Method, controller, and storage medium for predicting bridge segment road icing
By acquiring historical environmental data from bridge sections, performing grid division and data correction, and combining a three-dimensional variational assimilation objective function and a numerical prediction model, the problem of low accuracy in icing forecasts for bridge sections was solved, achieving high-precision icing prediction and early warning.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 湖南防灾科技有限公司
- Filing Date
- 2022-11-22
- Publication Date
- 2026-07-14
AI Technical Summary
Existing technologies fail to effectively account for data biases in special terrain areas such as bridges and water bodies, resulting in low accuracy in road icing forecasts for bridge sections.
By acquiring historical environmental data of bridge sections, dividing the grid according to a preset grid resolution, and combining a three-dimensional variational assimilation objective function and a numerical prediction model, the environmental prediction data of the bridge sections are determined. The numerical prediction model is established by selecting physical parameterization combination schemes through Taylor diagrams, and the environmental prediction data is corrected to determine the icing thickness and whether to issue an early warning.
It improves the accuracy of road icing prediction on bridge sections, enabling timely sending of early warning information and reducing the risk of traffic accidents.
Smart Images
Figure CN115809549B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of road safety prediction, and specifically to a method, controller, and storage medium for predicting road icing on bridge sections. Background Technology
[0002] Icy roads drastically reduce the friction between vehicles and the road surface, greatly increasing the risk of collisions and serious traffic accidents, causing significant loss of life and property. In recent years, accidents caused by icy roads have become increasingly frequent, making it one of the most serious hazards affecting traffic safety.
[0003] Field surveys revealed that, under the same conditions, elevated bridge decks are more prone to icing than normal road surfaces, frequently exhibiting a phenomenon where the bridge deck freezes while the road surface remains largely unaffected. This is primarily because elevated bridge decks are not in direct contact with the soil, lacking the soil's heating effect, resulting in lower temperatures and thus easier icing. Furthermore, bridges crossing (or adjacent to) bodies of water are more susceptible to icing than ordinary bridges. This is mainly because water bodies increase the moisture content of the surrounding air, making it easier for moisture to adhere to the bridge surface and cause icing. Existing forecasting models do not consider the differences between bridge decks and road surfaces, nor do they account for the significant impact of water bodies on bridge icing.
[0004] Therefore, existing technologies suffer from data bias due to the failure to consider special terrain areas with bridges and water bodies, resulting in low forecast accuracy. Summary of the Invention
[0005] The purpose of this application is to provide a method, controller, and storage medium for predicting road icing on bridge sections, in order to solve the problem that the accuracy of forecasts is low due to data bias that does not consider special terrain areas in the prior art.
[0006] To achieve the above objectives, the first aspect of this application provides a method for predicting road icing on bridge sections, the method comprising:
[0007] Obtain historical environmental data for bridge sections;
[0008] High-risk road sections for icing were identified based on historical environmental data.
[0009] The high-risk road sections for icing are divided into grids according to a preset grid resolution to obtain multiple grids;
[0010] Environmental prediction data for bridge sections are determined based on historical environmental data within each grid, combined with a three-dimensional variational assimilation objective function and a numerical prediction model.
[0011] The thickness of ice on bridge sections is determined based on environmental prediction data;
[0012] Whether to send an early warning message is determined based on the thickness of the ice layer.
[0013] In this embodiment of the application, the method further includes:
[0014] Obtain regional models of weather forecast patterns;
[0015] Multiple physical parameterization schemes are selected from the regional model of the weather forecast model;
[0016] Multiple physical parameterization schemes are combined to obtain a combined physical parameterization scheme;
[0017] Taylor diagrams are used to select combinations of physical parameters to establish numerical weather prediction models.
[0018] In this embodiment, the Taylor diagram satisfies formula (1):
[0019] RMSE = S x 2 +S y 2 -2S x S y r; (1)
[0020] Among them, S x S is the variance of the observed values. y Let be the variance of the environmental prediction data, r be the correlation coefficient between the observed values and the environmental prediction data, and RMSE be the root mean square error between the observed values and the environmental prediction data.
[0021] In this embodiment of the application, determining the environmental prediction data for bridge sections by combining the three-dimensional variational assimilation objective function and numerical prediction model further includes:
[0022] By combining the three-dimensional variational assimilation objective function, the numerical prediction model is optimized based on the environmental prediction data and observations of the previous sampling period to obtain the optimized numerical prediction model.
[0023] Environmental prediction data for the current sampling period are obtained using an optimized numerical weather prediction model.
[0024] In this embodiment, the three-dimensional variational assimilation objective function satisfies formula (2):
[0025]
[0026] Where x is the analysis variable, x b Let y0 be the initial field, B be the background error covariance matrix, R be the observation error covariance matrix, and H be the observation operator.
[0027] In this embodiment of the application, determining the icing thickness of bridge sections based on environmental prediction data includes:
[0028] Background data for bridge sections was determined using the inverse distance weighted interpolation method.
[0029] The icing thickness of bridge sections is determined based on environmental prediction data and background data.
[0030] In this embodiment of the application, determining the icing thickness of the bridge section based on environmental prediction data and background data includes:
[0031] The environmental prediction data is corrected to obtain the corrected environmental prediction data;
[0032] The icing thickness of the bridge section was determined based on the revised environmental prediction data and background data.
[0033] In this embodiment of the application, the high-risk road section for icing is divided into grids according to a preset grid resolution to obtain multiple grids, including:
[0034] Determine whether a high-risk road section for icing is a water section;
[0035] If a high-risk road section is a water section, the water section will be divided into one grid or multiple adjacent grids.
[0036] A second aspect of this application provides a controller, comprising:
[0037] The memory is configured to store instructions; and
[0038] The processor is configured to retrieve instructions from memory and, when executing the instructions, to implement the aforementioned method for predicting road icing on bridge sections.
[0039] A third aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to perform the aforementioned method for predicting road icing on bridge sections.
[0040] This application acquires historical environmental data for bridge sections and identifies high-risk icing sections based on this data. The high-risk icing sections are divided into multiple grids according to a preset grid resolution. Environmental prediction data for the bridge sections is determined based on historical environmental data within each grid, combined with a three-dimensional variational assimilation objective function and a numerical weather prediction model. The icing thickness for the bridge sections is then determined based on this prediction data. Whether to issue a warning is determined based on the icing thickness. This application, by using historical environmental data within each grid and a numerical weather prediction model to determine the environmental prediction data for bridge sections, and then using this prediction data to determine the icing thickness, can fully account for data biases in special terrain areas and improve the accuracy of predicting road icing conditions on bridge sections.
[0041] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description
[0042] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:
[0043] Figure 1 A flowchart illustrating a method for predicting road icing on bridge sections according to an embodiment of this application is shown schematically.
[0044] Figure 2 A schematic block diagram of a controller according to an embodiment of this application is shown. Detailed Implementation
[0045] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0046] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.
[0047] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.
[0048] Figure 1 A flowchart illustrating a method for predicting road icing on bridge sections according to an embodiment of this application is shown schematically. Figure 1As shown in the figure, this application provides a method for predicting road icing on bridge sections, which may include the following steps.
[0049] Step 101: Obtain historical environmental data for the bridge section;
[0050] Step 102: Identify high-risk road sections for icing based on historical environmental data;
[0051] Step 103: Divide the high-risk road sections for icing into grids according to the preset grid resolution to obtain multiple grids;
[0052] Step 104: Determine the environmental prediction data for the bridge section based on the historical environmental data within each grid, combined with the three-dimensional variational assimilation objective function and numerical prediction model.
[0053] Step 105: Determine the icing thickness of the bridge section based on environmental prediction data;
[0054] Step 106: Determine whether to send a warning message based on the thickness of the ice layer.
[0055] In this embodiment, since existing technologies do not consider the differences in environmental data between bridge sections and road surfaces, errors exist in icing forecasting. Therefore, the processor can determine whether to send a warning message by predicting the icing thickness of the bridge section. First, the processor can acquire historical environmental data of the bridge section and determine high-risk icing sections based on this data. Historical environmental data includes, but is not limited to, temperature data, humidity data, wind speed, radiation, and water vapor pressure data. Once high-risk icing sections are identified, they can be uniformly divided into one or more grids according to a preset grid resolution. The preset grid resolution refers to the preset grid edge length. In one example, the preset grid resolution is 30 meters. After completing the grid division of the high-risk icing sections, the processor can input the historical environmental data within each grid into the numerical weather prediction model to determine the environmental prediction data for the bridge section. The environmental prediction data is the environmental data for a preset future duration. Simultaneously, the processor can optimize the numerical weather prediction model using a three-dimensional variational assimilation objective function to improve the accuracy of the model's predictions. Furthermore, the processor can predict the icing thickness of bridge sections based on environmental prediction data, thereby obtaining icing prediction data, which is then sent to a visualization platform for visualization. If the processor's predicted icing thickness exceeds a preset value, the processor can send an early warning message. The preset thickness value refers to the maximum icing thickness determined based on actual conditions. In this way, the processor can achieve icing prediction for bridge sections.
[0056] This application acquires historical environmental data for bridge sections and identifies high-risk icing sections based on this data. The high-risk icing sections are divided into multiple grids according to a preset grid resolution. Environmental prediction data for the bridge sections is determined based on historical environmental data within each grid, combined with a three-dimensional variational assimilation objective function and a numerical weather prediction model. The icing thickness for the bridge sections is then determined based on this prediction data. Whether to issue a warning is determined based on the icing thickness. This application, by using historical environmental data within each grid and a numerical weather prediction model to determine the environmental prediction data for bridge sections, and then using this prediction data to determine the icing thickness, can fully account for data biases in special terrain areas and improve the accuracy of predicting road icing conditions on bridge sections.
[0057] In this embodiment of the application, the method may further include:
[0058] Obtain regional models of weather forecast patterns;
[0059] Multiple physical parameterization schemes are selected from the regional model of the weather forecast model;
[0060] Multiple physical parameterization schemes are combined to obtain a combined physical parameterization scheme;
[0061] Taylor diagrams are used to select combinations of physical parameters to establish numerical weather prediction models.
[0062] Specifically, the processor can establish a numerical weather prediction model to determine environmental prediction data for bridge sections. Historical environmental data includes, but is not limited to, temperature, humidity, wind speed, radiation, and water vapor pressure data. For sections with a high risk of icing, the processor can acquire a regional model of the weather forecast model. With the regional model available, the processor selects different physical parameterization schemes for radiation, convection, and boundary layer parameters from the model and combines these schemes to obtain a combined physical parameterization scheme. The processor can then use a Taylor series to filter the combined schemes to find the one that minimizes the deviation in the environmental prediction data, and establishes the numerical weather prediction model accordingly. This fulfills the requirement of determining environmental prediction data for bridge sections using numerical weather prediction models.
[0063] In this embodiment of the application, the Taylor diagram can satisfy formula (1):
[0064] RMSE = S x 2 +S y 2 -2S x S y r; (1)
[0065] Among them, S x S is the variance of the observed values. y Let be the variance of the environmental prediction data, r be the correlation coefficient between the observed values and the environmental prediction data, and RMSE be the root mean square error between the observed values and the environmental prediction data.
[0066] Specifically, the processor can use Taylor charts to filter combinations of physical parameters. Taylor charts can comprehensively consider the relationship between the variance, correlation coefficient, and root mean square error of observed values and environmental prediction data. Observed values refer to environmental data collected in real time by sensors and other devices. The variance of the observed values satisfies formula (3):
[0067]
[0068] Among them, S x Let x be the variance of the observed values. i Let X be the observed value, and let X be the average of the observed values.
[0069] The variance of the environmental prediction data satisfies formula (4):
[0070]
[0071] Among them, S y Let y be the variance of the environmental prediction data. i Y represents the environmental prediction data, and Y is the average value of the environmental prediction data.
[0072] The correlation coefficient between observed values and environmental prediction data satisfies formula (5):
[0073]
[0074] Where r is the correlation coefficient between observed values and environmental prediction data, x i Let X be the observed value, and y be the average of the observed values. i Y represents the environmental prediction data, and Y is the average value of the environmental prediction data.
[0075] Therefore, the root mean square error of the observed values and the environmental prediction data satisfies formula (6):
[0076]
[0077] Where RMSE is the root mean square error of the observed values and the environmental prediction data, x i For the observed value, y i For environmental prediction data.
[0078] In this embodiment of the application, determining the environmental prediction data for bridge sections by combining the three-dimensional variational assimilation objective function and numerical prediction model may further include:
[0079] By combining the three-dimensional variational assimilation objective function, the numerical prediction model is optimized based on the environmental prediction data and observations of the previous sampling period to obtain the optimized numerical prediction model.
[0080] Environmental prediction data for the current sampling period are obtained using an optimized numerical weather prediction model.
[0081] Specifically, to improve the accuracy of numerical weather prediction models in forecasting environmental data for bridge sections and surrounding areas, the processor can assimilate available environmental data in real time during the forecasting process to establish a more accurate initial field. The initial field, or initial value, in this embodiment refers to historical environmental data or environmental prediction data from the previous sampling period. Simultaneously, the processor can employ a cyclic assimilation method to acquire environmental prediction data in real time. In this embodiment, the processor can combine a three-dimensional variational assimilation objective function to optimize the numerical weather prediction model based on the environmental prediction data and observations from the previous sampling period, resulting in an optimized numerical weather prediction model. The optimized model is then used to acquire environmental prediction data for the current sampling period.
[0082] In one example, if a sampling period is 3 hours, in hour 0, the processor can determine an environmental prediction data point using a numerical weather prediction model. This environmental prediction data becomes the environmental prediction data for the previous sampling period. The environmental prediction data from the previous sampling period can predict environmental data for a predetermined duration in the future. If, in hour 1, the processor acquires observations—that is, environmental data collected in real time by sensors and other devices—the processor can optimize the numerical weather prediction model based on the environmental prediction data and observations from the previous sampling period to obtain an optimized numerical weather prediction model. This optimized model is then used to determine the environmental prediction data for the current sampling period, i.e., the environmental prediction data for hour 3. This approach allows for both optimization of the numerical weather prediction model and prediction of rolling icing on bridge sections.
[0083] In this embodiment, the three-dimensional variational assimilation objective function can satisfy formula (2):
[0084]
[0085] Where x is the analysis variable, x b Let y0 be the initial field, B be the background error covariance matrix, R be the observation error covariance matrix, and H be the observation operator.
[0086] Specifically, the processor can optimize the numerical weather prediction model using a three-dimensional variational assimilation objective function. The processor can acquire environmental data in real time during the forecasting process, thereby establishing a more accurate initial field. Simultaneously, the processor can combine the three-dimensional variational assimilation objective function with environmental prediction data and observations from the previous sampling period to optimize the numerical weather prediction model, obtaining an optimized model, and then using this optimized model to acquire environmental prediction data for the current sampling period. It should be noted that the observations refer to the real-time acquired environmental data.
[0087] In this embodiment of the application, step 105, determining the icing thickness of the bridge section based on environmental prediction data, may include:
[0088] Background data for bridge sections was determined using the inverse distance weighted interpolation method.
[0089] The icing thickness of bridge sections is determined based on environmental prediction data and background data.
[0090] Specifically, geographical attributes exhibit spatial correlation; data that are close together are more similar. Therefore, the processor can determine the background data of bridge sections using the inverse distance weighted interpolation method. The basic principle of inverse distance weighted interpolation is to interpolate the unknown information of the point to be calculated using known information from a finite number of data points, and then calculate the value of a new interpolation point based on the values of the interpolated points. Given the background data of the bridge section, the processor can establish a bridge icing calculation model. By inputting environmental prediction data into the bridge icing calculation model, the processor can obtain the icing thickness of the bridge section. By determining both the environmental prediction data and the background data, the processor can determine the icing thickness of the bridge section.
[0091] In this embodiment of the application, determining the icing thickness of a bridge section based on environmental prediction data and background data may include:
[0092] The environmental prediction data is corrected to obtain the corrected environmental prediction data;
[0093] The icing thickness of the bridge section was determined based on the revised environmental prediction data and background data.
[0094] Specifically, the environmental prediction data includes temperature prediction data, humidity prediction data, wind speed prediction data, radiation prediction data, and water vapor pressure prediction data. In this embodiment, the processor needs to correct the temperature prediction data, wind speed prediction data, and water vapor pressure prediction data. Therefore, the corrected environmental prediction data includes corrected temperature prediction data, corrected wind speed prediction data, and corrected water vapor pressure prediction data.
[0095] To ensure the computational stability of the numerical weather prediction model, the model smooths bridge sections, resulting in a difference between the terrain of the bridge sections in the numerical prediction model and the actual terrain, which causes deviations in the temperature prediction data. Furthermore, the numerical prediction model cannot incorporate the height of the bridge sections when determining environmental prediction data, further increasing the deviation in the temperature prediction data. Therefore, the processor needs to further correct the temperature prediction data obtained from the numerical prediction model. The corrected temperature prediction data satisfies formula (7):
[0096] T2 = T d0 -γ s (h2-h+ΔH); (7)
[0097] Where T2 is the corrected temperature prediction data, T d0 For temperature prediction data, h2 is the actual terrain elevation, h is the terrain elevation in the numerical formula, ΔH is the elevation of the bridge section, and γ is the elevation of the bridge section. s It is the dry adiabatic lapse rate of air, and its value is generally -1℃ / 100m.
[0098] Because there is a certain angle between the bridge section and the wind speed prediction data, the effective wind speed passing through the bridge section is reduced. Therefore, the processor needs to correct the wind speed prediction data. The corrected wind speed prediction data satisfies formula (8):
[0099] U = Vcos(α1-β); (8)
[0100] Where U is the corrected wind speed prediction data, V is the wind speed prediction data, α1 is the wind direction, and β is the direction of the bridge section. Both α and β range from 0 degrees to 360 degrees.
[0101] When a bridge crosses a water section or is close to a water section, the water vapor pressure near the bridge changes due to humidity variations as it passes over the water section. Therefore, the processor needs to correct the water vapor pressure prediction data. The corrected water vapor pressure prediction data satisfies formula (9):
[0102] P=ΔHα2(e0-e a )+P0; (9)
[0103] Where P is the corrected predicted water vapor pressure data, ΔH is the height of the bridge section, and e a α is the water vapor pressure of the air, e0 is the saturated water vapor pressure derived from the dew point temperature of the air, and α2 is the water vapor diffusion coefficient.
[0104] The relationship between saturated vapor pressure and dew point temperature satisfies formula (10):
[0105]
[0106] Where e0 is the saturated vapor pressure, in hectopascals, and T is the dew point temperature, in Kelvin.
[0107] By correcting the environmental prediction data, the processor can obtain the corrected environmental prediction data and determine the icing thickness of the bridge section based on the corrected environmental prediction data, humidity prediction data, radiation prediction data, and background data.
[0108] In this embodiment of the application, step 103, dividing the high-risk road section for icing into multiple grids according to a preset grid resolution, may include:
[0109] Determine whether a high-risk road section for icing is a water section;
[0110] If a high-risk road section is a water section, the water section will be divided into one grid or multiple adjacent grids.
[0111] Specifically, the processor can divide water sections into one or more adjacent grids. In this embodiment, it is necessary to comprehensively consider the impact of this special terrain area of water sections on road icing on bridge sections. Furthermore, the processor needs to determine environmental prediction data based on historical environmental data within each grid. Therefore, when the high-risk road section for icing is a water section, the processor can divide the water section into one or more adjacent grids so that the processor can consider the impact of the water section on road icing on bridge sections, thereby improving the accuracy of predicting road icing conditions on bridge sections.
[0112] In one specific embodiment of this application, the processor can acquire historical environmental data such as wind speed, temperature, humidity, radiation, and water vapor pressure of the bridge section and its surrounding area, and compile and summarize meteorological data during the icing period. The processor uses 30-meter resolution ASTER topographic data and the latest national land use and water body distribution data, established using the latest Landsat 8 imagery, as the basic geographic information dataset. It then uniformly divides high-risk icing sections into multiple grids with a 30-meter grid resolution. During the grid division process, the processor assigns water-covered sections—a special topographical area—to multiple adjacent grids.
[0113] For high-risk road sections prone to icing, the processor uses 9km resolution data from the European Centre for Medium-Range Weather Forecasts (ECMWF) as the driving force and constructs a numerical weather prediction model using a six-layer nested approach. For example, the resolutions from the outermost to the innermost layer can be 7290m, 2430m, 810m, 270m, 90m, and 30m. Furthermore, the processor can acquire regional models of the Weather Research and Forecasting Model (WRF), select different physical parameterization schemes for radiation, convection, and boundary layer parameters, and combine them to obtain a combined physical parameterization scheme. This combined scheme is then filtered using a Taylor chart. The filtering results show that when the RRTMG scheme is selected for radiation, the BMJ scheme for cumulus convection, and the YSU scheme for boundary layer, the average deviation between the environmental prediction data and the observed values is minimized. Therefore, the processor can use this combined physical parameterization scheme to construct a numerical weather prediction model.
[0114] To improve the accuracy of numerical weather prediction models in forecasting meteorological elements for bridge sections and surrounding areas, a three-dimensional variational assimilation objective function is used in the numerical weather prediction model to assimilate available environmental data, such as temperature, humidity, wind speed, radiation, and water vapor pressure, within high-risk icing sections. This allows the processor to determine a more accurate initial field. Furthermore, the processor employs a cyclical update method, initiating a forecast every hour to acquire environmental prediction data for the next three hours.
[0115] Simultaneously, the processor needs to determine the background data of the bridge section using the inverse distance weighted interpolation method and correct the environmental prediction data. Based on the background data and corrected environmental prediction data, the processor can then predict the icing thickness of the bridge section. After determining the icing thickness for all grids, the processor can obtain the icing prediction data for any point within the high-risk icing section and visualize the icing prediction data.
[0116] Figure 2 A schematic block diagram of a controller according to an embodiment of this application is shown. Figure 2 As shown in the figure, this application provides a controller that may include:
[0117] Memory 210 is configured to store instructions; and
[0118] Processor 220 is configured to retrieve instructions from memory 210 and, when executing the instructions, to implement the aforementioned method for predicting road icing on bridge sections.
[0119] Specifically, in this embodiment of the application, the processor 220 can be configured to:
[0120] Obtain historical environmental data for bridge sections;
[0121] High-risk road sections for icing were identified based on historical environmental data.
[0122] The high-risk road sections for icing are divided into grids according to a preset grid resolution to obtain multiple grids;
[0123] Environmental prediction data for bridge sections are determined based on historical environmental data within each grid, combined with a three-dimensional variational assimilation objective function and a numerical prediction model.
[0124] The thickness of ice on bridge sections is determined based on environmental prediction data;
[0125] Whether to send an early warning message is determined based on the thickness of the ice layer.
[0126] Furthermore, the processor 220 can also be configured to:
[0127] Obtain regional models of weather forecast patterns;
[0128] Multiple physical parameterization schemes are selected from the regional model of the weather forecast model;
[0129] Multiple physical parameterization schemes are combined to obtain a combined physical parameterization scheme;
[0130] Taylor diagrams are used to select combinations of physical parameters to establish numerical weather prediction models.
[0131] In this embodiment, the Taylor diagram satisfies formula (1):
[0132] RMSE = S x 2 +S y 2 -2S x S y r; (1)
[0133] Among them, S x S is the variance of the observed values. y Let be the variance of the environmental prediction data, r be the correlation coefficient between the observed values and the environmental prediction data, and RMSE be the root mean square error between the observed values and the environmental prediction data.
[0134] Furthermore, the processor 220 can also be configured to:
[0135] By combining the three-dimensional variational assimilation objective function, the numerical prediction model is optimized based on the environmental prediction data and observations of the previous sampling period to obtain the optimized numerical prediction model.
[0136] Environmental prediction data for the current sampling period are obtained using an optimized numerical weather prediction model.
[0137] In this embodiment, the three-dimensional variational assimilation objective function satisfies formula (2):
[0138]
[0139] Where x is the analysis variable, x b Let y0 be the initial field, B be the background error covariance matrix, R be the observation error covariance matrix, and H be the observation operator.
[0140] Furthermore, the processor 220 can also be configured to:
[0141] Background data for bridge sections was determined using the inverse distance weighted interpolation method.
[0142] The icing thickness of bridge sections is determined based on environmental prediction data and background data.
[0143] Furthermore, the processor 220 can also be configured to:
[0144] The environmental prediction data is corrected to obtain the corrected environmental prediction data;
[0145] The icing thickness of the bridge section was determined based on the revised environmental prediction data and background data.
[0146] Furthermore, the processor 220 can also be configured to:
[0147] Determine whether a high-risk road section for icing is a water section;
[0148] If a high-risk road section is a water section, the water section will be divided into one grid or multiple adjacent grids.
[0149] This application acquires historical environmental data for bridge sections and identifies high-risk icing sections based on this data. The high-risk icing sections are divided into multiple grids according to a preset grid resolution. Environmental prediction data for the bridge sections is determined based on historical environmental data within each grid, combined with a three-dimensional variational assimilation objective function and a numerical weather prediction model. The icing thickness for the bridge sections is then determined based on this prediction data. Whether to issue a warning is determined based on the icing thickness. This application, by using historical environmental data within each grid and a numerical weather prediction model to determine the environmental prediction data for bridge sections, and then using this prediction data to determine the icing thickness, can fully account for data biases in special terrain areas and improve the accuracy of predicting road icing conditions on bridge sections.
[0150] This application also provides a machine-readable storage medium storing instructions that cause a machine to perform the above-described method for predicting road icing on bridge sections.
[0151] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0152] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0153] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0154] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0155] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0156] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0157] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0158] It should also be noted that 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 process, method, article, or apparatus. Unless otherwise specified, 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 that element.
[0159] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A method for predicting road icing on bridge sections, characterized in that, The method includes: Obtain historical environmental data for bridge sections; High-risk road sections for icing were identified based on the aforementioned historical environmental data. The high-risk road section for icing is divided into multiple grids according to a preset grid resolution. The environmental prediction data for the bridge section is determined based on the historical environmental data within each grid, combined with a three-dimensional variational assimilation objective function and a numerical prediction model. The icing thickness of the bridge section is determined based on the environmental prediction data. Whether to send a warning message is determined based on the ice thickness. The determination of environmental prediction data for the bridge section by combining the three-dimensional variational assimilation objective function and numerical prediction model also includes: By combining the three-dimensional variational assimilation objective function, the numerical prediction model is optimized based on the environmental prediction data and observations of the previous sampling period to obtain the optimized numerical prediction model. Environmental prediction data for the current sampling period is obtained using the optimized numerical weather prediction model; Wherein, the three-dimensional variational assimilation objective function satisfies formula (2): ；（2） in, To analyze variables, For the initial field, For the observed values, B It is the background error covariance matrix. R It is the observation error covariance matrix. H It is an observation operator.
2. The method according to claim 1, characterized in that, The method further includes: Obtain regional models of weather forecast patterns; Multiple physical parameterization schemes are selected from the regional model of the weather forecast model; The multiple physical parameterization schemes are combined to obtain a physical parameterization combination scheme; The numerical prediction model is established by selecting the combination of physical parameters using Taylor diagrams.
3. The method according to claim 2, characterized in that, The Taylor diagram satisfies formula (1): ；（1） in, The variance of the observed values, The variance of environmental prediction data, r The correlation coefficient between observed values and environmental prediction data. RMSE The root mean square error is the sum of the observed values and the environmental prediction data.
4. The method according to claim 1, characterized in that, Determining the icing thickness of the bridge section based on the environmental prediction data includes: Background data for the bridge section was determined using the inverse distance weighted interpolation method. The icing thickness of the bridge section is determined based on the environmental prediction data and the background data.
5. The method according to claim 4, characterized in that, The process of determining the icing thickness of the bridge section based on the environmental prediction data and the background data includes: The environmental prediction data is corrected to obtain the corrected environmental prediction data; The icing thickness of the bridge section is determined based on the corrected environmental prediction data and the background data.
6. The method according to claim 1, characterized in that, The process of dividing the high-risk icing road section into multiple grids according to a preset grid resolution includes: Determine whether the high-risk road section for icing is a water section; If the high-risk road section for icing is a water section, the water section will be divided into one grid or multiple adjacent grids.
7. A controller, characterized in that, include: The memory is configured to store instructions; as well as The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the method for predicting road icing on bridge sections according to any one of claims 1 to 6.
8. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the method for predicting road icing on bridge sections according to any one of claims 1 to 6.