A system for monitoring vehicles traveling on highways over speeding

Abstract

The present application discloses a system for monitoring overspeeding of vehicles on highways throughout the journey, belonging to the field of traffic monitoring technology. The monitoring system includes the following modules: a multimodal sensor array module for all-round, multi-scale vehicle detection and speed measurement; an intelligent vehicle identification module for quickly and accurately identifying vehicle types and license plate numbers; an adaptive speed measurement strategy control module for dynamically adjusting speed measurement modes and parameters according to traffic flow, weather conditions and vehicle characteristics to ensure the accuracy and efficiency of speed measurement; a multi-scale speed measurement fusion module for generating continuous and accurate vehicle speed curves; an environmental factor compensation module for real-time correction of speed measurement results to improve speed measurement accuracy; a high-speed data transmission module for achieving real-time and secure transmission of speed measurement data; and a central data analysis platform for summarizing full-process data, conducting in-depth analysis, identifying overspeeding behavior, and generating monitoring reports.

Description

Technical Field

[0001] The present application relates to the technical field of traffic monitoring, and more specifically, to a system for monitoring overspeeding of vehicles traveling on a highway throughout the entire journey. Background Art

[0002] With the increase in traffic volume and the increasing importance of vehicle speed management, existing traffic monitoring systems have been widely used to detect and record vehicle speeding behavior. However, traditional speeding monitoring technology has some limitations, such as the limited coverage of a single speed measurement point, the inability to accurately monitor the vehicle throughout the entire journey, and the lack of speed measurement accuracy and efficiency under complex weather and high traffic conditions.

[0003] In the prior art, common speeding monitoring systems mainly rely on a single sensor or a limited combination of sensors to measure speed. For example, a single radar or laser rangefinder can provide speed data for a certain area, but cannot achieve continuous monitoring of the entire route. In addition, although some systems have introduced video monitoring, due to the complexity of image recognition and speed measurement, its application effect in actual scenarios is limited.

[0004] In terms of vehicle recognition, traditional methods often rely on simple image processing techniques or rule-based vehicle detection algorithms. These methods have certain limitations in recognition rate and processing speed, especially in complex traffic scenarios, such as night driving or high vehicle density, their accuracy and robustness need to be improved.

[0005] In addition, the existing system fails to effectively respond to changes in the dynamic traffic environment due to the fixedness and static nature of speed measurement parameters. The impact of factors such as changes in weather conditions and fluctuations in traffic flow on speed measurement accuracy has not been effectively dynamically adjusted and optimized.

[0006] Therefore, in view of the limitations and shortcomings of the above-mentioned existing technologies, it is necessary to propose a new type of full-time speeding monitoring system, which can achieve accurate monitoring and effective management of vehicles traveling on the highway throughout the entire process by integrating multiple sensors and advanced data processing technologies, thereby improving road traffic safety and management efficiency. Summary of the invention

[0007] In order to overcome a series of defects in the prior art, the purpose of this application is to provide a full-time overspeed monitoring system for vehicles traveling on a highway, including the following modules:

[0008] Multimodal sensor array module, integrating millimeter wave radar, high-speed camera and laser rangefinder to achieve all-round and multi-scale vehicle detection and speed measurement;

[0009] Intelligent vehicle identification module, using deep learning algorithms to quickly and accurately identify vehicle types and license plate numbers;

[0010] Adaptive speed measurement strategy control module dynamically adjusts speed measurement mode and parameters according to traffic flow, weather conditions and vehicle characteristics to ensure the accuracy and efficiency of speed measurement;

[0011] The multi-scale speed measurement fusion module comprehensively processes the speed measurement data at short, medium and long distances to generate a continuous and accurate vehicle speed curve;

[0012] Environmental factor compensation module collects and analyzes wind speed and slope data, performs real-time correction on speed measurement results, and improves speed measurement accuracy;

[0013] High-speed data transmission module, using 5G network technology to achieve real-time and secure transmission of speed measurement data;

[0014] The central data analysis platform aggregates all data, conducts in-depth analysis, identifies speeding behavior, and generates monitoring reports.

[0015] Furthermore, the multimodal sensor array module includes:

[0016] Millimeter-wave radar arrays use multiple millimeter-wave radar sensors to form an array to achieve large-scale, all-weather vehicle detection and speed measurement;

[0017] High-speed camera unit, which uses multiple high-frame-rate cameras to cover different angles and provide clear vehicle images for identification and speed verification;

[0018] A laser rangefinder set for precise measurement of vehicle position and speed;

[0019] The sensor synchronization controller coordinates the working timing of the millimeter wave radar array, high-speed camera unit and laser rangefinder group to ensure the synchronization of data collection;

[0020] Environmental sensing unit, which integrates temperature, humidity and light sensors to collect environmental data for subsequent measurement compensation and strategy adjustment;

[0021] The data preprocessing module performs preliminary processing and formatting on the raw data output by the millimeter wave radar array, high-speed camera unit, laser rangefinder group and environmental perception unit.

[0022] Furthermore, the intelligent vehicle identification module includes:

[0023] Image preprocessing unit, which performs denoising, enhancement and standardization on the input high-speed camera image to improve the accuracy of subsequent recognition;

[0024] Deep learning vehicle type recognition engine, using convolutional neural network models to quickly and accurately identify and classify vehicle types;

[0025] The vehicle detection network uses a deep learning target detection algorithm to quickly locate the position of vehicles in the image;

[0026] Vehicle type classifier, which uses convolutional neural network to classify detected vehicles and identify specific vehicle types;

[0027] License plate location and segmentation module, which accurately locates the license plate in the vehicle image and segments the license plate area for subsequent recognition;

[0028] License plate character recognition engine, using optical character recognition technology to accurately identify license plate numbers;

[0029] The result comprehensive analyzer integrates the vehicle model recognition and license plate recognition results, performs cross-validation and confidence assessment, and outputs the final recognition results.

[0030] Furthermore, the adaptive speed measurement strategy control module includes:

[0031] Real-time traffic flow analyzer, which continuously monitors and analyzes the traffic volume and speed distribution on the road, and provides basic data for adjusting speed measurement strategies;

[0032] The weather condition assessment unit integrates meteorological sensors and external weather data to assess the impact of current weather on speed measurement and adjust speed measurement parameters in a timely manner;

[0033] Dynamic parameter adjuster, which calculates and adjusts the optimal speed measurement parameters and thresholds in real time based on the current environment and vehicle characteristics;

[0034] Speed ​​measurement mode selector, intelligently switches different speed measurement modes according to comprehensive analysis results to adapt to various situations;

[0035] The strategy execution and feedback module executes the selected speed measurement strategy and collects real-time effect data to provide feedback for the continuous optimization of the strategy.

[0036] Furthermore, based on the current environment and vehicle characteristics, the optimal speed measurement parameters and thresholds are calculated and adjusted in real time, including the following steps:

[0037] Collect current environmental data and vehicle characteristic data and pre-process them to remove outliers and noise in preparation for subsequent analysis;

[0038] According to the preprocessed environmental data, the impact of the current environment on the speed measurement is evaluated, and an environmental impact factor is output for subsequent parameter adjustment;

[0039] Analyze the characteristics of vehicles on the current road, including vehicle type distribution, average speed and traffic density, and output a set of weight coefficients based on vehicle characteristics;

[0040] Compare current situations with historical data to identify similar scenarios and best practices;

[0041] Combining environmental impact factors, vehicle feature weights and historical data references, an optimization algorithm is used to calculate the optimal speed measurement parameters and thresholds under the current circumstances and apply them to the speed measurement equipment in real time;

[0042] Continuously monitor the application effect of new parameters, collect real-time data and conduct rapid analysis. If performance degradation or abnormal conditions are found, immediately trigger a new round of parameter optimization process.

[0043] Furthermore, the multi-scale speed measurement fusion module includes:

[0044] Multi-source data acquisition interface, synchronously receiving raw data from speed measuring devices at different distances to ensure the time consistency of data;

[0045] The data preprocessing and calibration unit performs noise filtering, unit unification and preliminary calibration on the speed measurement data of each distance segment to prepare for subsequent fusion;

[0046] The space-time registration algorithm maps the speed data measured at different distances into a unified space-time coordinate system to achieve spatial alignment of the data;

[0047] The multi-scale fusion engine uses the Kalman filter algorithm to comprehensively process the speed measurement data at short, medium and long distances to generate a preliminary continuous speed curve;

[0048] Outlier detection and processing module, which identifies and processes abnormal data points that appear during the fusion process, improving the smoothness and accuracy of the speed curve;

[0049] The curve optimizer and interpolator further smoothes and interpolates the fused speed curve to generate the final high-precision continuous vehicle speed curve.

[0050] Furthermore, the Kalman filter algorithm is used to comprehensively process the speed measurement data of short, medium and long distances to generate a preliminary continuous speed curve, including the following steps:

[0051] Organize the raw data of short, medium and long distance speed measuring equipment into time series, and perform data cleaning, format unification and time alignment to ensure the time series consistency and data quality of cross-distance measurement;

[0052] Establish a state space model and measurement model suitable for Kalman filtering to describe the vehicle motion characteristics and speed measurement error characteristics;

[0053] Iterate the Kalman filter on the time series data, fuse the speed measurement data at different distances, and generate an optimized speed estimate;

[0054] Based on the discrete velocity estimation of Kalman filter output, a smooth and continuous velocity curve is generated by interpolation method.

[0055] Furthermore, the environmental factor compensation module includes:

[0056] Environmental data collection unit, which collects environmental data including wind speed, wind direction and slope in real time, providing basic information for subsequent analysis;

[0057] Slope analyzer, which accurately calculates the instantaneous slope and slope change rate of the road, and evaluates the impact of slope on vehicle speed;

[0058] Wind impact evaluator, which analyses the impact of wind speed and direction on the speed of different types of vehicles and generates wind compensation factors;

[0059] Environmental factor modeling unit, which establishes a mathematical model of environmental data and vehicle speed deviation, providing a theoretical basis for real-time correction;

[0060] The real-time compensation calculator quickly calculates the required speed correction value based on the current environmental data and the established model.

[0061] Furthermore, the instantaneous slope θ is calculated by the following formula: Among them, △h is the height change of a section of the journey; △d is the horizontal distance of the section of the journey;

[0062] Instantaneous slope change rate By numerically differentiating the slope θ, we obtain: Among them, θ i+1 and θ i is the instantaneous slope of two adjacent points; △x is the horizontal distance between two adjacent points;

[0063] The vehicle's speed change is expressed by the following formula: Where a is the acceleration of the vehicle in the slope direction; g is the acceleration due to gravity; F resist is the total resistance including air resistance and friction; m is the mass of the vehicle;

[0064] The effect of slope on vehicle speed is expressed as: △v = a·△t, where △v is the change in speed; △t is the change in time;

[0065] Wind compensation factor C f for: Among them, F d Indicates the air resistance of the vehicle in windy conditions. F d0Indicates the air resistance of the vehicle in no wind conditions. V r Indicates the relative wind speed felt by the vehicle while driving. V w , represents wind speed, θ w Indicates wind direction, V v represents the forward speed of the vehicle; A is the frontal area of ​​the vehicle; C d is the air resistance coefficient of the vehicle; ρ is the air density; θ r Indicates the direction angle of the wind relative to the vehicle's forward direction. θ v Indicates the angle between the vehicle's current driving direction and the horizontal direction; through the wind compensation factor C f The speed change △V that needs to be compensated can be calculated c :

[0066] Furthermore, the central data analysis platform includes:

[0067] The data aggregation and storage center aggregates and stores the entire data from each module, providing a unified data source for subsequent analysis;

[0068] The vehicle behavior analysis module uses machine learning algorithms to analyze the vehicle's full driving data to identify potential speeding behaviors and abnormal driving patterns;

[0069] The speeding determination and assessment module accurately determines speeding behavior and assesses its severity based on preset rules and dynamic thresholds;

[0070] Report generator that automatically integrates analysis results and generates detailed monitoring reports including speeding statistics, trend analysis and abnormal events.

[0071] Compared with the prior art, this application has the following beneficial effects:

[0072] This application uses a multimodal sensor array module to integrate millimeter-wave radar, high-speed cameras and laser rangefinders to achieve all-round, multi-scale detection and speed measurement of vehicles. The intelligent vehicle identification module uses deep learning algorithms to quickly and accurately identify vehicle types and license plate numbers, while the adaptive speed measurement strategy control module dynamically adjusts the speed measurement mode and parameters according to traffic flow, weather conditions and vehicle characteristics to ensure the accuracy and efficiency of speed measurement. The multi-scale speed measurement fusion module comprehensively processes speed measurement data at different distances to generate a continuous and accurate vehicle speed curve, while the environmental factor compensation module corrects the speed measurement results in real time to improve accuracy. Through the high-speed data transmission module and the central data analysis platform, the system can transmit and deeply analyze the entire process data in real time, identify speeding behavior and generate detailed monitoring reports, thereby effectively improving the intelligence and efficiency of highway traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 The present invention discloses a schematic diagram of a system for monitoring overspeed of vehicles on a highway throughout the entire journey. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical scheme and advantages of the implementation of the present invention clearer, the technical scheme in the embodiment of the present invention will be described in more detail below in conjunction with the drawings in the embodiment of the present invention. In the drawings, the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of the present invention, not all of the embodiments.

[0075] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0076] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0077] like Figure 1 As shown, a system for monitoring overspeeding of vehicles on a highway includes the following modules:

[0078] Multimodal sensor array module, integrating millimeter wave radar, high-speed camera and laser rangefinder to achieve all-round and multi-scale vehicle detection and speed measurement;

[0079] Intelligent vehicle identification module, using deep learning algorithms to quickly and accurately identify vehicle types and license plate numbers;

[0080] Adaptive speed measurement strategy control module dynamically adjusts speed measurement mode and parameters according to traffic flow, weather conditions and vehicle characteristics to ensure the accuracy and efficiency of speed measurement;

[0081] The multi-scale speed measurement fusion module comprehensively processes the speed measurement data at short, medium and long distances to generate a continuous and accurate vehicle speed curve;

[0082] Environmental factor compensation module collects and analyzes wind speed and slope data, performs real-time correction on speed measurement results, and improves speed measurement accuracy;

[0083] High-speed data transmission module, using 5G network technology to achieve real-time and secure transmission of speed measurement data;

[0084] The central data analysis platform aggregates all data, conducts in-depth analysis, identifies speeding behavior, and generates monitoring reports.

[0085] The multimodal sensor array module integrates millimeter-wave radar, high-speed camera and laser rangefinder. Its main function is to achieve all-round, multi-scale vehicle detection and speed measurement. Millimeter-wave radar can effectively measure distance under various weather conditions, high-speed camera provides high-definition images for vehicle identification and behavior analysis, and laser rangefinder can provide high-precision distance measurement. Together, they can achieve accurate tracking and speed measurement of vehicles. This fusion of multimodal sensors not only improves the robustness and reliability of the monitoring system, but also ensures accurate monitoring capabilities for different types of vehicles and various driving scenarios.

[0086] The intelligent vehicle identification module uses deep learning algorithms to quickly and accurately identify the type and license plate number of vehicles. The application of deep learning technology enables the system to maintain high efficiency and accuracy in complex traffic environments, and even high-speed vehicles can be accurately identified. The use of this module not only helps improve the monitoring system's ability to identify vehicle identities, but also provides important basic information for subsequent data analysis and monitoring report generation.

[0087] The adaptive speed measurement strategy control module dynamically adjusts the speed measurement mode and parameters according to traffic flow, weather conditions and vehicle characteristics. This intelligent control capability ensures the accuracy and efficiency of speed measurement. The system can flexibly respond to real-time road conditions, whether it is high traffic period, bad weather or specific vehicle type, and can effectively control the speed measurement strategy, thereby improving the overall monitoring system's operating efficiency.

[0088] The multi-scale speed measurement fusion module is responsible for comprehensively processing the speed measurement data at short, medium and long distances to generate a continuous and accurate vehicle speed curve. By effectively integrating the speed measurement data at different distances, the system can accurately capture the speed changes of the vehicle and avoid speed errors caused by distance, thereby providing more accurate and consistent vehicle speed monitoring results.

[0089] The environmental factor compensation module is responsible for collecting and analyzing environmental data such as wind speed and slope, and is used to make real-time corrections to the speed measurement results, thereby improving the accuracy of the speed measurement. The introduction of this module effectively eliminates the potential interference of environmental factors on the speed measurement data, ensuring that the monitoring system can still provide reliable speed measurement results under various complex conditions.

[0090] The high-speed data transmission module uses 5G network technology to achieve real-time and secure transmission of speed measurement data. The high speed and low latency characteristics of the 5G network ensure the rapid transmission of monitoring data, and can update the data of the central data analysis platform in a timely manner, so that the monitoring system can quickly respond to various speeding behaviors and provide support for real-time decision-making.

[0091] The central data analysis platform is the core of the system, responsible for summarizing the entire process data and conducting in-depth analysis, identifying speeding behaviors and generating monitoring reports. Through the analysis and mining of large amounts of data, the platform can accurately capture and record speeding events, provide a basis for traffic management departments, and help optimize the operation of highway traffic safety management and monitoring systems.

[0092] In summary, the above modules work together to form an efficient highway speeding monitoring system, which can not only effectively monitor and record the vehicle's driving speed, but also timely warn and deal with speeding behaviors through intelligent analysis and data transmission, thereby improving the level of highway traffic safety and management efficiency.

[0093] Furthermore, the multimodal sensor array module includes:

[0094] Millimeter-wave radar arrays use multiple millimeter-wave radar sensors to form an array to achieve large-scale, all-weather vehicle detection and speed measurement;

[0095] High-speed camera unit, which uses multiple high-frame-rate cameras to cover different angles and provide clear vehicle images for identification and speed verification;

[0096] A laser rangefinder set for precise measurement of vehicle position and speed;

[0097] The sensor synchronization controller coordinates the working timing of the millimeter wave radar array, high-speed camera unit and laser rangefinder group to ensure the synchronization of data collection;

[0098] Environmental sensing unit, which integrates temperature, humidity and light sensors to collect environmental data for subsequent measurement compensation and strategy adjustment;

[0099] The data preprocessing module performs preliminary processing and formatting on the raw data output by the millimeter wave radar array, high-speed camera unit, laser rangefinder group and environmental perception unit.

[0100] In summary, the multimodal sensor array module effectively realizes full-time speeding monitoring of vehicles on highways through the synergy of millimeter-wave radar, high-speed camera, laser rangefinder, sensor synchronization controller, environmental perception unit and data preprocessing module, improves the reliability, accuracy and adaptability of the monitoring system, and provides important technical support and data foundation for traffic management.

[0101] Furthermore, the intelligent vehicle identification module includes:

[0102] Image preprocessing unit, which performs denoising, enhancement and standardization on the input high-speed camera image to improve the accuracy of subsequent recognition;

[0103] Deep learning vehicle type recognition engine, using convolutional neural network models to quickly and accurately identify and classify vehicle types;

[0104] The vehicle detection network uses a deep learning target detection algorithm to quickly locate the position of vehicles in the image;

[0105] Vehicle type classifier, which uses convolutional neural network to classify detected vehicles and identify specific vehicle types;

[0106] License plate location and segmentation module, which accurately locates the license plate in the vehicle image and segments the license plate area for subsequent recognition;

[0107] License plate character recognition engine, using optical character recognition technology to accurately identify license plate numbers;

[0108] The result comprehensive analyzer integrates the vehicle model recognition and license plate recognition results, performs cross-validation and confidence assessment, and outputs the final recognition results.

[0109] In summary, the intelligent vehicle recognition module realizes the accurate recognition and analysis of the identity and behavior of vehicles traveling on the highway through the organic combination of multiple technical modules such as image preprocessing, deep learning vehicle model recognition, vehicle detection network, vehicle model classifier, license plate positioning and segmentation, license plate character recognition and result comprehensive analyzer, providing important data support and decision-making basis for traffic management departments.

[0110] Furthermore, the adaptive speed measurement strategy control module includes:

[0111] Real-time traffic flow analyzer, which continuously monitors and analyzes the traffic volume and speed distribution on the road, and provides basic data for adjusting speed measurement strategies;

[0112] The weather condition assessment unit integrates meteorological sensors and external weather data to assess the impact of current weather on speed measurement and adjust speed measurement parameters in a timely manner;

[0113] Dynamic parameter adjuster, which calculates and adjusts the optimal speed measurement parameters and thresholds in real time based on the current environment and vehicle characteristics;

[0114] Speed ​​measurement mode selector, intelligently switches different speed measurement modes according to comprehensive analysis results to adapt to various situations;

[0115] The strategy execution and feedback module executes the selected speed measurement strategy and collects real-time effect data to provide feedback for the continuous optimization of the strategy.

[0116] In summary, the adaptive speed measurement strategy control module effectively realizes the intelligent monitoring and management of vehicles on highways through the synergy of multiple key technical modules such as real-time traffic flow analysis, weather condition evaluation, dynamic parameter adjustment, speed measurement mode selection, and strategy execution and feedback. These technical modules not only improve the responsiveness and adaptability of the speed measurement system, but also optimize traffic flow and safety, providing strong support and guarantee for drivers and traffic management departments.

[0117] Furthermore, based on the current environment and vehicle characteristics, the optimal speed measurement parameters and thresholds are calculated and adjusted in real time, including the following steps:

[0118] Collect current environmental data and vehicle characteristic data and pre-process them to remove outliers and noise in preparation for subsequent analysis;

[0119] According to the preprocessed environmental data, the impact of the current environment on the speed measurement is evaluated, and an environmental impact factor is output for subsequent parameter adjustment;

[0120] Analyze the characteristics of vehicles on the current road, including vehicle type distribution, average speed and traffic density, and output a set of weight coefficients based on vehicle characteristics;

[0121] Compare current situations with historical data to identify similar scenarios and best practices;

[0122] Combining environmental impact factors, vehicle feature weights and historical data references, an optimization algorithm is used to calculate the optimal speed measurement parameters and thresholds under the current circumstances and apply them to the speed measurement equipment in real time;

[0123] Continuously monitor the application effect of new parameters, collect real-time data and conduct rapid analysis. If performance degradation or abnormal conditions are found, immediately trigger a new round of parameter optimization process.

[0124] Furthermore, the multi-scale speed measurement fusion module includes:

[0125] Multi-source data acquisition interface, synchronously receiving raw data from speed measuring devices at different distances to ensure the time consistency of data;

[0126] The data preprocessing and calibration unit performs noise filtering, unit unification and preliminary calibration on the speed measurement data of each distance segment to prepare for subsequent fusion;

[0127] The space-time registration algorithm maps the speed data measured at different distances into a unified space-time coordinate system to achieve spatial alignment of the data;

[0128] The multi-scale fusion engine uses the Kalman filter algorithm to comprehensively process the speed measurement data at short, medium and long distances to generate a preliminary continuous speed curve;

[0129] Outlier detection and processing module, which identifies and processes abnormal data points that appear during the fusion process, improving the smoothness and accuracy of the speed curve;

[0130] The curve optimizer and interpolator further smoothes and interpolates the fused speed curve to generate the final high-precision continuous vehicle speed curve.

[0131] In summary, the multi-scale speed measurement fusion module achieves comprehensive, accurate and continuous monitoring of vehicle speed information on highways through the synergy of technical modules such as multi-source data acquisition interface, data preprocessing and calibration, spatiotemporal registration algorithm, multi-scale fusion engine, outlier detection and processing module, and curve optimization and interpolation. These technical means not only improve the accuracy and reliability of the monitoring system, but also provide an important data foundation for traffic management departments, helping to optimize road use efficiency and improve traffic safety.

[0132] Furthermore, the Kalman filter algorithm is used to comprehensively process the speed measurement data of short, medium and long distances to generate a preliminary continuous speed curve, including the following steps:

[0133] Organize the raw data of short, medium and long distance speed measuring equipment into time series, and perform data cleaning, format unification and time alignment to ensure the time series consistency and data quality of cross-distance measurement;

[0134] Establish a state space model and measurement model suitable for Kalman filtering to describe the vehicle motion characteristics and speed measurement error characteristics;

[0135] Iterate the Kalman filter on the time series data, fuse the speed measurement data at different distances, and generate an optimized speed estimate;

[0136] Based on the discrete velocity estimation of Kalman filter output, a smooth and continuous velocity curve is generated by interpolation method.

[0137] Furthermore, the environmental factor compensation module includes:

[0138] Environmental data collection unit, which collects environmental data including wind speed, wind direction and slope in real time, providing basic information for subsequent analysis;

[0139] Slope analyzer, which accurately calculates the instantaneous slope and slope change rate of the road, and evaluates the impact of slope on vehicle speed;

[0140] Wind impact evaluator, which analyzes the impact of wind speed and direction on the speed of different types of vehicles and generates wind compensation factors;

[0141] Environmental factor modeling unit, which establishes a mathematical model of environmental data and vehicle speed deviation, providing a theoretical basis for real-time correction;

[0142] The real-time compensation calculator quickly calculates the required speed correction value based on the current environmental data and the established model.

[0143] In summary, the environmental factor compensation module effectively improves the adaptability of the full-course speeding monitoring system to environmental factors and the accuracy of speed measurement through the synergy of multiple technical modules such as environmental data collection, slope analysis, wind impact assessment, modeling analysis and real-time compensation calculation. These technical means not only enhance the stability of the system under complex road conditions, but also provide more reliable data support for traffic management departments, helping to improve the level of road safety management.

[0144] Furthermore, the instantaneous slope θ is calculated by the following formula: Among them, △h is the height change of a section of the journey; △d is the horizontal distance of the section of the journey;

[0145] Instantaneous slope change rate By numerically differentiating the slope θ, we obtain: Among them, θ i+1 and θ i is the instantaneous slope of two adjacent points; △x is the horizontal distance between two adjacent points;

[0146] The vehicle's speed change is expressed by the following formula: Where a is the acceleration of the vehicle in the slope direction; g is the acceleration due to gravity; F resist is the total resistance including air resistance and friction; m is the mass of the vehicle;

[0147] The effect of slope on vehicle speed is expressed as: △v = a·△t, where △v is the change in speed; △t is the change in time;

[0148] Wind compensation factor C f for: Among them, F d Indicates the air resistance of the vehicle in windy conditions. F d0 Indicates the air resistance of the vehicle in no wind conditions. V r Indicates the relative wind speed felt by the vehicle while driving. V w represents wind speed, θ w Indicates wind direction, V v represents the forward speed of the vehicle; A is the frontal area of ​​the vehicle; C d is the air resistance coefficient of the vehicle; ρ is the air density; θ r Indicates the direction angle of the wind relative to the vehicle's forward direction. θ vIndicates the angle between the vehicle's current driving direction and the horizontal direction; through the wind compensation factor C f The speed change △V that needs to be compensated can be calculated c :

[0149] Furthermore, the central data analysis platform includes:

[0150] The data aggregation and storage center aggregates and stores the entire data from each module, providing a unified data source for subsequent analysis;

[0151] The vehicle behavior analysis module uses machine learning algorithms to analyze the vehicle's full driving data to identify potential speeding behaviors and abnormal driving patterns;

[0152] The speeding determination and assessment module accurately determines speeding behavior and assesses its severity based on preset rules and dynamic thresholds;

[0153] Report generator that automatically integrates analysis results and generates detailed monitoring reports including speeding statistics, trend analysis and abnormal events.

[0154] In summary, the central data analysis platform effectively realizes the comprehensive management and analysis of speeding monitoring throughout the expressway through the integration and synergy of multiple key modules such as data aggregation and storage, vehicle behavior analysis, speeding judgment and evaluation, and report generation. These technical modules not only improve the monitoring system's real-time monitoring capabilities of road traffic conditions, but also provide strong data support for traffic management departments, helping to optimize traffic management decisions and improve road safety levels.

[0155] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A system for monitoring vehicles traveling on a highway overspeeding, characterized in that: Includes the following modules: Multimodal sensor array module, integrating millimeter wave radar, high-speed camera and laser rangefinder to achieve all-round and multi-scale vehicle detection and speed measurement; Intelligent vehicle identification module, using deep learning algorithms to quickly and accurately identify vehicle types and license plate numbers; Adaptive speed measurement strategy control module dynamically adjusts speed measurement mode and parameters according to traffic flow, weather conditions and vehicle characteristics to ensure the accuracy and efficiency of speed measurement; The multi-scale speed measurement fusion module comprehensively processes the speed measurement data at short, medium and long distances to generate a continuous and accurate vehicle speed curve; Environmental factor compensation module collects and analyzes wind speed and slope data, performs real-time correction on speed measurement results, and improves speed measurement accuracy; High-speed data transmission module, using 5G network technology to achieve real-time and secure transmission of speed measurement data; The central data analysis platform aggregates all data, conducts in-depth analysis, identifies speeding behavior, and generates monitoring reports; Environmental factor compensation module includes: Environmental data collection unit, which collects environmental data including wind speed, wind direction and slope in real time, providing basic information for subsequent analysis; Slope analyzer, which accurately calculates the instantaneous slope and slope change rate of the road, and evaluates the impact of slope on vehicle speed; Wind impact evaluator, which analyzes the impact of wind speed and direction on the speed of different types of vehicles and generates wind compensation factors; Environmental factor modeling unit, which establishes a mathematical model of environmental data and vehicle speed deviation, providing a theoretical basis for real-time correction; Real-time compensation calculator, which quickly calculates the required speed correction value based on current environmental data and established models; The instantaneous slope θ is calculated by the following formula: , where Δh is the height change of a section of the journey; Δd is the horizontal distance of the section of the journey; Instantaneous slope change rate By numerically differentiating the slope θ, we obtain: ,in, and is the instantaneous slope of two adjacent points; Δx is the horizontal distance between two adjacent points; The vehicle's speed change is expressed by the following formula: , where a is the acceleration of the vehicle in the direction of the slope; is the acceleration due to gravity; is the total resistance including air resistance and friction; m is the mass of the vehicle; The effect of slope on vehicle speed is expressed as: Δv=a·Δt, where Δv is the change in speed; Δt is the change in time; Wind compensation factor for: ,in, Indicates the air resistance of the vehicle in windy conditions. ; Indicates the air resistance of the vehicle in no wind conditions. ; Indicates the relative wind speed felt by the vehicle while driving. , Indicates wind speed, Indicates wind direction. represents the forward speed of the vehicle; A is the frontal area of ​​the vehicle; C d is the air resistance coefficient of the vehicle; ρ is the air density; Indicates the direction angle of the wind relative to the vehicle's forward direction. ; Indicates the angle between the vehicle's current direction of travel and the horizontal direction; through the wind compensation factor The speed change that needs to be compensated can be calculated : .

2. A system for monitoring vehicles traveling on a highway overspeeding throughout the entire process according to claim 1, characterized in that: The multimodal sensor array module includes: Millimeter-wave radar arrays use multiple millimeter-wave radar sensors to form an array to achieve large-scale, all-weather vehicle detection and speed measurement; High-speed camera unit, which uses multiple high-frame-rate cameras to cover different angles and provide clear vehicle images for identification and speed verification; A laser rangefinder set for precise measurement of vehicle position and speed; The sensor synchronization controller coordinates the working timing of the millimeter wave radar array, high-speed camera unit and laser rangefinder group to ensure the synchronization of data collection; Environmental sensing unit, which integrates temperature, humidity and light sensors to collect environmental data for subsequent measurement compensation and strategy adjustment; The data preprocessing module performs preliminary processing and formatting on the raw data output by the millimeter wave radar array, high-speed camera unit, laser rangefinder group and environmental perception unit.

3. The system for monitoring vehicles traveling on a highway overspeeding throughout the entire process according to claim 1 is characterized in that: The intelligent vehicle identification module includes: Image preprocessing unit, which performs denoising, enhancement and standardization on the input high-speed camera image to improve the accuracy of subsequent recognition; Deep learning vehicle type recognition engine, using convolutional neural network models to quickly and accurately identify and classify vehicle types; The vehicle detection network uses a deep learning target detection algorithm to quickly locate the position of vehicles in the image; Vehicle type classifier, which uses convolutional neural network to classify detected vehicles and identify specific vehicle types; License plate location and segmentation module, which accurately locates the license plate in the vehicle image and segments the license plate area for subsequent recognition; License plate character recognition engine, using optical character recognition technology to accurately identify license plate numbers; The result comprehensive analyzer integrates the vehicle model recognition and license plate recognition results, performs cross-validation and confidence assessment, and outputs the final recognition results.

4. The system for monitoring vehicles traveling on a highway overspeeding throughout the entire process according to claim 1 is characterized in that: The adaptive speed measurement strategy control module includes: Real-time traffic flow analyzer, which continuously monitors and analyzes the traffic volume and speed distribution on the road, and provides basic data for adjusting speed measurement strategies; The weather condition assessment unit integrates meteorological sensors and external weather data to assess the impact of current weather on speed measurement and adjust speed measurement parameters in a timely manner; Dynamic parameter adjuster, which calculates and adjusts the optimal speed measurement parameters and thresholds in real time based on the current environment and vehicle characteristics; Speed ​​measurement mode selector, intelligently switches different speed measurement modes according to comprehensive analysis results to adapt to various situations; The strategy execution and feedback module executes the selected speed measurement strategy and collects real-time effect data to provide feedback for the continuous optimization of the strategy.

5. A system for monitoring vehicles traveling on a highway overspeeding throughout the entire process according to claim 4, characterized in that: Based on the current environment and vehicle characteristics, the optimal speed measurement parameters and thresholds are calculated and adjusted in real time, including the following steps: Collect current environmental data and vehicle characteristic data and pre-process them to remove outliers and noise in preparation for subsequent analysis; According to the preprocessed environmental data, the impact of the current environment on the speed measurement is evaluated, and an environmental impact factor is output for subsequent parameter adjustment; Analyze the characteristics of vehicles on the current road, including vehicle type distribution, average speed and traffic density, and output a set of weight coefficients based on vehicle characteristics; Compare current situations with historical data to identify similar scenarios and best practices; Combining environmental impact factors, vehicle feature weights and historical data references, an optimization algorithm is used to calculate the optimal speed measurement parameters and thresholds under the current circumstances and apply them to the speed measurement equipment in real time; Continuously monitor the application effect of new parameters, collect real-time data and conduct rapid analysis. If performance degradation or abnormal conditions are found, immediately trigger a new round of parameter optimization process.

6. The system for monitoring vehicles traveling on a highway overspeeding throughout the entire process according to claim 1 is characterized in that: The multi-scale speed measurement fusion module includes: Multi-source data acquisition interface, synchronously receiving raw data from speed measuring devices at different distances to ensure the time consistency of data; The data preprocessing and calibration unit performs noise filtering, unit unification and preliminary calibration on the speed measurement data of each distance segment to prepare for subsequent fusion; The spatiotemporal registration algorithm maps the speed data measured at different distances into a unified spatiotemporal coordinate system to achieve spatial alignment of the data; The multi-scale fusion engine uses the Kalman filter algorithm to comprehensively process the speed measurement data at short, medium and long distances to generate a preliminary continuous speed curve; Outlier detection and processing module, which identifies and processes abnormal data points that appear during the fusion process, improving the smoothness and accuracy of the speed curve; The curve optimizer and interpolator further smoothes and interpolates the fused speed curve to generate the final high-precision continuous vehicle speed curve.

7. A system for monitoring vehicles traveling on a highway overspeeding throughout the entire process according to claim 6, characterized in that: Using the Kalman filter algorithm, the speed measurement data of short, medium and long distances are comprehensively processed to generate a preliminary continuous speed curve, including the following steps: Organize the raw data of short, medium and long distance speed measuring equipment into time series, and perform data cleaning, format unification and time alignment to ensure the time series consistency and data quality of cross-distance measurement; Establish a state space model and measurement model suitable for Kalman filtering to describe the vehicle motion characteristics and speed measurement error characteristics; Iterate the Kalman filter on the time series data, fuse the speed measurement data at different distances, and generate an optimized speed estimate; Based on the discrete velocity estimate of the Kalman filter output, a smooth and continuous velocity curve is generated through interpolation method.

8. The system for monitoring vehicles traveling on a highway overspeeding throughout the entire process according to claim 1 is characterized in that: The central data analysis platform includes: The data aggregation and storage center aggregates and stores the entire data from each module, providing a unified data source for subsequent analysis; The vehicle behavior analysis module uses machine learning algorithms to analyze the vehicle's full driving data to identify potential speeding behaviors and abnormal driving patterns; The speeding determination and assessment module accurately determines speeding behavior and assesses its severity based on preset rules and dynamic thresholds; Report generator that automatically integrates analysis results and generates detailed monitoring reports including speeding statistics, trend analysis and abnormal events.

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