Highland collaborative automatic control method and system

Abstract

The present invention provides a highland coordinated automatic control method and system, wherein the method includes the following steps: in response to receiving a congestion inspection signal, controlling a drone to fly to a highway section for image acquisition; performing image recognition on the high-speed acquisition image, and determining a highway area and a ramp area located in the high-speed acquisition image; performing regional division on the high-speed area, obtaining a first section area and a second section area indicating a section before the ramp and a section after the ramp, respectively, obtaining a first congestion coefficient and a second congestion coefficient corresponding to the section before the ramp and the section after the ramp, respectively, and determining a flow attribute corresponding to the ramp section based on the first congestion coefficient and the second congestion coefficient; in response to the flow attribute being a congestion attribute, obtaining a vehicle light control device closest to the ramp section, and controlling the vehicle light control device to perform a light control operation corresponding to the passage of the vehicle. The present invention at least improves traffic management efficiency.

Description

Technical Field

[0001] The present invention relates to data processing technology, and in particular to a highland collaborative automatic control method and system. Background Art

[0002] With the rapid development of modern technology, Intelligent Transportation Systems (ITS) have become a key means to improve traffic efficiency, enhance road safety and optimize user experience worldwide. In this context, as an important traffic artery connecting cities and regions, the intelligent level of traffic management of highways is directly related to the efficient operation of the entire transportation network and the convenience of people's travel.

[0003] Traditional highway traffic management mainly relies on manual control, including obtaining the corresponding traffic conditions through road patrols, remote observation by surveillance cameras, and telephone alarms, and then making corresponding adjustments based on the traffic conditions. However, with the continuous expansion of the highway network and the sharp increase in the number of vehicles, this mode of manual control has gradually exposed many limitations. Faced with a large-scale, complex and intertwined road network structure, manual control is difficult to achieve comprehensive coverage, especially in the case of congestion caused by rush hour or sudden accidents. Manual control is unable to quickly identify the source of congestion, accurately locate the problem area, and quickly allocate resources to deal with congestion.

[0004] Therefore, how to automatically regulate the congestion on highways to ease traffic pressure has become an urgent problem to be solved. Summary of the invention

[0005] Based on the above problems, the present invention is proposed to provide a high-altitude collaborative automatic control method and system that overcomes the above problems or at least partially solves the above problems.

[0006] According to one aspect of the present invention, there is provided a highland collaborative automatic control method, comprising the following steps:

[0007] In response to receiving a congestion detection signal corresponding to any expressway section, controlling the drone to fly to the expressway section to collect images, and obtaining a high-speed collected image;

[0008] Performing region recognition on the high-speed acquisition image, and determining a high-speed region indicating a high-speed road section and a ramp region indicating a ramp road section having a connection relationship with the high-speed road section located in the high-speed acquisition image based on the recognition result;

[0009] Dividing the highway area into regions based on the ramp area to obtain a first section area and a second section area respectively indicating a section before the ramp and a section after the ramp, wherein the section before the ramp and the section after the ramp intersect at the ramp section;

[0010] Acquire a first congestion coefficient and a second congestion coefficient corresponding to a pre-ramp section and a post-ramp section respectively based on the first section area and the second section area, and determine a flow attribute corresponding to the ramp section based on the first congestion coefficient and the second congestion coefficient;

[0011] In response to the traffic attribute being a congestion attribute, a vehicle light control device closest to the ramp road segment is obtained along a ramp extension direction corresponding to the ramp road segment, and the vehicle light control device is controlled to perform a light control operation for the corresponding vehicle passing.

[0012] Optionally, in the method according to the present invention, in response to receiving a congestion check signal corresponding to any expressway section, controlling the drone to fly to the expressway section to collect images to obtain high-speed collected images, including:

[0013] In response to receiving a congestion check signal corresponding to any expressway section sent by any vehicle terminal, obtaining vehicle position information corresponding to the vehicle terminal, and comparing the vehicle position information with the retrieved section position information corresponding to the expressway section;

[0014] In response to determining that the vehicle is located at the expressway section based on the comparison result, determining that the vehicle satisfies a congestion reporting condition, and acquiring a sending time node corresponding to the congestion troubleshooting signal;

[0015] Establishing a timed task for a preset receiving period based on the sending time node, and determining the number of signals corresponding to all congestion troubleshooting signals that meet the congestion reporting condition and are received within the preset receiving period based on the timed task;

[0016] In response to the number of signals being greater than a preset number of signals, the drone is controlled to fly to the highway section to collect images, thereby obtaining high-speed collected images.

[0017] Optionally, in the method according to the present invention, the method further comprises:

[0018] In response to the number of signals being less than or equal to the preset number of signals, segmenting the preset receiving period based on a half value of the period to obtain a front period and a rear period;

[0019] Respectively determine the front quantity and the rear quantity of all congestion troubleshooting signals that meet the congestion reporting conditions and are received in the front time period and the rear time period, and compare the rear quantity with the half value of the quantity corresponding to the preset signal quantity;

[0020] In response to the rear quantity being greater than half the quantity value, a first update period having the same duration as the rear period is created, and the preset receiving period is extended based on the first update period to update the scheduled task.

[0021] Optionally, in the method according to the present invention, the method further comprises:

[0022] In response to the rear number being less than or equal to half the number value, acquiring each sending time node of each congestion troubleshooting signal corresponding to the rear number, and determining each sending time node as an interval determination group;

[0023] Adding the start time node and the end time node corresponding to the latter time period to the interval determination group respectively, and sorting the time nodes in the interval determination group based on the time sequence to obtain a node sequence;

[0024] Based on the obtained node sequence, the difference between each time node at adjacent positions is calculated to obtain the duration of each current interval;

[0025] Calculate the ratio of the preset signal quantity to the preset receiving period, and compare the current interval duration with the obtained reference interval duration;

[0026] In response to any current interval duration being less than or equal to the reference interval duration, determining the current interval duration as having a concentrated time attribute;

[0027] In response to any current interval duration being greater than the reference interval duration, determining the current interval duration as having a dispersed time attribute;

[0028] Determining the durations of the current intervals that are continuously arranged and correspond to the same time attribute as the same time attribute group based on the node sequence, and obtaining the dispersed time attribute groups and / or the concentrated time attribute groups arranged based on the node sequence;

[0029] In response to determining that the time attribute group at the end is a concentrated time attribute group based on the node sequence, obtaining the number of current durations corresponding to each current interval duration in the concentrated time attribute group, and comparing the number of current durations with the retrieved preset duration number;

[0030] In response to the current duration being greater than the preset duration, a second update period having the same duration as the preset receiving period is created, and the preset receiving period is extended based on the second update period to update the scheduled task.

[0031] Optionally, in the method according to the present invention, obtaining a first congestion coefficient and a second congestion coefficient corresponding to a pre-ramp section and a post-ramp section respectively based on the first section area and the second section area, and determining a flow attribute corresponding to the ramp section based on the first congestion coefficient and the second congestion coefficient, includes:

[0032] Inputting the high-speed acquisition image into a vehicle recognition model obtained after pre-training, and obtaining various vehicle elements respectively indicating different traveling vehicles in the high-speed acquisition image;

[0033] Coordinate processing is performed on the high-speed acquisition images, and each image coordinate point corresponding to any vehicle element is determined as a vehicle coordinate group, and each image coordinate point corresponding to any road section area is determined as a road section coordinate group, and coordinate comparison is performed between each vehicle coordinate group and each road section coordinate group;

[0034] In response to any vehicle coordinate group being completely included in any road segment coordinate group, determining the vehicle element corresponding to the vehicle coordinate group as being located in the road segment area corresponding to the road segment coordinate group;

[0035] In response to a portion of any vehicle coordinate group being partially included in the first road section area and another portion being partially included in the second road section area, obtaining a first road section proportion corresponding to the portion and a second road section proportion corresponding to the other portion;

[0036] Compare the first road section proportion with the second road section proportion, and determine the vehicle element corresponding to the vehicle coordinate group as being located in the road section area with the larger proportion;

[0037] Based on each vehicle element located in the first section area and the second section area respectively, a first congestion coefficient and a second congestion coefficient of a corresponding pre-ramp section and a corresponding post-ramp section are obtained, and in response to the first congestion coefficient being greater than the second congestion coefficient, a congestion coefficient difference between the corresponding first congestion coefficient and the second congestion coefficient is obtained;

[0038] In response to the congestion coefficient difference being greater than the retrieved preset congestion coefficient, the flow attribute corresponding to the ramp road segment is determined as a congestion attribute.

[0039] Optionally, in the method according to the present invention, obtaining a first congestion coefficient and a second congestion coefficient of a corresponding pre-ramp section and a post-ramp section based on each vehicle element located in the first section area and the second section area respectively includes:

[0040] Acquire the first number of vehicles corresponding to each vehicle element located in the first road section area, and acquire the vehicle distances of each vehicle element located in adjacent positions along the area extension direction of the first road section area to obtain each first vehicle distance;

[0041] Retrieving a preset road ahead congestion determination strategy to configure the strategy for the first road section area, and determining a first quantity congestion value corresponding to the first number of vehicles and a first spacing congestion value corresponding to the first vehicle spacing based on the preset road ahead congestion determination strategy;

[0042] Acquire the number of second vehicles corresponding to each vehicle element located in the second road section area, and acquire the vehicle distances of each vehicle element located in adjacent positions along the area extension direction of the second road section area to obtain each second vehicle distance;

[0043] Retrieving a preset back-road congestion determination strategy to configure the strategy for the second road section area, and determining a second quantity congestion value corresponding to the second number of vehicles and a second spacing congestion value corresponding to the second vehicle spacing based on the preset back-road congestion determination strategy;

[0044] The quantity congestion value and the spacing congestion value corresponding to the same road section are multiplied by the retrieved quantity weight value and the spacing weight value to obtain the first congestion coefficient and the second congestion coefficient of the corresponding section before the ramp and the section after the ramp.

[0045] Optionally, in the method according to the present invention, calling a preset front road congestion determination strategy to configure the strategy for the first road section area, and determining a first quantity congestion value corresponding to the first number of vehicles and a first spacing congestion value corresponding to each first vehicle spacing based on the preset front road congestion determination strategy, including:

[0046] Acquire lane areas respectively included in the first road section area, and sort lanes of the lane areas from near to far based on the ramp area to obtain a lane sequence;

[0047] Allocating weights from large to small to each lane region in the lane sequence to obtain lane weights corresponding to each lane region;

[0048] Determine the image coordinate points corresponding to the same lane area as a lane coordinate group, and compare the coordinates of each vehicle coordinate group located in the first road section area with each lane coordinate group;

[0049] In response to any vehicle coordinate group being completely included in any lane coordinate group, determining the vehicle element corresponding to the vehicle coordinate group as being located in the lane area corresponding to the lane coordinate group;

[0050] In response to a part of any vehicle coordinate group being partially included in any lane coordinate group and another part being partially included in another lane coordinate group, determining the image coordinate points located in the vehicle coordinate group with the longest corresponding distance along the area extension direction of the first road section area as the lane change coordinate point, and determining the vehicle element corresponding to the vehicle coordinate group as the lane area corresponding to the lane coordinate group including the lane change coordinate point;

[0051] Based on the first number of vehicles, the number of each vehicle element located in the same lane area is obtained to obtain the number of each vehicle sub-number corresponding to each different lane area, and the number of vehicle sub-numbers corresponding to the same lane area is multiplied by the lane weight to obtain the number coefficients of each lane corresponding to each lane area;

[0052] The coefficients of the number of lanes are summed up to obtain a first number congestion value corresponding to a first number of vehicles;

[0053] Determine each first vehicle distance corresponding to the same lane area as a first distance group, and in response to any first vehicle distance in the first distance group being greater than or equal to the retrieved preset congestion distance, screen out the first vehicle distance from the first distance group to obtain updated first distance groups;

[0054] Calculate the mean of each first vehicle spacing in the same first spacing group to obtain the mean of each first spacing corresponding to each lane area, and multiply the mean of the first spacing corresponding to the same lane area by the lane weight to obtain the lane spacing coefficient corresponding to each lane area;

[0055] The lane spacing coefficients are summed up and calculated to obtain first spacing congestion values ​​corresponding to first vehicle spacings.

[0056] Optionally, in the method according to the present invention, calling a preset back road congestion determination strategy to configure the strategy for the second road section area, and determining a second quantity congestion value corresponding to the second number of vehicles and a second spacing congestion value corresponding to the second vehicle spacing based on the preset back road congestion determination strategy, including:

[0057] Acquire a second number congestion value based on the second number of vehicles, and compare the distance between each second vehicle with the retrieved preset congestion distance;

[0058] In response to any second vehicle distance being smaller than the preset congestion distance, the second vehicle distance is aggregated into a second distance group to obtain each second vehicle distance in the second distance group;

[0059] An average value is calculated for each second vehicle interval in the second interval group, and a second interval congestion value corresponding to each second vehicle interval is obtained based on the obtained second interval average value.

[0060] Optionally, in the method according to the present invention, controlling the vehicle light control device to perform a light control operation corresponding to vehicle passage includes:

[0061] Retrieving a preset light control determination table, wherein the preset light control determination table includes different congestion coefficient intervals and light control durations corresponding to the different congestion coefficient intervals;

[0062] Traversing the preset light control determination table, obtaining a congestion coefficient interval including the congestion coefficient difference, and determining a corresponding light control duration based on the congestion coefficient interval;

[0063] The vehicle light control device is controlled to perform a light control operation corresponding to the light control time period for the vehicle to pass.

[0064] According to another aspect of the present invention, there is provided a highland coordinated automatic control system, comprising:

[0065] An image acquisition module is configured to, in response to receiving a congestion detection signal corresponding to any expressway section, control the drone to fly to the expressway section to acquire images and obtain a high-speed acquisition image;

[0066] a region recognition module configured to perform region recognition on the high-speed acquisition image, and determine, based on the recognition result, a high-speed region indicating a high-speed road section and a ramp region indicating a ramp road section having a connection relationship with the high-speed road section in the high-speed acquisition image;

[0067] A region division module is configured to divide the highway region into regions based on the ramp region to obtain a first section region and a second section region respectively indicating a pre-ramp section and a post-ramp section, wherein the pre-ramp section and the post-ramp section intersect at the ramp section;

[0068] an attribute determination module, configured to obtain a first congestion coefficient and a second congestion coefficient corresponding to a pre-ramp section and a post-ramp section respectively based on the first section area and the second section area, and determine a flow attribute corresponding to the ramp section based on the first congestion coefficient and the second congestion coefficient;

[0069] The light control operation module is configured to obtain the vehicle light control device closest to the ramp road section along the ramp extension direction corresponding to the ramp road section in response to the traffic attribute being a congestion attribute, and control the vehicle light control device to perform the light control operation for the corresponding vehicle passage.

[0070] The solution of the present invention has brought significant beneficial effects: First, the drone can quickly respond to the congestion inspection signal of the highway section, realize the real-time image acquisition of the target section, greatly shorten the monitoring response time, and improve the timeliness of traffic management. The high-altitude perspective and flexible maneuverability of the drone make the collected image information more comprehensive and accurate, providing a solid foundation for subsequent analysis and decision-making.

[0071] Secondly, by performing image recognition on high-speed images, the system can automatically distinguish between high-speed areas and ramp areas, and then make refined regional divisions of high-speed sections. This division not only helps to clarify the specific location of congestion, but also provides a clear definition range for subsequent congestion coefficient calculations, making the analysis results more accurate and reliable.

[0072] Furthermore, based on the first congestion coefficient and the second congestion coefficient calculated for the first section area and the second section area respectively, the system can accurately determine the flow attributes of the ramp section, thereby timely identifying potential congestion risks. This function is of great significance for early warning and timely diversion of traffic flow, which helps to avoid further deterioration of congestion.

[0073] Finally, when the system detects that the ramp section is in a congested state, it can quickly locate and control the nearest vehicle lighting control device to perform the corresponding lighting control operation. This intelligent lighting control adjustment can not only effectively alleviate the traffic pressure on the ramp section, but also improve the traffic efficiency of the entire highway network, providing drivers with a smoother travel experience. In summary, this method has shown significant beneficial effects in improving traffic management efficiency, optimizing road capacity, and improving public travel satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A flowchart of a highland collaborative automatic control method according to an embodiment of the present invention is shown;

[0075] Figure 2 A schematic diagram of a high-speed road section in this embodiment is shown;

[0076] Figure 3 A structural block diagram of a highland collaborative automatic control system according to another embodiment of the present invention is shown. DETAILED DESCRIPTION

[0077] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0078] To solve the problems in the prior art, the inventor proposes a solution of the present invention. An embodiment of the present invention provides a high-altitude collaborative automatic control method, which can be executed in a computing device, wherein the computing device can be understood as a terminal with data processing function, such as a mobile phone or a computer light.

[0079] Figure 1 A flowchart of a highland collaborative automatic control method according to an embodiment of the present invention is shown, and the method is suitable for being executed in a computing device, such as Figure 1 As shown, the method starts at step S101, wherein step S101 includes the following contents:

[0080] In response to receiving a congestion detection signal corresponding to any expressway section, the drone is controlled to fly to the expressway section to collect images and obtain high-speed collected images.

[0081] For example, in this embodiment, when the server receives a congestion check signal corresponding to any highway section, it indicates that the highway section is likely to have a corresponding congestion situation. Therefore, in order to further determine the authenticity of the information and implement congestion control on the highway section where the congestion occurs, the server can control the drone to fly to the corresponding highway section to perform corresponding image acquisition and obtain the corresponding high-speed acquisition image.

[0082] Furthermore, in this embodiment, the above-mentioned “in response to receiving a congestion check signal corresponding to any expressway section, controlling the drone to fly to the expressway section to collect images and obtain high-speed collected images” may also include the following steps:

[0083] In response to receiving a congestion check signal corresponding to any expressway section sent by any vehicle terminal, obtaining vehicle position information corresponding to the vehicle terminal, and comparing the vehicle position information with the retrieved section position information corresponding to the expressway section;

[0084] In response to determining that the vehicle is located at the expressway section based on the comparison result, determining that the vehicle satisfies a congestion reporting condition, and acquiring a sending time node corresponding to the congestion troubleshooting signal;

[0085] Establishing a timed task for a preset receiving period based on the sending time node, and determining the number of signals corresponding to all congestion troubleshooting signals that meet the congestion reporting condition and are received within the preset receiving period based on the timed task;

[0086] In response to the number of signals being greater than the preset number of signals, the drone is controlled to fly to the highway section to collect images, thereby obtaining high-speed collected images.

[0087] For example, in this embodiment, the congestion check signal can be sent by the corresponding vehicle end, for example, by pre-installing the corresponding signal sending plug-in in the moving vehicle. When the moving vehicle is located at the corresponding expressway section and it is believed that the expressway section has a corresponding congestion situation, the corresponding congestion check signal can be sent to the server based on the signal sending plug-in, in the hope of performing corresponding congestion regulation. After receiving the corresponding congestion check signal, the server can further obtain the vehicle position information corresponding to the vehicle end in order to determine the authenticity of the corresponding information, and compare the obtained vehicle position information with the retrieved section position information corresponding to the expressway section. When it is determined based on the comparison result that the vehicle end is located at the expressway section, it can be determined that the vehicle end meets the corresponding congestion reporting condition, so that the sending time node corresponding to the congestion check signal can be further obtained. The sending time node is used to establish a corresponding timing task for a continuous preset receiving time period, and the signal number of all congestion troubleshooting signals that meet the congestion reporting conditions and are searched within the preset receiving time period is determined based on the timing task; it can be explained that since the judgment of congestion conditions generally has corresponding subjectivity and timeliness, when any congestion troubleshooting signal is received, it is necessary to establish a corresponding timing task to further judge the authenticity of the congestion situation; when it is determined based on the timing task that the signal number of all congestion troubleshooting signals that meet the congestion reporting conditions and are received within the preset receiving time period is greater than the preset signal number pre-stored in the server, it indicates that the authenticity of the corresponding congestion situation on the expressway section is high. At this time, the server can control the drone to fly to the expressway section to perform corresponding image acquisition, and then obtain the corresponding high-speed acquisition image.

[0088] Furthermore, in this embodiment, the method further includes:

[0089] In response to the number of signals being less than or equal to the preset number of signals, segmenting the preset receiving period based on a half value of the period to obtain a front period and a rear period;

[0090] Respectively determine the front quantity and the rear quantity of all congestion troubleshooting signals that meet the congestion reporting conditions and are received in the front time period and the rear time period, and compare the rear quantity with the half value of the quantity corresponding to the preset signal quantity;

[0091] In response to the rear quantity being greater than half the quantity value, a first update period having the same duration as the rear period is created, and the preset receiving period is extended based on the first update period to update the scheduled task.

[0092] For example, in this embodiment, based on the above content, it can be known that the congestion situation has a certain timeliness, that is, it can be understood that the congestion situation is gradually formed, and each person has a different definition of the congestion situation. In order to improve the accuracy of determining the congestion situation, when the number of signals corresponding to the preset receiving time period is obtained and the corresponding number of signals is less than or equal to the preset number of signals, the preset receiving time period can be segmented into half the value of the corresponding time period to obtain the front time period and the rear time period (that is, when the preset receiving time period is 2 hours, the corresponding front time period and the rear time period are both 1 hour); after completing the corresponding timing task After the signal of the service is acquired, the front number and the rear number of all congestion troubleshooting signals that meet the congestion reporting conditions received in the front time period and the rear time period respectively can be further determined, and the corresponding rear number can be compared with the half value of the corresponding preset signal number; when the rear number is greater than the half value, it means that the corresponding received congestion troubleshooting signal is in a relatively concentrated state in the corresponding rear time period. Therefore, a first update period with the same duration as the rear time period can be created, and the preset receiving period can be extended accordingly based on the obtained first update period to complete the update of the scheduled task.

[0093] Here, it should be noted that updating the scheduled task can be understood as updating the preset receiving time period in the scheduled task, that is, extending the original preset receiving time period to the total length of the time period between the preset receiving time period and the first update time period; after completing the update of the preset receiving time period in the scheduled task, it is possible to determine whether the corresponding expressway section has corresponding congestion based on whether the number of signals corresponding to all congestion check signals received within the updated preset receiving time period is greater than the preset number of signals, thereby improving the accuracy of the judgment of the congestion situation.

[0094] Furthermore, in this embodiment, the method may further include the following steps:

[0095] In response to the rear number being less than or equal to half the number value, acquiring each sending time node of each congestion troubleshooting signal corresponding to the rear number, and determining each sending time node as an interval determination group;

[0096] Adding the start time node and the end time node corresponding to the latter time period to the interval determination group respectively, and sorting the time nodes in the interval determination group based on the time sequence to obtain a node sequence;

[0097] Based on the obtained node sequence, the difference between each time node at adjacent positions is calculated to obtain the duration of each current interval;

[0098] Calculate the ratio of the preset signal quantity to the preset receiving period, and compare the current interval duration with the obtained reference interval duration;

[0099] In response to any current interval duration being less than or equal to the reference interval duration, determining the current interval duration as having a concentrated time attribute;

[0100] In response to any current interval duration being greater than the reference interval duration, determining the current interval duration as having a dispersed time attribute;

[0101] Determining the durations of the current intervals that are continuously arranged and correspond to the same time attribute as the same time attribute group based on the node sequence, and obtaining the dispersed time attribute groups and / or the concentrated time attribute groups arranged based on the node sequence;

[0102] In response to determining that the time attribute group at the end is a concentrated time attribute group based on the node sequence, obtaining the number of current durations corresponding to each current interval duration in the concentrated time attribute group, and comparing the number of current durations with the retrieved preset duration number;

[0103] In response to the current duration being greater than the preset duration, a second update period having the same duration as the preset receiving period is created, and the preset receiving period is extended based on the second update period to update the scheduled task.

[0104] For example, in this embodiment, in order to further improve the accuracy of judging the congestion situation, the following method steps may be performed when the number of rear parts is less than or equal to half the number:

[0105] First, each sending time node of each congestion check signal corresponding to the rear number can be obtained, and each sending time node is determined as a corresponding interval determination group, wherein it can be understood that the sending time node can be understood as the time when each vehicle end sends the corresponding congestion check signal to the server;

[0106] Secondly, after obtaining the corresponding sending time nodes, the start time node and the end time node of the corresponding rear time period can be added to the interval determination group obtained above, and the time nodes in the interval determination group can be sorted according to the corresponding time sequence, so as to obtain the corresponding node sequence;

[0107] Next, after obtaining the corresponding node sequence, each time node in the node sequence can be respectively calculated based on the difference of adjacent positions, so as to obtain the corresponding current interval durations. For example, when any node sequence includes five nodes, namely, the starting time node A, the sending time node B, the sending time node C, the sending time node D and the ending time node E, it can be known that when the starting time node A is 16:00 and the sending time node B is 16:10, the corresponding current interval duration is 00:10;

[0108] Then, after the corresponding current interval durations are completed, the corresponding reference interval durations can be obtained by calculating the corresponding ratios of the preset signal quantity and the corresponding preset receiving time period;

[0109] Then, the corresponding current interval durations are compared with the benchmark interval durations one by one. When any current interval duration is less than or equal to the benchmark interval duration, it indicates that the sending interval of the congestion removal signal corresponding to the current interval duration is shorter, and it can be determined to be in a concentrated state. Therefore, the current interval duration can be further determined to have the corresponding concentrated time attribute; and when any current interval duration is greater than the benchmark interval duration, it indicates that the sending interval of the congestion removal signal corresponding to the current interval duration is longer, and it can be determined to be in a dispersed state. Therefore, the current interval duration can be further determined to have the corresponding dispersed time attribute;

[0110] Then, after the attribute determination of each current interval duration is completed, the current interval durations that are continuously arranged and correspond to the same time attribute can be further determined as the same time attribute group based on the obtained node sequence, and then the dispersed time attribute groups and / or concentrated time attribute groups arranged based on the node sequence can be obtained; it should be noted that, in this embodiment, the continuous arrangement can be understood as two or more current interval durations that have the same time attribute and are arranged in sequence;

[0111] Then, it can be determined whether the time attribute group at the end is a concentrated time attribute group based on the node sequence, and if so, the current duration quantity corresponding to each current interval duration in the concentrated attribute group is obtained, so as to further compare the current duration quantity with the retrieved preset duration quantity;

[0112] Finally, it can be explained that when it is determined based on the aforementioned quantity comparison that the corresponding current duration quantity is greater than the preset duration quantity, it means that more congestion troubleshooting signals are sent in a more concentrated manner at the end of the rear time period. Therefore, it can be judged that the corresponding congestion situation may occur at the end of the corresponding rear time period. At this time, in order to further improve the accuracy of the judgment of the congestion situation, a second update period with the same duration as the preset receiving period can be created, so that the preset receiving period can be extended by the corresponding period based on the second update period to complete the corresponding update of the scheduled task.

[0113] In step S103, the following contents are included:

[0114] Region recognition is performed on the high-speed acquisition image, and a high-speed region indicating a high-speed road section and a ramp region indicating a ramp road section connected to the high-speed road section located in the high-speed acquisition image are determined based on the recognition result.

[0115] For example, in this embodiment, when it is determined that the corresponding expressway section is congested, based on the above content, it can be known that the server will further control the drone to fly to the expressway section to perform corresponding image acquisition, thereby obtaining the corresponding expressway acquisition image. It should be noted that since any expressway section has a ramp section connected to the expressway section, and the ramp section is generally for vehicles to leave the expressway section, it can be understood that some of the vehicles traveling on the corresponding expressway section may expect to leave the corresponding expressway section through the ramp section, and the other part may expect to travel at high speed based on the expressway section. Therefore, when it is determined that the corresponding expressway section is congested, it can be determined that the corresponding congested section There are two reasons for the congestion: first, it is caused by each vehicle leaving the expressway section; second, it is caused by each vehicle continuing to travel on the expressway section; and because the improvement methods for different causes may be different, in order to improve the corresponding improvement effect, it is necessary to perform regional recognition on the acquired high-speed acquisition image, so as to determine the expressway area indicating the expressway section and the ramp area indicating the ramp section connected to the expressway section in the expressway acquisition image based on the recognition result, so as to further determine the cause of the congestion based on the expressway area and the ramp area in the expressway acquisition image; it can be explained that the ramp section is the section that can be used by vehicles to leave the expressway.

[0116] It should be noted that in the above method steps, the region recognition of the high-speed acquisition image can be performed by manual recognition, for example, sending the high-speed acquisition image to corresponding staff for corresponding recognition and region division.

[0117] In step S103, the following contents are included:

[0118] The high-speed area is divided into regions based on the ramp area to obtain a first section area and a second section area respectively indicating a section before the ramp and a section after the ramp, wherein the section before the ramp and the section after the ramp intersect at the ramp section.

[0119] For example, in this embodiment, it can be explained that, since all expressway sections have ramp sections connected to the expressway sections, the expressway sections can be segmented based on the ramp sections, that is, the corresponding ramp area can divide the expressway area into corresponding areas to obtain a first section area and a second section area indicating the section before the ramp and the section after the ramp, respectively, wherein the corresponding section before the ramp and the section after the ramp can intersect through the ramp section; here, the section before the ramp and the section after the ramp are relative to the section extension direction of the expressway section, the section before the ramp can be understood as the front part of the corresponding expressway section, and the section after the ramp can be understood as the rear part of the corresponding expressway section, that is, when a vehicle is traveling on the corresponding expressway section, the driving lane should travel from the section before the ramp to the section after the ramp; for example, Figure 2 As shown, Figure 2 The corresponding pre-ramp section, post-ramp section and corresponding ramp section are shown, wherein it can be seen that the pre-ramp section and post-ramp section are arranged based on the corresponding driving direction.

[0120] In step S104, the following contents are included:

[0121] A first congestion coefficient and a second congestion coefficient corresponding to a pre-ramp section and a post-ramp section respectively are obtained based on the first section area and the second section area, and a flow attribute corresponding to the ramp section is determined based on the first congestion coefficient and the second congestion coefficient.

[0122] For example, in this embodiment, based on the above content, it can be known that the corresponding causes of the congestion situation include two types: first, it is generated based on each vehicle leaving the expressway section; second, it is generated based on each vehicle continuing to travel on the expressway section; therefore, in order to determine the cause of the congestion situation, after completing the regional division of the expressway area, the first congestion coefficient and the second congestion coefficient of the corresponding pre-ramp section and post-ramp section can be respectively obtained based on the obtained first section area and the second section area, so as to further determine the flow attribute corresponding to the ramp section based on the first congestion coefficient and the second congestion coefficient; it should be noted that the flow attribute of the ramp section can include congestion attribute and unblocking attribute. When the flow attribute corresponding to the ramp section is the congestion attribute, it can be explained that the congestion situation is generated based on each vehicle leaving the expressway section; and when the flow attribute corresponding to the ramp section is the unblocking attribute, it can be explained that the congestion situation is generated based on each vehicle continuing to travel on the expressway section, so that the cause of the congestion situation can be determined.

[0123] For example, in this embodiment, the above-mentioned “obtaining a first congestion coefficient and a second congestion coefficient corresponding to a pre-ramp section and a post-ramp section respectively based on the first section area and the second section area, and determining a flow attribute corresponding to the ramp section based on the first congestion coefficient and the second congestion coefficient” may further include the following steps:

[0124] Inputting the high-speed acquisition image into a vehicle recognition model obtained after pre-training, and obtaining various vehicle elements respectively indicating different traveling vehicles in the high-speed acquisition image;

[0125] Coordinate processing is performed on the high-speed acquisition images, and each image coordinate point corresponding to any vehicle element is determined as a vehicle coordinate group, and each image coordinate point corresponding to any road section area is determined as a road section coordinate group, and coordinate comparison is performed between each vehicle coordinate group and each road section coordinate group;

[0126] In response to any vehicle coordinate group being completely included in any road segment coordinate group, determining the vehicle element corresponding to the vehicle coordinate group as being located in the road segment area corresponding to the road segment coordinate group;

[0127] In response to a portion of any vehicle coordinate group being partially included in the first road section area and another portion being partially included in the second road section area, obtaining a first road section proportion corresponding to the portion and a second road section proportion corresponding to the other portion;

[0128] Compare the first road section proportion with the second road section proportion, and determine the vehicle element corresponding to the vehicle coordinate group as being located in the road section area with the larger proportion;

[0129] Based on each vehicle element located in the first section area and the second section area respectively, a first congestion coefficient and a second congestion coefficient of a corresponding pre-ramp section and a corresponding post-ramp section are obtained, and in response to the first congestion coefficient being greater than the second congestion coefficient, a congestion coefficient difference between the corresponding first congestion coefficient and the second congestion coefficient is obtained;

[0130] In response to the congestion coefficient difference being greater than the retrieved preset congestion coefficient, the flow attribute corresponding to the ramp road segment is determined as a congestion attribute.

[0131] For example, in this embodiment, the congestion on the expressway is generally caused by the presence of a large number of moving vehicles. Therefore, the high-speed acquisition image can be input into a pre-trained vehicle recognition model to determine the vehicle elements in the high-speed acquisition image that indicate different moving vehicles.

[0132] Furthermore, in order to determine the first congestion coefficient and the second congestion coefficient corresponding to the first road section area and the second road section area, it is necessary to determine the number of vehicles in the vehicle element located in the first road section area and the number of vehicles in the vehicle element located in the second road section area. Therefore, the high-speed acquisition image can be subjected to corresponding coordinate processing to determine the image coordinate points corresponding to any vehicle element as a vehicle coordinate group, and the image coordinate points corresponding to any road section area (the first road section area or the second road section area) as a corresponding road section coordinate group, and the coordinates of each vehicle coordinate group are compared with each road section coordinate group respectively.

[0133] Here, when it is determined based on the comparison results that any vehicle coordinate group is completely included in any road section coordinate group, the vehicle element corresponding to the vehicle coordinate group can be determined to be located in the road section area corresponding to the road section coordinate group; and when it is determined based on the comparison results that a part of any vehicle coordinate group is partially included in the second road section area and the other part is partially included in the second road section area, it is necessary to obtain the first road section proportion of the corresponding part and the second road section proportion of the corresponding other part, that is, it is necessary to determine the number of coordinates of each image coordinate point constituting the vehicle element in the first road section area and the second road section area respectively; when it is determined based on the comparison results that any one proportion is larger, the vehicle element corresponding to the vehicle coordinate group can be determined to be located in the road section area with the larger corresponding proportion.

[0134] After completing the division of all vehicle elements based on the first road section area and the second road section area, the first congestion coefficient and the second congestion coefficient of the corresponding pre-ramp section and post-ramp section can be obtained based on the vehicle elements respectively located in the first road section area and the second road section area, and the corresponding difference between the first congestion coefficient and the second congestion coefficient can be further calculated, and the obtained congestion coefficient difference can be compared with the retrieved preset congestion coefficient.

[0135] It should be noted that when the congestion coefficient difference is greater than the preset congestion coefficient, it indicates that the number of vehicles in the vehicle element located in the first section area is much greater than the number of vehicles in the vehicle element located in the second section area, which can indirectly reflect that most of the vehicle elements may need to leave the expressway section from the ramp section. Therefore, the cause of the corresponding congestion situation is very likely to be based on the vehicles leaving the expressway section, and the flow attribute corresponding to the ramp section can be determined as the congestion attribute; and when the congestion coefficient difference is small, it indicates that the difference between the number of vehicles in the vehicle element located in the first section area and the number of vehicles in the vehicle element located in the second section area is small. At this time, it can indirectly reflect that most of the vehicle elements may need to continue traveling based on the expressway section. Therefore, the cause of the corresponding congestion situation is very likely to be based on the vehicles continuing to travel on the expressway section, and the flow attribute corresponding to the ramp section can be determined as the evacuation attribute.

[0136] Furthermore, in this embodiment, the above-mentioned “obtaining the first congestion coefficient and the second congestion coefficient of the corresponding pre-ramp section and post-ramp section based on each vehicle element located in the first section area and the second section area respectively” may further include the following steps:

[0137] Acquire the first number of vehicles corresponding to each vehicle element located in the first road section area, and acquire the vehicle distances of each vehicle element located in adjacent positions along the area extension direction of the first road section area to obtain each first vehicle distance;

[0138] Retrieving a preset road ahead congestion determination strategy to configure the strategy for the first road section area, and determining a first quantity congestion value corresponding to the first number of vehicles and a first spacing congestion value corresponding to the first vehicle spacing based on the preset road ahead congestion determination strategy;

[0139] Acquire the number of second vehicles corresponding to each vehicle element located in the second road section area, and acquire the vehicle distances of each vehicle element located in adjacent positions along the area extension direction of the second road section area to obtain each second vehicle distance;

[0140] Retrieving a preset back-road congestion determination strategy to configure the strategy for the second road section area, and determining a second quantity congestion value corresponding to the second number of vehicles and a second spacing congestion value corresponding to the second vehicle spacing based on the preset back-road congestion determination strategy;

[0141] The quantity congestion value and the spacing congestion value corresponding to the same road section are multiplied by the retrieved quantity weight value and the spacing weight value to obtain the first congestion coefficient and the second congestion coefficient of the corresponding section before the ramp and the section after the ramp.

[0142] For example, in this embodiment, since the expressway section may have different driving lanes, the corresponding driving vehicles that need to leave the expressway section (that is, the vehicle elements located in the first section area) generally need to drive to the lane area close to the ramp section in advance to facilitate driving. Therefore, it can be known that, for the first section area, the influence of the vehicle elements corresponding to each lane area on determining the flow attributes of the ramp section is different. For example, the vehicle elements in the lane area closer to the ramp section should have a higher influence on determining the flow attributes of the ramp section, and the vehicle elements in the lane area closer to the ramp section should have a higher influence on determining the flow attributes of the ramp section. The influence of the flow attributes of the road section should be lower; and for the second road section area, since all vehicle elements located in the second road section area are corresponding to continuing to travel in the expressway section, the influence of the vehicle elements existing in each lane area corresponding to the second road section area on determining the flow attributes of the ramp road section should be the same. Based on this, for the first road section area and the second road section area, the corresponding congestion coefficient can be obtained based on different congestion determination strategies to improve the accuracy of determining the congestion coefficients of different road section areas, thereby further improving the accuracy of determining the flow attributes of the ramp road section in the subsequent process.

[0143] Here, the specific implementation method for obtaining the coefficients can be described as follows:

[0144] First, it can be explained that in a highway section with congestion, the number of vehicles traveling should be relatively large, and the larger the number of vehicles, the more serious the corresponding congestion. Therefore, since the area division for each vehicle element has been completed in the aforementioned process, the first number of vehicles corresponding to each vehicle element located in the first section area and the second number of vehicles corresponding to each vehicle element located in the second section area can be further obtained;

[0145] Next, it can be explained that in a highway section without congestion, based on the high-speed characteristics of the highway section, adjacent vehicle elements generally maintain a large distance between each other to ensure a safe distance between each other, while in a highway section with congestion, adjacent vehicle elements will have a smaller distance between each other, and the more serious the congestion, the smaller the corresponding distance should be. Therefore, the distance between each vehicle element in the adjacent position can be obtained respectively along the regional extension direction of the first section area and the regional extension direction of the second section area, so as to obtain each first vehicle distance corresponding to the first section area and each second vehicle distance corresponding to the second section area;

[0146] Next, after the number of vehicles and the distances between vehicles corresponding to the same road section area are completed, the pre-stored preset front road congestion determination strategy and the preset rear road congestion determination strategy can be retrieved from the server. On the one hand, a first number congestion value corresponding to the first number of vehicles and a first distance congestion value corresponding to the first vehicle distances can be determined based on the preset front road congestion determination strategy. On the other hand, a second number congestion value corresponding to the second number of vehicles and a second distance congestion value corresponding to the second vehicle distances can be determined based on the preset front road congestion determination strategy.

[0147] Finally, by multiplying the quantity congestion value and the spacing congestion value corresponding to the same road section area with the retrieved quantity weight value and the spacing weight value respectively, the first congestion coefficient and the second congestion coefficient of the corresponding section before the ramp and the section after the ramp can be obtained.

[0148] Furthermore, in this embodiment, the above-mentioned “calling a preset front road congestion determination strategy to perform strategy configuration on the first road section area, and determining a first quantity congestion value corresponding to the first number of vehicles and a first spacing congestion value corresponding to each first vehicle spacing based on the preset front road congestion determination strategy” may also include the following steps:

[0149] Acquire lane areas respectively included in the first road section area, and sort lanes of the lane areas from near to far based on the ramp area to obtain a lane sequence;

[0150] Allocating weights from large to small to each lane region in the lane sequence to obtain lane weights corresponding to each lane region;

[0151] Determine the image coordinate points corresponding to the same lane area as a lane coordinate group, and compare the coordinates of each vehicle coordinate group located in the first road section area with each lane coordinate group;

[0152] In response to any vehicle coordinate group being completely included in any lane coordinate group, determining the vehicle element corresponding to the vehicle coordinate group as being located in the lane area corresponding to the lane coordinate group;

[0153] In response to a part of any vehicle coordinate group being partially included in any lane coordinate group and another part being partially included in another lane coordinate group, determining the image coordinate points located in the vehicle coordinate group with the longest corresponding distance along the area extension direction of the first road section area as the lane change coordinate point, and determining the vehicle element corresponding to the vehicle coordinate group as the lane area corresponding to the lane coordinate group including the lane change coordinate point;

[0154] Based on the first number of vehicles, the number of each vehicle element located in the same lane area is obtained to obtain the number of each vehicle sub-number corresponding to each different lane area, and the number of vehicle sub-numbers corresponding to the same lane area is multiplied by the lane weight to obtain the number coefficients of each lane corresponding to each lane area;

[0155] The coefficients of the number of lanes are summed up to obtain a first number congestion value corresponding to a first number of vehicles;

[0156] Determine each first vehicle distance corresponding to the same lane area as a first distance group, and in response to any first vehicle distance in the first distance group being greater than or equal to the retrieved preset congestion distance, screen out the first vehicle distance from the first distance group to obtain updated first distance groups;

[0157] Calculate the mean of each first vehicle spacing in the same first spacing group to obtain the mean of each first spacing corresponding to each lane area, and multiply the mean of the first spacing corresponding to the same lane area by the lane weight to obtain the congestion value of each spacing corresponding to each lane area;

[0158] The lane spacing coefficients are summed up and calculated to obtain first spacing congestion values ​​corresponding to first vehicle spacings.

[0159] It should be noted that, based on the above content, it can be known that, for the first road section area, the influence degree of the vehicle elements corresponding to each lane area on determining the flow attributes of the ramp road section is different. For example, the influence degree of the vehicle elements existing in the lane area closer to the ramp road section on determining the flow attributes of the ramp road section should be higher, and the influence degree of the vehicle elements existing in the lane area closer to the ramp road section on determining the flow attributes of the ramp road section should be lower; therefore, when determining the first quantity congestion value and the first spacing congestion value for the first road section area, it is necessary to first obtain the lane areas respectively included in the corresponding first road section area, and sort the lane areas from near to far based on the ramp area to obtain the corresponding lane sequence, and then the weight can be allocated from large to small according to the lane areas in the lane sequence to obtain the lane weights corresponding to each lane area.

[0160] Here, the lane weights corresponding to different lane areas can be set manually. For example, when the lane area includes corresponding areas A, B and C, and area A is close to the ramp area, area B is between area C and A, and area C is far away from the ramp area, then the lane weight corresponding to area A can be 0.6, the lane weight corresponding to area B can be 0.3, and the lane weight corresponding to area C can be 0.1.

[0161] After the determination of the lane weights corresponding to different lane areas is completed, it is necessary to further determine the lane elements included in each lane area. Here, similar to the above scheme, the determination can also be made based on coordinate comparison, that is, by determining the image coordinate points corresponding to the same lane area as the corresponding lane coordinate group based on the coordinateized high-speed acquisition image, so that the coordinate comparison can be made based on the vehicle coordinate groups and the lane coordinate groups located in the first road section area; wherein, when any vehicle coordinate group is completely included by any lane coordinate group, the vehicle element corresponding to the vehicle coordinate group can be determined as the lane area corresponding to the lane coordinate group; and when a part of any vehicle coordinate group is partially included by any lane coordinate group and the other part is partially included by other lane coordinate groups, it means that the corresponding vehicle element may be in a lane-changing driving state. At this time, the image coordinate points located in the vehicle coordinate group with the farthest distance can be determined as the lane-changing coordinate point along the area extension direction of the first road section area, so that the lane area corresponding to the lane coordinate group including the lane-changing coordinate point is determined as the lane area where the vehicle element is located;

[0162] After completing the acquisition of each vehicle element included in each lane area, the number of each vehicle element located in the same lane area can be acquired based on the corresponding first vehicle number, and then the number of lane sub-numbers corresponding to each different lane area can be obtained, so that the corresponding number product results can be obtained by multiplying the number of vehicle sub-numbers corresponding to the same lane area with the lane weight. Here, since the corresponding congestion coefficient needs to be obtained based on the obtained number congestion value and the spacing congestion value later, it is necessary to perform corresponding normalization processing on each number product result in advance, that is, to further multiply the obtained number product results with the retrieved number normalization value, and perform sum calculation based on the obtained lane number coefficients, and then the first number congestion value corresponding to the first number of vehicles can be obtained;

[0163] At the same time, based on the first vehicle spacings obtained above, the corresponding lane area can be determined as a first spacing group, and the first vehicle spacings in each first spacing group are respectively compared with the retrieved preset congestion spacings, wherein, when any first vehicle spacing is greater than or equal to the retrieved preset congestion spacing, it indicates that the first vehicle spacing is at a safe distance, and at this time, the first vehicle spacing can be screened out from the first spacing group to obtain updated first spacing groups;

[0164] Furthermore, by calculating the mean of each first vehicle spacing in the same first spacing group, the mean of each first spacing corresponding to each lane area can be obtained, and the first spacing mean corresponding to the same lane area is multiplied by the lane weight to obtain the corresponding spacing product results. Here, since it is necessary to obtain the corresponding congestion coefficient based on the obtained quantity congestion value and spacing congestion value in the future, it is necessary to perform corresponding normalization processing on the spacing product results in advance, that is, to further multiply the obtained spacing product results with the retrieved spacing normalization value, and perform sum calculation based on the obtained lane spacing coefficients, so as to obtain the first spacing congestion value corresponding to the first vehicle spacing.

[0165] In addition, in this embodiment, the above-mentioned "calling the preset back road congestion determination strategy to configure the strategy for the second road section area, and determining the second number congestion value corresponding to the second number of vehicles and the second spacing congestion value corresponding to the second vehicle spacing based on the preset back road congestion determination strategy" may also include the following steps:

[0166] Acquire a second number congestion value based on the second number of vehicles, and compare the distance between each second vehicle with the retrieved preset congestion distance;

[0167] In response to any second vehicle distance being smaller than the preset congestion distance, the second vehicle distance is aggregated into a second distance group to obtain each second vehicle distance in the second distance group;

[0168] An average value is calculated for each second vehicle interval in the second interval group, and a second interval congestion value corresponding to each second vehicle interval is obtained based on the obtained second interval average value.

[0169] It should be noted that, based on the above content, for the second road section area, since all vehicle elements located in the second road section area correspond to continuing to travel in the expressway section, the vehicle elements respectively existing in each lane area corresponding to the second road section area should have the same degree of influence on determining the flow attributes of the ramp section. Therefore, when determining the second quantity congestion value and the second spacing congestion value for the second road section area, there is no need to consider the different degrees of influence corresponding to different lane areas.

[0170] That is, in this embodiment, the acquisition of the second quantity congestion value can be directly based on the second vehicle quantity, for example, by multiplying the second vehicle quantity by the normalized quantity value retrieved above to obtain the corresponding second quantity congestion value; at the same time, the second vehicle spacings of the corresponding second road section area obtained above can be compared with the preset congestion spacing retrieved above, and when any second vehicle spacing is smaller than the preset congestion spacing, the second vehicle spacing is aggregated into the corresponding second spacing group to obtain the second vehicle spacings in the second spacing group; further, by calculating the mean of the second vehicle spacings in the second spacing group, the obtained second spacing mean is multiplied by the spacing normalization value retrieved above to obtain the second spacing congestion value corresponding to each second vehicle spacing.

[0171] In step S105, the following contents are included:

[0172] In response to the traffic attribute being a congestion attribute, a vehicle light control device closest to the ramp road segment is obtained along a ramp extension direction corresponding to the ramp road segment, and the vehicle light control device is controlled to perform a light control operation for the corresponding vehicle passing.

[0173] For example, in this embodiment, since the ramp road section is used for vehicles to leave the expressway section, and there is generally a corresponding vehicle light control device along the extension direction of the ramp road section to control the driving and stopping of vehicles, therefore, when the flow attribute of the ramp road section is a congestion attribute, the vehicle light control device closest to the corresponding distance of the ramp road section can be obtained along the ramp extension direction corresponding to the ramp road section, and further by controlling the vehicle light control device to perform the light control operation of the corresponding vehicle, the congestion situation of the expressway section can be relieved, which helps to avoid further deterioration of the congestion.

[0174] Furthermore, in this embodiment, the above-mentioned “controlling the vehicle light control device to perform the light control operation corresponding to the vehicle passing” may also include the following steps:

[0175] Retrieving a preset light control determination table, wherein the preset light control determination table includes different congestion coefficient intervals and light control durations corresponding to the different congestion coefficient intervals;

[0176] Traversing the preset light control determination table, obtaining a congestion coefficient interval including the congestion coefficient difference, and determining a corresponding light control duration based on the congestion coefficient interval;

[0177] The vehicle light control device is controlled to perform a light control operation corresponding to the light control time period for the vehicle to pass.

[0178] For example, in this embodiment, based on the above-mentioned congestion attribute acquisition process, it can be known that the corresponding congestion coefficient difference is greater than the retrieved preset congestion coefficient, then the flow attribute of the corresponding ramp road section is the congestion attribute, that is, the larger the congestion coefficient difference is, the more serious the congestion situation of the corresponding highway section is. Therefore, the proportional relationship between the congestion coefficient difference and the congestion situation can be used to pre-create the corresponding preset light control determination table and store it in the server accordingly, wherein the preset light control determination table includes different congestion coefficient intervals and light control durations corresponding to different congestion coefficient intervals; by traversing the preset light control determination table, the congestion coefficient interval including the congestion coefficient difference can be obtained, and then the light control duration corresponding to the vehicle light control device is determined based on the congestion coefficient interval, and the vehicle light control is controlled. The device performs a light control operation corresponding to the continuous light control duration of the vehicle passing; for example, when the corresponding congestion coefficient difference is 5, the corresponding preset light control determination table includes different congestion coefficient intervals including interval D, interval E and interval F, wherein interval D is [0-2], interval E is [3-5], interval F is [6-8], and the light control durations corresponding to interval D, interval E and interval F are 50s, 70s and 90s respectively. Through traversal, it can be known that the corresponding congestion coefficient difference is included in interval E. Therefore, the light control duration corresponding to the vehicle light control device is also 70s. Therefore, it is necessary to control the vehicle light control device to perform the light control operation corresponding to the vehicle passing for 70s to complete the relief of the congestion situation. Here, the light control operation can be specifically to keep displaying the green light.

[0179] In summary, the solution of the present invention has brought significant beneficial effects: First, the drone quickly responds to the congestion inspection signal of the highway section, realizing the real-time image acquisition of the target section, greatly shortening the monitoring response time and improving the timeliness of traffic management. The high-altitude perspective and flexible maneuverability of the drone make the collected image information more comprehensive and accurate, providing a solid foundation for subsequent analysis and decision-making.

[0180] Secondly, by performing image recognition on high-speed images, the system can automatically distinguish between high-speed areas and ramp areas, and then make refined regional divisions of high-speed sections. This division not only helps to clarify the specific location of congestion, but also provides a clear definition range for subsequent congestion coefficient calculations, making the analysis results more accurate and reliable.

[0181] Furthermore, based on the first congestion coefficient and the second congestion coefficient calculated for the first section area and the second section area respectively, the system can accurately determine the flow attributes of the ramp section, thereby timely identifying potential congestion risks. This function is of great significance for early warning and timely diversion of traffic flow, which helps to avoid further deterioration of congestion.

[0182] Finally, when the system detects that the ramp section is in a congested state, it can quickly locate and control the nearest vehicle lighting control device to perform the corresponding lighting control operation. This intelligent lighting control adjustment can not only effectively alleviate the traffic pressure on the ramp section, but also improve the traffic efficiency of the entire highway network, providing drivers with a smoother travel experience. In summary, this method has shown significant beneficial effects in improving traffic management efficiency, optimizing road capacity, and improving public travel satisfaction.

[0183] Another embodiment of the present invention provides a high-altitude collaborative automatic control system. Figure 3 As the corresponding system block diagram, the system includes:

[0184] An image acquisition module is configured to, in response to receiving a congestion detection signal corresponding to any expressway section, control the drone to fly to the expressway section to acquire images and obtain a high-speed acquisition image;

[0185] a region recognition module configured to perform region recognition on the high-speed acquisition image, and determine, based on the recognition result, a high-speed region indicating a high-speed road section and a ramp region indicating a ramp road section having a connection relationship with the high-speed road section in the high-speed acquisition image;

[0186] A region division module is configured to divide the highway region into regions based on the ramp region to obtain a first section region and a second section region respectively indicating a pre-ramp section and a post-ramp section, wherein the pre-ramp section and the post-ramp section intersect at the ramp section;

[0187] an attribute determination module, configured to obtain a first congestion coefficient and a second congestion coefficient corresponding to a pre-ramp section and a post-ramp section respectively based on the first section area and the second section area, and determine a flow attribute corresponding to the ramp section based on the first congestion coefficient and the second congestion coefficient;

[0188] The light control operation module is configured to obtain the vehicle light control device closest to the ramp road section along the ramp extension direction corresponding to the ramp road section in response to the traffic attribute being a congestion attribute, and control the vehicle light control device to perform the light control operation for the corresponding vehicle passage.

[0189] In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the examples of the present invention. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to implement the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the preferred embodiment of the present invention.

[0190] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0191] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0192] Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in the devices described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or may be divided into multiple submodules.

[0193] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore may be divided into a plurality of submodules or subunits or subcomponents.

[0194] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present invention and to form different embodiments.

[0195] In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the functions. Therefore, a processor with necessary instructions for implementing the method or method elements forms a device for implementing the method or method elements. In addition, the elements described herein of the device embodiments are examples of devices for implementing the functions performed by the elements for the purpose of implementing the invention.

[0196] As used herein, unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved, and is not intended to imply that the objects so described must have a given order in time, space, order, or in any other manner.

[0197] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is primarily selected for readability and instructional purposes, rather than for explaining or limiting the subject matter of the present invention.

Claims

1. A highland collaborative automatic control method, characterized in that: include: In response to the congestion checking signal of the corresponding expressway section sent by the vehicle end, the vehicle position information is obtained and compared with the section position information corresponding to the expressway section; Based on the comparison result, it is determined that the vehicle is located on a highway section, and the congestion reporting condition is met, and based on the timed task established at the sending time node, the number of signals of all congestion troubleshooting signals that meet the congestion reporting condition within the preset receiving period is determined; In response to the number of signals being less than or equal to the preset number of signals, segmenting the preset receiving period based on the half value of the period to obtain a front period and a rear period; Determine the front quantity and rear quantity of each congestion troubleshooting signal that meets the congestion reporting condition and is received in the front time period and the rear time period, and compare the rear quantity with a half value of the preset signal quantity; In response to the rear quantity being greater than the half value of the quantity, creating a first update period having the same duration as the rear period, and extending the preset receiving period; In response to the number of signals being greater than a preset number of signals, the drone is controlled to fly to a high-speed section for collection, thereby obtaining a high-speed collection image; Recognize the high-speed collected image to determine the high-speed area indicating the high-speed road section and the ramp area indicating the ramp road section; The highway area is divided based on the ramp area to obtain a first section area and a second section area respectively indicating a section before the ramp and a section after the ramp, wherein the section before the ramp and the section after the ramp intersect at the ramp section; Based on the first road section area and the second road section area, a first congestion coefficient and a second congestion coefficient of a corresponding pre-ramp section and a post-ramp section are obtained, and a flow attribute is determined; In response to the traffic attribute being a congestion attribute, a vehicle light control device closest to the corresponding ramp road segment is obtained along the ramp extension direction corresponding to the ramp road segment, and a light control operation for the corresponding vehicle to pass is controlled and executed.

2. The highland cooperative automatic control method according to claim 1 is characterized in that: The method further comprises: In response to the rear number being less than or equal to half the number value, acquiring each sending time node of each congestion removal signal corresponding to the rear number, and determining each sending time node as an interval determination group; Adding the start time node and the end time node corresponding to the latter time period to the interval determination group respectively, and sorting the time nodes in the interval determination group based on the time sequence to obtain a node sequence; Based on the obtained node sequence, the difference between each time node at adjacent positions is calculated to obtain the duration of each current interval; Calculate the ratio of the preset signal quantity to the preset receiving period, and compare the current interval duration with the obtained reference interval duration; In response to any current interval duration being less than or equal to the reference interval duration, determining the current interval duration as having a concentrated time attribute; In response to any current interval duration being greater than the reference interval duration, determining the current interval duration as having a dispersed time attribute; Determining the durations of the current intervals that are continuously arranged and correspond to the same time attribute as the same time attribute group based on the node sequence, and obtaining the dispersed time attribute groups and / or the concentrated time attribute groups arranged based on the node sequence; In response to determining that the time attribute group at the end is a concentrated time attribute group based on the node sequence, obtaining the number of current durations corresponding to each current interval duration in the concentrated time attribute group, and comparing the number of current durations with the retrieved preset duration number; In response to the current duration being greater than the preset duration, a second update period having the same duration as the preset receiving period is created, and the preset receiving period is extended based on the second update period to update the scheduled task.

3. The highland cooperative automatic control method according to claim 1, characterized in that: Based on the first road section area and the second road section area, a first congestion coefficient and a second congestion coefficient of a corresponding pre-ramp section and a post-ramp section are obtained, and a flow attribute is determined, including: Inputting the high-speed acquisition image into a vehicle recognition model obtained after pre-training, and obtaining various vehicle elements respectively indicating different traveling vehicles in the high-speed acquisition image; Coordinate processing is performed on the high-speed acquisition images, and each image coordinate point corresponding to any vehicle element is determined as a vehicle coordinate group, and each image coordinate point corresponding to any road section area is determined as a road section coordinate group, and coordinate comparison is performed between each vehicle coordinate group and each road section coordinate group; In response to any vehicle coordinate group being completely included in any road segment coordinate group, determining the vehicle element corresponding to the vehicle coordinate group as being located in the road segment area corresponding to the road segment coordinate group; In response to a portion of any vehicle coordinate group being partially included in the first road section area and another portion being partially included in the second road section area, obtaining a first road section proportion corresponding to the portion and a second road section proportion corresponding to the other portion; Compare the first road section proportion with the second road section proportion, and determine the vehicle element corresponding to the vehicle coordinate group as being located in the road section area with the larger proportion; Based on each vehicle element located in the first section area and the second section area respectively, a first congestion coefficient and a second congestion coefficient of a corresponding pre-ramp section and a corresponding post-ramp section are obtained, and in response to the first congestion coefficient being greater than the second congestion coefficient, a congestion coefficient difference between the corresponding first congestion coefficient and the second congestion coefficient is obtained; In response to the congestion coefficient difference being greater than the retrieved preset congestion coefficient, the flow attribute corresponding to the ramp road segment is determined as a congestion attribute.

4. The highland cooperative automatic control method according to claim 3 is characterized in that: Obtaining a first congestion coefficient and a second congestion coefficient of a corresponding pre-ramp section and post-ramp section based on each vehicle element located in the first section area and the second section area, including: Acquire the first number of vehicles corresponding to each vehicle element located in the first road section area, and acquire the vehicle distances of each vehicle element located in adjacent positions along the area extension direction of the first road section area to obtain each first vehicle distance; Retrieving a preset road ahead congestion determination strategy to configure the strategy for the first road section area, and determining a first quantity congestion value corresponding to the first number of vehicles and a first spacing congestion value corresponding to the first vehicle spacing based on the preset road ahead congestion determination strategy; Acquire the number of second vehicles corresponding to each vehicle element located in the second road section area, and acquire the vehicle distances of each vehicle element located in adjacent positions along the area extension direction of the second road section area to obtain each second vehicle distance; Retrieving a preset back-road congestion determination strategy to configure the strategy for the second road section area, and determining a second quantity congestion value corresponding to the second number of vehicles and a second spacing congestion value corresponding to the second vehicle spacing based on the preset back-road congestion determination strategy; The quantity congestion value and the spacing congestion value corresponding to the same road section are multiplied by the retrieved quantity weight value and the spacing weight value to obtain the first congestion coefficient and the second congestion coefficient of the corresponding section before the ramp and the section after the ramp.

5. The highland cooperative automatic control method according to claim 4 is characterized in that: Retrieving a preset road ahead congestion determination strategy to configure the strategy for the first road section area, and determining a first quantity congestion value corresponding to the first number of vehicles and a first spacing congestion value corresponding to the first vehicle spacing based on the preset road ahead congestion determination strategy, including: Acquire lane areas respectively included in the first road section area, and sort lanes of the lane areas from near to far based on the ramp area to obtain a lane sequence; Allocating weights from large to small to each lane region in the lane sequence to obtain lane weights corresponding to each lane region; Determine the image coordinate points corresponding to the same lane area as a lane coordinate group, and compare the coordinates of each vehicle coordinate group located in the first road section area with each lane coordinate group; In response to any vehicle coordinate group being completely included in any lane coordinate group, determining the vehicle element corresponding to the vehicle coordinate group as being located in the lane area corresponding to the lane coordinate group; In response to a part of any vehicle coordinate group being partially included in any lane coordinate group and another part being partially included in another lane coordinate group, determining the image coordinate points located in the vehicle coordinate group with the longest corresponding distance along the area extension direction of the first road section area as the lane change coordinate point, and determining the vehicle element corresponding to the vehicle coordinate group as the lane area corresponding to the lane coordinate group including the lane change coordinate point; Based on the first number of vehicles, the number of each vehicle element located in the same lane area is obtained to obtain the number of each vehicle sub-number corresponding to each different lane area, and the number of vehicle sub-numbers corresponding to the same lane area is multiplied by the lane weight to obtain the number coefficients of each lane corresponding to each lane area; The coefficients of the number of lanes are summed up to obtain a first number congestion value corresponding to a first number of vehicles; Determine each first vehicle distance corresponding to the same lane area as a first distance group, and in response to any first vehicle distance in the first distance group being greater than or equal to the retrieved preset congestion distance, screen out the first vehicle distance from the first distance group to obtain updated first distance groups; Calculate the mean of each first vehicle spacing in the same first spacing group to obtain the mean of each first spacing corresponding to each lane area, and multiply the mean of the first spacing corresponding to the same lane area by the lane weight to obtain the lane spacing coefficient corresponding to each lane area; The lane spacing coefficients are summed up and calculated to obtain first spacing congestion values ​​corresponding to first vehicle spacings.

6. The highland cooperative automatic control method according to claim 4 is characterized in that: Retrieving a preset back road congestion determination strategy to configure the strategy for the second road section area, and determining a second quantity congestion value corresponding to the second number of vehicles and a second spacing congestion value corresponding to the second vehicle spacing based on the preset back road congestion determination strategy, including: Acquire a second number congestion value based on the second number of vehicles, and compare the distance between each second vehicle with the retrieved preset congestion distance; In response to any second vehicle distance being smaller than the preset congestion distance, the second vehicle distance is aggregated into a second distance group to obtain each second vehicle distance in the second distance group; An average value is calculated for each second vehicle interval in the second interval group, and a second interval congestion value corresponding to each second vehicle interval is obtained based on the obtained second interval average value.

7. The highland cooperative automatic control method according to claim 3 is characterized in that: Control and execute the lighting control operations corresponding to the vehicle passing, including: Retrieving a preset light control determination table, wherein the preset light control determination table includes different congestion coefficient intervals and light control durations corresponding to the different congestion coefficient intervals; Traversing the preset light control determination table, obtaining a congestion coefficient interval including the congestion coefficient difference, and determining a corresponding light control duration based on the congestion coefficient interval; The vehicle light control device is controlled to perform a light control operation corresponding to the light control time period for the vehicle to pass.

8. A highland coordinated automatic control system, characterized in that: include: An image acquisition module is configured to obtain vehicle position information in response to a congestion detection signal of a corresponding highway section sent by a vehicle terminal, and compare the vehicle position information with the section position information corresponding to the highway section; Based on the comparison result, it is determined that the vehicle is located on a highway section, and the congestion reporting condition is met, and based on the timed task established at the sending time node, the number of signals of all congestion troubleshooting signals that meet the congestion reporting condition within the preset receiving period is determined; In response to the number of signals being less than or equal to the preset number of signals, segmenting the preset receiving period based on the half value of the period to obtain a front period and a rear period; Determine the front quantity and rear quantity of each congestion troubleshooting signal that meets the congestion reporting condition and is received in the front time period and the rear time period, and compare the rear quantity with a half value of the preset signal quantity; In response to the rear quantity being greater than the half value of the quantity, creating a first update period having the same duration as the rear period, and extending the preset receiving period; In response to the number of signals being greater than a preset number of signals, the drone is controlled to fly to a high-speed section for collection, thereby obtaining a high-speed collection image; A region recognition module is configured to recognize the high-speed acquisition image and determine a high-speed region indicating a high-speed road section and a ramp region indicating a ramp road section; The area division module is configured to divide the highway area based on the ramp area to obtain a first section area and a second section area respectively indicating a section before the ramp and a section after the ramp, wherein the section before the ramp and the section after the ramp intersect at the ramp section; An attribute determination module is configured to obtain a first congestion coefficient and a second congestion coefficient of a corresponding pre-ramp section and a post-ramp section based on the first section area and the second section area, and determine a flow attribute; The light control operation module is configured to obtain the vehicle light control device closest to the corresponding ramp road section along the ramp extension direction corresponding to the ramp road section in response to the traffic attribute being a congestion attribute, and control the execution of the light control operation for the corresponding vehicle to pass.

Images