CN115798247B - Intelligent public transportation cloud platform based on big data - Google Patents
Intelligent public transportation cloud platform based on big data Download PDFInfo
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- CN115798247B CN115798247B CN202211233108.6A CN202211233108A CN115798247B CN 115798247 B CN115798247 B CN 115798247B CN 202211233108 A CN202211233108 A CN 202211233108A CN 115798247 B CN115798247 B CN 115798247B
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Abstract
The invention discloses an intelligent public transportation cloud platform based on big data, which comprises the following components: the system comprises a bus monitoring unit, a communication unit, a cloud control service platform and a service terminal unit; the public transportation monitoring unit transmits the monitored data to the cloud control service platform through the communication unit, the cloud control service platform stores, distributes and manages the public transportation data, and a user checks the public transportation data through the service terminal unit and uses an intelligent public transportation service function. The information of the vehicle can be obtained in real time through the public transportation cloud platform, and functions such as vehicle operation monitoring, vehicle state monitoring, driver safe driving monitoring, real-time positioning and remote monitoring can be realized through the public transportation monitoring unit.
Description
Technical Field
The invention relates to the technical field of cloud platforms, in particular to an intelligent public transportation cloud platform based on big data.
Background
In recent years, with the development of electronic technology and intelligent transportation technology, smart cities fully utilize emerging technologies to create better urban lives for people. At present, the construction of smart cities has become the focus of research in various countries in the world, and the development of buses as a core travel tool of the cities is also the focus of the construction of the smart cities. With the rapid increase of the number of buses, demands such as vehicle operation condition monitoring, bus equipment monitoring, operation data monitoring and safe driving are rapidly increased, and the demands of buses, drivers, passengers and dispatch rooms cannot be met in the current working mode. Therefore, the functions of realizing data sharing, real-time monitoring, safe driving and the like through the intelligent public transportation cloud platform are becoming more and more urgent.
Disclosure of Invention
The invention provides a smart public transportation cloud platform based on big data, which aims to solve the problem that in recent years, along with the development of electronic technology and intelligent transportation technology, smart cities fully utilize the emerging technology to create better urban life for people. At present, the construction of smart cities has become the focus of research in various countries in the world, and the development of buses as a core travel tool of the cities is also the focus of the construction of the smart cities. With the rapid increase of the number of buses, demands such as vehicle operation condition monitoring, bus equipment monitoring, operation data monitoring and safe driving are rapidly increased, and the demands of buses, drivers, passengers and dispatch rooms cannot be met in the current working mode. Therefore, the functions of data sharing, real-time monitoring, safe driving and the like are realized through the intelligent public transportation cloud platform, and the problems are becoming more and more urgent.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an intelligent public transportation cloud platform based on big data, comprising: the system comprises a bus monitoring unit, a communication unit, a cloud control service platform and a service terminal unit;
the public transportation monitoring unit transmits the monitored data to the cloud control service platform through the communication unit, the cloud control service platform stores, distributes and manages the public transportation data, and a user checks the public transportation data through the service terminal unit and uses an intelligent public transportation service function.
Wherein, public transit monitoring unit includes: the system comprises a vehicle operation monitoring module, a vehicle state monitoring module and a driver monitoring module;
the vehicle operation monitoring module is used for monitoring operation dynamics of the bus, wherein the operation dynamics comprise driving routes, driving shifts, passenger flows, vehicle online rates and driving mileage information;
the vehicle state monitoring module is used for monitoring the current vehicle running state condition, the running speed and the passenger riding condition in the vehicle;
the driver monitoring module is used for monitoring whether abnormal actions exist or not in the driving process of a driver and whether fatigue driving exists or not.
Wherein the communication unit includes: the wireless network communication system comprises a data transmitting end, a wireless network communication module and a data receiving end;
the data transmitting end is used for processing the data monitored by the bus monitoring unit to generate a data set, encrypting the data set and transmitting the encrypted data set;
the wireless network communication module is used for transmitting the encrypted data set to the data receiving end in real time;
the data receiving end is used for receiving the encrypted data set, decrypting the data set and then sending the decrypted data set to the cloud control service platform.
Wherein, cloud accuse service platform includes: the system comprises a storage module, a background management and control module and a bus service module;
the storage module is used for storing monitored data, wherein the monitored data comprise monitoring data of bus operation vehicles, departure intervals and driving sequences, passenger information data and driver monitoring data;
the background management and control module is used for sending corresponding control instructions to the currently running vehicle according to the monitored data, wherein the control instructions comprise scheduling the vehicle, crossing the station, accelerating, decelerating and waiting;
the bus service module is used for providing corresponding services for buses, wherein the services comprise departure prompt, anti-theft alarm, regional alarm, monitoring and scheduling, determining operation lines and arranging bus running schedules.
Wherein the service terminal unit includes: the system comprises a user mobile terminal and a staff monitoring terminal;
the user mobile terminal is used for looking up route recommendation and duration to a destination, the current position of a bus to be taken and the predicted duration of the vehicle reaching a stop board where the user is located;
the staff monitoring end is used for checking the current bus condition, wherein the bus condition comprises the running condition of a bus, the state of a driver, the condition of passengers in the bus, the congestion condition of a running road section and an accident road section, and corresponding management measures are adopted for the current bus according to the checking condition.
Wherein, the backstage management and control module includes: a bus stop board guiding module;
the bus stop board guiding module acquires relevant data of all buses to be stopped from the cloud control service platform, wherein the relevant data comprise vehicle numbers, operation lines, stop arrival distances, estimated stop arrival time and estimated number of passengers;
the bus stop board guiding module calculates based on the past passenger flow volume and the number of people waiting for the stop board, obtains the stop time of the bus at the stop board, guides the stop vehicle to stop at a specified parking space to get on or off passengers, and sends a signal to the next stop based on the stop time of the bus at the stop board after the bus leaves the stop, and indicates to guide the stop-leaving vehicle.
Wherein, the driver monitoring module includes: the image acquisition module and the face detection model;
in the process of driving the bus, the face of the current driver is acquired through the image acquisition module, the face image is processed to obtain the cut face, the face detection model is used for positioning the face part, the blink state of the bus driver is judged by calculating the eye length-width ratio between the eye height and the eye width, and whether the driver has fatigue driving is judged by the blink state of the bus driver.
Wherein, include: the face detection model divides the face image data into 68 data points, each part of the face is provided with different labels, and whether the person blinks or not is judged by calculating the coordinate difference of 6 data points on eyes;
setting an eye coordinate difference threshold, and determining that the eyes of the bus driver are closed when the eye length-width ratio between the eye height and the eye width is smaller than the eye coordinate difference threshold; when the eye aspect ratio between the eye height and width is greater than the eye coordinate difference threshold, determining that the eyes of the bus driver are open; judging whether the bus driver has fatigue driving according to the eye closing time, and judging the bus driver has fatigue driving when the eye closing time of the bus driver exceeds the limit.
Wherein, the vehicle operation monitoring module includes: a passenger flow volume detection module; the passenger flow detection module acquires the identification of passengers on buses and off buses based on a computer vision technology, acquires videos or images of the passengers on buses through identification, acquires initial positions and candidate images of the passengers on the basis of the videos or images of the passengers on buses, tracks the passengers in a single video, associates the passengers with the same image sequence, acquires the motion trail information of the passengers in the single video, judges whether the detected targets belong to the boarding and disembarking behaviors according to the motion trail information, further judges whether the current passengers belong to the boarding and disembarking behaviors if the detected targets belong to the boarding and disembarking behaviors, generates a boarding or disembarking passenger data set according to the judgment result, and acquires the passenger flow of each station of buses according to the passenger data set.
Compared with the prior art, the invention has the following advantages:
an intelligent public transportation cloud platform based on big data, comprising: the system comprises a bus monitoring unit, a communication unit, a cloud control service platform and a service terminal unit; the public transportation monitoring unit transmits the monitored data to the cloud control service platform through the communication unit, the cloud control service platform stores, distributes and manages the public transportation data, and a user checks the public transportation data through the service terminal unit and uses an intelligent public transportation service function. The information of the vehicle can be obtained in real time through the public transportation cloud platform, and functions such as vehicle operation monitoring, vehicle state monitoring, driver safe driving monitoring, real-time positioning and remote monitoring can be realized through the public transportation monitoring unit.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of an intelligent public transportation cloud platform based on big data in an embodiment of the invention;
FIG. 2 is a flow chart of an intelligent public transportation cloud platform based on big data in an embodiment of the invention;
fig. 3 is a bus monitoring unit structure diagram of an intelligent bus cloud platform based on big data in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The embodiment of the invention provides an intelligent public transportation cloud platform based on big data, which comprises the following steps: the system comprises a bus monitoring unit, a communication unit, a cloud control service platform and a service terminal unit;
the public transportation monitoring unit transmits the monitored data to the cloud control service platform through the communication unit, the cloud control service platform stores, distributes and manages the public transportation data, and a user checks the public transportation data through the service terminal unit and uses an intelligent public transportation service function.
The working principle of the technical scheme is as follows: the public transportation monitoring unit transmits the monitored data to the cloud control service platform through the communication unit, the cloud control service platform stores, distributes and manages the public transportation data, and a user checks the public transportation data through the service terminal unit and uses an intelligent public transportation service function.
The beneficial effects of the technical scheme are as follows: the public transportation monitoring unit transmits the monitored data to the cloud control service platform through the communication unit, the cloud control service platform stores, distributes and manages the public transportation data, and a user checks the public transportation data through the service terminal unit and uses an intelligent public transportation service function. The information of the vehicle can be obtained in real time through the public transportation cloud platform, the functions of vehicle operation monitoring, vehicle state monitoring, driver safety driving monitoring, real-time positioning, remote monitoring and the like can be realized through the public transportation monitoring unit, and the management functions of the bus and the driver are realized through the cloud control service platform.
In another embodiment, a bus monitoring unit includes: the system comprises a vehicle operation monitoring module, a vehicle state monitoring module and a driver monitoring module;
the vehicle operation monitoring module is used for monitoring operation dynamics of the bus, wherein the operation dynamics comprise driving routes, driving shifts, passenger flows, vehicle online rates and driving mileage information;
the vehicle state monitoring module is used for monitoring the current vehicle running state condition, the running speed and the passenger riding condition in the vehicle;
the driver monitoring module is used for monitoring whether abnormal actions exist or not in the driving process of a driver and whether fatigue driving exists or not.
The working principle of the technical scheme is as follows: the vehicle operation monitoring module is used for monitoring operation dynamics of the bus, wherein the operation dynamics comprise driving routes, driving shifts, passenger flows, vehicle online rates and driving mileage information; the vehicle state monitoring module is used for monitoring the current vehicle running state condition, the running speed and the passenger riding condition in the vehicle; the driver monitoring module is used for monitoring whether abnormal actions exist or not in the driving process of a driver and whether fatigue driving exists or not. The bus monitoring unit can realize functions of vehicle operation monitoring, vehicle state monitoring, driver safe driving monitoring, real-time positioning, remote monitoring and the like.
The beneficial effects of the technical scheme are as follows: the vehicle operation monitoring module is used for monitoring operation dynamics of the bus, wherein the operation dynamics comprise driving routes, driving shifts, passenger flows, vehicle online rates and driving mileage information; the vehicle state monitoring module is used for monitoring the current vehicle running state condition, the running speed and the passenger riding condition in the vehicle; the driver monitoring module is used for monitoring whether abnormal actions exist or not in the driving process of a driver and whether fatigue driving exists or not. The intelligent and low-cost management of buses is realized by collecting bus data.
In another embodiment, the communication unit includes: the wireless network communication system comprises a data transmitting end, a wireless network communication module and a data receiving end;
the data transmitting end is used for processing the data monitored by the bus monitoring unit to generate a data set, encrypting the data set and transmitting the encrypted data set;
the wireless network communication module is used for transmitting the encrypted data set to the data receiving end in real time;
the data receiving end is used for receiving the encrypted data set, decrypting the data set and then sending the decrypted data set to the cloud control service platform.
The working principle of the technical scheme is as follows: the data transmitting end is used for processing the collected public transportation data to generate a data set, encrypting the data set and transmitting the encrypted data set; the wireless network communication module is used for transmitting the encrypted data set to the data receiving end in real time; the data receiving end is used for receiving the encrypted data set, decrypting the data set and then sending the decrypted data set to the cloud control service platform.
The beneficial effects of the technical scheme are as follows: the data transmitting end is used for processing the collected public transportation data to generate a data set, encrypting the data set and transmitting the encrypted data set; the wireless network communication module is used for transmitting the encrypted data set to the data receiving end in real time; the data receiving end is used for receiving the encrypted data set, decrypting the data set and then sending the decrypted data set to the cloud control service platform. Therefore, the vehicle operation monitoring data, the vehicle state monitoring data and the driver monitoring data are safely and effectively transmitted to the cloud control service platform.
In another embodiment, the cloud control service platform includes: the system comprises a storage module, a background management and control module and a bus service module;
the storage module is used for storing monitored data, wherein the monitored data comprise monitoring data of bus operation vehicles, departure intervals and driving sequences, passenger information data and driver monitoring data;
the background management and control module is used for sending corresponding control instructions to the currently running vehicle according to the monitored data, wherein the control instructions comprise scheduling the vehicle, crossing the station, accelerating, decelerating and waiting;
the bus service module is used for providing corresponding services for buses, wherein the services comprise departure prompt, anti-theft alarm, regional alarm, monitoring and scheduling, determining operation lines and arranging bus running schedules.
The working principle of the technical scheme is as follows: the storage module is used for storing monitored data, wherein the monitored data comprise monitoring data of bus operation vehicles, departure intervals and driving sequences, passenger information data and driver monitoring data; the background management and control module is used for sending corresponding control instructions to the currently running vehicle according to the monitored data, wherein the control instructions comprise scheduling the vehicle, crossing the station, accelerating, decelerating and waiting; the bus service module is used for providing corresponding services for buses, wherein the services comprise departure prompt, anti-theft alarm, regional alarm, monitoring and scheduling, determining operation lines and arranging bus running schedules.
The beneficial effects of the technical scheme are as follows: the storage module is used for storing monitored data, wherein the monitored data comprise monitoring data of bus operation vehicles, departure intervals and driving sequences, passenger information data and driver monitoring data; the background management and control module is used for sending corresponding control instructions to the currently running vehicle according to the monitored data, wherein the control instructions comprise scheduling the vehicle, crossing the station, accelerating, decelerating and waiting; the bus service module is used for providing corresponding services for buses, wherein the services comprise departure prompt, anti-theft alarm, regional alarm, monitoring and scheduling, determining operation lines and arranging bus running schedules. The cloud control service platform integrates static resources such as GIS maps and bus route data, and related bus data are continuously updated, so that functional services such as bus positioning and road condition inquiry based on bus routes are realized.
In another embodiment, the service terminal unit includes: the system comprises a user mobile terminal and a staff monitoring terminal;
the user mobile terminal is used for looking up route recommendation and duration to a destination, the current position of a bus to be taken and the predicted duration of the vehicle reaching a stop board where the user is located;
the staff monitoring end is used for checking the current bus condition, wherein the bus condition comprises the running condition of a bus, the state of a driver, the condition of passengers in the bus, the congestion condition of a running road section and an accident road section, and corresponding management measures are adopted for the current bus according to the checking condition.
The working principle of the technical scheme is as follows: the user mobile terminal is used for looking up route recommendation and duration to a destination, the current position of a bus to be taken and the predicted duration of the vehicle reaching a stop board where the user is located; the staff monitoring end is used for checking the current bus condition, wherein the bus condition comprises the running condition of a bus, the state of a driver, the condition of passengers in the bus, the congestion condition of a running road section and an accident road section, and corresponding management measures are adopted for the current bus according to the checking condition.
The beneficial effects of the technical scheme are as follows: the user mobile terminal is used for looking up route recommendation and duration to a destination, the current position of a bus to be taken and the predicted duration of the vehicle reaching a stop board where the user is located; the staff monitoring end is used for checking the current bus condition, wherein the bus condition comprises the running condition of a bus, the state of a driver, the condition of passengers in the bus, the congestion condition of a running road section and an accident road section, and corresponding management measures are adopted for the current bus according to the checking condition. And providing intelligent recommendation service for the user to travel public transportation through the service terminal unit.
In another embodiment, the background management module includes: a bus stop board guiding module;
the bus stop board guiding module acquires relevant data of all buses to be stopped from the cloud control service platform, wherein the relevant data comprise vehicle numbers, operation lines, stop arrival distances, estimated stop arrival time and estimated number of passengers;
the bus stop board guiding module calculates based on the past passenger flow volume and the number of people waiting for the stop board, obtains the stop time of the bus at the stop board, guides the stop vehicle to stop at a specified parking space to get on or off passengers, and sends a signal to the next stop based on the stop time of the bus at the stop board after the bus leaves the stop, and indicates to guide the stop-leaving vehicle.
The working principle of the technical scheme is as follows: the bus stop board guiding module acquires relevant data of all buses to be stopped from the cloud control service platform, wherein the relevant data comprise vehicle numbers, operation lines, stop distance, estimated stop time and estimated number of passengers; the system calculates the data of the previous passenger flow, the number of bus stop boards Hou Che and the like to obtain the estimated total time of boarding passengers, multiplies the estimated number of alighting passengers by the average time of alighting to obtain the estimated total time of alighting passengers, and compares the estimated total time with the estimated total time of alighting passengers to obtain a larger value as the parking time of the bus on the bus stop boards; the vehicles to be arrived at the station are arranged in sequence from short to long according to the estimated arrival time, and the arrival time interval between adjacent vehicles is calculated to be compared with the parking time of the preceding vehicle; when the arrival time interval is longer than the stop time of the front vehicle, the bus stop board guiding module does not intervene, and the front vehicle and the rear vehicle normally enter the station to get on or off the passengers in sequence; when the arrival time interval is smaller than the front vehicle stop time, the system synthesizes the arrival distance of two vehicles, the stop time of the rear vehicle and other factors, and judges whether the rear vehicle has overtaking conditions; if the rear vehicle has overtaking conditions, the system guides the front vehicle to properly decelerate, and the rear vehicle properly accelerates to realize overtaking, and corrects the estimated arrival time; if the overtaking condition is not met, the front vehicle is guided to accelerate properly, the rear vehicle is guided to decelerate properly, the arrival time interval of the front vehicle and the rear vehicle is prolonged, so that confusion and congestion in a stop board area are avoided, and the estimated arrival time is corrected; guiding a standing vehicle to accurately stop to get on and off passengers in a parking space appointed by a system; after the bus leaves the station, the bus stop board guiding module sends out a signal to the next station to instruct the next station to guide the bus leaving the station.
The beneficial effects of the technical scheme are as follows: the bus stop board guiding module acquires relevant data of all buses to be stopped from the cloud control service platform, wherein the relevant data comprise vehicle numbers, operation lines, stop arrival distances, estimated stop arrival time and estimated number of passengers; the bus stop board guiding module calculates based on the past passenger flow volume and the number of people waiting for the stop board, obtains the stop time of the bus at the stop board, guides the stop vehicle to stop at a specified parking space to get on or off passengers, and sends a signal to the next stop based on the stop time of the bus at the stop board after the bus leaves the stop, and indicates to guide the stop-leaving vehicle. The accurate guiding of the bus is realized, the possibility of confusion of the bus in a stop booth area is avoided, the invalid stop time of the bus in the stop board is reduced through the bus stop board guiding module, and the aim of improving the whole running efficiency of the urban bus is fulfilled. And the accurate stop of the bus is favorable for organizing passengers to get on or off the bus, the further definition of the stop program is also favorable for the accurate grasp of the intelligent bus cloud platform on the running state of the bus, and the estimated arrival time of the bus is more accurate and reliable.
In another embodiment, the driver monitoring module includes: the image acquisition module and the face detection model;
in the process of driving the bus, the face of the current driver is acquired through the image acquisition module, the face image is processed to obtain the cut face, the face detection model is used for positioning the face part, the blink state of the bus driver is judged by calculating the eye length-width ratio between the eye height and the eye width, and whether the driver has fatigue driving is judged by the blink state of the bus driver.
The working principle of the technical scheme is as follows: in the process of driving the bus, the face of the current driver is acquired through the image acquisition module, the face image is processed to obtain the cut face, the face detection model is used for positioning the face part, the blink state of the bus driver is judged by calculating the eye length-width ratio between the eye height and the eye width, and whether the driver has fatigue driving is judged by the blink state of the bus driver.
The method comprises the steps that a driver also collects video clips of the driver in the process of driving a bus, driving behaviors of the driver are set to be yawning, smoking, mobile phone beating and abnormal-free driving behaviors, for detecting abnormal behaviors of the bus driver, video data are divided into a plurality of video clips according to the time-constant type based on a TSN model, the video clips are used as inputs of a network to obtain a plurality of scores related to original videos, the score is fused and averaged to obtain a final result, and the driving behaviors of the current driver are determined according to the final result.
A given video is divided into K segments of equal duration, and then the segment sequence is modeled as follows, with the modeling formula:
wherein H (y, G) represents the value of the TSN model; c represents data of action category, y i Basic labels representing the relevant class i; g i A category score representing a related category i; g represents the use of an averaging function to infer a class score from the scores of the same class across all segments; y represents a basic tag; j represents all tags in a video clip;
and accurately judging the behavior type of the current driver through the value of the TSN model, reminding the driver according to the behavior type of the driver, and ensuring the safe driving of the driver.
The beneficial effects of the technical scheme are as follows: in the process of driving the bus, the face of the current driver is acquired through the image acquisition module, the face image is processed to obtain the cut face, the face detection model is used for positioning the face part, the blink state of the bus driver is judged by calculating the eye length-width ratio between the eye height and the eye width, and whether the driver has fatigue driving is judged by the blink state of the bus driver. Detecting abnormal behaviors of the bus driver and whether fatigue driving exists can early warn the improper behaviors of the driver, so that the behaviors of the bus driver are standardized, and traffic safety is guaranteed.
In another embodiment, the method comprises: the face detection model divides the face image data into 68 data points, each part of the face is provided with different labels, and whether the person blinks or not is judged by calculating the coordinate difference of 6 data points on eyes;
setting an eye coordinate difference threshold, and determining that the eyes of the bus driver are closed when the eye length-width ratio between the eye height and the eye width is smaller than the eye coordinate difference threshold; when the eye aspect ratio between the eye height and width is greater than the eye coordinate difference threshold, determining that the eyes of the bus driver are open; judging whether the bus driver has fatigue driving according to the eye closing time, and judging the bus driver has fatigue driving when the eye closing time of the bus driver exceeds the limit.
The working principle of the technical scheme is as follows: the face detection model divides the face image data into 68 data points, each part of the face is provided with different labels, and whether the person blinks or not is judged by calculating the coordinate difference of 6 data points on eyes; setting an eye coordinate difference threshold, and determining that the eyes of the bus driver are closed when the eye length-width ratio between the eye height and the eye width is smaller than the eye coordinate difference threshold; when the eye aspect ratio between the eye height and width is greater than the eye coordinate difference threshold, determining that the eyes of the bus driver are open; judging whether the bus driver has fatigue driving according to the eye closing time, and judging the bus driver has fatigue driving when the eye closing time of the bus driver exceeds the limit.
The beneficial effects of the technical scheme are as follows: the face detection model divides the face image data into 68 data points, each part of the face is provided with different labels, and whether the person blinks or not is judged by calculating the coordinate difference of 6 data points on eyes; setting an eye coordinate difference threshold, and determining that the eyes of the bus driver are closed when the eye length-width ratio between the eye height and the eye width is smaller than the eye coordinate difference threshold; when the eye aspect ratio between the eye height and width is greater than the eye coordinate difference threshold, determining that the eyes of the bus driver are open; judging whether the bus driver has fatigue driving according to the eye closing time, and judging the bus driver has fatigue driving when the eye closing time of the bus driver exceeds the limit. The accuracy and the reliability of the judgment and the prediction of the driving behavior and the driving state of the bus driver are further improved.
In another embodiment, a vehicle operation monitoring module includes: a passenger flow volume detection module; the passenger flow detection module acquires the identification of passengers on buses and off buses based on a computer vision technology, acquires videos or images of the passengers on buses through identification, acquires initial positions and candidate images of the passengers on the basis of the videos or images of the passengers on buses, tracks the passengers in a single video, associates the passengers with the same image sequence, acquires the motion trail information of the passengers in the single video, judges whether the detected targets belong to the boarding and disembarking behaviors according to the motion trail information, further judges whether the current passengers belong to the boarding and disembarking behaviors if the detected targets belong to the boarding and disembarking behaviors, generates a boarding or disembarking passenger data set according to the judgment result, and acquires the passenger flow of each station of buses according to the passenger data set.
The working principle of the technical scheme is as follows: the passenger flow detection module acquires the identification of passengers on buses and off buses based on a computer vision technology, acquires videos or images of the passengers on buses through identification, acquires initial positions and candidate images of the passengers on the basis of the videos or images of the passengers on buses, tracks the passengers in a single video, associates the passengers with the same image sequence, acquires the motion trail information of the passengers in the single video, judges whether the detected targets belong to the boarding and disembarking behaviors according to the motion trail information, further judges whether the current passengers belong to the boarding and disembarking behaviors if the detected targets belong to the boarding and disembarking behaviors, generates a boarding or disembarking passenger data set according to the judgment result, and acquires the passenger flow of each station of buses according to the passenger data set.
The beneficial effects of the technical scheme are as follows: the passenger flow detection module acquires the identification of passengers on buses and off buses based on a computer vision technology, acquires videos or images of the passengers on buses through identification, acquires initial positions and candidate images of the passengers on the basis of the videos or images of the passengers on buses, tracks the passengers in a single video, associates the passengers with the same image sequence, acquires the motion trail information of the passengers in the single video, judges whether the detected targets belong to the boarding and disembarking behaviors according to the motion trail information, further judges whether the current passengers belong to the boarding and disembarking behaviors if the detected targets belong to the boarding and disembarking behaviors, generates a boarding or disembarking passenger data set according to the judgment result, and acquires the passenger flow of each station of buses according to the passenger data set. The recognition of the passengers on buses and off buses is obtained by utilizing a computer vision technology, so that the OD information of the passengers can be obtained without a manual investigation method, and the labor cost is greatly reduced.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (5)
1. Intelligent public transportation cloud platform based on big data, characterized by comprising: the system comprises a bus monitoring unit, a communication unit, a cloud control service platform and a service terminal unit;
transmitting the monitored data to the cloud control service platform through the communication unit by the bus monitoring unit, storing, distributing and managing the bus data by the cloud control service platform, and checking the bus data through the service terminal unit by a user and using an intelligent bus service function;
the bus monitoring unit comprises: the system comprises a vehicle operation monitoring module, a vehicle state monitoring module and a driver monitoring module;
the vehicle operation monitoring module is used for monitoring operation dynamics of the bus, wherein the operation dynamics comprise driving routes, driving shifts, passenger flows, vehicle online rates and driving mileage information;
the vehicle state monitoring module is used for monitoring the current vehicle running state condition, the running speed and the passenger riding condition in the vehicle;
the driver monitoring module is used for monitoring whether abnormal actions exist or not in the driving process of a driver and whether fatigue driving exists or not;
the driver monitoring module includes: the image acquisition module and the face detection model;
in the process of driving the bus, the face of the current driver is acquired through an image acquisition module, the face image is processed to obtain a cut face, then the face detection model is utilized to position the face part, the blink state of the bus driver is judged through calculating the eye length-width ratio between the eye height and the eye width, and whether the driver has fatigue driving is judged through the blink state of the bus driver;
the face detection model divides the face image data into 68 data points, each part of the face is provided with different labels, and whether the person blinks or not is judged by calculating the coordinate difference of 6 data points on eyes;
setting an eye coordinate difference threshold, and determining that the eyes of the bus driver are closed when the eye length-width ratio between the eye height and the eye width is smaller than the eye coordinate difference threshold; when the eye aspect ratio between the eye height and width is greater than the eye coordinate difference threshold, determining that the eyes of the bus driver are open; judging whether the bus driver has fatigue driving according to the eye closing time, and judging the bus driver has fatigue driving behavior when the eye closing time of the bus driver exceeds the limit;
the method comprises the steps that a driver also collects video clips of the driver in the process of driving a bus, driving behaviors of the driver are set to be yawning, smoking, mobile phone beating and abnormal-free driving behaviors, for detecting abnormal behaviors of the bus driver, video data are divided into a plurality of video clips according to the time-constant type based on a TSN model, the video clips are used as inputs of a network to obtain a plurality of scores related to original videos, the score is fused and averaged to obtain a final result, and the driving behaviors of the current driver are determined according to the final result;
a given video is divided into K segments of equal duration, and then the segment sequence is modeled as follows, with the modeling formula:
wherein H (y, G) represents the value of the TSN model; c represents data of action category, y i Basic labels representing the relevant class i; g i Class score representing related class iThe method comprises the steps of carrying out a first treatment on the surface of the G represents the use of an averaging function to infer a class score from the scores of the same class across all segments; y represents a basic tag; j represents all tags in a video clip;
the vehicle operation monitoring module includes: a passenger flow volume detection module; the passenger flow detection module acquires the identification of passengers on buses and off buses based on a computer vision technology, acquires videos or images of the passengers on buses through identification, acquires initial positions and candidate images of the passengers on the basis of the videos or images of the passengers on buses, tracks the passengers in a single video, associates the passengers with the same image sequence, acquires the motion trail information of the passengers in the single video, judges whether the detected targets belong to the boarding and disembarking behaviors according to the motion trail information, further judges whether the current passengers belong to the boarding and disembarking behaviors if the detected targets belong to the boarding and disembarking behaviors, generates a boarding or disembarking passenger data set according to the judgment result, and acquires the passenger flow of each station of buses according to the passenger data set.
2. The big data based intelligent public transportation cloud platform of claim 1, wherein the communication unit comprises: the wireless network communication system comprises a data transmitting end, a wireless network communication module and a data receiving end;
the data transmitting end is used for processing the data monitored by the bus monitoring unit to generate a data set, encrypting the data set and transmitting the encrypted data set;
the wireless network communication module is used for transmitting the encrypted data set to the data receiving end in real time;
the data receiving end is used for receiving the encrypted data set, decrypting the data set and then sending the decrypted data set to the cloud control service platform.
3. The intelligent public transportation cloud platform based on big data according to claim 1, wherein the cloud control service platform comprises: the system comprises a storage module, a background management and control module and a bus service module;
the storage module is used for storing monitored data, wherein the monitored data comprise monitoring data of bus operation vehicles, departure intervals and driving sequences, passenger information data and driver monitoring data;
the background management and control module is used for sending corresponding control instructions to the currently running vehicle according to the monitored data, wherein the control instructions comprise scheduling the vehicle, crossing the station, accelerating, decelerating and waiting;
the bus service module is used for providing corresponding services for buses, wherein the services comprise departure prompt, anti-theft alarm, regional alarm, monitoring and scheduling, determining operation lines and arranging bus running schedules.
4. The intelligent public transportation cloud platform based on big data according to claim 1, wherein the service terminal unit comprises: the system comprises a user mobile terminal and a staff monitoring terminal;
the user mobile terminal is used for looking up route recommendation and duration to a destination, the current position of a bus to be taken and the predicted duration of the vehicle reaching a stop board where the user is located;
the staff monitoring end is used for checking the current bus condition, wherein the bus condition comprises the running condition of a bus, the state of a driver, the condition of passengers in the bus, the congestion condition of a running road section and an accident road section, and corresponding management measures are adopted for the current bus according to the checking condition.
5. The intelligent public transportation cloud platform based on big data according to claim 3, wherein the background management and control module comprises: a bus stop board guiding module;
the bus stop board guiding module acquires relevant data of all buses to be stopped from the cloud control service platform, wherein the relevant data comprise vehicle numbers, operation lines, stop arrival distances, estimated stop arrival time and estimated number of passengers;
the bus stop board guiding module calculates based on the past passenger flow volume and the number of people waiting for the stop board, obtains the stop time of the bus at the stop board, guides the stop vehicle to stop at a specified parking space to get on or off passengers, and sends a signal to the next stop based on the stop time of the bus at the stop board after the bus leaves the stop, and indicates to guide the stop-leaving vehicle.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018028068A1 (en) * | 2016-08-12 | 2018-02-15 | 深圳市元征科技股份有限公司 | Fatigue driving monitoring method and cloud server |
| CN109978741A (en) * | 2017-12-27 | 2019-07-05 | 上海宝康电子控制工程有限公司 | Wisdom traffic information service application system and method based on cloud platform |
| CN110378567A (en) * | 2019-06-21 | 2019-10-25 | 同济大学 | Wisdom public transit system and implementation method |
| CN111445669A (en) * | 2020-03-12 | 2020-07-24 | 杭州律橙电子科技有限公司 | Safety monitoring system of bus |
| CN111583699A (en) * | 2020-04-27 | 2020-08-25 | 深圳众维轨道交通科技发展有限公司 | Intelligent bus monitoring system |
| CN112241658A (en) * | 2019-07-17 | 2021-01-19 | 青岛大学 | Fatigue driving early warning system and method based on depth camera |
| CN113239754A (en) * | 2021-04-23 | 2021-08-10 | 泰山学院 | Dangerous driving behavior detection and positioning method and system applied to Internet of vehicles |
| WO2021223312A1 (en) * | 2020-05-08 | 2021-11-11 | 广东技术师范大学 | Public transportation smart monitoring cloud service platform |
-
2022
- 2022-10-10 CN CN202211233108.6A patent/CN115798247B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018028068A1 (en) * | 2016-08-12 | 2018-02-15 | 深圳市元征科技股份有限公司 | Fatigue driving monitoring method and cloud server |
| CN109978741A (en) * | 2017-12-27 | 2019-07-05 | 上海宝康电子控制工程有限公司 | Wisdom traffic information service application system and method based on cloud platform |
| CN110378567A (en) * | 2019-06-21 | 2019-10-25 | 同济大学 | Wisdom public transit system and implementation method |
| CN112241658A (en) * | 2019-07-17 | 2021-01-19 | 青岛大学 | Fatigue driving early warning system and method based on depth camera |
| CN111445669A (en) * | 2020-03-12 | 2020-07-24 | 杭州律橙电子科技有限公司 | Safety monitoring system of bus |
| CN111583699A (en) * | 2020-04-27 | 2020-08-25 | 深圳众维轨道交通科技发展有限公司 | Intelligent bus monitoring system |
| WO2021223312A1 (en) * | 2020-05-08 | 2021-11-11 | 广东技术师范大学 | Public transportation smart monitoring cloud service platform |
| CN113239754A (en) * | 2021-04-23 | 2021-08-10 | 泰山学院 | Dangerous driving behavior detection and positioning method and system applied to Internet of vehicles |
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