Motorcycle travel monitoring method and system based on internet of things

Abstract

The application provides a motorcycle driving monitoring method and system based on the Internet of Things, which collects traffic live sound of a road area first, extracts motorcycle driving sound components from the traffic live sound, judges the driving direction of the motorcycle on the road area, determines the road section where the motorcycle is likely to drive, prepares for shooting the corresponding road section in advance, and obtains completed motorcycle driving live images; analyzes the driving live images, judges whether the motorcycle has a rule-breaking driving behavior, and reports relevant motorcycle information to an Internet of Things platform, so that the Internet of Things platform instructs a monitoring device of the road area to track and locate the motorcycle according to the motorcycle information, effectively performs driving section pre-judgment shooting on the driving of the motorcycle, and improves the timeliness and reliability of the motorcycle driving monitoring.

Description

Technical Field

[0001] This invention relates to the technical field of traffic monitoring, and in particular to a method and system for monitoring motorcycle movement based on the Internet of Things. Background Technology

[0002] Motorcycles, as a small mode of transportation, are increasingly used. However, they frequently engage in speeding and running red lights, jeopardizing road safety. Their agility makes them easy to evade surveillance cameras, hindering effective and timely monitoring of illegal motorcycle activity. Existing road cameras typically capture incomplete images of motorcycles, failing to accurately analyze whether they are illegally operating. Furthermore, these cameras cannot effectively predict motorcycle routes before recording, reducing the timeliness and reliability of motorcycle monitoring. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a motorcycle driving monitoring method and system based on the Internet of Things (IoT). It extracts motorcycle driving sound component data from real-time traffic sound data in a road area to determine the motorcycle's driving status information and predict the road segments the motorcycle will travel on. It then collects and analyzes real-time images of the motorcycle traveling on these road segments to obtain real-time driving information. Based on this information, it determines whether the motorcycle is driving illegally and uploads the motorcycle's vehicle status information to the IoT platform for tracking and positioning. The system first collects real-time traffic sound data from the road area and extracts motorcycle driving sound components to determine the motorcycle's direction of travel and identify potential road segments. This allows for advance planning and filming of these segments, resulting in complete real-time motorcycle driving images. The system then analyzes these images to determine if the motorcycle is driving illegally and reports relevant motorcycle information to the IoT platform. This enables the IoT platform to instruct monitoring equipment in the road area to track and locate the motorcycle, effectively predicting and filming the motorcycle's travel segments and improving the timeliness and reliability of motorcycle driving monitoring.

[0004] This invention provides a method for monitoring motorcycle driving based on the Internet of Things, which includes the following steps:

[0005] Step S1: Obtain real-time traffic sound data of the road area; filter the real-time traffic sound data to extract the motorcycle driving sound component data contained in the real-time traffic sound data; analyze the motorcycle driving sound component data to determine the motorcycle driving status information of the road area.

[0006] Step S2: Based on the motorcycle driving status information, predict the road segment that the motorcycle will travel through in the road area, and collect real-time images of the motorcycle driving on the road segment; analyze and process the real-time images to obtain the motorcycle's real-time driving information.

[0007] Step S3: Based on the real-time driving information, determine whether the motorcycle is in a state of illegal driving; based on the determination result, upload the motorcycle's vehicle status information to the Internet of Things platform to track and locate the motorcycle.

[0008] Further, in step S1, real-time traffic sound data of the road area is acquired, and the real-time traffic sound data is filtered to extract motorcycle driving sound component data contained in the real-time traffic sound data; the motorcycle driving sound component data is analyzed and processed to determine the motorcycle driving status information of the road area, specifically including:

[0009] Sound data was collected from different directions in the road area to obtain real-time traffic sound data for each direction; sound frequency filtering was performed on the real-time traffic sound data for each direction to extract the motorcycle driving sound component data contained in the real-time traffic sound data for each direction.

[0010] The sound intensity change information of the motorcycle driving sound component data within a preset time period is obtained. Based on the sound intensity change information, the driving direction of the motorcycle in each direction in the road area is determined, and this is used as the motorcycle driving status information.

[0011] Furthermore, in step S2, based on the motorcycle's driving status information, the road segment the motorcycle will travel through in the road area is predicted, and real-time images of the motorcycle's driving on the road segment are collected; the real-time driving images are analyzed and processed to obtain the motorcycle's real-time driving information, specifically including:

[0012] Based on the direction of travel of the motorcycle in each direction of the road area and the direction of road extension of the road area, predict the road segments that the motorcycle may travel through in each direction of the road area.

[0013] The road section was scanned and photographed to obtain a real-time dynamic video of the motorcycle driving on the road section.

[0014] The driving dynamic video is segmented into frames to obtain several driving image frames; the driving image frames are processed to identify the motorcycle outline and lane line outline to obtain the relative positional relationship information between the motorcycle's wheels and the lane line during driving; any two adjacent driving image frames are compared to obtain the motorcycle's driving speed information.

[0015] Furthermore, in step S3, based on the real-time driving information, it is determined whether the motorcycle is in a state of illegal driving; based on the determination result, the vehicle status information of the motorcycle is uploaded to the Internet of Things platform to track and locate the motorcycle. This specifically includes:

[0016] Based on the relative position information, determine whether the motorcycle has crossed the line during its journey; based on the speed information, determine whether the motorcycle has exceeded the speed limit during its journey.

[0017] If there is any lane crossing or speeding, the motorcycle's license plate number and driver's appearance information are obtained based on the real-time driving video. The license plate number and driver's appearance information are then uploaded to the Internet of Things platform for tracking and locating the motorcycle.

[0018] The present invention also provides an Internet of Things-based motorcycle driving monitoring system, which includes:

[0019] The sound acquisition and analysis module is used to acquire real-time traffic sound data of the road area, filter the real-time traffic sound data, extract the motorcycle driving sound component data contained in the real-time traffic sound data, and analyze the motorcycle driving sound component data to determine the motorcycle driving status information of the road area.

[0020] A motorcycle driving prediction module is used to predict the road segment that the motorcycle will travel on in the road area based on the motorcycle driving status information.

[0021] The image acquisition and analysis module is used to acquire real-time images of the motorcycle driving on the road section; and to analyze and process the real-time images to obtain real-time information about the motorcycle's driving status.

[0022] The motorcycle driving status determination module is used to determine whether the motorcycle is in a violation of driving regulations based on the real-time driving information.

[0023] The Internet of Things (IoT) communication module is used to upload the motorcycle's vehicle status information to the IoT platform based on the judgment result, thereby tracking and locating the motorcycle.

[0024] Furthermore, the sound acquisition and analysis module acquires real-time traffic sound data of the road area, filters and processes the traffic sound data to extract motorcycle driving sound component data contained in the traffic sound data; and analyzes and processes the motorcycle driving sound component data to determine the motorcycle driving status information of the road area, specifically including:

[0025] Sound data was collected from different directions in the road area to obtain real-time traffic sound data for each direction; sound frequency filtering was performed on the real-time traffic sound data for each direction to extract the motorcycle driving sound component data contained in the real-time traffic sound data for each direction.

[0026] The sound intensity change information of the motorcycle driving sound component data within a preset time period is obtained. Based on the sound intensity change information, the driving direction of the motorcycle in each direction in the road area is determined, and this is used as the motorcycle driving status information.

[0027] Furthermore, the motorcycle driving prediction module predicts the specific road segments that the motorcycle will travel through in the road area based on the motorcycle driving status information, including:

[0028] Based on the direction of travel of the motorcycle in each direction of the road area and the direction of road extension of the road area, predict the road segments that the motorcycle may travel through in each direction of the road area.

[0029] The image acquisition and analysis module acquires real-time images of the motorcycle driving on the road section; the real-time driving images are analyzed and processed to obtain the motorcycle's driving information, specifically including:

[0030] The road section was scanned and photographed to obtain a real-time dynamic video of the motorcycle driving on the road section.

[0031] The driving dynamic video is segmented into frames to obtain several driving image frames; the driving image frames are processed to identify the motorcycle outline and lane line outline to obtain the relative positional relationship information between the motorcycle's wheels and the lane line during driving; any two adjacent driving image frames are compared to obtain the motorcycle's driving speed information.

[0032] Furthermore, the motorcycle driving status determination module determines whether the motorcycle is in a violation of driving regulations based on the real-time driving information, specifically including:

[0033] Based on the relative position information, determine whether the motorcycle has crossed the line during its journey; based on the speed information, determine whether the motorcycle has exceeded the speed limit during its journey.

[0034] The IoT communication module uploads the motorcycle's vehicle status information to the IoT platform based on the judgment result, thereby tracking and locating the motorcycle. Specifically, this includes:

[0035] If there is any lane crossing or speeding, the motorcycle's license plate number and driver's appearance information are obtained based on the real-time driving video. The license plate number and driver's appearance information are then uploaded to the Internet of Things platform for tracking and locating the motorcycle.

[0036] Compared to existing technologies, this IoT-based motorcycle driving monitoring method and system extracts motorcycle driving sound component data from real-time traffic sound data in road areas to determine the motorcycle driving status information in the road area, and then predicts the road segments the motorcycle will travel through in the road area; it collects and analyzes real-time images of the motorcycle driving on the said road segments to obtain real-time motorcycle driving information; based on the driving information, it determines whether the motorcycle is driving illegally, and uploads the motorcycle's vehicle status information to the IoT platform for tracking and positioning. It first collects real-time traffic sound data in the road area and extracts motorcycle driving sound components to determine the motorcycle's driving direction in the road area, and then determines the road segments the motorcycle might travel on. This allows for advance preparation to film the corresponding road segments and obtain complete real-time motorcycle driving images; then, it analyzes the driving images to determine whether the motorcycle has engaged in illegal driving behavior and reports relevant motorcycle information to the IoT platform. This facilitates the IoT platform to instruct the monitoring equipment in the road area to track and locate the motorcycle based on the motorcycle information, effectively predicting and filming the motorcycle's driving route, and improving the timeliness and reliability of motorcycle driving monitoring.

[0037] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0038] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating the Internet of Things-based motorcycle driving monitoring method provided by the present invention.

[0041] Figure 2This is a schematic diagram of the structure of the Internet of Things-based motorcycle driving monitoring system provided by the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] See Figure 1 This is a flowchart illustrating the IoT-based motorcycle driving monitoring method provided in an embodiment of the present invention. The IoT-based motorcycle driving monitoring method includes the following steps:

[0044] Step S1: Obtain real-time traffic sound data of the road area; filter the real-time traffic sound data to extract the motorcycle driving sound component data contained in the real-time traffic sound data; analyze and process the motorcycle driving sound component data to determine the motorcycle driving status information of the road area.

[0045] Step S2: Based on the motorcycle's driving status information, predict the road segment the motorcycle will travel through in the road area, and collect real-time images of the motorcycle's driving on that road segment; analyze and process the real-time images to obtain the motorcycle's driving information.

[0046] Step S3: Based on the real-time driving information, determine whether the motorcycle is in a state of illegal driving; based on the determination result, upload the motorcycle's vehicle status information to the Internet of Things platform to track and locate the motorcycle.

[0047] The beneficial effects of the above technical solution are as follows: This IoT-based motorcycle driving monitoring method extracts motorcycle driving sound component data from real-time traffic sound data in road areas to determine the motorcycle driving status information in the road areas, and then predicts the road segments the motorcycle will travel through in the road areas; it collects and analyzes real-time images of the motorcycle driving on the said road segments to obtain real-time motorcycle driving information; based on the real-time driving information, it determines whether the motorcycle is driving illegally, and uploads the motorcycle's vehicle status information to the IoT platform for tracking and locating the motorcycle. It first collects real-time traffic sound data in the road areas and extracts motorcycle driving sound components to determine the motorcycle's driving direction in the road areas, and then determines the road segments the motorcycle may travel on. This allows for advance preparation for filming the corresponding road segments to obtain complete real-time motorcycle driving images; then, it analyzes the driving images to determine whether the motorcycle has engaged in illegal driving behavior, and reports relevant motorcycle information to the IoT platform. This facilitates the IoT platform instructing the monitoring equipment in the road areas to track and locate the motorcycle based on the motorcycle information, effectively predicting and filming the motorcycle's driving route, and improving the timeliness and reliability of motorcycle driving monitoring.

[0048] Preferably, in step S1, real-time traffic sound data of the road area is acquired, the real-time traffic sound data is filtered, and the motorcycle driving sound component data contained in the real-time traffic sound data is extracted; the motorcycle driving sound component data is analyzed and processed to determine the motorcycle driving status information of the road area, specifically including:

[0049] Sound data was collected from different directions in the road area to obtain real-time traffic sound data for each direction; sound frequency filtering was performed on the real-time traffic sound data for each direction to extract the motorcycle driving sound component data contained in the real-time traffic sound data for each direction.

[0050] The sound intensity variation information of the motorcycle's driving sound components is obtained within a preset time period. Based on the sound intensity variation information, the driving direction of the motorcycle in each direction of the road area is determined, which is used as the driving status information of the motorcycle.

[0051] The beneficial effects of the above technical solution are as follows: the sounds produced by the engines of motorcycles and cars during operation are not the same in terms of audio frequency bands; that is, the sound produced by motorcycles during operation has a certain uniqueness in the audio frequency band. By scanning and collecting sound data from different directions in the road area, traffic sound data corresponding to each direction is obtained. Then, based on the coverage of the audio frequency band produced by the motorcycle engine, the motorcycle driving sound component data is extracted from the traffic sound data. Furthermore, the changes in the sound intensity of the motorcycle driving sound component data within a preset time period are identified. If the sound intensity increases, it indicates that the motorcycle is moving towards the direction of approach; if the sound intensity decreases, it indicates that the motorcycle is moving away from the direction of travel. This allows for the differentiation and identification of motorcycles moving towards or away from each direction.

[0052] Preferably, in step S2, based on the motorcycle's driving status information, the road segment the motorcycle will travel through in the road area is predicted, and real-time images of the motorcycle's driving on that road segment are collected; the real-time driving images are analyzed and processed to obtain the motorcycle's driving information, specifically including:

[0053] Based on the direction of travel of the motorcycle in each direction of the road area and the direction of road extension in the road area, predict the road segments that the motorcycle may travel through in each direction of the road area.

[0054] The road section was scanned and photographed to obtain real-time dynamic images of motorcycles traveling on that section of the road;

[0055] The dynamic video of the driving situation is segmented into frames to obtain several driving image frames; the motorcycle outline and lane line outline are identified in the driving image frames to obtain the relative positional relationship between the motorcycle's wheels and the lane lines during the driving process; any two adjacent driving image frames are compared to obtain the motorcycle's driving speed information.

[0056] The beneficial effects of the above technical solution are as follows: By combining and analyzing the driving direction of motorcycles in each direction within the road area with the road's extension direction, it is possible to determine whether a motorcycle is moving closer to or away from the road's extension direction, thereby predicting the road segments the motorcycle might traverse during its journey. Furthermore, by pre-scanning and photographing these road segments, a complete real-time dynamic image of the motorcycle's driving can be captured when it actually passes through them. Then, by performing frame-by-frame processing and recognition processing on this dynamic image, the relative positional relationship between the motorcycle's wheels and the lane lines, as well as the motorcycle's speed, can be accurately obtained.

[0057] Preferably, in step S3, based on the real-time driving information, it is determined whether the motorcycle is in a state of illegal driving; based on the determination result, the vehicle status information of the motorcycle is uploaded to the Internet of Things platform to track and locate the motorcycle. Specifically, this includes:

[0058] Based on the relative position information, determine whether the motorcycle crossed the line during its journey; based on the speed information, determine whether the motorcycle exceeded the speed limit during its journey.

[0059] If there is a violation of lane marking or speeding, the motorcycle's license plate number and driver's appearance information are obtained based on the real-time dynamic video footage. This information is then uploaded to the Internet of Things (IoT) platform for tracking and locating the motorcycle.

[0060] The beneficial effects of the above technical solution are as follows: This method enables accurate determination of whether a motorcycle is crossing lane lines or speeding during operation. Furthermore, by uploading the license plate number and driver appearance information of motorcycles exhibiting lane-crossing or speeding behavior to an IoT platform, the platform can broadcast this information to monitoring equipment in the road area, thereby facilitating continuous tracking and location of the corresponding motorcycles.

[0061] See Figure 2 This is a schematic diagram of the structure of a motorcycle driving monitoring system based on the Internet of Things (IoT) provided in an embodiment of the present invention. The IoT-based motorcycle driving monitoring system includes:

[0062] The sound acquisition and analysis module is used to acquire real-time traffic sound data in the road area, filter and process the real-time traffic sound data, extract the motorcycle driving sound component data contained in the real-time traffic sound data, and analyze and process the motorcycle driving sound component data to determine the motorcycle driving status information in the road area.

[0063] The motorcycle driving prediction module is used to predict the road segments that the motorcycle will travel on in the road area based on the motorcycle's driving status information.

[0064] The image acquisition and analysis module is used to acquire real-time images of motorcycles driving on this road section; and to analyze and process these images to obtain real-time information about the motorcycles' driving conditions.

[0065] The motorcycle driving status judgment module is used to determine whether the motorcycle is in a violation of driving regulations based on the real-time driving information.

[0066] The Internet of Things (IoT) communication module is used to upload the motorcycle's vehicle status information to the IoT platform based on the judgment result, thereby tracking and locating the motorcycle.

[0067] The beneficial effects of the above technical solution are as follows: This IoT-based motorcycle driving monitoring system extracts motorcycle driving sound component data from real-time traffic sound data in road areas to determine the motorcycle driving status information in the road area, and then predicts the road segments the motorcycle will travel through in the road area; it collects and analyzes real-time images of the motorcycle driving on the said road segments to obtain real-time motorcycle driving information; based on the real-time driving information, it determines whether the motorcycle is driving illegally, and uploads the motorcycle's vehicle status information to the IoT platform for tracking and locating the motorcycle. It first collects real-time traffic sound data in the road area and extracts motorcycle driving sound components to determine the motorcycle's driving direction in the road area, and then determines the road segments the motorcycle may travel on. This allows for advance preparation for filming the corresponding road segments, obtaining complete real-time motorcycle driving images; then, it analyzes the driving images to determine whether the motorcycle has engaged in illegal driving behavior, and reports relevant motorcycle information to the IoT platform. This facilitates the IoT platform instructing the monitoring equipment in the road area to track and locate the motorcycle based on the motorcycle information, effectively predicting and filming the motorcycle's driving route, and improving the timeliness and reliability of motorcycle driving monitoring.

[0068] Preferably, the sound acquisition and analysis module acquires real-time traffic sound data of the road area, filters and processes the traffic sound data to extract the motorcycle driving sound component data contained in the traffic sound data; and analyzes and processes the motorcycle driving sound component data to determine the motorcycle driving status information of the road area, specifically including:

[0069] Sound data was collected from different directions in the road area to obtain real-time traffic sound data for each direction; sound frequency filtering was performed on the real-time traffic sound data for each direction to extract the motorcycle driving sound component data contained in the real-time traffic sound data for each direction.

[0070] The sound intensity variation information of the motorcycle's driving sound components is obtained within a preset time period. Based on the sound intensity variation information, the driving direction of the motorcycle in each direction of the road area is determined, which is used as the driving status information of the motorcycle.

[0071] The beneficial effects of the above technical solution are as follows: the sounds produced by the engines of motorcycles and cars during operation are not the same in terms of audio frequency bands; that is, the sound produced by motorcycles during operation has a certain uniqueness in the audio frequency band. By scanning and collecting sound data from different directions in the road area, traffic sound data corresponding to each direction is obtained. Then, based on the coverage of the audio frequency band produced by the motorcycle engine, the motorcycle driving sound component data is extracted from the traffic sound data. Furthermore, the changes in the sound intensity of the motorcycle driving sound component data within a preset time period are identified. If the sound intensity increases, it indicates that the motorcycle is moving towards the direction of approach; if the sound intensity decreases, it indicates that the motorcycle is moving away from the direction of travel. This allows for the differentiation and identification of motorcycles moving towards or away from each direction.

[0072] Preferably, the motorcycle driving prediction module predicts the specific road segments that the motorcycle will travel on in the road area based on the motorcycle's driving status information, including:

[0073] Based on the direction of travel of the motorcycle in each direction of the road area and the direction of road extension in the road area, predict the road segments that the motorcycle may travel through in each direction of the road area.

[0074] The image acquisition and analysis module acquires real-time images of the motorcycle driving on this road section; it then analyzes and processes these images to obtain specific information about the motorcycle's driving conditions, including:

[0075] The road section was scanned and photographed to obtain real-time dynamic images of motorcycles traveling on that section of the road;

[0076] The dynamic video of the driving situation is segmented into frames to obtain several driving image frames; the motorcycle outline and lane line outline are identified in the driving image frames to obtain the relative positional relationship between the motorcycle's wheels and the lane lines during the driving process; any two adjacent driving image frames are compared to obtain the motorcycle's driving speed information.

[0077] The beneficial effects of the above technical solution are as follows: By combining and analyzing the driving direction of motorcycles in each direction within the road area with the road's extension direction, it is possible to determine whether a motorcycle is moving closer to or away from the road's extension direction, thereby predicting the road segments the motorcycle might traverse during its journey. Furthermore, by pre-scanning and photographing these road segments, a complete real-time dynamic image of the motorcycle's driving can be captured when it actually passes through them. Then, by performing frame-by-frame processing and recognition processing on this dynamic image, the relative positional relationship between the motorcycle's wheels and the lane lines, as well as the motorcycle's speed, can be accurately obtained.

[0078] Preferably, the motorcycle driving status determination module determines whether the motorcycle is in a violation of driving regulations based on the driving status information, specifically including:

[0079] Based on the relative position information, determine whether the motorcycle crossed the line during its journey; based on the speed information, determine whether the motorcycle exceeded the speed limit during its journey.

[0080] Based on the judgment result, the IoT communication module uploads the motorcycle's vehicle status information to the IoT platform to track and locate the motorcycle. Specifically, this includes:

[0081] If there is a violation of lane marking or speeding, the motorcycle's license plate number and driver's appearance information are obtained based on the real-time dynamic video footage. This information is then uploaded to the Internet of Things (IoT) platform for tracking and locating the motorcycle.

[0082] The beneficial effects of the above technical solution are as follows: This method enables accurate determination of whether a motorcycle is crossing lane lines or speeding during operation. Furthermore, by uploading the license plate number and driver appearance information of motorcycles exhibiting lane-crossing or speeding behavior to an IoT platform, the platform can broadcast this information to monitoring equipment in the road area, thereby facilitating continuous tracking and location of the corresponding motorcycles.

[0083] As can be seen from the above embodiments, this IoT-based motorcycle driving monitoring method and system extracts motorcycle driving sound component data from real-time traffic sound data in a road area to determine the motorcycle driving status information in the road area, and then predicts the road segment the motorcycle will travel through in the road area; it collects and analyzes real-time images of the motorcycle driving on the road segment to obtain real-time motorcycle driving information; based on the real-time driving information, it determines whether the motorcycle is driving illegally, and uploads the motorcycle's vehicle status information to the IoT platform for tracking and locating the motorcycle. It first collects real-time traffic sound data in the road area and extracts motorcycle driving sound components to determine the motorcycle's driving direction in the road area, and then determines the road segment the motorcycle may travel on. This allows for advance preparation for filming the corresponding road segment, obtaining complete real-time motorcycle driving images; then, it analyzes the driving images to determine whether the motorcycle has engaged in illegal driving behavior, and reports relevant motorcycle information to the IoT platform. This facilitates the IoT platform instructing the monitoring equipment in the road area to track and locate the motorcycle based on the motorcycle information, effectively predicting and filming the motorcycle's driving route, and improving the timeliness and reliability of motorcycle driving monitoring.

[0084] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for monitoring motorcycle driving based on the Internet of Things, characterized in that, It includes the following steps: Step S1: Obtain real-time traffic sound data of the road area, filter the real-time traffic sound data, and extract the motorcycle driving sound component data contained in the real-time traffic sound data. The analysis and processing of the motorcycle driving sound component data to determine the motorcycle driving status information in the road area includes: collecting sound data from different directions in the road area to obtain real-time traffic sound data corresponding to each direction; performing sound frequency filtering processing on the real-time traffic sound data from each direction to extract the motorcycle driving sound component data contained in the real-time traffic sound data from each direction; obtaining the sound intensity change information of the motorcycle driving sound component data within a preset time period; and determining the driving direction of the motorcycle in each direction in the road area based on the sound intensity change information, which is used as the motorcycle driving status information. Step S2: Based on the motorcycle's driving status information, predict the road segments the motorcycle will travel through in the road area, and collect real-time images of the motorcycle traveling on those road segments; analyze and process the real-time images to obtain the motorcycle's driving information, including: predicting the possible road segments the motorcycle might travel through in each direction of the road area based on the motorcycle's driving direction at each location and the road's extension direction in the road area; scanning and capturing the road segments to obtain dynamic images of the motorcycle's driving on those road segments; performing frame segmentation on the dynamic images to obtain several driving image frames; performing motorcycle outline and lane line outline recognition processing on the driving image frames to obtain the relative positional relationship information between the motorcycle's wheels and lane lines during driving; and comparing any two adjacent driving image frames to obtain the motorcycle's speed information. Step S3: Based on the real-time driving information, determine whether the motorcycle is in a state of illegal driving; based on the determination result, upload the motorcycle's vehicle status information to the Internet of Things platform to track and locate the motorcycle.

2. The motorcycle driving monitoring method based on the Internet of Things as described in claim 1, characterized in that: In step S3, based on the driving information, it is determined whether the motorcycle is in a state of illegal driving; Based on the judgment results, the vehicle status information of the motorcycle is uploaded to the Internet of Things platform for tracking and locating the motorcycle. Specifically, this includes: Based on the relative position information, determine whether the motorcycle has crossed the line during its journey; based on the speed information, determine whether the motorcycle has exceeded the speed limit during its journey. If there is any lane crossing or speeding, the motorcycle's license plate number and driver's appearance information are obtained based on the real-time driving video. The license plate number and driver's appearance information are then uploaded to the Internet of Things platform for tracking and locating the motorcycle.

3. A motorcycle driving monitoring system based on the Internet of Things, characterized in that, It includes: The sound acquisition and analysis module is used to acquire real-time traffic sound data in the road area, filter the real-time traffic sound data, and extract the motorcycle driving sound component data contained in the real-time traffic sound data. The analysis and processing of the motorcycle driving sound component data to determine the motorcycle driving status information in the road area includes: collecting sound data from different directions in the road area to obtain real-time traffic sound data corresponding to each direction; performing sound frequency filtering processing on the real-time traffic sound data from each direction to extract the motorcycle driving sound component data contained in the real-time traffic sound data from each direction; obtaining the sound intensity change information of the motorcycle driving sound component data within a preset time period; and determining the driving direction of the motorcycle in each direction in the road area based on the sound intensity change information, which is used as the motorcycle driving status information. The motorcycle driving prediction module is used to predict the road segments that the motorcycle will travel through in the road area based on the motorcycle driving status information, including: predicting the road segments that the motorcycle may travel through in each direction of the road area based on the motorcycle's driving direction in each direction of the road area and the road extension direction of the road area. The image acquisition and analysis module is used to acquire real-time images of the motorcycle driving on the road segment; analyze and process the real-time images to obtain real-time information about the motorcycle's driving situation, including: scanning and capturing images of the road segment to obtain dynamic images of the motorcycle driving on the road segment; performing frame segmentation processing on the dynamic images to obtain several driving image frames; performing motorcycle outline and lane line outline recognition processing on the driving image frames to obtain information on the relative positional relationship between the motorcycle's wheels and lane lines during driving; and comparing any two adjacent driving image frames to obtain the motorcycle's speed information. The motorcycle driving status determination module is used to determine whether the motorcycle is in a violation of driving regulations based on the real-time driving information. The Internet of Things (IoT) communication module is used to upload the motorcycle's vehicle status information to the IoT platform based on the judgment result, thereby tracking and locating the motorcycle.

4. The motorcycle driving monitoring system based on the Internet of Things as described in claim 3, characterized in that: The motorcycle driving status determination module determines whether the motorcycle is in a violation of driving regulations based on the real-time driving information, specifically including: Based on the relative position information, determine whether the motorcycle has crossed the line during its journey; based on the speed information, determine whether the motorcycle has exceeded the speed limit during its journey. The IoT communication module uploads the motorcycle's vehicle status information to the IoT platform based on the judgment result, thereby tracking and locating the motorcycle. Specifically, this includes: If there is any lane crossing or speeding, the motorcycle's license plate number and driver's appearance information are obtained based on the real-time driving video. The license plate number and driver's appearance information are then uploaded to the Internet of Things platform for tracking and locating the motorcycle.

Images