CN111967942A - Intelligent shopping method - Google Patents
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- G06Q20/401—Transaction verification
- G06Q20/4014—Identity check for transactions
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- G06Q30/00—Commerce
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- G—PHYSICS
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- 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
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- G07C9/00—Individual registration on entry or exit
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- G07C9/32—Individual registration on entry or exit not involving the use of a pass in combination with an identity check
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- G07C9/00—Individual registration on entry or exit
- G07C9/30—Individual registration on entry or exit not involving the use of a pass
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Abstract
The application discloses an intelligent shopping method, which belongs to the technical field of information; the technical key points are as follows: the method comprises the following steps: s1, identifying the target in the area; s2 controlling the information pickup device; s3 picking up object information using an information pickup device; s4, cleaning and standardizing the target information; s5 comparing the blacklists; s6 according to the judgment result of the first judgment module; s7, pushing the recommended commodity information according to the matched registered personnel and the attribute information thereof; and S8, matching the commodity image obtained by the commodity identification device with the commodity information base to obtain commodity price information, and completing self-service settlement. The application aims to provide an intelligent shopping method, which is used for realizing more personalized shopping service by training and constructing an accurate commodity recommendation model, and improving the shopping efficiency and convenience on the basis of saving resources.
Description
Technical Field
The application relates to the field of information technology (big data processing), in particular to an intelligent shopping method.
Background
With the development of biometric technology, mobile payment technology is applied deeply. The shopping mode is more and more intelligent and convenient by combining the internet of things technology, however, the existing intelligent shopping method is generally divided into two types, one type is a novel payment mode combining face brushing payment and man-machine interaction, and the other type is a self-service settlement mode under the commodity identification technology.
Although the traditional shopping process is simplified, the method is still insufficient in automation degree, and the existing commodity identification system and the shopping place intelligent access control system collect, process and analyze all data in a large environment and put high requirements on the storage space and the load pressure of the system.
In particular, processing and analyzing a large amount of poor quality data also results in wasted resources.
Therefore, there is a need to develop a new intelligent shopping method.
Disclosure of Invention
The application aims to provide an intelligent shopping method to solve the defects of the prior art.
An intelligent shopping method comprises the following steps:
s1, identifying the target in the area, and starting the control module when judging that the target object exists;
s2, controlling the information pick-up device, picking up information when it is started, and entering into sleep state when it is not started;
s3 picking up object information, which is single category information or a combination of multiple categories of information, using an information pickup device;
s4, cleaning and standardizing the target information;
s5, comparing the blacklists, determining whether suspicious persons are present or not, and performing system early warning when the suspicious persons are determined;
s6, comparing the history registration lists according to the judgment result of the first judgment module, and opening the entrance guard and entering the pushing module when the history registration personnel are judged to be history registration personnel; when the person is judged to be a non-historical registered person, performing identity authentication verification, inputting person identity information into a system and updating a registered person information base;
s7, pushing the recommended commodity information according to the matched registered personnel and the attribute information thereof;
and S8, matching the commodity image obtained by the commodity identification device with the commodity information base to obtain commodity price information, and completing self-service settlement.
The system further comprises a first storage module and a second storage module, wherein the first storage module is used for storing blacklist personnel information, and the second storage module is used for storing history registered personnel information.
Further, the information pickup device can be a certificate scanner, a fingerprint input instrument, an iris acquisition camera, a face snapshot machine and the like, and when the judgment module determines that a target object exists in the region, the control module controls the information pickup device to be started.
Further, the face snapshot machine uses structured light technology, and can perform depth detection to establish a three-dimensional model of the face.
Further, the information filtering module sets a first threshold value and a second threshold value, and carries out next-step authentication when the information scoring index is larger than the second threshold value; when the information scoring index is smaller than a first threshold value, deleting the information; and when the information index is greater than or equal to the first threshold value and less than or equal to the second threshold value, performing enhancement processing on the information, and performing next-step authentication.
Further, the intelligent shopping system further comprises a commodity information base, wherein the commodity information comprises commodity category, commodity name, commodity price and commodity picture information.
Further, the registered personnel information base comprises attribute information such as certificate information, fingerprint information, iris information, face information and historical purchase records of personnel, the pushing system inputs the historical purchase record data into the commodity recommendation model to obtain recommended commodity information, and the recommended commodity information is pushed to a user terminal interface.
And further, when the person is judged to be a non-history registered person, performing identity authentication verification, inputting person identity information into the system, opening the access control, and inputting a purchase record into the system after the user finishes shopping so as to finish updating the registered person information base.
Further, the commodity recommendation model is trained by taking historical commodity recommendation information and actual purchase record data of the registered personnel information base as samples.
The application has the advantages that:
first, a first basic concept of the present application is (another application for lack of unity with a second basic concept), providing an intelligent shopping system, comprising a judgment module, a control module, an information pickup module, an information filtering module, a first authentication module, a second authentication module, a pushing module, and a settlement module, wherein: the judging module is used for identifying the target in the area, and when the target object is judged to exist, the control module is started; the control module is used for controlling the information pickup device, picking up information when the information pickup device is started, and entering a dormant state when the information pickup device is not started; the information pickup module is used for picking up target information by using the information pickup device, wherein the target information is single-type information or combination of multiple types of information; the information filtering module is used for cleaning and standardizing the target information; the first authentication module is used for comparing blacklists, determining whether suspicious persons are behaving or not, and performing system early warning when the suspicious persons are determined; the second authentication module is used for comparing a historical registration list according to the judgment result of the first judgment module, and opening the entrance guard and entering the pushing module when the judgment result is that the person is a historical registration person; when the person is judged to be a non-historical registered person, performing identity authentication verification, inputting person identity information into a system and updating a registered person information base; the pushing module is used for pushing the recommended commodity information according to the matched registered personnel and the attribute information thereof; the settlement module is used for matching the commodity image acquired by the commodity identification device with the commodity information base to acquire commodity price information and finish self-service settlement; the system also comprises a first storage module and a second storage module, wherein the first storage module is used for storing blacklist personnel information, and the second storage module is used for storing history registered personnel information; the information pickup device can be a certificate scanner, a fingerprint input instrument, an iris acquisition camera, a face snapshot machine and the like, and when the judgment module determines that a target object exists in the region, the control module controls the information pickup device to be started; the face snapshot machine uses a structured light technology and can carry out depth detection to establish a three-dimensional model of the face; the information filtering module sets a first threshold value and a second threshold value, and carries out next-step authentication when the information scoring index is larger than the second threshold value; when the information scoring index is smaller than a first threshold value, deleting the information; when the information index is greater than or equal to a first threshold value and less than or equal to a second threshold value, performing enhancement processing on the information, and performing next-step authentication; the intelligent shopping system also comprises a commodity information base, wherein the commodity information comprises commodity category, commodity name, commodity price and commodity picture information; the registered personnel information base comprises attribute information such as certificate information, fingerprint information, iris information, face information, historical purchase records and the like of personnel, and the pushing system inputs historical purchase record data into a commodity recommendation model to obtain recommended commodity information and pushes the recommended commodity information to a user terminal interface; when the user is judged to be a non-history registered person, identity authentication verification is carried out, after personal identity information is input into the system and the registration list is updated, the entrance guard is opened, and after the user finishes shopping, a purchase record is input into the system to finish updating the information base of the registered person; the commodity recommendation model is trained by taking historical commodity recommendation information and actual purchase record data of a registered personnel information base as samples; according to the method and the system, multi-level personnel authentication is carried out through the first authentication module and the second authentication module, and the shopping efficiency and convenience are improved on the basis of saving resources by training and constructing an accurate commodity recommendation model to realize more personalized shopping service.
Second, the second basic idea of the present application is to provide an intelligent shopping solution (i.e. the solution of the present application).
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a schematic diagram of an embodiment of the method for intelligent shopping according to the present application.
Fig. 2 is a schematic diagram of an information pickup apparatus for controlling a state according to the present application.
Fig. 3 is a schematic diagram of the present application for performing pickup information filtering.
FIG. 4 is a schematic diagram of one embodiment of an intelligent shopping system architecture implementing the present application.
Fig. 5 is a schematic diagram illustrating commodity information pushing performed in the intelligent shopping system according to the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the application, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present application unless specifically stated otherwise.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting.
FIG. 1 is a schematic diagram of an embodiment of an intelligent shopping method of the present application. Preferably, the method steps of the present embodiment may be performed by a corresponding intelligent shopping system.
Identifying the target in the area, and starting a control module when judging that the target object exists;
controlling an information pickup device, picking up information when the information pickup device is started, and entering a dormant state when the information pickup device is not started;
picking up image data of a detection device, and cleaning and standardizing the image data;
carrying out blacklist comparison, determining whether behavior is suspicious personnel, and carrying out system early warning when the behavior is suspicious personnel;
comparing historical registration lists according to the judgment result of the first judgment module, and opening the access control when the historical registration personnel are judged to be the historical registration personnel; when the person is judged to be a non-history registered person, performing identity authentication verification, inputting person identity information into a system and updating a registration list;
carrying out recommended commodity information pushing according to the matched registered personnel and the attribute information thereof;
and matching the commodity image acquired by the commodity identification device with the commodity information base to acquire commodity price information and finish self-service settlement.
Step 102, judging whether a target object exists in the image.
And 103, when the target object is judged to exist in the image, the control module controls the information pickup device to be started so as to pick up information of the target object. When it is judged from the image that the target object does not exist for a long time, the control module brings the information pickup apparatus into a sleep state, and the above-described control process is illustrated in fig. 2.
Step 201, performing index evaluation on the picked information data, and performing next authentication when the image index is greater than a second threshold value; deleting the image when the image index is smaller than a first threshold value; and when the image index is greater than or equal to the first threshold value and less than or equal to the second threshold value, performing enhancement processing on the image, and performing next authentication.
The registered personnel information base comprises certificate information, fingerprint information, iris information, face information, historical purchase records and other attribute information of personnel.
Fig. 3 is a schematic diagram of information filtering according to the present application. The index evaluation is derived from a pre-established information evaluation model, the model is established through positive and negative sample training, the model can be classified by using an adaboost classifier, the categories comprise A, B, C, D, E, F six quality grades, and the evaluation scores of the corresponding indexes are from high to low. The positive and negative samples are from a conventional personnel information data source, and registered personnel newly-entered information is added for model enhancement training, wherein the personnel information can comprise biological information such as irises, fingerprints and faces, and personal attribute information such as sexes, names and regions.
Step 203, comparing blacklists, determining whether suspicious persons are behaving or not, and performing system early warning when the suspicious persons are determined;
the blacklist is derived from public criminal data and historical distrusted personnel registration data.
Step 204, comparing a historical registration list according to a comparison result, and opening an access control and entering a pushing module when the historical registration personnel are judged to be the historical registration personnel; when the person is judged to be a non-historical registered person, performing identity authentication verification, inputting person identity information into a system and updating a registered person information base;
and judging the attributes of the personnel identity, wherein the attributes comprise information such as gender, age, nationality and the like.
Step S205, carrying out recommended commodity information pushing according to the matched registered personnel and the attribute information thereof;
specifically, the information of registered personnel is input into a commodity recommendation model to obtain recommended commodity information, and the recommended commodity information is pushed to a user terminal interface.
The commodity recommendation model is trained by taking historical commodity recommendation information and actual purchase record data of a registered personnel information base as samples, and the samples are expanded according to the update of the personnel information base, so that the model has higher precision and timeliness;
and step S206, matching the commodity image acquired by the commodity identification device with the commodity information base to acquire commodity price information, and finishing self-service settlement.
Preferably, the commodity identification device adopts a target identification model, firstly performs image segmentation on the obtained commodity image to segment a background area and a target area, secondly performs contour feature extraction on the target area to construct a feature vector, secondly performs matching and similarity calculation on the feature vector and the commodity feature vector of the commodity information base, selects the commodity in the commodity base corresponding to the feature vector with the highest similarity, judges the commodity purchased by the user, identifies the number of the commodity and the commodity price, automatically generates a shopping receipt and a settlement price, and completes settlement.
As shown in fig. 4, according to another aspect of the present application, there is provided an intelligent shopping system, which includes a determining module 101, a control module 102, 201, an information pickup module, an information filtering module 202, a first authentication module 301, a second authentication module 302, a pushing module 401, and a settlement module 402, wherein:
the judging module 101 is used for identifying the target in the area, and when the target object is judged to exist, the control module is started;
the control module 102 is used for controlling the information pickup device, picking up information when the information pickup device is started, and entering a dormant state when the information pickup device is not started;
an information pickup module 201 for picking up target information which is single category information or a combination of multiple categories of information using an information pickup apparatus;
the information filtering module 202 is used for cleaning and standardizing the target information;
the first authentication module 301 is configured to perform blacklist comparison, determine whether there is a suspicious person acting, and perform system early warning when the suspicious person is determined;
the second authentication module 302 is used for comparing a historical registration list according to the judgment result of the first judgment module, and opening the entrance guard and entering the push module when the historical registration list is judged to be a historical registration person; when the person is judged to be a non-historical registered person, performing identity authentication verification, inputting person identity information into a system and updating a registered person information base;
the pushing module 401 is configured to push recommended commodity information according to the matched registered person and the attribute information thereof;
and the settlement module 402 is configured to match the commodity image acquired by the commodity identification device with the commodity information base to acquire commodity price information, and complete self-settlement.
Preferably, the information pickup device can be a certificate scanner, a fingerprint input instrument, an iris acquisition camera, a face snapshot machine and the like, and when the judgment module determines that a target object exists in the region, the control module controls the information pickup device to be started. The face snapshot machine uses structured light technology and can perform depth detection to build a three-dimensional model of the face.
The face snapshot machine uses structured light technology and can perform depth detection to build a three-dimensional model of the face.
Preferably, the information filtering module sets a first threshold and a second threshold, and performs the next authentication when the information scoring index is greater than the second threshold; when the information scoring index is smaller than a first threshold value, deleting the information; and when the information index is greater than or equal to the first threshold value and less than or equal to the second threshold value, performing enhancement processing on the information, and performing next-step authentication.
According to another aspect of the present application, blacklisted staff information is stored in a first storage module and historically registered staff information is stored in a second storage module.
Fig. 5 shows a preferred embodiment of the present application, wherein the intelligent shopping system further includes a commodity information base, and the commodity information includes commodity category, commodity name, commodity price, and commodity picture information. The registered personnel information base comprises attribute information such as certificate information, fingerprint information, iris information, face information and historical purchase records of personnel, the pushing system inputs the personnel information, commodity information and historical purchase record data into the commodity recommendation model to obtain recommended commodity information, and the recommended commodity information is pushed to a user terminal interface.
Through implementing this application, can realize automatic intelligent shopping safely high-efficiently. According to the method and the system, multi-level personnel authentication is carried out through the first authentication module and the second authentication module, and the shopping efficiency and convenience are improved on the basis of saving resources by training and constructing an accurate commodity recommendation model to realize more personalized shopping service.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The description of the present application has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the application in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. The embodiment was chosen and described in order to best explain the principles of the application and the practical application, and to enable others of ordinary skill in the art to understand the application for various embodiments with various modifications as are suited to the particular use contemplated.
Claims (9)
1. An intelligent shopping method is characterized by comprising the following steps:
s1, identifying the target in the area, and starting the control module when judging that the target object exists;
s2, controlling the information pick-up device, picking up information when it is started, and entering into sleep state when it is not started;
s3 picking up object information, which is single category information or a combination of multiple categories of information, using an information pickup device;
s4, cleaning and standardizing the target information;
s5, comparing the blacklists, determining whether suspicious persons are present or not, and performing system early warning when the suspicious persons are determined;
s6, comparing the history registration lists according to the judgment result of the first judgment module, and opening the entrance guard and entering the pushing module when the history registration personnel are judged to be history registration personnel; when the person is judged to be a non-historical registered person, performing identity authentication verification, inputting person identity information into a system and updating a registered person information base;
s7, pushing the recommended commodity information according to the matched registered personnel and the attribute information thereof;
and S8, matching the commodity image obtained by the commodity identification device with the commodity information base to obtain commodity price information, and completing self-service settlement.
2. The method of claim 1, further comprising a first storage module for storing blacklisted people information and a second storage module for storing historic enrolled people information.
3. The method according to claim 1, wherein the information pick-up device can be a certificate scanner, a fingerprint input device, an iris collecting camera, a face capturing machine, etc., and the control module controls the information pick-up device to be turned on when the judging module determines that the target object exists in the region.
4. The method of claim 3, wherein the face snapshot machine uses structured light technology to enable depth detection to build a three-dimensional model of the face.
5. The method according to claim 1, wherein the information filtering module sets a first threshold and a second threshold, and when the information scoring index is greater than the second threshold, the next authentication is performed; when the information scoring index is smaller than a first threshold value, deleting the information; and when the information index is greater than or equal to the first threshold value and less than or equal to the second threshold value, performing enhancement processing on the information, and performing next-step authentication.
6. The method of claim 1, wherein the intelligent shopping system further comprises a commodity information base, wherein the commodity information comprises commodity category, commodity name, commodity price and commodity picture information.
7. The method as claimed in claim 1, wherein the registered personnel information base includes certificate information, fingerprint information, iris information, face information, historical purchase records and other attribute information of personnel, and the pushing system inputs the historical purchase record data into the commodity recommendation model to obtain recommended commodity information and pushes the recommended commodity information to the user terminal interface.
8. The method of claim 7, wherein when it is judged that the registered person is not a history registered person, the authentication verification is performed, the person identification information is entered into the system and the door is opened, and the purchase record is entered into the system after the user finishes shopping to complete the update of the registered person information base.
9. The method of claim 7, wherein the product recommendation model is trained based on historical product recommendation information and actual purchase record data of a registered people information base as samples.
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