A global data processing system and method for real-time highway monitoring

Abstract

The present invention relates to the field of data processing technology, and in particular to a global data processing system and method for real-time highway monitoring. The present invention provides a real-time data acquisition module, a data preprocessing module, a data analysis module, and a data processing and control module. The real-time data acquisition module acquires vehicle driving data, the data preprocessing module determines the vehicle's driving characteristic tendency category, the data analysis module marks highway points, and the risk characteristic highway points are determined based on the comparison of each highway point, and the road surface abnormality risk sections are marked. The data processing and control module determines the data processing order of vehicle driving data for each monitored road section. The present invention realizes the identification of sections of the highway with road surface abnormality risks, adaptively adjusts the data processing order of vehicle driving data, and improves the timeliness and reliability of highway monitoring data processing.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a global data processing system and method for real-time highway monitoring. Background Art

[0002] With the acceleration of urbanization and the continuous growth of the number of motor vehicles, highway traffic volume is becoming increasingly large. Traditional monitoring data processing systems are unable to cope with the large-scale and high-frequency traffic data collection and processing needs. Factors such as severe weather and abnormal road conditions can easily lead to chain accidents. The existing monitoring methods have a lag in environmental perception and abnormal warning, and cannot provide real-time global road condition information. Due to the interference caused by environmental factors, the accuracy of judging abnormal conditions such as whether there are potholes on the road or abnormal weather causing local road abnormalities through images and other methods is low. At the same time, traditional highway monitoring systems are difficult to process the driving data of a large number of vehicles in real time, and only rely on single data for risk prediction, and have not formed a global collaborative analysis. Therefore, improving the timeliness and reliability of highway monitoring data processing is a technical problem that needs to be solved urgently.

[0003] For example, China Patent Authorization Announcement No.: CN117830063B, this invention discloses a mountain highway construction safety risk management system, which belongs to the field of data processing systems for management. This invention collects characteristic information of the slopes on both sides of a set section of a mountain highway and characteristic information of the highway surface, constructs a slope data analysis model to analyze the risk data of the slopes, and at the same time constructs a highway data analysis model to analyze the risk data of the highway. The risk analysis results of the slopes and the risk analysis results of the highway are integrated to analyze the safety risks in the construction process, and the safety risks obtained in the construction process are transmitted. By collecting and comprehensively analyzing road and slope data in real time, the efficiency and accuracy of mountain highway construction risk monitoring and early warning are improved.

[0004] The following problems also exist in the prior art:

[0005] The existing technology does not take into account how environmental factors may affect the reliability of the acquisition of images reflecting road conditions. The existing technology cannot accurately identify sections of highways with risks of road surface abnormalities based on real-time processing and analysis of vehicle driving data, and cannot adaptively adjust the data processing order of vehicle driving data based on sections with risks of road surface abnormalities, affecting the timeliness and reliability of highway monitoring data processing. Summary of the Invention

[0006] To this end, the present invention provides a global data processing system and method for real-time highway monitoring, which is used to overcome the problems that the existing technology cannot accurately identify sections of the highway with abnormal road surface risks based on real-time processing and analysis of vehicle driving data, and cannot adaptively adjust the data processing order of vehicle driving data, thereby affecting the timeliness and reliability of highway monitoring data processing.

[0007] To achieve the above objectives, the present invention provides a global data processing system and method for real-time highway monitoring, comprising:

[0008] A real-time data acquisition module is used to acquire vehicle driving data of a plurality of vehicles, wherein the vehicle driving data includes driving speed and driving trajectory;

[0009] a data preprocessing module connected to the real-time data acquisition module, configured to divide the highway into a plurality of monitoring sections, determine a driving characteristic tendency value based on the driving speed of the vehicle at a plurality of moments in the monitoring section, and thereby determine a driving characteristic tendency category of the vehicle;

[0010] a data analysis module, connected to the real-time data acquisition module and the data preprocessing module, respectively, for marking highway points based on the vehicle driving data of vehicles in different driving characteristic tendency categories within the monitored road section, screening risk characteristic highway points based on the comparison of each highway point, and marking abnormal road surface risk sections;

[0011] A data processing and control module is respectively connected to the real-time data acquisition module, the data preprocessing module, and the data analysis module, and is used to determine the data processing order of the vehicle driving data of each monitored section according to the abnormal road surface risk section within the monitored section, and transmit and process the vehicle driving data of each monitored section based on the data processing order.

[0012] Furthermore, the data preprocessing module compares the vehicle's driving characteristic tendency value with a preset first driving characteristic tendency value interval and a preset second driving characteristic tendency value interval to determine the vehicle's driving characteristic tendency category, wherein:

[0013] The driving characteristic tendency value is the average driving speed of the same vehicle at several moments in the monitored road section.

[0014] Furthermore, the data analysis module is used to mark the first highway point according to the vehicle driving data of the vehicle under the low-speed driving characteristic tendency category, wherein,

[0015] The data analysis module marks the highway point as the first highway point based on a determination result that the number of characteristic offsets of the same highway point in the driving trajectories of different vehicles meets the first highway point marking condition;

[0016] The first highway point marking condition is that the number of feature offsets exceeds a preset offset number threshold. The highway point is a point on the vehicle's driving trajectory where the curvature exceeds a preset curvature threshold. The number of feature offsets is the number of times the feature spacing between different vehicles at the same highway point does not exceed a preset feature spacing threshold.

[0017] Furthermore, the data analysis module is used to determine the feature spacing, wherein,

[0018] The data analysis module is used to construct a driving offset direction vector based on the vehicle's driving trajectory, wherein the driving offset direction vector uses the adjacent previous position point of the highway point in the vehicle's driving direction as the vector starting point and the highway point as the vector end point to construct the driving offset direction vector, and determines the lane pointed by the driving offset direction vector as the characteristic lane;

[0019] The characteristic distance is the distance between a vehicle on a characteristic lane and the highway point when the vehicle travels to the highway point.

[0020] Furthermore, the data analysis module is used to mark the second highway point according to the vehicle driving data of the vehicle under the high-speed driving characteristic tendency category, wherein,

[0021] The data analysis module marks the points as second highway points based on a determination result that the acceleration of the vehicle at the points on the driving trajectory meets the second highway point marking condition;

[0022] The second highway point marking condition is that the acceleration is less than zero, and the absolute value of the acceleration exceeds a preset first acceleration threshold.

[0023] Furthermore, the data analysis module is used to screen risk feature highway points, wherein:

[0024] The data analysis module selects the first highway point as a risk feature highway point based on a determination result that the first highway point and the second highway point meet the point constraint condition;

[0025] The point constraint condition is that the first highway point and the second highway point are distributed along the driving direction of the lane where they are located, and the interval distance between the first highway point and the second highway point does not exceed a preset interval distance reference value.

[0026] Furthermore, the data analysis module is used to determine the vehicle speed increase point, wherein:

[0027] The data analysis module determines the risk feature highway point as a speed increase point based on a determination result that the acceleration of each vehicle in the high-speed driving characteristic tendency category in the lane where the risk feature highway point is located meets the speed increase point marking conditions;

[0028] The speed increase point marking condition is to obtain the acceleration of the vehicle at several points on the driving trajectory after passing the risk characteristic highway point, the acceleration is greater than zero, and the absolute value of the acceleration exceeds a preset second acceleration threshold.

[0029] Furthermore, the data analysis module is used to mark abnormal risk sections of the road surface, wherein:

[0030] The road abnormality risk section starts from the risk characteristic highway point and the speed increase interval is the road abnormality risk section length;

[0031] The speed increase interval is the average distance between the speed increase point of each vehicle and the risk characteristic highway point.

[0032] Furthermore, the data processing control module is used to determine the data processing order of vehicle driving data of each monitored road section, wherein:

[0033] The data processing and control module is used to sort the risk coefficients of each monitored road section from large to small according to the numerical value, and the data processing order is the positive order of the sorting;

[0034] The risk coefficient is positively correlated with the number of abnormal road surface risk sections within the monitored road section, and is positively correlated with the maximum section length of the abnormal road surface risk sections within the monitored road section.

[0035] The present invention also provides a global data processing method for real-time highway monitoring, comprising:

[0036] Acquiring vehicle driving data of a plurality of vehicles, wherein the vehicle driving data includes driving speed and driving trajectory;

[0037] Divide the highway into several monitoring sections, determine the driving characteristic tendency value according to the driving speed of the vehicle at several moments in the monitoring section, and determine the driving characteristic tendency category of the vehicle;

[0038] Mark road points based on the vehicle driving data of vehicles under different driving characteristic tendency categories, screen risky road points based on the comparison of each road point, and mark abnormal risk sections of the road surface;

[0039] The data processing order of the vehicle driving data of each monitored section is determined according to the abnormal road surface risk section within the monitored section, and the vehicle driving data of each monitored section is transmitted and processed based on the data processing order.

[0040] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention sets a real-time data acquisition module, a data preprocessing module, a data analysis module, and a data processing and control module, obtains vehicle driving data through the real-time data acquisition module, determines the driving characteristic tendency value through the data preprocessing module, determines the driving characteristic tendency category of the vehicle, marks highway points based on the vehicle driving data of vehicles under different driving characteristic tendency categories through the data analysis module, determines the risk characteristic highway points according to the comparison of each highway point, and marks the abnormal road surface risk sections, determines the data processing order of vehicle driving data of each monitored section through the data processing and control module, and thus realizes the identification of sections in the highway with abnormal road surface risks, adaptively adjusts the data processing order of vehicle driving data, and improves the timeliness and reliability of highway monitoring data processing.

[0041] Furthermore, the present invention determines the driving characteristic tendency value according to the driving speed of the vehicle at several moments in the monitored section through the data preprocessing module to determine the driving characteristic tendency category of the vehicle. It can be understood that dividing the driving vehicles in the monitored section into different driving characteristic tendency categories according to the average driving speed of the vehicles at several moments can provide a more accurate classification basis for subsequent vehicle driving data analysis. Different driving characteristic tendency categories, that is, driving speeds, have differences. Since vehicles with different driving speeds have different driving trajectories when encountering road surface abnormalities, for example, when encountering road surface abnormalities, a vehicle with a faster driving speed will change its driving state later than a vehicle with a slower driving speed due to its faster speed. By dividing vehicles based on driving speed, a targeted driving data analysis method for vehicles with different speeds can be established, and a joint analysis can be performed based on the analysis results under the two categories to obtain more reliable identification analysis results, thereby achieving a more accurate classification basis for subsequent vehicle driving data analysis and improving the timeliness and reliability of highway monitoring data processing.

[0042] Furthermore, the present invention marks the first highway point based on the vehicle driving data of the vehicle in the low-speed driving characteristic tendency category. It can be understood that the curvature on the driving trajectory of the vehicle in the low-speed driving characteristic tendency category, that is, the vehicle with a slower driving speed, can represent the degree of deviation of the driving trajectory. When the vehicle encounters a road surface abnormality during driving, it usually avoids the road surface abnormality by adjusting the driving direction. When different vehicles have changed their driving trajectories at the same position point, and when there is a vehicle driving in the adjacent lane and it is unable to change lanes, the driving trajectory is also changed at this position point. It can be characterized that the reason for the driving trajectory change of multiple vehicles at the current position point is not a conventional lane change, but an encounter with a road surface abnormality. This position point is marked, thereby realizing the identification of sections of the highway with a risk of road surface abnormality, and improving the timeliness and reliability of highway monitoring data processing.

[0043] Furthermore, the present invention marks a second highway point based on the vehicle driving data of the vehicle in the high-speed driving characteristic tendency category, determines the risk characteristic highway point based on the comparison between the first highway point and the second highway point, and marks the road surface abnormality risk section. It can be understood that the high-speed driving characteristic tendency category vehicle is a vehicle with a faster driving speed. When a vehicle with a faster driving speed encounters a road surface abnormality, due to its faster speed, it will usually alleviate the impact of the road surface abnormality by slowing down. The acceleration at each point on the driving trajectory can be used to indicate whether the vehicle encounters a road surface abnormality at this point. Due to different vehicle speeds, the reaction position points to the road surface abnormality are different. The slower vehicle has a higher speed. The reaction point of a vehicle to abnormal road conditions will be earlier than that of a vehicle with a faster speed. The first highway point marked by a vehicle with a low-speed driving characteristic tendency category is verified by the second highway point marked by a vehicle with a high-speed driving characteristic tendency category to determine whether the first highway point is a risk characteristic highway point. A vehicle with a faster speed will reduce its speed when passing through an abnormal road section, such as a bumpy section, and increase its speed when leaving the abnormal road section due to improved road conditions. Therefore, the length of the abnormal road section can be determined by the position point where the speed is increased, thereby realizing the identification of sections of the highway with road abnormality risks and improving the timeliness and reliability of highway monitoring data processing.

[0044] Furthermore, the present invention determines the data processing order of vehicle driving data of each monitoring section according to the abnormal road surface risk section within the monitoring section. It can be understood that using the abnormal road surface risk section within the monitoring section as the basis for determining the vehicle driving data processing order can realize priority management of data processing based on the risk level, thereby more efficiently responding to highway safety hazards. The number of abnormal road surface risk sections and the maximum section length directly reflect the risk level of the monitoring section. The more abnormal road surface risk sections and the longer the longest section, the more serious the safety hazard in the monitoring section. The data processing order is determined by sorting the risk coefficients from large to small, which enables the system to give priority to processing vehicle driving data of high-risk sections, and realizes the optimal allocation of data processing resources under limited resources, ensuring that the system can concentrate on solving urgent safety problems and improving the overall efficiency and safety of highway monitoring. Furthermore, it realizes the identification of sections with abnormal road surface risks in the highway, adaptively adjusts the data processing order of vehicle driving data, and improves the timeliness and reliability of highway monitoring data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a functional block diagram of a global data processing system for real-time highway monitoring according to an embodiment of the present invention;

[0046] Figure 2 A logic flow chart of the data pre-processing module of an embodiment of the present invention for determining the driving characteristic tendency category of a vehicle;

[0047] Figure 3 A logic flow chart for marking a first highway point by a data analysis module according to an embodiment of the present invention;

[0048] Figure 4 This is a step diagram of a global data processing method for real-time highway monitoring according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0050] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0051] It should be noted that, in the description of the present invention, terms such as "upper", "lower", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0052] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted" and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0053] See also Figure 1 , which is a functional block diagram of a global data processing system for real-time highway monitoring according to an embodiment of the present invention. The global data processing system for real-time highway monitoring according to the present invention includes:

[0054] A real-time data acquisition module is used to acquire vehicle driving data of a plurality of vehicles, wherein the vehicle driving data includes driving speed and driving trajectory;

[0055] Specifically, the embodiment of the present invention does not specifically limit the structure of the real-time data acquisition module. Preferably, it can be a laser radar and video surveillance equipment deployed along the highway to obtain vehicle driving data of several vehicles, which will not be repeated here.

[0056] a data preprocessing module connected to the real-time data acquisition module, configured to divide the highway into a plurality of monitoring sections, determine a driving characteristic tendency value based on the driving speed of the vehicle at a plurality of moments in the monitoring section, and thereby determine a driving characteristic tendency category of the vehicle;

[0057] Specifically, the division length of the monitoring section is the product of the highway length and the monitoring section division factor. The monitoring section division factor can be set by technical personnel in this field based on the accuracy requirements of the global highway monitoring data acquisition. The higher the accuracy requirement, the smaller the monitoring section division factor. The value range of the monitoring section division factor can be [0.01, 0.03]. Preferably, the monitoring section division factor can be 0.02.

[0058] Specifically, the several moments can be several moments within the preset data acquisition period. The preset data acquisition period can be set by technical personnel in this field according to the accuracy requirements of highway monitoring full-area data acquisition. The higher the accuracy requirement, the shorter the preset data acquisition period. The value range of the preset data acquisition period can be [1,5], and the interval unit is min. Preferably, the preset data acquisition period can be 2 minutes. The value range of the interval between adjacent moments can be [20, 40], and the interval unit is s. Preferably, the interval length can be 30s.

[0059] Specifically, the embodiment of the present invention does not specifically limit the structure of the data preprocessing module. Preferably, it can be a microprocessor for dividing the monitored road sections, determining the driving characteristic tendency value, and determining the driving characteristic tendency category of the vehicle, which will not be repeated here.

[0060] a data analysis module, connected to the real-time data acquisition module and the data preprocessing module, respectively, for marking highway points based on the vehicle driving data of vehicles in different driving characteristic tendency categories within the monitored road section, screening risk characteristic highway points based on the comparison of each highway point, and marking abnormal road surface risk sections;

[0061] Specifically, the embodiment of the present invention does not specifically limit the structure of the data analysis module. Preferably, it can be a processor used in a computer to mark highway points, screen feature highway points, determine risk feature highway points, and mark abnormal risk sections of the road surface. This will not be repeated.

[0062] A data processing and control module is respectively connected to the real-time data acquisition module, the data preprocessing module, and the data analysis module, and is used to determine the data processing order of the vehicle driving data of each monitored section according to the abnormal road surface risk section within the monitored section, and transmit and process the vehicle driving data of each monitored section based on the data processing order.

[0063] Specifically, the embodiment of the present invention does not specifically limit the structure of the data processing and control module. Preferably, it can be a microprocessor to determine the data processing order of the vehicle driving data of each monitored section and transmit and process the vehicle driving data of each monitored section. This will not be repeated.

[0064] See also Figure 2 As shown, it is a logic flow chart of the data pre-processing module of an embodiment of the present invention for determining the driving characteristic tendency category of a vehicle. The data pre-processing module compares the driving characteristic tendency value of the vehicle with a preset first driving characteristic tendency value interval and a preset second driving characteristic tendency value interval to determine the driving characteristic tendency category of the vehicle, wherein:

[0065] The driving characteristic tendency value is the average driving speed of the same vehicle at several moments in the monitored road section.

[0066] Specifically, if the driving characteristic tendency value of the vehicle is within a preset first driving characteristic tendency value interval, the data preprocessing module determines that the driving characteristic tendency category of the vehicle is a low-speed driving characteristic tendency category;

[0067] If the driving characteristic tendency value of the vehicle is within a preset second driving characteristic tendency value interval, the data preprocessing module determines that the driving characteristic tendency category of the vehicle is a high-speed driving characteristic tendency category;

[0068] If the driving characteristic tendency value of the vehicle is neither within the preset first driving characteristic tendency value interval nor within the preset second driving characteristic tendency value interval, the data preprocessing module does not classify the vehicle into a driving characteristic tendency category;

[0069] The driving characteristic tendency value is the average driving speed of the same vehicle at several moments in the monitored road section.

[0070] Specifically, the same vehicle can be determined by the license plate number, and vehicles with the same license plate number can be determined as the same vehicle.

[0071] Specifically, the preset first driving characteristic tendency value interval and the preset second driving characteristic tendency value interval can be determined by technical personnel in this field based on several historical driving speed averages on the same road section. The preset first driving characteristic tendency value interval can be [1.1, 1.3) times the several historical driving speed averages, and the second driving characteristic tendency value interval can be [1.3, 1.5] times the several historical driving speed averages.

[0072] Specifically, the embodiment of the present invention determines the driving characteristic tendency value according to the driving speed of the vehicle at several moments in the monitored section through the data preprocessing module to determine the driving characteristic tendency category of the vehicle. It can be understood that dividing the driving vehicles in the monitored section into different driving characteristic tendency categories according to the average driving speed of the vehicles at several moments can provide a more accurate classification basis for subsequent vehicle driving data analysis. Different driving characteristic tendency categories, that is, driving speeds, have differences. Since vehicles with different driving speeds have different driving trajectories when encountering road surface abnormalities, for example, a vehicle with a faster driving speed will change its driving state later than a vehicle with a slower driving speed due to its faster speed when encountering road surface abnormalities. By dividing vehicles based on driving speed, a targeted driving data analysis method for vehicles with different speeds can be established, and a joint analysis can be performed based on the analysis results under the two categories to obtain more reliable identification analysis results, thereby achieving a more accurate classification basis for subsequent vehicle driving data analysis and improving the timeliness and reliability of highway monitoring data processing.

[0073] See also Figure 3 As shown, it is a logic flow chart of the data analysis module marking the first highway point according to an embodiment of the present invention, wherein the data analysis module is used to mark the first highway point according to the vehicle driving data of the vehicle under the low-speed driving characteristic tendency category, wherein:

[0074] The data analysis module marks the highway point as the first highway point based on a determination result that the number of characteristic offsets of the same highway point in the driving trajectories of different vehicles meets the first highway point marking condition;

[0075] If the number of characteristic deviations of the same highway point in the driving trajectories of different vehicles does not meet the first highway point marking condition, the data analysis module does not mark the highway point;

[0076] The first highway point marking condition is that the number of feature offsets exceeds a preset offset number threshold. The highway point is a point on the vehicle's driving trajectory where the curvature exceeds a preset curvature threshold. The number of feature offsets is the number of times the feature spacing between different vehicles at the same highway point does not exceed a preset feature spacing threshold.

[0077] Specifically, the preset offset number threshold can be set by technical personnel in this field based on the accuracy requirements of the global highway monitoring data acquisition. The higher the accuracy requirement, the smaller the preset overlap number threshold. The value range of the offset number threshold can be [5, 10]. Preferably, the offset number threshold can be 6.

[0078] Specifically, the preset curvature threshold is the product of the historical curvature average and the curvature factor. The historical curvature average is the average of the historical curvatures of several vehicles traveling in a straight line on the same road section. The curvature factor can be set by technical personnel in this field based on the accuracy requirements of the global highway monitoring data. The higher the accuracy requirement, the smaller the curvature factor. The value range of the curvature factor can be [1.4, 1.7]. Preferably, the curvature factor can be 1.5.

[0079] Specifically, the preset characteristic spacing threshold may be set by those skilled in the art based on the average characteristic spacing value under the same road section for several times, and the characteristic spacing threshold may be [1.3, 1.5] times the characteristic spacing value.

[0080] Specifically, if the interval distance between the same highway point in the driving trajectories of two vehicles does not exceed the preset interval distance reference value, it can be determined that there is an overlap at the highway point. The preset interval distance reference value can be set by technical personnel in this field based on the accuracy requirements of highway monitoring global data acquisition. The higher the accuracy requirement, the smaller the preset interval distance reference value. The value range of the interval distance reference value can be [0.2, 0.4], and the interval unit is m. Preferably, the interval distance reference value can be 0.3m.

[0081] Specifically, the data analysis module is used to determine the feature spacing, wherein,

[0082] The data analysis module is used to construct a driving offset direction vector based on the vehicle's driving trajectory, wherein the driving offset direction vector uses the adjacent previous position point of the highway point in the vehicle's driving direction as the vector starting point and the highway point as the vector end point to construct the driving offset direction vector, and determines the lane pointed by the driving offset direction vector as the characteristic lane;

[0083] Specifically, the driving offset direction vector can be determined by using laser radar and video surveillance equipment deployed along the highway in conjunction with a processor to determine the vehicle position in the characteristic lane. This will not be repeated here.

[0084] Specifically, the driving trajectory is the driving trajectory of the vehicle in the driving direction, wherein the previous position point is a position point that is farther away from the driving direction among adjacent position points.

[0085] The characteristic distance is the distance between a vehicle on a characteristic lane and the highway point when the vehicle travels to the highway point.

[0086] Specifically, the embodiment of the present invention marks the first highway point based on the vehicle driving data of the vehicle in the low-speed driving characteristic tendency category. It can be understood that the vehicle in the low-speed driving characteristic tendency category is a vehicle with a slower driving speed. The curvature on the driving trajectory can represent the degree of deviation of the driving trajectory. When the vehicle encounters a road surface abnormality during driving, it usually avoids the road surface abnormality by adjusting the driving direction. When different vehicles change their driving trajectories at the same position point, and when there is a vehicle driving in the adjacent lane and it is impossible to change lanes, the driving trajectory is also changed at this position point. It can be characterized that the reason for the driving trajectory change of multiple vehicles at the current position point is not a regular lane change, but an encounter with a road surface abnormality. This position point is marked, thereby realizing the identification of sections of the highway with a risk of road surface abnormality, and improving the timeliness and reliability of highway monitoring data processing.

[0087] Specifically, the data analysis module is used to mark the second highway point according to the vehicle driving data of the vehicle under the high-speed driving characteristic tendency category, wherein:

[0088] The data analysis module marks the points as second highway points based on a determination result that the acceleration of the vehicle at the points on the driving trajectory meets the second highway point marking condition;

[0089] If the acceleration of the vehicle at some points on the driving trajectory does not meet the second highway point marking condition, the data analysis module does not mark the points;

[0090] The second highway point marking condition is that the acceleration is less than zero, and the absolute value of the acceleration exceeds a preset first acceleration threshold.

[0091] Specifically, the preset first acceleration threshold can be set by those skilled in the art based on the average value of the absolute value of vehicle acceleration on the same road section for several times. The first acceleration threshold can be [1.1, 1.2] times the average value of the absolute value of acceleration.

[0092] Specifically, the data analysis module is used to screen risk feature highway points, where:

[0093] The data analysis module selects the first highway point as a risk feature highway point based on a determination result that the first highway point and the second highway point meet the point constraint condition;

[0094] If the first highway point and the second highway point do not meet the point constraint conditions, the data analysis module does not filter the first highway point;

[0095] The point constraint condition is that the first highway point and the second highway point are distributed along the driving direction of the lane where they are located, and the interval distance between the first highway point and the second highway point does not exceed a preset interval distance reference value.

[0096] Specifically, the preset interval distance reference value can be dynamically adjusted by technical personnel in this field according to real-time traffic flow, and set according to the average value of historical interval distances under several times of the same traffic flow. The value range of the interval distance reference value can be [1, 2], and the interval unit is m. Preferably, the value range of the interval distance reference value can be 1.5m.

[0097] Specifically, the data analysis module is used to determine the speed increase point, wherein:

[0098] The data analysis module determines the risk feature highway point as a speed increase point based on a determination result that the acceleration of each vehicle in the high-speed driving characteristic tendency category in the lane where the risk feature highway point is located meets the speed increase point marking conditions;

[0099] If the acceleration of each vehicle in the high-speed driving characteristic tendency category in the lane where the risk characteristic highway point is located does not meet the speed increase point marking conditions, the data analysis module will not filter the point;

[0100] The speed increase point marking condition is to obtain the acceleration of the vehicle at several points on the driving trajectory after passing the risk characteristic highway point, the acceleration is greater than zero, and the absolute value of the acceleration exceeds a preset second acceleration threshold.

[0101] Specifically, the preset second acceleration threshold can be set by those skilled in the art based on the average value of the absolute value of vehicle acceleration on the same road section for several times. The second acceleration threshold can be [1.3, 1.4] times the average value of the absolute value of acceleration.

[0102] Specifically, the data analysis module is used to mark abnormal risk sections of the road surface, wherein:

[0103] The road abnormality risk section starts from the risk characteristic highway point and the speed increase interval is the road abnormality risk section length;

[0104] The speed increase interval is the average distance between the speed increase point of each vehicle and the risk characteristic highway point.

[0105] Specifically, the embodiment of the present invention marks the second highway point according to the vehicle driving data of the vehicle in the high-speed driving characteristic tendency category, determines the risk characteristic highway point according to the comparison between the first highway point and the second highway point, and marks the road surface abnormality risk section. It can be understood that the high-speed driving characteristic tendency category vehicle is a vehicle with a faster driving speed. When a vehicle with a faster driving speed encounters a road surface abnormality, due to the faster speed, it will usually alleviate the impact of the road surface abnormality by slowing down. The acceleration at each point on the driving trajectory can be used to indicate whether the vehicle encounters a road surface abnormality at this point. Due to different vehicle speeds, the reaction position points to the road surface abnormality are different. The slower the vehicle, the more likely it is that the vehicle will encounter a road surface abnormality. The reaction position point of the vehicle with high speed to the abnormal road condition will be earlier than that of the vehicle with higher speed. The first highway point marked by the vehicle with low speed tendency category is verified by the second highway point marked by the vehicle with high speed tendency category to determine whether the first highway point is a risk characteristic highway point. When a vehicle with higher speed passes through a road section with abnormal road condition, such as a pothole section, it will reduce the speed. When it leaves the road section with abnormal road condition, it will increase the speed due to the improvement of road condition. Therefore, the length of the road section with abnormal road condition can be determined by the position point where the speed is increased, thereby realizing the identification of the road section with abnormal road condition risk and improving the timeliness and reliability of highway monitoring data processing.

[0106] Specifically, the data processing and control module is used to determine the data processing order of vehicle driving data of each monitored road section, wherein:

[0107] The data processing and control module is used to sort the risk coefficients of each monitored road section from large to small according to the numerical value, and the data processing order is the positive order of the sorting;

[0108] The risk coefficient is positively correlated with the number of abnormal road surface risk sections within the monitored road section, and is positively correlated with the maximum section length of the abnormal road surface risk sections within the monitored road section.

[0109] Specifically, the risk coefficient is quantity weight factor × quantity / quantity reference value + section length weight factor × maximum section length / maximum section length reference value, the quantity reference value is the average value of historical quantities, the maximum section length reference value is the average value of historical maximum section lengths, the quantity weight factor and the section length weight factor can be selected by those skilled in the art based on the degree of influence of the maximum quantity and section length in historical data on the calculation results. The quantity weight factor + section length weight factor = 1. Preferably, the quantity weight factor can be 0.5, and the section length weight factor can be 0.5.

[0110] Specifically, the embodiment of the present invention determines the data processing order of vehicle driving data of each monitoring section based on the abnormal road surface risk section within the monitoring section. It can be understood that using the abnormal road surface risk section within the monitoring section as the basis for determining the vehicle driving data processing order can realize priority management of data processing based on the risk level, thereby more efficiently responding to highway safety hazards. The number of abnormal road surface risk sections and the maximum section length directly reflect the risk level of the monitoring section. The more abnormal road surface risk sections and the longer the longest section, the more serious the safety hazard in the monitoring section. The data processing order is determined by sorting the risk coefficients from large to small, which enables the system to give priority to processing vehicle driving data of high-risk sections, and realizes the optimal allocation of data processing resources under limited resources, ensuring that the system can concentrate on solving urgent safety problems and improving the overall efficiency and safety of highway monitoring. Furthermore, it realizes the identification of sections with abnormal road surface risks in the highway, adaptively adjusts the data processing order of vehicle driving data, and improves the timeliness and reliability of highway monitoring data processing.

[0111] See also Figure 4 , which is a step diagram of a global data processing method for real-time highway monitoring according to an embodiment of the present invention. The present invention also provides a global data processing method for real-time highway monitoring, comprising:

[0112] Step S100, obtaining vehicle driving data of a plurality of vehicles, wherein the vehicle driving data includes driving speed and driving trajectory;

[0113] Step S200, dividing the highway into a plurality of monitoring sections, determining a driving characteristic tendency value based on the driving speed of the vehicle at a plurality of moments in the monitoring section, and determining a driving characteristic tendency category of the vehicle;

[0114] Step S300: marking road points based on the vehicle driving data of vehicles in different driving characteristic tendency categories, screening risky road points based on the comparison of each road point, and marking abnormal road sections;

[0115] Step S400 , determining a data processing order for vehicle driving data of each monitored section according to the road abnormality risk section within the monitored section, and transmitting and processing the vehicle driving data of each monitored section based on the data processing order.

[0116] Those skilled in the art will appreciate that the execution logic of the above method steps corresponds to the functional architecture of each module in the aforementioned system embodiment, and the implementation of each step can be completed based on the hardware structure or software module of the system.

[0117] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A global data processing system for real-time highway monitoring, characterized in that: include: A real-time data acquisition module is used to acquire vehicle driving data of a plurality of vehicles, wherein the vehicle driving data includes driving speed and driving trajectory; a data preprocessing module connected to the real-time data acquisition module, configured to divide the highway into a plurality of monitoring sections, determine a driving characteristic tendency value based on the driving speed of the vehicle at a plurality of moments in the monitoring section, and thereby determine a driving characteristic tendency category of the vehicle; a data analysis module, connected to the real-time data acquisition module and the data preprocessing module, respectively, for marking highway points based on the vehicle driving data of vehicles in different driving characteristic tendency categories within the monitored road section, screening risk characteristic highway points based on the comparison of each highway point, and marking abnormal road surface risk sections; The first highway point is marked according to the vehicle driving data of the vehicle under the low-speed driving characteristic tendency category, wherein: The data analysis module marks the highway point as the first highway point based on a determination result that the number of characteristic offsets of the same highway point in the driving trajectories of different vehicles meets the first highway point marking condition; The first highway point marking condition is that the number of feature offsets exceeds a preset offset number threshold, the highway point is a point on the vehicle's driving trajectory where the curvature exceeds a preset curvature threshold, and the number of feature offsets is the number of times that the feature spacing between different vehicles at the same highway point does not exceed a preset feature spacing threshold; The second highway point is marked according to the vehicle driving data of the vehicle under the high-speed driving characteristic tendency category, wherein: The data analysis module marks the points as second highway points based on a determination result that the acceleration of the vehicle at the points on the driving trajectory meets the second highway point marking condition; The second highway point marking condition is that the acceleration is less than zero, and the absolute value of the acceleration exceeds a preset first acceleration threshold; A data processing and control module is respectively connected to the real-time data acquisition module, the data preprocessing module, and the data analysis module, and is used to determine the data processing order of the vehicle driving data of each monitored section according to the abnormal road surface risk section within the monitored section, and transmit and process the vehicle driving data of each monitored section based on the data processing order.

2. The global data processing system for real-time highway monitoring according to claim 1, characterized in that: The data preprocessing module compares the vehicle's driving characteristic tendency value with a preset first driving characteristic tendency value interval and a preset second driving characteristic tendency value interval to determine the vehicle's driving characteristic tendency category, wherein: The driving characteristic tendency value is the average driving speed of the same vehicle at several moments in the monitored road section.

3. The global data processing system for real-time highway monitoring according to claim 2, characterized in that: The data analysis module is used to determine the feature spacing, wherein, The data analysis module is used to construct a driving offset direction vector based on the vehicle's driving trajectory, wherein the driving offset direction vector uses the adjacent previous position point of the highway point in the vehicle's driving direction as the vector starting point and the highway point as the vector end point to construct the driving offset direction vector, and determines the lane pointed by the driving offset direction vector as the characteristic lane; The characteristic distance is the distance between a vehicle on a characteristic lane and the highway point when the vehicle travels to the highway point.

4. The global data processing system for real-time highway monitoring according to claim 3, characterized in that: The data analysis module is used to screen risk feature highway points, wherein: The data analysis module selects the first highway point as a risk feature highway point based on a determination result that the first highway point and the second highway point meet the point constraint condition; The point constraint condition is that the first highway point and the second highway point are distributed along the driving direction of the lane where they are located, and the interval distance between the first highway point and the second highway point does not exceed a preset interval distance reference value.

5. The global data processing system for real-time highway monitoring according to claim 4, characterized in that: The data analysis module is used to determine the vehicle speed increase point, wherein: The data analysis module determines the risk feature highway point as a speed increase point based on a determination result that the acceleration of each vehicle in the high-speed driving characteristic tendency category in the lane where the risk feature highway point is located meets the speed increase point marking conditions; The speed increase point marking condition is to obtain the acceleration of the vehicle at several points on the driving trajectory after passing the risk characteristic highway point, the acceleration is greater than zero, and the absolute value of the acceleration exceeds a preset second acceleration threshold.

6. The global data processing system for real-time highway monitoring according to claim 5, characterized in that: The data analysis module is used to mark abnormal risk sections of the road surface, wherein: The road abnormality risk section starts from the risk characteristic highway point and the speed increase interval is the road abnormality risk section length; The speed increase interval is the average distance between the speed increase point of each vehicle and the risk characteristic highway point.

7. The global data processing system for real-time highway monitoring according to claim 6, characterized in that: The data processing control module is used to determine the data processing order of vehicle driving data of each monitored road section, wherein: The data processing and control module is used to sort the risk coefficients of each monitored road section from large to small according to the numerical value, and the data processing order is the positive order of the sorting; The risk coefficient is positively correlated with the number of abnormal road surface risk sections within the monitored road section, and is positively correlated with the maximum section length of the abnormal road surface risk sections within the monitored road section.

8. A global data processing method for real-time highway monitoring, applied to the global data processing system for real-time highway monitoring according to any one of claims 1 to 7, characterized in that: include: Acquiring vehicle driving data of a plurality of vehicles, wherein the vehicle driving data includes driving speed and driving trajectory; Divide the highway into several monitoring sections, determine the driving characteristic tendency value according to the driving speed of the vehicle at several moments in the monitoring section, and determine the driving characteristic tendency category of the vehicle; Mark road points based on the vehicle driving data of vehicles under different driving characteristic tendency categories, screen risky road points based on the comparison of each road point, and mark abnormal risk sections of the road surface; The data processing order of the vehicle driving data of each monitored section is determined according to the abnormal road surface risk section within the monitored section, and the vehicle driving data of each monitored section is transmitted and processed based on the data processing order.

Images