A numerical analysis method, device and medium for icing height variation in a complex terrain area

Abstract

The application provides a complex terrain icing height variation numerical analysis method, device and medium, the method comprises the following steps: simulating the meteorological parameters of the target area based on the mesoscale weather forecast model; calculating the icing thickness at different heights according to the meteorological parameters; fitting analysis is carried out on the icing thickness at different heights, and the icing height variation index is calculated; the icing height variation index is used to simulate the icing thickness data of the required height of the target area. The application proposes a method suitable for the icing simulation of complex terrain area, which is a combination of fine terrain data, WRF model physical parameterization scheme and boundary layer encryption, realizes high-precision reproduction of the icing growth process and meets the research demand of the icing height variation relationship.

Description

Technical Field

[0001] This invention relates to the field of disaster prevention and mitigation technology for power transmission lines, and in particular to a numerical analysis method, equipment, and medium for ice height variation in complex terrain areas. Background Technology

[0002] The thickness of icing on transmission lines is related to the conductor suspension height. Generally, near-ground wind speed increases with height. During the icing development period, under the same moisture conditions and within a certain wind speed range, the higher the wind speed, the more water droplets are captured by the conductor, resulting in a greater icing thickness. The ratio of icing thickness at two different heights is the height conversion factor for ice thickness. Extensive field data shows that the ratio of icing thickness at two different heights is a power function of the height ratio. The International Electrotechnical Commission's (IEC) Design criteria of overhead transmission lines states: "There is increasing evidence that certain types of icing may accumulate more icing at higher grounding wires."

[0003] The current power industry standard, "Specification for Icing Survey of Overhead Transmission Lines" (DL / T5509-2015), provides a formula for calculating the icing height conversion factor:

[0004]

[0005] In the formula, Design the conductor's height above the ground (m); For actual measurement or investigation of the height of ice-covered objects (m); The value for the icing height variation index is 0.17 for areas without measured icing data and can be taken as follows: 0.17 for areas within 10m above the ground and 0.14 for areas between 10m and 20m above the ground.

[0006] DL / T5509-2015 Index The reference index value was obtained by applying synchronous ice accumulation observation data of four layers (2, 9, 16, and 23 m) with the same diameter and direction at a certain ice observation station from 1988 to 1995, and synchronous ice accumulation observation data of three layers (2, 5, and 10 m) with the same diameter and direction at another ice observation station from 2006 to 2010. The average value of the standard ice thickness at each height was calculated and subjected to exponential fitting to obtain the reference index value.

[0007] Currently, my country has an increasing number of ultra-high voltage (UHV) and extra-high voltage (UHV) transmission lines. In particular, the conductor height of UHV lines is generally above 30 meters, and some crossing sections can reach 100 meters. Due to the lack of a suitable formula for correcting icing height variations, errors occur in the design ice thickness values ​​for UHV lines and other lines with high conductor suspension heights. With the significant increase in the scale of UHV line construction, this problem will become more prominent. Therefore, further analysis and research are needed on the icing height variation patterns for conductor suspension heights greater than 20 meters. Complex terrain makes them more susceptible to severe icing processes caused by orographic uplift, resulting in high icing levels, with the highest reaching 80 mm. The icing distribution is complex, with most areas above 1300 meters in altitude around the basin being heavy icing zones, significantly increasing the safety risks of transmission projects. To address the above issues, it is necessary to construct frost-covering towers covering the height of UHV transmission towers in typical areas (according to existing UHV line statistics, the height of the frost-covering towers needs to reach 98m to cover 95% of the anti-icing requirements of operating UHV transmission towers) for observation. However, this method involves high costs, with a single 100m high frost-covering tower costing tens of millions of yuan. In addition, given the complex terrain of the area, multiple frost-covering towers need to be built for observation to ensure that the obtained patterns are representative.

[0008] Therefore, it is urgent to develop new methods through numerical simulation to study the relationship between icing height variation in complex terrain areas, so as to support the engineering design of ultra-high voltage transmission lines in the heavy icing areas of this region. Summary of the Invention

[0009] This application provides a numerical analysis method, device, and medium for ice height variation in complex terrain areas to address the problems in the background art.

[0010] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0011] According to a first aspect of the embodiments of this application, a numerical analysis method for ice height variation in complex terrain areas is provided, including:

[0012] Meteorological parameters of the target area were simulated based on a mesoscale weather forecasting model;

[0013] Calculate the ice thickness at different altitudes based on the meteorological parameters;

[0014] The ice thickness at different altitudes is fitted and analyzed to calculate the ice height variation index; the ice height variation index is used to simulate the ice thickness data at the required altitude in the target area.

[0015] According to one embodiment of this application, the simulation of meteorological parameters of the target area based on the mesoscale weather forecasting model specifically includes:

[0016] The mesoscale weather forecasting model is set to a two-layer unidirectional nested grid with different resolutions.

[0017] Through comparative experiments, a combination of physical parameterization schemes for mesoscale weather forecasting models suitable for simulating icing meteorological elements in the target area was selected.

[0018] Meteorological parameters for the target area are obtained based on a defined combination of physical parameterization schemes.

[0019] According to one embodiment of this application, the combination of physical parameterization schemes includes:

[0020] The two-layer unidirectional nested grids employ unified physical parameter settings, including a vertical layer of 50 layers and an atmospheric top of 100 hPa; high-resolution digital elevation data set to SRTM 90m resolution; atmospheric circulation driving data set to ERA5 reanalysis data with a horizontal resolution of 0.25° and a time interval of 3 hours; cloud microphysics scheme set to the Morrison scheme; both longwave and shortwave radiation schemes set to the CAM scheme; near-surface layer scheme set to the MM5 Monin-Obukhov scheme; land surface process scheme set to the Noah-LSM scheme; and boundary layer scheme set to the MYJ scheme. The integration time steps for the two-layer unidirectional nested grids are 12 s and 4 s, respectively; and the boundary layer is also refined.

[0021] According to one embodiment of this application, the encryption process for the boundary layer includes:

[0022] The bottom atmospheric layer is set to be no more than 10m above the ground, and a denser vertical stratification of the boundary layer is achieved by controlling the maximum height of each layer and the scaling factor.

[0023] According to one embodiment of this application, when calculating the icing thickness, it is determined whether the grid is in the process of icing growth. If it is in the process of icing growth, the cumulative icing amount of the grid over 1 hour is calculated until the icing process ends. When the icing process exceeds 72 hours, the predicted icing data for 12-24 hours of each prediction time is extracted and the icing thickness is calculated cumulatively. The meteorological data must meet the following conditions: -13℃ ≤ air temperature ≤ 3℃, relative humidity ≥ 85%, and liquid water content > 0.07 g·m³. -3 This indicates that the ice cover is in the process of increasing.

[0024] According to one embodiment of this application, the method for calculating icing thickness includes:

[0025]

[0026] in, The mass of ice accumulation per unit length, expressed in kg·m. -1 , The time interval is expressed in seconds (s). The collision coefficient, The adhesion coefficient, The adsorption coefficient is . Liquid water content, in g·m -3 , The diameter of the conductor cross-section is in meters (m). Wind speed, in m / s -1 ;in, , These are direct output variables of mesoscale weather forecasting models. , , The ice thickness is calculated from variables such as humidity, wind speed, temperature, air pressure, and liquid water content output by the mesoscale weather forecast model; the ice thickness is determined based on the ice mass per unit length.

[0027] According to one embodiment of this application, the step of fitting analysis of icing thickness at different heights and calculating the icing height variation index specifically includes:

[0028] Grid point data with an ice thickness of not less than 1 mm were selected, and the average ice thickness data of each layer at seven height levels (4.7 m, 15.02 m, 27.4 m, 42.23 m, 60.0 m, 81.25 m, and 106.62 m) were calculated. Using a height of 10 m as the baseline, the average ice thickness data of each layer was normalized. The normalized average ice thickness data of each layer was then combined with a power function fitting analysis using the least squares method to obtain the ice height variation index. The ice thickness at the 10 m height was obtained by linear interpolation of the ice thickness at the 4.7 m and 15.02 m heights.

[0029] According to one embodiment of this application, the formula for calculating the icing height change index is as follows:

[0030]

[0031] in, The design conductor height above the ground is given in meters (m). For actual measurement or investigation of the height of ice-covered objects above the ground, in meters; for Ice thickness on the conductor at altitude, in mm; for Ice thickness on the conductor at altitude, in mm; This is the index of icing height variation.

[0032] According to a second aspect of this application, an electronic device is provided, comprising:

[0033] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the at least one processor performs the method as described in the first aspect by executing the instructions stored in the memory.

[0034] According to a third aspect of this application, a computer-readable storage medium is provided for storing instructions that, when executed, cause the method described in the first aspect to be implemented.

[0035] Compared with existing technologies, the advantages of adopting the above technical solution are as follows: 1) An innovative numerical simulation method for icing accretion is proposed, which includes detailed topographic data suitable for icing simulation in complex terrain areas, a combination of WRF model physical parameterization schemes, and boundary layer densification methods. This achieves high-precision reproduction of the icing growth process and meets the research needs of icing height variation relationships. 2) Significant engineering application value: Combining numerical simulation results with empirical formulas forms an icing thickness conversion relationship applicable to complex terrain and the height range of UHV lines. This provides a reliable reference for determining the ice thickness in the design of UHV transmission lines, improving the scientificity and safety of line anti-icing design. 3) Economic and practical advantages: Compared with constructing multiple 100-meter-level frost monitoring towers for full-height observation, this method is lower in cost and more applicable, and can widely support the line design and safe operation of UHV projects. Attached Figure Description

[0036] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0037] Figure 1 This is a flowchart of the numerical analysis method for ice height variation in complex terrain areas proposed in this application.

[0038] Figure 2 This is a map showing the altitude distribution of two nested regions of the target area and the geographical location of a certain ice observation station (scattered point) in an embodiment of this application.

[0039] Figure 3 This is a comparison diagram showing the heights of different vertical layers before and after boundary layer encryption in an embodiment of this application.

[0040] Figure 4This is a comparison of the absolute errors of the 2m air temperature and dew point temperature simulation at an ice observation station before and after boundary layer encryption in an embodiment of this application. (a) shows the comparison of the absolute errors of the 2m air temperature and the 2m dew point temperature with the observed values ​​under encryption and non-encryption control tests; (b) shows the relative changes of the absolute errors under encryption and non-encryption control tests.

[0041] Figure 5 This is a fitted curve of the icing height change index according to an embodiment of this application.

[0042] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0043] The embodiments of this application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar modules or modules having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. Rather, the embodiments of this application include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0044] This application proposes a numerical analysis method for ice height variation in complex terrain areas, mainly addressing the following two issues:

[0045] 1) Addressing the insufficient applicability of existing icing height conversion formulas: The current icing height conversion coefficient formula in DL / T 5509-2015 only covers conductor heights within 20 m above ground. However, the suspension heights of UHV transmission lines under rapid construction in my country are generally 30-100 m. The lack of applicable height correction relationships leads to errors in the design ice thickness values. 2) Meeting the design requirements of UHV lines in complex terrain: The target area has large terrain undulations, abundant moisture, and frequent icing disasters, posing a serious threat to the safe operation of UHV lines. By studying the variation of icing height with altitude in complex terrain areas, a scientific basis is provided for the design of high-suspension transmission lines, reducing the high cost of large-scale construction of ultra-high frost-covered towers.

[0046] For details, please refer to Figure 1 This numerical analysis method for ice height variation in complex terrain areas includes the following steps:

[0047] S101. Simulate meteorological parameters of the target area based on a mesoscale weather forecasting model.

[0048] This embodiment uses the mesoscale weather forecasting model WRF (Weather Research and Forecast) to simulate icing meteorological conditions in the target area. The method includes the following steps: First, determining the required spatial and temporal range for simulation based on the research objectives; second, selecting suitable combinations of WRF physical parameterization schemes for simulating icing meteorological elements in the target area through comparative experiments; finally, setting the parameters of the WRF model based on the determined combinations of physical parameterization schemes to simulate and extract meteorological parameters for the target area.

[0049] Specifically, in one embodiment, please refer to Figure 2 The simulation center was located at an ice observation station. The two unidirectional nested grid layers had horizontal resolutions of 9 km and 3 km, respectively, with grid points oriented east-west × north-south, at 90×90 and 160×160 points, respectively. Both grid layers used unified physical parameter settings, including a vertical layer of 50 layers and an atmospheric top of 100 hPa; high-resolution digital elevation data set to SRTM 90m resolution; atmospheric circulation driving data set to ERA5 reanalysis data with a horizontal resolution of 0.25° and a time interval of 3 hours; a cloud microphysics scheme set to the Morrison scheme; both longwave and shortwave radiation schemes set to the CAM scheme; a near-surface layer scheme set to the MM5 Monin-Obukhov scheme; a land surface process scheme set to the Noah-LSM scheme; and a boundary layer scheme set to the MYJ scheme. The integration time steps for the two unidirectional nested grid layers were 12 s and 4 s, respectively; and the boundary layer was also refined. Figure 2 The elevation of the two nested regions of the target area and the geographical distribution of a certain ice observation station (scattered points) are shown.

[0050] Two sets of numerical experiments were conducted: a control experiment and a sensitivity experiment. Specifically, the control experiment used the original vertical stratification of the WRF model, while the sensitivity experiment refined the boundary layer, setting the lowest atmospheric layer no higher than 10m above the ground, and achieving denser vertical stratification of the boundary layer by controlling the maximum height of each layer and the scaling factor. The vertical height differences between the two sets of experiments are as follows: Figure 3 As shown. In the control experiment, the lowest layer is 23.47m high, with only 2 layers of atmosphere within 100m above the ground and 8 layers within 1km; while in the sensitivity experiment, the lowest layer is 4.7m high, with 6 layers of atmosphere within 100m above the ground and 18 layers within 1km.

[0051] Based on this, this application utilizes the WRF model to design numerical experiments, more fully considering the variation of icing growth with altitude. It proposes a numerical simulation method using high-resolution topographic data, a combination of model physical parameterization schemes, and boundary layer intensification. The simulated meteorological conditions are more consistent with the observed data, resulting in higher simulation accuracy. Taking the icing growth process at a certain ice observation station from December 19th to December 23rd, 2024 as an example, compared with the control experiment without boundary layer intensification, the absolute error between the simulated 2m air temperature and the observed value decreased by 0.52℃ (a relative reduction of 34.45%), and the absolute error between the simulated 2m dew point temperature and the observed value decreased by 0.45℃ (a relative reduction of 29.63%). The simulated atmospheric temperature and humidity values ​​are closer to the observed values, specifically as follows: Figure 4 As shown.

[0052] S102. Calculate the icing thickness at different altitudes based on the meteorological parameters.

[0053] In this embodiment, the output of the second-layer grid in the WRF mode is the required meteorological data, which provides meteorological conditions such as temperature and humidity for calculating the icing thickness growth. The meteorological conditions at the grid points are: -13℃ ≤ air temperature ≤ 3℃, relative humidity ≥ 85%, and liquid water content > 0.07 g·m³. -3 If the icing occurs, the grid is determined to be in the process of icing growth. When icing occurs, the cumulative icing amount of the grid point over 1 hour is calculated until the icing process ends; when the icing process exceeds 72 hours, the predicted icing data for 12-24 hours of each prediction time is extracted and the icing thickness is calculated by accumulating the data.

[0054] Specifically, the formula for calculating icing thickness is:

[0055]

[0056] in, The mass of ice accumulation per unit length, expressed in kg·m. -1 , For time intervals, The collision coefficient, The adhesion coefficient, The adsorption coefficient is . Liquid water content, in g·m -3 , The diameter of the conductor cross-section is in meters (m). Wind speed, in m / s -1 . , For WRF mode, the direct output variables, , , The data is calculated from the humidity, wind speed, air temperature, air pressure, and liquid water content output by the WRF mode. For specific calculation methods, please refer to the algorithm recommended by ISO 12494.

[0057] In obtaining icing quality This is then further converted into ice thickness. In this embodiment, ice density is assumed. The constant is 0.9 × 10 3 kg·m -3 Furthermore, the ice-covered shape is a standard cylinder. Based on this, the volume of an ice-covered cylinder per unit length... Then, combined with the formula for the volume of a cylinder ( Take 1) Calculate the total radius of the ice-covered cylinder. Ultimately, the thickness of the ice cover... That is, the total radius With the initial radius of the conductor The difference: .

[0058] A certain ice observation station has a 10m high frost tower to observe the ice thickness at heights of 2.2m, 6.6m, and 10m. Taking the ice growth process at this observation station from December 19, 2024 to December 23, 2024 as an example, the ice thickness at 10m calculated using the original vertical stratification scheme based on meteorological data is 34.8mm. The ice thickness at 10m calculated using the meteorological data simulation method of this embodiment is 39.5mm. The actual observed ice thickness at 10m is 43.2mm. Obviously, the simulated ice thickness value of this embodiment is closer to the actual ice thickness value.

[0059] S103. Perform fitting analysis on the ice thickness at different heights and calculate the ice height variation index; the ice height variation index is used to simulate the ice thickness data at the required height in the target area.

[0060] To reduce random errors, in this embodiment, 7686 grid points with ice thickness ≥ 1 mm were selected for fitting. The average ice thickness data of each layer was calculated at seven height levels: 4.7 m, 15.02 m, 27.4 m, 42.23 m, 60.0 m, 81.25 m, and 106.62 m. Using a height of 10 m as the baseline, the average ice thickness data of each layer was normalized. The normalized average ice thickness data of each layer was then combined with a power function fitting analysis using the least squares method. The ice thickness at the 10 m height was obtained by linear interpolation of the ice thickness at the 4.7 m and 15.02 m heights.

[0061] Specifically, the formula for calculating the icing height change index is:

[0062]

[0063] in, The design conductor height above the ground is given in meters (m). For actual measurement or investigation of the height of ice-covered objects above the ground, in meters; for Ice thickness on the conductor at altitude, in mm; for Ice thickness on the conductor at altitude, in mm; This is the index of icing height variation.

[0064] The ice thickness was simulated with high precision, and the ice height variation index was obtained by fitting the data. This solved the problem of obtaining observation data on icing at heights above 20m, and addressed the lack of research on the icing height change index. Figure 5 The fitted curve of the icing height change index in this embodiment is shown.

[0065] The numerical analysis method for ice height variation in complex terrain areas proposed in this application can provide scientific numerical calculation support for designing ice thickness in complex terrain areas, thereby ensuring the safe operation of ultra-high voltage projects.

[0066] Based on the same technical concept, this application also provides an electronic device that can implement the numerical analysis method for ice height variation in complex terrain areas provided in the above embodiments of the present invention. In one embodiment, the electronic device can be a server, a terminal device, or other electronic equipment. Figure 6 As shown, the electronic device may include:

[0067] At least one processor and a memory connected to the at least one processor. In this embodiment of the invention, the specific connection medium between the processor and the memory is not limited. Figure 6 The example used is the connection between the processor and memory via a bus. The bus... Figure 6 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. Buses can be divided into address buses, data buses, control buses, etc., but for ease of representation, [the specific bus type is not shown here]. Figure 6 The processor is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, a processor can also be called a controller; there are no restrictions on the name.

[0068] In this embodiment of the invention, the memory stores instructions executable by at least one processor. By executing the instructions stored in the memory, the at least one processor can perform the numerical analysis method for ice height variation in complex terrain areas described above. The processor can implement... Figure 6 The functions of each module in the device shown.

[0069] The processor is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory and calling data stored in memory, it can monitor the device's various functions and process data, thereby enabling overall monitoring of the device.

[0070] In an alternative design, the processor may include one or more processing units. The processor may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.

[0071] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable array, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the numerical analysis method for ice height variation in complex terrain areas disclosed in the embodiments of this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0072] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, etc. Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. In embodiments of the present invention, memory can also be a circuit or any other device capable of implementing storage functions, used to store program instructions and / or data.

[0073] By designing and programming the processor, the code corresponding to the numerical analysis method for ice height variation in complex terrain areas described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute the steps of the methods described in the foregoing embodiments during runtime. How to design and program the processor is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0074] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform a numerical analysis method for ice height variation in complex terrain areas as described above.

[0075] In some alternative embodiments, the present invention also provides a method for numerical analysis of icing height variation in complex terrain areas, which can also be implemented as a program product including program code. When the program product is run on a device, the program code is used to cause the control device to perform the steps in the method for numerical analysis of icing height variation in complex terrain areas according to various exemplary embodiments of the present invention as described in this specification.

[0076] It should be noted that although several units or sub-units of the apparatus have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Furthermore, although the operation of the method of the invention is described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0077] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will 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 program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] Program code for performing the operations of this invention can be written using any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0080] In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0081] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0082] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A numerical analysis method for ice cover height variation in complex terrain areas, characterized in that, include: Meteorological parameters of the target area were simulated based on a mesoscale weather forecasting model; Calculate the ice thickness at different altitudes based on the meteorological parameters; The ice thickness at different heights is fitted and analyzed to calculate the ice height variation index; the ice height variation index is used to simulate the ice thickness data at the required height in the target area. The meteorological parameters of the target area simulated based on the mesoscale weather forecasting model specifically include: The mesoscale weather forecasting model is set to a two-layer unidirectional nested grid with different resolutions. Through comparative experiments, a combination of physical parameterization schemes for mesoscale weather forecasting models suitable for simulating icing meteorological elements in the target area was selected. Meteorological parameters for the target area are obtained based on a defined combination of physical parameterization schemes; The combination of physical parameterization schemes includes: The two-layer unidirectional nested grids employ unified physical parameter settings, including a vertical layer of 50 layers and an atmospheric top of 100 hPa; high-resolution digital elevation data set to SRTM 90m resolution; atmospheric circulation driving data set to ERA5 reanalysis data with a horizontal resolution of 0.25° and a time interval of 3 hours; cloud microphysics scheme set to the Morrison scheme; both longwave and shortwave radiation schemes set to the CAM scheme; near-surface layer scheme set to the MM5 Monin-Obukhov scheme; land surface process scheme set to the Noah-LSM scheme; and boundary layer scheme set to the MYJ scheme. The integration time steps for the two-layer unidirectional nested grids are 12 s and 4 s, respectively; and the boundary layer is also refined. The fitting analysis of ice thickness at different altitudes and the calculation of the ice height variation index specifically include: Grid point data with an ice thickness of not less than 1 mm were selected. The average ice thickness of each layer was calculated at seven height levels: 4.7 m, 15.02 m, 27.4 m, 42.23 m, 60.0 m, 81.25 m, and 106.62 m. Using a height of 10 m as the baseline, the average ice thickness of each layer was normalized. The normalized average ice thickness of each layer was then combined with a power function fitting analysis using the least squares method to obtain the ice height variation index. The ice thickness at the 10 m height was obtained by linear interpolation of the ice thickness at heights of 4.7 m and 15.02 m. The formula for calculating the icing height change index is: in, The design conductor height above the ground is given in meters (m). For actual measurement or investigation of the height of ice-covered objects above the ground, in meters; for Ice thickness on the conductor at altitude, in mm; for Ice thickness on the conductor at altitude, in mm; This is the index of icing height variation.

2. The numerical analysis method for ice cover height variation in complex terrain areas according to claim 1, characterized in that, The encryption process for the boundary layer includes: The bottom atmospheric layer is set to be no more than 10m above the ground, and a denser vertical stratification of the boundary layer is achieved by controlling the maximum height of each layer and the scaling factor.

3. The numerical analysis method for ice cover height variation in complex terrain areas according to claim 1, characterized in that, When calculating the icing thickness, it is determined whether the grid is in the process of icing growth. If it is in the process of icing growth, the cumulative icing amount of the grid in 1 hour is calculated until the icing process ends. When the icing process exceeds 72 hours, extract the predicted icing data for 12-24 hours for each prediction period and calculate the icing thickness by summing them up; where the meteorological data meets the following conditions: Temperature range: 13℃ ≤ air temperature ≤ 3℃; relative humidity: ≥ 85%; liquid water content: > 0.07 g·m³ -3 This indicates that the ice cover is in the process of increasing.

4. The numerical analysis method for ice cover height variation in complex terrain areas according to claim 3, characterized in that, The method for calculating the ice thickness includes: in, The mass of ice accumulation per unit length, expressed in kg·m. -1 , The time interval is expressed in seconds (s). The collision coefficient, The adhesion coefficient, The adsorption coefficient is . Liquid water content, in g·m -3 , The diameter of the conductor cross-section is in meters (m). Wind speed, unit: m / s -1 ;in, , These are direct output variables of mesoscale weather forecasting models. , , The icing thickness is calculated from the humidity, wind speed, air temperature, air pressure, and liquid water content output by the mesoscale weather forecast model; the icing thickness is determined based on the icing mass per unit length.

5. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which executes the instructions stored in the memory to perform the method as described in any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store instructions that, when executed, cause the method as described in any one of claims 1 to 4 to be implemented.

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