CN113888254A - Shelf commodity management method and electronic equipment - Google Patents
Shelf commodity management method and electronic equipment Download PDFInfo
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- CN113888254A CN113888254A CN202111070178.XA CN202111070178A CN113888254A CN 113888254 A CN113888254 A CN 113888254A CN 202111070178 A CN202111070178 A CN 202111070178A CN 113888254 A CN113888254 A CN 113888254A
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Abstract
The application discloses a shelf commodity management method and electronic equipment, wherein the shelf management method comprises the following steps: acquiring a real-time state image of the goods shelf; inputting the real-time state image into a pre-trained commodity state detection model to obtain commodity state information, wherein the commodity state information comprises at least one of the out-of-stock degree, out-of-stock commodity information, out-of-stock position and commodity uniformity; determining a commodity management policy based on the commodity state information. After the commodity state information is detected, a commodity management strategy can be determined in real time based on the commodity state information, excessive human participation is not needed for commodity management, commodity state analysis is carried out on a real-time image of a shelf, and the management strategy is directly determined in real time according to the predicted commodity state information.
Description
Technical Field
The application relates to the technical field of intelligent retail, in particular to a goods shelf commodity management method and electronic equipment.
Background
With the continuous and steady development of the economy of retail industry in China, the scale of a large supermarket is continuously enlarged, and the problem of shelf commodity management is also generated. Traditional supermarket management mainly depends on manpower, and shelf commodity management requires a large amount of labor force; the 'unmanned supermarket' is mainly based on a radio frequency identification (FRID) scheme, can complete basic intelligent management, such as intelligent cash register, but the management of goods on shelves, such as lack of goods detection, replenishment, goods placement and the like, still has no corresponding intelligent processing scheme. In the prior art, identification is carried out on the basis of scanning bar codes containing cargo information, and the method is an indirect identification method and is complex to operate in practical application.
Therefore, how to realize intelligent management of shelf products is a technical problem to be solved urgently.
Disclosure of Invention
The application provides a shelf commodity management method and electronic equipment, which are used for at least solving the technical problems in the related art.
According to a first aspect of the present application, there is provided a shelf commodity management method including: acquiring a real-time state image of the goods shelf; inputting the real-time state image into a pre-trained commodity state detection model to obtain commodity state information, wherein the commodity state information comprises at least one of the out-of-stock degree, out-of-stock commodity information, out-of-stock position and commodity uniformity; determining a commodity management policy based on the commodity state information.
Optionally, the determining the goods management policy based on the goods status information includes: analyzing the sales popularity of each commodity based on the out-of-stock degree and the out-of-stock commodity information; analyzing the matched commodities of the commodities with the sales heat greater than the preset heat; and adjusting the commodity type and/or the placing position on the goods shelf based on the matched commodity.
Optionally, the determining a product management policy based on the product status information further includes: determining the sales heat of each commodity and the sales heat of the shelf position based on the out-of-stock degree, the out-of-stock commodity information and the out-of-stock position; and determining the type of replenishment commodities and the placement positions of various commodities on the current shelf by integrating the sales heat of the commodities and the sales heat of the positions of the shelves.
Optionally, the determining a product management policy based on the product status information further includes: determining a packaging type of the goods placed on the shelf based on the goods tidiness.
Optionally, the determining a product management policy based on the product status information further includes: counting commodity state information of a plurality of shelves; performing big data analysis based on the commodity state information of the plurality of shelves to obtain the distribution state of the commodity sales heat in the area where each shelf is located and the customer shopping habits of the area where each shelf is located; and adjusting the commodity replenishment strategy and the commodity placement strategy of each shelf based on the distribution state and the customer purchasing habit.
Optionally, the shelf product management method further includes: acquiring attribute information of a newly opened area; and determining a commodity replenishment strategy and a commodity placement strategy of the shelves in the newly opened area based on the distribution state, the customer purchasing habits and the attribute information of the newly opened area.
Optionally, the acquiring the real-time status image of the shelf includes: acquiring a video stream containing shelves; and intercepting shelf images in the video stream as the real-time state images at intervals of preset time according to preset coordinates.
Optionally, the inputting the real-time state image into a pre-trained commodity state detection model to obtain commodity state information includes: inputting the real-time state image into the commodity detection model, and detecting the commodity information and the vacant position to obtain the commodity information and the vacant position information; and calculating the out-of-stock degree, the out-of-stock commodity information, the out-of-stock position and the commodity uniformity based on the commodity information and the vacancy position information.
According to a second aspect of the present application, embodiments of the present application further provide a shelf product management device, including: the acquisition module is used for acquiring a real-time state image of the goods shelf; the detection module is used for inputting the real-time state image into a pre-trained commodity state detection model to obtain commodity state information, wherein the commodity state information comprises at least one of the out-of-stock degree, out-of-stock commodity information, out-of-stock position and commodity uniformity; and the determining module is used for determining the commodity management strategy based on the commodity state information.
According to a third aspect of the present application, an embodiment of the present application further provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the shelf good management method of any one of the first aspect.
According to a fourth aspect of the present application, there is also provided a computer-readable storage medium storing a computer program which, when executed by a processor, implements the shelf goods management method according to any one of the first aspects.
The shelf commodity management method comprises the steps of detecting a real-time state image of a shelf by utilizing a pre-trained commodity state detection model to obtain commodity state information comprising at least one of the degree of shortage, the information of the shortage commodity, the position of the shortage and the uniformity of the commodity, determining a commodity management strategy in real time based on the commodity state information after the commodity state information is detected, performing commodity state analysis on the real-time image of the shelf without excessive human participation in commodity management, and further directly determining the management strategy in real time according to predicted commodity state information, for example, determining the type and the quantity of commodities needing replenishment according to shortage reminding. And the goods placing strategy can be formulated and adjusted according to the goods state information. The shelf goods are managed more intelligently.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart of a shelf goods management method provided by the present application;
FIG. 2 is a schematic view of a real-time status image capture of a shelf provided herein;
FIG. 3 is a schematic view of a shelf merchandise management device provided herein;
fig. 4 is a schematic diagram of an electronic device provided in the present application.
Detailed Description
In order to more clearly explain the overall concept of the present application, the following detailed description is given by way of example in conjunction with the accompanying drawings.
In order to make the technical solutions better understood by those skilled in the art, 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 partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the application provides a shelf commodity management method, and referring to fig. 1, the method may include the following steps:
and S10, acquiring a real-time state image of the goods shelf. As an exemplary embodiment, the shelf may be a vending machine or a store shelf, etc. Wherein, the front surface or the periphery of the goods shelf is provided with a camera for collecting video information or image information of the goods shelf by a user. The camera can acquire the real-time state image of goods shelves in real time. As an alternative embodiment, a video stream containing shelves is obtained; and intercepting shelf images in the video stream as the real-time state images at intervals of preset time according to preset coordinates. For example, a video stream with a preset number of frames may be captured at preset time intervals, and the position of the shelf in the image is calibrated in the image of the video stream, as shown in fig. 2, the coordinates of the upper left corner (x1, y1) and the coordinates of the lower right corner (x2, y2) of the shelf are calibrated in the image, and the image of the shelf is captured by the calibrated coordinates to serve as the real-time status image of the shelf.
And S20, inputting the real-time state image into a pre-trained commodity state detection model to obtain commodity state information, wherein the commodity state information comprises at least one of the shortage degree, the shortage commodity information, the shortage position and the commodity uniformity. As an exemplary embodiment, the commodity state detection model may employ a neural network model, for example, a convolutional neural network model, a deep convolutional neural network model, or the like. The commodity state detection model may be pre-trained using training samples. Specifically, the data of the out-of-stock degree, the out-of-stock commodity information, the out-of-stock position and the commodity regularity of the label can be collected to train the model, and in the embodiment, the out-of-stock detection can be taken as an example to explain the training process:
and manually marking commodities and the positions of the out-of-stock in the training images, and training the shelf model in a hardware environment with a GPU (graphics processing unit) so as to improve the convergence rate of model training. Using small Momentum factors (Momentum)Batch (Mini-batch) Stochastic Gradient Descent (SGD) to train the network. Wherein the number of image samples per batch (Batchsize) is set to 8, the momentum factor is set to a fixed value of 0.95, and the weight Decay (Decay) is 4X 10-4. The initialization of the weights affects the convergence rate of the model training, and the initial learning rate (Learningrate) is set to 2 × 10-4And in the training process, data enhancement is carried out in a random cutting and horizontal turning mode. The size of the finally obtained training image is 608 × 608, and Loss basically converges to a stable value and is less than 0.1, which indicates that the network model has reached the expected training effect and stops training, so that the final model is obtained. In the embodiment, a Histogram of Oriented Gradients (HOG) feature is used, so that the image feature does not need to be analyzed artificially, and meanwhile, the sensitivity to the change of the color and the brightness of the image is small, and the robustness is high.
As an optional embodiment, during training, the degree of out-of-stock, the information of out-of-stock goods, the position of out-of-stock, and the label of goods uniformity may be marked in the training sample image, respectively, for training, in this embodiment, a separate model may be used for detecting each kind of goods state information, or one model may be used.
And S30, determining a commodity management strategy based on the commodity state information. As an exemplary embodiment, after the commodity state information is detected, the commodity management strategy may be determined in real time based on the commodity state information, the commodity state analysis is performed on the real-time shelf image without excessive human involvement for commodity management, and the management strategy is determined in real time directly according to the predicted commodity state information, for example, the type and the number of the commodities needing replenishment are determined according to the shortage reminding. And the goods placing strategy can be formulated and adjusted according to the goods state information. The shelf goods are managed more intelligently.
As an exemplary embodiment, determining the goods management policy based on the goods status information may include: analyzing the sales popularity of each commodity based on the out-of-stock degree and the out-of-stock commodity information; analyzing the matched commodities of the commodities with the sales heat greater than the preset heat; and adjusting the commodity type and/or the placing position on the goods shelf based on the matched commodity.
For example, since the sales of the commodities on the shelves are often different in degree of hotness, the sales of the commodities on the shelves can be analyzed based on the commodity state information output from the model in order to increase the sales volume of the whole shelves. Specifically, the sales popularity may be determined based on the commodity sales rate, the remaining quantity or the sales quantity of each commodity in a certain time period, for example, a day, a week, or a longer or shorter time period, may be detected, the sales quantity of each commodity in a preset time period may be determined, and then the sales popularity may be determined. The remaining quantity or sales quantity of the goods can be detected based on the model to detect the stock shortage degree of a certain position of the shelf, for example, the volume of stock shortage space of a certain position can be detected to determine the remaining quantity or sales quantity.
After the sales popularity of each commodity is obtained, the commodity with the sales popularity larger than the preset popularity is selected as a hot-sold commodity, and the collocation commodity of the current commodity is analyzed. For example, if the milk is a hot sale, the matching commodity can be breakfast bread or breakfast biscuits; for example, drinks are hot sales, and the matching goods can be corresponding snacks, snacks and the like. When the collocated goods are obtained, the shelf goods management strategy can be executed based on the collocated goods obtained through analysis, and the goods type and/or the placing position on the shelf are adjusted, for example, the collocated goods are placed at a position close to hot-sell goods.
As an exemplary embodiment, determining the goods management policy based on the goods status information further comprises: determining the sales heat of each commodity and the sales heat of the shelf position based on the out-of-stock degree, the out-of-stock commodity information and the out-of-stock position; and determining the type of replenishment commodities and the placement positions of various commodities on the current shelf by integrating the sales heat of the commodities and the sales heat of the positions of the shelves. For example, after obtaining the commodity status information, the shortage degree, shortage position, and the like of each commodity in a certain time period, for example, a day, a week, or a longer or shorter time period, may be counted to determine the sales heat of each commodity and the sales heat of the shelf position, where the sales heat of the shelf position may be a position where the probability of the occurrence of the shortage position on the shelf is large, for example, a shelf top position, a shelf middle position, and the like.
In some embodiments, in general, the hot-pin positions of the shelves (the shelf positions with sales heat greater than the preset heat, in particular, see the description of the hot-pin products in the above embodiments) are often easy to see or take by customers who shop for products, and therefore, the placement strategy of the goods in the prior art is to place the hot-pin products in the middle layer of the shelves, or to place the hot-pin positions of the shelves with shorter heights in the upper layer of the shelves. However, for different zones, such as a child-prone zone such as a casino, the hot-pin locations for the vending machine may be located in the lower level of the shelves. Therefore, a situation may occur in which the hot-pinned commodity corresponds to the hot-pinning area. And, due to different distribution of people in different areas, hot-market goods tend to be different. Therefore, hot-sold commodities and hot-sold positions in the current area can be determined according to the commodity sales state, and the replenishment commodity types and various commodity placement positions of the current shelf can be determined based on the hot-sold commodities and the hot-sold positions.
As an alternative embodiment, the shopping habits of the area where the shelf is located may also be analyzed based on the obtained hot marketer and the hot marketing position, where the shopping habits may include the preference of the type of the customer goods and the preference of the shopping position on the shelf, and the shelf goods placement and replenishment strategy may be adjusted based on the shopping habits.
As an exemplary embodiment, the packaging type of the goods placed on the shelf may be determined based on the goods tidiness, which may be, for example, the tidiness of a certain position of the shelf or the tidiness of the entire shelf. For example, the commodity regularity may be detected based on the commodity state detection model to obtain regularity at a certain position of the shelf or regularity of the entire shelf. The commodity regularity can represent that the commodity on the current position or the current goods shelves is prone to toppling, skewing and other conditions when being selected for purchase, so that the corresponding packaging type can be selected just for the commodity regularity, for example, square packaged commodities can be placed in the position or the area with lower regularity, and round packaged or bagged commodities can be placed in the area with higher regularity. So as to improve the overall appearance of the goods on the shelf.
As an exemplary embodiment, if there are multiple shelves, e.g., multiple autonomous vending machines, the product management policies may be more finely adjusted based on the product status of the multiple shelves with big data analysis. Illustratively, counting commodity state information of a plurality of shelves; performing big data analysis based on the commodity state information of the plurality of shelves to obtain the distribution state of the commodity sales heat in the area where each shelf is located and the customer shopping habits of the area where each shelf is located; and adjusting the commodity replenishment strategy and the commodity placement strategy of each shelf based on the distribution state and the customer purchasing habit. The distribution state of the sales heat of the commodities can comprise the sales heat of the commodities at various positions on the shelf and the types of hot commodities. Specifically, after the commodity state information of a large number of shelf commodities is obtained, hot-sold commodities of the shelf in a certain type of area and customer purchasing habits of the certain type of area are analyzed based on big data, and commodity placement and replenishment are carried out on the shelf of the corresponding type area according to the hot-sold commodities and the purchasing habits aiming at the hot-sold commodity distribution state of the certain type of area and the customer purchasing habits of the certain type of area. As an exemplary embodiment, when a shelf is arranged in a newly opened area, attribute information of the newly opened area, for example, attributes such as an area location and a customer group, may be determined first, a corresponding area of an existing shelf is found based on the attribute information, and a goods replenishment policy and a goods placement policy of the shelf in the newly opened area are made for a distribution state of hot sold goods in the existing shelf area and a customer shopping habit.
An embodiment of the present application further provides a shelf product management method and apparatus, as shown in fig. 3, including: the acquisition module 10 is used for acquiring a real-time state image of the shelf; the detection module 20 is configured to input the real-time state image to a pre-trained commodity state detection model to obtain commodity state information, where the commodity state information includes at least one of a shortage degree, shortage commodity information, a shortage position, and commodity uniformity; a determining module 30, configured to determine a product management policy based on the product status information.
It should be noted that the obtaining module 10 in this embodiment may be configured to execute the step S10, the detecting module 20 in this embodiment may be configured to execute the step S20, and the determining module 30 in this embodiment may be configured to execute the step S30.
It should be noted here that the modules described above are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above embodiments. It should be noted that the modules described above as a part of the apparatus may be run in a hardware environment as shown in fig. 4, may be implemented by software, and may also be implemented by hardware, where the hardware environment includes a network environment.
Therefore, according to still another aspect of the embodiments of the present application, there is also provided an electronic device for implementing the shelf goods management method, where the electronic device may be a server, a terminal, or a combination thereof.
Fig. 4 is a block diagram of an alternative electronic device according to an embodiment of the present application, as shown in fig. 4, including a processor 401, a communication interface 402, a memory 403, and a communication bus 404, where the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404, where,
a memory 403 for storing a computer program;
the processor 401, when executing the computer program stored in the memory 403, implements the following steps:
acquiring a real-time state image of the goods shelf;
inputting the real-time state image into a pre-trained commodity state detection model to obtain commodity state information, wherein the commodity state information comprises at least one of the out-of-stock degree, out-of-stock commodity information, out-of-stock position and commodity uniformity;
determining a commodity management policy based on the commodity state information.
Alternatively, in this embodiment, the communication bus may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The memory may include RAM, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory. Alternatively, the memory may be at least one memory device located remotely from the processor.
The processor may be a general-purpose processor, and may include but is not limited to: a CPU (Central Processing Unit), an NP (Network Processor), and the like; but also a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
It can be understood by those skilled in the art that the structure shown in fig. 4 is only an illustration, and the device implementing the shelf-based commodity management method may be a terminal device, and the terminal device may be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 4 is a diagram illustrating the structure of the electronic device. For example, the terminal device may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 4, or have a different configuration than shown in FIG. 4.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disk, ROM, RAM, magnetic or optical disk, and the like.
According to still another aspect of an embodiment of the present application, there is also provided a storage medium. Alternatively, in the present embodiment, the storage medium may be a program code for executing the shelf commodity management method.
Optionally, in this embodiment, the storage medium may be located on at least one of a plurality of network devices in a network shown in the above embodiment.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps:
acquiring a real-time state image of the goods shelf;
inputting the real-time state image into a pre-trained commodity state detection model to obtain commodity state information, wherein the commodity state information comprises at least one of the out-of-stock degree, out-of-stock commodity information, out-of-stock position and commodity uniformity;
determining a commodity management policy based on the commodity state information.
Optionally, the specific example in this embodiment may refer to the example described in the above embodiment, which is not described again in this embodiment.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a U disk, a ROM, a RAM, a removable hard disk, a magnetic disk, or an optical disk.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including instructions for causing one or more computer devices (which may be personal computers, servers, network devices, or the like) to execute all or part of the steps of the method described in the embodiments of the present application.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, and may also be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution provided in the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
Where not mentioned in this application, can be accomplished using or referencing existing technology.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A shelf product management method, comprising:
acquiring a real-time state image of the goods shelf;
inputting the real-time state image into a pre-trained commodity state detection model to obtain commodity state information, wherein the commodity state information comprises at least one of the out-of-stock degree, out-of-stock commodity information, out-of-stock position and commodity uniformity;
determining a commodity management policy based on the commodity state information.
2. The shelf-commodity management method according to claim 1, wherein the determining the commodity management policy based on the commodity state information includes:
analyzing the sales popularity of each commodity based on the out-of-stock degree and the out-of-stock commodity information;
analyzing the matched commodities of the commodities with the sales heat greater than the preset heat;
and adjusting the commodity type and/or the placing position on the goods shelf based on the matched commodity.
3. The method of managing as set forth in claim 1, wherein the determining a merchandise management policy based on the merchandise status information further comprises:
determining the sales heat of each commodity and the sales heat of the shelf position based on the out-of-stock degree, the out-of-stock commodity information and the out-of-stock position;
and determining the type of replenishment commodities and the placement positions of various commodities on the current shelf by integrating the sales heat of the commodities and the sales heat of the positions of the shelves.
4. The management method according to any one of claims 1 to 3, wherein the determining of the commodity management policy based on the commodity state information further comprises:
determining a packaging type of the goods placed on the shelf based on the goods tidiness.
5. The method of managing as set forth in claim 4, wherein the determining a merchandise management policy based on the merchandise status information further comprises:
counting commodity state information of a plurality of shelves;
performing big data analysis based on the commodity state information of the plurality of shelves to obtain the distribution state of the commodity sales heat in the area where each shelf is located and the customer shopping habits of the area where each shelf is located;
and adjusting the commodity replenishment strategy and the commodity placement strategy of each shelf based on the distribution state and the customer purchasing habit.
6. The management method of claim 5, further comprising:
acquiring attribute information of a newly opened area;
and determining a commodity replenishment strategy and a commodity placement strategy of the shelves in the newly opened area based on the distribution state, the customer purchasing habits and the attribute information of the newly opened area.
7. The shelf merchandise management method of claim 1 wherein said obtaining a real-time status image of the shelf comprises:
acquiring a video stream containing shelves;
and intercepting shelf images in the video stream as the real-time state images at intervals of preset time according to preset coordinates.
8. The shelf commodity management method according to claim 1, wherein the inputting the real-time status image into a pre-trained commodity status detection model to obtain commodity status information comprises:
inputting the real-time state image into the commodity detection model, and detecting the commodity information and the vacant position to obtain the commodity information and the vacant position information;
and calculating the out-of-stock degree, the out-of-stock commodity information, the out-of-stock position and the commodity uniformity based on the commodity information and the vacancy position information.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the shelf merchandise management method of any one of claims 1-8.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the shelf good management method according to any one of claims 1 to 7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114511820A (en) * | 2022-04-14 | 2022-05-17 | 美宜佳控股有限公司 | Goods shelf commodity detection method and device, computer equipment and storage medium |
| CN115965324A (en) * | 2023-03-16 | 2023-04-14 | 浙江天柜科技有限公司 | Commodity selling method and system based on vending machine |
| CN116629979A (en) * | 2023-07-21 | 2023-08-22 | 深圳市方度电子有限公司 | Digital store management system and method |
| CN116629979B (en) * | 2023-07-21 | 2024-04-26 | 深圳市方度电子有限公司 | Digital store management system and method |
-
2021
- 2021-09-13 CN CN202111070178.XA patent/CN113888254A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114511820A (en) * | 2022-04-14 | 2022-05-17 | 美宜佳控股有限公司 | Goods shelf commodity detection method and device, computer equipment and storage medium |
| CN115965324A (en) * | 2023-03-16 | 2023-04-14 | 浙江天柜科技有限公司 | Commodity selling method and system based on vending machine |
| CN115965324B (en) * | 2023-03-16 | 2023-06-06 | 浙江天柜科技有限公司 | Commodity sales method and system based on vending machine |
| CN116629979A (en) * | 2023-07-21 | 2023-08-22 | 深圳市方度电子有限公司 | Digital store management system and method |
| CN116629979B (en) * | 2023-07-21 | 2024-04-26 | 深圳市方度电子有限公司 | Digital store management system and method |
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