Winter snow removal early warning method, early warning model, model optimization method and snow removal system for airport runway

Abstract

The invention discloses an airport runway winter snow removal early warning method, an early warning model, a model optimization method and a snow removal system. The method comprises the following steps: dividing snow conditions based on airport weather early warning data; determining snow removal observation time and association time among different runways based on the historical snow removal whole process record data and the runway model; determining the snow removal time and sequence of different runways based on the airport snow removal guarantee capability data and the runway model; determining the first snow removal time based on the snowfall time, the snow removal observation time, the correlation time and the snow removal sequence; determining runway safety retention time based on the GRF monitoring data; based on the first snow removal time, the snow removal time and the runway safety keeping time, the predicted next snow removal time of the different runways is determined. According to the method, the working efficiency of snow removal commanders can be improved, irrationality of human subjective consciousness judgment is avoided, the commanders are assisted to give snow removal decisions, the snow removal cost is reduced, and the operation efficiency of airport runways is improved.

Description

Technical Field

[0001] The present invention belongs to the field of snow removal guarantee for airport runway in winter, and more specifically, relates to a snow removal early warning method, an early warning model, a model optimization method and a snow removal system for airport runway in winter. Background Art

[0002] For northern airports, runway snow removal and aircraft de-icing are one of the most important tasks in winter operation, directly affecting aircraft flight safety, airport operation safety and flight operation efficiency. With the progress of runway snow removal equipment, methods and materials, northern transport airports have certain snow removal guarantee capabilities, and in most cases, they can ensure the normal operation of flights in snowy weather.

[0003] Currently, quite a number of airports adopt hot air blowing snow removal operation on the runway. Briefly, for a runway with a length of 3,800 meters and a width of 60 meters, 4 vehicles are grouped for hot air blowing operation. In light to moderate snow weather, a single vehicle consumes 1,500 L of aviation kerosene for one-time operation on one runway, and 4 vehicles consume a total of 6,000 L; converted to 4.65 tons of aviation kerosene, calculated at 7,000 yuan / ton, the cost of a single operation on one runway is 32,500 yuan. If the runway is snow-removed for the first time during long-time snowfall at night, or in moderate to heavy snow weather, the aviation kerosene consumption increases by about 30%, approximately 42,000 yuan. If the snow removal interval time of the runway is long, the risk of runway icing, aircraft skidding or running off the runway will increase greatly; if the snow removal interval time of the runway is short, the airport cost input will increase greatly, and the relatively frequent snow removal occupies the runway and greatly affects the flight operation efficiency during snow removal. Therefore, with the annual development of snow removal work, improving snow removal efficiency, reducing snow removal cost and reducing runway occupation time have become the key problems to be solved at present.

[0004] In order to reduce the runway occupation time and improve the runway safety maintenance time after snow removal work, airport de-icing fluid has been widely used in transport airports. Airport de-icing fluid has become an important measure for airports to cope with ice and snow weather and improve snow removal guarantee capabilities in winter operation. However, according to the snowfall weather characteristics, flight operation scale and economic conditions of the airport location, the usage plans are different. At the same time, the effective time of runway de-icing fluid is greatly affected by the superposition of various factors such as different runways, environmental temperature, wind speed, snowfall amount and snow type. Precise quantification of the effective time of de-icing fluid affected by the real-time change of environmental temperature is the top priority for reducing snow removal cost and evaluating the runway safety maintenance time.

[0005] Therefore, it is necessary to propose a runway snow removal early warning technology covering the whole process of runway snow removal, improve the work efficiency of snow removal commanders, avoid the irrationality of human subjective consciousness judgment, assist snow removal commanders to issue snow removal decisions, and improve the operation efficiency of airport runways while reducing snow removal costs. Summary of the Invention

[0006] The object of the present invention is to provide a winter snow removal warning method, a warning model, a model optimization method and a snow removal system for airport pavement, so as to accurately locate the start and end times of each stage of snow removal work, guide the orderly progress of snow removal work, improve the work efficiency of snow removal commanders, avoid the irrationality of human subjective judgment, assist the commander to issue snow removal decisions, reduce snow removal costs and improve the operation efficiency of airport runways at the same time.

[0007] To achieve the above object, in the first aspect, the present invention provides a winter snow removal warning method for airport pavement, including:

[0008] Obtaining airport weather warning data, GRF monitoring data, airport snow removal support capacity data, historical records of the entire snow removal process, and airport runway data;

[0009] Establishing a runway model based on the airport runway data;

[0010] Dividing snow conditions based on the airport weather warning data, where the snow conditions include light to moderate snow, heavy snow and blizzard;

[0011] Based on the historical records of the entire snow removal process data and the runway model, determining the snow removal observation time for different runways and the associated time between different runways under different snow conditions;

[0012] Based on the airport snow removal support capacity data and the runway model, determining the snow removal time for different runways and the order of snow removal for multiple runways under different snow conditions;

[0013] Based on the snowfall time, the snow removal observation time for different runways, the associated time between different runways and the order of snow removal for multiple runways, determining the first snow removal time for different runways in sequence;

[0014] Based on the GRF monitoring data, determining the runway safety holding time for different deicing fluid application amounts under different snow conditions;

[0015] Based on the first snow removal time for different runways, the snow removal time for different runways and the runway safety holding time for different deicing fluid application amounts, determining the estimated next snow removal time for different runways.

[0016] Optionally, the dividing the snow conditions based on the airport weather warning data includes:

[0017] Dividing the snow conditions according to the snowfall amount in the airport weather warning data, where:

[0018] When the snowfall amount is 0.1 mm / 24 h to 4.9 mm / 24 h, the snow condition is divided into light to moderate snow;

[0019] When the snowfall is 5.0 mm / 24 h to 9.9 mm / 24 h, the snow condition is classified as heavy snow;

[0020] When the snowfall is above 10 mm / 24 h, the snow condition is classified as blizzard.

[0021] Optionally, the historical full-process snow removal record data includes: each snowfall time, snowfall amount, snow removal sequence, the first snow removal time of different runways, the snow removal end time, and the associated time between different runways;

[0022] If it is still snowing after the runway safety holding time ends, the historical full-process snow removal record data also includes the second snow removal time of each runway until the snowfall ends.

[0023] Optionally, when there is only one runway at the airport, the airport snow removal guarantee capacity data includes: the runway snow removal time under different snow conditions;

[0024] When there are multiple runways at the airport, the airport snow removal guarantee capacity data includes: the snow removal time of different runways and the snow removal sequence of different runways under different snow conditions and different runway operation modes;

[0025] The acquisition method of the airport snow removal guarantee capacity data is:

[0026] Each airport evaluates the runway snow removal time of each runway under different snow conditions according to snow removal equipment, methods, materials, and management capabilities, and verifies the operation capabilities of different runways against the full-process snow removal record data; when there are multiple runways at the airport, the snow removal sequence of the runways is determined in combination with the runway operation modes under different snow conditions.

[0027] Optionally, the snow removal sequence conforms to the permutation and combination rules. If there are a total of n runway resources at the airport and all runways operate normally during snowfall, there are a total of snow removal sequences in this snow condition;

[0028] The associated time between different runways includes: the transfer time of snow removal vehicles, snow removal equipment, and snow removal staff;

[0029] Based on the snowfall time, the snow removal observation time of different runways, the associated time between different runways, and the snow removal sequence of multiple runways, the first snow removal time of different runways is determined in sequence, including:

[0030] Under different snow conditions, the first snow removal time of the first runway is the snowfall time plus the snow removal observation time; the first snow removal time of the second runway is the first snow removal time of the first runway plus the snow removal time of the first runway and the correlation time between the first runway and the second runway, and so on. Except for the first runway, the first snow removal time of the nth runway is the first snow removal time of the (n - 1)th runway plus the snow removal time of the (n - 1)th runway and the correlation time between the nth runway and the (n - 1)th runway.

[0031] Optionally, the GRF monitoring data includes the atmospheric temperature and humidity, pavement temperature, water film thickness, and snow depth data obtained in real time by GRF monitoring devices installed near the runway.

[0032] Based on the GRF monitoring data, determining the runway safety holding time under different snow conditions with different deicing fluid spreading amounts includes:

[0033] Quantifying the effective time of the deicing fluid according to different deicing fluid spreading amounts, different pavement surfaces, ambient temperature, wind speed, snow amount, and snow type, and determining the runway safety holding time under different snow conditions based on the effective time of the deicing fluid.

[0034] Among them, the different deicing fluid spreading amounts are determined based on the actual gears of the airport spreader.

[0035] Optionally, determining the estimated next snow removal time for different runways based on the first snow removal time of different runways, the snow removal time of different runways, and the runway safety holding time with different deicing fluid spreading amounts includes:

[0036] The estimated next snow removal time for any runway is the first snow removal time of that runway plus the snow removal time of that runway and the runway safety holding time of that runway.

[0037] In a second aspect, the present invention proposes an airport runway winter snow removal early warning model, including:

[0038] A data acquisition module for acquiring airport weather early warning data, GRF monitoring data, airport snow removal guarantee capacity data, historical whole-process snow removal record data, and airport runway data.

[0039] A runway model establishment module for establishing a runway model based on the airport runway data.

[0040] A snow condition division module for dividing snow conditions based on the airport weather early warning data, where the snow conditions include light to moderate snow, heavy snow, and blizzard.

[0041] The snow removal observation time and runway correlation time calculation module is used to determine the snow removal observation time of different runways and the correlation time between different runways under different snow conditions based on the historical full-process record data of snow removal and the runway model;

[0042] The snow removal time and snow removal sequence calculation module is used to determine the snow removal time of different runways and the snow removal sequence of multiple runways under different snow conditions based on the airport snow removal support capacity data and the runway model;

[0043] The first snow removal time calculation module is used to sequentially determine the first snow removal time of different runways based on the snowfall time, the snow removal observation time of different runways, the correlation time between different runways, and the snow removal sequence of multiple runways;

[0044] The runway safety maintenance time calculation module is used to determine the runway safety maintenance time with different deicing fluid application amounts under different snow conditions based on the GRF monitoring data;

[0045] The next snow removal time calculation module is used to determine the estimated next snow removal time of different runways based on the first snow removal time of different runways, the snow removal time of different runways, and the runway safety maintenance time with different deicing fluid application amounts.

[0046] Thirdly, the present invention proposes an optimization method for the winter snow removal warning model of airport pavement, including:

[0047] According to the actual implementation of snow removal work, affected by the superposition of personnel, vehicle equipment, weather factors and management factors, a correction function is added; the winter snow removal warning model of airport pavement is corrected according to the full-process record data of each snow removal operation, so as to improve the accuracy of the warning model and avoid warning errors caused by deviations in the pre-set procedures.

[0048] Fourthly, the present invention proposes a winter snow removal system for airport pavement, including: a general view level, a deployment level and a guidance level;

[0049] The general view level includes a display screen, which is used to display the runway model, personnel standby situation, vehicle status and the pavement snow removal warning in real time; the GRF monitoring data, the input data of runway diseases and the repair data of different areas at different points on each runway are displayed in real time in the runway model;

[0050] The deployment level plans the snow removal time for each operation based on the winter snow removal warning model of airport pavement as described in the second aspect, according to the airport weather warning data, GRF monitoring data, airport runway data and the application amount of deicing fluid;

[0051] The guidance level includes the guidance data embedded in each stage of the entire snow removal process. Among them, the snow removal observation time embeds the real-time change curves of the runway water film and snow depth during this time period, the snow removal time embeds the snow removal formations of different runways under different snow conditions, and the runway safety maintenance time embeds the real-time change curve of the deicing fluid concentration and the real-time change curve of the freezing point.

[0052] The beneficial effects of the present invention are as follows:

[0053] Based on the airport weather warning data, GRF monitoring data, airport snow removal guarantee ability data, historical snow removal process record data, and airport runway data, the present invention can determine the start and end times of each stage in the entire process of airfield snow removal in real time. The cross-utilization of historical snow removal experience data and on-site real-time monitoring data improves the reliability of the airport snow removal warning method and warning model. At the same time, the present invention establishes an airport airfield winter snow removal system with three levels: overview, deployment, and guidance. Each level is coupled and interacts with each other, which is of great significance for the smooth progress of the airport snow removal work. In addition, in order to improve the credibility of the warning model, the present invention also proposes an optimization method for continuously correcting the model through snow removal record data, which can effectively guide the orderly progress of snow removal work, improve the work efficiency of snow removal commanders, assist commanders in issuing snow removal decisions, reduce snow removal costs, and improve the operation efficiency of airport runways.

[0054] The system of the present invention has other characteristics and advantages, which will be obvious from the accompanying drawings incorporated herein and the subsequent specific embodiments, or will be described in detail in the accompanying drawings incorporated herein and the subsequent specific embodiments. These accompanying drawings and specific embodiments are used together to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more obvious. In the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0056] Figure 1 Shows a step diagram of a method for warning of winter snow removal on an airport airfield according to an embodiment of the present invention.

[0057] Figure 2 Shows a logical relationship diagram of a warning model for winter snow removal on an airport airfield according to an embodiment of the present invention.

[0058] Figure 3 Shows a schematic diagram of three levels of an airport airfield winter snow removal system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] For northern airports, snow removal from the airport apron and taxiway is one of the most important tasks during winter operations, directly affecting the flight safety of aircraft, the operational safety of the airport, and the efficiency of flight operations. Taking the Capital Airport as an example, with the accumulation of years of snow removal experience, the snow removal plan for the apron and taxiway at the Capital Airport has become relatively mature, the snow removal personnel have become more professional, and a large number of advanced snow removal vehicles and equipment have been introduced at home and abroad, aiming to ensure the normal operation of flights under harsh winter ice and snow weather conditions. With the proposal of the concept of "Four Types of Airports", higher requirements have been put forward for the airport apron and taxiway snow removal work. How to refine the snow removal work process, reduce the risks of snow removal work, and improve the quality of snow removal work is crucial. The winter snow removal warning method, warning model, model optimization method, and snow removal system provided by the present invention can accurately locate the start and end times of each stage of the snow removal work, guide the orderly progress of the snow removal work, improve the work efficiency of snow removal commanders, avoid the irrationality of human subjective judgment, assist the commander in making snow removal decisions, reduce the snow removal cost, and improve the operational efficiency of the airport runway.

[0060] The present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0061] Embodiment 1

[0062] As Figure 1 shown, this embodiment provides a winter snow removal warning method for an airport apron and taxiway, including:

[0063] S1: Obtain airport weather warning data, GRF monitoring data, airport snow removal guarantee capacity data, historical snow removal full-process record data, and airport runway data;

[0064] In this embodiment, the airport weather warning data includes snowfall data.

[0065] In this embodiment, the airport snow removal guarantee capacity data is that each airport evaluates the runway snow removal time of each runway under different snow conditions according to actual situations, considering various factors such as snow removal equipment, methods, materials, and management capabilities, and verifies the operation capabilities of different runways by referring to the snow removal full-process record data.

[0066] When there is only one runway at the airport, the airport snow removal guarantee capacity data includes: the runway snow removal time under different snow conditions; when there are multiple runways at the airport, the airport snow removal guarantee capacity data includes: the snow removal time and the snow removal sequence of different runways under different snow conditions and different runway operation modes;

[0067] The method for obtaining the airport snow removal guarantee ability data is as follows: Each airport evaluates the runway snow removal time of each runway under different snow conditions according to snow removal equipment, methods, materials, and management capabilities, and verifies the operation capabilities of different runways by comparing with the data recorded throughout the snow removal process; When there are multiple runways at the airport, the order of runway snow removal is determined in combination with the runway operation modes under different snow conditions.

[0068] In this embodiment, the GRF monitoring data includes the atmospheric temperature and humidity, pavement temperature, water film thickness, and snow depth data obtained in real time by GRF monitoring equipment installed near the runway; The GRF monitoring data is mainly obtained in real time by GRF monitoring equipment installed near the runway. By installing a communication type temperature and humidity transmitter, a laser ranging sensor, and a runway status sensor, real-time monitoring of data such as atmospheric temperature and humidity, pavement temperature, water film thickness, and snow depth is achieved.

[0069] In this embodiment, the historical snow removal process record data includes: the time of each snowfall, the snowfall amount, the order of snow removal, the first snow removal time of different runways, the snow removal end time, and the associated time between different runways; If it is still snowing after the runway safety holding time ends, the historical snow removal process record data also includes the second snow removal time of each runway until the snowfall ends.

[0070] S2: Establish a runway model based on the airport runway data;

[0071] S3: Divide the snow conditions based on the airport weather warning data, where the snow conditions include light to moderate snow, heavy snow, and blizzard;

[0072] In this step, the division of snow conditions based on the airport weather warning data includes:

[0073] Divide the snow conditions according to the snowfall amount in the airport weather warning data, where:

[0074] When the snowfall amount is 0.1mm / 24h - 4.9mm / 24h, the snow condition is divided into light to moderate snow;

[0075] When the snowfall amount is 5.0mm / 24h - 9.9mm / 24h, the snow condition is divided into heavy snow;

[0076] When the snowfall amount is above 10mm / 24h, the snow condition is divided into blizzard.

[0077] S4: Based on the historical snow removal process record data and the runway model, determine the snow removal observation time of different runways and the associated time between different runways under different snow conditions;

[0078] Among them, the associated time between different runways includes the transfer time of snow removal vehicles, snow removal equipment, and snow removal staff.

[0079] S5: Based on the airport snow removal guarantee ability data and the runway model, determine the snow removal time of different runways and the snow removal sequence of multiple runways under different snow conditions;

[0080] Among them, the snow removal sequence mainly considers that some airports have multiple runway resources and the snow removal guarantee ability cannot meet the simultaneous snow removal of multiple runways. When the snow condition is severe, the snow removal decision to close some runways is made to ensure safe operation; the snow removal sequence conforms to the permutation and combination rules. If the airport has a total of n runway resources and all runways are guaranteed to operate normally during snowfall, then there are a total of snow removal sequences under this snow condition.

[0081] S6: Based on the snowfall time, the snow removal observation time of different runways, the associated time between different runways, and the snow removal sequence of multiple runways, determine the first snow removal time of different runways in sequence;

[0082] Specifically, under different snow conditions, the first snow removal time of the first runway is the snowfall time plus the snow removal observation time; the first snow removal time of the second runway is the first snow removal time of the first runway plus the snow removal time of the first runway and the associated time between the first runway and the second runway, and so on. Except for the first runway, the first snow removal time of the nth runway is the first snow removal time of the n - 1th runway plus the snow removal time of the n - 1th runway and the associated time between the nth runway and the n - 1th runway.

[0083] S7: Based on the GRF monitoring data, determine the runway safety holding time under different snow conditions for different deicing fluid spreading amounts;

[0084] In this step, the effective time of the deicing fluid is quantified according to different deicing fluid spreading amounts, different pavement surfaces, environmental temperature, wind speed, snow volume, and snow type, and the runway safety holding time under different snow conditions is determined based on the effective time of the deicing fluid; among them, the different deicing fluid spreading amounts are determined based on the actual gear positions of the airport spreader trucks.

[0085] Specifically, the runway safety holding time is mainly determined based on the effective time of the deicing fluid, and the pavement deicing fluid is greatly affected by the superposition of various factors such as different deicing fluid spreading amounts, different pavement surfaces, environmental temperature, wind speed, snow volume, and snow type. The effective time of the deicing fluid affected by the above various factors should be quantified; the different deicing fluid spreading amounts are determined based on the actual gear positions of the airport spreader trucks. Generally, each interval of 5 g / m2 is a gear position.

[0086] S8: Determine the estimated next snow removal time for different runways based on the first snow removal time for different runways, the snow removal time for different runways, and the runway safety maintenance time for different deicing fluid application amounts.

[0087] Specifically, the estimated next snow removal time for any runway is the sum of the first snow removal time for that runway, the snow removal time for that runway, and the runway safety maintenance time for that runway; that is, the estimated next snow removal time for the nth runway is the sum of the first snow removal time for the nth runway, the snow removal time for the nth runway, and the runway safety maintenance time for the nth runway.

[0088] The warning method of this embodiment is based on airport weather warning data, GRF monitoring data, airport snow removal guarantee capacity data, historical whole-process snow removal record data, and airport runway data, and determines the start and end times of each stage in the whole process of airfield snow removal work in real time. The cross-utilization of historical snow removal experience data and on-site real-time monitoring data improves the reliability of the airport snow removal warning method and warning model.

[0089] Embodiment 2

[0090] This embodiment provides an airport airfield winter snow removal warning model, including:

[0091] A data acquisition module for acquiring airport weather warning data, GRF monitoring data, airport snow removal guarantee capacity data, historical whole-process snow removal record data, and airport runway data;

[0092] A runway model establishment module for establishing a runway model based on the airport runway data;

[0093] A snow condition division module for dividing snow conditions based on the airport weather warning data, where the snow conditions include light to moderate snow, heavy snow, and blizzard;

[0094] A snow removal observation time and runway correlation time calculation module for determining the snow removal observation time for different runways and the correlation time between different runways under different snow condition conditions based on the historical whole-process snow removal record data and the runway model;

[0095] A snow removal time and snow removal order calculation module for determining the snow removal time for different runways and the snow removal order of multiple runways under different snow condition conditions based on the airport snow removal guarantee capacity data and the runway model;

[0096] A first snow removal time calculation module for sequentially determining the first snow removal time for different runways based on the snowfall time, the snow removal observation time for different runways, the correlation time between different runways, and the snow removal order of multiple runways;

[0097] The runway safety holding time calculation module is used to determine the runway safety holding time under different deicing fluid application rates under different snow conditions based on the GRF monitoring data;

[0098] The next snow removal time calculation module is used to determine the expected next snow removal time for different runways based on the first snow removal time of different runways, the snow removal time of different runways, and the runway safety holding time under different deicing fluid application rates.

[0099] The warning model of this embodiment is a software model for implementing the winter snow removal warning method for airport aprons in Embodiment 1.

[0100] In one example, the snow condition division module determines the snow condition of the day based on the snowfall amount in the airport weather warning data. If the snowfall amount is 0.1 mm / 24 h to 4.9 mm / 24 h, the snow condition in the snow removal warning model is defined as light to moderate snow. If the snowfall amount is 5.0 mm / 24 h to 9.9 mm / 24 h, the snow condition in the snow removal warning model is defined as heavy snow; if the snowfall amount is above 10 mm / 24 h, the snow condition in the snow removal warning model is defined as blizzard. The snow removal observation time, runway snow removal time, and runway safety holding time in the snow removal warning model are all confirmed based on different snow condition working conditions. Therefore, confirming the snow condition of the day is the beginning of the entire model.

[0101] In one example, the snow removal observation time and runway correlation time calculation module determines the observation time under different snow conditions based on the historical full-process snow removal record data.

[0102] Specifically, if there are multiple airport actual snow removal record data under a certain snow condition, the average value of all data under this snow condition is taken. Based on the historical full-process snow removal record data, the snow removal time when the first runway is different runways under different snow conditions is determined respectively. Its specific form is as follows:

[0103] Table 1: Snow removal observation time table

[0104]

[0105] Based on the historical snow removal record data, the transfer time between different runways is determined. Taking three runways of an airport as an example, its specific form is as follows:

[0106] Table 2: Correlation time table between different runways

[0107]

[0108] Specifically, if there are multiple snow removal record data that meet this snow condition, it is advisable to take the average value of all correlation times as the correlation time between two runways under this snow condition.

[0109] In one example, the snow removal time and order calculation module determines the snow removal times for different runways under different snow conditions based on the airport snow removal support capacity data.

[0110] Specifically, each airport determines the snow removal times for different runways under different snow conditions according to the snow removal vehicles and equipment, personnel, and the reserve of snow removal materials, and in actual snow removal operations and drills. If the airport has multiple runways, the runway operation modes under different snow conditions should also be considered to determine the order of snow removal. Taking an airport with a total of three runways as an example, the airport snow removal support capacity data under different snow conditions is determined as follows:

[0111] Table 3: Runway Snow Removal Time and Order Table

[0112]

[0113]

[0114] In one example, the runway safety holding time calculation module determines the runway safety holding times for different deicing fluid application amounts under different snow conditions based on the GRF monitoring data. The runway safety holding time is affected by the superposition of factors such as atmospheric temperature, snowfall amount, and deicing fluid application amount. The GRF fixed-end device can monitor the atmospheric temperature in real time, thereby quantifying the runway safety holding time, that is, the effective time of the deicing fluid.

[0115] Specifically, a mathematical model in which the effective time of the deicing fluid is affected by atmospheric temperature, snowfall amount, and deicing fluid application amount is established. The freezing point of the deicing fluid is affected by the change in its concentration, and when the freezing point of the deicing fluid reaches the atmospheric temperature, the deicing fluid fails. The relationship between the freezing point of the deicing fluid and the concentration of the deicing fluid is fitted as y = -47.34x 2 - 4.762x - 1.6952. Assuming the snowfall amount is z mm / 24h, the application amount of the deicing fluid is s g, the effective time of the deicing fluid, that is, the runway safety holding time is t h, and the atmospheric temperature is T °C. Based on the relationship between the freezing point of the deicing fluid and the concentration of the deicing fluid, the mathematical model for the runway safety holding time affected by the snowfall amount, the application amount of the deicing fluid, and the atmospheric temperature can be determined as:

[0116]

[0117] Therefore, if the snowfall amount, the atmospheric temperature during snowfall, and the deicing fluid application amount are determined, the runway safety holding time can be determined.

[0118] In one example, the method for the first snow removal time calculation module to determine the first snow removal time of different runways is as follows: under different snow conditions, the first snow removal time of the first runway is the snowfall time plus the snow removal observation time; the first snow removal time of the second runway is the first snow removal time of the first runway plus the snow removal time of the first runway and the associated time between the first runway and the second runway. By analogy, except for the first runway, the first snow removal time of the nth runway is the first snow removal time of the (n - 1)th runway plus the snow removal time of the (n - 1)th runway and the associated time between the nth runway and the (n - 1)th runway.

[0119] The method for the next snow removal time calculation module to determine the expected next snow removal time of different runways is as follows: the expected next snow removal time of any runway is the first snow removal time of that runway plus the snow removal time of that runway and the runway safety maintenance time of that runway.

[0120] Specifically, as Figure 2 shown, by determining the runway observation time, runway snow removal time, runway safety maintenance time, and runway associated time under different snow conditions, the snow removal time for each round under different snow conditions at a certain airport can be preliminarily determined. Based on the snow conditions information such as the snowfall time and snowfall amount in the airport weather warning data, the snow removal order of the runways is determined according to the runway priority. After the observation time of the first runway under this snow condition ends, it is the first snow removal time of that runway. Based on the airport support capacity data, the snow removal time of that runway under this snow condition is determined. After the snow removal is completed, the snow removal vehicles, personnel, and equipment are transferred to the second runway. After the associated time between the two runways ends, it is the first snow removal time of the second runway. By analogy, the first snow removal time and the expected next snow removal time of each runway are determined. After the snow removal is completed, it is the runway safety maintenance time of that runway. Before the runway safety maintenance time ends, the command post is reminded that the next snow removal work on that runway is about to start, playing the role of snow removal warning.

[0121] The warning model in this embodiment determines the snow conditions based on the predicted snowfall time and snowfall amount in the airport weather warning data, determines the snow removal observation time under different snow conditions and the snow removal correlation time between different runways based on the historical records of the entire snow removal process data, determines the snow removal time for different runways under different snow conditions based on the airport snow removal guarantee capacity data, establishes a mathematical model in which the effective time of deicing fluid is affected by factors such as atmospheric temperature, snow conditions, and deicing fluid spreading amount, and quantifies the influence law of the runway safety maintenance time by the GRF real-time monitoring data. Taking the start snowfall time as the starting point of the entire snow removal process, the first snow removal time of the first runway of the airport is after the snow removal observation time ends. After the snow removal of the first runway is completed, the vehicles, equipment, and personnel are transferred to the second runway, that is, the snow removal correlation time between the two runways. After the snow removal correlation time between the two runways ends, it is the first snow removal time of the second runway, and so on to determine the first snow removal time of the last runway. If the snowfall stops within the safety maintenance time after the first snow removal is completed, the current snow removal work ends; otherwise, the second round of snow removal work continues to ensure that the snowfall stops before the end of the runway safety maintenance time after the last round of snow removal. And before the end of each runway safety maintenance time, the system automatically issues a warning to the snow removal command room. The snow removal command room determines the second round of snow removal work according to the actual situation, and continuously optimizes and corrects the warning model through this method to improve the accuracy of the warning model. The airfield snow removal warning model can effectively guide the orderly development of snow removal work, improve the work efficiency of snow removal commanders, assist the commanders in making snow removal decisions, reduce the snow removal cost while improving the operation efficiency of the airport runway.

[0122] Embodiment 3

[0123] This embodiment provides an optimization method for the airport airfield winter snow removal warning model described in the above Embodiment 2, including:

[0124] According to the actual development of the snow removal work, affected by the superposition of personnel, vehicle equipment, weather factors and management factors, a correction function is added; the airport airfield winter snow removal warning model is corrected according to the full-process record data of each snow removal actual combat to improve the accuracy of the warning model and avoid warning errors caused by deviations in the pre-set procedures.

[0125] Specifically, due to the complexity of the on-site snow removal work and the large number of emergencies, a small change in a certain factor is very likely to cause a large deviation in the entire warning model. Therefore, an optimization method of continuous correction is proposed. The correction function can correct each stage in the entire snow removal process separately, and the warning model can continuously improve its accuracy based on the corrected data.

[0126] Embodiment 4

[0127] As Figure 3As shown in the figure, this embodiment provides an airport runway winter snow removal system, including: an overview level, a deployment level, a guidance level, and the airport runway winter snow removal warning model described in the second aspect;

[0128] The overview level includes a display screen, which is used to display the runway model, personnel standby situation, vehicle status, and the runway snow removal warning in real time; in the runway model, the GRF monitoring data of different points on each runway, the input data of runway diseases, and the repair data of different regions are displayed in real time;

[0129] Based on the warning model, the deployment level plans the snow removal time for each time according to the airport weather warning data, GRF monitoring data, airport runway data, and the spreading amount of deicing fluid;

[0130] The guidance level embeds guidance data in each stage of the entire snow removal process. Among them, the real-time change curves of the runway water film and snow depth are embedded in the snow removal observation time, the snow removal formation of different runways under different snow conditions is embedded in the snow removal time, and the real-time change curves of the deicing fluid concentration and the freezing point are embedded in the runway safety maintenance time.

[0131] Specifically, this system has an airport runway snow removal system with three levels: overview, deployment, and guidance. The overview level mainly monitors the runway, personnel, and vehicle status in real time, and updates the runway snow removal order and the snow removal time for each round of each runway in real time based on the forecast information of the deployment level. The guidance level mainly assists the snow removal commander in issuing instructions during the entire snow removal process. For example, the real-time monitoring data of GRF equipment, including key runway status information such as snow depth and water film, are mainly embedded in the snow removal observation time period. The snow removal plan and formation for this runway under this snow condition are embedded in the snow removal time period, and the freezing point data change curve of the deicing fluid is embedded in the runway safety maintenance time period.

[0132] In summary, the airport runway winter snow removal warning method, warning model, model optimization method, and snow removal system of the present invention can accurately locate the start and end times of each stage of the snow removal work, guide the orderly development of the snow removal work, improve the work efficiency of snow removal commanders, avoid the irrationality of human subjective judgment, assist the commander in making snow removal decisions, reduce the snow removal cost, and improve the operation efficiency of the airport runway.

[0133] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments.

Claims

1. A winter snow removal warning method for airport runways, characterized in that: include: Obtain airport weather warning data, GRF monitoring data, airport snow removal support data, historical snow removal process record data, and airport runway data; Establishing a runway model based on the airport runway data; Classify snow conditions based on the airport weather warning data, the snow conditions including light snow, heavy snow and blizzard; Based on the historical snow removal process record data and the runway model, determining the snow removal observation time of different runways under different snow conditions and the correlation time between different runways; Based on the airport snow removal support capability data and the runway model, determining the snow removal time for different runways and the snow removal sequence for multiple runways under different snow conditions; Based on the snowfall time, the snow removal observation time of different runways, the correlation time between different runways and the snow removal sequence of multiple runways, the first snow removal time of different runways is determined in sequence; Based on the GRF monitoring data, determine the runway safety holding time under different snow conditions and different de-icing fluid spreading amounts; The expected next snow removal time for different runways is determined based on the first snow removal time for different runways, the snow removal time for different runways and the runway safety maintenance time for different deicing fluid spreading amounts.

2. The winter snow removal early warning method for airport runways according to claim 1, characterized in that: The classification of snow conditions based on the airport weather warning data includes: Snow conditions are classified according to the snowfall in the airport weather warning data, where: When the snowfall is 0.1mm / 24h~4.9mm / 24h, the snow condition is classified as light or moderate snow; When the snowfall is 5.0 mm / 24h to 9.9 mm / 24h, the snow condition is classified as heavy snow; When the snowfall is above 10 mm / 24 h, the snow conditions are classified as heavy snow.

3. The winter snow removal early warning method for airport runways according to claim 1 is characterized in that: The historical snow removal process record data includes: each snowfall time, snowfall amount, snow removal sequence, first snow removal time of different runways, snow removal end time and the correlation time between different runways; If snow is still falling after the runway safety maintenance time ends, the historical snow removal process record data also includes the second snow removal time for each runway until the snowfall ends.

4. The winter snow removal early warning method for airport runways according to claim 1 is characterized in that: When the airport has only one runway, the airport snow removal support capability data includes: runway snow removal time under different snow conditions; When there are multiple runways at an airport, the airport snow removal support capability data includes: snow removal time for different runways under different snow conditions and different runway operation modes and snow removal sequence for different runways; The method for obtaining the airport snow removal support capability data is as follows: Each airport evaluates the snow-removal time for each runway under different snow conditions based on its snow-removal equipment, methods, materials and management capabilities, and verifies the operating capabilities of different runways by comparing the data recorded throughout the snow-removal process; when an airport has multiple runways, the order of snow-removal is determined based on the runway operation modes under different snow conditions.

5. The winter snow removal early warning method for airport runways according to claim 1 is characterized in that: The snow removal sequence complies with the permutation and combination rules. If the airport has a total of n runway resources and they are all guaranteed to operate normally during snowfall, then the total number of runways under this snow condition is Snow removal sequence; The associated time between different runways includes: the transfer time of snow removal vehicles, snow removal equipment and snow removal staff; The method of sequentially determining the first snow removal time for different runways based on the snowfall time, the snow removal observation time for different runways, the correlation time between different runways, and the snow removal sequence for multiple runways includes: Under different snow conditions, the first snow removal time of the first runway is the snowfall time plus the snow removal observation time; the first snow removal time of the second runway is the first snow removal time of the first runway plus the snow removal time of the first runway and the association time between the first runway and the second runway, and so on. Except for the first runway, the first snow removal time of the nth runway is the first snow removal time of the n-1th runway plus the snow removal time of the n-1th runway and the association time between the nth runway and the n-1th runway.

6. The winter snow removal early warning method for airport runways according to claim 1, characterized in that: The GRF monitoring data includes atmospheric temperature and humidity, pavement temperature, water film thickness and snow depth data obtained in real time by GRF monitoring equipment installed near the runway; Determining the runway safety holding time under different snow conditions and different deicing fluid spreading amounts based on the GRF monitoring data includes: Quantify the effective time of deicing fluid according to different deicing fluid spreading amounts, different pavement surfaces, ambient temperatures, wind speeds, snow amounts and snow types, and determine the runway safety holding time under different snow conditions based on the effective time of the deicing fluid; The different deicing fluid spreading amounts are determined based on the actual gear position of the airport spreading vehicle.

7. The winter snow removal early warning method for airport runways according to claim 1, characterized in that: Determining the estimated next snow removal time for different runways based on the first snow removal time for different runways, the snow removal time for different runways, and the runway safety maintenance time for different deicing fluid spreading amounts includes: The estimated next snow removal time for any runway is the first snow removal time of the runway plus the snow removal time of the runway and the runway safety maintenance time of the runway.

8. An airport runway winter snow removal warning model, characterized in that: include: Data acquisition module, used to obtain airport weather warning data, GRF monitoring data, airport snow removal support data, historical snow removal process record data and airport runway data; A runway model building module, used to build a runway model based on the airport runway data; A snow condition classification module, used to classify snow conditions based on the airport weather warning data, wherein the snow conditions include light snow, heavy snow and blizzard; A snow removal observation time and runway association time calculation module, for determining the snow removal observation time of different runways and the association time between different runways under different snow conditions based on the historical snow removal process record data and the runway model; A snow removal time and snow removal sequence calculation module, used to determine the snow removal time of different runways and the snow removal sequence of multiple runways under different snow conditions based on the airport snow removal support capability data and the runway model; A first snow removal time calculation module is used to determine the first snow removal time of different runways in sequence based on the snowfall time, the snow removal observation time of different runways, the correlation time between different runways and the snow removal sequence of multiple runways; A runway safety holding time calculation module, used to determine the runway safety holding time under different snow conditions and different deicing fluid spreading amounts based on the GRF monitoring data; The next snow removal time calculation module is used to determine the expected next snow removal time for different runways based on the first snow removal time for different runways, the snow removal time for different runways and the runway safety maintenance time for different deicing fluid spreading amounts.

9. A winter snow removal warning model optimization method for airport runways as claimed in claim 8, characterized in that: include: According to the actual progress of snow removal work, affected by the combined influence of personnel, vehicle equipment, weather factors and management factors, a correction function is added; The winter snow removal warning model for the airport runway is revised based on the full process record data of each snow removal operation to improve the accuracy of the warning model and avoid warning errors caused by deviations in the previous program.

10. An airport runway snow removal system in winter, characterized in that: include: Overview level, deployment level and guidance level; The overview level includes a display screen, which is used to display the runway model, personnel standby status, vehicle status and snow removal warning of the runway in real time; The runway model displays in real time the GRF monitoring data of different points on each runway, the input data of runway diseases and the repair data of different areas; The deployment level is based on the winter snow removal warning model for airport runways as claimed in claim 8, and plans each snow removal time according to airport weather warning data, GRF monitoring data, airport runway data and the amount of deicing fluid to be spread; The guidance level embeds guidance data for each stage of the entire snow removal process, wherein the snow removal observation time is embedded with a real-time change curve of the runway water film and snow depth during the time period, the snow removal time is embedded with snow removal formations for different runways under different snow conditions, and the runway safety holding time is embedded with a real-time change curve of the deicing fluid concentration and the real-time change curve of the freezing point.

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