Prediction method and processor for ice thickness on roads near water and cliffs

Abstract

The embodiment of the present application provides a method and processor for predicting the thickness of ice on a road near water or cliffs. The method includes: determining a road section to be predicted for which ice prediction needs to be carried out, the road section to be predicted includes a road section near water and a road section near cliffs, dividing the area where the road section to be predicted is located into equidistant grids according to the area where the road section to be predicted is located and the water body and / or cliff position adjacent to the road section to be predicted, obtaining terrain data and land use data of the area where the road section to be predicted is located, determining the target meteorological boundary field conditions and target initial field conditions of the equidistant grids, and inputting them into a weather prediction model to determine the meteorological prediction data for each preset time point, determining the temperature and humidity at each preset time point, and when ice is determined to occur at the preset time point according to the temperature and humidity, determining the ice growth thickness at each preset time point, and integrating the ice growth thickness at each preset time point to determine the final ice end thickness value for the future preset time period.

Description

Technical Field

[0001] The present application relates to the technical field of road icing disaster warning, and specifically to a method and processor for predicting the thickness of road ice on a road section near water or cliffs. Background Art

[0002] Road sections near water are affected by the evaporation of water bodies, and the liquid water content in the air is high, making it more likely to freeze when low temperatures come. Road sections near cliffs are affected by the increase in local wind speed that carries away more heat, and the ambient atmospheric temperature is lower, making it easier to reach a state of water vapor saturation, resulting in a large amount of liquid water in the air, which freezes on the road surface to form road ice.

[0003] In the existing technology, there is a lack of corresponding road icing prediction models and refined meteorological numerical calculation models for road sections near water and cliffs, which makes it impossible to provide the refined meteorological data required for calculating icing on roads near water and cliffs, and it is impossible to achieve quantitative prediction of icing on roads near water and cliffs. At present, the main method of road icing warning in my country is to conduct empirical road icing warning based on large-scale meteorological forecast data and meteorological monitoring data of road environment. This technology has no effective prediction for a period of time in the future, resulting in serious traffic accidents and huge losses of people's lives and property. Summary of the invention

[0004] The purpose of the embodiment of the present application is to provide a method and processor for predicting the thickness of ice on a road near water or cliffs.

[0005] In order to achieve the above-mentioned purpose, the first aspect of the present application provides a method for predicting the thickness of ice on a road section near water or cliffs, comprising:

[0006] Determine the road sections that need to be predicted for icing, including sections near water and sections near cliffs;

[0007] Divide the area where the road section to be predicted is located into equidistant grids according to the area where the road section to be predicted is located and the locations of water bodies and / or cliffs adjacent to the road section to be predicted;

[0008] Obtain terrain data and land use data of the area where the road section to be predicted is located;

[0009] Interpolate terrain data and land use data to determine target meteorological boundary field conditions and target initial field conditions at equidistant grids;

[0010] Inputting the target meteorological boundary field conditions and the target initial field conditions into the weather prediction model, so as to output the meteorological prediction data at each preset time point in the future preset time period through the weather prediction model;

[0011] Determine the temperature and humidity at each preset time point based on weather forecast data;

[0012] In the case where ice formation occurs at a preset time point according to the temperature and humidity, determining the ice growth thickness at each preset time point;

[0013] The ice growth thickness at each preset time point is integrated and calculated to determine the final ice thickness value in the future preset time period.

[0014] In an embodiment of the present application, terrain data and land use data are interpolated to determine target meteorological boundary field conditions and target initial field conditions of equidistant grids, including: interpolating terrain data and land use data to equidistant grids to determine the lower boundary conditions of the equidistant grids for conducting icing predictions, obtaining initial field data for conducting icing predictions, and determining initial meteorological boundary field conditions and initial values ​​of initial field conditions of a segment icing numerical calculation model based on the initial field data, interpolating the initial field data to equidistant grids, and adjusting the interpolation based on the lower boundary conditions to obtain target meteorological boundary field conditions and target initial field conditions for driving numerical calculations.

[0015] In an embodiment of the present application, when the road section to be predicted is a water-adjacent road section, the liquid water content W at each preset time point is calculated according to the following formula (1):

[0016] W=1.33q+E / S (1),

[0017] Among them, q refers to the liquid content, E is the water surface evaporation, and S is the grid area of ​​the road section to be predicted.

[0018] In the embodiments of the present application, q and E are calculated according to the following formula (2) and formula (3), respectively:

[0019] q=0.622E w / P (2),

[0020] E = 0.00017A (e w -E w )P0 / P×10 3 (3)

[0021] Where A is the total water surface area, e w is the saturated water vapor pressure corresponding to the water temperature, E w is the micro-topography temperature corresponding to the saturated water vapor pressure, P is the air pressure, P0 is the standard atmospheric pressure,

[0022] e w and E w Calculated according to the following formula (4) and formula (5):

[0023]

[0024]

[0025] Among them, C1=0.1079574×102, C2=-0.5028×10, C3=0.150475×10-3, C4=0.42873×10-3, C5=0.78614, C6=-0.82969×10, C7=0.476955×10, T1 is the triple point temperature of water. Normally, T1=273.16K, T=273.15+t(K), K is the temperature unit Kelvin, t is the temperature in Celsius.

[0026] In an embodiment of the present application, when the road section to be predicted is a cliff section, the liquid water content W at each preset time point is calculated according to the following formula (6):

[0027] W=1.33q (6)

[0028] Where q refers to the liquid content, q = 0.622E w / P,E w is the micro-topography temperature corresponding to the saturated water vapor pressure, and P is the air pressure.

[0029] In the embodiment of the present application, E is calculated according to the following formula (7): w :

[0030]

[0031] Among them, C1=0.1079574×102, C2=-0.5028×10, C3=0.150475×10-3, C4=0.42873×10-3, C5=0.78614, C6=-0.82969×10, C7=0.476955×10, T1 is the triple point temperature of water. Under normal circumstances, T1=273.16K, T=273.15+t(K), K is the temperature unit Kelvin, t is the temperature in Celsius.

[0032] In an embodiment of the present application, the weather forecast data includes wind speed and predicted temperature. When the road section to be predicted is a cliff section, the wind speed and temperature T at each preset time point are calculated according to the following formula (8) and formula (9), respectively:

[0033]

[0034]

[0035] Among them, U is the wind speed of the cliff section at an altitude of h, U0 is the average wind speed in the future preset time period, r0 is the length of the cliff mouth, L is the width of the cliff mouth, θ is the angle from the bottom of the cliff mouth to the road, H0 refers to the altitude corresponding to the preset time point, h0 is the altitude of the bottom of the cliff mouth, and T0 is the predicted temperature corresponding to the preset time point.

[0036] In an embodiment of the present application, the meteorological forecast data includes wind speed and predicted temperature. When the road section to be predicted is a water section, the wind speed U and temperature T at each preset time point are calculated according to the following formula (10) and formula (11), respectively:

[0037]

[0038]

[0039] Among them, U is the wind speed of the cliff section at an altitude of h, T is the temperature at a preset time point, U0 is the average wind speed at an altitude of H0 in the future preset time period, α is the empirical coefficient of ground roughness, H0 refers to the altitude corresponding to the preset time point, h0 is the altitude at the bottom of the cliff, and T0 is the predicted temperature corresponding to the preset time point.

[0040] In an embodiment of the present application, the meteorological forecast data includes predicted precipitation intensity, and determining the ice growth thickness at each preset time point includes calculating the ice growth thickness at each preset time point according to the following formula (12):

[0041]

[0042] Where dI is the ice growth thickness at the preset time point, ρ i is the density of ice, π is the pi, E1 is the freezing rain collision efficiency coefficient, r is the predicted precipitation intensity, ρ is the density of water, E2 is the supercooled water collision efficiency coefficient, U is the wind speed of the cliff section at an altitude of h, and W is the liquid water content at a preset time point.

[0043] In an embodiment of the present application, a data collection module is used to determine a road section to be predicted for which icing prediction needs to be carried out, the road section to be predicted includes a water section and a cliff section, and obtains terrain data and land use data of the area where the road section to be predicted is located; a data grid division module is used to divide the area where the road section to be predicted is located into equidistant grids according to the area where the road section to be predicted is located and the water body and / or cliff position adjacent to the road section to be predicted; a data pre-processing module is used to interpolate the terrain data and land use data to determine the target meteorological boundary field conditions and target initial field conditions of the equidistant grids; a numerical calculation module is used to input the target meteorological boundary field conditions and the target initial field conditions into a weather prediction model to output meteorological prediction data for each preset time point in a future preset time period through the weather prediction model; an icing calculation module is used to determine the temperature and humidity at each preset time point according to the meteorological prediction data; when it is determined that icing occurs at the preset time point according to the temperature and humidity, determine the icing growth thickness at each preset time point; and integrate the icing growth thickness at each preset time point to determine the final icing thickness value for the future preset time period.

[0044] Through the above-mentioned method for predicting ice thickness on roads near water and cliffs, by determining the road sections to be predicted for which ice prediction needs to be carried out, the road sections to be predicted include road sections near water and road sections near cliffs, and dividing the area where the road sections to be predicted are located into equidistant grids according to the area where the road sections to be predicted are located and the water bodies and / or cliffs adjacent to the road sections to be predicted, obtaining terrain data and land use data of the area where the road sections to be predicted are located, interpolating the terrain data and land use data to determine target meteorological boundary field conditions and target initial field conditions of the equidistant grids, inputting the target meteorological boundary field conditions and target initial field conditions into a weather prediction model, so as to output meteorological prediction data for each preset time point in a preset time period in the future through the weather prediction model, determining the temperature and humidity at each preset time point according to the meteorological prediction data, and in the case where it is determined that ice occurs at the preset time point according to the temperature and humidity, determining the ice growth thickness at each preset time point, and integrating the ice growth thickness at each preset time point to determine the final ice thickness value for the preset time period in the future. Through this method, the thickness of ice on roads near water and cliffs in the future can be predicted, reducing the occurrence of disasters and accidents and ensuring the safety of people's lives and property.

[0045] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0047] Figure 1 A schematic diagram of a process flow of a method for predicting ice thickness on a road section near water or cliffs according to an embodiment of the present application is schematically shown;

[0048] Figure 2 A structural block diagram of a device for predicting ice thickness on a road section near water or cliffs according to an embodiment of the present application is schematically shown;

[0049] Figure 3 The internal structure diagram of a computer device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0051] Figure 1 The flowchart of the method for predicting the thickness of ice on a road near a cliff or water is schematically shown in the embodiment of the present application. Figure 1 As shown, in one embodiment of the present application, a method for predicting the thickness of ice on a road section near water or cliffs is provided, comprising the following steps:

[0052] Step 101, determining the road sections to be predicted for which icing prediction needs to be carried out, the road sections to be predicted include waterside sections and cliffside sections.

[0053] Step 102 : dividing the area where the road section to be predicted is located into equidistant grids according to the area where the road section to be predicted is located and the locations of water bodies and / or cliffs adjacent to the road section to be predicted.

[0054] Step 103, obtaining terrain data and land use data of the area where the road section to be predicted is located.

[0055] Step 104 , interpolating the terrain data and the land use data to determine the target meteorological boundary field conditions and the target initial field conditions of the equidistant grids.

[0056] Step 105, inputting the target meteorological boundary field conditions and the target initial field conditions into the weather prediction model, so as to output the meteorological prediction data for each preset time point in the future preset time period through the weather prediction model.

[0057] Step 106, determining the temperature and humidity at each preset time point according to the weather forecast data.

[0058] Step 107, when it is determined that ice occurs at a preset time point according to the temperature and humidity, determine the ice growth thickness at each preset time point.

[0059] Step 108 , integrating the ice growth thickness at each preset time point to determine the final ice thickness value in the future preset time period.

[0060] Determine the road sections to be predicted where ice prediction needs to be carried out. The road sections to be predicted include water sections and cliff sections. Water sections refer to places close to water sources, and cliff sections refer to sections close to cliffs. According to the area where the road section to be predicted is located and the location of the water body and / or cliffs adjacent to the road section to be predicted, the area where the road section to be predicted is located is divided into equidistant grids. The grid is divided into 30m×30m grids with equal distances in the east-west direction and equal distances in the north-south direction. Furthermore, obtain the terrain data and land use data of the area where the road section to be predicted is located. The terrain data includes latitude and longitude data and altitude data, etc. The land use data includes information data such as rivers, rice fields, houses, etc. in the region. Then, the terrain data and land use data are interpolated into the equally divided distance grids to determine the lower boundary conditions for the equidistant grids to carry out ice prediction, and the initial field data for carrying out ice prediction is obtained to determine the initial meteorological boundary field conditions and initial values ​​of the initial field conditions of the segment ice numerical calculation mode according to the initial field data, and the initial field data is interpolated into the divided equidistant grids, and the interpolation of the equidistant grids is adjusted according to the lower boundary conditions to obtain the target meteorological boundary field conditions and target initial field conditions of the equidistant grid driven numerical calculation. The target meteorological boundary field conditions and the target initial field conditions are input into the weather prediction model to output the meteorological prediction data of each preset time point in each grid in the future preset time period through the weather prediction model, and the meteorological prediction data includes wind speed, predicted temperature, humidity, etc. Further, the temperature and humidity of each preset time point of each grid are determined according to the meteorological prediction data. Then, the temperature and humidity of each preset time point of each grid determine the ice growth thickness of each preset time point when ice occurs at the preset time point, and the ice growth thickness of each preset time point is integrated to determine the final ice thickness value of the future preset time period. The integral calculation includes time-step integration. For example, one hour is divided into four segments, i.e., four 15-minute segments, and the integral is determined in segments, and then the cumulative calculation is performed. The calculation result is more accurate. This method can be used to predict the thickness of ice on roads near water and cliffs in the future, reduce the occurrence of disasters, and ensure the safety of people's lives and property.

[0061] In one embodiment, interpolating terrain data and land use data to determine target meteorological boundary field conditions and target initial field conditions of equidistant grids includes: interpolating terrain data and land use data to equidistant grids to determine lower boundary conditions of the equidistant grids for conducting icing predictions, obtaining initial field data for conducting icing predictions, and determining initial meteorological boundary field conditions and initial values ​​of initial field conditions of a segment icing numerical calculation model based on the initial field data, interpolating the initial field data to equidistant grids, and adjusting the interpolation based on the lower boundary conditions to obtain target meteorological boundary field conditions and target initial field conditions for driving numerical calculations.

[0062] Determine the road sections to be predicted where ice prediction needs to be carried out. The road sections to be predicted include water sections and cliff sections. According to the area where the road sections to be predicted are located and the water bodies and / or cliffs adjacent to the road sections to be predicted, the area where the road sections to be predicted are located is divided into equidistant grids, which are 30m×30m grids with equal distances in the east-west direction and the north-south direction. Furthermore, obtain the terrain data and land use data of the area where the road sections to be predicted are located. The terrain data includes latitude and longitude data and altitude data, etc. The land use data includes information data such as rivers, rice fields, houses, etc. in the region. The terrain data and land use data are then interpolated into equally divided distance grids to determine the lower boundary conditions of the equally divided grids for conducting icing predictions, and the initial field data for conducting icing predictions are obtained to determine the initial meteorological boundary field conditions and initial values ​​of the initial field conditions of the segment icing numerical calculation model based on the initial field data. The initial field data are interpolated into the equally divided grids, and the interpolation of the equally divided grids is adjusted according to the lower boundary conditions to obtain the target meteorological boundary field conditions and target initial field conditions for the equally divided grid-driven numerical calculations.

[0063] In one embodiment, when the road section to be predicted is a water-adjacent road section, the liquid water content W at each preset time point is calculated according to the following formula (1):

[0064] W=1.33q+E / S (1),

[0065] Among them, q refers to the liquid content, E is the water surface evaporation, and S is the grid area of ​​the road section to be predicted.

[0066] In one embodiment, q and E are calculated according to the following formula (2) and formula (3), respectively:

[0067] q=0.622E w / P (2),

[0068] E = 0.00017A (e w -E w )P0 / P×10 3 (3)

[0069] Where A is the total water surface area, e w is the saturated water vapor pressure corresponding to the water temperature, E w is the micro-topography temperature corresponding to the saturated water vapor pressure, P is the air pressure, P0 is the standard atmospheric pressure,

[0070] e w and E w Calculated according to the following formula (4) and formula (5):

[0071]

[0072]

[0073] Among them, C1=0.1079574×102, C2=-0.5028×10, C3=0.150475×10-3, C4=0.42873×10-3, C5=0.78614, C6=-0.82969×10, C7=0.476955×10, T1 is the triple point temperature of water. Normally, T1=273.16K, T=273.15+t(K), K is the temperature unit Kelvin, t is the temperature in Celsius.

[0074] Determine the road section to be predicted where ice prediction needs to be carried out. The road section to be predicted includes the road section near water and the road section near cliff. When the road section to be predicted is the road section near water, calculate the liquid water content W at each preset time point according to the following formula (1):

[0075] W=1.33q+E / S (1),

[0076] Among them, q refers to the liquid content, E is the water surface evaporation, and S is the grid area of ​​the road section to be predicted.

[0077] In one embodiment, q and E are calculated according to the following formula (2) and formula (3), respectively:

[0078] q=0.622E w / P (2),

[0079] E = 0.00017A (e w -E w )P0 / P×10 3 (3)

[0080] Where A is the total water surface area, e w is the saturated water vapor pressure corresponding to the water temperature, E w is the micro-topography temperature corresponding to the saturated water vapor pressure, P is the air pressure, P0 is the standard atmospheric pressure,

[0081] e w and Ew Calculated according to the following formula (4) and formula (5):

[0082]

[0083]

[0084] Among them, C1=0.1079574×102, C2=-0.5028×10, C3=0.150475×10-3, C4=0.42873×10-3, C5=0.78614, C6=-0.82969×10, C7=0.476955×10, T1 is the triple point temperature of water. Normally, T1=273.16K, T=273.15+t(K), K is the temperature unit Kelvin, t is the temperature in Celsius.

[0085] In one embodiment, when the road section to be predicted is a road section near a cliff, the liquid water content W at each preset time point is calculated according to the following formula (6):

[0086] W=1.33q (6)

[0087] Where q refers to the liquid content, q = 0.622E w / P,E w is the micro-topography temperature corresponding to the saturated water vapor pressure, and P is the air pressure.

[0088] In one embodiment, E is calculated according to the following formula (7): w :

[0089]

[0090] Among them, C1=0.1079574×102, C2=-0.5028×10, C3=0.150475×10-3, C4=0.42873×10-3, C5=0.78614, C6=-0.82969×10, C7=0.476955×10, T1 is the triple point temperature of water. Under normal circumstances, T1=273.16K, T=273.15+t(K), K is the temperature unit Kelvin, t is the temperature in Celsius.

[0091] Determine the road section to be predicted where ice prediction needs to be carried out. The road section to be predicted includes a road section near water and a road section near a cliff. When the road section to be predicted is a road section near a cliff, calculate the liquid water content W at each preset time point according to the following formula (6):

[0092] W=1.33q (6)

[0093] Where q refers to the liquid content, q = 0.622Ew / P,E w is the micro-topography temperature corresponding to the saturated water vapor pressure, and P is the air pressure.

[0094] In one embodiment, E is calculated according to the following formula (7): w :

[0095]

[0096] Among them, C1=0.1079574×102, C2=-0.5028×10, C3=0.150475×10-3, C4=0.42873×10-3, C5=0.78614, C6=-0.82969×10, C7=0.476955×10, T1 is the triple point temperature of water. Under normal circumstances, T1=273.16K, T=273.15+t(K), K is the temperature unit Kelvin, t is the temperature in Celsius.

[0097] In one embodiment, the weather forecast data includes wind speed and predicted temperature. When the road section to be predicted is a cliff section, the wind speed and temperature T at each preset time point are calculated according to the following formula (8) and formula (9), respectively:

[0098]

[0099]

[0100] Among them, U is the wind speed of the cliff section at an altitude of h, U0 is the average wind speed in the future preset time period, r0 is the length of the cliff mouth, L is the width of the cliff mouth, θ is the angle from the bottom of the cliff mouth to the road, H0 refers to the altitude corresponding to the preset time point, h0 is the altitude of the bottom of the cliff mouth, and T0 is the predicted temperature corresponding to the preset time point.

[0101] The target meteorological boundary conditions and the target initial conditions are input into the weather prediction model, so as to output the meteorological prediction data for each preset time point in each grid within the preset time period in the future through the weather prediction model. The meteorological prediction data includes wind speed and predicted temperature. When the road section to be predicted is a cliff section, the wind speed and temperature T at each preset time point are calculated according to the following formula (8) and formula (9) respectively:

[0102]

[0103]

[0104] Among them, U is the wind speed of the cliff section at an altitude of h, U0 is the average wind speed in the future preset time period, r0 is the length of the cliff mouth, L is the width of the cliff mouth, λ is the angle from the bottom of the cliff mouth to the road, H0 refers to the altitude corresponding to the preset time point, h0 is the altitude of the bottom of the cliff mouth, and T0 is the predicted temperature corresponding to the preset time point.

[0105] In one embodiment, the meteorological forecast data includes wind speed and predicted temperature. When the road section to be predicted is a road section near water, the wind speed U and temperature T at each preset time point are calculated according to the following formula (10) and formula (11), respectively:

[0106]

[0107]

[0108] Among them, U is the wind speed of the cliff section at an altitude of h, T is the temperature at a preset time point, U0 is the average wind speed at an altitude of H0 in the future preset time period, α is the empirical coefficient of ground roughness, H0 refers to the altitude corresponding to the preset time point, h0 is the altitude at the bottom of the cliff, and T0 is the predicted temperature corresponding to the preset time point.

[0109] The target meteorological boundary conditions and the target initial conditions are input into the weather prediction model, so as to output the meteorological prediction data for each preset time point in each grid within the preset time period in the future through the weather prediction model. The meteorological prediction data includes wind speed and predicted temperature. When the road section to be predicted is a water section, the wind speed U and temperature T at each preset time point are calculated according to the following formulas (10) and (11), respectively:

[0110]

[0111]

[0112] Among them, U is the wind speed of the cliff section at an altitude of h, T is the temperature at a preset time point, U0 is the average wind speed at an altitude of H0 in the future preset time period, α is the empirical coefficient of ground roughness, H0 refers to the altitude corresponding to the preset time point, h0 is the altitude at the bottom of the cliff, and T0 is the predicted temperature corresponding to the preset time point.

[0113] In one embodiment, the meteorological forecast data includes predicted precipitation intensity, and determining the ice growth thickness at each preset time point includes calculating the ice growth thickness at each preset time point according to the following formula (12):

[0114]

[0115] Where dI is the ice growth thickness at the preset time point, ρi is the density of ice, π is the pi, E1 is the freezing rain collision efficiency coefficient, r is the predicted precipitation intensity, ρ is the density of water, E2 is the supercooled water collision efficiency coefficient, U is the wind speed of the cliff section at an altitude of h, and W is the liquid water content at a preset time point.

[0116] The target meteorological boundary field conditions and the target initial field conditions are input into the weather prediction model, so as to output the meteorological prediction data for each preset time point in each grid within the future preset time period through the weather prediction model, wherein the meteorological prediction data includes the predicted precipitation intensity, and determining the ice growth thickness at each preset time point includes calculating the ice growth thickness at each preset time point according to the following formula (12):

[0117]

[0118] Where dI is the ice growth thickness at the preset time point, ρ i is the density of ice, π is the pi, E1 is the freezing rain collision efficiency coefficient, r is the predicted precipitation intensity, ρ is the density of water, E2 is the supercooled water collision efficiency coefficient, U is the wind speed of the cliff section at an altitude of h, and W is the liquid water content at a preset time point.

[0119] In one embodiment, Figure 2 As shown, a device for predicting the thickness of ice on a road near water or cliff is provided, comprising:

[0120] The data collection module 201 is used to determine the road sections to be predicted where ice prediction needs to be carried out, the road sections to be predicted include water sections and cliff sections, and obtain the terrain data and land use data of the area where the road sections to be predicted are located;

[0121] The data grid division module 202 divides the area where the road section to be predicted is located into equidistant grids according to the area where the road section to be predicted is located and the water body and / or cliff position adjacent to the road section to be predicted;

[0122] The data pre-processing module 203 is used to perform interpolation processing on the terrain data and the land use data to determine the target meteorological boundary field conditions and the target initial field conditions of the equidistant grid;

[0123] The numerical calculation module 204 is used to input the target meteorological boundary field conditions and the target initial field conditions into the weather prediction model, so as to output the meteorological prediction data at each preset time point in the future preset time period through the weather prediction model;

[0124] The icing calculation module 205 is used to determine the temperature and humidity at each preset time point based on the meteorological forecast data. When it is determined that icing will occur at the preset time point based on the temperature and humidity, the icing growth thickness at each preset time point is determined, and the icing growth thickness at each preset time point is integrated to determine the final icing thickness value for the future preset time period.

[0125] In one embodiment, the data pre-processing module 203 is also used to interpolate the terrain data and land use data to determine the target meteorological boundary field conditions and target initial field conditions of the equidistant grid, including: interpolating the terrain data and land use data to the equidistant grid to determine the lower boundary conditions of the equidistant grid for conducting icing prediction, obtaining the initial field data for conducting icing prediction, and determining the initial meteorological boundary field conditions and initial values ​​of the initial field conditions of the segment icing numerical calculation model based on the initial field data, interpolating the initial field data to the equidistant grid, and adjusting the interpolation according to the lower boundary conditions to obtain the target meteorological boundary field conditions and target initial field conditions for driving the numerical calculation.

[0126] In one embodiment, when the road section to be predicted is a water-adjacent road section, the liquid water content W at each preset time point is calculated according to the following formula (1):

[0127] W=1.33q+E / S (1),

[0128] Among them, q refers to the liquid content, E is the water surface evaporation, and S is the grid area of ​​the road section to be predicted.

[0129] In one embodiment, q and E are calculated according to the following formula (2) and formula (3), respectively:

[0130] q=0.622E w / P (2),

[0131] E = 0.00017A (e w -E w )P0 / P×10 3 (3)

[0132] Where A is the total water surface area, e w is the saturated water vapor pressure corresponding to the water temperature, E w is the micro-topography temperature corresponding to the saturated water vapor pressure, P is the air pressure, P0 is the standard atmospheric pressure,

[0133] e w and E w Calculated according to the following formula (4) and formula (5):

[0134]

[0135]

[0136] Among them, C1=0.1079574×102, C2=-0.5028×10, C3=0.150475×10-3, C4=0.42873×10-3, C5=0.78614, C6=-0.82969×10, C7=0.476955×10, T1 is the triple point temperature of water. Normally, T1=273.16K, T=273.15+t(K), K is the temperature unit Kelvin, t is the temperature in Celsius.

[0137] In one embodiment, when the road section to be predicted is a road section near a cliff, the liquid water content W at each preset time point is calculated according to the following formula (6):

[0138] W=1.33q (6)

[0139] Where q refers to the liquid content, q = 0.622E w / P,E w is the micro-topography temperature corresponding to the saturated water vapor pressure, and P is the air pressure.

[0140] In one embodiment, E is calculated according to the following formula (7): w :

[0141]

[0142] Among them, C1=0.1079574×102, C2=-0.5028×10, C3=0.150475×10-3, C4=0.42873×10-3, C5=0.78614, C6=-0.82969×10, C7=0.476955×10, T1 is the triple point temperature of water. Under normal circumstances, T1=273.16K, T=273.15+t(K), K is the temperature unit Kelvin, t is the temperature in Celsius.

[0143] In one embodiment, the weather forecast data includes wind speed and predicted temperature. When the road section to be predicted is a cliff section, the wind speed and temperature T at each preset time point are calculated according to the following formula (8) and formula (9), respectively:

[0144]

[0145]

[0146] Among them, U is the wind speed of the cliff section at an altitude of h, U0 is the average wind speed in the future preset time period, r0 is the length of the cliff mouth, L is the width of the cliff mouth, θ is the angle from the bottom of the cliff mouth to the road, H0 refers to the altitude corresponding to the preset time point, h0 is the altitude of the bottom of the cliff mouth, and T0 is the predicted temperature corresponding to the preset time point.

[0147] In one embodiment, the meteorological forecast data includes wind speed and predicted temperature. When the road section to be predicted is a road section near water, the wind speed U and temperature T at each preset time point are calculated according to the following formula (10) and formula (11), respectively:

[0148]

[0149]

[0150] Among them, U is the wind speed of the cliff section at an altitude of h, T is the temperature at a preset time point, U0 is the average wind speed at an altitude of H0 in the future preset time period, α is the empirical coefficient of ground roughness, H0 refers to the altitude corresponding to the preset time point, h0 is the altitude at the bottom of the cliff, and T0 is the predicted temperature corresponding to the preset time point.

[0151] In one embodiment, the meteorological forecast data includes predicted precipitation intensity, and determining the ice growth thickness at each preset time point includes calculating the ice growth thickness at each preset time point according to the following formula (12):

[0152]

[0153] Where dI is the ice growth thickness at the preset time point, ρ i is the density of ice, π is the pi, E1 is the freezing rain collision efficiency coefficient, r is the predicted precipitation intensity, ρ is the density of water, E2 is the supercooled water collision efficiency coefficient, U is the wind speed of the cliff section at an altitude of h, and W is the liquid water content at a preset time point.

[0154] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05 and a memory (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A06. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor A01, a method for predicting the thickness of ice on a road section near water and cliffs is implemented. The display screen A04 of the computer device can be a liquid crystal display or an electronic ink display, and the input device A05 of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0155] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0156] An embodiment of the present application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, steps of a method for predicting the thickness of ice on a road section near water or cliffs are implemented.

[0157] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0158] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0159] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0161] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0162] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0163] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0164] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0165] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for predicting the thickness of ice on a road near water or cliffs, characterized in that: The prediction method comprises: Determine the road sections to be predicted where ice prediction needs to be carried out, wherein the road sections to be predicted include road sections near water and road sections near cliffs; Dividing the area where the road section to be predicted is located into equidistant grids according to the area where the road section to be predicted is located and the locations of water bodies and / or cliffs adjacent to the road section to be predicted; Acquire terrain data and land use data of the area where the road section to be predicted is located; interpolating the terrain data and the land use data to the equidistant grid to determine a lower boundary condition of the equidistant grid for performing icing prediction; Acquiring initial field data for icing prediction, so as to determine initial meteorological boundary field conditions and initial values ​​of initial field conditions of a segment icing numerical calculation model according to the initial field data; interpolating the initial field data to the equidistant grid, and adjusting the interpolation according to the lower boundary condition to obtain target meteorological boundary field conditions and target initial field conditions for driving numerical calculation; Inputting the target meteorological boundary field conditions and the target initial field conditions into a weather prediction model, so as to output meteorological prediction data for each preset time point in a future preset time period through the weather prediction model; Determine the temperature and humidity at each preset time point according to the weather forecast data; In the case where ice formation occurs at the preset time point according to the temperature and humidity, determining the ice growth thickness at each preset time point; The ice growth thickness at each preset time point is integrated and calculated to determine the final ice thickness value for the future preset time period.

2. The method according to claim 1, characterized in that When the road section to be predicted is a water-adjacent road section, the liquid water content W at each preset time point is calculated according to the following formula (1): （1）； in, It refers to the liquid content, E is the water surface evaporation, and S is the grid area of ​​the road section to be predicted.

3. The method according to claim 2, characterized in that Calculate q and E according to the following formula (2) and formula (3) respectively: （2）； （3）； Where A is the total water surface area, is the water temperature corresponding to the saturated water vapor pressure, is the micro-topography temperature corresponding to the saturated water vapor pressure, P is the air pressure, is standard atmospheric pressure; and Calculated according to the following formulas (4) and (5): （4） （5） Among them, C1 = 0.1079574 × 102, C2 = - 0.5028 × 10, C3 = 0.150475 × 10-3, C4 =0.42873 × 10-3, C5 = 0.78614, C6 = - 0.82969 × 10, C7 = 0.476955 × 10, is the triple point temperature of water. Usually, , T=273.15+t (K), K is the temperature unit Kelvin, is the temperature in degrees Celsius.

4. The method according to claim 1, characterized in that: When the road section to be predicted is a cliff section, the liquid water content W at each preset time point is calculated according to the following formula (6): （6） in, It refers to the liquid content. , is the micro-topography temperature corresponding to the saturated water vapor pressure, and P is the air pressure.

5. The method according to claim 4, characterized in that Calculate according to the following formula (7) : （7） Among them, C1 = 0.1079574 × 102, C2 = - 0.5028 × 10, C3 = 0.150475 × 10-3, C4 =0.42873 × 10-3, C5 = 0.78614, C6 = - 0.82969 × 10, C7 = 0.476955 × 10, is the triple point temperature of water. Usually, , T=273.15+t (K), K is the temperature unit Kelvin, is the temperature in degrees Celsius.

6. The method according to claim 1, characterized in that The meteorological forecast data includes wind speed and predicted temperature. When the road section to be predicted is a cliff section, the wind speed and temperature T at each preset time point are calculated according to the following formula (8) and formula (9) respectively: （8）； （9）； Among them, U is the wind speed of the cliff section at an altitude of h, is the average wind speed in the future preset time period, is the length of the cliff mouth, is the width of the cliff mouth, is the angle from the bottom of the cliff to the road, It refers to the altitude corresponding to the preset time point. is the altitude of the bottom of the cliff, It is the predicted temperature corresponding to the preset time point.

7. The method according to claim 1, characterized in that The meteorological forecast data includes wind speed and predicted temperature. When the road section to be predicted is a road section near water, the wind speed U and temperature T at each preset time point are calculated according to the following formula (10) and formula (11) respectively: （10）； （11）； Among them, U is the wind speed at the cliff section at an altitude of h, T is the temperature at a preset time point, The future preset time period is at a height of The average wind speed at is the empirical coefficient of ground roughness, It refers to the altitude corresponding to the preset time point. is the altitude of the bottom of the cliff, It is the predicted temperature corresponding to the preset time point.

8. The method according to claim 1, characterized in that: The meteorological forecast data includes the predicted precipitation intensity, and determining the ice growth thickness at each preset time point includes calculating the ice growth thickness at each preset time point according to the following formula (12): （12）； in, The ice growth thickness at the preset time point, is the density of ice, is the circumference of a circle, is the freezing rain collision efficiency coefficient, is the predicted precipitation intensity, is the density of water, is the supercooled water collision efficiency coefficient, U is the wind speed of the cliff section at an altitude of h, and W is the liquid water content at a preset time point.

9. A device for predicting the thickness of ice on a road near water or cliffs, characterized in that: include: A data collection module is used to determine the road sections to be predicted where ice prediction needs to be carried out, the road sections to be predicted include water sections and cliff sections, and obtain terrain data and land use data of the area where the road sections to be predicted are located; A data grid division module, which divides the area where the road section to be predicted is located into equidistant grids according to the area where the road section to be predicted is located and the water body and / or cliff position adjacent to the road section to be predicted; A data pre-processing module, for interpolating the terrain data and the land use data to the equidistant grid to determine the lower boundary conditions of the equidistant grid for conducting icing prediction, obtaining initial field data for conducting icing prediction, determining the initial meteorological boundary field conditions and initial values ​​of the initial field conditions of the segment icing numerical calculation mode according to the initial field data, interpolating the initial field data to the equidistant grid, and adjusting the interpolation according to the lower boundary conditions to obtain the target meteorological boundary field conditions and target initial field conditions for driving the numerical calculation; A numerical calculation module, used for inputting the target meteorological boundary field conditions and the target initial field conditions into a weather prediction model, so as to output meteorological prediction data at each preset time point in a future preset time period through the weather prediction model; An icing calculation module, used to determine the temperature and humidity at each preset time point according to the meteorological forecast data; In the case where ice formation occurs at the preset time point according to the temperature and humidity, determining the ice growth thickness at each preset time point; The ice growth thickness at each preset time point is integrated and calculated to determine the final ice thickness value for the future preset time period.

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