponding to the dynamic risk value is at a medium risk level (R2). When the dynamic risk value is greater than or equal to the second threshold B, determine that the airport pavement corresponding to the dynamic risk value is at a high risk level (R3).

[0026] Step S103: Determine the pavement maintenance decision for each airport pavement in the target airport based on the risk assessment level, wherein the pavement maintenance decision corresponds to the risk assessment level.

[0027] As an optional implementation of this application, for airport pavements of low risk level (R1), only the spatiotemporal data of the airport pavement needs to be uploaded to the database; for airport pavements of medium risk level (R2), the corresponding pavement maintenance decision is to push inspection alerts and optimal arrival paths to ground terminals; for airport pavements of high risk level (R3), the corresponding pavement maintenance decision is to activate the pre-embedded spray device of the target airport pavement or dispatch the nearest cleaning vehicle to the GPS coordinates (X,Y,Z) of the airport pavement to clean the de-icing fluid.

[0028] By implementing steps S101 to S103 above, this application first calculates the dynamic risk value of each airport pavement using various monitoring data, enabling rapid identification and response to anti-icing fluid residue in high-risk areas, significantly shortening the residence time of high-concentration corrosive liquids, and effectively inhibiting the chemical erosion process of concrete surfaces. Then, based on this dynamic risk value, a risk assessment is conducted on each airport pavement. Finally, maintenance decisions are determined based on the assessment results. An automated monitoring network replaces high-frequency manual inspections, and cleaning resources are dynamically adapted. The flushing intensity is intelligently adjusted based on real-time risk levels to avoid resource waste caused by excessive water usage. Furthermore, as... Figure 4 As shown, this application also includes generating a corrosion heat map based on the first monitoring data and the second monitoring data. The corrosion heat map is used to characterize the risk prediction level of each airport pavement during a preset time period. The step of generating the corrosion heat map based on the first monitoring data and the second monitoring data includes steps S501 to S504. Step S501: Train the corrosion risk prediction model using historical datasets of the first and second monitoring data; in this embodiment, the first and second monitoring data from the past 3-5 winter operating periods are selected.

[0029] As an optional implementation, after collecting the first and second monitoring data, data preprocessing is required to clean the raw data. The data preprocessing method is as follows: First, the sensor data of different frequencies are time-aligned with the meteorological data using timestamps; second, noise data that has been verified as abnormal (outlier handling), such as disconnected and abrupt values, are removed; finally, the multi-source data is normalized to construct a standardized historical dataset as the training sample set for the prediction model.

[0030] In this embodiment of the application, the corrosion risk prediction model is a time-series prediction model constructed using a Long Short-Term Memory Network (LSTM) or a Gated Recurrent Unit (GRU). The training method of the corrosion risk prediction model is as follows: the historical dataset is used as the training set input to the model, and the model weights are continuously optimized through the backpropagation algorithm until the prediction error (RMSE) of the model on the validation set data converges to below a preset threshold, thereby obtaining the trained corrosion risk prediction model.

[0031] Step S502: Input the observation data of the preset duration into the trained corrosion risk prediction model to output the corrosion medium concentration change curve and pavement condition prediction data of each airport pavement during the preset time period.

[0032] In step S502 of this application embodiment, the observation data for the preset duration uses the time series data of the past 72 hours as the input vector. After the data is input, the corrosion risk prediction model outputs the corrosion medium concentration change curve and pavement condition prediction data of the area in the next 48 hours based on the time series evolution law learned during the training process. It can be understood that the next 48 hours is the preset time period.

[0033] Step S503: Calculate the corrosion medium concentration change curve and pavement condition prediction data using the dynamic risk value calculation formula to obtain the risk prediction level of each airport pavement during a preset time period.

[0034] It should be noted that in step S503 of this application embodiment, the corrosive medium concentration change curve includes the predicted value of the antifreeze concentration gradient of the airport pavement at a future time (preset time), and the pavement condition prediction data includes the predicted values ​​of temperature and humidity of the airport pavement at a future time. In addition, when calculating the risk prediction level of the airport pavement during a preset period, the future weather forecast data and future flight plan data of the target airport are also required. The future weather forecast data is obtained by pulling the hourly weather forecast (including precipitation, temperature, and wind speed) for the next 48 hours issued by the airport meteorological station in real time through the API interface, and is used to calculate the future environmental trend factor P(M). The future flight plan data is obtained by synchronously reading the flight schedule and gate allocation plan for the next 48 hours from the airport A-CDM system, and is used to calculate the future operational urgency coefficient K(F).

[0035] Step S504: Map the risk prediction level of each airport pavement to the two-dimensional grid of the target airport, assign a color value corresponding to the risk prediction level of each airport pavement to the two-dimensional grid, and generate the corrosion heat map after smoothing.

[0036] In step S504 of this embodiment, the risk prediction level of each airport pavement at future times can be divided into three levels: red (high risk), yellow (medium risk), and green (low risk). After the level division, the risk prediction level of each airport pavement is mapped onto a two-dimensional grid of the entire airport pavement using inverse distance weighted interpolation (IDW) or Kriging interpolation. Then, based on the risk value of each grid, a corresponding color value is assigned through rendering, and smoothing is performed to finally generate a dynamic corrosion heat map that intuitively reflects the distribution of pavement corrosion risk. The dynamic corrosion heat map intuitively displays the corrosion accumulation trend of the pavement area, guiding the targeted allocation of preventive maintenance resources.

[0037] Furthermore, this application can also generate a spatiotemporal heat map based on the cumulative duration of corrosive media in the pavement area. The spatiotemporal heat map is used to guide long-term maintenance, while the dynamic corrosion heat map is used for real-time scheduling. The method for generating the spatiotemporal heat map is as follows: areas where the total duration of corrosive media presence accounts for more than 30% of the total monitoring period are marked as red high-risk areas; areas with a proportion between 10% and 30% are marked as yellow medium-risk areas; and areas with a proportion less than 10% are marked as green low-risk areas.

[0038] By implementing steps S501 to S504 above, this application generates a visual corrosion risk heat map, which intuitively presents the chemical corrosion accumulation trend in different areas of the pavement; provides a quantitative basis for preventive maintenance strategies (such as sealing coating replacement and drainage system renovation); supports the scientific planning of infrastructure renovation, and reduces the risk of liquid retention from the source.

[0039] Second Embodiment Based on the same inventive concept, such as Figure 2 as well as Figure 3 As shown in the illustration, this application also provides an intelligent monitoring and active corrosion prevention system for residual anti-icing fluid on airport pavements. The system includes a sensing layer, an edge layer, and a control center. The edge layer communicates with the control center via the LoRaWAN communication protocol. The sensing layer is used to acquire first monitoring data of multiple airport pavements in the target airport and second monitoring data of the target airport. The first monitoring data includes temperature data, humidity data, and characteristic data of corrosive media of the airport pavements. The second monitoring data includes multidimensional meteorological information and flight data of the target airport. The characteristic data of the corrosive media includes conductivity and spectral characteristics. The corrosive media is an alcohol or salt de-icing fluid.

[0040] It should be noted that the sensing layer consists of several integrated sensor nodes, and each sensor node is arranged as needed at specific locations on multiple airport pavements of the target airport. As an optional implementation, the airport pavement can be key pavement areas such as runway entrances, taxiway waiting points, and parking positions. The specific locations include low-lying water accumulation areas, pavement joints, and high-risk corrosion areas such as areas with poor drainage.

[0041] That is, Figure 3 As shown in the embodiments of this application, the sensor node highly integrates and encapsulates a composite conductivity sensing unit (conductivity sensor EC), a miniature near-infrared spectroscopy detection unit (spectral sensor SP), and a temperature and humidity detection unit (temperature and humidity sensor TH). The composite conductivity sensing unit is used to detect changes in solution ion concentration in real time to identify residual ethylene glycol, propylene glycol, and acetate-based anti-icing fluids. The miniature near-infrared spectroscopy detection unit is used to identify the chemical composition of the anti-icing fluid through specific wavelength absorption characteristics. The integrated temperature and humidity sensor is used to simultaneously collect ambient temperature and humidity data to assess the liquid evaporation rate and freezing risk.

[0042] In an exemplary embodiment, the sensor node includes a sensor housing 10, a waterproof cable 13, and a miniature near-infrared spectral probe 11, a temperature and humidity sensor 12, and a conductivity electrode 17 encapsulated inside the sensor housing 10. The sensor housing 10 is made of stainless steel, and the main body of the sensor housing 10 is embedded in the subgrade soil 20 of the concrete pavement 30. The top end face 19 of the sensor housing 10 is flush with the surface of the concrete pavement 30 to ensure that the de-icing fluid flowing through the pavement can cover the sensing area.

[0043] The sensor housing 10 has an optical detection port at its top, and a high-strength glass window 15 is sealed and embedded in the optical detection port. The high-strength glass window 15 is made of pressure-resistant and light-transmitting sapphire glass, and its surface is hydrophobically treated to reduce droplet residue. A sealant 18 is provided at the contact points between the high-strength glass window 15, the sensor housing 10, and the runway surface. The high-strength glass window 15 is directly below the miniature near-infrared spectral probe 11, which acts as a miniature near-infrared spectral detection unit to collect the spectral characteristics of liquids on the airport runway surface, thereby identifying the chemical composition of the liquid. A coplanar conductivity electrode 17 is embedded in the top end face 19 of the sensor housing 10. The conductivity electrode 17 is located adjacent to the high-strength glass window 15 and is used to contact the runway surface liquid and as a composite conductivity sensing unit to identify anti-icing fluid residue. A waterproof and ventilated hole 14 is provided on the side of the sensor housing 10, and the temperature and humidity sensor 12 is located inside the sensor housing 10 at the waterproof and ventilated hole 14.

[0044] In addition, to prevent pavement oil stains and rubber debris from obscuring the high-strength glass window 15, the sensor node is also equipped with a self-cleaning system. In this embodiment, the cleaning system adopts ultrasonic vibration descaling technology, specifically including a piezoelectric ceramic vibrator 16 attached to the inner edge of the high-strength glass window 15. When the monitoring data indicates an abnormal signal, the processor of the cleaning system controls the piezoelectric ceramic vibrator 16 to generate high-frequency micro-vibration, shaking off or crushing the attached dirt. At the same time, the piezoelectric ceramic vibrator 16 can be linked with the existing cleaning nozzles on the pavement for auxiliary rinsing.

[0045] In this embodiment, sensor nodes collect data and transmit it to the edge layer via a wireless network. After receiving the data, the edge gateway of the edge layer uses its built-in edge computing module to perform localized preprocessing on the raw sensor data, including conductivity threshold determination, spectral feature matching, and temperature and humidity compensation calculation. The data is then fused to generate a residual liquid concentration distribution map, which is compressed and transmitted to the control center.

[0046] The sensing layer is used to determine whether the characteristic data of the corrosive medium on the airport pavement is abnormal. If the determination result is yes, the first monitoring data and the second monitoring data are uploaded to the edge layer.

[0047] In this embodiment, to achieve comprehensive and differentiated accurate monitoring of residual anti-icing fluid on airport pavements, the system plans the deployment of sensor nodes as follows: For high-risk areas (R3), such as pavement joints and the area around drainage outlets, the system divides the monitoring area into a fine grid of 1 m × 1 m and deploys one sensor node to ensure accurate location of high-concentration corrosive media accumulation points and guide the automatic spraying device to perform precise operations; For medium-risk areas (R2), the system uses a standard grid of 5 m × 5 m to deploy one sensor node (corresponding to the size of a single pavement concrete slab; domestically, 4.5 m × 5 m or 5 m × 5 m pavement panels are used), and pushes inspection instructions to the ground terminal using this unit, facilitating rapid manual location and inspection; For low-risk areas (R1), the system uses a sparse grid of 10 m × 10 m for macroscopic monitoring, mainly used to record long-term environmental parameter change trends, thereby effectively reducing the system's computational load while ensuring full monitoring coverage.

[0048] To implement the above-described deployment method for sensor nodes, this application employs a risk-based multi-scale adaptive mesh partitioning technique for sensor network deployment planning, resulting in the integrated network as follows: Figure 4 The airport BIM system shown provides a precise 3D geometric model of the airport pavement (including structural information such as runway slabs, joints, and drainage ditches). The corrosion risk data generated in this application is overlaid onto the surface of the BIM pavement model as a dynamic texture using geographic coordinate mapping technology to form the airport BIM system. This allows maintenance personnel to intuitively view the distribution of corrosion risks by combining the pavement's physical structure (such as whether it is located in a low-lying area or near drainage outlets), thereby making more scientific maintenance decisions.

[0049] An edge layer is used to calculate the dynamic risk value of the airport pavement based on the first monitoring data and the second monitoring data, compare the dynamic risk value with a preset threshold, and determine the risk level of the airport pavement based on the comparison result. It should be noted that, as... Figure 3 As shown, the edge layer uses an ARM Cortex-M7 processor to calculate dynamic risk values ​​based on a dynamic risk value calculation model. When acquiring data uploaded from the sensing layer to the edge layer, the Kalman filter algorithm is used to perform real-time noise reduction and smoothing on the raw time-series data collected by the sensor, and the sensor data is stored locally for 72 hours.

[0050] The step of calculating the dynamic risk value of the airport pavement based on the first monitoring data and the second monitoring data includes: The characteristic data of the corrosive medium are processed to obtain the pavement concentration gradient of the corrosive medium; Dynamic risk values ​​are calculated based on the airport pavement temperature and humidity data, the multidimensional meteorological information, the flight data, and the pavement concentration gradient.

[0051] The control center is used to determine pavement maintenance decisions for each airport pavement in the target airport based on the risk assessment level, and the pavement maintenance decisions correspond to the risk assessment level.

[0052] It should be noted that the pavement maintenance decision includes uploading the spatiotemporal data of the airport pavement to the database (for low-risk levels (R1)) and performing route planning for the dispatch system. For medium-risk levels (R2) airport pavements, the corresponding pavement maintenance decision is to push inspection alerts and the optimal arrival route to the ground terminal. For high-risk levels (R3) airport pavements, the corresponding pavement maintenance decision is to activate the pre-embedded spray device on the target airport pavement or dispatch the nearest cleaning vehicle to the GPS coordinates (X,Y,Z) of the airport pavement to clean the de-icing fluid.

[0053] In addition, the control center also predicts the dynamic risk value at future moments using a time-series prediction model (LSTM prediction engine) built on a long short-term memory network, thereby generating a BIM system heat map. This BIM system heat map includes a spatiotemporal heat map and a dynamic corrosion heat map. The spatiotemporal heat map is used to guide long-term maintenance, while the dynamic corrosion heat map is used for real-time scheduling.

[0054] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0055] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0056] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0057] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0058] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0059] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0060] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0061] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for intelligent monitoring and active corrosion prevention of residual anti-icing fluid on airport pavements, characterized in that, The intelligent monitoring and active corrosion prevention method for residual anti-icing fluid on airport pavements includes: Acquire first monitoring data of multiple airport pavements at the target airport and second monitoring data of the target airport. The first monitoring data includes temperature data, humidity data, and characteristic data of corrosive media on the airport pavements. The second monitoring data includes multidimensional meteorological information and flight data of the target airport. The characteristic data of the corrosive media includes electrical conductivity and spectral characteristics. Determine whether the characteristic data of the corrosive medium of the airport pavement is abnormal. If the determination result is yes, calculate the dynamic risk value of the airport pavement based on the first monitoring data and the second monitoring data, compare the dynamic risk value with a preset threshold, and determine the risk level of the airport pavement based on the comparison result. Based on the risk assessment level, pavement maintenance decisions are made for each airport pavement in the target airport, and the pavement maintenance decisions correspond to the risk assessment level. The step of calculating the dynamic risk value of the airport pavement based on the first monitoring data and the second monitoring data includes: The characteristic data of the corrosive medium are processed to obtain the pavement concentration gradient of the corrosive medium; Dynamic risk values ​​are calculated based on the airport pavement temperature and humidity data, the multidimensional meteorological information, the flight data, and the pavement concentration gradient.

2. The intelligent monitoring and active corrosion prevention method for residual anti-icing fluid on airport pavements according to claim 1, characterized in that, The determination of whether the characteristic data of the corrosive medium on the airport pavement is abnormal includes: The system determines whether the characteristic data of the corrosive medium on the airport pavement meets preset anomaly judgment conditions. If the judgment result is yes, the characteristic data of the corrosive medium on the airport pavement is marked as anomaly data. The anomaly judgment conditions are concentration threshold judgment conditions, composition fingerprint judgment conditions, or critical risk judgment conditions. The concentration threshold judgment condition is that the conductivity of the corrosive medium is greater than a first preset value. The composition fingerprint judgment condition is that the spectral characteristics of the corrosive medium match the preset corrosion medium fingerprint with a degree greater than a second preset value. The critical risk judgment condition is that the difference between the temperature data of the airport pavement and the freezing point of the liquid is less than a third preset value.

3. The intelligent monitoring and active corrosion prevention method for residual anti-icing fluid on airport pavements according to claim 1, characterized in that, The process of processing the characteristic data of the corrosive medium to obtain the pavement concentration gradient of the corrosive medium includes: The electrical conductivity and spectral characteristics of the corrosive medium are inverted to obtain the concentration of the corrosive medium; The pavement concentration gradient of the corrosive medium is calculated based on the concentration of the corrosive medium and the geographical location information of the airport pavement.

4. The intelligent monitoring and active corrosion prevention method for residual anti-icing fluid on airport pavements according to claim 3, characterized in that, The formula for calculating the dynamic risk value based on the airport pavement temperature and humidity data, the multidimensional meteorological information, the flight data, and the pavement concentration gradient is as follows: ； In the formula, Indicates dynamic risk value; Represents flight data. This represents the operational urgency coefficient calculated based on flight data; Indicates the pavement concentration gradient; This represents the surface concentration gradient of the corrosive medium. Represents multidimensional meteorological information. This represents the trend factor calculated based on multidimensional meteorological information; Indicates the weighting coefficient; Indicates based on pavement concentration gradient The physical corrosion rate is calculated based on temperature T and humidity H.

5. The intelligent monitoring and active corrosion prevention method for residual anti-icing fluid on airport pavements according to claim 4, characterized in that, The step of comparing the dynamic risk value with a preset threshold and determining the risk level of the airport pavement based on the comparison result includes: A preset threshold is determined, which includes a first threshold and a second threshold; wherein the first threshold is less than the second threshold. The dynamic risk value is compared with a first threshold and a second threshold. When the dynamic risk value is less than the first threshold, the airport pavement corresponding to the dynamic risk value is determined to be of low risk level; when the dynamic risk value is greater than or equal to the first threshold and less than the second threshold, the airport pavement corresponding to the dynamic risk value is determined to be of medium risk level; when the dynamic risk value is greater than or equal to the second threshold, the airport pavement corresponding to the dynamic risk value is determined to be of high risk level.

6. The intelligent monitoring and active corrosion prevention method for residual anti-icing fluid on airport pavements according to any one of claims 1-5, characterized in that, It also includes generating a corrosion heat map based on first and second monitoring data, the corrosion heat map being used to characterize the risk prediction level of each airport pavement during a preset time period; wherein: generating the corrosion heat map based on the first and second monitoring data includes: The corrosion risk prediction model was trained using historical datasets of the first and second monitoring data. The observation data for a preset duration is input into the trained corrosion risk prediction model to output the corrosion medium concentration change curve and pavement condition prediction data of each airport pavement during the preset time period. The risk prediction level of each airport pavement during a preset time period is obtained by using the dynamic risk value calculation formula to calculate the concentration change curve of the corrosive medium and the pavement condition prediction data. The risk prediction level of each airport pavement is mapped onto a two-dimensional grid of the target airport. A color value corresponding to the risk prediction level of each airport pavement is assigned to the two-dimensional grid, and the corrosion heat map is generated after smoothing.

7. A smart monitoring and active corrosion prevention system for residual anti-icing fluid on airport pavements, characterized in that, The intelligent monitoring and active corrosion prevention system for residual anti-icing fluid on airport pavements includes: The sensing layer is used to acquire first monitoring data of multiple airport pavements in the target airport and second monitoring data of the target airport. The first monitoring data includes temperature data, humidity data, and characteristic data of corrosive media of the airport pavements. The second monitoring data includes multi-dimensional meteorological information and flight data of the target airport. The characteristic data of the corrosive media includes electrical conductivity and spectral characteristics. In addition, determine whether the characteristic data of the corrosive medium on the airport pavement is abnormal; An edge layer is used to calculate the dynamic risk value of the airport pavement based on the first monitoring data and the second monitoring data when the characteristic data of the corrosive medium on the airport pavement is judged to be abnormal. The dynamic risk value is compared with a preset threshold, and the risk level of the airport pavement is determined based on the comparison result. The control center is used to determine pavement maintenance decisions for each airport pavement in the target airport based on the risk assessment level, and the pavement maintenance decisions correspond to the risk assessment level. The step of calculating the dynamic risk value of the airport pavement based on the first monitoring data and the second monitoring data includes: The characteristic data of the corrosive medium are processed to obtain the pavement concentration gradient of the corrosive medium; Dynamic risk values ​​are calculated based on the airport pavement temperature and humidity data, the multidimensional meteorological information, the flight data, and the pavement concentration gradient.

8. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of any one of claims 1-6.

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