Universe data processing system and method for real-time road monitoring

Abstract

The invention relates to the technical field of data processing, in particular to a global data processing system and method for real-time road monitoring, and the system is provided with a data real-time acquisition module, a data preprocessing module, a data analysis module and a data processing regulation and control module. The driving characteristic tendency category of the vehicle is determined through the data preprocessing module, the road point positions are marked through the data analysis module, the risk characteristic road point positions are judged according to the comparison condition of the road point positions, the road sections with abnormal road surface risks are marked, and the data processing sequence of the vehicle driving data of the monitored road sections is determined through the data processing regulation and control module. According to the invention, the road section with the abnormal road surface risk in the road is identified, the data processing sequence of the vehicle driving data is adaptively adjusted, and the timeliness and reliability of road monitoring data processing are improved.

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: 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

[0005] 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 in the prior art that it is unable to accurately identify sections with abnormal road surface risks on the highway based on real-time processing and analysis of vehicle driving data, and is unable to adaptively adjust the data processing order of vehicle driving data, affecting the timeliness and reliability of highway monitoring data processing.

[0006] To achieve the above object, the present invention provides a global data processing system and method for real-time highway monitoring, including: A data real-time acquisition module, which is used to acquire vehicle driving data of a number of vehicles, and the vehicle driving data includes driving speed and driving trajectory; A data preprocessing module, which is connected to the data real-time acquisition module, and is used to divide the highway into a number of monitoring sections, determine the driving characteristic tendency value according to the driving speeds of vehicles at several moments within the monitoring section, so as to determine the driving characteristic tendency category of the vehicle; A data analysis module, which is respectively connected to the data real-time acquisition module and the data preprocessing module, and is used to mark highway points based on the vehicle driving data of vehicles under different driving characteristic tendency categories within the monitoring section, screen out risk characteristic highway points according to the comparison situation of each highway point, and mark sections with abnormal road surface risks; A data processing control module, which is respectively connected to the data real-time 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 monitoring section according to the sections with abnormal road surface risks within the monitoring section, and perform transmission processing on the vehicle driving data of each monitoring section based on the data processing order.

[0007] Further, the data preprocessing 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, The driving characteristic tendency value is the average driving speed of the same vehicle at several moments within the monitoring section.

[0008] Further, the data analysis module is used to mark the first highway points according to the vehicle driving data of vehicles under the low-speed driving characteristic tendency category, wherein, The data analysis module marks the highway point as the first highway point based on the 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 driving trajectory of a vehicle where the curvature exceeds a preset curvature threshold. 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.

[0009] Further, the data analysis module is used to determine the feature spacing. Among them, The data analysis module is used to construct a driving offset direction vector according to the driving trajectory of the vehicle. The driving offset direction vector takes the adjacent previous position point of the highway point in the vehicle driving direction as the vector starting point and the highway point as the vector ending point to construct the driving offset direction vector, and determines the lane pointed by the driving offset direction vector as the feature lane; The feature spacing is the spacing between the vehicle on the feature lane and the highway point at the moment when the vehicle travels to the highway point.

[0010] Further, the data analysis module is used to mark the second highway point according to the vehicle driving data of the vehicles under the high-speed driving feature tendency category. Among them, The data analysis module marks the point as the second highway point based on the determination result that the acceleration of the vehicle at several 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.

[0011] Further, the data analysis module is used to screen the risk feature highway points. Among them, The data analysis module screens the first highway point as the risk feature highway point based on the 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.

[0012] Further, the data analysis module is used to determine the vehicle speed increase point. Among them, The data analysis module determines the point as the vehicle speed increase point based on the determination result that the acceleration of each vehicle under the high-speed driving feature tendency category in the lane where the risk feature highway point is located meets the vehicle speed increase point marking condition; The vehicle speed increase point marking condition is to obtain the acceleration of the vehicle at several points on the driving trajectory after passing the risk feature highway point. The acceleration is greater than zero, and the absolute value of the acceleration exceeds a preset second acceleration threshold.

[0013] Furthermore, the data analysis module is used to mark the road sections with abnormal risks, where the road sections with abnormal risks start from the road points with risk characteristics and the vehicle speed increase interval is the road section length of the road sections with abnormal risks; the vehicle speed increase interval is the average value of the interval distances between the vehicle speed increase points of each vehicle and the road points with risk characteristics.

[0014] Furthermore, the data processing and regulation module is used to determine the data processing order of the vehicle driving data of each monitoring section, where the data processing and regulation module is used to sort the risk coefficients of each monitoring section from large to small according to the numerical values, and the data processing order is the positive order of the sorting; the risk coefficients are respectively in a positive correlation with the number of road sections with abnormal risks in the monitoring section and in a positive correlation with the maximum value of the road section length of the road sections with abnormal risks in the monitoring section.

[0015] The present invention also provides a global data processing method for real-time highway monitoring, including: Obtaining the vehicle driving data of a number of vehicles, where the vehicle driving data includes the driving speed and the driving trajectory; Dividing the highway into a number of monitoring sections, and determining the driving characteristic tendency value according to the driving speeds of the vehicles at several moments in the monitoring section to determine the driving characteristic tendency category of the vehicles; Marking the road points based on the vehicle driving data of the vehicles under different driving characteristic tendency categories, screening the road points with risk characteristics according to the comparison situation of each road point, and marking the road sections with abnormal risks; Determining the data processing order of the vehicle driving data of each monitoring section according to the road sections with abnormal risks in the monitoring section, and performing transmission processing on the vehicle driving data of each monitoring section based on the data processing order.

[0016] Compared with the prior art, the beneficial effects of the present invention are that the present invention sets up a data real-time acquisition module, a data preprocessing module, a data analysis module, and a data processing and regulation module. The vehicle driving data is acquired through the data real-time acquisition module, the driving characteristic tendency value is determined through the data preprocessing module to determine the driving characteristic tendency category of the vehicles. The road points are marked based on the vehicle driving data of the vehicles under different driving characteristic tendency categories through the data analysis module, the road points with risk characteristics are determined according to the comparison situation of each road point, and the road sections with abnormal risks are marked. The data processing order of the vehicle driving data of each monitoring section is determined through the data processing and regulation module. Furthermore, the road sections with abnormal risks existing in the highway are identified, the data processing order of the vehicle driving data is adaptively adjusted, and the timeliness and reliability of highway monitoring data processing are improved.

[0017] 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.

[0018] 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.

[0019] Furthermore, the present invention marks the second highway point according to the vehicle driving data of the vehicle under the high-speed driving characteristic tendency category, determines the risk characteristic highway point according to the comparison situation between the first highway point and the second highway point, and marks the road surface abnormal risk section. It can be understood that the vehicle under the high-speed driving characteristic tendency category is a vehicle with a relatively fast driving speed. When a vehicle with a relatively fast driving speed encounters an abnormal road surface, due to the relatively fast vehicle speed, it usually reduces the speed to relieve the influence brought by the abnormal road surface. Whether the vehicle encounters an abnormal road surface at a certain point can be characterized by the acceleration at each point on the driving trajectory. Due to different vehicle speeds, the response position points for abnormal road surface conditions are different. The response position points of vehicles with a slower speed for abnormal road surface conditions will be earlier than those of vehicles with a faster speed. The second highway point marked by the vehicle under the high-speed driving characteristic tendency category is used to verify the first highway point marked by the vehicle under the low-speed driving characteristic tendency category to determine whether the first highway point is a risk characteristic highway point. When a vehicle with a relatively fast speed drives on an abnormal road surface section such as a pothole section, it usually reduces the speed, and when it drives out of the abnormal road surface section, the speed will increase due to the improvement of the road condition. Therefore, the length of the abnormal road surface section can be determined by the position point where the vehicle speed increases. Furthermore, the section of the road with abnormal road surface risk in the highway is identified, and the timeliness and reliability of highway monitoring data processing are improved.

[0020] Furthermore, the present invention determines the data processing order of the vehicle driving data of each monitoring section according to the road surface abnormal risk section within the monitoring section. It can be understood that using the road surface abnormal risk section within the monitoring section as the basis for determining the data processing order of the vehicle driving data can realize the priority management of data processing based on the risk level, so as to more efficiently respond to highway safety hazards. The number and the maximum section length of the road surface abnormal risk section directly reflect the risk level of the monitoring section. The more the number of road surface abnormal risk sections and the longer the longest section, the more serious the safety hazards existing in the monitoring section. Sorting the risk coefficients from large to small to determine the data processing order can enable the system to give priority to processing the vehicle driving data of high-risk sections, realizing 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, the section of the road with abnormal road surface risk in the highway is identified, the data processing order of the vehicle driving data is adaptively adjusted, and the timeliness and reliability of highway monitoring data processing are improved. Description of the Drawings

[0021] Figure 1 It is a functional block diagram of the global data processing system for real-time highway monitoring according to an embodiment of the present invention; Figure 2 It is a logic flowchart for the data preprocessing module to determine the driving characteristic tendency category of the vehicle according to an embodiment of the present invention; Figure 3The logic flow chart for the data analysis module of the present invention's embodiment to mark the first highway point; Figure 4 The step diagram of the method for processing global data for real-time highway monitoring according to the embodiment of the present invention. Detailed implementation manners

[0022] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] The preferred implementation manners of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these implementation manners are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0024] It should be noted that in the description of the present invention, the terms indicating the direction or positional relationship such as "upper", "lower", "inner", "outer", etc. are based on the direction or positional relationship shown in the drawings. This is only for 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, and thus should not be construed as a limitation of the present invention.

[0025] In addition, it should also be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0026] Please refer to Figure 1 As shown, it is the functional block diagram of the system for processing global data for real-time highway monitoring according to the embodiment of the present invention. A system for processing global data for real-time highway monitoring of the present invention includes: A data real-time acquisition module, which is used to acquire the vehicle driving data of a number of vehicles, and the vehicle driving data includes driving speed and driving trajectory; [[ID=2,6]]Specifically, the structure of the data real-time acquisition module in the embodiment of the present invention is not specifically limited. Preferably, it can be lidar and video monitoring equipment deployed along the highway to acquire the vehicle driving data of a number of vehicles, which will not be elaborated here.

[0027] A data preprocessing module, which is connected to the data real-time acquisition module, is used to divide a road into several monitoring sections, determine a driving characteristic tendency value according to the driving speeds of vehicles at several moments within a monitoring section, so as to determine the driving characteristic tendency category of the vehicle; Specifically, the division length of a monitoring section is the product of the road length and a monitoring section division factor. The monitoring section division factor can be set by those skilled in the art according to the accuracy requirement for obtaining the overall road monitoring data. 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.

[0028] Specifically, several moments can be several moments within a preset data acquisition period. The preset data acquisition period can be set by those skilled in the art according to the accuracy requirement for obtaining the overall road monitoring data. 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 min. The value range of the interval duration between adjacent moments can be [20, 40], and the interval unit is s. Preferably, the interval duration can be 30 s.

[0029] Specifically, the structure of the data preprocessing module in the embodiments of the present invention is not specifically limited. Preferably, it can be a microprocessor, which is used to divide the monitoring section, determine the driving characteristic tendency value, and determine the driving characteristic tendency category of the vehicle, which will not be elaborated here.

[0030] A data analysis module, which is respectively connected to the data real-time acquisition module and the data preprocessing module, is used to mark road points based on the vehicle driving data of vehicles under different driving characteristic tendency categories within a monitoring section, screen risk characteristic road points according to the comparison situation of each road point, and mark a road section with abnormal road surface risk; Specifically, the structure of the data analysis module in the embodiments of the present invention is not specifically limited. Preferably, it can be a processor used in a computer, which is used to mark road points, screen characteristic road points, determine risk characteristic road points, and mark a road section with abnormal road surface risk, which will not be elaborated here.

[0031] A data processing and control module, which is respectively connected to the data real-time acquisition module, the data preprocessing module, and the data analysis module, is used to determine the data processing sequence of the vehicle driving data of each monitoring section according to the road section with abnormal road surface risk within the monitoring section, and perform transmission processing on the vehicle driving data of each monitoring section based on the data processing sequence.

[0032] Specifically, the structure of the data processing and regulation module in the embodiments of the present invention is not specifically limited. Preferably, it can be a microprocessor, which is used to determine the data processing sequence of the vehicle driving data of each monitored section and perform transmission processing on the vehicle driving data of each monitored section, which will not be elaborated here.

[0033] Please refer to Figure 2 as shown, which is a logic flowchart for the data preprocessing module in the embodiments of the present invention to determine the driving characteristic tendency category of a vehicle. The data preprocessing 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. Among them, the driving characteristic tendency value is the average driving speed of the same vehicle at several moments within the monitored section.

[0034] Specifically, if the driving characteristic tendency value of the vehicle is within the preset first driving characteristic tendency value interval, the data preprocessing module determines that the driving characteristic tendency category of the vehicle is the low-speed driving characteristic tendency category; if the driving characteristic tendency value of the vehicle is within the preset second driving characteristic tendency value interval, the data preprocessing module determines that the driving characteristic tendency category of the vehicle is the high-speed driving characteristic tendency category; if the driving characteristic tendency value of the vehicle is not within the preset first driving characteristic tendency value interval and not within the preset second driving characteristic tendency value interval, the data preprocessing module does not classify the driving characteristic tendency category of the vehicle; the driving characteristic tendency value is the average driving speed of the same vehicle at several moments within the monitored section.

[0035] Specifically, the same vehicle can be determined by the license plate number, and the vehicle with the same license plate number is determined as the same vehicle.

[0036] Specifically, the preset first driving characteristic tendency value interval and the preset second driving characteristic tendency value interval can be determined by those skilled in the art according to the average historical driving speeds at several moments on the same section. The preset first driving characteristic tendency value interval can be [1.1, 1.3) times the average historical driving speeds, and the second driving characteristic tendency value interval can be [1.3, 1.5] times the average historical driving speeds.

[0037] 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, 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.

[0038] 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: 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; 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; 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.

[0039] 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.

[0040] 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 driving straight on the same road section. The curvature factor can be set by those skilled in the art according to the accuracy requirements obtained from 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.

[0041] Specifically, the preset feature spacing threshold can be set by those skilled in the art according to the average feature spacing in several identical road sections. The feature spacing threshold can be [1.3, 1.5] times the average feature spacing.

[0042] Specifically, when 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 a coincidence at this highway point. The preset interval distance reference value can be set by those skilled in the art according to the accuracy requirements obtained from the global highway monitoring data. 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 0.3 m.

[0043] Specifically, the data analysis module is used to determine the feature spacing, where the data analysis module is used to construct a driving offset direction vector based on the driving trajectory of the vehicle. The driving offset direction vector takes the adjacent previous position point of the highway point in the vehicle driving direction as the vector starting point and the highway point as the vector ending point to construct the driving offset direction vector, and determines the lane pointed to by the driving offset direction vector as the feature lane; Specifically, the driving offset direction vector and the vehicle position of the feature lane can be determined by cooperating the lidar and video monitoring devices deployed along the highway with the processor, which will not be elaborated here.

[0044] Specifically, the driving trajectory is the driving trajectory of the vehicle in the driving direction, where the previous position point is the position point farther from the driving direction among the adjacent position points.

[0045] The feature spacing is the spacing between the vehicle on the feature lane and the highway point at the moment when the vehicle travels to the highway point.

[0046] Specifically, in the embodiment of the present invention, the first highway point is marked according to the vehicle driving data of the vehicle under the low-speed driving characteristic tendency category. It can be understood that the vehicle under the low-speed driving characteristic tendency category is a vehicle with a relatively low driving speed. The curvature on the driving track can characterize the deviation degree of the driving track. When a vehicle encounters a road surface anomaly during driving, it usually adjusts the driving direction to avoid the road surface anomaly. When different vehicles all make changes to the driving track at the same position point, and when there are vehicles driving in the adjacent lane and it is impossible to change lanes, changes to the driving track are also made at this position point, which can characterize that the reason for the change in the driving track of multiple vehicles at the current position point is not a conventional lane change, but encountering a road surface anomaly. This position point is marked, and thus, a section of the road with a risk of road surface anomaly in the highway is identified, improving the timeliness and reliability of highway monitoring data processing.

[0047] 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, where the data analysis module marks the point as the second highway point based on the determination result that the acceleration of the vehicle at several points on the driving track meets the second highway point marking condition; if the acceleration of the vehicle at several points on the driving track does not meet the second highway point marking condition, the data analysis module does not mark the point; 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.

[0048] Specifically, the preset first acceleration threshold can be set by those skilled in the art according to the average value of the absolute values of the vehicle accelerations under several identical road sections. The first acceleration threshold can be [1.1, 1.2] times the average value of the absolute values of the accelerations.

[0049] Specifically, the data analysis module is used to screen the risk characteristic highway points, where the data analysis module screens the first highway point as a risk characteristic highway point based on the determination result that the first highway point and the second highway point meet the point constraint condition; if the first highway point and the second highway point do not meet the point constraint condition, the data analysis module does not screen the first highway point; 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.

[0050] Specifically, the preset reference value of the interval distance can be dynamically adjusted by those skilled in the art according to the real-time traffic flow, and is set according to the average value of the historical interval distances under several identical traffic flows. The value range of the reference value of the interval distance can be [1, 2], and the interval unit is m. Preferably, the value range of the reference value of the interval distance can be 1.5 m.

[0051] Specifically, the data analysis module is used to determine the vehicle speed increase points, where the data analysis module determines the point as a vehicle speed increase point based on the determination result that the accelerations of the vehicles in the high-speed driving characteristic tendency category in the lane where the risk characteristic highway point is located meet the vehicle speed increase point marking condition; If the accelerations of the vehicles in the high-speed driving characteristic tendency category in the lane where the risk characteristic highway point is located do not meet the vehicle speed increase point marking condition, the data analysis module does not screen the point; The vehicle speed increase point marking condition is to obtain the accelerations at several points on the driving trajectory of the vehicle after passing the risk characteristic highway point, the acceleration is greater than zero, and the absolute value of the acceleration exceeds the preset second acceleration threshold.

[0052] Specifically, the preset second acceleration threshold can be set by those skilled in the art according to the average value of the absolute values of the vehicle accelerations under several identical road sections, and the second acceleration threshold can be [1.3, 1.4] times the average value of the absolute values of the accelerations.

[0053] Specifically, the data analysis module is used to mark the road surface abnormal risk section, where the road surface abnormal risk section starts from the risk characteristic highway point, and the vehicle speed increase interval is the section length of the road surface abnormal risk section; The vehicle speed increase interval is the average value of the interval distances between the vehicle speed increase points of each vehicle and the risk characteristic highway point.

[0054] Specifically, in the embodiments of the present invention, the second highway points are marked according to the vehicle driving data of the vehicles under the high-speed driving characteristic tendency category. The risk characteristic highway points are determined according to the comparison between the first highway points and the second highway points, and the road surface abnormal risk sections are marked. It can be understood that the vehicles under the high-speed driving characteristic tendency category are vehicles with relatively fast driving speeds. When vehicles with relatively fast driving speeds encounter road surface abnormalities, due to the relatively fast vehicle speeds, they usually decelerate to alleviate the impact brought by the road surface abnormalities. The acceleration at each point on the driving trajectory can be used to characterize whether the vehicle encounters a road surface abnormality at this point. Due to different vehicle speeds, the response position points for road surface abnormality situations are different. The response position points of vehicles with slower speeds for road surface abnormality situations will be earlier than those of vehicles with faster speeds. The first highway points marked by vehicles with low-speed driving characteristic tendency categories are verified by the second highway points marked by vehicles with high-speed driving characteristic tendency categories to determine whether the first highway points are risk characteristic highway points. When vehicles with relatively fast speeds drive on road surface abnormal sections such as pothole sections, they usually reduce their speeds, and when leaving the road surface abnormal sections, their speeds will increase due to the improvement of the road conditions. Therefore, the section length of the road surface abnormal section can be determined through the position point where the vehicle speed increases. Furthermore, the sections of the road with road surface abnormal risks are identified, and the timeliness and reliability of highway monitoring data processing are improved.

[0055] Specifically, the data processing and regulation module is used to determine the data processing order of the vehicle driving data of each monitored section, where the data processing and regulation module is used to sort the risk coefficients of each monitored section from large to small according to the numerical values, and the data processing order is the positive order of the sorting; The risk coefficients are respectively in a positive correlation relationship with the number of road surface abnormal risk sections in the monitored section and in a positive correlation relationship with the maximum value of the section length of the road surface abnormal risk sections in the monitored section.

[0056] Specifically, the risk coefficient is the 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, and 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 according to the influence degree of the quantity and the maximum section length in the historical data on the calculation result. 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.

[0057] Specifically, in the embodiments of the present invention, the data processing order of the vehicle driving data of each monitoring section is determined according to the road surface abnormal risk sections in the monitoring section. It can be understood that taking the road surface abnormal risk sections in the monitoring section as the basis for determining the data processing order of vehicle driving data can realize the priority management of data processing based on the risk level, so as to more efficiently respond to highway safety hazards. The number and the maximum section length of the road surface abnormal risk sections directly reflect the risk level of the monitoring section. The more the number of road surface abnormal risk sections and the longer the longest section, the more serious the safety hazards existing in the monitoring section. Sorting according to the risk coefficient from large to small to determine the data processing order can enable the system to give priority to processing the vehicle driving data of high-risk sections, realizing the optimal allocation of data processing resources under the condition of limited resources, ensuring that the system can concentrate on solving urgent safety problems, improving the overall efficiency and safety of highway monitoring. Furthermore, it realizes the identification of the road sections with road surface abnormal 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.

[0058] Please refer to Figure 4 , which is the step diagram of the global data processing method for real-time highway monitoring in the embodiments of the present invention. The present invention also provides a global data processing method for real-time highway monitoring, including: Step S100, obtaining the vehicle driving data of a plurality of vehicles, where the vehicle driving data includes the driving speed and the driving trajectory; Step S200, dividing the highway into a plurality of monitoring sections, and determining the driving characteristic tendency value according to the driving speeds of the vehicles at several moments in the monitoring section to determine the driving characteristic tendency category of the vehicles; Step S300, marking highway points based on the vehicle driving data of the vehicles under different driving characteristic tendency categories, screening out the risk characteristic highway points according to the comparison situation of each highway point, and marking the road surface abnormal risk sections; Step S400, determining the data processing order of the vehicle driving data of each monitoring section according to the road surface abnormal risk sections in the monitoring section, and performing transmission processing on the vehicle driving data of each monitoring section based on the data processing order.

[0059] Those skilled in the art can understand that the execution logic of the above method steps corresponds to the functional architecture of each module in the foregoing system embodiments, and the implementation of each step can be completed based on the hardware structure or software module of the system.

[0060] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easily understood by those skilled in the art that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. A global data processing system for real-time highway monitoring, characterized in that, Including: A real-time data acquisition module for acquiring vehicle driving data of a number of vehicles, where the vehicle driving data includes driving speed and driving trajectory; A data preprocessing module connected to the real-time data acquisition module for dividing a road into a number of monitored sections, determining a driving characteristic tendency value based on the driving speeds of vehicles at several moments within the monitored sections, so as to determine the driving characteristic tendency category of the vehicles; A data analysis module connected to the real-time data acquisition module and the data preprocessing module respectively for marking road points based on the vehicle driving data of vehicles under different driving characteristic tendency categories within the monitored sections, screening risk characteristic road points according to the comparison situation of each road point, and marking abnormal road surface risk sections; A data processing and control module connected to the real-time data acquisition module, the data preprocessing module, and the data analysis module respectively for determining the data processing order of the vehicle driving data of each monitored section according to the abnormal road surface risk sections within the monitored sections, and performing transmission processing on 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, wherein The data preprocessing module compares the driving characteristic tendency value of the vehicle with a preset first driving characteristic tendency value range and a preset second driving characteristic tendency value range to determine the driving characteristic tendency category of the vehicle, where The driving characteristic tendency value is the average driving speed of the same vehicle at several moments within the monitored section.

3. The global data processing system for real-time highway monitoring according to claim 2, wherein, The data analysis module is used to mark a first road point based on the vehicle driving data of vehicles under the low-speed driving characteristic tendency category, where The data analysis module marks the road point as a first road point based on the determination result that the number of characteristic offsets of the same road point in the driving trajectories of different vehicles meets the first road point marking condition; The first road point marking condition is that the number of characteristic offsets exceeds a preset offset number threshold, the road point is a point on the driving trajectory of the vehicle where the curvature exceeds a preset curvature threshold, and the number of characteristic offsets is the number of times that the characteristic spacing between different vehicles at the same road point does not exceed a preset characteristic spacing threshold.

4. The global data processing system for real-time highway monitoring according to claim 3, wherein, The data analysis module is used to determine the characteristic spacing, where The data analysis module constructs a driving offset direction vector according to the driving trajectory of the vehicle. The driving offset direction vector takes the adjacent previous position point of the road point in the vehicle driving direction as the vector starting point and the road point as the vector ending 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 spacing is the spacing between the vehicle on the characteristic lane and the road point at the moment when the vehicle travels to the road point.

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 mark a second road point based on the vehicle driving data of vehicles under the high-speed driving characteristic tendency category, where The data analysis module marks the point as a second road point based on the determination result that the acceleration of the vehicle at several points on the driving trajectory meets the second road point marking condition; The second road point marking condition is that the acceleration is less than zero, and the absolute value of the acceleration exceeds a preset first 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 screen out highway points with risk characteristics. Among them, the data analysis module screens the first highway point as a highway point with risk characteristics based on the determination result that the first highway point and the second highway point meet the point constraint conditions; the point constraint conditions are 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.

7. The global data processing system for real-time highway monitoring according to claim 6, characterized in that, The data analysis module is used to determine speed increase points. Among them, the data analysis module determines the point as a speed increase point based on the determination result that the acceleration of each vehicle in the high-speed driving characteristic tendency category within the lane where the highway point with risk characteristics is located meets the speed increase point marking condition; the speed increase point marking condition is to obtain the acceleration at several points on the driving trajectory of the vehicle after passing the highway point with risk characteristics, the acceleration is greater than zero, and the absolute value of the acceleration exceeds a preset second acceleration threshold.

8. The global data processing system for real-time highway monitoring according to claim 7, characterized in that The data analysis module is used to mark road surface abnormal risk sections. Among them, [[ID= ​ 9. The global data processing system for real-time highway monitoring according to claim 8, wherein ​ ​ ​ 10. A method for processing global data for real-time highway monitoring, which is applied to the global data processing system for real-time highway monitoring described in any one of claims 1-9 above, characterized in that, ​ ​ ​ ​ ​

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