Methods, devices, equipment and storage media for predicting icing thickness of transmission lines

Abstract

This invention provides a method, apparatus, device, and storage medium for predicting icing thickness of transmission lines. First, it acquires cloud and fog images, temperature values, and wind speed values ​​of micro-topographic points of the target transmission line at a first time point, and determines the image spatial uniformity coefficient corresponding to the cloud and fog images. Then, it determines the adhesion coefficient based on the temperature value and the image spatial uniformity coefficient, and determines the cloud and fog particle concentration corresponding to the micro-topographic points based on the image spatial uniformity coefficient and an icing thickness model. The icing thickness model is obtained by inverting multiple sample data within the target time period, including cloud and fog particle concentration samples and the image spatial uniformity coefficient corresponding to the cloud and fog image samples. Next, based on the cloud and fog particle concentration, adhesion coefficient, and wind speed values ​​corresponding to the micro-topographic points, it determines the icing thickness increment corresponding to the micro-topographic points, and uses this icing thickness increment to predict the icing thickness of the micro-topographic points at a second time point.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, equipment, and storage medium for predicting the icing thickness of transmission lines. Background Technology

[0002] In recent years, extreme and abnormal weather events caused by global warming have become more frequent, with drastic fluctuations in temperature during winter. Furthermore, power transmission lines are prone to icing disasters in winter, especially in micro-topographical locations such as valleys, mountain passes, and narrow wind tunnels, which exacerbate the icing disasters. Therefore, carrying out power transmission line icing monitoring and keeping abreast of the icing situation is of great significance for preventing power grid disasters and ensuring power supply.

[0003] Therefore, how to predict the changes in ice thickness of transmission lines at micro-topographic locations is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] To address the aforementioned technical problems, embodiments of the present invention provide a method, apparatus, device, and storage medium for predicting icing thickness of transmission lines. This method enables the analysis of icing calculation variables such as cloud and fog particle concentration, and then determines the icing thickness increment at micro-topographic locations based on the calculated cloud and fog particle concentration, adhesion coefficient, and field-collected wind speed values. The icing thickness increment is then used to predict the icing thickness at micro-topographic locations at the next time step.

[0005] In a first aspect, embodiments of the present invention provide a method for predicting the icing thickness of transmission lines, the method comprising: Acquire cloud and fog images, temperature values, and wind speed values ​​of the micro-topographic points of the target transmission line at the first time point, and determine the image spatial uniformity coefficient corresponding to the cloud and fog images; The adhesion coefficient is determined based on the temperature value and the image spatial uniformity coefficient; wherein, the adhesion coefficient is used to characterize the adhesion efficiency of supercooled cloud particles after impacting the target transmission line; Based on the image spatial uniformity coefficient and the ice thickness model, the cloud and fog particle concentration corresponding to the micro-topography point is determined; wherein, the ice thickness model is obtained by inversion using multiple sample data within the target time period, and the sample data includes cloud and fog particle concentration samples and image spatial uniformity coefficients corresponding to cloud and fog image samples. Based on the cloud and fog particle concentration, adhesion coefficient, and wind speed value corresponding to the micro-topography point, the ice thickness increment corresponding to the micro-topography point is determined; wherein, the ice thickness increment is used to predict the ice thickness of the micro-topography point at a second time point.

[0006] In one optional implementation, determining the adhesion coefficient based on the temperature value and the image spatial uniformity coefficient includes: Based on the temperature value and the image spatial uniformity coefficient, the particle size range corresponding to the micro-topography point is determined; The adhesion coefficient is determined based on the particle size range and temperature value corresponding to the micro-topography point.

[0007] In one optional implementation, determining the grain size range corresponding to the micro-topography point based on the temperature value and the image spatial uniformity coefficient includes: The temperature value and the image spatial uniformity coefficient are used to query a first data table to determine the particle size range corresponding to the micro-topography point; wherein, the first data table includes multiple particle size ranges, each particle size range corresponds to multiple temperature intervals, and multiple temperature intervals of the same particle size range correspond to different coefficient ranges, and the coefficient range is determined based on the image spatial uniformity coefficient of multiple sample data corresponding to its respective particle size range and temperature interval.

[0008] In one optional implementation, determining the adhesion coefficient based on the particle size range corresponding to the micro-topography point and the temperature value includes: The adhesion coefficient is obtained by querying a second data table using the particle size range and temperature value corresponding to the micro-topography point; wherein the second data table includes multiple particle size ranges, each particle size range corresponds to multiple temperature intervals, and multiple temperature intervals of the same particle size range correspond to different adhesion coefficients.

[0009] In one optional implementation, the particle size range in the first data table and the second data table is obtained by dividing the multiple sample data into tiers based on particle size, and the sample data contained in the same tier belong to the same particle size range.

[0010] In an optional implementation, after determining the grain size range corresponding to the micro-topography point based on the temperature value and the image spatial uniformity coefficient, the method further includes: The median of the particle size range corresponding to the micro-topography point is used as the average particle size, and substituted into the spherical volume calculation formula to obtain the average particle volume. Accordingly, determining the icing thickness increment corresponding to the micro-topographic point based on the cloud particle concentration, the adhesion coefficient, and the wind speed value corresponding to the micro-topographic point includes: Based on the cloud and fog particle concentration, adhesion coefficient, wind speed value, and average particle volume corresponding to the micro-topography point, the ice thickness increment corresponding to the micro-topography point is determined.

[0011] In one optional implementation, determining the image spatial uniformity coefficient corresponding to the cloud and fog image includes: Obtain the pixel value corresponding to each pixel in the cloud and fog image, and determine the pixel perturbation value between the pixel value of each pixel and the pixel time average value of the cloud and fog image; The pixel perturbation values ​​of each pixel are normalized to obtain the image spatial uniformity coefficient corresponding to the cloud image.

[0012] Secondly, the present invention provides a device for predicting the icing thickness of transmission lines, the device comprising: The acquisition module is used to acquire cloud and fog images, temperature values ​​and wind speed values ​​of the micro-topography points of the target transmission line at the first time point, and to determine the image spatial uniformity coefficient corresponding to the cloud and fog image. The first determining module is used to determine the adhesion coefficient based on the temperature value and the image spatial uniformity coefficient; wherein the adhesion coefficient is used to characterize the adhesion efficiency of supercooled cloud particles after impacting the target transmission line; The second determining module is used to determine the cloud and fog particle concentration corresponding to the micro-topography point based on the image spatial uniformity coefficient and the ice thickness model; wherein, the ice thickness model is obtained by inversion using multiple sample data within the target time period, and the sample data includes cloud and fog particle concentration samples and image spatial uniformity coefficients corresponding to cloud and fog image samples. The third determining module is used to determine the ice thickness increment corresponding to the micro-topography point based on the cloud and fog particle concentration, the adhesion coefficient and the wind speed value corresponding to the micro-topography point; wherein, the ice thickness increment is used to predict the ice thickness of the micro-topography point at a second time point.

[0013] Thirdly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a terminal device, cause the terminal device to perform the above-described method.

[0014] Fourthly, the present invention provides an ice thickness prediction device for transmission lines, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method.

[0015] Fifthly, the present invention provides a computer program product comprising a computer program / instruction that, when executed by a processor, implements the above-described method.

[0016] The technical solution provided by the embodiments of the present invention has at least the following advantages compared with the prior art: This invention provides a method, apparatus, device, and storage medium for predicting icing thickness of transmission lines. Specifically, firstly, cloud and fog images, temperature values, and wind speed values ​​of micro-topographic points of the target transmission line at a first time point are acquired, and the image spatial uniformity coefficient corresponding to the cloud and fog images is determined. Then, based on the temperature values ​​and the image spatial uniformity coefficient, the adhesion coefficient is determined, and based on the image spatial uniformity coefficient and the icing thickness model, the cloud and fog particle concentration corresponding to the micro-topographic points is determined. The icing thickness model is obtained by inverting multiple sample data within the target time period, including cloud and fog particle concentration samples and image spatial uniformity coefficients corresponding to cloud and fog image samples. Next, based on the cloud and fog particle concentration, adhesion coefficient, and wind speed values ​​corresponding to the micro-topographic points, the icing thickness increment corresponding to the micro-topographic points is determined, so as to predict the icing thickness of the micro-topographic points at a second time point using the icing thickness increment.

[0017] As can be seen, the embodiments of the present invention input the spatial uniformity coefficient and temperature data of the cloud and fog image at the current moment into a pre-constructed prediction model. After processing by the prediction model, the cloud and fog particle concentration and adhesion coefficient of the micro-topographic point are output, realizing the analysis of icing calculation variables such as cloud and fog particle concentration. Then, based on the calculated cloud and fog particle concentration, adhesion coefficient and the wind speed value collected on site, the icing thickness increment of the micro-topographic point is determined, so as to use the icing thickness increment to predict the icing thickness of the micro-topographic point at the next moment. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a method for predicting the icing thickness of a power transmission line according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of an ice thickness prediction device for power transmission lines provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a transmission line icing thickness prediction device provided in an embodiment of the present invention. Detailed Implementation

[0021] To better understand the above-mentioned objectives, features, and advantages of the present invention, the solutions of the present invention will be further described below. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0022] Many specific details are set forth in the following description in order to provide a full understanding of the invention, but the invention may also be practiced in other ways different from those described herein; obviously, the embodiments in the specification are only some embodiments of the invention, and not all embodiments.

[0023] In recent years, extreme weather events caused by global warming have become more frequent, with dramatic fluctuations in temperature during winter. Furthermore, power transmission lines are prone to icing disasters in winter, especially in locations with poor topography such as valleys, mountain passes, and narrow wind tunnels, where icing is exacerbated. Therefore, predicting the thickness of icing on power transmission lines is crucial. Based on these predictions, relevant personnel can monitor the icing situation in real time, which is of great significance for preventing power grid disasters and ensuring power supply.

[0024] To address this, this invention provides a method for predicting icing thickness of transmission lines. First, cloud and fog images, temperature values, and wind speed values ​​of micro-topographic points along the target transmission line at a first time point are acquired, and the image spatial uniformity coefficient corresponding to the cloud and fog images is determined. Then, an adhesion coefficient is determined based on the temperature value and the image spatial uniformity coefficient, and the cloud and fog particle concentration corresponding to the micro-topographic point is determined based on the image spatial uniformity coefficient and the icing thickness model. The icing thickness model is obtained by inverting multiple sample data within the target time period, including cloud and fog particle concentration samples and the image spatial uniformity coefficient corresponding to the cloud and fog image samples. Next, based on the cloud and fog particle concentration, adhesion coefficient, and wind speed values ​​corresponding to the micro-topographic point, the icing thickness increment corresponding to the micro-topographic point is determined, and the icing thickness increment is used to predict the icing thickness of the micro-topographic point at a second time point.

[0025] As can be seen, in this embodiment of the invention, after determining the image spatial uniformity coefficient corresponding to the cloud and fog image at the first time point, the image spatial uniformity coefficient is input into the icing thickness model to obtain the cloud and fog particle concentration, and the adhesion coefficient is determined based on the temperature value and the image spatial uniformity coefficient. Then, the icing thickness increment corresponding to the micro-topographic point is calculated using the cloud and fog particle concentration, adhesion coefficient and wind speed value, so as to use the icing thickness increment to predict the icing thickness of the micro-topographic point at the second time point.

[0026] Based on the above method embodiments, the present invention provides a method for predicting the icing thickness of transmission lines, such as... Figure 1 The diagram shown is a flowchart illustrating a method for predicting the icing thickness of a power transmission line according to an embodiment of the present invention.

[0027] S101: Obtain cloud and fog images, temperature values, and wind speed values ​​of the micro-topographic points of the target transmission line at the first time point, and determine the image spatial uniformity coefficient corresponding to the cloud and fog images.

[0028] The icing thickness prediction method provided in this embodiment of the invention can be applied to a client, such as a client deployed on a smartphone, a client deployed on a tablet, etc.

[0029] In this embodiment of the invention, the target transmission line can be any transmission line. Micro-topography refers to a localized, narrow area within a large terrain that can cause changes in climate parameters. For the target transmission line, a micro-topographic point refers to a specific small terrain area near the ground where local geographical features cause drastic changes in meteorological factors, significantly enhancing certain meteorological factors (such as wind speed and humidity), thereby significantly increasing icing on transmission lines passing through this small terrain area.

[0030] Cloud and fog images refer to visual images of cloud and fog clusters taken at micro-topographic locations using cameras or other equipment. Temperature values ​​refer to real-time temperature data measured at micro-topographic locations. Wind speed values ​​refer to real-time wind speed measured at micro-topographic locations.

[0031] In this embodiment of the invention, the first time point is the current time. By acquiring cloud and fog images, temperature values, and wind speed values ​​of the micro-topographic points of the target transmission line at the current time, these parameters can be used to calculate the ice thickness increment corresponding to the micro-topographic points.

[0032] After acquiring the cloud and fog image of the micro-topography point at the current moment, the image spatial uniformity coefficient corresponding to the cloud and fog image is determined. The image spatial uniformity coefficient is used to characterize the spatial uniformity of pixels in the cloud and fog image.

[0033] In one optional implementation, the image spatial uniformity coefficient of the cloud and fog image can be determined by calculating the spatial uniformity of the RGB three channels of the cloud and fog image. Specifically, firstly, the pixel values ​​corresponding to each pixel in the cloud and fog image are obtained, and the pixel perturbation value between the pixel value of each pixel and the pixel time average value of the cloud and fog image is determined. Then, the pixel perturbation value of each pixel is normalized to obtain the image spatial uniformity coefficient corresponding to the cloud and fog image.

[0034] In practical applications, the spatial uniformity coefficient corresponding to the cloud and fog image can be determined using the following formula (1).

[0035]

[0036]

[0037]

[0038] (1); Where R, G, and B represent the original pixel grayscale values ​​(pixel values) of the pixel in the R, G, and B channels, and i represents the i-th pixel in the cloud image. , , Indicates pixel perturbation value, , , Represents the average pixel time value. , , This represents the spatial average value of the pixel perturbation.

[0039] As can be seen, the embodiments of the present invention extract the pixel values ​​corresponding to each pixel in the cloud and fog image, and obtain the image spatial uniformity coefficient based on the pixel interference value between the pixel value of each pixel and the pixel time average value, so as to use the coefficient to characterize the spatial uniformity of the pixels in the cloud and fog image.

[0040] S102: Determine the adhesion coefficient based on the temperature value and the image spatial uniformity coefficient.

[0041] The adhesion coefficient is used to characterize the adhesion efficiency of supercooled cloud particles after they collide with the target transmission line.

[0042] In this embodiment of the invention, after determining the image spatial uniformity coefficient corresponding to the cloud and fog image, the adhesion coefficient can be determined based on the temperature value and the image spatial uniformity coefficient. The adhesion coefficient is used to characterize the adhesion efficiency of supercooled cloud and fog particles after impacting the target transmission line. Under different temperature and particle size conditions, the adhesion efficiency of supercooled cloud and fog particles after impacting the transmission line surface is different. The adhesion efficiency is the proportion of supercooled cloud and fog particles that can adhere, remain and participate in freezing into ice after impacting the conductor surface.

[0043] In one optional implementation, the adhesion coefficient is determined based on the temperature value and the image spatial uniformity coefficient. Specifically, this may include: first, determining the particle size range corresponding to the micro-topography point based on the temperature value and the image spatial uniformity coefficient, and then determining the adhesion coefficient based on the particle size range corresponding to the micro-topography point and the temperature value.

[0044] As can be seen, the embodiments of the present invention can use the real-time temperature value measured at the current moment and the image spatial uniformity coefficient calculated based on the pixel value of the cloud and fog image to determine the adhesion coefficient of the target transmission line at the micro-topographic point, so that the ice thickness increment at the micro-topographic point can be calculated based on parameters such as the adhesion coefficient and cloud and fog particle concentration.

[0045] S103: Based on the image spatial uniformity coefficient and the ice thickness model, determine the cloud and fog particle concentration corresponding to the micro-topography point.

[0046] The ice thickness model is obtained by inversion using multiple sample data within the target time period. The sample data includes cloud and fog particle concentration samples and image spatial uniformity coefficients corresponding to cloud and fog image samples.

[0047] In this embodiment of the invention, after obtaining the image spatial uniformity coefficient corresponding to the cloud and fog image, the cloud and fog particle concentration corresponding to the micro-topography point can be determined based on the image spatial uniformity coefficient and the ice thickness model.

[0048] Among them, the ice thickness model can be an exponential relationship constructed using multiple sample data within the target time period, such as the following formula (2).

[0049] (2); Where n represents the cloud particle concentration, r represents the image spatial uniformity coefficient, and a, b, and c are coefficients obtained by inversion using cloud particle concentration samples and image spatial uniformity coefficients from multiple sample data.

[0050] In some embodiments, before constructing the above-mentioned ice thickness model, the obtained multiple image spatial uniformity coefficients can be sorted and the cloud particle concentration corresponding to each coefficient can be measured to obtain multiple sets of data (i.e., image spatial uniformity coefficient and cloud particle concentration samples), and the multiple sets of data can be used for exponential regression fitting to obtain the above formula (2).

[0051] As can be seen, the embodiments of the present invention can utilize multiple sample data within a target time period to fit a fixed exponential formula between the image spatial uniformity coefficient and the cloud and fog particle concentration samples. Subsequently, when determining the cloud and fog particle concentration, it is only necessary to acquire the cloud and fog images of the micro-topographic points and calculate the corresponding image spatial uniformity coefficient to directly calculate the cloud and fog particle concentration without the need for real-time measurement using a particle size spectrometer. This can significantly reduce the costs of on-site deployment, periodic calibration, and long-term operation and maintenance of the particle size spectrometer, reduce the interference errors caused by complex micro-topography and variable meteorological environments on the particle measurement results, and achieve rapid, non-contact, real-time inversion of cloud and fog particle concentration. It can be adapted to the routine online monitoring of micro-topography along the entire transmission line, and significantly improve the continuity of cloud and fog icing monitoring and the timeliness of early warning.

[0052] S104: Based on the cloud and fog particle concentration, the adhesion coefficient, and the wind speed value corresponding to the micro-topography point, determine the ice thickness increment corresponding to the micro-topography point.

[0053] The ice thickness increment is used to predict the ice thickness at the micro-topography point at the second time point.

[0054] In this embodiment of the invention, after calculating the cloud and fog particle concentration and adhesion coefficient, the ice thickness increment corresponding to the micro-topographic point is determined based on the cloud and fog particle concentration, adhesion coefficient and wind speed value, so as to use the ice thickness increment to predict the ice thickness of the micro-topographic point at the second time point.

[0055] In the method for predicting icing thickness of transmission lines provided in this embodiment of the invention, the cloud and fog images, temperature values, and wind speed values ​​of the micro-topographic points of the target transmission line at a first time point are first acquired, and the image spatial uniformity coefficient corresponding to the cloud and fog images is determined. Then, the adhesion coefficient is determined based on the temperature value and the image spatial uniformity coefficient, and the cloud and fog particle concentration corresponding to the micro-topographic points is determined based on the image spatial uniformity coefficient and the icing thickness model. The icing thickness model is obtained by inverting multiple sample data within the target time period, and the sample data includes cloud and fog particle concentration samples and the image spatial uniformity coefficient corresponding to the cloud and fog image samples. Next, based on the cloud and fog particle concentration, adhesion coefficient, and wind speed value corresponding to the micro-topographic points, the icing thickness increment corresponding to the micro-topographic points is determined, so as to predict the icing thickness of the micro-topographic points at a second time point using the icing thickness increment.

[0056] As can be seen, in this embodiment of the invention, after determining the image spatial uniformity coefficient corresponding to the cloud and fog image at the first time point, the image spatial uniformity coefficient is input into the icing thickness model to obtain the cloud and fog particle concentration, and the adhesion coefficient is determined based on the temperature value and the image spatial uniformity coefficient. Then, the icing thickness increment corresponding to the micro-topographic point is calculated using the cloud and fog particle concentration, adhesion coefficient and wind speed value, and the icing thickness increment of the micro-topographic point is predicted based on the icing thickness increment.

[0057] In related technologies, icing prediction mainly relies on kilometer-level grid weather forecasts. The icing thickness is inferred by inverting large-area meteorological elements (such as ambient temperature, humidity, wind speed, precipitation, etc.). Although this prediction method can achieve kilometer-level forecasts, the prediction error for the icing thickness at local micro-topographic points of transmission lines is relatively large. Furthermore, due to the extremely harsh observation environment at these micro-topographic points, it is difficult to obtain accurate observation data in a timely manner for predicting the icing thickness.

[0058] In this embodiment of the invention, the particle size range corresponding to the micro-topography point is first determined based on the temperature value and the image spatial uniformity coefficient, and then the adhesion coefficient is determined based on the particle size range corresponding to the micro-topography point and the temperature value.

[0059] In one alternative implementation, after obtaining the temperature value and the image spatial uniformity coefficient, the particle size range corresponding to the micro-topography point can be determined by querying the system's built-in data table.

[0060] Specifically, the temperature value and the image spatial uniformity coefficient are used to query the first data table to determine the particle size range corresponding to the micro-topography point. The first data table includes multiple particle size ranges, each particle size range corresponds to multiple temperature intervals, and multiple temperature intervals of the same particle size range correspond to different coefficient ranges. As shown in Table (1) below, this is an example of the contents of the first data table.

[0061]

[0062] Among them, [ra 1min ,ra 1max ]、[ra 2min ,ra 2max The contents of the table items are the coefficient ranges corresponding to the temperature range and particle size range. The coefficient range can be determined by the image spatial uniformity coefficient based on the multiple sample data corresponding to its respective particle size range and temperature range.

[0063] In practical applications, multiple sample data can be determined based on temperature range and particle size range. For example, based on a temperature range of [-2, 0] and a particle size range of (0, 50], 10 sample data can be determined. Using the above formula (1), the image spatial uniformity coefficient corresponding to each sample data can be calculated. Then, the maximum and minimum values ​​are determined from the image uniformity coefficients of each sample data to obtain the coefficient range when the temperature range is [-2, 0] and the particle size range is (0, 50]. For example, [ra] can be used. 1min ,ra 1max [Use ] to represent.

[0064] For example, assuming a temperature of -3 degrees Celsius and an image spatial uniformity coefficient of 0.78, it belongs to [rc]. 2min ,rc 2max If the particle size is within the range of 200, then by querying the first data table mentioned above, the particle size range can be obtained as (200, 1000).

[0065] In another alternative implementation, the adhesion coefficient can be obtained by querying a built-in data table in the system.

[0066] Specifically, the adhesion coefficient is obtained by querying a second data table using the particle size range and temperature value corresponding to the micro-topography point. The second data table includes multiple particle size ranges, each corresponding to multiple temperature ranges, and different adhesion coefficients for different temperature ranges within the same particle size range.

[0067] For example, Table (2) below is a table of adhesion coefficients at different levels and temperatures based on different particle sizes and established through field observation or experimentation, i.e., the second data table.

[0068]

[0069] Assuming the particle size range determined by querying the first data table is (50, 200], and the actual measured temperature is -8 degrees Celsius, then by querying the second data table mentioned above, the corresponding adhesion coefficient is b3.

[0070] In one optional implementation, the particle size range in the first data table and the second data table is obtained by dividing multiple sample data into tiers based on particle size, and the sample data contained in the same tier belong to the same particle size range.

[0071] In another optional implementation, after selecting micro-topographic points, the concentration and size of cloud particles are measured at preset time intervals. At the same time, the temperature value at each measurement is recorded and the cloud cluster is photographed to obtain cloud images. The cloud images, temperature values, cloud particle concentration and particle size are used as a sample data. The measurements are continuously performed for a period of time, and an initial sample library is constructed using the multiple sample data obtained from the measurements.

[0072] Then, for each sample data temperature value in the initial sample library, a sample set with a temperature value below 0 degrees Celsius was selected, and the sample set was divided into grades, and sample data of multiple grades were obtained according to different particle size ranges. As shown in Table (3) below, the particle size ranges corresponding to different grades are shown.

[0073]

[0074] In practical applications, a particle size analyzer can be deployed near the micro-topography tower to measure the concentration and size of cloud particles every 10 minutes. At the same time, a visualization device at the tower location can be used to capture images of the cloud, and a thermometer can be used to collect the current temperature value.

[0075] In practical applications, after determining the particle size range corresponding to the micro-topographic point based on the temperature value and the image spatial uniformity coefficient, the median of the particle size range corresponding to the micro-topographic point can be used as the average particle size and substituted into the spherical volume calculation formula to obtain the average particle volume. Then, based on the cloud and fog particle concentration, adhesion coefficient, wind speed value and average particle volume corresponding to the micro-topographic point, the ice thickness increment corresponding to that point can be determined. The ice thickness calculation formula is shown in formula (3) below.

[0076] (3); Where I represents the icing thickness increment, ρ represents the supercooled water droplet density, V represents the average particle volume, N represents the cloud and fog particle concentration, and w represents the wind speed.

[0077] As can be seen, the embodiments of the present invention can calculate the ice thickness increment corresponding to the micro-topographic point using cloud and fog particle concentration, adhesion coefficient and wind speed value, so as to use the ice thickness increment to predict the ice thickness of the micro-topographic point at the second time point, thereby further improving the accuracy and precision of ice thickness prediction.

[0078] Based on the above method embodiments, the present invention also provides a device for predicting the icing thickness of transmission lines, with reference to... Figure 2 This is a schematic diagram of a device for predicting the icing thickness of a power transmission line according to an embodiment of the present invention. The device includes: The acquisition module 201 is used to acquire the cloud and fog image, temperature value and wind speed value of the micro-topography point of the target transmission line at the first time point, and determine the image spatial uniformity coefficient corresponding to the cloud and fog image; The first determining module 202 is used to determine the adhesion coefficient based on the temperature value and the image spatial uniformity coefficient; wherein the adhesion coefficient is used to characterize the adhesion efficiency of supercooled cloud particles after impacting the target transmission line; The second determining module 203 is used to determine the cloud and fog particle concentration corresponding to the micro-topography point based on the image spatial uniformity coefficient and the ice thickness model; wherein, the ice thickness model is obtained by inversion using multiple sample data within the target time period, and the sample data includes cloud and fog particle concentration samples and image spatial uniformity coefficients corresponding to cloud and fog image samples. The third determining module 204 is used to determine the ice thickness increment corresponding to the micro-topography point based on the cloud and fog particle concentration, the adhesion coefficient and the wind speed value corresponding to the micro-topography point; wherein, the ice thickness increment is used to predict the ice thickness of the micro-topography point at a second time point.

[0079] In one optional implementation, the first determining module includes: The first determining submodule is used to determine the particle size range corresponding to the micro-topography point based on the temperature value and the image spatial uniformity coefficient; The second determining submodule is used to determine the adhesion coefficient based on the particle size range and temperature value corresponding to the micro-topography point.

[0080] In one optional implementation, the first determining submodule is specifically used for: The temperature value and the image spatial uniformity coefficient are used to query a first data table to determine the particle size range corresponding to the micro-topography point; wherein, the first data table includes multiple particle size ranges, each particle size range corresponds to multiple temperature intervals, and multiple temperature intervals of the same particle size range correspond to different coefficient ranges, and the coefficient range is determined based on the image spatial uniformity coefficient of multiple sample data corresponding to its respective particle size range and temperature interval.

[0081] In one optional implementation, the second determining submodule is specifically used for: The adhesion coefficient is obtained by querying a second data table using the particle size range and temperature value corresponding to the micro-topography point; wherein the second data table includes multiple particle size ranges, each particle size range corresponds to multiple temperature intervals, and multiple temperature intervals of the same particle size range correspond to different adhesion coefficients.

[0082] In one optional implementation, the particle size range in the first data table and the second data table is obtained by dividing the multiple sample data into tiers based on particle size, and the sample data contained in the same tier belong to the same particle size range.

[0083] In one optional embodiment, the apparatus further includes: The substitution module is used to take the median value of the particle size range corresponding to the micro-topography point as the average particle size, and substitute it into the spherical volume calculation formula to obtain the average particle volume. Accordingly, the third determining module is specifically used for: Based on the cloud and fog particle concentration, adhesion coefficient, wind speed value, and average particle volume corresponding to the micro-topography point, the ice thickness increment corresponding to the micro-topography point is determined.

[0084] In one optional implementation, the acquisition module includes: The acquisition submodule is used to acquire the pixel value corresponding to each pixel in the cloud and fog image, and determine the pixel perturbation value between the pixel value of each pixel and the pixel time average value of the cloud and fog image; The normalization processing submodule is used to normalize the pixel perturbation values ​​of each pixel to obtain the image spatial uniformity coefficient corresponding to the cloud image.

[0085] In the transmission line icing thickness prediction device provided in this embodiment of the invention, the cloud and fog images, temperature values, and wind speed values ​​of the micro-topographic points of the target transmission line at a first time point are first acquired, and the image spatial uniformity coefficient corresponding to the cloud and fog images is determined. Then, the adhesion coefficient is determined based on the temperature value and the image spatial uniformity coefficient, and the cloud and fog particle concentration corresponding to the micro-topographic points is determined based on the image spatial uniformity coefficient and the icing thickness model. The icing thickness model is obtained by inverting multiple sample data within the target time period. The sample data includes cloud and fog particle concentration samples and the image spatial uniformity coefficient corresponding to the cloud and fog image samples. Next, based on the cloud and fog particle concentration, adhesion coefficient, and wind speed value corresponding to the micro-topographic points, the icing thickness increment corresponding to the micro-topographic points is determined, so as to predict the icing thickness of the micro-topographic points at a second time point using the icing thickness increment.

[0086] As can be seen, in this embodiment of the invention, after determining the image spatial uniformity coefficient corresponding to the cloud and fog image at the first time point, the image spatial uniformity coefficient is input into the icing thickness model to obtain the cloud and fog particle concentration, and the adhesion coefficient is determined based on the temperature value and the image spatial uniformity coefficient. Then, the icing thickness increment corresponding to the micro-topographic point is calculated using the cloud and fog particle concentration, adhesion coefficient and wind speed value, so as to use the icing thickness increment to predict the icing thickness of the micro-topographic point at the second time point.

[0087] In addition to the methods and apparatus described above, embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a terminal device, cause the terminal device to implement the method for predicting the icing thickness of transmission lines as described in the embodiments of the present invention.

[0088] This invention also provides a computer program product, which includes a computer program / instruction. When the computer program / instruction is executed by a processor, it implements the method for predicting the icing thickness of transmission lines according to this invention.

[0089] In addition, embodiments of the present invention also provide a device for predicting the icing thickness of transmission lines, see [link to relevant documentation]. Figure 3 As shown, it may include: The device includes a processor 301, a memory 302, an input device 303, and an output device 304. The number of processors 301 in the transmission line icing thickness prediction device can be one or more. Figure 3 Taking a processor as an example. In some embodiments of the present invention, the processor 301, memory 302, input device 303, and output device 304 can be connected via a bus or other means, wherein, Figure 3 Taking the example of a connection between China and Israel via a bus.

[0090] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications of the transmission line icing thickness prediction device and the prediction of icing thickness of the transmission line by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. The input device 303 can be used to receive input digital or character information, and generate signal inputs related to user settings and function control of the transmission line icing thickness prediction device.

[0091] Specifically in this embodiment, the processor 301 will load the executable files corresponding to the processes of one or more applications into the memory 302 according to the following instructions, and the processor 301 will run the applications stored in the memory 302 to realize the various functions of the above-mentioned transmission line icing thickness prediction device.

[0092] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0093] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the icing thickness of transmission lines, characterized in that, The method includes: Acquire cloud and fog images, temperature values, and wind speed values ​​of the micro-topographic points of the target transmission line at the first time point, and determine the image spatial uniformity coefficient corresponding to the cloud and fog images; The adhesion coefficient is determined based on the temperature value and the image spatial uniformity coefficient; wherein, the adhesion coefficient is used to characterize the adhesion efficiency of supercooled cloud particles after impacting the target transmission line; Based on the image spatial uniformity coefficient and the ice thickness model, the cloud and fog particle concentration corresponding to the micro-topography point is determined; wherein, the ice thickness model is obtained by inversion using multiple sample data within the target time period, and the sample data includes cloud and fog particle concentration samples and image spatial uniformity coefficients corresponding to cloud and fog image samples. Based on the cloud and fog particle concentration, adhesion coefficient, and wind speed value corresponding to the micro-topography point, the ice thickness increment corresponding to the micro-topography point is determined; wherein, the ice thickness increment is used to predict the ice thickness of the micro-topography point at a second time point.

2. The method according to claim 1, characterized in that, The determination of the adhesion coefficient based on the temperature value and the image spatial uniformity coefficient includes: Based on the temperature value and the image spatial uniformity coefficient, the particle size range corresponding to the micro-topography point is determined; The adhesion coefficient is determined based on the particle size range and temperature value corresponding to the micro-topography point.

3. The method according to claim 2, characterized in that, Determining the grain size range corresponding to the micro-topography point based on the temperature value and the image spatial uniformity coefficient includes: The temperature value and the image spatial uniformity coefficient are used to query a first data table to determine the particle size range corresponding to the micro-topography point; wherein, the first data table includes multiple particle size ranges, each particle size range corresponds to multiple temperature intervals, and multiple temperature intervals of the same particle size range correspond to different coefficient ranges, and the coefficient range is determined based on the image spatial uniformity coefficient of multiple sample data corresponding to its respective particle size range and temperature interval.

4. The method according to claim 2, characterized in that, The determination of the adhesion coefficient based on the particle size range and temperature value corresponding to the micro-topography point includes: The adhesion coefficient is obtained by querying a second data table using the particle size range and temperature value corresponding to the micro-topography point; wherein the second data table includes multiple particle size ranges, each particle size range corresponds to multiple temperature intervals, and multiple temperature intervals of the same particle size range correspond to different adhesion coefficients.

5. The method according to claim 3 or 4, characterized in that, The particle size range in the first data table and the second data table is obtained by dividing the multiple sample data into different tiers based on particle size. Sample data within the same tier belong to the same particle size range.

6. The method according to claim 2, characterized in that, After determining the grain size range corresponding to the micro-topography point based on the temperature value and the image spatial uniformity coefficient, the method further includes: The median of the particle size range corresponding to the micro-topography point is used as the average particle size, and substituted into the spherical volume calculation formula to obtain the average particle volume. Accordingly, determining the icing thickness increment corresponding to the micro-topographic point based on the cloud particle concentration, the adhesion coefficient, and the wind speed value corresponding to the micro-topographic point includes: Based on the cloud and fog particle concentration, adhesion coefficient, wind speed value, and average particle volume corresponding to the micro-topography point, the ice thickness increment corresponding to the micro-topography point is determined.

7. The method according to claim 1, characterized in that, Determining the image spatial uniformity coefficient corresponding to the cloud and fog image includes: Obtain the pixel value corresponding to each pixel in the cloud and fog image, and determine the pixel perturbation value between the pixel value of each pixel and the pixel time average value of the cloud and fog image; The pixel perturbation values ​​of each pixel are normalized to obtain the image spatial uniformity coefficient corresponding to the cloud image.

8. A device for predicting the icing thickness of a power transmission line, characterized in that, The device includes: The acquisition module is used to acquire cloud and fog images, temperature values ​​and wind speed values ​​of the micro-topography points of the target transmission line at the first time point, and to determine the image spatial uniformity coefficient corresponding to the cloud and fog image. The first determining module is used to determine the adhesion coefficient based on the temperature value and the image spatial uniformity coefficient; wherein the adhesion coefficient is used to characterize the adhesion efficiency of supercooled cloud particles after impacting the target transmission line; The second determining module is used to determine the cloud and fog particle concentration corresponding to the micro-topography point based on the image spatial uniformity coefficient and the ice thickness model; wherein, the ice thickness model is obtained by inversion using multiple sample data within the target time period, and the sample data includes cloud and fog particle concentration samples and image spatial uniformity coefficients corresponding to cloud and fog image samples. The third determining module is used to determine the ice thickness increment corresponding to the micro-topography point based on the cloud and fog particle concentration, the adhesion coefficient and the wind speed value corresponding to the micro-topography point; wherein, the ice thickness increment is used to predict the ice thickness of the micro-topography point at a second time point.

9. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method as described in any one of claims 1-7.

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