Intelligent traffic monitoring data optimization method and system

Abstract

The application discloses an intelligent traffic monitoring data optimization method and system, which comprises the following steps: acquiring monitoring data; distributing the monitoring data into hierarchical data and basic data; distributing the basic data into first-level data and second-level data according to the hierarchical data; transmitting the first-level data to vehicles and / or a monitoring center, performing edge processing on the second-level data to obtain edge analysis data, and then transmitting the edge analysis data to the vehicles and / or the monitoring center; and transmitting the second-level data to the vehicles and / or the monitoring center in a transmission gap between the first-level data and the edge analysis data. The application can distribute the basic data into hierarchical data, obtain relatively urgent first-level data, and obtain edge analysis data by using second-level data, so that data distribution is completed according to different urgency.

Description

Technical Field

[0001] This application relates to a method and system for optimizing intelligent traffic monitoring data. Background Technology

[0002] With the rapid development of highways, the scheduling of highways themselves is receiving increasing attention, especially with the rise of vehicle-road cooperation (V2L) technology, which is increasingly important in the context of autonomous driving. V2L utilizes advanced wireless communication and next-generation internet technologies to implement comprehensive, real-time dynamic information exchange between vehicles and infrastructure. Based on the collection and fusion of dynamic traffic information across all times and spaces, it enables active vehicle safety control and collaborative road management, fully realizing effective coordination between people, vehicles, and roads. This ensures traffic safety, improves traffic efficiency, and ultimately forms a safe, efficient, and environmentally friendly road traffic system.

[0003] In road testing, vehicle-to-infrastructure (V2I) communication primarily serves two purposes: real-time monitoring data collection and data transmission. Data transmission occurs in two directions: to the monitoring center and to the vehicles themselves. The monitoring center requires complete and timely data, while the vehicles prioritize timely access to critical data. Therefore, data transmission needs to be configured differently depending on the specific circumstances. Current edge data monitoring centers lack this capability, making it difficult to simultaneously meet the data transmission requirements of V2I communication. Summary of the Invention

[0004] To address the aforementioned problems, this application discloses a method for optimizing intelligent traffic monitoring data, comprising the following steps:

[0005] Obtain monitoring data;

[0006] The monitoring data is divided into hierarchical data and basic data;

[0007] Based on the hierarchical data, the basic data is allocated into primary data and secondary data;

[0008] The primary data is transmitted to the vehicle and / or monitoring center, the secondary data is processed at the edge to obtain edge analysis data, and then the edge analysis data is transmitted to the vehicle and / or monitoring center.

[0009] During the interval between primary data and edge analysis data transmission, secondary data is transmitted to the vehicle and / or monitoring center. This application obtains tiered data from the monitoring data itself, and then tiers the basic data. This method allows for the tiering of basic data to obtain relatively urgent primary data, and edge analysis data is obtained from the secondary data, thus completing data tiering according to different levels of urgency.

[0010] Preferably, the grading data includes evaluation values ​​and emergency measurement values.

[0011] Preferably, the hierarchical data is obtained in the following manner:

[0012] The monitoring data are ranked according to importance (Z). i Assign a value, and combine it with the size B of the data packet corresponding to the monitoring data. i The evaluation value P was calculated. i =Z i / B i ;

[0013] Based on road congestion level Y i And the corresponding vehicle speed V i The emergency measurement value J was obtained. i =Y i *V i ;

[0014] Based on the evaluation value P i The monitoring data are sorted, and then ranked according to the magnitude of the emergency measurement value, based on the evaluation value P. i The data is sorted and allocated into hierarchical and basic data. The evaluation values ​​in this application take into account the degree of importance and the size of the data packets to balance the data load and importance. For emergency measurements, the degree of road congestion and the speed of individual vehicles are taken into account, so that as much effective data as possible can be obtained in the shortest possible time under relatively urgent conditions.

[0015] Preferably, the road congestion level Y i =Predicted travel time for the current road segment / Predicted travel time for the road segment under smooth traffic conditions.

[0016] Preferably, based on the size of the emergency measurement value, the values ​​are sorted in (J) i The monitoring data before 5% is used as primary data, and the remaining data is used as secondary data.

[0017] Preferably, if the monitoring data is video data, it is automatically classified as secondary data; after the target vehicle acquires primary data, if automatic optimization of the driving strategy is not possible or the optimization effect is poor, then in the next segmentation of primary and secondary data, it will be sorted in (2J) i The monitoring data before 5% is used as primary data, and the remaining data is used as secondary data.

[0018] Preferably, the secondary data processing obtains edge analysis data as follows: for monitoring data that is video or image, emergency data analysis and extraction are performed to obtain edge analysis data. This application addresses the issue that while videos and images generally have large bit values, they may be the only source of data in certain situations. Therefore, edge analysis data is extracted by setting up emergency data analysis and extraction to meet the timeliness requirement of data integrity when decision-making is needed.

[0019] Preferably, the emergency data includes the following data: vehicle information, distance between vehicles, distance between vehicles and the roadside, vehicle speed, vehicle acceleration, and the time, location, and probability of the vehicle risk occurring.

[0020] Preferably, the time, location, and probability of vehicle risks are calculated and predicted while keeping the target vehicle's driving strategy unchanged and the surrounding environment unchanged.

[0021] On the other hand, a smart traffic monitoring data optimization system is also disclosed, including:

[0022] The acquisition module is used to acquire monitoring data;

[0023] The hierarchical processing module is used to allocate monitoring data into hierarchical data and basic data, and to allocate basic data according to hierarchical data, further allocating basic data into primary data and secondary data; and to perform edge processing on secondary data to obtain edge analysis data.

[0024] The transmission module is used to transmit primary data to the vehicle and / or monitoring center, and then transmit edge analysis data to the vehicle and / or monitoring center; during the interval between the transmission of primary data and edge analysis data, it transmits secondary data to the vehicle and / or monitoring center.

[0025] This application can bring the following beneficial effects:

[0026] 1. This application obtains graded data through monitoring data itself, and then grades the basic data. This method can grade the basic data to obtain relatively urgent first-level data and use second-level data to obtain edge analysis data, thereby completing the data grading according to different urgency levels.

[0027] 2. The evaluation values ​​in this application take into account the degree of importance and the size of the data packets, balancing the data load and importance. For emergency measurement values, the degree of road congestion and the speed of individual vehicles are taken into account, so that as much effective data as possible can be obtained in the shortest possible time under relatively urgent conditions.

[0028] 3. This application addresses the issue that while videos and images generally have large bit values, they may be the only source of data in certain situations. Therefore, it extracts edge analysis data by setting up emergency data analysis and extraction to meet the timeliness requirement of data integrity when decision-making is needed. Attached Figure Description

[0029] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0030] Figure 1 This is a schematic diagram of Example 1;

[0031] Figure 2 This is a schematic diagram of Example 2. Detailed Implementation

[0032] To clearly illustrate the technical features of this solution, the following detailed description of specific implementation methods will be provided.

[0033] In the first embodiment, as Figure 1 As shown, a method for optimizing intelligent traffic monitoring data includes the following steps:

[0034] S101 acquires monitoring data;

[0035] S102 divides the monitoring data into hierarchical data and basic data;

[0036] The grading data includes evaluation values ​​and emergency measurement values.

[0037] The hierarchical data is obtained in the following manner:

[0038] The monitoring data are ranked according to importance (Z). i Assign a value, and combine it with the size B of the data packet corresponding to the monitoring data. i The evaluation value P was calculated. i =Z i / B i ;

[0039] The importance level is currently assigned between 50 and 100. At present, the importance is set at 100 for vehicle speed, and 90-100 for the importance of various entities or between entities and road tests. The value is assigned separately according to road conditions. For vehicle type, the value is 50. The values ​​for other aspects are also within this range.

[0040] For B iGenerally, the unit of measurement is kb. Since the comparison here is essentially a horizontal comparison, the difference in the unit of measurement will not affect the sorting. As long as it remains consistent during use, the requirements can be met.

[0041] Based on road congestion level Y i And the corresponding vehicle speed V i The emergency measurement value J was obtained. i =Y i *V i Travel speed V i The unit of measurement is km / h;

[0042] Based on the evaluation value P i The monitoring data are sorted, and then ranked according to the magnitude of the emergency measurement value, based on the evaluation value P. i The sorting is used to allocate hierarchical and basic data.

[0043] S103 allocates basic data based on hierarchical data, dividing the basic data into primary data and secondary data;

[0044] The road congestion level Y i =Predicted travel time for the current road segment / Predicted travel time for the road segment under smooth traffic conditions.

[0045] Based on the size of the emergency measurement value, they will be sorted in (J) i The monitoring data before 5%) is used as primary data, and the remaining data is used as secondary data. i The values ​​used here are absolute values, that is, values ​​after removing the km / h unit.

[0046] If the monitoring data is video data, it will be automatically classified as secondary data. If, after acquiring primary data for the target vehicle, automatic optimization of the driving strategy is not possible or the optimization effect is poor, then during the next segmentation of primary and secondary data, it will be sorted in (2J). i The monitoring data before ( / 5)% is used as primary data, and the remaining data is used as secondary data. (2J) i / 5) No more than 1 / 3.

[0047] S104 processes the secondary data to obtain edge analysis data:

[0048] The secondary data processing is carried out as follows to obtain edge analysis data. For monitoring data that is video or image, emergency data analysis and extraction are performed to obtain edge analysis data.

[0049] The emergency data includes the following: vehicle information, distance between vehicles, distance between vehicles and the roadside, vehicle speed, vehicle acceleration, and the time, location, and likelihood of vehicle risks occurring.

[0050] The timing, location, and probability of vehicle risks are calculated and predicted while keeping the target vehicle's driving strategy and the surrounding environment constant.

[0051] S105 Data Hierarchical Transmission

[0052] The primary data is transmitted to the vehicle and / or monitoring center, the secondary data is processed at the edge to obtain edge analysis data, and then the edge analysis data is transmitted to the vehicle and / or monitoring center.

[0053] Between primary data and edge analytics data transmission, secondary data is transmitted to vehicles and / or monitoring centers.

[0054] In the second embodiment, as Figure 2 As shown, a smart traffic monitoring data optimization system includes:

[0055] Module 201 is used to acquire monitoring data;

[0056] The hierarchical processing module 202 is used to allocate monitoring data into hierarchical data and basic data, and to allocate basic data according to hierarchical data, and to allocate basic data into first-level data and second-level data; and to perform edge processing on the second-level data to obtain edge analysis data.

[0057] The transmission module 203 is used to transmit primary data to the vehicle and / or monitoring center, and then transmit edge analysis data to the vehicle and / or monitoring center; during the interval between the transmission of primary data and edge analysis data, it transmits secondary data to the vehicle and / or monitoring center.

[0058] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for optimizing intelligent traffic monitoring data, characterized in that: The steps include the following: Obtain monitoring data; Hierarchical data is generated based on monitoring data, with monitoring data serving as the basic data. Based on the hierarchical data, the basic data is allocated into primary data and secondary data; The primary data is transmitted to the vehicle and / or monitoring center, the secondary data is processed at the edge to obtain edge analysis data, and then the edge analysis data is transmitted to the vehicle and / or monitoring center. During the gap between primary data and edge analytics data transmission, secondary data is transmitted to the vehicle and / or monitoring center; The grading data includes evaluation values ​​and emergency measurement values; The hierarchical data is obtained in the following manner: The monitoring data are ranked according to importance (Z). i Assign a value, and combine it with the size B of the data packet corresponding to the monitoring data. i The evaluation value P was calculated. i =Z i / B i ; Based on road congestion level Y i And the corresponding vehicle speed V i The emergency measurement value J was obtained. i =Y i *V i ; Based on the evaluation value P i The monitoring data is sorted, and then, based on the magnitude of the emergency measurement value, the data is ranked in (J). i The monitoring data before 5%) is used as primary data, and the remaining data is used as secondary data.

2. The method for optimizing intelligent traffic monitoring data according to claim 1, characterized in that: The road congestion level Y i =Predicted travel time for the current road segment / Predicted travel time for the road segment under smooth traffic conditions.

3. The method for optimizing intelligent traffic monitoring data according to claim 1, characterized in that: If the monitoring data is video data, it will be automatically classified as secondary data. If, after acquiring primary data for the target vehicle, automatic optimization of the driving strategy is not possible or the optimization effect is poor, then during the next segmentation of primary and secondary data, it will be sorted in (2J). i The monitoring data before 5%) is used as primary data, and the remaining data is used as secondary data.

4. The method for optimizing intelligent traffic monitoring data according to claim 1, characterized in that: The secondary data processing is performed as follows to obtain edge analysis data: for monitoring data that is video or image, emergency data analysis and extraction are performed to obtain edge analysis data.

5. The method for optimizing intelligent traffic monitoring data according to claim 4, characterized in that: The emergency data includes the following: vehicle information, distance between vehicles, distance between vehicles and the roadside, vehicle speed, vehicle acceleration, and the time, location, and likelihood of vehicle risks occurring.

6. The method for optimizing intelligent traffic monitoring data according to claim 5, characterized in that: The timing, location, and probability of vehicle risks are calculated and predicted while keeping the target vehicle's driving strategy and the surrounding environment constant.

7. A smart traffic monitoring data optimization system for implementing the smart traffic monitoring data optimization method according to any one of claims 1-6, characterized in that: include: The acquisition module is used to acquire monitoring data; The hierarchical processing module is used to generate hierarchical data based on monitoring data, using the monitoring data as basic data, and allocating the basic data into first-level data and second-level data according to the hierarchical data. The secondary data is then processed to obtain edge analysis data. The transmission module is used to transmit primary data to the vehicle and / or monitoring center, and then transmit edge analysis data to the vehicle and / or monitoring center; during the interval between the transmission of primary data and edge analysis data, it transmits secondary data to the vehicle and / or monitoring center.

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