Campus vehicle driving management method and system based on internet of things

Abstract

This invention provides a campus vehicle driving management method and system based on the Internet of Things (IoT). It determines the planned driving route of a vehicle within the campus based on its historical driving habits, provides predictive driving instructions based on the distribution of key locations along the planned route, and obtains information on vehicle violations based on actual driving images within the campus. This updates the vehicle's historical driving route information on the IoT platform. It provides predictive instructions during vehicle operation, ensuring vehicle safety within the campus and enabling comprehensive management and control of vehicle driving conditions.

Description

Technical Field

[0001] This invention relates to the technical field of intelligent traffic management, and in particular to a campus vehicle driving management method and system based on the Internet of Things. Background Technology

[0002] The campus has a large and widely distributed flow of people, requiring drivers to constantly be aware of pedestrian traffic. This lack of readily available information provides no early warning for vehicles, leading to slow-moving traffic or congestion. Current technology can only provide drivers with traffic information signs, but it cannot offer predictive guidance or provide comprehensive management and control over real-time vehicle traffic on campus. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a campus vehicle driving management method and system based on the Internet of Things (IoT). It retrieves historical driving route information of vehicles on campus from the IoT based on license plate information obtained through visual recognition, and determines the planned driving route within the campus based on response messages from the vehicles. Combining this with current time information, it identifies key locations along the planned route and sends driving instruction information to the corresponding terminal of the vehicle based on the distance between the vehicle and these key locations. Furthermore, it obtains driving violation information of the vehicle based on real-time driving images within the campus, updating the vehicle's historical driving route information on the IoT platform. Based on the vehicle's historical driving habits within the campus, it determines the planned driving route for the current trip within the campus and provides predictive driving instruction information to the vehicle based on the distribution of key locations along the planned route. Finally, it obtains driving violation information of the vehicle based on actual driving images within the campus, updating the vehicle's historical driving route information on the IoT platform. This provides predictive instruction information for vehicles during driving, ensuring the safety of vehicles driving within the campus and enabling comprehensive management and control of the real-time driving situation of vehicles on campus.

[0004] This invention provides a campus vehicle driving management method based on the Internet of Things, which includes the following steps:

[0005] Step S1: Perform visual recognition processing on vehicles entering the campus to determine the vehicle's license plate information; retrieve the vehicle's historical driving route information within the campus from the Internet of Things platform based on the license plate information; determine the vehicle's planned driving route within the campus based on the vehicle's response message and the historical driving route information.

[0006] Step S2: Based on the current time information and the planned driving route, determine the key locations on the planned driving route; retrieve the real-time location of the vehicle within the campus from the IoT platform, and send driving instruction information to the terminal corresponding to the vehicle based on the real-time location and the key locations;

[0007] Step S3: Retrieve the actual driving video of the vehicle within the campus from the IoT platform, analyze and process the actual driving video to obtain the driving violation information of the vehicle during the driving process; update the historical driving route information of the vehicle on the IoT platform based on the driving violation information.

[0008] Further, in step S1, visual recognition processing is performed on vehicles entering the campus to determine their license plate information; based on the license plate information, the historical driving route information of the vehicle within the campus is retrieved from the IoT platform; based on the response message from the vehicle and the historical driving route information, the planned driving route of the vehicle within the campus is determined, specifically including:

[0009] Collect images of the front or rear of vehicles entering the campus, and perform recognition processing on the images to obtain the vehicle's license plate information.

[0010] Using the license plate information as an index, the historical driving route information of the vehicle within the campus during historical time periods is retrieved from the Internet of Things platform; a historical driving route list is generated according to the order of the shortest and longest paths of all historical driving routes, and sent to the terminal corresponding to the vehicle; based on the response message from the terminal regarding the historical driving route list, one of the historical driving routes is selected as the planned driving route of the vehicle within the campus.

[0011] Furthermore, in step S2, determining key locations on the planned driving route based on the current time information and the planned driving route; retrieving the vehicle's real-time location within the campus from the IoT platform, and sending driving instruction information to the vehicle's corresponding terminal based on the real-time location and the key locations, specifically includes:

[0012] From the historical distribution database of campus pedestrian traffic on the IoT platform, retrieve campus pedestrian traffic distribution data that matches the current time information; compare the campus pedestrian traffic distribution data with the planned driving route to determine the pedestrian traffic value at each intersection of the planned driving route;

[0013] If the pedestrian flow value is greater than or equal to the preset pedestrian flow threshold, the corresponding intersection location will be identified as a key location.

[0014] The real-time location of the vehicle within the campus is retrieved from the distributed positioning devices connected to the IoT platform. The real-time location is compared with the key location to determine the actual distance between the real-time location and the key location. If the actual distance is less than or equal to a preset distance threshold, a driving deceleration instruction is sent to the terminal corresponding to the vehicle.

[0015] Furthermore, in step S3, the actual driving video of the vehicle within the campus is retrieved from the IoT platform, and the actual driving video is analyzed and processed to obtain the driving violation information of the vehicle during the driving process; based on the driving violation information, the historical driving path information of the vehicle on the IoT platform is further analyzed, including:

[0016] The system retrieves actual driving footage of the vehicle within the campus from distributed positioning devices connected to the Internet of Things (IoT), and extracts the actual driving speed of the vehicle at all key locations from the actual driving footage. If the actual driving speed at any key location is greater than or equal to a preset speed threshold, it is determined that the vehicle has committed a driving violation during the driving process.

[0017] If the vehicle commits a driving violation during its journey, the planned route for that vehicle will be deleted from the historical route information of the IoT platform.

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

[0019] The visual recognition module is used to perform visual recognition processing on vehicles entering the campus and determine the vehicle's license plate information.

[0020] The driving route determination module is used to retrieve the vehicle's historical driving route information within the campus from the Internet of Things platform based on the license plate information; and to determine the vehicle's planned driving route within the campus based on the response message from the vehicle and the historical driving route information.

[0021] The driving instruction module is used to determine key locations on the planned driving route based on the current time information and the planned driving route; retrieve the real-time location of the vehicle within the campus from the Internet of Things platform; and send driving instruction information to the terminal corresponding to the vehicle based on the real-time location and the key locations.

[0022] The driving violation judgment module is used to retrieve the actual driving video of the vehicle within the campus from the Internet of Things platform, analyze and process the actual driving video, and obtain the driving violation information of the vehicle during the driving process.

[0023] The historical driving route information update module is used to update the historical driving route information of the vehicle on the Internet of Things platform based on the driving violation information;

[0024] Furthermore, the visual recognition module performs visual recognition processing on vehicles entering the campus to determine the vehicle's license plate information, specifically including:

[0025] Collect images of the front or rear of vehicles entering the campus, and perform recognition processing on the images to obtain the vehicle's license plate information.

[0026] The driving route determination module retrieves the vehicle's historical driving route information within the campus from the IoT platform based on the license plate information; based on the vehicle's response message and the historical driving route information, the module determines the vehicle's planned driving route within the campus, specifically including:

[0027] Using the license plate information as an index, the historical driving route information of the vehicle within the campus during historical time periods is retrieved from the Internet of Things platform; a historical driving route list is generated according to the order of the shortest and longest paths of all historical driving routes, and sent to the terminal corresponding to the vehicle; based on the response message from the terminal regarding the historical driving route list, one of the historical driving routes is selected as the planned driving route of the vehicle within the campus.

[0028] Furthermore, the driving instruction module determines key locations on the planned driving route based on the current time information and the planned driving route; it retrieves the real-time location of the vehicle within the campus from the IoT platform, and sends driving instruction information to the terminal corresponding to the vehicle based on the real-time location and the key locations. Specifically, this includes:

[0029] From the historical distribution database of campus pedestrian traffic on the IoT platform, retrieve campus pedestrian traffic distribution data that matches the current time information; compare the campus pedestrian traffic distribution data with the planned driving route to determine the pedestrian traffic value at each intersection of the planned driving route;

[0030] If the pedestrian flow value is greater than or equal to the preset pedestrian flow threshold, the corresponding intersection location will be identified as a key location.

[0031] The real-time location of the vehicle within the campus is retrieved from the distributed positioning devices connected to the IoT platform. The real-time location is compared with the key location to determine the actual distance between the real-time location and the key location. If the actual distance is less than or equal to a preset distance threshold, a driving deceleration instruction is sent to the terminal corresponding to the vehicle.

[0032] Furthermore, the driving violation judgment module retrieves the actual driving video of the vehicle within the campus from the IoT platform, analyzes and processes the actual driving video, and obtains the driving violation information of the vehicle during the driving process, specifically including:

[0033] The system retrieves actual driving footage of the vehicle within the campus from distributed positioning devices connected to the Internet of Things (IoT), and extracts the actual driving speed of the vehicle at all key locations from the actual driving footage. If the actual driving speed at any key location is greater than or equal to a preset speed threshold, it is determined that the vehicle has committed a driving violation during the driving process.

[0034] The historical driving route information update module updates the vehicle's historical driving route information on the IoT platform based on the driving violation information, specifically including:

[0035] If the vehicle commits a driving violation during its journey, the planned route for that vehicle will be deleted from the historical route information of the IoT platform.

[0036] Compared to existing technologies, this IoT-based campus vehicle traffic management method and system retrieves historical driving route information of vehicles on campus from the IoT based on license plate information obtained through visual recognition. Based on response messages from the vehicles, it determines the planned driving route within the campus. Combining this with current time information, it identifies key locations along the planned route and sends driving instructions to the corresponding terminals of the vehicles based on the distance between the vehicles and these key locations. Furthermore, it obtains information on vehicle violations based on real-time driving images within the campus, updating the historical driving route information of the vehicles on the IoT platform. Based on the vehicles' historical driving habits within the campus, it determines the planned driving route for the current trip within the campus and provides predictive driving instructions to the vehicles based on the distribution of key locations along the planned route. It also obtains information on vehicle violations based on actual driving images within the campus, updating the historical driving route information of the vehicles on the IoT platform. This provides predictive instructions during vehicle operation, ensuring the safety of vehicles driving within the campus and enabling comprehensive management and control of vehicle traffic conditions on campus.

[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 campus vehicle driving management method provided by the present invention.

[0041] Figure 2 This is a schematic diagram of the structure of the Internet of Things-based campus vehicle driving management 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 an IoT-based campus vehicle traffic management method provided in an embodiment of the present invention. The IoT-based campus vehicle traffic management method includes the following steps:

[0044] Step S1: Perform visual recognition processing on vehicles entering the campus to determine the vehicle's license plate information; retrieve the vehicle's historical driving route information within the campus from the IoT platform based on the license plate information; determine the vehicle's planned driving route within the campus based on the vehicle's response message and the historical driving route information.

[0045] Step S2: Based on the current time information and the planned driving route, determine the key locations on the planned driving route; retrieve the real-time location of the vehicle within the campus from the IoT platform, and send driving instruction information to the terminal corresponding to the vehicle based on the real-time location and the key location;

[0046] Step S3: Retrieve the actual driving video of the vehicle within the campus from the IoT platform, analyze and process the actual driving video to obtain the driving violation information of the vehicle during the driving process; based on the driving violation information, update the historical driving route information of the vehicle on the IoT platform.

[0047] The beneficial effects of the above technical solution are as follows: This IoT-based campus vehicle driving management method retrieves historical driving route information of vehicles on campus from the IoT based on license plate information obtained through visual recognition, and determines the planned driving route of the vehicle within the campus based on the response messages from the vehicle; combined with the current time information, it identifies key locations on the planned driving route, and sends driving instruction information to the corresponding terminal of the vehicle based on the distance between the vehicle and the key locations; it also obtains driving violation information of the vehicle based on the real-time driving images of the vehicle within the campus, thereby updating the historical driving route information of the vehicle on the IoT platform; based on the vehicle's historical driving habits within the campus, it determines the planned driving route of the vehicle within the campus for the current time, and provides predictive driving instruction information to the vehicle based on the distribution of key locations on the planned driving route; it also obtains driving violation information of the vehicle based on the actual driving images of the vehicle within the campus for the current time, thereby updating the historical driving route information of the vehicle on the IoT platform. This can provide predictive instruction information for vehicles during driving, ensuring the safety of vehicles driving within the campus and enabling comprehensive management and control of the actual driving situation of vehicles on campus.

[0048] Preferably, in step S1, visual recognition processing is performed on vehicles entering the campus to determine the vehicle's license plate information; based on the license plate information, the vehicle's historical driving route information within the campus is retrieved from the IoT platform; based on the vehicle's response message and the historical driving route information, the planned driving route of the vehicle within the campus is determined, specifically including:

[0049] Collect images of the front or rear of vehicles entering the campus, process these images to obtain the vehicle's license plate information;

[0050] Using the license plate information as an index, the system retrieves the vehicle's historical driving route information within the campus during historical time periods from the IoT platform; based on the order of the shortest and longest paths of all historical driving routes, a historical driving route list is generated and sent to the terminal corresponding to the vehicle; based on the terminal's response message regarding the historical driving route list, one of the historical driving routes is selected as the planned driving route for the vehicle within the campus.

[0051] The beneficial effects of the above technical solution are as follows: When a vehicle enters the campus, images of the front or rear of the vehicle are captured at the campus entrance / exit, obtaining the corresponding front or rear images. These images are then analyzed to obtain the vehicle's license plate number. The IoT platform pre-stores the vehicle's past driving route information within the campus. Using the vehicle's license plate number as an index, the platform filters out the vehicle's historical driving routes within the campus for specific historical periods, generating a corresponding list of historical driving routes, which is then returned to the vehicle's human-machine interface terminal. The driver performs corresponding touch operations on the human-machine interface terminal, selecting one of the historical driving routes from the list as the vehicle's planned driving route within the campus.

[0052] Preferably, in step S2, based on the current time information and the planned driving route, key locations on the planned driving route are determined; the real-time location of the vehicle within the campus is retrieved from the IoT platform, and driving instruction information is sent to the terminal corresponding to the vehicle based on the real-time location and the key locations. This specifically includes:

[0053] Retrieve campus pedestrian flow distribution data that matches the current time information from the historical campus pedestrian flow distribution database of the IoT platform; compare the campus pedestrian flow distribution data with the planned driving route to determine the pedestrian flow value at each intersection along the planned driving route;

[0054] If the pedestrian flow value is greater than or equal to the preset pedestrian flow threshold, the corresponding intersection location will be identified as a key location.

[0055] The vehicle's real-time location within the campus is retrieved from the distributed positioning devices connected to the IoT platform. This real-time location is compared with the key location to determine the actual distance between them. If the actual distance is less than or equal to a preset distance threshold, a deceleration instruction is sent to the terminal corresponding to the vehicle.

[0056] The beneficial effects of the above technical solution are as follows: When the driver is driving along the planned route, based on the current time information, the system retrieves campus pedestrian flow distribution data matching the current time information from the campus pedestrian flow historical distribution database of the IoT platform. This campus pedestrian flow historical distribution database includes pedestrian flow statistics for different locations within the campus at different times throughout the 24 hours of the day. The retrieved campus pedestrian flow distribution data can accurately reflect the actual pedestrian flow distribution on campus at the current time. Threshold comparisons are then performed on the pedestrian flow values ​​at each intersection along the planned route to identify key locations. Furthermore, distributed positioning devices (such as distributed camera positioning devices) installed within the campus are used to determine the vehicle's real-time location. When the vehicle's real-time location is close to the key location, a deceleration instruction is sent to the vehicle's corresponding human-machine interface terminal to remind the driver to slow down in advance.

[0057] Preferably, in step S3, the actual driving video of the vehicle within the campus is retrieved from the IoT platform, and the video is analyzed to obtain driving violation information of the vehicle during its journey; based on this driving violation information, the historical driving route information of the vehicle on the IoT platform is further analyzed, including:

[0058] The system retrieves the vehicle's actual driving footage within the campus from distributed positioning devices connected to the Internet of Things, and extracts the vehicle's actual driving speed at all key locations from the footage. If the actual driving speed at any key location is greater than or equal to a preset speed threshold, it is determined that the vehicle has committed a driving violation.

[0059] If the vehicle commits a traffic violation during its journey, the planned route for that particular trip will be removed from the historical route information of the IoT platform.

[0060] The beneficial effects of the above technical solution are as follows: By monitoring the actual driving speed of the vehicle along the planned driving route through each key location, if the vehicle is found to be speeding when passing through a key location, it is determined that the vehicle has violated driving regulations. At this time, the planned driving route corresponding to this vehicle is deleted from the historical driving route information of the Internet of Things platform. In this way, the vehicle will not be recommended the current driving route again when it enters the campus next time, thus avoiding accidents caused by the vehicle driving on campus.

[0061] See Figure 2 This is a schematic diagram of the structure of an IoT-based campus vehicle driving management system provided in an embodiment of the present invention. The IoT-based campus vehicle driving management system includes:

[0062] The visual recognition module is used to perform visual recognition processing on vehicles entering the campus and determine the vehicle's license plate information.

[0063] The driving route determination module is used to retrieve the vehicle's historical driving route information within the campus from the Internet of Things platform based on the license plate information; and to determine the vehicle's planned driving route within the campus based on the response message from the vehicle and the historical driving route information.

[0064] The driving instruction module is used to determine key locations on the planned driving route based on the current time information and the planned driving route; retrieve the real-time location of the vehicle within the campus from the Internet of Things platform, and send driving instruction information to the terminal corresponding to the vehicle based on the real-time location and the key location;

[0065] The driving violation judgment module is used to retrieve the actual driving video of the vehicle within the campus from the Internet of Things platform, analyze and process the actual driving video, and obtain the driving violation information of the vehicle during the driving process.

[0066] The historical driving route information update module is used to update the vehicle's historical driving route information on the Internet of Things platform based on the driving violation information.

[0067] The beneficial effects of the above technical solution are as follows: This IoT-based campus vehicle driving management system retrieves historical driving route information of vehicles on campus from the IoT based on license plate information obtained through visual recognition, and determines the planned driving route of the vehicle within the campus based on the response messages from the vehicle; combined with the current time information, it identifies key locations on the planned driving route, and sends driving instruction information to the corresponding terminal of the vehicle based on the distance between the vehicle and the key locations; it also obtains driving violation information of the vehicle based on real-time driving images of the vehicle within the campus, thereby updating the historical driving route information of the vehicle on the IoT platform; based on the vehicle's historical driving habits within the campus, it determines the planned driving route of the vehicle within the campus for the current time, and provides predictive driving instruction information to the vehicle based on the distribution of key locations on the planned driving route; it also obtains driving violation information of the vehicle based on actual driving images of the vehicle within the campus for the current time, thereby updating the historical driving route information of the vehicle on the IoT platform. It can provide predictive instruction information for vehicles during driving, ensuring the safety of vehicles driving within the campus and enabling comprehensive management and control of the actual driving situation of vehicles on campus.

[0068] Preferably, the visual recognition module performs visual recognition processing on vehicles entering the campus, and the specific information of the vehicle's license plate includes:

[0069] Collect images of the front or rear of vehicles entering the campus, process these images to obtain the vehicle's license plate information;

[0070] The route determination module retrieves the vehicle's historical route information within the campus from the IoT platform based on the license plate information. Based on the vehicle's response message and the historical route information, it determines the vehicle's planned route within the campus, specifically including:

[0071] Using the license plate information as an index, the system retrieves the vehicle's historical driving route information within the campus during historical time periods from the IoT platform; based on the order of the shortest and longest paths of all historical driving routes, a historical driving route list is generated and sent to the terminal corresponding to the vehicle; based on the terminal's response message regarding the historical driving route list, one of the historical driving routes is selected as the planned driving route for the vehicle within the campus.

[0072] The beneficial effects of the above technical solution are as follows: When a vehicle enters the campus, images of the front or rear of the vehicle are captured at the campus entrance / exit, obtaining the corresponding front or rear images. These images are then analyzed to obtain the vehicle's license plate number. The IoT platform pre-stores the vehicle's past driving route information within the campus. Using the vehicle's license plate number as an index, the platform filters out the vehicle's historical driving routes within the campus for specific historical periods, generating a corresponding list of historical driving routes, which is then returned to the vehicle's human-machine interface terminal. The driver performs corresponding touch operations on the human-machine interface terminal, selecting one of the historical driving routes from the list as the vehicle's planned driving route within the campus.

[0073] Preferably, the driving instruction module determines key locations on the planned driving route based on the current time information and the planned driving route; it retrieves the vehicle's real-time location within the campus from the IoT platform, and sends driving instruction information to the terminal corresponding to the vehicle based on the real-time location and the key locations. Specifically, this includes:

[0074] Retrieve campus pedestrian flow distribution data that matches the current time information from the historical campus pedestrian flow distribution database of the IoT platform; compare the campus pedestrian flow distribution data with the planned driving route to determine the pedestrian flow value at each intersection along the planned driving route;

[0075] If the pedestrian flow value is greater than or equal to the preset pedestrian flow threshold, the corresponding intersection location will be identified as a key location.

[0076] The vehicle's real-time location within the campus is retrieved from the distributed positioning devices connected to the IoT platform. This real-time location is compared with the key location to determine the actual distance between them. If the actual distance is less than or equal to a preset distance threshold, a deceleration instruction is sent to the terminal corresponding to the vehicle.

[0077] The beneficial effects of the above technical solution are as follows: When the driver is driving along the planned route, based on the current time information, the system retrieves campus pedestrian flow distribution data matching the current time information from the campus pedestrian flow historical distribution database of the IoT platform. This campus pedestrian flow historical distribution database includes pedestrian flow statistics for different locations within the campus at different times throughout the 24 hours of the day. The retrieved campus pedestrian flow distribution data can accurately reflect the actual pedestrian flow distribution on campus at the current time. Threshold comparisons are then performed on the pedestrian flow values ​​at each intersection along the planned route to identify key locations. Furthermore, distributed positioning devices (such as distributed camera positioning devices) installed within the campus are used to determine the vehicle's real-time location. When the vehicle's real-time location is close to the key location, a deceleration instruction is sent to the vehicle's corresponding human-machine interface terminal to remind the driver to slow down in advance.

[0078] Preferably, the driving violation judgment module retrieves the actual driving video of the vehicle within the campus from the Internet of Things platform, analyzes and processes the actual driving video, and obtains the driving violation information of the vehicle during the driving process, specifically including:

[0079] The system retrieves the vehicle's actual driving footage within the campus from distributed positioning devices connected to the Internet of Things, and extracts the vehicle's actual driving speed at all key locations from the footage. If the actual driving speed at any key location is greater than or equal to a preset speed threshold, it is determined that the vehicle has committed a driving violation.

[0080] The historical driving route information update module updates the vehicle's historical driving route information on the IoT platform based on the driving violation information, specifically including:

[0081] If the vehicle commits a traffic violation during its journey, the planned route for that particular trip will be removed from the historical route information of the IoT platform.

[0082] The beneficial effects of the above technical solution are as follows: By monitoring the actual driving speed of the vehicle along the planned driving route through each key location, if the vehicle is found to be speeding when passing through a key location, it is determined that the vehicle has violated driving regulations. At this time, the planned driving route corresponding to this vehicle is deleted from the historical driving route information of the Internet of Things platform. In this way, the vehicle will not be recommended the current driving route again when it enters the campus next time, thus avoiding accidents caused by the vehicle driving on campus.

[0083] As can be seen from the above embodiments, this IoT-based campus vehicle driving management method and system retrieves historical driving route information of vehicles on campus from the IoT based on license plate information obtained through visual recognition, and determines the planned driving route of the vehicle within the campus based on the response messages from the vehicle; combined with the current time information, it determines key locations on the planned driving route, and sends driving instruction information to the corresponding terminal of the vehicle based on the distance between the vehicle and the key locations; it also obtains driving violation information of the vehicle based on the real-time driving images of the vehicle within the campus, thereby updating the historical driving route information of the vehicle on the IoT platform. Based on the vehicle's historical driving habits within the campus, it determines the planned driving route of the vehicle within the campus for the current time, and provides predictive driving instruction information to the vehicle based on the distribution of key locations along the planned driving route; it also obtains driving violation information of the vehicle based on the actual driving images of the vehicle within the campus for the current time, thereby updating the historical driving route information of the vehicle on the IoT platform. This provides predictive instruction information for the vehicle during driving, ensuring the safety of vehicles driving within the campus and enabling comprehensive management and control of the real-time driving situation of vehicles on campus.

[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 campus vehicle driving management method based on the Internet of Things, characterized in that, It includes the following steps: Step S1: Perform visual recognition processing on vehicles entering the campus to determine the vehicle's license plate information; based on the license plate information, retrieve the vehicle's historical driving route information within the campus from the Internet of Things platform; Based on the response message from the vehicle and the historical driving route information, the planned driving route of the vehicle within the campus is determined, including: acquiring images of the front or rear of the vehicle entering the campus; performing recognition processing on the front or rear images to obtain the vehicle's license plate information; using the license plate information as an index, retrieving the historical driving route information of the vehicle within the campus for a historical time period from the IoT platform; generating a historical driving route list according to the order of the shortest and longest paths of all historical driving routes, and sending it to the terminal corresponding to the vehicle; and selecting one of the historical driving routes as the planned driving route of the vehicle within the campus based on the response message from the terminal regarding the historical driving route list. Step S2: Based on the current time information and the planned driving route, determine the key locations on the planned driving route; retrieve the real-time location of the vehicle within the campus from the IoT platform, and send driving instruction information to the terminal corresponding to the vehicle based on the real-time location and the key locations; Step S3: Retrieve the actual driving video of the vehicle within the campus from the IoT platform, analyze and process the actual driving video to obtain the driving violation information of the vehicle during the driving process; update the historical driving route information of the vehicle on the IoT platform based on the driving violation information.

2. The campus vehicle driving management method based on the Internet of Things as described in claim 1, characterized in that: In step S2, based on the current time information and the planned driving route, key locations on the planned driving route are determined; the real-time location of the vehicle within the campus is retrieved from the IoT platform, and driving instruction information is sent to the terminal corresponding to the vehicle based on the real-time location and the key locations. Specifically, this includes: From the historical distribution database of campus pedestrian traffic on the IoT platform, retrieve campus pedestrian traffic distribution data that matches the current time information; compare the campus pedestrian traffic distribution data with the planned driving route to determine the pedestrian traffic value at each intersection of the planned driving route; If the pedestrian flow value is greater than or equal to the preset pedestrian flow threshold, the corresponding intersection location will be identified as a key location. The real-time location of the vehicle within the campus is retrieved from the distributed positioning devices connected to the IoT platform. The real-time location is compared with the key location to determine the actual distance between the real-time location and the key location. If the actual distance is less than or equal to a preset distance threshold, a driving deceleration instruction is sent to the terminal corresponding to the vehicle.

3. The campus vehicle driving management method based on the Internet of Things as described in claim 2, characterized in that: In step S3, the actual driving video of the vehicle within the campus is retrieved from the Internet of Things platform, and the actual driving video is analyzed and processed to obtain the driving violation information of the vehicle during the driving process. Updating the vehicle's historical driving route information on the IoT platform based on the aforementioned driving violation information specifically includes: The system retrieves actual driving footage of the vehicle within the campus from distributed positioning devices connected to the Internet of Things (IoT), and extracts the actual driving speed of the vehicle at all key locations from the actual driving footage. If the actual driving speed at any key location is greater than or equal to a preset speed threshold, it is determined that the vehicle has committed a driving violation during the driving process. If the vehicle commits a driving violation during its journey, the planned route for that vehicle will be deleted from the historical route information of the IoT platform.

4. A campus vehicle driving management system based on the Internet of Things, characterized in that, It includes: The visual recognition module is used to perform visual recognition processing on vehicles entering the campus and determine the vehicle's license plate information. The driving route determination module is used to retrieve the vehicle's historical driving route information within the campus from the Internet of Things platform based on the license plate information; and to determine the vehicle's planned driving route within the campus based on the response message from the vehicle and the historical driving route information. A driving instruction module is used to determine key locations on the planned driving route based on the current time information and the planned driving route; The system retrieves the real-time location of the vehicle within the campus from the Internet of Things platform, and sends driving instruction information to the terminal corresponding to the vehicle based on the real-time location and the key location. The driving violation judgment module is used to retrieve the actual driving video of the vehicle within the campus from the Internet of Things platform, analyze and process the actual driving video, and obtain the driving violation information of the vehicle during the driving process. The historical driving route information update module is used to update the historical driving route information of the vehicle on the Internet of Things platform based on the driving violation information; The visual recognition module performs visual recognition processing on vehicles entering the campus, and determines the vehicle's license plate information, specifically including: Collect images of the front or rear of vehicles entering the campus, and perform recognition processing on the images to obtain the vehicle's license plate information. The driving route determination module retrieves the vehicle's historical driving route information within the campus from the IoT platform based on the license plate information. The determination of the vehicle's planned driving route within the campus, based on the vehicle's response message and the historical driving route information, specifically includes: retrieving the vehicle's historical driving route information within the campus for a historical time period from the IoT platform using the license plate information as an index; generating a historical driving route list according to the shortest to longest order of all historical driving routes and sending it to the terminal corresponding to the vehicle; and selecting one of the historical driving routes as the vehicle's planned driving route within the campus based on the terminal's response message regarding the historical driving route list.

5. The campus vehicle driving management system based on the Internet of Things as described in claim 4, characterized in that: The driving instruction module determines key locations on the planned driving route based on the current time information and the planned driving route; it retrieves the real-time location of the vehicle within the campus from the IoT platform, and sends driving instruction information to the terminal corresponding to the vehicle based on the real-time location and the key locations. Specifically, this includes: From the historical distribution database of campus pedestrian traffic on the IoT platform, retrieve campus pedestrian traffic distribution data that matches the current time information; compare the campus pedestrian traffic distribution data with the planned driving route to determine the pedestrian traffic value at each intersection of the planned driving route; If the pedestrian flow value is greater than or equal to the preset pedestrian flow threshold, the corresponding intersection location will be identified as a key location. The real-time location of the vehicle within the campus is retrieved from the distributed positioning devices connected to the IoT platform. The real-time location is compared with the key location to determine the actual distance between the real-time location and the key location. If the actual distance is less than or equal to a preset distance threshold, a driving deceleration instruction is sent to the terminal corresponding to the vehicle.

6. The campus vehicle driving management system based on the Internet of Things as described in claim 5, characterized in that: The driving violation judgment module retrieves the actual driving video of the vehicle within the campus from the Internet of Things platform, analyzes and processes the actual driving video, and obtains the driving violation information of the vehicle during the driving process, specifically including: The system retrieves actual driving footage of the vehicle within the campus from distributed positioning devices connected to the Internet of Things (IoT), and extracts the actual driving speed of the vehicle at all key locations from the actual driving footage. If the actual driving speed at any key location is greater than or equal to a preset speed threshold, it is determined that the vehicle has committed a driving violation during the driving process. The historical driving route information update module updates the vehicle's historical driving route information on the IoT platform based on the driving violation information, specifically including: If the vehicle commits a driving violation during its journey, the planned route for that vehicle will be deleted from the historical route information of the IoT platform.

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