A vehicle speed monitoring and prompting method in a vehicle networking scenario

Abstract

The present invention discloses a vehicle speed monitoring and prompting method in a vehicle networking scenario, which relates to the technical field of vehicle speed monitoring, and comprises the following steps: integrating multi-dimensional data collected by sensors or cameras on traffic roads through multi-source data fusion technology to form a comprehensive traffic status view; uploading the integrated multi-dimensional data to a cloud storage system; using a machine learning algorithm in artificial intelligence to conduct an in-depth analysis of traffic flow and vehicle speed; using virtual simulation technology to establish a dynamic simulation model of urban traffic; generating real-time traffic information based on multi-source data fusion, artificial intelligence and dynamic simulation models, and a traffic management center performing real-time signal control based on the real-time traffic information; designing a smart contract for traffic violation detection based on real-time traffic information and blockchain technology; the present invention realizes efficient monitoring of vehicle speed and timely prompting.

Description

Technical Field

[0001] The present invention relates to the technical field related to vehicle speed monitoring, and in particular to a vehicle speed monitoring prompt method in a vehicle networking scenario. Background Art

[0002] With the acceleration of urbanization and the continuous growth of vehicle ownership, urban traffic management is facing unprecedented challenges. Traditional traffic management methods mainly rely on signal light control and manual monitoring at fixed time intervals. This method is not only inefficient, but also difficult to cope with sudden traffic incidents and complex traffic flow changes. In addition, the collection and processing of traffic data are usually scattered in different systems and equipment, lacking effective integration and analysis methods, which makes it difficult for traffic management departments to obtain comprehensive traffic status information in a timely manner, thus affecting the real-time and accuracy of traffic management.

[0003] In recent years, with the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, the Internet of Vehicles has gradually become an important part of the intelligent transportation system. By connecting vehicles, road infrastructure, and traffic management centers, the Internet of Vehicles has realized real-time collection, transmission, and processing of traffic data, providing new solutions for intelligent transportation management.

[0004] However, how to effectively integrate multi-source data, conduct in-depth analysis and achieve real-time traffic control remains the main technical challenge facing current Internet of Vehicles technology. Summary of the invention

[0005] In order to solve the defects of the prior art, the present invention provides a vehicle speed monitoring and prompting method in a vehicle networking scenario.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: The present invention provides a vehicle speed monitoring and prompting method in a vehicle networking scenario, the method comprising the following steps: S1: Integrate the multi-dimensional data collected by sensors or cameras on the traffic road through multi-source data fusion technology to form a comprehensive view of the traffic status; S2: uploading the multi-dimensional data integrated in step S1 to a cloud storage system, and using big data processing technology to analyze and process the multi-dimensional data; S3: Combines historical data and real-time data in the cloud storage system, uses machine learning algorithms in artificial intelligence to conduct in-depth analysis of traffic flow and vehicle speed, and predict future traffic conditions; S4: Based on step S3, a dynamic simulation model of urban traffic is established using virtual simulation technology to simulate traffic conditions under different flow rates and behaviors; S5: Generate real-time traffic information based on multi-source data fusion in S1, artificial intelligence in S3, and dynamic simulation model in S4. The traffic management center performs real-time signal control based on the real-time traffic information. S6: Based on the real-time traffic information in S5 and blockchain technology, a smart contract for traffic violation detection is designed. When speeding or other violations are detected, the smart contract automatically executes a pre-set response mechanism.

[0007] As a preferred technical solution of the present invention, step S1 specifically includes the following steps: S11: Data collection equipment deployment: Intelligent data collection equipment will be deployed at traffic intersections, main roads and transportation hubs. The intelligent data collection equipment will obtain multi-dimensional data of vehicles; S12: Real-time data transmission, using wireless communication technology to transmit the collected multi-dimensional data to the central data processing system in real time; S13: Data fusion and processing: multi-dimensional data is integrated through multi-source data fusion technology to form a traffic status view.

[0008] As a preferred technical solution of the present invention, the intelligent collection equipment includes sensors and cameras, and the multi-dimensional data includes vehicle speed, vehicle flow, vehicle type, and vehicle driving direction.

[0009] As a preferred technical solution of the present invention, step S3 specifically includes the following steps: S31: Establish a multi-dimensional data set, integrate historical traffic data and real-time traffic data in the cloud storage system, and establish a multi-dimensional data set; S32: Model training and optimization: for multi-dimensional data sets, use machine learning algorithms to train multi-dimensional data sets and gradually optimize the model; S33: Real-time prediction, using trained models to analyze current traffic conditions, predict future flow and speed changes, and automatically adjust model parameters based on real-time data feedback.

[0010] As a preferred technical solution of the present invention, step S4 specifically includes the following steps: S41: Establish a dynamic simulation model of urban traffic, including various traffic flow and behavior patterns; S42: Simulate the effects of various management strategies under different traffic flow conditions; S43: Summarize simulation results, analyze the impact of strategies on traffic flow and safety, and determine the best traffic management solution.

[0011] As a preferred technical solution of the present invention, a dynamic simulation model of urban traffic based on virtual simulation technology utilizes a traffic flow simulation algorithm to display the dynamic operation status of the traffic system under different traffic flows and driving behaviors.

[0012] As a preferred technical solution of the present invention, step S5 specifically includes the following steps: S51: Data analysis interface, real-time data exchange between the traffic management center and the integrated multi-source data, AI analysis and virtual simulation layer; S52: signal control decision, automatically adjust the traffic light cycle according to the comprehensive traffic status and set the priority vehicles; S53: Implementation and feedback, execute signal control in real time and adjust strategies through real-time data feedback to adapt to traffic flow changes.

[0013] As a preferred technical solution of the present invention, the real-time signal control system is integrated with the user's mobile application to provide the driver with real-time speed monitoring prompts and optimal route suggestions.

[0014] As a preferred technical solution of the present invention, step S6 specifically includes the following steps: S61: Protocol formulation, based on real-time traffic information and blockchain technology, design smart contracts for traffic violation detection, define violation trigger conditions and penalty measures; S62: Agreement execution. After the monitoring system detects a violation, the smart contract automatically executes the corresponding measures, generates a notification to the law enforcement agency, or sends real-time prompt information to traffic participants.

[0015] As a preferred technical solution of the present invention, the smart contract generates a visual report while processing violations.

[0016] The beneficial effects of the present invention are: The present invention realizes efficient monitoring of vehicle speed and timely prompts through the comprehensive application of multi-source data fusion technology, cloud computing platform, artificial intelligence, virtual simulation technology, smart contracts and real-time signal control, thereby achieving comprehensive, efficient, accurate, dynamic and intelligent traffic management, with significant beneficial effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a flow chart of the vehicle speed monitoring and prompting method of the present invention.

[0018] Figure 2 Schematic diagram of the specific process of step S1.

[0019] Figure 3 FIG. 4 is a schematic diagram of a specific flow chart of step S3.

[0020] Figure 4 FIG. 4 is a schematic diagram of a specific flow chart of step S4.

[0021] Figure 5 FIG. 5 is a schematic diagram of a specific flow chart of step S5.

[0022] Figure 6 FIG. 4 is a schematic diagram of a specific flow chart of step S6. DETAILED DESCRIPTION

[0023] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0024] Example 1

[0025] like Figure 1 As shown, a vehicle speed monitoring and prompting method in a vehicle networking scenario includes the following steps: S1: Integrate the multi-dimensional data collected by sensors or cameras on the traffic road through multi-source data fusion technology to form a comprehensive view of the traffic status; S2: Upload the multi-dimensional data integrated in step S1 to the cloud storage system, and use big data processing technology to analyze and process the multi-dimensional data; cloud computing technology can store and process massive amounts of data, making it more reliable and scalable. In the cloud storage system, big data processing technology (such as Hadoop and Spark frameworks) is used to analyze and process the integrated data. This process includes data mining, trend analysis, and anomaly detection. At the same time, based on the cloud platform, data can be quickly accessed and updated in real time, making it easier for traffic management departments to obtain the latest traffic intelligence at any time. This processing method not only reduces the load on local devices, but also enables complex data analysis through the powerful computing power of cloud computing, thereby improving traffic management efficiency; S3: Combines historical data and real-time data in the cloud storage system, uses machine learning algorithms in artificial intelligence to conduct in-depth analysis of traffic flow and vehicle speed, and predict future traffic conditions; S4: Based on step S3, a dynamic simulation model of urban traffic is established using virtual simulation technology to simulate traffic conditions under different flow rates and behaviors; S5: Generate real-time traffic information based on multi-source data fusion in S1, artificial intelligence in S3, and dynamic simulation model in S4. The traffic management center performs real-time signal control based on the real-time traffic information. S6: Based on the real-time traffic information in S5 and blockchain technology, a smart contract for traffic violation detection is designed. When speeding or other violations are detected, the smart contract automatically executes a pre-set response mechanism.

[0026] The above steps constitute a systematic and intelligent vehicle speed monitoring and prompting method in the Internet of Vehicles scenario, which has unique foresight and practicality, and can effectively improve the intelligence and instant response capabilities of urban traffic management. Through the comprehensive application of multi-source data fusion technology, cloud computing platform, artificial intelligence, virtual simulation technology, smart contracts and real-time signal control, it can realize efficient monitoring of vehicle speed and timely prompts, thus realizing comprehensive, efficient, accurate, dynamic and intelligent traffic management.

[0027] Further, if Figure 2 As shown, the step S1 specifically includes the following steps: S11: Data collection equipment deployment: Intelligent data collection equipment will be deployed at traffic intersections, main roads and transportation hubs. The intelligent data collection equipment will obtain multi-dimensional data of vehicles; S12: Real-time data transmission, using wireless communication technology to transmit the collected multi-dimensional data to the central data processing system in real time; S13: Data fusion and processing: multi-dimensional data is integrated through multi-source data fusion technology to form a traffic status view.

[0028] The multi-dimensional data of vehicles usually comes from different devices, and the measurement technologies and data formats used are different, which may cause data inconsistency and noise. The application of multi-source data fusion technology will effectively solve this problem. Through data cleaning, feature extraction and weighted fusion, data from different sources can be integrated to generate a comprehensive traffic status view. This view not only covers the real-time traffic conditions, but also dynamically reflects the overall operation of the road network, providing a solid data foundation for subsequent data analysis and decision-making; Specifically, data cleaning is the first step of multi-source data fusion, which aims to remove noise and outliers. Common data cleaning methods include median filtering, Gaussian filtering, etc. Feature extraction is the process of extracting useful information from raw data. Common feature extraction methods include principal component analysis (PCA) and linear discriminant analysis (LDA), which are all existing technologies, and the specific working process will not be repeated; Weighted fusion is to integrate data from different sources according to certain weights. Preferably, the weighted fusion method of this embodiment uses the Kalman filter method, and its formula is as follows: ; in, It is represented as the state estimate at the current time k, which is the output of the Kalman filter and represents the optimal estimate of the system state at time k. Represented as time-based The state estimate of the vehicle motion model of the system is obtained from the state estimate of the previous moment. The predicted state value at the current moment, is the Kalman gain, which determines the current observation value and predicted values The weight in the state estimation, is the observation matrix, which describes the system state How to map to observations , in vehicle speed estimation, It can be an identity matrix, indicating that the state directly corresponds to the observation value; Therefore, the Kalman filter method realizes state estimation through the following steps: Prediction: An estimate of the state based on the previous moment , predict the current state through the state transition model ; Update: Combine the current observations , through the Kalman gain Correct the predicted value to obtain the optimal state estimate at the current moment ; In multi-source data fusion, Kalman filtering can effectively integrate data from different devices (such as radars, cameras, ultrasonic sensors, etc.), solve data inconsistency and noise problems, and dynamically adjust the weights of predicted values ​​and observed values. Kalman filtering can generate a more accurate and comprehensive view of traffic status, providing reliable data support for traffic management and decision-making.

[0029] Furthermore, the intelligent collection equipment in step S11 includes sensors (such as radars, ultrasonic sensors), cameras (such as video analysis cameras), traffic flow meters, etc. The multi-dimensional data includes multi-dimensional data such as vehicle speed, vehicle flow, vehicle type, vehicle driving direction, and vehicle flow density. In addition, the intelligent collection equipment may also include a weather station for collecting more dimensional data such as real-time weather conditions (such as temperature, precipitation, wind speed, etc.).

[0030] Further, if Figure 3 As shown, step S3 specifically includes the following steps: S31: Establish a multi-dimensional data set, integrate historical traffic data and real-time traffic data in the cloud storage system, and establish a multi-dimensional data set; S32: Model training and optimization: for multi-dimensional data sets, use machine learning algorithms to train multi-dimensional data sets and gradually optimize the model; S33: Real-time prediction, using trained models to analyze current traffic conditions, predict future flow and speed changes, and automatically adjust model parameters based on real-time data feedback.

[0031] Among them, in the cloud storage system, historical data and real-time data are integrated, and artificial intelligence technology is used for in-depth analysis. This process relies on machine learning algorithms, such as regression analysis, decision trees, deep learning, etc., to identify and analyze traffic flow and vehicle speed patterns. Through training models, the system can identify peak hours, traffic bottlenecks and accident-prone areas based on historical data. More importantly, combined with real-time data, the system can dynamically adjust the prediction model to achieve accurate prediction of upcoming traffic conditions. This prediction is not limited to short-term traffic flow predictions, but can also help traffic managers formulate long-term traffic optimization strategies and improve the level of intelligence in traffic management.

[0032] Preferably, in this embodiment, a recurrent neural network (RNN) is used to train the multidimensional data set, which specifically includes the following steps: Input sequence: Input time series data (such as hourly traffic volume and vehicle speed) into the RNN. Assuming the time step is T, the input sequence can be expressed as: ; in, is the input vector at time step t, such as: =[Traffic flow t , vehicle speed t ]; Hidden state update: the hidden state at each time step From the current input and the hidden state at the previous time step Calculated, the core of RNN is the hidden state , which saves the historical information from time step 1 to t, and the hidden state update formula is: ;

[0033] in, is represented as the hidden state at time step t, is represented as the hidden state at time step t-1, is represented as the input vector at time step t, is represented as the weight matrix of the hidden state, is represented as the weight matrix of the input vector, It is represented as the bias term of the hidden state, and tanh is the hyperbolic tangent activation function, which is used for nonlinear transformation; Output prediction: Based on the hidden state Calculate the output for the current time step (such as predicted traffic flow or vehicle speed), the formula is as follows: ; in, Represented as the output at time step t (such as predicted traffic flow or vehicle speed), is the weight matrix of the output vector, Represented as the bias term of the output; Back propagation: The model parameters (weight matrix) are optimized through the back propagation through time (BPTT) algorithm to minimize the loss function. In this embodiment, the mean square error is used as the loss function: ; in, Represented as the true value at time step t (such as actual traffic flow or vehicle speed , , and the bias term , ), L is used to measure the difference between the model prediction value and the true value. By calculating L, the prediction effect of the model can be evaluated, so that the prediction result of the model is closer to the true value; Through the above steps, RNN can effectively process time series data and play an important role in traffic flow and vehicle speed prediction. In addition, in addition to excellent data processing capabilities, the artificial intelligence analysis module can also self-learn and continuously optimize the model to improve its adaptability to complex traffic conditions.

[0034] Further, if Figure 4 As shown, step S4 specifically includes the following steps: S41: Establish a dynamic simulation model of urban traffic, including various traffic flow and behavior patterns; S42: Simulate the effects of various management strategies (such as speed limit, lane change, signal light control, etc.) under different traffic flow conditions; S43: Summarize simulation results, analyze the impact of strategies on traffic flow and safety, and determine the best traffic management solution.

[0035] Based on the above-mentioned predictive analysis, virtual simulation technology can be used to create a dynamic simulation model of urban traffic. This model integrates a variety of traffic flow simulation algorithms and can display the dynamic operation status of the traffic system under different traffic flows and driving behaviors. By continuously updating the model input and providing real-time feedback on traffic status and flow changes, simulation technology can help traffic management departments conduct multi-scenario simulation tests when making decisions. This process effectively promotes the evaluation of the flexibility and feasibility of traffic management measures, provides a scientific basis for complex urban traffic management, and improves the foresight and accuracy of traffic management.

[0036] Optionally, the traffic flow simulation algorithm uses cellular automata and a microscopic traffic flow model, which are the core components of the dynamic simulation model, wherein the cellular automaton divides the road into discrete cells, each cell represents a road segment of fixed length, the state of each cell is determined by the vehicle occupancy situation, and the vehicle moves between cells; the microscopic traffic flow model simulates the driving behavior of each vehicle, including speed, acceleration, lane change, etc., and reflects the dynamic changes of traffic flow through the interaction between vehicles (such as following, lane changing), and at the same time, the urban traffic dynamic simulation model can be updated in real time, and the new traffic flow and speed information can be fed back to the simulation model to ensure the timeliness and accuracy of the model.

[0037] The following is an example of an algorithm that combines cellular automata and a microscopic traffic flow model to simulate the dynamic operation of urban traffic: The cellular automaton model divides the road into discrete cells. The state of each cell is determined by the vehicle occupancy. The cell state update rule is: ; in, represents the state of cell i at time t (0 means empty, 1 means occupied by a vehicle), f is the state update function, which updates the state of the adjacent cells according to their states ( , , ) determines the state of cell i at the next moment; Vehicle Movement Rules: ; in, It is represented as the speed of vehicle i at time t, is the maximum speed of the vehicle, is the acceleration of vehicle i at time t, is the distance between vehicle i and the preceding vehicle; Acceleration adjustment rules: ; in, is the maximum acceleration of the vehicle; In summary, the cellular state update rule combined with the vehicle movement rule and acceleration adjustment rule can be used to simulate the vehicle distribution and movement on urban roads.

[0038] The microscopic traffic flow model reflects the dynamic changes of traffic flow by simulating the driving behavior of each vehicle. The first is the car-following model: ; Among them, α is the weight coefficient of speed difference, which is used to adjust the influence of the speed of the preceding vehicle on the acceleration of the current vehicle; β is the weight coefficient of distance difference, which is used to adjust the influence of the distance from the preceding vehicle on the acceleration of the current vehicle. It is represented as the speed of the preceding vehicle i-1 at time t, Indicated as safe distance, it indicates the minimum distance that should be maintained between the vehicle and the vehicle ahead; The second is the lane change decision rule: ; Among them, γ is a tuning parameter used to control the sensitivity of lane change probability; The car-following model and lane-changing decision rules are used to simulate the behavior of vehicles in congestion or free-flow conditions.

[0039] Finally, by continuously updating the model input and providing real-time feedback on traffic status and flow changes, simulation technology can help traffic management departments conduct multi-scenario simulation tests. The following is the core formula of the test process: Scenario evaluation indicators: ;

[0040] in, , , , are weight coefficients used to balance the importance of different objectives (such as delay time, number of stops, fuel consumption, and safety risks). The goal of the scenario evaluation index is to minimize delay time, number of stops, fuel consumption, and safety risks. Optimal strategy selection: ; in, Denotes the optimal strategy, represents the traffic management measure that minimizes the evaluation index, and π represents the traffic management strategy, such as signal light control, speed limit adjustment, lane management, etc. The optimal strategy selects the best traffic management measure by minimizing the evaluation index (such as delay time, number of stops, fuel consumption, safety risk); By establishing the above rules and models, traffic management departments can more effectively simulate and optimize traffic flows, reduce congestion and improve road safety.

[0041] In addition, a real-time feedback mechanism can be set up to monitor traffic flow and speed in real time through sensors and cameras, and dynamically adjust traffic lights or speed limit strategies.

[0042] Further, if Figure 5 As shown, step S5 specifically includes the following steps: S51: Data analysis interface, real-time data exchange between the traffic management center and the integrated multi-source data, AI analysis and virtual simulation layer; S52: signal control decision, automatically adjust the traffic light cycle according to the comprehensive traffic status and set the priority vehicles; S53: Implementation and feedback, execute signal control in real time and adjust strategies through real-time data feedback to adapt to traffic flow changes.

[0043] After completing steps S1 to S4, the traffic management center will generate real-time traffic information based on the above analysis and simulation results. Based on the real-time traffic information, the system can intelligently adjust and control the traffic lights. The real-time signal control module is not only based on the flow conditions but also considers pedestrian priority and the passage needs of special vehicles (such as ambulances and fire trucks) to achieve dynamic optimization control of traffic signals. Such a decision-making mechanism is intuitive and flexible, and is an effective measure to reduce traffic congestion and ensure traffic safety. Through this mechanism, traffic managers can respond to changes in traffic flow in a timely manner and intelligently dispatch traffic resources, thereby effectively improving the efficiency of the entire urban traffic operation.

[0044] In addition, the real-time signal control system is integrated with the user's mobile application to provide drivers with real-time speed monitoring prompts and optimal route suggestions; The mobile application can obtain the current traffic status and signal light information of the road in real time through the data interface with the real-time signal control system. When the driver approaches a certain intersection, the application will provide real-time speed monitoring prompts based on the current signal light status and vehicle speed. For example, if the traffic light ahead is about to turn red, the application will prompt the driver to slow down to avoid sudden braking and potential traffic accidents. Based on the traffic data provided by the real-time signal control system, the mobile application can provide drivers with optimal route recommendations. The system will comprehensively consider factors such as current traffic conditions, traffic light cycles, road congestion, etc. to calculate the fastest and safest driving route. For example, when the system detects severe congestion on a certain section of road, the application will suggest that the driver take other routes to reduce driving time and fuel consumption.

[0045] It should be noted that all modules of the system can be integrated with other traffic management systems through open API interfaces, sharing data and resources to enhance the comprehensive benefits of urban intelligent transportation. Data sharing between mobile applications and real-time signal control systems not only provides convenience for drivers, but also provides opportunities for collaborative optimization of the entire traffic management system.

[0046] Further, if Figure 6 As shown, step S6 specifically includes the following steps: S61: Protocol formulation, based on real-time traffic information and blockchain technology, design smart contracts for traffic violation detection, define violation triggering conditions and punishment measures; blockchain technology is used to store violations and processing results to ensure data transparency and security. Each violation will be recorded as a block and linked to the previous block through a hash value, forming an unalterable chain structure; S62: Agreement execution. After the monitoring system detects a violation, the smart contract automatically executes the corresponding measures, generates a notification to the law enforcement agency, or sends real-time prompt information to traffic participants.

[0047] With the help of the real-time traffic information obtained in the above steps and blockchain technology, the designed smart contract can realize automatic detection and intelligent response to traffic violations. When the system detects violations such as speeding and running red lights through real-time data, a series of response mechanisms can be automatically executed based on the preset smart contract. The application of smart contracts ensures the transparency and automation of the punishment process after the violation is detected. For example, it can automatically generate a violation notification and send it to the driver's mobile device, providing relevant evidence and handling suggestions. At the same time, the system can update the blockchain in real time, record all violations and processing results, and ensure the data is tamper-proof and traceable. This function not only improves the degree of automation of traffic management, but also enhances the compliance awareness of traffic participants, thereby promoting the effective implementation of traffic rules.

[0048] Furthermore, the smart contract automatically handles different levels of violations through specific rule settings and defines response measures for different levels of violations.

[0049] Smart contract is one of the core modules in the vehicle speed monitoring and prompting method. The core of smart contract is to define the violation trigger conditions and penalty measures. Through specific rule settings, it can automatically handle violations of different levels and define response measures for different violation levels. Its working principle is as follows: Rule setting: Smart contracts use pre-defined rules to monitor and analyze vehicle speed, traffic flow and other data in real time. For example, when the vehicle speed exceeds the speed limit, the smart contract will automatically trigger the violation handling process. The rules can be graded according to different violation levels (such as minor speeding, serious speeding, multiple violations, etc.); Automated processing: Smart contracts can automatically execute corresponding response measures based on the severity of the violation. For example, for vehicles with minor speeding, the system will send a warning notification; for vehicles with serious speeding or multiple violations, the system will automatically record the violation and generate a penalty notice; Definition of violation level: The violation level is usually defined based on factors such as the speeding extent, number of violations, and time. For example, minor speeding: the speed exceeds the speed limit by less than 10%; severe speeding: the speed exceeds the speed limit by more than 10%; multiple violations: multiple speeding violations on the same road section; Response measures: For different levels of violations, the smart contract will execute different response measures. For example, for minor speeding: send a warning notification to remind the driver to slow down; for serious speeding: record the violation, generate a penalty notice, and restrict the vehicle's driving rights; for multiple violations: generate a detailed report and submit it to the traffic management department for further processing.

[0050] Furthermore, the smart contract can generate visual reports while handling violations to help decision makers conduct behavioral analysis.

[0051] While processing violations, smart contracts can generate visual reports to help decision makers conduct behavioral analysis. These reports may include the following: Violation statistics: The report will count the number of violations in different time periods and on different road sections to help decision makers identify high-incidence areas and high-incidence periods; Violation level distribution: The report will show the distribution of different violation levels to help decision makers understand the severity of the violations; Vehicle speed and flow relationship analysis: The report will analyze the relationship between vehicle speed and flow to help decision makers identify traffic bottlenecks and high-risk areas; Violation trend prediction: Based on historical data, the report will predict future violation trends to help decision makers develop preventive measures.

[0052] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A vehicle speed monitoring and prompting method in a vehicle networking scenario, characterized in that: The method comprises the following steps: S1: Integrate the multi-dimensional data collected by sensors or cameras on the traffic road through multi-source data fusion technology to form a comprehensive view of the traffic status; S2: uploading the multi-dimensional data integrated in step S1 to a cloud storage system, and using big data processing technology to analyze and process the multi-dimensional data; S3: Combines historical data and real-time data in the cloud storage system, uses machine learning algorithms in artificial intelligence to conduct in-depth analysis of traffic flow and vehicle speed, and predict future traffic conditions; S4: Based on step S3, a dynamic simulation model of urban traffic is established using virtual simulation technology to simulate traffic conditions under different flow rates and behaviors; S5: Generate real-time traffic information based on multi-source data fusion in S1, artificial intelligence in S3, and dynamic simulation model in S4. The traffic management center performs real-time signal control based on the real-time traffic information. S6: Based on the real-time traffic information in S5 and blockchain technology, a smart contract for traffic violation detection is designed. When speeding or other violations are detected, the smart contract automatically executes a pre-set response mechanism.

2. The vehicle speed monitoring and prompting method in a vehicle networking scenario according to claim 1 is characterized in that: The step S1 specifically includes the following steps: S11: Data collection equipment deployment: Intelligent data collection equipment will be deployed at traffic intersections, main roads and transportation hubs. The intelligent data collection equipment will obtain multi-dimensional data of vehicles; S12: Real-time data transmission, using wireless communication technology to transmit the collected multi-dimensional data to the central data processing system in real time; S13: Data fusion and processing, integrating multi-dimensional data through multi-source data fusion technology to form a traffic status view.

3. The vehicle speed monitoring and prompting method in a vehicle networking scenario according to claim 2 is characterized in that: The intelligent collection equipment includes sensors and cameras, and the multi-dimensional data includes vehicle speed, vehicle flow, vehicle type, and vehicle driving direction.

4. The vehicle speed monitoring and prompting method in a vehicle networking scenario according to claim 1 is characterized in that: The step S3 specifically comprises the following steps: S31: Establish a multi-dimensional data set, integrate historical traffic data and real-time traffic data in the cloud storage system, and establish a multi-dimensional data set; S32: Model training and optimization: for multi-dimensional data sets, use machine learning algorithms to train multi-dimensional data sets and gradually optimize the model; S33: Real-time prediction, using trained models to analyze current traffic conditions, predict future flow and speed changes, and automatically adjust model parameters based on real-time data feedback.

5. The vehicle speed monitoring and prompting method in a vehicle networking scenario according to claim 1 is characterized in that: The step S4 specifically comprises the following steps: S41: Establish a dynamic simulation model of urban traffic, including various traffic flow and behavior patterns; S42: Simulate the effects of various management strategies under different traffic flow conditions; S43: Summarize simulation results, analyze the impact of strategies on traffic flow and safety, and determine the best traffic management solution.

6. The vehicle speed monitoring and prompting method in a vehicle networking scenario according to claim 5 is characterized in that: The urban traffic dynamic simulation model based on virtual simulation technology uses traffic flow simulation algorithm to demonstrate the dynamic operation status of the traffic system under different traffic flows and driving behaviors.

7. The vehicle speed monitoring and prompting method in a vehicle networking scenario according to claim 1 is characterized in that: The step S5 specifically comprises the following steps: S51: Data analysis interface, real-time data exchange between the traffic management center and the integrated multi-source data, AI analysis and virtual simulation layer; S52: signal control decision, automatically adjust the traffic light cycle according to the comprehensive traffic status and set the priority vehicles; S53: Implementation and feedback, execute signal control in real time and adjust strategies through real-time data feedback to adapt to traffic flow changes.

8. The vehicle speed monitoring and prompting method in a vehicle networking scenario according to claim 7 is characterized in that: The real-time signal control system is integrated with the user's mobile application to provide drivers with real-time speed monitoring prompts and optimal route suggestions.

9. The vehicle speed monitoring and prompting method in a vehicle networking scenario according to claim 1 is characterized in that: The step S6 specifically comprises the following steps: S61: Protocol formulation, based on real-time traffic information and blockchain technology, design smart contracts for traffic violation detection, define violation trigger conditions and penalty measures; S62: Agreement execution. After the monitoring system detects a violation, the smart contract automatically executes the corresponding measures, generates a notification to the law enforcement agency, or sends real-time prompt information to traffic participants.

10. The vehicle speed monitoring and prompting method in a vehicle networking scenario according to claim 9 is characterized in that: The smart contract generates a visual report while processing violations.

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