An intelligent monitoring method and system based on a highway entrance

Abstract

The present invention relates to the technical field of traffic monitoring, and specifically discloses an intelligent monitoring method and system based on a highway entrance. Based on the abnormal signal of vehicle merging, the vehicle driving stability value is obtained and compared with a threshold value to generate a signal indicating whether the vehicle driving is stable. Based on the signal indicating unstable vehicle driving, the accident occurrence probability value in the merging area is obtained and compared with a threshold value to generate a signal indicating the size of the accident occurrence probability. Based on the signal indicating a large accident occurrence probability, the merging driving speed of the vehicle is adjusted to obtain an adjusted value of the merging driving speed, thereby completing the optimization adjustment of the vehicle merging driving. By monitoring the vehicle driving stability and adjusting the merging driving speed when instability is detected, the accident risk in the merging area caused by vehicle out of control or improper operation can be effectively reduced, and the traffic congestion caused by accidents is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic monitoring, and particularly to an intelligent monitoring method and system based on a highway entrance. Background Art

[0002] With the continuous growth of traffic flow and the increasing improvement of the highway network, the real-time monitoring and management of highway entrances and exits have become particularly important. The intelligent monitoring method and system based on highway entrances have emerged as the times require. It realizes the real-time monitoring and analysis of information such as vehicle flow, speed, and driving trajectory at highway entrances by integrating advanced sensor technology, data processing technology, and communication technology, provides effective decision-making support for traffic management departments, and improves the traffic efficiency and safety of highways.

[0003] The Chinese invention patent with the publication number CN110503826B discloses an intelligent induction method based on highway traffic flow monitoring and prediction. Based on the real-time monitoring of traffic conditions, a new model algorithm is used to predict traffic flow, and then according to the prediction results and combined with relevant highway design and management plans, it is published according to the highway induction information release process. At the same time, the traffic flow state is monitored in real time, the induction results are evaluated, and finally the results are fed back and updated to the induction knowledge base.

[0004] However, there is no monitoring device that can collect traffic data in the merging area in real time, reflect the actual traffic conditions in this area, and avoid traffic accidents in the later stage. There is no monitoring of vehicle driving stability and adjusting the merging driving speed when instability is detected, so as to effectively reduce the accident risk in the merging area caused by vehicle out of control or improper operation, and reduce traffic congestion caused by accidents. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent monitoring method and system based on a highway entrance to solve the above technical problems in the background.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] In the first aspect, the present invention provides an intelligent monitoring method based on a highway entrance, including the following steps:

[0008] Step 1: Monitor the merging area in front of the highway entrance through a monitoring device, obtain the regional congestion value, compare it with a threshold, and generate a signal indicating whether the area is congested;

[0009] If the regional congestion value is greater than or equal to the regional congestion threshold, generate a regional congestion signal;

[0010] Step 2: Based on the regional congestion signal, obtain the vehicle merging value, compare it with a threshold, and generate a signal indicating whether the vehicle merging is abnormal;

[0011] If the vehicle merging value is greater than or equal to the vehicle merging threshold, generate a vehicle merging abnormal signal, and mark the vehicle that generates the vehicle merging abnormal signal as a merging abnormal vehicle;

[0012] Step 3: Based on the vehicle merging abnormal signal, obtain the vehicle driving stability value, compare it with the threshold, and generate a signal indicating whether the vehicle driving is stable;

[0013] If the vehicle driving stability value is greater than or equal to the vehicle driving stability threshold, generate a vehicle driving instability signal;

[0014] Step 4: Based on the vehicle driving instability signal, obtain the accident occurrence probability value in the merging area, compare it with the threshold, and generate a signal indicating the size of the accident occurrence probability;

[0015] If the accident occurrence probability value in the merging area is greater than or equal to the accident occurrence probability threshold in the merging area, generate a signal indicating a high accident occurrence probability;

[0016] Step 5: Based on the signal indicating a high accident occurrence probability, adjust the vehicle merging driving speed to obtain an adjusted value of the merging driving speed, and complete the optimization adjustment of the vehicle merging driving;

[0017] In Step 5, when generating a signal indicating a high accident occurrence probability, adjust the vehicle merging driving, multiply the vehicle driving speed in the merging area by the adjustment coefficient to obtain an adjusted value of the merging driving speed;

[0018] The way to obtain the adjustment coefficient is:

[0019] Obtain the vehicle driving stability deviation coefficient and the accident occurrence deviation coefficient in the merging area, add the vehicle driving stability deviation coefficient and the accident occurrence deviation coefficient in the merging area to sum, and obtain the adjustment coefficient.

[0020] As a further solution of the present invention: In Step 1, the way to obtain the regional congestion value is:

[0021] During the monitoring period, obtain the area occupied by each vehicle in the merging area, add up the areas occupied by all vehicles in the merging area to obtain the total area occupied by vehicles in the merging area;

[0022] Obtain the area of the merging area, calculate the ratio of the total area occupied by vehicles in the merging area to the area of the merging area to obtain the regional congestion value.

[0023] As a further solution of the present invention: In Step 2, the way to obtain the vehicle merging value is:

[0024] During the monitoring period, obtain the vehicle regional driving speed and the vehicle path deviation value, add the vehicle regional driving speed and the vehicle path deviation value together to obtain the vehicle confluence value;

[0025] The vehicle regional driving speed is obtained as follows:

[0026] Within the confluence area, the vehicle driving speed is monitored in real time, and the vehicle driving speeds are added together and averaged to obtain the vehicle regional driving speed.

[0027] As a further solution of the present invention: In step two, the vehicle path deviation value is obtained as follows:

[0028] Within the confluence area, divide the confluence area in the form of a grid, and mark the actual position coordinates of the vehicle in the confluence area as ( ), mark the preset position coordinates of the vehicle as ( ), obtain the initial position of the vehicle within the confluence area, and mark the initial position coordinates of the vehicle as ( );

[0029] During the monitoring period, the actual position coordinates of the vehicle in the confluence area ( ), divide the monitoring period into several time nodes, obtain the actual position coordinates of the vehicle corresponding to each time node ( ), connect the actual position coordinates of the vehicle corresponding to each time node to obtain the actual driving path of the vehicle;

[0030] Based on the actual driving path of the vehicle, compare the actual driving path of the vehicle with the preset driving path of the vehicle, and the comparison process is as follows:

[0031] Subtract the preset position coordinates of the vehicle from the actual position coordinates of the vehicle corresponding to each time node to obtain the vehicle position deviation value;

[0032] Add up and average the vehicle position deviation values to obtain the vehicle path deviation value.

[0033] As a further solution of the present invention: In step three, the vehicle driving stability value is obtained as follows:

[0034] During the monitoring period, obtain the number of hasty driving times and the number of off-track driving times of the vehicle in the confluence area, add the number of hasty driving times and the number of off-track driving times together to obtain the vehicle driving stability value.

[0035] As a further solution of the present invention: The process of obtaining the number of hasty driving times of the vehicle in step three is as follows:

[0036] During the monitoring period, obtain the real-time speed of the confluence abnormal vehicle, establish an X-Y axis coordinate system, where the X-axis represents the monitoring time point, and the Y-axis represents the speed of the confluence abnormal vehicle corresponding to each monitoring time point within the monitoring period. Substitute the real-time speed of the confluence abnormal vehicle into the X-Y axis coordinate system to obtain the real-time speed change curve of the confluence abnormal vehicle;

[0037] Based on the real-time speed change curve of the confluence abnormal vehicle, extract the peak coordinate values in the real-time speed change curve of the confluence abnormal vehicle, and mark them as ( , ), and the valley coordinate values, marked as ( , );

[0038] Calculate the distance between two adjacent peak coordinate values and valley coordinate values to obtain the speed change value;

[0039] Compare the speed change value with the speed change threshold. The comparison process is as follows:

[0040] If the speed change value is greater than or equal to the speed change threshold, the probability of haste is relatively high, and mark this time period as the hasty time period;

[0041] Obtain the number of times the hasty vehicle time period appears in the real-time speed change curve of the confluence abnormal vehicle to get the number of times the vehicle travels hastily.

[0042] As a further solution of the present invention: In step three, the method for obtaining the number of times the vehicle travels off-track is as follows:

[0043] Based on the vehicle position deviation value in step two above, sum up the vehicle position deviation values within the monitoring period and take the average to obtain the vehicle position deviation threshold;

[0044] Compare the vehicle position deviation value with the vehicle position deviation threshold. The comparison process is as follows:

[0045] If the vehicle position deviation value is greater than or equal to the vehicle position deviation threshold, mark the time point when the large position deviation signal is generated as the position deviation time point;

[0046] If the vehicle position deviation value is less than the vehicle position deviation threshold, mark the time period when the small position deviation signal is generated as the non-position deviation time point;

[0047] Obtain the number of position deviation time points within the monitoring period to get the number of times the vehicle travels off-track.

[0048] As a further solution of the present invention: In step four, the method for obtaining the probability value of an accident occurring in the confluence area is as follows:

[0049] Obtain the influence value of the number of merging abnormal vehicles and the influence value of the time of merging abnormal vehicles, add the influence value of the number of merging abnormal vehicles and the influence value of the time of merging abnormal vehicles to sum, and obtain the accident occurrence probability value in the merging area;

[0050] The process of obtaining the influence value of the number of merging abnormal vehicles is as follows:

[0051] During the monitoring period, obtain the area ratio of the merging abnormal vehicles to the affected vehicles and the area ratio of the merging abnormal vehicles, add the area ratio of the merging abnormal vehicles to the affected vehicles and the area ratio of the merging abnormal vehicles to sum, and obtain the influence value of the number of merging abnormal vehicles;

[0052] The process of obtaining the area ratio of the merging abnormal vehicles is as follows:

[0053] During the monitoring period, obtain the area occupied by the merging abnormal vehicles, calculate the ratio of the area occupied by the merging abnormal vehicles to the area value occupied by the vehicles, and obtain the area ratio of the merging abnormal vehicles;

[0054] The process of obtaining the area ratio of the merging abnormal vehicles to the affected vehicles is as follows:

[0055] During the monitoring period, obtain the area of the merging abnormal vehicles affecting the vehicles, calculate the ratio of the area of the merging abnormal vehicles affecting the vehicles to the area value occupied by the vehicles, and obtain the area ratio of the merging abnormal vehicles to the affected vehicles.

[0056] As a further illustration of the present invention: In step four, the process of obtaining the influence value of the time of merging abnormal vehicles is as follows:

[0057] During the monitoring period, obtain the passing time length value of the merging abnormal vehicles and the passing time length value of the merging abnormal vehicles affecting the vehicles, add the passing time length value of the merging abnormal vehicles and the passing time length value of the merging abnormal vehicles affecting the vehicles to sum, and obtain the influence value of the time of merging abnormal vehicles;

[0058] The process of obtaining the passing time length value of the merging abnormal vehicles is as follows:

[0059] During the monitoring period, obtain the passing time of the merging abnormal vehicles in the merging area, add the passing time of the merging abnormal vehicles to sum, and obtain the total passing time of the merging abnormal vehicles;

[0060] Calculate the ratio of the total passing time of the merging abnormal vehicles to the monitoring period time, and obtain the passing time length value of the merging abnormal vehicles;

[0061] The process of obtaining the passing time length value of the merging abnormal vehicles affecting the vehicles is as follows:

[0062] During the monitoring period, obtain the vehicle passing time affected by the confluence abnormal vehicle, sum up the vehicle passing time affected by the confluence abnormal vehicle to obtain the total vehicle passing time affected by the confluence abnormal vehicle;

[0063] Calculate the ratio of the total vehicle passing time affected by the confluence abnormal vehicle to the monitoring period time to obtain the value of the vehicle passing time length affected by the confluence abnormal vehicle;

[0064] The determination process of the vehicle affected by the confluence abnormal vehicle is as described in the process of obtaining the area ratio of the vehicle affected by the confluence abnormal vehicle above.

[0065] In a second aspect, the present invention provides an intelligent monitoring system based on a highway entrance, and the system includes the following modules:

[0066] Congestion monitoring module: Monitor the confluence area in front of the highway entrance through monitoring equipment, obtain the area congestion value, compare it with the threshold, and generate a signal indicating whether the area is congested;

[0067] If the area congestion value is greater than or equal to the area congestion threshold, generate an area congestion signal;

[0068] Confluence analysis module: Based on the area congestion signal, obtain the vehicle confluence value, compare it with the threshold, and generate a signal indicating whether the vehicle confluence is abnormal;

[0069] If the vehicle confluence value is greater than or equal to the vehicle confluence threshold, generate a vehicle confluence abnormal signal, and mark the vehicle generating the vehicle confluence abnormal signal as a confluence abnormal vehicle;

[0070] Stability evaluation module: Based on the vehicle confluence abnormal signal, obtain the vehicle driving stability value, compare it with the threshold, and generate a signal indicating whether the vehicle driving is stable;

[0071] If the vehicle driving stability value is greater than or equal to the vehicle driving stability threshold, generate a vehicle driving instability signal;

[0072] Probability prediction module: Based on the vehicle driving instability signal, obtain the accident occurrence probability value in the confluence area, compare it with the threshold, and generate a signal indicating the size of the accident occurrence probability;

[0073] If the accident occurrence probability value in the confluence area is greater than or equal to the accident occurrence probability threshold in the confluence area, generate a signal indicating a high accident occurrence probability;

[0074] Optimization and adjustment module: Based on the signal indicating a high accident occurrence probability, adjust the vehicle confluence driving speed to obtain the adjusted value of the confluence driving speed, and complete the optimization and adjustment of the vehicle confluence driving;

[0075] In step five, when a signal indicating a high probability of an accident occurs, the confluence driving of the vehicle is adjusted. The driving speed of the vehicle in the confluence area is multiplied by the adjustment coefficient to obtain the adjusted value of the confluence driving speed.

[0076] The adjustment coefficient is obtained as follows:

[0077] Obtain the vehicle driving stability deviation coefficient and the accident occurrence deviation coefficient in the confluence area. Add the vehicle driving stability deviation coefficient and the accident occurrence deviation coefficient in the confluence area to obtain the adjustment coefficient.

[0078] Advantages of the present invention:

[0079] (1) The present invention monitors the confluence area in front of the highway entrance through a monitoring device, obtains the area congestion value, compares it with a threshold value to generate a signal indicating whether the area is congested, and based on the area congestion signal, obtains the vehicle confluence value, compares it with a threshold value to generate a signal indicating whether the vehicle confluence is abnormal. The monitoring device can collect traffic data in the confluence area in real time, reflect the actual traffic conditions in this area, and avoid traffic accidents in the later stage.

[0080] (2) Based on the vehicle confluence abnormal signal, the present invention obtains the vehicle driving stability value, compares it with a threshold value to generate a signal indicating whether the vehicle is driving stably, based on the vehicle driving instability signal, obtains the accident occurrence probability value in the confluence area, compares it with a threshold value to generate a signal indicating the size of the accident occurrence probability, and based on the signal indicating a high probability of an accident, adjusts the confluence driving speed of the vehicle to obtain the adjusted value of the confluence driving speed, completing the optimization adjustment of the vehicle confluence driving. By monitoring the vehicle driving stability and adjusting the confluence driving speed when instability is detected, the risk of accidents in the confluence area caused by vehicle out of control or improper operation can be effectively reduced, and traffic congestion caused by accidents can be reduced. Description of the Drawings

[0081] The present invention will be further described below with reference to the drawings.

[0082] Figure 1 is the flowchart of the first embodiment of the present invention;

[0083] Figure 2 is the flowchart of the second embodiment of the present invention;

[0084] Figure 3 is the system schematic diagram of the present invention. Detailed Embodiments

[0085] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0086] Embodiment 1:

[0087] Please refer to Figure 1 As shown, a kind of intelligent monitoring method based on the highway entrance in the embodiment of the present invention, the specific method is as follows:

[0088] Step 1: Monitor the confluence area in front of the highway entrance through the monitoring device, obtain the area congestion value, compare it with the threshold value, and generate a signal indicating whether the area is congested;

[0089] In some embodiments, detect the confluence area in front of the highway entrance through the monitoring device, obtain the area congestion value, and compare the area congestion value with the area congestion threshold value. The comparison process is as follows:

[0090] If the area congestion value is greater than or equal to the area congestion threshold value, it indicates that there are more vehicles in the confluence area and the safety distance between vehicles is smaller, and a signal indicating area congestion is generated;

[0091] If the area congestion value is less than the area congestion threshold value, it indicates that there are fewer vehicles in the confluence area and the safety distance between vehicles is larger, and a signal indicating that the area is not congested is generated;

[0092] Among them, the way to obtain the area congestion value is:

[0093] During the monitoring period, obtain the area occupied by each vehicle in the confluence area, add up the areas occupied by all vehicles in the confluence area to obtain the total area occupied by vehicles in the confluence area;

[0094] Obtain the confluence area, calculate the ratio of the total area occupied by vehicles in the confluence area to the confluence area to obtain the area congestion value;

[0095] Step 2: Based on the area congestion signal, obtain the vehicle confluence value, compare it with the threshold value, and generate a signal indicating whether the vehicle confluence is abnormal;

[0096] Among them, the signal indicating whether the vehicle confluence is abnormal includes a signal indicating abnormal vehicle confluence and a signal indicating normal vehicle confluence;

[0097] In some embodiments, when a signal indicating area congestion is generated, obtain the vehicle confluence value, and compare the vehicle confluence value with the vehicle confluence threshold value. The comparison process is as follows:

[0098] If the vehicle merging value is greater than or equal to the vehicle merging threshold, it indicates that the vehicle speed is relatively fast and the degree of vehicle path deviation is relatively large. A vehicle merging anomaly signal is generated, and the vehicle that generates the vehicle merging anomaly signal is marked as a merging anomaly vehicle;

[0099] If the vehicle merging value is less than the vehicle merging threshold, it indicates that the vehicle speed is relatively slow and the degree of vehicle path deviation is relatively small. A vehicle merging normal signal is generated, and the vehicle that generates the vehicle merging normal signal is marked as a merging normal vehicle;

[0100] Among them, the method for obtaining the vehicle merging value is as follows:

[0101] During the monitoring period, obtain the vehicle regional driving speed and the vehicle path deviation value, add and sum the vehicle regional driving speed and the vehicle path deviation value to obtain the vehicle merging value;

[0102] Exemplarily, the method for obtaining the vehicle regional driving speed is as follows:

[0103] Within the merging area, monitor the vehicle driving speed in real time, add and sum the vehicle driving speeds and take the average value to obtain the vehicle regional driving speed;

[0104] The method for obtaining the vehicle path deviation value is as follows:

[0105] Within the merging area, divide the merging area in the form of a grid, and mark the actual position coordinates of the vehicle in the merging area as ( ), mark the preset position coordinates of the vehicle as ( ), obtain the initial position of the vehicle within the merging area, and mark the initial position coordinates of the vehicle as ( );

[0106] Among them, it should be explained that: ( ) is marked as the position reached by the vehicle in the merging area at each time point during the monitoring period, and n can be 1, 2, 3,......;

[0107] During the monitoring period, monitor the actual position coordinates of the vehicle in the merging area ( ), divide the monitoring period into several time nodes, obtain the actual position coordinates of the vehicle corresponding to each time node ( ), and connect the actual position coordinates of the vehicle corresponding to each time node to obtain the actual driving path of the vehicle;

[0108] Based on the actual driving path of the vehicle, compare and process the actual driving path of the vehicle with the preset driving path of the vehicle. The process of comparison and processing is as follows:

[0109] Subtract the preset position coordinates of the vehicle from the actual position coordinates of the vehicle corresponding to each time node to obtain the vehicle position deviation value;

[0110] Add up the vehicle position deviation values and take the average to obtain the vehicle path deviation value;

[0111] Specific implementation of the embodiment of the present invention: Monitor the merging area in front of the highway entrance through a monitoring device, obtain the area congestion value, compare it with a threshold value to generate a signal indicating whether the area is congested, and based on the area congestion signal, obtain the vehicle merging value, compare it with the threshold value to generate a signal indicating whether the vehicle merging is abnormal. The monitoring device can collect traffic data in the merging area in real time, reflect the actual traffic conditions in this area, and avoid traffic accidents in the later stage.

[0112] Embodiment Two:

[0113] Based on Embodiment One, please refer to Figure 2 As shown, a method for intelligent monitoring based on a highway entrance according to an embodiment of the present invention further includes the following specific steps:

[0114] Step Three: Based on the vehicle merging abnormal signal, obtain the vehicle driving stability value, compare it with a threshold value to generate a signal indicating whether the vehicle is driving stably;

[0115] In some embodiments, when a vehicle merging abnormal signal is generated, obtain the vehicle driving stability value, and compare the vehicle driving stability value with the vehicle driving stability threshold value. The comparison process is as follows:

[0116] If the vehicle driving stability value is greater than or equal to the vehicle driving stability threshold value, it indicates that the vehicle has a relatively large number of hasty driving times and deviation times during driving, and a signal indicating unstable vehicle driving is generated;

[0117] If the vehicle driving stability value is less than the vehicle driving stability threshold value, it indicates that the vehicle has a relatively small number of hasty driving times and deviation times during driving, and a signal indicating stable vehicle driving is generated;

[0118] Among them, the method for obtaining the vehicle driving stability value is:

[0119] During the monitoring period, obtain the number of hasty driving times and the number of deviation times of vehicles in the merging area, add up the number of hasty driving times and the number of deviation times of vehicles to obtain the vehicle driving stability value;

[0120] Exemplarily, the process for obtaining the number of hasty driving times of a vehicle is as follows:

[0121] During the monitoring period, obtain the real-time speed of the merging abnormal vehicle, establish an X-Y axis coordinate system, where the X-axis represents the monitoring time point, and the Y-axis represents the speed of the merging abnormal vehicle corresponding to each monitoring time point during the monitoring period. Substitute the real-time speed of the merging abnormal vehicle into the X-Y axis coordinate system to obtain the real-time speed change curve of the merging abnormal vehicle;

[0122] Based on the real-time speed change curve of the merging abnormal vehicle, extract the peak coordinate values in the real-time speed change curve of the merging abnormal vehicle and mark them as ( , ), and the valley coordinate values and mark them as ( , );

[0123] Calculate the distance between two adjacent peak coordinate values and valley coordinate values to obtain the speed change value;

[0124] Compare the speed change value with the speed change threshold. The comparison process is as follows:

[0125] If the speed change value is greater than or equal to the speed change threshold, it means that the speed change of the merging abnormal vehicle is large and the probability of rushing is high. Mark this time period as the hasty vehicle time period;

[0126] If the speed change value is less than the speed change threshold, it means that the speed change of the merging abnormal vehicle is small and the probability of rushing is low. Mark this time period as the non-hasty vehicle time period;

[0127] Obtain the number of times the hasty vehicle time period appears in the real-time speed change curve of the merging abnormal vehicle to get the number of times the vehicle travels hastily;

[0128] The method for obtaining the number of times the vehicle travels off-track is as follows:

[0129] Based on the vehicle position deviation value in the above step two, sum up the vehicle position deviation values within the monitoring period and take the average to obtain the vehicle position deviation threshold;

[0130] Compare the vehicle position deviation value with the vehicle position deviation threshold. The comparison process is as follows:

[0131] If the vehicle position deviation value is greater than or equal to the vehicle position deviation threshold, it means that the actual driving path of the merging vehicle deviates greatly from the safe driving path, generate a large position deviation signal, and mark the time point when the large position deviation signal is generated as the position deviation time point;

[0132] If the vehicle position deviation value is less than the vehicle position deviation threshold, it means that the actual driving path of the merging vehicle deviates slightly from the safe driving path, generate a small position deviation signal, and mark the time period when the small position deviation signal is generated as the non-position deviation time point;

[0133] Obtain the number of position deviation time points within the monitoring period to get the number of times the vehicle travels off-track;

[0134] Step Four: Based on the vehicle driving instability signal, obtain the accident occurrence probability value in the merging area, compare it with the threshold, and generate a signal indicating the size of the accident occurrence probability;

[0135] Among them, the accident occurrence probability signal includes a large accident occurrence probability signal and a small accident occurrence probability signal;

[0136] In some embodiments, when a vehicle driving instability signal is generated, the accident occurrence probability value in the merging area is obtained, and the accident occurrence probability value in the merging area is compared with the accident occurrence probability threshold in the merging area. The comparison process is as follows:

[0137] If the accident occurrence probability value in the merging area is greater than or equal to the accident occurrence probability threshold in the merging area, it indicates that the merging abnormal vehicle causes a relatively large accident occurrence probability in the merging area, and a large accident occurrence probability signal is generated;

[0138] If the accident occurrence probability value in the merging area is less than the accident occurrence probability threshold in the merging area, it indicates that the merging abnormal vehicle causes a small accident occurrence probability in the merging area, and a small accident occurrence probability signal is generated;

[0139] Among them, the method for obtaining the accident occurrence probability value in the merging area is as follows:

[0140] Obtain the influence value of the number of merging abnormal vehicles and the ratio of the passing time of the merging abnormal vehicles, add and sum the ratio of the number of merging abnormal vehicles and the ratio of the passing time of the merging abnormal vehicles to obtain the accident occurrence probability value in the merging area;

[0141] Among them, the process for obtaining the influence value of the number of merging abnormal vehicles is as follows:

[0142] During the monitoring period, obtain the ratio of the area of the vehicles affected by the merging abnormal vehicles and the ratio of the area of the merging abnormal vehicles, add and sum the ratio of the area of the vehicles affected by the merging abnormal vehicles and the ratio of the area of the merging abnormal vehicles to obtain the influence value of the number of merging abnormal vehicles;

[0143] Exemplarily, the process for obtaining the ratio of the area of the merging abnormal vehicles is as follows:

[0144] During the monitoring period, obtain the area occupied by the merging abnormal vehicles, and calculate the ratio of the area occupied by the merging abnormal vehicles to the area value of the vehicles to obtain the ratio of the area of the merging abnormal vehicles;

[0145] The process for obtaining the ratio of the area of the vehicles affected by the merging abnormal vehicles is as follows:

[0146] During the monitoring period, obtain the area of the vehicles affected by the merging abnormal vehicles, and calculate the ratio of the area of the vehicles affected by the merging abnormal vehicles to the area value of the vehicles to obtain the ratio of the area of the vehicles affected by the merging abnormal vehicles;

[0147] Specifically, the determination process of the vehicles affected by the merging abnormal vehicles is as follows:

[0148] During the monitoring period, centering on the confluence abnormal vehicle, obtain the driving stability value of the vehicles around the confluence abnormal vehicle, and mark it as the vehicle to-be-abnormal value;

[0149] Compare the vehicle to-be-abnormal value with the vehicle to-be-abnormal threshold, and the comparison process is as follows:

[0150] If the vehicle to-be-abnormal value is greater than or equal to the vehicle to-be-abnormal threshold, it indicates that the vehicles around the confluence abnormal vehicle are driving unstably, generate a to-be-abnormal signal, and mark the vehicle corresponding to the generated to-be-abnormal signal as the vehicle affected by the confluence abnormal vehicle;

[0151] If the vehicle to-be-abnormal value is less than the vehicle to-be-abnormal threshold, it indicates that the vehicles around the confluence abnormal vehicle are driving stably, generate a non-to-be-abnormal signal, and mark the vehicle corresponding to the generated non-to-be-abnormal signal as the vehicle not affected by the confluence abnormal vehicle;

[0152] The acquisition method of the time influence value of the confluence abnormal vehicle is as follows:

[0153] During the monitoring period, obtain the passing time length value of the confluence abnormal vehicle and the passing time length value of the vehicles affected by the confluence abnormal vehicle, add the passing time length value of the confluence abnormal vehicle and the passing time length value of the vehicles affected by the confluence abnormal vehicle, and obtain the time influence value of the confluence abnormal vehicle;

[0154] Exemplarily, the acquisition process of the passing time length value of the confluence abnormal vehicle is as follows:

[0155] During the monitoring period, obtain the passing time of the confluence abnormal vehicle in the confluence area, add the passing time of the confluence abnormal vehicle, and obtain the total passing time of the confluence abnormal vehicle;

[0156] Calculate the ratio of the total passing time of the confluence abnormal vehicle to the monitoring period time to obtain the passing time length value of the confluence abnormal vehicle;

[0157] The acquisition process of the passing time length value of the vehicles affected by the confluence abnormal vehicle is as follows:

[0158] During the monitoring period, obtain the passing time of the vehicles affected by the confluence abnormal vehicle, add the passing time of the vehicles affected by the confluence abnormal vehicle, and obtain the total passing time of the vehicles affected by the confluence abnormal vehicle;

[0159] Calculate the ratio of the total passing time of the vehicles affected by the confluence abnormal vehicle to the monitoring period time to obtain the passing time length value of the vehicles affected by the confluence abnormal vehicle;

[0160] The determination process of the vehicles affected by the confluence abnormal vehicle is as described in the acquisition process of the area ratio of the vehicles affected by the confluence abnormal vehicle above;

[0161] Step Five: Based on the large accident probability signal, adjust the merging driving speed of the vehicle to obtain the adjusted value of the merging driving speed, and complete the optimization adjustment of the vehicle's merging driving;

[0162] In some embodiments, when the large accident probability signal is generated, the merging driving of the vehicle is adjusted, and the driving speed of the vehicle in the merging area is multiplied by the adjustment coefficient to obtain the adjusted value of the merging driving speed;

[0163] Among them, the acquisition method of the adjustment coefficient is as follows:

[0164] Obtain the vehicle driving stability deviation coefficient and the accident occurrence deviation coefficient in the merging area, add the vehicle driving stability deviation coefficient and the accident occurrence deviation coefficient in the merging area to sum, and obtain the adjustment coefficient;

[0165] Exemplarily, the acquisition process of the vehicle driving stability deviation coefficient is as follows:

[0166] Obtain the vehicle driving stability value, subtract the vehicle driving stability value from the vehicle driving stability threshold to obtain the vehicle driving stability deviation value;

[0167] Perform a ratio calculation on the vehicle driving stability deviation value and the vehicle driving stability threshold to obtain the vehicle driving stability deviation coefficient;

[0168] The acquisition process of the accident occurrence deviation coefficient in the merging area is as follows:

[0169] Obtain the accident occurrence probability value in the merging area, subtract the accident occurrence probability value in the merging area from the accident occurrence probability threshold in the merging area to obtain the accident occurrence probability deviation value in the merging area;

[0170] Perform a ratio process on the accident occurrence probability deviation value in the merging area and the accident occurrence probability threshold in the merging area to obtain the accident occurrence deviation coefficient in the merging area;

[0171] The specific implementation scheme of the embodiment of the present invention: Based on the abnormal signal of vehicle merging, obtain the vehicle driving stability value, compare it with the threshold value to generate a signal indicating whether the vehicle is driving stably. Based on the signal indicating that the vehicle is driving unstably, obtain the accident occurrence probability value in the merging area, compare it with the threshold value to generate a signal indicating the size of the accident probability. Based on the large accident probability signal, adjust the merging driving speed of the vehicle to obtain the adjusted value of the merging driving speed, and complete the optimization adjustment of the vehicle's merging driving. By monitoring the vehicle driving stability and adjusting the merging driving speed when instability is detected, the accident risk in the merging area caused by vehicle out-of-control or improper operation can be effectively reduced, and the traffic congestion caused by accidents can be reduced.

[0172] Embodiment Three:

[0173] Based on the first and second embodiments, please refer to Figure 3 As shown, an intelligent monitoring system based on a highway entrance according to an embodiment of the present invention includes the following steps:

[0174] Congestion monitoring module: Monitor the confluence area in front of the highway entrance through monitoring devices, obtain the area congestion value, compare it with a threshold, and generate a signal indicating whether the area is congested;

[0175] If the area congestion value is greater than or equal to the area congestion threshold, generate an area congestion signal;

[0176] Confluence analysis module: Based on the area congestion signal, obtain the vehicle confluence value, compare it with a threshold, and generate a signal indicating whether the vehicle confluence is abnormal;

[0177] If the vehicle confluence value is greater than or equal to the vehicle confluence threshold, generate a vehicle confluence abnormal signal, and mark the vehicle that generates the vehicle confluence abnormal signal as a confluence abnormal vehicle;

[0178] Stability evaluation module: Based on the vehicle confluence abnormal signal, obtain the vehicle driving stability value, compare it with a threshold, and generate a signal indicating whether the vehicle driving is stable;

[0179] If the vehicle driving stability value is greater than or equal to the vehicle driving stability threshold, generate a vehicle driving unstable signal;

[0180] Probability prediction module: Based on the vehicle driving unstable signal, obtain the accident occurrence probability value in the confluence area, compare it with a threshold, and generate a signal indicating the size of the accident occurrence probability;

[0181] If the accident occurrence probability value in the confluence area is greater than or equal to the accident occurrence probability threshold in the confluence area, generate a signal indicating a high accident occurrence probability;

[0182] Optimization and adjustment module: Based on the signal indicating a high accident occurrence probability, adjust the vehicle confluence driving speed to obtain a confluence driving speed adjustment value, and complete the optimization and adjustment of the vehicle confluence driving;

[0183] In step five, when a signal indicating a high accident occurrence probability is generated, adjust the vehicle confluence driving, multiply the vehicle driving speed in the confluence area by an adjustment coefficient to obtain a confluence driving speed adjustment value;

[0184] The way to obtain the adjustment coefficient is as follows:

[0185] Obtain the vehicle driving stability deviation coefficient and the accident occurrence deviation coefficient in the confluence area, add the vehicle driving stability deviation coefficient and the accident occurrence deviation coefficient in the confluence area, and obtain the adjustment coefficient.

[0186] The above has described in detail an embodiment of the present invention, but the above content is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention shall still fall within the scope covered by the patent of the present invention.

Claims

1. An intelligent monitoring method based on high-speed entrance, characterized in that: The following steps are involved: Step 1: Use monitoring equipment to monitor the merging area before the highway entrance, obtain the regional congestion value, compare it with the threshold, and generate a signal to determine whether the area is congested; If the regional congestion value is greater than or equal to the regional congestion threshold, a regional congestion signal is generated; Step 2: Based on the regional congestion signal, obtain the vehicle merging value, compare it with the threshold, and generate a signal whether the vehicle merging is abnormal; If the vehicle merging value is greater than or equal to the vehicle merging threshold, a vehicle merging abnormality signal is generated, and the vehicle generating the vehicle merging abnormality signal is marked as a merging abnormality vehicle; During the monitoring period, the vehicle area driving speed and the vehicle path deviation value are obtained, and the vehicle area driving speed and the vehicle path deviation value are added and summed to obtain the vehicle merging value; The vehicle regional driving speed is obtained as follows: In the merging area, the vehicle speed is monitored in real time, and the vehicle speeds are added and averaged to obtain the vehicle area speed; In the merging area, the merging area is divided into grids, and the actual position coordinates of the vehicles in the merging area are marked as (X n , Y n ), mark the vehicle preset position coordinates as (X s , Y s ), obtain the initial position of the vehicle in the merging area, and mark the initial position coordinates of the vehicle as (X c , Y c ); During the monitoring period, the actual position coordinates (X n , Y n ), divide the monitoring period into several time nodes, and obtain the actual position coordinates of the vehicle corresponding to each time node (X n , Y n ), connect the actual position coordinates of the vehicle corresponding to each time node to obtain the actual driving path of the vehicle; Based on the actual driving path of the vehicle, the actual driving path of the vehicle is compared with the preset driving path of the vehicle. The comparison process is as follows: The actual position coordinates of the vehicle corresponding to each time node are subtracted from the preset position coordinates of the vehicle to obtain the vehicle position deviation value; The vehicle position deviation values ​​are added and averaged to obtain the vehicle path deviation value; Step 3: Based on the abnormal vehicle merging signal, obtain the vehicle driving stability value, compare it with the threshold, and generate a vehicle driving stability signal; If the vehicle driving stability value is greater than or equal to the vehicle driving stability threshold, a vehicle driving instability signal is generated; Step 4: Based on the unstable vehicle driving signal, obtain the probability value of the accident in the merging area, compare it with the threshold, and generate an accident probability signal; If the probability value of an accident occurring in the merging area is greater than or equal to the threshold value of the probability value of an accident occurring in the merging area, a large signal of the probability of an accident occurring is generated; Step 5: Based on the large signal of the accident probability, the merging speed of the vehicle is adjusted to obtain the merging speed adjustment value, thereby completing the optimization adjustment of the merging speed of the vehicle; In step 5, when a high accident probability signal is generated, the merging speed of the vehicle is adjusted, and the vehicle speed in the merging area is multiplied by the adjustment coefficient to obtain the merging speed adjustment value; The adjustment coefficient is obtained as follows: The vehicle driving stability deviation coefficient and the merging area accident occurrence deviation coefficient are obtained, and the vehicle driving stability deviation coefficient and the merging area accident occurrence deviation coefficient are added together to obtain an adjustment coefficient.

2. The intelligent monitoring method based on high-speed entrance according to claim 1 is characterized in that: In step 1, the regional congestion value is obtained as follows: During the monitoring period, the area occupied by each vehicle in the merging area is obtained, and the areas occupied by all vehicles in the merging area are added together to obtain the total area occupied by vehicles in the merging area; The area of ​​the merging area is obtained, and the ratio of the total area occupied by vehicles in the merging area to the area of ​​the merging area is calculated to obtain the regional congestion value.

3. The intelligent monitoring method based on high-speed entrance according to claim 1 is characterized in that: In step 3, the vehicle driving stability value is obtained as follows: During the monitoring period, the number of hasty vehicle driving and the number of vehicle derailment in the merging area are obtained, and the number of hasty vehicle driving and the number of vehicle derailment are added together to obtain the vehicle driving stability value.

4. The intelligent monitoring method based on high-speed entrance according to claim 3 is characterized in that: In step 3, the process of obtaining the number of hasty driving times of the vehicle is as follows: During the monitoring period, the real-time speed of the abnormal merging vehicle is obtained, and an XY axis coordinate system is established. The X axis represents the monitoring time point, and the Y axis represents the speed of the abnormal merging vehicle corresponding to each monitoring time point during the monitoring period. The real-time speed of the abnormal merging vehicle is substituted into the XY axis coordinate system to obtain the real-time speed change curve of the abnormal merging vehicle; Based on the real-time speed change curve of abnormal merging vehicles, the peak coordinate value in the real-time speed change curve of abnormal merging vehicles is extracted and marked as (X f , Y f ), the trough coordinate value, marked as (X g , Y g ); Calculate the distance between the coordinate values ​​of two adjacent wave crests and the coordinate values ​​of wave troughs to obtain the speed change value; The speed change value is compared with the speed change threshold. The comparison process is as follows: If the speed change value is greater than or equal to the speed change threshold, the probability of rush is high, and the time period is marked as a rush time period; The number of times the hasty vehicle time period appears in the real-time speed change curve of the abnormal merging vehicles is obtained, and the number of hasty vehicle driving times is obtained.

5. The intelligent monitoring method based on high-speed entrance according to claim 3 is characterized in that: In step 3, the number of times the vehicle deviates from the track is obtained as follows: Based on the vehicle position deviation value in the above step 2, the vehicle position deviation values ​​in the monitoring period are added and averaged to obtain the vehicle position deviation threshold; The vehicle position deviation value is compared with the vehicle position deviation threshold. The comparison process is as follows: If the vehicle position deviation value is greater than or equal to the vehicle position deviation threshold, the time point at which the position deviation large signal is generated is marked as the position deviation time point; If the vehicle position deviation value is less than the vehicle position deviation threshold, the time period in which the position deviation small signal is generated is marked as a non-position deviation time point; The number of position deviation time points within the monitoring period is obtained to obtain the number of times the vehicle deviates from the track.

6. The intelligent monitoring method based on high-speed entrance according to claim 1 is characterized in that: In step 4, the probability value of an accident occurring in the merging area is obtained in the following way: Obtaining the impact value of the number of abnormal merging vehicles and the impact value of the time of abnormal merging vehicles, adding the impact value of the number of abnormal merging vehicles and the impact value of the time of abnormal merging vehicles to obtain the probability value of an accident occurring in the merging area; The process of obtaining the impact value of the number of abnormal merging vehicles is as follows: During the monitoring period, the vehicle area ratio affected by the abnormal merging vehicle and the area ratio of the abnormal merging vehicle are obtained, and the vehicle area ratio affected by the abnormal merging vehicle and the area ratio of the abnormal merging vehicle are added together to obtain the impact value of the number of abnormal merging vehicles; The process of obtaining the area ratio of abnormal merging vehicles is as follows: During the monitoring period, the area occupied by the abnormal merging vehicle is obtained, and the area occupied by the abnormal merging vehicle is calculated to be a ratio of the area occupied by the abnormal merging vehicle to the area occupied by the vehicle, so as to obtain the abnormal merging vehicle area ratio; The process of obtaining the area ratio of vehicles affected by abnormal merging vehicles is as follows: During the monitoring period, the vehicle area affected by the abnormal merging vehicle is obtained, and the ratio of the vehicle area affected by the abnormal merging vehicle to the area occupied by the vehicle is calculated to obtain the vehicle area ratio affected by the abnormal merging vehicle.

7. The intelligent monitoring method based on high-speed entrance according to claim 6 is characterized in that: In step 4, the time impact value of the abnormal merging vehicle is obtained as follows: During the monitoring period, the length of time for the abnormal merging vehicle to pass and the length of time for the abnormal merging vehicle to affect the vehicle to pass are obtained, and the length of time for the abnormal merging vehicle to pass and the length of time for the abnormal merging vehicle to affect the vehicle to pass are added together to obtain the time impact value of the abnormal merging vehicle; The process of obtaining the length of time for abnormal merging vehicles is as follows: During the monitoring period, the travel time of abnormal merging vehicles in the merging area is obtained, and the travel time of abnormal merging vehicles is added up to obtain the total travel time of abnormal merging vehicles; The total time of abnormal merging vehicles passing is calculated by ratio with the monitoring cycle time to obtain the length of the abnormal merging vehicle passing time; The process of obtaining the length of time that abnormal merging vehicles affect the passage of vehicles is as follows: During the monitoring period, the time that the abnormal merging vehicles affect the passage of vehicles is obtained, and the time that the abnormal merging vehicles affect the passage of vehicles is added up to obtain the total time that the abnormal merging vehicles affect the passage of vehicles; The total time that the abnormal merging vehicle affects the passage of vehicles is calculated by ratio with the monitoring cycle time, so as to obtain the length of time that the abnormal merging vehicle affects the passage of vehicles; The process of determining the vehicles affected by the abnormal merging vehicle is as described above in the process of obtaining the area ratio of the vehicles affected by the abnormal merging vehicle.

8. An intelligent monitoring system based on high-speed entrances, characterized in that: The system is used to execute the method described in any one of claims 1 to 7, and the system includes the following modules: Congestion monitoring module: monitors the merging area before the highway entrance through monitoring equipment, obtains the regional congestion value, compares it with the threshold, and generates a signal whether the area is congested; If the regional congestion value is greater than or equal to the regional congestion threshold, a regional congestion signal is generated; Merging analysis module: Based on the regional congestion signal, the vehicle merging value is obtained, compared with the threshold, and a signal is generated to determine whether the vehicle merging is abnormal; If the vehicle merging value is greater than or equal to the vehicle merging threshold, a vehicle merging abnormality signal is generated, and the vehicle generating the vehicle merging abnormality signal is marked as a merging abnormality vehicle; Stability assessment module: Based on the abnormal vehicle merging signal, obtain the vehicle driving stability value, compare it with the threshold, and generate a signal to determine whether the vehicle is stable; If the vehicle driving stability value is greater than or equal to the vehicle driving stability threshold, a vehicle driving instability signal is generated; Probability prediction module: Based on the unstable vehicle driving signal, the probability value of the accident in the merging area is obtained, compared with the threshold, and the probability signal of the accident is generated; If the probability value of an accident occurring in the merging area is greater than or equal to the threshold value of the probability value of an accident occurring in the merging area, a large signal of the probability of an accident occurring is generated; Optimization and adjustment module: Based on the large signal of accident probability, the merging speed of vehicles is adjusted to obtain the merging speed adjustment value, thus completing the optimization and adjustment of the merging speed of vehicles; In step 5, when a high accident probability signal is generated, the merging speed of the vehicle is adjusted, and the vehicle speed in the merging area is multiplied by the adjustment coefficient to obtain the merging speed adjustment value; The adjustment coefficient is obtained as follows: The vehicle driving stability deviation coefficient and the merging area accident occurrence deviation coefficient are obtained, and the vehicle driving stability deviation coefficient and the merging area accident occurrence deviation coefficient are added together to obtain an adjustment coefficient.

Images