Retail management method and system based on Internet of Things

Abstract

The invention discloses a retail management method and system based on the Internet of Things, and the method comprises the steps: capturing a shopping video of a consumer in a supermarket through a camera device, obtaining commodity data and sensor data, and obtaining the commodity data matched with the shopping video; constructing a three-dimensional digital twinborn model according to the shelf position and commodity distribution in the supermarket, and carrying out commodity category division according to whether commodities are scattered commodities or not; analyzing a personal shopping list of the consumer according to the shopping video, matching the commodity data or the sensor data according to the commodity type in the personal shopping list, and counting the personal expenditure in the personal shopping list; when it is recognized that the consumer enters the settlement area according to the total consumption amount, whether the consumer has an associated consumer or not is judged according to the shopping video, and the final total consumption amount is determined, and the method has the advantages of improving retail management efficiency and optimizing the consumer satisfaction degree.

Description

Technical Field

[0001] The present invention relates to the technical field of retail management, and particularly to an Internet of Things-based retail management method and system. Background Art

[0002] The shopping peak periods in shopping malls often concentrate on weekends, public holidays, and special promotion periods. During these periods, the number of people in the shopping mall increases significantly, resulting in extremely long queues in front of the cashiers. To effectively manage the large number of customers during this period, shopping malls usually implement various strategies, such as hiring temporary cashiers, opening additional checkout channels, and optimizing the customer flow route, aiming to maintain the normal operation of the shopping mall and ensure customer satisfaction. Despite these measures taken by shopping malls to relieve the pressure during peak periods, consumers may still have to endure long waiting times when shopping during these periods, which not only affects their shopping experience but also may affect their overall impression of the shopping mall. Long queues may make some customers impatient and thus choose to leave, which poses a potential risk of losing customers to the shopping mall. Therefore, it is necessary to design an Internet of Things-based retail management method and system to improve retail management efficiency and optimize consumer satisfaction. Summary of the Invention

[0003] The purpose of the present invention is to provide an Internet of Things-based retail management method and system to solve the problems raised in the above background art.

[0004] To solve the above technical problems, the present invention provides the following technical solution: An Internet of Things-based retail management method, and the running steps of the method include:

[0005] Step S1: Capture the shopping videos of consumers in the supermarket through a camera device, obtain product data and sensor data according to the shopping videos, and obtain product data matching the shopping videos;

[0006] Step S2: Construct a three-dimensional digital twin model according to the shelf positions and product distributions in the supermarket, and classify product categories according to whether the products are bulk products;

[0007] Step S3: Analyze the personal shopping list of the consumer according to the shopping videos, match the product data or the sensor data according to the product categories in the personal shopping list, and calculate the personal consumption amount in the personal shopping list;

[0008] Step S4: When it is identified according to the total consumption amount that the consumer enters the settlement area, judge whether the consumer has associated consumers according to the shopping videos and determine the final total consumption amount. The associated consumers refer to other shoppers who shop, travel together with the consumer, and jointly complete the payment.

[0009] Further, step S1 further includes the following steps:

[0010] Step S11: Establish an identity identification number for the consumer;

[0011] Step S12: Analyze whether the consumer has associated consumers based on the usage pattern of the shopping cart (shopping basket) according to the shopping video. The usage pattern is divided into an exclusive mode and a shared mode;

[0012] Step S13: List the commodities in the shopping cart into the personal shopping list and the joint shopping list respectively according to the usage pattern, and calculate the total consumption amount of the associated user group.

[0013] Further, step S2 further includes the following steps:

[0014] Step S21: Generate a three-dimensional digital twin model of the supermarket by scanning the internal space of the supermarket, and obtain the spatial coordinate range of the shelves according to the three-dimensional digital twin model;

[0015] Step S22: Based on the spatial coordinate range and the sizes of the commodities displayed on each shelf, obtain the spatial coordinate set of each type of commodity on the shelf, and obtain the basic information of each type of commodity. Among them, the basic information includes: external features, price data;

[0016] Step S23: Classify the commodities into first commodities and second commodities according to whether the commodities are bulk commodities;

[0017] Step S24: Deploy sensors on the shelves storing the second commodities, and update the original weight of the commodities on the shelves in real time according to the sensors.

[0018] Further, step S3 further includes the following steps:

[0019] Step S31: Extract the hand coordinates of the consumer through the skeleton key point tracking algorithm according to the shopping video. When the distance between the hand coordinates and the shelf is less than the distance threshold, obtain the time series of the hand coordinates, and judge the hand vector according to the hand coordinate time series;

[0020] Step S32: Identify and analyze the operation type of the consumer on the commodity according to the direction of the hand vector combined with the state of the commodity in the hand, and dynamically update the personal shopping list. The operation type includes: taking operation and returning operation;

[0021] Step S33: When the consumer performs the taking operation, a differential pricing strategy is adopted for the types of goods taken, and the personal consumption amount is updated.

[0022] Further, step S32 further includes: when it is determined that the consumer's operation on the goods to be returned is the return operation, the basic information of the placement location of the goods to be returned is matched, the attribution of the goods to be returned is verified, and when the attribution is abnormal, the spatial coordinate set is reconstructed according to the basic information of the goods to be returned.

[0023] Further, step S4 further includes the following steps:

[0024] Step S41: Detect the goods handover behavior between the consumer and other consumers according to the shopping video, establish the associated user group, and synchronize the total consumption amount of the associated user group;

[0025] Step S42: When it is recognized that the consumer enters the settlement area, it is recognized whether the consumer has an associated consumer. When the consumer does not have an associated consumer, the personal consumption amount of the consumer is the settlement amount for this settlement area;

[0026] When it is recognized that the consumer has an associated consumer, verify the integrity of the goods in the associated user group and determine the settlement amount for this settlement area.

[0027] Further, the system includes a data acquisition module and a digital twin modeling module:

[0028] The data acquisition module is used to capture the shopping video of consumers in the supermarket through a camera device, obtain commodity data and sensor data according to the shopping video, and obtain commodity data matching the shopping video;

[0029] The digital twin modeling module is used to construct a three-dimensional digital twin model according to the shelf positions and commodity distributions in the supermarket, and classify the types of goods according to whether the goods are bulk goods.

[0030] Further, the system further includes a behavior analysis and list generation module and a collaborative settlement processing module:

[0031] The behavior analysis and list generation module is used to analyze the personal shopping list of the consumer according to the shopping video, match the commodity data or the sensor data according to the types of goods in the personal shopping list, and count the personal consumption amount in the personal shopping list;

[0032] The collaborative settlement processing module is configured to, when it is recognized that the consumer enters the settlement area according to the total consumption amount, determine whether there is an associated consumer for the consumer and determine the final total consumption amount according to the shopping video. The associated consumer refers to other shoppers who shop together with the consumer, travel together, and jointly complete the payment. The associated consumer refers to other shoppers who shop together with the consumer, travel together, and jointly complete the payment.

[0033] In a third aspect of the present application, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electronic device implements the method described in the first aspect of the present application.

[0034] In a fourth aspect of the present application, there is provided a computer-readable storage medium for storing a computer program. When the computer program runs on a computer, the computer is caused to execute the method described in the first aspect of the present application.

[0035] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By capturing shopping videos through a camera device and combining sensor data, the present invention realizes real-time monitoring and accurate identification of consumers' shopping behaviors. Consumers do not need to manually scan goods or wait for manual settlement. The system can automatically update the shopping list and calculate the consumption amount, significantly reducing the queuing waiting time and enhancing the shopping experience. By introducing the concept of associated consumers and identifying the behavior of transferring goods through hand coordinates and shopping videos, the system can automatically synchronize the commodity information to the shopping lists of the corresponding consumers, effectively solving the problem of unclear ownership of goods when multiple people shop together and simplifying the settlement process. For bulk goods, traditional supermarkets need to weigh and scan the goods one by one at the cashier, increasing the settlement time and the usage amount of plastic bags. The present invention directly adds the commodity information to the shopping list through real-time weight monitoring and price calculation, reducing the weighing and scanning steps, lowering the operating cost, and at the same time reducing resource waste. Furthermore, it improves the retail management efficiency and optimizes the consumer satisfaction. Description of the Drawings

[0036] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0037] Figure 1 It is a schematic flowchart diagram of a retail management method based on the Internet of Things provided in Embodiment 1 of the present invention.

[0038] Figure 2 It is a schematic diagram of the module composition of a retail management system based on the Internet of Things provided in Embodiment 2 of the present invention.

[0039] Figure 3 It is a schematic diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0041] This embodiment can be applied to the scenario where consumers check out in the retail industry. This method can be executed by a retail management system based on the Internet of Things provided in this embodiment. Figure 1 It is a schematic flowchart of a retail management method based on the Internet of Things provided in Embodiment 1 of the present invention. This method specifically includes the following steps:

[0042] Step S1: Capture the shopping video of consumers in the supermarket through a camera device, obtain product data and sensor data according to the shopping video, and obtain product data matching the shopping video.

[0043] Step S2: Construct a three-dimensional digital twin model according to the shelf positions and product distributions in the supermarket, and classify product types according to whether the products are bulk products.

[0044] Step S3: Analyze the personal shopping list of the consumer according to the shopping video, match the product data or the sensor data according to the product types in the personal shopping list, and count the personal consumption amount in the personal shopping list.

[0045] Step S4: When it is recognized according to the total consumption amount that the consumer enters the settlement area, judge whether the consumer has associated consumers according to the shopping video and determine the final total consumption amount. The associated consumer refers to other shoppers who shop, travel together with the consumer and complete the payment together. For example, a family may consist of parents and children, or several friends shop together. They may put the products in the same shopping cart or go to the settlement area together.

[0046] Specifically, a three-dimensional digital twin model is constructed based on the supermarket shelf positions and product distribution, and the products are classified according to whether they are bulk products, realizing the digital mapping of the shopping environment. This model not only improves the efficiency of product management but also provides accurate spatial information for consumer behavior analysis. By analyzing the consumer behavior in the shopping video and combining with the product distribution in the three-dimensional digital twin model, the system can generate and update the consumer's personal shopping list in real time. This intelligent recognition and update mechanism not only improves the accuracy of the shopping list but also reduces the need for manual intervention, enhancing the shopping experience. When the consumer enters the settlement area, the system can determine whether there are associated consumers through the shopping video and determine the final total consumption amount based on the association relationship. This intelligent settlement method not only simplifies the settlement process for multiple-person shopping but also reduces the settlement time and improves the settlement efficiency.

[0047] In some preferred embodiments, the step S1 further includes the following steps:

[0048] Step S11: When it is recognized that the consumer enters the area of the supermarket, assign a unique number to the consumer;

[0049] Step S12: Determine whether the shopping cart (shopping basket) is used separately by the consumer according to the shopping video. When the shopping cart is used exclusively, add the consumer's number to the shopping cart. When the shopping cart is shared by multiple people, add an associated number set to the shopping cart. The associated number set contains the numbers of all consumers who put products into the shopping cart, and determine that there is an association between the consumers represented in the associated number set;

[0050] Step S13: When the shopping cart is used separately, list the products in the shopping cart in the personal shopping list of the consumer with the same number. When the shopping cart is used by multiple people, list the products in the shopping cart in the common shopping list of all consumers in the associated number set, and preferentially count the total consumption amount in the personal shopping lists of all consumers in the associated number set.

[0051] Specifically, through the consumer unique identifier assignment technology, the accurate tracking and data isolation of individual consumption behaviors are realized, effectively avoiding the data confusion problem in the multi-user cross-shopping scenario. Through the shopping cart usage pattern dynamic detection algorithm, the real-time perception of the shared shopping scenario is realized. By adopting the associated number set construction technology, the system can automatically identify the consumer groups with payment associations. Through the intelligent shopping list allocation mechanism, the problem of determining the ownership of products shared by multiple people in traditional retail is solved. For the shopping cart in the exclusive mode, a direct mapping strategy is adopted, and for the shopping cart in the shared mode, a distributed list management technology is adopted, enabling the common products to be synchronously included in the personal consumption records of multiple associated users.

[0052] In some preferred embodiments, step S2 further includes the following steps:

[0053] Step S21: By scanning the internal space of the supermarket, a three-dimensional digital twin model of the supermarket is generated, the positions and sizes of the shelves in the supermarket are identified, and the spatial coordinate ranges of the shelves in the three-dimensional digital twin model are obtained according to the positions and sizes of the shelves. For example, the shelf coordinates in the fresh food area are significantly different from those in the daily necessities area, facilitating the system to quickly identify the categories of the goods taken by consumers in different areas;

[0054] Step S22: Based on the spatial coordinate ranges and the sizes of each commodity displayed in each shelf, a set of spatial coordinates for each category of commodity in the shelf is obtained, and basic information of each category of commodity is obtained, where the basic information includes: external features, price data;

[0055] Step S23: Classify the commodity types according to whether the commodity is a bulk commodity. The commodities that do not require weighing by the intelligent scale are classified as the first commodities, and the commodities that require weighing by the intelligent scale are classified as the second commodities;

[0056] Step S24: Deploy sensors in the shelves storing the second commodities, and update the original weights of the commodities in the shelves according to the sensors in real time.

[0057] Specifically, through three-dimensional space modeling and coordinate mapping technology, commodity spatial positioning with centimeter-level accuracy is achieved. Through the construction technology of the commodity feature space database, multi-dimensional commodity management is achieved. Through the intelligent commodity classification system, a differentiated management strategy is achieved. Through the Internet of Things real-time monitoring network, the visualization of the inventory status is achieved.

[0058] In some preferred embodiments, step S3 further includes the following steps:

[0059] Step S31: Map the position in the shopping video to the three-dimensional digital twin model in real time, extract the hand coordinates (x1, y1, z1) of the consumer through the skeleton key point tracking algorithm. When the distance between the hand coordinates and the shelf is less than the distance threshold, obtain the time series of the hand coordinates Judge the hand vector v according to the time series of the hand coordinates t ;

[0060] Step S32: Judge whether the consumer is performing a taking operation or a returning operation on the commodity according to the direction of the hand vector and whether there is a commodity in the hand. Add or subtract the personal shopping list respectively according to the taking operation and the putting-back operation, and update the personal consumption amount of the consumer;

[0061] Step S33: When the consumer performs the taking operation, based on the spatial coordinate points where the distance is less than the threshold, obtain the basic information of the goods to be shopped according to the spatial coordinate points. When the consumer performs the taking operation on the first commodity, add the basic information to the personal shopping list. When the consumer performs the taking operation on the second commodity, obtain the weight of the sensor data in the shelf after the taking operation, calculate the difference between the original weight and the weight, and calculate the price of the second commodity for the consumer in combination with the price data in the basic information, and add it to the personal shopping list. Among them, in existing supermarkets, for the second commodity, after being put into the same bag at a unified price and weighed one by one, it needs to be scanned one by one at the cashier's desk for price calculation, which increases the waiting time for price settlement and increases the usage amount of plastic bags, resulting in waste of resources. Some retail supermarkets provide unified settlement at the cashier's desk. The cashier needs to weigh the second commodity one by one according to its different prices and categories, which increases the time for price calculation and further increases the waiting time for consumers to queue up.

[0062] Specifically, by mapping the position of the consumer in the shopping video to the three-dimensional digital twin model in real time and combining the bone key point tracking algorithm to extract the hand coordinates, the accurate recognition of the consumer's behavior of taking and returning goods is realized. When the distance between the hand coordinates and the shelf is less than the threshold, the system can judge the direction of the hand vector through the time series of the hand coordinates, and combine whether the hand holds a commodity to judge the taking or returning operation of the consumer. Further, the system obtains the basic information of the commodity according to the spatial coordinates of the commodity, and directly adds it to the personal shopping list when the consumer takes the first type of commodity; when taking the second type of commodity, the weight data is updated in real time through the shelf sensor, the commodity price is calculated and added to the shopping list. This not only improves the timeliness and accuracy of the shopping list, but also optimizes the shopping experience, reduces manual intervention, and at the same time provides more accurate data support for the supermarket's inventory management and sales data analysis.

[0063] In some optional embodiments, the step S32 further includes the following steps:

[0064] Step S321: When the direction of the hand vector is towards the shelf, if the direction is towards, it is determined that the hand is approaching the shelf. By judging from the shopping video whether there is a commodity in the hand, when there is a commodity in the hand, the commodity in the consumer's hand is regarded as the commodity to be returned, and the external features of the commodity to be returned are obtained and matched with the external features of the commodity in the personal shopping list. The basic information of the commodity to be returned is obtained. When it is obtained in real time that when the direction is away, if the commodity to be returned still exists, no operation is performed on the personal shopping list; otherwise, the basic information of the commodity to be returned is deleted.

[0065] Step S322: When there is no commodity in the hand, when it is obtained in real time that the direction is away, analyze whether there is a commodity in the hand. When there is a commodity in the hand, it is the taking operation. The commodity in the consumer's hand is regarded as the commodity to be shopped. The spatial coordinate points where the distance between the hand coordinates and the spatial coordinate set is less than the threshold. The basic information of the commodity to be shopped is obtained according to the spatial coordinate points and added to the personal shopping list. Otherwise, no processing is performed on the personal shopping list.

[0066] In some preferred embodiments, step S321 further includes: when it is determined that the consumer performs the return operation on the commodity to be returned, obtain the spatial coordinate points where the distance between the hand coordinates and the spatial coordinate set is less than the threshold, obtain the basic information of the commodity set by the spatial coordinate points, and match it with the basic information of the commodity to be returned. When the match is consistent, it is determined that the commodity to be returned is normal; when the match is inconsistent, it is judged that the commodity to be returned is abnormal. According to the external features of the commodity to be returned, a new spatial coordinate set is reconstructed for the commodity to be returned and the corresponding basic information is associated.

[0067] Specifically, when the consumer performs the commodity return operation, by obtaining the distance between the hand coordinates and the spatial coordinate set of the shelf and matching the basic information of the commodity set by the spatial coordinate points, the accurate identification and status judgment of the returned commodity are realized. When the match is consistent, it is determined that the returned commodity is normal, ensuring the accuracy of the shopping list; when the match is inconsistent, it is judged that the returned commodity is abnormal, and a new spatial coordinate set is reconstructed according to the external features of the commodity and the corresponding basic information is associated. This effectively avoids errors in the shopping list caused by commodity misplacement or incorrect operations, improves the robustness and reliability of the system, optimizes the consumer's shopping experience at the same time, and reduces potential problems brought by the commodity return operation.

[0068] In some preferred embodiments, step S4 further includes the following steps:

[0069] Step S41: When it is detected according to the shopping video that there is a product in the consumer's hand, and when the hand distance between the hand coordinates of other consumers and the hand coordinates of the consumer is less than the threshold, and when it is recognized through the shopping video that the consumer transfers the product in the hand to another consumer, the consumer is regarded as having an associated consumer, the basic information of the transferred product is synchronously transferred to the personal shopping list of the corresponding associated consumer, and the total consumption amount between the consumer and the associated consumer is calculated and the associated number set is generated;

[0070] Step S42: When it is recognized that the consumer enters the settlement area, it is recognized whether the consumer has an associated consumer. When the consumer does not have an associated consumer, the personal consumption amount of the consumer is the settlement amount for this settlement area;

[0071] When it is recognized that the consumer has an associated consumer, it is recognized whether all the personal shopping lists of all consumers in the associated number set and the common shopping list are all placed in the settlement area. When it is recognized that all are placed in the settlement area, the total consumption amount is the settlement amount for this settlement area;

[0072] When it is recognized that not all are placed in the settlement area, the basic information of all products in the personal shopping lists of all consumers in the associated number set and the common shopping list is extracted, and the consumption amount of the products in the settlement area is recognized as the settlement amount for this settlement area according to the basic information.

[0073] Specifically, by detecting the distance between the hand coordinates of consumers and combining the shopping video to identify the product transfer behavior, the system can automatically identify the association relationship between consumers, and synchronously transfer the basic information of the product to the personal shopping list of the corresponding associated consumer. When the consumer enters the settlement area, the system can quickly identify whether there is an associated consumer, and accurately calculate the total consumption amount according to the personal shopping list and the common shopping list in the associated number set. When all the products of all associated consumers are placed in the settlement area, the system directly takes the total consumption amount as the settlement amount; if some products are not placed in the settlement area, the system identifies the consumption amount of the products in the settlement area according to the basic information of the products. By automatically identifying and processing the shopping behavior of associated consumers, the system reduces the need for manual intervention and reduces the settlement problems caused by human errors.

[0074] Based on the same inventive concept as the above method embodiment, the embodiment of the present invention also provides an Internet of Things-based retail management system, Figure 2 which is a schematic diagram of the module composition of an Internet of Things-based retail management system provided by the embodiment of the present invention. As Figure 2 shown, the system includes a data acquisition module, a digital twin modeling module, a behavior analysis and list generation module, and a collaborative settlement processing module:

[0075] The data acquisition module is used to capture the shopping videos of consumers in the supermarket through a camera device, obtain product data and sensor data according to the shopping videos, and obtain product data matching the shopping videos;

[0076] The digital twin modeling module is used to construct a three-dimensional digital twin model according to the shelf positions and product distributions in the supermarket, and classify product categories according to whether the products are bulk products;

[0077] The behavior analysis and list generation module is used to analyze the personal shopping list of the consumer according to the shopping video, match the product data or the sensor data according to the product categories in the personal shopping list, and calculate the personal consumption amount in the personal shopping list;

[0078] The collaborative settlement processing module is used to, when it is recognized according to the total consumption amount that the consumer enters the settlement area, judge whether the consumer has associated consumers according to the shopping video and determine the final total consumption amount. The associated consumer refers to other shoppers who shop, travel together with the consumer and jointly complete the payment. The associated consumer refers to other shoppers who shop, travel together with the consumer and jointly complete the payment.

[0079] It should be noted that although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more of the above-mentioned units can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0080] Based on the same inventive concept as the above method embodiment, an electronic device is further provided in the embodiments of the present application, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electronic device implements the control method in the above embodiment.

[0081] In one embodiment, the electronic device may be a server. In this embodiment, the structure of the electronic device may be as Figure 3 shown, including a memory 2001, a communication module 2003, and one or more processors 2002.

[0082] A memory 2001 for storing a computer program executed by a processor 2002. The memory 2001 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, programs required for running an instant messaging function, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0083] The memory 2001 may be a volatile memory, such as a random-access memory (RAM); the memory 2001 may also be a non-volatile memory, such as a read-only memory, a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); or the memory 2001 is any other medium that can be used to carry or store a desired computer program in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2001 may be a combination of the above memories.

[0084] The processor 2002 may include one or more central processing units (CPUs) or be a digital processing unit, etc. The processor 2002 is used to implement the above method for processing audio data when calling the computer program stored in the memory 2001.

[0085] A communication module 2003 is used to communicate with a terminal device and other servers.

[0086] In the embodiments of the present application, the specific connection medium between the above-mentioned memory 2001, communication module 2003, and processor 2002 is not limited. In the embodiments of the present application Figure 3 it is described that the memory 2001 and the processor 2002 are connected through a bus 2004, and the bus 2004 is described by an arrow in Figure 3 The connection manners between other components are only for illustrative purposes and are not to be construed as limitations. The bus 2004 may be divided into an address bus, a data bus, a control bus, etc. For ease of description, Figure 3 only one arrow is used to describe it in

[0087] Based on the same inventive concept as the above method embodiments, an embodiment of the present invention further provides a computer-readable storage medium for storing a computer program. When the computer program runs on a computer, the electronic device is enabled to implement the control method in the above embodiments. The computer-readable storage medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0088] Based on the same inventive concept as the above method embodiments, an embodiment of the present invention further provides a computer program product. The computer program product includes a computer program. When the program product runs on an electronic device, the computer program is used to enable the electronic device to execute the steps in the control method according to various exemplary embodiments described above in this specification. The program product may adopt any combination of one or more readable media. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for realizing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or a device for the functions specified in multiple blocks.

[0089] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

Claims

1. An Internet of Things-based retail management method, characterized in that: Capturing the shopping videos of consumers in the supermarket through a camera device, obtaining product data and sensor data according to the shopping videos, and obtaining product data matching the shopping videos; Constructing a three-dimensional digital twin model according to the shelf positions and product distributions in the supermarket, and classifying product categories according to whether the products are bulk products; Analyzing the personal shopping list of the consumer according to the shopping video, matching the product data or the sensor data according to the product categories in the personal shopping list, and counting the personal consumption amount in the personal shopping list; When it is recognized according to the total consumption amount that the consumer enters the settlement area, judging whether the consumer has associated consumers according to the shopping video and determining the final total consumption amount, where the associated consumer refers to other shoppers who shop, travel together with the consumer and jointly complete the payment.

2. The retail management method based on the Internet of Things according to claim 1, characterized in that: The capturing the shopping videos of consumers in the supermarket through a camera device, obtaining product data and sensor data according to the shopping videos, and obtaining product data matching the shopping videos includes: Establishing an identity identification number for the consumer; Analyzing whether the consumer has associated consumers according to the usage pattern of the shopping cart (shopping basket) judged from the shopping video, where the usage pattern is divided into an exclusive mode and a shared mode; Listing the products in the shopping cart into the personal shopping list and the joint shopping list respectively according to the usage pattern, and counting the total consumption amount of the associated user group.

3. The retail management method based on the Internet of Things according to claim 2, characterized in that: The constructing a three-dimensional digital twin model according to the shelf positions and product distributions in the supermarket, and classifying product categories according to whether the products are bulk products includes: Scanning the internal space of the supermarket to generate a three-dimensional digital twin model of the supermarket, and obtaining the spatial coordinate range of the shelves according to the three-dimensional digital twin model; Based on the spatial coordinate range and the sizes of the products displayed in each shelf, obtaining the spatial coordinate set of each type of product in the shelf, and obtaining the basic information of each type of product, where the basic information includes: external features, price data; And classifying the products into first products and second products according to whether the products are bulk products; Deploying sensors in the shelves storing the second products, and updating the original weights of the products in the shelves in real time according to the sensors.

4. A retail management method based on the Internet of Things according to claim 3, characterized in that: The analyzing the personal shopping list of the consumer according to the shopping video, matching the product data or the sensor data according to the product categories in the personal shopping list, and counting the personal consumption amount in the personal shopping list includes: Extracting the hand coordinates of the consumer through a skeleton key point tracking algorithm according to the shopping video. When the distance between the hand coordinates and the shelf is less than the distance threshold, obtaining the time series of the hand coordinates, and judging the hand vector according to the hand coordinate time series; Identifying and analyzing the operation type of the consumer on the product according to the direction of the hand vector combined with the state of the product in the hand, and dynamically updating the personal shopping list, where the operation type includes: taking operation and returning operation; When the consumer performs the taking operation, a differential pricing strategy is adopted for the types of goods taken, and the individual consumption amount is updated.

5. The retail management method based on the Internet of Things according to claim 4, characterized in that: Analyzing the operation type of the consumer on the goods according to the direction of the hand vector in combination with the state of the goods in the hand and dynamically updating the personal shopping list, including: when it is determined that the consumer is performing the return operation on the goods to be returned, matching the basic information of the placement position of the goods to be returned, verifying the attribution of the goods to be returned, and reconstructing the spatial coordinate set according to the basic information of the goods to be returned when the attribution is abnormal.

6. The retail management method based on the Internet of Things according to claim 5, characterized in that: When it is recognized according to the total consumption amount that the consumer enters the settlement area, it is judged according to the shopping video whether the consumer has associated consumers and the final total consumption amount is determined. The associated consumer refers to other shoppers who shop, travel together with the consumer and jointly complete the payment, including: Detecting the goods handover behavior between the consumer and other consumers according to the shopping video, establishing the associated user group and synchronizing the total consumption amount of the associated user group; When it is recognized that the consumer enters the settlement area, it is judged whether the consumer has the associated consumer. When the consumer does not have the associated consumer, the individual consumption amount of the consumer is used as the settlement amount for this settlement area; When it is recognized that the consumer has the associated consumer, verifying the integrity of the goods of the associated user group and determining the settlement amount for this settlement area.

7. An Internet of Things-based retail management system, characterized in that: The system includes a data acquisition module and a digital twin modeling module: The data acquisition module is used to capture the shopping video of consumers in the supermarket through a camera device, obtain commodity data and sensor data according to the shopping video, and obtain commodity data matching the shopping video; The digital twin modeling module is used to construct a three-dimensional digital twin model according to the shelf positions and commodity distributions in the supermarket, and classify the commodity types according to whether the commodities are bulk commodities.

8. An Internet of Things-based retail management system according to claim 7, characterized in that: The system further includes a behavior analysis and list generation module and a collaborative settlement processing module: The behavior analysis and list generation module is used to analyze the personal shopping list of the consumer according to the shopping video, match the commodity data or the sensor data according to the commodity types in the personal shopping list, and count the individual consumption amount in the personal shopping list; The collaborative settlement processing module is used to judge according to the shopping video whether the consumer has associated consumers and determine the final total consumption amount when it is recognized according to the total consumption amount that the consumer enters the settlement area. The associated consumer refers to other shoppers who shop, travel together with the consumer and jointly complete the payment. The associated consumer refers to other shoppers who shop, travel together with the consumer and jointly complete the payment.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the electronic device realizes the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program. When the computer program runs on a computer, the computer is made to execute the method according to any one of claims 1 to 6.

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