CN113160603A - Intelligent bus management based on Internet of vehicles - Google Patents

Intelligent bus management based on Internet of vehicles Download PDF

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CN113160603A
CN113160603A CN202110457203.3A CN202110457203A CN113160603A CN 113160603 A CN113160603 A CN 113160603A CN 202110457203 A CN202110457203 A CN 202110457203A CN 113160603 A CN113160603 A CN 113160603A
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赵岩
孙宏飞
张世强
钱贵涛
南京
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Hualu Zhida Technology Co Ltd
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Abstract

The invention discloses an intelligent bus management system based on internet of vehicles, which is characterized by comprising an intelligent bus management subsystem and an arrival reminding system, wherein the intelligent bus management subsystem comprises a mobile phone terminal system for acquiring a transfer list of passengers, a data association module for acquiring identity information of the passengers, a position tracking module for tracking the positions of the passengers by using a face recognition method, and a passenger flow volume counting module for acquiring passenger flow information in a vehicle. The arrival reminding module comprises a station analysis module, an optimization selection module and a loudspeaker module, the station analysis module receives information of the position tracking module and the passenger flow volume counting module, generates a reminding scheme and sends the reminding scheme to the optimization selection module, the optimization selection module is used for determining the optimal reminding scheme and reminding passengers by using the loudspeaker module, and the passengers can set a transfer list according to the demands of the passengers, so that the satisfaction degree of the passengers is improved.

Description

Intelligent bus management based on Internet of vehicles
Technical Field
The invention relates to the field of public transportation, in particular to intelligent public transportation management based on Internet of vehicles.
Background
The existing public transport transfer is not high in usability to a mobile phone terminal generally, and the requirement of providing transfer reminding point to point cannot be met.
Disclosure of Invention
The invention provides intelligent bus management based on the internet of vehicles, which aims to overcome the technical problem.
The invention discloses intelligent bus management based on internet of vehicles, which is characterized by comprising an intelligent bus management subsystem and an arrival reminding system, wherein the intelligent bus management subsystem comprises a mobile phone terminal system for acquiring a transfer list of passengers, a data association module for acquiring identity information of the passengers, a position tracking module for tracking the positions of the passengers by using a face recognition method, and a passenger flow volume counting module for acquiring passenger flow information in a vehicle. The arrival reminding module comprises a site analysis module, an optimization selection module and a loudspeaker module, wherein the site analysis module receives information of the position tracking module and the passenger flow volume counting module, generates a reminding scheme and sends the reminding scheme to the optimization selection module, and the optimization selection module is used for determining an optimal reminding scheme and reminding passengers by using the loudspeaker module.
The mobile phone terminal system is used for acquiring a transfer list of passengers, the step of acquiring the transfer list of the passengers is realized by selecting a destination and a current boarding station through a mobile phone terminal, creating the transfer list, taking a mobile phone user identification code as an ID (identity), storing the facial image data of the passengers, sending the transfer list, the facial image data and the ID to the data association module, and updating the transfer list according to whether the passengers get off or not;
the data association module is used for acquiring identity information of passengers, wherein the acquisition of the identity information of the passengers comprises the steps of establishing a face database, storing face image data and ID (identity) into the face database, establishing data association between station arrival information and facial features of the passengers, and sending the associated information to the position tracking module;
the position tracking module is used for tracking the position of a passenger by using a face recognition method, the position tracking of the passenger by using the face recognition method comprises a monitoring device arranged in the vehicle, and the data association of the station arrival station information and the facial features of the passenger, which is sent by the data association module, is received, the number of people about to arrive at the station is counted, and the information of the position of the people about to arrive at the station in the vehicle is respectively tracked according to the ID;
the passenger flow volume counting module is used for acquiring passenger flow information in the automobile, acquiring the number of passengers getting on the automobile, acquiring a video in the door opening process of the automobile through a monitoring device arranged on the automobile door, identifying and counting the number of passengers getting off the automobile appearing in the video, and calculating the number of passengers in the automobile after the door is closed and the ID of the passengers getting on the automobile according to the number of passengers getting on the automobile, the number of passengers getting off the automobile and the number of passengers in the automobile before the door is opened;
the arrival reminding module comprises a station analysis module, an optimization selection module and a loudspeaker module,
the station analysis module divides the time of arriving at the next station into different sampling periods, acquires the current speed in the same sampling period, acquires the distance between the current speed and the previous vehicle through GPS positioning, acquires the current passenger flow in the vehicle counted by the passenger flow counting module, acquires the number statistics of the people about to arrive at the station transmitted by the position tracking module, acquires the information of the positions of the people about to arrive at the station in the vehicle transmitted by the position tracking module, and generates a reminding scheme according to the data;
the optimization selection module calculates the matching degree of data generated in two adjacent sampling periods in the station analysis module, the formula for calculating the matching degree is (1), if the matching value is greater than the threshold value, the matching degree of the two schemes is higher, the reminding scheme of the first sampling period is selected as the optimal reminding scheme, if the matching value is less than or equal to the threshold value, the scheme of the second sampling period is selected as the optimal reminding scheme, and the passenger corresponding to the optimal reminding scheme is marked;
Figure BDA0003040941340000021
wherein, txi,txi-1Respectively data generated in two adjacent sampling periods, zm is the matching degree, k is the position information of the person coming to the station in the vehicle, VxRepresenting the confidence of the first sampling period characteristic, and the value range [0,1 ]]，VyRepresenting the confidence of the second sampling period characteristic, and the value range [0,1 ]]L (×) represents the features extracted using the convolutional network, f (×) represents the serialization of the data;
the speaker module is used for acquiring the optimal reminding scheme of the optimal selection module and reminding the passengers to arrive at the station;
preferably, the current vehicle speed is acquired in the same sampling period, the station passenger flow information of the next station is acquired, the current in-vehicle passenger flow counted by the passenger flow counting module is acquired, the number of people coming to the station, which is transmitted by the position tracking module, is counted, the in-vehicle position information of the people coming to the station, which is transmitted by the position tracking module, is acquired, and a reminding scheme is generated according to the data, wherein the generation formula of the reminding scheme is (2):
Figure BDA0003040941340000031
wherein TX is a reminding scheme, p is station passenger flow information of a next station, o is inter-bus distance between buses, s is vehicle speed, h is current passenger flow in the bus, k is position information of a person to arrive at the station in the bus, i represents the ith station, 1 ≦ i ≦ n, fun represents a residual error network using cross entropy loss training, f isn(x) represents the features extracted using a residual network, fm(. x) represents the features extracted using a convolutional network.
Preferably, the data association module is used for acquiring the identity information of the passenger, the acquiring of the identity information of the passenger comprises establishing a face database, storing the face image data and the ID into the face database, extracting the face facial features of the passenger, establishing data association between the station arrival site information and the face facial features of the passenger, the formula for acquiring the face facial features of the passenger is (3),
Figure BDA0003040941340000032
wherein, x is an image, y is a universal face template, in particular to a three-dimensional point cloud consisting of 256 points, the points in the point cloud comprise eye, nose and mouth and cheek contours, r is the number of blocks divided by the face image of the passenger, fea is the facial feature of the passenger, and V isiCharacterizing image feature confidence, VjRepresenting the confidence coefficient of the face template features, and the value range [0,1 ]]，ln(. indicates the features extracted using the twin network trained with contrast loss, ff(x) represents extracting face information from the face image, and concat (x) represents that all the features are longitudinally connected;
preferably, the step of reminding the passenger about to arrive at the station includes reminding the passenger through a miniature voice player of a vehicle-mounted terminal, wherein the miniature voice players are uniformly distributed in the whole carriage, and the position is taken as the center, and the nearest one is selected for voice playing;
preferably, the passenger face image data is stored, the transfer list, the face image data and the ID are sent to the data association module, and the transfer list is updated according to whether the passenger gets off or not, wherein whether the passenger gets off or not is judged to receive the ID corresponding to the person getting off sent by the passenger flow volume counting module, and if the ID is matched with the current mobile phone terminal ID, the passenger gets off is judged;
preferably, the passenger facial image data are stored, the transfer list, the facial image data and the ID are sent to the data association module, and the transfer list is updated according to whether the passenger gets off the bus or not, wherein the change of the transfer list means that the passenger can select whether to transfer according to the transfer list next time after getting off the bus again, if so, the transfer list is stored as a common list, otherwise, the current transfer list is not stored, and the transfer list still needs to be created again when the mobile phone terminal system is used next time;
the invention provides intelligent bus management based on the internet of vehicles, and passengers can set transfer lists according to the requirements of the passengers, so that the satisfaction degree of the passengers is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a block diagram of an intelligent bus management system based on the internet of vehicles.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a block diagram of an intelligent bus management system based on internet of vehicles, as shown in fig. 1, the method of this embodiment may include:
the intelligent bus management system comprises a mobile phone terminal system 101 for acquiring a transfer list of passengers, a data association module 102 for acquiring identity information of the passengers, a position tracking module 103 for tracking the positions of the passengers by using a face recognition method, and a passenger flow volume counting module 104 for acquiring passenger flow information in a bus. The arrival reminding module comprises a station analysis module 201, an optimization selection module 202 and a loudspeaker module 203, the station analysis module receives information of the position tracking module and the passenger flow volume statistic module, generates a reminding scheme and sends the reminding scheme to the optimization selection module, and the optimization selection module is used for determining an optimal reminding scheme and reminding passengers by using the loudspeaker module.
The mobile phone terminal system 101 is used for obtaining a transfer list of passengers, the step of obtaining the transfer list of the passengers is used for selecting a destination and a current boarding station through a mobile phone terminal, creating the transfer list, taking a mobile phone user identification code as an ID (identity), storing the facial image data of the passengers, sending the transfer list, the facial image data and the ID to the data association module, and updating the transfer list according to whether the passengers get off the train or not;
specifically, whether getting-off is judged to receive the ID corresponding to the person getting-off sent by the passenger flow volume counting module, if the ID is matched with the current mobile phone terminal ID, the person getting-off is judged, the change of the transfer list means that after getting-off again, the passenger can select whether to transfer according to the transfer list next time, if so, the transfer list is stored as a common list, otherwise, the current transfer list is not stored, and the transfer list still needs to be created again when the mobile phone terminal system is used next time;
the data association module 102 is used for acquiring identity information of passengers, wherein the acquisition of the identity information of the passengers comprises the steps of establishing a face database, storing face image data and ID (identity) into the face database, establishing data association between station arrival information and facial features of the passengers, and sending the associated information to the position tracking module;
specifically, all the face recognition data can be collected only after the consent of the passenger, the formula for obtaining the face features of the passenger is (1),
Figure BDA0003040941340000051
wherein, x is an image, y is a universal face template, in particular to a three-dimensional point cloud consisting of 256 points, the points in the point cloud comprise eye, nose and mouth and cheek contours, r is the number of blocks divided by the face image of the passenger, fea is the facial feature of the passenger, and V isiCharacterizing image feature confidence, VjRepresenting the confidence coefficient of the face template features, and the value range [0,1 ]]，ln(. indicates the features extracted using the twin network trained with contrast loss, ff(x) represents extracting face information from the face image, and concat (x) represents that all the features are longitudinally connected;
the position tracking module 103 is used for tracking the position of a passenger by using a face recognition method, comprises a monitoring device arranged in the vehicle and used for receiving data association between the station arrival station information and the facial features of the passenger, which is sent by the data association module, counting the number of people about to arrive at the station, and respectively tracking the position information of the people about to arrive at the station in the vehicle according to the ID;
the passenger flow volume counting module 104 is used for acquiring passenger flow information in the vehicle, acquiring the number of passengers getting on the vehicle, acquiring a video in the door opening process of the vehicle through a monitoring device arranged on the door, identifying and counting the number of passengers getting off the vehicle appearing in the video, and calculating the number of passengers in the vehicle after the door is closed and the ID of the passengers getting on the vehicle according to the number of passengers getting on the vehicle, the number of passengers getting off the vehicle and the number of passengers in the vehicle before the door is opened;
specifically, the formula for counting the number of getting-off people is (2):
Figure BDA0003040941340000061
t1n is the number of blocks into which the picture is cut, n is 15,
Figure BDA0003040941340000064
is a block t1The feature matching function of the method specifically uses Euclidean distance to calculate the matching degree of features, W is a universal face template, specifically refers to a three-dimensional point cloud consisting of 256 points, the points in the point cloud comprise linear regression matrixes of eye, nose and mouth contours and cheek contours, omega represents weight, f represents the image features extracted by convolution, M represents the first M maximum feature values, c represents the first M maximum feature values1The number of people getting off the bus;
the formula for counting the number of passengers getting on the train is (3):
Figure BDA0003040941340000062
t2for the input boarding image, n represents a pictureThe number of pieces into which the block is cut, n 15,
Figure BDA0003040941340000065
is a block t2The feature matching function of the method specifically uses Euclidean distance to calculate the matching degree of features, W is a universal face template, specifically is a three-dimensional point cloud consisting of 256 points, the points in the point cloud comprise linear regression matrixes of eye, nose and mouth and cheek contours, omega represents weight, f represents convolution extraction image features, M represents the first M maximum feature values, c represents the first M maximum feature values2The number of passengers getting on the bus;
the formula for counting the number of people in the vehicle is (4):
Figure BDA0003040941340000063
Figure BDA0003040941340000071
t3n represents the number of blocks into which a picture is cut, n is 15,
Figure BDA0003040941340000072
is a block t3The feature matching function of the method specifically uses Euclidean distance to calculate the matching degree of features, W is a universal face template specifically refers to a three-dimensional point cloud consisting of 256 points, the points in the point cloud comprise linear regression matrixes of eye, nose and mouth and cheek contours, omega represents weight, f represents the image features extracted by convolution, M represents the first M maximum feature values, c represents the first M maximum feature values3The number of people in the vehicle;
the arrival reminding system comprises a station analysis module, an optimization selection module and a loudspeaker module,
the station analysis module 201 is used for dividing the time of arriving at the next station into different sampling periods, acquiring the current speed in the same sampling period, acquiring the distance between the current speed and the previous vehicle through GPS positioning, acquiring the current passenger flow in the vehicle counted by the passenger flow counting module, acquiring the number statistics of the people coming to the station transmitted by the position tracking module, acquiring the position information of the people coming to the station in the vehicle transmitted by the position tracking module, and generating a reminding scheme according to the data;
specifically, the generation formula of the reminding scheme is (5):
Figure BDA0003040941340000073
wherein TX is a reminding scheme, p is station passenger flow information of a next station, o is inter-bus distance between buses, s is vehicle speed, h is current passenger flow in the bus, k is position information of a person to arrive at the station in the bus, i represents the ith station, 1 ≦ i ≦ n, fun represents a residual error network using cross entropy loss training, f isn(x) represents the features extracted using a residual network, fm(-) represents the features extracted using a convolutional network;
the optimization selection module 202 is used for calculating the matching degree of data generated in two adjacent sampling periods in the station analysis module, the formula for calculating the matching degree is (6), if the matching value is greater than the threshold value, the matching degree of the two schemes is higher, the reminding scheme of the first sampling period is selected as the optimal reminding scheme, if the matching value is less than or equal to the threshold value, the scheme of the second sampling period is selected as the optimal reminding scheme, and the passenger corresponding to the optimal reminding scheme is marked;
Figure BDA0003040941340000074
wherein, txi,txi-1Respectively data generated in two adjacent sampling periods, zm is the matching degree, k is the position information of the person coming to the station in the vehicle, VxRepresenting the confidence of the first sampling period characteristic, and the value range [0,1 ]]，VyRepresenting the confidence of the second sampling period characteristic, and the value range [0,1 ]]L (×) represents the features extracted using the convolutional network, f (×) represents the serialization of the two data;
the speaker module 203 is used for acquiring the optimal reminding scheme of the optimal selection module and reminding the passengers to arrive at the station;
specifically, the miniature voice players are uniformly distributed in the whole carriage, and the closest one is selected to play voice by taking the position as the center;
the invention provides intelligent bus management based on the internet of vehicles, and passengers can set transfer lists according to the requirements of the passengers, so that the satisfaction degree of the passengers is improved.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (6)

1. An intelligent bus management based on the Internet of vehicles is characterized by comprising an intelligent bus management subsystem and an arrival reminding system, wherein the intelligent bus management subsystem comprises a mobile phone terminal system, a data association module, a position tracking module and a passenger flow volume statistic module,
the mobile phone terminal system is used for acquiring a transfer list of passengers, the step of acquiring the transfer list of the passengers is realized by selecting a destination and a current boarding station through a mobile phone terminal, creating the transfer list, taking a mobile phone user identification code as an ID (identity), storing the facial image data of the passengers, sending the transfer list, the facial image data and the ID to the data association module, and updating the transfer list according to whether the passengers get off or not;
the data association module is used for acquiring identity information of passengers, wherein the acquisition of the identity information of the passengers comprises the steps of establishing a face database, storing face image data and ID (identity) into the face database, establishing data association between station arrival information and facial features of the passengers, and sending the associated information to the position tracking module;
the position tracking module is used for tracking the position of a passenger by using a face recognition method, the position tracking of the passenger by using the face recognition method comprises a monitoring device arranged in the vehicle, and the data association of the station arrival station information and the facial features of the passenger, which is sent by the data association module, is received, the number of people about to arrive at the station is counted, and the information of the position of the people about to arrive at the station in the vehicle is respectively tracked according to the ID;
the passenger flow volume counting module is used for acquiring passenger flow information in the automobile, acquiring the number of passengers getting on the automobile, acquiring a video in the door opening process of the automobile through a monitoring device arranged on the automobile door, identifying and counting the number of passengers getting off the automobile appearing in the video, and calculating the number of passengers in the automobile after the door is closed and the ID of the passengers getting on the automobile according to the number of passengers getting on the automobile, the number of passengers getting off the automobile and the number of passengers in the automobile before the door is opened;
the arrival reminding module comprises a station analysis module, an optimization selection module and a loudspeaker module,
the station analysis module divides the time of arriving at the next station into different sampling periods, acquires the current speed in the same sampling period, acquires the distance between the current speed and the previous vehicle through GPS positioning, acquires the current passenger flow in the vehicle counted by the passenger flow counting module, acquires the number statistics of the people about to arrive at the station transmitted by the position tracking module, acquires the information of the positions of the people about to arrive at the station in the vehicle transmitted by the position tracking module, and generates a reminding scheme according to the data;
the optimization selection module calculates the matching degree of data generated in two adjacent sampling periods in the station analysis module, the formula for calculating the matching degree is (1), if the matching value is greater than the threshold value, the matching degree of the two schemes is higher, the reminding scheme of the first sampling period is selected as the optimal reminding scheme, if the matching value is less than or equal to the threshold value, the scheme of the second sampling period is selected as the optimal reminding scheme, and the passenger corresponding to the optimal reminding scheme is marked;
Figure FDA0003040941330000021
wherein, txi,txi-1Respectively data generated in two adjacent sampling periods, zm is the matching degree, k is the position information of the person coming to the station in the vehicle, VxRepresenting the confidence of the first sampling period characteristic, and the value range [0,1 ]]，VyRepresenting the confidence of the second sampling period characteristic, and the value range [0,1 ]]L (×) represents the features extracted using the convolutional network, f (×) represents the serialization of the data;
the speaker module is used for acquiring the optimal reminding scheme of the optimal selection module and reminding the passengers coming to the station.
2. The intelligent bus management system based on the internet of vehicles as claimed in claim 1, wherein the current vehicle speed and the station passenger flow information at the next station are obtained in the same sampling period, the current in-vehicle passenger flow counted by the passenger flow counting module is obtained, the number of people coming to the station counted by the position tracking module is obtained, the in-vehicle position information of the people coming to the station, which is sent by the position tracking module, is obtained, and a reminding scheme is generated according to the above data, wherein the generation formula of the reminding scheme is (2):
Figure FDA0003040941330000022
wherein TX is a reminding scheme, p is station passenger flow information of a next station, o is inter-bus distance between buses, s is vehicle speed, h is current passenger flow in the bus, k is position information of a person to arrive at the station in the bus, i represents the ith station, 1 ≦ i ≦ n, fun represents a residual error network using cross entropy loss training, f isn(x) represents the features extracted using a residual network, fm(. x) represents the features extracted using a convolutional network.
3. The intelligent bus management system based on the internet of vehicles as claimed in claim 1, wherein the data association module is used for obtaining the identity information of the passenger, the obtaining of the identity information of the passenger comprises establishing a face database, storing the face image data and the ID into the face database, extracting the face feature of the passenger, establishing the data association between the arrival station information and the face feature of the passenger, the formula for obtaining the face feature of the passenger is (3),
Figure FDA0003040941330000031
wherein, x is image, y is universal face template, r is number of blocks divided by passenger face image, fea is passenger face characteristic, ViCharacterizing image feature confidence, VjRepresenting the confidence coefficient of the face template features, and the value range [0,1 ]]，ln(. indicates the features extracted using the twin network trained with contrast loss, ff(. indicates that face information is extracted from the image, and concat (indicates that all features are longitudinally connected).
4. The intelligent bus management system based on the internet of vehicles as claimed in claim 1, wherein the reminding of the arrival of the coming passenger comprises reminding the passenger through a miniature voice player of a vehicle-mounted terminal, the miniature voice players are uniformly distributed in the whole carriage, and the position is used as the center, and the nearest one is selected for voice playing.
5. The intelligent bus management system based on the internet of vehicles as claimed in claim 1, wherein the passenger facial image data is stored and the transfer list, the facial image data and the ID are sent to the data association module, and the transfer list is updated according to whether to get off or not, wherein the judgment whether to get off is to receive the ID corresponding to the person getting off sent by the passenger flow volume statistics module, and if the ID is matched with the current mobile phone terminal ID, the judgment of getting off is made.
6. The intelligent bus management system based on the internet of vehicles as claimed in claim 1, wherein the passenger facial image data is stored, the transfer list, the facial image data and the ID are sent to the data association module, and the transfer list is updated according to whether the passenger gets off the bus or not, wherein the change of the transfer list means that after the passenger gets off the bus again, whether the passenger transfers according to the transfer list next time can be selected, if so, the transfer list is stored as a common list, otherwise, the current transfer list is not stored, and the transfer list still needs to be created again when the mobile phone terminal system is used next time.
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