Cross-wind frequently-occurring road section safety early warning method and device

Abstract

The application discloses a cross-wind frequently-occurring road section safety early warning method and device, and belongs to the technical field of road traffic safety. The application automatically detects whether a passing object is a vehicle, and monitors a vehicle condition in real time, calculates a cross-wind resistance area of the vehicle, judges a wind scale, obtains a cross-wind force size to which the vehicle is subjected under a condition of not taking measures through analysis and processing of data, and if the cross-wind force size exceeds a set threshold value, the application warns the measured vehicle, can greatly reduce the occurrence of road accidents, and has prominent advantages under the conditions of night and bad weather, is widely applicable to cross-wind frequently-occurring road sections, and has strong universality. The application effectively detects a wind force condition, monitors a vehicle condition, provides a cross-wind weather warning for a driving vehicle, avoids the occurrence of traffic accidents, and guarantees road traffic safety.

Description

Technical Field

[0001] This invention relates to a safety early warning method and device for road sections prone to crosswinds, belonging to the field of road traffic safety technology. Background Technology

[0002] Crosswinds are a frequent weather condition affecting road traffic. They are characterized by their sudden onset and strong force, reducing vehicle grip and causing vehicles to deviate from their intended direction, making them highly susceptible to skidding and sideslip. This impact is even greater on vehicles with high centers of gravity and high speeds, significantly increasing the risk of road traffic accidents. Common areas where crosswind accidents occur include: tunnel entrances and exits, valleys, bridges, high embankments, and coastal areas—windy or wide-open areas are numerous.

[0003] Currently, there are few measures for early warning and prevention of crosswinds. Traffic management departments set up crosswind signs on road sections where crosswinds are frequent to warn oncoming vehicles to pay attention to crosswinds. Existing technologies mostly detect the current wind level and issue corresponding warnings to oncoming vehicles. Such technologies mostly have the following problems: (1) The detection accuracy is too low and the wind direction cannot be determined; (2) In the dark or in bad weather, the driver's vision is limited and crosswinds cannot be prevented in advance, which can easily cause accidents; (3) It cannot monitor real-time vehicle conditions and traffic conditions, and cannot report and contact the relevant departments in a timely manner to handle traffic accidents, so as to minimize the losses caused by accidents.

[0004] In order to reduce traffic accidents caused by crosswinds and improve the comfort of drivers, this invention designs a method for automatically detecting wind level and monitoring vehicle conditions in road sections with frequent crosswinds, and issuing corresponding early warnings in a timely manner. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a safety early warning method and device for road sections prone to crosswinds. This method can effectively detect wind conditions, monitor vehicle status, provide crosswind weather warnings for vehicles, prevent traffic accidents, and ensure road traffic safety.

[0006] The technical solution adopted by this invention to solve its technical problem is as follows:

[0007] In a first aspect, the present invention provides a safety early warning method for road sections prone to crosswinds, comprising the following steps:

[0008] Step S1: When an object enters a road section with frequent crosswinds, the vehicle detection module is used to detect and analyze whether the detected object is a vehicle.

[0009] Step S2: When the detected object is not a vehicle and stays in the crosswind-prone section for more than the set threshold time and affects the driving of vehicles behind, it is determined that there is an unidentified object staying in the crosswind-prone section.

[0010] Step S3: When the object being tested is a vehicle, the vehicle detection module detects the wind resistance area of ​​the vehicle, and the wind force detection module detects the wind speed and direction of the current crosswind-prone road section. The microcontroller uploads the detection data to the information processing module. The information processing module calculates the crosswind force experienced by the vehicle without taking any measures. At the same time, the COCD algorithm is used to analyze and determine whether the object being tested is a normal vehicle. If the vehicle is determined to be a normal vehicle, proceed to step S4; otherwise, proceed to step S5.

[0011] Step S4: The vehicle passes through the section of road with frequent crosswinds normally, and the display screen shows the current wind speed and direction.

[0012] Step S5: The display screen prompts that there is crosswind in the section of road where crosswinds are frequent, reminding drivers to adjust their steering wheel and slow down in time. At the same time, the voice warning device activates the voice warning to prevent the vehicle from deviating or skidding.

[0013] Step S6: When a vehicle accident occurs on a road section prone to crosswinds, or when an unidentified object is lodged on the road section that affects the normal driving of vehicles behind, the road condition information of the road section prone to crosswinds is uploaded to the traffic management department through the communication module for further processing.

[0014] As one possible implementation of this embodiment, the crosswind-prone road sections include road A and road B. The vehicle detection module, wind detection module, information processing module, display screen prompt device, voice warning device, communication module and microcontroller each have two groups, A and B, which provide safety warnings for road A and road B respectively.

[0015] As one possible implementation of this embodiment, the wind resistance area S of the vehicle = the cross-sectional area of ​​one side of the vehicle = the radar target cross-sectional area, and its calculation formula is as follows:

[0016]

[0017] Among them, P t For transmission power, P r R is the received power, G is the distance from the vehicle to the vehicle detection module, G is the gain of the transmitting and receiving antennas, and λ is the wavelength of the transmitted wave.

[0018] As one possible implementation of this embodiment, the process of determining whether the detected object is a vehicle involves using a vehicle detection module to measure the wind resistance area Sn of the detected object and analyzing it to determine whether the detected object is a vehicle.

[0019] As one possible implementation of this embodiment, the analysis and determination of whether the detected object is a vehicle includes:

[0020] The wind resistance area data Sn of the object to be detected is compared with the pre-set wind resistance area threshold σi. Based on the classification interval of the object, it is determined whether the object is a vehicle, i = 1, 2, 3, 4.

[0021] The basis for determining whether an object is a vehicle based on its classification range is as follows:

[0022] The classification intervals for non-vehicle objects are (0, σ1) and (σ4, +∞), the classification intervals for small vehicles are [σ1, σ2), the classification intervals for medium-sized vehicles are [σ2, σ3), and the classification intervals for large vehicles are [σ3, σ4].

[0023] As one possible implementation of this embodiment, the specific process of calculating the crosswind force experienced by the vehicle without taking any measures is as follows: the relative speed between the vehicle and the air and the direction of the crosswind force experienced by the vehicle are measured by wind speed and wind direction sensors, and the formula for calculating the crosswind force is:

[0024]

[0025] In the formula, Fx is the lateral force on the vehicle under test, C is the air resistance coefficient, ρ is the air density, S is the area of ​​the vehicle subject to wind resistance, v is the relative velocity between the object and the air, and θ is the angle between the vehicle's direction of travel and the wind direction.

[0026] As one possible implementation of this embodiment, in the process of using the COCD algorithm to analyze and determine whether the detected object is a normal vehicle, the objects in the detection area are classified into non-vehicles, small vehicles, medium-sized vehicles and large vehicles according to the wind resistance area of ​​the detected vehicle.

[0027] As one possible implementation of this embodiment, the step of using the COCD algorithm to analyze and determine whether the detected object is a normal vehicle includes:

[0028] The wind resistance area data Sn of the object to be detected is compared with the pre-set wind resistance area threshold σi to determine the classification interval of the object to be detected, i = 1, 2, 3, 4;

[0029] The classification range is used to determine whether the detected object is a normal vehicle.

[0030] The data on the wind resistance area of ​​the tested vehicle, vehicle speed, crosswind speed and direction in the current crosswind-prone road sections are processed to obtain data A(X,Y,Z):

[0031] A(X,Y,Z)=RXY 2 sinZ

[0032] Where X represents the area of ​​the object subject to wind resistance, Y represents the relative speed between the vehicle and the crosswind in the direction of travel, Z represents the angle between the crosswind and the direction of travel of the vehicle, A represents the lateral force of the crosswind on the vehicle being detected, and R represents the distance from the vehicle to the vehicle detection module.

[0033] Let data point P(A,B), B be the type of object being detected, B = (B1,B2,B3,B4), where B1 represents non-vehicle objects, B2 represents small vehicles, B3 represents medium-sized vehicles, and B4 represents large vehicles.

[0034] Create the following datasets: normal vehicles (centroid Q1), skidding vehicles (centroid Q2), and overturned vehicles (centroid Q3).

[0035] Establish similarity relationships using the Euclidean distance method. Calculate the distance ΔX between P(A,B) and the three centroids Q1, Q2, and Q3;

[0036] Depending on the data B, the corresponding data are calculated and processed. When B = B2, ΔX21 = |A2 - Q1|, ΔX21 = |A2 - Q2|; when B = B3, ΔX31 = |A3 - Q1|, ΔX32 = |A3 - Q2|, ΔX33 = |A3 - Q3|; when B = B4, ΔX41 = |A3 - Q1|, ΔX42 = |A3 - Q2|, ΔX43 = |A4 - Q3|; in particular, when B = B1,

[0037] After the calculation is completed, compare the size of ΔX within the defined value group corresponding to data B. The smaller ΔX is, the closer the data point is to its corresponding dataset, and it is then classified into that dataset.

[0038] The COCD algorithm model is trained using a dataset, and through repeated classification and analysis, a more accurate threshold range is obtained.

[0039] As one possible implementation of this embodiment, the classification interval is:

[0040] The classification intervals for non-vehicle objects are (0, σ1) and (σ4, +∞), the classification interval for small vehicles is [σ1, σ2), the classification interval for medium-sized vehicles is [σ2, σ3), and the classification interval for large vehicles is [σ3, σ4].

[0041] When the classification interval is (σ4, +∞), the detected object is occluded or there are other conditions, where σ1 = 4000000 mm. 2 σ² = 4725000 mm 2 σ3=5400000mm2 σ4=18900000mm 2 .

[0042] Secondly, an embodiment of the present invention provides a safety early warning device for road sections prone to crosswinds, comprising a pillar device and a vehicle detection module.

[0043] The pillar device is equipped with a wind detection module, an information processing module, a display screen, a voice warning device, a communication module, and a power supply unit. The vehicle detection module includes a millimeter-wave radar, a signal processor, a microcontroller, and a battery pack. The pillar device is installed on the outer side of the road in a section prone to crosswinds for detecting wind force, analyzing data, providing vehicle warnings, and facilitating information communication. The crosswind-prone road sections include Road A and Road B. The vehicle detection modules consist of two sets, A and B, installed 60m in front of the pillar device on Road A and Road B respectively, for detecting whether passing objects are vehicles and monitoring vehicle and road conditions in real time.

[0044] The wind detection module includes a wind speed sensor and a wind direction sensor, which are installed above the column device to detect real-time crosswind speed and direction.

[0045] The information processing module includes an STC-51 series microcontroller, which is installed inside the column and used to analyze and process information.

[0046] The display screen device includes a 3500mm*2660mm conventional LED display screen, which is installed in front of the column and is used to display real-time weather conditions and warnings of crosswinds.

[0047] The voice warning device is installed above the display screen and has a working volume of 80 decibels. The warning voice can be clearly received within a 100m radius of the sound source.

[0048] The technical solutions of the embodiments of the present invention can have the following beneficial effects:

[0049] This invention provides a safety warning method for road sections prone to crosswinds. It automatically detects whether passing objects are vehicles, monitors vehicle conditions in real time, calculates the crosswind resistance area of ​​the vehicle, determines the wind force, and analyzes the data to determine the magnitude of the lateral force on the vehicle without intervention. If the force exceeds a set threshold, the tested vehicle is alerted. This method significantly reduces the occurrence of road accidents, with particularly strong advantages in darkness and severe weather conditions. It is widely applicable to road sections prone to crosswinds and has high versatility. This invention effectively detects wind conditions, monitors vehicle status, and provides crosswind weather warnings for vehicles, preventing traffic accidents and ensuring road traffic safety.

[0050] This invention utilizes the COCD (Classification of Crosswind Data) algorithm for analysis. Through repeated classification and analysis, a more precise threshold range is obtained, thereby improving the accuracy of the algorithm model. Based on the wind resistance area of ​​the measured object, objects in the detection area are classified into non-vehicles, small vehicles, medium-sized vehicles, and large vehicles, which greatly improves the detection accuracy of vehicles. Furthermore, the magnitude of the lateral force on the corresponding vehicle is calculated, and timely warnings are issued to vehicles, reducing traffic hazards caused by crosswinds. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating a safety early warning method for road sections prone to crosswinds, according to an exemplary embodiment.

[0052] Figure 2 This is a schematic block diagram illustrating a safety early warning device for road sections prone to crosswinds, according to an exemplary embodiment.

[0053] Figure 3 This is an installation schematic diagram of a safety early warning device for road sections prone to crosswinds, according to an exemplary embodiment.

[0054] Figure 4 This is a schematic diagram of a column device structure according to an exemplary embodiment;

[0055] Figure 5 This is a schematic diagram of the overall appearance of a vehicle detection module device according to an exemplary embodiment;

[0056] Figure 6 This is a force analysis diagram illustrating the calculation of lateral forces in a vehicle, according to an exemplary embodiment.

[0057] Figure 7 This is a flowchart illustrating a specific method for providing safety warnings for road sections prone to crosswinds using the crosswind-prone road section safety warning device of the present invention, according to an exemplary embodiment.

[0058] In the diagram, AA road, BB road, 1-pillar device, 2, 3-vehicle detection module, 4-solar panel, 5-wind detection module, 6-voice warning device, 7-information processing module and communication module, 8-display display device, 9-power supply unit, 10-signal processor, 11-microcontroller, 12-millimeter wave radar, 13-battery pack, 14-vehicle being detected. Detailed Implementation

[0059] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0060] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure of the invention, components and arrangements of specific examples are described below. Furthermore, reference numerals and / or letters may be repeated in different examples. This repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known components, processing techniques, and processes are omitted in this invention to avoid unnecessarily limiting the invention.

[0061] like Figure 1 As shown in the figure, an embodiment of the present invention provides a safety early warning method for road sections prone to crosswinds, comprising the following steps:

[0062] Step S1: When an object enters a road section with frequent crosswinds, the vehicle detection module is used to detect and analyze whether the detected object is a vehicle.

[0063] Step S2: When the detected object is not a vehicle and stays in the crosswind-prone section for more than the set threshold time and affects the driving of vehicles behind, it is determined that there is an unidentified object staying in the crosswind-prone section.

[0064] Step S3: When the object being tested is a vehicle, the vehicle detection module detects the wind resistance area of ​​the vehicle, and the wind force detection module detects the wind speed and direction of the current crosswind-prone road section. The microcontroller uploads the detection data to the information processing module. The information processing module calculates the crosswind force experienced by the vehicle without taking any measures. At the same time, the COCD algorithm is used to analyze and determine whether the object being tested is a normal vehicle. If the vehicle is determined to be a normal vehicle, proceed to step S4; otherwise, proceed to step S5.

[0065] Step S4: The vehicle passes through the section of road with frequent crosswinds normally, and the display screen shows the current wind speed and direction.

[0066] Step S5: The display screen prompts that there is crosswind in the section of road where crosswinds are frequent, reminding drivers to adjust their steering wheel and slow down in time. At the same time, the voice warning device activates the voice warning to prevent the vehicle from deviating or skidding.

[0067] Step S6: When a vehicle accident occurs on a road section prone to crosswinds, or when an unidentified object is lodged on the road section that affects the normal driving of vehicles behind, the road condition information of the road section prone to crosswinds is uploaded to the traffic management department through the communication module for further processing.

[0068] As one possible implementation of this embodiment, the crosswind-prone road sections include road A and road B. The vehicle detection module, wind detection module, information processing module, display screen prompt device, voice warning device, communication module and microcontroller each have two groups, A and B, which provide safety warnings for road A and road B respectively.

[0069] As one possible implementation of this embodiment, the wind resistance area S of the vehicle = the cross-sectional area of ​​one side of the vehicle = the radar target cross-sectional area, and its calculation formula is as follows:

[0070]

[0071] Among them, P t For transmission power, P r R is the received power, G is the distance from the vehicle to the vehicle detection module, G is the gain of the transmitting and receiving antennas, and λ is the wavelength of the transmitted wave.

[0072] As one possible implementation of this embodiment, the process of determining whether the detected object is a vehicle involves using a vehicle detection module to measure the wind resistance area Sn of the detected object and analyzing it to determine whether the detected object is a vehicle.

[0073] As one possible implementation of this embodiment, the analysis and determination of whether the detected object is a vehicle includes:

[0074] The wind resistance area data Sn of the object to be detected is compared with the pre-set wind resistance area threshold σi. Based on the classification interval of the object, it is determined whether the object is a vehicle, i = 1, 2, 3, 4.

[0075] The basis for determining whether an object is a vehicle based on its classification range is as follows:

[0076] The classification intervals for non-vehicle objects are (0, σ1) and (σ4, +∞), the classification intervals for small vehicles are [σ1, σ2), the classification intervals for medium-sized vehicles are [σ2, σ3), and the classification intervals for large vehicles are [σ3, σ4].

[0077] As one possible implementation of this embodiment, the specific process of calculating the crosswind force experienced by the vehicle without taking any measures is as follows: the relative speed between the vehicle and the air and the direction of the crosswind force experienced by the vehicle are measured by wind speed and wind direction sensors, and the formula for calculating the crosswind force is:

[0078]

[0079] In the formula, Fx is the lateral force on the vehicle under test, C is the air resistance coefficient, ρ is the air density, S is the area of ​​the vehicle subject to wind resistance, v is the relative velocity between the object and the air, and θ is the angle between the vehicle's direction of travel and the wind direction.

[0080] As one possible implementation of this embodiment, in the process of using the COCD algorithm to analyze and determine whether the detected object is a normal vehicle, the objects in the detection area are classified into non-vehicles, small vehicles, medium-sized vehicles and large vehicles according to the wind resistance area of ​​the detected vehicle.

[0081] As one possible implementation of this embodiment, the step of using the COCD algorithm to analyze and determine whether the detected object is a normal vehicle includes:

[0082] The wind resistance area data Sn of the object to be detected is compared with the pre-set wind resistance area threshold σi to determine the classification interval of the object to be detected, i = 1, 2, 3, 4;

[0083] The classification range is used to determine whether the detected object is a normal vehicle.

[0084] The data on the wind resistance area of ​​the tested vehicle, vehicle speed, crosswind speed and direction in the current crosswind-prone road sections are processed to obtain data A(X,Y,Z):

[0085] A(X,Y,Z)=RXY 2 sin Z

[0086] Where X represents the area of ​​the object subject to wind resistance, Y represents the relative speed between the vehicle and the crosswind in the direction of travel, Z represents the angle between the crosswind and the direction of travel of the vehicle, A represents the lateral force of the crosswind on the vehicle being detected, and R represents the distance from the vehicle to the vehicle detection module.

[0087] Let data point P(A,B), B be the type of object being detected, B = (B1,B2,B3,B4), where B1 represents non-vehicle objects, B2 represents small vehicles, B3 represents medium-sized vehicles, and B4 represents large vehicles.

[0088] Create the following datasets: normal vehicles (centroid Q1), skidding vehicles (centroid Q2), and overturned vehicles (centroid Q3).

[0089] Establish similarity relationships using the Euclidean distance method. Calculate the distance ΔX between P(A,B) and the three centroids Q1, Q2, and Q3;

[0090] Depending on the data B, the corresponding data are calculated and processed. When B = B2, ΔX21 = |A2 - Q1|, ΔX21 = |A2 - Q2|; when B = B3, ΔX31 = |A3 - Q1|, ΔX32 = |A3 - Q2|, ΔX33 = |A3 - Q3|; when B = B4, ΔX41 = |A3 - Q1|, ΔX42 = |A3 - Q2|, ΔX43 = |A4 - Q3|; in particular, when B = B1,

[0091] After the calculation is completed, compare the size of ΔX within the defined value group corresponding to data B. The smaller ΔX is, the closer the data point is to its corresponding dataset, and it is then classified into that dataset.

[0092] The COCD algorithm model is trained using a dataset, and through repeated classification and analysis, a more accurate threshold range is obtained.

[0093] As one possible implementation of this embodiment, the classification interval is:

[0094] The classification intervals for non-vehicle objects are (0, σ1) and (σ4, +∞), the classification interval for small vehicles is [σ1, σ2), the classification interval for medium-sized vehicles is [σ2, σ3), and the classification interval for large vehicles is [σ3, σ4].

[0095] When the classification interval is (σ4, +∞), the detected object is occluded or there are other conditions, where σ1 = 4000000 mm. 2 σ² = 4725000 mm 2 σ3=5400000mm 2 σ4=18900000mm 2 .

[0096] This invention provides a safety early warning device for road sections prone to crosswinds, comprising a support column and a vehicle detection module.

[0097] The pillar device is equipped with a wind detection module, an information processing module, a display screen, a voice warning device, a communication module, a microcontroller, and a power supply unit. The vehicle detection module includes a millimeter-wave radar, a signal processor, and a power supply unit. The pillar device is installed on the outer side of the road in a section prone to crosswinds for detecting wind force, analyzing data, providing vehicle warnings, and facilitating information communication. The crosswind-prone road sections include Road A and Road B. The vehicle detection modules consist of two sets, A and B, installed 60m in front of the pillar device on Road A and Road B respectively, for detecting whether passing objects are vehicles and monitoring vehicle and road conditions in real time.

[0098] The wind detection module includes a wind speed sensor and a wind direction sensor, which are installed above the column device to detect real-time crosswind speed and direction.

[0099] The information processing module includes an STC-51 series microcontroller, which is installed inside the column and used to analyze and process information.

[0100] The display screen device includes a 3500mm*2660mm conventional LED display screen, which is installed in front of the column and is used to display real-time weather conditions and warnings of crosswinds.

[0101] The voice warning device is installed above the display screen and has a working volume of 80 decibels. The warning voice can be clearly received within a 100m radius of the sound source.

[0102] like Figures 2 to 7 As shown in the figure, an embodiment of the present invention provides a safety early warning device for road sections prone to crosswinds, including a column device 1 and vehicle detection modules 2 and 3. The column device is equipped with a wind detection module 5, an information processing module 7, a display screen device 8, a voice warning device 6, a communication module 7, and a power supply unit 9. The road sections prone to crosswinds include road A and road B. The vehicle detection modules include a millimeter-wave radar 12, a signal processor 10, a microcontroller 11, and a battery pack 13, with two groups, A and B. The remaining devices are assembled into columns and installed on the outside of the road. There is a group of vehicle detection modules 2 on road A and a group of vehicle detection modules 3 on road B.

[0103] The column device 1 is installed on the outside of the lane in road sections prone to crosswinds, and is used to detect wind force, analyze data, warn vehicles, and communicate information.

[0104] The vehicle detection module includes a millimeter-wave radar 12, a signal processor 10, a microcontroller 11, and a battery pack 13. It is positioned 60m in front of the pillar device. Two sets of vehicle detection modules, A and B, are respectively arranged parallel to each other on both sides of roads A and B. The millimeter-wave radar 12 is a 77Hz millimeter-wave radar, and the microcontroller 11 is an STC-51 series microcontroller used to detect whether passing objects 14 are vehicles and to monitor vehicle and road conditions in real time. The vehicle detection module identifies and detects vehicles and transmits corresponding data and signals to the pillar device and the backend server. When the wind force on a passing vehicle reaches a threshold set in the backend server, a warning signal is displayed on the screen, and a voice warning device is activated to alert the vehicle to prevent crosswinds. This provides a significant dual warning effect through both visual and auditory means, and can be widely used in valleys, bridges, tunnel entrances, and other sections prone to crosswinds. Its advantages are particularly pronounced at night and in severe weather conditions, warning against vehicle rollovers and skidding, greatly improving road safety.

[0105] The vehicle detection modules 2 and 3 are in working state by default. When an object passes by, the vehicle detection modules 2 and 3 determine the type of object by detecting the wind resistance area of ​​the object, and classify it into four categories: non-vehicle, small vehicle, medium vehicle, and large vehicle.

[0106] The wind detection module 5 includes a wind speed sensor and a wind direction sensor, which are installed above the column device to detect real-time crosswind speed and direction.

[0107] The communication module 7 (the information processing module and the communication module are combined, and both are labeled 7 in the attached diagram) is an NB-IoT Internet of Things transmission module. The IoT module is connected to the back-end server, installed inside the column, and is used to communicate with the outside world and upload the current road conditions in a timely manner.

[0108] The information processing module 7, i.e., the microcontroller, is an STC-51 series single-chip microcomputer, installed inside the column, and is used to analyze and process information.

[0109] The display screen device 8 is a 3500mm*2660mm conventional LED display screen, installed in front of the column, used to display real-time weather conditions and warnings of crosswinds.

[0110] The voice warning device 6 is installed above the display screen prompt device. Its operating volume is 80 decibels, and the warning voice can be clearly received within a 100m radius of the sound source.

[0111] The aforementioned safety early warning device for road sections prone to crosswinds is installed in road sections prone to crosswinds, such as: tunnel entrances and exits, valleys, bridges, high embankments, seaside and other windy or wide areas.

[0112] like Figure 7 As shown, the specific process of using the crosswind-prone road section safety early warning device of the present invention for crosswind-prone road section safety early warning method is as follows.

[0113] Step 1: When an object enters the detection range of the vehicle detection module, vehicle detection modules 2 and 3 automatically determine whether the object is a vehicle. If it is not a vehicle, proceed to step 2; if it is a vehicle, proceed to step 3. This device... Figure 4 As shown;

[0114] Step 2: When the object being detected is not a vehicle, vehicle detection modules 2 and 3 work normally. When the object stays within the detection range for a longer period than the set threshold and affects the driving of vehicles behind, proceed to step 6.

[0115] Step 3: When the object being tested is a vehicle, vehicle detection modules 2 and 3 detect the wind resistance area of ​​the vehicle. The wind force detection module 5 in the column device detects the wind speed and direction of the current road section and uploads the corresponding data to the information processing module 7 via the microcontroller. This module then calculates the lateral wind force experienced by the vehicle without intervention. The data is analyzed using the COCD algorithm on the backend server. If the vehicle is determined to be normal, proceed to step 4; if it is determined to be abnormal, proceed to step 5. This device... Figure 3 As shown.

[0116] Step 4: The vehicle passes through the crosswind section normally, and the display screen device 8 shows the current wind speed and direction.

[0117] Step 5: The display screen prompts device 8 to indicate that there is crosswind in this section of the road, reminding the vehicle to adjust the steering wheel in time and slow down. The voice warning device 6 activates the voice warning to prevent the vehicle from deviating or skidding.

[0118] Step 6: When a vehicle is involved in an accident or an unidentified object is left on the road section, affecting the normal driving of vehicles behind, the communication module 7 will immediately upload the information to the traffic police department for further processing.

[0119] In step one, the millimeter-wave radar in the vehicle detection module detects the crosswind drag area of ​​the passing object and transmits the data to the back-end server via the microcontroller.

[0120] In step one, the specific process of determining whether an object passing through the detection area is a vehicle is as follows:

[0121] The backend server can receive the data Sn of the wind resistance area of ​​the object detected by the vehicle detection module, compare it with the wind resistance area threshold σi (i=1,2,3,4) set in the server in advance, and thus determine its classification interval.

[0122] The specific range for determining wind resistance area is as follows:

[0123] Non-vehicle objects: (0, σ1), small vehicles: [σ1, σ2), medium vehicles: [σ2, σ3), large vehicles: [σ3, σ4], detection device obstructed or other situations: (σ4, +∞), where σ1 = 4000000 mm 2 σ² = 4725000 mm 2 σ3=5400000mm 2 σ4=18900000mm 2 .

[0124] Suppose that the backend server receives a set of data Sn = {S1, S2, S3, S4,} consisting of four objects in the same detection area;

[0125] If S1 belongs to the interval (0, σ1), the server determines that the object is not a vehicle. Similarly, if S2, S3, and S4 belong to the intervals [σ1, σ2), [σ2, σ3), and [σ3, σ4] respectively, the server determines that the detected object is a vehicle.

[0126] To improve the accuracy of determining whether an object is a vehicle, a vehicle detection module can be used to detect the object's real-time speed, and the speed information can be combined to make a comprehensive judgment on whether the object is a vehicle.

[0127] To accurately determine the range to which the detected object belongs, the existing thresholds in the backend server can be compared with real-time data, and the threshold range can be continuously updated and refined by importing a large amount of data to train the system.

[0128] When the backend server determines that the detection device is obstructed or otherwise obstructed, it will activate the communication module to promptly report to the relevant departments and take measures to avoid affecting road traffic.

[0129] The server can receive four parameters: the area of ​​the object subjected to wind resistance detected by the information acquisition device, the vehicle speed data, the crosswind speed data of the road segment, and the crosswind direction data of the road segment. Let the data calculated and processed in the information processing module be A(X,Y,Z):

[0130] A(X,Y,Z)=RXY 2 sin Z

[0131] Where X represents the area of ​​the object subjected to wind resistance, Y represents the relative speed between the vehicle and the crosswind in the direction of travel, Z represents the angle between the crosswind and the direction of travel of the vehicle, and A represents the lateral force of the crosswind on the vehicle being tested.

[0132] In step three, the process of classifying vehicles passing through the detection area as normal vehicles is as follows:

[0133] The server receives a set of data P(A,B) consisting of two parameters: the magnitude of the crosswind lateral force on the vehicle and the vehicle type data. Here, A represents the value of the crosswind lateral force on the detected vehicle, and B represents the vehicle type. B = (B1,B2,B3,B4), where B1 represents non-vehicle objects, B2 represents small vehicles, B3 represents medium-sized vehicles, and B4 represents large vehicles.

[0134] Create a dataset: a set of normal vehicles with centroid Q1, a set of vehicles skidding with centroid Q2, and a set of vehicles overturned with centroid Q3.

[0135] Based on the data analyzed by the information processing module, similarity relationships are established, and the Euclidean distance method is used to confirm these relationships. Calculate the distance ΔX between P(A,B) and the three centroids Q1, Q2, and Q3.

[0136] Based on the different values ​​of data B, the corresponding data are calculated and processed. When B = B2, ΔX21 = |A2 - Q1|, ΔX21 = |A2 - Q2|; when B = B3, ΔX31 = |A3 - Q1|, ΔX32 = |A3 - Q2|, ΔX33 = |A3 - Q3|; when B = B4, ΔX41 = |A3 - Q1|, ΔX42 = |A3 - Q2|, ΔX43 = |A4 - Q3|; in particular, when B = B1,

[0137] After the calculation is completed, compare the magnitude of ΔX within the defined value group corresponding to data B. The smaller ΔX is, the closer the data point is to its corresponding dataset, and it is then classified into that dataset.

[0138] The dataset is imported into the server of the integrated algorithm to train the COCD algorithm model. Through repeated classification and analysis, a more accurate threshold range is obtained, thereby improving the accuracy of the algorithm model.

[0139] The column device and vehicle detection module device of the present invention are both designed with a windproof shape that is narrow at the top, wide at the bottom, and has a low center of gravity, which greatly reduces the damage to the device in crosswind weather and improves the service life of the device.

[0140] This invention enables early warning and real-time monitoring of vehicles and road conditions in areas prone to crosswinds through wireless transmission and intelligent control, greatly improving road safety.

[0141] This invention is installed on road sections prone to crosswinds. It can effectively detect wind conditions, monitor vehicle status, provide crosswind weather warnings for vehicles, prevent traffic accidents, and ensure road traffic safety.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A safety early warning method for road sections prone to crosswinds, characterized in that, Includes the following steps: Step S1: When an object enters a road section with frequent crosswinds, the vehicle detection module is used to detect and analyze whether the detected object is a vehicle. Step S2: When the detected object is not a vehicle and stays in the crosswind-prone section for more than the set threshold time and affects the driving of vehicles behind, it is determined that there is an unidentified object staying in the crosswind-prone section. Step S3: When the object being tested is a vehicle, the vehicle detection module detects the wind resistance area of ​​the vehicle, and the wind force detection module detects the wind speed and direction of the current crosswind-prone road section. The microcontroller uploads the detection data to the information processing module. The information processing module calculates the crosswind force experienced by the vehicle without taking any measures. At the same time, the COCD algorithm is used to analyze and determine whether the object being tested is a normal vehicle. If the vehicle is determined to be a normal vehicle, proceed to step S4; otherwise, proceed to step S5. Step S4: The vehicle passes through the section of road with frequent crosswinds normally, and the display screen shows the current wind speed and direction. Step S5: The display screen prompts that there is crosswind in the section of road where crosswinds are frequent, reminding drivers to adjust their steering wheel and slow down in time. At the same time, the voice warning device activates the voice warning to prevent the vehicle from deviating or skidding. Step S6: When a vehicle accident occurs on a road section prone to crosswinds or an unidentified object is left on the road section prone to crosswinds and affects the normal driving of vehicles behind, the road condition information of the road section prone to crosswinds is uploaded to the traffic management department through the communication module for subsequent processing. The method of using the COCD algorithm to analyze and determine whether the detected object is a normal vehicle includes: The wind resistance area data Sn of the object to be tested and the pre-set wind resistance area threshold By making comparisons, the classification interval of the detected object is determined. ; The classification range is used to determine whether the detected object is a normal vehicle. The data on the wind resistance area of ​​the tested vehicle, vehicle speed, crosswind speed and direction in the current crosswind-prone road sections are processed to obtain data A(X,Y,Z): Where X represents the area of ​​the object subject to wind resistance, Y represents the relative speed between the vehicle and the crosswind in the direction of travel, Z represents the angle between the crosswind and the direction of travel of the vehicle, A represents the lateral force of the crosswind on the vehicle being detected, and R represents the distance from the vehicle to the vehicle detection module. Let data point P(A,B), B be the type of object being detected, B=(B1,B2,B3,B4), where B1 represents non-vehicle objects, B2 represents small vehicles, B3 represents medium-sized vehicles, and B4 represents large vehicles. Create the following datasets: normal vehicles (centroid Q1), skidding vehicles (centroid Q2), and overturned vehicles (centroid Q3). Establish similarity relationships using the Euclidean distance method. Calculate the distances between P(A,B) and the three centroids Q1, Q2, and Q3. X; Based on the different values ​​of data B, the corresponding data are calculated and processed. When B = B2, X21=|A2-Q1|, When X22=|A2-Q2|; B=B3, X31=|A3-Q1|, X32 = |A3 - Q2|, When X33 = |A3 - Q3|; B = B4 X41=|A3-Q1|, X42 = |A3 - Q2| X43 = |A4 - Q3|; When B = B1, P(A1, B1) ; After the calculation is completed, compare the values ​​within the defined value group corresponding to data B. X size, The smaller X is, the closer the data point is to its corresponding dataset, and therefore it is classified into that dataset. The COCD algorithm model is trained using a dataset, and through repeated classification and analysis, a more accurate threshold range is obtained.

2. The safety early warning method for road sections prone to crosswinds according to claim 1, characterized in that, The crosswind-prone road sections include Road A and Road B. The vehicle detection module, wind detection module, information processing module, display screen prompt device, voice warning device, communication module and microcontroller each have two sets, A and B, which provide safety warnings for Road A and Road B respectively.

3. The safety early warning method for road sections prone to crosswinds according to claim 1, characterized in that, The wind resistance area S of a vehicle = the cross-sectional area of ​​one side of the vehicle = the radar target cross-sectional area, and its calculation formula is as follows: in, For transmission power, R is the received power, R is the distance from the vehicle to the vehicle detection module, and G is the gain of the transmitting and receiving antennas. The wavelength of the emitted wave is denoted as .

4. The safety early warning method for road sections prone to crosswinds according to claim 1, characterized in that, The process of determining whether the detected object is a vehicle involves using a vehicle detection module to measure the wind resistance area Sn of the detected object and analyzing it to determine whether the detected object is a vehicle.

5. The safety early warning method for road sections prone to crosswinds according to claim 4, characterized in that, The analysis to determine whether the detected object is a vehicle includes: The wind resistance area data Sn of the object to be tested and the pre-set wind resistance area threshold By comparison, the system determines whether the detected object is a vehicle based on its classification range. , The basis for determining whether an object is a vehicle based on its classification range is as follows: The classification interval for non-vehicle objects is (0, ...). 1) and ( 4, +∞), the classification range for small vehicles is [ 1, 2) The classification range for medium-sized vehicles is [ 2, 3) The classification range for large vehicles is [ 3, 4).

6. The safety early warning method for road sections prone to crosswinds according to claim 1, characterized in that, The specific process for calculating the crosswind force experienced by the vehicle without taking any action is as follows: the relative speed between the vehicle and the air and the direction of the crosswind force are measured using wind speed and wind direction sensors. The formula for calculating the crosswind force is: In the formula, Fx is the lateral force acting on the vehicle under test, C is the air drag coefficient, ρ is the air density, S is the area of ​​the vehicle subject to wind resistance, and v is the relative velocity between the object and the air. The angle between the vehicle's direction of travel and the wind direction.

7. The safety early warning method for road sections prone to crosswinds according to claim 1, characterized in that, In the process of using the COCD algorithm to analyze and determine whether the detected object is a normal vehicle, the objects in the detection area are classified into non-vehicles, small vehicles, medium-sized vehicles, and large vehicles according to the wind resistance area of ​​the detected vehicle.

8. The safety early warning method for road sections prone to crosswinds according to claim 1, characterized in that, The classification interval is: The classification interval for non-vehicle objects is (0, ...). 1) and ( 4, +∞), the classification range for small vehicles is [ 1, 2) The classification range for medium-sized vehicles is [ 2, 3) The classification range for large vehicles is [ 3, 4], Among them, when the classification interval is ( When the range is 4, +∞, the detected object is occluded or other conditions exist, where, 1 = 4,000,000 , 2 = 4725000 , 3 = 5400000 , 4 = 18,900,000 .

9. A safety early warning device for road sections prone to crosswinds, used to implement the safety early warning method for road sections prone to crosswinds as described in any one of claims 1 to 8, characterized in that, Includes column assembly and vehicle detection module. The pillar device is equipped with a wind detection module, an information processing module, a display screen, a voice warning device, a communication module, and a power supply unit. The vehicle detection module includes a millimeter-wave radar, a signal processor, a microcontroller, and a battery pack. The pillar device is installed on the outer side of the road in a crosswind-prone section for detecting wind force, analyzing data, providing vehicle warnings, and facilitating information communication. The crosswind-prone section includes Road A and Road B. The vehicle detection modules consist of two sets, A and B, installed 60m in front of the pillar device on Road A and Road B respectively, for detecting whether passing objects are vehicles and monitoring vehicle and road conditions in real time. The wind detection module includes a wind speed sensor and a wind direction sensor, which are installed above the column device to detect real-time crosswind speed and direction. The information processing module includes an STC-51 series microcontroller, which is installed inside the column and used to analyze and process information. The display screen device includes a 3500mm*2660mm conventional LED display screen, which is installed in front of the column and is used to display real-time weather conditions and warnings of crosswinds. The voice warning device is installed above the display screen and has a working volume of 80 decibels. The warning voice can be clearly received within a 100m radius of the sound source.

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