Aircraft-based highway accident emergency method and system

Abstract

The invention discloses a highway accident emergency method and system based on an aircraft, relates to the technical field of emergency disposal, and discloses the highway accident emergency method based on the aircraft. The three-dimensional environment sensing data is used for extracting accident characteristics and carrying out grade judgment, rescue resources are reasonably allocated, warning is carried out through the navigation system and the aircraft, accident information is timely and effectively transmitted to other vehicles, the vehicles are guided to detour, and therefore secondary accidents are effectively avoided.

Description

Technical Field

[0001] This application relates to the technical field of emergency response, and particularly to an emergency method and system for highway accidents based on an aircraft. Background Art

[0002] In the prior art, once a traffic accident occurs on a highway, due to the injuries of on-site personnel and accident handling experience, it is often impossible to timely and effectively feedback accident-related information (such as the time of the accident, location, and the condition of personnel injuries) to the traffic management department. And after the traffic management department receives the feedback of accident-related information, it generally takes a long time from starting the emergency response mechanism until arriving at the scene for relevant handling (this time may be further extended due to traffic congestion), and it is extremely easy to have secondary accidents during this period. Summary of the Invention

[0003] The main purpose of this application is to provide an emergency method and system for highway accidents based on an aircraft, aiming to improve the response speed of highway accidents and reduce the probability of secondary accidents.

[0004] To achieve the above object, this application proposes an emergency method for highway accidents based on an aircraft, and the method includes:

[0005] Obtain the original accident trigger data, and perform spatio-temporal alignment processing on the original accident trigger data to generate accident location information including the accident geographical coordinates;

[0006] Determine the flight path of the aircraft according to the accident location information;

[0007] Control the aircraft to reach the accident geographical coordinate position based on the flight path, and collect three-dimensional environmental perception data of the accident area corresponding to the accident geographical coordinate position;

[0008] Extract accident characteristics from the three-dimensional environmental perception data, and determine the corresponding accident level;

[0009] Determine the corresponding rescue resource allocation data and dynamic warning area based on the accident level;

[0010] Dispatch the corresponding rescue resources according to the rescue resource allocation data, control the aircraft to reach the corresponding warning position for warning according to the dynamic warning area, and synchronize the dynamic warning area to the navigation system to prompt other vehicles to detour.

[0011] In an embodiment, the original accident trigger data includes a collision trigger signal, a trajectory anomaly signal, and a road condition anomaly signal; the step of obtaining the original accident trigger data and performing spatio-temporal alignment processing on the original accident trigger data to generate accident location information including the accident geographical coordinates includes:

[0012] Obtain vehicle collision acceleration data through the in-vehicle OBD interface, and generate a collision trigger signal when the vehicle collision acceleration data exceeds a first threshold;

[0013] Obtain vehicle positioning anomaly data based on the navigation system, and generate a trajectory anomaly signal when the continuous positioning deviation exceeds a second threshold;

[0014] Obtain vehicle stagnation data through the roadside millimeter-wave radar, and generate a road condition anomaly signal when the stagnation time exceeds a third threshold;

[0015] Perform a logical OR operation on the collision trigger signal, the trajectory anomaly signal, and the road condition anomaly signal to generate the original accident trigger data;

[0016] Establish a time synchronization window to align the timestamps of the three signals to the same time reference;

[0017] Adopt a weighted decision algorithm to evaluate the confidence levels of the collision trigger signal, the trajectory anomaly signal, and the road condition anomaly signal, and generate accident location information including the accident geographical coordinates when the confidence level exceeds a preset confidence threshold.

[0018] In one embodiment, the step of determining the flight path of the aircraft according to the accident location information includes:

[0019] Obtain the obstacle information between the accident geographical coordinates and the aircraft deployment location;

[0020] Fuse the accident geographical coordinates and the obstacle information, and generate a horizontal obstacle avoidance path through the A* algorithm;

[0021] Obtain the first meteorological information between the accident geographical coordinates and the aircraft deployment location, and determine the flight altitude based on the obstacle information and the first meteorological information;

[0022] Combine the horizontal obstacle avoidance path and the flight altitude to generate a three-dimensional waypoint sequence of the aircraft to determine the flight path of the aircraft.

[0023] In one embodiment, when the accident geographical coordinates are located in a tunnel, the step of controlling the aircraft to reach the accident geographical coordinate position based on the flight path and collecting three-dimensional environmental perception data of the accident area corresponding to the accident geographical coordinate position includes:

[0024] Obtain the feature point image data of the tunnel top;

[0025] Match the feature point image data with the pre-stored three-dimensional tunnel model to generate visual positioning coordinate data;

[0026] Obtain the inertial data of the aircraft and fuse it with the visual positioning coordinate data, and use the Kalman filter algorithm to generate compensated positioning data;

[0027] Calculate the positioning error index based on the feature point image data and the inertial data;

[0028] When the positioning error index exceeds the preset range, obtain the distance between the aircraft and the tunnel sidewall through the on-board ultrasonic ranging device to assist in positioning the aircraft.

[0029] In one embodiment, the positioning error index includes visual matching degree and inertial cumulative offset;

[0030] Among them, the visual matching degree is calculated according to the following formula:

[0031] ;

[0032] Among them represents the visual matching degree; represents the number of feature points with successful matching; represents the total number of feature points in the pre-stored model; represents the sidewall spacing measured visually; represents the theoretical sidewall spacing of the model;

[0033] The inertial cumulative offset is calculated according to the following formula:

[0034] ;

[0035] Among them represents the inertial cumulative offset; represents the acceleration of the aircraft in the x-axis direction of the horizontal plane; represents the acceleration of the aircraft in the y-axis direction of the horizontal plane; represents time.

[0036] In one embodiment, the three-dimensional environment perception data includes lidar point cloud data, image data, and infrared thermal imaging data; the steps of extracting accident characteristics from the three-dimensional environment perception data and determining the corresponding accident level include:

[0037] Convert the lidar point cloud data in the coordinate system to generate three-dimensional grid model data;

[0038] Extract the deformed feature data of the accident vehicle in the image data through the image recognition algorithm;

[0039] Analyze the temperature anomaly area in the infrared thermal imaging data to generate fire risk data;

[0040] Based on the three-dimensional grid model data, vehicle deformation feature data, and fire risk data described above, determine the accident level.

[0041] In one embodiment, the three-dimensional network model data includes a vehicle structure deformation index and an obstacle distribution density, the vehicle deformation feature data includes an airbag deployment state and a maximum depression depth, and the fire risk data includes the proportion of the area exceeding the critical temperature value and the highest temperature value; the steps of determining the accident level based on the three-dimensional grid model data, vehicle deformation feature data, and fire risk data specifically include:

[0042] Determine the accident level according to the following formula:

[0043] ;

[0044] where, represents the accident level; represents the vehicle structure deformation index; represents the weight coefficient of the vehicle structure deformation index; represents the maximum depression depth; represents the critical depression depth; represents the weight coefficient of the ratio of the maximum depression depth to the critical depression depth; represents the airbag deployment state; represents the weight coefficient of the airbag deployment state; represents the proportion of the area exceeding the critical temperature value; represents the weight coefficient of the proportion of the area exceeding the critical temperature value; represents the highest temperature value; represents the critical temperature value; represents the weight coefficient of the ratio of the highest temperature value to the critical temperature value; represents the obstacle distribution density; represents the weight proportion of the obstacle distribution density.

[0045] In one embodiment, the steps of determining the dynamic warning area based on the accident level include:

[0046] Determine the initial warning distance according to the accident level;

[0047] Obtain the second meteorological information of the accident address coordinates, and adjust the initial warning distance based on the second meteorological information to generate the final warning distance;

[0048] Determine the warning width according to the number of lanes occupied by the accident;

[0049] Determine the corresponding dynamic warning area according to the final warning distance and the warning width.

[0050] In one embodiment, the aircraft is equipped with a warning sign and / or an alarm; the step of controlling the aircraft to reach the corresponding warning position for warning according to the dynamic warning area includes:

[0051] Hover the aircraft above the corresponding lane at the boundary position of the dynamic warning area, and use the warning sign and / or alarm to prompt other vehicles to detour.

[0052] In addition, to achieve the above object, the present application also proposes a highway accident emergency system based on an aircraft, and the system includes:

[0053] An accident location information determination module, configured to obtain original accident trigger data, perform spatio-temporal alignment processing on the original accident trigger data, and generate accident location information including accident geographical coordinates;

[0054] A flight path determination module, configured to determine the flight path of the aircraft according to the accident location information;

[0055] A three-dimensional environment perception data acquisition module, configured to control the aircraft to reach the accident geographical coordinate position based on the flight path, and acquire three-dimensional environment perception data of the accident area corresponding to the accident geographical coordinate position;

[0056] An accident level determination module, configured to extract accident characteristics from the three-dimensional environment perception data and determine the corresponding accident level;

[0057] A rescue allocation data and dynamic warning area determination module, configured to determine corresponding rescue resource allocation data and dynamic warning area based on the accident level;

[0058] A warning module, configured to dispatch corresponding rescue resources according to the rescue resource allocation data, control the aircraft to reach the corresponding warning position for warning according to the dynamic warning area, and synchronize the dynamic warning area to the navigation system to prompt other vehicles to detour.

[0059] The emergency method for highway accidents based on an aircraft proposed in this application obtains the original accident trigger data, performs spatio-temporal alignment processing on the original accident trigger data to generate accident location information including the geographical coordinates of the accident, determines the flight path of the aircraft based on the accident location information, controls the aircraft to reach the accident geographical coordinate position based on the flight path, and collects three-dimensional environmental perception data of the accident area corresponding to the accident geographical coordinate position. Then, accident features are extracted from the three-dimensional environmental perception data, and the corresponding accident level is determined. Based on the accident level, the corresponding rescue resource allocation data and dynamic warning area are determined. Finally, the corresponding rescue resources are dispatched according to the rescue resource allocation data, the aircraft is controlled to reach the corresponding warning position for warning according to the dynamic warning area, and the dynamic warning area is synchronized to the navigation system to prompt other vehicles to detour. In this way, the response speed of highway accidents can be significantly improved, the rescue response time can be reduced, and the probability of secondary accidents can be decreased. Specifically, in this application, after an accident occurs, the aircraft quickly reaches the accident scene and collects three-dimensional environmental perception data, uses the three-dimensional environmental perception data to extract accident features and determine the level, thereby reasonably allocating rescue resources, and warns through the navigation system and the aircraft, effectively conveying the accident information to other vehicles in a timely manner and guiding them to detour, thus effectively avoiding the occurrence of secondary accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application and, together with the specification, are used to explain the principles of the present application.

[0061] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0062] Figure 1 It is a schematic flowchart of an embodiment of the emergency method for highway accidents based on an aircraft according to the present application.

[0063] Figure 2 For the present application Figure 1 It is a detailed schematic flowchart of step S100.

[0064] Figure 3 For the present application Figure 1 It is a detailed schematic flowchart of step S200.

[0065] Figure 4 It is a schematic flowchart of another embodiment of the emergency method for highway accidents based on an aircraft according to the present application.

[0066] Figure 5 For this application Figure 1 is a detailed process schematic diagram of step S400 in this application.

[0067] Figure 6 is a process schematic diagram provided for another embodiment of the highway accident emergency method based on an aircraft in this application.

[0068] The realization of the purpose, functional features, and advantages of this application will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific embodiments

[0069] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not used to limit this application.

[0070] To better understand the technical solutions of this application, the following will be described in detail in conjunction with the accompanying drawings of the specification and specific embodiments.

[0071] The main solution of the embodiment of this application is: obtaining original accident trigger data, performing spatio-temporal alignment processing on the original accident trigger data to generate accident location information including accident geographical coordinates, determining the flight path of the aircraft based on the accident location information, then controlling the aircraft to reach the accident geographical coordinate position based on the flight path, collecting three-dimensional environmental perception data of the accident area corresponding to the accident geographical coordinate position, then extracting accident features from the three-dimensional environmental perception data and determining the corresponding accident level, determining the corresponding rescue resource allocation data and dynamic warning area based on the accident level, finally dispatching corresponding rescue resources according to the rescue resource allocation data, controlling the aircraft to reach the corresponding warning position for warning according to the dynamic warning area, and synchronizing the dynamic warning area to the navigation system to prompt other vehicles to detour.

[0072] In the embodiment of this application, for the convenience of description, the following will be described with the recognition of the highway accident emergency system based on an aircraft as the execution subject.

[0073] Since in the prior art, once a traffic accident occurs on a highway, limited by the injuries of on-site personnel and the accident handling experience, it is often impossible to timely and effectively feedback accident-related information (such as the time, location, and injury conditions of the accident) to the traffic management department, and after the traffic management department receives the feedback of accident-related information, it generally takes a long time from starting the emergency response mechanism until arriving at the scene for relevant handling (this time may be further extended due to traffic congestion), and it is extremely easy to have secondary accidents during this period.

[0074] The solution provided by this application can significantly improve the response speed to highway accidents, reduce the rescue response time, and lower the probability of secondary accidents. Specifically, after an accident occurs, this application enables an aircraft to quickly reach the accident scene and collect three-dimensional environmental perception data, extracts accident characteristics and determines the level using the three-dimensional environmental perception data, then reasonably allocates rescue resources, and issues warnings through the navigation system and the aircraft to convey accident information to other vehicles in a timely and effective manner, guiding them to detour, thereby effectively avoiding the occurrence of secondary accidents.

[0075] Based on this, an embodiment of this application provides an aircraft-based highway accident emergency method. Referring to Figure 1 , in this embodiment, the aircraft-based highway accident emergency method includes steps S100 to S600, where:

[0076] Step S100, obtain the original accident trigger data, and perform spatio-temporal alignment processing on the original accident trigger data to generate accident location information including the accident geographical coordinates.

[0077] In this embodiment, the original accident trigger data may include collision trigger signals, trajectory anomaly signals, and road condition anomaly signals, etc. Among them, the collision trigger signal can be generated by the in-vehicle OBD interface obtaining vehicle collision acceleration data and when the vehicle collision acceleration data exceeds the first threshold; the trajectory anomaly signal can be generated by the navigation system obtaining vehicle positioning anomaly data when the continuous positioning deviation exceeds the second threshold; the road condition anomaly signal can be generated by the roadside millimeter-wave radar obtaining vehicle stagnation data when the stagnation time exceeds the third threshold. Among them, the first threshold, the second threshold, and the third threshold can be obtained through actual road tests and statistical analysis of historical accident data, and can be dynamically adjusted according to the actual situation. The original accident trigger data is used to trigger the entire emergency response process to ensure that relevant mechanisms can be quickly activated after an accident occurs. By performing spatio-temporal alignment processing on the original accident trigger data, the geographical location information of the accident occurrence, that is, the accident geographical coordinates, can be accurately obtained, thereby laying the foundation for determining the flight path of the aircraft and collecting three-dimensional environmental perception data at the accident scene in subsequent steps. The spatio-temporal alignment processing can comprehensively consider the time stamp and geographical location information of the accident trigger data to ensure the accuracy and consistency of the data, providing a reliable basis for subsequent accident emergency handling.

[0078] In a feasible implementation manner, referring to Figure 2 , step S100 includes steps S110 to S160, where:

[0079] Step S110, obtain vehicle collision acceleration data through the in-vehicle OBD interface, and generate a collision trigger signal when the vehicle collision acceleration data exceeds the first threshold.

[0080] In this embodiment, the vehicle OBD interface, i.e., the on-vehicle diagnostic system interface, can monitor the vehicle running state in real time, including key parameters such as engine speed, vehicle speed, fuel consumption, and collision acceleration. When a vehicle collides, a large acceleration change will occur, and the collision acceleration data obtained through the OBD interface can accurately reflect this change. When the collision acceleration data exceeds a preset first threshold, it can be determined that a collision event has occurred. At this time, a collision trigger signal is generated, which is one of the important trigger conditions for starting the emergency response process. The setting of the first threshold comprehensively considers factors such as vehicle type, road conditions, and historical accident data to ensure that collision events can be accurately identified while avoiding false alarms and missed reports. Obtaining collision acceleration data through the vehicle OBD interface has the advantages of high real-time performance and strong accuracy, providing reliable data support for subsequent determination of accident location information and flight path planning of the aircraft.

[0081] Step S120: Obtain vehicle positioning abnormal data based on the navigation system, and generate a trajectory abnormal signal when the continuous positioning deviation exceeds a second threshold.

[0082] In this embodiment, the navigation system is usually used to provide real-time position information of the vehicle, including coordinate data such as longitude, latitude, and altitude. The generation of the trajectory abnormal signal is based on the continuous monitoring of the vehicle positioning data. During normal driving, the vehicle positioning data will show a continuous and smooth change trend. However, when the vehicle has an abnormal trajectory, such as sharp turning, rollover, or running off the road, its positioning data will show significant deviations. By analyzing the vehicle positioning data obtained by the navigation system in real time, when it is detected that the continuous positioning deviation exceeds a preset second threshold, it can be determined that the trajectory is abnormal. At this time, a trajectory abnormal signal is generated. The setting of the second threshold considers factors such as road conditions, vehicle performance, and historical accident data to ensure that trajectory abnormal events can be accurately identified while avoiding false alarms. The generation of the trajectory abnormal signal provides timely and accurate accident warnings for the emergency response system, helping the aircraft quickly reach the accident scene and perform subsequent processing. In addition, the trajectory abnormal signal can also be combined with other accident trigger data, such as collision trigger signals or road condition abnormal signals, to further improve the accuracy and reliability of accident identification.

[0083] Step S130: Obtain vehicle stagnation data through the roadside millimeter-wave radar, and generate a road condition abnormal signal when the stagnation time exceeds a third threshold.

[0084] In this embodiment, millimeter-wave radar devices are usually equipped on the roadside of highways, which can continuously monitor the driving status of vehicles on the road. By transmitting and receiving millimeter-wave signals, the millimeter-wave radar can accurately measure the speed, distance, and position information of vehicles. When a vehicle stalls due to an accident, the millimeter-wave radar can quickly detect this abnormal state and record the time when the vehicle stalls. If the stall time exceeds a preset third threshold, the system determines that the road condition is abnormal and generates a corresponding road condition abnormal signal. The setting of the third threshold comprehensively considers factors such as the traffic flow, vehicle speed, and historical accident data of the highway to ensure that road condition abnormalities can be identified in a timely manner while avoiding false alarms caused by short-term stalls. The generation of the road condition abnormal signal indicates that a traffic accident may have occurred. By combining multiple data sources such as in-vehicle OBD interfaces, navigation systems, and roadside millimeter-wave radars, the solution provided in the embodiment of this application can achieve fast and accurate identification of highway accidents, providing strong support for subsequent emergency responses.

[0085] Step S140, perform a logical OR operation on the collision trigger signal, the trajectory abnormal signal, and the road condition abnormal signal to generate the original accident trigger data.

[0086] In this embodiment, in order to achieve a fast response and effective handling of highway accidents, the emergency method proposed in this application combines multiple data sources and generates original accident trigger data through a logical OR operation. When any one or more of the collision trigger signal, the trajectory abnormal signal, or the road condition abnormal signal are triggered, it is regarded as an accident occurring. At this time, the relevant signals are subjected to a logical OR operation to generate the original accident trigger data containing key accident information, ensuring the comprehensiveness and accuracy of the accident information, and providing a solid foundation for subsequent steps such as determining the accident location information, planning the flight path of the aircraft, and collecting three-dimensional environmental perception data of the accident scene. By integrating multiple data sources, the solution provided in the embodiment of this application can achieve all-round monitoring and fast response to highway accidents, effectively improving the efficiency and accuracy of accident handling.

[0087] Step S150, establish a time synchronization window and align the timestamps of the three signals to the same time reference.

[0088] In this embodiment, to ensure the spatio-temporal consistency of accident-triggered data, it is necessary to align the timestamps of the collision-trigger signal, trajectory anomaly signal, and road condition anomaly signal. By setting a time window, the timestamps of the three signals are uniformly adjusted to the start time or the center time of the time window, thereby eliminating the time deviation caused by signal transmission delay or device clock difference. The width of the time synchronization window can be set according to the actual requirements of highway accident emergency response to ensure that it can cover the timestamps of all relevant signals while avoiding introducing unnecessary time delay. The signals processed by the time synchronization window will have the same time reference, providing an accurate time reference for subsequent determination of accident location information and flight path planning of the aircraft.

[0089] Step S160, use a weighted decision algorithm to evaluate the confidence of the collision-trigger signal, trajectory anomaly signal, and road condition anomaly signal, and generate accident location information including the geographical coordinates of the accident when the confidence exceeds the preset confidence threshold.

[0090] In this embodiment, to further improve the accuracy and reliability of accident emergency response, the embodiment of the present application uses a weighted decision algorithm to evaluate the confidence of the collision-trigger signal, trajectory anomaly signal, and road condition anomaly signal. By analyzing the characteristics of each signal and combining factors such as historical accident data and road conditions, a weight value is assigned to each signal, and then a comprehensive confidence is calculated. When the comprehensive confidence exceeds the preset confidence threshold, it can be determined that a real accident has occurred, and accident location information including the geographical coordinates of the accident is generated.

[0091] In the weighted decision algorithm, the weight values of different signals are determined according to their importance and accuracy in accident identification. For example, the collision-trigger signal usually has a higher weight because the collision event is the most common and most harmful type in highway accidents. The weights of the trajectory anomaly signal and the road condition anomaly signal may be relatively lower, but they still play an important role in accident identification, especially when the collision-trigger signal is missing or unreliable. By evaluating the confidence of the signal using the weighted decision algorithm, the situations of false alarms and missed alarms can be effectively reduced, and the accuracy and reliability of accident emergency response can be improved. When the comprehensive confidence exceeds the confidence threshold, the system can generate accident location information including the geographical coordinates of the accident, providing an accurate target location for the determination of the flight path of the aircraft and the acquisition of three-dimensional environmental perception data of the accident scene in the subsequent steps.

[0092] The embodiments of the present application consider the complementarity among multiple signals. In practical applications, due to the complexity and diversity of highway accidents, a single signal often fails to comprehensively and accurately reflect the accident situation. Therefore, the present application combines multiple data sources such as collision trigger signals, trajectory anomaly signals, and road condition anomaly signals, and performs comprehensive evaluation through a weighted decision algorithm to achieve rapid and accurate identification of highway accidents.

[0093] Step S200: Determine the flight path of the aircraft according to the accident location information.

[0094] In this embodiment, when the accident location information including the accident geographical coordinates is obtained, the aircraft-based highway accident emergency system needs to plan an optimal flight path for the aircraft according to this information. The determination of the flight path can comprehensively consider multiple factors, including the takeoff position of the aircraft, the geographical location of the accident site, the flight speed, flight altitude, flight time of the aircraft, and the safety during the flight process, etc. To ensure that the aircraft can quickly and accurately reach the accident site, the aircraft-based highway accident emergency system can adopt advanced path planning algorithms, such as the A* algorithm, Dijkstra algorithm, or heuristic search algorithm, etc., to calculate an optimal flight path. During the path planning process, the aircraft-based highway accident emergency system can also consider factors such as weather conditions, air traffic conditions, and the performance parameters of the aircraft in real time to ensure the safety and feasibility of the flight path. By comprehensively considering multiple factors, a fast and safe flight path can be planned for the aircraft to provide support for subsequent rescue operations.

[0095] In a feasible implementation manner, referring to Figure 3 , step S200 includes steps S210 to S240, where:

[0096] Step S210: Obtain the obstacle information between the accident geographical coordinates and the aircraft deployment position.

[0097] In this embodiment, to ensure that the aircraft can safely and efficiently reach the accident site, before planning the flight path, it is first necessary to obtain the obstacle information between the accident geographical coordinates and the aircraft deployment position. Obstacles may include natural or man-made structures such as mountains, high-rise buildings, and bridges, which may pose obstacles or safety hazards to the flight path of the aircraft. By obtaining detailed obstacle information, important reference can be provided for subsequent flight path planning to ensure the safety and feasibility of the flight path.

[0098] Step S220: Integrate the accident geographical coordinates and the obstacle information, and generate a horizontal obstacle avoidance path through the A* algorithm.

[0099] In this embodiment, after obtaining the obstacle information between the accident geographical coordinates and the aircraft deployment location, the highway accident emergency system based on the aircraft will combine this information with the accident geographical coordinates and use them together as the input conditions for path planning. Through the A* algorithm, the system can comprehensively consider the takeoff position of the aircraft, the geographical location of the target accident site, and the obstacle distribution between the two to generate a flight path that can avoid obstacles horizontally. The A* algorithm has the characteristics of high search efficiency and optimal path, which can ensure that the aircraft avoids obstacles during flight and at the same time minimizes the flight distance and time as much as possible. Through the application of the A* algorithm, a safe and efficient flight path can be planned for the aircraft, providing strong support for the subsequent three-dimensional environmental perception data collection and rescue operations at the accident site.

[0100] Step S230: Obtain the first meteorological information between the accident geographical coordinates and the aircraft deployment location, and determine the flight altitude based on the obstacle information and the first meteorological information.

[0101] In this embodiment, it can be understood that the flight altitude of the aircraft is not only affected by ground obstacles but also restricted by meteorological conditions. Therefore, when determining the flight altitude, multiple factors need to be comprehensively considered. The first meteorological information includes wind speed, wind direction, cloud height, precipitation conditions, etc. These factors may all affect the flight stability and safety of the aircraft. For example, strong winds may cause the aircraft to deviate from the predetermined path, and low clouds or precipitation may affect the aircraft's line of sight and flight performance.

[0102] To determine the appropriate flight altitude, the highway accident emergency system based on the aircraft will first obtain the real-time meteorological data between the accident geographical coordinates and the aircraft deployment location. These data can be obtained through meteorological satellites, ground meteorological stations, or meteorological detection equipment carried by the aircraft. Then, the system will combine the obstacle information and meteorological information to calculate the optimal flight altitude that can both avoid ground obstacles and adapt to the current meteorological conditions. When determining the flight altitude, the system will also consider the performance parameters of the aircraft, such as the maximum flight altitude, minimum safe flight altitude, etc., to ensure that the flight altitude is within the performance range of the aircraft.

[0103] Step S240: Combine the horizontal obstacle avoidance path and the flight altitude to generate a three-dimensional waypoint sequence of the aircraft to determine the flight path of the aircraft.

[0104] In this embodiment, after determining the horizontal obstacle avoidance path and flight altitude, the aircraft-based highway accident emergency system combines these two pieces of information to generate a three-dimensional aircraft waypoint sequence. The three-dimensional waypoint sequence includes all the key flight points between the takeoff position of the aircraft and the accident scene, and each waypoint contains key parameters such as the longitude, latitude, altitude, and flight speed of the aircraft. By generating the three-dimensional waypoint sequence, the precise description and control of the aircraft flight path can be realized, ensuring that the aircraft can reach the accident scene safely and efficiently according to the predetermined path. During the process of generating the three-dimensional waypoint sequence, the aircraft-based highway accident emergency system also performs real-time flight path optimization, dynamically adjusting the flight path according to the actual situation during flight, such as changes in meteorological conditions and adjustments in air traffic conditions, to ensure the optimality and safety of the flight path. By generating the three-dimensional aircraft waypoint sequence, an accurate target path can be provided for subsequent flight control and the acquisition of three-dimensional environmental perception data at the accident scene, thereby improving the speed of highway accident emergency response.

[0105] Step S300, control the aircraft to reach the accident geographical coordinate position based on the flight path, and collect three-dimensional environmental perception data of the accident area corresponding to the accident geographical coordinate position.

[0106] In this embodiment, when the aircraft reaches the accident geographical coordinate position according to the predetermined flight path, the aircraft-based highway accident emergency system will start the collection work of three-dimensional environmental perception data to facilitate subsequent accident analysis, rescue operations, and road repair work. In order to achieve a comprehensive perception of the three-dimensional environment of the accident area, various sensor devices are usually installed on the aircraft, such as high-resolution cameras, lidar, and infrared sensors. These sensor devices can capture information such as images, distances, and temperatures at the accident scene in real time, providing detailed data support for subsequent accident handling.

[0107] In this embodiment, when collecting three-dimensional environmental perception data, the aircraft-based highway accident emergency system comprehensively considers factors such as the flight attitude of the aircraft, the working parameters of the sensors, and the specific situation of the accident scene to ensure the accuracy and integrity of the data. At the same time, the system also performs real-time preprocessing and analysis on the collected data, such as image stitching, noise filtering, and target recognition, to further improve the usability of the data. By collecting three-dimensional environmental perception data of the accident area corresponding to the accident geographical coordinate position, the aircraft-based highway accident emergency system can provide accurate target positions and environmental information for subsequent rescue operations, helping rescue personnel quickly understand the situation at the accident scene, formulate reasonable rescue plans, and improve rescue efficiency and safety.

[0108] In a feasible implementation, when the accident geographical coordinates are located in a tunnel, it can be understood that in a tunnel environment, due to factors such as limited space, insufficient light, and signal interference, the positioning accuracy of the aircraft is often greatly affected. To ensure that the aircraft can accurately collect three-dimensional environment perception data in the tunnel, the embodiments of the present application propose a targeted positioning method. Refer to Figure 4 , step S300 further includes steps S310 to S350, where:

[0109] Step S310, obtain the feature point image data of the tunnel top.

[0110] In this embodiment, a high-resolution camera is mounted on the aircraft, and the aircraft obtains the feature point image data of the tunnel top through the high-resolution camera. Specifically, there are usually some obvious feature points on the tunnel top, such as lamps, signs, or structural nodes, etc. These feature points have high contrast and recognition in the image, so they can be used as the basis for visual positioning. By capturing the image data of these feature points, it can provide a basis for subsequent matching and positioning work.

[0111] Step S320, match the feature point image data with the pre-stored tunnel three-dimensional model to generate visual positioning coordinate data.

[0112] In this embodiment, the pre-stored tunnel three-dimensional model is usually generated by professional surveying and mapping equipment during the tunnel construction stage or maintenance stage, and contains the accurate geometric shape and spatial position information of the tunnel. By comparing and matching the feature point image data captured in real time with the pre-stored three-dimensional model, the visual positioning coordinate data of the aircraft can be generated, and the position of the aircraft can be initially determined.

[0113] Step S330, obtain the inertial data of the aircraft and fuse it with the visual positioning coordinate data, and use the Kalman filter algorithm to generate compensated positioning data.

[0114] It can be understood that relying solely on visual positioning data often fails to meet the requirements of high-precision positioning. Therefore, in this embodiment, the inertial data of the aircraft is also introduced to fuse with the visual positioning data to improve the positioning accuracy. The inertial data can include information such as the attitude and acceleration of the aircraft measured by sensors such as accelerometers and gyroscopes mounted on the aircraft. By fusing the inertial data with the visual positioning coordinate data and processing it using the Kalman filter algorithm, the visual data and inertial data sources are integrated to generate an optimal estimation result, and then more accurate compensated positioning data is generated.

[0115] Step S340, calculate the positioning error index based on the feature point image data and the inertial data.

[0116] In this embodiment, to evaluate the accuracy of the positioning result, the embodiment of the present application also calculates a positioning error index. The positioning error index can be obtained by comparing the difference between the actual positioning result and the theoretical position. When the positioning error index exceeds the preset range, it is considered that there is a large uncertainty or error in the current positioning result, and further correction or assisted positioning is required. By analyzing the positioning error index, problems that may exist in the positioning process can be discovered in a timely manner, and corresponding measures can be taken for correction to improve the accuracy and reliability of the positioning result.

[0117] In a feasible implementation manner, the positioning error index includes a visual matching degree, and the visual matching degree is calculated according to the following formula:

[0118] ;

[0119] Where represents the visual matching degree; represents the number of successfully matched feature points; represents the total number of feature points in the pre-stored model; represents the side wall spacing measured visually; represents the theoretical side wall spacing of the model.

[0120] In this embodiment, the visual matching degree is comprehensively evaluated by the number of successfully matched feature points and the degree of consistency of the spatial relationship (such as side wall spacing) between the feature points with the pre-stored model. When the visual matching degree is high, it indicates that the number of successfully matched feature points is large and the measured value of the side wall spacing is close to the theoretical value, and the positioning result is relatively accurate. On the contrary, when the visual matching degree is low, further correction or other assisted positioning means may be required to improve the positioning accuracy.

[0121] In a feasible implementation manner, the positioning error index includes an inertial cumulative offset, and the inertial cumulative offset is calculated according to the following formula:

[0122] ;

[0123] Where represents the inertial cumulative offset; represents the acceleration of the aircraft in the x-axis direction of the horizontal plane; represents the acceleration of the aircraft in the y-axis direction of the horizontal plane; represents time.

[0124] In this embodiment, the inertial cumulative offset is calculated by integrating the acceleration data of the aircraft in two orthogonal directions (x-axis and y-axis) in the horizontal plane. As time goes by, the accumulation of acceleration data over time will cause the position offset to gradually increase, and this offset reflects the inaccuracy when relying solely on inertial data for positioning. By calculating the inertial cumulative offset, the influence degree of inertial data on the positioning result can be quantitatively evaluated, and then corresponding compensation measures can be taken during the positioning process to improve the accuracy and stability of the overall positioning system.

[0125] Step S350, when the positioning error index exceeds the preset range, obtain the distance between the aircraft and the tunnel sidewall through the on-board ultrasonic ranging device to assist in positioning the aircraft.

[0126] In this embodiment, the preset range is set according to the specific environmental conditions of the highway tunnel and the positioning requirements of the aircraft. For example, the visual matching degree is greater than 0.6 or the inertial cumulative offset is less than 1.2 meters. When the positioning error index exceeds this preset range, such as when the visual matching degree is less than or equal to 0.6 or the inertial cumulative offset is greater than or equal to 1.2 meters, it means that the current positioning result may have large uncertainties or errors and cannot meet the requirements of high-precision positioning.

[0127] To make up for this deficiency, this embodiment proposes a method of using the on-board ultrasonic ranging device for assisted positioning. The ultrasonic ranging device measures the distance between the aircraft and the tunnel sidewall by emitting and receiving ultrasonic signals. Since the propagation speed of ultrasonic signals in the air is relatively stable and less affected by the environment, it can be used as a reliable means of assisted positioning.

[0128] In specific implementation, when the positioning error index exceeds the preset range, the highway accident emergency system based on the aircraft will activate the ultrasonic ranging device and obtain the distance data between the aircraft and the tunnel sidewall in real time. Then, the system will combine these distance data with information such as the flight attitude and inertial data of the aircraft, so as to correct and optimize the position of the aircraft, which can further improve the positioning accuracy and stability of the aircraft in the tunnel and provide more accurate target position and environmental information for subsequent rescue operations.

[0129] Step S400, extract accident characteristics from the three-dimensional environment perception data and determine the corresponding accident level.

[0130] In this embodiment, after capturing information such as images, distances, and temperatures of the accident scene through devices such as high-resolution cameras, lidar, and infrared sensors carried by the aircraft, the highway accident emergency system based on the aircraft will identify accident characteristics related to the accident from these three-dimensional environmental perception data, such as three-dimensional grid models, vehicle deformation characteristics, fire risk levels, etc. After determining the accident characteristics, the system will classify the accident according to the preset accident level determination criteria. The accident level is usually determined based on factors such as the severity of the accident, the scope of influence, and potential casualties, and can be divided into multiple levels such as minor accidents, general accidents, major accidents, and especially major accidents. Different levels of accidents correspond to different emergency response processes and resource allocation plans. By extracting accident characteristics and determining the level from the three-dimensional environmental perception data, the highway accident emergency system based on the aircraft can provide more accurate and comprehensive information support for subsequent rescue operations, helping rescue personnel quickly understand the situation at the accident scene, formulate reasonable rescue plans, and thus minimize casualties and property losses to the greatest extent.

[0131] In a feasible implementation manner, the three-dimensional environmental perception data includes lidar point cloud data, image data, and infrared thermal imaging data. Among them, the lidar data is generated by the lidar device carried on the aircraft, and the three-dimensional coordinate information of each point at the accident scene is measured by emitting and receiving laser pulses to generate high-precision three-dimensional point cloud data. The image data is captured by a high-resolution camera and can record detailed image information of the accident scene, including the damage condition of the vehicle, the casualty situation of the personnel, and the scene environment. The infrared thermal imaging data is collected by an infrared sensor and can reflect the temperature distribution at the accident scene. Refer to Figure 5 , step S400 includes steps S410 to S440, where:

[0132] Step S410, convert the lidar point cloud data in the coordinate system to generate three-dimensional grid model data.

[0133] In this embodiment, the lidar point cloud data is obtained by scanning the accident scene with the lidar device during the flight of the aircraft. These data contain the three-dimensional coordinate information of each point at the accident scene, but are usually represented in the coordinate system of the lidar itself. For the convenience of subsequent processing and analysis, it is necessary to convert these point cloud data to a unified global coordinate system. The process of coordinate system conversion usually involves operations such as rotation and translation to ensure the accuracy and consistency of the point cloud data. Through coordinate system conversion, three-dimensional grid model data of the accident scene can be generated, which can intuitively display the three-dimensional shape and structure of the accident scene and provide a basis for subsequent accident level determination.

[0134] Step S420, extract the deformation feature data of the accident vehicle from the image data through an image recognition algorithm.

[0135] In this embodiment, the image data captured by the high-resolution camera contains detailed image information of the accident scene, including the damage condition of the vehicle. In order to extract the features related to the deformation of the accident vehicle from these image data, an advanced image recognition algorithm is adopted in this embodiment. This algorithm can automatically detect and identify the vehicle in the image, and further analyze the shape and structure of the vehicle to extract the feature data of the vehicle deformation, such as the degree of distortion of the vehicle body and the damage condition of the window. These data can intuitively reflect the damage condition of the accident vehicle and provide a basis for the subsequent determination of the accident level.

[0136] Step S430, analyze the temperature abnormal areas in the infrared thermal imaging data to generate fire risk data.

[0137] In this embodiment, the infrared thermal imaging data is collected by an infrared sensor and can reflect the temperature distribution of the accident scene. By analyzing the infrared thermal imaging data, the areas with abnormal temperatures can be identified, and these areas are often related to fires or high-temperature accidents. The system analyzes these temperature abnormal areas, including the area of the abnormal area, the temperature peak value, and the change trend of the temperature distribution, etc., so as to generate fire risk data and provide a basis for the subsequent determination of the accident level.

[0138] Step S440, comprehensively consider the three-dimensional grid model data, the vehicle deformation feature data, and the fire risk data to determine the accident level.

[0139] In this embodiment, when determining the accident level, the highway accident emergency system based on the aircraft comprehensively considers multiple aspects of information such as the three-dimensional grid model data, the vehicle deformation feature data, and the fire risk data. The three-dimensional grid model data can intuitively display the three-dimensional shape and structure of the accident scene and help rescue personnel quickly understand the overall situation of the accident scene. The vehicle deformation feature data can reflect the damage condition of the accident vehicle and provide a basis for judging the severity and influence range of the accident. The fire risk data can evaluate whether there is a fire or high-temperature risk at the accident scene, as well as the potential scale and harm degree of the fire. By comprehensively analyzing and evaluating these data, the system can accurately determine the accident level, so as to provide more accurate and comprehensive information support for the subsequent rescue operations. Different levels of accidents correspond to different emergency response processes and resource allocation plans, which helps to improve the rescue efficiency and safety.

[0140] In a feasible implementation, the three-dimensional network model data includes a vehicle structure deformation index and an obstacle distribution density. The vehicle deformation characteristic data includes an airbag deployment state and a maximum dent depth. The fire risk data includes the proportion of the area exceeding the critical temperature value and the highest temperature value. Among them, the vehicle structure deformation index evaluates the damage of the vehicle by measuring the deformation degree of key parts of the vehicle. The higher this index, the more severely the vehicle structure is damaged. The obstacle distribution density reflects the density of obstacles at the accident scene. The greater the density, the more complex the scene environment. The airbag deployment state is an important basis for judging whether the vehicle occupants are protected. If the airbag deploys normally, it usually means that the occupants are protected to a certain extent. The maximum dent depth directly reflects the magnitude of the impact force on the vehicle. The greater the depth, the more serious the accident. The proportion of the area exceeding the critical temperature value and the highest temperature value respectively reflect the scale and intensity of the fire.

[0141] Optionally, the steps of determining the accident level by comprehensively considering the three-dimensional grid model data, vehicle deformation characteristic data, and fire risk data specifically include:

[0142] Determine the accident level according to the following formula:

[0143] ;

[0144] Wherein, represents the accident level; represents the vehicle structure deformation index; represents the weight coefficient of the vehicle structure deformation index; represents the maximum dent depth; represents the critical dent depth; represents the weight coefficient of the ratio of the maximum dent depth to the critical dent depth; represents the airbag deployment state; represents the weight coefficient of the airbag deployment state; represents the proportion of the area exceeding the critical temperature value; represents the weight coefficient of the proportion of the area exceeding the critical temperature value; represents the highest temperature value; represents the critical temperature value; represents the weight coefficient of the ratio of the highest temperature value to the critical temperature value; represents the obstacle distribution density; represents the weight proportion of the obstacle distribution density.

[0145] In this embodiment, the critical dent depth and the critical temperature value It is obtained based on a large amount of experimental data and empirical analysis, and is a critical value (such as a critical depression depth of 50 cm and a critical temperature value of 300 °C) for judging the degree of vehicle damage and fire risk. Airbag deployment status Includes the airbag not deployed, the airbag partially deployed, and the airbag fully deployed, corresponding to the three discrete values of 0, 1, and 2 respectively. Each weight coefficient is set according to the importance of each factor in the accident level determination to ensure the accuracy of the accident level determination. In a feasible embodiment, the weight coefficient of the vehicle structure deformation index 、The weight coefficient of the ratio of the maximum depression depth to the critical depression depth 、The weight coefficient of the airbag deployment status 、The weight coefficient of the proportion of the area exceeding the critical temperature value 、The weight coefficient of the ratio of the highest temperature value to the critical temperature value And the weight ratio of the obstacle distribution density Are 0.3, 0.2, 0.15, 0.15, 0.1, and 0.1 respectively. By comprehensively calculating these data through the above formula, a comprehensive index reflecting the severity of the accident can be obtained, that is, the accident level. Different accident levels correspond to different emergency response processes and resource allocation plans, which helps to improve the rescue efficiency and safety. For example, when the accident level is relatively high, it may be necessary to initiate a higher-level emergency response process and allocate more rescue resources and personnel to ensure that the accident site is handled promptly and effectively.

[0146] Step S500, determine the corresponding rescue resource allocation data and dynamic warning area based on the accident level.

[0147] In this embodiment, after determining the accident level, the highway accident emergency system based on the aircraft will allocate corresponding rescue resources for accidents of different levels according to the preset rescue resource allocation rules. These resources may include rescue vehicles, fire-fighting equipment, medical personnel, professional rescue teams, etc. For example, the accident level can be divided into levels 1 to 5. When the accident level is 1, a minor accident may occur and there is no need to deploy rescue resources and personnel for on-site handling; while when the accident level is 5, a comprehensive emergency response process needs to be initiated, deploying one fire truck, two ambulances, two groups of professional rescue teams, and necessary medical equipment and fire-fighting equipment to ensure that the accident site is handled in a timely and effective manner. At the same time, the system will also dynamically delimit the warning area according to the situation at the accident site to ensure the safety of rescue personnel and on-site personnel. By determining the rescue resource allocation data based on the accident level, the highway accident emergency system based on the aircraft can allocate reasonable rescue resources for subsequent rescue operations, avoiding the situation of insufficient or excessive rescue resources, or continuously dispatching additional rescue forces after arriving at the scene, or the situation of excessive provision of rescue resources. At the same time, through the dynamic warning area, the accident information can be conveyed to other vehicles in a timely and effective manner to guide them to detour, thus effectively avoiding the occurrence of secondary accidents.

[0148] In a feasible implementation manner, the aircraft is wirelessly communicatively connected to the emergency command center, enabling the emergency command center to share the accident scene data collected by the aircraft, including lidar point cloud data, image data, infrared thermal imaging data, and the determined accident level and other information. The wireless communication connection can be implemented based on existing mobile communication networks, satellite communication networks, or dedicated emergency communication networks to ensure real-time data transmission and sharing. By sharing this data, the emergency command center can quickly understand the situation at the accident scene, including information such as the type, scale, impact range, and potential casualties of the accident, enabling the staff of the emergency command center to further precisely adjust the rescue resources and formulate corresponding rescue plans and strategies. In addition, the emergency command center can also dynamically adjust and optimize the emergency response process according to the real-time transmitted accident scene data to adapt to the changing on-site situation, not only improving the efficiency and accuracy of the rescue operation, but also strengthening the coordinated combat ability between the emergency command center and the on-site rescue team, which helps to better protect the lives and property safety of the people.

[0149] In a feasible implementation manner, referring to Figure 6 , the steps of determining the dynamic warning area based on the accident level include steps S510 to S540, where:

[0150] Step S510, determining the initial warning distance according to the accident level.

[0151] In this embodiment, the initial warning distance is determined according to the accident level and a preset warning distance rule. The higher the accident level, the more serious the accident is, and the greater the potential risk. Therefore, the initial warning distance to be set is farther. The preset warning distance rule is formulated based on multiple factors such as historical accident data, road conditions, and traffic flow to ensure that the warning area can effectively cover the potential dangerous area while avoiding excessive expansion of the warning range and affecting the normal traffic flow. By setting a reasonable initial warning distance, it can provide a basis for the subsequent dynamic adjustment of the warning area, ensure the safety of rescue personnel and on-site personnel, and minimize the impact of the accident on traffic as much as possible. Specifically, when the traffic accident level is divided into levels 1 to 5, when a level 1 accident occurs, the initial warning distance is determined to be 200 meters, and for each increase in level, the warning distance is increased by 50 meters. Such a setting aims to dynamically adjust the range of the warning area according to the severity of the accident to ensure the maximization of the warning effect while avoiding unnecessary interference with normal traffic.

[0152] Step S520: Obtain the second meteorological information of the accident address coordinates, and adjust the initial warning distance based on the second meteorological information to generate the final warning distance.

[0153] In this embodiment, the second meteorological information can also be obtained through meteorological satellites, ground meteorological stations, or meteorological detection equipment carried by aircraft, including key meteorological parameters such as wind speed, wind direction, and visibility. These factors may all affect the actual effect of the warning area. For example, under harsh weather conditions such as strong winds or heavy rains, warning signs may be more easily blocked or damaged, so it is necessary to appropriately increase the warning distance to ensure that the warning information can be conveyed to passing vehicles in a timely and effective manner. At the same time, situations such as slippery or icy road surfaces may also affect the braking performance and driving stability of vehicles, so the warning distance also needs to be adjusted accordingly. By obtaining the meteorological information of the accident site in real time and dynamically adjusting the initial warning distance based on this information, a more accurate and reasonable final warning distance can be generated, thus better protecting the safety of rescue personnel and on-site personnel while reducing the impact of the accident on traffic.

[0154] Step S530: Determine the warning width according to the number of lanes occupied by the accident.

[0155] In this embodiment, the more lanes occupied by the accident, the greater the impact range of the accident on traffic. Therefore, the warning width to be set is wider. By setting a reasonable warning width, it can ensure that the warning area can effectively cover the accident site and its surrounding areas, guide passing vehicles to detour safely, and thus effectively avoid the occurrence of secondary accidents.

[0156] Step S540: Determine the corresponding dynamic warning area according to the final warning distance and the warning width.

[0157] In this embodiment, after determining the final warning distance and the warning width, a rectangular dynamic warning area can be constructed with the final warning distance as the length and the warning width as the width. This dynamic warning area can intuitively reflect the dangerous range of the accident scene and provide clear warning information for passing vehicles.

[0158] Step S600: Dispatch corresponding rescue resources according to the rescue resource allocation data, control the aircraft to reach the corresponding warning position for warning according to the dynamic warning area, and synchronize the dynamic warning area to the navigation system to prompt other vehicles to detour.

[0159] In this embodiment, after determining the final rescue resource allocation data and the dynamic warning area, the highway accident emergency system based on the aircraft will immediately initiate the corresponding dispatching and warning processes. The system will quickly dispatch resources such as rescue vehicles, fire-fighting equipment, medical personnel, and professional rescue teams according to the rescue resource allocation data to ensure that they can reach the accident scene at the fastest speed. At the same time, the system will also control the aircraft to reach the corresponding warning position for aerial warning according to the information of the dynamic warning area. The aircraft can provide clear warning information to passing vehicles by hanging warning signs, alarms, etc., guiding them to safely detour around the accident scene, thus effectively avoiding the occurrence of secondary accidents. In addition, the system will also synchronize the information of the dynamic warning area to the navigation system, and through the prompt function of the navigation system, further remind other vehicles to pay attention to avoiding the accident scene to ensure the smoothness and safety of traffic.

[0160] In a feasible implementation manner, the aircraft is equipped with a warning sign and / or an alarm; the step of controlling the aircraft to reach the corresponding warning position for warning according to the dynamic warning area includes: hovering the aircraft above the corresponding lane at the boundary position of the dynamic warning area, and prompting other vehicles to detour through the warning sign and / or the alarm. In this embodiment, the boundary position of the dynamic warning area refers to the side of the boundary of the rectangular dynamic warning area that is far from the accident geographical location. By hovering the aircraft above the corresponding lane at the boundary position of the dynamic warning area and prompting other vehicles to detour through the warning sign and / or the alarm, not only the warning effect is improved, the occurrence of secondary accidents is effectively avoided, but also the impact of the accident on traffic can be minimized to the greatest extent, ensuring the smoothness and safety of the road.

[0161] In a feasible implementation, after obtaining the accident location information, multiple aircraft can be dispatched to jointly reach the accident scene for coordination to handle the accident more comprehensively. For example, among the dispatched aircraft, two aircraft are responsible for hovering at the boundary positions of the dynamic warning area for warning, and one aircraft is responsible for continuously conducting aerial reconnaissance over the accident scene and transmitting the real-time images of the accident scene to the emergency command center, providing intuitive and comprehensive accident information for the staff in the emergency command center so that they can make more accurate emergency decisions. In addition, the multiple aircraft can also share information and conduct coordinated operations through a wireless communication module to ensure the efficiency and safety of the rescue operation.

[0162] In this embodiment, by obtaining the original accident trigger data, performing spatio-temporal alignment processing on the original accident trigger data to generate accident location information including accident geographical coordinates, determining the flight path of the aircraft according to the accident location information, controlling the aircraft to reach the accident geographical coordinate position based on the flight path, collecting three-dimensional environmental perception data of the accident area corresponding to the accident geographical coordinate position, then extracting accident features from the three-dimensional environmental perception data and determining the corresponding accident level, determining the corresponding rescue resource allocation data and dynamic warning area based on the accident level, and finally dispatching the corresponding rescue resources according to the rescue resource allocation data, controlling the aircraft to reach the corresponding warning position for warning according to the dynamic warning area, and synchronizing the dynamic warning area to the navigation system to prompt other vehicles to detour. In this way, the response speed of highway accidents can be significantly improved, the rescue response time can be reduced, and the probability of secondary accidents can be decreased. Specifically, in this application, after an accident occurs, the aircraft quickly reaches the accident scene and collects three-dimensional environmental perception data, extracts accident features from the three-dimensional environmental perception data and conducts level determination, then reasonably allocates rescue resources, and warns through the navigation system and the aircraft, and conveys the accident information to other vehicles in a timely and effective manner to guide them to detour, thereby effectively avoiding the occurrence of secondary accidents.

[0163] It should be noted that the above examples are only for understanding this application and do not constitute a limitation to the method for highway accident emergency based on aircraft in this application. Any simple transformation in more forms based on this technical concept is within the protection scope of this application.

[0164] This application also provides a highway accident emergency system based on aircraft, and the system includes:

[0165] An accident location information determination module, configured to obtain the original accident trigger data, and perform spatio-temporal alignment processing on the original accident trigger data to generate accident location information including accident geographical coordinates;

[0166] A flight path determination module, configured to determine the flight path of the aircraft according to the accident location information;

[0167] A three-dimensional environment perception data acquisition module, configured to control the aircraft to reach the accident geographical coordinate position based on the flight path, and acquire three-dimensional environment perception data of the accident area corresponding to the accident geographical coordinate position;

[0168] An accident level determination module, configured to extract accident characteristics from the three-dimensional environment perception data and determine the corresponding accident level;

[0169] A rescue allocation data and dynamic warning area determination module, configured to determine the corresponding rescue resource allocation data and dynamic warning area based on the accident level;

[0170] A warning module, configured to dispatch corresponding rescue resources according to the rescue resource allocation data, control the aircraft to reach the corresponding warning position for warning according to the dynamic warning area, and synchronize the dynamic warning area to the navigation system to prompt other vehicles to detour.

[0171] The highway accident emergency system based on an aircraft provided in this application adopts the highway accident emergency method based on an aircraft in the above embodiment, which can improve the response speed of highway accidents and reduce the occurrence probability of secondary accidents. Compared with the prior art, the beneficial effects of the highway accident emergency system based on an aircraft provided in this application are the same as those of the highway accident emergency method based on an aircraft provided in the above embodiment, and other technical features in the highway accident emergency system based on an aircraft are the same as the features disclosed in the method of the above embodiment, which will not be elaborated here.

[0172] The above are only partial embodiments of this application, and thus do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application by using the content of the specification and drawings of this application, or direct / indirect applications in other related technical fields are included in the patent protection scope of this application.

Claims

1. An aircraft-based emergency response method for highway accidents, characterized in that, The method described above includes: Obtaining original accident trigger data, performing spatio-temporal alignment processing on the original accident trigger data, and generating accident location information including accident geographical coordinates; Determining the flight path of the aircraft according to the accident location information; Controlling the aircraft to reach the accident geographical coordinate position based on the flight path, and collecting three-dimensional environmental perception data of the accident area corresponding to the accident geographical coordinate position; Extracting accident features from the three-dimensional environmental perception data, and determining the corresponding accident level; Determining the corresponding rescue resource allocation data and dynamic warning area based on the accident level; Dispatching corresponding rescue resources according to the rescue resource allocation data, controlling the aircraft to reach the corresponding warning position for warning according to the dynamic warning area, and synchronizing the dynamic warning area to the navigation system to prompt other vehicles to detour.

2. The aircraft-based highway accident emergency method according to claim 1, wherein The original accident trigger data includes a collision trigger signal, a trajectory anomaly signal, and a road condition anomaly signal; the steps of obtaining the original accident trigger data and performing spatio-temporal alignment processing on the original accident trigger data to generate accident location information including accident geographical coordinates include: Obtaining vehicle collision acceleration data through an in-vehicle OBD interface, and generating a collision trigger signal when the vehicle collision acceleration data exceeds a first threshold; Obtaining vehicle positioning anomaly data based on the navigation system, and generating a trajectory anomaly signal when the continuous positioning deviation exceeds a second threshold; Obtaining vehicle stagnation data through a roadside millimeter-wave radar, and generating a road condition anomaly signal when the stagnation time exceeds a third threshold; Performing a logical OR operation on the collision trigger signal, the trajectory anomaly signal, and the road condition anomaly signal to generate the original accident trigger data; Establishing a time synchronization window to align the timestamps of the three signals to the same time reference; Adopting a weighted decision-making algorithm to evaluate the confidence levels of the collision trigger signal, the trajectory anomaly signal, and the road condition anomaly signal, and generating accident location information including accident geographical coordinates when the confidence level exceeds a preset confidence threshold.

3. The aircraft-based highway accident emergency method according to claim 1, wherein The steps of determining the flight path of the aircraft according to the accident location information include: Obtaining obstacle information between the accident geographical coordinates and the aircraft deployment position; Fusing the accident geographical coordinates and the obstacle information, and generating a horizontal obstacle avoidance path through the A* algorithm; Obtaining first meteorological information between the accident geographical coordinates and the aircraft deployment position, and determining the flight altitude based on the obstacle information and the first meteorological information; Combining the horizontal obstacle avoidance path and the flight altitude to generate a three-dimensional waypoint sequence of the aircraft to determine the flight path of the aircraft.

4. The aircraft-based highway accident emergency method according to claim 1, wherein When the accident geographical coordinates are located in a tunnel, the steps of controlling the aircraft to reach the accident geographical coordinate position based on the flight path and collecting three-dimensional environmental perception data of the accident area corresponding to the accident geographical coordinate position include: Obtaining feature point image data of the tunnel top; Matching the feature point image data with a pre-stored three-dimensional tunnel model to generate visual positioning coordinate data; Obtaining the inertial data of the aircraft and fusing it with the visual positioning coordinate data, and adopting the Kalman filtering algorithm to generate compensated positioning data; Calculate the positioning error index based on the feature point image data and the inertial data; When the positioning error index exceeds the preset range, obtain the distance between the aircraft and the tunnel sidewall through the airborne ultrasonic ranging device to assist in positioning the aircraft.

5. The aircraft-based highway accident emergency method according to claim 4, characterized in that, The positioning error index includes the visual matching degree and the inertial cumulative offset; Among them, the visual matching degree is calculated according to the following formula: ； wherein represents the visual matching degree; represents the number of feature points with successful matching; represents the total number of feature points in the pre - stored model; represents the side - wall spacing measured visually; represents the theoretical side - wall spacing of the model; The inertial cumulative offset is calculated according to the following formula: ； wherein represents the inertial cumulative offset; represents the acceleration of the aircraft in the x-axis direction of the horizontal plane; represents the acceleration of the aircraft in the y-axis direction of the horizontal plane; represents time.

6. The aircraft-based highway accident emergency method according to claim 1, characterized in that, The three-dimensional environment perception data includes lidar point cloud data, image data, and infrared thermal imaging data; the steps of extracting accident features from the three-dimensional environment perception data and determining the corresponding accident level include: Convert the lidar point cloud data in the coordinate system to generate three-dimensional grid model data; Extract the accident vehicle deformation feature data in the image data through an image recognition algorithm; Analyze the temperature anomaly area in the infrared thermal imaging data to generate fire risk data; Comprehensively determine the accident level based on the three-dimensional grid model data, vehicle deformation feature data, and fire risk data.

7. The aircraft-based highway accident emergency method according to claim 6, characterized in that, The three-dimensional network model data includes the vehicle structure deformation index and the obstacle distribution density, the vehicle deformation feature data includes the airbag deployment state and the maximum depression depth, and the fire risk data includes the area ratio of the area exceeding the critical temperature value and the highest temperature value; the steps of comprehensively determining the accident level based on the three-dimensional grid model data, vehicle deformation feature data, and fire risk data specifically include: Determine the accident level according to the following formula: ； Among them, represents the accident level; represents the vehicle structure deformation index; represents the weight coefficient of the vehicle structure deformation index; represents the maximum depression depth; represents the critical depression depth; represents the weight coefficient of the ratio of the maximum depression depth to the critical depression depth; represents the airbag deployment state; represents the weight coefficient of the airbag deployment state; represents the proportion of the area of the region exceeding the critical temperature value; represents the weight coefficient of the proportion of the area of the region exceeding the critical temperature value; represents the highest temperature value; represents the critical temperature value; represents the weight coefficient of the ratio of the highest temperature value to the critical temperature value; represents the obstacle distribution density; represents the weight proportion of the obstacle distribution density.

8. The aircraft-based highway accident emergency method according to claim 1, wherein The step of determining the dynamic warning area based on the accident level includes: Determine the initial warning distance according to the accident level; Obtain the second meteorological information of the accident address coordinates, and adjust the initial warning distance based on the second meteorological information to generate the final warning distance; Determine the warning width according to the number of lanes occupied by the accident; Determine the corresponding dynamic warning area according to the final warning distance and the warning width.

9. The aircraft-based highway accident emergency method according to claim 1, wherein The aircraft is equipped with a warning sign and / or an alarm; the step of controlling the aircraft to reach the corresponding warning position for warning according to the dynamic warning area includes: Hover the aircraft above the corresponding lane at the boundary position of the dynamic warning area, and use the warning sign and / or alarm to prompt other vehicles to detour.

10. An aircraft-based highway accident emergency system, characterized in that, The system includes: An accident location information determination module, configured to obtain the original accident trigger data, perform spatio-temporal alignment processing on the original accident trigger data, and generate accident location information including accident geographical coordinates; A flight path determination module, configured to determine the flight path of the aircraft according to the accident location information; A three-dimensional environment perception data acquisition module, configured to control the aircraft to reach the accident geographical coordinate position based on the flight path, and acquire three-dimensional environment perception data of the accident area corresponding to the accident geographical coordinate position; An accident level determination module, configured to extract accident features from the three-dimensional environment perception data and determine the corresponding accident level; A rescue allocation data and dynamic warning area determination module, configured to determine the corresponding rescue resource allocation data and dynamic warning area based on the accident level; The warning module is used to dispatch corresponding rescue resources according to the rescue resource allocation data, control the aircraft to reach the corresponding warning position for warning according to the dynamic warning area, and synchronize the dynamic warning area to the navigation system to prompt other vehicles to detour.

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