A retail market business system that incorporates cultural elements

Abstract

This invention belongs to the field of market management technology and discloses a retail market business system incorporating cultural elements, including a server, a user terminal, and a merchant terminal. The server includes a database and a main control module. The database stores user information, merchant information, cultural element datasets, and digital collection datasets. The main control module communicates with both the user terminal and the merchant terminal, sending order information from the user terminal to the merchant terminal and feeding back order execution information from the merchant terminal to the user terminal. The server also includes a business interaction module based on cultural elements, a product recommendation module, and a customer flow allocation module. This invention introduces cultural elements into the market business system, increasing the interaction between merchants and users in the market, enhancing the market's cultural atmosphere while promoting local cultural characteristics, increasing the interactivity and experience of the market economy, and attracting consumer interest.

Description

Technical Field

[0001] This invention belongs to the field of market management technology and relates to a retail market business system that incorporates cultural elements. Background Technology

[0002] In the specific commercial context of retail markets such as night markets and tourist attractions, customers' consumption behavior mainly involves making small-amount food and beverage purchases and experiences among different mobile vendors. Due to the mobility and variability of vendors, merchants often do not have fixed storefronts or fixed operating hours and locations, making it difficult for management to regulate them in a standardized manner. Customers also find it easy to not be able to find their preferred vendor among a large number of vendors with high repetition rates.

[0003] Meituan, Ele.me, and Dianping, along with other food promotion and online ordering apps, and Gaode Maps and Baidu Maps, offering nearby recommendations and navigation, represent the most similar solutions currently available. These apps all involve learning about merchants online and placing orders. However, firstly, due to limitations in their operating models, these apps require merchants to have fixed storefronts and business locations, which doesn't align well with the mobile and scattered nature of market stalls. Secondly, these apps limit markets to food and retail, failing to incorporate local culture, and the goods sold in markets across different regions tend to be similar, leading to repetition and a lack of appeal. Furthermore, these apps result in merchants only competing on the platform itself, with the platform rarely providing opportunities for collaboration. Moreover, the non-real-time nature of these apps makes it difficult for administrators to effectively manage and coordinate the market in real time, potentially leading to long queues and overcrowding. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a retail market business system that incorporates cultural elements. This system can enhance the cultural atmosphere of retail markets (especially night markets) through mathematical modeling of business models and the exploration of local cultural elements, promote local culture, rationally allocate pedestrian flow to optimize the customer's consumption experience, improve the overall integrity and connectivity of the market, and increase the market's space utilization and economic benefits.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions.

[0006] This invention provides a retail market business system incorporating cultural elements, comprising a server, a user terminal, and a merchant terminal. The server includes a database and a main control module. The database stores user information, merchant information, a cultural element dataset, and a digital collection dataset. The main control module is communicatively connected to both the user terminal and the merchant terminal, and is used to send product order information from the user terminal to the merchant terminal and to feed back order execution information from the merchant terminal to the user terminal. The user terminal sends product order information to the merchant terminal via the server. The merchant terminal confirms and executes the product order information.

[0007] The server also includes:

[0008] A business interaction module based on cultural elements is used to build interactive mechanisms based on cultural elements and reward customers; this business interaction module includes:

[0009] The Common Cultural Elements and Combination Scheme Generation Submodule is used to determine several cultural elements as common cultural elements based on market transaction data, and to construct common cultural element combination schemes.

[0010] The reward generation module is used to associate common cultural element combination schemes with digital collectibles, design related rewards, and save them to the Common Cultural Element Combination Scheme - Digital Collectibles - Reward Association List;

[0011] The cultural assignment submodule is used to assign certain common cultural elements to several products of different merchants in the market, and then save them in the product-cultural element association list in the database.

[0012] The linkage submodule is used to send common cultural elements associated with the products in the order to the user terminal via the main control module, based on the order execution information received by the main control module.

[0013] The first reward submodule is used to send the corresponding digital collectibles and associated rewards to the user terminal via the main control module, based on the common cultural elements received by the main control module.

[0014] The above-mentioned sub-module for generating common cultural elements and matching schemes is executed according to the following steps:

[0015] 1) Obtain the recommended number of cultural elements;

[0016] 2) Obtain the recommended number of cultural elements to combine;

[0017] 3) Select several cultural elements from the cultural element dataset in the database that meet the recommended values ​​for the number of cultural elements and the recommended values ​​for the number of artistic elements to be combined as ordinary cultural elements, and arrange and combine these ordinary cultural elements to construct several sets of ordinary cultural element combination schemes.

[0018] In the above step 1), the recommended value of the number of cultural elements calculated according to the following formula is at least:

[0019]

[0020] In the formula, P1 represents the per capita consumption amount in the market during the operation period; P2 represents the average price of goods in the market during the operation period; represents the t-value when the right area under the t-distribution density curve is α represents the significance level, generally taking a value of 0.05; σ represents the standard deviation of the data in the sample set {A} composed of the number of ordinary cultural elements obtained by each customer collected during the operation period, n represents the number of data in {A}; p represents the set proportion, L represents the interval length,

[0021] Initially, the operation period refers to the trial operation period, and the data during the trial operation period is used as the initial quantity; subsequently, it can be corrected according to the marketing situation during the set operation period (such as one month).

[0022] In the above step 2), the recommended value of the number of cultural element combinations is calculated according to the following formula:

[0023]

[0024]

[0025] In the above step 3), several cultural elements that meet the requirements of the recommended value of the number of cultural elements and the recommended value of the number of literary element combinations are selected from the cultural element dataset in the database as ordinary cultural elements, and the ordinary cultural elements are arranged and combined to construct several sets of ordinary cultural element combination schemes to ensure the appropriate difficulty of collecting digital collections.

[0026] The cultural elements and digital collections used in the present invention can be set in combination with local culture, and different themed cultural night markets can also be created according to the same model. Large-scale cultural phenomena, such as famous routes like the Silk Road and Emperor Qianlong's南巡 (Southern Inspection Tour), can also be used to create large-scale linkages of the same theme in different regions. For example, in combination with local culture, it can be divided into natural, humanistic, etc. Cultural elements can be selected from nature (such as natural landscapes, special agricultural products, specific animals and plants, etc.), while digital collections can be selected from humanistic aspects (such as famous people, famous paintings, calligraphy works, folk customs, historical events, historical sites, etc.).

[0027] The above reward generation module associates the ordinary cultural element combination scheme with the unique digital collection in the digital collection dataset. The reward can be coupons of different amounts, etc.

[0028] The aforementioned reward generation module can also combine two or more digital collectibles to form a catalog, design rewards associated with the catalog, and save them to the catalog-reward association list to further attract visitors' interest. At this time, the business interaction module also includes a second reward submodule, used to send the rewards associated with the catalog to the user terminal via the main control module, based on the catalog received by the main control module.

[0029] The aforementioned cultural assignment submodule is used to assign common cultural elements to several products from different merchants in the market. Since the types of cultural elements are limited, initially, each common cultural element can be evenly distributed among several products from several merchants in different areas of the market; the common cultural elements, merchants, and products are then associated and stored in a product-cultural element association list in the database. After the cultural assignment submodule completes the assignment of values ​​to the products, the main control module pushes the common cultural elements to the corresponding merchants.

[0030] The aforementioned first reward submodule also assigns a unique code to each digital collectible, which is then sent to the user's end along with the digital collectible. The digital collectible code is generated by directly combining information such as the merchant's ID, / and the time the cultural element was submitted, / and / or the user's ID, or by using this information as a seed to generate a pseudo-random number.

[0031] The aforementioned server also includes:

[0032] The product recommendation module generates recommended products and their order based on user behavior data, which is then pushed to the user's device via the main control module. In its implementation, user behavior data includes at least one of the following: click behavior, browsing behavior, purchase behavior, rating behavior, favorites behavior, search history, sharing behavior, dwell time, and location data. Recommended products and their order can be generated based on an Item Collaborative Filtering (ItemCF) algorithm.

[0033] In its specific implementation, the product recommendation module performs the following steps:

[0034] 4) Construct a product-user rating matrix;

[0035] 5) Obtain the similarity between different products;

[0036] 6) Sort the products in descending order of their similarity scores and recommend them to the user through the main control module.

[0037] In step 4) above, a product-user rating matrix is ​​constructed based on the user behavior data within a specified time period. The rows in the matrix represent products, the columns represent users, and each element represents the user's behavior score for the product.

[0038] In the preferred implementation, to improve the reliability of the behavior score, the behavior score is corrected according to the following formula:

[0039]

[0040] In the formula, M k,i M′ k,i Let Q represent the row scores before and after correction in the k-th row and i-th column, respectively. j Let J represent the weight of the j-th objective factor, and J represent the number of objective factors.

[0041] In step 5) above, the similarity between two products is represented by the cosine similarity. This similarity is calculated based on the number of users sharing the same product.

[0042] For two products A and B:

[0043] First, calculate the vector dot product of product A and product B:

[0044]

[0045] In the formula, A i B i Let represent the behavior scores of the i-th user for the two items, and I represent the number of users.

[0046] Next, we calculate the vector norm (Euclidean norm) for both product A and product B.

[0047]

[0048] Finally, calculate the cosine similarity:

[0049]

[0050] These cosine similarity values ​​represent the degree of similarity between each pair of products. The higher the value, the higher the similarity, meaning that a user who purchases one type of product is more likely to purchase the other related type.

[0051] In this step, the similarity between each product purchased by the user and other products is calculated, and the products are sorted in descending order of similarity scores. The sorted results are then recommended to the user by the main control module.

[0052] A drawback of collaborative filtering algorithms is the high computational complexity of the similarity matrix when the number of users or products is large. To address this issue, this invention determines a user quantity threshold p and a product quantity threshold q based on computational load. When a new user or product is added, if the existing user quantity has reached p or the product quantity has reached q, a similarity comparison is performed. The comparison method involves calculating the similarity difference s, i.e.

[0053] or

[0054] In the formula, I and K represent the number of users and the number of products, respectively;

[0055] The similarity matrix selects the user or product with the smallest similarity difference, binds the new user or product to it, and replaces the original user or product's behavior score in the matrix with the average of the new user or product's behavior score and that of the original product. Under this simplified scheme, the size of the similarity matrix can always be kept within p×q, thus simplifying computational complexity.

[0056] The aforementioned server-side components also include:

[0057] The customer flow allocation module determines the placement locations of several rare cultural elements based on the market's ideal and actual weight centers, thereby guiding customer movement. These rare cultural elements are selected from the cultural element dataset, excluding common cultural elements. This approach aims to attract customers and promote load balancing within the area.

[0058] The steps to obtain the ideal weight center of gravity of the above market are as follows:

[0059] 7) Draw the outline image of the entire market area onto a two-dimensional plane coordinate system, convert the image to a grayscale image, and treat it as a homogeneous plane, that is, set the grayscale value to the same value everywhere.

[0060] 8) Traverse each pixel of the image and obtain its grayscale value and coordinates;

[0061] 9) Weight the coordinates of each pixel by multiplying the coordinates of each pixel by its corresponding gray value;

[0062] 10) Sum all the weighted coordinate values ​​and divide them by the sum of the total gray values ​​of the image to obtain the coordinates of the ideal weight center of gravity of the market.

[0063] The steps for obtaining the actual weight center of gravity of the above-mentioned market are as follows:

[0064] 11) First, calculate the total foot traffic R in the entire market. Then, divide the entire market into u areas and calculate the foot traffic R in each area. u Then calculate the pedestrian flow weight for each area.

[0065] 12) Use the pedestrian flow weights of each area as grayscale values ​​to redraw the contour image;

[0066] 13) Traverse each pixel of the image and obtain its grayscale value and coordinates;

[0067] 14) Weight the coordinates of each pixel by multiplying the coordinates of each pixel by its corresponding gray value;

[0068] 15) Add all the weighted coordinate values ​​together and divide them by the sum of the total gray values ​​of the image to obtain the coordinates of the actual weight center position of the market.

[0069] The aforementioned customer flow allocation module determines the placement location of rare cultural elements using the following method: the point symmetrical to the actual weight centroid coordinates about the ideal weight centroid coordinates is the placement location of the rare cultural element. At this time, the commercial interaction module based on cultural elements also includes a rare cultural element generation module, which selects several cultural elements from the cultural element dataset (excluding ordinary cultural elements) as rare cultural elements; the number of rare cultural elements does not exceed 10% of the total number of cultural elements in the dataset. The reward generation module designs rewards related to the number of rare cultural elements and saves them to a rare cultural element-reward association list. The cultural assignment submodule assigns the determined rare cultural elements to several products of different merchants in the designated market placement locations, and then saves them to a product-cultural element association list in the database; since the types of cultural elements are limited, each rare cultural element can be randomly assigned to several products of several merchants in the placement locations. After the cultural assignment submodule completes the assignment of values ​​to the products, the main control module pushes the rare cultural elements to the corresponding merchants. The linkage submodule is also used to send rare cultural elements associated with the products in the order to the user terminal via the main control module, based on the order execution information received by the main control module; the first reward submodule is also used to send the associated reward to the user terminal via the main control module, based on the number of rare cultural elements received by the main control module.

[0070] The aforementioned client application can also rate purchased products and send feedback to the server, as well as save the data to the user information database. Furthermore, the client application also sends user click behavior, browsing behavior, favorites behavior, search history, sharing behavior, dwell time, location data, etc., to the server and saves them to the user information database. The client application includes a display interface, a storage module, a first collection module, a second collection module, and a first sending module. The display interface is used to display the market map page, merchant introduction page, and online ordering page. The storage module is used to store product order information and behavioral data. The first collection module is used to store common cultural elements. The second collection module is used to store digital collectibles. The first sending module is used to send two or more different types of common cultural elements selected by the user according to the set common cultural element combination rules (i.e., the common cultural element combination schemes given above) to the server. The first collection module of the client application is also used to store rare cultural elements; the first sending module is also used to send the rare cultural elements selected by the user to the server.

[0071] The aforementioned second collection module can also select two or more different types of digital collectibles to form an illustrated guide according to the set digital collectible pairing rules; the user terminal also includes a second sending module, used to send the illustrated guide to the server.

[0072] The aforementioned merchant client can be installed on the stall cart; the user client can be installed on the user's mobile phone, such as an APP or WeChat mini program; and the server is installed on the computer that serves as the central server.

[0073] The aforementioned stall cart includes a sensor assembly, a control device, a display module, and an LED light strip mounted on the vehicle body. The sensor assembly includes a temperature and humidity sensor for detecting ambient temperature and humidity, a gas sensor for detecting flammable gases, a smoke alarm, and a camera module group. The control device includes a housing, a controller installed inside the housing, and a touch screen mounted on the housing. The controller is equipped with merchant-side software and is connected to the touch screen to display product order information.

[0074] The aforementioned camera module group is used to collect real-time data on the situation within the coverage area of ​​the stall cart and upload the collected information to the server via the controller; the server analyzes and statistically analyzes the collected image information, obtains the number of people, and feeds back the number of people to the controller.

[0075] The controller adjusts the LED light strip color based on the number of online orders (i.e., the sum of orders being executed and those yet to be executed) or the number of people, to indicate the current level of crowding at the stall; for example, red indicates very crowded, and green indicates smooth traffic. The LED light strip is positioned around the perimeter of the vehicle.

[0076] The aforementioned control device also includes an NFC card reader installed on one side of the housing for authentication between the merchant and the server. To facilitate merchant operation of the controller, a button box connected to the controller can also be provided, with buttons in the button box connected to the controller for accepting or canceling orders.

[0077] The aforementioned display module is used to show information such as the cultural element logos of the current stall, the cultural elements themselves, the products associated with the cultural elements, and the explanations of the cultural elements.

[0078] In summary, this invention provides a novel retail market business system that integrates cultural elements. While exploring and disseminating local cultural characteristics, it optimizes the customer's consumption experience and enhances the market's economic benefits. Compared with existing technologies, this invention has the following advantages:

[0079] 1) This invention introduces cultural elements into the market business system, which can increase the interaction between merchants and users in the market, promote local characteristic culture while increasing the cultural atmosphere of the market, increase the interactivity and experience of the market economy, and attract consumer interest.

[0080] 2) This invention can improve the problems of traditional markets being difficult to manage and scattered, and is more suitable for highly mobile commercial scenarios such as night markets, making it easier for managers to centrally schedule and manage them;

[0081] 3) By adjusting the placement of cultural elements, this invention can enhance the overall economic integrity of the market, rationally plan customer flow, improve the utilization rate of night market space, increase the overall economic benefits of the night market, and optimize the customer consumption experience.

[0082] 4) This invention can leverage shared cultural values ​​to facilitate cross-regional collaboration and expand its influence.

[0083] 5) This invention is uniquely designed for night markets and can also be extended to other tourist attractions and retail markets. Attached Figure Description

[0084] Figure 1 This is a schematic diagram of a retail market business system incorporating cultural elements provided in Embodiment 1 of the present invention;

[0085] Figure 2 A diagram illustrating the transmission of data types from the user end to the server end;

[0086] Figure 3 This is a schematic diagram of the stall cart structure;

[0087] Figure 4 This is a schematic diagram of the control device structure;

[0088] Figure 5 This is a block diagram of the server-side structure in one approach;

[0089] Figure 6 This is a diagram of the server-side architecture in another approach. Detailed Implementation

[0090] The technical solutions of various embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0091] Example 1

[0092] This embodiment provides a retail market business system that incorporates cultural elements, integrating cultural elements, digital collectibles, and rewards into the business model. This enhances the market's cultural atmosphere, promotes local cultural characteristics, increases the interactivity and experience of the market economy, and attracts consumer interest.

[0093] Cultural elements and digital artifacts can be integrated with local culture, and different themed cultural night markets can be created based on the same model. Large-scale cultural phenomena, such as the Silk Road and Emperor Qianlong's southern tours, can also be utilized to create large-scale collaborative projects with the same theme in different regions. For example, local culture can be categorized into natural and cultural elements. Cultural elements can be selected from natural elements (such as natural landscapes, specialty agricultural products, specific flora and fauna), while digital artifacts can be selected from cultural elements (such as famous figures, paintings, calligraphy, folk customs, historical events, historical sites, etc.). The selected cultural elements are stored in a cultural element dataset; the selected digital artifacts are stored in a digital artifact dataset.

[0094] Rewards can include coupons of varying amounts.

[0095] like Figure 1 As shown, the retail market business system incorporating cultural elements provided in this embodiment includes a server, a user, and a merchant.

[0096] (I) User Terminal

[0097] The client application can be installed on the user's mobile phone, such as an app or WeChat mini-program. Users register and log in through the client, then enter the client's display interface. The client's display interface mainly includes a market (such as a night market) map page, a vendor introduction page, and an online ordering page. These pages can use standard settings in this field. The map page can display the distribution of market vendors, and can also display cultural elements corresponding to the vendors' products.

[0098] The user interface also includes a collection page, which displays collected cultural elements, digital collections, and / or illustrated guides.

[0099] like Figure 2 As shown, the user client sends product order information to the merchant client via the server (i.e., the purchase behavior). The user client can also rate the purchased products (i.e., rating behavior) and provide feedback to the server, and can also save it to the user information in the database. Furthermore, the user client also feeds back user behavior data such as click behavior, browsing behavior, favorite behavior, search history, sharing behavior, dwell time, and location data to the server and saves it to the user information in the database.

[0100] The user-side also includes a storage module for storing product order information and behavioral data.

[0101] The user terminal also includes a first collection module, a second collection module, a first sending module, and a second sending module:

[0102] The first collection module is used to store common cultural elements.

[0103] The second collection module is used to store digital collectibles; it can also select two or more different types of digital collectibles to form an illustrated catalog according to the set digital collectible pairing rules.

[0104] The first sending module is used to send two or more different types of common cultural elements selected by the user according to the set common cultural element combination rules to the server.

[0105] The second sending module is used to send the encyclopedia to the server.

[0106] Table 1 Explanation of User Behavior Data

[0107] Behavior explain Click behavior Record user actions when clicking on shops and dishes on the page, as well as the number of clicks. Browsing behavior Records the merchant pages and dishes that users have viewed. Purchasing behavior Track users' purchase history, including the food items purchased, the time of purchase, and the number of purchases. rating behavior Allows users to rate businesses and food. collecting behavior Record user behavior of adding food items to their shopping cart or wish list. Search History Record users' search queries to understand what types of businesses and food they are looking for. Sharing behavior Understand users' behavior in sharing information about businesses or food. Duration of stay Record the time users spend on specific merchant pages or food detail pages. Location data With user authorization, their location data can be used to recommend local businesses or food.

[0108] (II) Merchant side

[0109] The merchant client can be installed on the stall cart. Merchants register and log in through the client, then access the merchant interface. The merchant interface displays product information, order information, daily transaction volume, etc. Merchants confirm and execute orders through the client.

[0110] like Figure 3-4 As shown, the stall cart includes a sensor assembly 1, a control device 2, a display module 3, and an LED light strip 4 mounted on the vehicle body. The LED light strip 4 is arranged around the perimeter of the vehicle body.

[0111] Sensor assembly 1 includes a temperature and humidity sensor for detecting ambient temperature and humidity, a gas sensor for detecting flammable gases, a smoke alarm, and a camera module assembly. The camera module assembly is used to monitor the area covered by the stall cart.

[0112] The control device 2 includes a housing 21, a controller 22 installed inside the housing, a touchscreen 23 mounted on the housing, and an NFC reader 24 installed on one side of the housing. The controller 22 is a Raspberry Pi controller; it has merchant-side software installed and is connected to the touchscreen 23 to display product order information. The NFC reader 24 is used for authentication between the merchant and the server, and can employ conventional authentication methods in the art. To facilitate merchant operation of the controller, a button box 25 connected to the controller can also be provided. Buttons in the button box are connected to the controller and used to accept or cancel orders.

[0113] Display module 3 is used to display information such as the cultural element logo of the current stall, the cultural element, the products associated with the cultural element, and the explanation of the cultural element.

[0114] The aforementioned camera module group collects information and uploads it to the server via the controller; the server analyzes and statistically analyzes the collected image information to obtain the number of people, and then feeds the number of people back to the controller.

[0115] The system allows for setting several pedestrian flow levels and several order quantity levels based on online order volume. The controller can adjust the LED light strip color based on the greater of the pedestrian flow level or the online order quantity level to indicate the current crowding level of the stall. For example, setting the pedestrian flow level and order quantity level to 1-3, level 1 indicates smooth pedestrian flow, and the LED light strip displays green; level 2 indicates crowded pedestrian flow, and the LED light strip displays yellow; level 3 indicates very crowded pedestrian flow, and the LED light strip displays red.

[0116] (III) Server

[0117] The server is installed on the computer that serves as the central server. The server assists users and merchants in completing registration, login, and other tasks.

[0118] like Figure 5 As shown, the server-side includes a database and a main control module. The database stores user and merchant information, cultural element datasets, and digital collection datasets. The main control module communicates with both the user and merchant ends, sending product order information from the user end to the merchant end and feeding back order execution information from the merchant end to the user end.

[0119] The server-side also includes a business interaction module based on cultural elements and a product recommendation module.

[0120] 1. Business interaction module based on cultural elements

[0121] A business interaction module based on cultural elements is used to build interactive mechanisms based on cultural elements and reward customers.

[0122] The business interaction module includes: a sub-module for generating common cultural elements and matching schemes, a reward generation module, a cultural value assignment sub-module, a linkage sub-module, a first reward sub-module, and a second reward sub-module.

[0123] (1) Submodule for generating common cultural elements and matching schemes

[0124] The Common Cultural Elements and Combination Scheme Generation Submodule is used to identify several cultural elements as common cultural elements based on market transaction data, and to construct common cultural element combination schemes.

[0125] Common cultural elements and matching schemes can be generated based on transaction data within the operating cycle. Initially, the operating cycle refers to the trial operation period, using data from the trial operation period as the initial data; subsequent adjustments can be made based on marketing performance within the set operating cycle (e.g., one month).

[0126] The submodule for generating common cultural elements and matching schemes is executed according to the following steps:

[0127] 1) Obtain the recommended number of cultural elements.

[0128] The average spending per person in the market area is calculated by dividing the market area's total sales revenue by the market area's customer traffic during the operating period, denoted as P1.

[0129]

[0130] In the formula, W1 represents the market area's turnover during the operating period; N2 represents the market area's foot traffic during the operating period.

[0131] The estimated number of goods purchased per person in the market area is obtained by dividing the average per capita consumption during the operating period by the average price of goods in the market area, denoted as N3.

[0132]

[0133] In the formula, P2 represents the average price of goods in the market area during the operating period.

[0134] This data N3 represents the estimated number of cultural elements that an average person may be able to access.

[0135] At a 95% confidence level, i.e., when α = 0.05, the confidence interval for calculating the average number of cultural elements acquired per person in the market area is:

[0136]

[0137] In the formula, The area under the right side of the t-distribution density curve is... The t-value is given by α, which represents the significance level and is typically set to 0.05; σ represents the standard deviation of the data in the sample set {A} consisting of the number of common cultural elements collected from each customer during the operating period; and n represents the number of data points in {A}.

[0138] Let the upper limit of its confidence interval be N5, that is

[0139] Let the length of its interval be L, that is

[0140] Suppose we want to ensure that a customer base of proportion p can collect all the element combinations.

[0141] The number of combinations N7 should be the number of combinations that can be generated by at least N6 = N5 - p * L (rounded down) elements, i.e., N7 = C. 2 n6 ;

[0142] Therefore, the recommended minimum number of cultural elements is:

[0143]

[0144] Recommended number of cultural elements to include:

[0145]

[0146]

[0147] 3) Select several cultural elements from the cultural element dataset in the database that meet the recommended values ​​for the number of cultural elements and the number of artistic elements to be combined as ordinary cultural elements. Arrange and combine these ordinary cultural elements to construct several sets of ordinary cultural element combination schemes to ensure that the difficulty of collecting digital collections is moderate.

[0148] (2) Reward Generation Module

[0149] The reward generation module is used to associate common cultural element combination schemes with digital collectibles, design related rewards, and save them to the Common Cultural Element Combination Scheme - Digital Collectibles - Reward Association List.

[0150] Here, common cultural element combinations are linked to unique digital artifacts within the digital artifact dataset. For example, the combination of cultural elements "Jinjiang River," "Mount Qingcheng," and "silk" is linked to the digital artifact "Shu Embroidery," which includes a description of Shu embroidery: "According to Chang Qu's *Huayang Guozhi* (Records of the States South of the Yangtze River) from the Jin Dynasty, 'The Shu Records* states that because brocade weavers washed their brocade in the river, the brocade colors became brighter, while other rivers did not produce good results; hence, this place was named Jinli (Brocade Village).'" Rewards associated with the digital artifacts can be set, such as coupons of a certain amount, or specific merchants where coupons are limited to be used, thus driving traffic to these merchants.

[0151] The rewards generation module allows users to combine two or more digital collectibles into a catalog, design rewards associated with the catalog, and save them to the catalog-reward association list to further attract visitors' interest. Digital collectible pairing rules can be categorized by type, with each type requiring a given number of digital collectibles. Rewards associated with the collection catalog can be larger coupons or cultural and creative works, etc.

[0152] (3) Cultural Assignment Submodule

[0153] The cultural assignment submodule is used to assign certain common cultural elements to several products of different merchants in the market, and then save them in the product-cultural element association list in the database.

[0154] This cultural assignment submodule is used to assign common cultural elements to several products from different merchants in the market. Since the types of cultural elements are limited, initially, each common cultural element can be evenly distributed to several products from several merchants in various areas of the market; the common cultural elements, merchants, and products are associated and stored in the product-cultural element association list in the database.

[0155] For example, each stall is given a unique cultural element as a symbol, and each type of food is categorized under a corresponding cultural element, such as water for drinks, fire for barbecue, and wood for fried rice. When customers purchase the corresponding items at the night market, they receive the corresponding cultural element.

[0156] After the cultural value assignment submodule completes the value assignment for the product, the main control module pushes the common cultural elements to the corresponding merchants and synchronizes them to the merchant information in the database.

[0157] (4) Linkage Submodule

[0158] The linkage submodule is used to send common cultural elements associated with the products in the order to the user terminal via the main control module, based on the order execution information received by the main control module, and synchronize them to the user information in the database.

[0159] (5) First reward submodule

[0160] The first reward submodule is used to send the corresponding digital collectibles and associated rewards to the user terminal via the main control module, based on the common cultural elements received by the main control module.

[0161] The first reward submodule also assigns a unique code to each digital collectible, which is sent to the user along with the digital collectible. This code can be directly combined based on information such as the merchant ID, / and the time the cultural element was submitted, / and the user ID, or it can be generated using these information as a seed for pseudo-random number generation.

[0162] The first reward submodule also synchronizes the digital collection code, digital collection, and reward to the user information in the database.

[0163] (6) Second reward submodule

[0164] The second reward submodule is used to send the rewards associated with the illustrations to the user terminal via the main control module, based on the illustrations received by the main control module, and synchronize them to the user information in the database.

[0165] 2. Product Recommendation Module

[0166] The product recommendation module generates recommended products and their order based on user behavior data, which is then pushed to the user's device via the main control module. Specifically, user behavior data includes click behavior, browsing behavior, purchase behavior, rating behavior, favorites behavior, search history, sharing behavior, dwell time, and location data. Recommended products and their order can be generated based on an ItemCF (Item Collaborative Filtering) algorithm.

[0167] In its specific implementation, the product recommendation module follows these steps:

[0168] 4) Construct a product-user rating matrix.

[0169] Based on user behavior data within a specified time period, construct a product-user rating matrix. In the matrix, rows represent products, columns represent users, and each element represents a user's behavior score for a product.

[0170] Different behaviors can be mapped to different scores. For example, a purchase can be mapped to a high score, a click to a medium score, and no behavior to a low or zero score. The sum of the scores obtained from all user behaviors is the user's behavior score for that product. Assume there are 5 users (User1, User2, User3, User4, User5) and 5 products (ItemA, ItemB, ItemC, ItemD, ItemE). Taking rating behavior as an example, users rate these products, with ratings ranging from 1 to 5, where 5 represents the most liked and 1 represents the least liked.

[0171]

[0172] In this example, we have the transpose of the product-user rating matrix, where each row represents a user and each column represents an item. The values ​​in the matrix represent the user's rating of the item. If a user has not rated or acted on a product, the corresponding position can be represented by 0. Based on this matrix, we can calculate the similarity between users and between items, and use it to implement collaborative filtering algorithms.

[0173] Similarity recommendations based on user ratings are relatively subjective. To improve the reliability of behavioral scores, objective factors (such as distance from the user and sales volume of nearby stores) can be added as weighting factors to influence the rating.

[0174] The behavior score can be adjusted using the following formula:

[0175]

[0176] In the formula, M k,i M′k,i Q represents the behavior scores before and after correction in the k-th row (product) and i-th column (user), respectively. j Let J represent the weight of the j-th objective factor, and J represent the number of objective factors.

[0177] For example, the distance to the user should be inversely proportional to the rating. First, calculate the distance L from the current user to each merchant. i Then, take the minimum distance among them, set its weight to 1, and the remaining weights are the ratios of the minimum distance to the current distance, that is:

[0178]

[0179] Taking store sales as an example, store sales should be directly proportional to the rating. Therefore, following the method described above, the highest sales value is assigned a weight of 1, and the remaining weights are the ratios of the current sales volume to the highest sales volume, i.e.:

[0180]

[0181] Therefore, the J objective factors can be divided into two categories: direct and inverse, and their respective influence weights can be determined.

[0182] Furthermore, a drawback of collaborative filtering algorithms is the high computational complexity of the similarity matrix when the number of users or products is large. To address this issue, this invention determines a user quantity threshold p and a product quantity threshold q based on computational load. When a new user or product is added, if the existing user quantity has reached p or the product quantity has reached q, a similarity comparison is performed. The comparison method involves calculating the similarity difference s, i.e.

[0183] or

[0184] In the formula, I and K represent the number of users and the number of products, respectively;

[0185] The similarity matrix selects the user or product with the smallest similarity difference, binds the new user or product to it, and replaces the original user or product's behavior score in the matrix with the average of the new user or product's behavior score and that of the original product. Under this simplified scheme, the size of the similarity matrix can always be kept within p×q, thus simplifying computational complexity.

[0186] For example, when the number of users first reaches the user threshold p, a similarity comparison is performed. The comparison method calculates the similarity difference s, which is defined as the sum of the absolute values ​​of the differences between each rating of the current user and each rating of all existing users.

[0187]

[0188] Select the user with the smallest similarity difference (let's call it the l-th user), bind the new user p to the l-th user, and replace the l-th user's rating in the original matrix with the average of the ratings of the new user (i.e., the p-th user) and the l-th user. That is:

[0189]

[0190] In the formula, M″ k,l M′ represents the updated rating of the l-th user for the k-th product, and also reflects the shared rating preference between the l-th and p-th users in the original system. k,l M′ is the rating of the l-th user for the k-th product before the update. k,p This is the rating of the p-th user for the k-th product before the update. This data is then used as the basis for recommending products to the l-th and p-th users.

[0191] Next, when the number of products reaches a threshold q, a similarity comparison is performed. The comparison method calculates the similarity difference s, defined as the sum of the absolute values ​​of the differences between each rating of the current user and each rating of all existing users.

[0192]

[0193] Select the product with the smallest similarity difference (let's call it the r-th product), bind the new product q to the r-th product, and replace the rating of the r-th product in the original matrix with the average of the ratings of the new product (i.e., the q-th product) and the r-th product.

[0194]

[0195] In the formula, M″ r,i M′ represents the updated rating of the i-th user for the r-th product, and also indicates the user's shared rating preference for both the r-th and q-th products. r,i M′ is the rating of the i-th user for the r-th product before the update. q,i This is the rating of the i-th user for the q-th product before the update. This data is then used as the basis for recommending products to the i-th user.

[0196] 5) Obtain the similarity between different products.

[0197] The similarity between two products is expressed as the cosine similarity. This similarity is calculated based on the number of users who share the same accounts with the products.

[0198] For two products A and B:

[0199] First, calculate the vector dot product of product A and product B:

[0200]

[0201] In the formula, A i B i Let represent the behavior scores of the i-th user for the two items, and I represent the number of users.

[0202] Next, we calculate the vector norm (Euclidean norm) for both product A and product B.

[0203]

[0204] Finally, calculate the cosine similarity:

[0205]

[0206] These cosine similarity values ​​represent the degree of similarity between each pair of products. The higher the value, the higher the similarity, meaning that a user who purchases one type of product is more likely to purchase the other related type.

[0207] In this step, the similarity between each product purchased by the user and other products is calculated.

[0208] 6) Sort the products in descending order of their similarity scores and recommend them to customers via the main control module.

[0209] In this step, products that the user has not purchased are sorted in descending order of similarity score and then recommended to the customer by the main control module.

[0210] In this embodiment, the process for obtaining common cultural elements is as follows:

[0211] A1) Users send product order information to merchants via the server-side main control module through the user terminal;

[0212] B1) The merchant confirms and executes the product order information;

[0213] C1) The server-side linkage sub-module sends the common cultural elements associated with the products in the order to the user terminal through the main control module based on the order execution information received by the main control module.

[0214] The process for redeeming digital collectibles and rewards is as follows:

[0215] A2) Users send combinations of common cultural elements from the user's end to the server;

[0216] B2) The first reward submodule, based on the common cultural elements received by the main control module, sends the corresponding digital collections, digital collection codes, and associated rewards to the user terminal via the main control module.

[0217] The process for redeeming rewards for the illustrated guide is as follows:

[0218] A3) The user sends the encyclopedia from the user's device to the server;

[0219] B3) The second reward submodule sends the rewards associated with the illustrations to the user terminal via the main control module, based on the illustrations received by the main control module.

[0220] Example 2

[0221] This embodiment is a further improvement on the retail market business system incorporating cultural elements, based on Embodiment 1. The retail market business system incorporating cultural elements provided in this embodiment includes a server, a user terminal, and a merchant terminal. In addition to the components described in Embodiment 1, such as... Figure 6 As shown, the server also includes a customer flow allocation module to achieve effective allocation of customer traffic.

[0222] The customer flow allocation module determines the placement locations of several rare cultural elements based on the market's ideal and actual weight centers, thereby guiding customer movement. These rare cultural elements are selected from the cultural element dataset, excluding common cultural elements, and their number does not exceed 10% of the total number of cultural elements in the dataset. This approach aims to attract customers and promote load balancing within the area.

[0223] The above-mentioned passenger flow allocation module determines the placement location of rare cultural elements according to the following method: calculate the point symmetrical to the actual weight centroid coordinates about the ideal weight centroid coordinates, which is the placement location of the rare cultural elements.

[0224] The steps to obtain the ideal weight center of gravity of the above market are as follows:

[0225] 7) Draw the outline image of the entire market area onto a two-dimensional plane coordinate system, convert the image to a grayscale image, and treat it as a homogeneous plane, that is, set the grayscale value to the same value everywhere.

[0226] 8) Traverse each pixel of the image and obtain its grayscale value and coordinates;

[0227] 9) Weight the coordinates of each pixel by multiplying the coordinates of each pixel by its corresponding gray value;

[0228] 10) Sum all the weighted coordinate values ​​and divide them by the sum of the total gray values ​​of the image to obtain the coordinates of the ideal weight center of gravity of the market.

[0229] This embodiment uses Python and the OpenCV library to calculate its centroid. The specific code implementation is as follows:

[0230]

[0231]

[0232] The ideal centroid can be calculated using the above method, with coordinates G1(x1,y1).

[0233] The steps for obtaining the actual weight center of gravity of the above-mentioned market are as follows:

[0234] 11) First, calculate the total foot traffic R in the entire market. Then, divide the entire market into u areas and calculate the foot traffic R in each area. u Then calculate the pedestrian flow weight for each area.

[0235] People flow statistics can be achieved by combining the camera modules installed on the carts at each stall with the statistics of online orders for each stall.

[0236] 12) Use the pedestrian flow weight of each area as the grayscale value and redraw the outline image.

[0237] 13) Traverse each pixel of the image and obtain its grayscale value and coordinates.

[0238] 14) Weight the coordinates of each pixel by multiplying the coordinates of each pixel by its corresponding gray value.

[0239] 15) Sum all the weighted coordinate values ​​and divide them by the sum of the total gray values ​​of the image to obtain the coordinates of the actual weight centroid of the market, denoted as G2(x2,y2).

[0240] For the calculation of the actual weight center position of the market, please refer to the calculation of the ideal weight center position of the market.

[0241] The above-mentioned passenger flow allocation module determines the placement location of rare cultural elements according to the following method: calculate the point G3 (2x1-x2, 2y1-y2) that is symmetrical to the actual weight centroid coordinate G2 about the ideal weight centroid coordinate G1, which is the placement location of the rare cultural element.

[0242] G3 is the location for placing rare cultural elements, which can guide the flow of people and promote the load balance of the area.

[0243] After determining the placement locations of rare cultural elements, the rare cultural elements are placed and used based on the rare cultural element generation module, reward generation module, cultural value assignment sub-module, linkage sub-module, and first reward sub-module of the business interaction module, as well as the first collection module and first sending module of the user end.

[0244] The rare cultural element generation module selects several cultural elements from the cultural element dataset other than ordinary cultural elements as rare cultural elements; the number of rare cultural elements shall not exceed 10% of the total number of cultural elements in the cultural element dataset.

[0245] The reward generation module is used to design rewards related to the quantity of rare cultural elements and save them to the rare cultural element-reward association list.

[0246] The Culture Assignment submodule assigns a set of rare cultural elements to a number of goods from different merchants at designated market locations, and then saves these values ​​in a product-culture element association list in the database. Since the types of cultural elements are limited, each rare cultural element can be randomly assigned to a number of goods from several merchants at a given location. After the Culture Assignment submodule completes the assignment, the main control module pushes the rare cultural elements to the corresponding merchants and synchronizes them to the merchant information in the database.

[0247] The linkage submodule is also used to send rare cultural elements associated with the products in the order to the user terminal via the main control module, based on the order execution information received by the main control module, and synchronize them to the user information in the database.

[0248] The first reward submodule is also used to send the associated rewards to the user terminal via the main control module based on the number of rare cultural elements received by the main control module, and synchronize them to the user information in the database.

[0249] The first collection module on the user side is also used to store rare cultural elements; the first sending module is also used to send the rare cultural elements selected by the user to the server.

[0250] In this embodiment, the process for obtaining rare cultural elements is as follows:

[0251] A4) Users send product order information to merchants via the server-side main control module through the user terminal;

[0252] B4) The merchant confirms and executes the product order information;

[0253] C4) The server-side linkage sub-module sends the rare cultural elements associated with the products in the order to the user terminal through the main control module based on the order execution information received by the main control module.

[0254] The process for redeeming rare cultural elements as rewards is as follows:

[0255] A5) Users send rare cultural elements from the user's end to the server;

[0256] B5) The first reward submodule sends the associated reward to the user terminal via the main control module based on the number of rare cultural elements received by the main control module.

[0257] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A retail market business system incorporating cultural elements, characterized in that, It includes a server, a user, and a merchant; the server includes a database and a main control module; the database is used to store user information, merchant information, cultural element datasets, and digital collection datasets; the main control module is connected to the user and merchant terminals respectively, and is used to send product order information from the user terminal to the merchant terminal, and to feed back order execution information from the merchant terminal to the user terminal. The user terminal sends the product order information to the merchant terminal via the server. The merchant terminal confirms and executes the product order information; The server also includes: A business interaction module based on cultural elements is used to build interactive mechanisms based on cultural elements and reward customers; this business interaction module includes: The Common Cultural Elements and Combination Scheme Generation Submodule is used to determine several cultural elements as common cultural elements based on market transaction data, and to construct common cultural element combination schemes. The reward generation module is used to associate common cultural element combination schemes with digital collectibles, design related rewards, and save them to the Common Cultural Element Combination Scheme - Digital Collectibles - Reward Association List; The cultural assignment submodule is used to assign certain common cultural elements to several products of different merchants in the market, and then save them in the product-cultural element association list in the database. The linkage submodule is used to send common cultural elements associated with the products in the order to the user terminal via the main control module, based on the order execution information received by the main control module. The first reward submodule is used to send the corresponding digital collections and associated rewards to the user terminal through the main control module based on the combination of common cultural elements received by the main control module. The product recommendation module is used to generate recommended products and their order based on user behavior data, and push them to the user's end via the main control module; user behavior data includes at least one of the following: click behavior, browsing behavior, purchase behavior, rating behavior, collection behavior, search history, sharing behavior, dwell time and location data; The passenger flow allocation module is used to determine the placement location of rare cultural elements based on the ideal weight center and the actual weight center of the market; the rare cultural elements are selected from other cultural elements in the cultural element dataset besides ordinary cultural elements. The product recommendation module executes the following steps: 4) Construct a product-user rating matrix; Based on user behavior data within a specified time period, construct a product-user rating matrix. In the matrix, rows represent products, columns represent users, and each element represents a user's behavior score for a product. 5) Obtain the similarity between different products; 6) Sort the products in descending order of their similarity scores and recommend them to the user through the main control module; The behavioral scores were adjusted according to the following formula: ； In the formula, , Let Q represent the row scores before and after correction in the k-th row and i-th column, respectively. j Let J represent the weight of the j-th objective factor, and J represent the number of objective factors. Define a user quantity threshold p and a product quantity threshold q. When adding a new user or product, if the existing user quantity has already reached p or the product quantity has already reached q, a similarity comparison is performed. The comparison method is to calculate the similarity difference s, i.e. or ; In the formula, I and K represent the number of users and the number of products, respectively; k represents the k-th product, i represents the i-th user, p represents the p-th user, and q represents the q-th product; Select the user or product with the smallest similarity difference, bind the new user or product to it, and replace the behavior score of the user or product in the original matrix with the average behavior score of the new user or product and the same behavior score of the new user or product. The steps for obtaining the ideal weight center of the marketplace are as follows: 7) Draw the outline image of the entire market area onto a two-dimensional plane coordinate system, convert the image to a grayscale image, and treat it as a homogeneous plane, that is, set the grayscale value to the same value everywhere; 8) Traverse each pixel of the image and obtain its grayscale value and coordinates; 9) Weight the coordinates of each pixel by multiplying the coordinates of each pixel by its corresponding gray value; 10) Sum all the weighted coordinate values ​​and divide them by the sum of the total gray values ​​of the image to obtain the coordinates of the ideal weight center of gravity of the market. The steps for obtaining the actual weight center of the market are as follows: 11) First, calculate the total foot traffic R in the entire market. Then, divide the entire market into u areas and calculate the foot traffic R in each area. u Then calculate the pedestrian flow weight for each area. ; 12) Use the pedestrian flow weights of each area as grayscale values ​​to redraw the outline image; 13) Traverse each pixel of the image and obtain its grayscale value and coordinates; 14) Weight the coordinates of each pixel, that is, multiply the coordinates of each pixel by its corresponding gray value; 15) Sum all the weighted coordinate values ​​and divide them by the sum of the total gray values ​​of the image to obtain the coordinates of the actual weight center position of the market. The passenger flow allocation module determines the placement location of rare cultural elements using the following method: calculate the point symmetrical to the actual weight centroid coordinates about the ideal weight centroid coordinates, which is the placement location of the rare cultural element.

2. The retail market business system incorporating cultural elements according to claim 1, characterized in that, The sub-module for generating common cultural elements and matching schemes is executed according to the following steps: 1) Obtain the recommended number of cultural elements; 2) Obtain the recommended number of cultural elements to combine; 3) Select several cultural elements from the cultural element dataset in the database that meet the recommended values ​​for the number of cultural elements and the recommended values ​​for the number of artistic elements to be combined as ordinary cultural elements, and arrange and combine these ordinary cultural elements to construct several sets of ordinary cultural element combination schemes.

3. The retail market business system incorporating cultural elements according to claim 2, characterized in that, In step 1), the recommended minimum number of cultural elements is calculated using the following formula: ； In the formula, P1 represents the average consumption per person in the market during the operating period; P2 represents the average price of goods in the market during the operating period. The area under the right side of the t-distribution density curve is... The t-value at time t, where α represents the significance level, is typically taken as 0.05; Let n represent the standard deviation of the data in the sample set {A}, which consists of the number of common cultural elements collected from each customer during the operating period; n represents the number of data points in {A}; p represents the set proportion; and L represents the interval length. ; In step 2), the recommended number of cultural elements to be combined is calculated using the following formula: 。 4. The retail market business system incorporating cultural elements according to claim 2, characterized in that, In step 3), two or more digital collectibles will be combined to form a catalog, and rewards associated with the catalog will be designed and saved to the catalog-reward association list. At this point, the business interaction module also includes a second reward submodule, which is used to send the rewards associated with the illustrations to the user terminal via the main control module, based on the illustrations received by the main control module.

5. The retail market business system incorporating cultural elements according to any one of claims 1 to 4, characterized in that, The user terminal includes a display interface, a storage module, a first collection module, a second collection module, and a first sending module; The display interface shows the market map page, merchant introduction page, and online ordering page; the storage module stores product order information and behavioral data; the first collection module stores general cultural elements. The second collection module is used to store digital collections; the first sending module is used to send two or more different types of common cultural elements selected by the user according to the set common cultural element matching rules to the server.

6. The retail market business system incorporating cultural elements according to claim 5, characterized in that, The first collection module of the user terminal is also used to store rare cultural elements; the first sending module is also used to send the rare cultural elements selected by the user to the server. The second collection module also selects two or more different types of digital collectibles to form an illustrated guide according to the set digital collectible pairing rules; the user terminal also includes a second sending module for sending the illustrated guide to the server.

7. The retail market business system incorporating cultural elements according to claim 1, characterized in that, The merchant's client is installed on the stall cart; The stall cart includes a sensor assembly, a control device, a display module, and an LED light strip mounted on the vehicle body. The sensor assembly includes a temperature and humidity sensor for detecting ambient temperature and humidity, a gas sensor for detecting combustible gases, a smoke alarm, and a camera module group. The control device includes a housing, a controller installed inside the housing, and a touch screen mounted on the housing. The controller is equipped with merchant-side software and is connected to the touch screen to display product order information. The camera module group is used to collect real-time data on the situation within the coverage area of ​​the stall cart and upload the collected information to the server via the controller. The server analyzes and statistically analyzes the collected image information to obtain the number of people and feeds the number of people back to the controller. The controller controls the color of the LED light strip based on the number of online orders or the number of people to indicate the current level of crowding at the stall. The LED light strip is set around the perimeter of the vehicle.

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