A highway road condition warning method

Abstract

The present invention discloses a highway road condition early warning method. The highway road condition early warning method comprises the following steps: information collection; comprehensive road condition analysis; result feedback. The present invention collects initial highway information in real time and transmits it to a central server, then pre-processes the initial highway information transmitted to the central server to obtain highway information, and performs road condition analysis on the highway to be warned according to the highway information to obtain a comprehensive road condition analysis index, and finally compares and analyzes the obtained comprehensive road condition analysis index with a corresponding threshold value, if the comprehensive road condition analysis index meets the corresponding threshold value, no special accident occurs on the highway to be warned, otherwise the real-time road condition of the highway to be warned is annotated and the visualized result is fed back to the preset highway personnel, thereby achieving the effect of improving the efficiency of highway road condition early warning, and solving the problem of low efficiency of highway road condition early warning in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a highway road condition early warning method. Background Art

[0002] With the development of unmanned aircraft technology, unmanned aircraft controlled by radio remote control equipment and self-contained program control devices, or operated completely or intermittently autonomously by on-board computers. Expressways have become an important part of the modern transportation system and play an important role in traffic safety and efficiency. In recent years, expressway construction has achieved rapid development, but at the same time, a series of traffic safety accidents occur on expressways every year. In addition to road quality problems and vehicle safety problems, there are also human factors such as speeding, illegal lane changes and illegal intrusions by pedestrians. Therefore, road condition warning for expressways has become particularly critical. With the development of science and technology, UAV technology has made great innovations in fuselage structure, battery technology, control system and image transmission, enabling UAVs to play an important role in various fields, including road inspection.

[0003] The existing highway accident warning system is set up to ensure the safety of highway driving. The system mainly uses a device composed of a transmitter and a receiver arranged on both sides of the highway. The transmitter continuously emits lasers, and when the receiver receives the signal, everything is normal. If the fog reduces visibility to a certain limit, the laser is blocked so that the receiver cannot receive the signal. The receiver immediately lights up the yellow warning light and sounds a warning sound, requiring the driver to slow down to a reasonable speed to prevent a series of collisions. In addition, the receiver can also promptly feedback the road condition information to the traffic command center through wired or wireless communication means for the traffic command center to refer to and direct driving.

[0004] For example, the road condition warning method and device disclosed in the invention patent with the announcement number CN115131957B include: dividing the road surface image around the target vehicle at the current moment into multiple areas based on an image segmentation algorithm; classifying each area in the road surface image based on a classification algorithm to obtain the road surface condition of each area, and issuing a warning if the road surface condition of at least one area is abnormal; if the road surface condition of any area is abnormal, the area is labeled according to a pre-associated labeling style according to the type of abnormal condition in the area, and the labeled road surface image is displayed on the display screen of the target vehicle; the labeling style is predetermined according to the severity or manifestation of the abnormal condition.

[0005] For example, the invention patent announcement with announcement number: CN112700662B discloses a highway intersection traffic warning adjustment device and method, including: establishing an optimal road condition model and a detection system to process data; the establishment of the optimal road condition model includes the following steps: collecting color image data of multiple road conditions in different time periods of a day through a video camera; collecting depth image data under the road conditions corresponding to the color image data through a laser radar; inputting the multiple color image data and the multiple depth image data into a generative adversarial network respectively; defining one of the multiple groups of road condition data in each time period input into the generative adversarial network as the optimal road condition data in the time period; outputting the optimal road condition data through the generative adversarial network. The detection system processes data including the following steps: using a video camera to collect color image data of the corresponding road section when there are no vehicles at different time periods of the day; using a video camera to collect color image data of road conditions in real time; using a lidar to collect depth image data of road conditions in real time; comparing the color image data collected in real time with the color image data without vehicles, and removing the overlapping pixels in the real-time collected image and the color image without vehicles to obtain separate vehicle image data; fusing the separate vehicle image data with the depth image to obtain specific road condition data, which is then input into the optimal road condition model, and outputting adjustment parameters for controlling the warning components and signal lights through judging the optimal road condition model.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, different types of sensors are deployed using traditional highway traffic condition warning systems. Highway traffic conditions are obtained and warnings are issued based on the data uploaded by different types of sensors and combined with real-time images of the highway within a preset time interval collected by fixed-point cameras. The collected data and images are fixed at a single point, resulting in low efficiency of highway traffic condition warnings. Summary of the invention

[0008] The embodiment of the present application solves the problem of low efficiency of highway road condition warning in the prior art by providing a highway road condition warning method, thereby improving the efficiency of highway road condition warning.

[0009] The embodiment of the present application provides a highway road condition warning method, comprising the following steps: S1, real-time collection of initial highway information on a preset drone of a highway to be warned and transmission to a central server, the initial highway information comprising initial sensor data and an initial highway image sequence, the initial sensor data being data acquired in real time by a preset sensor mounted on a preset drone, and the initial highway image sequence being an image sequence acquired in real time by a preset image acquisition device mounted on a preset drone; S2, pre-processing the initial highway information transmitted to the central server to obtain highway information, and performing road condition analysis on the highway to be warned based on the highway information to obtain a comprehensive road condition analysis index, the comprehensive road condition analysis index being used to comprehensively describe the traffic conditions, vehicle conditions and weather conditions of the highway to be warned within a preset time interval; S3, comparing and analyzing the obtained comprehensive road condition analysis index with a corresponding threshold value, if the comprehensive road condition analysis index meets the corresponding threshold value, it indicates that no special accident has occurred on the highway to be warned, otherwise, information annotation is performed on the real-time road condition of the highway to be warned and the visualized results are fed back to the preset highway personnel.

[0010] Furthermore, the specific acquisition process of the initial highway image sequence is as follows: a preset image acquisition device mounted on a preset drone is used to shoot a highway video of the highway to be warned in real time and record shooting information when shooting the highway video; the highway video is transmitted to a central server, and the highway video is divided into a number of highway images according to a preset time interval; the divided highway images are sorted in chronological order to obtain an initial highway image sequence.

[0011] Furthermore, the shooting information includes position information and parameter information; the position information includes the flight altitude, flight angle and flight path of the preset UAV, and the flight path is used to record the real-time position of the preset UAV and generate the next flight path of the preset UAV; the parameter information includes the magnification and focal length of the preset image acquisition device during real-time shooting.

[0012] Furthermore, the specific generation process of the flight path is as follows: path points are set according to the shooting information recorded in real time, and the path generated by all the set path points is the path of the current round of the preset drone; the flight height of the preset drone is adjusted in order from large to small in combination with the highest point and the lowest point of the preset path, and the path for the next round is generated.

[0013] Furthermore, the specific method for obtaining the comprehensive road condition analysis index is as follows: by analyzing the obtained highway information, a traffic condition evaluation index, a vehicle condition evaluation index and a weather condition evaluation index of the highway to be warned are obtained, and corresponding thresholds and reference deviations are obtained, the highway information includes sensor data and highway images, the traffic condition evaluation index is used to describe the traffic condition of the highway to be warned within a preset time interval, the vehicle condition evaluation index is used to describe the vehicle condition of the highway to be warned within a preset time interval, and the weather condition evaluation index is used to describe the weather condition of the highway to be warned within a preset time interval; the comprehensive road condition analysis index of the highway to be warned is calculated according to the obtained data and combined with the corresponding thresholds and reference deviations, and the comprehensive road condition analysis index is calculated using the following formula:

[0014] Wherein, e represents a natural constant, m represents the number of the highway to be warned, m=1,2,...,M, M is the total number of highways to be warned, LKm is the comprehensive road condition analysis index of the mth highway to be warned, JTm is the traffic condition evaluation index of the mth highway to be warned, JT0 is the threshold of the traffic condition evaluation index, ΔJT is the reference deviation of the traffic condition evaluation index, CLm is the vehicle condition evaluation index of the mth highway to be warned, CL0 is the threshold of the vehicle condition evaluation index, ΔCL is the reference deviation of the vehicle condition evaluation index, TQm is the weather condition evaluation index of the mth highway to be warned, TQ0 is the threshold of the weather condition evaluation index, ΔTQ is the reference deviation of the weather condition evaluation index, and ε0 is the reference relative deviation of the weather condition evaluation index.

[0015] Furthermore, a specific method for obtaining the traffic condition assessment index is as follows: obtaining traffic monitoring data of the highway to be warned within a preset time interval from the highway information and performing data normalization processing, the traffic monitoring data including vehicle flow and vehicle density, and the highway information including sensor data and highway images; combining the obtained vehicle flow with the reference vehicle flow to determine whether the corresponding difference is less than the reference relative deviation of the vehicle flow; if it is less than the reference relative deviation of the vehicle flow, performing a traffic condition assessment based on the obtained data to obtain the traffic condition assessment index of the highway to be warned; otherwise, the traffic condition assessment index of the highway to be warned is directly 0.

[0016] Furthermore, a specific method for obtaining the vehicle condition assessment index is as follows: obtaining vehicle driving data and vehicle quality scores of the highway to be warned within a preset time interval from highway information, the vehicle driving data including the number of vehicle braking times, vehicle speed and vehicle acceleration, and the vehicle quality score being a score for evaluating the vehicle quality condition by preset professionals; calculating the vehicle condition assessment index based on the obtained vehicle driving data and vehicle quality score in combination with vehicle weights, the vehicle weights including driving weights and quality weights, the driving weights being used to describe the degree of influence of vehicle driving data on the vehicle condition assessment index, the quality weights being used to describe the degree of influence of the vehicle quality score on the vehicle condition assessment index, and the driving weights being used to describe the degree of influence of the vehicle driving data on the vehicle condition assessment index, and the driving weights being used to describe the degree of influence of the vehicle quality score on the vehicle condition assessment index, and the driving weights being included first driving weights and second driving weights.

[0017] Furthermore, the vehicle condition assessment index is calculated using the following formula:

[0018]

[0019] Wherein, my represents the vehicle number of the mth highway to be warned, my=m.1,m.2,...,mY, mY is the total number of vehicles on the mth highway to be warned, z represents the number of the preset professional personnel, z=1,2,...,Z, Z is the total number of preset professional personnel, Cm.y is the number of vehicle braking of the myth vehicle on the mth highway to be warned, is the vehicle quality score of the my-th vehicle on the m-th highway to be warned obtained by the z-th preset professional, Vm.y is the vehicle speed of the my-th vehicle on the m-th highway to be warned, Am.y is the vehicle acceleration of the my-th vehicle on the m-th highway to be warned, Δt is the preset time interval of the highway image sequence of the highway to be warned, b1 is the first driving weight, b2 is the quality weight, and b3 is the second driving weight.

[0020] Furthermore, a specific method for obtaining the weather condition assessment index is as follows: obtaining weather data and weather quality scores of the highway to be warned within a preset time interval from highway information, the weather data including rainfall, road surface temperature and visibility, and the weather quality score being a score for assessing the weather quality conditions by preset professionals; judging whether the corresponding difference between the acquired visibility and the reference visibility is greater than the reference relative deviation of visibility, if it is greater than the reference relative deviation of visibility, performing data evaluation on the weather conditions of the highway to be warned based on the acquired data to obtain the corresponding weather condition assessment index, otherwise the weather condition assessment index of the highway to be warned is directly 0; the weather condition assessment index is obtained based on the acquired weather data and weather quality score in combination with weather weights, the weather weights including weather data weights and weather quality weights, the weather data weights being used to describe the degree of influence of weather data on the weather condition assessment index, the weather quality weights being used to describe the degree of influence of the weather quality score on the weather condition assessment index, and the weather data weights being included weather first weights, weather second weights and weather third weights.

[0021] Furthermore, the weather condition assessment index is calculated using the following formula:

[0022] Where Jm is the rainfall on the mth highway to be warned. is the reference rainfall of the mth highway to be warned, Tm is the road surface temperature of the mth highway to be warned, is the reference road surface temperature of the mth highway to be warned, Dm is the visibility of the mth highway to be warned, is the reference visibility of the mth highway to be warned, Uzm is the weather quality score of the mth highway to be warned obtained by the zth preset professional, c1 is the first weather weight, c2 is the second weather weight, c3 is the third weather weight, c4 is the weather quality weight, and β0 is the reference relative deviation of visibility.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0024] 1. The highway information is obtained by preprocessing the initial highway information transmitted to the central server, and then the road condition analysis of the highway to be warned is carried out according to the highway information to obtain a comprehensive road condition analysis index. Finally, the obtained comprehensive road condition analysis index is compared and analyzed with the corresponding threshold and the result is fed back to the preset highway personnel, thereby realizing a rapid warning of the highway to be warned, and then realizing the improvement of the efficiency of highway road condition warning, which effectively solves the problem of low efficiency of highway road condition warning in the prior art.

[0025] 2. By obtaining the vehicle driving data and vehicle quality score of the highway to be warned within a preset time interval from the highway information, and then calculating the vehicle condition evaluation index based on the obtained vehicle driving data and vehicle quality score in combination with the vehicle weight, finally evaluating the vehicle condition of the highway to be warned by the calculated vehicle condition evaluation index, thereby realizing the numerical evaluation of the vehicle condition of the highway to be warned, and then realizing a more accurate evaluation of the vehicle condition of the highway to be warned.

[0026] 3. By obtaining the weather data and weather quality scores of the highways to be warned within a preset time interval from the highway information, and then calculating the weather condition assessment index based on the obtained weather data and weather quality scores in combination with the weather weight, finally evaluating the weather conditions of the highways to be warned by the calculated weather condition assessment index, thereby realizing a numerical evaluation of the weather conditions of the highways to be warned, and then realizing a more accurate evaluation of the weather conditions of the highways to be warned. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart of a highway traffic warning method provided in an embodiment of the present application;

[0028] Figure 2 A flowchart for obtaining an initial highway image sequence provided in an embodiment of the present application;

[0029] Figure 3 A conceptual diagram of shooting information provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiment of the present application solves the problem of low efficiency of highway road condition warning in the prior art by providing a highway road condition warning method. The initial highway information on a preset drone of the highway to be warned is collected in real time and transmitted to a central server. The initial highway information transmitted to the central server is then preprocessed to obtain highway information. A road condition analysis is performed on the highway to be warned based on the highway information to obtain a comprehensive road condition analysis index. Finally, the obtained comprehensive road condition analysis index is compared with a corresponding threshold value for analysis. If the comprehensive road condition analysis index meets the corresponding threshold value, it indicates that no special accident has occurred on the highway to be warned. Otherwise, the real-time road condition of the highway to be warned is annotated and the visualized results are fed back to the preset highway personnel, thereby improving the efficiency of highway road condition warning.

[0031] The technical solution in the embodiment of the present application is to solve the problem of low efficiency of highway road condition warning, and the overall idea is as follows:

[0032] The highway information is obtained by preprocessing the initial highway information transmitted to the central server, and the road condition analysis of the highway to be warned is performed based on the highway information to obtain a comprehensive road condition analysis index. Finally, the obtained comprehensive road condition analysis index is compared and analyzed with the corresponding threshold, thereby achieving the effect of improving the efficiency of highway road condition warning.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] like Figure 1 As shown, it is a flow chart of a highway road condition warning method provided by an embodiment of the present application, and the method includes the following steps: S1, information collection, real-time collection of initial highway information on a preset drone of the highway to be warned and transmission to a central server, the initial highway information includes initial sensor data and initial highway image sequence, the initial sensor data is data acquired in real time by a preset sensor mounted on a preset drone, and the initial highway image sequence is an image sequence acquired in real time by a preset image acquisition device mounted on a preset drone; S2, comprehensive road condition analysis, pre-processing the initial highway information transmitted to the central server to obtain highway information, and performing road condition analysis on the highway to be warned based on the highway information to obtain a comprehensive road condition analysis index, which is used to comprehensively describe the traffic conditions, vehicle conditions and weather conditions of the highway to be warned within a preset time interval; S3, result feedback, comparing and analyzing the obtained comprehensive road condition analysis index with the corresponding threshold value, if the comprehensive road condition analysis index meets the corresponding threshold value, it indicates that no special accident has occurred on the highway to be warned, otherwise, the real-time road condition of the highway to be warned is annotated and the visualized result is fed back to the preset highway personnel.

[0035] In this embodiment, the road condition analysis specifically includes analysis of traffic conditions, vehicle conditions and weather conditions; the preset highway personnel include highway managers and users to be warned; information annotation is used to visualize special accidents occurring on the highway to be warned; and the efficiency of highway road condition warning is improved.

[0036] Further, such as Figure 2As shown, it is a flow chart for obtaining the initial highway image sequence provided in an embodiment of the present application, and the specific process of obtaining the initial highway image sequence is as follows: highway video shooting: using a preset image acquisition device mounted on a preset drone to shoot a highway video of the highway to be warned in real time and record the shooting information when shooting the highway video; highway video segmentation: transmitting the highway video to a central server, and segmenting the highway video into a number of highway images according to preset time intervals; highway image sorting: sorting the segmented highway images in chronological order to obtain an initial highway image sequence.

[0037] In this embodiment, the preset image acquisition device is a high-definition camera, which has the advantages of strong automation, small distortion, strong anti-shake power, high resolution and speed measurement; the preset drone is equipped with a high-definition camera to shoot videos of highway road conditions, with a wider shooting range and a wider field of view; the real-time video shooting of highway road conditions to be warned is achieved.

[0038] Further, such as Figure 3 As shown, it is a conceptual diagram of shooting information provided in an embodiment of the present application, and the shooting information includes position information and parameter information; the position information includes the flight altitude, flight angle and flight path of the preset UAV, and the flight path is used to record the real-time position of the preset UAV and generate the next flight path of the preset UAV; the parameter information includes the magnification and focal length multiple of the preset image acquisition device during real-time shooting.

[0039] In this embodiment, the internal and external parameters of the preset image acquisition device can be calculated by obtaining parameter information and position information in combination with the performance parameters of the preset image acquisition device, thereby improving the efficiency of high-speed road condition video shooting.

[0040] Furthermore, the specific generation process of the flight path is as follows: path points are set according to the real-time recorded shooting information, and the path generated by all the set path points is the path of the current round of the preset drone; the flight height of the preset drone is adjusted in order from large to small in combination with the highest and lowest points of the preset path, and the next round of path is generated.

[0041] In this embodiment, the highest point of the preset path can capture the highway video with the maximum viewing angle, so that the traffic flow on the highway and other conditions can be obtained; the lowest point of the preset path can capture a clear and effective highway video; and the real-time generation of the preset drone shooting path is realized.

[0042] Furthermore, the specific method for obtaining the comprehensive road condition analysis index is as follows: by analyzing the obtained highway information, the traffic condition evaluation index, vehicle condition evaluation index and weather condition evaluation index of the highway to be warned are obtained, and the corresponding thresholds and reference deviations are obtained. The highway information includes sensor data and highway images. The traffic condition evaluation index is used to describe the traffic condition of the highway to be warned within a preset time interval. The vehicle condition evaluation index is used to describe the vehicle condition of the highway to be warned within a preset time interval. The weather condition evaluation index is used to describe the weather condition of the highway to be warned within a preset time interval; the comprehensive road condition analysis index of the highway to be warned is calculated according to the obtained data and combined with the corresponding thresholds and reference deviations. The comprehensive road condition analysis index describes the comprehensive road condition. In addition to being directly obtained through the traffic performance index (TPI), the comprehensive road condition can also be more accurately calculated through a formula. The comprehensive road condition analysis index is calculated using the following formula:

[0043]

[0044] Wherein, e represents a natural constant, m represents the number of the highway to be warned, m=1,2,...,M, M is the total number of highways to be warned, LKm is the comprehensive road condition analysis index of the mth highway to be warned, JTm is the traffic condition evaluation index of the mth highway to be warned, JT0 is the threshold of the traffic condition evaluation index, ΔJT is the reference deviation of the traffic condition evaluation index, CLm is the vehicle condition evaluation index of the mth highway to be warned, CL0 is the threshold of the vehicle condition evaluation index, ΔCL is the reference deviation of the vehicle condition evaluation index, TQm is the weather condition evaluation index of the mth highway to be warned, TQ0 is the threshold of the weather condition evaluation index, ΔTQ is the reference deviation of the weather condition evaluation index, and ε0 is the reference relative deviation of the weather condition evaluation index.

[0045] In this embodiment, a numerical evaluation of the comprehensive road conditions of the expressway to be warned is achieved.

[0046] Furthermore, a specific method for obtaining the traffic condition assessment index is as follows: traffic monitoring data of the highway to be warned within a preset time interval is obtained from the highway information and the data is normalized, the traffic monitoring data includes vehicle flow and vehicle density, and the highway information includes sensor data and highway images; combining the obtained vehicle flow with the reference vehicle flow to determine whether the corresponding difference is less than the reference relative deviation of the vehicle flow; if it is less than the reference relative deviation of the vehicle flow, a traffic condition assessment is performed based on the obtained data to obtain the traffic condition assessment index of the highway to be warned; otherwise, the traffic condition assessment index of the highway to be warned is directly 0.

[0047] In this embodiment, the traffic condition evaluation index describes the traffic condition. In addition to being directly obtained by taking an image with a camera, the traffic condition can also be more accurately calculated by a formula. The traffic condition evaluation index is calculated using the following formula:

[0048]

[0049] Where mn represents the highway image number of the mth highway to be warned, mn=m.1,m.2,...,mN, mN is the total number of highway images of the mth highway to be warned, Im is the traffic volume of the mth highway to be warned, is the reference traffic flow of the mth highway to be warned, ρm.n is the vehicle density of the mnth highway image of the mth highway to be warned, is the reference vehicle density of the mn-th highway image of the m-th highway to be warned, a1 is the influence of the square of the traffic flow difference on the traffic condition assessment index, a2 is the influence of the square of the vehicle density difference on the traffic condition assessment index, α0 is the reference relative deviation of the traffic flow; the reference relative deviation of the traffic flow is the maximum value of the highway to be warned relative to the traffic flow, if the maximum value of the traffic flow is exceeded, the highway to be warned is in a closed state, and the closed state indicates that the highway to be warned is closed due to a special accident; the numerical evaluation of the traffic conditions of the highway to be warned is realized.

[0050] Furthermore, a specific method for obtaining the vehicle condition assessment index is as follows: obtaining vehicle driving data and vehicle quality scores of the highway to be warned within a preset time interval from the highway information, the vehicle driving data including the number of vehicle braking times, vehicle speed and vehicle acceleration, and the vehicle quality score being the score of the vehicle quality condition assessed by preset professionals; calculating the vehicle condition assessment index based on the obtained vehicle driving data and vehicle quality score in combination with vehicle weights, the vehicle weights including driving weights and quality weights, the driving weights being used to describe the degree of influence of the vehicle driving data on the vehicle condition assessment index, the quality weights being used to describe the degree of influence of the vehicle quality score on the vehicle condition assessment index, and the driving weights being used to describe the degree of influence of the vehicle quality score on the vehicle condition assessment index, and the driving weights being included first driving weights and second driving weights.

[0051] In this embodiment, a numerical evaluation of the vehicle conditions on the highway to be warned is implemented.

[0052] Furthermore, the vehicle condition evaluation index describes the vehicle condition. In addition to being directly obtained through the vehicle dial, the vehicle condition can also be more accurately calculated through a formula. The vehicle condition evaluation index is calculated using the following formula:

[0053]

[0054] Wherein, my represents the vehicle number of the mth highway to be warned, my=m.1,m.2,...,mY, mY is the total number of vehicles on the mth highway to be warned, z represents the number of the preset professional personnel, z=1,2,...,Z, Z is the total number of preset professional personnel, Cm.y is the number of vehicle braking of the myth vehicle on the mth highway to be warned, is the vehicle quality score of the my-th vehicle on the m-th highway to be warned obtained by the z-th preset professional, Vm.y is the vehicle speed of the my-th vehicle on the m-th highway to be warned, Am.y is the vehicle acceleration of the my-th vehicle on the m-th highway to be warned, Δt is the preset time interval of the highway image sequence of the highway to be warned, b1 is the first driving weight, b2 is the quality weight, and b3 is the second driving weight.

[0055] In this embodiment, the first driving weight is the influence of the average number of vehicle braking times on the vehicle condition assessment index, the quality weight is the influence of the average vehicle quality score on the vehicle condition assessment index, and the second driving weight is the influence of the square of the time interval difference on the vehicle condition assessment index; a more accurate assessment of the vehicle condition on the highway to be warned is achieved.

[0056] Furthermore, a specific method for obtaining the weather condition assessment index is as follows: obtaining weather data and weather quality scores of the highway to be warned within a preset time interval from the highway information, the weather data including rainfall, road surface temperature and visibility, and the weather quality score being the score of the weather quality conditions assessed by preset professionals; combining the obtained visibility with the reference visibility to determine whether the corresponding difference is greater than the reference relative deviation of visibility, if it is greater than the reference relative deviation of visibility, then performing data evaluation on the weather conditions of the highway to be warned based on the obtained data to obtain the corresponding weather condition assessment index, otherwise the weather condition assessment index of the highway to be warned is directly 0; the weather condition assessment index is obtained based on the obtained weather data and weather quality score in combination with the weather weight, the weather weight including the weather data weight and the weather quality weight, the weather data weight is used to describe the degree of influence of the weather data on the weather condition assessment index, the weather quality weight is used to describe the degree of influence of the weather quality score on the weather condition assessment index, and the weather data weight includes the first weather weight, the second weather weight and the third weather weight.

[0057] In this embodiment, the reference relative deviation of visibility is the minimum relative visibility of the highway to be warned. If it is lower than the minimum visibility, the weather is abnormal and the weather condition assessment index is zero, thereby realizing the numerical assessment of the weather condition of the highway to be warned.

[0058] Furthermore, the weather condition assessment index describes the weather conditions. In addition to being directly obtained through meteorological indicators, the weather conditions can also be more accurately calculated through formulas. The weather condition assessment index is calculated using the following formula:

[0059] Where Jm is the rainfall on the mth highway to be warned. is the reference rainfall of the mth highway to be warned, Tm is the road surface temperature of the mth highway to be warned, is the reference road surface temperature of the mth highway to be warned, Dm is the visibility of the mth highway to be warned, is the reference visibility of the mth highway to be warned, is the weather quality score of the mth highway to be warned obtained by the zth preset professional, c1 is the first weather weight, c2 is the second weather weight, c3 is the third weather weight, c4 is the weather quality weight, and β0 is the reference relative deviation of visibility.

[0060] In this embodiment, the first weather weight is the influence of the absolute difference in rainfall on the weather condition assessment index, the second weather weight is the influence of the absolute difference in road surface temperature on the weather condition assessment index, the third weather weight is the influence of the absolute difference in visibility on the weather condition assessment index, and the weather quality weight is the influence of the average weather quality score on the weather condition assessment index; thereby achieving a more accurate assessment of the weather conditions of the highway to be warned.

[0061] The technical scheme in the above-mentioned embodiment of the present application has at least the following technical effects or advantages: relative to the road condition warning method and device of the invention patent announcement with the publication number: CN115131957B, the embodiment of the present application obtains the vehicle driving data and vehicle quality score of the highway to be warned within a preset time interval from the highway information, and then calculates the vehicle condition evaluation index according to the obtained vehicle driving data and vehicle quality score in combination with the vehicle weight, and finally evaluates the vehicle condition of the highway to be warned by the calculated vehicle condition evaluation index, thereby realizing the numerical evaluation of the vehicle condition of the highway to be warned, and then realizing a more accurate evaluation of the vehicle condition of the highway to be warned; relative to a highway intersection traffic warning adjustment device and method announced by the invention patent announcement with the publication number: CN112700662B, the embodiment of the present application obtains the weather data and weather quality score of the highway to be warned within a preset time interval from the highway information, and then calculates the weather condition evaluation index according to the obtained weather data and weather quality score in combination with the weather weight, and finally evaluates the weather condition of the highway to be warned by the calculated weather condition evaluation index, thereby realizing the numerical evaluation of the weather condition of the highway to be warned, and then realizing a more accurate evaluation of the weather condition of the highway to be warned.

[0062] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0064] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0065] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0066] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0067] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A highway road condition warning method, It is characterized in that The following steps are involved: S1, collecting initial highway information on a preset drone of a highway to be warned in real time and transmitting it to a central server, wherein the initial highway information includes initial sensor data and an initial highway image sequence, wherein the initial sensor data is data acquired in real time by a preset sensor carried on a preset drone, and the initial highway image sequence is an image sequence acquired in real time by a preset image acquisition device carried on a preset drone; S2, preprocessing the initial highway information transmitted to the central server to obtain highway information, and performing a road condition analysis on the highway to be warned based on the highway information to obtain a comprehensive road condition analysis index, wherein the comprehensive road condition analysis index is used to comprehensively describe the traffic conditions, vehicle conditions and weather conditions of the highway to be warned within a preset time interval; S3, comparing and analyzing the obtained comprehensive road condition analysis index with the corresponding threshold value. If the comprehensive road condition analysis index meets the corresponding threshold value, it indicates that no special accident has occurred on the highway to be warned. Otherwise, the real-time road condition of the highway to be warned is annotated and the visualized result is fed back to the preset highway personnel. The specific method for obtaining the comprehensive road condition analysis index is as follows: By analyzing the obtained highway information, a traffic condition evaluation index, a vehicle condition evaluation index and a weather condition evaluation index of the highway to be warned are obtained, and corresponding thresholds and reference deviations are obtained, wherein the highway information includes sensor data and highway images, the traffic condition evaluation index is used to describe the traffic condition of the highway to be warned within a preset time interval, the vehicle condition evaluation index is used to describe the vehicle condition of the highway to be warned within a preset time interval, and the weather condition evaluation index is used to describe the weather condition of the highway to be warned within a preset time interval; The comprehensive road condition analysis index of the highway to be warned is calculated based on the acquired data and in combination with the corresponding threshold and reference deviation. The comprehensive road condition analysis index is calculated using the following formula: In the formula, e represents a natural constant, m represents the number of the highway to be warned, m = 1, 2, ..., M, M is the total number of highways to be warned, LK m is the comprehensive road condition analysis index of the mth highway to be warned, JT m is the traffic situation evaluation index of the mth highway to be warned, JT 0 is the threshold of the traffic situation evaluation index, ΔJT is the reference deviation of the traffic situation evaluation index, CL m is the vehicle situation evaluation index of the mth highway to be warned, CL 0 is the threshold of the vehicle condition evaluation index, ΔCL is the reference deviation of the vehicle condition evaluation index, TQ m is the weather condition evaluation index of the mth highway to be warned, TQ 0 is the threshold of the weather condition evaluation index, ΔTQ is the reference deviation of the weather condition evaluation index, and ε 0 Reference relative deviation for weather condition assessment index.

2. A highway road condition warning method as claimed in claim 1, It is characterized in that The specific acquisition process of the initial highway image sequence is as follows: Using a preset image acquisition device mounted on a preset drone to shoot a highway video of the highway to be warned in real time and record shooting information when shooting the highway video; Transmitting the highway video to a central server, and dividing the highway video into a number of highway images according to a preset time interval; The segmented highway images are sorted in chronological order to obtain an initial highway image sequence.

3. A highway road condition warning method as claimed in claim 2, Features: The shooting information includes location information and parameter information; The location information includes the flight altitude, flight angle and flight path of the preset UAV, and the flight path is used to record the real-time location of the preset UAV and generate the next flight path of the preset UAV; The parameter information includes a magnification and a focal length multiple when the image acquisition device is preset to take real-time photos.

4. A highway road condition warning method as claimed in claim 3, It is characterized in that The specific generation process of the flight path is as follows: Set the path points according to the real-time recorded shooting information, and the path generated by all the set path points is the path of the current round of the preset drone; Combine the highest and lowest points of the preset path and adjust the preset drone flight altitude in order from large to small, and generate the next round of paths.

5. A highway road condition warning method as claimed in claim 4, It is characterized in that The specific method for obtaining the traffic situation evaluation index is as follows: Acquire traffic monitoring data of the highway to be warned within a preset time interval from the highway information and perform data normalization processing, wherein the traffic monitoring data includes vehicle flow and vehicle density, and the highway information includes sensor data and highway images; The obtained traffic flow is combined with the reference traffic flow to determine whether the corresponding difference is less than the reference relative deviation of the traffic flow. If it is less than the reference relative deviation of the traffic flow, a traffic condition assessment is performed based on the obtained data to obtain the traffic condition assessment index of the highway to be warned. Otherwise, the traffic condition assessment index of the highway to be warned is directly 0.

6. A highway road condition warning method as claimed in claim 4, It is characterized in that The specific method for obtaining the vehicle condition assessment index is as follows: Obtaining vehicle driving data and vehicle quality scores of the highway to be warned within a preset time interval from the highway information, wherein the vehicle driving data includes the number of vehicle braking times, vehicle speed and vehicle acceleration, and the vehicle quality score is a score evaluated by preset professionals on the vehicle quality situation; A vehicle condition assessment index is calculated based on the acquired vehicle driving data and vehicle quality score in combination with vehicle weights, wherein the vehicle weights include driving weights and quality weights, wherein the driving weights are used to describe the degree of influence of the vehicle driving data on the vehicle condition assessment index, and the quality weights are used to describe the degree of influence of the vehicle quality score on the vehicle condition assessment index, and the driving weights include first driving weights and second driving weights.

7. A highway road condition warning method as claimed in claim 6, It is characterized in that The vehicle condition assessment index is calculated using the following formula: In the formula, my represents the vehicle number of the mth highway to be warned, my=m.1,m.2,...,mY, mY is the total number of vehicles on the mth highway to be warned, z represents the number of the preset professional personnel, z=1,2,...,Z, Z is the total number of preset professional personnel, C m.y is the number of vehicle braking of the my-th vehicle on the m-th highway to be warned, is the vehicle quality score of the my-th vehicle on the m-th highway to be warned obtained by the z-th preset professional, V m.y is the speed of the my-th vehicle on the m-th highway to be warned, A m.y is the vehicle acceleration of the my-th vehicle on the m-th highway to be warned, Δt is the preset time interval of the highway image sequence of the highway to be warned, b 1 is the first travel weight, b 2 is the quality weight, b 3 is the second driving weight.

8. A highway road condition warning method as claimed in claim 7, It is characterized in that The specific method for obtaining the weather condition assessment index is as follows: Acquire weather data and weather quality scores of the highway to be warned within a preset time interval from the highway information, wherein the weather data includes rainfall, road surface temperature and visibility, and the weather quality score is a score evaluated by preset professionals on the weather quality conditions; Combine the obtained visibility with the reference visibility to determine whether the corresponding difference is greater than the reference relative deviation of visibility. If it is greater than the reference relative deviation of visibility, then the weather condition of the highway to be warned is evaluated based on the obtained data to obtain the corresponding weather condition evaluation index. Otherwise, the weather condition evaluation index of the highway to be warned is directly 0. The weather condition assessment index is obtained based on the acquired weather data and weather quality score in combination with weather weights. The weather weights include weather data weights and weather quality weights. The weather data weights are used to describe the degree of influence of weather data on the weather condition assessment index. The weather quality weights are used to describe the degree of influence of the weather quality score on the weather condition assessment index. The weather data weights include weather first weights, weather second weights and weather third weights.

9. A highway road condition warning method as claimed in claim 8, It is characterized in that The weather condition assessment index is calculated using the following formula: In the formula, J m is the rainfall of the mth highway to be warned, is the reference rainfall for the mth highway to be warned, T m is the road surface temperature of the mth highway to be warned, is the reference road surface temperature of the mth highway to be warned, D m is the visibility of the mth highway to be warned, is the reference visibility of the mth highway to be warned, is the weather quality score of the mth highway to be warned obtained by the zth preset professional, c 1 is the first weight of weather, c 2 is the second weight of weather, c 3 is the third weight of weather, c 4 is the weather quality weight, β 0 is the reference relative deviation of visibility.

Images