Meteorological element correction method for electrified railway contact network
By constructing a meteorological correction model and using topographic data and historical observation data to correct the meteorological simulation values of the electrified railway catenary, the problem of insufficient forecast accuracy of traditional models in complex terrain areas has been solved, the forecast accuracy of wind speed and temperature has been improved, and the disaster early warning capability has been enhanced.
Patent Information
- Application Number
- CN202511490288.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-17
- Publication Date
- 2026-02-10
AI Technical Summary
Traditional meteorological numerical models exhibit systematic deviations from actual observations in the complex micro-topographical areas of electrified railway catenary systems, making it difficult to meet the needs for refined early warning of railway meteorological disasters.
By acquiring topographic data of the electrified railway catenary area, a meteorological correction model is constructed. The fitting coefficients are determined using topographic parameters and historical meteorological observation data to correct the meteorological simulation values, thereby improving the spatial resolution and temporal accuracy of meteorological elements such as wind speed and temperature.
It significantly improves the forecast accuracy of meteorological elements under complex terrain conditions, enhances the accuracy of icing dance risk prediction and the foresight of disaster early warning, and is applicable to various operating environments such as high-speed rail, intercity rail and mountain rail.
Smart Images
Figure CN121504130A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present disclosure relates to the technical field of railway disaster prevention and mitigation, and particularly relates to a meteorological element correction method for an electrified railway catenary. BACKGROUND
[0002] The electrified railway widely adopts a catenary power supply system. The catenary wire is easily affected by meteorological factors, forms ice and causes galloping, arc jumping and even equipment damage. In complex micro-topographic areas such as high altitudes, valleys, wind outlets and passes, the distribution of meteorological elements presents strong non-uniformity. Due to the limited resolution and simplified terrain, the traditional numerical weather prediction model causes systematic deviation between the simulation results and the actual observation, and it is difficult to meet the fine early warning needs of railway meteorological disasters. SUMMARY
[0003] In order to solve the above technical problems, the present disclosure provides a meteorological element correction method for an electrified railway catenary.
[0004] In a first aspect, the present disclosure provides a meteorological element correction method for an electrified railway catenary, comprising: obtaining terrain data of an electrified railway catenary area, and determining terrain parameters and terrain categories of the electrified railway catenary area according to the terrain data; for each terrain category, determining fitting coefficients of a meteorological correction model according to the terrain parameters and historical meteorological observation data of the electrified railway catenary area; the meteorological correction model is used to determine a meteorological correction amount according to the fitting coefficients and the terrain parameters; in response to a risk prediction request for a current area, obtaining meteorological simulation values of the current area; correcting the meteorological simulation values according to a target meteorological correction model corresponding to the terrain category of the current area to obtain meteorological prediction data of the current area.
[0005] In a second aspect, the present disclosure provides a meteorological element correction device for an electrified railway catenary, comprising: an acquisition module, configured to obtain terrain data of an electrified railway catenary area, and determine terrain parameters and terrain categories of the electrified railway catenary area according to the terrain data; a training module, configured to, for each terrain category, determine fitting coefficients of a meteorological correction model according to the terrain parameters and historical meteorological observation data of the electrified railway catenary area; the meteorological correction model is used to determine a meteorological correction amount according to the fitting coefficients and the terrain parameters; a prediction module, configured to, in response to a risk prediction request for a current area, obtain meteorological simulation values of the current area; The correction module is used to correct the meteorological simulation values according to the target meteorological correction model corresponding to the terrain category of the current area, so as to obtain the meteorological forecast data of the current area.
[0006] Thirdly, embodiments of this disclosure provide an electronic device, including: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the meteorological element correction method for electrified railway catenary described in the first aspect above.
[0007] Fourthly, embodiments of this disclosure provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the meteorological element correction method for electrified railway catenary described in the first aspect.
[0008] Compared with the prior art, the technical solution provided in this disclosure has the following advantages: It provides a meteorological element correction scheme suitable for electrified railway catenary systems. By comprehensively considering micro-topographic features, terrain classification, and historical meteorological observation data, a meteorological correction model for complex terrain areas is constructed. This solves the problem of insufficient forecast accuracy of existing numerical meteorological models in fine-scale terrain areas. In railway catenary systems, for key sections with high risks of extreme weather such as icing and galloping, it can significantly improve the spatial resolution and temporal accuracy of key meteorological elements such as wind speed and temperature, improve the forecast accuracy of meteorological variables such as wind speed and temperature under complex terrain conditions, further enhance the accuracy and practicality of catenary icing and galloping risk prediction, and enhance the foresight and accuracy of disaster early warning. This method has the advantages of strong versatility, high computational efficiency, and easy integration. It can be widely applied to various operating environments such as high-speed rail, intercity railways, and mountain railways, providing technical support for the monitoring, forecasting, and emergency dispatching of railway meteorological disasters. Attached Figure Description
[0009] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0010] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying 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.
[0011] Figure 1 This is a flowchart illustrating a meteorological element correction method for an electrified railway overhead contact system provided in an embodiment of this disclosure. Figure 2This is a flowchart illustrating another method for correcting meteorological elements in an electrified railway overhead contact system, provided by an embodiment of this disclosure. Figure 3 This is a schematic diagram of the structure of a meteorological element correction device for an electrified railway catenary provided in an embodiment of this disclosure. Detailed Implementation
[0012] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.
[0013] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.
[0014] Figure 1 This is a flowchart illustrating a meteorological element correction method for an electrified railway catenary provided in an embodiment of this disclosure. The method provided in this embodiment can be executed by a meteorological element correction device for an electrified railway catenary. This device can be implemented using software and / or hardware and can be integrated into any electronic device with computing capabilities.
[0015] like Figure 1 As shown, the meteorological element correction method for electrified railway catenary provided in this embodiment may include: Step 101: Obtain topographic data of the electrified railway catenary area, and determine the topographic parameters and topographic category of the electrified railway catenary area based on the topographic data.
[0016] The method of this disclosure is applied to the catenary system of electrified railways to improve the forecast accuracy of meteorological elements such as wind speed and temperature under complex terrain conditions, and to enhance the accuracy and practicality of predicting the risk of catenary icing and galloping.
[0017] In this embodiment, micro-topographic parameters are calculated based on Digital Elevation Model (DEM) data, and then the regional micro-topography is clustered and divided into terrain categories. These terrain categories include wind gaps, ridges, and water edges, providing a classification basis for subsequent meteorological element correction. Optionally, the terrain parameters and terrain categories of the electrified railway catenary area are determined based on the terrain data. This includes: calculating the slope, aspect, topographic relief, and valley pass index of the electrified railway catenary area based on the DEM data; and then using a clustering algorithm to cluster the electrified railway catenary area based on the slope, aspect, topographic relief, and valley pass index, resulting in regions with multiple terrain categories.
[0018] As an example, based on a 30-meter resolution digital elevation model, the following micro-topographic parameters are calculated using a GIS (Geographic Information System) platform: slope S, aspect A, topographic relief R, and valley / pass index V. Here, slope S reflects the degree of terrain inclination, aspect A indicates the orientation of the slope, topographic relief R indicates the change in elevation within a certain range, and valley / pass index V quantitatively identifies wind tunnel-type terrain.
[0019] In this example, the formula for calculating the slope S is:
[0020] Where Z is the elevation value (from the DEM raster), and X and Y are the horizontal coordinates (usually projected coordinates, in meters).
[0021] and These represent the rates of elevation change in the east-west and north-south directions (partial derivatives of elevation with respect to X and Y), respectively.
[0022] The formula for calculating the terrain relief R is:
[0023] in, and These represent the maximum and minimum elevations within the surrounding area, respectively.
[0024] Among them, the clustering algorithm can be the K-means clustering algorithm to classify the micro-topography of the study area into typical micro-topography types such as wind gaps, ridges, and water edges.
[0025] Step 102: For each terrain category, determine the fitting coefficients of the meteorological correction model based on terrain parameters and historical meteorological observation data of the electrified railway catenary area.
[0026] Among them, the meteorological correction model is used to determine the meteorological correction amount based on the fitting coefficient and topographic parameters.
[0027] In this embodiment, meteorological elements include, but are not limited to, wind speed, temperature, humidity, wind direction, air pressure, and precipitation. A meteorological correction model for the meteorological elements is pre-constructed. The meteorological correction model adopts a nonlinear polynomial regression model, which is used to estimate the correction between the output value of the meteorological numerical model and the observed value. The meteorological numerical model is, for example, WRF (Weather Research and Forecasting Model).
[0028] The meteorological correction model will be explained below in the context of specific application scenarios.
[0029] As an example, a meteorological correction model for wind speed and topographic relief is constructed. The expression for the meteorological correction model is as follows: Where R is the topographic relief. , , For regression coefficients, This is the wind speed correction factor. In this example, the regression coefficients are obtained by fitting historical data. During the training phase, historical datasets (R_measured, wind speed_measured, wind speed_WRF simulation) are collected, and the polynomial coefficients are fitted. , , Independent regression coefficients are used for different terrain categories (wind gaps / ridges / valleys). For example, the terrain relief R has a higher weight in the wind speed correction term in wind gap areas. Terrain parameters such as slope S and aspect A have been implicitly included in the terrain categories through the clustering process, while terrain relief R is explicitly included in the regression model because of its direct impact on local circulation (such as wind acceleration ratio).
[0030] As another example, a meteorological correction model for temperature and altitude is constructed. The expression for the meteorological correction model is as follows: Where H is the altitude. , , For regression coefficients, This is the temperature correction factor. In this example, the regression coefficients are obtained by fitting historical data. During the training phase, historical datasets (H_measured, Temperature_measured, Temperature_WRF simulation) are collected, and polynomial coefficients are fitted. , , Independent regression coefficients are used for different terrain types (wind gaps / ridges / valleys). For example, in ridge areas, the coefficient of the elevation term H in the temperature correction model is affected by elevation. Larger than other areas.
[0031] This can improve the simulation accuracy of key variables such as wind speed and temperature in complex terrain areas, thereby supporting the forecasting and prevention work of railway catenary.
[0032] In one embodiment of this disclosure, the dataset is divided according to clustering results (e.g., K=6 classes), and regression coefficients for each terrain type are trained independently. After training, the model parameters are further optimized. Optionally, locations where ice floes have historically occurred are selected, and the root mean square error (RMSE) of the data before and after correction is compared using a meteorological correction model. The RMSE of each terrain type after correction is compared. If the error of a certain type is greater than a threshold (e.g., a large residual after humidity correction in a river valley), the weight of that type of sample is increased, and retraining is performed. The formula for calculating the RMSE is:
[0033] in, For the observed values, Here, is the forecast value, and N is the sample size. Therefore, the model parameters are iteratively optimized based on different micro-topographical categories and meteorological conditions to establish a regional correction coefficient library for rapid table lookup and correction.
[0034] Optionally, a terrain category constraint coefficient range can be introduced, for example, the coefficient |b| for windy areas should be greater than the threshold to avoid overfitting.
[0035] Step 103: In response to the risk forecast request for the current area, obtain the meteorological simulation value for the current area.
[0036] Step 104: Correct the meteorological simulation values according to the target meteorological correction model corresponding to the terrain category of the current area to obtain the meteorological forecast data of the current area.
[0037] In this embodiment, the meteorological simulation value of the current area can be obtained according to the actual scenario needs. Optionally, the WRF output variable can be obtained and spatial interpolation can be performed between the WRF output variable and historical observations. Then, a pre-trained meteorological correction model can be applied to correct the WRF simulation value. This achieves post-correction processing of meteorological elements such as wind speed and temperature output by the WRF model by combining historical meteorological station observation data and using statistical methods, thereby improving the accuracy of meteorological forecast data in micro-topographic areas.
[0038] As an example, in the prediction stage, wind speed simulation values are obtained, a target meteorological correction model corresponding to the terrain category of the current area is determined from multiple meteorological correction models, the wind speed correction amount is calculated using the fitting coefficient of the target meteorological correction model and the terrain relief of the current area, and then the wind speed simulation values are corrected according to the wind speed correction amount to obtain the corrected wind speed prediction data.
[0039] As another example, in the forecasting phase, temperature simulation values are obtained, a target meteorological correction model corresponding to the terrain category of the current area is determined from multiple meteorological correction models, the temperature correction amount is calculated using the fitting coefficient of the target meteorological correction model and the altitude of the current area, and then the temperature simulation values are corrected according to the temperature correction amount to obtain the corrected temperature forecast data.
[0040] In this embodiment, after determining the meteorological forecast data for the current area, it is further used for catenary risk prediction, such as for catenary icing and galloping risk prediction. The risk prediction can be implemented by connecting to an existing risk prediction system.
[0041] According to the technical solution of this disclosure, a meteorological element correction scheme suitable for electrified railway catenary systems is provided. By comprehensively considering micro-topographic features, terrain classification, and historical meteorological observation data, a meteorological correction model for complex terrain areas is constructed. This solves the problem of insufficient forecast accuracy of existing numerical meteorological models in fine-scale terrain areas. Furthermore, this method is not only applicable to the refinement of large-scale numerical simulation results but also to dynamic meteorological service systems in actual operations, possessing good scalability and practicality. In railway catenary systems, for key sections with high risks of extreme weather such as icing and galloping, it can significantly improve the spatial resolution and temporal accuracy of key meteorological elements such as wind speed and temperature, improve the forecast accuracy of meteorological variables such as wind speed and temperature under complex terrain conditions, enhance the accuracy and practicality of catenary icing and galloping risk prediction, and strengthen the foresight and accuracy of disaster early warning. This method has advantages such as strong versatility, high computational efficiency, and ease of integration, and can be widely applied to various operating environments such as high-speed railways, intercity railways, and mountain railways, providing technical support for the monitoring, forecasting, and emergency dispatching of railway meteorological disasters.
[0042] Based on the above embodiments, Figure 2 This is a flowchart illustrating another method for correcting meteorological elements in an electrified railway overhead contact system, as provided in this embodiment of the disclosure. Figure 2 As shown, in this method, the fitting coefficients of the meteorological correction model are determined based on topographic parameters and historical meteorological observation data of the electrified railway catenary area, including: Step 201: Obtain the structural parameters of the overhead contact system when it is located in any of the following areas: mountain pass, tunnel exit, or bridge area.
[0043] Step 202: Determine the fitting coefficients of the meteorological correction model based on topographic parameters, structural parameters, and historical meteorological observation data of the electrified railway catenary area.
[0044] In this embodiment, considering the special stress characteristics of the catenary system in mountain passes, tunnel exits, and bridge areas, catenary structural parameters are further introduced for coupling correction, and the nonlinear regression model is optimized by combining the special terrain features of the railway catenary.
[0045] As an example, a meteorological correction model is constructed for wind speed in relation to topographic relief, tower height, and conductor tension. The expression for the meteorological correction model is as follows: Where R is the topographic relief. , , For regression coefficients, The height of the iron tower, For conductor tension, , The coupling coefficient is... This is the wind speed correction factor. In this example, the regression coefficients and coupling coefficients are obtained by fitting historical data. During the training phase, the fitting polynomial coefficients of the historical dataset are collected, and independent fitting coefficients are used for different terrain categories (wind gaps / ridges / valleys).
[0046] As another example, a meteorological correction model is constructed for temperature in relation to altitude and tower height. The expression for the meteorological correction model is as follows: Where H is the altitude. , , For regression coefficients, The height of the iron tower, The coupling coefficient is... This is the temperature correction factor. In this example, the regression coefficients and coupling coefficients are obtained by fitting historical data. During the training phase, the fitting polynomial coefficients of the historical dataset are collected, and independent fitting coefficients are used for different terrain categories (wind gaps / ridges / valleys).
[0047] In this embodiment, a meteorological correction model for complex terrain regions is constructed by comprehensively considering micro-topographic features, terrain classification, structural parameters, and historical meteorological observation data. Through the comprehensive modeling of micro-topographic parameters and structural parameters, the simulation accuracy of WRF under complex terrain is effectively improved. It is highly adaptable and can be adapted to railway sections with different terrain types, enabling rapid response to regional correction coefficient lookup tables. It provides data support for refined early warning of railway meteorological disasters, reduces icing and galloping accidents, and improves the safe and stable operation of the line. The model structure supports the introduction of meteorological elements such as relative humidity, wind shear, and snow depth to expand application scenarios, and has good scalability.
[0048] The following examples illustrate the practical application process.
[0049] Taking a section of electrified railway catenary in a certain region as an example, the line traverses multiple complex micro-topographical areas, such as river valleys, mountain passes, hills, and narrow wind tunnels, which are high-risk areas for icing and galloping. The collected data mainly includes: topographic data, derived from a digital elevation model with a resolution of 30m; meteorological observation data, including regional automatic weather stations and micro-weather towers (observing wind speed, temperature, and humidity); historical icing event data, obtained through meteorological bureau disaster reports and railway operation and maintenance records; and numerical simulation output, using a WRF model with nested resolutions of 9km / 3km / 1km, and a physical scheme combination of Thompson microphysics, YSU boundary layer, and RRTMG radiation scheme. The micro-topographic feature extraction steps include: loading DEM data into a GIS platform and calculating micro-topographic parameters, including slope (expressed in degrees), aspect (divided into eight directions: N, NE, E, SE, S, SW, W, NW), topographic relief (calculated in units of 5×5 windows to determine the maximum local elevation difference), and wind tunnel index (generated through river network extraction and slope aggregation analysis). Then, K-means clustering (K=4-6) is used to classify the study area into typical micro-topographic categories based on the above parameters, such as ridges, wind gaps, valleys, and plateau plains.
[0050] Historical data and nonlinear regression model construction: Typical weather events from 2015 to 2024 were selected, and the corresponding values of observed wind speed / temperature at meteorological stations and their respective micro-topographic parameters were extracted. Using the scipy.optimize.curve_fit and statsmodels modules in Python, the following models were constructed: The wind speed correction model is... The temperature correction model is ,in, The height of the iron tower, Let R be the conductor tension, R be the terrain relief, and H be the altitude. All the above parameters were obtained through fitting to obtain coefficients, and the goodness of fit was... >0.82.
[0051] Post-correction processing of meteorological elements output from WRF: Bilinear interpolation is performed on the original WRF output to match the latitude and longitude of the observation points; a constructed regression model is applied to the simulated wind speed and temperature values to output the corrected meteorological element field; the results are output in GeoTIFF format and can be directly integrated into a visualization platform or railway meteorological disaster early warning system. Taking an icing and dancing event on a certain railway section as an example, the predicted and measured wind speed / temperature values before and after correction are compared. Before correction, the RMSE of wind speed was approximately 2.3 m / s, which decreased to 1.4 m / s after correction; the temperature error decreased from 2.2℃ to 1.3℃; the wind speed error decreased by approximately 39%, and the temperature error decreased by approximately 41%.
[0052] Figure 3This is a schematic diagram of the structure of a meteorological element correction device for an electrified railway catenary provided in an embodiment of this disclosure, as shown below. Figure 3 As shown, the meteorological element correction device for electrified railway catenary includes: acquisition module 31, training module 32, prediction module 33, and correction module 34.
[0053] Module 31 is used to acquire terrain data of the electrified railway catenary area and determine the terrain parameters and terrain category of the electrified railway catenary area based on the terrain data. Training module 32 is used to determine the fitting coefficients of the meteorological correction model for each terrain category based on terrain parameters and historical meteorological observation data of the electrified railway catenary area; the meteorological correction model is used to determine the meteorological correction amount based on the fitting coefficients and terrain parameters. Prediction module 33 is used to obtain the meteorological simulation value of the current area in response to a risk prediction request for the current area; The correction module 34 is used to correct the meteorological simulation values according to the target meteorological correction model corresponding to the terrain category of the current area, so as to obtain the meteorological forecast data of the current area.
[0054] In one embodiment of this disclosure, the acquisition module 31 is specifically used to: calculate the slope, aspect, topographic relief, and valley pass index of the electrified railway contact network area based on the digital elevation model data of the contact network area; and use a clustering algorithm to cluster the contact network area of the electrified railway to obtain areas with multiple topographic categories based on the slope, aspect, topographic relief, and valley pass index.
[0055] In one embodiment of this disclosure, the expression for the weather correction model is as follows:
[0056] Where R represents the topographic relief. , , For regression coefficients, This is the wind speed correction amount.
[0057] In one embodiment of this disclosure, the expression for the weather correction model is as follows:
[0058] Where H represents altitude. , , For regression coefficients, This is the temperature correction amount.
[0059] In one embodiment of this disclosure, the training module 32 is specifically used for: Obtain the structural parameters of the overhead contact system when it is located in any of the following situations: mountain pass, tunnel exit, or bridge area. Based on topographic parameters, structural parameters, and historical meteorological observation data of the electrified railway catenary area, the fitting coefficients of the meteorological correction model are determined.
[0060] In one embodiment of this disclosure, the expression for the weather correction model is as follows:
[0061] Where R represents the topographic relief. , , For regression coefficients, The height of the iron tower, For conductor tension, , The coupling coefficient is... This is the wind speed correction amount.
[0062] In one embodiment of this disclosure, the expression for the weather correction model is as follows:
[0063] Where H represents altitude. , , For regression coefficients, The height of the iron tower, The coupling coefficient is... This is the temperature correction amount.
[0064] The meteorological element correction device for electrified railway catenary provided in this disclosure can execute any meteorological element correction method for electrified railway catenary provided in this disclosure, and has the corresponding functional modules and beneficial effects for executing the method. Contents not described in detail in the device embodiments of this disclosure can be referred to the descriptions in any method embodiments of this disclosure.
[0065] This disclosure also provides an electronic device including one or more processors and a memory. The processor may be a central processing unit (CPU) or other processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the methods of the embodiments of this disclosure above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.
[0066] In one example, the electronic device may also include input and output devices, which are interconnected via a bus system and / or other forms of connection. Furthermore, the input device may include, for example, a keyboard, a mouse, etc. The output device can output various information to the outside, including determined distance information, direction information, etc. The output device may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc. In addition, depending on the specific application, the electronic device may include any other suitable components such as a bus, input / output interfaces, etc.
[0067] In addition to the methods and apparatus described above, embodiments of this disclosure may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform any of the methods provided in the embodiments of this disclosure.
[0068] Computer program products can be written in any combination of one or more programming languages to perform the operations of embodiments of this disclosure. The programming languages include object-oriented programming languages such as Java and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.
[0069] Furthermore, embodiments of this disclosure may also be computer-readable storage media storing computer program instructions that, when executed by a processor, cause the processor to perform any of the methods provided in the embodiments of this disclosure.
[0070] Computer-readable storage media may take the form of any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0071] 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.
[0072] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. 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 this disclosure. Therefore, this disclosure 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 correcting meteorological elements in the overhead contact system of electrified railways, characterized in that, The method includes: Obtain topographic data of the electrified railway catenary area, and determine the topographic parameters and topographic category of the electrified railway catenary area based on the topographic data; For each terrain category, the fitting coefficients of the meteorological correction model are determined based on the terrain parameters and historical meteorological observation data of the electrified railway catenary area; the meteorological correction model is used to determine the meteorological correction amount based on the fitting coefficients and the terrain parameters. In response to a risk forecast request for the current area, obtain the meteorological simulation value for the current area; The meteorological simulation values are corrected according to the target meteorological correction model corresponding to the terrain category of the current area to obtain the meteorological forecast data of the current area.
2. The method as described in claim 1, characterized in that, The step of determining the terrain parameters and terrain category of the electrified railway catenary area based on the terrain data includes: Based on the digital elevation model data of the electrified railway catenary area, the slope, aspect, topographic relief, and valley pass index of the electrified railway catenary area are calculated. Based on the slope, aspect, topographic relief, and valley pass index, a clustering algorithm is used to divide the electrified railway catenary area into multiple topographic categories.
3. The method as described in claim 1, characterized in that, The expression for the meteorological correction model is as follows: Where R represents the topographic relief. , , For regression coefficients, This is the wind speed correction amount.
4. The method as described in claim 1, characterized in that, The expression for the meteorological correction model is as follows: Where H represents altitude. , , For regression coefficients, This is the temperature correction amount.
5. The method as described in claim 1, characterized in that, The determination of the fitting coefficients of the meteorological correction model based on the terrain parameters and historical meteorological observation data of the electrified railway catenary area includes: When the contact network is located in any one of the following areas: mountain pass, tunnel exit, or bridge area, obtain the structural parameters of the contact network; Based on the terrain parameters, the structural parameters, and historical meteorological observation data of the electrified railway catenary area, the fitting coefficients of the meteorological correction model are determined.
6. The method as described in claim 5, characterized in that, The expression for the meteorological correction model is as follows: Where R represents the topographic relief. , , For regression coefficients, The height of the iron tower, For conductor tension, , The coupling coefficient is... This is the wind speed correction amount.
7. The method as described in claim 5, characterized in that, The expression for the meteorological correction model is as follows: Where H represents altitude. , , For regression coefficients, The height of the iron tower, The coupling coefficient is... This is the temperature correction amount.
8. A meteorological element correction device for electrified railway catenary, characterized in that, include: The acquisition module is used to acquire terrain data of the electrified railway catenary area and determine the terrain parameters and terrain category of the electrified railway catenary area based on the terrain data. The training module is used to determine the fitting coefficients of the meteorological correction model for each terrain category based on the terrain parameters and historical meteorological observation data of the electrified railway catenary area; the meteorological correction model is used to determine the meteorological correction amount based on the fitting coefficients and the terrain parameters. The prediction module is used to obtain the meteorological simulation value of the current area in response to a risk prediction request for the current area; The correction module is used to correct the meteorological simulation values according to the target meteorological correction model corresponding to the terrain category of the current area, so as to obtain the meteorological forecast data of the current area.
9. An electronic device, characterized in that, include: 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 meteorological element correction method for the catenary of electrified railways 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, which, when executed by a processor, implements the meteorological element correction method for the catenary of electrified railways as described in any one of claims 1-7.