Emergency traffic scheduling management method and system based on unmanned aerial vehicle monitoring

Abstract

The invention discloses an emergency traffic scheduling management method and system based on unmanned aerial vehicle monitoring, and relates to the technical field of traffic management control. Comprising the steps of determining an emergency traffic position in an urban main road area based on traffic image data, and planning driving data of a target object in the urban main road area according to the emergency traffic position. According to the invention, the unmanned aerial vehicle collects the image data of different positions of the target object in the urban main road area, and combines the time interval between the image data of different positions, so that the unmanned aerial vehicle positions the position of the target object; according to the invention, an instruction channel can be connected between a public transport communication device interacting with a target object and an unmanned aerial vehicle, so that the unmanned aerial vehicle can send a regulation instruction to the public transport communication device in stages based on the position of the target object and the position between the unmanned aerial vehicles, and rapid response and accurate scheduling of an emergency traffic condition are realized. And the traffic scheduling efficiency is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management and control, and in particular to an emergency traffic dispatch management method and system based on unmanned aerial vehicle monitoring. Background Art

[0002] In the field of traffic management and control, the use of drones to monitor and record traffic conditions in real time can quickly reach the scene of an accident or congestion, providing the dispatch center with first-hand on-site conditions. It can also help managers accurately judge traffic conditions and develop more precise dispatch plans.

[0003] After searching, the Chinese invention patent with publication number "CN118486198A" discloses "UAV traffic control method, equipment and medium based on regional division". This application can confirm which locations are working areas that can achieve work goals based on the analysis of point cloud data of target buildings, and set warning areas outside the working areas. Through reasonable regional division, the driving range of working drones is restricted, which facilitates traffic control of drones in various areas. Compared with manual control methods, it does not rely on the operator's operating level, making traffic control more efficient and accurate, and increasing the level of automation control of equipment.

[0004] In addition, the Chinese invention patent with publication number "CN119207087A" discloses "a method, device, electronic device and storage medium for identifying highway traffic jams". The application uses real-time drone video as a data source, identifies and analyzes the vehicles in the data source, and calculates the distance relationship between adjacent vehicles to determine whether a traffic jam has occurred. Compared with traditional methods that only rely on traffic flow or average speed, this method is closer to the physical characteristics of actual traffic jams, and can therefore more accurately detect actual traffic jams and reduce the occurrence of false alarms and missed alarms.

[0005] When an emergency traffic incident occurs in a city's main road area, the above two disclosed patents and similar patent methods can usually only identify relatively stationary targets in actual applications, resulting in the traffic control instructions based on these stationary targets being unable to respond to real-time changes in traffic conditions in a timely manner, thereby affecting the efficiency of traffic control. Summary of the invention

[0006] The purpose of the present invention is to provide an emergency traffic dispatch management method and system based on drone monitoring to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] In the first aspect, an emergency traffic dispatch management method based on drone monitoring is proposed, including: a number of drones set up over the urban trunk road area, the drones are used to obtain traffic image data and transmit control instructions to public transportation communication equipment in the urban trunk road area;

[0009] Determine the location of emergency traffic in the city's main road area based on traffic image data;

[0010] According to the emergency traffic location, plan the driving data of the target object in the urban main road area;

[0011] Selecting a marked drone corresponding to the driving data from among the multiple drones;

[0012] Collecting primary traffic image data, which is acquired by a marking drone located at the starting node in the driving data to record the traffic conditions of the target object at the starting node in the driving data;

[0013] Collecting secondary traffic image data, which is acquired by a marking drone located at any node in the driving data to record the traffic conditions of the target object at any node in the driving data;

[0014] calculating the time interval between the primary traffic image data and the secondary traffic image data;

[0015] The marking drone sends a control instruction to the corresponding public transportation communication equipment based on the time interval, the primary traffic image data and the secondary traffic image data;

[0016] Public transportation communication equipment adjusts traffic flow in the city's main road areas based on control instructions.

[0017] As a further preferred embodiment of the present technical solution, the method for determining the emergency traffic location includes:

[0018] An initial image segment is selected, where the initial image segment is any static image frame in the traffic image data collected by a single UAV;

[0019] Annotate the moving feature points in the initial image clips, including vehicles and pedestrians;

[0020] Taking the initial image segment as a benchmark, extract multiple static image segments of traffic image data at the same location and different time points;

[0021] Using the initial image segment as a template, multiple frames of static image segments are superimposed to form a discriminative image;

[0022] Determine a discrimination target in the discrimination image, where the discrimination target is any continuously moving feature point in the discrimination image;

[0023] Compare the positions of the discrimination target at different time points in the discrimination image to obtain the movement attribute data of the discrimination target;

[0024] Based on the mobile attribute data of the identified target and combined with traffic rules, it is evaluated whether there is an emergency traffic situation in the traffic image data collected by a single drone;

[0025] Based on the evaluation results, the moving range of the discrimination target is marked as an emergency traffic location.

[0026] As a further preferred embodiment of the present technical solution, a method for evaluating whether there is an emergency traffic situation in the traffic image data collected by a single drone includes:

[0027] According to traffic rules, the threshold data in the urban main road area is extracted;

[0028] Mapping the moving attribute data and threshold data of the target to be judged into a two-dimensional coordinate system, wherein the horizontal axis of the two-dimensional coordinate system represents the moving speed of the target, and the vertical axis of the two-dimensional coordinate system represents the moving direction of the target;

[0029] In a two-dimensional coordinate system, normal moving objects and emergency moving objects are defined based on threshold data;

[0030] Based on the position evaluation of the movement attribute data of the target in the two-dimensional coordinate system, it is determined whether the target has an emergency traffic situation.

[0031] As a further preferred embodiment of the present technical solution, the planning method of driving data includes:

[0032] The drone retrieves the traffic image data covering the area of ​​emergency traffic location and uploads it to the map system of the city's main roads;

[0033] The map system of the city's main roads forms a terminal node based on the area of ​​the emergency traffic location, and the terminal node is used to mark the end point of the driving data;

[0034] Based on the attribute type of the target object, the drone retrieves the starting node of the target object in the urban main road area. The starting node is used to identify the starting point of the driving data;

[0035] Based on the road attributes of the starting node, the ending node and the urban main roads, a series of basic data sequences are constructed;

[0036] A series of basic data sequences are used to filter out corresponding driving data according to the attributes of emergency traffic locations;

[0037] The drone transmits the filtered driving data to the target object to guide the target object to navigate according to the driving data.

[0038] As a further preferred embodiment of the present technical solution, the attributes of the emergency traffic location include: dynamic attributes and static attributes;

[0039] A series of basic data sequences The method of filtering driving data based on the dynamic attributes of emergency traffic locations includes:

[0040] Based on the dynamic attributes of emergency traffic locations in urban trunk roads, the movement trend of emergency traffic locations in traffic image data is obtained;

[0041] According to the movement trend, a driving data subset matching the movement trend is selected from the basic data sequence, and the driving data subset is used to represent the driving data consistent with the driving direction of the target object;

[0042] The UAV identifies the driving data in the driving data subset that is consistent with the driving direction of the target object as the optimal driving data.

[0043] As a further preferred embodiment of the present technical solution, a method for filtering travel data based on static attributes of emergency traffic locations in a series of basic data sequences includes:

[0044] Based on the static attributes of the emergency traffic location in the urban trunk road area, the distribution location of public transportation communication equipment in the emergency traffic location in the urban trunk road area in the traffic image data is obtained;

[0045] Based on the distribution locations of public transportation communication equipment, a travel data subset matching the distribution locations of public transportation communication equipment is selected from the basic data sequence, where the travel data subset is the travel data corresponding to the public transportation communication equipment closest to the target object;

[0046] The UAV takes the driving data closest to the target object in the driving data subset as the optimal driving data.

[0047] As a further preferred embodiment of the present technical solution, the method for selecting the marked drone includes:

[0048] Based on the driving data of target objects in the urban main road area, several drones are selected as candidate drones;

[0049] Calculate the relative distance between the candidate UAV and the target object, as well as the angle between the candidate UAV and the target object’s travel direction, and conduct quantitative analysis;

[0050] Based on the quantitative analysis results, the priority evaluation rules of candidate drones are constructed. The numerical values ​​assigned by the priority evaluation rules are negatively correlated with the priority levels.

[0051] The candidate drone with the highest priority is selected as the marked drone.

[0052] As a further preferred embodiment of the present technical solution, the method of marking the drone to send a control instruction to the public transportation communication equipment includes:

[0053] According to the location information of the target object in the primary traffic image data and the secondary image data, the corresponding two marked drone serial numbers are extracted;

[0054] Match the extracted two marked drone serial numbers with the location information of the target object in the driving data, and build a location matching relationship;

[0055] Based on the position matching relationship, locate the target position of the target object in the driving data set;

[0056] According to the distance between the target location and the two marked drones, the command channels between the marked drones and the public transportation communication devices that have interacted with the target objects in the driving data are activated in order from near to far;

[0057] According to the time interval, the two tagged drones send control instructions to the public transportation communication equipment in stages.

[0058] Secondly, in order to improve the above technical solution, an emergency traffic dispatch management system based on drone monitoring is also proposed, wherein the emergency traffic dispatch management system based on drone monitoring uses the above emergency traffic dispatch management method based on drone monitoring, and includes:

[0059] The drone monitoring module is responsible for collecting traffic image data;

[0060] The traffic image data processing module is responsible for extracting static image segments, marking feature points, and superimposing images on the acquired traffic image data;

[0061] The UAV collaboration module is designed to achieve mutual connection and collaborative operation between UAVs;

[0062] Emergency traffic location identification and assessment module, responsible for analyzing traffic image data, and identifying and assessing emergency traffic locations;

[0063] The target object driving data planning module is used to plan the target object's driving data in the urban main road area according to the emergency traffic location and traffic rules;

[0064] The drone command sending module sends control commands to the public transportation communication equipment based on the location, driving data and image data of the target object;

[0065] The public transportation communication equipment module is responsible for receiving control instructions from the marking drone and adjusting the traffic flow in the main road area of ​​the city according to the instructions;

[0066] The data interaction module realizes the data interaction function between drones, public transportation communication equipment and map systems.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] The emergency traffic dispatch management method and system based on drone monitoring collects image data of target objects at different positions in the urban main road area by drones, and combines the time intervals between image data at different positions, so that the drone can locate the position of the target object while connecting the command channel between the public transportation communication equipment that interacts with the target object and the drone, so that the drone can send control instructions to the public transportation communication equipment in stages based on the position of the target object and the positions between the drones, thereby achieving rapid response and accurate dispatch to emergency traffic conditions, which not only ensures the efficiency of traffic dispatch, but also ensures the timeliness and accuracy of dispatch instructions. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A flowchart of the steps of the method disclosed in the present invention;

[0070] Figure 2 It is a module composition diagram of the system disclosed in the present invention;

[0071] Figure 3 This is an auxiliary illustration of step S107.1 of the present invention. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] Before understanding the technical solution proposed in this application, it should be clear that the purpose of the drone in the present invention is specifically to provide aerial surveillance and dispatch command when emergency traffic incidents occur in the main road area of ​​the city. The drone monitors the traffic conditions in real time in the air.

[0074] It should be noted that, in the present invention, a number of drones are set up above the urban main road area, and the drones are used to obtain traffic image data and transmit control instructions to public transportation communication equipment in the urban main road area.

[0075] Specifically, in the present invention, the drone captures traffic image data by carrying a high-definition camera, and the traffic image data is then transmitted to a ground control center. Therefore, the present invention proposes that the emergency traffic dispatch management system based on drone monitoring also includes a ground control center module, wherein the ground control center module is used to receive, analyze and process the traffic image data transmitted by the drone. In addition, in actual use, the ground control center module can also monitor the flight status of the drone to ensure that it performs the monitoring task safely and stably.

[0076] It should be added that reference Figure 2 It can be seen that the emergency traffic dispatch management system based on drone monitoring proposed by the present invention includes, in addition to the ground control center module:

[0077] The drone monitoring module is responsible for collecting traffic image data. It should be understood that the drone monitoring module in the present invention is specifically a high-definition camera mounted outside the drone.

[0078] The traffic image data processing module is responsible for performing static image segment extraction, feature point annotation and image superposition on the acquired traffic image data. It should be clear that the traffic image data processing module in the present invention is an image processing unit inside the high-definition camera. The image processing unit can perform preliminary processing on the real-time traffic images captured by the high-definition camera and extract key information. It should be added that since the image processing unit is a common technical component at this stage, the applicant will not elaborate on its specific structure and working principle.

[0079] The UAV collaboration module is designed to achieve mutual connection and collaborative operation between UAVs. It should be clear that the UAV collaboration module in the present invention is specifically a communication component installed inside the UAV. Through the communication component, the UAVs share location information, flight status and captured traffic image data in real time to ensure that in emergency traffic incidents, multiple UAVs can conduct collaborative monitoring efficiently and orderly. In addition, in actual use, the UAV collaboration module can also receive instructions from the ground control center module to adjust the flight trajectory or monitoring objects.

[0080] The emergency traffic location identification and assessment module is responsible for analyzing traffic image data and identifying and assessing emergency traffic locations.

[0081] The target object driving data planning module is used to plan the driving data of the target object in the urban main road area according to the emergency traffic location and traffic rules.

[0082] The drone command sending module sends control instructions to public transportation communication equipment based on the location, driving data and image data of the target object.

[0083] The public transportation communication equipment module is responsible for receiving control instructions from the marking drone and adjusting the traffic flow in the main road area of ​​the city according to the instructions.

[0084] The data interaction module realizes the data interaction function between drones, public transportation communication equipment and map systems.

[0085] It should be pointed out that in the present invention, the emergency traffic position identification and evaluation module, the target object driving data planning module, the drone command sending module, the public transportation communication equipment module and the data interaction module are all specifically summarized into the technical category of the integrated processing chip, wherein the integrated processing chip is installed in the drone, the public transportation communication equipment and the ground control center in the present invention.

[0086] In addition, it should be noted that the present invention provides an emergency traffic dispatch management system based on drone monitoring. During actual operation, the drone continuously captures traffic image data in the air, and the traffic image data is transmitted to the ground control center in real time through the drone monitoring module. After the ground control center module receives these data, it starts the traffic image data processing module to perform preliminary processing such as static image fragment extraction, feature point annotation and image overlay on the captured real-time traffic images, so as to quickly extract key traffic information.

[0087] Subsequently, the emergency traffic location identification and assessment module works to analyze the processed traffic image data, identify the location of the emergency traffic incident, and assess the traffic conditions at that location. Based on the assessment results, the target object driving data planning module quickly plans the optimal driving data for the target object in the city's main road area based on the emergency traffic location and traffic rules.

[0088] The drone command sending module intelligently generates control instructions based on the target object's location, driving data and real-time image data, and sends these instructions to the public transportation communication equipment through the data interaction module. After receiving the instructions, the public transportation communication equipment module immediately adjusts the traffic flow in the city's main road area according to the instructions to ensure smooth and safe traffic.

[0089] Based on the emergency traffic dispatch management system based on drone monitoring proposed by the present invention, Figure 1 As shown, the present invention provides a technical solution: an emergency traffic dispatch management method based on drone monitoring, and includes: steps S100 to S800.

[0090] Step S100: Determine the emergency traffic location in the urban main road area based on the traffic image data.

[0091] It should be understood that the method for determining the emergency traffic position in step S400 includes: steps S101 to S108.

[0092] Step S101: Select an initial image segment.

[0093] It should be noted that the initial image segment in step S101 is any frame of static image in the traffic image data collected by a single drone.

[0094] Step S102: marking moving feature points in the initial image segment.

[0095] It should be noted that the moving feature points in step S102 include vehicles and pedestrians.

[0096] Step S103: Taking the initial image segment as a reference, extracting multiple static image segments of the traffic image data at the same location but at different time points.

[0097] It should be clear that step S103 in the present invention is used to compare static image segments at different time points and analyze the position changes of moving feature points, so as to determine the state of traffic flow and whether there are emergency traffic conditions such as abnormal congestion or accidents.

[0098] Step S104: using the initial image segment as a template, superimposing multiple frames of static image segments to form a discrimination image.

[0099] Step S105: determining a discrimination target in the discrimination image, where the discrimination target is any continuously moving feature point in the discrimination image.

[0100] Step S106: comparing the positions of the target at different time points in the detection image to obtain movement attribute data of the target.

[0101] It should be clear that step S106 is used in the present invention to analyze the moving speed, moving direction and moving trajectory of the target, and then determine whether the target is in an abnormal state, such as sudden braking, sharp turns and reverse driving to cause traffic accidents.

[0102] Step S107: Based on the movement attribute data of the identified target and in combination with traffic rules, it is evaluated whether there is an emergency traffic situation in the traffic image data collected by a single drone.

[0103] Step S108: Based on the evaluation result, the moving range of the determination target is marked as an emergency traffic location.

[0104] It should be noted that in the present invention, steps S101 to S108 can be automatically executed by the emergency traffic location identification and evaluation module without human intervention, thereby ensuring the efficiency and accuracy of handling emergency traffic events. After determining the emergency traffic location, the system immediately enters step S200, that is, according to the emergency traffic location and traffic rules, plans the driving data of the target object in the urban main road area.

[0105] In addition, it is necessary to further explain that the method for evaluating whether there is an emergency traffic situation in the traffic image data collected by a single drone in step S107 of the present invention includes: steps S107.1 to S107.4.

[0106] Step S107.1: extracting threshold data in the urban main road area according to traffic rules.

[0107] It should be noted that in step S107.1, the threshold data covers any two or more of the maximum and minimum values ​​of the vehicle's driving speed, the safe distance between vehicles, and the indication rules of traffic signs and signal lights.

[0108] For example: Reference Figure 3 It can be seen that when the traffic rules are specific, in the area of ​​urban main roads, the vehicle speed must not exceed 60 kilometers per hour and must not be less than 30 kilometers per hour. At the same time, vehicles traveling in the same direction should maintain a safe distance of at least 50 meters. At this time, the threshold data is specifically expressed as (60-30) km / h±50m.

[0109] Step S107.2: Mapping the movement attribute data and threshold data of the target to be identified into a two-dimensional coordinate system.

[0110] It should be clear that reference Figure 3 It can be seen that in step S107.2 of the present invention, the horizontal axis of the two-dimensional coordinate system represents the moving speed of the discrimination target, wherein the two ends of the horizontal axis respectively represent the moving speeds of the discrimination target, and the vertical axis of the two-dimensional coordinate system represents the distance between the two discrimination targets, wherein a positive value of the vertical axis in the two-dimensional coordinate system indicates that the two discrimination targets are in the same direction of travel, and a negative value of the vertical axis in the two-dimensional coordinate system indicates that the two discrimination targets are in opposite directions of travel.

[0111] Step S107.3: In the two-dimensional coordinate system, normal moving objects and emergency moving objects are defined based on the threshold data.

[0112] Step S107.4: Determine whether the target has an emergency traffic situation based on the position evaluation of the target's movement attribute data in the two-dimensional coordinate system.

[0113] It should be added that, during the actual operation of steps S107.1 to S107.4 of the present invention, by comparing the mobile attribute data of the identification target with the preset threshold data, it is possible to quickly and accurately identify traffic participants who deviate from the normal driving state, such as speeding, illegal lane changing or abnormal parking. When an emergency traffic situation is discovered, the evaluation result is fed back to the target object driving data planning module, and the target object driving data planning module then plans a driving route or avoidance plan for vehicles and pedestrians in the main road area of ​​the city based on the current traffic conditions and traffic rules, thereby reducing the impact of emergency traffic events on the overall traffic flow to a certain extent, and ensuring the safety and smooth travel of all road users.

[0114] Step S200: planning the driving data of the target object in the urban main road area according to the emergency traffic location.

[0115] It should be understood that the planning method for driving data in step S200 includes: steps S201 to S206.

[0116] Step S201: The drone retrieves the area covering the emergency traffic location in the traffic image data and uploads it to the map system of the city's main roads.

[0117] Step S202: The map system of the city's main roads forms a termination node based on the area of ​​the emergency traffic location.

[0118] It should be clear that in the present invention, the termination node is used to identify the end point of the driving data. In addition, in the present invention, the map system of the city's main roads is recorded into the integrated processing chip.

[0119] Step S203: The drone searches for the starting node of the target object in the urban main road area based on the attribute type of the target object.

[0120] It should be clear that in step S203 , the start node is used to identify the starting point of the driving data.

[0121] Step S204: construct a series of basic data sequences based on the road attributes of the starting node, the ending node and the urban main roads.

[0122] It should be clear that the road attributes of the city's main roads in step S204 cover the specific distribution locations of the public transportation communication equipment in the city's main roads and the operating logic of the public transportation communication equipment in a normal state.

[0123] Step S205: A series of basic data sequences are filtered out corresponding travel data according to the attributes of the emergency traffic location.

[0124] Step S206: The drone transmits the filtered driving data to the target object to guide the target object to navigate according to the driving data.

[0125] It should be noted that in the present invention, steps S201 to S206 are jointly executed by the target object driving data planning module and the drone command sending module, ensuring the timeliness and accuracy of the driving data planning. It should be added that the target objects, including but not limited to vehicles and pedestrians, can quickly receive the optimal driving path or avoidance plan, so as to effectively avoid or approach the emergency traffic location.

[0126] It should be further supplemented that, in step S205, the attributes of the emergency traffic location include: dynamic attributes and static attributes. It should be clear that the dynamic attributes of the emergency traffic location represent the mobile state of the emergency traffic location in the actual scenario. Specifically, in the existing emergency traffic scenario, it can be the vehicle involved in the traffic accident. The dynamic attributes of the emergency traffic location in this traffic scenario will change with the movement of the emergency traffic event.

[0127] In contrast, the static attributes of the emergency traffic location represent the static state of the emergency traffic location in the actual scenario. Specifically, the existing emergency traffic events may be road construction, obstacles after traffic accidents, and traffic control areas. At this time, the static attributes of the emergency traffic location will remain unchanged until the emergency traffic event is handled. By analyzing the dynamic and static attributes of the emergency traffic location, the target object driving data planning module can more accurately screen out driving data suitable for the current traffic conditions, thereby further improving the practicality and reliability of the emergency traffic dispatch management system.

[0128] Therefore, a method for filtering travel data based on the dynamic attributes of an emergency traffic location using a series of basic data sequences includes: Step S205.1 to Step S205.3.

[0129] Step S205.1: Based on the dynamic attributes of the emergency traffic positions in the urban trunk road area, the movement trend of the emergency traffic positions in the traffic image data is obtained.

[0130] Step S205.2: According to the movement trend, a driving data subset matching the movement trend is selected from the basic data sequence.

[0131] It should be clear that in step S205.2, the driving data subset is used to represent driving data consistent with the driving direction of the target object.

[0132] Step S205.3: The drone identifies the driving data in the driving data subset that is consistent with the driving direction of the target object as the optimal driving data.

[0133] In contrast, a method for filtering travel data based on static attributes of emergency traffic locations using a series of basic data sequences includes: Step S205.A-Step S205.C.

[0134] Step S205.A: Based on the static attributes of the emergency traffic location in the urban main road area, the distribution position of the public transportation communication equipment in the emergency traffic location in the urban main road area in the traffic image data is obtained.

[0135] Step S205.B: Based on the distribution locations of the public transportation communication equipment, a travel data subset matching the distribution locations of the public transportation communication equipment is selected from the basic data sequence.

[0136] It should be clear that in step S205.B, the travel data subset is the travel data corresponding to the public transportation communication device that is closest to the target object.

[0137] Step S206.C: The drone uses the driving data in the driving data subset that is closest to the target object as the optimal driving data.

[0138] Step S300: Selecting a marked drone corresponding to the driving data from among multiple drones.

[0139] It should be understood that the method for selecting the marked drone in step S300 includes: steps S301 to S304.

[0140] Step S301: Based on the driving data of the target object in the urban main road area, several drones are selected as candidate drones.

[0141] Step S302: Calculate the relative distance between the candidate UAV and the target object and the angle between the candidate UAV and the target object's travel direction, and perform quantitative analysis.

[0142] Step S303: Based on the quantitative analysis results, a priority evaluation rule for the candidate drones is constructed, and the priority evaluation rule assigns a numerical value that is negatively correlated with the priority level.

[0143] It should be clear that the negative correlation in step S300 means that the closer the relative distance between the candidate UAV and the target object, and the smaller the angle between the candidate UAV and the target object's driving direction, the higher the priority of the candidate UAV.

[0144] Step S304: Select the candidate drone with the highest priority as the marked drone.

[0145] Step S400: Collect primary traffic image data.

[0146] It should be clear that the primary traffic image data is acquired by a marked drone located at the starting node in the driving data to record the traffic conditions of the target object when it is at the starting node in the driving data.

[0147] Step S500: Collect secondary traffic image data.

[0148] It should be clear that the secondary traffic image data is acquired by a marking drone located at any node in the driving data to record the traffic conditions of the target object at any node in the driving data.

[0149] Step S600: Calculate the time interval between the primary traffic image data and the secondary traffic image data.

[0150] It should be noted that the time interval between the primary traffic image data and the secondary traffic image data in step S600 is obtained by comparing the timestamp information of the traffic image data, wherein in the prior art, the traffic impact data includes the timestamp information.

[0151] Step S700: The marked drone sends a control instruction to the corresponding public transportation communication equipment based on the time interval, the primary traffic image data and the secondary traffic image data.

[0152] It should be clear that in step S700, the method of marking the drone to send control instructions to the public transportation communication equipment includes: steps S701-S705.

[0153] Step S701: extracting two corresponding marked drone serial numbers according to the location information of the target object in the primary traffic image data and the secondary image data.

[0154] Step S702: Match the extracted two marked drone serial numbers with the location information of the target object in the driving data, and establish a location matching relationship.

[0155] Step S703: Based on the position matching relationship, locate the target position of the target object in the driving data set.

[0156] Step S704: according to the distance between the target location and the two marked drones, the command channels between the marked drone and the public transportation communication devices that have interacted with the target objects in the driving data are activated in order from near to far.

[0157] Step S705: Based on the time interval, the two marked drones send control instructions to the public transportation communication equipment in stages.

[0158] It should be noted that in step S702, the position matching relationship is determined according to the relative distance between the marked drone and the position information in the driving data. Specifically, when the relative distance between the marked drone and the position information is less than the distance between the two marked drones, it is determined that the position matching relationship is established.

[0159] Step S800: The public transportation communication equipment adjusts the traffic flow in the urban main road area according to the control instruction.

[0160] It should be clear that during the actual operation of step S800, the specific content of the control instruction will be dynamically adjusted according to the current traffic conditions in the city's main road area and the driving needs of the target object. For example, if the target object is an ambulance that urgently needs to pass through a congested road section, the control instruction will instruct the public transportation communication equipment to adjust the traffic light timing to open a green channel for the ambulance.

[0161] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is limited by the attached embodiments and their equivalents.

Claims

1. An emergency traffic dispatch management method based on drone monitoring, comprising: Several drones are set up over the main roads of the city. The drones are used to obtain traffic image data and transmit control instructions to public transportation communication equipment in the main roads of the city. The characteristics are: Determine the location of emergency traffic in the city's main road area based on traffic image data; According to the emergency traffic location, plan the driving data of the target object in the urban main road area; Selecting a marked drone corresponding to the driving data from among the multiple drones; Collecting primary traffic image data, which is acquired by a marking drone located at the starting node in the driving data to record the traffic conditions of the target object at the starting node in the driving data; Collecting secondary traffic image data, which is acquired by a marking drone located at any node in the driving data to record the traffic conditions of the target object at any node in the driving data; calculating the time interval between the primary traffic image data and the secondary traffic image data; The marking drone sends a control instruction to the corresponding public transportation communication equipment based on the time interval, the primary traffic image data and the secondary traffic image data; Public transportation communication equipment adjusts traffic flow in the city's main road areas based on control instructions.

2. The method for emergency traffic dispatch management based on drone monitoring according to claim 1 is characterized in that: Methods for determining emergency transportation locations include; An initial image segment is selected, where the initial image segment is any static image frame in the traffic image data collected by a single UAV; Annotate the moving feature points in the initial image clips, including vehicles and pedestrians; Taking the initial image segment as a benchmark, extract multiple static image segments of traffic image data at the same location and different time points; Using the initial image segment as a template, multiple frames of static image segments are superimposed to form a discriminative image; Determine a discrimination target in the discrimination image, where the discrimination target is any continuously moving feature point in the discrimination image; Compare the positions of the discrimination target at different time points in the discrimination image to obtain the movement attribute data of the discrimination target; Based on the mobile attribute data of the identified target and combined with traffic rules, it is evaluated whether there is an emergency traffic situation in the traffic image data collected by a single drone; Based on the evaluation results, the moving range of the discrimination target is marked as an emergency traffic location.

3. The method for emergency traffic dispatch management based on drone monitoring according to claim 2 is characterized in that: Methods for evaluating whether emergency traffic conditions exist in traffic image data collected by a single drone include: According to traffic rules, the threshold data in the urban main road area is extracted; Mapping the moving attribute data and threshold data of the target to be judged into a two-dimensional coordinate system, wherein the horizontal axis of the two-dimensional coordinate system represents the moving speed of the target, and the vertical axis of the two-dimensional coordinate system represents the moving direction of the target; In a two-dimensional coordinate system, normal moving objects and emergency moving objects are defined based on threshold data; Based on the position evaluation of the movement attribute data of the target in the two-dimensional coordinate system, it is determined whether the target has an emergency traffic situation.

4. The method for emergency traffic dispatch management based on drone monitoring according to claim 1 is characterized in that: The planning methods for driving data include: The drone retrieves the traffic image data covering the area of ​​emergency traffic location and uploads it to the map system of the city's main roads; The map system of the city's main roads forms a terminal node based on the area of ​​the emergency traffic location, and the terminal node is used to mark the end point of the driving data; Based on the attribute type of the target object, the drone retrieves the starting node of the target object in the urban main road area. The starting node is used to identify the starting point of the driving data; Based on the road attributes of the starting node, the ending node and the urban main roads, a series of basic data sequences are constructed; A series of basic data sequences are used to filter out corresponding driving data according to the attributes of emergency traffic locations; The drone transmits the filtered driving data to the target object to guide the target object to navigate according to the driving data.

5. The method for emergency traffic dispatch management based on drone monitoring according to claim 4 is characterized in that: The attributes of emergency traffic locations include: dynamic attributes and static attributes; A series of basic data sequences The method of filtering driving data based on the dynamic attributes of emergency traffic locations includes: Based on the dynamic attributes of emergency traffic locations in urban trunk roads, the movement trend of emergency traffic locations in traffic image data is obtained; According to the movement trend, a driving data subset matching the movement trend is selected from the basic data sequence, and the driving data subset is used to represent the driving data consistent with the driving direction of the target object; The UAV identifies the driving data in the driving data subset that is consistent with the driving direction of the target object as the optimal driving data.

6. The method for emergency traffic dispatch management based on drone monitoring according to claim 5 is characterized in that: A series of basic data sequences Methods for filtering driving data based on static attributes of emergency traffic locations include: Based on the static attributes of the emergency traffic location in the urban trunk road area, the distribution location of public transportation communication equipment in the emergency traffic location in the urban trunk road area in the traffic image data is obtained; Based on the distribution locations of public transportation communication equipment, a travel data subset matching the distribution locations of public transportation communication equipment is selected from the basic data sequence, where the travel data subset is the travel data corresponding to the public transportation communication equipment closest to the target object; The UAV takes the driving data closest to the target object in the driving data subset as the optimal driving data.

7. The method for emergency traffic dispatch management based on drone monitoring according to claim 1 is characterized in that: Selected methods for tagging drones include: Based on the driving data of target objects in the urban main road area, several drones are selected as candidate drones; Calculate the relative distance between the candidate UAV and the target object, as well as the angle between the candidate UAV and the target object’s travel direction, and conduct quantitative analysis; Based on the quantitative analysis results, the priority evaluation rules of candidate drones are constructed. The numerical values ​​assigned by the priority evaluation rules are negatively correlated with the priority levels. The candidate drone with the highest priority is selected as the marked drone.

8. The method for emergency traffic dispatch management based on drone monitoring according to claim 1 is characterized in that: Methods for sending control instructions from a tagged drone to a public transportation communication device include: According to the location information of the target object in the primary traffic image data and the secondary image data, the corresponding two marked drone serial numbers are extracted; Match the extracted two marked drone serial numbers with the location information of the target object in the driving data, and build a location matching relationship; Based on the position matching relationship, locate the target position of the target object in the driving data set; According to the distance between the target location and the two marked drones, the command channels between the marked drones and the public transportation communication devices that have interacted with the target objects in the driving data are activated in order from near to far; According to the time interval, the two tagged drones send control instructions to the public transportation communication equipment in stages.

9. An emergency traffic dispatch management system based on drone monitoring, using an emergency traffic dispatch management method based on drone monitoring as described in any one of claims 1 to 8, characterized in that: include: The drone monitoring module is responsible for collecting traffic image data; The traffic image data processing module is responsible for extracting static image segments, marking feature points, and superimposing images on the acquired traffic image data; The UAV collaboration module is designed to achieve mutual connection and collaborative operation between UAVs; Emergency traffic location identification and assessment module, responsible for analyzing traffic image data, and identifying and assessing emergency traffic locations; The target object driving data planning module is used to plan the target object's driving data in the urban main road area according to the emergency traffic location and traffic rules; The drone command sending module sends control commands to the public transportation communication equipment based on the location, driving data and image data of the target object; The public transportation communication equipment module is responsible for receiving control instructions from the marking drone and adjusting the traffic flow in the main road area of ​​the city according to the instructions; The data interaction module realizes the data interaction function between drones, public transportation communication equipment and map systems.

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