A highway network operation monitoring method

Abstract

This application relates to a method for monitoring the operation of a highway network. The method includes: identifying the data acquisition module that collects vehicle data corresponding to an abnormal event as the target module among multiple data acquisition modules; analyzing the vehicle data collected by each data acquisition module to obtain the congestion coefficient of each area; performing a linkage analysis between the congestion coefficient of each target area and the congestion coefficients of adjacent areas to obtain the congestion level of the abnormal event occurring in each target area; the target area is the area corresponding to the target module; and determining the power consumption of the corresponding target module based on the congestion level and duration of the abnormal event occurring in each target area. This method can reduce the generation of duplicate or useless data, thereby completing the monitoring of road network conditions while overall reducing the amount of data collected.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method for monitoring the operation of a highway network. Background Technology

[0002] With the gradual improvement of the highway network and the continuous growth of traffic volume, traditional road condition monitoring methods relying on manual inspections and single sensors are no longer sufficient to meet the higher requirements for traffic safety, traffic efficiency, and intelligent management. To overcome these limitations, it is urgent to integrate multi-source heterogeneous data from cameras, radar, vehicle-mounted terminals, weather stations, etc., and to conduct in-depth analysis using advanced technologies such as artificial intelligence and big data. Achieving more comprehensive, accurate, and real-time perception and early warning of road conditions, traffic incidents, and environmental factors is a key path to improving the safety and efficiency of road network operation, supporting vehicle-road cooperation and intelligent maintenance, and empowering the intelligent upgrade of modern transportation systems.

[0003] However, when using multimodal data to analyze highway network conditions, the amount of data collected is large, while the system's real-time processing capability is limited. If the amount of collected multimodal data exceeds the system's processing capacity, it may cause edge computing to crash, leading to processing delays and thus affecting the real-time performance and reliability of highway network condition monitoring. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method for monitoring the operation of a highway network, the specific technical solution of which is as follows:

[0005] Firstly, a method for monitoring the operation of a highway network is provided, the method comprising:

[0006] Among multiple data acquisition modules, the data acquisition module that collects vehicle data corresponding to the abnormal event is identified as the target module; the multiple data acquisition modules correspond one-to-one with multiple areas in the road network;

[0007] The vehicle data collected by each data acquisition module is analyzed to obtain the congestion coefficient for each area;

[0008] A linkage analysis is performed on the congestion coefficient of each target area and the congestion coefficient of adjacent areas to obtain the congestion level of abnormal events occurring in each target area; the target area is the area corresponding to the target module.

[0009] The power consumption of the corresponding target module is determined based on the congestion level of the abnormal events occurring in each target area and the duration of the abnormal events in each target area.

[0010] Optionally, among the multiple data acquisition modules, the data acquisition module that acquires vehicle data corresponding to the abnormal event is identified as the target module, including:

[0011] An anomaly index for the corresponding region is determined based on the displacement velocity of each vehicle collected by each data acquisition module at the target time; the vehicle data includes the displacement velocity of each vehicle; the target time is any moment during the data acquisition process of the data acquisition module.

[0012] When the anomaly index of a region is greater than a preset anomaly threshold, it is determined that an anomaly event has occurred in that region, and the data acquisition module corresponding to that region is identified as the target module.

[0013] Optionally, determining the anomaly index of the corresponding area based on the displacement velocity of each vehicle collected by each data acquisition module at the target time includes:

[0014] Based on the displacement velocity of each vehicle collected by each data acquisition module at the target time and the displacement velocity of the vehicle at the previous time before the target time, the velocity change value of each vehicle at the target time is determined, and the vehicle with the largest velocity change value in the corresponding area is identified as the target vehicle.

[0015] The anomaly index of the corresponding area is determined by the ratio of the speed change of the target vehicle at the target time to the average displacement speed of all vehicles in the corresponding area at the target time.

[0016] Optionally, the analysis of vehicle data collected by each data acquisition module to obtain the congestion coefficient for each area includes:

[0017] The congestion coefficient of the corresponding area is obtained by analyzing the displacement speed of each vehicle collected by each data acquisition module at the target time, the number of multiple vehicles in the corresponding area, the speed limit of the corresponding area, and the number of vehicles among the multiple vehicles whose displacement speed is less than the preset speed; the vehicle data includes the number of the multiple vehicles.

[0018] Optionally, the step of performing a linked analysis of the congestion coefficient of each target area and the congestion coefficient of adjacent areas to obtain the congestion level of abnormal events occurring in each target area includes:

[0019] A linkage analysis is performed on the congestion coefficient of each target area at the target time and the congestion coefficient of adjacent areas at the target time, as well as the distance between the center point of each target area and the center point of adjacent areas, to obtain the connection coefficient between each target area and adjacent areas; the connection coefficient represents the correlation between abnormal events occurring in each target area and abnormal events occurring in adjacent areas.

[0020] By analyzing the connectivity coefficients between each target area and its adjacent areas, the congestion level of abnormal events occurring in each target area can be obtained.

[0021] Optionally, the step of performing a linked analysis of the congestion coefficient of each target area at the target time and the congestion coefficient of adjacent areas at the target time, as well as the distance between the center point of each target area and the center points of adjacent areas, to obtain the connection coefficient between each target area and adjacent areas includes:

[0022] The connection coefficient between each target area and its adjacent areas is determined by taking the reciprocal of the product of the coefficient difference and the interval distance. The coefficient difference indicates the absolute difference between the congestion coefficient of the adjacent area at the target time and the sum of the congestion coefficients of the corresponding target area at the target time. The interval distance indicates the distance between the center point of the adjacent area and the center point of the corresponding target area.

[0023] Optionally, the analysis of the connection coefficients between each target area and its adjacent areas to obtain the congestion level of abnormal events occurring in each target area includes:

[0024] In response to the connection coefficient being greater than a preset connection threshold, the number of multiple regions between each target region and its corresponding associated region, and the average congestion coefficient between the target region and its corresponding associated region are analyzed to obtain the congestion score of the abnormal event occurring in each target region; the associated region indicates the region with a high degree of correlation with the corresponding target region, and the associated region corresponding to each target region is obtained by analyzing the connection coefficient between each target region and its adjacent regions;

[0025] The congestion level of each target area is determined based on the congestion score of the abnormal events occurring in each target area.

[0026] Optionally, determining the congestion level of each target area based on the congestion score of the abnormal events occurring in each target area includes:

[0027] If the congestion score of an abnormal event occurring in the target area is less than a preset first threshold, the congestion level of the abnormal event occurring in the target area is determined to be Level 3 congestion.

[0028] If the congestion score of an abnormal event occurring in the target area is between a preset first threshold and a preset second threshold, the congestion level of the abnormal event occurring in the target area is determined to be level two congestion; the second threshold is greater than the first threshold.

[0029] If the congestion score of an abnormal event occurring in the target area is not less than a preset second threshold, the congestion level of the abnormal event occurring in the target area is determined to be Level 1 congestion.

[0030] Optionally, determining the power consumption of the corresponding target module based on the congestion level of the abnormal event occurring in each target area and the duration of the abnormal event in each target area includes:

[0031] The impact weight of the abnormal events in each target area is determined based on the duration of the abnormal events in each target area and the average congestion coefficient of each target area at multiple moments within the duration.

[0032] The impact weight of abnormal events in each target area and the congestion level of abnormal events in each target area are analyzed to obtain the power consumption of the corresponding target module.

[0033] Optionally, the analysis of the impact weight of abnormal events in each target area and the congestion level of abnormal events occurring in each target area to obtain the power consumption of the corresponding target module includes:

[0034] The power consumption of the corresponding target module is determined by multiplying the impact weight of the abnormal event in each target area by the value corresponding to the congestion level of the abnormal event in each target area.

[0035] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of the present invention.

[0036] This application has the following beneficial effects: it analyzes the data collected by the data acquisition module in the road network traffic condition monitoring system, determines the power of the data acquisition module based on the road monitoring situation, increases the power consumption of the equipment in the data acquisition module to increase the sampling frequency when encountering abnormal events, and ensures real-time and accurate monitoring of abnormal conditions. Under normal conditions, the data acquisition module operates with low power consumption to reduce the generation of duplicate or useless data, thereby completing the monitoring of road network traffic conditions while reducing the overall amount of data collected. Attached Figure Description

[0037] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 A flowchart illustrating a highway network operation monitoring method provided in one embodiment of this application;

[0039] Figure 2 A road diagram illustrating a method for monitoring highway network operation provided in one embodiment of this application;

[0040] Figure 3 This is a schematic diagram of the structure of a highway network operation monitoring system provided in one embodiment of this application;

[0041] Figure 4 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0042] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a highway network operation monitoring method proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0044] The specific scheme of the highway network operation monitoring method provided in this application is described below with reference to the accompanying drawings. Figure 1 As shown, the method includes:

[0045] S11. Among multiple data acquisition modules, the data acquisition module that acquires vehicle data corresponding to the abnormal event is identified as the target module.

[0046] Among them, multiple data acquisition modules correspond one-to-one with multiple areas in the road network, such as Figure 2 As shown, the entire road network traffic condition monitoring system includes multiple roads, such as Road A, Road B, Road C, etc. Each road includes multiple monitoring areas. For example, Road A includes monitoring area 1, monitoring area 2, monitoring area 3, etc., and Road B includes monitoring area 4, monitoring area 5, monitoring area 6, etc. Multiple data acquisition modules can be set up for each monitoring area of ​​each road in the road network. Each area can be set up with one data acquisition module to collect multimodal data such as vehicle data and road data of the monitoring area.

[0047] The data acquisition module mainly consists of visual sensing devices and radar sensing devices. The visual sensing devices include high-definition cameras for acquiring video images, such as traffic flow and road images. The radar sensing devices include millimeter-wave radar for target tracking in rainy and foggy weather. The data acquisition module transmits the acquired multimodal data to the data edge computing module within the road network condition monitoring system for data processing. The data edge computing module performs local real-time processing, reducing the load on the cloud. The data edge computing module and the data acquisition module are in a one-to-one correspondence, ensuring that data from the data acquisition module can be processed by the data edge computing module.

[0048] When monitoring road network conditions, the types of abnormal events detected are complex and diverse, including unexpected traffic accidents, road surface wear, emergency braking of vehicles, traffic jams, and other abnormal events. If the data acquisition module collects vehicle data corresponding to the abnormal event, it is determined that an abnormal event has occurred in the area corresponding to the data acquisition module, and the data acquisition module is identified as the target module.

[0049] The impact of various abnormal events on traffic conditions and safety varies. For abnormal events with high hazard, the road network traffic monitoring system should ensure real-time and accurate monitoring. For abnormal events with lower hazard, the system can appropriately reduce the frequency of data monitoring. This ensures 100% response to major events while reducing resource consumption for minor events, achieving optimal allocation of monitoring resources. Within the overall road network traffic monitoring system, when an abnormal event is detected, the monitored multimodal data determines the level of importance of the abnormal event. Different abnormal events have different levels of importance. For high-level important abnormal events, the data generated by the event is more important, while the data generated by low-level important abnormal events is less important. The system can automatically adjust the data acquisition capabilities of corresponding devices based on the level of importance of the abnormal event, thereby reducing the amount of multimodal data collected by the system at any given time. This alleviates the system's resource processing pressure to some extent while completing road network traffic monitoring.

[0050] In all-weather, all-modal mode, a lot of invalid data will be generated (such as empty shots in video surveillance). Therefore, low-power data acquisition modules can be used to collect data first. For example, the power consumption of each sensor in the data acquisition module can be set to 10% of the rated power consumption to reduce the total amount of data collected. When the road network traffic monitoring system detects an abnormal event, the power consumption can be increased to increase the amount of data collected and achieve accurate monitoring of the abnormal event.

[0051] In one embodiment, among multiple data acquisition modules, the data acquisition module that acquires vehicle data corresponding to the abnormal event is identified as the target module, including:

[0052] Based on the displacement velocity of each vehicle collected by each data acquisition module at the target time, the anomaly index of the corresponding area is determined; vehicle data includes the displacement velocity of each vehicle; the target time is any moment in the data acquisition process of the data acquisition module.

[0053] When the anomaly index of a region exceeds a preset anomaly threshold, an anomaly event is determined to have occurred in that region, and the data acquisition module corresponding to that region is identified as the target module.

[0054] The abnormal threshold can be set according to the actual situation, for example, it can be 0.3 or 0.35.

[0055] In one embodiment, based on the displacement velocity of each vehicle collected by each data acquisition module at the target time, an anomaly index for the corresponding area is determined, including:

[0056] Based on the displacement velocity of each vehicle collected by each data acquisition module at the target time and the displacement velocity of the vehicle at the previous time before the target time, the velocity change value of each vehicle at the target time is determined, and the vehicle with the largest velocity change value in the corresponding area is identified as the target vehicle.

[0057] The anomaly index of the corresponding area is determined by the ratio of the speed change of the target vehicle at the target time to the average displacement speed of all vehicles in the corresponding area at the target time.

[0058] It can collect data located in the road network. The first on the road The data acquisition module in each area The vehicle data monitored at any given time, understandably, is the first The data acquisition module and the first Each region corresponds one-to-one, the first The data acquisition module is set at the first In the region, when the first If the data acquisition module in each area collects vehicle data corresponding to the abnormal event, then the... Anomalies may have occurred in certain areas. Different anomalies have different impacts. For anomalies with significant impact (such as accidents), real-time and accurate monitoring is required to ensure high-quality data. For anomalies with minor impact (such as short-distance temporary congestion), the quality of the monitoring data should be appropriately improved. Therefore, calculations are performed based on data collected by data acquisition modules in different areas of the entire lane to complete the analysis of... Analysis of abnormal events in each region at any given time. The specific steps are as follows:

[0059] based on At this moment The first on the road The video cameras of the data acquisition modules in each area capture images, and the YOLO object detection algorithm is used to identify the number of objects present in the images and to count them. At this moment The first on the road Number of vehicles in the image of each area Simultaneously utilizing tracking algorithms Calculate the first The image of the first region The car is displacement velocity at time t Normally, vehicles on the road move at a constant speed or with a gradual change in speed. However, when an abnormal event occurs, it is usually accompanied by a sudden decrease in vehicle speed. Therefore, the analysis of the first... The first on the road Abnormal index of each region Abnormal index The calculation formula is as follows:

[0060] ;

[0061] in, Instruction No. The first on the road Abnormal indices for each region instruct The speed change of the target vehicle in the image at a given time can be calculated first by measuring the speed of the vehicle in the image at the specified time. The car is Time and The displacement velocity at time t, and then according to the t... The car is Time and The absolute difference in displacement velocity at time t is used as the first... The car is Calculate the change in velocity at time t. The speed changes of all vehicles in the image at any given time are recorded, and the vehicle with the largest speed change is identified as the target vehicle. The speed change of the target vehicle is... . express The average displacement velocity of all vehicles in the image at a given time can be calculated. The displacement and velocity of all vehicles in the image at a given time are calculated, and then the position of all vehicles at that time is determined. The average displacement velocity at time t is obtained The norm function indicates that normalization processing is performed (the normalization formula can be norm(x)=min(1,x / K), where K is a preset saturation value, and the specific value can be obtained from experience, for example, it can be set to 3), which is used to map the value to the interval [0,1], and +0.1 prevents the denominator from being 0.

[0062] when At this moment The first on the road Abnormal index of each region If the value exceeds the preset abnormal threshold, then determine the first... An abnormal event may have occurred in a certain area. The data acquisition module corresponding to that area has been identified as the target module. The target area is defined as a specific region. The power consumption of each sensor in the target module is increased to 30% of its rated power for data monitoring, thereby ensuring more accurate analysis of abnormal events.

[0063] S12. Analyze the vehicle data collected by each data acquisition module to obtain the congestion coefficient for each area.

[0064] The data edge computing module is used to analyze the vehicle data collected by each data module to determine the hierarchical importance of abnormal events that may occur in different areas, thereby helping to complete the subsequent road condition monitoring of the road network.

[0065] In one embodiment, the vehicle data collected by each data acquisition module is analyzed to obtain the congestion coefficient for each area, including:

[0066] The congestion coefficient of the corresponding area is obtained by analyzing the displacement speed of each vehicle collected by each data acquisition module at the target time, the number of multiple vehicles in the corresponding area, the speed limit of the corresponding area, and the number of multiple vehicles whose displacement speed is less than the preset speed; the vehicle data includes the number of the multiple vehicles.

[0067] The preset speed can be set according to the actual situation, for example, it can be set to... .

[0068] Following the steps above, statistics are performed. At this moment The number of vehicles with displacement speeds less than a preset speed in the images captured by the data acquisition module in each area. When vehicles on the road are traveling at slow speeds (e.g., significantly slower than the speed limit for the corresponding area) and the vehicle density is high, it indicates that there may be traffic congestion at that moment. Therefore, this calculation... At this moment The first on the road Congestion coefficient of each area Congestion coefficient The calculation formula is as follows:

[0069] ;

[0070] in, instruct At this moment The first on the road The congestion coefficient of each area instruct At this moment The first on the road The displacement velocity of each vehicle in each region is related to the first... The average of the absolute differences in the speed limits of each region. instruct At this moment The number of vehicles with displacement speeds less than a preset speed in the images captured by the data acquisition module in each area. instruct At this moment The first on the road The number of vehicles in the image of each region, +0.1 to prevent the denominator from being 0, the norm function indicates that normalization processing is performed. Normalization processing is intended to map the numerical values ​​of the data to the interval [0,1], so as to facilitate the merging of data obtained by different methods, while eliminating the difference in units between different data and retaining the characteristics of the data obtained by each method.

[0071] This application calculates the road congestion coefficient based on the monitored vehicle speed and number of vehicles. When it is detected that the vehicle speed is slow in a certain area of ​​the road, the speed is much lower than the road speed limit, and the proportion of vehicles with speeds lower than the preset speed is higher, it indicates that the vehicles in that area are moving slowly and the road is relatively congested at the current moment.

[0072] S13. Perform a linkage analysis on the congestion coefficient of each target area and the congestion coefficient of adjacent areas to obtain the congestion level of abnormal events occurring in each target area.

[0073] Wherein, the target region is the region corresponding to the target module, if the first If the data acquisition module in each area collects vehicle data corresponding to the abnormal event, then the data acquisition module in the first area collects vehicle data corresponding to the abnormal event. The target area is one region.

[0074] In one embodiment, a linked analysis is performed on the congestion coefficient of each target area and the congestion coefficients of adjacent areas to obtain the congestion level of abnormal events occurring in each target area, including:

[0075] A linkage analysis is performed on the congestion coefficient of each target area at the target time and the congestion coefficient of adjacent areas at the target time, as well as the distance between the center point of each target area and the center point of adjacent areas, to obtain the connection coefficient between each target area and adjacent areas; the connection coefficient represents the correlation between abnormal events occurring in each target area and abnormal events occurring in adjacent areas.

[0076] By analyzing the connectivity coefficients of each target area, the congestion level of abnormal events occurring in each target area can be obtained.

[0077] In one embodiment, a linkage analysis is performed on the congestion coefficient of each target area at the target time, the congestion coefficient of adjacent areas at the target time, and the distance between the center point of each target area and the center points of adjacent areas to obtain the connection coefficient between each target area and adjacent areas, including:

[0078] The connection coefficient between each target area and its adjacent areas is determined by taking the reciprocal of the product of the coefficient difference and the interval distance. The coefficient difference indicates the absolute difference between the congestion coefficient of the adjacent area at the target time and the sum of the congestion coefficients of the corresponding target area at the target time. The interval distance indicates the distance between the center point of the adjacent area and the center point of the corresponding target area.

[0079] The congestion threshold can be set according to the actual situation, for example, it can be 0.4 or 0.45.

[0080] When a significant unexpected event occurs (such as a car accident or traffic incident), the vehicles involved in the accident occupy part of the lanes, reducing road traffic efficiency and easily causing long-distance congestion. Further analysis is needed to determine the next steps. At this moment The first road The classification level of abnormal events occurring in each region. Select the first... At this moment The first on the road The analysis of the first region The first road The connection coefficient between region j and region j The j-th region is the j-th region. The connectivity coefficient between adjacent regions of each region. The calculation formula is as follows:

[0081] ;

[0082] in, instruct At this moment The first on the road The region and the first The connectivity coefficient of each region Instruction No. The first on the road The center point of the region and the first The distance between the center points of each region can be understood as the midpoint of the length of each region along the road direction, or the centroid of each region. instruct At this moment The congestion coefficient of the region and the first The absolute difference in congestion coefficients among the regions. If the first... The first on the road The region and the first Congestion coefficient between regions If the number of elements is 0, then If the first The region and the first Congestion coefficient between regions If the number of elements is greater than 0, then +0.1 to prevent the denominator from being 0.

[0083] This application's embodiments utilize congestion coefficients calculated from adjacent locations on the same road to analyze whether abnormal events detected in adjacent areas constitute the same abnormal event. The closer the distance between two areas, the smaller the difference in their calculated congestion coefficients, and the more congested the road conditions detected in both areas, the more likely the abnormal events in the two areas are the same event.

[0084] In one embodiment, the connectivity coefficients between each target area and its adjacent areas are analyzed to obtain the congestion level of abnormal events occurring in each target area, including:

[0085] In response to a connection coefficient greater than a preset connection threshold, the number of multiple regions between each target region and its corresponding associated region, and the average congestion coefficient between the target region and its corresponding associated region are analyzed to obtain a congestion score for abnormal events occurring in each target region. The associated region indicates the region with a high degree of correlation with the corresponding target region. The associated region corresponding to each target region is obtained by analyzing the connection coefficient between each target region and its adjacent regions.

[0086] The congestion level of each target area is determined based on the congestion score of the abnormal events occurring in each target area.

[0087] The connection threshold can be set according to the actual situation, for example, it can be 0.7 or 0.75.

[0088] The above calculations Normalization to Set the link threshold to When the normalized result is greater than If the abnormal events detected in the two areas are considered to be the same abnormal event, then the abnormal events detected in the two areas are considered to be the same abnormal event.

[0089] Determine the first The steps for linking regions in a given region include: calculating the associated region of the first region. The congestion coefficient of adjacent areas in the first region, the first The connection coefficient between a region and its neighboring regions, where the neighboring region can be the i-th region. -1 area, or the first In the first region, if the congestion coefficient of the adjacent region is >0.4, and the first region is <0.4, then the congestion coefficient of the adjacent region is >0.4. If the connectivity coefficient between a region and its adjacent regions is greater than 0.7, then the congestion coefficient of the regions adjacent to the adjacent regions, and the connectivity coefficient between the adjacent regions and their adjacent regions are calculated, and so on, until the congestion coefficient of the y-th region is ≤0.4, and the y-th region... If the connection coefficient between a region and its adjacent regions is ≤0.7, then the adjacent regions of region y in the direction closer to region i are defined as associated regions of region i. Understandably, if the distance between region y and region (i-1) is closer than the distance between region y and region i, then region (y+1) is defined as an associated region of region i; if the distance between region y and region (i+1) is closer than the distance between region y and region i, then region (y-1) is defined as an associated region of region i. Understandably, if the road runs east-west, and both the eastern and western adjacent regions of region i experience abnormal events, then region i has two associated regions.

[0090] When the The region and its adjacent regions, that is, the first region The connectivity coefficient of each region Greater than And the first If the congestion coefficient of a region is greater than 0.4, then the region is determined to be the first region. The region and the first If the anomalous events occurring in each region are the same anomalous event, then the calculation is repeated for the nth region. The congestion coefficient of adjacent areas in the first region, the first The connectivity coefficients of the nth region and its adjacent regions are used to determine the nth region. Whether the anomalous events occurring in each region and its adjacent regions are the same anomalous event, and so on, until the first anomalous event is determined. The associated regions of the first region, the first Multiple regions between a region and its associated regions are related to the first region. The abnormal events occurring in one region are multiple regions experiencing the same abnormal event. Therefore, it can be calculated. At this moment The first on the road Congestion score of abnormal events occurring in each area Congestion score The calculation formula is as follows:

[0091] ;

[0092] in, instruct At this moment The first on the road Congestion scores for abnormal events occurring in each area Instruction No. The average congestion coefficient between a region and its related regions. Instruction No. The number of regions between the first region and the a-th region, where the a-th region is the number of regions between the first and a-th regions. The associated region of each region, norm is a normalization function used to map the values ​​to the interval [0,1]. The normalization formula can be norm(x)=min(1,x / K), where K is a preset saturation value. The specific value can be obtained by experience, for example, the value range is 4~5, and a suitable value is selected from this value range.

[0093] This application's embodiments calculate based on the congested road sections of abnormal events occurring in the monitoring area. The longer the road length involved in the abnormal event (i.e., the more areas where the monitored abnormal event is the same), and the greater the degree of road congestion, the greater the impact of the abnormal event, and the higher the grade score is given to the abnormal event.

[0094] In one embodiment, the congestion level of an anomaly occurring in each target area is determined based on the congestion score of the anomaly occurring in each target area, including:

[0095] If the congestion score of an abnormal event occurring in the target area is less than a preset first threshold, the congestion level of the abnormal event occurring in the target area is determined to be Level 3 congestion.

[0096] If the congestion score of an abnormal event occurring in the target area is between a preset first threshold and a preset second threshold, the congestion level of the abnormal event occurring in the target area is determined to be Level 2 congestion; the second threshold is greater than the first threshold.

[0097] If the congestion score of an abnormal event occurring in the target area is not less than a preset second threshold, the congestion level of the abnormal event occurring in the target area is determined to be Level 1 congestion.

[0098] The first threshold can be set according to the actual situation, for example, it can be 0.3. The second threshold can be set according to the actual situation, for example, it can be 0.6.

[0099] When a high congestion score indicates an abnormal event occurring in the target area, it signifies a significant impact from the event. The data acquisition capability of the target area's data acquisition module should be adjusted to ensure accurate and real-time monitoring of the area's information. The higher the congestion score, the greater the increase in power consumption for the data acquisition module. Therefore, based on the calculated congestion score, the congestion level of the target area is categorized, and the power consumption of the data acquisition module is adjusted accordingly. This intelligent control of the data acquisition module's power consumption dynamically regulates the amount of data collected by the system.

[0100] based on At this moment The first on the road The congestion scores of abnormal events occurring in each area are used to classify the areas into three levels, with the congestion score of abnormal events ranking among the highest. The interval is classified as Level 3 congestion, and the congestion score for abnormal events is... The interval is classified as Level 2 congestion, and the congestion score for abnormal events is... The interval is classified as Level 1 congestion, which indicates that the abnormal event is the most severe.

[0101] S14. Determine the power consumption of the corresponding target module based on the congestion level of the abnormal event occurring in each target area and the duration of the abnormal event in each target area.

[0102] If the anomaly index of a region is greater than the preset anomaly threshold, then the region is the target region. The duration of the abnormal event in the target region can be obtained based on the duration for which the anomaly index of the region is greater than the anomaly threshold.

[0103] In one embodiment, the power consumption of the corresponding target module is determined based on the congestion level of the abnormal event occurring in each target area and the duration of the abnormal event in each target area, including:

[0104] The impact weight of the abnormal events in each target area is determined based on the duration of the abnormal events in each target area and the average congestion coefficient of each target area at multiple target times within the duration.

[0105] The impact weight of abnormal events in each target area and the congestion level of abnormal events in each target area are analyzed to obtain the power consumption of the corresponding target module.

[0106] Influence weight The calculation formula is as follows:

[0107] ;

[0108] in, instruct At this moment The first on the road The impact weight of anomalous events in each region, T indicates the weight of the impact of anomalous events in the region. The duration of the abnormal event in each region is determined by the following formula: T is 2 when the duration of the abnormal event in the i-th region is ≥ 2 hours, and T is the actual duration when the duration of the abnormal event in the i-th region is < 2 hours. Instruction No. The average congestion coefficient of a region at multiple moments within a duration, based on the... The average congestion coefficient of each region is calculated at each moment within the duration. The norm function indicates that normalization processing is performed. The purpose of normalization processing is to map the data values ​​to the interval [0,1] to facilitate the merging of data obtained using different methods, eliminating the differences in units between different data while retaining the characteristics of the data obtained by each method.

[0109] In one embodiment, the impact weight of abnormal events in each target area and the congestion level of abnormal events occurring in each target area are analyzed to obtain the power consumption of the corresponding target module, including:

[0110] The power consumption of the corresponding target module is determined by multiplying the impact weight of the abnormal event in each target area by the value corresponding to the congestion level of the abnormal event in each target area.

[0111] At this moment The first on the road The power consumption calculation method for each region is as follows:

[0112] ;

[0113] in, instruct At this moment The first on the road Power consumption of each region express At this moment The first on the road The minimum score for the congestion level of an abnormal event in a given area; if the abnormal event is level three congestion, If the abnormal event is a level 2 congestion, If the abnormal event is a level one congestion, . instruct At this moment The first on the road Congestion scores for abnormal events occurring in each area instruct At this moment The first on the road The impact weight of abnormal events in each region. The maximum value of the calculation result is 1. When the calculation result exceeds 1, .

[0114] This application embodiment calculates the power consumption of the data acquisition module by assigning different power consumption to abnormal events of different congestion levels and combining the time performance. When the duration of an abnormal event on a road is long, even if the analysis shows that the congestion score of the abnormal event is low, the long duration of the abnormal event and the high degree of road congestion still indicate that the impact of the abnormal event is significant. Therefore, the power consumption of the data acquisition module is appropriately adjusted based on the time performance.

[0115] The above process achieves intelligent control of the power consumption of the data acquisition module. By intelligently controlling the power consumption of the data acquisition module in real time, the collected data is analyzed and calculated by the data edge computing module, and the analysis results (including traffic flow information and other data) are transmitted to the system data display terminal. This changes the sampling frequency of the data acquisition module, thereby helping to complete the monitoring of road network conditions while reducing the overall amount of sampled data.

[0116] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0117] This application also provides a highway network operation monitoring system, such as Figure 3 As shown, the system includes:

[0118] The first determining module 31 is used to determine the data acquisition module that has acquired vehicle data corresponding to the abnormal event as the target module among multiple data acquisition modules; the multiple data acquisition modules correspond one-to-one with multiple areas in the road network;

[0119] The first analysis module 32 is used to analyze the vehicle data collected by each data acquisition module to obtain the congestion coefficient of each area;

[0120] The second analysis module 33 is used to perform a linkage analysis on the congestion coefficient of each target area and the congestion coefficient of adjacent areas to obtain the congestion level of abnormal events occurring in each target area; the target area is the area corresponding to the target module.

[0121] The second determining module 34 is used to determine the power consumption of the corresponding target module based on the congestion level of the abnormal event occurring in each target area and the duration of the abnormal event in each target area.

[0122] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs.

[0123] Figure 4 This is a schematic diagram of the structure of an electronic device according to an example embodiment of this application. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, it implements the method described in any of the above embodiments. Figure 4 The electronic device 40 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0124] like Figure 4 As shown, the electronic device 40 can be manifested in the form of a general-purpose computing device, such as a server device. The components of the electronic device 40 may include, but are not limited to: at least one processor 41, at least one memory 42, and a bus 43 connecting different system components (including memory 42 and processor 41).

[0125] Bus 43 includes a data bus, an address bus, and a control bus.

[0126] The memory 42 may include volatile memory, such as random access memory (RAM) 421 and / or cache memory 422, and may further include read-only memory (ROM) 423.

[0127] The memory 42 may also include a program tool 425 (or utility) having a set (at least one) program module 424, such program module 424 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0128] The processor 41 performs various functional applications and data processing, such as the methods provided in any of the above embodiments, by running computer programs stored in the memory 42.

[0129] Electronic device 40 can also communicate with one or more external devices 44 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 45. Furthermore, electronic device 40 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) via network adapter 46. As shown, network adapter 46 communicates with other modules of electronic device 40 via bus 43. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 40, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0130] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0131] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided in any of the above embodiments.

[0132] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0133] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0134] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method described in any of the above embodiments.

[0135] The program code for executing the computer program product of this application can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0136] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0137] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

[0138] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for monitoring the operation of a highway network, characterized in that, The method includes: Among multiple data acquisition modules, the data acquisition module that collects vehicle data corresponding to the abnormal event is identified as the target module; the multiple data acquisition modules correspond one-to-one with multiple areas in the road network; The vehicle data collected by each data acquisition module is analyzed to obtain the congestion coefficient for each area; A linkage analysis is performed on the congestion coefficient of each target area and the congestion coefficient of adjacent areas to obtain the congestion level of abnormal events occurring in each target area; the target area is the area corresponding to the target module. The power consumption of the corresponding target module is determined based on the congestion level of the abnormal events occurring in each target area and the duration of the abnormal events in each target area. The linked analysis of the congestion coefficient of each target area and the congestion coefficient of adjacent areas yields the congestion level of abnormal events occurring in each target area, including: A linkage analysis is performed on the congestion coefficient of each target area at the target time and the congestion coefficient of adjacent areas at the target time, as well as the distance between the center point of each target area and the center point of adjacent areas, to obtain the connection coefficient between each target area and adjacent areas; the connection coefficient represents the correlation between abnormal events occurring in each target area and abnormal events occurring in adjacent areas. The connection coefficients between each target area and its adjacent areas are analyzed to obtain the congestion level of abnormal events occurring in each target area; The analysis of the connectivity coefficients between each target area and its adjacent areas yields the congestion level of abnormal events occurring in each target area, including: In response to the connection coefficient being greater than a preset connection threshold, the number of multiple regions between each target region and its corresponding associated region, and the average congestion coefficient between the target region and its corresponding associated region are analyzed to obtain the congestion score of the abnormal event occurring in each target region; the associated region indicates the region with a high degree of correlation with the corresponding target region, and the associated region corresponding to each target region is obtained by analyzing the connection coefficient between each target region and its adjacent regions; Based on the congestion score of the abnormal events occurring in each target area, the congestion level of the abnormal events occurring in each target area is determined; The step of determining the power consumption of the corresponding target module based on the congestion level of the abnormal events occurring in each target area and the duration of the abnormal events in each target area includes: The impact weight of the abnormal events in each target area is determined based on the duration of the abnormal events in each target area and the average congestion coefficient of each target area at multiple moments within the duration. The power consumption of the corresponding target module is determined by multiplying the impact weight of the abnormal event in each target area with the value corresponding to the congestion level of the abnormal event in each target area. Influence weight The calculation formula is as follows: ； in, instruct At this moment The first on the road The impact weight of anomalous events in each region, T indicates the weight of the impact of anomalous events in the region. The duration of the abnormal event in each region is determined by the following formula: T is 2 when the duration of the abnormal event in the i-th region is ≥ 2 hours, and T is the actual duration when the duration of the abnormal event in the i-th region is < 2 hours. Instruction No. The average congestion coefficient of a region at multiple moments within a duration, based on the... The average congestion coefficient of each region is calculated at each moment within the duration, and the norm function indicates that normalization is performed. At this moment The first on the road The power consumption calculation method for each region is as follows: ； in, instruct At this moment The first on the road Power consumption of each region express At this moment The first on the road The minimum congestion score for the congestion level of abnormal events in a given area. instruct At this moment The first on the road Congestion scores for abnormal events occurring in each area instruct At this moment The first on the road The impact weight of abnormal events in each region The maximum value of the calculation result is 1. When the calculation result exceeds 1, .

2. The method for monitoring the operation of a highway network as described in claim 1, characterized in that, Among the multiple data acquisition modules, the data acquisition module that acquires vehicle data corresponding to the abnormal event is identified as the target module, including: An anomaly index for the corresponding region is determined based on the displacement velocity of each vehicle collected by each data acquisition module at the target time; the vehicle data includes the displacement velocity of each vehicle; the target time is any moment during the data acquisition process of the data acquisition module. When the anomaly index of a region is greater than a preset anomaly threshold, it is determined that an anomaly event has occurred in that region, and the data acquisition module corresponding to that region is identified as the target module.

3. The method for monitoring the operation of a highway network as described in claim 2, characterized in that, The step of determining the anomaly index of the corresponding area based on the displacement velocity of each vehicle collected by each data acquisition module at the target time includes: Based on the displacement velocity of each vehicle collected by each data acquisition module at the target time and the displacement velocity of the vehicle at the previous time before the target time, the velocity change value of each vehicle at the target time is determined, and the vehicle with the largest velocity change value in the corresponding area is identified as the target vehicle. The anomaly index of the corresponding area is determined by the ratio of the speed change of the target vehicle at the target time to the average displacement speed of all vehicles in the corresponding area at the target time.

4. The method for monitoring the operation of a highway network as described in claim 3, characterized in that, The analysis of vehicle data collected by each data acquisition module to obtain the congestion coefficient for each area includes: The congestion coefficient of the corresponding area is obtained by analyzing the displacement speed of each vehicle collected by each data acquisition module at the target time, the number of multiple vehicles in the corresponding area, the speed limit of the corresponding area, and the number of vehicles among the multiple vehicles whose displacement speed is less than the preset speed; the vehicle data includes the number of the multiple vehicles.

5. A method for monitoring the operation of a highway network as described in claim 1, characterized in that, The connection coefficient between each target area and its neighboring areas is obtained by performing a linkage analysis on the congestion coefficient of each target area at the target time and the congestion coefficient of its neighboring areas at the target time, as well as the distance between the center point of each target area and the center point of its neighboring areas. This includes: The connection coefficient between each target area and its adjacent areas is determined by taking the reciprocal of the product of the coefficient difference and the interval distance. The coefficient difference indicates the absolute difference between the congestion coefficient of the adjacent area at the target time and the sum of the congestion coefficients of the corresponding target area at the target time. The interval distance indicates the distance between the center point of the adjacent area and the center point of the corresponding target area.

6. The method for monitoring the operation of a highway network as described in claim 1, characterized in that, The process of determining the congestion level of abnormal events occurring in each target area based on the congestion score of the abnormal events occurring in each target area includes: If the congestion score of an abnormal event occurring in the target area is less than a preset first threshold, the congestion level of the abnormal event occurring in the target area is determined to be Level 3 congestion. If the congestion score of an abnormal event occurring in the target area is between a preset first threshold and a preset second threshold, the congestion level of the abnormal event occurring in the target area is determined to be level two congestion; the second threshold is greater than the first threshold. If the congestion score of an abnormal event occurring in the target area is not less than a preset second threshold, the congestion level of the abnormal event occurring in the target area is determined to be Level 1 congestion.

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