Traffic processing method and system, terminal and storage medium

Abstract

The invention relates to the technical field of intelligent traffic, and discloses a traffic processing method and system, a terminal and a storage medium, and the method comprises the steps: dynamically adjusting traffic lights and public traffic scheduling information based on multi-dimensional traffic data and a first algorithm model; wherein the multi-dimensional traffic data comprises traffic prediction data, environment state data and vehicle running state data; and acquiring navigation path planning information and emergency braking information of each vehicle based on the multi-dimensional traffic data, the traffic light adjustment condition, the public traffic scheduling information and the vehicle operation information. By means of the method, safe and efficient traffic signal adjustment, public traffic scheduling and path planning work can be achieved.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and in particular to a traffic processing method, system, terminal and storage medium. Background Art

[0002] In the existing technology, the intelligent transportation system mainly transmits data through 4G LTE communication technology. When dealing with high-density traffic flow and complex traffic scenes, its real-time performance cannot be guaranteed, which will cause data transmission delays and thus fail to meet current traffic needs. In addition, the existing technology usually only predicts or processes a certain traffic situation, such as only planning vehicle routes and processing emergency braking, etc., so it cannot be comprehensive. Summary of the invention

[0003] In view of this, the embodiments of the present application provide a traffic processing method, system, terminal and storage medium to achieve safe and efficient traffic and avoid traffic congestion.

[0004] In a first aspect, an embodiment of the present application provides a traffic processing method, comprising: dynamically adjusting traffic lights and public transportation scheduling information based on multi-dimensional traffic data and a first algorithm model; wherein the multi-dimensional traffic data includes traffic prediction data, environmental status data, and vehicle operation status data;

[0005] Based on the multi-dimensional traffic data, the adjustment of the traffic lights, the public transportation dispatch information and the vehicle operation information, the navigation path planning information and the emergency braking information of each vehicle are obtained and sent to the corresponding vehicle.

[0006] In some embodiments, the method further comprises:

[0007] The multi-dimensional traffic data is obtained by analyzing and processing the traffic information obtained by the traffic information acquisition device deployed in the urban traffic.

[0008] In some embodiments, the dynamically adjusting traffic lights and public transportation dispatch information based on the multi-dimensional traffic data and the first algorithm model includes:

[0009] Inputting the multi-dimensional traffic data into a first algorithm model to obtain traffic flow data of each road section in a specific future time period; wherein the road section is a road between two traffic lights;

[0010] According to the traffic flow data of each road section in a specific time period in the future, the switching cycle and duration of the traffic lights of each road section are formulated;

[0011] Based on the vehicle operation status data of public transportation, the traffic forecast data and the switching cycle and duration of the traffic lights on each road section, the switching cycle and duration of the traffic lights are adjusted to allow the public transportation to pass first.

[0012] In some embodiments, the obtaining of navigation path planning information and emergency braking information of each vehicle based on the multi-dimensional traffic data, the traffic light adjustment status, the public transportation dispatch information and the vehicle operation information includes:

[0013] Acquiring the vehicle operation information of each vehicle through a V2V communication module installed on each vehicle;

[0014] According to the vehicle operation information of each vehicle, if a vehicle in the same lane has an emergency brake, a reminder message of the emergency brake of the vehicle in front is sent to the vehicle behind the vehicle currently having the emergency brake;

[0015] The multi-dimensional traffic data, the traffic light adjustment status, the public transportation scheduling information and the vehicle operation information are input into a second algorithm model to analyze the path of each vehicle, and the path of each vehicle is optimized and sent to the corresponding vehicle.

[0016] In some embodiments, the traffic information acquired by the traffic information acquisition device deployed in the urban traffic and the multi-dimensional traffic data acquired after analyzing and processing the traffic information include:

[0017] Receiving the traffic information acquired by the traffic information acquisition device; wherein the traffic information acquisition device includes a camera, a vehicle-mounted sensor, an intelligent road sign and a meteorological sensor; the traffic information includes traffic flow data, environmental data and vehicle status data;

[0018] Preprocess the traffic information by data cleaning and format conversion through edge computing to obtain preprocessed traffic information;

[0019] The pre-processed traffic information is deeply analyzed through a preset learning model to obtain the multi-dimensional traffic data.

[0020] In a second aspect, an embodiment of the present application provides a traffic processing system, including: a traffic prediction management module and a vehicle networking module;

[0021] The traffic prediction management module is used to dynamically adjust traffic lights and public transportation scheduling information based on multi-dimensional traffic data and a first algorithm model; wherein the multi-dimensional traffic data includes traffic prediction data, environmental status data, and vehicle operation status data;

[0022] The vehicle networking module is used to obtain navigation path planning information and emergency braking information of each vehicle based on multi-dimensional traffic data, the traffic light adjustment status, the public transportation scheduling information and vehicle operation information.

[0023] In some embodiments, the system further comprises: a data acquisition and analysis module;

[0024] The data acquisition and analysis module includes a data acquisition unit and a central data processing server;

[0025] The data acquisition unit is used to acquire traffic information based on a traffic information acquisition device deployed in urban traffic;

[0026] The central data processing server is used to receive the traffic information sent by the data acquisition unit, and obtain multi-dimensional traffic data after analyzing and processing the traffic information.

[0027] In some embodiments, the system further includes a traffic incident detection module;

[0028] The traffic incident detection module is used to detect traffic incidents based on the multi-dimensional traffic data and in combination with a third preset AI algorithm.

[0029] In a third aspect, an embodiment of the present application provides a terminal device, which includes a processor and a memory, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the above-mentioned traffic processing method.

[0030] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which implements the above-mentioned traffic processing method when executed on a processor.

[0031] The embodiments of the present application have the following beneficial effects. The embodiments of the present application process the processed traffic data using an AI algorithm through a traffic prediction management module and a vehicle networking module to dynamically adjust traffic signals and public transportation scheduling strategies, thereby effectively reducing urban traffic congestion and improving the overall efficiency of the transportation system. It also realizes collaborative communication between autonomous driving vehicles, optimizes navigation path planning, and improves driving safety and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 A first structural schematic diagram of a traffic processing system according to an embodiment of the present application is shown;

[0034] Figure 2 A second structural schematic diagram of the traffic processing system according to an embodiment of the present application is shown;

[0035] Figure 3 A first flow chart of the traffic processing method according to an embodiment of the present application is shown;

[0036] Figure 4 A second flow chart of the traffic processing method according to an embodiment of the present application is shown;

[0037] Figure 5 A third flow chart of the traffic processing method according to an embodiment of the present application is shown;

[0038] Figure 6 A fourth flow chart of the traffic processing method according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0040] The components of the embodiments of the present application generally described and shown in the drawings herein may be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0041] Hereinafter, the terms "including", "having" and their cognates that can be used in various embodiments of the present application are intended only to indicate specific features, numbers, steps, operations, elements, components or a combination of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or a combination of the foregoing items or increasing the possibility of one or more features, numbers, steps, operations, elements, components or a combination of the foregoing items. In addition, the terms "first", "second", "third" and the like are only used to distinguish descriptions and cannot be understood as indicating or implying relative importance.

[0042] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present application.

[0043] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0044] Based on the high latency of 4G LTE networks in the prior art, they cannot meet the requirements of autonomous driving and dynamic traffic management with high real-time requirements. In addition, the data transmission efficiency of the prior art is reduced during peak hours. In addition, traffic management in the prior art is usually one-sided, and there is no unified system to obtain comprehensive traffic information and process and analyze the traffic to improve the accuracy of traffic prediction, reduce traffic congestion, improve the overall traffic efficiency, and enhance driving safety. Based on this, the present application proposes a traffic processing method, system, terminal and storage medium to improve the accuracy of traffic prediction, reduce traffic congestion, improve the overall traffic efficiency, and enhance driving safety.

[0045] The traffic processing system is described below in conjunction with some specific embodiments.

[0046] Figure 1 FIG. 1 shows a schematic diagram of a traffic processing system according to an embodiment of the present application. Figure 1 As shown, the traffic processing system includes a traffic prediction management module 100 and a vehicle networking module 200; wherein the traffic prediction management module 100 is used to dynamically adjust traffic lights and public transportation scheduling information based on multi-dimensional traffic data and a first algorithm model.

[0047] Among them, multi-dimensional traffic data includes traffic prediction data, environmental status data and vehicle operation status data.

[0048] like Figure 2 As shown, the system also includes a data acquisition and analysis module 300, which includes a data acquisition unit 310 and a central data processing server 320; wherein the data acquisition unit 310 is used to acquire traffic information according to a traffic information acquisition device deployed in urban traffic; wherein the traffic information includes traffic flow data, environmental data and vehicle status data. The central data server is used to receive the traffic information acquired by the data acquisition unit 310, and obtain multi-dimensional traffic data after analyzing and processing the traffic information.

[0049] In some embodiments, Figure 3 As shown, the processing flow of the data acquisition and analysis module 300 includes:

[0050] Step S310: receiving traffic information acquired by a traffic information acquisition device.

[0051] Among them, the traffic information acquisition devices include cameras, vehicle-mounted sensors, smart road signs and meteorological sensors; traffic information includes traffic flow data, environmental data and vehicle status data.

[0052] Step S320, pre-processing the traffic information by data cleaning, data aggregation and format conversion to obtain pre-processed traffic information.

[0053] Step S330, performing in-depth analysis on the pre-processed traffic information using a first algorithm model to obtain multi-dimensional traffic data.

[0054] In some embodiments, Figure 2 As shown, traffic information acquisition devices are deployed at various traffic nodes in the city, where traffic nodes include but are not limited to intersections, main roads, public transportation hubs, etc.; traffic information acquisition devices include but are not limited to cameras, vehicle-mounted sensors, smart road signs, meteorological sensors, etc. These traffic information acquisition devices serve as edge nodes to obtain various traffic information. It should be noted that the traffic information acquisition device can also be a laser radar (LIDAR), millimeter wave radar (MMW), etc., to replace traditional cameras and vehicle-mounted sensors to improve the accuracy and reliability of data collection.

[0055] Among them, cameras can be used for vehicle identification, pedestrian behavior, traffic flow acquisition, traffic accident identification, etc.; vehicle identification is different types of vehicles (such as cars, trucks, buses, etc.) and license plate numbers; pedestrian behavior is to monitor pedestrian activities, including illegal crossing and waiting to cross the road, in order to enhance pedestrian safety; traffic flow acquisition is to count the number of vehicles passing through a specific intersection or section, and analyze the changes in traffic flow during peak and trough periods; traffic accident identification is to monitor possible traffic accidents in real time in order to respond quickly to emergencies.

[0056] On-board sensors can be used to calculate vehicle speed through GPS or other sensors; determine the vehicle's location and direction of travel to help with route planning and navigation; monitor the vehicle's acceleration and deceleration to help predict and avoid collisions; identify possible obstacles around the vehicle to improve the driver's perception; and detect whether the vehicle is parked correctly in the parking area to prevent illegal parking. It can also be used to collect fuel consumption and driving behavior.

[0057] Smart road signs can be used to provide real-time traffic information, traffic rules, emergency announcements, etc. to help drivers make decisions; adjust speed limit signs according to factors such as weather and traffic flow to optimize traffic efficiency; guide vehicles to their destinations to reduce the phenomenon of getting lost; and indicate possible maintenance areas and accident sites ahead, so that drivers can be prepared in advance.

[0058] Meteorological sensors are used to monitor road surface and ambient temperature to prevent driving safety issues caused by ice, snow or heat waves; understand air humidity and assess the slipperiness of the road surface, which has a direct impact on driving safety; monitor wind speed, which is especially important for special sections such as bridges and tunnels to avoid dangers caused by strong winds; report rainfall in a timely manner to help traffic management decisions, such as whether to close certain sections of the road or take other preventive measures; and measure visibility levels, which has a direct impact on driving vision and helps to formulate traffic control measures.

[0059] After the traffic information acquisition device deployed in urban traffic acquires the traffic information, the acquired traffic information is transmitted to the central data processing server 320 in real time through the 5G / 6G network.

[0060] Among them, traffic information includes but is not limited to traffic flow data, environmental data and vehicle status data, of which traffic flow data is mainly obtained through cameras and smart road signs; environmental data is mainly obtained through meteorological sensors, and vehicle status data is mainly obtained through on-board sensors; the frequency at which the central data processing server 320 obtains traffic information can be preset in advance, such as obtaining traffic information once per second, once every 5 seconds, etc. Under normal circumstances, in order to obtain the timeliness of the traffic information, it is usually set to obtain traffic information once per second.

[0061] After receiving the traffic information, each traffic information acquisition device sends the traffic information to the central data processing server 320, so that the central data processing server 320 first cleans the traffic information, including removing abnormal values ​​(checking abnormal values ​​in the data (such as extreme values ​​caused by sensor failure or communication error), and correcting or deleting them according to preset rules), filling missing values ​​(interpolating missing values ​​in traffic data or filling them with average values ​​to ensure the integrity of traffic data), noise filtering (removing noise in traffic data through filtering algorithms (such as Kalman filtering, sliding average, etc.) to improve the accuracy of traffic data) and so on to ensure data quality. After cleaning the data, the cleaned traffic data is aggregated, and the aggregation form can be aggregation by time, aggregation by geographical location, etc.; wherein, aggregation by time means sorting the traffic data by timestamp, and then aggregating the data by time interval (such as hourly, daily, monthly, etc.), such as calculating statistical indicators such as average value, maximum value, minimum value, and sum, so that the real-time aggregated traffic data can quickly respond to traffic emergencies. Aggregation by geographic location can be to divide traffic data into different areas according to geographic coordinates (such as by district, by street, etc.), and then aggregate the relevant data in these areas to provide regional analysis reports or services. The format is converted to convert the processed traffic data into the data format required by the model used for in-depth analysis in the following steps.

[0062] It should be noted that the above-mentioned process of preprocessing traffic information can also be processed in each edge node. The processing process is consistent with the process of preprocessing traffic information in the central data processing server 320, and will not be explained here. However, if the traffic information is preprocessed at each edge node, it will no longer be preprocessed in the central data processing server 320.

[0063] When performing deep processing on the pre-processed traffic data, the first algorithm model to be used must be determined. For example, machine learning models (such as regression analysis, classification models (such as decision trees, random forests) can be used to predict traffic flow and environmental change trends; for the prediction of traffic congestion, deep learning models (such as LSTM, CNN) can be used for time series prediction or image recognition, or CNN can be used to identify image data of real-time traffic conditions. It should be noted that the machine learning models, deep learning models, etc. used are all pre-trained models, and their training process is trained using the training methods in the prior art, which will not be repeated here.

[0064] Among them, the prediction of traffic flow and traffic congestion can be refined to the streets and road sections, so as to facilitate the subsequent analysis of the traffic on each street and road.

[0065] In addition, in this embodiment, vehicle operating status data can also be obtained through vehicle data collected by vehicle sensors, such as vehicle operating efficiency, fuel consumption, fault prediction, etc., so that the driver can obtain the vehicle status in real time and play an auxiliary role in subsequent vehicle path planning.

[0066] The following will continue to describe the traffic prediction management module 100, which is connected to the central data processing server 320 and is used to dynamically adjust traffic lights and public transportation scheduling information based on multi-dimensional traffic data and a second algorithm model.

[0067] In some embodiments, the multi-dimensional traffic data obtained through the above-mentioned in-depth analysis (including but not limited to traffic prediction data, environmental status data and vehicle operation status data) and the second algorithm model are used to adjust traffic lights and schedule public transportation, so that vehicles on the main roads have priority and public transportation has priority.

[0068] like Figure 4 As shown, the traffic prediction management module 100 is specifically used to execute:

[0069] Step S110, inputting the multi-dimensional traffic data into the second algorithm model to obtain the traffic flow data of each road section in a specific future time period.

[0070] The road section is a road between two traffic lights.

[0071] Step S120, according to the traffic flow data of each road section in a specific time period in the future, the switching cycle and duration of the traffic lights of each road section are determined.

[0072] Step S130, based on the vehicle operation status data of public transportation, traffic prediction data and the switching cycle and duration of traffic lights on each road section, the switching cycle and duration of traffic lights are adjusted to give priority to public transportation.

[0073] In some embodiments, after the traffic forecast management module 100 obtains multi-dimensional traffic data through machine learning models, deep learning models, etc., it first calculates the vehicle flow and vehicle density of each road section according to the predicted traffic flow, and then adjusts the traffic lights at the corresponding intersection according to the calculated vehicle flow and vehicle density of each road section. If the prediction results show that a certain main road will have high traffic in the future preset time period, the green light duration of the main road will be extended first to speed up vehicle traffic. The preset time period can be set as needed, such as 5 minutes, 6 minutes, 8 minutes or 10 minutes. If the traffic flow information shows that the traffic flow in each direction of the intersection is uneven, the cycle ratio of the signal light can be adjusted, for example, the green light time in the direction with a larger vehicle density is extended by 5-10 seconds, while the corresponding time is shortened in other directions.

[0074] For example, suppose that at a certain intersection, the traffic flow in the north and south directions is higher, while that in the east-west direction is lower. The traffic prediction management module 100 predicts that the north and south traffic will continue to increase in the next 15 minutes. Based on this prediction, the traffic prediction management module 100 increases the green light duration in the north and south directions from 30 seconds to 40 seconds, while the green light duration in the east-west direction is reduced from 30 seconds to 20 seconds. At a complex intersection, the traffic prediction management module 100 predicts that in the next 10 minutes, the traffic flow on the main road A will increase significantly, and congestion may occur. The traffic prediction management module 100 automatically adjusts the traffic light cycle of the main road A from 2 minutes to 1 minute and 30 seconds, and gives priority to ensuring the green light duration of the main road A. It should be noted that after adjusting each traffic light, the traffic prediction management module 100 sends the adjusted rules to the corresponding traffic lights.

[0075] Alternatively, the traffic prediction management module 100 sends the multi-dimensional traffic data after in-depth analysis to the traffic light management system. The traffic light management system calculates the vehicle flow and vehicle density of each road section through the received multi-dimensional traffic data, and then adjusts the traffic lights at the corresponding intersections according to the calculated vehicle flow and vehicle density of each road section.

[0076] For the dispatching of public transportation, the vehicle operation status data (including but not limited to location, speed, estimated arrival time, etc.) obtained by the on-board sensors on public transportation vehicles is combined with the traffic flow prediction data to obtain the traffic conditions in the current and future preset time periods. When it is predicted that a bus or rail transit vehicle is about to arrive at a certain section of road and the traffic flow of the section is large, the traffic lights of the section will be adjusted first, and the green light time will be extended to ensure the smooth passage of the bus. For example, if a bus is approaching a busy intersection, it is predicted that the intersection will be congested within 5 minutes. Based on this, the traffic lights at the intersection are adjusted, and the green light time in the direction of the bus is extended to ensure the priority passage of the bus. For another example, at a rail transit intersection, it is predicted that the next subway train will be blocked by other vehicles. The traffic prediction management module 100 automatically adjusts the track signal and sends it to the corresponding rail transit signal management system to ensure the priority passage of the subway train and reduce the delay time.

[0077] In some embodiments, the system of the embodiment of the present application further includes a traffic event detection module 400, which can use a pre-trained YOLOv5 (You Only Look Once) model to detect traffic events, wherein the YOLOv5 (You Only Look Once) model is a real-time object detection model based on deep learning, which is widely used in traffic monitoring and event detection, and the model can detect vehicles, pedestrians, traffic accidents, etc. on the road. When using the YOLOv5 model, the camera data and the vehicle-mounted sensor data can be input into the model after pre-processing and deep analysis, and the real-time monitoring video stream and sensor data are processed by the YOLOv5 model. The model identifies traffic events and generates event detection results, such as the location of the accident, the vehicle status, etc. Once a traffic event is detected, the traffic event detection module 400 automatically generates warning information and sends it to the traffic management center or relevant emergency departments; in addition, if an accident is found, the system can automatically adjust the traffic lights of the adjacent sections to evacuate the traffic flow and reduce the impact of the accident. The traffic lights of the adjacent sections can also be automatically adjusted to evacuate the traffic flow and reduce the impact of the accident. When pre-training the YOLOv5 model, the YOLOv5 model can be used as a basis to further optimize the model to adapt to specific traffic environments. For example, the model can be fine-tuned by adding more traffic accident data to improve its accuracy in accident recognition, that is, YOLOv5 is trained using a labeled data set containing a large number of traffic events to ensure that the model can accurately detect various traffic events (such as traffic accidents, vehicle breakdowns, road obstacles, etc.).

[0078] Compared with the prior art which only monitors and controls traffic flow by combining on-board equipment and cloud systems, or controls traffic lights based on sensor data and fixed rules to achieve switching of power traffic lights, or uses simple statistical methods and rules to monitor road network traffic flow to warn of potential congestion points, this application focuses on the application of AI algorithms (such as deep learning and time series analysis) in traffic prediction and management. These AI algorithms can process complex traffic data, conduct multi-dimensional real-time analysis, and provide dynamic and intelligent traffic management strategies, rather than just control based on fixed rules. That is, the embodiments of this application are based on the analysis of multi-source data by AI algorithms to achieve more accurate prediction and management than traditional statistical methods, especially in dynamic traffic signal adjustment and traffic event detection. Furthermore, the currently mentioned prior art usually uses predetermined rules to switch traffic lights, while the embodiment of the present application introduces an adaptive traffic signal control system. The embodiment of the present application uses an AI algorithm to adjust the duration and cycle of traffic lights in real time to optimize traffic flow distribution. This dynamic adjustment can automatically adapt according to real-time traffic conditions to provide a more flexible and effective traffic management solution. That is, the embodiment of the present application emphasizes that when facing emergencies (such as traffic accidents, emergencies), it can quickly respond and adjust through AI algorithms to improve the intelligence level of traffic management; further, in the embodiment of the present application, data collection is no longer limited to a single traffic flow, but integrates multi-dimensional information such as environmental data and vehicle status data, and performs in-depth analysis through edge computing nodes and distributed databases. That is, through the integration of multi-source data, the embodiment of the present application, the AI ​​model can more accurately predict traffic flow changes and dynamically adjust traffic management strategies to further optimize urban traffic.

[0079] In some embodiments, the embodiment of the present application further includes a security protection module 500, which can implement end-to-end encrypted communication, that is, quantum encryption technology can be used to encrypt all communications between the vehicle and the network to ensure high security during data transmission. Through end-to-end encryption, the entire process from the collection of traffic information acquisition devices deployed in urban traffic to data storage and processing can be avoided from data leakage or tampering; further, the security protection module 500 of the embodiment of the present application is also used to implement security authentication and identity management. Specifically, a unique digital identity is configured on each vehicle and device, and security authentication is performed through the 5G / 6G network, and the identity authentication process of all devices is recorded using blockchain technology to prevent identity forgery and illegal access; further, the security protection module 500 of the embodiment of the present application can also be preset through an AI algorithm (such as Autoencoders or Generative Adversarial The security protection module 500 can detect abnormal communication behaviors, such as network attacks and data packet forgery, and generate alarm information in real time. After detecting abnormal communication behaviors, the security protection module 500 can automatically start the emergency response program, prevent unauthorized access, and notify relevant management departments; further, the security protection module 500 of the embodiment of the present application can be used for privacy protection and data management. It implements strict privacy protection strategies to ensure the security of personal data during transmission, storage and use. Data is only accessible to users with specific permissions, and data privacy is protected through encryption and hierarchical management strategies. The embodiment of the present application can ensure the communication security, identity authentication, event response and data privacy of the Internet of Vehicles system through the security protection module 500, and provide users with a safe and reliable Internet of Vehicles environment.

[0080] The security protection module 500 of the embodiment of the present application addresses the data transmission security issues of the Internet of Vehicles system. The embodiment of the present application introduces end-to-end encryption technology and a multi-level security authentication mechanism to prevent illegal access and data leakage, thereby ensuring the overall security of the system. It also utilizes the decentralization and immutability of blockchain technology to ensure the security, transparency and traceability of traffic data, thereby effectively preventing traffic data tampering and information leakage.

[0081] Of course, the data encryption technology of the security protection module 500 is not limited to end-to-end encryption. It can also use public key infrastructure (PKI) for device identity authentication to ensure the legitimacy of each networked device and communication node; data transmission is encrypted using the Advanced Encryption Standard (AES) to ensure the integrity and confidentiality of data during transmission.

[0082] The following is a description of the vehicle networking module 200 of an embodiment of the present application. The vehicle networking module 200 is used to obtain navigation path planning information and emergency braking information of each vehicle based on multi-dimensional traffic data, traffic light adjustment conditions, public transportation scheduling information and vehicle operation information.

[0083] In the embodiment of the present application, the Internet of Vehicles module 200 is combined with the traffic prediction management module 100. When performing vehicle route planning, the real-time road condition data provided by the traffic prediction management module 100 is utilized. The vehicle network optimizes the route of each vehicle based on this data combined with the operating information of each vehicle and multi-dimensional traffic data, thereby achieving more efficient traffic management.

[0084] In some embodiments, Figure 5 As shown, the specific implementation step S200 of the vehicle networking module 200 includes:

[0085] Step S210, obtaining vehicle operation information of each vehicle through the V2V communication module installed on each vehicle.

[0086] A V2V communication module is installed on the vehicle that needs to use the method of the embodiment of the present application for path planning. The V2V communication module supports vehicle-to-vehicle communication based on the 5G / 6G network and can transmit real-time information of the vehicle within a few milliseconds, including but not limited to speed, acceleration / deceleration status, vehicle position, direction, etc. Through the real-time position tracking system of the V2V communication module, each vehicle can identify the position and driving direction of surrounding vehicles, thereby forming a dynamic traffic environment model.

[0087] Step S220, based on the vehicle operation information of each vehicle, if a vehicle in the same lane experiences emergency braking, a reminder message of the emergency braking of the vehicle in front is sent to the vehicle behind the vehicle currently experiencing emergency braking.

[0088] In this step, when multiple vehicles or vehicles in the same fleet are in the same lane, the vehicle networking module 200 determines whether the vehicle has an emergency brake according to the received vehicle operation information, and determines which road and which vehicle the emergency brake is on, and sends the judgment result to the vehicle in the same lane as the vehicle sending the emergency brake, and sends speed and direction recommendations for each vehicle to the rear vehicle according to the emergency brake situation to ensure a safe distance between vehicles. That is, if a vehicle needs emergency braking, the system will transmit the braking signal to the vehicles in the same lane, and other vehicles will slow down or stop in advance according to the received signal to avoid chain collisions.

[0089] Step S230, input the multi-dimensional traffic data, traffic light adjustment status, public transportation scheduling information and vehicle operation information into the third preset AI algorithm to analyze the path of each vehicle, and optimize the path of each vehicle and send it to the corresponding vehicle.

[0090] In this step, the third preset AI algorithm can be a Dijkstra algorithm. In the embodiment of the present application, multiple factors (such as the shortest path, the lowest fuel consumption, and the least congestion) can be considered to calculate the optimal path. After obtaining the traffic light adjustment and public transportation scheduling information according to the multi-dimensional traffic data in the above steps, and obtaining the operation information of each vehicle, it can be determined which sections can be quickly passed. After the vehicle networking module 200 obtains the multi-dimensional traffic data, the traffic light adjustment, the public transportation scheduling information, and the vehicle operation information and inputs them into the Dijkstra algorithm, the vehicle path can be optimized and the navigation guidance can be updated through the vehicle display or HUD. For example, when vehicle A is driving, the system detects that the road ahead is seriously congested, and recalculates the optimal route through the third preset AI algorithm, and guides vehicle A through an alternative section. For example, the navigation system of vehicle B dynamically adjusts the driving route in combination with historical traffic data during the morning rush hour, giving priority to sections with less traffic flow, thereby shortening the driving time.

[0091] This application first obtains traffic information in real time through a traffic information acquisition device deployed in urban traffic, and then processes the collected traffic information, thereby improving the accuracy of traffic flow prediction and realizing more intelligent traffic management. In addition, the embodiment of the present application uses the traffic prediction management module 100 and the Internet of Vehicles module 200 to process the processed traffic data using an AI algorithm to dynamically adjust traffic signals and public transportation scheduling strategies, effectively reducing urban traffic congestion, improving the overall efficiency of the traffic system, and realizing collaborative communication between autonomous driving vehicles, optimizing navigation path planning, and improving driving safety and driving efficiency. In addition, the transmission of data in the embodiment of the present application is all based on 5G / 6G communication technology, realizing real-time, low-latency communication between Internet of Vehicles equipment and infrastructure, and adopting 5G / 6G communication technology in a complex urban traffic environment. Not only the transmission speed of traffic information is improved, but also the reliability of communication is enhanced.

[0092] Figure 6 A flow chart of a traffic processing method according to an embodiment of the present application is shown. Exemplarily, the traffic processing method includes the following steps:

[0093] Step S100, dynamically adjusting traffic lights and public transportation scheduling information based on multi-dimensional traffic data and a first algorithm model.

[0094] Among them, multi-dimensional traffic data includes traffic prediction data, environmental status data and vehicle operation status data.

[0095] Step S200, based on multi-dimensional traffic data, traffic light adjustment conditions, public transportation scheduling information and vehicle operation information, obtain navigation path planning information and emergency braking information of each vehicle.

[0096] It can be understood that the method of this embodiment corresponds to the system of the above embodiment, and the options in the above embodiment are also applicable to this embodiment, so they will not be described repeatedly here.

[0097] The present application also provides a terminal device. Exemplarily, the terminal device includes a processor and a memory, wherein the memory stores a computer program, and the processor runs the computer program, thereby enabling the terminal device to execute the above-mentioned traffic processing method.

[0098] Among them, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU) and a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or at least one of other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application.

[0099] The memory may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The memory is used to store a computer program, and the processor may execute the computer program accordingly after receiving an execution instruction.

[0100] The present application also provides a computer-readable storage medium for storing the computer program used in the above-mentioned terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0101] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or the flow diagram, and the combination of boxes in the structure diagram and / or the flow diagram, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] In addition, the functional modules or units in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0103] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application.

[0104] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A traffic processing method, characterized in that: include: Dynamically adjust traffic lights and public transportation dispatch information based on multi-dimensional traffic data and a first algorithm model; wherein the multi-dimensional traffic data includes traffic prediction data, environmental status data, and vehicle operation status data; Based on the multi-dimensional traffic data, the adjustment of the traffic lights, the public transportation dispatch information and the vehicle operation information, the navigation path planning information and the emergency braking information of each vehicle are obtained and sent to the corresponding vehicle.

2. The traffic processing method according to claim 1, characterized in that: The method further comprises: The multi-dimensional traffic data is obtained by analyzing and processing the traffic information obtained by the traffic information acquisition device deployed in the urban traffic.

3. The traffic processing method according to claim 1, characterized in that: The method of dynamically adjusting traffic lights and public transportation dispatch information based on multi-dimensional traffic data and the first algorithm model includes: Inputting the multi-dimensional traffic data into a first algorithm model to obtain traffic flow data of each road section in a specific future time period; wherein the road section is a road between two traffic lights; According to the traffic flow data of each road section in a specific time period in the future, the switching cycle and duration of the traffic lights of each road section are formulated; Based on the vehicle operation status data of public transportation, the traffic forecast data and the switching cycle and duration of the traffic lights on each road section, the switching cycle and duration of the traffic lights are adjusted to allow the public transportation to pass first.

4. The traffic processing method according to claim 1, characterized in that: The obtaining of navigation path planning information and emergency braking information of each vehicle based on the multi-dimensional traffic data, the traffic light adjustment status, the public transportation dispatch information and the vehicle operation information includes: Acquiring the vehicle operation information of each vehicle through a V2V communication module installed on each vehicle; According to the vehicle operation information of each vehicle, if a vehicle in the same lane has an emergency brake, a reminder message of the emergency brake of the vehicle in front is sent to the vehicle behind the vehicle currently having the emergency brake; The multi-dimensional traffic data, the traffic light adjustment status, the public transportation scheduling information and the vehicle operation information are input into a second algorithm model to analyze the path of each vehicle, and the path of each vehicle is optimized and sent to the corresponding vehicle.

5. The traffic processing method according to claim 2, characterized in that: The traffic information acquired by the traffic information acquisition device deployed in the urban traffic and the multi-dimensional traffic data obtained after analyzing and processing the traffic information include: Receiving the traffic information acquired by the traffic information acquisition device; wherein the traffic information acquisition device includes a camera, a vehicle-mounted sensor, an intelligent road sign and a meteorological sensor; the traffic information includes traffic flow data, environmental data and vehicle status data; Preprocess the traffic information by data cleaning and format conversion through edge computing to obtain preprocessed traffic information; The pre-processed traffic information is deeply analyzed through a preset learning model to obtain the multi-dimensional traffic data.

6. A traffic processing system, characterized in that: include: Traffic prediction management module and vehicle networking module; The traffic prediction management module is used to dynamically adjust traffic lights and public transportation scheduling information based on multi-dimensional traffic data and a first algorithm model; wherein the multi-dimensional traffic data includes traffic prediction data, environmental status data, and vehicle operation status data; The vehicle networking module is used to obtain navigation path planning information and emergency braking information of each vehicle based on multi-dimensional traffic data, the traffic light adjustment status, the public transportation scheduling information and vehicle operation information.

7. The traffic processing system according to claim 6, characterized in that: The system also includes: a data acquisition and analysis module; The data acquisition and analysis module includes a data acquisition unit and a central data processing server; The data acquisition unit is used to acquire traffic information based on a traffic information acquisition device deployed in urban traffic; The central data processing server is used to receive the traffic information sent by the data acquisition unit, and obtain multi-dimensional traffic data after analyzing and processing the traffic information.

8. The traffic processing system according to claim 6, characterized in that: The system also includes a traffic incident detection module; The traffic incident detection module is used to detect traffic incidents based on the multi-dimensional traffic data and in combination with a third preset AI algorithm.

9. A terminal device, characterized in that: The terminal device comprises a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the traffic processing method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer program is stored therein, and when the computer program is executed on a processor, the traffic processing method according to any one of claims 1 to 5 is implemented.

Images