A method, system, and computer equipment for predicting ground icing temperature.

Abstract

This invention belongs to the field of civil aviation ground icing data identification technology, and discloses a method, system, and computer equipment for predicting ground icing temperature. The method includes: acquiring and outputting historical temperature data of a solid surface, and simultaneously outputting the collected relevant data to an icing temperature determination module; obtaining the predicted solid surface temperature through a triple exponential smoothing method of the time series, and outputting it; after receiving the transmitted data, the icing temperature determination module acquires the ice phase coverage rate on the solid surface, and, combined with the output predicted solid surface temperature, obtains the coupling relationship between the ice phase coverage rate, temperature, and time; based on the proportion of the ice phase coverage rate and the coupling relationship, the prediction result is obtained. This invention does not require the collection of large amounts of data and does not have excessively high requirements on the distribution of the required data. The obtained prediction result can provide an effective basis for operational support under near-surface icing conditions, enabling early and efficient de-icing preparation.

Description

Technical Field

[0001] This invention belongs to the field of civil aviation ground icing data recognition technology, and particularly relates to a ground icing temperature prediction method, prediction system and computer equipment. Background Technology

[0002] With the rapid development of airports, highways, and power transmission lines, these areas are also frequently threatened by icing. When icing occurs, ice on airports and highways alters pavement properties, reducing the surface friction coefficient and affecting the safety of aircraft takeoffs and vehicle driving. Icing on power lines increases their windward position in the atmosphere, which, when winds are favorable, can cause them to sway and become unstable, impacting power and communications. Therefore, to protect people's lives and national property and minimize the impact of icing on various industries, accurate icing prediction is crucial for efficient de-icing operations. Given the complex environments across various sectors, numerous factors influence surface icing, making it a complex problem with multiple coupled factors.

[0003] Based on the above analysis, the problems and shortcomings of existing technologies for dealing with icing in various fields are as follows:

[0004] (1) At present, airports and highways are relatively passive in dealing with road icing. They only start to issue warnings and remove ice after icing has occurred and a certain amount of ice has accumulated. They do not start to prevent icing before it occurs.

[0005] (2) Most of the meteorological systems equipped at airports can only collect various meteorological information in the atmospheric environment and use it as a basis for predicting whether solid surfaces will accumulate ice. This method has a lot of uncertainty.

[0006] (3) It is generally believed that surface ice will occur when the temperature is below 0℃, but in reality, water freezing will have supercooling, that is, when the temperature is below 0℃, water is still in the liquid phase.

[0007] (4) Complex conditions on solid surfaces, such as the presence of contaminants like mud, sand, and rubber deposits, or the presence of special solutions like de-icing fluid mixed in with the surface liquid, can all affect the final freezing temperature of the liquid, making it more difficult to determine. Depending on meteorological data and surface characteristics, the freezing temperature of the liquid also varies, so the conditions for determining freezing cannot be generalized.

[0008] (5) Existing technology prediction results cannot provide effective basis for operation support under near-surface ice conditions, and the accuracy of the data provided for early de-icing preparation is low, which affects work efficiency. Summary of the Invention

[0009] To overcome the problems existing in related technologies, the present invention discloses an embodiment of a method, system and computer device for predicting ground icing temperature.

[0010] The technical solution is as follows: A method for predicting ground icing temperature includes the following steps:

[0011] S1: The solid surface data acquisition module acquires historical temperature data of the solid surface and outputs it to the solid surface temperature prediction module; the solid surface material property data and liquid accumulation property data acquired by the solid surface data acquisition module are output to the freezing temperature determination module.

[0012] S2, the solid surface temperature prediction module obtains the predicted solid surface temperature through the triple exponential smoothing method of the time series and outputs it to the freezing temperature determination module.

[0013] S3, after receiving the data transmitted by the solid surface data acquisition module, the freezing temperature determination module obtains the ice phase coverage rate on the solid surface, and combines it with the predicted solid surface temperature output by the solid surface temperature prediction module to obtain the coupling relationship between the ice phase coverage rate and temperature and time. Based on the proportion of the ice phase coverage rate and the coupling relationship, the prediction result is obtained.

[0014] In one embodiment, in step S1, the material property data and liquid accumulation property data of the solid surface acquired by the solid surface data acquisition module include: the surface free energy of the solid surface. The roughness factor z and the liquid accumulation characteristic data include: the type of liquid accumulation on the surface, the concentration C% and its corresponding density, the equilibrium temperature and the latent heat of solidification parameters.

[0015] In one embodiment, in step S1, the historical surface temperature data x collected by the solid surface data acquisition module i The output is sent to the solid surface temperature prediction module, and the following cubic exponential smoothing calculation formula is substituted into it:

[0016] Given a smoothing coefficient 'a', the formulas for calculating the smoothing values ​​of each order are as follows:

[0017]

[0018] Where, x i This represents the actual temperature at the current time i. These represent the smoothed values ​​of the first, second, and third exponential smoothing models at time i, respectively; and the predicted value x at time t. i+t The calculation formula is:

[0019] x i+t =A i +Bi t+C i t 2

[0020] Where coefficient A i B i C i They are represented as follows:

[0021]

[0022]

[0023]

[0024] The predicted future temperature of the solid surface is T(t) = x i+t .

[0025] In one embodiment, obtaining the prediction result in step S3 includes the following steps:

[0026] 1) Based on the characteristic data of the surface liquid collected by the solid surface data acquisition module (1), the homogeneous nucleation work G of the ice nucleus is calculated by combining the nucleation theory;

[0027] 2) Based on the material property data of the solid surface collected by the solid surface data acquisition module, calculate the contact angle θ when the ice nucleus forms on the solid surface. IS With heterogeneous nucleation surface factor a epy And the heterogeneous nucleation work G of ice nuclei on a rough solid surface was obtained. * =a epy G;

[0028] 3) Regarding the heterogeneous nucleation work G in step 2), * By combining the Boltzmann energy distribution law, the formation rate J of ice nuclei on the solid surface can be obtained. a And calculate the radius growth rate u of the ice nucleus on the solid surface. is ;

[0029] 4) Combining the nucleation rate J from step 3) a and ice core radius growth rate u is The coverage of the ice phase on the solid surface can be obtained.

[0030] 5) For the ice phase coverage in step 4), the predicted surface temperature T(t) from the solid surface temperature prediction module is introduced to obtain the relationship between coverage and time. Using 100% ice coverage as the criterion for determining icing, the system outputs the predicted temperature and time when the ice coverage reaches 100%.

[0031] In one embodiment, in step 1), the characteristic data of the surface liquid further include: ice-water specific surface free energy γ. iw Phase transition equilibrium temperature T eq Latent heat of solidification per unit mass (h) f And the density ρ of ice after freezing ice ;

[0032] The homogeneous nucleation work G of ice nuclei, calculated using relevant nucleation theories, is:

[0033]

[0034] In one embodiment, in step 2), under ideal smooth surface conditions, the contact angle is:

[0035]

[0036] When the surface roughness of the solid is such that cosθ rIS =zcosθ IS The roughness factor z is the ratio of the actual surface area of ​​the solid surface to the smooth surface area.

[0037] The surface factor is:

[0038]

[0039] In the above formula, The dispersion force component of the substrate surface energy is expressed in J / m. 2 , The dispersion force component of the ice surface energy is expressed in J / m². 2 , The dispersion force component of the surface energy of water, expressed in J / m². 2 γ I This refers to the surface energy of ice, measured in J / m³. 2 γ W The surface energy of water, measured in J / m³ 2 γ iw The surface free energy of ice-water ratio is expressed in J / m². 2 .

[0040] In one embodiment, in step 3), for the heterogeneous nucleation work G in step 2), * By combining the Boltzmann energy distribution law, the formation rate J of ice nuclei on the solid surface can be obtained. a As shown in the following formula:

[0041]

[0042] in, The area number density of the critical nucleus, in units of 1 / m. 2 Na The surface number density of water molecules, expressed in J / m². 2 ;ρ w This is the density of water, expressed in kg / m³. 3 ;ρ i This is the density of water, expressed in kg / m³. 3 k is Boltzmann's constant; T is absolute temperature in Kelvin; ΔG is the critical nucleation work. The number of times a water molecule jumps out of the potential well per unit time, expressed in J / s, k = 1.380622 × 10⁻⁶. -23 J / ℃ is the Boltzmann constant; N A =6.022045×10 23 Let h be Avogadro's constant; h = 6.626176 × 10⁻⁶. -34 J·s is Planck's constant; E w =1.3×10 4 J / mol is the diffusion activation energy of water;

[0043] And calculate the radius growth rate u of a single ice nucleus. iw As shown in the following formula:

[0044]

[0045] In the formula, L m λ is the latent heat of solidification per unit mass, expressed in J / kg. iw M represents the average distance between water phase molecules and between ice phase molecules, in meters. w Let be the molar mass of water, expressed in kg / mol; then the rate of increase in the radius of the ice nucleus due to the expansion of ice nuclei on the ice surface is u. is =u iw sinθ Is .

[0046] In one embodiment, in step 4), the nucleation rate J in step 3) is combined with... a and ice core radius growth rate u is The coverage of the ice phase on the solid surface was obtained. for:

[0047]

[0048] In step 5), for the ice phase coverage rate in step 4), the predicted surface temperature T(t) from the solid surface temperature prediction module is introduced to obtain the relationship between coverage rate and time. As shown in the following formula:

[0049]

[0050] Where t is time; the condition for determining freezing is that the coverage reaches 100%, i.e., the appearance of ice in a macroscopic sense. The output of the temperature and time when the coverage reaches 100% is the prediction result.

[0051] Another object of the present invention is to provide a ground icing temperature prediction system according to the aforementioned ground icing temperature prediction method, the ground icing temperature prediction system comprising:

[0052] The system includes a solid surface data acquisition module, a solid surface temperature prediction module, and an icing temperature prediction module. The solid surface data acquisition module is connected to both the solid surface temperature prediction module and the icing temperature prediction module. The solid surface temperature prediction module is then connected to the icing temperature prediction module.

[0053] The solid surface data acquisition module is used to acquire material property data and liquid accumulation property data of the solid surface; wherein the material property data includes the surface free energy of the solid surface. And roughness factor z, liquid accumulation characteristics include the type of liquid accumulation on the surface, concentration C% and its corresponding density, equilibrium temperature and latent heat of solidification parameters;

[0054] The solid surface temperature prediction module is used to calculate the future temperature T(t) of the solid surface using historical surface temperature data collected by the data acquisition module and a cubic exponential smoothing formula.

[0055] The freezing temperature determination module is used to determine the freezing temperature and obtain the temperature and time prediction results when the coverage reaches a certain proportion.

[0056] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the ground icing temperature prediction method.

[0057] Combining all the above technical solutions, the advantages and positive effects of this invention are as follows:

[0058] First, in view of the technical problems existing in the prior art and the difficulty of solving these problems, and closely combining the technical solution to be protected by this invention with the results and data in the research and development process, this invention provides a detailed and in-depth analysis of how the technical solution of this invention solves the technical problems and the creative technical effects brought about after solving the problems: In order to solve the above-mentioned problems of existing icing prediction methods, this invention adopts a ground icing temperature prediction method. This method is a mechanism model that does not require the collection of a large amount of data and does not have excessive requirements on the distribution of the required data. After obtaining the prediction results, it can provide an effective basis for the operation guarantee under the condition of near-surface icing, and carry out de-icing preparation in advance and efficiently.

[0059] Secondly, considering the technical solution as a whole or from a product perspective, the technical effects and advantages of the technical solution to be protected by this invention are specifically described as follows: The ground icing temperature prediction system provided by this invention includes a solid surface data acquisition module, a solid surface temperature prediction module, and an icing temperature determination module. The data acquisition module acquires historical temperature data of the solid surface and outputs it to the solid temperature prediction module, which then uses a triple exponential smoothing method for the time series to obtain the predicted solid surface temperature. The relevant data collected by the data acquisition module is output to the freezing temperature determination module. Using the characteristics of liquid accumulation on the solid surface—liquid type and concentration—the homogeneous nucleation work of ice nuclei is calculated. Using the physical parameters of the solid surface—surface free energy and roughness factor—the surface factor of heterogeneous nucleation is calculated. Combining the homogeneous nucleation work and the surface factor, the heterogeneous nucleation work of ice nuclei on the solid surface is obtained, and then the heterogeneous nucleation rate of ice nuclei on the surface is obtained. Then, using the characteristics of liquid accumulation on the solid surface, the growth rate of ice nuclei radius is obtained. Combined with the above nucleation rate, the coverage of ice phase on the solid surface is obtained. Finally, the predicted surface temperature output by the solid surface temperature prediction module is substituted to obtain the coupling relationship between ice phase coverage, temperature and time. When the coverage reaches 100%, freezing is judged, and the temperature and time at this time are output, which is the predicted condition for freezing.

[0060] Third, as supplementary evidence of the inventive step of the claims of this invention, it is also reflected in the fact that many scholars' research on the solidification and freezing of liquid water is based on experiments, and the nucleation process usually only lasts for tens of milliseconds. From a temporal perspective, this process is usually ignored. However, in reality, the nucleation process determines the starting point of the solidification and freezing stage and is an indispensable part of the entire freezing process. This invention analyzes the nucleation process, improves the evolution of the entire liquid water freezing process, and defines the actual starting point of liquid water freezing. Attached Figure Description

[0061] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0062] Figure 1 This is a flowchart of the ground icing temperature prediction method provided in an embodiment of the present invention;

[0063] Figure 2 This is a schematic diagram of the ground icing temperature prediction system provided in an embodiment of the present invention;

[0064] Figure 3 This is a flowchart illustrating the principle of the freezing temperature determination module provided in this embodiment of the invention.

[0065] Figure 4The simulation results are shown in the figure below, which is provided by the embodiment of the present invention for a small area of ​​a running track of one square meter, in the case of the simultaneous presence of tire rubber and cement, and considering that the roughness is not unique.

[0066] In the diagram: 1. Solid surface data acquisition module; 2. Solid surface temperature prediction module; 3. Icing temperature prediction module. Detailed Implementation

[0067] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0068] I. Explanation of the Implementation Example:

[0069] The ground icing temperature prediction system provided in this embodiment of the invention includes: a solid surface data acquisition module 1, a solid surface temperature prediction module 2, and an icing temperature prediction module 3; wherein, the solid surface data acquisition module 1 is connected to both the solid surface temperature prediction module 2 and the icing temperature prediction module 3; and the solid surface temperature prediction module 2 is further connected to the icing temperature prediction module 3.

[0070] In this embodiment of the invention, the solid surface data acquisition module 1 is used to acquire material property data and liquid accumulation property data of the solid surface; wherein the material property data includes the surface free energy of the solid surface. And roughness factor z, liquid accumulation characteristics include parameters such as the type of liquid accumulation on the surface, concentration C% and its corresponding density, equilibrium temperature and latent heat of solidification;

[0071] The solid surface temperature prediction module 2 is used to calculate the future temperature T(t) of the solid surface using historical surface temperature data collected by the data acquisition module and the time series-cubic exponential smoothing calculation formula.

[0072] Freezing temperature determination module 3 is used to determine the freezing temperature, and its determination principle includes:

[0073] 1) Based on the characteristic data of the surface liquid collected by the solid surface data acquisition module 1—liquid type, solubility C%, and relevant parameters obtained by consulting relevant materials, the homogeneous nucleation work G of the ice nucleus is calculated by combining the nucleation theory.

[0074] 2) Based on the material property data of the solid surface collected by solid surface data acquisition module 1—surface free energy Given the roughness factor z, calculate the contact angle θ when the ice nucleus forms on the solid surface. IS With heterogeneous nucleation surface factor a epy And the heterogeneous nucleation work G of ice nuclei on a rough solid surface was obtained. * =a epy G;

[0075] 3) Regarding the heterogeneous nucleation work G in step 2), * By combining the Boltzmann energy distribution law, the formation rate J of ice nuclei on the solid surface can be obtained. a And calculate the radius growth rate u of the ice nucleus on the solid surface. is ;

[0076] 4) Combining the nucleation rate J from step 3) a and ice core radius growth rate u is The coverage of the ice phase on the solid surface can be obtained.

[0077] 5) For the ice phase coverage in step 4), the surface temperature T(t) predicted in the solid surface temperature prediction module 2 is introduced to obtain the relationship between coverage and time. Using 100% ice coverage as the criterion for determining icing, the system outputs the predicted temperature and time when the ice coverage reaches 100%.

[0078] like Figure 1 As shown, the ground icing temperature prediction method provided in this embodiment of the invention includes the following steps:

[0079] S101, the historical temperature data of the solid surface is acquired through the solid surface data acquisition module 1 and output to the solid surface temperature prediction module 2; at the same time, the relevant data acquired through the solid surface data acquisition module 1 is output to the freezing temperature determination module 3.

[0080] S102, the solid surface temperature prediction module 2 obtains the predicted solid surface temperature through the triple exponential smoothing method of the time series and outputs it to the freezing temperature determination module 3.

[0081] S103, after receiving the data transmitted by the solid surface data acquisition module 1, the freezing temperature determination module 3 uses the characteristics of the liquid accumulation on the solid surface, including the type and concentration of the liquid accumulation, to solve for the homogeneous nucleation work of the ice nuclei; using the physical properties of the solid surface, including surface free energy and roughness factor, to solve for the surface factor of heterogeneous nucleation, and combining the homogeneous nucleation work and the surface factor to obtain the heterogeneous nucleation work of the ice nuclei on the solid surface; then, the heterogeneous nucleation rate of the ice nuclei on the surface is obtained; then, the growth rate of the ice nucleus radius is obtained using the characteristics of the liquid accumulation on the solid surface, and combined with the above-mentioned nucleation rate, the coverage rate of the ice phase on the solid surface is obtained; finally, the predicted surface temperature output by the solid surface temperature prediction module 2 is substituted to obtain the coupling relationship between the ice phase coverage rate and temperature and time. When the coverage rate reaches 100%, freezing is determined, and the temperature and time at this time are output, which is the prediction result.

[0082] Example 1

[0083] The ground icing temperature prediction method provided in this invention is applicable to the prediction of icing on solid surfaces. The ground icing temperature prediction system in this invention is as follows: Figure 2 As shown, it includes a solid surface data acquisition module 1, a solid surface temperature prediction module 2, and an icing temperature determination module 3;

[0084] The solid surface data acquisition module 1 acquires historical temperature data of the solid surface and outputs it to the solid surface temperature prediction module 2. The predicted solid temperature is obtained by using the triple exponential smoothing method of the time series.

[0085] The relevant data collected by the solid surface data acquisition module 1 is output to the freezing temperature determination module 3. Using the characteristics of the liquid accumulation on the solid surface—liquid type and concentration—the homogeneous nucleation work of the ice nuclei is calculated. Using the physical properties of the solid surface—surface free energy and roughness factor—the surface factor of heterogeneous nucleation is calculated. Combining the homogeneous nucleation work and the surface factor, the heterogeneous nucleation work of the ice nuclei on the solid surface is obtained. Then, the heterogeneous nucleation rate of the ice nuclei on the surface is obtained. Next, the growth rate of the ice nucleus radius is obtained using the characteristics of the liquid accumulation on the solid surface. Combined with the above-mentioned nucleation rate, the coverage of the ice phase on the solid surface is obtained. Finally, the predicted surface temperature output by the solid surface temperature prediction module 2 is substituted to obtain the coupling relationship between the ice phase coverage and temperature and time. When the coverage reaches 100%, freezing is determined, and the temperature and time at this time are output, which is the prediction result.

[0086] The solid surface data acquisition module 1 needs to collect material property data and liquid accumulation property data of the solid surface; the material property data includes the surface free energy of the solid surface. And roughness factor z, liquid accumulation characteristics include parameters such as the type of liquid accumulation on the surface, concentration C% and its corresponding density, equilibrium temperature and latent heat of solidification;

[0087] In addition, the solid surface temperature prediction module 2 uses historical surface temperature data x collected by the solid surface data acquisition module 1. i The output is sent to solid surface temperature prediction module 2, and substituted into the calculation formula for the third exponential smoothing:

[0088] Given a smoothing coefficient 'a', the formulas for calculating the smoothing values ​​of each order are as follows:

[0089]

[0090] Where, x i This represents the actual temperature at the current time i. These represent the smoothed values ​​of the first, second, and third exponential smoothing models at time i, respectively; and the predicted value x at time t. i+t The calculation formula is:

[0091] x i+t =A i +B i t+C i t 2

[0092] Where coefficient A i B i C i They are represented as follows:

[0093]

[0094]

[0095]

[0096] The predicted future temperature of the solid surface is T(t) = x i+t .

[0097] Finally, the determination principle and steps for the freezing temperature determination module 3 are as follows: Figure 3 As shown:

[0098] 1) Based on the characteristic data of the surface liquid collected by solid surface data acquisition module 1—liquid type, solubility C%, and by consulting relevant literature, the relevant parameters are obtained: ice-water specific surface free energy γ. iw Phase transition equilibrium temperature T eq Latent heat of solidification per unit mass (h) f And the density ρ of ice after freezing ice Based on relevant nucleation theories, the homogeneous nucleation work G of ice nuclei is calculated as follows:

[0099]

[0100] Based on the material property data of the solid surface collected by solid surface data acquisition module 1—surface free energy Given the roughness factor z, calculate the contact angle θ when the ice nucleus forms on the solid surface. IS With heterogeneous nucleation surface factor a epy And the heterogeneous nucleation work G of ice nuclei on a rough solid surface was obtained. * =a epy G;

[0101] Under ideal smooth surface conditions, the contact angle is:

[0102]

[0103] When the surface roughness of the solid is such that cosθ rIS =zcosθ IS The roughness factor z is the ratio of the actual surface area to the smooth surface area of ​​a solid surface. Therefore, the surface factor is:

[0104]

[0105] In the above formula, The dispersion force component of the substrate surface energy is expressed in J / m. 2 , The dispersion force component of the ice surface energy is expressed in J / m². 2 , The dispersion force component of the surface energy of water, expressed in J / m². 2 γ I This refers to the surface energy of ice, measured in J / m³. 2 γ W The surface energy of water, measured in J / m³ 2 γ iw The surface free energy of ice-water ratio is expressed in J / m². 2 .

[0106] 3) Regarding the heterogeneous nucleation work in step 2), the formation rate J of ice nuclei on the solid surface can be obtained by combining the Boltzmann energy distribution law. a As shown in the following formula:

[0107]

[0108] in, The area number density of the critical nucleus, in units of 1 / m. 2 N a The surface number density of water molecules, expressed in J / m². 2 ;ρ w This is the density of water, expressed in kg / m³. 3 ;ρ i This is the density of water, expressed in kg / m³. 3k is Boltzmann's constant; T is absolute temperature in Kelvin; ΔG is the critical nucleation work. The number of times a water molecule jumps out of the potential well per unit time, expressed in J / s, k = 1.380622 × 10⁻⁶. -23 J / ℃ is the Boltzmann constant; N A =6.022045×10 23 Let h be Avogadro's constant; h = 6.626176 × 10⁻⁶. -34 J·s is Planck's constant; E w =1.3×10 4 J / mol is the diffusion activation energy of water;

[0109] And calculate the radius growth rate u of a single ice nucleus. iw As shown in the following formula:

[0110]

[0111] In the formula, L m λ is the latent heat of solidification per unit mass, expressed in J / kg. iw M represents the average distance between water phase molecules and between ice phase molecules, in meters. w Let be the molar mass of water, expressed in kg / mol; then the rate of increase in the radius of the ice nucleus due to the expansion of ice nuclei on the ice surface is u. is =u iw sinθ Is .

[0112] 4) Combining the nucleation rate J from step 3) a and ice core radius growth rate u is The coverage of the ice phase on the solid surface was obtained. for:

[0113]

[0114] For the ice phase coverage in step 4), the surface temperature T(t) predicted in the solid surface temperature prediction module (2) is introduced to obtain the relationship between coverage and time t. As shown in the following formula:

[0115]

[0116] Using 100% coverage, i.e. the appearance of ice in a macroscopic sense, as the condition for determining icing, the output temperature and time when the coverage reaches 100% is the prediction result.

[0117] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0118] The information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0119] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0120] II. Application Examples:

[0121] Application Example 1

[0122] An application embodiment of the present invention provides a computer device comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above-described method embodiments.

[0123] Application Example 2

[0124] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the above-described method embodiments.

[0125] Application Example 3

[0126] The present invention also provides an information data processing terminal, which is used to provide a user input interface to implement the steps in the above method embodiments when executed on an electronic device. The information data processing terminal is not limited to mobile phones, computers, and switches.

[0127] Application Example 4

[0128] The present invention also provides a server that, when executed on an electronic device, provides a user input interface to implement the steps described in the above method embodiments.

[0129] Application Example 5

[0130] The present invention provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the various method embodiments above.

[0131] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0132] III. Evidence of the relevant effects of the embodiments:

[0133] The application examples of this invention can determine the actual icing initiation temperature by considering pavement conditions. For instance, on a concrete runway surface with tire residue from aircraft tire friction, the icing temperature is not unique because the type of material and roughness of the material in contact with the runway surface by liquid water are not uniform. This invention, applied to a small area of ​​one square meter on a runway surface, addresses the simultaneous presence of tire residue and concrete, and considers that the roughness is not unique. The simulation results are as follows: Figure 4 This indicates that the freezing temperature within this range is not unique, varying from -0.9450℃ to -0.6722℃.

[0134] This invention can determine the icing initiation temperature at a specific location, and by combining it with the pavement temperature predicted for future times using the triple exponential smoothing method of the time series, the starting time of the icing process can be predicted. This provides an effective basis for ensuring operation under near-surface icing conditions, and allows for early and efficient de-icing preparation.

[0135] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for predicting ground icing temperature, characterized in that, The method includes the following steps: The solid surface data acquisition module (1) acquires historical temperature data of the solid surface and outputs it to the solid surface temperature prediction module (2); the solid surface data acquisition module (1) acquires material property data and liquid accumulation property data of the solid surface and outputs them to the freezing temperature determination module (3). The solid surface temperature prediction module (2) obtains the predicted solid surface temperature through the triple exponential smoothing method of the time series and outputs it to the freezing temperature determination module (3). After receiving the data transmitted by the solid surface data acquisition module (1), the freezing temperature determination module (3) obtains the ice phase coverage rate of the ice phase on the solid surface. Combined with the predicted solid surface temperature output by the solid surface temperature prediction module (2), the coupling relationship between the ice phase coverage rate and temperature and time is obtained. Based on the proportion of the ice phase coverage rate, the prediction result is obtained in combination with the coupling relationship. In the steps In the middle, the solid surface data acquisition module (1) collects historical surface temperature data. The output is sent to the solid surface temperature prediction module (2), and substituted into the following cubic exponential smoothing calculation formula: Given smoothing coefficient The formulas for calculating the smoothing values ​​of each order are as follows: ； in, Indicates the current time The actual temperature They represent Smoothing values ​​of linear, quadratic, and cubic exponential smoothing models at any given time; predicting the future. The value of time The calculation formula is: ； Where the coefficient They are represented as follows: ； ； ； The predicted future temperature of the solid surface is obtained. .

2. The method for predicting ground icing temperature according to claim 1, characterized in that, In the steps In the solid surface data acquisition module (1), the material property data and liquid accumulation property data of the solid surface are collected. The material property data includes the surface free energy of the solid surface. and roughness factor The liquid accumulation characteristic data includes: the type and concentration of the liquid accumulation on the surface. And its corresponding density, equilibrium temperature and latent heat of solidification.

3. The method for predicting ground icing temperature according to claim 1, characterized in that, In the steps In this process, obtaining the prediction results includes the following steps: 1) Based on the characteristic data of the surface liquid collected by the solid surface data acquisition module (1), the homogeneous nucleation work of the ice nucleus is calculated using the nucleation theory. ; 2) Based on the material property data of the solid surface collected by the solid surface data acquisition module (1), the contact angle of the ice nucleus when it forms on the solid surface is calculated. With heterogeneous nucleation surface factor And obtain the heterogeneous nucleation work of ice nuclei on rough solid surfaces. ; 3) Regarding the heterogeneous nucleation work in step 2), By combining the Boltzmann energy distribution law, the formation rate of ice nuclei on the solid surface can be obtained. And determine the radius growth rate of the ice nucleus on the solid surface. ; 4) Combining the nucleation rate from step 3) and ice core radius growth rate The coverage of the ice phase on the solid surface can be obtained. ; 5) For the ice phase coverage in step 4), the surface temperature predicted in the solid surface temperature prediction module (2) is introduced. The relationship between coverage and time was obtained. Using 100% ice coverage as the criterion for determining icing, the system outputs the predicted temperature and time when the ice coverage reaches 100%.

4. The method for predicting ground icing temperature according to claim 3, characterized in that, In step 1), the characteristic data of the surface liquid also include: ice-water specific surface free energy. Phase transition equilibrium temperature Latent heat of solidification per unit mass and the density of ice after freezing ; The calculation based on the nucleation theory yielded the homogeneous nucleation work of the ice nucleus. for: 。 5. The method for predicting ground icing temperature according to claim 3, characterized in that, In step 2), under ideal smooth surface conditions, the contact angle is: ； When the surface roughness of the solid is... Roughness factor It is the ratio of the actual surface area of ​​a solid surface to the smooth surface area. The surface factor is: ； In the above formula, The dispersion force component of the substrate surface energy is given by units of . , The dispersion force component of the ice surface energy, in units of . , The dispersion force component of the surface energy of water, in units of , The surface energy of ice, expressed in units of . , The surface energy of water, measured in units of . , The specific surface free energy of ice-water is expressed in units of... .

6. The method for predicting ground icing temperature according to claim 5, characterized in that, In step 3), for the heterogeneous nucleation work in step 2), By combining the Boltzmann energy distribution law, the formation rate of ice nuclei on the solid surface can be obtained. As shown in the following formula: ； in, The area number density of the critical nucleus, in units of ; The surface number density of water molecules, in units of ; The density of water, in units of ; The density of water, in units of ; Boltzmann's constant; Absolute temperature, unit: ; For critical nucleation work, The number of times a water molecule jumps out of its potential well per unit time, expressed in units of... , Boltzmann's constant; It is Avogadro's constant; It is Planck's constant; The activation energy for water diffusion; And calculate the radius growth rate of a single ice nucleus. As shown in the following formula: ； In the formula, The latent heat of solidification per unit mass, in units of ; This represents the average distance between water phase molecules and the average distance between ice phase molecules, in units of... ; The molar mass of water, in units of The rate of increase in the radius of the ice nucleus due to its expansion on the ice surface is: .

7. The method for predicting ground icing temperature according to claim 6, characterized in that, In step 4), the nucleation rate in step 3) is combined with the nucleation rate. and ice core radius growth rate The coverage of the ice phase on the solid surface was obtained. for: ； In step 5), for the ice phase coverage in step 4), the surface temperature predicted in the solid surface temperature prediction module (2) is introduced. The relationship between coverage and time was obtained. As shown in the following formula: ； Where t is time, and the condition for determining freezing is that the coverage reaches 100%, that is, the appearance of ice in a macroscopic sense. The output of the temperature and time when the coverage reaches 100% is the prediction result.

8. A system for predicting ground icing temperature according to any one of claims 1-7, characterized in that, The ground icing temperature prediction system includes: a solid surface data acquisition module (1), a solid surface temperature prediction module (2), and an icing temperature determination module (3); the solid surface data acquisition module (1) is connected to both the solid surface temperature prediction module (2) and the icing temperature determination module (3); the solid surface temperature prediction module (2) is then connected to the icing temperature determination module (3); The solid surface data acquisition module (1) is used to acquire material property data and liquid accumulation property data of the solid surface; wherein the material property data includes the surface free energy of the solid surface. and roughness factor Liquid accumulation characteristics include the type and concentration of liquid accumulation on the surface. And their corresponding density, equilibrium temperature and latent heat of solidification; The solid surface temperature prediction module (2) is used to calculate the future temperature of the solid surface using historical surface temperature data collected by the data acquisition module and a cubic exponential smoothing formula. ; The freezing temperature determination module (3) is used to determine the freezing temperature and obtain the temperature and time prediction results when the coverage reaches a certain proportion.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the ground icing temperature prediction method according to any one of claims 1-7.

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