Road section weather prediction method, device, equipment and medium

Abstract

The invention provides a road section weather prediction method and device, equipment and a medium. The method comprises the following steps: acquiring road section information of a target road; acquiring grid weather prediction data corresponding to each road section of the target road according to the road section information; and generating weather prediction information of each road section based on the grid weather prediction data corresponding to each road section. Therefore, the method can achieve the refined prediction of the weather conditions of each road, achieves the precision of the road level, and provides an accurate decision support basis for a traffic management department and a driver.

Description

Technical Field

[0001] This application relates to the technical field of road traffic weather, and particularly to a road section weather prediction method, device, equipment, and medium. Background Art

[0002] Weather prediction information can be used to predict the weather conditions of roads, thereby providing decision-making support for traffic departments, helping traffic management departments and drivers take preventive measures in advance, reducing traffic accidents, and ensuring driving safety. However, due to the insufficient spatial resolution of weather prediction information, the refined prediction of the weather conditions of each road cannot be achieved, so accurate decision-making support cannot be provided for traffic management departments and drivers. Summary of the Invention

[0003] In view of this, this application provides a road section weather prediction method, device, equipment, and medium, which can achieve the refined prediction of the weather conditions of each road, accurate to the road section level, and provide an accurate decision-making support basis for traffic management departments and drivers.

[0004] In a first aspect, a road section weather prediction method is provided, including: obtaining the road section information of a target road; obtaining the grid weather prediction data corresponding to each road section of the target road according to the road section information; generating the weather prediction information of each road section based on the grid weather prediction data corresponding to each road section.

[0005] In a second aspect, a road section weather prediction device includes: a road section information obtaining module for obtaining the road section information of a target road; a prediction data obtaining module for obtaining the grid weather prediction data corresponding to each road section of the target road according to the road section information; and a prediction information generating module for generating the weather prediction information of each road section based on the grid weather prediction data corresponding to each road section.

[0006] In a third aspect, an electronic device is provided, including a processor, a memory, and a program stored on the memory and capable of running on the processor, where when the program is executed by the processor, the steps of any one of the road section weather prediction methods provided in the embodiments of this application are implemented.

[0007] In a fourth aspect, a computer-readable storage medium is provided, where instructions are stored on the computer-readable storage medium, and when the instructions are executed by a processor, the steps of any one of the road section weather prediction methods provided in the embodiments of this application are implemented.

[0008] In summary, the road weather prediction method, device, electronic device, and medium provided by this application have the following beneficial effects: By obtaining the specific road section information of the target road, and obtaining the grid weather prediction data corresponding to each road section of the target road according to this road section information, and by analyzing the grid weather prediction data of each road section to accurately predict the weather of each road section, weather prediction at the road section level is achieved. This is more accurate than traditional regional or urban-level weather forecasts, improves the spatial resolution of road weather prediction, and provides an accurate decision-making support basis for traffic management departments and drivers. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the specific embodiments of this application or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of this application. For those skilled in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0010] Figure 1 The flowchart showing a road weather prediction method provided by an embodiment of this application;

[0011] Figure 2 The interface schematic diagram showing a road section weather prediction provided by an embodiment of this application;

[0012] Figure 3 The structural schematic diagram showing a road section weather prediction device provided by an embodiment of this application;

[0013] Figure 4 The structural schematic diagram showing an electronic device provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] In order to make the above and other features and advantages of this application clearer, the following further describes this application with reference to the drawings. It should be understood that the specific embodiments given herein are for the purpose of explaining to those skilled in the art and are merely exemplary, not restrictive.

[0015] In the following description, many specific details are set forth to provide a thorough understanding of this application. However, it is obvious to those skilled in the art that specific details do not need to be employed to practice this application. In other cases, well-known steps or operations are not described in detail to avoid obscuring this application.

[0016] An embodiment of this application provides a road section weather prediction method, which is applied to a road section weather prediction device. Figure 1 The flowchart showing a road weather prediction method provided by an embodiment of this application, asFigure 1 As shown in the figure, the road section weather prediction method may include the following steps.

[0017] Step S11: Obtain the section information of the target road.

[0018] In an embodiment of the present application, the target road involved may be any road on the map, including but not limited to rural roads, town roads, county roads, national roads, and highways. The target road may include one or more road sections. The section information may include relevant information of all road sections of the target road, including but not limited to section names, section lengths, section longitude and latitude coordinates, etc. Among them, the section longitude and latitude coordinates refer to the set of longitude and latitude coordinates of the entire road section.

[0019] Step S12: Obtain the grid weather prediction data corresponding to each road section of the target road according to the section information.

[0020] The grid weather prediction data involved in an embodiment of the present application refers to the weather forecast data obtained by dividing the forecast area into grids. The grid weather prediction data may include but not limited to basic weather elements and weather phenomena. Among them, the basic weather elements may include temperature, humidity, precipitation, wind direction, and wind speed, etc. The weather phenomena may be one of sunny, cloudy, overcast, fog, haze, rain, snow, thunderstorm, etc. This grid may be referred to as a weather grid. A weather grid refers to a square with a fixed side length. Optionally, the fixed side length may be 1 km × 1 km, 2 km × 2 km, 5 km × 5 km, or 10 km × 10 km.

[0021] In an embodiment of the present application, the road section weather prediction device may obtain the grid weather prediction data of the weather grid where each road section is located from the meteorological platform according to the section information of the target road.

[0022] Step S13: Generate the weather prediction information for each road section based on the grid weather prediction data corresponding to each road section.

[0023] The weather prediction information involved in an embodiment of the present application includes basic weather elements, weather meteorology, icing probability, and occurrence probability of patchy fog, etc.

[0024] In an embodiment of the present application, the road section weather prediction device may comprehensively analyze the grid weather prediction data corresponding to each road section to obtain the weather prediction information for each road section.

[0025] Figure 2 Show an interface schematic diagram of a road section weather prediction provided by an embodiment of the present application, as Figure 2 As shown in the figure, the weather prediction information of this road may include prediction time, rainfall, temperature, wind force level, etc.

[0026] In each of the above embodiments, by obtaining the specific section information of the target road, and obtaining the grid weather prediction data corresponding to each section of the target road according to the section information, and by analyzing the grid weather prediction data of each section to accurately predict the weather of each section, weather prediction at the section level is achieved. This is more accurate than traditional regional or urban-level weather forecasts, improves the spatial resolution of road weather prediction, and provides accurate decision-making support basis for traffic management departments and drivers.

[0027] When a section is long and involves many grids, there may be differences in the grid weather prediction data corresponding to the starting grid where the starting point of the section is located and the ending grid where the ending point of the section is located, thus affecting the weather prediction of this section. Therefore, when the section is long, the long section is divided into at least two short sections to improve the accuracy and pertinence of weather prediction.

[0028] In some embodiments, the section information includes the section length. Before obtaining the grid weather prediction data corresponding to each section of the target road according to the section information in step S12, this section weather prediction method further includes: comparing the section lengths of each section with the grid side length of the weather grid; in the case where the section length of a section is greater than the grid side length, breaking this section to obtain at least two short sections, and obtaining the corresponding section information.

[0029] In an embodiment of the present application, the section weather prediction device compares the section length of each section with the grid side length of the weather grid. If the section length is not greater than the grid side length, step S12 is continued. If the section length is greater than the grid side length, the section corresponding to this section length is broken at least once to obtain at least two short sections, and the section information of each short section is obtained.

[0030] In an embodiment of the present application, the section breaking step may specifically be to set a breaking point every first preset length starting from the starting point of the section until the ending point of the section. The section weather prediction device breaks the section according to the breaking points. Among them, the first preset length is less than the grid side length and greater than 2 kilometers. In this way, the section can be divided into multiple short sections, and each short section can involve at most two grids, so that the weather conditions of each short section change less, thereby improving the accuracy and local pertinence of section weather prediction.

[0031] In another embodiment of the present application, the road segment interruption step may specifically be to obtain the historical grid weather data of each grid where the road segment is located, compare the historical grid weather data of the grid where the starting point of the road segment is located with the historical grid weather data of other grids, identify the grid positions with significant changes in weather data compared to the grid where the starting point of the road segment is located to obtain meteorological change grids, set interruption points at the road segments corresponding to the identified meteorological change grids, and the road segment weather prediction device interrupts the road segment according to the interruption points. In this way, the interruption points are accurately determined based on the historical grid weather data, and the long road segment is divided into shorter paragraphs, thereby improving the accuracy and local pertinence of road segment weather prediction.

[0032] In the above embodiment, dividing the long road segment into multiple short road segments can make the weather conditions of each road segment change little, thereby improving the accuracy and local pertinence of road segment weather prediction.

[0033] In some embodiments, the road segment information includes the longitude and latitude coordinates of the road segment. In step S12, obtaining the grid weather prediction data corresponding to each road segment of the target road includes: determining the central longitude and latitude coordinates of each road segment according to the longitude and latitude coordinates of each road segment of the target road; determining the weather grid corresponding to each central longitude and latitude coordinate according to the central longitude and latitude coordinates of each road segment, and obtaining the corresponding grid weather prediction data.

[0034] In an embodiment of the present application, the road segment weather prediction device extracts the longitude and latitude coordinates of each road segment from the road segment information. For each road segment, the central longitude and latitude coordinates of the road segment are calculated using the longitude and latitude coordinates of the starting point and the ending point, or, a longitude and latitude coordinate in the middle of each road segment is selected as the central longitude and latitude coordinate. For example, a road segment corresponds to 5 longitude and latitude coordinates, and the 3rd longitude and latitude coordinate is selected as the central longitude and latitude coordinate.

[0035] The road segment weather prediction device matches the preset weather grid system according to the central longitude and latitude coordinates of each road segment, determines the weather grid corresponding to each central longitude and latitude coordinate, and obtains the grid weather prediction data of the weather grid corresponding to each road segment from the weather platform. Among them, the weather grid system refers to dividing the geographical area into a series of regular grids. Each grid has a unique identifier and is associated with a specific longitude and latitude range.

[0036] In the above embodiments, the weather grid corresponding to each road segment is determined through the central longitude and latitude coordinates of each road segment, so that the grid weather prediction data corresponding to each road segment can be accurately obtained. In this way, by matching each road segment of the target road with a specific weather grid, it can be ensured that each road segment can obtain weather prediction data closely related to its geographical location, which helps to reduce errors caused by insufficient spatial resolution of weather prediction data and improve the accuracy of weather prediction data at the road segment level.

[0037] In some embodiments, the road segment information includes the longitude and latitude coordinates of the road segment. In step S12, obtaining the grid weather prediction data corresponding to each road segment of the target road according to the road segment information includes: determining at least one weather grid corresponding to each road segment according to the longitude and latitude coordinates of each road segment of the target road; for each road segment, fusing the grid weather prediction data of at least one weather grid of the road segment according to the proportion of the road segment in each weather grid to obtain the grid weather prediction information of the road segment.

[0038] In an embodiment of the present application, the road segment weather prediction device extracts the longitude and latitude coordinates of each road segment from the road segment information, matches the longitude and latitude coordinates of each road segment with a predefined weather grid system to obtain all weather grids corresponding to each road segment, and obtains the grid weather prediction data of the corresponding weather grid from the weather platform.

[0039] When a road segment corresponds to two or more weather grids, calculate the proportion of the road segment in each grid. Specifically, calculate the proportion of the length of the road segment in each grid to the total length of the road segment. And according to the proportion of the road segment in each grid, fuse the grid weather prediction data of multiple grids to obtain the grid weather prediction data of the road segment. Among them, the fusion can adopt a weighted fusion algorithm, that is, using the proportion as the weighting parameter.

[0040] In the above embodiment, for each road segment, fusion processing is performed according to its proportion in different weather grids, and more refined grid weather prediction data can be obtained, improving the spatial accuracy and refined application level of the weather prediction data.

[0041] In some embodiments, in step S13, generating the weather prediction information of each road segment based on the grid weather prediction data corresponding to each road segment includes: inputting the grid weather prediction data corresponding to each road segment into a weather prediction model to obtain the weather prediction information of each road segment.

[0042] The weather prediction model involved in an embodiment of the present application is trained according to historical grid weather data and road condition data of each road segment. Specifically, the weather prediction model extracts features from the historical grid weather data for training and corrects the model parameters according to the road condition data. Among them, the feature refers to the parameter that affects the weather condition of the road segment.

[0043] In addition, the weather prediction model can be constructed based on a deep learning model. The weather prediction model can be used to predict specific weather meteorological conditions such as heavy rain level, snow level, road segment icing probability, and occurrence probability of group fog.

[0044] In an embodiment of the present application, the road weather prediction device transmits the grid weather prediction data corresponding to each road segment as input parameters to the weather prediction model. The weather prediction model predicts the weather conditions of each road segment according to the rules learned internally and outputs the weather prediction information of each road segment. In this way, the weather prediction information of each road segment can be accurately predicted using the weather prediction model.

[0045] In some embodiments, in step S13, after generating the weather prediction information of each road segment based on the grid weather prediction data corresponding to each road segment, the road weather prediction method further includes: according to the weather prediction information of each road segment, counting the proportion of road segments with bad weather in the target road.

[0046] The road segments with bad weather involved in an embodiment of the present application may refer to road segments in bad weather. Bad weather can be defined according to user needs. For example, road segments with an ice formation probability exceeding a certain threshold (such as 30%), heavy snowfall level, heavy rainfall level greater than or a relatively high probability of occurrence of group fog (such as reaching or exceeding 20%) are defined as road segments with bad weather. Road response strategies may include but are not limited to various treatment strategies for roads with bad weather. For example, increasing the patrol frequency, adjusting traffic signal timing, setting warning signs, deicing, issuing road closure or traffic restriction notices, etc.

[0047] In an embodiment of the present application, the road weather prediction device classifies each road segment into a road segment with bad weather and a road segment without bad weather based on the defined bad weather standard and the weather prediction information of each road segment, counts the number of road segments with bad weather in the target road, and divides it by the total number of road segments to obtain the proportion of road segments with bad weather. And according to the proportion of road segments with bad weather, analyze the degree and scope of the impact of bad weather on the target road, and determine the corresponding road response strategy.

[0048] In addition, when there are multiple road response strategies, sort them according to the priority of the road response strategies and select the road response strategy with the highest level.

[0049] In the above embodiments, the proportion of road segments with bad weather in the target road can be accurately counted according to the weather prediction information of each road segment, and an effective road response strategy can be formulated accordingly to ensure the safety and smoothness of the road.

[0050] In some embodiments, in step S13, after generating the weather prediction information of each road segment based on the grid weather prediction data corresponding to each road segment, the road weather prediction method further includes: when the weather prediction information meets the preset warning conditions, generating and pushing warning prompt information.

[0051] The preset warning conditions involved in an embodiment of the present application may be warning thresholds for different weather phenomena (such as icing, group fog, heavy rain, etc.). The warning threshold can be the occurrence probability, intensity level or other relevant indicators of a specific weather phenomenon.

[0052] In an embodiment of the present application, the road weather prediction device compares the set alarm threshold with the weather prediction information. When the prediction information reaches or exceeds the threshold, the alarm condition is satisfied.

[0053] The early warning prompt information involved in an embodiment of the present application may include the type of weather phenomenon, occurrence time, location (specific to the road section), predicted intensity, possible impacts, and recommended measures, etc.

[0054] In an embodiment of the present application, when the alarm condition is satisfied, the road weather prediction device immediately activates the push mechanism and selects a suitable push channel to push the early warning prompt information according to the preferences and actual situations of the target audience. For example, for drivers, it can be pushed through channels such as in-vehicle navigation systems and mobile phone APPs; for the public, it can be pushed through channels such as social media, radio, and television; for traffic management departments, it is pushed to the management platform through the network.

[0055] In the above embodiment, when the weather prediction information meets the preset alarm condition, it can generate and push alarm prompt information in a timely and accurate manner, providing effective early warnings and decision-making support for road managers, drivers, and the public.

[0056] Another aspect of the embodiments of the present application provides a road weather prediction device. Figure 3 The structural schematic diagram of a road weather prediction device provided by an embodiment of the present application is shown in Figure 3 As shown, the road weather prediction device 30 may include the following several modules.

[0057] The road section information acquisition module 31 is used to acquire the road section information of the target road.

[0058] The prediction data acquisition module 32 is used to acquire the grid weather prediction data corresponding to each road section of the target road according to the road section information.

[0059] The prediction information generation module 33 is used to generate the weather prediction information of each road section based on the grid weather prediction data corresponding to each road section.

[0060] In the above embodiments, by acquiring the specific road section information of the target road, acquiring the grid weather prediction data corresponding to each road section of the target road according to the road section information, and accurately predicting the weather of each road section by analyzing the grid weather prediction data of each road section, road section-level weather prediction is realized. This is more accurate than traditional regional or urban-level weather forecasts, improves the spatial resolution of road weather prediction, and provides an accurate decision-making support basis for traffic management departments and drivers.

[0061] In some embodiments, the road segment weather prediction device 30 may further include the following several modules.

[0062] A comparison module, configured to compare the lengths of each road segment with the side length of the weather grid.

[0063] An interruption module, configured to, before obtaining the grid weather prediction data corresponding to each road segment of the target road according to the road segment information, when the length of a road segment is greater than the side length of the grid, interrupt the road segment to obtain at least two short road segments, and obtain the corresponding road segment information.

[0064] In some embodiments, the prediction data acquisition module 32 is specifically configured to determine the central longitude and latitude coordinates of each road segment according to the longitude and latitude coordinates of each road segment of the target road; determine the weather grid corresponding to each central longitude and latitude coordinate according to the central longitude and latitude coordinates of each road segment, and obtain the corresponding grid weather prediction data.

[0065] In some embodiments, the prediction data acquisition module 32 is specifically configured to determine at least one weather grid corresponding to each road segment according to the longitude and latitude coordinates of each road segment of the target road; for each road segment, fuse the grid weather prediction data of at least one weather grid of the road segment according to the proportion of the road segment in each weather grid to obtain the grid weather prediction information of the road segment.

[0066] In some embodiments, the road segment weather prediction device 30 may further include the following several modules.

[0067] A proportion calculation module, configured to, after generating the weather prediction information of each road segment based on the grid weather prediction data corresponding to each road segment, count the proportion of the road segments with bad weather in the target road according to the weather prediction information of each road segment.

[0068] A response strategy determination module, configured to determine the corresponding road response strategy based on this proportion.

[0069] In some embodiments, the road segment weather prediction device 30 may further include the following several modules.

[0070] An information push module, configured to generate and push a warning prompt message when the weather prediction information meets the preset warning conditions.

[0071] In some embodiments, the prediction information generation module 33 is specifically configured to input the grid weather prediction data corresponding to each road segment into a weather prediction model to obtain the weather prediction information of each road segment, and this weather prediction model is trained according to historical grid weather data.

[0072] It should be understood that the specific features, operations, and details described above regarding the method of the present application can also be similarly applied to the apparatus and system of the present application, or vice versa. Additionally, each step of the method of the present application described above can be executed by the corresponding components or units of the apparatus or system of the present application.

[0073] It should be understood that each module / unit of the apparatus of the present application can be implemented in whole or in part by software, hardware, firmware, or a combination thereof. Each module / unit can be embedded in the processor of the electronic device in the form of hardware or firmware, or independent of the processor, and can also be stored in the memory of the electronic device in the form of software for the processor to call to execute the operations of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0074] In another aspect of the present application, an electronic device is provided. Figure 4 The structural schematic diagram of an electronic device provided according to an embodiment of the present application is shown, as Figure 4 shown, the electronic device 40 includes a processor 41, a memory 42, and a program stored on the memory and capable of running on the processor. When the program is executed by the processor, it implements the steps of the road section weather prediction method provided in any of the above embodiments.

[0075] In one embodiment, the electronic device 40 may include a processor, a memory, a network interface, a communication interface, etc. connected through a system bus. The processor of the electronic device 40 can be used to provide necessary computing, processing, and / or control capabilities. The memory of the electronic device 40 can include a non-volatile storage medium and an internal memory. The non-volatile storage medium can store an operating system, a computer program, etc. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the electronic device 40 can be used to connect and communicate with external devices through a network.

[0076] In another aspect of the present application, a computer-readable storage medium is provided. Instructions are stored on the computer-readable storage medium. When the instructions are executed by the processor, they implement the steps of the road section weather prediction method provided in any of the above embodiments.

[0077] Those skilled in the art can understand that the method steps of this application can be completed by a computer program instructing relevant hardware such as electronic devices or processors. The computer program can be stored in a non-transitory computer-readable storage medium. When the computer program is executed, the steps of this application are caused to be executed. Depending on the situation, any reference to a memory, storage, or other medium herein may include non-volatile or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tapes, floppy disks, magneto-optical data storage devices, optical data storage devices, hard disks, solid-state disks, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0078] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification as long as such a combination does not exist in contradiction.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for predicting road section weather, characterized in that, Including: Obtain the section information of the target road; Obtain the grid weather prediction data corresponding to each section of the target road according to the section information; Generate the weather prediction information of each section based on the grid weather prediction data corresponding to each section.

2. The road section weather prediction method according to claim 1, wherein The section information includes the section length. Before obtaining the grid weather prediction data corresponding to each section of the target road according to the section information, it further includes: Compare the section length of each section with the grid side length of the weather grid; In the case where the section length of a section is greater than the grid side length, break the section to obtain at least two short sections, and obtain the corresponding section information.

3. The road section weather prediction method according to claim 1 or 2, characterized in that, The section information includes the longitude and latitude coordinates of the section. The obtaining of the grid weather prediction data corresponding to each section of the target road according to the section information includes: Determine the central longitude and latitude coordinates of each section according to the longitude and latitude coordinates of each section of the target road; Determine the weather grid corresponding to each central longitude and latitude coordinate according to the central longitude and latitude coordinates of each section, and obtain the corresponding grid weather prediction data.

4. The road section weather prediction method according to claim 1 or 2, characterized in that, The section information includes the longitude and latitude coordinates of the section. The obtaining of the grid weather prediction information corresponding to each section of the target road according to the section information includes: Determine at least one weather grid corresponding to each section according to the longitude and latitude coordinates of each section of the target road; For each section, fuse the grid weather prediction data of at least one weather grid of the section according to the proportion of the section in each weather grid to obtain the grid weather prediction information of the section.

5. The road section weather prediction method according to claim 1, characterized in that, After generating the weather prediction information of each section based on the grid weather prediction data corresponding to each section, it further includes: Statistically calculate the proportion of sections with bad weather on the target road according to the weather prediction information of each section; Determine the corresponding road response strategy based on the proportion.

6. The road section weather prediction method according to claim 1, characterized in that After generating the weather prediction information of each section based on the grid weather prediction data corresponding to each section, it further includes: When the weather prediction information meets the preset early warning conditions, generate and push early warning prompt information.

7. The road segment weather prediction method according to claim 1, characterized in that The generating of the weather prediction information of each section based on the grid weather prediction data corresponding to each section includes: Input the grid weather prediction data corresponding to each section into a weather prediction model to obtain the weather prediction information of each section, and the weather prediction model is trained according to historical grid weather data.

8. A road section weather prediction device, characterized in that, The device includes: A section information acquisition module for acquiring the section information of the target road; A prediction data acquisition module for acquiring the grid weather prediction data corresponding to each section of the target road according to the section information; A prediction information generation module for generating the weather prediction information of each section based on the grid weather prediction data corresponding to each section.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program stored on the memory and executable on the processor. When the program is executed by the processor, it implements the steps of the section weather prediction method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Instructions are stored on the computer-readable storage medium, and when the instructions are executed by a processor, the steps of the road segment weather prediction method according to any one of claims 1-7 are implemented.

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