CN113053023A - Unmanned vending machine based on artificial intelligence technique - Google Patents

Unmanned vending machine based on artificial intelligence technique Download PDF

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CN113053023A
CN113053023A CN202110280576.8A CN202110280576A CN113053023A CN 113053023 A CN113053023 A CN 113053023A CN 202110280576 A CN202110280576 A CN 202110280576A CN 113053023 A CN113053023 A CN 113053023A
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CN113053023B (en
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陈云
周梓荣
龚庆祝
尹波
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Guangdong Convenisun Technology Co ltd
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07F—COIN-FREED OR LIKE APPARATUS
    • G07F9/00—Details other than those peculiar to special kinds or types of apparatus
    • G07F9/006—Details of the software used for the vending machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4014—Identity check for transactions
    • G06Q20/40145—Biometric identity checks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
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    • 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
    • G06V40/12—Fingerprints or palmprints
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    • 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
    • G06V40/161—Detection; Localisation; Normalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships

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Abstract

The invention provides an unmanned vending machine based on artificial intelligence technology, which comprises: the binding module is used for binding the user identity and the payment account of the user based on the face information or the palm print information of the user to obtain a binding relationship; the identification module is used for identifying the user identity by utilizing the binding relationship by utilizing a face identification technology or a palm print identification technology; the monitoring module is used for monitoring the purchasing behavior of the user after the identity of the user is successfully identified; the payment module is used for generating a payment list and sending the payment list to a payment account of the user after the user purchases the goods, and through the artificial intelligence technology, the payment mode is convenient and efficient, the shopping experience of the user is improved, the safety of the user in the purchase process is guaranteed, the damage to the unmanned vending machine body can be prevented, and the safety of the unmanned vending machine body is guaranteed.

Description

Unmanned vending machine based on artificial intelligence technique
Technical Field
The invention relates to the technical field of unmanned vending machines, in particular to an unmanned vending machine based on an artificial intelligence technology.
Background
With the change of consumption mode, the unmanned vending machine is more and more appeared in the life of people. The vending machine can fully supplement the shortage of human resources and adapt to the change of consumption environment and consumption mode. The vending machine can be operated for 24 hours all weather, the investment capital is small, the area is small, the novel shopping mode is adopted, and the convenient and quick shopping mode attracts a great number of young people with curiosity and desire to buy goods.
The traditional unmanned vending machine has the following defects in the operation process: when the vending machine receives money, the vending machine needs to pay by inserting coins or scanning codes, particularly coin inserting, the time is long, bad experience is caused to users, and the payment mode is not convenient and efficient; meanwhile, in the self-service purchase process of the user, the safety of the user in the purchase process cannot be guaranteed, and the safety of the unmanned vending machine body cannot be guaranteed.
Therefore, the invention provides the unmanned vending machine based on the artificial intelligence technology.
Disclosure of Invention
The invention provides an unmanned vending machine based on an artificial intelligence technology, which is convenient and efficient in payment mode, improves the shopping experience of a user, ensures the safety of the user in the purchasing process, can prevent the unmanned vending machine from being damaged, and ensures the safety of the unmanned vending machine.
The invention provides an unmanned vending machine based on artificial intelligence technology, which comprises:
the binding module is used for binding the user identity and the payment account of the user based on the face information or the palm print information of the user to obtain a binding relationship;
the identification module is used for identifying the user identity by utilizing the binding relationship by utilizing a face identification technology or a palm print identification technology;
the monitoring module is used for monitoring the purchasing behavior of the user after the identity of the user is successfully identified;
and the checkout module is used for generating a payment list and sending the payment list to the payment account of the user after the user finishes purchasing.
In one possible way of realisation,
the binding module comprises:
the request unit is used for receiving an account binding request of a user and acquiring real-name information and payment account information of the user;
the first acquisition unit is used for acquiring the face information and the palm print information of the user;
an authentication unit configured to determine whether the face information coincides with a photograph in the real-name information;
if so, indicating that the authentication is successful, and establishing a binding relationship between the facial information or the palm print information and the user payment account information;
otherwise, the authentication is failed;
and the storage unit is used for storing the binding relationship and respectively storing the face information and the palm print information into a face database and a palm print database.
In one possible way of realisation,
the identification module comprises: the face recognition submodule is used for recognizing the identity of the user by using a face recognition technology and comprises the following steps:
the second acquisition unit is used for acquiring a first facial image of the user;
the brightness adjusting unit is used for acquiring the brightness value of each pixel point in the first face image, calculating the average brightness value of the first face image based on the brightness value of each pixel point in the image, and determining the brightness adjusting proportion value of the first face image based on the difference between the average brightness value and a target brightness value;
the brightness adjusting unit is further configured to set a weight value of the brightness adjustment proportion value based on a brightness value of each pixel point in the first face image to obtain a specific brightness adjustment proportion value of the brightness value of each pixel point, and adjust the brightness of each pixel point in the first face image based on the specific brightness adjustment proportion value to obtain a second face image;
the image intercepting unit is used for identifying the face part of the second face image after carrying out scale conversion processing and gray level normalization processing on the second face image, determining image intercepting parameters based on the face part, and intercepting the face part in the second face image based on the image intercepting parameters to obtain a third face image;
the feature extraction unit is used for carrying out face detection on the third face image, acquiring key points of a face, positioning the key points to acquire positioning information, carrying out feature extraction on the key points based on the positioning information to acquire feature vectors corresponding to the key points, and establishing a corresponding relation between the feature vectors and the positioning information corresponding to the key points;
the feature extraction unit is further used for preprocessing the images in the face database and extracting standard feature vectors of the images in the face database;
the face recognition unit is used for comparing and matching the feature vector of the third face image with the standard feature vector of the image in the face database based on the corresponding relation to obtain a matching value;
the first judgment unit is used for selecting the maximum matching value in the matching values and judging whether the maximum matching value is larger than a preset matching value or not;
if so, determining the face information of the image corresponding to the maximum matching value, and determining the identity and the payment account number of the user based on the binding relationship of the face information;
otherwise, judging that the user does not have the binding relationship, and not determining the identity of the user.
In one possible way of realisation,
the identification module comprises: the palm print recognition submodule is used for recognizing the identity of the user by using a palm print recognition technology and comprises the following steps:
the third acquisition unit is used for acquiring a first palm print image of the user;
the palm print analysis unit is used for preprocessing the first palm print image, extracting a palm print outline, obtaining a second palm print image, carrying out global analysis on the palm print based on the second palm print image to obtain a first feature point of the palm print, carrying out local analysis on the palm print based on the first feature point, extracting a line of the palm print and obtaining a second feature point of the line;
the region determining unit is used for segmenting the second palm print image according to a preset rule to obtain a plurality of local regions, selecting a region with a second feature point in the local region based on the second feature point as a key region, inputting the key region into a stability judging model, determining a feature stability value of the key region, and selecting the key region with the feature stability value larger than a preset feature stability value as a stable region;
the coding unit is used for carrying out characteristic coding on the palm print information in the palm print database to obtain a standard coding result;
the encoding unit is further configured to perform feature encoding on the stable region to obtain an encoding result;
the second judgment unit is used for carrying out similarity judgment on the standard coding result and the coding result, selecting the maximum similarity in the similarities and judging whether the maximum similarity is greater than the preset similarity or not;
if so, determining the palm print information corresponding to the maximum similarity, and determining the identity and the payment account number of the user based on the binding relationship of the palm print information;
otherwise, judging that the user does not have the binding relationship, and not determining the identity of the user.
In one possible way of realisation,
the monitoring module includes:
the behavior monitoring unit is used for acquiring the purchasing behavior of the user within a preset time length based on the camera, and performing parameter detection on the purchasing behavior of the user based on the sensor to acquire a detection result;
the third judging unit is used for comparing and fitting the detection result with the abnormal behavior detection result to obtain a fitting result and judging whether the fitting result meets a preset fitting result or not;
if so, indicating that the purchasing behavior of the user is abnormal, locking the unmanned vending machine, and alarming and reminding;
otherwise, the purchasing behavior of the user is normal, and the unmanned vending machine is controlled to normally operate.
In one possible way of realisation,
the checkout module comprising:
the generating unit is used for generating a payment list of the user based on an artificial intelligence technology after receiving a purchase completion signal of the user;
and the sending unit is used for sending the payment list to a payment account of the user based on the binding relationship so as to assist the user in completing payment.
In one possible way of realisation,
still include, management module for after the user accomplishes the shopping each time, based on artificial intelligence technique, manage the commodity in the unmanned vending machine, the stock condition of real-time update commodity includes:
the confirmation unit is used for acquiring payment feedback information of the user on a payment account within preset time, generating an electronic two-dimensional code ticket based on the payment feedback information, inputting the electronic two-dimensional code ticket into a verification system to obtain a verification result, and confirming whether the user completes payment and purchases successfully based on the verification result;
the inventory updating unit is used for taking the last inventory result as the latest inventory result after confirming that the user does not finish payment or fails to purchase;
the first calculation unit is used for determining the name and the quantity of commodities purchased by the user based on a payment list of the user after the user is confirmed to finish payment and the purchase is successful, and calculating the theoretical variation of the commodities purchased by the user based on the last inventory result;
the detection unit is used for determining the quantity of historical commodities corresponding to the commodity names according to the last inventory result based on the commodity names purchased by the users, detecting the position information of the purchased commodities in the unmanned vending machine according to the commodity names, and determining the area where the purchased commodities are located based on the position information;
the detection unit is further used for emitting a first infrared ray to a first direction of the area where the purchased commodities are located, emitting a second infrared ray to a second direction of the area where the purchased commodities are located, and obtaining a time difference between the first infrared ray and the second infrared ray;
a correction unit, configured to obtain a first light intensity of the first infrared ray and a second light intensity of the second infrared ray, obtain an average light intensity of the first light intensity and the second light intensity, obtain an intensity difference between the average light intensity and a preset light intensity, and correct the time difference based on the intensity difference, so as to obtain a corrected time difference;
the detection unit is further configured to acquire external parameters and placement parameters of the purchased commodities, determine the current commodity quantity of the purchased commodities based on the corrected time difference, and subtract the current commodity quantity from the historical commodity quantity to obtain an actual variation of the purchased commodities;
the judging unit is used for judging whether the theoretical fluctuation amount is consistent with the actual fluctuation amount or not to obtain a judging result;
the inventory updating unit is further configured to, when the determination result indicates consistency, cover the current quantity of the purchased commodities over the historical quantity of the purchased commodities in the inventory result of the last time, keep the quantities of other commodities unchanged, and generate a latest inventory result;
and the reminding unit is used for sending the judgment result to a remote terminal and carrying out alarm reminding when the judgment result shows that the judgment result is inconsistent.
In one possible way of realisation,
further comprising: the pushing module is used for analyzing the shopping of the user after the user settles accounts and pushing the commodities which are interested by the user to the payment account of the user according to the analysis result, and comprises the following steps:
the information acquisition unit is used for acquiring facial emotion information and behavior data information of a user facing to the commodity in the shopping process based on an artificial intelligence technology;
the information analysis unit is used for analyzing the facial emotion information, determining the facial emotion of the user, and determining an emotion value corresponding to the face of the user based on a preset emotion numerical value comparison table;
the information analysis unit is further used for analyzing the behavior data information and determining the commodity selection speed of the user and the behavior liveness when the user selects the commodity;
the second calculation unit is used for calculating the satisfaction value of the user for the current shopping according to the following formula based on the analysis result of the information analysis unit;
Figure BDA0002978145700000061
wherein Q represents the satisfaction value of the user for the shopping, n represents the number of the types of the commodities purchased by the user, and epsilon1Represents a first weight value of [0.5,0.7 ]]，ε2Represents the second weight value, and takes the value of [0.5, 0.7%]，SiIndicating a facial emotion value, v, of said user at the time of purchase of an ith merchandise typeiIndicating the selection speed, v, of said user when purchasing the ith type of merchandise0Represents the standard selection speed, tau represents the reaction index of the user and takes the value of [1.0, 1.5%]，DiRepresenting the activity of the user when purchasing the ith commodity type, and the value is [0.3,0.9 ]]；
The second calculating unit is further used for calculating the satisfaction value of the user to the unmanned vending machine based on the satisfaction value of the user to the current shopping;
Figure BDA0002978145700000071
wherein R represents a satisfaction value of the user with the vending machine, c0The average richness value of the vending machine is represented and is (0.6, 0.8), and caThe commodity type richness value of the vending machine is represented and is [0.5,1 ]]；
The pushing unit is used for determining a pushing time interval based on the satisfaction value of the user to the unmanned vending machine, determining pushed commodity information based on a shopping list of the user, and pushing the pushed commodity information to a payment account of the user periodically based on the pushing time interval.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a block diagram of an unmanned aerial vehicle based on artificial intelligence technology according to an embodiment of the present invention;
FIG. 2 is a block diagram of a binding module in an embodiment of the invention;
FIG. 3 is a block diagram of a face recognition sub-module in an embodiment of the present invention;
FIG. 4 is a block diagram of a palm print recognition sub-module according to an embodiment of the present invention;
FIG. 5 is a block diagram of a monitoring module in an embodiment of the present invention;
FIG. 6 is a block diagram of a checkout module in an embodiment of the present invention;
FIG. 7 is a block diagram of a management module in an embodiment of the invention;
fig. 8 is a structural diagram of a push module according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example 1
An embodiment of the present invention provides an artificial intelligence technology-based vending machine, as shown in fig. 1, including:
the binding module is used for binding the user identity and the payment account of the user based on the face information or the palm print information of the user to obtain a binding relationship;
the identification module is used for identifying the user identity by utilizing the binding relationship by utilizing a face identification technology or a palm print identification technology;
the monitoring module is used for monitoring the purchasing behavior of the user after the identity of the user is successfully identified;
and the checkout module is used for generating a payment list and sending the payment list to the payment account of the user after the user finishes purchasing.
In this embodiment, the payment account may be, for example, a WeChat account, a Payment Bank account, or the like.
The beneficial effect of above-mentioned design is: through binding the payment account of user, when the user purchases, discern user's identity through binding the relation, accomplish the back at shopping, with payment list direct routing to user's payment account, the payment method is convenient high-efficient, improve user's shopping experience and feel to in-process at shopping is through monitoring module monitoring shopping process, guarantee user purchase process's safety, also can prevent the destruction to unmanned vending machine body to appear, guarantee unmanned vending machine body's safety.
Example 2
Based on embodiment 1, an embodiment of the present invention provides an artificial intelligence technology-based unmanned vending machine, as shown in fig. 2, where the binding module includes:
the request unit is used for receiving an account binding request of a user and acquiring real-name information and payment account information of the user;
the first acquisition unit is used for acquiring the face information and the palm print information of the user;
an authentication unit configured to determine whether the face information coincides with a photograph in the real-name information;
if so, indicating that the authentication is successful, and establishing a binding relationship between the facial information or the palm print information and the user payment account information;
otherwise, the authentication is failed;
and the storage unit is used for storing the binding relationship and respectively storing the face information and the palm print information into a face database and a palm print database.
In this embodiment, the real-name information is identity card information.
The beneficial effect of above-mentioned design is: after the face information and the palm print information of the user are collected and the identity of the user is successfully authenticated, the payment account of the user is bound, the accuracy of the binding relationship is ensured, and a relationship basis is established for payment through the payment account.
Example 3
Based on embodiment 1, an embodiment of the present invention provides an artificial intelligence technology-based unmanned vending machine, as shown in fig. 3, where the identification module includes: the face recognition submodule is used for recognizing the identity of the user by using a face recognition technology and comprises the following steps:
the second acquisition unit is used for acquiring a first facial image of the user;
the brightness adjusting unit is used for acquiring the brightness value of each pixel point in the first face image, calculating the average brightness value of the first face image based on the brightness value of each pixel point in the image, and determining the brightness adjusting proportion value of the first face image based on the difference between the average brightness value and a target brightness value;
the brightness adjusting unit is further configured to set a weight value of the brightness adjustment proportion value based on a brightness value of each pixel point in the first face image to obtain a specific brightness adjustment proportion value of the brightness value of each pixel point, and adjust the brightness of each pixel point in the first face image based on the specific brightness adjustment proportion value to obtain a second face image;
the image intercepting unit is used for identifying the face part of the second face image after carrying out scale conversion processing and gray level normalization processing on the second face image, determining image intercepting parameters based on the face part, and intercepting the face part in the second face image based on the image intercepting parameters to obtain a third face image;
the feature extraction unit is used for carrying out face detection on the third face image, acquiring key points of a face, positioning the key points to acquire positioning information, carrying out feature extraction on the key points based on the positioning information to acquire feature vectors corresponding to the key points, and establishing a corresponding relation between the feature vectors and the positioning information corresponding to the key points;
the feature extraction unit is further used for preprocessing the images in the face database and extracting standard feature vectors of the images in the face database;
the face recognition unit is used for comparing and matching the feature vector of the third face image with the standard feature vector of the image in the face database based on the corresponding relation to obtain a matching value;
the first judgment unit is used for selecting the maximum matching value in the matching values and judging whether the maximum matching value is larger than a preset matching value or not;
if so, determining the face information of the image corresponding to the maximum matching value, and determining the identity and the payment account number of the user based on the binding relationship of the face information;
otherwise, judging that the user does not have the binding relationship, and not determining the identity of the user.
In this embodiment, the brightness of the first face image is adjusted to ensure the sharpness of the face image, which is beneficial to face recognition.
In this embodiment, the image capture parameters are coordinates of image capture edges, capture accuracy, and the like.
In this embodiment, the key points of the face include eyes, nose, mouth, and the like.
The beneficial effect of above-mentioned design is: the identity of the user using the unmanned vending machine is identified through the face identification technology, so that the payment account corresponding to the identity of the user is determined, the identification efficiency is high, the user does not need to perform any operation, and the shopping process of the user is facilitated.
Example 4
Based on embodiment 1, an embodiment of the present invention provides an artificial intelligence technology-based unmanned vending machine, as shown in fig. 4, where the identification module includes: the palm print recognition submodule is used for recognizing the identity of the user by using a palm print recognition technology and comprises the following steps:
the third acquisition unit is used for acquiring a first palm print image of the user;
the palm print analysis unit is used for preprocessing the first palm print image, extracting a palm print outline, obtaining a second palm print image, carrying out global analysis on the palm print based on the second palm print image to obtain a first feature point of the palm print, carrying out local analysis on the palm print based on the first feature point, extracting a line of the palm print and obtaining a second feature point of the line;
the region determining unit is used for segmenting the second palm print image according to a preset rule to obtain a plurality of local regions, selecting a region with a second feature point in the local region based on the second feature point as a key region, inputting the key region into a stability judging model, determining a feature stability value of the key region, and selecting the key region with the feature stability value larger than a preset feature stability value as a stable region;
the coding unit is used for carrying out characteristic coding on the palm print information in the palm print database to obtain a standard coding result;
the encoding unit is further configured to perform feature encoding on the stable region to obtain an encoding result;
the second judgment unit is used for carrying out similarity judgment on the standard coding result and the coding result, selecting the maximum similarity in the similarities and judging whether the maximum similarity is greater than the preset similarity or not;
if so, determining the palm print information corresponding to the maximum similarity, and determining the identity and the payment account number of the user based on the binding relationship of the palm print information;
otherwise, judging that the user does not have the binding relationship, and not determining the identity of the user.
In this embodiment, the first feature point is a point where a pixel value of a pixel point is within a preset range.
In this embodiment, the second feature point is a first feature point located on a line of a palm print.
In this embodiment, the preset rule is to perform segmentation according to the texture of the second palm print image.
The beneficial effect of above-mentioned design does: the identity of the user using the unmanned vending machine is identified through a palm print identification technology, so that the payment account corresponding to the identity of the user is determined, the identification efficiency is high, the user does not need to perform any operation, and the shopping process of the user is facilitated.
Example 5
Based on embodiment 1, an embodiment of the present invention provides an artificial intelligence technology-based unmanned vending machine, as shown in fig. 5, where the monitoring module includes:
the behavior monitoring unit is used for acquiring the purchasing behavior of the user within a preset time length based on the camera, and performing parameter detection on the purchasing behavior of the user based on the sensor to acquire a detection result;
the third judging unit is used for comparing and fitting the detection result with the abnormal behavior detection result to obtain a fitting result and judging whether the fitting result meets a preset fitting result or not;
if so, indicating that the purchasing behavior of the user is abnormal, locking the unmanned vending machine, and alarming and reminding;
otherwise, the purchasing behavior of the user is normal, and the unmanned vending machine is controlled to normally operate.
The beneficial effect of above-mentioned design is: through monitoring user's action in the shopping process, guarantee user's purchase process's safety, simultaneously, if the abnormal conditions appears, lock unmanned vending machine, the prevention appears the destruction to unmanned vending machine, guarantees unmanned vending machine's safety.
Example 6
Based on embodiment 1, an embodiment of the present invention provides an unmanned vending machine based on an artificial intelligence technology, and as shown in fig. 6, the checkout module includes:
the generating unit is used for generating a payment list of the user based on an artificial intelligence technology after receiving a purchase completion signal of the user;
and the sending unit is used for sending the payment list to a payment account of the user based on the binding relationship so as to assist the user in completing payment.
The beneficial effect of above-mentioned design is: after shopping is finished, payment is carried out through the payment account bound by the user, the payment mode is convenient and efficient, and the shopping experience of the user is improved.
Example 7
Based on embodiment 1, an embodiment of the present invention provides an unmanned vending machine based on an artificial intelligence technology, as shown in fig. 7, further including a management module, configured to manage goods in the unmanned vending machine based on the artificial intelligence technology after each time a user finishes shopping, and update stock conditions of the goods in real time, where the management module includes:
the confirmation unit is used for acquiring payment feedback information of the user on a payment account within preset time, generating an electronic two-dimensional code ticket based on the payment feedback information, inputting the electronic two-dimensional code ticket into a verification system to obtain a verification result, and confirming whether the user completes payment and purchases successfully based on the verification result;
the inventory updating unit is used for taking the last inventory result as the latest inventory result after confirming that the user does not finish payment or fails to purchase;
the first calculation unit is used for determining the name and the quantity of commodities purchased by the user based on a payment list of the user after the user is confirmed to finish payment and the purchase is successful, and calculating the theoretical variation of the commodities purchased by the user based on the last inventory result;
the detection unit is used for determining the quantity of historical commodities corresponding to the commodity names according to the last inventory result based on the commodity names purchased by the users, detecting the position information of the purchased commodities in the unmanned vending machine according to the commodity names, and determining the area where the purchased commodities are located based on the position information;
the detection unit is further used for emitting a first infrared ray to a first direction of the area where the purchased commodities are located, emitting a second infrared ray to a second direction of the area where the purchased commodities are located, and obtaining a time difference between the first infrared ray and the second infrared ray;
a correction unit, configured to obtain a first light intensity of the first infrared ray and a second light intensity of the second infrared ray, obtain an average light intensity of the first light intensity and the second light intensity, obtain an intensity difference between the average light intensity and a preset light intensity, and correct the time difference based on the intensity difference, so as to obtain a corrected time difference;
the detection unit is further configured to acquire external parameters and placement parameters of the purchased commodities, determine the current commodity quantity of the purchased commodities based on the corrected time difference, and subtract the current commodity quantity from the historical commodity quantity to obtain an actual variation of the purchased commodities;
the judging unit is used for judging whether the theoretical fluctuation amount is consistent with the actual fluctuation amount or not to obtain a judging result;
the inventory updating unit is further configured to, when the determination result indicates consistency, cover the current quantity of the purchased commodities over the historical quantity of the purchased commodities in the inventory result of the last time, keep the quantities of other commodities unchanged, and generate a latest inventory result;
and the reminding unit is used for sending the judgment result to a remote terminal and carrying out alarm reminding when the judgment result shows that the judgment result is inconsistent.
In this embodiment, the first direction scans the area where the whole purchased commodity type is located, the second direction scans the area where the existing purchased commodity type is located, and the scanning directions of the first direction and the second direction are the same.
In this embodiment, the light intensity of the infrared ray is too weak, which may cause an error in the time difference between the first infrared ray and the second infrared ray, and the error may be reduced by correcting the time difference by the light intensity.
In this embodiment, the external parameters of the purchased goods include area, length, volume of the goods package; the placing parameters comprise placing intervals and placing directions among commodities.
In this embodiment, the corrected time difference is multiplied by the speed of light to obtain a distance difference, and the distance difference is subtracted by the placement distance between the commodities and then divided by the length of the single purchased commodity in the first direction, which is the number of the commodities.
The beneficial effect of above-mentioned design is: the goods of the unmanned vending machine are updated in real time, so that the remote terminal can know the inventory condition of the unmanned vending machine in real time, timely replenishment is facilitated, abundant goods types are provided for a user, and the shopping experience of the user is improved.
Example 8
Based on embodiment 1, an embodiment of the present invention provides an unmanned vending machine based on an artificial intelligence technology, as shown in fig. 8, further including: the pushing module is used for analyzing the shopping of the user after the user settles accounts and pushing the commodities which are interested by the user to the payment account of the user according to the analysis result, and comprises the following steps:
the information acquisition unit is used for acquiring facial emotion information and behavior data information of a user facing to the commodity in the shopping process based on an artificial intelligence technology;
the information analysis unit is used for analyzing the facial emotion information, determining the facial emotion of the user, and determining an emotion value corresponding to the face of the user based on a preset emotion numerical value comparison table;
the information analysis unit is further used for analyzing the behavior data information and determining the commodity selection speed of the user and the behavior liveness when the user selects the commodity;
the second calculation unit is used for calculating the satisfaction value of the user for the current shopping according to the following formula based on the analysis result of the information analysis unit;
Figure BDA0002978145700000151
wherein Q represents the satisfaction value of the user for the shopping, n represents the number of the types of the commodities purchased by the user, and epsilon1Represents a first weight value of [0.5,0.7 ]]，ε2Represents the second weight value, and takes the value of [0.5, 0.7%]，SiIndicating a facial emotion value, v, of said user at the time of purchase of an ith merchandise typeiIndicating the selection speed, v, of said user when purchasing the ith type of merchandise0Represents the standard selection speed, tau represents the reaction index of the user and takes the value of [1.0, 1.5%]，DiRepresenting the activity of the user when purchasing the ith commodity type, and the value is [0.3,0.9 ]]；
The second calculating unit is further used for calculating the satisfaction value of the user to the unmanned vending machine based on the satisfaction value of the user to the current shopping;
Figure BDA0002978145700000152
wherein R represents a satisfaction value of the user with the vending machine, c0The average richness value of the vending machine is represented and is (0.6, 0.8), and caThe commodity type richness value of the vending machine is represented and is [0 ].5,1]；
The pushing unit is used for determining a pushing time interval based on the satisfaction value of the user to the unmanned vending machine, determining pushed commodity information based on a shopping list of the user, and pushing the pushed commodity information to a payment account of the user periodically based on the pushing time interval.
In this embodiment, the higher the emotion, the larger the value in the emotion value lookup table.
In this embodiment, the magnitude of the first weighted value is selected according to the degree of influence of emotion on the satisfaction of the user, and the larger the numerical value is, the larger the degree of influence is; the second weighted value is selected according to the influence degree of the purchasing behavior on the satisfaction degree of the user, and the larger the numerical value is, the larger the influence degree is.
The working principle of the design scheme is as follows: the satisfaction degree of the user to the shopping is determined through analyzing the emotion and the purchasing behavior of the user, the influence of the richness degree of the commodity types of the unmanned vending machine is added according to the satisfaction degree of the shopping, the satisfaction degree of the user to the unmanned vending machine is determined, the pushing time interval is determined, the boring emotion of the user caused by frequent pushing is avoided, the user cannot obtain timely commodity information due to untimely pushing, and the experience feeling of the user is improved.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (8)

1. An unmanned vending machine based on artificial intelligence technology, comprising:
the binding module is used for binding the user identity and the payment account of the user based on the face information or the palm print information of the user to obtain a binding relationship;
the identification module is used for identifying the user identity by utilizing the binding relationship by utilizing a face identification technology or a palm print identification technology;
the monitoring module is used for monitoring the purchasing behavior of the user after the identity of the user is successfully identified;
and the payment module is used for generating a payment list and sending the payment list to a payment account of the user for payment after the user finishes purchasing.
2. The unmanned vending machine based on artificial intelligence technology of claim 1, wherein the binding module comprises:
the request unit is used for receiving an account binding request of a user and acquiring real-name information and payment account information of the user;
the first acquisition unit is used for acquiring the face information and the palm print information of the user;
an authentication unit configured to determine whether the face information coincides with a photograph in the real-name information;
if so, indicating that the authentication is successful, and establishing a binding relationship between the facial information or the palm print information and the user payment account information;
otherwise, the authentication is failed;
and the storage unit is used for storing the binding relationship and respectively storing the face information and the palm print information into a face database and a palm print database.
3. The unmanned vending machine based on artificial intelligence technology of claim 1, wherein the identification module comprises: the face recognition submodule is used for recognizing the identity of the user by using a face recognition technology and comprises the following steps:
the second acquisition unit is used for acquiring a first facial image of the user;
the brightness adjusting unit is used for acquiring the brightness value of each pixel point in the first face image, calculating the average brightness value of the first face image based on the brightness value of each pixel point in the image, and determining the brightness adjusting proportion value of the first face image based on the difference between the average brightness value and a target brightness value;
the brightness adjusting unit is further configured to set a weight value of the brightness adjustment proportion value based on a brightness value of each pixel point in the first face image to obtain a specific brightness adjustment proportion value of the brightness value of each pixel point, and adjust the brightness of each pixel point in the first face image based on the specific brightness adjustment proportion value to obtain a second face image;
the image intercepting unit is used for identifying the face part of the second face image after carrying out scale conversion processing and gray level normalization processing on the second face image, determining image intercepting parameters based on the face part, and intercepting the face part in the second face image based on the image intercepting parameters to obtain a third face image;
the feature extraction unit is used for carrying out face detection on the third face image, acquiring key points of a face, positioning the key points to acquire positioning information, carrying out feature extraction on the key points based on the positioning information to acquire feature vectors corresponding to the key points, and establishing a corresponding relation between the feature vectors and the positioning information corresponding to the key points;
the feature extraction unit is further used for preprocessing the images in the face database and extracting standard feature vectors of the images in the face database;
the face recognition unit is used for comparing and matching the feature vector of the third face image with the standard feature vector of the image in the face database based on the corresponding relation to obtain a matching value;
the first judgment unit is used for selecting the maximum matching value in the matching values and judging whether the maximum matching value is larger than a preset matching value or not;
if so, determining the face information of the image corresponding to the maximum matching value, and determining the identity and the payment account number of the user based on the binding relationship of the face information;
otherwise, judging that the user does not have the binding relationship, and not determining the identity of the user.
4. The unmanned vending machine based on artificial intelligence technology of claim 1, wherein the identification module comprises: the palm print recognition submodule is used for recognizing the identity of the user by using a palm print recognition technology and comprises the following steps:
the third acquisition unit is used for acquiring a first palm print image of the user;
the palm print analysis unit is used for preprocessing the first palm print image, extracting a palm print outline, obtaining a second palm print image, carrying out global analysis on the palm print based on the second palm print image to obtain a first feature point of the palm print, carrying out local analysis on the palm print based on the first feature point, extracting a line of the palm print and obtaining a second feature point of the line;
the region determining unit is used for segmenting the second palm print image according to a preset rule to obtain a plurality of local regions, selecting a region with a second feature point in the local region based on the second feature point as a key region, inputting the key region into a stability judging model, determining a feature stability value of the key region, and selecting the key region with the feature stability value larger than a preset feature stability value as a stable region;
the coding unit is used for carrying out characteristic coding on the palm print information in the palm print database to obtain a standard coding result;
the encoding unit is further configured to perform feature encoding on the stable region to obtain an encoding result;
the second judgment unit is used for carrying out similarity judgment on the standard coding result and the coding result, selecting the maximum similarity in the similarities and judging whether the maximum similarity is greater than the preset similarity or not;
if so, determining the palm print information corresponding to the maximum similarity, and determining the identity and the payment account number of the user based on the binding relationship of the palm print information;
otherwise, judging that the user does not have the binding relationship, and not determining the identity of the user.
5. The unmanned vending machine based on artificial intelligence technology of claim 1, wherein the monitoring module comprises:
the behavior monitoring unit is used for acquiring the purchasing behavior of the user within a preset time length based on the camera, and performing parameter detection on the purchasing behavior of the user based on the sensor to acquire a detection result;
the third judging unit is used for comparing and fitting the detection result with the abnormal behavior detection result to obtain a fitting result and judging whether the fitting result meets a preset fitting result or not;
if so, indicating that the purchasing behavior of the user is abnormal, locking the unmanned vending machine, and alarming and reminding;
otherwise, the purchasing behavior of the user is normal, and the unmanned vending machine is controlled to normally operate.
6. The automated intelligence technology-based vending machine according to claim 1, wherein the checkout module comprises:
the generating unit is used for generating a payment list of the user based on an artificial intelligence technology after receiving a purchase completion signal of the user;
and the sending unit is used for sending the payment list to a payment account of the user based on the binding relationship so as to assist the user in completing payment.
7. The unmanned vending machine based on artificial intelligence technology as claimed in claim 1, further comprising a management module, configured to manage the commodities in the unmanned vending machine based on artificial intelligence technology after each time a user finishes shopping, and update the inventory of the commodities in real time, including:
the confirmation unit is used for acquiring payment feedback information of the user on a payment account within preset time, generating an electronic two-dimensional code ticket based on the payment feedback information, inputting the electronic two-dimensional code ticket into a verification system to obtain a verification result, and confirming whether the user completes payment and purchases successfully based on the verification result;
the inventory updating unit is used for taking the last inventory result as the latest inventory result after confirming that the user does not finish payment or fails to purchase;
the first calculation unit is used for determining the name and the quantity of commodities purchased by the user based on a payment list of the user after the user is confirmed to finish payment and the purchase is successful, and calculating the theoretical variation of the commodities purchased by the user based on the last inventory result;
the detection unit is used for determining the quantity of historical commodities corresponding to the commodity names according to the last inventory result based on the commodity names purchased by the users, detecting the position information of the purchased commodities in the unmanned vending machine according to the commodity names, and determining the area where the purchased commodities are located based on the position information;
the detection unit is further used for emitting a first infrared ray to a first direction of the area where the purchased commodities are located, emitting a second infrared ray to a second direction of the area where the purchased commodities are located, and obtaining a time difference between the first infrared ray and the second infrared ray;
a correction unit, configured to obtain a first light intensity of the first infrared ray and a second light intensity of the second infrared ray, obtain an average light intensity of the first light intensity and the second light intensity, obtain an intensity difference between the average light intensity and a preset light intensity, and correct the time difference based on the intensity difference, so as to obtain a corrected time difference;
the detection unit is further configured to acquire external parameters and placement parameters of the purchased commodities, determine the current commodity quantity of the purchased commodities based on the corrected time difference, and subtract the current commodity quantity from the historical commodity quantity to obtain an actual variation of the purchased commodities;
the judging unit is used for judging whether the theoretical fluctuation amount is consistent with the actual fluctuation amount or not to obtain a judging result;
the inventory updating unit is further configured to, when the determination result indicates consistency, cover the current quantity of the purchased commodities over the historical quantity of the purchased commodities in the inventory result of the last time, keep the quantities of other commodities unchanged, and generate a latest inventory result;
and the reminding unit is used for sending the judgment result to a remote terminal and carrying out alarm reminding when the judgment result shows that the judgment result is inconsistent.
8. The unmanned vending machine based on artificial intelligence technology of claim 1, further comprising: the pushing module is used for analyzing the shopping of the user after the user settles accounts and pushing the commodities which are interested by the user to the payment account of the user according to the analysis result, and comprises the following steps:
the information acquisition unit is used for acquiring facial emotion information and behavior data information of a user facing to the commodity in the shopping process based on an artificial intelligence technology;
the information analysis unit is used for analyzing the facial emotion information, determining the facial emotion of the user, and determining an emotion value corresponding to the face of the user based on a preset emotion numerical value comparison table;
the information analysis unit is further used for analyzing the behavior data information and determining the commodity selection speed of the user and the behavior liveness when the user selects the commodity;
the second calculation unit is used for calculating the satisfaction value of the user for the current shopping according to the following formula based on the analysis result of the information analysis unit;
Figure FDA0002978145690000061
wherein Q represents the satisfaction value of the user for the shopping, n represents the number of the types of the commodities purchased by the user, and epsilon1Represents a first weight value of [0.5,0.7 ]]，ε2Represents the second weight value, and takes the value of [0.5, 0.7%]，SiIndicating that the user is purchasingValue of facial emotion, v, in the ith type of articleiIndicating the selection speed, v, of said user when purchasing the ith type of merchandise0Represents the standard selection speed, tau represents the reaction index of the user and takes the value of [1.0, 1.5%]，DiRepresenting the activity of the user when purchasing the ith commodity type, and the value is [0.3,0.9 ]]；
The second calculating unit is further used for calculating the satisfaction value of the user to the unmanned vending machine based on the satisfaction value of the user to the current shopping;
Figure FDA0002978145690000062
wherein R represents a satisfaction value of the user with the vending machine, c0The average richness value of the vending machine is represented and is (0.6, 0.8), and caThe commodity type richness value of the vending machine is represented and is [0.5,1 ]]；
The pushing unit is used for determining a pushing time interval based on the satisfaction value of the user to the unmanned vending machine, determining pushed commodity information based on a shopping list of the user, and pushing the pushed commodity information to a payment account of the user periodically based on the pushing time interval.
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