An airport pavement freezing point temperature real-time monitoring method, device and system

Abstract

The application provides an airport pavement freezing point temperature real-time monitoring method, device and system, and relates to the technical field of airport pavement monitoring. The method comprises the following steps: identifying water accumulation areas contained in each collection area in an airport pavement image, determining a water accumulation distribution main axis of the water accumulation areas, and each collection area containing a water film sensor; determining a water accumulation position influence degree based on a water accumulation total area change trend of the collection area and distances of the water accumulation areas from the water accumulation distribution main axis, and determining a water accumulation area sensitivity in combination with area ratios of the water accumulation areas to the collection areas; determining a water film sensor water film thickness sensitivity based on the water accumulation area sensitivity, a pavement water film thickness, and an average value and a change rate of the water film thickness, and determining a water film thickness sensitivity consistency in combination with an average water film thickness sensitivity; determining a water film sensor parameter sensitivity, determining a data accuracy in combination with an average parameter sensitivity, correcting the pavement water film thickness, and determining the freezing point temperature. The application can improve the accuracy of freezing point temperature monitoring.

Description

Technical Field

[0001] This application relates to the field of airport pavement monitoring technology, specifically to a method, device, and system for real-time monitoring of airport pavement freezing point temperature. Background Technology

[0002] Airport pavement icing is a core risk factor threatening aviation safety in winter. Iced pavements significantly reduce the friction between aircraft tires and the pavement, leading to safety risks such as loss of steering control and increased braking distances during takeoff, landing, and taxiing. Therefore, timely and accurate pavement icing warnings and efficient de-icing and anti-icing operations through airport pavement monitoring technology are crucial for ensuring normal airport operations and flight safety during winter.

[0003] The relevant technology mainly monitors the freezing point temperature of airport pavement by deploying water film sensors at fixed locations. Specifically, it collects multi-dimensional parameters such as pavement temperature, water film thickness, conductivity, pavement physical state, and pollution concentration by deploying water film sensors at fixed locations. Then, it combines the built-in algorithm to calculate the pavement freezing point temperature by integrating all influencing factors, so as to realize pavement icing early warning.

[0004] In the above methods, the accuracy of freezing point temperature calculation directly depends on the accuracy of the parameters collected by the water film sensor. However, because the water film sensor is deployed in a fixed location, its response to parameters under different pavement physical conditions varies, resulting in inconsistent measurement accuracy under different pavement conditions, failing to uniformly reflect the true pavement situation. The water film sensor measures the sum of all soluble substances in the water film; the presence of contaminants causes the parameters collected by the water film sensor to deviate from the true water film characteristics, thus causing a deviation between the calculated freezing point temperature and the actual freezing point. The calculation of pavement freezing point temperature requires multi-parameter collaborative analysis, but the above methods do not fully consider the response of each monitoring parameter to changes in pavement condition; differences in parameter responsiveness further amplify measurement errors and reduce data reliability. Summary of the Invention

[0005] To address the shortcomings in related technologies, such as measurement deviations due to fixed water film sensor positions, interference from pavement water film contaminants, and failure to consider the impact of parameter responsiveness on accuracy, which result in insufficient accuracy of airport pavement freezing point temperature monitoring data and fail to provide precise support for icing early warning and de-icing / anti-icing operations, this application provides a method, device, and system for real-time monitoring of airport pavement freezing point temperature. The specific technical solution adopted is as follows: Monitoring data is collected by multiple water film sensors deployed on the airport pavement; the monitoring data includes the thickness of the water film on the pavement. Airport pavement images are acquired and divided into multiple acquisition areas. Water accumulation areas are identified within the acquisition areas, and the main axis of water accumulation distribution is determined based on the centroid coordinates of all the water accumulation areas. Each acquisition area contains one water film sensor. The influence of water location is determined based on the changing trend of the total area of ​​water accumulation in the collection area during multiple consecutive collections and the distance between each water accumulation area and the main axis of water accumulation distribution. The water area sensitivity of the collection area is determined based on the influence of water location in each water accumulation area and its ratio to the area of ​​the collection area. The water film thickness sensitivity is determined based on the water accumulation area sensitivity and the pavement water film thickness, average pavement water film thickness, and pavement water film thickness change rate collected by the water film sensor. The water film thickness sensitivity consistency is determined based on the deviation between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity of all water film sensors. The parameter sensitivity is determined based on the water film thickness sensitivity of the water film sensor and the water film thickness sensitivity consistency. The data accuracy is determined based on the deviation between the parameter sensitivity of the water film sensor and the average parameter sensitivity of all water film sensors. The pavement water film thickness is corrected based on the average data accuracy probability of all water film sensors. The freezing point temperature of the airport pavement is determined based on the corrected pavement water film thickness.

[0006] For example, determining the main axis of water accumulation distribution based on the centroid coordinates of all the water accumulation areas includes: constructing a two-dimensional spatial point set based on the centroid coordinates of all the water accumulation areas; extracting the principal component directions of the two-dimensional spatial point set using a principal component analysis algorithm, which are denoted as the main axis of water accumulation distribution. The main axis of water accumulation distribution is used to characterize the linear distribution characteristics of water accumulation on the airport runway along the direction of aircraft takeoff and landing, and its extension direction is consistent with the direction of aircraft taxiing trajectory on the airport runway.

[0007] For example, determining the degree of influence of water accumulation location based on the changing trend of the total area of ​​water accumulation in the collection area during multiple consecutive collections and the distance between each water accumulation area and the main axis of water accumulation distribution includes: calculating the sum of the areas of all water accumulation areas included in the collection area at each data collection time, and recording it as the total area of ​​water accumulation in the corresponding collection number; calculating and summing the area differences of the total area of ​​water accumulation in each adjacent collection number, and determining the regional stability of the collection area based on the sum of the area differences; determining the Euclidean distance between each water accumulation area and the main axis of water accumulation distribution for each water accumulation area in the collection area, and recording it as the distance influence factor; and determining the degree of influence of water accumulation location of the water accumulation area based on the regional stability and the distance influence factor corresponding to the water accumulation area.

[0008] For example, determining the water area sensitivity of the collection area based on the influence of the water location of each water accumulation area and its area ratio with that of the collection area includes: calculating the ratio of the area of ​​a first region of each water accumulation area to the area of ​​a second region of the collection area for each water accumulation area in the collection area, and recording it as the area contribution factor of the water accumulation area; determining its sensitivity contribution factor based on the area contribution factor of each water accumulation area and the influence of the water location; and determining the water area sensitivity of the collection area based on the sensitivity contribution factor of each water accumulation area.

[0009] For example, determining the water film thickness sensitivity based on the water accumulation area sensitivity and the pavement water film thickness, average pavement water film thickness, and pavement water film thickness change rate collected by the water film sensor includes: acquiring the pavement water film thickness collected by the water film sensor multiple times consecutively; calculating the average value of the pavement water film thickness collected multiple times consecutively, and recording it as the average pavement water film thickness; calculating the thickness difference between the pavement water film thickness currently collected by the water film sensor and the pavement water film thickness collected previously, and the collection time interval, and recording the absolute value of the ratio of the thickness difference to the collection time interval as the water film thickness change rate; determining the dynamic stability of the water film sensor based on the pavement water film thickness currently collected by the water film sensor, the average pavement water film thickness, and the water film thickness change rate; and determining the water film thickness sensitivity of the water film sensor based on the water accumulation area sensitivity and the dynamic stability.

[0010] For example, determining the water film thickness sensitivity consistency based on the deviation between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity of all water film sensors includes: calculating the average value of the water film thickness sensitivity of the water film sensors corresponding to each collection area, denoted as the average water film thickness sensitivity; for each water film sensor, calculating the absolute value of the difference between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity, denoted as the water film thickness sensitivity deviation factor; and determining the water film thickness sensitivity consistency of the water film sensors based on the water film thickness sensitivity deviation factor.

[0011] For example, determining the data accuracy based on the deviation between the parameter sensitivity of the water film sensor and the average parameter sensitivity of all water film sensors includes: calculating the average value of the parameter sensitivity of the water film sensors corresponding to each collection area, denoted as the average parameter sensitivity; for each water film sensor, calculating the absolute value of the difference between the parameter sensitivity of the water film sensor and the average parameter sensitivity, denoted as the parameter sensitivity deviation factor; and determining the data accuracy of the water film sensor based on the parameter sensitivity of the water film sensor and the parameter sensitivity deviation factor.

[0012] For example, the step of correcting the pavement water film thickness based on the average data accuracy probability of all the water film sensors, and determining the freezing point temperature of the airport pavement based on the corrected pavement water film thickness, includes: calculating the average of the data accuracy rates of all the water film sensors, denoted as the average data accuracy probability; determining a correction coefficient based on the average data accuracy probability; correcting the pavement water film thickness based on the correction coefficient; and substituting the corrected pavement water film thickness into the freezing point reduction formula to calculate the freezing point temperature.

[0013] Correspondingly, this application also provides a real-time monitoring device for airport pavement freezing point temperature, comprising: The data acquisition module is used to collect monitoring data through multiple water film sensors deployed on the airport pavement; the monitoring data includes the thickness of the water film on the pavement. The image processing module is used to acquire airport pavement images and divide them into multiple acquisition areas, identify water accumulation areas in the acquisition areas, and determine the main axis of water accumulation distribution based on the centroid coordinates of all the water accumulation areas; each acquisition area includes one water film sensor; The freezing point monitoring module is used to determine the degree of influence of water accumulation location based on the changing trend of the total area of ​​water accumulation area in the collection area during multiple consecutive collections and the distance between each water accumulation area and the main axis of water accumulation distribution; and to determine the water accumulation area sensitivity of the collection area based on the degree of influence of water accumulation location of each water accumulation area and its area ratio with that of the collection area. The freezing point monitoring module is further configured to determine its water film thickness sensitivity based on the water accumulation area sensitivity and the pavement water film thickness, average pavement water film thickness, and pavement water film thickness change rate collected by the water film sensor; determine its water film thickness sensitivity consistency based on the deviation between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity of all the water film sensors; and determine its parameter sensitivity based on the water film thickness sensitivity of the water film sensor and the water film thickness sensitivity consistency. The freezing point monitoring module is further configured to determine the data accuracy based on the deviation between the parameter sensitivity of the water film sensor and the average parameter sensitivity of all the water film sensors, correct the pavement water film thickness based on the average data accuracy probability of all the water film sensors, and determine the freezing point temperature of the airport pavement based on the corrected pavement water film thickness.

[0014] In addition, this application also provides a real-time monitoring system for airport pavement freezing point temperature, which determines the freezing point temperature of the airport pavement by performing any of the above-described real-time monitoring methods for airport pavement freezing point temperature.

[0015] This application may have some or all of the following beneficial effects: In the real-time monitoring method for airport pavement freezing point temperature provided in this application, airport pavement images are acquired and divided into multiple acquisition areas (each acquisition area corresponds to a water film sensor). This allows for the accurate establishment of a correspondence between the monitoring range of the water film sensor and the actual distribution of water accumulation on the pavement, solving the problem of the fixed water film sensor monitoring range being disconnected from the pavement condition. By determining the main axis of water accumulation distribution, the linear distribution law of water accumulation along the aircraft taxiing trajectory is clarified. The stability of the acquisition area is determined by the trend of the total water accumulation area change. The influence of water accumulation location is calculated by combining the distance between each water accumulation area and the main axis of water accumulation distribution. This can identify the interference of high-pollutant areas such as the aircraft main landing gear track zone on the water film sensor measurement. Furthermore, by combining the area ratio of the water accumulation area to the acquisition area to determine the water accumulation area sensitivity, parameter anomalies caused by pollutants can be effectively eliminated, improving the acquisition of water film-related parameters. The accuracy of pavement water film thickness monitoring is improved by combining real-time values, historical averages, and rates of change of water film thickness collected by water film sensors. This allows for a comprehensive assessment of parameter reliability based on the spatial distribution and dynamic changes of pavement water. Furthermore, the sensitivity consistency is calculated by comparing the sensitivity of a single water film sensor with the average level of all sensors, further validating the spatial consistency of the parameters. This multi-dimensional optimization of parameter sensitivity assessment enhances the reliability of parameter monitoring. The data accuracy is determined by comparing the sensitivity of each water film sensor with the average sensitivity. The pavement water film thickness is then corrected using the average accuracy probability of all sensors, and the corrected pavement water film thickness is used to determine the freezing point temperature of the airport pavement. This improves the accuracy of freezing point temperature calculation, ensuring that the calculated freezing point temperature matches the actual icing risk and enhancing the accuracy of airport pavement freezing point temperature monitoring.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a method for real-time monitoring of airport pavement freezing point temperature according to an exemplary embodiment of this application is shown; Figure 2 A schematic block diagram of an airport pavement freezing point temperature real-time monitoring device according to an exemplary embodiment of this application is shown. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method, apparatus, and system for real-time monitoring of airport pavement freezing point temperature proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0021] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method, device, and system for real-time monitoring of airport pavement freezing point temperature provided in this application.

[0022] Please see Figure 1 The document illustrates a flowchart of a real-time monitoring method for airport pavement freezing point temperature according to an embodiment of this application. Figure 1 As shown, the real-time monitoring method for the freezing point temperature of the airport pavement specifically includes the following steps: S110: Monitoring data is collected through multiple water film sensors deployed on the airport pavement; the monitoring data includes the thickness of the water film on the pavement. S120: Acquires images of the airport pavement and divides them into multiple acquisition areas, identifies water accumulation areas within the acquisition areas, and determines the main axis of water accumulation distribution based on the centroid coordinates of all water accumulation areas; each acquisition area contains a water film sensor. S130: Determine the degree of influence of water accumulation location based on the changing trend of the total area of ​​water accumulation area in the collection area during multiple consecutive collections and the distance between each water accumulation area and the main axis of water accumulation distribution; determine the water accumulation area sensitivity of the collection area based on the degree of influence of water accumulation location of each water accumulation area and its area ratio with that of the collection area. S140: The water film thickness sensitivity is determined based on the water accumulation area sensitivity and the pavement water film thickness, average pavement water film thickness and pavement water film thickness change rate collected by the water film sensor. The water film thickness sensitivity consistency is determined based on the deviation between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity of all water film sensors. The parameter sensitivity is determined based on the water film thickness sensitivity of the water film sensor and the water film thickness sensitivity consistency. S150: Determine the data accuracy based on the deviation between the parameter sensitivity of the water film sensor and the average parameter sensitivity of all water film sensors; correct the pavement water film thickness based on the average data accuracy probability of all water film sensors; and determine the freezing point temperature of the airport pavement based on the corrected pavement water film thickness.

[0023] The following is a detailed explanation of each step in the above-mentioned method for real-time monitoring of airport pavement freezing point temperature: In step S110, monitoring data is collected by multiple water film sensors deployed on the airport pavement; the monitoring data includes the thickness of the water film on the pavement.

[0024] In this embodiment, the water film sensor is a monitoring device for collecting parameters related to the water film on the airport pavement. For example, the water film sensor can be an embedded water film sensor, which is installed at a designated location on the airport pavement by core drilling to obtain multi-source monitoring data related to the pavement water film required for the freezing point temperature of the airport pavement.

[0025] In this embodiment, the monitoring data includes multi-source basic data on the freezing point temperature of the airport pavement collected by a water film sensor. Exemplarily, the monitoring data may include the pavement water film thickness and the pavement physical state. The pavement water film thickness refers to the thickness of the liquid water layer formed on the airport pavement surface due to precipitation, de-icing, etc. The pavement physical state includes five typical states: dry, wet, waterlogged, snow-covered, and icy, used to assist in determining the form of the water film and the basic scenarios for pavement icing risk.

[0026] In one specific implementation of this application embodiment, the above-mentioned data collection and monitoring through multiple water film sensors deployed on the airport pavement can be achieved as follows: The aforementioned embedded water film sensors are installed on one shoulder of the airport road using core drilling, with a total of K sensors deployed. Each water film sensor is ensured to be in contact with the pavement surface to accurately collect parameters such as the water film thickness and physical state of the pavement in its designated area. Continuous data collection is performed using the K deployed embedded water film sensors, with a total of A data collection operations. During each data collection, each water film sensor simultaneously outputs two types of data: the water film thickness in the area where the sensor is located, denoted as... (The value of a ranges from 1 to A, and the value of k ranges from 1 to K). The physical state of the pavement in the area where the water film sensor is located is matched and recorded from five states: "dry, wet, waterlogged, snow-covered, and icy". The pavement water film thickness and pavement physical state collected from K water film sensors each time are classified and temporarily stored according to the number of collections and the water film sensor number. This lays the foundation for the alignment and fusion of the water film sensor data with the airport pavement image data, ensuring that the water film sensor monitoring data can be associated with the image information of specific pavement areas.

[0027] In step S120, an image of the airport pavement is acquired and divided into multiple acquisition areas. Water accumulation areas are identified within the acquisition areas, and the main axis of water accumulation distribution is determined based on the centroid coordinates of all water accumulation areas. Each acquisition area contains a water film sensor.

[0028] In this embodiment of the application, the aforementioned airport pavement image is image data covering the airport pavement monitoring range, captured by a drone equipped with a high-definition camera.

[0029] In this embodiment, the aforementioned acquisition area is a sub-region of the pavement image obtained by dividing the airport pavement image according to the principle of matching the number of water film sensors. The acquisition area is the same as the number of water film sensors, and each acquisition area uniquely contains one water film sensor. By establishing the correspondence between the monitoring range of the water film sensor and the actual water accumulation distribution on the pavement, the problem of the fixed monitoring range of the water film sensor being disconnected from the pavement condition is solved.

[0030] In this embodiment of the application, the aforementioned water accumulation area is a local area on the pavement where liquid water exists, identified from the collection area by an image processing algorithm. Each collection area may contain one or more water accumulation areas.

[0031] In one specific implementation of this application embodiment, the above-mentioned acquisition of airport pavement images and division into multiple acquisition areas, and the identification of water accumulation areas within the acquisition areas, can be achieved as follows: A drone equipped with a high-definition camera is used to photograph the airport pavement covering the entire deployment range of the water film sensors, obtaining a complete airport pavement image; the photographed airport pavement image is preprocessed, and based on the principle of one-to-one correspondence between acquisition areas and water film sensors, the preprocessed image is uniformly divided into N acquisition areas (N=K, where K is the total number of water film sensors); for each acquisition area, an image threshold segmentation algorithm (Otsu method or adaptive threshold method) is used to segment the water accumulation areas and non-water accumulation areas within the acquisition area at the pixel level by setting a pixel grayscale threshold, and the area of ​​each water accumulation area is calculated and recorded. Specifically, taking the i-th water accumulation area in the n-th acquisition area of ​​the a-th acquisition data in A consecutive acquisitions as an example, the area of ​​this water accumulation area is recorded as... The water film sensor data and image data are aligned and fused to form a multi-source dataset.

[0032] During takeoff and landing, the main landing gear tires experience continuous and intense friction with specific areas of the runway, creating a track zone on the pavement. This track zone, due to its physical characteristics (such as texture variations and rubber deposits), is more prone to water accumulation, and the water within it exhibits a roughly linear distribution along the aircraft's direction of flight, rather than a random distribution. Furthermore, the friction process generates contaminants such as rubber particles, hydraulic fluid residue, and dust. Therefore, the water film in the track zone is more likely to contain these contaminants, and the closer the water accumulation is to the tire track zone, the higher the probability of contaminants and the stronger the interference with measurement results.

[0033] In this embodiment of the application, the aforementioned main axis of water accumulation distribution is used to reflect the approximate linear distribution characteristics of water accumulation on the airport runway along the direction of aircraft takeoff and landing. It is consistent with the trajectory of the aircraft's main landing gear tires on the runway and serves as a reference benchmark for determining whether the water accumulation area is close to a high-pollutant area.

[0034] For example, the above method of determining the main axis of water accumulation distribution based on the centroid coordinates of all water accumulation areas can be achieved as follows: construct a two-dimensional spatial point set based on the centroid coordinates of all water accumulation areas; extract the principal component directions of the two-dimensional spatial point set through principal component analysis algorithm, and denote them as the main axis of water accumulation distribution.

[0035] In one specific implementation of this application embodiment, determining the main axis of water accumulation distribution can be achieved as follows: For each water accumulation area, the centroid coordinates of its pixel contour are determined through geometric calculation. Taking the i-th water accumulation area in the n-th acquisition area of ​​the a-th acquisition data in A consecutive acquisitions as an example, the centroid coordinates of this water accumulation area are marked as follows: Summarize the centroid coordinates of all water-filled areas within all sampling areas during the a-th sampling period to construct a two-dimensional spatial point set of centroid coordinates of the water-filled areas; extract the main distribution direction of the constructed two-dimensional spatial point set using principal component analysis algorithm, denoted as the main axis of water distribution. The direction of the extension of this main axis is consistent with the taxiing trajectory of aircraft taking off and landing on the airport runway, which can characterize the main distribution pattern of water accumulation on the runway.

[0036] In step S130, the influence of water location is determined based on the changing trend of the total area of ​​water accumulation in the collection area during multiple consecutive collections and the distance between each water accumulation area and the main axis of water accumulation distribution. The water area sensitivity of the collection area is determined based on the influence of water location in each water accumulation area and its ratio to the area of ​​the collection area.

[0037] In this embodiment, the aforementioned influence of water accumulation location is an index used to quantify the degree of interference of the water accumulation area on the measurement results of the water film sensor. This influence can be determined based on the distance between the water accumulation area and the main axis of water distribution, as well as the stability of the sampling area. Specifically, the closer the water accumulation area is to the main axis of water distribution and the more stable the water in the sampling area, the higher the probability that it contains contaminants such as rubber particles and hydraulic oil, and the stronger the interference to the measurement.

[0038] For example, the above-mentioned determination of the influence of water accumulation location based on the changing trend of the total area of ​​water accumulation in the collection area during multiple consecutive collections and the distance between each water accumulation area and the main axis of water accumulation distribution can be achieved as follows: Calculate the sum of the areas of all water accumulation areas included in the collection area at each data collection time, and record it as the total area of ​​water accumulation in the corresponding collection number; calculate and sum the area differences of the total area of ​​water accumulation in each adjacent collection number, and determine the regional stability of the collection area based on the sum of the area differences; for each water accumulation area in the collection area, determine the Euclidean distance between the water accumulation area and the main axis of water accumulation distribution, and record it as the distance influence factor; determine the influence of water accumulation location in the water accumulation area based on the regional stability and the distance influence factor corresponding to the water accumulation area.

[0039] In one specific implementation of this application embodiment, taking the nth collection area in the ath collection data of A consecutive collections as an example, the process of determining the influence of water accumulation location can be achieved as follows: obtain the total area of ​​all water accumulation areas in the nth collection area of ​​A consecutive collections. (a=1,2,...,A); the regional stability of the nth acquisition area is calculated using the following formula: in, The stability of the nth collection area is used to characterize the smoothness of water accumulation fluctuations in the nth collection area during A consecutive monitoring sessions. It is an indicator for quantifying the stability of water accumulation in the collection area. This represents the sum of the areas of all waterlogged areas in the nth data collection region during the a-th data collection. It is the sum of the areas of all waterlogged areas in the nth collection region in the (a-1)th data collection; This represents the area difference between the total area of ​​the waterlogged area in the nth sampling region among adjacent sampling times; The formula is a natural exponential function; it accumulates the fluctuation range of water accumulation in adjacent sampling times and maps it to water accumulation stability. , The larger the value, the more likely the water accumulation status of the nth sampling area is to remain stable over a long period.

[0040] After determining the regional stability of the collection area, this specific implementation method calculates the influence of the water accumulation location of the i-th water accumulation area in the n-th collection area of ​​the a-th data collection using the following formula: in, The degree of influence of the water accumulation location on the i-th water accumulation area within the n-th data collection area in the a-th data collection; This represents the regional stability of the nth sampling area, reflecting the stability of the water accumulation area within that area. Stable water accumulation area (corresponding to...) Larger values ​​indicate a higher likelihood of being located in areas with high pollutant incidence (such as near the main landing gear track area of ​​an aircraft), resulting in higher confidence levels regarding interference with measurements. Unstable areas (corresponding to...) (If the value is small) Because the water accumulation is temporary, the predictability of pollutant interference is low, and the interference weight needs to be reduced. The i-th waterlogged area within the n-th data collection region in the a-th data collection session and the main axis of waterlogging distribution. The smaller the Euclidean distance between them, the closer the i-th waterlogged area is to the main axis of water distribution, the greater the likelihood that it contains pollutants, and the greater its impact on parameter monitoring. The purpose of adding 1 to the denominator is to prevent the denominator from being 0.

[0041] In this embodiment of the application, the above-mentioned water accumulation area sensitivity is an index used to quantify the influence of water accumulation in the collection area on the reliability of water film sensor parameter measurement. The water accumulation area sensitivity can be determined based on the influence of water accumulation location in each water accumulation area and its area contribution weight. Specifically, the larger the value of the water accumulation area sensitivity, the more representative the parameters measured by the water film sensor in the corresponding collection area are of the true water film state, and the higher the data reliability.

[0042] For example, the above-mentioned determination of the water area sensitivity of the collection area based on the influence of the water location of each water accumulation area and its ratio to the area of ​​the collection area can be achieved as follows: For each water accumulation area in the collection area, calculate the ratio of the area of ​​the first area of ​​each water accumulation area to the area of ​​the second area of ​​the collection area, and record it as the area contribution factor of the water accumulation area; determine its sensitivity contribution factor based on the area contribution factor of each water accumulation area and the influence of the water location; and determine the water area sensitivity of the collection area based on the sensitivity contribution factor of each water accumulation area.

[0043] In one specific implementation of this application embodiment, taking the nth collection area in the a-th collection data of A consecutive collections as an example, the water area sensitivity of the nth collection area can be calculated by the following formula: in, The sensitivity of the water accumulation area in the nth collection area in the data collection of the ath time; This represents the number of waterlogged areas in the nth data collection region. This represents the influence of the water accumulation location of the i-th water accumulation area within the n-th data collection area in the a-th data collection. A larger value indicates a stronger interference from the i-th water accumulation area on parameter monitoring, and its weight should be reduced accordingly. Adding 1 to the denominator term is to prevent the denominator from being 0; This refers to the area of ​​the i-th waterlogged area within the n-th data collection area in the a-th data collection (i.e., the area of ​​the first area mentioned above). This is the area of ​​the nth collection region in the data collection of the ath time (that is, the area of ​​the second region mentioned above); The area contribution factor of the i-th water accumulation area is used to quantify the representativeness of a single water accumulation area to the overall water film state of the collection area. The larger the area of ​​the water accumulation area, the better it reflects the overall water film characteristics (such as thickness and pollutant content) of the collection area, and it needs to be given a higher weight. The above formula quantifies the overall reliability contribution of the acquisition area by summing the reliability contributions of all waterlogged areas in the nth acquisition area, for the reliability contribution of the i-th waterlogged area (i.e., the sensitivity contribution factor mentioned above). The larger the value, the larger the water accumulation area in the nth collection area and the farther away from the main axis it is. The less likely it is to contain pollutants, the more representative the data measured by the water film sensors deployed in the collection area is of the true water film state of the entire collection area, and the higher the data reliability.

[0044] In step S140, the water film thickness sensitivity is determined based on the water accumulation area sensitivity and the pavement water film thickness, average pavement water film thickness, and pavement water film thickness change rate collected by the water film sensor. The water film thickness sensitivity consistency is determined based on the deviation between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity of all water film sensors. The parameter sensitivity is determined based on the water film thickness sensitivity of the water film sensor and the water film thickness sensitivity consistency.

[0045] In the embodiments of this application, the above-mentioned water film thickness sensitivity is the reliability sensitivity of the pavement water film thickness measured by the corresponding water film sensor, which can be used to evaluate the reliability of the water film sensor's response to changes in water film thickness; the larger the value of the water film thickness sensitivity, the more the water film thickness measured by the corresponding water film sensor can reflect the real dynamic changes of the pavement water film (it is less affected by water accumulation and can accurately respond to thickness fluctuations), and the higher the data reliability.

[0046] For example, the above-mentioned determination of pavement water film thickness sensitivity based on water accumulation area sensitivity and pavement water film thickness, average pavement water film thickness, and pavement water film thickness change rate collected by the water film sensor can be achieved as follows: obtain pavement water film thickness collected by the water film sensor multiple times consecutively; calculate the average pavement water film thickness collected multiple times consecutively, and record it as the average pavement water film thickness; calculate the thickness difference between the pavement water film thickness currently collected by the water film sensor and the previous pavement water film thickness, and the collection time interval, and record the absolute value of the ratio of the thickness difference to the collection time interval as the water film thickness change rate; determine the dynamic stability of the water film sensor based on the pavement water film thickness currently collected by the water film sensor, the average pavement water film thickness, and the water film thickness change rate; determine the water film thickness sensitivity of the water film sensor based on water accumulation area sensitivity and dynamic stability.

[0047] Specifically, taking the k-th water film sensor in the a-th data acquisition from A consecutive acquisitions as an example, the water film thickness sensitivity of the k-th water film sensor in the a-th data acquisition can be calculated using the following formula: in, Let be the water film thickness sensitivity of the k-th water film sensor in the data collected during the a-th acquisition; The sensitivity of the water area in the nth sampling area in the data collection a is the larger the value, the smaller the interference of the water in the nth sampling area on the water film sensor measurement during the ath sampling. Let be the thickness of the pavement water film measured by the k-th water film sensor in the a-th data acquisition; It is the average value of the pavement water film thickness measured by the k-th water film sensor in A consecutive acquisitions (that is, the average value of the pavement water film thickness mentioned above). This is the deviation between the current pavement water film thickness collected by the water film sensor and the historical average pavement water film thickness. The smaller this value, the higher the reliability of the pavement water film thickness data collected by the water film sensor. Let be the difference in thickness of the pavement water film collected by the k-th water film sensor during the a-th and a-1-th data acquisition processes. The time interval between the a-th and a-1-th data collections; The smaller the value of the water film thickness change rate, the smoother the thickness fluctuation, and the more the data conforms to the natural change law of the pavement water film (such as slow evaporation and uniform precipitation), and the stronger the data stability; e is the natural exponential function. To achieve the aforementioned dynamic stability, the reliability of the water film thickness data acquired by the water film sensor is dynamically adjusted. The above formula quantifies the reliability sensitivity of the water film thickness data by comprehensively considering water accumulation interference constraints and thickness dynamic characteristic correction. The larger the value, the more representative the data measured by the water film sensor is of the water film condition in the entire collection area, and the higher the accuracy of the collected data.

[0048] Airport runways are large, open areas. Multiple water film sensors are deployed on the runway surface to collect data on the thickness of the water film. Since the physical conditions of the runway surface (such as dryness, humidity, and icing) are relatively uniform in spatial distribution, if the monitoring data collected by all water film sensors under the same environmental conditions are similar, it indicates that each water film sensor is functioning normally, and the entire monitoring system is reliable. Conversely, if the reading of a particular water film sensor differs significantly from the others, it indicates that the sensor may be malfunctioning or obstructed by foreign objects, resulting in low data accuracy. In other words, under the same runway surface physical conditions, the better the consistency of the data monitored by multiple water film sensors, the more accurate the data.

[0049] In this embodiment, the aforementioned water film thickness sensitivity consistency is used to evaluate the spatial consistency between the data of a single water film sensor and the overall water film sensor data; the larger the value, the closer the water film thickness sensitivity of the water film sensor is to the average sensitivity level of all water film sensors, and the more reliable the data.

[0050] For example, the determination of water film thickness sensitivity consistency based on the deviation between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity of all water film sensors can be achieved as follows: calculate the average value of the water film thickness sensitivity of the water film sensors corresponding to each collection area, and denot it as the average water film thickness sensitivity; for each water film sensor, calculate the absolute value of the difference between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity, and denot it as the water film thickness sensitivity deviation factor; determine the water film thickness sensitivity consistency of the water film sensors based on the water film thickness sensitivity deviation factor.

[0051] Specifically, taking the k-th water film sensor in the a-th data acquisition of A consecutive acquisitions as an example, the water film thickness sensitivity consistency of the k-th water film sensor in the a-th data acquisition is determined by the following formula: in, The water film thickness sensitivity consistency of the k-th water film sensor in the data collected in the a-th time; Let be the membrane thickness sensitivity of the k-th water film sensor in the data collected during the a-th acquisition; This represents the average membrane thickness sensitivity of all water film sensors in the a-th data collection; adding 1 to the denominator is to avoid the denominator being 0.

[0052] After determining the membrane thickness sensitivity and water film thickness sensitivity consistency of the k-th water film sensor in the a-th data acquisition using the above method, its parameter sensitivity can be determined using the following formula: in, The parameter sensitivity of the k-th water film sensor in the data collected in the a-th time is used to quantify the overall reliability and sensitivity of the pavement water film thickness parameter measured by the k-th water film sensor. The larger the value, the higher the possibility that the reading of the k-th water film sensor is affected by environmental fluctuations or local abnormal interference, and the lower the data accuracy. The water film thickness sensitivity consistency of the k-th water film sensor in the data collected in the a-th time; Let be the water film thickness sensitivity of the k-th water film sensor in the a-th data acquisition; e is the natural exponential function.

[0053] In step S150, the data accuracy is determined based on the deviation between the parameter sensitivity of the water film sensor and the average parameter sensitivity of all water film sensors. The pavement water film thickness is corrected based on the average data accuracy probability of all water film sensors. The freezing point temperature of the airport pavement is determined based on the corrected pavement water film thickness.

[0054] In this embodiment of the application, the above-mentioned data accuracy rate is an indicator used to evaluate the reliability of the data measured by the corresponding water film sensor. The data accuracy rate can be determined based on the parameter sensitivity of its corresponding water film sensor and the average parameter sensitivity of all water film sensors. The larger the value, the more consistent the data measured by its corresponding water film sensor is with the overall monitoring level, and the higher the probability that the measured data is close to the true state of the pavement.

[0055] For example, the above-mentioned determination of data accuracy based on the deviation between the parameter sensitivity of the water film sensor and the average parameter sensitivity of all water film sensors can be achieved as follows: calculate the average value of the parameter sensitivity of the water film sensors corresponding to each collection area, and denot it as the average parameter sensitivity; for each water film sensor, calculate the absolute value of the difference between the parameter sensitivity of the water film sensor and the average parameter sensitivity, and denot it as the parameter sensitivity deviation factor; determine the data accuracy of the water film sensor based on the parameter sensitivity of the water film sensor and the parameter sensitivity deviation factor.

[0056] Specifically, taking the k-th water film sensor in the a-th data acquisition from A consecutive acquisitions as an example, the data accuracy of the k-th water film sensor in the a-th data acquisition is determined by the following formula: in, For the first The first data collection in the second collection Data accuracy of individual water film sensors; For the first The first data collection in the second collection The parameter sensitivity of a water film sensor; Indicates the first All data collected in this batch The average value of the parameter sensitivity of each water film sensor (i.e., the average parameter sensitivity mentioned above). This is the normalization function; adding 1 to the denominator term avoids the denominator being 0; the above formula is based on the first... Parameter sensitivity of a water film sensor and its deviation from the average parameter sensitivity Quantify the data accuracy of the k-th water film sensor; parameter sensitivity The smaller the value, the less the sensitivity of the parameters of the k-th water film sensor has to affect the accuracy of the data, and the higher the accuracy of the collected data. The smaller the value, the more consistent the data status of the k-th water film sensor is with the overall level, and the higher the accuracy of the collected data.

[0057] After determining the data accuracy of each water film sensor using the above method, the data accuracy is used as the data weight to adjust the pavement freezing point calculation formula, and the original collected data is corrected to eliminate measurement errors caused by the fixed position of the water film sensor and the sensitivity of the measurement parameters themselves, so as to obtain more accurate measurement data for subsequent computer-controlled pavement freezing point temperature calculation.

[0058] For example, the above-mentioned method of correcting the pavement water film thickness based on the average data accuracy probability of all water film sensors and determining the freezing point temperature of the airport pavement based on the corrected pavement water film thickness can be achieved as follows: calculate the average data accuracy of all water film sensors, denoted as the average data accuracy probability; determine the correction coefficient based on the average data accuracy probability; correct the pavement water film thickness based on the correction coefficient; and substitute the corrected pavement water film thickness into the freezing point reduction formula to calculate the freezing point temperature.

[0059] In one specific implementation of this application embodiment, taking the a-th data acquisition as an example, the process of determining the freezing point temperature can be implemented as follows: Calculate the average data accuracy of all water film sensors in the a-th data acquisition, and denot it as the average data accuracy probability. The correction factor is calculated using the following formula: in, This is the correction coefficient for the thickness of the pavement water film collected by the k-th water film sensor in the a-th data acquisition. The above average data accuracy is denoted as ln; ln is the natural logarithm function. The above formula determines the correction coefficient based on the average data accuracy. This correction coefficient is used to eliminate the original data deviation caused by the interference of the sensor's fixed position and the difference in parameter sensitivity, so that the corrected thickness value is closer to the real water film state of the pavement, providing accurate input for freezing point temperature calculation.

[0060] After determining the correction coefficient using the above method, the collected pavement water film thickness is corrected using the correction coefficient. The corrected water film thickness parameter is then substituted into the freezing point reduction formula to calculate the pavement freezing point temperature. The freezing point reduction formula is the same as that in the prior art and will not be described again here.

[0061] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0062] Correspondingly, embodiments of this application also provide a real-time monitoring device for airport pavement freezing point temperature, for reference. Figure 2As shown, the airport pavement freezing point temperature real-time monitoring device 200 may include a data acquisition module 210, an image processing module 220, and a freezing point monitoring module 230, wherein: The data acquisition module is used to collect monitoring data through multiple water film sensors deployed on the airport pavement; the monitoring data includes the thickness of the water film on the pavement. The image processing module is used to acquire images of the airport pavement and divide them into multiple acquisition areas, identify water accumulation areas within the acquisition areas, and determine the main axis of water accumulation distribution based on the centroid coordinates of all water accumulation areas; each acquisition area contains a water film sensor. The freezing point monitoring module is used to determine the degree of influence of water accumulation location based on the changing trend of the total area of ​​water accumulation area in the collection area during multiple consecutive collections and the distance of each water accumulation area from the main axis of water accumulation distribution. It also determines the water accumulation area sensitivity of the collection area based on the degree of influence of water accumulation location of each water accumulation area and its ratio to the area of ​​the collection area. The freezing point monitoring module is also used to determine the water film thickness sensitivity based on the water accumulation area sensitivity and the pavement water film thickness, the average pavement water film thickness and the pavement water film thickness change rate collected by the water film sensor; to determine the water film thickness sensitivity consistency based on the deviation between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity of all water film sensors; and to determine the parameter sensitivity based on the water film thickness sensitivity of the water film sensor and the water film thickness sensitivity consistency. The freezing point monitoring module is also used to determine the data accuracy based on the deviation between the parameter sensitivity of the water film sensor and the average parameter sensitivity of all water film sensors, to correct the pavement water film thickness based on the average data accuracy probability of all water film sensors, and to determine the freezing point temperature of the airport pavement based on the corrected pavement water film thickness.

[0063] The specific implementation details of the aforementioned airport pavement freezing point temperature real-time monitoring device have been explained in detail in the corresponding section of the airport pavement freezing point temperature real-time monitoring method, so they will not be repeated here.

[0064] In addition, this application also provides a real-time monitoring system for airport pavement freezing point temperature, which determines the freezing point temperature of the airport pavement by executing any of the above-described real-time monitoring methods for airport pavement freezing point temperature.

[0065] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for real-time monitoring of the freezing point temperature of airport pavement, characterized in that, The method includes: Monitoring data is collected by multiple water film sensors deployed on the airport pavement; the monitoring data includes the thickness of the water film on the pavement. Airport pavement images are acquired and divided into multiple acquisition areas. Water accumulation areas are identified within the acquisition areas, and the main axis of water accumulation distribution is determined based on the centroid coordinates of all the water accumulation areas. Each acquisition area contains one water film sensor. The influence of water location is determined based on the changing trend of the total area of ​​water accumulation in the collection area during multiple consecutive collections and the distance between each water accumulation area and the main axis of water accumulation distribution. The water area sensitivity of the collection area is determined based on the influence of water location in each water accumulation area and its ratio to the area of ​​the collection area. The water film thickness sensitivity is determined based on the water accumulation area sensitivity and the pavement water film thickness, average pavement water film thickness, and pavement water film thickness change rate collected by the water film sensor. The water film thickness sensitivity consistency is determined based on the deviation between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity of all water film sensors. The parameter sensitivity is determined based on the water film thickness sensitivity of the water film sensor and the water film thickness sensitivity consistency. The data accuracy is determined based on the deviation between the parameter sensitivity of the water film sensor and the average parameter sensitivity of all water film sensors. The pavement water film thickness is corrected based on the average data accuracy probability of all water film sensors. The freezing point temperature of the airport pavement is determined based on the corrected pavement water film thickness.

2. The method for real-time monitoring of airport pavement freezing point temperature according to claim 1, characterized in that, The determination of the main axis of water accumulation distribution based on the centroid coordinates of all the water accumulation areas includes: A two-dimensional spatial point set is constructed based on the centroid coordinates of all the water accumulation areas; The principal component directions of the two-dimensional spatial point set are extracted by principal component analysis algorithm and denoted as the main axis of water accumulation distribution. The main axis of water accumulation distribution is used to characterize the linear distribution characteristics of water accumulation on the airport runway along the direction of aircraft take-off and landing. Its extension direction is consistent with the direction of aircraft taxiing trajectory on the airport runway.

3. The method for real-time monitoring of airport pavement freezing point temperature according to claim 2, characterized in that, The determination of the influence of water accumulation location based on the changing trend of the total area of ​​water accumulation in the collection area during multiple consecutive collections and the distance of each water accumulation area from the main axis of water accumulation distribution includes: Calculate the sum of the areas of all the waterlogged areas included in the collection area during each data collection, and record it as the total area of ​​the waterlogged areas for the corresponding number of collections; Calculate and sum the area differences of the total area of ​​the waterlogged area for each adjacent sampling number, and determine the regional stability of the sampling area based on the sum of the area differences; For each water accumulation area in the collection area, the Euclidean distance between the water accumulation area and the main axis of water accumulation distribution is determined and denoted as the distance influence factor; The degree of influence of the water accumulation location in the water accumulation area is determined based on the stability of the region and the distance influence factor corresponding to the water accumulation area.

4. The method for real-time monitoring of airport pavement freezing point temperature according to claim 3, characterized in that, The determination of the water area sensitivity of the collection area based on the influence of the water location in each water accumulation area and its ratio to the area of ​​the collection area includes: For each waterlogged area in the collection area, the ratio of the area of ​​the first region of each waterlogged area to the area of ​​the second region of the collection area is calculated and recorded as the area contribution factor of the waterlogged area. The sensitivity contribution factor is determined based on the area contribution factor of each water accumulation area and the degree of influence of the water accumulation location. The water accumulation area sensitivity of the collection area is determined based on the sensitivity contribution factor of each water accumulation area.

5. The method for real-time monitoring of airport pavement freezing point temperature according to claim 4, characterized in that, The determination of water film thickness sensitivity based on the water accumulation area sensitivity and the pavement water film thickness, average pavement water film thickness, and pavement water film thickness change rate collected by the water film sensor includes: The thickness of the water film on the pavement is obtained by the water film sensor through multiple consecutive data acquisitions. Calculate the average value of the pavement water film thickness collected multiple times consecutively, and record it as the average pavement water film thickness; Calculate the thickness difference between the current thickness of the pavement water film collected by the water film sensor and the thickness of the pavement water film collected previously, as well as the collection time interval. The absolute value of the ratio of the thickness difference to the collection time interval is recorded as the water film thickness change rate. The dynamic stability of the water film sensor is determined based on the current water film thickness on the pavement, the average water film thickness on the pavement, and the rate of change of the water film thickness. The water film thickness sensitivity of the water film sensor is determined based on the water accumulation area sensitivity and the dynamic stability.

6. The method for real-time monitoring of airport pavement freezing point temperature according to claim 5, characterized in that, The determination of water film thickness sensitivity consistency based on the deviation between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity of all water film sensors includes: Calculate the average value of the water film thickness sensitivity of the water film sensor corresponding to each of the acquisition areas, and record it as the average water film thickness sensitivity; For each of the aforementioned water film sensors, the absolute value of the difference between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity is calculated and denoted as the water film thickness sensitivity deviation factor. The water film thickness sensitivity consistency of the water film sensor is determined based on the water film thickness sensitivity deviation factor.

7. The method for real-time monitoring of airport pavement freezing point temperature according to claim 6, characterized in that, The determination of data accuracy based on the deviation between the parameter sensitivity of the water film sensor and the average parameter sensitivity of all water film sensors includes: Calculate the average value of the parameter sensitivity of the water film sensor corresponding to each of the acquisition areas, and denot it as the average parameter sensitivity; For each of the aforementioned water film sensors, the absolute value of the difference between the parameter sensitivity of the water film sensor and the average parameter sensitivity is calculated and denoted as the parameter sensitivity deviation factor. The data accuracy of the water film sensor is determined based on the parameter sensitivity and the parameter sensitivity deviation factor of the water film sensor.

8. The method for real-time monitoring of airport pavement freezing point temperature according to claim 7, characterized in that, The process of accurately correcting the pavement water film thickness based on the average data from all the water film sensors, and determining the freezing point temperature of the airport pavement based on the corrected pavement water film thickness, includes: Calculate the average of the data accuracy rates of all the water film sensors, and denote it as the average data accuracy probability; Based on the accuracy probability of the average data, a correction coefficient is determined, and the pavement water film thickness is corrected based on the correction coefficient. The corrected pavement water film thickness is then substituted into the freezing point reduction formula to calculate the freezing point temperature.

9. A real-time monitoring device for the freezing point temperature of airport pavement, characterized in that, The device includes: The data acquisition module is used to collect monitoring data through multiple water film sensors deployed on the airport pavement; the monitoring data includes the thickness of the water film on the pavement. The image processing module is used to acquire airport pavement images and divide them into multiple acquisition areas, identify water accumulation areas in the acquisition areas, and determine the main axis of water accumulation distribution based on the centroid coordinates of all the water accumulation areas; each acquisition area includes one water film sensor; The freezing point monitoring module is used to determine the degree of influence of water accumulation location based on the changing trend of the total area of ​​water accumulation area in the collection area during multiple consecutive collections and the distance between each water accumulation area and the main axis of water accumulation distribution; and to determine the water accumulation area sensitivity of the collection area based on the degree of influence of water accumulation location of each water accumulation area and its area ratio with that of the collection area. The freezing point monitoring module is further configured to determine its water film thickness sensitivity based on the water accumulation area sensitivity and the pavement water film thickness, average pavement water film thickness, and pavement water film thickness change rate collected by the water film sensor; determine its water film thickness sensitivity consistency based on the deviation between the water film thickness sensitivity of the water film sensor and the average water film thickness sensitivity of all the water film sensors; and determine its parameter sensitivity based on the water film thickness sensitivity of the water film sensor and the water film thickness sensitivity consistency. The freezing point monitoring module is further configured to determine the data accuracy based on the deviation between the parameter sensitivity of the water film sensor and the average parameter sensitivity of all the water film sensors, correct the pavement water film thickness based on the average data accuracy probability of all the water film sensors, and determine the freezing point temperature of the airport pavement based on the corrected pavement water film thickness.

10. A real-time monitoring system for the freezing point temperature of airport pavement, characterized in that, The system determines the freezing point temperature of the airport pavement by executing the real-time monitoring method for airport pavement freezing point temperature as described in any one of claims 1-8.

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