Wind condition early warning method, storage medium, program product, equipment, system and carrier

Abstract

The invention relates to a wind condition early warning method, a storage medium, a program product, equipment, a system and a carrier. The wind regime early warning method comprises the step of carrying out wind regime early warning processing on a carrier according to current wind regime data and wind regime feature data of an environment where the carrier is located. According to the method, wind condition early warning can be carried out on the carrier in time through wind condition early warning processing, a user or passerby and the like can be helped to take measures in time to protect the carrier, and the safety of the carrier is improved. Moreover, during wind regime early warning processing, real-time data of the current wind regime are considered, the influence of the environment where the carrier is located on the wind regime is also considered, and the accuracy and reliability of wind regime early warning are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a wind condition warning method, storage medium, program product, device, system and carrier. Background Art

[0002] With the advancement of modern technology, the safety of vehicles and other vehicles is receiving increasing attention. In strong winds, trees and branches in areas like green belts and parking lots can collapse or fall due to the strong winds, threatening the safety of parked vehicles below. Therefore, providing early warnings of wind conditions for vehicles to improve their safety is a pressing technical challenge. Summary of the Invention

[0003] The embodiments of the present application provide a wind condition warning method, storage medium, program product, device, system and vehicle, which can provide wind condition warning for the vehicle, improve the safety of the vehicle, and at least partially solve the above-mentioned technical problems.

[0004] In order to achieve the above-mentioned purpose, according to the first aspect of the present application, a wind condition warning method is provided, comprising: performing wind condition warning processing on the vehicle based on current wind condition data and wind condition characteristic data of the environment in which the vehicle is located.

[0005] Optionally, the current wind condition data includes at least one of the following: current wind speed, current wind direction, and current wind force.

[0006] Optionally, the wind condition characteristic data includes at least one of the following: wind condition prediction data, geographical environment data, historical wind condition data, and wind condition fluctuation data.

[0007] Optionally, the wind condition prediction data includes at least one of the following: predicted wind speed, predicted wind speed intensity, predicted wind force, and predicted wind direction.

[0008] Optionally, the geographic environment data includes at least one of the following: vegetation density, building density.

[0009] Optionally, the historical wind condition data includes at least one of the following: historical wind speed, historical wind direction, and historical wind force.

[0010] Optionally, the wind condition fluctuation data includes at least one of the following: wind speed standard deviation, wind speed change rate.

[0011] Optionally, the wind condition warning processing is performed on the vehicle based on the current wind condition data and the wind condition characteristic data of the environment in which the vehicle is located, including: determining the wind condition warning threshold value based on the current wind condition data and the wind condition characteristic data of the environment in which the vehicle is located; and performing wind condition warning processing on the vehicle based on the current wind condition data and the wind condition warning threshold value.

[0012] Optionally, determining the wind condition warning threshold based on the current wind condition data and the wind condition characteristic data of the vehicle's environment includes: correcting the initial warning threshold based on the current wind condition data and the wind condition characteristic data of the vehicle's environment to obtain the wind condition warning threshold.

[0013] Optionally, the initial warning threshold is corrected according to the current wind condition data and the wind condition characteristic data of the vehicle's environment to obtain the wind condition warning threshold, including: determining at least one wind condition correction parameter according to the current wind condition data and the wind condition characteristic data of the vehicle's environment; and correcting the initial warning threshold according to at least one of the wind condition correction parameters to obtain the wind condition warning threshold.

[0014] Optionally, the initial warning threshold is corrected according to at least one of the wind condition correction parameters to obtain the wind condition warning threshold, including: performing statistical processing on at least one of the wind condition correction parameters to obtain a wind condition correction statistic; and summing the initial warning threshold and the wind condition correction statistic to obtain the wind condition warning threshold.

[0015] Optionally, the correcting the initial warning threshold according to at least one of the wind condition correction parameters to obtain the wind condition warning threshold includes: performing product processing on the initial warning threshold and at least one of the wind condition correction parameters to obtain the wind condition warning threshold.

[0016] Optionally, the initial warning threshold is corrected according to the current wind condition data and the wind condition characteristic data of the vehicle's environment to obtain the wind condition warning threshold, including: correcting the initial warning threshold according to the current wind condition data and the wind condition characteristic data of the vehicle's environment to obtain an intermediate warning threshold; and determining the wind condition warning threshold based on the intermediate warning threshold within the first time period.

[0017] Optionally, determining the wind condition warning threshold value based on the intermediate warning threshold value within the first time period includes: averaging the intermediate warning threshold values ​​within the first time period to obtain the wind condition warning threshold value.

[0018] Optionally, the method further includes: determining the initial warning threshold value based on the site design wind speed and vehicle structure parameters; wherein the site design wind speed refers to the maximum wind speed that may occur within the recurrence period.

[0019] Optionally, the vehicle structure parameters include a vehicle structure protection level.

[0020] Optionally, performing wind condition warning processing on the vehicle based on the current wind condition data and the wind condition warning threshold includes: if the comparison result of the current wind condition data and the wind condition warning threshold meets a first condition, performing a wind condition warning operation on the vehicle.

[0021] Optionally, the first condition includes: within a second time period, the current wind condition data exceeds the wind condition warning threshold.

[0022] Optionally, the first condition includes: within a third time period, the difference between the current wind condition data and the wind condition warning threshold is within a first range, and the current wind condition data shows a deteriorating trend.

[0023] Optionally, the wind condition warning processing is performed on the vehicle based on the current wind condition data and the wind condition warning threshold, including: determining the wind condition warning level based on the current wind condition data and the wind condition warning threshold; and performing wind condition warning operations on the vehicle according to the wind condition warning level.

[0024] Optionally, for different wind condition warning levels, the parameters of the wind condition warning operation are different, and / or the types of the wind condition warning operation are different.

[0025] Optionally, after performing the wind condition warning operation on the vehicle, the method further includes: if a second condition is met, stopping performing the wind condition warning operation on the vehicle.

[0026] Optionally, the second condition includes: within a fourth time period, the current wind condition data does not exceed the wind condition warning threshold.

[0027] Optionally, the second condition includes: receiving an early warning confirmation instruction and / or an early warning closing instruction.

[0028] Optionally, the wind condition warning operation includes at least one of the following: adjusting the lighting of the vehicle, adjusting the audio of the vehicle, adjusting the display content of the vehicle, controlling the vibration of a target module in the vehicle, and sending wind condition warning information to a target device.

[0029] Optionally, the wind condition warning information includes at least one of the following: current wind condition data, historical wind condition data, and wind condition warning level.

[0030] According to a second aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned wind condition warning method is implemented.

[0031] According to a third aspect of the present application, a computer program product is provided, comprising a computer program, wherein the computer program implements the above-mentioned wind condition warning method when executed by a processor.

[0032] According to a fourth aspect of the present application, an electronic device is provided, comprising: a memory storing a computer program; and a processor for executing the computer program in the memory to implement the above-mentioned wind condition warning method.

[0033] According to the fifth aspect of the present application, a wind condition warning system is provided, which includes a controller and a vehicle; wherein the controller is used to perform wind condition warning processing on the vehicle based on current wind condition data and wind condition characteristic data of the environment in which the vehicle is located.

[0034] Optionally, the controller is arranged on the vehicle.

[0035] Optionally, the controller includes a threshold determination module and a wind condition warning module; wherein, the threshold determination module is used to determine the wind condition warning threshold based on current wind condition data and wind condition characteristic data of the vehicle's environment; the wind condition warning module is used to perform wind condition warning processing on the vehicle based on the current wind condition data and the wind condition warning threshold.

[0036] Optionally, the wind condition warning module includes a first warning module and a second warning module; wherein, the first warning module is used to perform at least one of the following: adjusting the lighting of the vehicle, adjusting the audio of the vehicle, adjusting the display content of the vehicle, and controlling the vibration of the target module in the vehicle; the second warning module is used to send wind condition warning information to the target device.

[0037] Optionally, the wind condition warning system further includes a detection device; wherein the detection device is used to send the current wind condition data to the controller.

[0038] Optionally, the detection device includes an ultrasonic anemometer.

[0039] Optionally, the detection device is arranged on the carrier.

[0040] According to a sixth aspect of the present application, a vehicle is provided, comprising the above-mentioned electronic device, or comprising the above-mentioned wind condition warning system.

[0041] The technical solution provided by the embodiments of the present application performs wind condition warning processing for a vehicle based on current wind condition data and wind condition characteristic data of the vehicle's environment. This wind condition warning processing allows for timely wind condition warnings for the vehicle, helping users and passersby take timely measures to protect the vehicle and improve vehicle safety. Furthermore, the embodiments of the present application consider not only the current wind condition data (real-time data) but also the impact of the vehicle's environment on wind conditions, improving the accuracy and reliability of wind condition warnings.

[0042] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0044] In order to more completely understand the present application and its beneficial effects, the following description will be given in conjunction with the accompanying drawings, wherein the same drawing numbers represent the same parts in the following description.

[0045] Figure 1 This is a flow chart of a wind condition warning method provided in an embodiment of the present application;

[0046] Figure 2 is a schematic diagram of a wind condition warning system provided in an embodiment of the present application;

[0047] Figure 3 This is a schematic diagram of the working principle of an ultrasonic anemometer provided in an embodiment of the present application;

[0048] Figure 4 Schematic diagram of a wind condition warning method provided in an embodiment of the present application;

[0049] Figure 5 It is a schematic diagram of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0051] According to the first aspect of the present application, an embodiment of the present application provides a wind condition warning method.

[0052] See also Figure 1 , Figure 1 This is a flow chart of a wind warning method provided by an embodiment of the present application. Figure 1 As shown, the wind condition warning method may include the following steps:

[0053] Step S100: Perform wind condition warning processing on the vehicle based on the current wind condition data and the wind condition characteristic data of the environment in which the vehicle is located.

[0054] The current wind condition data is used to indicate the current actual wind condition of the environment in which the vehicle is located. The current wind condition data can be obtained by measuring using sensors onboard the vehicle, for example, an air pressure sensor, an anemometer, an ultrasonic anemometer, or the like.

[0055] The wind condition characteristic data of the vehicle's environment is used to indicate the impact of the vehicle's environment on the wind conditions. Different environments have different impacts on wind conditions. For example, the wind condition limits that can be achieved or have been achieved in the past in different environments are different. For example, the wind condition limits that can be achieved or have been achieved in the past in an indoor parking lot environment and an urban open road environment are different. In addition, the same wind conditions in different environments have different impacts on the safety of the vehicle. For example, in an indoor parking lot environment and an urban green belt environment, even if the wind conditions are the same, the urban green belt environment has more trees and is prone to falling branches, which is more likely to affect the safety of the vehicle.

[0056] The embodiments of the present application do not limit the environment in which the vehicle is located. In some embodiments, the environment in which the vehicle is located includes, but is not limited to, any of the following: an indoor parking environment, an outdoor parking environment, an urban green belt environment, a rural road environment, a sandy environment, etc. The embodiments of the present application can obtain corresponding wind condition characteristic data after determining the environment in which the vehicle is located. For example, there is a mapping relationship between different environments and different wind condition characteristic data, and the corresponding wind condition characteristic data can be searched and obtained from the mapping relationship based on the environment in which the vehicle is located.

[0057] In step S100, a wind condition warning process is performed on the vehicle based on the current wind condition data and the wind condition characteristic data of the environment in which the vehicle is located.

[0058] Among them, the wind condition warning processing may include wind condition warning analysis and / or wind condition warning operation. The wind condition warning analysis is used to determine whether to execute the wind condition warning operation based on the current wind condition data and the wind condition characteristic data of the vehicle's environment. The embodiments of the present application do not limit the specific method of wind condition warning analysis, and in actual application, it can be flexibly selected based on the needs. For example, the current wind condition data can be updated based on the wind condition characteristic data of the vehicle's environment, and then the updated current wind condition data and the threshold comparison result are used to determine whether to execute the wind condition warning operation; or, a threshold can be determined based on the current wind condition data and / or the wind condition characteristic data of the vehicle's environment, and then the current wind condition data and the threshold comparison result are used to determine whether to execute the wind condition warning operation. The embodiments of the present application do not limit the specific method of wind condition warning operation, and in actual application, it can be flexibly selected based on the needs. For example, the wind condition warning operation can include at least one of the following: flashing lights, honking the horn, steering wheel vibration, and seat vibration. For other introductions and descriptions of wind condition warning processing, please refer to the following embodiments, which will not be elaborated here.

[0059] In summary, the technical solution provided by the embodiments of the present application performs wind condition warning processing for a vehicle based on current wind condition data and wind condition characteristic data of the vehicle's environment. This wind condition warning processing allows for timely wind condition warnings for the vehicle, helping users and passersby take timely measures to protect the vehicle and improve vehicle safety. Furthermore, when processing wind condition warnings, the embodiments of the present application not only consider the real-time data of the current wind conditions, but also consider the impact of the vehicle's environment on the wind conditions, thereby improving the accuracy and reliability of wind condition warnings.

[0060] In some embodiments, the current wind condition data includes at least one of the following: current wind speed, current wind direction, and current wind force. Multi-dimensional wind condition data can provide richer and more comprehensive considerations when providing wind condition warnings, thereby improving the accuracy of wind condition warnings.

[0061] In some embodiments, the wind characteristic data for the vehicle's environment includes at least one of the following: wind forecast data, geographic environment data, historical wind data, and wind fluctuation data. Multidimensional wind characteristic data can more comprehensively and fully characterize the impact of the vehicle's environment on wind conditions, facilitating more accurate wind warning processing.

[0062] Wind condition prediction data refers to predicted wind conditions in the vehicle's environment, and can be obtained through weather forecasts, for example. In some embodiments, the wind condition prediction data includes at least one of the following: predicted wind speed, predicted wind speed intensity, predicted wind force, and predicted wind direction. By combining this wind condition prediction data, potential vehicle safety risks can be predicted in advance, enabling more intelligent wind condition warning processing.

[0063] Geographical environment data indicates the environmental characteristics of the vehicle's environment and can be obtained through navigation or sensors. In some embodiments, the geographic environment data includes at least one of the following: vegetation density and building density. By incorporating geographic environment data, the impact of different environmental characteristics on vehicle safety can be fully considered, improving the accuracy of wind warning processing.

[0064] Historical wind data indicates the historical wind conditions of the vehicle's environment and can be obtained through navigation or other means. In some embodiments, the historical wind data includes at least one of the following: historical wind speed, historical wind direction, and historical wind force. By incorporating this historical wind data, the potential risk of extreme wind conditions in the environment can be considered, making wind warning processing more comprehensive and accurate.

[0065] Wind fluctuation data indicates fluctuations in wind conditions in the vehicle's environment. This data can be further determined based on historical wind data. In some embodiments, this data includes at least one of the following: wind speed standard deviation and wind speed rate of change. This data can reflect the fluctuation trend of wind conditions, helping to proactively identify potentially dangerous wind conditions.

[0066] In some embodiments, the above step S100 may include the following steps:

[0067] Step S110: determining a wind condition warning threshold based on current wind condition data and wind condition characteristic data of the vehicle's environment;

[0068] Step S120: Perform wind condition warning processing on the vehicle based on the current wind condition data and the wind condition warning threshold.

[0069] The embodiments of the present application can combine current wind data with wind characteristic data of the vehicle's environment to determine a wind warning threshold. Based on the current wind data and the wind warning threshold, a wind warning process can be performed on the vehicle. For example, a comparison of the current wind data with the wind warning threshold can be used to determine whether to perform a wind warning operation on the vehicle. Determining the wind warning threshold facilitates wind warning analysis and helps improve wind warning efficiency.

[0070] It should be understood that the wind warning threshold may include one or more thresholds. Embodiments of the present application may determine some or all of the thresholds in the wind warning threshold based on current wind condition data and wind characteristic data of the vehicle's environment. For example, the wind warning threshold may include a first threshold and a second threshold. The first threshold may be a preset threshold, and the second threshold may be determined based on current wind condition data and wind characteristic data of the vehicle's environment. In addition, when determining multiple thresholds in the wind warning threshold in embodiments of the present application, the data referenced by each threshold may be the same or different. For example, the wind warning threshold may include a first threshold and a second threshold. The first threshold may be determined based on current wind condition data and historical wind condition data and wind condition fluctuation data in the wind characteristic data, and the second threshold may be determined based on current wind condition data and geographical environment data in the wind characteristic data. Alternatively, the first threshold and the second threshold may be determined based on current wind condition data and the same wind characteristic data, but the degree of influence of each data on the threshold may be different, such as giving more weight to current wind condition data when determining the first threshold, and giving more weight to wind condition characteristic data when determining the second threshold.

[0071] In some embodiments, the above step S110 may include: modifying the initial warning threshold according to the current wind condition data and the wind condition characteristic data of the vehicle's environment to obtain the wind condition warning threshold.

[0072] The initial warning threshold may be preset or determined based on the vehicle's environment, etc., and is not limited in this embodiment of the present application. For example, the initial warning threshold may be 8 m / s, 10 m / s, or 12 m / s. For further information on how to determine the initial warning threshold, please refer to the following embodiments and will not be elaborated upon here.

[0073] The embodiment of the present application corrects the initial warning threshold based on the current wind condition data and the wind condition characteristic data of the vehicle's environment, dynamically adjusts the warning threshold, optimizes the triggering conditions of the wind condition warning, and improves the accuracy of wind condition warning processing.

[0074] In some embodiments, the above-mentioned correction of the initial warning threshold based on the current wind condition data and the wind condition characteristic data of the vehicle environment to obtain the wind condition warning threshold may include the following steps:

[0075] Step S111: determining at least one wind condition correction parameter based on current wind condition data and wind condition characteristic data of the vehicle's environment;

[0076] Step S112: Correcting the initial warning threshold according to at least one wind condition correction parameter to obtain a wind condition warning threshold.

[0077] In this embodiment, at least one wind condition correction parameter can be determined for each factor affecting the warning threshold, and the initial warning threshold can be corrected based on the at least one wind condition correction parameter. This method can quantify the degree to which the current wind condition data and the wind condition characteristic data of the vehicle's environment affect the wind condition warning.

[0078] In some embodiments, step S112 may include: performing statistical processing on at least one wind condition correction parameter to obtain a wind condition correction statistic; and summing the initial warning threshold and the wind condition correction statistic to obtain the wind condition warning threshold. Statistical processing includes, but is not limited to, summing, averaging, or weighted summing. By calculating the wind condition correction statistic, multiple factors influencing the warning threshold are integrated.

[0079] For example, the wind condition warning threshold can be calculated using the following formula 1.

[0080] Formula 1:

[0081] Among them, T adjusted is the wind warning threshold; T init is the initial warning threshold; △T i is the i-th wind condition correction parameter; w i is the weight of the i-th wind condition correction parameter. The sum of the weights of n wind condition correction parameters is equal to 1, and n is a positive integer.

[0082] For example, if the current wind speed is greater than the initial warning threshold, the initial warning threshold can be adjusted upward. Otherwise, the initial warning threshold can be adjusted downward. The wind correction parameter corresponding to the current wind speed can be calculated using the following formula 2.

[0083] Formula 2: ΔT wind =k1×(v current -T init )

[0084] Among them, △T wind is the wind correction parameter corresponding to the current wind data; k1 is the adjustment coefficient; v current is the current wind speed.

[0085] For example, if the wind condition characteristic data for the vehicle's environment, including wind forecast data, includes extreme weather conditions such as storms and typhoons, the warning threshold can be lowered to enhance the sensitivity of the wind warning. The wind correction parameter corresponding to the wind forecast data can be calculated using the following formula 3.

[0086] Formula 3: ΔT forecast =k²×forecast_severity

[0087] Among them, △T forecast is the wind correction parameter corresponding to the wind forecast data; k2 is the adjustment coefficient; forecast_severity is the predicted wind speed intensity, such as the predicted wind speed intensity of typhoons and storms.

[0088] For example, the wind condition characteristic data for the vehicle's environment, including geographic data, can be used to assess risk based on this data. Factors such as tree density will influence the warning threshold. If the vehicle's environment is unsafe, the warning threshold is lowered. The wind correction parameter corresponding to the geographic data can be calculated using the following formula 4.

[0089] Formula 4: ΔT env =k3×tree_density

[0090] Among them, △T env is the wind condition correction parameter corresponding to the geographical environment data; k3 is the adjustment coefficient; tree_density is the vegetation density, such as tree density.

[0091] Taking the wind characteristic data of the vehicle's environment, including wind fluctuation data, as an example, if the wind speed fluctuates significantly, the warning threshold can be lowered. The wind correction parameter corresponding to the wind fluctuation data can be calculated using the following formula 5.

[0092] Formula 5: ΔT fluctuation =k4×σ(vwind )

[0093] Among them, △T fluctuation is the wind correction parameter corresponding to the wind fluctuation data; k4 is the adjustment coefficient; σ(v wind ) is the standard deviation of wind speed; v wind The historical wind speed.

[0094] Each of these adjustment coefficients can be derived through regression analysis of historical data and automatically adjusted through machine learning. In practical applications, machine learning and big data analysis can be used to predict warning thresholds based on historical data and wind speed patterns. This can be combined with intelligent learning based on historical vehicle damage (e.g., whether a vehicle has been damaged by wind in the past) to optimize the adjustment of warning thresholds.

[0095] In some embodiments, step S112 may include performing a quadrature process on the initial warning threshold and at least one wind condition correction parameter to obtain the wind condition warning threshold. The quadrature process enables integration of multiple factors affecting the warning threshold.

[0096] Taking the current wind condition data including the current wind speed and the wind condition prediction data including the predicted wind speed and the predicted wind speed intensity as an example, the wind condition correction parameters corresponding to the current wind condition data and the wind condition prediction data can be calculated using the following formula 6.

[0097] Formula 6:

[0098] Among them, W factor v is the wind correction parameter corresponding to the current wind condition data and the wind condition forecast data; forecast is the predicted wind speed; v current is the current wind speed; Strom_level is the predicted wind speed intensity, such as typhoon levels 0 to 4; γ is the credibility coefficient of the wind forecast data. For example, when a typhoon red alert appears in the wind forecast data, γ can be set to 0.7 or 0.8, and in other cases it can be set to 0.4, 0.5 or 0.6.

[0099] Taking geographic environment data including vegetation density and building density as an example, the wind condition correction parameter corresponding to the geographic environment data can be calculated using the following formula 7.

[0100] Formula 7: G factor =1-(tree_density×0.3+building_density×0.2)

[0101] G_factor is the wind correction parameter corresponding to the geographic environment data; tree_density is the vegetation density, such as tree density; and building_density is the building density. Vegetation density can be the vegetation coverage within a radius of 80m, 90m, or 100m, and can range from 0 to 1. Building density can be the product of the average building height (m) and the coverage ratio (range 0 to 1).

[0102] Taking the historical wind condition data including historical wind speed and the wind condition fluctuation data including wind speed standard deviation as an example, the wind condition correction parameters corresponding to the historical wind condition data and the wind condition fluctuation data can be calculated using the following formula 8.

[0103] Formula 8:

[0104] Among them, T_factor is the wind correction parameter corresponding to the historical wind data and wind fluctuation data; σ is the wind speed standard deviation, such as the wind speed standard deviation between historical wind speeds within a range of 4 minutes or 5 minutes; is the wind speed change rate, which can be calculated using the following formula 9.

[0105] Formula 9:

[0106] Among them, v_current is the current wind speed; v_ago is the historical wind speed, such as the wind speed half a minute or one minute ago; △t is the time interval between the current moment and the moment corresponding to the historical wind speed. If the historical wind speed is the wind speed one minute ago, △t can be 60 seconds.

[0107] Based on the above formulas 6 to 9, the wind condition warning threshold can be calculated using the following formula 10.

[0108] Formula 10:Dynamic_Threshold=Base_Threshold×W factor ×G factir ×T factor

[0109] Among them, Dynamic_Threshold is the wind warning threshold; Base_Threshold is the initial warning threshold; W_factor is the wind correction parameter corresponding to the current wind condition data and the wind condition forecast data, which can be calculated by the above formula 6; G_factor is the wind correction parameter corresponding to the geographical environment data, which can be calculated by the above formula 7; T_factor is the wind correction parameter corresponding to the historical wind condition data and the wind condition fluctuation data, which can be calculated by the above formulas 8 and 9.

[0110] In some embodiments, the above step S110 may include: correcting the initial warning threshold according to the current wind condition data and the wind condition characteristic data of the vehicle environment to obtain an intermediate warning threshold; and determining the wind condition warning threshold according to the intermediate warning threshold within the first time period.

[0111] The embodiment of the present application does not limit the length of the first time period. For example, the length of the first time period can be 10 minutes, 15 minutes, or 30 minutes, etc., and can be flexibly set according to actual needs in actual applications. In addition, the method for determining the intermediate warning threshold can refer to the method for determining the wind condition warning threshold in the above embodiment, and will not be further described here.

[0112] By setting the first time period as a time window and processing the corrected intermediate warning threshold within the first time period to determine the wind warning threshold, frequent or excessive adjustments to the wind warning threshold can be avoided, the adjustment process of the wind warning threshold can be smoothed, and frequent wind warnings due to short-term wind fluctuations can be avoided.

[0113] In some embodiments, determining the wind warning threshold based on the intermediate warning thresholds within the first time period includes averaging the intermediate warning thresholds within the first time period to obtain the wind warning threshold. Of course, in actual applications, the intermediate warning thresholds within the first time period may also be weighted averaged to determine the wind warning threshold.

[0114] For example, the wind condition warning threshold can be calculated using the following formula 11.

[0115] Formula 11:

[0116] Among them, T adjusted,smoothed is the wind warning threshold; T adjusted(i) is the intermediate warning threshold at each moment in the first time period; N is the number of correction time points in the first time period, and the initial warning threshold is corrected at each correction time point to obtain the intermediate warning threshold.

[0117] In some embodiments, the wind warning method may further include determining an initial warning threshold based on the site design wind speed and vehicle structural parameters. By combining the site design wind speed and vehicle structural parameters, the initial warning threshold can be specifically determined based on the vehicle and its environment, improving the compatibility of the initial warning threshold with the vehicle and its environment, thereby improving the accuracy of the wind speed warning threshold.

[0118] The site design wind speed (DWS) refers to the maximum wind speed that is expected to occur within a return period. Based on long-term meteorological observations and probabilistic statistical methods, the maximum wind speed expected to occur within a specific return period can be determined at a specific location. This DWS reflects the potential risk of extreme wind conditions in that environment.

[0119] The return period can refer to the statistically significant time interval over which a certain wind speed may occur. For example, a 50-year return period wind speed is a wind speed that is likely to be exceeded once every 50 years. Furthermore, the site's design wind speed may also be affected by the base height and landform type. The base height is typically set at approximately 10 meters above the ground (e.g., the standard measurement height of a meteorological station). Landform types include, but are not limited to, open areas, urban areas, forests, and other areas with varying surface roughness.

[0120] The site design wind speed can be obtained by looking up a table. For example, it can be obtained based on standards issued by relevant institutions, wind speed contour maps, global wind atlas tools, Global Wind Atlas (Global Wind Energy Association), WINDSPEEDDIRECTION (European Wind Atlas), etc., and the corresponding site design wind speed can be found through the above tools or data according to the location coordinates of the vehicle's environment. For example, the basic wind pressure during the recurrence period can be found based on the vehicle's environment, such as 0.55kN / m 2 , convert the basic wind pressure into the site design wind speed, such as 28.1m / s.

[0121] Alternatively, the site design wind speed can be estimated using short-term observations combined with statistical methods. The short-term observations can be based on an observation period of six months, one year, or two years or more. For example, the site design wind speed can be calculated using the following formula 12.

[0122] Formula 12: V τ =V avg +K×σ

[0123] Among them, V τ Design wind speed for the site; V avg is the average maximum wind speed during the observation period; σ is the standard deviation of wind speed; K is the coefficient related to the recurrence period, which can be obtained by looking up the table.

[0124] For example, the basic wind pressure of the vehicle's environment within a 50-year return period is 0.75 kN / m 2 , convert the basic wind pressure into wind speed using the following formula 13.

[0125] Formula 13:

[0126] In Formula 13, the air density can be taken as 1.25 kg / m.

[0127] Afterwards, the wind speed can be corrected according to the benchmark height and landform type using the following formula 14 to obtain the site design wind speed.

[0128] Formula 14: V site =V met ×K z ×K zt

[0129] Among them, V site Design wind speed for the site; V met The wind speed measured by the weather station, such as obtained by converting the above formula 13; K z is the height correction factor, which can be referred to the building code; K zt It is the terrain correction coefficient. For example, the valley terrain can be set to 1.3, the urban terrain can be set to 0.85, etc.

[0130] Taking the urban terrain as an example, based on the above formulas 13 and 14, it can be obtained that the site design wind speed of the vehicle's environment is 34.6×0.85≈29.4m / s.

[0131] In addition to the site design wind speed, the initial warning threshold is also determined in combination with the vehicle structure parameters. In some embodiments, the vehicle structure parameters include the vehicle structure protection level. The vehicle structure protection level is used to indicate the structural strength of the vehicle. The vehicle structure protection level can be obtained by looking up a table, etc. For example, taking the vehicle as an example, the structural protection level of an ordinary vehicle can be set to 0.6, the structural protection level of a multi-purpose vehicle and a multi-purpose vehicle can be set to 0.8, and the structural protection level of a truck and other special vehicles can be set to 1.2.

[0132] Exemplarily, the initial warning threshold can be calculated using the following formula 15.

[0133] Formula 15: Base_Threshold=0.3×C protect ×V design +0.7×V design

[0134] Among them, Base_Threshold is the initial warning threshold; C protect V is the vehicle structure protection level; design Design the wind speed for the site. For example, if the vehicle is located in a coastal city, the design wind speed for the site can be 32m / s; if the vehicle is located in an inland city, the design wind speed for the site can be 28m / s.

[0135] The following describes the process of determining wind warning parameters using a specific example.

[0136] Taking the vehicle structure protection level of 0.7, the vehicle environment of an inland urban parking lot, and the site design wind speed of 28m / s as an example, the initial warning threshold can be calculated using the following formula 16.

[0137] Formula 16: 0.3 × 0.8 × 28 + 0.7 × 28 = 6.72 + 19.6 = 26.322 m / s

[0138] The current wind condition data includes the current wind speed, such as the current wind speed is 25m / s; the predicted wind condition data includes the predicted wind speed, such as the predicted wind speed is 30m / s; the predicted wind condition data also includes the predicted wind speed intensity, such as a typhoon level 2 warning; the wind condition correction parameters corresponding to the current wind condition data and the predicted wind condition data can be calculated using the following formula 17.

[0139] Formula 17:

[0140] The geographical environment data includes vegetation density, such as vegetation density of 0.4; the geographical environment data also includes building density, such as building density of 0.3; then the wind condition correction parameter corresponding to the geographical environment data can be calculated using the following formula 18.

[0141] Formula 18: G factor =1-(0.4×0.3+0.3×0.2)=1-0.18=0.82

[0142] The wind condition fluctuation data includes the wind speed standard deviation, such as the wind speed standard deviation is 3m / s; the wind speed fluctuation data also includes the wind speed change rate. Integrating the wind speed change rate can obtain the risk factor, such as the risk factor is 0.2; then the wind condition correction parameter corresponding to the wind condition fluctuation data can be calculated using the following formula 19.

[0143] Formula 19: T factor =1+0.2+3×0.1=1.5

[0144] Based on this, the wind warning threshold can be calculated using the following formula 20.

[0145] Formula 20: 26.32×1.5×0.82×1.5≈48.6m / s

[0146] To sum up, the technical solution provided in the embodiment of the present application automatically adjusts the wind warning threshold by integrating current wind condition data, wind condition forecast data, geographical environment data, and wind condition fluctuation data, so as to automatically optimize the wind warning strategy for different weather and environments, provide timely wind condition warnings, and reduce the risk of vehicle damage.

[0147] In some embodiments, the above step S120 may include: if the comparison result between the current wind condition data and the wind condition warning threshold satisfies a first condition, performing a wind condition warning operation on the vehicle.

[0148] The present embodiment compares current wind data with a wind warning threshold to determine whether to initiate a wind warning operation for a vehicle. This is simple and efficient. For example, the current wind data includes the current wind speed, and the current wind speed is compared with the wind warning threshold to determine whether to initiate a wind warning operation.

[0149] It should be understood that the wind condition warning threshold may include one or more thresholds. Taking the example of the wind condition warning threshold including one threshold, if the current wind speed is greater than or equal to the wind condition warning threshold, a wind condition warning operation may be performed on the vehicle. Taking the example of the wind condition warning threshold including a first threshold and a second threshold, if the current wind speed is greater than or equal to the first threshold and less than the second threshold, a wind condition warning operation may be performed on the vehicle in a first manner, such as flashing lights, honking horns, etc.; if the current wind speed is greater than or equal to the second threshold, a wind condition warning operation may be performed on the vehicle in a second manner, such as steering wheel vibration, seat vibration, etc.

[0150] In some embodiments, the first condition includes: within the second time period, current wind condition data exceeds the wind condition warning threshold.

[0151] The embodiment of the present application does not limit the length of the second time period. In actual applications, it can be flexibly set according to needs. For example, the length of the second time period can be set to 5 seconds, 6 seconds, or 8 seconds.

[0152] During the second time period, if the current wind condition data exceeds the wind warning threshold, a wind warning operation can be performed on the vehicle. By setting this second time period as a time window, a delayed warning can be implemented to avoid instantaneous wind fluctuations triggering unnecessary wind warning operations.

[0153] It should be understood that if the wind condition warning threshold includes multiple thresholds, then the current wind condition data exceeds the wind condition warning threshold, which may mean that the current wind condition data exceeds the threshold with the largest value among the wind condition warning thresholds, or it may mean that the current wind condition data exceeds the threshold with the smallest value among the wind condition warning thresholds. The embodiments of the present application do not limit this.

[0154] In some embodiments, the first condition includes: within a third time period, the difference between the current wind condition data and the wind condition warning threshold is within a first range, and the current wind condition data shows a deteriorating trend.

[0155] The embodiment of the present application does not limit the duration of the third time period. In actual application, it can be flexibly set according to needs. For example, the duration of the third time period can be set to 3 seconds, 5 seconds, or 9 seconds. The embodiment of the present application does not limit the first range. In actual application, it can be flexibly set according to needs. For example, the first range can be 1m / s, 2m / s, or 2.5m / s.

[0156] During the third time period, if the difference between the current wind data and the wind warning threshold falls within the first range and the current wind data shows a deteriorating trend—that is, if the current wind data approaches the wind warning threshold and continues to rise—a wind warning operation can be initiated for the vehicle. Setting these conditions allows for redundant wind condition determination, triggering warnings in advance for deteriorating wind conditions, thus avoiding missed opportunities to protect vehicles due to sudden wind exceeding the threshold.

[0157] In some embodiments, the above step S120 may include: determining a wind warning level based on current wind condition data and a wind warning threshold; and performing a wind warning operation on the vehicle according to the wind warning level.

[0158] The embodiment of the present application does not limit the number of wind warning levels, and the number of wind warning levels may be related to the number of wind warning thresholds. For example, when the wind warning threshold includes one threshold, the wind warning level may include two levels; when the wind warning threshold includes two thresholds, the wind warning level may include three levels. For different wind warning levels, the execution method of the wind warning operation is different. For example, the embodiment of the present application may establish a mapping relationship between the wind warning level and the wind warning operation. After determining the wind warning level, the corresponding wind warning operation is searched and obtained according to the mapping relationship.

[0159] By dividing wind warning levels and executing wind warning operations according to the wind warning levels, a multi-level warning response can be achieved, which improves the pertinence and effectiveness of wind warnings.

[0160] In some embodiments, the execution method of the wind warning operation includes parameters and / or types of the wind warning operation. Thus, for different wind warning levels, the parameters of the wind warning operation are different and / or the types of wind warning operations are different. For example, the types of wind warning operations include flashing lights, playing audio, honking horns, and seat vibrations; and the parameters of the wind warning operation include light brightness, audio volume, vibration frequency, etc.

[0161] Exemplarily, the wind condition warning threshold includes a first threshold and a second threshold, and the first threshold is less than the second threshold; the wind condition warning levels include a safety level, a warning level, and a critical level from low to high. When the current wind condition data is less than the first threshold, the wind condition warning level is a safety level, and the wind condition warning operation may not be performed. When the current wind condition data is greater than or equal to the first threshold and less than the second threshold, the wind condition warning level is a warning level, and wind condition warning operations such as flashing warning lights (such as yellow lights or green lights) and playing soft audio (such as short beeps) may be performed. When the current wind condition data is greater than or equal to the second threshold, the wind condition warning level is a critical level, and wind condition warning operations such as flashing warning lights (such as red lights), playing strong audio (such as strong beeps), steering wheel vibration, seat vibration, etc. may be performed.

[0162] In some embodiments, after performing the wind warning operation on the vehicle, the system further includes: if a second condition is met, then stopping the wind warning operation on the vehicle. By setting the second condition, the wind warning operation can be flexibly stopped at any time, improving the intelligence of wind warning. The specific content of the second condition is not limited in this embodiment of the application, and it can be flexibly set according to actual needs in actual application.

[0163] In some embodiments, the second condition includes: within a fourth time period, the current wind condition data does not exceed the wind condition warning threshold. The length of the fourth time period can be flexibly set based on needs. For example, the length of the fourth time period can be 5 seconds, 7 seconds, 8 seconds, or 10 seconds. If the current wind condition data does not exceed the wind condition warning threshold within the fourth time period, the wind condition warning operation can be automatically stopped.

[0164] In some embodiments, the second condition includes: receiving an early warning confirmation instruction and / or an early warning closing instruction. The early warning confirmation instruction and the early warning closing instruction may be generated by user operations. For example, the early warning confirmation instruction may be an instruction generated by the user's confirmation operation on the wind warning information, and the early warning closing instruction may be an instruction generated by the user's closing operation on the wind warning operation. Among them, the user's confirmation operation and closing operation may be based on the user's input such as clicking or sliding on the display screen, control panel or mobile device in the vehicle, or based on the user's input such as voice or gesture. If the early warning confirmation operation and / or the early warning closing operation are received, the wind warning operation can be stopped in time, which provides users with more room for independent choice and improves the flexibility of wind warning.

[0165] In some embodiments, wind warning operations include at least one of the following: adjusting the vehicle's lighting, adjusting the vehicle's audio, adjusting the vehicle's display content, controlling the vibration of a target module in the vehicle, and sending a wind warning message to a target device. By implementing multiple wind warning operations, the flexibility of wind warnings can be enhanced, and wind warnings can be targeted.

[0166] Adjusting the vehicle's lighting includes, but is not limited to, adjusting the color and / or brightness of the lighting; adjusting the vehicle's audio includes, but is not limited to, adjusting the audio type and / or volume; adjusting the vehicle's display content includes, but is not limited to, displaying wind warning information on the vehicle's multimedia device; controlling the vibration of a target module in the vehicle includes, but is not limited to, controlling the vibration of the steering wheel and / or seat; and sending wind warning information to a target device includes, but is not limited to, sending wind warning information to a user's and / or administrator's mobile device. In some embodiments, the wind warning information includes at least one of the following: current wind condition data, historical wind condition data, and a wind warning level.

[0167] To sum up, the technical solution provided in the embodiments of the present application effectively identifies potential dangers of wind conditions and promptly warns users or passers-by through multi-level warnings, delayed warnings, early warnings and / or multiple warning methods, thereby improving the safety of vehicles in severe weather environments, reducing the probability of false warnings, improving the timeliness of wind condition warnings, and ensuring the stability and reliability of wind condition warnings.

[0168] According to a second aspect of the present application, embodiments of the present application further provide a non-transitory computer-readable storage medium having a computer program stored thereon. When executed by a processor, the program implements the steps of the aforementioned wind condition warning method. This non-transitory computer-readable storage medium has all the beneficial effects of the aforementioned wind condition warning method, which are not further elaborated herein.

[0169] According to the third aspect of the present application, an embodiment of the present application further provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the above-mentioned wind condition warning method and has all the beneficial effects of the above-mentioned wind condition warning method. This application will not go into details here.

[0170] According to a fourth aspect of the present application, embodiments of the present application further provide an electronic device comprising: a memory and a processor, wherein the memory stores a computer program; the processor is configured to execute the computer program in the memory to implement the steps of the aforementioned wind condition warning method. This electronic device has all the beneficial effects of the aforementioned wind condition warning method, and this application will not further elaborate on them.

[0171] The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof, and this application does not specifically limit this. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0172] In some embodiments of the present application, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0173] The computer-readable storage medium may be included in the electronic device or may exist independently without being incorporated into the electronic device. The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0174] Based on the current wind condition data and the wind condition characteristic data of the vehicle's environment, the vehicle is given a wind condition warning.

[0175] Computer program code for performing the operations of some embodiments of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network (including a local area network (LAN) or a wide area network (WAN)), or can be connected to an external computer (for example, using an Internet service provider to connect via the Internet).

[0176] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function.

[0177] It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.

[0178] For example, two blocks shown in succession may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block in the block diagrams and / or flow charts, and combinations of blocks in the block diagrams and / or flow charts, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or may be implemented using a combination of dedicated hardware and computer instructions.

[0179] The units described in some embodiments of the present application may be implemented in software or hardware, and may also be provided in a processor.

[0180] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), Application Specific Standard Parts (ASSP), System on Chip (SOC), Complex Programmable Logic Device (CPLD), and the like.

[0181] According to the fifth aspect of the present application, an embodiment of the present application also provides a wind condition warning system.

[0182] See also Figure 2 , Figure 2 This is a schematic diagram of a wind warning system provided by an embodiment of the present application. Figure 2 As shown, the wind condition warning system may include a controller 110 and a vehicle 100 .

[0183] The controller 110 is used to perform wind condition warning processing on the vehicle 100 based on the current wind condition data and the wind condition characteristic data of the environment in which the vehicle 100 is located.

[0184] In some embodiments, the controller 110 is disposed on the vehicle 100 .

[0185] In some embodiments, as Figure 2 As shown, the controller 110 includes a threshold determination module 111 and a wind condition warning module 112 .

[0186] Among them, the threshold determination module 111 is used to determine the wind warning threshold based on the current wind condition data and the wind condition characteristic data of the vehicle's environment; the wind warning module 112 is used to perform wind warning processing on the vehicle based on the current wind condition data and the wind warning threshold.

[0187] In some embodiments, as Figure 2 As shown, the wind condition warning module 112 includes a first warning module 1121 and a second warning module 1122 .

[0188] Among them, the first warning module 1121 is used to perform at least one of the following: adjusting the lights of the vehicle 100, adjusting the audio of the vehicle 100, adjusting the display content of the vehicle 100, and controlling the vibration of the target module in the vehicle 100; the second warning module 1122 is used to send wind condition warning information to the target device.

[0189] In some embodiments, as Figure 2 As shown, the wind condition warning system further includes a detection device 120 .

[0190] The detection device 120 is used to send current wind condition data to the controller 110 .

[0191] In some embodiments, the detection device 120 includes an ultrasonic anemometer. Of course, the detection device 120 may also include a sensor, a laser Doppler anemometer, a mechanical anemometer, etc., which is not limited in this embodiment of the present application.

[0192] In some embodiments, the detection device 120 is disposed on the carrier 100 .

[0193] For an introduction to the execution steps and beneficial effects of each module in the wind warning system, please refer to the above embodiments and will not be elaborated here.

[0194] For example, if detection device 120 includes an ultrasonic anemometer, the ultrasonic anemometer uses an ultrasonic sensor to measure current wind conditions, such as wind speed. The ultrasonic anemometer operates based on the Doppler effect, and the current wind conditions can be determined based on the ultrasonic anemometer's transmitted signal and the corresponding echo signal. For example, the wind speed data can be determined based on the time difference between the ultrasonic anemometer's transmitted signal and the corresponding echo signal.

[0195] For example, an ultrasonic anemometer consists of two sets of transceiver transducers. The time difference between the transmitted and received signals at a fixed distance is calculated as the ratio of the distance to the time difference to determine the actual wind speed relative to the vehicle (calibration and verification are required to account for the Doppler effect). The actual wind speed is then calculated using the actual vehicle speed. Using two sets of ultrasonic transducers capable of both transmitting and receiving, horizontal wind speed in the X and Y directions can be determined.

[0196] like Figure 3 As shown, when the wind speed vector V and the line connecting the transceiver make an angle θ when blowing through the detection unit, the time t1 required for the sound wave emitted from T1 at an angle α to reach R1 along the wind can be calculated by the following formula 21.

[0197] Formula 21: t1 = L / (C*cosα+Vd)

[0198] Where C is the speed of sound in air, V d is the component of V on line L.

[0199] Similarly, for the T2-R2 transducer, the corresponding sound wave propagation time t2 in headwind can be calculated using the following formula 22.

[0200] Formula 22: t2 = L / (C*cosα-V d )

[0201] Based on sinα=V n / C and V 2 =V N 2 +V d 2 The acoustic time difference △t for windward and headwind propagation can be obtained according to the following formula 23.

[0202] Formula 23: △t=t2-t1=2LV d / (C 2 -V 2 )

[0203] The basic formula for wind speed measurement can be derived from formula 23. Then, the wind speed can be measured using the time difference method according to the following formula 24. The wind speed component V in the L direction can be calculated first. d .

[0204] Formula 24: V d =△t*(C 2 -V 2 ) / 2L

[0205] Since usually C 2 Much larger than V 2 , therefore, the following formula 25 can be obtained.

[0206] Formula 25: V d =(C 2 / 2L)*△t(V d Directly proportional to the time difference of sound (△t)

[0207] Because C 2 V minus 2 This is a nonlinear error, with an error of 1% at a wind speed of 35 m / s, 2% at a wind speed of 50 m / s, and 3% at a wind speed of 60 m / s. Therefore, when measuring wind speed using the transit time method, the error primarily stems from nonlinear errors in strong winds and the effects of temperature and humidity on the speed of sound C. Therefore, appropriate compensation strategies are necessary to improve accuracy.

[0208] Based on the above working principle, the ultrasonic anemometer measures wind speed, wind direction and possible wind speed change trends in real time.

[0209] Below, an example is used to introduce and illustrate the wind condition warning system and wind condition warning method provided in the embodiments of the present application.

[0210] like Figure 4 As shown, detection device 120 monitors wind conditions in real time to determine data such as wind speed, wind direction, and wind force. The wind condition data collected in real time by detection device 120 can be transmitted to controller 110 for further processing. In addition, controller 110 can also obtain weather forecasts and other data, such as images of the surrounding environment and historical wind condition data. Based on the obtained weather forecasts and other data, controller 110 can further determine wind condition prediction data, geographical environment data, historical wind condition data, and wind condition fluctuation data.

[0211] Threshold determination module 111 in controller 110 automatically adjusts wind warning thresholds by integrating real-time current wind data, wind forecast data, geographic data, historical wind data, and wind fluctuation data. This allows for timely wind warnings and reduces the risk of vehicle damage. This module can also incorporate intelligent methods such as machine learning to continuously optimize wind warning strategies based on actual conditions, achieving more flexible and accurate wind warnings.

[0212] Threshold determination module 111 considers that the risk to vehicles in high winds is not solely due to wind speed itself, but also includes wind duration, suddenness, and other environmental factors (such as terrain and tree density). Wind speeds and wind impacts may vary across different environments, so a single, fixed warning threshold is often inadequate for all situations. Threshold determination module 111 can optimize the wind warning system's response in real time based on current wind data, weather forecasts, and other data, providing more accurate warnings. For example, when the weather forecast indicates a typhoon or storm, the threshold determination module 111 can lower the warning threshold in advance, triggering a wind warning even if the wind speed increases slightly. The warning threshold is adjusted based on the specific parking location of the vehicle (e.g., based on GPS positioning). Tall buildings in cities may have a shielding effect on wind speed, while wind speed in open areas may directly affect the parked vehicle. If the vehicle is parked in an area with many trees, the warning threshold can be set lower because branches or trees may fall when the wind speed is low. Real-time analysis of wind speed change trends is also taken into account. If the wind speed suddenly increases, the wind speed fluctuation in a short period of time may pose a threat to the vehicle. Based on historical data and current wind speed changes, the wind speed trend in the next few minutes or hours is calculated, and the warning threshold is automatically adjusted to respond. When the wind speed fluctuation is large, the warning threshold is temporarily lowered to increase sensitivity to the risk. The core goal of the threshold determination module 111 is to automatically adjust the warning threshold based on factors such as real-time wind conditions, weather forecasts, geographical environment, and wind speed fluctuations. Through intelligent adjustment, a flexible response mechanism can be achieved to provide the most appropriate wind condition warning in different situations.

[0213] Wind warning module 112 in controller 110 triggers the corresponding wind warning mechanism based on the current wind data collected by detection device 120 and the wind warning threshold determined by threshold determination module 111. Wind warning module 112 comprises a first warning module 1121 and a second warning module 1122. First warning module 1121 primarily provides vehicle-side warnings, such as flashing warning lights, honking horns, and seat and steering wheel vibrations. Second warning module 1122 primarily provides remote warnings.

[0214] The second warning module 1122 can be implemented as a multi-platform remote monitoring and data synchronization system, transmitting wind condition data and wind warning information to the user's target device via network communications, enabling remote interaction. For example, data can be transmitted to the user's mobile app via wireless communications such as WiFi or 4G / 5G, enabling interaction. The user's mobile app can display real-time wind condition data around the vehicle, such as the current wind speed. If the wind speed exceeds the standard, the wind speed display will turn red, and detailed wind speed values ​​can be provided. A wind speed trend chart can also be displayed to help the user understand wind speed changes. When the second warning module 1122 pushes wind condition warning information to the target device, it can generate different sound and icon prompts based on the wind condition warning level. In addition, the target device can also have a historical data viewing function, allowing users to view wind condition data over a period of time, understand wind speed trends and historical exceedances, and display past wind condition data using charts (such as line charts and bar charts) to facilitate user analysis of historical wind condition changes. Users can select date ranges, wind speed ranges, and other filter options for viewing. After the wind condition warning information arrives, the user can manually cancel the warning or perform other operations as appropriate.

[0215] As an application example, the embodiment of the present application can provide wind condition warnings for vehicles in a stationary state, such as wind condition warnings for vehicles in a parked state, and has high feasibility, application and promotion value. The wind condition warning system achieves collaborative work through data sharing and real-time feedback. Each module relies on real-time data flow to ensure the most effective response. The wind condition warning system can monitor and analyze wind condition data around the vehicle in real time, provide instant wind condition warnings and remote interaction. The embodiment of the present application can not only improve the safety of vehicles when parked, but also effectively reduce the risk of damage caused by strong winds, thereby reducing losses for users.

[0216] Compared with the related art, the beneficial effects of the embodiments of the present application include at least:

[0217] (1) Traditional regional wind speed measurement relies on mechanical anemometers or weather radars, while the present embodiment can use ultrasonic anemometers to monitor wind conditions around the vehicle in real time. Ultrasonic anemometers can achieve high-precision, non-contact, all-weather wind measurement, overcoming the wear and mechanical component failure problems of mechanical anemometers.

[0218] (2) The embodiment of the present application provides an efficient, accurate, stable and intelligent wind warning system. In windy weather, the risk faced by vehicles is not only the wind speed itself, but also the duration and suddenness of the wind and other environmental factors (such as terrain, tree density, etc.). The wind speed and wind impact in different environments may be different, so a single fixed warning threshold is often not suitable for all situations. The embodiment of the present application dynamically adjusts the wind warning threshold, and can optimize the response of the warning system in real time based on actual wind data, weather forecasts and other data, providing more accurate warnings, with strong market competitiveness and broad application prospects;

[0219] (3) The embodiments of the present application can realize real-time data upload and remote monitoring, not only providing local warnings, but also realizing multi-platform data sharing and remote warning functions, greatly enhancing the flexibility and application scenarios of the system. The real-time data upload and remote monitoring of the system enable users to understand the wind conditions around the vehicle at any time and identify potential risks in advance;

[0220] (4) In related technologies, anemometers are only used to recommend speed limits or assist in optimizing driving behavior during vehicle driving, and do not monitor the potential risks of excessive wind speed when the vehicle is parked.

[0221] According to a fifth aspect of the present application, an embodiment of the present application further provides a vehicle including the electronic device or wind warning system described above. The vehicle has all the beneficial effects of the electronic device or wind warning system described above, and the embodiments of the present application are not further described here.

[0222] The embodiments of this application do not limit the type of vehicle. In some embodiments, the vehicle includes, but is not limited to, at least one of the following: a vehicle, an aircraft, a motor vehicle, a ship, etc. For example, the vehicle used in a vehicle scene is a car, a motor vehicle, etc. The vehicle used in a flight scene is an aircraft, a drone, etc.

[0223] Take the vehicle as an example, Figure 5 As shown, the vehicle 10 includes the above-mentioned electronic device or wind condition warning system. The vehicle has all the beneficial effects of the above-mentioned electronic device or wind condition warning system, etc., and this application will not repeat them here.

[0224] The vehicle may be a fuel vehicle, a plug-in hybrid vehicle or a new energy vehicle, etc., and this application does not make any specific restrictions on this.

[0225] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0226] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0227] The embodiments, implementation methods and related technical features of the present application can be combined and replaced with each other without conflict.

[0228] The above are merely preferred embodiments of the present application and do not constitute any form of limitation to the present application. Although the descriptions of each embodiment in the embodiments of the present application have different focuses, for parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application are still within the scope of the technical solution of the present application.

Claims

1. A wind condition early warning method, characterized in that: The method comprises: Based on the current wind condition data and the wind condition characteristic data of the vehicle's environment, the vehicle is given a wind condition warning.

2. The wind condition warning method according to claim 1, characterized in that: The current wind condition data includes at least one of the following: current wind speed, current wind direction, and current wind force.

3. The wind condition warning method according to claim 1, characterized in that: The wind condition characteristic data includes at least one of the following: wind condition prediction data, geographical environment data, historical wind condition data, and wind condition fluctuation data.

4. The wind condition warning method according to claim 3, characterized in that: The wind condition prediction data includes at least one of the following: predicted wind speed, predicted wind speed intensity, predicted wind force, and predicted wind direction.

5. The wind condition warning method according to claim 3, characterized in that: The geographical environment data includes at least one of the following: vegetation density and building density.

6. The wind condition warning method according to claim 3, characterized in that: The historical wind condition data includes at least one of the following: historical wind speed, historical wind direction, and historical wind force.

7. The wind condition warning method according to claim 3, characterized in that: The wind condition fluctuation data includes at least one of the following: wind speed standard deviation and wind speed change rate.

8. The wind condition warning method according to claim 1, characterized in that: The wind condition warning processing for the vehicle based on the current wind condition data and the wind condition characteristic data of the environment in which the vehicle is located includes: Determine the wind warning threshold based on the current wind condition data and the wind characteristic data of the vehicle's environment; Perform wind condition warning processing on the vehicle according to the current wind condition data and the wind condition warning threshold.

9. The wind condition warning method according to claim 8, characterized in that: Determining the wind warning threshold based on the current wind condition data and the wind characteristic data of the vehicle's environment includes: According to the current wind condition data and the wind condition characteristic data of the vehicle's environment, the initial warning threshold is corrected to obtain the wind condition warning threshold.

10. The wind condition warning method according to claim 9, characterized in that: The initial warning threshold is corrected based on the current wind condition data and the wind condition characteristic data of the vehicle's environment to obtain the wind condition warning threshold, including: Determining at least one wind condition correction parameter based on current wind condition data and wind condition characteristic data of the vehicle's environment; The initial warning threshold is corrected according to at least one of the wind condition correction parameters to obtain a wind condition warning threshold.

11. The wind condition warning method according to claim 10, characterized in that: The step of correcting the initial warning threshold value according to at least one wind condition correction parameter to obtain the wind condition warning threshold value includes: performing statistical processing on at least one of the wind condition correction parameters to obtain a wind condition correction statistic; The initial warning threshold and the wind condition correction statistic are summed to obtain the wind condition warning threshold.

12. The wind condition warning method according to claim 10, characterized in that: The step of correcting the initial warning threshold value according to at least one wind condition correction parameter to obtain the wind condition warning threshold value includes: The initial warning threshold and at least one of the wind condition correction parameters are quadratured to obtain the wind condition warning threshold.

13. The wind condition warning method according to claim 9, characterized in that: The initial warning threshold is corrected based on the current wind condition data and the wind condition characteristic data of the vehicle's environment to obtain the wind condition warning threshold, including: Based on the current wind condition data and the wind characteristic data of the vehicle's environment, the initial warning threshold is modified to obtain the intermediate warning threshold; A wind condition warning threshold is determined according to the intermediate warning threshold within the first time period.

14. The wind condition warning method according to claim 13, characterized in that: Determining the wind condition warning threshold according to the intermediate warning threshold within the first time period includes: The intermediate warning thresholds within the first time period are averaged to obtain a wind condition warning threshold.

15. The wind condition warning method according to claim 9, characterized in that: The method further comprises: The initial warning threshold is determined based on the site design wind speed and vehicle structure parameters; wherein the site design wind speed refers to the maximum wind speed that may occur within the recurrence period.

16. The wind condition warning method according to claim 15, characterized in that: The vehicle structure parameters include the vehicle structure protection level.

17. The wind condition warning method according to claim 8, characterized in that: The performing wind condition warning processing on the vehicle according to the current wind condition data and the wind condition warning threshold comprises: If the comparison result between the current wind condition data and the wind condition warning threshold satisfies a first condition, a wind condition warning operation is performed on the vehicle.

18. The wind condition warning method according to claim 17, characterized in that: The first condition includes: within the second time period, the current wind condition data exceeds the wind condition warning threshold.

19. The wind condition warning method according to claim 17, characterized in that: The first condition includes: within a third time period, the difference between the current wind condition data and the wind condition warning threshold is within a first range, and the current wind condition data shows a deteriorating trend.

20. The wind condition warning method according to claim 8, characterized in that: The performing wind condition warning processing on the vehicle according to the current wind condition data and the wind condition warning threshold comprises: Determining a wind warning level based on the current wind condition data and the wind warning threshold; Perform wind warning operations on the vehicle according to the wind warning level.

21. The wind condition warning method according to claim 20, characterized in that: For different wind warning levels, the parameters of the wind warning operation are different, and / or the types of the wind warning operation are different.

22. The wind condition warning method according to any one of claims 17 to 21, characterized in that: After performing the wind condition warning operation on the vehicle, the method further includes: If the second condition is met, the wind condition warning operation on the vehicle is stopped.

23. The wind condition warning method according to claim 22, characterized in that: The second condition includes: within a fourth time period, the current wind condition data does not exceed the wind condition warning threshold.

24. The wind condition warning method according to claim 22, characterized in that: The second condition includes: receiving an early warning confirmation instruction and / or an early warning closing instruction.

25. The wind condition warning method according to any one of claims 17 to 21, characterized in that: The wind condition warning operation includes at least one of the following: adjusting the lighting of the vehicle, adjusting the audio of the vehicle, adjusting the display content of the vehicle, controlling the vibration of the target module in the vehicle, and sending wind condition warning information to the target device.

26. The wind condition warning method according to claim 25, characterized in that: The wind condition warning information includes at least one of the following: current wind condition data, historical wind condition data, and wind condition warning level.

27. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the wind condition warning method according to any one of claims 1 to 26 is implemented.

28. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the wind condition warning method according to any one of claims 1 to 26 is implemented.

29. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the wind condition warning method according to any one of claims 1 to 26.

30. A wind condition warning system, characterized in that: The wind condition warning system includes a controller (110) and a vehicle (100); wherein, The controller (110) is used to perform wind condition warning processing on the vehicle (100) based on current wind condition data and wind condition characteristic data of the environment in which the vehicle (100) is located.

31. The wind condition warning system according to claim 30, characterized in that: The controller (110) is arranged on the carrier (100).

32. The wind condition warning system according to claim 30, characterized in that: The controller (110) includes a threshold determination module (111) and a wind condition warning module (112); wherein, The threshold determination module (111) is used to determine the wind condition warning threshold based on current wind condition data and wind condition characteristic data of the vehicle's environment; The wind condition warning module (112) is used to perform wind condition warning processing on the vehicle based on the current wind condition data and the wind condition warning threshold.

33. The wind condition warning system according to claim 32, characterized in that: The wind condition warning module (112) comprises a first warning module (1121) and a second warning module (1122); wherein, The first warning module (1121) is used to perform at least one of the following: adjusting the lighting of the vehicle (100), adjusting the audio of the vehicle (100), adjusting the display content of the vehicle (100), and controlling the vibration of a target module in the vehicle (100); The second warning module (1122) is used to send wind condition warning information to the target device.

34. The wind condition warning system according to claim 30, characterized in that: The wind condition warning system further includes a detection device (120); wherein, The detection device (120) is used to send the current wind condition data to the controller (110).

35. The wind condition warning system according to claim 34, characterized in that: The detection device (120) includes an ultrasonic anemometer.

36. The wind condition warning system according to claim 34, characterized in that: The detection device (120) is arranged on the carrier (100).

37. A vehicle, characterized in that: The vehicle includes the electronic device as described in claim 29, or includes the wind condition warning system as described in any one of claims 30 to 36.

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