CN108269369A - Settle accounts case and its settlement method - Google Patents
Settle accounts case and its settlement method Download PDFInfo
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- CN108269369A CN108269369A CN201810050328.2A CN201810050328A CN108269369A CN 108269369 A CN108269369 A CN 108269369A CN 201810050328 A CN201810050328 A CN 201810050328A CN 108269369 A CN108269369 A CN 108269369A
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
- G07G1/00—Cash registers
- G07G1/0018—Constructional details, e.g. of drawer, printing means, input means
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
- G07G1/00—Cash registers
- G07G1/0036—Checkout procedures
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47F—SPECIAL FURNITURE, FITTINGS, OR ACCESSORIES FOR SHOPS, STOREHOUSES, BARS, RESTAURANTS OR THE LIKE; PAYING COUNTERS
- A47F9/00—Shop, bar, bank or like counters
- A47F9/02—Paying counters
- A47F9/04—Check-out counters, e.g. for self-service stores
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G—PHYSICS
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- 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
- G06Q20/08—Payment architectures
- G06Q20/20—Point-of-sale [POS] network systems
- G06Q20/208—Input by product or record sensing, e.g. weighing or scanner processing
-
- 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
- G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
- G06Q20/32—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using wireless devices
- G06Q20/327—Short range or proximity payments by means of M-devices
- G06Q20/3276—Short range or proximity payments by means of M-devices using a pictured code, e.g. barcode or QR-code, being read by the M-device
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C9/00—Individual registration on entry or exit
- G07C9/30—Individual registration on entry or exit not involving the use of a pass
- G07C9/32—Individual registration on entry or exit not involving the use of a pass in combination with an identity check
- G07C9/37—Individual registration on entry or exit not involving the use of a pass in combination with an identity check using biometric data, e.g. fingerprints, iris scans or voice recognition
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
- G07G1/00—Cash registers
- G07G1/0036—Checkout procedures
- G07G1/0045—Checkout procedures with a code reader for reading of an identifying code of the article to be registered, e.g. barcode reader or radio-frequency identity [RFID] reader
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
- G07G1/00—Cash registers
- G07G1/0036—Checkout procedures
- G07G1/0045—Checkout procedures with a code reader for reading of an identifying code of the article to be registered, e.g. barcode reader or radio-frequency identity [RFID] reader
- G07G1/0054—Checkout procedures with a code reader for reading of an identifying code of the article to be registered, e.g. barcode reader or radio-frequency identity [RFID] reader with control of supplementary check-parameters, e.g. weight or number of articles
- G07G1/0072—Checkout procedures with a code reader for reading of an identifying code of the article to be registered, e.g. barcode reader or radio-frequency identity [RFID] reader with control of supplementary check-parameters, e.g. weight or number of articles with means for detecting the weight of the article of which the code is read, for the verification of the registration
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
- G07G1/00—Cash registers
- G07G1/01—Details for indicating
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
- G07G1/00—Cash registers
- G07G1/12—Cash registers electronically operated
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07G—REGISTERING THE RECEIPT OF CASH, VALUABLES, OR TOKENS
- G07G3/00—Alarm indicators, e.g. bells
-
- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47F—SPECIAL FURNITURE, FITTINGS, OR ACCESSORIES FOR SHOPS, STOREHOUSES, BARS, RESTAURANTS OR THE LIKE; PAYING COUNTERS
- A47F9/00—Shop, bar, bank or like counters
- A47F9/02—Paying counters
- A47F9/04—Check-out counters, e.g. for self-service stores
- A47F2009/041—Accessories for check-out counters, e.g. dividers
-
- 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
- G06N3/088—Non-supervised learning, e.g. competitive learning
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Abstract
The invention discloses a kind of clearing casees and its settlement method, the clearing case to include:Including:Babinet, the camera for being set to top of the box and the picture recognition module being set in babinet, picture recognition module are connected with camera, and dynamic code module is connected with picture recognition module;The region of commodity is placed in the shooting angle covering babinet of camera;Picture recognition module is connect with camera, is identified the merchandise news in the image of commodity to be detected and is generated settlement information.The babinet of commodity to be settled accounts can be accommodated by setting, is covered commodity by babinet, so as to avoid interference of other light to commodity image, improves the accuracy of gained checkout result.
Description
Technical field
The present invention relates to a kind of clearing case and its settlement methods, belong to payment mechanism technical field.
Background technology
With the raising of human cost, human cost is just becoming the main burden of many simple duplication of labour industries.Such as
Convenience store, the cashier of convenience store is on the one hand on duty for a long time, on the other hand is only capable of being engaged in the cash register tally simply repeated again
Deng labour, cashier can not both obtain more higher vocational skills to improve income, while human cost also heavy compression quotient
The profit of family.
Existing unattended settlement device, mostly by commodity are placed under light shot after obtain commodity image,
To be settled accounts.Background residing for captured commodity is complicated, and ambient combines complicated background during shooting so that obtains commodity
Image interference is serious, and existing method can not accurately obtain commodity amount and type in image, be easy to cause settlement amounts mistake.
Invention content
According to an aspect of the invention, there is provided a kind of clearing case, which is avoided that other light to commodity institute
The interference of image is obtained, improves the accuracy rate of checkout result.
Including:Babinet, the camera being set in babinet and the picture recognition module being set in babinet, camera
The region of commodity is placed in shooting angle covering babinet;Picture recognition module is connect with camera, identifies the figure of commodity to be detected
As in merchandise news and generate settlement information.
Preferably, camera obtains the image containing commodity to be detected;Picture recognition module will contain commodity to be detected
Image inputs the identifying system based on neural network, and the identifying system based on neural network exports merchandise news to be detected.
Preferably, it obtains the image containing commodity to be detected and includes at least angle and/or different first images of the depth of field to N
Image;N≥2;
Neural network recognization system includes the first nerves network based on region convolutional neural networks;Commodity recognition method packet
Include step:
(a1) the first image is inputted into first nerves network, first nerves network exports the first merchandise news;By N images
Input first nerves network, first nerves network output N merchandise newss;
(b1) judge whether N merchandise newss are included in the first merchandise news;
If judging result is yes, then exported the first merchandise news as merchandise news to be detected;As judging result be it is no,
Then export feedback result.
Preferably, judge that whether the method being included in the first merchandise news is N merchandise newss, judges in step (b1)
Whether the type of merchandize in N merchandise newss is present in the first merchandise news.
Preferably, judge that whether the method being included in the first merchandise news is N merchandise newss, judges in step (b1)
Whether the commodity amount in N merchandise newss is less than or equal to the commodity amount in the first merchandise news.
Preferably, clearing case further includes trigger module, and trigger module is connected with camera triggering;Clearing case is further included and is schemed
As identification module connect dynamic code module, dynamic code module obtain settlement information after generate payment code, and show payment code and
Settlement information.
Preferably, clearing case further includes consumption data analysis module, consumption data analysis module be set in babinet and with
Dynamic code module is connected, and consumption data analysis module is for statistical analysis to settlement information, obtains consumption time in the unit interval
Number and/or consumption quantity are more than the trade name of preset value.
Another aspect of the present invention additionally provides a kind of settlement method, includes the following steps:
Step S100:Commodity to be settled accounts are put into clearing case, obtain the image containing commodity to be detected;
Step S200:It identifies the merchandise news in the image of commodity to be detected and generates settlement information;
Step S300:Payment code is generated, and show payment code and settlement information after obtaining settlement information.
Preferably, the commodity letter in images of the step S200 by running neural network recognization system identification commodity to be detected
Breath;
Step S200 is further comprising the steps of:It is for statistical analysis to settlement information, obtain consumption number of times in the unit interval
And/or consumption quantity is more than the trade name of preset value.
Preferably, step S100 is further comprising the steps of:
Step S110:Clearing case inner light source is opened after being put into commodity;
Step S120:Camera obtains the image of commodity to be detected.
The advantageous effect that the present invention can generate includes:
1) clearing case provided by the present invention can accommodate the babinets of commodity to be settled accounts by setting, by babinet by commodity
It covers, so as to avoid interference of other light to commodity image, improves the accuracy of gained checkout result.
2) clearing case provided by the present invention, only just opens light after commodity enter babinet, can play energy saving
Effect.
3) clearing case provided by the present invention suitable for unattended restaurant or convenience store, can reduce cost of labor, with
Lower cost realizes 24 hours on duty, raising profit margins.
4) settlement method provided by the present invention is counted by the commodity high to sales volume in generated order, for paving
Goods provides data, realizes compartmentalization paving goods.
Description of the drawings
Fig. 1 is the clearing case stereoscopic schematic diagram provided in the preferred embodiment of the present invention;
Fig. 2 is the settlement method flow diagram schematic diagram provided in the preferred embodiment of the present invention.
Component and reference numerals list:
Component names | Number |
Babinet | 100 |
Commodity settle accounts chamber | 110 |
Opening | 111 |
Trigger module | 120 |
Dynamic code module | 130 |
Picture recognition module | 140 |
Operating space | 150 |
Specific embodiment
The present invention is described in detail, but the invention is not limited in these embodiments with reference to embodiment.
Referring to Fig. 1, the present invention provides a kind of clearing case, including:Babinet 100, the camera for being set to 100 top of babinet
And the picture recognition module 140 in babinet 100 is set to, picture recognition module 140 is connected with camera, dynamic code module
130 are connected with picture recognition module 140;
The region of commodity is placed in the shooting angle covering babinet 100 of camera;
Picture recognition module 140 is connect with camera, is identified the merchandise news in the image of commodity to be detected and is generated knot
Calculate information.
Picture recognition module 140, for the pre-stored image stored in acquired commodity image and database to be compared
It is right, find corresponding pre-stored image, and read the label of the pre-stored image, so that it is determined that in commodity image contained commodity title
And its price.The quantity of contained commodity in commodity image can also be obtained by being compared by commodity image and pre-stored image, so as to combine
The price of gained commodity obtains the settlement information of order.Merchandise news herein includes the type and quantity of commodity.
Background, light source and the camera angle of pre-stored image generate image with commodity in actual use middle case 100
It is completely the same, so as to reduce the generation of matching error.
Preferably, camera obtains the image containing commodity to be detected;
Picture recognition module 140 will input the identifying system based on neural network containing the image of commodity to be detected, be based on
The identifying system of neural network exports merchandise news to be detected.
In order to improve recognition efficiency and precision, it is preferred to use neural network recognization system is identified.Neural network recognization
System belongs to deep learning method, and the method learnt using the sustainability based on deep learning is not needed to by any third
Side's mark identification commodity, as long as the free choice of goods is placed on desktop by user can be realized identification.Commodity are captured by common camera
Picture significantly reduces cost and speed that commodity identify, it can be achieved that the quick detection of batch commodity.
Preferably, it obtains the image containing commodity to be detected and includes at least angle and/or different the first image of the depth of field extremely
N images;N≥2;
Neural network recognization system includes the first nerves network based on region convolutional neural networks;Commodity recognition method packet
Include step:
(a1) the first image is inputted into first nerves network, first nerves network exports the first merchandise news;By N images
Input first nerves network, first nerves network output N merchandise newss;
(b1) judge whether N merchandise newss are included in the first merchandise news;
If judging result is yes, then exported the first merchandise news as merchandise news to be detected;
If judging result is no, then feedback result is exported.Feedback result herein includes stacking in prompting, error reporting
It is at least one.
According to said method it is identified, it can accurate acquisition recognition result.
Preferably, judge that whether the method being included in the first merchandise news is N merchandise newss, judges in step (b1)
Whether the type of merchandize in N merchandise newss is present in the first merchandise news.
Preferably, judge that whether the method being included in the first merchandise news is N merchandise newss, judges in step (b1)
Whether the commodity amount in N merchandise newss is less than or equal to the commodity amount in the first merchandise news.
The quantity of camera is not limited to one in the present invention, can be arranged as required to multiple.Picture acquired in camera
Can be two dimension or three-dimensional.
Camera right over objective table is main camera, is denoted as the first camera;Commodity recognition method includes following step
Suddenly:
When using N number of camera respectively from different angles, obtain the picture of article to be identified, be denoted as respectively P1,
P2.....PN, wherein the picture of main camera shooting is P1;
P1, P2......PN are uploaded into local identification server or high in the clouds identification server, each pictures are known
Not, the merchandise news identified is denoted as R1, R2....RN respectively, and merchandise news includes the classification information of commodity and quantity letter
Breath, wherein, the recognition result R1 of main camera is the first merchandise news, and the recognition result R2......RN of other cameras distinguishes
For the second merchandise news ... N merchandise newss;
By taking two cameras as an example, judge whether R2 (the second merchandise news) is included in R1 (the first merchandise news);
If if judging result is yes, exported R1 as merchandise news to be detected;
If judging result is no, then exports final recognition result and calculate the total weight of commodity in R1, with practical weighing
Commodity total weight compare to obtain differential data, judge differential data whether be less than or equal to predetermined threshold value:
It if judging result is yes, is then exported R1 as merchandise news to be detected, shows the information in R1, i.e., comprising commodity
Classification information, quantity information and pricing information items list;
If judging result is no, then stacking prompt message is shown.
Preferably, the commodity clearing chamber 110 being set to inside babinet 100, the light source for being set to 100 top of babinet are further included
With dynamic code module 130, picture recognition module 140 is connected with camera, dynamic code module 130 and picture recognition module 140
It is connected.Commodity clearing chamber 110 opens up opening 111 to place/take away commodity on one side.
Preferably, clearing case further includes the dynamic code module 130 being connect with picture recognition module 140, dynamic code module 130
Payment code is generated, and show payment code and settlement information after obtaining settlement information.
Dynamic code module 130 is used for the code information that can be paid according to the generation of the settlement information of commodity with barcode scanning, and shows branch
Code is paid to pay for user's barcode scanning.Payment code can be shown on the outer surface of babinet 100, can also be shown in other equipment.
100 outer surface of babinet refers to herein, when user carries out barcode scanning action for payment, the arbitrary surfaces of babinet 100 that arrive of institute's energy barcode scanning.
The generating process of the module can be carried out by the prior art.By showing settlement information in 100 outer surface of babinet, convenient for being used for core
To checkout result, and carry out barcode scanning payment.The realization of picture recognition module 140 can be carried out by existing method.
In order to improve the accuracy of the recognition result of picture recognition module 140, commodity can be set in commodity clearing chamber 110
Rest area, so as to improve the matching result accuracy of acquired commodity image and pre-stored image.Obvious is provided by the invention
Settle accounts case, however it is not limited to unattended consumer environment, or used in the environment for having cashier.
Clearing case provided by the invention includes babinet 100 and the commodity being set in babinet 100 clearing chamber 110, image are known
Other module 140, dynamic code module 130, light source and camera.Commodity clearing chamber 110 can accommodate commodity, and closing chamber as needed
It opens after being set to the light source in babinet 100, commodity is shot, to obtain the image of commodity behind the door.100 1 surface side of babinet
Wall is hatch door to be opened/closed, and hatch door face commodity clearing chamber 110 is set.Commodity 110 inner top of clearing chamber setting light source and camera shooting
Head, light source and camera are subject to shadow surface or projection surface can effectively cover commodity placement area.Babinet 100 is closing,
It can prevent interference of the extraneous light to commodity image, improve picture recognition module 140 to type of merchandize in image and quantity
Recognition efficiency.Picture recognition module 140 is connected with camera.The image transmitting that camera obtains is to picture recognition module 140
Afterwards, which can identify contained commodity and its quantity in image, and according to existing algorithm in numerous stored commodity figures
The type for finding out commodity in image is compared as in.Using cashier after this equipment, can be replaced substantially, customer is helped to complete self-service
Payment.Remove the expense for employing cashier from, and can realize full-time employment.
For the ease of operation, operating space 150 is additionally provided on the outer surface of cupular part of babinet 100.
Preferably, clearing case further includes trigger module 120, and trigger module 120 is connected with camera triggering.When commodity enter
Camera is triggered after commodity clearing chamber 110 by trigger module 120 to shoot commodity.
Preferably, light source is LED light.
Preferably, camera is single wide-angle camera, is set at commodity clearing 110 top center of chamber.It sets single
Camera is conducive to improve the recognition result accuracy of image.Camera is set to the center of commodity clearing chamber 110, can obtain standard
True commodity front view paves the direct picture information for placing commodity convenient for identification.
Preferably, picture recognition module 140 includes the settlement information of gained commodity to settle accounts area in dynamic code, and show use
In the dynamic code of payment.It is used to that clearing can be realized by barcode scanning later.
Preferably, the consumption data analysis mould for being set to and being connected in babinet 100 and with dynamic code module 130 is further included
Block, consumption data analysis module are used for, acquisition unit interval in consumption number of times and/or consumption for statistical analysis to settlement information
Quantity is more than the trade name of preset value.Unit interval herein refers to have year, month, day set by user, Zhou Junke.The module
Can be used to statistics consumption number of times can be used for statistics consumption quantity.Preset value can be set according to user.Needle
The commodity more to consumption number of times and consumption quantity increase the paving goods amount of corresponding commodity, improve customer's purchase rate again.Improve convenience store
Purchase rate again.Acquire and analyze the consumption data of the region cash register.Region commodity purchasing power and purchase hobby digitization.It completes
During commodity are paid, the details such as the amount of money and the type of merchandize of commodity are purchased and server, shape are uploaded to by the form of image
Into the purchase data in the region.
Referring to Fig. 2, another aspect of the present invention additionally provides a kind of clearing case settlement method, includes the following steps:
Step S100:Commodity to be settled accounts are put into clearing case, obtain the image containing commodity to be detected;
Step S200:It identifies the commodity kind information in the image of commodity to be detected and generates settlement information;
Step S300:Payment code is generated, and show payment code and settlement information after obtaining settlement information.
Preferably, the commodity letter in images of the step S200 by running neural network recognization system identification commodity to be detected
Breath.
Step S200 is further comprising the steps of:It is for statistical analysis to settlement information, obtain consumption number of times in the unit interval
And/or consumption quantity is more than the trade name of preset value.So as to fulfill the analysis to consumer's consumer behavior, paving trader's product are improved
Sales volume.For the more commodity of consumption number of times and consumption quantity, increase the paving goods amount of corresponding commodity, improve customer and purchase again
Rate.Improve convenience store's purchase rate again.Acquire and analyze the consumption data of the region cash register.
Preferably, step S100 is further comprising the steps of:
Step S110:Commodity to be settled accounts are put into commodity clearing intracavitary triggering light source and open;
Step S120:Camera obtains commodity image.
More than, be only several embodiments of the present invention, any type of limitation not done to the present invention, although the present invention with
Preferred embodiment discloses as above, however not to limit the present invention, any person skilled in the art is not departing from this
In the range of inventive technique scheme, make a little variation using the technology contents of the disclosure above or modification is equal to equivalent reality
Case is applied, is belonged in the range of technical solution.
Claims (10)
1. a kind of clearing case, which is characterized in that including:It babinet, the camera being set in the babinet and is set to described
Picture recognition module in babinet,
The shooting angle of the camera covers the region that commodity are placed in the babinet;
Described image identification module is connect with the camera, is identified the merchandise news in the image of commodity to be detected and is generated knot
Calculate information.
2. clearing case according to claim 1, which is characterized in that
The camera obtains the image containing commodity to be detected;
The image containing commodity to be detected is inputted the identifying system based on neural network by described image identification module, described
Identifying system based on neural network exports merchandise news to be detected.
3. clearing case according to claim 2, which is characterized in that
The image for containing commodity to be detected that obtains includes at least angle and/or different first images of the depth of field to N images;N
≥2;
The neural network recognization system includes the first nerves network based on region convolutional neural networks;The commodity identification side
Method includes step:
(a1) described first image is inputted into the first nerves network, the first nerves network exports the first merchandise news;
The N images are inputted into the first nerves network, the first nerves network exports N merchandise newss;
(b1) judge whether the N merchandise newss are included in first merchandise news;
If judging result is yes, then exported first merchandise news as the merchandise news to be detected;
If judging result is no, then feedback result is exported.
4. clearing case according to claim 3, which is characterized in that judge the N merchandise newss in the step (b1)
Whether the method being included in first merchandise news is to judge whether the type of merchandize in the N merchandise newss deposits
It is in first merchandise news.
5. clearing case according to claim 3, which is characterized in that judge the N merchandise newss in the step (b1)
Whether the method being included in first merchandise news is to judge whether the commodity amount in the N merchandise newss is less than
Equal to the commodity amount in first merchandise news.
6. clearing case according to claim 1, which is characterized in that the clearing case further includes trigger module, the triggering
Module is connected with camera triggering;
The clearing case further includes dynamic code module connect with described image identification module, described in the dynamic code module acquisition
Payment code is generated after settlement information, and shows the payment code and the settlement information.
7. according to clearing case according to any one of claims 1 to 6, which is characterized in that the clearing case further includes consumption number
According to analysis module, the consumption data analysis module is set in the babinet and is connected with the dynamic code module, described
Consumption data analysis module is for statistical analysis to the settlement information, obtains consumption number of times and/or consumption number in the unit interval
Amount is more than the trade name of preset value.
8. a kind of settlement method, which is characterized in that include the following steps:
Step S100:Commodity to be settled accounts are put into clearing case, obtain the image containing commodity to be detected;
Step S200:It identifies the merchandise news in the image of the commodity to be detected and generates settlement information;
Step S300:Payment code is generated after obtaining the settlement information, and shows the payment code and the settlement information.
9. settlement method according to claim 8, which is characterized in that the step S200 is by running neural network recognization
Merchandise news in the image of commodity to be detected described in system identification.
10. settlement method according to claim 8, which is characterized in that the step S200 is further comprising the steps of:To institute
It states that settlement information is for statistical analysis, obtains the trade name that consumption number of times and/or consumption quantity in the unit interval are more than preset value
Claim.
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CN201810050328.2A Pending CN108269369A (en) | 2017-09-27 | 2018-01-18 | Settle accounts case and its settlement method |
CN201810135196.3A Expired - Fee Related CN108320404B (en) | 2017-09-27 | 2018-02-09 | Commodity identification method and device based on neural network and self-service cash register |
CN201811133576.XA Expired - Fee Related CN109559457B (en) | 2017-09-27 | 2018-09-27 | Neural network-based commodity identification cash registering method and self-service cash registering desk |
CN201811133556.2A Expired - Fee Related CN109559454B (en) | 2017-09-27 | 2018-09-27 | Neural network-based commodity identification cash registering method and self-service cash registering desk |
CN201811134347.XA Expired - Fee Related CN109559458B (en) | 2017-09-27 | 2018-09-27 | Neural network-based commodity identification cash registering method and self-service cash registering desk |
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CN201811133576.XA Expired - Fee Related CN109559457B (en) | 2017-09-27 | 2018-09-27 | Neural network-based commodity identification cash registering method and self-service cash registering desk |
CN201811133556.2A Expired - Fee Related CN109559454B (en) | 2017-09-27 | 2018-09-27 | Neural network-based commodity identification cash registering method and self-service cash registering desk |
CN201811134347.XA Expired - Fee Related CN109559458B (en) | 2017-09-27 | 2018-09-27 | Neural network-based commodity identification cash registering method and self-service cash registering desk |
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Also Published As
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CN108320404A (en) | 2018-07-24 |
CN109559457B (en) | 2021-09-21 |
CN109559457A (en) | 2019-04-02 |
CN109559458A (en) | 2019-04-02 |
CN209132890U (en) | 2019-07-19 |
CN109559454A (en) | 2019-04-02 |
CN109559454B (en) | 2021-09-21 |
CN109559458B (en) | 2021-09-21 |
CN108320404B (en) | 2020-04-03 |
WO2019062017A1 (en) | 2019-04-04 |
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