A data processing method for monitoring events

Abstract

The embodiment of the present application relates to a kind of data processing methods for monitoring event, the method comprises: cloud platform receives the first vehicle driving data sent by any vehicle in the first road section and saves;And receive the first road monitoring data sent by the roadside equipment of the first road section and save;And according to the first vehicle driving data, driving event analysis processing is generated corresponding first event message set to roadside equipment sending;And according to the first road monitoring data, comprehensive event analysis processing is generated corresponding second event message set to roadside equipment sending;Roadside equipment according to first event message set is sent to the specified vehicle in the first road section and carries out message push;And according to second event message set, message push is carried out to all vehicles in the first road section.The present application can push the real-time road traffic risk information to the vehicle driving in front, so as to achieve the purpose of guiding safe driving, improving driving safety and improving driving efficiency.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data processing method for monitoring events. Background Technology

[0002] Current conventional traffic monitoring solutions involve deploying cameras at the front end to capture real-time video, playing the video back on the back end, and having back-end staff manually analyze the traffic conditions. This conventional monitoring solution uses a unidirectional network architecture that does not support high-frequency data feedback from the back end to the front end. Therefore, this conventional solution inherently has two technical limitations: 1) it cannot provide driving guidance to vehicles ahead; 2) it cannot push any road traffic risk information to vehicles ahead.

[0003] With the maturity and development of vehicle-to-everything (V2X) technology, we have found that vehicle-mounted devices, roadside devices, and cloud platforms in V2X can communicate with each other and support high-frequency data transmission. Based on this network architecture, adding analysis and feedback processing mechanisms for monitoring events to the traditional monitoring scheme can solve the two technical defects mentioned above. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing a data processing method, electronic device, and computer-readable storage medium for monitoring events. The cloud platform analyzes vehicle driving data transmitted from front-end vehicle-mounted devices to obtain a first set of event messages containing abnormal driving events, which is then returned to the front-end roadside equipment in real time. Simultaneously, it performs comprehensive event analysis on road monitoring data transmitted from the roadside equipment to obtain a second set of event messages containing vehicle, road, and traffic congestion risk events, which is also returned to the front-end roadside equipment in real time. Upon receiving the first set of event messages, the roadside equipment determines the notification range based on the abnormal event type and pushes corresponding warning messages to all vehicles within that range. Upon receiving the second set of event messages, it performs a real-time one-time push of vehicle event message sets related to vehicle risk events to all vehicles in the current road segment, a cyclical broadcast of congestion event message sets related to traffic congestion risk events to all vehicles in the current road segment for a specified duration, and a long-term cyclical broadcast of road event message sets related to road events to all vehicles in the current road segment. This invention enables the monitoring and analysis of real-time perceived data, provides timely driving risk warnings to vehicles ahead based on the analysis results, and pushes real-time road traffic risk information to vehicles ahead, thereby guiding safe driving, improving driving safety, and increasing driving efficiency.

[0005] To achieve the above objectives, a first aspect of the present invention provides a data processing method for monitoring events, the method comprising:

[0006] The cloud platform receives and saves first vehicle driving data sent by any vehicle in the first road segment; receives and saves first road monitoring data sent by roadside equipment in the first road segment; performs driving event analysis and processing based on the first vehicle driving data to generate a corresponding first event message set and sends it to the roadside equipment; and performs comprehensive event analysis and processing based on the first road monitoring data to generate a corresponding second event message set and sends it to the roadside equipment.

[0007] The roadside equipment pushes messages to designated vehicles in the first road segment based on the first event message set; and pushes messages to all vehicles in the first road segment based on the second event message set.

[0008] Preferably, each vehicle traveling on the road is equipped with an on-board terminal; corresponding roadside devices are pre-installed along each section of the road; the roadside devices are connected to the cloud platform and each on-board terminal; multiple monitoring devices are pre-installed near each roadside device, and each monitoring device is connected to the corresponding roadside device. The types of monitoring devices include cameras, lidar, millimeter-wave radar, temperature and humidity sensors, weather phenomenon sensors, and traffic lights.

[0009] The vehicle-mounted terminal is used to collect vehicle driving data, generate corresponding first vehicle driving data, and send it to the platform.

[0010] The monitoring equipment is used to acquire real-time perception data of the road and transmit it to the roadside equipment; specifically: the monitoring equipment of the camera type is used to capture video of the road and transmit the captured video to the roadside equipment; the monitoring equipment of the lidar or millimeter-wave radar type is used to perform radar scanning of the road and transmit the scanned point cloud to the roadside equipment; the monitoring equipment of the temperature and humidity sensor type is used to collect temperature and humidity data of the road and transmit the collected temperature and humidity data to the roadside equipment; the monitoring equipment of the weather phenomenon sensor type is used to analyze the visibility and weather phenomena of the road environment and transmit the corresponding visibility and weather type to the roadside equipment; the monitoring equipment of the traffic light type is used to collect and transmit the traffic light location, traffic light intersection type, total number of traffic light entrances, traffic light entrance identification, and traffic light group information at the intersection entrance to the roadside equipment.

[0011] The roadside equipment is used to perform spatiotemporal feature fusion of the captured video and the scanned point cloud to obtain corresponding fused features; and based on the fused features, to perform vehicle and license plate recognition to obtain multiple first vehicle monitoring data to form a corresponding monitoring data set, and to set the monitoring data type corresponding to the current monitoring data set as the vehicle monitoring data type, and to form a corresponding first type of monitoring data by the current monitoring data type and the current monitoring data set; and based on the fused features, to perform pedestrian and non-motorized vehicle recognition to obtain multiple traffic participant monitoring data to form a corresponding monitoring data set, and to set the monitoring data type corresponding to the current monitoring data set as the other traffic participant monitoring data type, and to form a corresponding first type of monitoring data by the current monitoring data type and the current monitoring data set; and based on the fused features and a preset high-precision road map, to perform lane status recognition to obtain multiple first lane monitoring data to form a corresponding monitoring dataset. The system combines the current monitoring data set with the current monitoring data type, setting it as the road monitoring data type. The current monitoring data type and the current monitoring data set together form the corresponding first type of monitoring data. The system also combines the temperature and humidity data, visibility, and weather type to form the corresponding monitoring data set, setting it as the meteorological monitoring data type. The system further combines the current monitoring data type and the current monitoring data set together to form the corresponding first type of monitoring data. Finally, the system combines the traffic light location, traffic light intersection type, total number of traffic light entrances, traffic light entrance identifiers, and traffic light group information to form the corresponding monitoring data set. The monitoring data type corresponding to the current monitoring data set is set as the traffic light monitoring data type. The current monitoring data type and the current monitoring data set together form the corresponding first type of monitoring data. All the obtained first type of monitoring data are then combined to form the corresponding first road monitoring data, which is sent to the platform.

[0012] Preferably, the first vehicle driving data includes a first license plate number and a first driving data sequence; the first driving data sequence includes multiple first driving data; the first driving data includes a first time, a first driving mode, a first positioning, a first heading angle, a first vehicle speed, a first throttle opening, a first brake pedal opening, a first steering wheel angle, a first driving gear, a first longitudinal acceleration, a first lateral acceleration, a first yaw rate, a first vehicle roll rate, and a first vehicle light status;

[0013] The first road monitoring data includes multiple types of first-class monitoring data; the first-class monitoring data includes monitoring data types and monitoring data sets; the monitoring data types include vehicle monitoring data types, other traffic participant monitoring data types, road monitoring data types, meteorological monitoring data types, and traffic light monitoring data types;

[0014] When the monitoring data type is a vehicle monitoring data type, the corresponding monitoring data set includes multiple first vehicle monitoring data; the first vehicle monitoring data includes first vehicle license plate, first vehicle type, first body color, first vehicle speed, first vehicle heading angle, first vehicle running trajectory, first vehicle driving lane marking, first license plate type and first license plate color;

[0015] When the monitoring data type is other traffic participant monitoring data type, the corresponding monitoring data set includes multiple traffic participant monitoring data; the traffic participant monitoring data includes participant type, participant location, participant shape and size, and participant speed; the participant type includes pedestrians, non-motorized vehicles, and static obstacles.

[0016] When the monitoring data type is a road monitoring data type, the corresponding monitoring data set includes multiple first lane monitoring data; the first lane monitoring data includes first lane marking, first lane type, first lane speed limit range, first lane construction location, first lane water accumulation location, first lane pothole location, and first lane congestion section start location;

[0017] When the monitored data type is a meteorological monitored data type, the corresponding monitored data set includes temperature, humidity, visibility, and weather type; the weather type includes rainy day, snowy day, sunny day, cloudy day, and overcast day;

[0018] When the monitored data type is a traffic light monitoring data type, the corresponding monitored data set includes traffic light location, traffic light intersection type, total number of traffic light entrances, traffic light entrance identifier, and traffic light group information; the traffic light location includes latitude, longitude, and altitude; the traffic light intersection type is the intersection type where the current traffic light is located; the total number of traffic light entrances is the total number of entrances at the intersection where the current traffic light is located; the traffic light entrance identifier is the unique identifier of the entrance where the current traffic light is located; the traffic light group information includes one or more traffic light information; the traffic light information includes traffic light type, traffic light status, and remaining time of the traffic light; the traffic light type includes at least left turn light type, straight light type, and right turn light type; the traffic light status includes red light status, green light status, yellow light status, and flashing yellow light status;

[0019] The first event message set includes the first license plate number and the first, second, third, and fourth abnormal event messages;

[0020] The second event message set includes a vehicle event message set, a road event message set, and a congestion event message set;

[0021] The vehicle event message set includes multiple first vehicle message groups; each first vehicle message group includes the license plate of the first vehicle and multiple first vehicle risk event messages; the first vehicle risk event messages include forward collision event messages, intersection collision event messages, lane change collision event messages, lane left departure event messages, lane right departure event messages, emergency braking event messages, pedestrian collision event messages, vehicle loss of control event messages, vehicle speeding event messages, road restriction event messages, and license plate restriction event messages;

[0022] The road event message set includes multiple first lane message groups; each first lane message group includes a first lane identifier and one or more first lane risk event messages; the first lane risk event messages include construction road occupation event messages with construction location information, road water accumulation event messages with water accumulation location information, road pothole event messages with pothole location information, and severe weather risk messages with level information;

[0023] The congestion event message set includes multiple second lane message groups; the second lane message group includes the first lane identifier and a second lane risk event message with the starting position of the congestion segment.

[0024] Preferably, the step of generating a corresponding first event message set based on the first vehicle driving data through driving event analysis and processing, and sending it to the roadside equipment, specifically includes:

[0025] The cloud platform performs autonomous driving event recognition processing on the first driving data sequence of the first vehicle driving data based on a preset autonomous driving event model to generate corresponding autonomous driving event identifiers; and performs braking event recognition processing on the first driving data sequence based on a preset braking event model to generate corresponding braking event identifiers; and performs steering event recognition processing on the first driving data sequence based on a preset steering event model to generate corresponding steering event identifiers; and performs accident event recognition processing on the first driving data sequence based on a preset accident event model to generate corresponding accident event identifiers; the autonomous driving event identifiers include normal autonomous driving identifiers, autonomous driving disengagement identifiers, and autonomous driving exit identifiers; the braking event identifiers include normal braking identifiers, rapid deceleration identifiers, rapid acceleration identifiers, and emergency braking identifiers; the steering event identifiers include normal steering identifiers, sharp left turn identifiers, and sharp right turn identifiers; the accident event identifiers include no accident identifiers, vehicle collision accident identifiers, vehicle rollover accident identifiers, and vehicle loss of control accident identifiers;

[0026] If the autonomous driving event identifier is not a normal autonomous driving identifier, then the corresponding first abnormal event message is set according to the autonomous driving event identifier; if the braking event identifier is not a normal braking identifier, then the corresponding second abnormal event message is set according to the braking event identifier; if the steering event identifier is not a normal steering identifier, then the corresponding third abnormal event message is set according to the steering event identifier; if the accident event identifier is not an accident-free identifier, then the corresponding fourth abnormal event message is set according to the accident event identifier.

[0027] The first license plate number from the first vehicle's driving data, along with the first, second, third, and fourth abnormal event messages, form a corresponding first event message set, which is then sent to the roadside equipment.

[0028] Preferably, the step of generating a corresponding second event message set based on the comprehensive event analysis and processing of the first road monitoring data and sending it to the roadside equipment specifically includes:

[0029] The cloud platform performs risk event assessments on the vehicle collision risk, lane departure risk, emergency braking risk, pedestrian collision risk, vehicle loss of control risk, vehicle speeding risk, road type restriction risk, and license plate restriction risk of each vehicle in the first road segment based on the first road monitoring data, and generates the corresponding vehicle event message set.

[0030] Based on the first road monitoring data, the road construction risk, road water accumulation risk, road pothole risk and road weather risk in the first road section are assessed to generate the corresponding set of road event messages;

[0031] Based on the first road monitoring data, the traffic congestion risk in the first road segment is assessed to generate the corresponding set of congestion event messages.

[0032] The second event message set, composed of the vehicle event message set, the road event message set, and the congestion event message set, is sent to the roadside equipment.

[0033] Preferably, the roadside equipment pushes messages to designated vehicles within the first road segment based on the first event message set, specifically including:

[0034] The roadside equipment extracts the corresponding first license plate number and the first, second, third, and fourth abnormal event messages from the first event message set; records the vehicle corresponding to the first license plate number as the current vehicle; and performs real-time positioning on the current vehicle to obtain the corresponding current location;

[0035] If the first, second, or third abnormal event message is not empty, then the first license plate number, the current location, and the first, second, or third abnormal event message are combined to form a corresponding first warning message, and the first warning message is pushed to the surrounding vehicles of the current vehicle in the first road segment at once.

[0036] If the fourth abnormal event message is not empty, then the first warning message is composed of the first license plate number, the current location and the fourth abnormal event message, and the first warning message is pushed to the vehicles behind the current vehicle in the first road segment at one time.

[0037] Preferably, message push is performed to all vehicles within the first road segment based on the second event message set, specifically including:

[0038] The roadside equipment extracts the corresponding vehicle event message set, road event message set, and congestion event message set from the second event message set;

[0039] The vehicle event message set is pushed to all vehicles within the first road segment at once;

[0040] The congestion event message set is temporarily cached; and within a preset broadcast duration, the congestion event message set is periodically pushed to all vehicles in the first road segment based on a preset second broadcast frequency; and after the preset broadcast duration, the temporarily cached congestion event message set is deleted.

[0041] The system identifies whether a set of historical road event messages is stored locally. If so, it replaces the set of historical road event messages with the set of road event messages to obtain a new set of historical road event messages. Otherwise, it saves the set of historical road event messages to obtain a new set of historical road event messages. The system then periodically pushes the set of historical road event messages to all vehicles in the first road segment based on a preset first broadcast frequency.

[0042] A second aspect of the present invention provides an electronic device, including: a memory, a processor, and a transceiver;

[0043] The processor is used to couple with the memory, read and execute instructions in the memory to implement the steps of the method described in the first aspect above;

[0044] The transceiver is coupled to the processor, and the processor controls the transceiver to send and receive messages.

[0045] A third aspect of the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a computer, cause the computer to perform the instructions described in the first aspect.

[0046] This invention provides a data processing method for monitoring events, an electronic device, and a computer-readable storage medium. A cloud platform performs driving event analysis on vehicle driving data transmitted from front-end vehicle-mounted devices to obtain a first set of event messages containing abnormal driving events, which is then returned to the front-end roadside equipment in real time. The platform also performs comprehensive event analysis on road monitoring data transmitted from the roadside equipment to obtain a second set of event messages containing vehicle, road, and traffic congestion risk events, which is also returned to the front-end roadside equipment in real time. Upon receiving the first set of event messages, the roadside equipment determines the scope of vehicles to be notified based on the type of abnormal event and pushes corresponding warning messages to all vehicles within that scope. Upon receiving the second set of event messages, it performs a real-time one-time push of vehicle event message sets related to vehicle risk events to all vehicles in the current road segment, a cyclical broadcast of congestion event message sets related to traffic congestion risk events to all vehicles in the current road segment for a specified duration, and a long-term cyclical broadcast of road event message sets related to road events to all vehicles in the current road segment. This invention enables the monitoring and analysis of real-time perceived data, provides timely driving risk warnings to vehicles ahead based on the analysis results, and pushes real-time road traffic risk information to vehicles ahead, thus providing safe driving guidance and improving driving safety and efficiency. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of a data processing method for monitoring events provided in Embodiment 1 of the present invention;

[0048] Figure 2 This is a schematic diagram of the structure of an electronic device provided in Embodiment 2 of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0050] Embodiment 1 of the present invention provides a data processing method for monitoring events, such as... Figure 1 The diagram illustrates a data processing method for monitoring events provided in Embodiment 1 of the present invention. This method mainly includes the following steps:

[0051] Step 1: The cloud platform receives and saves the first vehicle driving data sent by any vehicle in the first road segment; it also receives and saves the first road monitoring data sent by the roadside equipment in the first road segment; it performs driving event analysis and processing based on the first vehicle driving data to generate a corresponding first event message set and sends it to the roadside equipment; and it performs comprehensive event analysis and processing based on the first road monitoring data to generate a corresponding second event message set and sends it to the roadside equipment.

[0052] Specifically, this includes: Step 11, the cloud platform receives and saves the first vehicle driving data sent by any vehicle in the first road segment;

[0053] Here, a simple vehicle-to-everything (V2X) structure is presented in Embodiment 1 of the present invention, including an onboard unit (OBU), monitoring equipment, roadside equipment, and a cloud platform. Each vehicle traveling on the road is equipped with an onboard unit (OBU). Corresponding roadside equipment (RSU) is pre-installed along each section of the road. The roadside equipment can have its own data processing module or can achieve data processing functions by connecting with other mobile edge computing (MEC) devices. The roadside equipment is connected to the cloud platform and each onboard unit. Multiple monitoring devices are pre-installed near each roadside equipment. Each monitoring device is connected to its corresponding roadside equipment. The types of monitoring equipment include cameras, lidar, millimeter-wave radar, temperature and humidity sensors, weather phenomenon sensors, and traffic lights.

[0054] The vehicle-mounted terminal is used to collect vehicle driving data, generate corresponding first vehicle driving data, and send it to the platform. The first vehicle driving data includes a first license plate number and a first driving data sequence. The first driving data sequence includes multiple first driving data points. The first driving data includes a first time, a first driving mode, a first positioning, a first heading angle, a first vehicle speed, a first throttle opening, a first brake pedal opening, a first steering wheel angle, a first driving gear, a first longitudinal acceleration, a first lateral acceleration, a first yaw rate, a first vehicle roll rate, and a first headlight status. The first driving mode includes manual driving mode and automatic driving mode.

[0055] The monitoring equipment is used to acquire real-time perception data of the road and transmit it to roadside equipment. Specifically: camera-type monitoring equipment is used to capture video of the road and transmit the captured video to roadside equipment; lidar or millimeter-wave radar-type monitoring equipment is used to perform radar scanning of the road and transmit the scanned point cloud to roadside equipment; temperature and humidity sensor-type monitoring equipment is used to collect temperature and humidity data of the road and transmit the collected temperature and humidity data to roadside equipment; weather phenomenon sensor-type monitoring equipment is used to analyze the visibility and weather phenomena of the road environment and transmit the corresponding visibility and weather type to roadside equipment; traffic light-type monitoring equipment is used to collect and transmit the location of traffic lights at the intersection entrance, traffic light intersection type, total number of traffic light entrances, traffic light entrance identification, and traffic light group information to roadside equipment.

[0056] Roadside equipment is used to perform spatiotemporal feature fusion of captured video and scanned point cloud to obtain corresponding fused features; based on the fused features, it performs vehicle and license plate recognition to obtain multiple first vehicle monitoring data to form a corresponding monitoring data set, and sets the monitoring data type corresponding to the current monitoring data set as the vehicle monitoring data type, and the current monitoring data type and the current monitoring data set together form the corresponding first type of monitoring data; based on the fused features, it performs pedestrian and non-motorized vehicle recognition to obtain multiple traffic participant monitoring data to form a corresponding monitoring data set, and sets the monitoring data type corresponding to the current monitoring data set as the other traffic participant monitoring data type, and the current monitoring data type and the current monitoring data set together form the corresponding first type of monitoring data; based on the fused features and a preset high-precision road map, it performs lane status recognition to obtain multiple first lane monitoring data to form the corresponding monitoring data. The system collects and sets the monitoring data set corresponding to the current monitoring data set as the road monitoring data type, and the current monitoring data set and the current monitoring data set together form the corresponding first type of monitoring data; it also sets the monitoring data set corresponding to temperature and humidity data, visibility, and weather type, and sets the monitoring data set corresponding to the current monitoring data set as the meteorological monitoring data type, and sets the current monitoring data set and the current monitoring data set together form the corresponding first type of monitoring data; it further sets the monitoring data set corresponding to traffic light location, traffic light intersection type, total number of traffic light entrances, traffic light entrance identifiers, and traffic light group information, and sets the monitoring data set corresponding to the current monitoring data set as the traffic light monitoring data type, and sets the current monitoring data set and the current monitoring data set together form the corresponding first type of monitoring data; and finally, it sends the first type of road monitoring data, composed of all the obtained first type of monitoring data, to the platform.

[0057] Step 12: Receive and save the first road monitoring data sent by the roadside equipment of the first road section;

[0058] Here, the first road monitoring data includes multiple first-category monitoring data; the first-category monitoring data includes monitoring data types and monitoring data sets; the monitoring data types include vehicle monitoring data types, other traffic participant monitoring data types, road monitoring data types, meteorological monitoring data types, and traffic light monitoring data types;

[0059] When the monitored data type is vehicle monitoring data type, the corresponding monitoring data set includes multiple first vehicle monitoring data; the first vehicle monitoring data includes the first vehicle license plate, first vehicle type, first vehicle body color, first vehicle speed, first vehicle heading angle, first vehicle trajectory, first vehicle lane marking, first license plate type, and first license plate color; the first vehicle speed and first vehicle heading angle are the latest speed and heading angle of the corresponding vehicle, and the first vehicle trajectory is the movement trajectory of the corresponding vehicle in the most recent time period (including the trajectory points at the latest moment corresponding to the first vehicle speed and first vehicle heading angle);

[0060] When the monitoring data type is other traffic participant monitoring data type, the corresponding monitoring data set includes multiple traffic participant monitoring data; the traffic participant monitoring data includes participant type, participant location, participant shape and size, and participant speed; participant types include pedestrians, non-motorized vehicles, and static obstacles;

[0061] When the monitoring data type is road monitoring data type, the corresponding monitoring data set includes multiple first lane monitoring data; the first lane monitoring data includes first lane markings, first lane type, first lane speed limit range, first lane construction location, first lane water accumulation location, first lane pothole location, and first lane congestion section start location;

[0062] When the monitored data type is a meteorological monitoring data type, the corresponding monitoring data set includes temperature, humidity, visibility, and weather type; the weather type includes rainy, snowy, sunny, cloudy, and overcast.

[0063] When the monitoring data type is traffic light monitoring data type, the corresponding monitoring data set includes traffic light location, traffic light intersection type, total number of traffic light entrances, traffic light entrance identifier, and traffic light group information; traffic light location includes latitude and longitude and altitude; traffic light intersection type is the intersection type where the current traffic light is located; total number of traffic light entrances is the total number of entrances at the intersection where the current traffic light is located; traffic light entrance identifier is the unique identifier of the entrance where the current traffic light is located; traffic light group information includes one or more traffic light information; traffic light information includes traffic light type, traffic light status, and remaining time of traffic light; traffic light type includes at least left turn light type, straight light type, and right turn light type; traffic light status includes red light status, green light status, yellow light status, and flashing yellow light status;

[0064] Step 13: Analyze and process driving events based on the first vehicle's driving data to generate a corresponding first event message set and send it to the roadside equipment;

[0065] The first event message set includes the first license plate number and the first, second, third, and fourth abnormal event messages;

[0066] Specifically, this includes: Step 131, whereby the cloud platform performs autonomous driving event recognition processing on the first driving data sequence of the first vehicle driving data based on a preset autonomous driving event model to generate corresponding autonomous driving event identifiers; and performs braking event recognition processing on the first driving data sequence based on a preset braking event model to generate corresponding braking event identifiers; and performs steering event recognition processing on the first driving data sequence based on a preset steering event model to generate corresponding steering event identifiers; and performs accident event recognition processing on the first driving data sequence based on a preset accident event model to generate corresponding accident event identifiers;

[0067] Among them, autonomous driving event identifiers include normal autonomous driving identifiers, autonomous driving disengagement identifiers, and autonomous driving exit identifiers; braking event identifiers include normal braking identifiers, rapid deceleration identifiers, rapid acceleration identifiers, and emergency braking identifiers; steering event identifiers include normal steering identifiers, sharp left turn identifiers, and sharp right turn identifiers; and accident event identifiers include no accident identifiers, vehicle collision accident identifiers, vehicle rollover accident identifiers, and vehicle loss of control accident identifiers.

[0068] Here, the autonomous driving event model, braking event model, steering event model, and accident event model in Embodiment 1 of the present invention are all abnormal event identification models pre-defined based on set rules;

[0069] The autonomous driving event model is used to determine whether the current vehicle has switched from autonomous driving to manual driving based on the driving modes at multiple time points provided by the first driving data sequence. If not, it means that the current vehicle is always in autonomous driving, and the autonomous driving event identifier output by the model is a normal autonomous driving identifier. If so, it identifies the real-time motion state difference values ​​(including vehicle speed difference, brake pedal opening difference, longitudinal acceleration difference, etc.) at the time points before and after the state switch based on a set of preset first motion state difference threshold ranges (including vehicle speed difference threshold range, brake pedal opening difference threshold range, longitudinal acceleration difference threshold range, etc.). If the real-time motion state difference values ​​at the time points before and after the switch are all within the preset first motion state difference threshold range, it means that the current vehicle has exited autonomous driving by tapping the brake, and the autonomous driving event identifier output by the model is an autonomous driving exit identifier. If the real-time motion state difference values ​​at the time points before and after the switch are not all within the preset first motion state difference threshold range, it means that the current vehicle has exited autonomous driving by slamming on the brake, and the autonomous driving event identifier output by the model is an autonomous driving disengagement identifier.

[0070] The braking event model is used to perform differential calculations on the real-time motion state information (including vehicle speed, throttle opening, brake pedal opening, longitudinal acceleration, etc.) at multiple time points provided by the first driving data sequence to obtain the corresponding real-time motion state differential values ​​(including vehicle speed differential value, throttle opening differential value, brake pedal opening differential value, longitudinal acceleration differential value, etc.); and based on four preset motion state differential threshold ranges: normal braking motion state differential threshold range (including normal braking vehicle speed differential threshold range, normal braking throttle opening differential threshold range, normal brake pedal opening differential threshold range, normal braking longitudinal acceleration differential threshold range, etc.), rapid deceleration motion state differential threshold range (including rapid deceleration braking vehicle speed differential threshold range, rapid deceleration braking throttle opening differential threshold range, rapid deceleration braking brake pedal opening differential threshold range, rapid deceleration braking longitudinal acceleration differential threshold range, etc.), and rapid acceleration motion state differential threshold range (including rapid acceleration braking vehicle speed differential threshold range, rapid acceleration braking throttle opening differential threshold range, rapid acceleration braking longitudinal acceleration differential threshold range, rapid acceleration braking throttle opening ... The threshold ranges for various real-time motion state differences (including the threshold ranges for vehicle speed, throttle opening, brake pedal opening, and longitudinal acceleration) are matched, and the motion state difference threshold range with the closest range is taken as the matched motion state difference threshold range. If the matched motion state difference threshold range is the normal braking motion state difference threshold range, the braking event identifier output by the model is a normal braking identifier. If the matched motion state difference threshold range is the threshold range for rapid deceleration braking motion state difference, the braking event identifier output by the model is a rapid deceleration braking identifier. If the matched motion state difference threshold range is the threshold range for rapid acceleration braking motion state difference, the braking event identifier output by the model is a rapid acceleration braking identifier. If the matched motion state difference threshold range is the threshold range for emergency braking motion state difference, the braking event identifier output by the model is an emergency braking identifier.

[0071] The steering event model is used to perform differential calculations on the real-time motion state information (including heading angle, steering wheel angle, lateral acceleration, etc.) at multiple time points provided by the first driving data sequence to obtain the corresponding real-time motion state differential values ​​(including heading angle differential values, steering wheel angle differential values, lateral acceleration differential values, etc.); and according to three preset motion state differential threshold ranges: normal steering motion state differential threshold range (including normal steering heading angle differential threshold range, normal steering steering wheel angle differential threshold range, normal steering lateral acceleration differential threshold range, etc.), sharp left turn motion state differential threshold range (including sharp left turn heading angle differential threshold range, sharp left turn steering wheel angle differential threshold range, sharp left turn lateral acceleration differential threshold range, etc.), sharp right turn... The motion state difference threshold range (including the right turn heading angle difference threshold range, right turn steering wheel angle difference threshold range, right turn lateral acceleration difference threshold range, etc.) is matched with each real-time motion state difference value, and the motion state difference threshold range with the closest range is taken as the matched motion state difference threshold range; if the matched motion state difference threshold range is the normal turn motion state difference threshold range, the model outputs a normal turn identifier; if the matched motion state difference threshold range is the left turn motion state difference threshold range, the model outputs a left turn identifier; if the matched motion state difference threshold range is the right turn motion state difference threshold range, the model outputs a right turn identifier.

[0072] The accident event model is used to perform differential calculations on real-time motion state information (including positioning, heading angle, vehicle speed, throttle opening, brake pedal opening, steering wheel angle, longitudinal acceleration, lateral acceleration, yaw rate, and vehicle roll rate) at multiple time points provided by the first driving data sequence to obtain corresponding real-time motion state differential values ​​(including positioning differential value, heading angle differential value, vehicle speed differential value, throttle opening differential value, brake pedal opening differential value, steering wheel angle differential value, longitudinal acceleration differential value, lateral acceleration differential value, yaw rate differential value, and vehicle roll rate differential value, etc.); and based on four preset motion state differential thresholds... Scope: Threshold ranges for accident-free motion state differentials (including accident-free positioning differential threshold ranges, accident-free heading angle differential threshold ranges, accident-free vehicle speed differential threshold ranges, accident-free throttle opening differential threshold ranges, accident-free brake pedal opening differential threshold ranges, accident-free steering wheel angle differential threshold ranges, accident-free longitudinal acceleration differential threshold ranges, accident-free lateral acceleration differential threshold ranges, accident-free yaw rate differential threshold ranges, accident-free vehicle roll rate differential values, etc.), and threshold ranges for collision motion state differentials (including collision positioning differential threshold ranges, collision heading angle differential threshold ranges, collision vehicle speed differential threshold ranges, collision throttle opening differential threshold ranges, etc.). The threshold ranges for collision braking pedal opening, collision steering wheel angle, collision longitudinal acceleration, collision lateral acceleration, collision yaw rate, and collision vehicle rollover velocity are included. The threshold ranges for rollover motion state include: rollover positioning, rollover heading angle, rollover speed, rollover throttle opening, rollover braking pedal opening, rollover steering wheel angle, rollover longitudinal acceleration, rollover lateral acceleration, and rollover yaw rate. The differential threshold ranges for various real-time motion states (including differential threshold ranges for differential threshold ...If the matching motion state difference threshold range is within the accident-free motion state difference threshold range, the braking event identifier output by the model is an accident-free identifier. If the matching motion state difference threshold range is within the collision motion state difference threshold range, the braking event identifier output by the model is a vehicle collision accident identifier. If the matching motion state difference threshold range is within the rollover motion state difference threshold range, the braking event identifier output by the model is a vehicle rollover accident identifier. If the matching motion state difference threshold range is within the out-of-control motion state difference threshold range, the braking event identifier output by the model is a vehicle out-of-control accident identifier.

[0073] Step 132: If the autonomous driving event identifier is not a normal autonomous driving identifier, then set the corresponding first abnormal event message according to the autonomous driving event identifier; if the braking event identifier is not a normal braking identifier, then set the corresponding second abnormal event message according to the braking event identifier; if the steering event identifier is not a normal steering identifier, then set the corresponding third abnormal event message according to the steering event identifier; if the accident event identifier is not an accident-free identifier, then set the corresponding fourth abnormal event message according to the accident event identifier.

[0074] Step 133: The first license plate number of the first vehicle driving data and the first, second, third and fourth abnormal event messages are combined to form a corresponding first event message set and sent to the roadside equipment;

[0075] Step 14: Based on the first road monitoring data, perform comprehensive event analysis and processing to generate a corresponding second event message set and send it to the roadside equipment;

[0076] The second event message set includes a vehicle event message set, a road event message set, and a congestion event message set. The vehicle event message set includes multiple first vehicle message groups; each first vehicle message group includes a first vehicle license plate and multiple first vehicle risk event messages; first vehicle risk event messages include forward collision event messages, intersection collision event messages, lane change collision event messages, lane left departure event messages, lane right departure event messages, emergency braking event messages, pedestrian collision event messages, vehicle loss of control event messages, vehicle speeding event messages, road restriction event messages, and license plate restriction event messages. The road event message set includes multiple first lane message groups; each first lane message group includes a first lane marking and one or more first lane risk event messages; first lane risk event messages include construction road occupation event messages with construction location information, road water accumulation event messages with water accumulation location information, road pothole event messages with pothole location information, and severe weather risk messages with level information. The congestion event message set includes multiple second lane message groups; each second lane message group includes a first lane marking and second lane risk event messages with the starting location of the congestion segment.

[0077] Specifically, this includes: Step 141, where the cloud platform assesses the risk events of each vehicle in the first road segment based on the first road monitoring data, including vehicle collision risk, lane departure risk, emergency braking risk, pedestrian collision risk, vehicle loss of control risk, vehicle speeding risk, road type restriction risk, and license plate restriction risk, and generates a corresponding set of vehicle event messages.

[0078] Here, in Embodiment 1 of this method, when assessing the risk of vehicle collisions for each vehicle within a first road segment based on first road monitoring data, the current monitoring data set is defined as the monitoring data set whose data type is vehicle monitoring data. Based on the first vehicle speed, first vehicle heading angle, and first vehicle trajectory of each first vehicle monitoring data point in the current monitoring data set, the probability of future pairwise collisions is predicted to obtain the corresponding first assessment probability. Two vehicles whose first assessment probability exceeds a set collision threshold are recorded as the corresponding first collision pair. The collision type of the current collision pair is determined based on the collision location—whether it is a forward collision or a lane-change collision. Based on the confirmation result, the two vehicles in the current collision pair are assigned corresponding... Specifically, the first vehicle risk event message, which is either a forward collision event message or a lane change collision event message, needs to be set. In addition, if the monitoring data set, which is a traffic light monitoring data type, is not empty, it is also necessary to predict the risk probability of vehicles colliding with each other at the intersection entrance corresponding to the traffic light in the future based on the first vehicle speed, first vehicle heading angle, first vehicle trajectory, and the position of the traffic light ahead of each first vehicle monitoring data in the current monitoring data set. The two vehicles whose second assessment probability exceeds the set collision threshold are recorded as the corresponding second collision vehicle pair. The first vehicle risk event message, which is specifically an intersection collision event message, corresponding to the two vehicles of the current collision vehicle pair is set.

[0079] In Embodiment 1 of this method, when assessing the lane departure risk of each vehicle in a first road segment based on the first road monitoring data, the monitoring data set with the vehicle monitoring data type as the current monitoring data set is used. Based on the first vehicle speed, first vehicle heading angle, first vehicle trajectory, and first vehicle lane marking of each first vehicle monitoring data in the current monitoring data set, the probability of the vehicle deviating to the left or right in the future is predicted to obtain the corresponding third assessment probability. The first vehicle risk event message corresponding to the vehicle whose third assessment probability exceeds the set collision threshold is set as either a lane left departure event message or a lane right departure event message.

[0080] In Embodiment 1 of this method, when assessing the risk of emergency braking of each vehicle in a first road segment based on the first road monitoring data, the monitoring data set with the vehicle monitoring data type as the current monitoring data set is used. Based on the first vehicle speed, first vehicle heading angle, and first vehicle trajectory of each first vehicle monitoring data in the current monitoring data set, the probability of the vehicle experiencing emergency braking in the future is predicted to obtain the corresponding fourth assessment probability. The first vehicle risk event message corresponding to the emergency braking event message for vehicles whose fourth assessment probability exceeds the set collision threshold is set.

[0081] In Embodiment 1 of this method, when assessing the pedestrian collision risk of each vehicle in a first road segment based on first road monitoring data, a first monitoring data set is defined as the monitoring data set with vehicle monitoring data type as the monitoring data type, and a second monitoring data set is defined as the monitoring data set with other traffic participants monitoring data type as the monitoring data type. Based on the first vehicle speed, first vehicle heading angle, and first vehicle trajectory of each first vehicle monitoring data in the first monitoring data set, the future trajectory of each vehicle is predicted to obtain a corresponding first predicted trajectory. Based on the participant position, participant shape and size, and participant speed of each traffic participant monitoring data in the second monitoring data set, the future trajectory of each participant is predicted to obtain a corresponding second predicted trajectory. Furthermore, it identifies whether each pair of first and second predicted trajectories intersects; if so, a first vehicle risk event message corresponding to the pedestrian collision event message for the vehicle of the currently intersecting first and second predicted trajectories is set.

[0082] In Embodiment 1 of this method, when assessing the risk of vehicle loss of control for each vehicle in a first road segment based on the first road monitoring data, the monitoring data set with the vehicle monitoring data type as the current monitoring data set is used. Based on the first vehicle speed, first vehicle heading angle, first vehicle trajectory, and first vehicle lane marking of each first vehicle monitoring data in the current monitoring data set, the probability of a vehicle rapidly leaving the road or rapidly crossing a lane is predicted to obtain the corresponding fifth assessment probability. For vehicles whose fifth assessment probability exceeds a set collision threshold, a first vehicle risk event message corresponding to a vehicle loss of control event message is set.

[0083] In Embodiment 1 of this method, when assessing the risk of vehicle speeding risk for each vehicle in a first road segment based on the first road monitoring data, a monitoring data set with vehicle monitoring data type as the monitoring data type is designated as the first monitoring data set, and a monitoring data set with road monitoring data type as the monitoring data type is designated as the second monitoring data set. Based on the first lane speed limit range of each first lane monitoring data in the second monitoring data set, the system identifies whether the speed of the first vehicle in each first vehicle monitoring data in the first monitoring data set exceeds the speed limit. Furthermore, it sets a first vehicle risk event message corresponding to the vehicle identified as speeding, specifically a vehicle speeding event message.

[0084] In Embodiment 1 of this method, when assessing the risk event of road vehicle type restriction for each vehicle in a first road segment based on the first road monitoring data, the monitoring data set with vehicle monitoring data type as the monitoring data type is the first monitoring data set, and the monitoring data set with road monitoring data type as the monitoring data type is the second monitoring data set. Based on a preset first correspondence table reflecting the correspondence between lane type and vehicle type and the first lane type of each first lane monitoring data in the second monitoring data set, the method identifies whether the first vehicle type of each first vehicle monitoring data in the first monitoring data set satisfies the lane-vehicle correspondence, and sets a first vehicle risk event message corresponding to the specific road restriction event message for vehicles identified as not satisfying the correspondence.

[0085] In Embodiment 1 of this method, when assessing the risk of license plate restrictions for each vehicle in a first road segment based on the first road monitoring data, the current monitoring data set is the monitoring data set whose monitoring data type is vehicle monitoring data type. Based on a preset second correspondence table reflecting the correspondence between date, license plate type, license plate color, and the last digit of the vehicle license plate, the system identifies whether the first vehicle license plate, first license plate type, and first license plate color of each first vehicle monitoring data in the first monitoring data set for the day meet the agreed correspondence. For vehicles identified as not meeting the correspondence, a first vehicle risk event message corresponding to a license plate restriction event message is set.

[0086] Step 142: Based on the first road monitoring data, assess the road construction risk, road water accumulation risk, road pothole risk and road weather risk within the first road section to generate a corresponding set of road event messages;

[0087] Here, in Embodiment 1 of the present invention, when assessing the road construction risk in the first road segment based on the first road monitoring data, the monitoring data set with the monitoring data type of road monitoring data type is used as the current monitoring data set. The first lane construction location of each first lane monitoring data in the current monitoring data set is identified as empty. The first lane risk event message corresponding to the lane whose identification result is not empty is set as a construction road occupation event message. The corresponding first lane construction location is added as the corresponding construction location information to the current first lane risk event message.

[0088] In Embodiment 1 of the present invention, when assessing the risk of road water accumulation in a first road segment based on first road monitoring data, the monitoring data set with the monitoring data type of road monitoring data type is used as the current monitoring data set. The system identifies whether the first lane water accumulation location of each first lane monitoring data in the current monitoring data set is empty. The system sets the first lane risk event message corresponding to the lane whose identification result is not empty, and adds the corresponding first lane water accumulation location as the corresponding road water accumulation location information to the current first lane risk event message.

[0089] In Embodiment 1 of the present invention, when assessing the risk of road water accumulation in a first road segment based on first road monitoring data, the monitoring data set with the monitoring data type of road monitoring data type is used as the current monitoring data set. The system identifies whether the location of the first lane pothole in each first lane monitoring data of the current monitoring data set is empty. The system sets the first lane risk event message corresponding to the lane whose identification result is not empty, and adds the corresponding first lane pothole location as the corresponding road pothole location information to the current first lane risk event message.

[0090] In Embodiment 1 of the present invention, when assessing the road meteorological risk within a first road segment based on first road monitoring data, the current monitoring data set is a monitoring data set whose monitoring data type is meteorological monitoring data type. Based on a preset third correspondence table reflecting the correspondence between temperature, humidity, visibility, weather type, and severe weather level, the severe weather level corresponding to the temperature, humidity, visibility, and weather type of the current monitoring data set is identified to obtain the corresponding first level. When the first level is higher than a set level threshold, a first lane risk event message corresponding to the lane, specifically a severe weather risk message, is set, and the corresponding first level is added as corresponding level information to the current first lane risk event message.

[0091] Step 143: Assess the traffic congestion risk in the first road segment based on the first road monitoring data and generate a corresponding set of congestion event messages;

[0092] Here, in Embodiment 1 of the present invention, when assessing the traffic congestion risk in the first road segment based on the first road monitoring data, the monitoring data set with the monitoring data type of road monitoring data type is used as the current monitoring data set; and it identifies whether the starting position of the first lane congestion segment of each first lane monitoring data in the current monitoring data set is empty. If it is not empty, the corresponding starting position of the first lane congestion segment is used as the corresponding starting position of the congestion segment, and the corresponding first lane identifier and the starting position of the congestion segment are combined to form the corresponding second lane risk event message;

[0093] Step 144: The second event message set, composed of the vehicle event message set, the road event message set, and the congestion event message set, is sent to the roadside equipment.

[0094] Step 2: The roadside equipment pushes messages to designated vehicles in the first road segment based on the first event message set; and pushes messages to all vehicles in the first road segment based on the second event message set.

[0095] Specifically, this includes: Step 21, where the roadside equipment pushes messages to designated vehicles in the first road segment based on the first event message set;

[0096] Specifically, this includes: Step 211, whereby the roadside equipment extracts the corresponding first license plate number and the first, second, third, and fourth abnormal event messages from the first event message set; records the vehicle corresponding to the first license plate number as the current vehicle; and performs real-time positioning on the current vehicle to obtain the corresponding current location;

[0097] Step 212: If the first, second, or third abnormal event messages are not empty, then the first license plate number, the current location, and the first, second, or third abnormal event messages are combined to form the corresponding first warning message, and the first warning message is pushed to the surrounding vehicles of the current vehicle in the first road segment at once.

[0098] Step 213: If the fourth abnormal event message is not empty, the first alarm message is composed of the first license plate number, the current location and the fourth abnormal event message, and the first alarm message is pushed to the vehicles behind the current vehicle in the first road segment at one time.

[0099] Step 22: Push messages to all vehicles in the first road segment based on the second event message set;

[0100] Specifically, this includes: Step 221, where the roadside equipment extracts the corresponding vehicle event message set, road event message set, and congestion event message set from the second event message set;

[0101] Step 222: Push a set of vehicle event messages to all vehicles in the first road segment at once;

[0102] Step 223: Temporarily cache the congestion event message set; and periodically push the congestion event message set to all vehicles in the first road segment based on the preset second broadcast frequency within the preset broadcast duration; and delete the temporarily cached congestion event message set after the preset broadcast duration.

[0103] Step 224: Identify whether a set of historical road event messages is saved locally. If so, replace the set of historical road event messages with the set of road event messages to obtain a new set of historical road event messages. Otherwise, save the set of road event messages to obtain a new set of historical road event messages. And push the set of historical road event messages to all vehicles in the first road segment periodically based on a preset first broadcast frequency.

[0104] Figure 2 This is a schematic diagram of an electronic device provided in Embodiment 2 of the present invention. This electronic device can be the aforementioned terminal device or server, or it can be a terminal device or server connected to the aforementioned terminal device or server that implements the method of the embodiments of the present invention. Figure 2 As shown, the electronic device may include: a processor 301 (e.g., CPU), a memory 302, and a transceiver 303; the transceiver 303 is coupled to the processor 301, and the processor 301 controls the transmission and reception operations of the transceiver 303. The memory 302 may store various instructions for performing various processing functions and implementing the processing steps described in the foregoing method embodiments. Preferably, the electronic device involved in the embodiments of the present invention further includes: a power supply 304, a system bus 305, and a communication port 306. The system bus 305 is used to realize communication connections between components. The communication port 306 is used for communication between the electronic device and other peripherals.

[0105] exist Figure 2 The system bus 305 mentioned can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 2 The symbol is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. The communication interface is used to enable communication between the database access device and other devices (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk drive.

[0106] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), graphics processing units (GPUs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0107] It should be noted that the embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when run on a computer, cause the computer to perform the methods and processes provided in the above embodiments.

[0108] This invention also provides a chip for executing instructions, which is used to perform the processing steps described in the foregoing method embodiments.

[0109] This invention provides a data processing method for monitoring events, an electronic device, and a computer-readable storage medium. A cloud platform performs driving event analysis on vehicle driving data transmitted from front-end vehicle-mounted devices to obtain a first set of event messages containing abnormal driving events, which is then returned to the front-end roadside equipment in real time. The platform also performs comprehensive event analysis on road monitoring data transmitted from the roadside equipment to obtain a second set of event messages containing vehicle, road, and traffic congestion risk events, which is also returned to the front-end roadside equipment in real time. Upon receiving the first set of event messages, the roadside equipment determines the scope of vehicles to be notified based on the type of abnormal event and pushes corresponding warning messages to all vehicles within that scope. Upon receiving the second set of event messages, it performs a real-time one-time push of vehicle event message sets related to vehicle risk events to all vehicles in the current road segment, a cyclical broadcast of congestion event message sets related to traffic congestion risk events to all vehicles in the current road segment for a specified duration, and a long-term cyclical broadcast of road event message sets related to road events to all vehicles in the current road segment. This invention enables the monitoring and analysis of real-time perceived data, provides timely driving risk warnings to vehicles ahead based on the analysis results, and pushes real-time road traffic risk information to vehicles ahead, thus providing safe driving guidance and improving driving safety and efficiency.

[0110] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0111] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0112] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A data processing method for monitoring events, characterized in that, The method includes: The cloud platform receives and saves first vehicle driving data sent by any vehicle in the first road segment; receives and saves first road monitoring data sent by roadside equipment in the first road segment; performs driving event analysis and processing based on the first vehicle driving data to generate a corresponding first event message set and sends it to the roadside equipment; and performs comprehensive event analysis and processing based on the first road monitoring data to generate a corresponding second event message set and sends it to the roadside equipment. The roadside equipment pushes messages to designated vehicles in the first road segment based on the first event message set; and pushes messages to all vehicles in the first road segment based on the second event message set. Specifically, the step of generating a corresponding first event message set based on the first vehicle driving data through driving event analysis and processing, and sending it to the roadside equipment, includes: The cloud platform performs autonomous driving event recognition processing on the first driving data sequence of the first vehicle driving data based on a preset autonomous driving event model to generate corresponding autonomous driving event identifiers; and performs braking event recognition processing on the first driving data sequence based on a preset braking event model to generate corresponding braking event identifiers; and performs steering event recognition processing on the first driving data sequence based on a preset steering event model to generate corresponding steering event identifiers; and performs accident event recognition processing on the first driving data sequence based on a preset accident event model to generate corresponding accident event identifiers; the autonomous driving event identifiers include normal autonomous driving identifiers, autonomous driving disengagement identifiers, and autonomous driving exit identifiers; the braking event identifiers include normal braking identifiers, rapid deceleration identifiers, rapid acceleration identifiers, and emergency braking identifiers; the steering event identifiers include normal steering identifiers, sharp left turn identifiers, and sharp right turn identifiers; the accident event identifiers include no accident identifiers, vehicle collision accident identifiers, vehicle rollover accident identifiers, and vehicle loss of control accident identifiers; If the autonomous driving event identifier is not a normal autonomous driving identifier, then a corresponding first abnormal event message is set according to the autonomous driving event identifier; if the braking event identifier is not a normal braking identifier, then a corresponding second abnormal event message is set according to the braking event identifier; if the steering event identifier is not a normal steering identifier, then a corresponding third abnormal event message is set according to the steering event identifier; if the accident event identifier is not an accident-free identifier, then a corresponding fourth abnormal event message is set according to the accident event identifier. The first event message set, composed of the first license plate number of the first vehicle driving data and the first, second, third and fourth abnormal event messages, is sent to the roadside equipment. The step of generating a corresponding second event message set based on the first road monitoring data through comprehensive event analysis and processing, and sending it to the roadside equipment, specifically includes: The cloud platform assesses the risks of vehicle collisions, lane departures, emergency braking, pedestrian collisions, vehicle loss of control, speeding, road type restrictions, and license plate restrictions for each vehicle within the first road segment based on the first road monitoring data, generating corresponding vehicle event message sets; it also assesses the risks of road construction, road flooding, potholes, and road weather within the first road segment based on the first road monitoring data, generating corresponding road event message sets; and it assesses the risks of traffic congestion within the first road segment based on the first road monitoring data, generating corresponding congestion event message sets. Finally, the cloud platform sends a second event message set, composed of the vehicle event message set, the road event message set, and the congestion event message set, to the roadside equipment. The roadside equipment pushes messages to designated vehicles within the first road segment based on the first event message set, specifically including: The roadside equipment extracts the corresponding first license plate number and the first, second, third, and fourth abnormal event messages from the first event message set; records the vehicle corresponding to the first license plate number as the current vehicle; and performs real-time positioning on the current vehicle to obtain the corresponding current location; if the first, second, or third abnormal event message is not empty, then the first license plate number, the current location, and the first, second, or third abnormal event message are combined to form a corresponding first warning message, and the first warning message is pushed to the surrounding vehicles of the current vehicle in the first road segment at once; if the fourth abnormal event message is not empty, then the first license plate number, the current location, and the fourth abnormal event message are combined to form a corresponding first warning message, and the first warning message is pushed to the vehicles behind the current vehicle in the first road segment at once; The step of pushing messages to all vehicles in the first road segment based on the second event message set specifically includes: The roadside equipment extracts the corresponding vehicle event message set, road event message set, and congestion event message set from the second event message set; The vehicle event message set is pushed to all vehicles within the first road segment at once; The congestion event message set is temporarily cached; and within a preset broadcast duration, the congestion event message set is periodically pushed to all vehicles in the first road segment based on a preset second broadcast frequency; and after the preset broadcast duration, the temporarily cached congestion event message set is deleted. The system identifies whether a set of historical road event messages is stored locally. If so, it replaces the set of historical road event messages with the set of road event messages to obtain a new set of historical road event messages. Otherwise, it saves the set of historical road event messages to obtain a new set of historical road event messages. The system then periodically pushes the set of historical road event messages to all vehicles in the first road segment based on a preset first broadcast frequency.

2. The data processing method for monitoring events according to claim 1, characterized in that, Each vehicle traveling on the road is equipped with an on-board terminal; corresponding roadside devices are pre-installed along each section of the road; the roadside devices are connected to the cloud platform and each of the on-board terminals; multiple monitoring devices are pre-installed near each of the roadside devices, and each monitoring device is connected to the corresponding roadside device. The types of monitoring devices include cameras, lidar, millimeter-wave radar, temperature and humidity sensors, weather phenomenon sensors, and traffic lights. The vehicle-mounted terminal is used to collect vehicle driving data, generate corresponding first vehicle driving data, and send it to the platform. The monitoring equipment is used to acquire real-time perception data of the road and transmit it to the roadside equipment; specifically: the monitoring equipment of the camera type is used to capture video of the road and transmit the captured video to the roadside equipment; the monitoring equipment of the lidar or millimeter-wave radar type is used to perform radar scanning of the road and transmit the scanned point cloud to the roadside equipment; the monitoring equipment of the temperature and humidity sensor type is used to collect temperature and humidity data of the road and transmit the collected temperature and humidity data to the roadside equipment; the monitoring equipment of the weather phenomenon sensor type is used to analyze the visibility and weather phenomena of the road environment and transmit the corresponding visibility and weather type to the roadside equipment; the monitoring equipment of the traffic light type is used to collect and transmit the traffic light location, traffic light intersection type, total number of traffic light entrances, traffic light entrance identification, and traffic light group information at the intersection entrance to the roadside equipment. The roadside equipment is used to perform spatiotemporal feature fusion of the captured video and the scanned point cloud to obtain corresponding fused features; and based on the fused features, to perform vehicle and license plate recognition to obtain multiple first vehicle monitoring data to form a corresponding monitoring data set, and to set the monitoring data type corresponding to the current monitoring data set as the vehicle monitoring data type, and to form a corresponding first type of monitoring data by the current monitoring data type and the current monitoring data set; and based on the fused features, to perform pedestrian and non-motorized vehicle recognition to obtain multiple traffic participant monitoring data to form a corresponding monitoring data set, and to set the monitoring data type corresponding to the current monitoring data set as the other traffic participant monitoring data type, and to form a corresponding first type of monitoring data by the current monitoring data type and the current monitoring data set; and based on the fused features and a preset high-precision road map, to perform lane status recognition to obtain multiple first lane monitoring data to form a corresponding monitoring dataset. The system combines the current monitoring data set with the current monitoring data type, setting it as the road monitoring data type. The current monitoring data type and the current monitoring data set together form the corresponding first type of monitoring data. The system also combines the temperature and humidity data, visibility, and weather type to form the corresponding monitoring data set, setting it as the meteorological monitoring data type. The system further combines the current monitoring data type and the current monitoring data set together to form the corresponding first type of monitoring data. Finally, the system combines the traffic light location, traffic light intersection type, total number of traffic light entrances, traffic light entrance identifiers, and traffic light group information to form the corresponding monitoring data set. The monitoring data type corresponding to the current monitoring data set is set as the traffic light monitoring data type. The current monitoring data type and the current monitoring data set together form the corresponding first type of monitoring data. All the obtained first type of monitoring data are then combined to form the corresponding first road monitoring data, which is sent to the platform.

3. The data processing method for monitoring events according to claim 1, characterized in that, The first vehicle driving data includes a first license plate number and a first driving data sequence; the first driving data sequence includes multiple first driving data; the first driving data includes a first time, a first driving mode, a first positioning, a first heading angle, a first vehicle speed, a first throttle opening, a first brake pedal opening, a first steering wheel angle, a first driving gear, a first longitudinal acceleration, a first lateral acceleration, a first yaw rate, a first vehicle roll rate, and a first vehicle light status; The first road monitoring data includes multiple types of first-class monitoring data; the first-class monitoring data includes monitoring data types and monitoring data sets; the monitoring data types include vehicle monitoring data types, other traffic participant monitoring data types, road monitoring data types, meteorological monitoring data types, and traffic light monitoring data types; When the monitoring data type is a vehicle monitoring data type, the corresponding monitoring data set includes multiple first vehicle monitoring data; the first vehicle monitoring data includes first vehicle license plate, first vehicle type, first body color, first vehicle speed, first vehicle heading angle, first vehicle running trajectory, first vehicle driving lane marking, first license plate type and first license plate color; When the monitoring data type is other traffic participant monitoring data type, the corresponding monitoring data set includes multiple traffic participant monitoring data; the traffic participant monitoring data includes participant type, participant location, participant shape and size, and participant speed; the participant type includes pedestrians, non-motorized vehicles, and static obstacles. When the monitoring data type is a road monitoring data type, the corresponding monitoring data set includes multiple first lane monitoring data; the first lane monitoring data includes first lane marking, first lane type, first lane speed limit range, first lane construction location, first lane water accumulation location, first lane pothole location, and first lane congestion section start location; When the monitored data type is a meteorological monitored data type, the corresponding monitored data set includes temperature, humidity, visibility, and weather type; the weather type includes rainy day, snowy day, sunny day, cloudy day, and overcast day; When the monitored data type is a traffic light monitoring data type, the corresponding monitored data set includes traffic light location, traffic light intersection type, total number of traffic light entrances, traffic light entrance identifier, and traffic light group information; the traffic light location includes latitude, longitude, and altitude; the traffic light intersection type is the intersection type where the current traffic light is located; the total number of traffic light entrances is the total number of entrances at the intersection where the current traffic light is located; the traffic light entrance identifier is the unique identifier of the entrance where the current traffic light is located; the traffic light group information includes one or more traffic light information; the traffic light information includes traffic light type, traffic light status, and remaining time of the traffic light; the traffic light type includes at least left turn light type, straight light type, and right turn light type; the traffic light status includes red light status, green light status, yellow light status, and flashing yellow light status; The first event message set includes the first license plate number and the first, second, third, and fourth abnormal event messages; The second event message set includes a vehicle event message set, a road event message set, and a congestion event message set; The vehicle event message set includes multiple first vehicle message groups; each first vehicle message group includes the license plate of the first vehicle and multiple first vehicle risk event messages. The first vehicle risk event message includes forward collision event message, intersection collision event message, lane change collision event message, lane left departure event message, lane right departure event message, emergency braking event message, pedestrian collision event message, vehicle loss of control event message, vehicle speeding event message, road restriction event message, and license plate restriction event message; The road event message set includes multiple first lane message groups; each first lane message group includes a first lane identifier and one or more first lane risk event messages; the first lane risk event messages include construction road occupation event messages with construction location information, road water accumulation event messages with water accumulation location information, road pothole event messages with pothole location information, and severe weather risk messages with level information; The congestion event message set includes multiple second lane message groups; the second lane message group includes the first lane identifier and a second lane risk event message with the starting position of the congestion segment.

4. An electronic device, characterized in that, include: Memory, processor, and transceiver; The processor is configured to be coupled to the memory, read and execute instructions in the memory to implement the method according to any one of claims 1-3; The transceiver is coupled to the processor, and the processor controls the transceiver to send and receive messages.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a computer, cause the computer to perform the method described in any one of claims 1-3.

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