Method and apparatus for predicting line icing thickness
By analyzing ground and cloud top meteorological data and selecting appropriate forecasting methods to predict the thickness of ice accumulation on power lines, the problem of scarce power line ice accumulation observation stations has been solved, enabling accurate and timely prediction of ice accumulation thickness and reducing the risk of ice disasters.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ANHUI PROVINCIAL PUBLIC METEOROLOGICAL SERVICE CENT
- Filing Date
- 2023-03-14
- Publication Date
- 2026-06-12
AI Technical Summary
In the current technology, there are few power line icing observation stations, which leads to a lack of accurate estimation of power line icing thickness in power engineering design. In addition, the observation time interval is long, making it impossible to obtain the icing thickness in a timely manner, which increases the difficulty of rescue in ice disasters.
By analyzing surface meteorological data, standard isobaric surface temperature, and cloud top temperature data, freezing rain conditions are determined, an appropriate thickness increment prediction method is selected, and the icing thickness of the line is predicted in combination with the initial icing thickness.
It improves the accuracy and timeliness of ice thickness prediction, reduces observation time, enhances prediction efficiency, and helps maintenance personnel respond to ice disasters in a timely manner.
Smart Images
Figure CN116362389B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of meteorological forecasting technology, specifically to a method and apparatus for predicting the thickness of icing on power lines. Background Technology
[0002] Standard ice thickness is a key indicator used by the power sector to measure the severity of icing on power lines. Standard ice thickness is expressed as a density of 0.9 g / cm³. 3 The thickness of the ice accretion is measured and evenly covers the area around the conductor. Typically, the ice thickness can be calculated based on the weight of the ice, the diameter of the ice, or the length of the wire. However, due to the scarcity of power line icing monitoring stations, most of which are located in plains or basins where icing is less frequent, power engineering design often faces the problem of lacking accurate measurements of power line icing thickness.
[0003] To address the aforementioned issues, empirical models are typically used to estimate the icing thickness of telecommunications power lines. These empirical models calculate the icing thickness based on the statistical relationship between power line icing data and meteorological factors, employing methods such as statistical regression, neural networks, or support vector machines.
[0004] The aforementioned empirical models are simple and easy to implement, and have good simulation effects. However, in areas where observational data on power line icing is lacking, it is impossible to establish empirical models, resulting in the inability to obtain accurate icing thickness on the power lines. Furthermore, the observation intervals for most power line icing data are relatively long, making it impossible for maintenance personnel to obtain timely and accurate icing thickness, thus increasing the difficulty of emergency response to ice storms. Summary of the Invention
[0005] Therefore, this application provides a method and apparatus for predicting line icing thickness, an electronic device, and a storage medium to solve the problems of inaccurate and untimely prediction of line icing thickness in the prior art.
[0006] To achieve the above objectives, the first aspect of this application provides a method for predicting the thickness of icing on power lines. The method includes: analyzing acquired surface meteorological data, standard isobaric surface temperature, and cloud top temperature data to determine whether conditions for freezing rain are present; if conditions for freezing rain are present, selecting a target thickness increment prediction method from multiple preset thickness increment prediction methods based on surface meteorological data; predicting the increase in icing thickness on the power line using the target thickness increment prediction method, and obtaining the icing thickness of the power line by combining the initial icing thickness.
[0007] In some optional embodiments, the line icing thickness increment is predicted using a target thickness increment prediction method, and the line icing thickness is obtained by combining the initial icing thickness, including:
[0008] Obtain the initial thickness of the ice layer at the first moment;
[0009] The target thickness increment prediction method is used to predict the line icing thickness increment, and the line icing thickness increment is obtained.
[0010] Based on the increase in line icing thickness and the initial icing thickness, the line icing thickness at the second moment is predicted, and the line icing thickness at the second moment is obtained. The second moment is the moment following the first moment.
[0011] In some optional embodiments, the surface meteorological data includes precipitation information and surface temperature information;
[0012] The acquired surface meteorological data, standard isobaric surface temperature, and cloud top temperature data are analyzed to determine whether conditions are suitable for freezing rain, including:
[0013] Analyze precipitation and surface temperature information to obtain surface data analysis results;
[0014] The cloud top temperature data and the temperatures of various standard isobaric surfaces were analyzed to obtain the cloud top analysis results;
[0015] Based on the analysis results of ground data and cloud top analysis, it is determined whether the conditions for freezing rain are met.
[0016] In some optional embodiments, precipitation information includes precipitation amount within a preset duration, and ground temperature information includes ground air temperature;
[0017] Analyzing precipitation and surface temperature information yields surface data analysis results, including:
[0018] Given that the ground temperature is within a preset temperature range and the precipitation within a preset duration is greater than a preset precipitation threshold, determine whether the atmospheric thickness between two standard isobaric surfaces is less than or equal to a preset threshold, and obtain the determination result.
[0019] The results of the ground data analysis are determined based on the judgment results.
[0020] In some optional embodiments, the ground meteorological information also includes ground wind speed; the preset thickness increment prediction method includes any one of the first prediction method, the second prediction method, and the third prediction method;
[0021] The first prediction method is a prediction method based on surface wind speed, precipitation and icing density, with icing density being a value determined based on surface air temperature.
[0022] The second prediction method is a prediction method based on icing density, surface air temperature and a first preset threshold;
[0023] The third prediction method is a prediction method based on ice density and a second preset threshold.
[0024] In some optional embodiments, the cloud top temperature data includes cloud top temperature values;
[0025] The cloud top temperature data and the temperatures of various standard isobaric surfaces were analyzed to obtain the cloud top analysis results, including:
[0026] If the cloud top temperature value is determined to be greater than the preset temperature threshold, or if the cloud top temperature value is less than or equal to the preset temperature threshold and the temperature values corresponding to multiple standard isobaric surfaces meet the melting layer conditions, the cloud top analysis result is determined to be that the cloud top temperature data and the temperatures of each standard isobaric surface meet the conditions for freezing rain.
[0027] The melting layer conditions include: the temperature value corresponding to the first standard isobaric surface is less than the first preset temperature value, and the temperature value corresponding to the second standard isobaric surface is less than the second preset temperature value, and the temperature value corresponding to the third standard isobaric surface is less than the third preset temperature value, and the temperature value corresponding to the fourth standard isobaric surface is greater than or equal to the third preset temperature value.
[0028] Alternatively, the melting layer conditions include: the temperature value corresponding to the first standard isobaric surface is less than the fourth preset temperature value, and the temperature value corresponding to the third standard isobaric surface is greater than or equal to the fifth preset temperature value.
[0029] In some optional embodiments, before analyzing precipitation and surface temperature information to obtain surface data analysis results, the method further includes:
[0030] Determine the correlation between ground temperature and altitude;
[0031] Determine the altitude difference between the preset terrain elevation and the actual terrain elevation.
[0032] Based on the height difference value and the correspondence between ground air temperature and altitude, the temperature correction value corresponding to the ground temperature information is determined;
[0033] The surface air temperature is updated based on the temperature correction value corresponding to the surface temperature information.
[0034] In some optional embodiments, determining the correspondence between ground temperature and altitude includes:
[0035] Obtain the first mode altitude and first mode ground temperature corresponding to the first measurement point, as well as the second mode altitude and second mode ground temperature corresponding to multiple second measurement points, wherein the second measurement points are observation points adjacent to the first measurement point;
[0036] Linear fitting was performed on the first model altitude, multiple second model altitudes, the first model surface temperature, and multiple second model surface temperatures to determine the linear relationship of the decrease in surface temperature with altitude.
[0037] To achieve the above objectives, a second aspect of this application provides a line icing thickness prediction device, comprising: an analysis module configured to analyze acquired surface meteorological data, standard isobaric surface temperature, and cloud top temperature data to determine whether conditions for freezing rain are present; a selection module configured to, if conditions for freezing rain are determined to be present, select a target thickness increment prediction method from multiple preset thickness increment prediction methods based on surface meteorological data; and a prediction module configured to predict the line icing thickness increment using the target thickness increment prediction method, and obtain the line icing thickness by combining the initial icing thickness.
[0038] To achieve the above objectives, in a third aspect, this application provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores one or more computer programs executable by the at least one processor, the one or more computer programs being executed by the at least one processor to enable the at least one processor to perform the above-described method for predicting line icing thickness.
[0039] To achieve the above objectives, in a fourth aspect, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor / processing core, implements the above-described method for predicting line icing thickness.
[0040] The method, device, electronic equipment, and storage medium for predicting line icing thickness in this application analyze acquired surface meteorological data, standard isobaric surface temperature, and cloud top temperature data to determine whether conditions for freezing rain are present. If freezing rain conditions are confirmed, a target thickness increment prediction method is selected from multiple preset thickness increment prediction methods based on the surface meteorological data. This allows for the selection of a target thickness increment prediction method that matches the surface meteorological data to predict the increase in line icing thickness. This combines the initial icing thickness with the predicted increase in line icing thickness, resulting in a more accurate prediction of the line icing thickness. Simultaneously, it reduces the observation time for power line icing data in multiple regions, shortens the prediction cycle for line icing thickness, improves prediction efficiency, and enables maintenance personnel to obtain the line icing thickness promptly and accurately, accelerating the rescue speed for ice disasters. Attached Figure Description
[0041] The accompanying drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. The above and other features and advantages will become more apparent to those skilled in the art from the detailed description of exemplary embodiments with reference to the accompanying drawings, in which:
[0042] Figure 1 This is a flowchart illustrating a method for predicting the thickness of icing on power lines, as provided in an embodiment of this application.
[0043] Figure 2 This is a schematic diagram showing the distribution of multiple test points provided in an embodiment of this application.
[0044] Figure 3 This is a flowchart illustrating a method for predicting the thickness of icing on power lines, as provided in an embodiment of this application.
[0045] Figure 4 This is a schematic diagram illustrating the relationship between icing density and ground temperature, provided for an embodiment of this application.
[0046] Figure 5 This is a schematic diagram showing the comparison between predicted and actual values of line icing thickness provided in an embodiment of this application.
[0047] Figure 6 This is a block diagram of a line icing thickness prediction device provided in an embodiment of this application.
[0048] Figure 7 This is a block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation
[0049] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the application. Those skilled in the art can implement this application without requiring some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.
[0050] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.
[0051] Freezing rain, also known as rime, is a precipitation pattern composed of supercooled water droplets that freeze immediately upon collision with objects below 0°C. This freezing can form a hard, frosted glass-like or transparent layer of ice. When freezing rain falls to the ground, it can freeze on power lines or communication lines. Over time, the accumulated frozen material can cause power lines to break or communication lines to be interrupted.
[0052] In severe cases, freezing rain can collapse houses, damage trees, and kill crops and vegetables. Furthermore, its smooth, hard ice layer can severely impact transportation and aviation, such as causing aircraft to be damaged by freezing rain.
[0053] Given the severity of the disasters caused by freezing rain, scholars have been actively exploring the patterns and causes of freezing rain in recent years. The thickness of ice accumulation on power lines or communication lines can be determined based on the extent of damage caused by freezing rain.
[0054] The density can be set to 0.9 g / cm³. 3 The standard ice thickness is defined as the thickness of the ice that evenly covers the conductor. Typically, the ice thickness can be calculated based on the weight of the ice, the diameter of the ice, or the length of the wire. However, due to the scarcity of power line icing monitoring stations, and the fact that most are located in plains or basins where icing is less frequent, power engineering design often faces the problem of lacking accurate measurements of power line icing thickness.
[0055] To address the aforementioned issues, empirical models are typically used to estimate the icing thickness of telecommunications power lines. These empirical models calculate the icing thickness based on the statistical relationship between power line icing data and meteorological factors, employing methods such as statistical regression, neural networks, or support vector machines.
[0056] The aforementioned empirical models are simple and easy to implement, and have good simulation effects. However, in areas where observational data on power line icing is lacking, it is impossible to establish empirical models, resulting in the inability to obtain accurate icing thickness on the power lines. Furthermore, the observation intervals for most power line icing data are relatively long, making it impossible for maintenance personnel to obtain timely and accurate icing thickness, thus increasing the difficulty of emergency response to ice storms.
[0057] In view of this, this application provides a method and apparatus for predicting line icing thickness, an electronic device, and a storage medium to solve the problems of inaccurate and untimely prediction of line icing thickness in the prior art.
[0058] The first aspect of this application provides a method for predicting the thickness of icing on power lines. Figure 1 This is a flowchart illustrating a method for predicting line icing thickness according to an embodiment of this application. This method can be applied to a line icing thickness prediction device. Figure 1 As shown, the method for predicting the icing thickness of this line includes, but is not limited to, the following steps.
[0059] Step S 101: Analyze the acquired surface meteorological data, standard isobaric surface temperature and cloud top temperature data to determine whether the conditions for freezing rain are met.
[0060] First, the standard isobaric surface temperature and cloud top temperature data can be confirmed to determine whether there is a possibility of precipitation (e.g., rain or snow) in a certain area or location. If precipitation is determined to be possible, the analysis of ground meteorological data can be used to determine whether the conditions for freezing rain will occur when rain or snow falls to the ground due to drastic temperature changes.
[0061] Step S102: If the conditions for freezing rain are determined to be met, select the target thickness increment prediction method from multiple preset thickness increment prediction methods based on ground meteorological data.
[0062] Among them, several preset thickness increment prediction methods are methods for predicting the thickness increment of line icing, which are determined in advance based on historical data or experience data.
[0063] By selecting a target thickness increment prediction method from multiple preset thickness increment prediction methods, the target thickness increment prediction method can be matched with ground meteorological data, thereby improving the accuracy of predicting the thickness of line icing.
[0064] Step S 103: Use the target thickness increment prediction method to predict the line icing thickness increment, and combine it with the initial icing thickness to obtain the line icing thickness.
[0065] One method for predicting the target thickness increment is to directly predict the increase in line icing thickness, thereby obtaining the line icing thickness increment. Alternatively, this method can predict the decrease in line icing thickness (i.e., when the increment is negative), thereby obtaining the decrease in line icing thickness at a certain moment. Furthermore, based on the initial icing thickness at that moment and the line icing thickness increment at that moment, the line icing thickness increment for the next moment is predicted.
[0066] For example, the step S103, which uses the target thickness increment prediction method to predict the line icing thickness increment and combines it with the initial icing thickness to obtain the line icing thickness, can be implemented as follows: obtain the initial icing thickness at the first moment; use the target thickness increment prediction method to predict the line icing thickness increment to obtain the line icing thickness increment; and based on the line icing thickness increment and the initial icing thickness, predict the line icing thickness at the second moment to obtain the line icing thickness at the second moment.
[0067] Here, the second time point is the time point following the first time point. For example, if the first time point is set as time point t, then the second time point is time point t+1. The target thickness increment prediction method is used to predict the line icing thickness increment, so that the line icing thickness increment can reflect the change in icing thickness from time point t to time point t+1. Then, the line icing thickness increment is summed with the initial icing thickness at time point t to obtain the predicted line icing thickness at time point t+1. t is a real number greater than or equal to 0 and less than or equal to 24 hours (or, 1440 minutes, or, 86400 seconds).
[0068] Based on the increase in line icing thickness between two different moments, it is possible to predict the line icing thickness at a future moment (e.g., time t+1). This allows for the determination of the possible severity (or level) of the disaster based on the predicted line icing thickness, enabling the development of contingency plans in advance based on different disaster severity levels and reducing the losses caused by freezing rain disasters.
[0069] In this embodiment, by analyzing the acquired surface meteorological data, standard isobaric surface temperature, and cloud top temperature data, it is determined whether conditions for freezing rain are present. If conditions for freezing rain are confirmed, a target thickness increment prediction method is selected from multiple preset thickness increment prediction methods based on the surface meteorological data. This allows for the selection of a target thickness increment prediction method that matches the surface meteorological data to predict the increase in line icing thickness. This combines the initial icing thickness with the predicted increase in line icing thickness, resulting in a more accurate prediction of the line icing thickness. Simultaneously, it reduces the observation time for power line icing data in multiple regions, shortens the prediction cycle for line icing thickness, improves prediction efficiency, and enables maintenance personnel to obtain the line icing thickness promptly and accurately, accelerating the rescue speed for ice disasters.
[0070] In some optional embodiments, the surface meteorological data includes precipitation information and surface temperature information; step S101, which involves analyzing the acquired surface meteorological data, standard isobaric surface temperature, and cloud top temperature data to determine whether conditions for freezing rain are met, includes: analyzing the precipitation information and surface temperature information to obtain surface data analysis results; analyzing the cloud top temperature data and the temperatures of each standard isobaric surface to obtain cloud top analysis results; and determining whether conditions for freezing rain are met based on the surface data analysis results and the cloud top analysis results.
[0071] Among them, the ground data analysis results are used to characterize the analysis results of precipitation information and ground temperature information of a certain measurement point, which can reflect the ground precipitation situation of the measurement point; while the cloud top analysis results are used to characterize the cloud top temperature data and the temperature of each standard isobaric surface of the measurement point, that is, they can characterize the analysis results of the cloud top temperature data and the temperature of each standard isobaric surface corresponding to the measurement point.
[0072] By comprehensively considering the above ground data analysis results and cloud top analysis results, we can fully measure the ground precipitation at the test point and the corresponding cloud top temperature data, thereby more accurately determining whether the test point has the conditions for freezing rain, so as to make accurate predictions of freezing rain.
[0073] In some optional embodiments, precipitation information includes precipitation amount within a preset duration, and ground temperature information includes ground air temperature. The precipitation information and ground temperature information are analyzed to obtain ground data analysis results, including: if the ground air temperature is determined to be within a preset temperature range and the precipitation amount within a preset duration is greater than a preset precipitation threshold, determining whether the atmospheric thickness between two standard isobaric surfaces is less than or equal to a preset thickness threshold, and obtaining a determination result; and determining the ground data analysis results based on the determination result.
[0074] The preset temperature range can be -10℃ to -0.1℃; the preset precipitation threshold can be set to 0.1 mm / hour; and the preset thickness threshold can be set to 1300 geopotential meters (gpm).
[0075] It should be noted that in meteorology, geopotential height (in meters) is often used instead of geometric height. The geopotential height at a point in space is the work done against gravity to raise a unit mass of object from sea level (i.e., when the geopotential height is zero) to a certain height in space; this work is also called gravitational potential. The unit of geopotential height is gpm.
[0076] If the ground temperature is determined to be within the range of -10℃ to -0.1℃, and the precipitation within the preset duration is greater than 0.1 mm / hour, and the atmospheric thickness between two standard isobaric surfaces is less than or equal to 1300 gpm, then the ground data analysis results indicate that the conditions for freezing rain are met, and further analysis of cloud top temperature data and the temperatures of each standard isobaric surface is required.
[0077] In some optional embodiments, the ground meteorological information also includes ground wind speed; the preset thickness increment prediction method includes any one of the first prediction method, the second prediction method, and the third prediction method.
[0078] The first prediction method is based on surface wind speed, precipitation, and icing density, with icing density determined based on surface air temperature. The second prediction method is based on icing density, surface air temperature, and a first preset threshold. The third prediction method is based on icing density and a second preset threshold.
[0079] The first and second preset thresholds are both set based on ground temperature. However, ground temperature changes constantly. The first or second preset threshold can be set according to the changes in ground temperature at different times to meet the need to predict the increase in line icing thickness at different times.
[0080] By employing multiple different prediction methods as preset thickness increment prediction methods, it is possible to select the appropriate thickness increment prediction method to predict the icing thickness of the line based on actual needs, thereby improving the accuracy of the prediction.
[0081] In some alternative embodiments, the cloud top temperature data includes cloud top temperature values, which are temperature values located on an isobaric surface.
[0082] The cloud top temperature data and the temperatures of various standard isobaric surfaces are analyzed to obtain cloud top analysis results, including: if the cloud top temperature value is greater than the preset temperature threshold, or the cloud top temperature value is less than or equal to the preset temperature threshold, and the temperature values corresponding to multiple standard isobaric surfaces meet the melting layer conditions, the cloud top analysis results are determined to be that the cloud top temperature data and the temperatures of various standard isobaric surfaces meet the conditions for freezing rain.
[0083] The preset temperature threshold can be a value determined based on a preset temperature range. For example, if the preset temperature range is -10℃ to -0.1℃, the preset temperature threshold can be set to -10℃.
[0084] It should be noted that when the cloud top temperature is greater than or equal to -10℃, it indicates that the cloud top is low and the liquid water content in the cloud is relatively high, thus confirming that the cloud top temperature data meets the conditions for freezing rain.
[0085] When the cloud top temperature is less than -10℃, it indicates that the cloud top is relatively high and the solid water content in the cloud is relatively high. At this time, it is necessary to continue to determine whether the temperatures of multiple standard isobaric surfaces meet the melting layer conditions.
[0086] The melting layer conditions include: the temperature value corresponding to the first standard isobaric surface is less than the first preset temperature value, and the temperature value corresponding to the second standard isobaric surface is less than the second preset temperature value, and the temperature value corresponding to the third standard isobaric surface is less than the third preset temperature value, and the temperature value corresponding to the fourth standard isobaric surface is greater than or equal to the third preset temperature value.
[0087] Alternatively, the melting layer conditions include: the temperature value corresponding to the first standard isobaric surface is less than the fourth preset temperature value, and the temperature value corresponding to the third standard isobaric surface is greater than or equal to the fifth preset temperature value.
[0088] For example, the melting layer conditions include: T1000hPa < 1℃ and T925hPa < -2℃ and T850hPa < 0℃ and T700hPa ≥ 0℃; or, the melting layer conditions include: T1000hPa < -1℃ and T850hPa ≥ 0.5℃.
[0089] Wherein, T1000hPa represents the temperature corresponding to a standard isobaric surface of 1000 hPa, T925hPa represents the temperature corresponding to a standard isobaric surface of 925 hPa, T850hPa represents the temperature corresponding to a standard isobaric surface of 850 hPa, and T700hPa represents the temperature corresponding to a standard isobaric surface of 700 hPa.
[0090] The altitude corresponding to 850 hPa is approximately between 1200 and 1900 meters; the altitude corresponding to 1000 hPa is approximately 0 meters above sea level; the altitude corresponding to 925 hPa is approximately between 600 and 800 meters; and the altitude corresponding to 700 hPa is approximately between 2700 and 3000 meters.
[0091] Therefore, when the cloud top temperature is less than -10℃, and the temperature values corresponding to multiple standard isobaric surfaces meet any of the above-mentioned melting layer conditions, it can be determined that the cloud top temperature data meets the conditions for freezing rain.
[0092] In some optional embodiments, before analyzing precipitation and surface temperature information to obtain surface data analysis results, the method further includes:
[0093] Determine the correspondence between ground temperature and altitude; determine the altitude difference between the altitude corresponding to the preset terrain and the altitude corresponding to the actual terrain; based on the altitude difference and the correspondence between ground temperature and altitude, determine the temperature correction value corresponding to the ground temperature information; update the ground temperature based on the temperature correction value corresponding to the ground temperature information.
[0094] For example, set the altitude corresponding to the preset terrain mode to H. M The actual terrain corresponds to an altitude of H. R The height difference between the altitude corresponding to the preset terrain and the altitude corresponding to the actual terrain is H. R -H M .
[0095] For any time t, it can be based on the ground temperature (T) corresponding to time t. Mt ) and H M The relationship between them can be expressed as: temperature decreases with increasing altitude. Here, the decrease rate can be set to α. t .
[0096] Furthermore, it can be based on H R -H M and α t Calculate the temperature correction value corresponding to the obtained ground temperature information, and then compare the temperature correction value with T. Mt Add them together to obtain the updated surface temperature (T). Dt ).
[0097] Wherein, the ground temperature T corresponds to time t. Mt It can be represented as: T Mt =α t *H Mt +β, where β represents the preset correction coefficient, H Mt This represents the altitude corresponding to the preset terrain at time t.
[0098] The updated surface temperature T at time t Dt It can be represented as: T Dt =T Mt +α t *(H R -H M ).
[0099] The above processing steps update the temperature values corresponding to the ground temperature information, thereby providing the ground air temperature to be updated and improving its accuracy.
[0100] In some optional embodiments, determining the correspondence between ground temperature and altitude includes: obtaining a first mode altitude and a first mode ground temperature corresponding to a first measurement point, and a second mode altitude and a second mode ground temperature corresponding to multiple second measurement points; performing linear fitting on the first mode altitude, multiple second mode altitudes, the first mode ground temperature, and multiple second mode ground temperatures to determine a linear relationship in which ground temperature decreases with altitude.
[0101] The second point to be measured is the observation point adjacent to the first point to be measured.
[0102] For example, Figure 2 This is a schematic diagram illustrating the distribution of multiple test points provided in an embodiment of this application. Figure 2 As shown, the first test point (x, y) represents the position point with x as the abscissa and y as the ordinate. Multiple second test points adjacent to the first test point (x, y) are as follows: Figure 2 The test points are shown as (x-1, y), (x-1, y-1), (x-1, y+1), (x, y+1), (x, y-1), (x+1, y), (x+1, y+1), and (x+1, y-1).
[0103] Each point to be measured corresponds to a mode height, which is used to characterize the altitude of the preset mode terrain to which the point to be measured belongs.
[0104] Furthermore, the model altitude and the model ground temperature corresponding to each test point are obtained respectively; that is, the first model altitude and the first model ground temperature corresponding to the first test point, and the second model altitude and the second model ground temperature corresponding to multiple second test points.
[0105] For example, the model ground temperature at the measurement point is set to be related only to altitude. If the first model altitude corresponding to the first measurement point is 2000 meters above sea level, the model ground temperature at 2000 meters above sea level (i.e., the first model ground temperature) is 10℃; if the second model altitude corresponding to the second measurement point (x+1, y) is 2100 meters above sea level, the second model ground temperature corresponding to the second measurement point (x+1, y) is 9.4℃; if the second model altitude corresponding to the second measurement point (x+1, y+1) is 2200 meters above sea level, the second model ground temperature corresponding to the second measurement point (x+1, y+1) is 8.8℃, and so on.
[0106] The model altitude and model surface temperature are measured sequentially at each of the second measurement points to obtain the first model altitude, multiple second model altitudes, the first model surface temperature, and multiple second model surface temperatures. Furthermore, the first model altitude, multiple second model altitudes, the first model surface temperature, and the multiple second model surface temperatures are linearly fitted to determine the linear relationship of the decrease in surface temperature with altitude.
[0107] Figure 3 This is a flowchart illustrating a method for predicting line icing thickness provided in an embodiment of this application. Figure 3 As shown, the method for predicting the icing thickness of this line includes, but is not limited to, the following steps.
[0108] Step S301: Determine the correspondence between ground temperature and altitude.
[0109] The process of determining the correspondence between ground temperature and altitude includes: obtaining the first model altitude and first model ground temperature corresponding to the first measurement point, as well as the second model altitude and second model ground temperature corresponding to multiple second measurement points, wherein the second measurement points are observation points adjacent to the first measurement point; and performing linear fitting on the first model altitude, multiple second model altitudes, the first model ground temperature, and multiple second model ground temperatures to determine the linear relationship in which ground temperature decreases with altitude.
[0110] Step S302: Determine the altitude difference between the preset terrain elevation and the actual terrain elevation.
[0111] Step S303: Based on the height difference value and the correspondence between ground temperature and altitude, determine the temperature correction value corresponding to the ground temperature information.
[0112] Step S304: Based on the temperature correction value corresponding to the ground temperature information, update the ground temperature to obtain the updated ground temperature.
[0113] Step S305: Determine whether the updated ground temperature is within the preset temperature range.
[0114] The preset temperature range can be -10℃ to -0.1℃.
[0115] If the updated ground temperature is determined to be within the preset temperature range, proceed to step S306; otherwise, if the updated ground temperature is determined to be outside the preset temperature range, proceed to step S307.
[0116] Step S306: Determine whether the precipitation within the preset time period is greater than 0.1 mm / hour.
[0117] For example, determine whether the precipitation within a preset time period (e.g., one hour) is greater than 0.1 mm / h. If the precipitation within one hour is determined to be greater than 0.1 mm / h, proceed to step S308; otherwise, proceed to step S312.
[0118] It should be noted that when the precipitation in one hour is less than 0.1 mm / h, the precipitation conditions are determined not to be suitable for freezing rain, meaning that freezing rain will not occur.
[0119] Step S307: Determine whether the updated ground temperature is greater than the preset temperature threshold.
[0120] The preset temperature threshold can be -0.1℃.
[0121] If the updated ground temperature is determined to be greater than the preset temperature threshold, proceed to step S313; otherwise, proceed to step S314.
[0122] Step S308: Determine whether the atmospheric thickness between the two standard isobaric surfaces is less than or equal to 130 potential meters.
[0123] For example, given two standard isobaric surfaces of 850 hPa and 1000 hPa, determine whether the atmospheric thickness between 850 hPa and 1000 hPa is greater than 1300 gpm.
[0124] It should be noted that in meteorology, geopotential height is often used instead of geometric height (e.g., meters). The geopotential height at a point in space is the work done against gravity to raise a unit mass of object from sea level (i.e., when the geopotential height is zero) to a certain height in space; this work is also called gravitational potential. The unit of geopotential height is gpm.
[0125] If the atmospheric thickness between 850 hPa and 1000 hPa is determined to be greater than 1300 gpm, then the lower troposphere is determined to be warmer and freezing rain will not occur. Proceed to step S312.
[0126] If the atmospheric thickness between 850 hPa and 1000 hPa is determined to be less than or equal to 1300 gpm, then the conditions for freezing rain to occur in the lower troposphere are determined, and step S309 is executed.
[0127] Step S309: Determine whether the cloud top temperature value is less than or equal to -10℃.
[0128] It should be noted that when the cloud top temperature is greater than -10℃, it indicates that the cloud top is low and the liquid water content in the cloud is relatively high, which meets the conditions for freezing rain. Step S311 can be executed.
[0129] When the cloud top temperature is less than or equal to -10℃, it indicates that the cloud top is relatively high and the solid water content in the cloud is relatively high. Proceed to step S310.
[0130] Step S310: Determine whether the temperature values corresponding to multiple standard isobaric surfaces meet the melting layer conditions.
[0131] The melting layer conditions are: T1000hPa < 1℃ and T925hPa < -2℃ and T850hPa < 0℃ and T700hPa ≥ 0℃; or T1000hPa < -1℃ and T850hPa ≥ 0.5℃.
[0132] If the cloud top temperature value is greater than or equal to a preset temperature threshold (e.g., -10℃), or if the cloud top temperature value is less than the preset temperature threshold and the temperature values corresponding to multiple standard isobaric surfaces meet the melting layer conditions, then the cloud top temperature data can be determined to meet the conditions for freezing rain.
[0133] When the temperature values corresponding to multiple standard isobaric surfaces meet the melting layer condition, step S311 can be executed. When the temperature values corresponding to multiple standard isobaric surfaces do not meet the melting layer condition, it is determined that there is no freezing rain, and step S312 is executed.
[0134] Step S311: Based on precipitation information and ground temperature information, select the first prediction method from multiple preset thickness increment prediction methods as the target thickness increment prediction method, and use the target thickness increment prediction method to predict the line icing thickness increment to obtain the line icing thickness increment.
[0135] The first prediction method is based on ground wind speed, precipitation, and icing density, with icing density determined based on ground temperature.
[0136] For example, formula (1) can be used to predict the increase in line icing thickness using the first prediction method, and obtain the increase in line icing thickness D.
[0137]
[0138] Where D represents the increase in line icing thickness; V represents ground wind speed; P represents precipitation; and π represents pi, a constant.
[0139] Where ρ represents the ice cover density, which is related to... The corresponding numerical values are in grams per cubic centimeter (g / cm³). 3 ); T represents the ground temperature.
[0140] For example, Figure 4 This is a schematic diagram illustrating the relationship between icing density and ground temperature, provided as an embodiment of this application. Figure 4 As shown, the density of ice covering (unit: g / cm³) 3 A coordinate system is established with the vertical axis representing the surface temperature (in °C) and the horizontal axis representing the air temperature. Multiple sample surface temperatures obtained from historical observations, along with the corresponding icing densities, are plotted within this coordinate system to obtain the following data: Figure 4 The multiple sample points shown can be further linearly fitted to obtain the relationship between the ice density ρ and the given data. There is a corresponding relationship (e.g.) Figure 4 (The solid line in the diagram represents the linear relationship).
[0141] The horizontal axis represents the air temperature at ground level.
[0142] In some alternative embodiments, the ice density ρ described above can be used as a reference. The correspondence between the two is established by creating a table of reference between ice density ρ and ground temperature T. By looking up this table, the ice density ρ corresponding to the ground temperature T can be quickly obtained, and the change in ice thickness can be calculated in a timely and accurate manner according to formula (1).
[0143] After calculating the line icing thickness increment D, step S315 is executed.
[0144] Step S312: Determine that the change in the line icing thickness is 0.
[0145] After calculating the line icing thickness increment D, step S315 is executed.
[0146] Step S313: The second prediction method is used as the target thickness increment prediction method to predict the line icing thickness increment and obtain the line icing thickness increment.
[0147] The second prediction method is a prediction method based on icing density, ground temperature and a first preset threshold.
[0148] The first preset threshold is a threshold set based on the ground temperature. For example, the first preset threshold can be set to -0.08. For example, formula (2) can be used to predict the increase in line icing thickness using the second prediction method to obtain the increase in line icing thickness D.
[0149] D=(-0.087-0.08*T) / ρ (2)
[0150] After calculating the line icing thickness increment D, step S315 is executed.
[0151] Step S314: The third prediction method is used as the target thickness increment prediction method to predict the line icing thickness increment and obtain the line icing thickness increment.
[0152] The third prediction method is based on icing density and a second preset threshold. The second preset threshold is a threshold set according to ground temperature; for example, the first preset threshold can be set to -0.007. Formula (3) can be used to predict the increase in line icing thickness using the third prediction method to obtain the increase in line icing thickness D.
[0153] D = -0.007 / ρ (3)
[0154] After calculating the line icing thickness increment D, step S315 is executed.
[0155] Step S315: Determine the line icing thickness based on the obtained initial icing thickness and the line icing thickness increment.
[0156] The initial icing thickness can be the icing thickness at the first moment, while the line icing thickness increment is obtained by predicting the change in line icing thickness at the second moment (e.g., the moment after the first moment).
[0157] For example, the line icing thickness at the second moment can be obtained by summing the initial icing thickness with the line icing thickness increment.
[0158] Figure 5 This is a schematic diagram showing the comparison between predicted and actual values of line icing thickness provided in an embodiment of this application. (See attached diagram.) Figure 5 As shown, when predicting the icing thickness of a line at a certain test point, information can be obtained in multiple dimensions, including time, geographical location, parameters used in the prediction, thickness prediction method used, predicted icing thickness (unit: mm), actual icing thickness (unit: mm), and the corresponding disaster level (or grade).
[0159] The predicted icing thickness is the icing thickness of the line obtained by predicting the line using the above prediction method; the actual icing thickness is the thickness obtained by measuring the actual icing thickness of the line.
[0160] Specifically, at 20:00, the icing thickness of the line at the geographical location "118.16°E; 30.14°N" (i.e., 118.16 degrees east longitude and 30.14 degrees north latitude) was predicted. A second prediction method was used to determine the predicted icing thickness as 0 millimeters (mm). The parameters used in the prediction could include terrain elevation, ground air temperature, upper-air temperature, and initial ice thickness. In actual testing, the actual icing thickness was found to be 0 mm, confirming that no freezing rain disaster had occurred.
[0161] At 23:00, the icing thickness of the power line at "118.16°E; 30.14°N" is predicted. The first prediction method is used, resulting in a predicted icing thickness of 11mm. Parameters used in the prediction include terrain elevation, surface air temperature, wind speed, precipitation, upper-level temperature, upper-level humidity, and upper-level geopotential height. However, during actual testing, the actual icing thickness was found to be 8mm. Since the predicted icing thickness of 11mm exceeds a preset thickness threshold (e.g., a preset threshold of 10mm), a freezing rain disaster of "moderate icing" is identified. Appropriate rescue equipment and plans need to be prepared based on this disaster level to address the "moderate icing" freezing rain disaster. This allows maintenance personnel to anticipate freezing rain disasters in advance and reduce losses caused by them.
[0162] At 8:00 AM, the icing thickness of the power line at "118.16°E; 30.14°N" was predicted using a second prediction method, resulting in a predicted icing thickness of 10 mm. Parameters used in the prediction included terrain elevation, ground air temperature, and upper-air temperature. During actual testing, the actual icing thickness was found to be 8 mm. Since the predicted icing thickness of 10 mm equals the preset thickness threshold, a freezing rain disaster of "light icing" level was identified. Based on the moderate level, appropriate reductions were made to the prepared rescue equipment and rescue plans to adapt to the "light icing" disaster response scenario.
[0163] It should be noted that, through Figure 5The method shown for predicting the thickness of ice accretion on power lines at different times can promptly change the corresponding disaster level and take appropriate rescue equipment and plans for different disaster levels. It can accurately measure the impact of freezing rain on the thickness of ice accretion on power lines, improve the accuracy of power line protection, speed up the rescue of ice disasters, and reduce the losses caused by the disaster.
[0164] The second aspect of this application provides a device for predicting the thickness of icing on power lines. Figure 6 This is a block diagram illustrating the composition of a line icing thickness prediction device provided in an embodiment of this application. Figure 6 As shown, the line icing thickness prediction device 600 includes, but is not limited to, the following modules.
[0165] Analysis module 601 is configured to analyze the acquired surface meteorological data, standard isobaric surface temperature and cloud top temperature data to determine whether conditions are suitable for freezing rain.
[0166] Selection module 602 is configured to select a target thickness increment prediction method from multiple preset thickness increment prediction methods based on ground meteorological data when it is determined that conditions for freezing rain have been met.
[0167] The prediction module 603 is configured to predict the line icing thickness increment using the target thickness increment prediction method, and obtain the line icing thickness by combining the initial icing thickness.
[0168] It should be noted that the line icing thickness prediction device 600 in this embodiment can execute any of the line icing thickness prediction methods in this application embodiment, which will not be described in detail here.
[0169] In this embodiment, the analysis module 601 analyzes the acquired surface meteorological data, standard isobaric surface temperature, and cloud top temperature data to determine whether conditions for freezing rain are present. Then, the selection module 602, based on the surface meteorological data, selects a target thickness increment prediction method from multiple preset thickness increment prediction methods to predict the increase in line icing thickness. This allows for the selection of a target thickness increment prediction method that matches the surface meteorological data. The prediction module 603 then combines the initial icing thickness with the predicted increase in line icing thickness, making the obtained line icing thickness more accurate. Simultaneously, it reduces the observation time for power line icing data in multiple regions, shortens the prediction cycle for line icing thickness, improves prediction efficiency, and enables maintenance personnel to obtain line icing thickness promptly and accurately, accelerating the rescue speed for ice disasters.
[0170] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problem proposed in this application; however, this does not mean that other units are absent from this embodiment.
[0171] The third aspect of this application provides an electronic device and a computer-readable storage medium, both of which can be used to implement any of the line icing thickness prediction methods in this application. The corresponding technical solutions and descriptions are described in the corresponding descriptions in the method section and will not be repeated here.
[0172] Figure 7 This is a block diagram illustrating the composition of an electronic device according to an embodiment of this application. Figure 7 As shown, this application provides an electronic device 700, which includes: at least one processor 701; at least one memory 702; and one or more I / O interfaces 703 connected between the processor 701 and the memory 702; wherein the memory 702 stores one or more computer programs that can be executed by at least one processor 701, and the one or more computer programs are executed by at least one processor 701 to enable at least one processor 701 to perform the above-described method for predicting line icing thickness.
[0173] This application also provides a computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor / processor core, implements the above-described method for predicting line icing thickness. The computer-readable storage medium can be volatile or non-volatile.
[0174] This application also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described method for predicting line icing thickness.
[0175] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).
[0176] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable program instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.
[0177] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.
[0178] The computer program instructions used to perform the operations of this application may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuits, such as programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), are personalized by utilizing the status information of the computer-readable program instructions. These electronic circuits can execute the computer-readable program instructions to implement various aspects of this application.
[0179] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.
[0180] Various aspects of this application are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.
[0181] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.
[0182] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.
[0183] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0184] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for general illustrative purposes only and should not be construed as limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this application as set forth by the appended claims.
Claims
1. A method for predicting the thickness of icing on power lines, characterized in that, The method includes: The obtained surface meteorological data, standard isobaric surface temperature and cloud top temperature data are analyzed to determine whether the conditions for freezing rain are met. If the conditions for freezing rain to occur are determined, a target thickness increment prediction method is selected from multiple preset thickness increment prediction methods based on the ground meteorological data. The target thickness increment prediction method is used to predict the line icing thickness increment, and the line icing thickness is obtained by combining the initial icing thickness. The surface meteorological data includes precipitation information and surface temperature information; The analysis of the acquired surface meteorological data, standard isobaric surface temperature, and cloud top temperature data to determine whether conditions are suitable for freezing rain includes: The precipitation information and the ground temperature information are analyzed to obtain ground data analysis results; The cloud top temperature data and the temperatures of each of the standard isobaric surfaces are analyzed to obtain cloud top analysis results; Based on the ground data analysis results and the cloud top analysis results, determine whether the conditions for freezing rain are met; The precipitation information includes the amount of precipitation within a preset time period, and the ground temperature information includes the ground air temperature; The analysis of the precipitation information and the ground temperature information to obtain ground data analysis results includes: If the ground temperature is determined to be within a preset temperature range and the precipitation within a preset duration is greater than a preset precipitation threshold, then determine whether the atmospheric thickness between two standard isobaric surfaces is less than or equal to a preset thickness threshold, and obtain the determination result. The ground data analysis results are determined based on the judgment results; The ground meteorological information also includes ground wind speed; the preset thickness increment prediction method includes any one of the first prediction method, the second prediction method, and the third prediction method; The first prediction method is a prediction method based on the ground wind speed, the precipitation and the icing density, wherein the icing density is a value determined based on the ground temperature. The second prediction method is a prediction method determined based on the ice density, the ground temperature, and the first preset threshold. The third prediction method is a prediction method determined based on the ice density and the second preset threshold.
2. The method according to claim 1, characterized in that, The step of predicting the line icing thickness increment using the target thickness increment prediction method and obtaining the line icing thickness by combining it with the initial icing thickness includes: Obtain the initial thickness of the ice layer at the first moment; The target thickness increment prediction method is used to predict the line icing thickness increment, and the line icing thickness increment is obtained. Based on the line icing thickness increment and the initial icing thickness, the line icing thickness at the second moment is predicted to obtain the line icing thickness at the second moment, which is the next moment after the first moment.
3. The method according to claim 1, characterized in that, The cloud top temperature data includes cloud top temperature values; The analysis of the cloud top temperature data and the temperatures of each of the standard isobaric surfaces to obtain cloud top analysis results includes: If the cloud top temperature value is determined to be greater than a preset temperature threshold, or if the cloud top temperature value is less than or equal to the preset temperature threshold and the temperature values corresponding to multiple standard isobaric surfaces meet the melting layer conditions, the cloud top analysis result is determined to be that the cloud top temperature data and the temperatures of each of the standard isobaric surfaces meet the conditions for the occurrence of freezing rain. The melting layer conditions include: the temperature value corresponding to the first standard isobaric surface is less than the first preset temperature value, and the temperature value corresponding to the second standard isobaric surface is less than the second preset temperature value, and the temperature value corresponding to the third standard isobaric surface is less than the third preset temperature value, and the temperature value corresponding to the fourth standard isobaric surface is greater than or equal to the third preset temperature value. Alternatively, the melting layer conditions include: the temperature value corresponding to the first standard isobaric surface is less than the fourth preset temperature value, and the temperature value corresponding to the third standard isobaric surface is greater than or equal to the fifth preset temperature value.
4. The method according to claim 1, characterized in that, Before analyzing the precipitation information and the surface temperature information to obtain the surface data analysis results, the method further includes: Determine the correlation between ground temperature and altitude; Determine the altitude difference between the preset terrain elevation and the actual terrain elevation. Based on the height difference value and the correspondence between the ground air temperature and altitude, determine the temperature correction value corresponding to the ground temperature information; The ground air temperature is updated based on the temperature correction value corresponding to the ground temperature information.
5. The method according to claim 4, characterized in that, Determining the correspondence between ground temperature and altitude includes: Obtain the first mode altitude and first mode ground temperature corresponding to the first measurement point, and the second mode altitude and second mode ground temperature corresponding to multiple second measurement points, wherein the second measurement points are observation points adjacent to the first measurement point; Linear fitting is performed on the first mode altitude, multiple second mode altitudes, the first mode ground temperature, and multiple second mode ground temperatures to determine the linear relationship in which the ground temperature decreases with the change of altitude.
6. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed by the at least one processor, the one or more of the computer programs being executed by the at least one processor to enable the at least one processor to perform the line icing thickness prediction method as described in any one of claims 1-5.
7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the line icing thickness prediction method as described in any one of claims 1-5.