CN116823378A - Visual marketing method for unmanned retail equipment - Google Patents
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- CN116823378A CN116823378A CN202310353891.8A CN202310353891A CN116823378A CN 116823378 A CN116823378 A CN 116823378A CN 202310353891 A CN202310353891 A CN 202310353891A CN 116823378 A CN116823378 A CN 116823378A
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- 238000000034 method Methods 0.000 title claims abstract description 20
- 230000000007 visual effect Effects 0.000 title claims abstract description 16
- 238000007405 data analysis Methods 0.000 claims abstract description 7
- 238000012549 training Methods 0.000 claims description 22
- 238000012544 monitoring process Methods 0.000 claims description 7
- 238000007781 pre-processing Methods 0.000 claims description 6
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 3
- 238000005206 flow analysis Methods 0.000 claims description 3
- 230000002093 peripheral effect Effects 0.000 claims description 3
- 238000011176 pooling Methods 0.000 claims description 3
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- 239000002699 waste material Substances 0.000 claims description 3
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- 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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
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- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/53—Recognition of crowd images, e.g. recognition of crowd congestion
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/178—Human faces, e.g. facial parts, sketches or expressions estimating age from face image; using age information for improving recognition
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Abstract
The invention provides a visual marketing method of unmanned retail equipment, which comprises the following steps: collecting image information through video identification equipment; image recognition and data analysis are carried out on the image information through the trained model, and surrounding environment information and user characteristic information are obtained; outputting service strategies through the surrounding environment information and the user characteristic information; the image information collection specifically includes: acquiring surrounding environment information and customer information through video identification equipment; the mounting position of the video recognition device includes: the hanging arm above the cabinet body is integrated in the display screen; the surrounding environment information includes: people flow and environment summary; the client information includes: the face and clothing of the user. The invention can rapidly acquire the basic personal information of the user, thereby rapidly and accurately presuming the marketing scheme of the user preference, and carrying out targeted commodity marketing strategies and commodity selections through predicting the client preference.
Description
Technical Field
The invention relates to the technical field of unmanned retail equipment, in particular to a visual marketing method of unmanned retail equipment.
Background
The current collection modes of user preferences are a direct scheme and an indirect scheme.
The direct scheme is as follows: an unmanned retail system or applet provides "wisdom sheets" to directly obtain SKU requirement information for the user group.
The indirect scheme is as follows: the system can acquire the commodity preference condition of the customer group according to the actual sales condition; or by a system or applet querying, collecting the customer's age, occupation, snack time, snack class, etc., to obtain relevant demand information.
For example, chinese patent CN201910475319.2 provides an unmanned retail device, which cannot accurately obtain user information, collect user information in time, and collect information with difficulty, so that a targeted marketing method cannot be implemented.
Accordingly, there is a need in the art to provide a new approach to solving the above-mentioned problems.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a visual marketing method for unmanned retail equipment.
In order to achieve the above object, the present invention is specifically as follows:
the invention provides a visual marketing method of unmanned retail equipment, which comprises the following steps:
s1, collecting image information through video identification equipment;
s2, carrying out image recognition and data analysis on the image information through a trained model to acquire surrounding environment information and user characteristic information;
s3, outputting the service strategy through the surrounding environment information and the user characteristic information.
Further, in step S1, the image information collection specifically includes: acquiring surrounding environment information and customer information through video identification equipment;
the mounting position of the video recognition device includes: the hanging arm above the cabinet body is integrated in the display screen;
the surrounding environment information includes: people flow and environment summary;
the client information includes: the face and clothing of the user.
Note that: when a customer uses the unmanned retail equipment for the first time, a screen pops up an informed license agreement, wherein the informed license agreement comprises a user information acquisition range and options of whether to agree or not, the user is required to read the whole text, the clearly known content is checked, after the user reads the information, the user selects to agree and authorize the license to use, the information acquisition can be carried out, the information acquisition is only used for the subsequent personalized marketing of the user, and the data confidentiality is not used for other purposes;
further, in step S2, the training model specifically includes the following steps:
s201, extracting each frame of picture in a video, and preprocessing by adopting the same parameters to obtain a large number of environment pictures and face pictures, wherein the preprocessing comprises the following steps: scaling, cutting, graying and normalizing;
s202, establishing a CNN model, and determining parameters of the model, wherein the parameters comprise: depth, number of layers, convolution kernel size, pooling size;
s203, determining training parameters and an optimizer algorithm, and training the model, wherein the training parameters comprise: batch size, learning rate, training wheel number; the optimizer algorithm adopts: adam or SGD;
s204, importing training data, performing multi-round training on the model, adjusting parameters, and improving the accuracy of the model;
s205, evaluating the performance of the model by calculating the accuracy, recall or F1-score.
Further, in step S2, image recognition and data analysis are performed through the model, specifically including:
inputting recorded video of the video identification equipment into a model, and automatically analyzing and processing the model:
analyzing the surrounding environment information to obtain further information, including: environmental type (office building, school, etc.), people stream density, people stream time distribution, residence time before equipment;
analyzing the video operated by the user to obtain the user characteristics, including: gender, age range, clothing style, color value; and establishing a customer attribute label for predicting user preference.
Further, in step S3, the service policy output includes: marketing recommendation, option updating, screen personalized display and passenger flow monitoring.
Further, the marketing recommendation refers to:
forming a portrait based on the user information, and performing commodity discount promotion recommendation;
predicting favorite commodities of a user based on personal characteristics, and providing a personalized recommended sales promotion scheme;
wherein the personal characteristics include: age, sex, looks, clothing.
Further, the option update means:
acquiring information of surrounding scenes and client preference information through scene and character information, and predicting sales volume; optimizing a selection scheme and improving sales level of the selection; further reducing the operation cost and the trial and error cost of the selection.
Further, the screen personalized presentation means:
displaying corresponding personalized commodity sales promotion and coupon package activities on a device screen according to the customer image;
and meanwhile, the screen self-lighting and marketing video playing are carried out, so that the promotion information is accepted by the clients.
Further, the passenger flow monitoring means:
by collecting images of the periphery of the unmanned retail equipment, the peripheral passenger flow condition is obtained,
judging whether the risk of resource waste exists or not by comprehensively analyzing the low point positions of sales information; and (3) carrying out time-lapse passenger flow analysis to obtain commodity sales levels under different passenger flows, so as to further optimize the options and marketing schemes.
The technical scheme of the invention has the following beneficial effects:
the method can quickly obtain the basic personal information of the user, so that the marketing scheme of the user preference can be quickly and accurately presumed; through predicting the preference of the clients, targeted commodity marketing strategies and commodity selections are carried out.
Drawings
FIG. 1 is a schematic diagram of an unmanned retail device of the present invention;
FIG. 2 is an overall flow chart of the present invention;
FIG. 3 is a model training flow chart of the present invention.
In the figure: 1. a cabinet body; 2. a suspension arm; 3. a display screen; 4. video recognition equipment.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
In the description of the present invention, unless explicitly stated and limited otherwise, the terms "connected," "connected," and "fixed" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In the present invention, unless expressly stated or limited otherwise, a first feature "above" or "below" a second feature may include both the first and second features being in direct contact, as well as the first and second features not being in direct contact but being in contact with each other through additional features therebetween. Moreover, a first feature being "above," "over" and "on" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature includes the first feature being directly under and obliquely below the second feature, or simply means that the first feature is less level than the second feature.
In the description of the present embodiment, the terms "upper", "lower", "front", "rear", "left", "right", and the like are orientation or positional relationships based on those shown in the drawings, and are merely for convenience of description and simplicity of operation, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used merely for distinguishing between descriptions and not for distinguishing between them.
As shown in fig. 1-3, the present invention provides a method of visual marketing of an unmanned retail device, the method comprising the steps of:
s1, collecting image information through video identification equipment;
s2, carrying out image recognition and data analysis on the image information through a trained model to acquire surrounding environment information and user characteristic information;
s3, outputting the service strategy through the surrounding environment information and the user characteristic information.
In step S1, the image information collection specifically includes: acquiring surrounding environment information and client information through the video identification equipment 4;
the mounting position of the video recognition apparatus 4 includes: a hanging arm 2 above the cabinet body 1 is integrated in a display screen 3;
the video recognition apparatus includes: a cabinet camera and a screen camera;
cabinet camera: the device can be installed above or inside the retail device for limited monitoring, and the service condition of the device can be effectively monitored;
screen camera: the operation screen is installed, and the operation record can be effectively carried out on the use of a user like the front camera of the mobile equipment.
The surrounding environment information includes: people flow and environment summary;
the client information includes: the face and clothing of the user.
Note that: when a customer uses the unmanned retail equipment for the first time, a screen pops up an informed license agreement, wherein the informed license agreement comprises a user information acquisition range and options of whether to agree or not, the user is required to read the whole text, the clearly known content is checked, after the user reads the information, the user selects to agree and authorize the license to use, the information acquisition can be carried out, the information acquisition is only used for the subsequent personalized marketing of the user, and the data confidentiality is not used for other purposes;
in step S2, the training model specifically includes the following steps:
s201, extracting each frame of picture in a video, and preprocessing by adopting the same parameters to obtain a large number of environment pictures and face pictures, wherein the preprocessing comprises the following steps: scaling, cutting, graying and normalizing;
s202, establishing a CNN model, and determining parameters of the model, wherein the parameters comprise: depth, number of layers, convolution kernel size, pooling size;
s203, determining training parameters and an optimizer algorithm, and training the model, wherein the training parameters comprise: batch size, learning rate, training wheel number; the optimizer algorithm adopts: adam or SGD;
s204, importing training data, performing multi-round training on the model, adjusting parameters, and improving the accuracy of the model;
s205, evaluating the performance of the model by calculating the accuracy, recall or F1-score.
In step S2, image recognition and data analysis are performed through a model, specifically including:
inputting recorded video of the video identification device 4 into a model, and automatically analyzing and processing the model:
analyzing the surrounding environment information to obtain further information, including: environmental type (office building, school, etc.), people stream density, people stream time distribution, residence time before equipment;
analyzing the video operated by the user to obtain the user characteristics, including: gender, age range, clothing style, color value; and establishing a customer attribute label for predicting user preference.
In step S3, the service policy output includes: marketing recommendation, option updating, screen personalized display and passenger flow monitoring.
The marketing recommendation refers to:
forming a portrait based on the user information, and performing commodity discount promotion recommendation;
predicting favorite commodities of a user based on personal characteristics, and providing a personalized recommended sales promotion scheme;
wherein the personal characteristics include: age, sex, looks, clothing.
The option update means:
acquiring information of surrounding scenes and client preference information through scene and character information, and predicting sales volume; optimizing a selection scheme and improving sales level of the selection; further reducing the operation cost and the trial and error cost of the selection.
The screen personalized display means that:
displaying corresponding personalized commodity sales promotion and coupon package activities on a device screen according to the customer image;
and meanwhile, the screen self-lighting and marketing video playing are carried out, so that the promotion information is accepted by the clients.
The passenger flow monitoring means that:
by collecting images of the periphery of the unmanned retail equipment, the peripheral passenger flow condition is obtained,
judging whether the risk of resource waste exists or not by comprehensively analyzing the low point positions of sales information;
and (3) carrying out time-lapse passenger flow analysis to obtain commodity sales levels under different passenger flows, so as to further optimize the options and marketing schemes.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the description of the present invention and the accompanying drawings or direct/indirect application in other related technical fields are included in the scope of the present invention.
Claims (9)
1. A method of visual marketing of an unmanned retail device, the method comprising the steps of:
s1, collecting image information through video identification equipment;
s2, carrying out image recognition and data analysis on the image information through a trained model to acquire surrounding environment information and user characteristic information;
s3, outputting the service strategy through the surrounding environment information and the user characteristic information.
2. The method of visual marketing of an unmanned retail device of claim 1,
in step S1, the image information collection specifically includes: acquiring surrounding environment information and customer information through video identification equipment (4);
the mounting position of the video recognition device (4) includes: the hanging arm (2) above the cabinet body (1) is integrated in the display screen (3);
the surrounding environment information includes: people flow and environment summary;
the client information includes: the face and clothing of the user.
3. The method of visual marketing of an unmanned retail device of claim 1,
in step S2, the training model specifically includes the following steps:
s201, extracting each frame of picture in a video, and preprocessing by adopting the same parameters to obtain a large number of environment pictures and face pictures, wherein the preprocessing comprises the following steps: scaling, cutting, graying and normalizing;
s202, establishing a CNN model, and determining parameters of the model, wherein the parameters comprise: depth, number of layers, convolution kernel size, pooling size;
s203, determining training parameters and an optimizer algorithm, and training the model, wherein the training parameters comprise: batch size, learning rate, training wheel number; the optimizer algorithm adopts: adam or SGD;
s204, importing training data, performing multi-round training on the model, adjusting parameters, and improving the accuracy of the model;
s205, evaluating the performance of the model by calculating the accuracy, recall or F1-score.
4. The method of visual marketing of unmanned retail equipment according to claim 1, characterized in that in step S2, image recognition and data analysis are performed by means of a model, comprising in particular:
inputting recorded video of the video identification equipment (4) into a model, and automatically analyzing and processing the model:
analyzing the surrounding environment information to obtain further information, including: environmental type, people stream density, people stream time distribution, pre-equipment residence time;
analyzing the video operated by the user to obtain the user characteristics, including: gender, age range, clothing style, color value; and establishing a customer attribute label for predicting user preference.
5. The method of visual marketing of an unmanned retail device of claim 1,
in step S3, the service policy output includes: marketing recommendation, option updating, screen personalized display and passenger flow monitoring.
6. The method of visual marketing of an unmanned retail device of claim 5,
the marketing recommendation refers to:
forming a portrait based on the user information, and performing commodity discount promotion recommendation;
predicting favorite commodities of a user based on personal characteristics, and providing a personalized recommended sales promotion scheme;
wherein the personal characteristics include: age, sex, looks, clothing.
7. The method of visual marketing of an unmanned retail device of claim 5,
the option update means:
acquiring information of surrounding scenes and client preference information through scene and character information, and predicting sales volume; optimizing a selection scheme and improving sales level of the selection; further reducing the operation cost and the trial and error cost of the selection.
8. The method of visual marketing of an unmanned retail device of claim 5,
the screen personalized display means that:
displaying corresponding personalized commodity sales promotion and coupon package activities on a device screen according to the customer image;
and meanwhile, the screen self-lighting and marketing video playing are carried out, so that the promotion information is accepted by the clients.
9. The method of visual marketing of an unmanned retail device of claim 5,
the passenger flow monitoring means that:
by collecting images of the periphery of the unmanned retail equipment, the peripheral passenger flow condition is obtained,
judging whether the risk of resource waste exists or not by comprehensively analyzing the low point positions of sales information;
and (3) carrying out time-lapse passenger flow analysis to obtain commodity sales levels under different passenger flows, so as to further optimize the options and marketing schemes.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310353891.8A CN116823378A (en) | 2023-03-24 | 2023-03-24 | Visual marketing method for unmanned retail equipment |
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| CN202310353891.8A CN116823378A (en) | 2023-03-24 | 2023-03-24 | Visual marketing method for unmanned retail equipment |
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