CN115410252A - Living body face recognition charging system and method for charging and replacing station - Google Patents

Living body face recognition charging system and method for charging and replacing station Download PDF

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CN115410252A
CN115410252A CN202211016901.0A CN202211016901A CN115410252A CN 115410252 A CN115410252 A CN 115410252A CN 202211016901 A CN202211016901 A CN 202211016901A CN 115410252 A CN115410252 A CN 115410252A
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data
charging
user
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face recognition
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赵景宝
江涛
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Hefei Miaoxu Intelligent Technology Co ltd
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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
    • 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
    • G06V40/40—Spoof detection, e.g. liveness detection
    • G06V40/45—Detection of the body part being alive
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
    • H04L63/0861—Network architectures or network communication protocols for network security for authentication of entities using biometrical features, e.g. fingerprint, retina-scan
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations

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Abstract

The invention discloses a charging station living body face recognition charging system and a charging station living body face recognition charging method. In the invention: the image acquisition module acquires living human face image data entering the battery replacement station; the central processing unit is used for comparing the face image data collected by the image collection module and controlling the operation of the charging control module according to the comparison result; the charging control module is used for controlling and monitoring the vehicle condition of a user; the cloud server is used for grouping the user face image data stored in the battery swapping terminal and mapping the accessed user to the power station terminal. According to the method and the device, the power station terminal is arranged to control the power exchanging station, face recognition is carried out on the entering user, the user does not need to carry out recognition through extra operation, the deployment cost of the cloud data center data can be effectively reduced by optimizing a data deployment scheme, the data with high read-write frequency similarity is grouped by comprehensively considering the property of the data read-write frequency, and the influence of read data and write data on the overall cost is reduced.

Description

Living body face recognition charging system and method for charging and replacing station
Technical Field
The invention belongs to the technical field of face recognition, and particularly relates to a living body face recognition charging system and a living body face recognition charging method for a charging and replacing station.
Background
The offline face recognition technology is not affected by network stability, the offline face recognition technology is greatly affected by the network in application, and if the network is unstable when a user performs face recognition, the user experience is affected due to recognition failure. The off-line face recognition technology does not need to use a network in application at all times, and face information can be synchronized to equipment when a user registers a face. When a user identifies a face, the currently acquired face information is compared with the face information of the equipment repository, so that the face information can be identified and passed, and then the face information enters a charging system to select charging.
Disclosure of Invention
The invention aims to provide a living body face recognition charging system and a living body face recognition charging method for a charging and swapping station.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a living body face recognition charging system for a charging and replacing station, which comprises a power station terminal for controlling the operation of the replacing station; the power station terminal comprises a central processing unit, an image acquisition module, a data storage module, a charging control module and a communication module; the image acquisition module is used for acquiring living human face image data entering the battery replacement station; the data storage module is used for storing face image data; the central processing unit is used for comparing the face image data collected by the image collecting module and controlling the operation of the charging control module according to the comparison result; the charging control module is used for controlling and monitoring the charging condition of the user vehicle; the central processing unit performs information interaction with a cloud server or other battery replacement stations through a communication module; the cloud server is used for grouping user face image data stored by the battery swapping terminal and mapping accessed users to the power station terminal; and the personal terminal receives the face recognition result and the vehicle charging data by performing information interaction with the cloud server.
Preferably, the personal terminal is a mobile phone or a smart tablet or a computer.
Preferably, the personal terminal transmits the user registration information to a cloud server for storage; the user registration information includes an account ID and a password, face image data, vehicle identification data, and payment account data.
Preferably, the charging control module comprises a charging control switch and a charging detection unit; the charging control switch is used for controlling a vehicle charging switch, the charging detection unit is used for detecting vehicle charging data, including charging voltage data, charging current data and charging electric quantity data, the central processing unit uploads charging switch state data and vehicle charging data to the cloud server in real time, and the cloud server receives the data and then transmits the data to the personal terminal.
A living body face recognition method for a charging and battery replacing station is characterized by comprising the following steps:
SS01, a user registers an account through a personal terminal, the personal terminal transmits registered user registration information to a cloud server, and the cloud server groups the received user registration information and transmits the user registration information to a power station terminal for storage;
SS02, a user enters the battery replacement station, the power station terminal collects face image data of the user through the image collection module and carries out recognition comparison, and after the face image data are recognized, the central controller controls the charging control module to charge the vehicle and transmits the data to the personal terminal in real time.
Preferably, the data deployment policy of the cloud server includes the following steps:
stp1, using power station terminal as data center, assuming data deploymentk, and the SLA levels selectable by the users are divided into 5, the N data centers are divided into k areas according to the SLA levels of the users accessing the data, and the number of the data centers of the k areas is { N 1 ，N 2 ，..N k Then the data can be collected as N in the candidate data center set 1 *N 2 *，..N k Seed growing;
stp2, for each data m, select from k regions a set S of data centers that can satisfy all user delays for accessing data m m ；
Stp3, sorting and grouping the data according to the reading and writing frequency to obtain data groups with high similarity, and calculating a data center priority list of each group;
stp4, sequentially deploying the data in the group according to the group sequence, and finding the data contained in S according to the priority list of the group data center m A medium and high priority data center collection.
Preferably, the step Stp1 is comprised of the following substeps:
stp11, supposing that a data center is randomly selected as a first central point, and a user set capable of being served is found;
stp12, comparing the rest data centers with the previous center points, and selecting the data center with the smallest coincidence degree with the user set which can be served by the center point as a new center point until k center points are found;
stp12, finding the central point with the maximum contact ratio of the user set for all the remaining N-k data centers to divide.
Preferably, step Stp3 comprises the following substeps:
stp31, calculating the rank value of each data, and arranging and forming a queue Q according to a non-ascending order;
stp32, selecting the first data in the queue as the first group, G1;
stp33, sequentially selecting other data, comparing the similarity with the previous group, and if the similarity exceeds a threshold value, independently dividing the groups; if the similarity is smaller than the threshold, selecting the group combination with the maximum similarity until all the data are grouped;
stp34, the data center set of each group candidate is
Figure BDA0003812886090000041
Averaging the request quantity of all data in the group to k data centers of each data center set in the S, calculating corresponding data deployment cost, wherein the smaller the deployment cost is, the higher the priority is, and finally obtaining a data center priority list of each group.
Preferably, the cost of deployment in step Stp34 is the electric charge of the data center generated by the read-write request of the user and the update transmission cost of the network copy between the data centers.
The invention has the following beneficial effects:
1. according to the invention, the power station terminal is arranged to control the battery replacement station, the face of the entering user is identified, the user does not need to identify through additional operation, the charging and battery replacement operation is convenient, the deployment cost of cloud data center data can be effectively reduced by optimizing a data deployment scheme, the data with high read-write frequency similarity is grouped by comprehensively considering the property of data read-write frequency, and the influence of the read data and the write data on the overall cost is reduced.
2. The invention deploys all data into fixed copy number based on access delay, data center cost and network transmission cost, thereby not only ensuring service reliability, but also minimizing deployment cost under the condition of ensuring the consistent copy number.
Of course, it is not necessary for any product to practice the invention to achieve all of the above-described advantages at the same time.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a system block diagram of a living body face recognition charging system of a charging and swapping station;
FIG. 2 is a system block diagram of a power station terminal;
FIG. 3 is a flowchart of a live face recognition method for a charging and swapping station;
FIG. 4 is a flowchart of a data deployment method of a live body face recognition method of a charging and swapping station
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
For data intensive application, the method is mainly characterized by large data volume and high user access rate, in order to reduce the time complexity of a deployment scheme, before data deployment, data is subjected to sequencing and grouping preprocessing, the number of copies of the data deployment is set to be a fixed number, a candidate data center set is selected as a candidate deployment position of the data after region division is performed on a data center, and the most appropriate data center is found.
From the perspective of the data center, under the condition that the maximum tolerant time delay condition of user access data is met, by taking the electricity charge of the data center generated by a user read-write request and the network copy updating transmission charge between the data centers as an objective function, the delay, the energy consumption and the network transmission cost are comprehensively considered, and an economical and effective data deployment scheme is provided.
The first embodiment is as follows:
referring to fig. 1, the present invention is a living body face recognition charging system for a charging and swapping station, including a power station terminal for controlling the operation of the swapping station, a cloud server and a personal terminal;
the cloud server is used for grouping user face image data stored in the battery swapping terminal and mapping accessed users to the power station terminal; the personal terminal receives a face recognition result and vehicle charging data by performing information interaction with the cloud server, adopts a mobile phone or an intelligent tablet or a computer, and transmits user registration information to the cloud server for storage; the user registration information includes account ID and password, face image data, vehicle identification data, and payment account data
As shown in fig. 2, the power station terminal includes a central processing unit, an image acquisition module, a data storage module, a charging control module, and a communication module; the image acquisition module is used for acquiring living human face image data entering the battery replacement station; the data storage module is used for storing the face image data; the central processing unit is used for comparing the face image data collected by the image collecting module and controlling the operation of the charging control module according to the comparison result;
the charging control module is used for controlling and monitoring the charging condition of the user vehicle; the charging control module comprises a charging control switch and a charging detection unit; the charging control switch is used for controlling a vehicle charging switch, the charging detection unit is used for detecting vehicle charging data which comprise charging voltage data, charging current data and charging electric quantity data, the central processing unit uploads charging switch state data and the vehicle charging data to the cloud server in real time, and the cloud server receives the data and then transmits the data to the personal terminal;
the central processing unit performs information interaction with the cloud server or other power exchange stations through the communication module; the communication module adopts a GPRS mobile communication module or a WIFI communication module or a wired network communication module.
Example two:
as shown in fig. 3, a method for recognizing a living human face in a charging and swapping station is characterized by comprising the following steps:
SS01, a user registers an account through a personal terminal, the personal terminal transmits registered user registration information to a cloud server, and the cloud server groups the received user registration information and transmits the user registration information to a power station terminal for storage;
SS02, the user enters the battery replacement station, the power station terminal collects face image data of the user through the image collection module, identification and comparison are carried out, after identification is passed, the central controller controls the charging control module to charge the vehicle, and data are transmitted to the personal terminal in real time.
Example three:
as shown in fig. 4, there are D parts of data, M users, N data centers in the cloud server, the process of data deployment is a process of mapping users accessing the data to the data centers, the data deployment process is to divide the data centers into k regions, where k is the number of data copies, the data is pre-processed in groups according to the read-write frequency, and then each group is respectively data-deployed in sequence, with the data center electricity charges generated by the read-write request of the user and the network copy update transmission charges between the data centers as the data deployment cost, and includes the following steps:
stp1, taking a power station terminal as a data center, assuming that k data are deployed, dividing SLA levels selectable by users into 5 types, dividing N data centers into k areas according to the SLA levels of the users accessing data, wherein the number of the data centers of the k areas is { N 1 ，N 2 ，..N k H, the data can be set as N in the candidate data center 1 *N 2 *，..N k Seed growing;
the method comprises the following substeps:
stp11, supposing that a data center is randomly selected as a first central point, and a user set capable of being served is found;
stp12, comparing the rest data centers with the previous center points, and selecting the data center with the smallest coincidence degree with the user set which can be served by the center point as a new center point until k center points are found;
stp12, finding the central point with the maximum contact ratio of the user set for all the remaining N-k data centers to divide;
stp2, for each data m, select from k regions a set S of data centers that can satisfy all user delays for accessing data m m ；
Stp3, sorting and grouping the data according to the reading and writing frequency to obtain data groups with high similarity, and calculating a data center priority list of each group; the method comprises the following substeps:
stp31, calculating the rank value of each data, and arranging and forming a queue Q according to a non-ascending order;
stp32, selecting the first data in the queue as a first group, G1;
stp33, sequentially selecting other data, comparing the similarity with the previous group, and if the similarity exceeds a threshold value, independently dividing the groups; if the similarity is smaller than the threshold, selecting the group combination with the maximum similarity until all the data are grouped;
stp34, the data center set of each group candidate is
Figure BDA0003812886090000081
Averaging the request quantity of all data in the group to k data centers of each data center set in S, calculating corresponding data deployment cost, wherein the smaller the deployment cost is, the higher the priority is, and finally obtaining a data center priority list of each group, wherein the deployment cost is the electricity charge of the data centers generated by the read-write request of the user and the update transmission cost of the network copies among the data centers.
Stp4, sequentially deploying the data in the group according to the group sequence, and finding the data contained in S according to the priority list of the group data center m A medium and high priority data center collection.
It should be noted that, in the foregoing system embodiment, each unit included is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In addition, it can be understood by those skilled in the art that all or part of the steps in the method for implementing the embodiments described above can be implemented by instructing the relevant hardware through a program, and the corresponding program can be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, or the like.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims (9)

1. A living body face recognition charging system for a charging and replacing station is characterized by comprising
The power station terminal is used for controlling the operation of the battery replacement station; the power station terminal comprises a central processing unit, an image acquisition module, a data storage module, a charging control module and a communication module; the image acquisition module is used for acquiring living human face image data entering the power conversion station; the data storage module is used for storing the face image data; the central processing unit is used for comparing the face image data collected by the image collection module and controlling the operation of the charging control module according to the comparison result;
the charging control module is used for controlling and monitoring the charging condition of the user vehicle; the central processing unit is in information interaction with a cloud server or other power exchange stations through a communication module;
the cloud server is used for grouping user face image data stored by the battery swapping terminal and mapping accessed users to the power station terminal;
and the personal terminal receives the face recognition result and the vehicle charging data through information interaction with the cloud server.
2. The living body face recognition charging system for the charging and replacing station as claimed in claim 1, wherein the personal terminal is a mobile phone, an intelligent tablet or a computer.
3. The live body face recognition charging system for the charging and replacing station as claimed in claim 2, wherein the personal terminal transmits the user registration information to a cloud server for storage; the user registration information includes an account ID and a password, face image data, vehicle identification data, and payment account data.
4. The living body face recognition charging system for the charging and replacing station as claimed in claim 1, wherein the charging control module comprises a charging control switch and a charging detection unit; the charging control switch is used for controlling a vehicle charging switch, the charging detection unit is used for detecting vehicle charging data, including charging voltage data, charging current data and charging electric quantity data, the central processing unit uploads charging switch state data and vehicle charging data to the cloud server in real time, and the cloud server receives the data and then transmits the data to the personal terminal.
5. The live face recognition method for the charging and replacing station as claimed in any one of claims 1 to 4, characterized by comprising the following steps:
SS01, a user registers an account through a personal terminal, the personal terminal transmits registered user registration information to a cloud server, and the cloud server groups the received user registration information and transmits the user registration information to a power station terminal for storage;
SS02, a user enters the battery replacement station, the power station terminal collects face image data of the user through the image collection module and carries out recognition comparison, and after the face image data are recognized, the central controller controls the charging control module to charge the vehicle and transmits the data to the personal terminal in real time.
6. The live face recognition method for the charging and replacing station as claimed in claim 5, wherein the data deployment strategy of the cloud server comprises the following steps:
stp1, using a power station terminal as a data center, assuming that k parts of data are deployed and SLA levels selectable by users are divided into 5, and dividing N SLA levelsThe data center is divided into k areas according to the SLA level of a user accessing the data, and the number of the data centers of the k areas is { N } 1 ，N 2 ，..N k H, the data can be set as N in the candidate data center 1 *N 2 *，..N k Seed;
stp2, for each data m, select from k regions a set S of data centers that can satisfy all user delays for accessing data m m ；
Stp3, sorting and grouping the data according to the reading and writing frequency to obtain data groups with high similarity, and calculating a data center priority list of each group;
stp4, sequentially deploying the data in the group according to the group sequence, and finding the data contained in S according to the priority list of the group data center m A medium and high priority data center collection.
7. The live face recognition method for the charging and replacing station as claimed in claim 6, wherein the step Stp1 comprises the following substeps:
stp11, supposing that a data center is randomly selected as a first central point, and a user set capable of being served is found;
stp12, comparing the rest data centers with the previous center points, and selecting the data center with the smallest contact ratio with the user set which can be served by the center point as a new center point until k center points are found;
stp12, finding the central point with the maximum contact ratio of the user set for all the remaining N-k data centers to divide.
8. The live face recognition method for the charging and replacing station as claimed in claim 6, wherein the step Stp3 comprises the following substeps:
stp31, calculating the rank value of each data, and arranging the rank values in a non-ascending order to form a queue Q;
stp32, selecting the first data in the queue as a first group, G1;
stp33, sequentially selecting other data, comparing the similarity with the previous group, and if the similarity exceeds a threshold value, independently dividing the groups; if the similarity is smaller than the threshold, selecting the group combination with the maximum similarity until all the data are grouped;
stp34, the data center set of each group candidate is
Figure FDA0003812886080000031
Averaging the request quantity of all data in the group to k data centers of each data center set in the S, calculating corresponding data deployment cost, wherein the smaller the deployment cost is, the higher the priority is, and finally obtaining a data center priority list of each group.
9. The live face recognition method for charging and replacing power stations as claimed in claim 8, wherein the deployment cost in step Stp34 is the electricity fee of the data center generated by the read-write request of the user and the update transmission cost of the network copy between the data centers.
CN202211016901.0A 2022-08-24 2022-08-24 Living body face recognition charging system and method for charging and replacing station Pending CN115410252A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117841762A (en) * 2024-03-08 2024-04-09 北京电有引力大数据科技有限公司 Automatic charging system and method based on artificial intelligence

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117841762A (en) * 2024-03-08 2024-04-09 北京电有引力大数据科技有限公司 Automatic charging system and method based on artificial intelligence

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