Methods, devices, and software products for predicting visibility during icing periods for overhead power lines in mountainous areas

Abstract

This application relates to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for predicting visibility during the icing period of mountainous overhead power lines. The method includes: acquiring a historical weather forecast dataset for a target area, inputting it into a weather forecasting system to simulate historical weather background field data, and determining a historical visibility dataset for the target area based on the historical weather background field data and a preset algorithm; inputting the historical weather background field data and historical visibility data into a random forest model to revise the historical visibility dataset and determine the nonlinear relationship between the historical weather background field data and the historical visibility dataset; acquiring a real-time weather forecast dataset for the target area within a preset time period and simulating real-time weather background field data; and predicting the visibility data for the target area based on the real-time weather background field data and the nonlinear relationship. This method can improve the accuracy of visibility prediction.

Description

Technical Field

[0001] This application relates to the field of visibility prediction technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for predicting visibility during the icing period of mountain overhead power lines. Background Technology

[0002] Complex mountainous terrain significantly impacts winter weather conditions and visibility. In mountainous regions, due to the complexity of the terrain and the diversity of local weather processes, low visibility occurs frequently in winter, causing great inconvenience to local transportation, aviation, and residents' lives, and even threatening safety and economic development. Furthermore, low visibility can cause icing on power lines, increasing their weight and potentially leading to the breakage of power lines and poles, thus affecting power transmission and communications, and posing a significant threat to power grid safety.

[0003] Among related technologies, visibility prediction methods mainly include the FSL algorithm, which combines multiple factors such as relative humidity and temperature. However, in mountainous areas, the vertical distribution of temperature and dew point temperature and local terrain changes are complex, resulting in a large error in its quantitative characterization of visibility, leading to inaccurate final visibility prediction results. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for predicting visibility during the icing period of mountain overhead lines, which can improve the accuracy of visibility prediction during the icing period of mountain overhead lines, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for predicting visibility during the icing period of mountain overhead power lines, including:

[0006] Obtain historical weather forecast datasets for the target area;

[0007] The historical meteorological forecast dataset is input into the weather forecast system to simulate the historical meteorological background field data of the target area;

[0008] Based on the historical meteorological background field data and the preset algorithm, the historical visibility dataset of the target area is determined;

[0009] The historical meteorological background data and the historical visibility data are input into a random forest model to revise the historical visibility dataset and determine the nonlinear relationship between the historical meteorological background data and the historical visibility dataset.

[0010] Obtain the real-time weather forecast dataset for the target area within a preset time period, and simulate the real-time weather background field data based on the weather forecast system;

[0011] Based on the real-time meteorological background field data and the nonlinear relationship, the visibility data of the target area is predicted.

[0012] In one embodiment, the weather forecasting system includes an outer layer network, a middle layer network, an inner layer network, and a large eddy simulation network; the step of inputting the historical weather forecast dataset into the weather forecasting system to simulate the historical weather background field data of the target area includes:

[0013] The historical weather forecast dataset is converted into a preset format, and the converted historical weather forecast dataset is input into the outer network to simulate the weather evolution trend of the target area.

[0014] The weather evolution trend is input into the middle layer network to simulate weather phenomenon data of the target area;

[0015] The weather phenomenon data is input into the inner network to simulate the meteorological background field data of the target area based on a preset resolution.

[0016] The meteorological background field data is input into the large eddy simulation network to simulate the turbulent motion of the target area, thereby obtaining the historical meteorological background field data of the target area.

[0017] In one embodiment, the historical meteorological background field data includes: cloud-water mixing ratio data, temperature data, water vapor pressure data, and water vapor mixing ratio data of the target area.

[0018] In one embodiment, the preset algorithm includes a cloud microphysics visibility algorithm; the step of determining the historical visibility dataset of the target area based on the historical meteorological background field data and the preset algorithm includes:

[0019] ;

[0020] in, For visibility data, This is data on the cloud-water mixing ratio.

[0021] In one embodiment, the preset algorithm includes a temperature and humidity empirical visibility algorithm; the step of determining the historical visibility dataset of the target area based on the historical meteorological background field data and the preset algorithm includes:

[0022] ;

[0023] in, The data represents visibility, and T represents the measured ambient temperature from the temperature data. RH represents the relative humidity in the temperature data; the relative humidity is the ratio of the actual water vapor pressure in the air to the saturated water vapor pressure at the same temperature in the water vapor pressure data.

[0024] In one embodiment, the preset algorithm includes an Air Force Weather Bureau algorithm; the step of determining the historical visibility dataset of the target area based on the historical meteorological background field data and the preset algorithm includes:

[0025] ;

[0026] in, The data represents visibility, and Mix represents the water vapor mixing ratio.

[0027] Secondly, this application also provides a visibility prediction device for mountain overhead power lines during icing periods, comprising:

[0028] The acquisition module is used to acquire historical weather forecast datasets for the target area;

[0029] The simulation module is used to input the historical meteorological forecast dataset into the weather forecast system to simulate the historical meteorological background field data of the target area;

[0030] The determination module is used to determine the historical visibility dataset of the target area based on the historical meteorological background field data and a preset algorithm;

[0031] The determining module is used to input the historical meteorological background field data and the historical visibility data into the random forest model, revise the historical visibility dataset, and determine the nonlinear relationship between the historical meteorological background field data and the historical visibility dataset.

[0032] The acquisition module is used to acquire the real-time weather forecast dataset of the target area within a preset time period, and to simulate the real-time weather background field data based on the weather forecast system.

[0033] The prediction module is used to predict the visibility data of the target area based on the real-time meteorological background field data and the nonlinear relationship.

[0034] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0035] The acquisition module is used to acquire historical weather forecast datasets for the target area;

[0036] The simulation module is used to input the historical meteorological forecast dataset into the weather forecast system to simulate the historical meteorological background field data of the target area;

[0037] The determination module is used to determine the historical visibility dataset of the target area based on the historical meteorological background field data and a preset algorithm;

[0038] The determining module is used to input the historical meteorological background field data and the historical visibility data into the random forest model, revise the historical visibility dataset, and determine the nonlinear relationship between the historical meteorological background field data and the historical visibility dataset.

[0039] The acquisition module is used to acquire the real-time weather forecast dataset of the target area within a preset time period, and to simulate the real-time weather background field data based on the weather forecast system.

[0040] The prediction module is used to predict the visibility data of the target area based on the real-time meteorological background field data and the nonlinear relationship.

[0041] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0042] The acquisition module is used to acquire historical weather forecast datasets for the target area;

[0043] The simulation module is used to input the historical meteorological forecast dataset into the weather forecast system to simulate the historical meteorological background field data of the target area;

[0044] The determination module is used to determine the historical visibility dataset of the target area based on the historical meteorological background field data and a preset algorithm;

[0045] The determining module is used to input the historical meteorological background field data and the historical visibility data into the random forest model, revise the historical visibility dataset, and determine the nonlinear relationship between the historical meteorological background field data and the historical visibility dataset.

[0046] The acquisition module is used to acquire the real-time weather forecast dataset of the target area within a preset time period, and to simulate the real-time weather background field data based on the weather forecast system.

[0047] The prediction module is used to predict the visibility data of the target area based on the real-time meteorological background field data and the nonlinear relationship.

[0048] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0049] The acquisition module is used to acquire historical weather forecast datasets for the target area;

[0050] The simulation module is used to input the historical meteorological forecast dataset into the weather forecast system to simulate the historical meteorological background field data of the target area;

[0051] The determination module is used to determine the historical visibility dataset of the target area based on the historical meteorological background field data and a preset algorithm;

[0052] The determining module is used to input the historical meteorological background field data and the historical visibility data into the random forest model, revise the historical visibility dataset, and determine the nonlinear relationship between the historical meteorological background field data and the historical visibility dataset.

[0053] The acquisition module is used to acquire the real-time weather forecast dataset of the target area within a preset time period, and to simulate the real-time weather background field data based on the weather forecast system.

[0054] The prediction module is used to predict the visibility data of the target area based on the real-time meteorological background field data and the nonlinear relationship.

[0055] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for predicting visibility during the icing period of elevated mountain lines first acquire a historical weather forecast dataset for the target area; input the historical weather forecast dataset into a weather forecast system to simulate historical background weather field data for the target area; based on the historical background weather field data and a preset algorithm, determine the historical visibility dataset for the target area; input the historical background weather field data and historical visibility data into a random forest model to revise the historical visibility dataset and determine the nonlinear relationship between the historical background weather field data and the historical visibility dataset; acquire a real-time weather forecast dataset for the target area within a preset time period, and simulate real-time background weather field data based on the weather forecast system; based on the real-time background weather field data and the nonlinear relationship, predict the visibility data for the target area. In this way, by performing a series of processes on the weather forecast data through the weather forecast system, and using multiple different preset algorithms to comprehensively predict the visibility data for the target area based on the background weather field data, the accuracy of visibility data prediction for the target area is improved. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is an application environment diagram of the visibility prediction method for mountain overhead lines during the icing period in one embodiment;

[0058] Figure 2 This is a flowchart illustrating a method for predicting visibility during icing periods for overhead power lines in mountainous areas, as shown in one embodiment.

[0059] Figure 3 This is a structural block diagram of a visibility prediction device for an overhead power line in a mountainous area during the icing period, as shown in one embodiment.

[0060] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0063] The visibility prediction method for mountain overhead lines during icing periods provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located on the cloud or other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0064] In one exemplary embodiment, such as Figure 2 As shown, a method for predicting visibility during the icing period of mountain overhead power lines is provided, and this method is applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps 202 to 212. Wherein:

[0065] Step 202: Obtain the historical weather forecast dataset for the target area.

[0066] The historical weather forecast dataset includes a series of environmental data such as temperature, humidity, air pressure, altitude, and wind field data of the target area during historical periods. These data can be obtained by the user according to the actual situation, and this application embodiment does not limit them.

[0067] For example, obtain the historical weather forecast dataset for the target area.

[0068] In one embodiment, historical weather forecast datasets collected by the GFS (Global Forecasting System) developed by NOAA (National Oceanic and Atmospheric Administration) are obtained, with a time resolution of 6 hours and a spatial resolution of 0.25°*0.25°. Historical visibility datasets for the target area are also obtained, with the visibility observation device 2 meters above the ground and a data resolution of 10 minutes.

[0069] Step 204: Input the historical weather forecast dataset into the weather forecast system to simulate the historical weather background field data of the target area.

[0070] Optionally, historical weather forecast datasets can be input into the WRF model (The Weather Research and Forecasting Model) in the weather forecasting system to simulate historical weather background field data for the target area.

[0071] Historical meteorological background field data shows the distribution of meteorological elements over a large spatial area (such as regional or global scale), including temperature, humidity, wind field, etc., providing a macro background for the evolution of weather systems. For mountainous areas, meteorological background field data can reflect regional weather system changes, such as the movement of pressure systems and the activity of cold and warm air. It also includes cloud-water mixing ratio data, water vapor pressure data, and water vapor mixing ratio data for the target area.

[0072] Step 206: Based on historical meteorological background field data and preset algorithms, determine the historical visibility dataset for the target area.

[0073] For example, historical meteorological background field data is cleaned and normalized to obtain processed meteorological background field data. Based on the processed meteorological background field data and preset algorithms, the visibility data corresponding to each algorithm is determined, and finally, a historical visibility dataset for the target area is formed.

[0074] The preset algorithms include the cloud microphysics visibility algorithm (SW99 algorithm), the temperature and humidity empirical visibility algorithm (FSL algorithm), and the Air Force Weather Bureau algorithm (AFWA algorithm). Other algorithms that can determine visibility data through meteorological background field data are also possible, and this application embodiment does not limit them.

[0075] Step 208: Input historical meteorological background field data and historical visibility data into the random forest model, revise the historical visibility dataset, and determine the nonlinear relationship between the historical meteorological background field data and the historical visibility dataset.

[0076] Optionally, historical meteorological background field data and historical visibility data are input into a random forest model to revise the historical visibility dataset and determine the nonlinear relationship between the historical meteorological background field data and the historical visibility dataset.

[0077] Step 210: Obtain the real-time weather forecast dataset for the target area within a preset time period, and simulate the real-time weather background field data based on the weather forecast system.

[0078] For example, real-time weather forecast datasets for the target area within a preset time period are obtained, and real-time weather background field data are simulated based on the weather forecast system.

[0079] Step 212: Based on real-time meteorological background field data and nonlinear relationships, the visibility data of the target area is predicted.

[0080] Optionally, visibility data for the target area can be predicted based on the real-time meteorological background field data and nonlinear relationships obtained from the simulation.

[0081] The aforementioned method for predicting visibility during the icing period of elevated mountain lines involves: acquiring historical weather forecast datasets for the target area; inputting these datasets into a weather forecasting system to simulate historical background weather data for the target area; determining historical visibility datasets for the target area based on the historical background weather data and a pre-defined algorithm; inputting the historical background weather data and historical visibility data into a random forest model to revise the historical visibility datasets and determine the nonlinear relationship between them; acquiring real-time weather forecast datasets for the target area within a preset time period and simulating real-time background weather data using the weather forecasting system; and predicting visibility data for the target area based on the real-time background weather data and the nonlinear relationship. In this way, by processing the weather forecast data through a series of steps using the weather forecasting system, and by comprehensively predicting the visibility data of the target area using the background weather data and multiple pre-defined algorithms, the accuracy of visibility data prediction for the target area is improved.

[0082] In an exemplary embodiment, the weather forecasting system includes an outer layer network, a middle layer network, an inner layer network, and a large eddy simulation network. Inputting historical weather forecast datasets into the weather forecasting system to simulate historical meteorological background field data for a target area includes: converting the historical weather forecast datasets into a preset format, and inputting the converted historical weather forecast datasets into the outer layer network to simulate the weather evolution trend of the target area; inputting the weather evolution trend into the middle layer network to simulate weather phenomenon data for the target area; inputting the weather phenomenon data into the inner layer network to simulate meteorological background field data for the target area at a preset resolution; and inputting the meteorological background field data into the large eddy simulation network to simulate turbulent motion in the target area to obtain historical meteorological background field data for the target area.

[0083] In practical implementation, the initial field and boundary condition data of historical meteorological background field data are determined based on historical weather forecast datasets. The geographical range of the target area is defined, nested networks are set up, and corresponding physical parameterization schemes (such as boundary layer schemes and microphysical schemes) are selected. Through format conversion, spatial interpolation, etc., the data is converted into a preset format, matched with the corresponding grid and time step, and input into the outer network to simulate the weather evolution trend of the target area. The weather evolution trend is input into the middle network to simulate the weather phenomenon data of the target area. The weather phenomenon data is input into the inner network to simulate the meteorological background field data of the target area based on the preset resolution. The meteorological background field data is input into the large eddy simulation network to simulate the turbulent motion of the target area and obtain the historical meteorological background field data of the target area.

[0084] The Large Eddy Simulation Network (LES) is nested within the initial atmospheric forecasting system.

[0085] In one embodiment, the Mellor-Yamada-Janjic boundary layer parameterization scheme and the Morrison 2-mom microphysics parameterization scheme are selected. The Mellor-Yamada-Janjic scheme improves the simulation capability of momentum, energy, and vertical water vapor transport within the planetary boundary layer by improving the nonsingular form and accurately describing the boundary layer turbulent motion; the Morrison 2-mom scheme describes the formation, growth, and phase transition processes of cloud droplets and ice crystals in more detail by predicting the mass mixing ratio and number concentration of water species such as cloud water, rain, and snow.

[0086] In the above embodiments, by capturing more refined meteorological element distribution characteristics through multi-layer networks and gradually refining the resolution, it is possible to gradually capture meteorological features from large scale to small and medium scale, and ultimately more finely characterize the visibility evolution under complex mountainous terrain. At the same time, by nesting large eddy simulation networks, it is possible to further capture local small-scale turbulent motion and fine changes in meteorological elements, providing more accurate meteorological field data for visibility data prediction.

[0087] In one exemplary embodiment, historical meteorological background field data includes: cloud-water mixing ratio data, temperature data, water vapor pressure data, and water vapor mixing ratio data for the target area.

[0088] In practice, historical meteorological background data includes: cloud-water mixing ratio data, temperature data, water vapor pressure data, and water vapor mixing ratio data for the target area.

[0089] In another embodiment, historical meteorological background field data may also include wind field data, temperature and humidity data, geopotential height field, air pressure data, precipitation and water vapor flux, convective parameters and instability indicators, etc., and may also include other relevant meteorological data. This application embodiment does not limit this.

[0090] In the above embodiments, by comprehensively considering various meteorological data, the meteorological background field data obtained through data processing is more accurate.

[0091] In an exemplary embodiment, the preset algorithm includes the cloud microphysics visibility algorithm (SW99 algorithm); based on historical meteorological background field data and the SW99 algorithm, the historical visibility dataset of the target area is determined, and the specific calculation formula is shown in formula (1):

[0092]

[0093] in, For visibility data, This is data on the cloud-water mixing ratio.

[0094] Among them, the SW99 algorithm is based on the empirical relationship between the density of various liquid water contents in the atmosphere and the extinction coefficient. It believes that visibility is inversely proportional to the extinction coefficient, that is, the larger the extinction coefficient, the lower the visibility.

[0095] In the above embodiments, the total extinction coefficient is obtained by calculating and summing the extinction coefficients of different liquid water and ice phase particles, thereby further obtaining visibility data and making the results more accurate.

[0096] In an exemplary embodiment, the preset algorithm includes the empirical visibility algorithm (FSL algorithm); based on historical meteorological background field data and the FSL algorithm, the historical visibility dataset of the target area is determined, and the specific calculation formula is shown in formula (2):

[0097]

[0098] in, The data represents visibility, and T represents the measured ambient temperature from the temperature data. RH represents the dew point temperature in the temperature data and relative humidity; relative humidity is the ratio of the actual water vapor pressure in the air to the saturated water vapor pressure at the same temperature in the water vapor pressure data.

[0099] The FSL algorithm was developed by the Forecast Systems Laboratory of NOAA (National Oceanic and Atmospheric Administration) in the United States.

[0100] In the above embodiments, by using temperature-dew point difference (T-Td) and relative humidity (RH) as core variables, the changes in the degree of water vapor saturation in the air can be directly reflected. It is highly sensitive to low visibility weather such as fog, haze, and precipitation, and is especially suitable for visibility estimation under high humidity, low temperature, and stable weather conditions, making the final prediction results more accurate.

[0101] In an exemplary embodiment, the preset algorithm includes the Air Force Weather Bureau algorithm (AFWA algorithm); based on historical meteorological background field data and the AFWA algorithm, the historical visibility dataset of the target area is determined, and the specific formula is shown in formula (3):

[0102]

[0103] in, The data represents visibility, and Mix represents the water vapor mixing ratio.

[0104] The AFWA algorithm calculates visibility based on relative humidity and the water vapor mixing ratio. Relative humidity reflects the degree to which the water vapor content in the air approaches saturation. When relative humidity increases, the water vapor content in the air increases, and the possibility of water vapor condensing into small water droplets increases. This scatters and absorbs light, reducing visibility.

[0105] In the above embodiments, the AFWA algorithm only requires two conventional meteorological elements, relative humidity (RH) and water vapor mixing ratio (Mix), to estimate visibility. It does not require microphysical quantities such as cloud water and rainwater, and can still run stably in areas with sparse observations or where the model does not output the water condensate field, making it more adaptable.

[0106] To illustrate the visibility prediction method for mountain overhead lines during icing periods in this application in detail, an embodiment is described below. For example, this application describes the visibility prediction method for mountain overhead lines during icing periods in a specific scenario.

[0107] First, acquire a series of environmental data for the target area, including temperature, humidity, air pressure, altitude, and wind field data over historical periods.

[0108] Historical weather forecast datasets are input into the WRF model (The Weather Research and Forecasting Model) of the weather forecasting system to simulate historical weather background field data for the target area.

[0109] Historical meteorological background field data shows the distribution of meteorological elements over a large spatial area (such as regional or global scale), including temperature, humidity, wind field, etc., providing a macro background for the evolution of weather systems. For mountainous areas, meteorological background field data can reflect regional weather system changes, such as the movement of pressure systems and the activity of cold and warm air. It also includes cloud-water mixing ratio data, water vapor pressure data, and water vapor mixing ratio data for the target area.

[0110] Historical meteorological background field data is cleaned and normalized to obtain processed meteorological background field data. Based on the processed meteorological background field data and preset algorithms, the visibility data corresponding to each algorithm is determined, and finally, a historical visibility dataset for the target area is formed.

[0111] The preset algorithms include the cloud microphysics visibility algorithm (SW99 algorithm), the temperature and humidity empirical visibility algorithm (FSL algorithm), and the Air Force Weather Bureau algorithm (AFWA algorithm).

[0112] Historical meteorological background data and historical visibility data are input into a random forest model to revise the historical visibility dataset and determine the nonlinear relationship between the historical meteorological background data and the historical visibility dataset.

[0113] The system acquires real-time weather forecast datasets for the target area within a preset time period and simulates real-time weather background field data based on a weather forecasting system. Based on the simulated real-time weather background field data and nonlinear relationships, it predicts the visibility data for the target area.

[0114] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0115] Based on the same inventive concept, this application also provides a device for predicting visibility during the icing period of mountain overhead lines, which is used to implement the aforementioned method for predicting visibility during the icing period of mountain overhead lines. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for predicting visibility during the icing period of mountain overhead lines provided below can be found in the limitations of the method for predicting visibility during the icing period of mountain overhead lines described above, and will not be repeated here.

[0116] In one exemplary embodiment, such as Figure 3 As shown, a visibility prediction device for icing periods of mountain overhead power lines is provided, comprising: an acquisition module 301, a simulation module 302, a determination module 303, and a prediction module 304, wherein:

[0117] The acquisition module is used to acquire historical weather forecast datasets for the target area.

[0118] The simulation module is used to input the historical meteorological forecast dataset into the weather forecast system to simulate the historical meteorological background field data of the target area.

[0119] The determination module is used to determine the historical visibility dataset of the target area based on the historical meteorological background field data and a preset algorithm.

[0120] The determining module is used to input the historical meteorological background field data and the historical visibility data into the random forest model, revise the historical visibility dataset, and determine the nonlinear relationship between the historical meteorological background field data and the historical visibility dataset.

[0121] The acquisition module is used to acquire the real-time weather forecast dataset of the target area within a preset time period, and to simulate the real-time weather background field data based on the weather forecast system.

[0122] The prediction module is used to predict the visibility data of the target area based on the real-time meteorological background field data and the nonlinear relationship.

[0123] In one exemplary embodiment, the simulation module is further configured to convert the historical weather forecast dataset into a preset format and input the converted historical weather forecast dataset into the outer network to simulate the weather evolution trend of the target area.

[0124] The weather evolution trend is input into the middle layer network to simulate weather phenomenon data of the target area;

[0125] The weather phenomenon data is input into the inner network to simulate the meteorological background field data of the target area based on a preset resolution.

[0126] The meteorological background field data is input into the large eddy simulation network to simulate the turbulent motion of the target area, thereby obtaining the historical meteorological background field data of the target area.

[0127] In one exemplary embodiment, cloud-water mixing ratio data, temperature data, water vapor pressure data, and water vapor mixing ratio data of the target area are included.

[0128] In one exemplary embodiment, the determining module is further configured to determine a historical visibility dataset for the target area based on historical meteorological background field data and a preset algorithm, including:

[0129] ;

[0130] in, For visibility data, This is data on the cloud-water mixing ratio.

[0131] In one exemplary embodiment, the determining module is further configured to determine a historical visibility dataset for the target area based on historical meteorological background field data and a preset algorithm, including:

[0132] ;

[0133] in, The data represents visibility, and T represents the measured ambient temperature from the temperature data. RH represents the relative humidity in the temperature data; the relative humidity is the ratio of the actual water vapor pressure in the air to the saturated water vapor pressure at the same temperature in the water vapor pressure data.

[0134] In one exemplary embodiment, the determining module is further configured to determine a historical visibility dataset for the target area based on historical meteorological background field data and a preset algorithm, including:

[0135] ;

[0136] in, The data represents visibility, and Mix represents the water vapor mixing ratio.

[0137] The various modules in the aforementioned visibility prediction device for icing periods of mountain overhead power lines can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0138] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output interfaces (I / O), a communication interface, a display unit, and input devices. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface, display unit, and input devices are also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores visibility prediction data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for predicting visibility during the icing period of an overhead power line in a mountainous area.

[0139] The display unit of this computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of this computer device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad set on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0140] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0141] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0142] Obtain historical weather forecast datasets for the target area;

[0143] The historical meteorological forecast dataset is input into the weather forecast system to simulate the historical meteorological background field data of the target area;

[0144] Based on the historical meteorological background field data and the preset algorithm, the historical visibility dataset of the target area is determined;

[0145] The historical meteorological background data and the historical visibility data are input into a random forest model to revise the historical visibility dataset and determine the nonlinear relationship between the historical meteorological background data and the historical visibility dataset.

[0146] Obtain the real-time weather forecast dataset for the target area within a preset time period, and simulate the real-time weather background field data based on the weather forecast system;

[0147] Based on the real-time meteorological background field data and the nonlinear relationship, the visibility data of the target area is predicted.

[0148] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0149] Obtain historical weather forecast datasets for the target area;

[0150] The historical meteorological forecast dataset is input into the weather forecast system to simulate the historical meteorological background field data of the target area;

[0151] Based on the historical meteorological background field data and the preset algorithm, the historical visibility dataset of the target area is determined;

[0152] The historical meteorological background data and the historical visibility data are input into a random forest model to revise the historical visibility dataset and determine the nonlinear relationship between the historical meteorological background data and the historical visibility dataset.

[0153] Obtain the real-time weather forecast dataset for the target area within a preset time period, and simulate the real-time weather background field data based on the weather forecast system;

[0154] Based on the real-time meteorological background field data and the nonlinear relationship, the visibility data of the target area is predicted.

[0155] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0156] Obtain historical weather forecast datasets for the target area;

[0157] The historical meteorological forecast dataset is input into the weather forecast system to simulate the historical meteorological background field data of the target area;

[0158] Based on the historical meteorological background field data and the preset algorithm, the historical visibility dataset of the target area is determined;

[0159] The historical meteorological background data and the historical visibility data are input into a random forest model to revise the historical visibility dataset and determine the nonlinear relationship between the historical meteorological background data and the historical visibility dataset.

[0160] Obtain the real-time weather forecast dataset for the target area within a preset time period, and simulate the real-time weather background field data based on the weather forecast system;

[0161] Based on the real-time meteorological background field data and the nonlinear relationship, the visibility data of the target area is predicted.

[0162] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0163] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0164] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0165] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for predicting visibility during icing periods for overhead power lines in mountainous areas, characterized in that, The method includes: Obtain historical weather forecast datasets for the target area; the weather forecast system includes an outer layer network, a middle layer network, an inner layer network, and a large eddy simulation network; The historical weather forecast dataset is converted into a preset format, and the converted historical weather forecast dataset is input into the outer network to simulate the weather evolution trend of the target area. The weather evolution trend is input into the middle layer network to simulate weather phenomenon data of the target area; The weather phenomenon data is input into the inner network to simulate the meteorological background field data of the target area based on a preset resolution. The meteorological background field data is input into the large eddy simulation network to simulate the turbulent motion of the target area and obtain the historical meteorological background field data of the target area. Based on the historical meteorological background field data and the preset algorithm, the historical visibility dataset of the target area is determined; The historical meteorological background data and the historical visibility data are input into a random forest model to revise the historical visibility dataset and determine the nonlinear relationship between the historical meteorological background data and the historical visibility dataset. Obtain the real-time weather forecast dataset for the target area within a preset time period, and simulate the real-time weather background field data based on the weather forecast system; Based on the real-time meteorological background field data and the nonlinear relationship, the visibility data of the target area is predicted.

2. The method according to claim 1, characterized in that, The historical meteorological background field data includes: cloud-water mixing ratio data, temperature data, water vapor pressure data, and water vapor mixing ratio data for the target area.

3. The method according to claim 2, characterized in that, The preset algorithm includes a cloud microphysics visibility algorithm; the step of determining the historical visibility dataset of the target area based on the historical meteorological background field data and the preset algorithm includes: ； in, For visibility data, This is data on the cloud-water mixing ratio.

4. The method according to claim 2, characterized in that, The preset algorithm includes a temperature and humidity empirical visibility algorithm; the step of determining the historical visibility dataset of the target area based on the historical meteorological background field data and the preset algorithm includes: ； in, The data represents visibility, and T represents the measured ambient temperature from the temperature data. RH represents the relative humidity in the temperature data; the relative humidity is the ratio of the actual water vapor pressure in the air to the saturated water vapor pressure at the same temperature in the water vapor pressure data.

5. The method according to claim 4, characterized in that, The preset algorithm includes the Air Force Weather Bureau algorithm; the determination of the historical visibility dataset for the target area based on the historical meteorological background field data and the preset algorithm includes: ； in, The data represents visibility, and Mix represents the water vapor mixing ratio.

6. A device for predicting visibility during icing periods for overhead power lines in mountainous areas, characterized in that, The device includes: The acquisition module is used to acquire historical weather forecast datasets for the target area; the weather forecast system includes an outer layer network, a middle layer network, an inner layer network, and a large eddy simulation network; The simulation module is used to convert the historical weather forecast dataset into a preset format, and input the converted historical weather forecast dataset into the outer layer network to simulate the weather evolution trend of the target area; input the weather evolution trend into the middle layer network to simulate the weather phenomenon data of the target area; input the weather phenomenon data into the inner layer network to simulate the meteorological background field data of the target area based on a preset resolution; and input the meteorological background field data into the large eddy simulation network to simulate the turbulent motion of the target area to obtain the historical meteorological background field data of the target area. The determination module is used to determine the historical visibility dataset of the target area based on the historical meteorological background field data and a preset algorithm; The determining module is used to input the historical meteorological background field data and the historical visibility data into the random forest model, revise the historical visibility dataset, and determine the nonlinear relationship between the historical meteorological background field data and the historical visibility dataset. The acquisition module is used to acquire the real-time weather forecast dataset of the target area within a preset time period, and to simulate the real-time weather background field data based on the weather forecast system. The prediction module is used to predict the visibility data of the target area based on the real-time meteorological background field data and the nonlinear relationship.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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