CN112381956A - AI intelligent face recognition ticket selling and checking system and method based on cloud computing - Google Patents
AI intelligent face recognition ticket selling and checking system and method based on cloud computing Download PDFInfo
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- CN112381956A CN112381956A CN201911050411.0A CN201911050411A CN112381956A CN 112381956 A CN112381956 A CN 112381956A CN 201911050411 A CN201911050411 A CN 201911050411A CN 112381956 A CN112381956 A CN 112381956A
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
- G07B11/00—Apparatus for validating or cancelling issued tickets
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/76—Television signal recording
- H04N5/78—Television signal recording using magnetic recording
- H04N5/781—Television signal recording using magnetic recording on disks or drums
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
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- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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Abstract
The invention relates to a ticket selling and checking system and a ticket selling and checking method, belongs to the field of rail transit, and particularly relates to an AI intelligent face recognition ticket selling and checking system and an AI intelligent face recognition ticket selling and checking method based on cloud computing. Under the premise of fully utilizing the advantages of cloud computing, the invention combines an AI face recognition system and a deep learning system to achieve elastic expansion and contraction, resource sharing and resource utilization rate improvement, thereby saving the cost. The invention provides a new convenient mode for subway payment, provides technical support for creating intelligent subways and service concepts, and further improves passenger experience.
Description
Technical Field
The invention relates to a ticket selling and checking system and a ticket selling and checking method, belongs to the field of rail transit, and particularly relates to an AI intelligent face recognition ticket selling and checking system and an AI intelligent face recognition ticket selling and checking method based on cloud computing.
Background
With the rapid development of internet technologies, technologies such as cloud computing, big data, IoT, 5G, AI face recognition and the like are also changing day by day. On the basis of shared resource pool, demand allocation, tenant isolation and SDN provided by the cloud computing virtualization technology, the method provides possibility for large-scale computing of big data, IoT and face recognition, and is widely practiced on the premise of rapid technology iteration in recent years. With the construction of subway rail transit, a cloud computing architecture design is gradually adopted, and from the aspects of improving the resource utilization rate and reducing the system implementation complexity, a new technology is applied to modernization, so that the experience degree and the service level of passengers are improved, and the method becomes the target of subway development at present.
Cloud computing is a product under the high-speed development of the internet, the internet is used as a support, virtualized resources can be provided for users, network access services can be provided for the users according to the actual needs of the users, and authorized users can directly enter a resource sharing pool through the network to use the resources in the resource sharing pool. Cloud computing has gained rapid development in short years since its appearance to date and has gained application in many fields, and the characteristics of cloud computing can be summarized in the following aspects.
One is large scale. The cloud computing represents the internet, so that the cloud computing has the characteristic of large scale, for example, google cloud computing has millions of servers, and the private clouds of common enterprises also have servers with different numbers, namely hundreds of servers and thousands of servers, so that the cloud computing brings strong computing power to users to a certain extent.
Second, virtualization. The cloud computing is not limited by time and place, and a user can obtain corresponding services as long as a network exists. The cloud can provide massive resources, but the cloud is not a tangible entity, all applications run somewhere in the cloud, and a user does not need to know the specific running position of the applications, and can complete tasks by means of network services only through one terminal device.
Thirdly, the reliability is high. The cloud computing uses various technical measures such as data copy fault tolerance, isomorphism of computing nodes and the like, and reliability of the provided service is ensured.
The AFC system is combined with computer technology, so that the AFC system can play a plurality of roles of ticket sellers, ticket inspectors, accountants and the like, is a ticket administration manager, collects information, provides a large amount of data information for each department of traffic, provides data information support for decision making of each department of traffic, and is a real automatic system.
In the prior art, some cloud-based ticket checking systems exist, but a new face recognition technology is not incorporated as a new solution in most cases, and the computing performance of the system cannot meet the requirements of a large amount of real-time computing capacity and concurrency capacity.
Disclosure of Invention
The invention mainly solves the problems in the prior art and provides an AI intelligent face recognition ticket selling and checking system and method based on cloud computing. The system and the method have performability, and can improve the utilization of resources at the same time, thereby reducing the input cost.
The technical problem of the invention is mainly solved by the following technical scheme:
an AI intelligent face identification ticket selling and checking system based on cloud computing comprises:
the system comprises a station cloud platform, a bus station cloud platform and a bus station cloud biological characteristic identification system, wherein the station cloud platform is deployed at a station side, a station cloud biological characteristic identification database and the biological characteristic identification system are arranged in the station cloud platform, and the biological characteristic identification system is used for acquiring biological characteristic information to be identified of passengers;
the central cloud platform is connected with the station cloud platform through a bearing network, a big data system and a behavior analysis system are arranged in the central cloud platform, the behavior analysis system analyzes habit riding routes of different users according to user behavior data collected by the big data system, and sends biological characteristics of the users to a station cloud biological characteristic recognition database of a starting station cloud platform and a station cloud biological characteristic recognition database of an end station vehicle cloud platform of the habit riding routes corresponding to the users;
the station cloud platform carries out local matching to identify the user, and the local matching is to match the biological characteristic information to be identified with biological characteristic information in a station cloud biological characteristic identification database.
Preferably, in the above AI intelligent face recognition ticket selling and checking system based on cloud computing, after local matching fails, the station cloud platform sends the biometric information to be recognized to the central cloud platform for remote matching, and the remote matching is to match the biometric information to be recognized with the biometric information in the central cloud biometric database of the central cloud platform.
Preferably, in the above AI intelligent face recognition ticket vending and checking system based on cloud computing, the biometric information to be recognized includes
The peripheral biological characteristic information of passengers when entering the station is acquired by the peripheral acquisition and identification device and the ticket checking biological characteristic information of passengers when passing the gate is acquired by the gate acquisition and identification device;
wherein the station cloud platform maintains an outdoor tag biometric database that stores passenger biometric information that matches the peripheral biometric information;
and the station cloud platform matches the ticket checking biological characteristic information with the passenger biological characteristic information in the outdoor marking biological characteristic database so as to identify the user identity information of the passenger to be checked.
Preferably, in the above AI intelligent face recognition ticket selling and checking system based on cloud computing, the station cloud platform first matches the outdoor marker biometric data locally, and matches the outdoor marker biometric data with the biometric feature in the central cloud biometric feature recognition database of the central cloud platform when the matching fails.
Preferably, in the above AI intelligent face recognition sale ticket checking system based on cloud computing, when the matching of the ticket checking biological characteristic information with the passenger biological characteristic information in the outdoor marker biological characteristic database fails, the station cloud platform further matches the ticket checking biological characteristic information with a biological characteristic in a central cloud biological characteristic recognition database of a central cloud platform.
Preferably, in the above AI intelligent face recognition ticket vending and checking system based on cloud computing, the central cloud platform predicts the target station of the user by using the behavior analysis system after the passenger enters the station, and sends the biological feature information of the passenger to the station cloud platform of the target station.
Preferably, in the above AI intelligent face recognition ticket vending system based on cloud computing, the biometric information is face recognition information.
Preferably, the above AI intelligent face recognition ticket selling and checking system based on cloud computing includes a vehicle segment cloud platform and a control center cloud platform, where the vehicle segment cloud platform and the control center cloud platform are designed by a double-active cloud architecture, and adopt a double-active high-performance storage, a GPU resource pool, and an AI storage pool.
Preferably, in the above AI intelligent face recognition ticket selling and checking system based on cloud computing, the station cloud platform is responsible for station-level task processing, and the central cloud platform deploys the AFC system and the face recognition algorithm system in a cluster manner for core computing, account clearing and large-scale face computing processing.
An AI intelligent face recognition ticket selling and checking method based on cloud computing comprises the following steps:
peripheral identification, namely, carrying out acquisition and identification when the station is entered through peripheral acquisition and identification, if the identification is successful, storing the human face into an outdoor marked human face database, and preprocessing the gate verification
A gate-in identification step, wherein secondary grabbing identification is carried out on the outdoor marked face database, and if the identification is successful, an AFC ticket interface is called, and passengers enter the station; if the identification fails, pushing the data to a central cloud for comparison, calling an AFC ticket business interface after the identification succeeds, and allowing passengers to enter the station;
in the target prediction step, after a passenger successfully arrives at a station by face brushing, deep mining is carried out through a large data platform of a central cloud, the terminal station of the passenger is judged in advance, and a face service is informed to push the face to a station cloud face database of the terminal station;
the gate-out identification step, wherein the gate machine at the station out takes a snapshot of the human face, and firstly, the identification is carried out based on a local database, and the fee deduction and the station out are successfully identified; and if the identification fails, pushing the cloud identification of the center, and returning a result to deduct the fee and outbound.
Therefore, the invention has the following advantages: under the premise of fully utilizing the advantages of cloud computing, the invention combines an AI face recognition system and a deep learning system to achieve elastic expansion and contraction, resource sharing and resource utilization rate improvement, thereby saving the cost. The invention provides a new convenient mode for subway payment, provides technical support for creating intelligent subways and service concepts, and further improves passenger experience.
Drawings
FIG. 1 is a prior art cloud-based ticketing system;
FIG. 2 is a schematic diagram of a cloud platform deployment architecture of the present invention;
FIG. 3 is a system scheduling process of the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b):
fig. 1 shows a cloud-based ticket checking system in the prior art. The system does not have advanced technologies such as an AI high-performance storage pool, a GPU resource pool, a neural network, a deep training model, an outdoor face recognition ratio database and the like, and does not have specific recognition and verification strategies and flows. The technology can not meet the requirements of a large amount of real-time computing capacity and concurrency capacity in terms of computing performance.
In order to overcome the defects, the embodiment improves the prior art and provides an AI intelligent face recognition ticket selling and checking system and method based on cloud computing.
The cloud platform deployment architecture of the system of the present embodiment is shown in fig. 2. The system mainly comprises a station cloud platform and a central cloud platform.
The station cloud platform is deployed on a station side, a station cloud biological characteristic recognition database and a biological characteristic recognition system are arranged in the station cloud platform, and the biological characteristic recognition system is used for collecting biological characteristic information to be recognized of passengers;
the central cloud platform is connected with the station cloud platform through a bearing network, a big data system and a behavior analysis system are arranged in the central cloud platform, the behavior analysis system analyzes habit riding routes of different users according to user behavior data collected by the big data system, and sends biological characteristics of the users to a station cloud biological characteristic recognition database of a starting station cloud platform and a station cloud biological characteristic recognition database of an end station vehicle cloud platform of the habit riding routes corresponding to the users;
in the embodiment, a vehicle section and a control center adopt a double-active cloud architecture design, and double-active high-performance storage, a GPU resource pool and an AI storage pool are adopted. The station cloud communicates with the central cloud through a dedicated high-speed bearing network. The central cloud deploys an AFC system and a face recognition algorithm system on the cloud platform in a cluster mode, and the AFC system and the face recognition algorithm system are respectively used for core computing, account clearing and large-scale face computing processing. Meanwhile, thresholds of a CPU, a GPU, a memory, a network, a disk and the like are set, for example, the CPU threshold is set to be 0.8, early warning is carried out (namely, the resource utilization rate exceeds 80%), elastic expansion is carried out, a cluster is guaranteed to have a certain elastic expansion strategy, transverse expansion of computing capacity can be carried out in a peak period and a valley period, and effectiveness of service is guaranteed. The station cloud deploys subsystems (such as AFC, face recognition and the like) of each system and is used for being responsible for task processing at the station level. And the station cloud deploys an outdoor labeled face database (Labled Faces in the Wild: LFW), and the data is obtained by the behavior analysis system of the station identification and the central cloud together. Meanwhile, the central cloud is stored for analyzing the behavior of each passenger at each station, so that classification is performed, the data range of each station is reduced, and the comparison speed and accuracy are improved. And a two-layer identification system, namely pre-identification and gate identification is deployed at the station. Pre-recognition, namely, the peripheral camera of the station carries out snapshot recognition, and the recognition is stored in an outdoor labeled face database (Labled Faces in the Wild: LFW) after being successfully recognized; the gate identification is carried out through the gate camera.
In the embodiment, the station face library is firstly established, and through repeated learning and collection, resident people flows of different stations can be subdivided, so that the recognition rate and the recognition speed can be effectively improved.
In this embodiment, central face big data is also established, and the central face big data utilizes sufficient cloud computing resources and massive face big data to perform feature extraction, deep learning, model training completion on comparison tasks submitted by each station, and pushes computed results to a corresponding station cloud database.
Fig. 3 shows a system scheduling process according to the present invention. The implementation thereof is described in detail below.
The station cloud platform carries out local matching to identify the user, and the local matching is to match the biological characteristic information to be identified with biological characteristic information in a station cloud biological characteristic identification database. And after the local matching fails, the station cloud platform sends the biological feature information to be identified to a central cloud platform for remote matching, wherein the remote matching is to match the biological feature information to be identified with biological features in a central cloud biological feature identification database of the central cloud platform.
In this embodiment, the biometric information to be recognized includes the peripheral biometric information of the passenger when entering the station, which is collected by the peripheral collection and recognition device, and the ticket checking biometric information of the passenger when passing through the gate, which is collected by the gate collection and recognition device.
When ticket checking is carried out, firstly, acquisition and identification are carried out through peripheral acquisition and identification when a user enters a station, firstly, the identification is based on a local face database (a small-scale face range is formed through multiple deep learning and classification), if the identification is successful, the face is stored in an outdoor marked face database (Labled Faces in the Wild: LFW), and preprocessing is carried out for gate machine verification; and if the identification is not successful, pushing the data to a center for comparison, and returning a result to the station face database after the center cloud comparison is successful.
Secondly, the gate machine conducts secondary grabbing recognition in a local database, if the recognition is successful, an AFC ticket business interface is called, and passengers enter the station; if the identification fails, the AFC ticket business interface is pushed to the central cloud for comparison, and after the identification succeeds, the AFC ticket business interface is called, and passengers enter the station.
In this embodiment, the central cloud platform predicts a target station of a user by using a behavior analysis system after a passenger enters the station, and sends biological characteristic information of the passenger to a station cloud platform of the target station. Specifically, after a passenger successfully arrives at a station by swiping the face, deep mining is carried out through a large data platform of the central cloud, the terminal station of the passenger is judged in advance, and the face service is informed to push the face to a station cloud face database of the terminal station.
In the embodiment, the gate machine at the station is used for snapshotting the face, firstly, the face is identified based on the local database, and the gate machine is used for successfully deducting the fee and then the station is out; and if the identification fails, pushing the cloud identification of the center, and returning a result to deduct the fee and outbound. And simultaneously, recording results, submitting the results to a deep learning system, performing model training, forming the portrait and the action track of the passenger, and further improving the accuracy and the efficiency of recognition.
In the embodiment, when the tasks for central cloud comparison are submitted in the peak period of each station, due to the fact that a large number of tasks are needed, the data volume is large, the time is short, a large number of computing resources are bound to be called, at the moment, the cloud platform monitoring system controls (elastically expands) the thresholds of different resources, the concurrent computing speed is improved, and the tasks of each station are fed back in time. When the passenger flow is reduced, the resource utilization rate is reduced, and the cloud platform monitoring system contracts all cluster resources. During the period of stopping operation, the big data and deep learning service cluster is called to start calculation, and a required data model is formed.
According to the embodiment, on the premise of fully utilizing the advantages of cloud computing, the purposes of elastic expansion, resource sharing and resource utilization rate improvement are achieved by combining an AI face recognition system and a deep learning system, and further the cost is saved. The invention provides a new convenient mode for subway payment, provides technical support for creating intelligent subways and service concepts, and further improves passenger experience.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (10)
1. An AI intelligent face identification ticket checking system based on cloud computing is characterized by comprising:
the system comprises a station cloud platform, a bus station cloud platform and a bus station cloud biological characteristic identification system, wherein the station cloud platform is deployed at a station side, a station cloud biological characteristic identification database and the biological characteristic identification system are arranged in the station cloud platform, and the biological characteristic identification system is used for acquiring biological characteristic information to be identified of passengers;
the central cloud platform is connected with the station cloud platform through a bearing network, a big data system and a behavior analysis system are arranged in the central cloud platform, the behavior analysis system analyzes habit riding routes of different users according to user behavior data collected by the big data system, and sends biological characteristics of the users to a station cloud biological characteristic recognition database of a starting station cloud platform and a station cloud biological characteristic recognition database of an end station vehicle cloud platform of the habit riding routes corresponding to the users;
the station cloud platform carries out local matching to identify the user, and the local matching is to match the biological characteristic information to be identified with biological characteristic information in a station cloud biological characteristic identification database.
2. The AI intelligent face recognition ticket checking system based on cloud computing as claimed in claim 1 wherein the station cloud platform sends the biometric information to be recognized to a central cloud platform for remote matching after a local matching failure, the remote matching being the matching of the biometric information to be recognized with the biometric information in the central cloud biometric database of the central cloud platform.
3. The AI intelligent face recognition ticket system based on cloud computing as in claim 1, wherein the biometric information to be recognized comprises
The peripheral biological characteristic information of passengers when entering the station is acquired by the peripheral acquisition and identification device and the ticket checking biological characteristic information of passengers when passing the gate is acquired by the gate acquisition and identification device;
wherein the station cloud platform maintains an outdoor tag biometric database that stores passenger biometric information that matches the peripheral biometric information;
and the station cloud platform matches the ticket checking biological characteristic information with the passenger biological characteristic information in the outdoor marking biological characteristic database so as to identify the user identity information of the passenger to be checked.
4. The AI intelligent face recognition ticket checking system based on cloud computing as in claim 3, wherein the station cloud platform first matches the outdoor tag biometric data locally and matches the outdoor tag biometric data with the biometric feature in the central cloud biometric database of the central cloud platform when the matching fails.
5. The AI intelligent face recognition ticket checking system based on cloud computing as claimed in claim 3 wherein the station cloud platform further matches the ticket checking biometric information with the biometric feature in the central cloud biometric database of the central cloud platform if the matching of the ticket checking biometric information with the passenger biometric information in the outdoor tag biometric database fails.
6. The AI intelligent face recognition ticket checking system based on cloud computing as claimed in claim 1 wherein the central cloud platform predicts the target station of the user by using the behavior analysis system after the passenger arrives at the station and sends the biological feature information of the passenger to the station cloud platform of the target station.
7. The cloud-computing-based AI intelligent face recognition ticketing system of claim 1, wherein the biometric information is face recognition information.
8. The AI intelligent face recognition ticket checking system based on cloud computing as claimed in claim 1 wherein the central cloud platform comprises a vehicle segment cloud platform and a control center cloud platform, the vehicle segment cloud platform and the control center cloud platform are designed using a dual active cloud architecture, and are designed using a dual active high performance storage, a GPU resource pool and an AI storage pool.
9. The AI intelligent face recognition ticket checking system based on cloud computing as in claim 1 wherein a station cloud platform is responsible for station level task processing, said central cloud platform deploying AFC system and face recognition algorithm system in a cluster for core computing, account clearing and large scale face computing processing.
10. An AI intelligent face recognition ticket selling and checking method based on cloud computing is characterized by comprising the following steps:
peripheral identification, namely, carrying out acquisition and identification when the station is entered through peripheral acquisition and identification, if the identification is successful, storing the human face into an outdoor marked human face database, and preprocessing the gate verification
A gate-in identification step, wherein secondary grabbing identification is carried out on the outdoor marked face database, and if the identification is successful, an AFC ticket interface is called, and passengers enter the station; if the identification fails, pushing the data to a central cloud for comparison, calling an AFC ticket business interface after the identification succeeds, and allowing passengers to enter the station;
in the target prediction step, after a passenger successfully arrives at a station by face brushing, deep mining is carried out through a large data platform of a central cloud, the terminal station of the passenger is judged in advance, and a face service is informed to push the face to a station cloud face database of the terminal station;
the gate-out identification step, wherein the gate machine at the station out takes a snapshot of the human face, and firstly, the identification is carried out based on a local database, and the fee deduction and the station out are successfully identified; and if the identification fails, pushing the cloud identification of the center, and returning a result to deduct the fee and outbound.
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