CN113516814A - Intelligent supply method and terminal based on face recognition - Google Patents

Intelligent supply method and terminal based on face recognition Download PDF

Info

Publication number
CN113516814A
CN113516814A CN202110422007.2A CN202110422007A CN113516814A CN 113516814 A CN113516814 A CN 113516814A CN 202110422007 A CN202110422007 A CN 202110422007A CN 113516814 A CN113516814 A CN 113516814A
Authority
CN
China
Prior art keywords
goods
image
taking
similarity
face
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110422007.2A
Other languages
Chinese (zh)
Other versions
CN113516814B (en
Inventor
曾勇
乔国坤
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Core Computing Integrated Shenzhen Technology Co ltd
Original Assignee
Xinjiang Aiwinn Information Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xinjiang Aiwinn Information Technology Co Ltd filed Critical Xinjiang Aiwinn Information Technology Co Ltd
Priority to CN202110422007.2A priority Critical patent/CN113516814B/en
Publication of CN113516814A publication Critical patent/CN113516814A/en
Application granted granted Critical
Publication of CN113516814B publication Critical patent/CN113516814B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07F—COIN-FREED OR LIKE APPARATUS
    • G07F17/00—Coin-freed apparatus for hiring articles; Coin-freed facilities or services
    • G07F17/10—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for means for safe-keeping of property, left temporarily, e.g. by fastening the property
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • 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
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Software Systems (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Image Analysis (AREA)

Abstract

The application discloses intelligent supply method and terminal based on face identification, wherein the intelligent supply terminal can collect registered face images, a preset registered image library is used for storage, when the intelligent supply terminal supplies goods, a shooting module can be controlled to shoot faces of goods-taking users located in a goods-taking area so as to obtain goods-taking face images, the first image similarity of each registered face image in the registered image library and the goods-taking face images is identified, when the first image similarity is detected to be larger than or equal to a first similarity threshold value, a goods-taking module is controlled to execute goods-out operation, and goods supply is carried out in time, the supply process is effectively simplified, the supply efficiency is improved, labor cost is saved, and goods-taking experience is improved.

Description

Intelligent supply method and terminal based on face recognition
Technical Field
The application relates to the technical field of face recognition, in particular to an intelligent goods supply method and terminal based on face recognition.
Background
In many organizations such as companies, supply of articles such as office supplies, daily supplies, and daily foods requires management by a dedicated person, and it is difficult for the person who manages the articles to be on duty all day long, and the supply efficiency is unstable due to factors such as experience and environment. Some organization groups introduce self-service goods supplying terminals provided by external merchants along with the rise of the self-service goods supplying terminals, and the self-service goods supplying terminals are not limited by time and can respond to the goods taking requirements of users to a certain extent.
However, the self-service goods supply terminal provided by the external merchant usually needs means such as coin and/or mobile phone electronic payment to realize payment to obtain the required goods, it is difficult to respond to the user's demand for use under the condition that no coin or mobile phone is powered off, and the payment method using coin and/or mobile phone electronic payment is relatively complicated, and the shipment efficiency is easily affected.
Disclosure of Invention
In view of this, the present application provides an intelligent supply method and terminal based on face recognition, so as to simplify the supply process and improve the supply efficiency.
One aspect of the present application provides an intelligent supply method based on face recognition, which is applied to an intelligent supply terminal, where the intelligent supply terminal includes a shipment module, and the method includes:
s100, acquiring a face image shot by a shooting module on a goods-taking user in a goods-taking area at present to obtain a goods-taking face image;
s200, identifying the first image similarity of each registered face image in a prestored registered image library and the goods-taking face image respectively; the registered image library records face images of all goods supplying objects;
and S300, controlling the delivery module to execute delivery operation when the first image similarity which is greater than or equal to the first similarity threshold is detected.
In one embodiment, the intelligent supply method based on face recognition further includes:
s400, if the similarity of each first image is smaller than the first similarity threshold, when the similarity of the first images is larger than or equal to the second similarity threshold, determining the registered face image corresponding to the similarity of the first images larger than or equal to the second similarity threshold as an image to be compared;
s500, inputting the goods-taking face image into a preprocessing model for filtering to obtain a preprocessing image; the preprocessing model is used for filtering noise information of the face image;
s600, identifying the second image similarity between the preprocessed image and the image to be compared, and controlling the shipment module to execute shipment operation if the second image similarity is larger than or equal to a first similarity threshold value.
Specifically, the training process of the preprocessing model comprises the following steps:
s510, acquiring face images shot for a plurality of test users in a specific shooting environment to obtain input sample images, and acquiring high-definition face images corresponding to the input sample images to obtain output sample images; the high-definition face image is a face image with quality parameters meeting preset quality conditions;
s520, training a convolutional neural network by taking the input sample image as input and the output sample image as output, and acquiring a loss function of the convolutional neural network in a training process;
and S540, when the loss function reaches a preset state, determining a preprocessing model according to the network parameters of the convolutional neural network.
Specifically, the intelligent supply method based on face recognition further includes:
s530, acquiring the minimum value of the loss function, and if the minimum value is smaller than or equal to a convergence threshold, judging that the loss function reaches a preset state; if the minimum value is greater than the convergence threshold, increasing the number of the input sample images and the number of the output sample images, and returning to perform step S520 until the minimum value of the loss function is less than or equal to the convergence threshold.
In one embodiment, the intelligent supply terminal further includes a pickup window, and the controlling the shipment module to perform shipment operations includes:
s310, outputting a selection range of supplied goods, and reading target goods selected by the goods taking user in the selection range;
s320, controlling the goods outlet module to push the target object to a goods taking window.
Specifically, the registered image library records balance data of each registered face image; the controlling the shipment module to push the target item to a pickup window comprises:
and obtaining the selling price data of the target object, if the selling price data is less than or equal to the balance data, controlling the shipment module to push the target object to a pickup window, and updating the balance data into the difference between the balance data and the selling price data.
Specifically, the intelligent supply method based on face recognition further includes:
s700, if the similarity of each first image is smaller than the second similarity threshold, acquiring a credit evaluation parameter corresponding to the goods-taking face image from an externally-connected credit evaluation platform, acquiring a contact account number of the goods-taking user through a dialog window of the collected contact account number when the credit evaluation parameter is larger than or equal to the credit threshold, controlling the goods-taking module to execute goods-taking operation, acquiring goods-taking information, pushing the contact account number, the goods-taking information and the goods-taking face image to a management terminal, and enabling the management terminal to provide a payment path and/or a registration path for the goods-taking user through the contact account number.
Specifically, the intelligent supply method based on face recognition further includes:
s800, if the registration information of the goods-taking face image fed back by the management terminal is detected within a set time period, adding the goods-taking face image as a registration face image to the registration image library; if the registration information or payment information of the goods-taking face image fed back by the management terminal is not detected within a set time period, adding the goods-taking face image to a limited image library; the limited image library is used for recording face images without goods taking authority.
Specifically, when each first image similarity is smaller than the second similarity threshold, the intelligent supply method based on face recognition further includes:
and S900, identifying the similarity between each limited face image in the limited image library and a third image of the goods-taking face image, and outputting a prompt message of the end of shipment if the similarity of the third image is greater than or equal to a third similarity threshold.
Another aspect of the present application provides an intelligent supply terminal, which includes a shooting module, a shipment module, a processor, and a storage medium; the storage medium having program code stored thereon; the shooting module is used for shooting the face image of a goods taking user in the goods taking area to obtain a goods taking face image and uploading the goods taking face image to the processor; the processor is used for calling the program codes stored in the storage medium to execute any one of the intelligent goods supplying methods based on the face recognition; the shipment module is configured to perform shipment operations under control of the processor.
The application provides an intelligent supply method and terminal based on face identification, can control its shooting module to shoot the face of the user of getting goods that is located the area of getting goods, in order to obtain the face image of getting goods, identify each registered face image in the registered image storehouse respectively with the first image similarity of the face image of should getting goods, when detecting that first image similarity is greater than or equal to first similarity threshold value, control shipment module and carry out shipment operation, and supply goods in time, this supply goods process is effectively simplified, supply goods efficiency obtains promoting, be favorable to saving the cost of labor, promote and get goods and experience.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of an intelligent goods supply method based on face recognition according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an intelligent goods supply system based on face recognition according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an intelligent supply terminal according to an embodiment of the present application.
Detailed Description
As described in the background art, the scheme of managing articles such as office documents, living users and/or daily food by a specially-assigned person is difficult to realize on duty all day long, and is liable to cause unstable supply efficiency, and the supply manner of the self-service supply terminal is relatively complicated, and liable to affect the shipment efficiency.
The application aims at the problems and provides an intelligent goods supply method and a terminal based on face recognition, wherein the intelligent goods supply method based on face recognition can be operated on the intelligent goods supply terminal. The intelligent goods supply terminal can be arranged in a corresponding area according to the goods taking characteristics of a target user needing to supply goods, such as goods receiving use in a certain office area, a front desk of a certain office building or a free goods sending place of a certain public service organization; it is also possible to provide a recharge function as a vending terminal at a related vending place, etc. The intelligent goods supply terminal can shoot (or upload) the face image of the user or collect the registered face image by related management personnel during the registration of the user, the registered face image is stored in a preset registered image library, when the intelligent goods supply terminal supplies goods, the shooting module can be controlled to shoot the face of a goods taking user in a goods taking area so as to obtain the goods taking face image, the similarity between each registered face image in the registered image library and the first image of the goods taking face image is identified, when the similarity of the first image is detected to be larger than or equal to a first similarity threshold value, the goods output module is controlled to carry out goods output operation, timely goods supply is realized, the goods supply process is effectively simplified, the goods supply efficiency is improved, the labor cost is saved, and the goods taking experience is improved.
The technical solutions in the embodiments of the present application are clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application. The following embodiments and their technical features may be combined with each other without conflict.
The first aspect of the present application provides an intelligent goods supply method based on face recognition, which is applied to an intelligent goods supply terminal, and specifically can be run in a processor of the intelligent goods supply terminal, where the intelligent goods supply terminal includes a shipment module, as shown in fig. 1, and the method includes:
s100, acquiring a face image shot by the shooting module on the goods taking user in the goods taking area at present to obtain the goods taking face image.
The goods taking area is usually arranged right in front of the intelligent goods supplying terminal, and the shooting module can be arranged at the front side of the intelligent goods supplying terminal and close to the upper position, so that the face of the goods taking user can be shot smoothly when the goods taking user is located in the goods taking area.
Specifically, above-mentioned shooting module includes the camera lens, and this camera lens's mounted position can preset, and this mounted position includes horizontal position and vertical position, and horizontal position can set up on the central line of intelligent supply terminal front side or the symmetry line of vertical direction, and the vertical position can refer to target user's height equipment, for example sets up at target user's average height. In one example, an infrared detector can be further arranged on the front side of the intelligent goods supply terminal to detect height information and station information of a goods-taking user and upload the height information and the station information to a processor of the intelligent goods supply terminal; the shooting module can control the shooting lens to move in a certain range of the front side surface of the intelligent supply terminal, for example, in a range taking the installation position as the center of a circle and taking the set length as the radius. Therefore, after the processor receives the height information and the station information detected by the infrared detector, the optimal shooting position of the shooting lens is calculated by combining the moving range of the shooting lens, the optimal shooting position is sent to the shooting module, the shooting module controls the shooting lens to move to the optimal shooting position, the face of a goods taking user is accurately and completely shot, and an effective goods taking face image is obtained.
S200, identifying the first image similarity of each registered face image in a prestored registered image library and the goods-taking face image respectively; the registered image library records face images of all goods supplying objects.
The registered image library can be preset and can record face images of all target users (goods supply objects) needing goods supply; the system can be set according to the characteristics of a goods supply object (target user), for example, if the goods supply object is all employees of a certain company, the administrative staff of the company can collect the face images of all employees, so that a registered image stock is constructed and stored in an intelligent goods supply terminal; for example, if the goods supply object is a public service goods receiving person, an active registration channel can be provided for the person to provide registration information and a face image, a registration image library is constructed according to the corresponding face image after the registration information is checked, and the registration image library is updated according to the corresponding face image after the new registration information is checked and passed when the new registration information and the corresponding face image are received subsequently.
The above steps respectively calculate the similarity (first image similarity) between each registered face image in the registered image library and the pickup face image, so as to search the registered face image matched with the pickup face image in the registered image library. The process for determining the similarity between a certain registered face image and a goods-taking face image comprises the following steps: respectively extracting feature codes of the registered face image and the goods-taking face image, and calculating the similarity of the registered face image and the goods-taking face image according to the feature codes of the registered face image and the goods-taking face image; or respectively comparing the pixel similarity of corresponding pixel points between the registered face image and the goods-taking face image, determining the pixel similarity between the registered face image and the goods-taking face image according to the pixel similarity, and the like. It should be noted that the determination process of the similarity between two images is applicable to the identification of the similarity of each image (e.g., the similarity of the first image, the similarity of the second image, and the similarity of the third image) in the present application.
And S300, controlling a discharging module (also called as a discharging module) to execute discharging operation when the first image similarity which is greater than or equal to the first similarity threshold is detected.
The first similarity threshold may be set to a value close to 1, such as 95%. The similarity of a certain first image is greater than or equal to a first similarity threshold value, the registered face image with the similarity of the first image and the goods-taking face image are represented to be images of the same user, the goods-taking user is a goods-supplying object of the intelligent goods-supplying terminal, and at the moment, the goods-outputting module can be controlled to execute goods-outputting operation, so that corresponding goods are provided, and the goods-supplying efficiency is ensured.
In one embodiment, the intelligent supply method based on face recognition further includes:
s400, if the similarity of each first image is smaller than the first similarity threshold, when the similarity of the first images is larger than or equal to the second similarity threshold, determining the registered face image corresponding to the similarity of the first images larger than or equal to the second similarity threshold as an image to be compared;
s500, inputting the goods-taking face image into a preprocessing model for filtering to obtain a preprocessing image; the preprocessing model is used for filtering noise information of the face image;
s600, identifying the second image similarity between the preprocessed image and the image to be compared, and controlling the shipment module to execute shipment operation if the second image similarity is larger than or equal to a first similarity threshold value.
The second similarity threshold may be set to a value slightly smaller than the first similarity threshold, such as 92%. Each first image similarity is smaller than a first similarity threshold, which indicates that each registered face image in the registered image library cannot be directly matched with the goods-taking face image, if the first image similarity is larger than or equal to a second similarity threshold, the corresponding registered face image (the image to be compared) with the corresponding goods-taking face image has higher similarity with the corresponding first image similarity of the second similarity threshold, and the goods-taking face image has high noise and/or low quality due to shooting angles, shooting environments and the like, at the moment, the goods-taking face image can be input into a preprocessing model for filtering so as to carry out denoising and/or repairing, so that a preprocessed image with quality higher than that of the original goods-taking face image is obtained, and then the preprocessed image is compared with the image to be compared, and obtaining the second image similarity between the two images, if the second image similarity is greater than or equal to the first similarity threshold, indicating that the preprocessed image and the image to be compared can be matched, and the corresponding goods-taking user is one of the goods-supplying objects, and at the moment, controlling the goods-outputting module to execute goods-outputting operation, supplying goods in time and improving the user experience brought by the intelligent goods-supplying terminal.
Specifically, the training process of the preprocessing model comprises the following steps:
s510, acquiring face images shot for a plurality of test users in a specific shooting environment to obtain input sample images, and acquiring high-definition face images corresponding to the input sample images to obtain output sample images; the high-definition face image is a face image with quality parameters meeting preset quality conditions;
s520, training a convolutional neural network by taking the input sample image as input and the output sample image as output, and acquiring a loss function of the convolutional neural network in a training process;
and S540, when the loss function reaches a preset state, determining a preprocessing model according to the network parameters of the convolutional neural network. The preset state may be set according to a specific type of the convolutional neural network, for example, the loss function is set to reach a convergence state, or a value of the loss function is smaller than a certain convergence threshold, and the like.
The specific shooting environment may be determined according to a preprocessing requirement, and generally includes a plurality of environments with different environmental factors, such as brightness and/or light, for example, an environment close to a goods-taking area of an intelligent goods-supplying terminal under different brightness conditions, and in one example, shooting devices with different specifications may be used to shoot a user for testing under the specific environment to obtain a plurality of input sample images with different qualities, where most of the images are low-quality images. After the required input sample images are obtained by shooting a plurality of test users in a plurality of specific shooting environments with incompletely consistent environmental parameters, a high-definition shooting device is required to be adopted for each test user respectively to shoot the face images of the test users in a professional shooting environment with sufficient light and low background noise so as to obtain output sample images corresponding to each input sample image respectively, and each output sample image is a high-definition face image with the quality requirements of a certain standard (preset quality conditions) such as resolution, pixel depth, an image color model and/or an image file storage format. Therefore, by taking each input sample image as input and the output sample image corresponding to each input sample image as output, the convolutional neural network is trained, and a preprocessing model capable of effectively performing filtering processing such as denoising and/or repairing on the face image can be obtained.
Specifically, the intelligent supply method based on face recognition further includes:
s530, acquiring the minimum value of the loss function, and if the minimum value is smaller than or equal to a convergence threshold, judging that the loss function reaches a preset state; if the minimum value is greater than the convergence threshold, increasing the number of the input sample images and the number of the output sample images, and returning to perform step S520 until the minimum value of the loss function is less than or equal to the convergence threshold. The convergence threshold may be set (for example, set to a smaller value such as 0.1) according to the preprocessing precision of the preprocessing model, and the higher the preprocessing precision is, the smaller the value of the convergence threshold is.
In the training process of the convolutional neural network, if the minimum value of the loss function is smaller than or equal to the convergence threshold value, the determined network model can effectively preprocess the input image of the network model, at the moment, the loss function is judged to reach the preset state, the preprocessing model is determined, the needed preprocessing model can be obtained in time, and the corresponding training efficiency is improved. The minimum value is greater than the convergence threshold, which indicates that the preprocessing performed by the current network model has not yet reached the relevant requirements, where the number of the input sample images and the number of the corresponding output sample images need to be increased (for example, the number of the input sample images and the number of the corresponding output sample images are respectively increased to two times of the original number), and then the process returns to step S520 to retrain the convolutional neural network until the minimum value of the loss function is less than or equal to the convergence threshold, that is, the determined network model can effectively preprocess the input images thereof, and then the determined preprocessing model is determined according to the current network parameters of the convolutional neural network, which can ensure the quality of the determined preprocessing model, thereby improving the quality of the subsequent preprocessing of the corresponding goods-taking face images.
In one embodiment, the intelligent supply terminal further includes a pickup window, and the controlling the shipment module to perform shipment operations includes:
s310, outputting a selection range of supplied goods, and reading target goods selected by the goods taking user in the selection range;
s320, controlling the goods outlet module to push the target object to a goods taking window.
In this embodiment, multiple article such as office supplies, articles for daily use and/or daily food can be deposited to intelligence supply terminal, and the intelligent supply terminal can be through setting up the selection range of mode output supplies article such as selection button or selection interface this moment, and the target article that the user of getting goods can select to get oneself required in this selection range, makes intelligence supply terminal control shipment module with this target article propelling movement to getting the goods window, lets the user of getting goods get required article, in order to guarantee the accuracy of supplying goods.
Specifically, the registered image library records balance data of each registered face image; the controlling the shipment module to push the target item to a pickup window comprises:
and obtaining the selling price data of the target object, if the selling price data is less than or equal to the balance data, controlling the shipment module to push the target object to a pickup window, and updating the balance data into the difference between the balance data and the selling price data.
The registered image library can record the balance data of each registered face image, so that the intelligent goods supply terminal provided by the embodiment can have a goods selling function. Specifically, in one example, when the user registers for the intelligent supply terminal, the user may pay, so that when the intelligent supply terminal constructs a registered image library including registered face images of each supply object, the payment fee corresponding to each registered face image is recorded, and balance data corresponding to each registered face image is determined. In other examples, the relevant management user may also allocate an initial balance to each offering object when acquiring the face image of the offering object, so that the constructed registered image library records each registered face image and balance data corresponding to each registered face image. Correspondingly, the intelligent supply terminal can also prestore the selling price data of each supplied article so as to obtain the selling price data of the target article in time after reading the target article selected by the goods-taking user and sell the target article.
As an embodiment, the intelligent supply method based on face recognition further includes:
s700, if the similarity of each first image is smaller than the second similarity threshold, acquiring a credit evaluation parameter corresponding to the goods-taking facial image from an externally-connected credit evaluation platform, acquiring a contact account (such as a mobile phone number and/or a mailbox address) of the goods-taking user through a dialog window of an acquisition contact account when the credit evaluation parameter is larger than or equal to the credit threshold, controlling a goods-taking module to execute goods-taking operation, acquiring goods-taking information, pushing the contact account, the goods-taking information and the goods-taking facial image to a management terminal, and enabling the management terminal to provide a payment path and/or a registration path for the goods-taking user through the contact account; wherein the shipment information may include the time of the shipment, the identity information of the goods and the corresponding intelligent supply terminal, and so on.
The external credit evaluation platform can be a credit recording platform which is in contact with more users in daily life, such as a credit recording platform of a bank with the user number ranked at the front in a corresponding area, a credit recording platform adopted by precious sesame credit or WeChat payment, and the like. The credit threshold value can be set according to the credit record characteristics of the credit evaluation platform, and is generally set as a parameter threshold value capable of representing the corresponding user without bad records such as arrears and/or defaults.
In this embodiment, the intelligent supply terminal may include a communication module, so as to communicate with an external platform (e.g., a credit evaluation platform) and an external device (e.g., a management terminal) through the communication module. Each first image similarity is smaller than the second similarity threshold, which indicates that the current goods-taking user is almost impossible to be a goods-supplying object of the intelligent goods-supplying terminal, but is possible to be a potential goods-supplying object, such as a staff newly working in a certain company, a frequently-coming client, a trial user of a certain public goods or a customer who is not registered in time when the intelligent goods-supplying terminal serves as a goods-selling function, and the like. At the moment, the identity information of the goods-taking face image can be further identified, the credit evaluation parameter corresponding to the identity information is obtained from an external credit evaluation platform, if the credit evaluation parameter is larger than or equal to a credit threshold value, it is indicated that the current goods-taking user has no problem of poor credit, a dialogue window for collecting a contact account number can be displayed at the moment, so that the goods-taking user inputs the contact account number through the dialogue window, and after the contact account number of the goods-taking user is obtained at the intelligent goods-supplying terminal, the goods-outputting module can be controlled to execute goods-outputting operation and supply goods, so that the user experience brought by goods-supplying is further improved. After timely supply, the intelligent supply terminal can acquire shipment information, and pushes the shipment information of the pickup user, the pickup face image and the shipment information to the management terminal, so that the management terminal can timely receive the special supply behavior of the intelligent supply terminal for subsequent processing.
The management terminal is an intelligent terminal for managing an intelligent supply terminal, such as a management terminal of an intelligent supply terminal used by administrative staff of a certain company. The management terminal receives the contact account number, the shipment information and the goods taking face image of the goods taking user, and the management user (such as administrative staff of a certain company) can know the contact account number, the shipment information and the goods taking face image through display or short message push and other modes. The management user judges the goods-taking user, the goods-taking authority of the goods-taking user can be directly and newly set (such as registration and corresponding balance data setting) according to the judgment result, and the goods-taking face image is used as a registered face image and added to the registration image library; and a payment channel and a registration channel can be provided, so that the user can pay in time, registration and cost storage can be performed if a goods taking requirement exists subsequently, the intelligent goods supply terminal can obtain registration information and paid cost, and registered face images and balance data corresponding to the goods taking user are recorded in a registered image library. Therefore, the registered image library of the intelligent goods supply terminal comprises the registered face image corresponding to the goods taking user, and the next goods taking requirement of the goods taking user can be responded by the intelligent goods taking terminal in time.
Specifically, the intelligent supply method based on face recognition further includes:
s800, if the registration information of the goods-taking face image fed back by the management terminal is detected within a set time period, adding the goods-taking face image as a registration face image to the registration image library; if the registration information or payment information of the goods-taking face image fed back by the management terminal is not detected within a set time period, adding the goods-taking face image to a limited image library; the limited image library is used for recording face images without goods taking authority; the setting period can be set according to the management characteristics of the management user or the related characteristics of the supply group (all supply objects), such as setting to 5 days or 7 days, etc.
In this embodiment, after receiving the special goods supply behavior of the intelligent goods supply terminal, the management terminal may enable a management user (e.g., an administrative staff of a certain company) to know the special goods supply behavior through displaying or short message pushing, so that the management user may charge the corresponding goods pickup user or perform new registration, and feed back the charge and/or new registration result to the intelligent goods supply terminal through information pushing or manual input. If the management user judges that the goods-taking user is not a potential goods-supplying object and does not smoothly collect corresponding fees, other operations can be omitted, so that the goods-supplying terminal does not receive the related feedback of the management terminal, and if the intelligent goods-supplying terminal does not detect the registration information or payment information of the goods-taking face image fed back by the management terminal within a set time period, the goods-taking face image shot at the time can be added to the limited image library to be used as a basis for checking the subsequent goods-supplying object.
Further, when each first image similarity is smaller than the second similarity threshold, the method further includes:
s900, identifying the similarity between each limit face image in the limit image library and a third image of the goods-taking face image, and outputting a prompt message of the end of shipment if the similarity of the third image is greater than or equal to a third similarity threshold value; wherein the prompt message can be output in the form of display and/or audio playing.
The third image similarity may be set to a value close to 1, such as 95%. The similarity of a certain third image is greater than or equal to a third similarity threshold value, the fact that the goods taking user is not a potential goods supplying object of the intelligent goods supplying terminal is represented, the prompt information of goods discharging completion can be achieved at the moment, invalid goods supplying is avoided, goods supplying effectiveness is improved, the user can be informed in time, other arrangements can be conducted by the user, and waste of time and energy of the user is avoided.
Specifically, the intelligent supply method based on face recognition further includes: and acquiring the temperature of the face area of the goods taking user. In S300, when the first image similarity greater than or equal to the first similarity threshold is detected and the temperature is lower than the preset value, the shipment module is controlled to perform shipment operation. And if the temperature exceeds the preset value, controlling the delivery module not to respond to the instruction for executing the delivery operation, namely not delivering the product. Therefore, the possibility of other users being infected is reduced by limiting the goods taking of the goods taking users with health problems. Alternatively, the preset value may be 37 ℃.
Or, intelligence supply of material terminal is including the electron bin that is used for storing protective gloves, and electron storage box is equipped with the trompil, if the temperature exceeds the default, control the electron bin stretches out protective gloves trompil, detect the protective gloves who stretches out and be taken out the back, control shipment module carries out shipment operation, herein, if get goods user temperature and exceed the default, stretch out protective gloves trompil through control electron bin, can remind the user of getting goods to wear protective gloves and get goods, furtherly, detect the back that the protective gloves is taken out, just shipment, can let the user of getting goods who has the health problem get goods, can reduce the probability that other users are infected again.
Or, the intelligent supply terminal further comprises a disinfection article electronic bearing table, and in the step S300, if the temperature exceeds a preset value, the delivery module can be controlled to execute delivery operation; however, when the step S100 is returned to detect the next pickup user, before the next pickup user controls the pickup module to perform the pickup operation, the prompt message for prompting the sterilization is sent first, and the electronic carrying platform is returned within a preset time (for example, 3 minutes) after the sterilized product of the electronic carrying platform is detected to be taken out, and this detection result indicates that the next pickup user has taken the sterilized product according to the prompt message, sterilizes pickup positions such as the pickup window, and returns the sterilized product to the original position after the sterilization is completed, and at this time, the pickup module can be controlled to perform the pickup operation. During the concrete realization, the electron plummer includes pressure sensor, can detect whether the articles for use of disinfecting are taken out and put back through pressure change information. If the current goods taking user has a health problem, the next goods taking user is prompted to perform disinfection operation, so that the probability of infection can be effectively reduced.
The intelligent goods supply method based on face recognition can preset a registered image library comprising face images of target goods supply objects to be stored, when a user needs to take goods, the user only needs to stand in a goods taking area to enable a shooting module to shoot the goods taking face images, and the goods taking face images are uploaded to a processor of an intelligent goods supply terminal.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not limited to be performed in the exact order described, and may be performed in other orders, unless otherwise indicated herein. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least a portion of the sub-steps or stages of other steps.
In a second aspect, the present application provides an intelligent goods supply system based on face recognition, as shown in fig. 2, the intelligent goods supply system based on face recognition includes:
the acquisition unit 100 is configured to acquire a face image of a pickup user currently picked up by a pickup module in a pickup area, so as to obtain a pickup face image;
the identification unit 200 is configured to identify first image similarities between each registered face image in a pre-stored registered image library and the pickup face image; the registered image library records face images of all goods supplying objects;
the control unit 300 is configured to control the shipment module to perform a shipment operation when the first image similarity greater than or equal to the first similarity threshold is detected.
For specific limitations of the intelligent delivery system based on face recognition, reference may be made to the above limitations of the intelligent delivery method based on face recognition, and details are not repeated here. All or part of the modules in the intelligent goods supply system based on the face recognition can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
The division of each unit in the intelligent supply system based on face recognition is only used for illustration, and in other embodiments, the intelligent supply system based on face recognition may be divided into different units as needed to complete all or part of the functions of the intelligent supply system based on face recognition.
In a third aspect, the present application provides an intelligent goods supply terminal based on face recognition, and as shown in fig. 3, the intelligent goods supply terminal may include a department shooting module, a shipment module, a processor, and a storage medium; the storage medium having program code stored thereon; the shooting module and the delivery module are respectively connected with the processor; the shooting module is used for shooting a face image of a goods-taking user in a goods-taking area to obtain a goods-taking face image and uploading the goods-taking face image to the processor; the processor is used for calling the program codes stored in the storage medium to execute the intelligent goods supply method based on face recognition provided by any one of the above embodiments.
The intelligent goods supply terminal can be arranged in a corresponding area according to the goods taking characteristics of a target user needing to supply goods, such as the goods receiving use of a set office area, the goods sending place of a set office building or the free goods sending place of a set public service organization, and the like. The goods taking area of the intelligent goods supplying terminal is usually arranged right in front of the intelligent goods supplying terminal, and the shooting module can be arranged at the position close to the upper side of the front side of the intelligent goods supplying terminal so as to smoothly shoot the face of the goods taking user when the goods taking user is located in the goods taking area.
Specifically, the intelligent supply terminal may further include a communication module, so as to communicate with an external platform (e.g., a credit evaluation platform) and an external device (e.g., a management terminal) through the communication module.
Furthermore, the intelligent supply terminal can also comprise a display module for displaying the delivery end and other prompting information, and can also be used for displaying the selection range of the articles supplied by the display and the like.
The intelligent goods supply terminal can collect the registered face images in a mode of shooting the face images of a user when the user provides or registers the intelligent goods supply terminal, the registered face images are stored in a preset registered image library, when the intelligent goods supply terminal supplies goods, the shooting module can be controlled to shoot the faces of goods taking users located in a goods taking area, so that goods taking face images are obtained, the similarity between each registered face image in the registered image library and a first image of the goods taking face image is identified, when the similarity of the first image is detected to be larger than or equal to a first similarity threshold value, the goods taking module is controlled to execute goods taking operation, goods supply is carried out in time, the goods supply process is effectively simplified, the goods supply efficiency is improved, labor cost is saved, and goods taking experience of the user is improved.
The intelligent supply terminal can also comprise a display module, when game software is operated, the display module is used for displaying a game interface, at the moment, screenshot can be carried out on the displayed game interface at regular intervals, required game display pictures can be continuously obtained, the intelligent supply method based on face recognition is respectively carried out on each game display picture, each game display picture is subjected to saturation enhancement processing, and a corresponding fusion image is obtained. In the fused images, target elements required to be extracted are enhanced, background elements including noise are weakened, and therefore the intelligent supply terminal can accurately extract the target elements.
Although the application has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. This application is intended to embrace all such modifications and variations and is limited only by the scope of the appended claims. In particular regard to the various functions performed by the above described components, the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary implementations of the specification.
That is, the above description is only an embodiment of the present application, and not intended to limit the scope of the present application, and all equivalent structures or equivalent flow transformations made by using the contents of the specification and the drawings, such as mutual combination of technical features between various embodiments, or direct or indirect application to other related technical fields, are included in the scope of the present application.
In addition, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first" and "second" may explicitly or implicitly include one or more features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
The previous description is provided to enable any person skilled in the art to make and use the present application. In the foregoing description, various details have been set forth for the purpose of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known processes have not been described in detail so as not to obscure the description of the present application with unnecessary detail. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

Claims (10)

1. An intelligent supply method based on face recognition is characterized in that the intelligent supply method is applied to an intelligent supply terminal, the intelligent supply terminal comprises a shipment module, and the method comprises the following steps:
s100, acquiring a face image shot by a shooting module on a goods-taking user in a goods-taking area at present to obtain a goods-taking face image;
s200, identifying the first image similarity of each registered face image in a prestored registered image library and the goods-taking face image respectively; the registered image library records face images of all goods supplying objects;
and S300, controlling the delivery module to execute delivery operation when the first image similarity which is greater than or equal to the first similarity threshold is detected.
2. The intelligent supply method based on face recognition as claimed in claim 1, further comprising:
s400, if the similarity of each first image is smaller than the first similarity threshold, when the similarity of the first images is larger than or equal to the second similarity threshold, determining the registered face image corresponding to the similarity of the first images larger than or equal to the second similarity threshold as an image to be compared;
s500, inputting the goods-taking face image into a preprocessing model for filtering to obtain a preprocessing image; the preprocessing model is used for filtering noise information of the face image;
s600, identifying the second image similarity between the preprocessed image and the image to be compared, and controlling the delivery module to execute delivery operation if the second image similarity is larger than or equal to a first similarity threshold value.
3. The intelligent supply method based on face recognition as claimed in claim 2, wherein the training process of the preprocessing model comprises:
s510, acquiring face images shot for a plurality of test users in a specific shooting environment to obtain input sample images, and acquiring high-definition face images corresponding to the input sample images to obtain output sample images; the high-definition face image is a face image with quality parameters meeting preset quality conditions;
s520, training a convolutional neural network by taking the input sample image as input and the output sample image as output, and acquiring a loss function of the convolutional neural network in the training process;
and S540, when the loss function reaches a preset state, determining a preprocessing model according to the network parameters of the convolutional neural network.
4. The intelligent supply method based on face recognition as claimed in claim 3, further comprising:
s530, obtaining the minimum value of the loss function, and if the minimum value is smaller than or equal to a convergence threshold value, judging that the loss function reaches a preset state; if the minimum value is greater than the convergence threshold, increasing the number of the input sample images and the number of the output sample images, and returning to execute step S520 until the minimum value of the loss function is less than or equal to the convergence threshold.
5. The intelligent supply method based on face recognition as claimed in claim 1, wherein the intelligent supply terminal further comprises a goods-taking window, and the controlling of the shipment module to perform shipment operation comprises:
s310, outputting a selection range of supplied goods, and reading a target goods selected by the goods taking user in the selection range;
s320, controlling the goods outlet module to push the target object to the goods taking window.
6. The intelligent supply method based on face recognition as claimed in claim 5, wherein the registered image library records the balance data of each registered face image; the controlling the shipment module to push the target item to a pickup window comprises:
and obtaining the selling price data of the target object, if the selling price data is less than or equal to the balance data, controlling the shipment module to push the target object to the pickup window, and updating the balance data into the difference between the balance data and the selling price data.
7. The intelligent supply method based on face recognition as claimed in claim 6, further comprising:
s700, if the similarity of each first image is smaller than the second similarity threshold, acquiring a credit evaluation parameter corresponding to the goods-taking face image from an external credit evaluation platform, acquiring a contact account number of the goods-taking user through a dialogue window for acquiring the contact account number when the credit evaluation parameter is larger than or equal to the credit threshold, controlling the goods-taking module to execute goods-taking operation, acquiring goods-taking information, pushing the contact account number, the goods-taking information and the goods-taking face image to a management terminal, and enabling the management terminal to provide a payment path and/or a registration path for the goods-taking user through the contact account number.
8. The intelligent supply method based on face recognition as claimed in claim 7, further comprising:
s800, if the registration information of the goods-taking face image fed back by the management terminal is detected within a set time period, adding the goods-taking face image as a registration face image to the registration image library; if the registration information or payment information of the goods-taking face image fed back by the management terminal is not detected within a set time period, adding the goods-taking face image to a limited image library; the limited image library is used for recording face images without goods taking authority.
9. The intelligent supply method based on face recognition as claimed in claim 8, wherein when each first image similarity is smaller than the second similarity threshold, the method further comprises:
and S900, identifying the similarity between each limit face image in the limit image library and a third image of the goods-taking face image, and outputting a prompt message of the end of shipment if the similarity of the third image is greater than or equal to a third similarity threshold.
10. An intelligent goods supply terminal based on face recognition is characterized by comprising a shooting module, a goods delivery module, a processor and a storage medium; the storage medium having program code stored thereon;
the shooting module is used for shooting a face image of a goods taking user in a goods taking area to obtain a goods taking face image and uploading the goods taking face image to the processor;
the processor is used for calling the program codes stored in the storage medium to execute the intelligent goods supply method based on the face recognition according to any one of claims 1 to 9;
the shipment module is used for executing shipment operation under the control of the processor.
CN202110422007.2A 2021-04-19 2021-04-19 Intelligent supply method and terminal based on face recognition Active CN113516814B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110422007.2A CN113516814B (en) 2021-04-19 2021-04-19 Intelligent supply method and terminal based on face recognition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110422007.2A CN113516814B (en) 2021-04-19 2021-04-19 Intelligent supply method and terminal based on face recognition

Publications (2)

Publication Number Publication Date
CN113516814A true CN113516814A (en) 2021-10-19
CN113516814B CN113516814B (en) 2023-04-18

Family

ID=78062418

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110422007.2A Active CN113516814B (en) 2021-04-19 2021-04-19 Intelligent supply method and terminal based on face recognition

Country Status (1)

Country Link
CN (1) CN113516814B (en)

Citations (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1971630A (en) * 2006-12-01 2007-05-30 浙江工业大学 Access control device and check on work attendance tool based on human face identification technique
CN102609678A (en) * 2011-01-24 2012-07-25 台湾色彩与影像科技股份有限公司 Intelligent self-service system for face recognition
CN103559758A (en) * 2013-11-06 2014-02-05 上海煦荣信息技术有限公司 Intelligent vending system and intelligent vending method
CN107633600A (en) * 2017-08-11 2018-01-26 深兰科技（上海）有限公司 A kind of unattended intelligent purchase method and self-help shopping cabinet
CN107749115A (en) * 2017-10-30 2018-03-02 中米（北京）农业科技股份有限公司 One kind opens automatic selling equipment door method and vending equipment based on credit
CN108376434A (en) * 2018-02-22 2018-08-07 陈崇 A kind of intelligent home control system based on Internet of Things
CN108648377A (en) * 2018-04-10 2018-10-12 合肥美的智能科技有限公司 Automatically vending system based on unmanned retail units and method
CN109241720A (en) * 2018-09-05 2019-01-18 广州小楠科技有限公司 A kind of tripper based on recognition of face
CN109801438A (en) * 2019-01-18 2019-05-24 创新奇智(南京)科技有限公司 A kind of intelligent sales counter based on recognition of face and interactive voice
CN110211302A (en) * 2019-04-18 2019-09-06 江苏图云智能科技发展有限公司 The control method and device of self-service storage cabinet
CN110765866A (en) * 2019-09-18 2020-02-07 新疆爱华盈通信息技术有限公司 Face recognition method and face recognition equipment
CN111724522A (en) * 2020-05-25 2020-09-29 浙江大华技术股份有限公司 Access control system, method and device, control equipment and storage medium
CN111814735A (en) * 2020-07-24 2020-10-23 深圳市爱深盈通信息技术有限公司 Ticket taking method, device and equipment based on face recognition and storage medium
CN211979685U (en) * 2020-05-15 2020-11-20 广州致远电子有限公司 Face detection equipment and access control system
CN112052780A (en) * 2020-09-01 2020-12-08 北京嘀嘀无限科技发展有限公司 Face verification method, device and system and storage medium
CN112329679A (en) * 2020-11-12 2021-02-05 济南博观智能科技有限公司 Face recognition method, face recognition system, electronic equipment and storage medium

Patent Citations (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1971630A (en) * 2006-12-01 2007-05-30 浙江工业大学 Access control device and check on work attendance tool based on human face identification technique
CN102609678A (en) * 2011-01-24 2012-07-25 台湾色彩与影像科技股份有限公司 Intelligent self-service system for face recognition
CN103559758A (en) * 2013-11-06 2014-02-05 上海煦荣信息技术有限公司 Intelligent vending system and intelligent vending method
CN107633600A (en) * 2017-08-11 2018-01-26 深兰科技（上海）有限公司 A kind of unattended intelligent purchase method and self-help shopping cabinet
CN107749115A (en) * 2017-10-30 2018-03-02 中米（北京）农业科技股份有限公司 One kind opens automatic selling equipment door method and vending equipment based on credit
CN108376434A (en) * 2018-02-22 2018-08-07 陈崇 A kind of intelligent home control system based on Internet of Things
CN108648377A (en) * 2018-04-10 2018-10-12 合肥美的智能科技有限公司 Automatically vending system based on unmanned retail units and method
CN109241720A (en) * 2018-09-05 2019-01-18 广州小楠科技有限公司 A kind of tripper based on recognition of face
CN109801438A (en) * 2019-01-18 2019-05-24 创新奇智(南京)科技有限公司 A kind of intelligent sales counter based on recognition of face and interactive voice
CN110211302A (en) * 2019-04-18 2019-09-06 江苏图云智能科技发展有限公司 The control method and device of self-service storage cabinet
CN110765866A (en) * 2019-09-18 2020-02-07 新疆爱华盈通信息技术有限公司 Face recognition method and face recognition equipment
CN211979685U (en) * 2020-05-15 2020-11-20 广州致远电子有限公司 Face detection equipment and access control system
CN111724522A (en) * 2020-05-25 2020-09-29 浙江大华技术股份有限公司 Access control system, method and device, control equipment and storage medium
CN111814735A (en) * 2020-07-24 2020-10-23 深圳市爱深盈通信息技术有限公司 Ticket taking method, device and equipment based on face recognition and storage medium
CN112052780A (en) * 2020-09-01 2020-12-08 北京嘀嘀无限科技发展有限公司 Face verification method, device and system and storage medium
CN112329679A (en) * 2020-11-12 2021-02-05 济南博观智能科技有限公司 Face recognition method, face recognition system, electronic equipment and storage medium

Also Published As

Publication number Publication date
CN113516814B (en) 2023-04-18

Similar Documents

Publication Publication Date Title
CN108922026B (en) Replenishment management method and device for vending machine and user terminal
CN107464116B (en) Order settlement method and system
US20200258070A1 (en) Purchased product checkout support system
US20210056498A1 (en) Method and device for identifying product purchased by user and intelligent shelf system
CN101160576B (en) Method and system for measuring retail store display conditions
CN207624039U (en) A kind of vending system
CN105869278A (en) A method, a system and a selling machine for automatic tobacco selling
CN109801438A (en) A kind of intelligent sales counter based on recognition of face and interactive voice
CN108805495A (en) Article storage management method and system and computer-readable medium
CN108198315A (en) A kind of auth method and authentication means
CN109147174A (en) A kind of self-service method, server and self-service cabinet
US11379903B2 (en) Data processing method, device and storage medium
CN113128898A (en) Vending machine management method and device, electronic equipment and storage medium
CN113887884A (en) Business-super service system
CN108510673A (en) A kind of the express checkout method, apparatus and electronic equipment of automated radio frequency
CN113066225A (en) Shopping method based on shared refrigerator and shared refrigerator
CN108052928A (en) A kind of user credit degree evaluation method, device and electronic equipment
US20230102033A1 (en) Payment processing system, payment processing method, and recording medium
CN111523348A (en) Information generation method and device and equipment for man-machine interaction
CN110415129A (en) Damage identification method, device, equipment and storage medium based on video image
WO2022034298A1 (en) Recycling mobile devices
CN113516814B (en) Intelligent supply method and terminal based on face recognition
CN112884332A (en) Intelligent terminal management system based on integrated circuit perception technology
KR102335918B1 (en) Digital signage to sell fashion goods using big data and artificial intelligence algorithm
CN113129501B (en) Anti-theft early warning method for unmanned retail terminal

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
TR01 Transfer of patent right

Effective date of registration: 20240508

Address after: Building 9, Building 203B, Phase II, Nanshan Yungu Entrepreneurship Park, No. 2 Pingshan 1st Road, Pingshan Community, Taoyuan Street, Nanshan District, Shenzhen, Guangdong Province, 518033

Patentee after: Core Computing Integrated (Shenzhen) Technology Co.,Ltd.

Country or region after: China

Address before: Room 1010, 10 / F, building a, Chuangzhi building, Xinjiang Software Park, 455 Kanas Hubei Road, Urumqi Economic and Technological Development Zone, 830000 Xinjiang Uygur Autonomous Region

Patentee before: XINJIANG AIHUA YINGTONG INFORMATION TECHNOLOGY Co.,Ltd.

Country or region before: China

TR01 Transfer of patent right