An intelligent ordering method and system based on multi-party information synchronization in a back kitchen

Abstract

The application relates to the technical field of intelligent ordering, and particularly discloses an intelligent ordering method and system based on multi-party information synchronization of a back kitchen. The application synchronously updates multi-party information of the back kitchen, synchronously optimizes a complete restaurant menu, obtains historical ordering data of multiple customers at the same table, performs dish intersection analysis on the multiple historical ordering data, selects multiple intersection dishes of the same table, performs correlation analysis, selects multiple correlation dishes of the same table, arranges and optimizes the synchronously optimized menu, generates an arranged and optimized menu, and performs ordering display. The historical ordering data of multiple customers at the same table can be subjected to dish intersection analysis, multiple intersection dishes of the same table can be selected, multiple correlation dishes of the same table can be selected, the arranged and optimized menu can be subjected to arrangement optimization and ordering display, the dish preferences of customers at the same table can be deeply mined and analyzed, and then accurate and personalized menu recommendation and optimization can be provided for the customers at the same table, so that a customer-centered ordering experience is realized.

Description

Technical Field

[0001] This invention belongs to the field of intelligent ordering technology, and in particular relates to an intelligent ordering method and system based on the synchronization of information from multiple parties in the kitchen. Background Technology

[0002] Intelligent ordering is a catering service system built using advanced technologies such as artificial intelligence, big data, and the Internet of Things. It integrates menu display, voice recognition, image recognition, user profiling, personalized recommendations, order management, and payment settlement to achieve an efficient, convenient, and personalized ordering experience.

[0003] Compared with traditional manual or self-service ordering, intelligent ordering can be completed through mobile devices, smart terminals or voice assistants, and is widely used in restaurants, food delivery platforms, unmanned catering stores and other scenarios. It has the advantages of reducing labor costs and shortening waiting time.

[0004] In existing technologies, the application of intelligent ordering is mostly just a simple upgrade of traditional menu ordering. It can only make limited dynamic adjustments based on the merchant's marketing strategy and customer evaluations of the dishes. In essence, it is still a menu-centric service model. It lacks in-depth mining and analysis of the food preferences of customers at the same table, and cannot provide accurate and personalized menu recommendations and optimizations for customers at the same table, thus failing to achieve a customer-centric ordering experience. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent ordering method and system based on the synchronization of information from multiple parties in the kitchen, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] An intelligent ordering method based on multi-party information synchronization in the kitchen, the method specifically includes the following steps:

[0008] Obtain the complete restaurant menu, synchronously update information from multiple parties in the kitchen, and synchronously optimize the complete restaurant menu based on the information from multiple parties in the kitchen to generate a synchronously optimized menu;

[0009] Identify multiple customers who order food at the same table, request data access permissions for these customers, and obtain multiple historical order data after the request is approved;

[0010] Based on the synchronized optimized menu, perform dish intersection analysis on multiple historical order data, and select multiple dishes that intersect at the same table from the synchronized optimized menu;

[0011] Based on the multiple dishes that overlap at the same table, perform correlation analysis, and select multiple related dishes from the synchronized optimized menu;

[0012] Based on the multiple dishes that overlap at the same table and the multiple dishes that are related to each other at the same table, the synchronized optimized menu is arranged and optimized to generate an optimized menu, which is then displayed for ordering.

[0013] As a further limitation of the technical solution of this invention, the steps of obtaining the complete restaurant menu, synchronously updating multiple kitchen information, and synchronously optimizing the complete restaurant menu based on the multiple kitchen information to generate a synchronously optimized menu specifically include the following steps:

[0014] Get the full restaurant menu;

[0015] Determine the synchronization period;

[0016] According to the aforementioned synchronization cycle, the kitchen material information is periodically updated synchronously.

[0017] According to the aforementioned synchronization cycle, the kitchen duty information is periodically updated synchronously.

[0018] By integrating the kitchen material information and the kitchen duty information, multi-party kitchen information is generated periodically;

[0019] Based on information from various sources in the kitchen, the complete restaurant menu is simultaneously optimized to generate a synchronized optimized menu.

[0020] As a further limitation of the technical solution of this embodiment of the invention, the step of identifying multiple customers ordering food at the same table, applying for data access permissions for multiple customers ordering food at the same table, and obtaining multiple historical ordering data after the application is approved specifically includes the following steps:

[0021] Obtain the scan data of your deskmate;

[0022] Based on the scan data of the same table, multiple customers who ordered food at the same table were identified;

[0023] Generate and send data permission requests to the mobile devices of multiple customers ordering food at the same table;

[0024] After the data permission request is approved, data access permissions are granted to multiple customers ordering food at the same table.

[0025] Based on data access permissions for multiple customers ordering food at the same table, multiple historical ordering data can be obtained through the mobile devices of multiple customers ordering food at the same table.

[0026] As a further limitation of the technical solution of this invention embodiment, the step of performing dish intersection analysis on multiple historical order data based on the synchronized optimized menu, and selecting multiple dishes that intersect at the same table from the synchronized optimized menu, specifically includes the following steps:

[0027] According to the preset intersecting time periods, the historical order data is extracted and processed to obtain order data for multiple time periods;

[0028] Analyze the ordering data from multiple time periods to identify the dishes ordered by multiple users and record the corresponding ordering data.

[0029] Identify the intersecting factors for multiple dishes, including: identical dish names, identical price ranges, and identical interest recovery value ranges;

[0030] Based on the synchronized optimized menu, the ordering data of multiple dishes are analyzed according to multiple intersecting factors, and multiple intersecting dishes at the same table are selected.

[0031] As a further limitation of the technical solution of this invention, the step of performing correlation analysis on multiple dishes that overlap at the same table, and selecting multiple related dishes from the synchronized optimized menu, specifically includes the following steps:

[0032] Obtain historical order data from restaurants;

[0033] Based on the historical data, a dish association analysis is performed on multiple dishes that overlap at the same table to identify multiple directly related dishes and record the dish association data;

[0034] Based on the synchronized optimized menu, multiple directly related dishes are matched and multiple matching related dishes are filtered.

[0035] Based on multiple matched and associated dishes, the dish association data is optimized to generate optimized association data;

[0036] Based on the optimized association data and the preset number of associations, multiple matched and associated dishes are compared and selected to be associated dishes at the same table.

[0037] As a further limitation of the technical solution of this invention embodiment, the step of arranging and optimizing the synchronously optimized menu according to multiple overlapping dishes at the same table and multiple related dishes at the same table, generating an optimized menu, and displaying it for ordering specifically includes the following steps:

[0038] Create a sub-menu for dishes that appear at the same table, based on the multiple dishes mentioned above.

[0039] Create a table-related sub-menu based on multiple dishes associated with the same table;

[0040] Based on the multiple dishes that intersect at the same table and the multiple dishes that are related to each other at the same table, select multiple other dishes from the synchronized optimized menu;

[0041] Create additional sub-menus based on the other dishes mentioned above;

[0042] The arrangement of the overlapping sub-menu, the related sub-menu, and the other sub-menus is optimized to generate an optimized menu.

[0043] The optimized menu is displayed to multiple customers at the same table.

[0044] An intelligent ordering system based on multi-party information synchronization in the kitchen, the system specifically includes a menu synchronization optimization module, a historical data acquisition module, a dish intersection analysis module, a dish association analysis module, and a menu arrangement optimization module, wherein:

[0045] The menu synchronization optimization module is used to obtain the complete restaurant menu, synchronize and update information from multiple parties in the kitchen, and optimize the complete restaurant menu based on the information from multiple parties in the kitchen to generate a synchronized optimized menu.

[0046] The historical data acquisition module is used to identify multiple customers who order food at the same table, apply for data access permissions for multiple customers who order food at the same table, and acquire multiple historical order data after the application is approved;

[0047] The dish intersection analysis module is used to perform dish intersection analysis on multiple historical order data based on the synchronized optimized menu, and select multiple intersecting dishes at the same table from the synchronized optimized menu;

[0048] The dish association analysis module is used to perform association analysis based on multiple dishes that overlap at the same table, and to select multiple dishes that overlap at the same table from the synchronized optimized menu;

[0049] The menu arrangement optimization module is used to arrange and optimize the synchronously optimized menu based on multiple overlapping dishes at the same table and multiple related dishes at the same table, generate an optimized menu, and display it for ordering.

[0050] As a further limitation of the technical solution of this embodiment of the invention, the dish intersection analysis module specifically includes:

[0051] The time period data extraction unit is used to extract and process multiple historical order data according to preset intersecting time periods to obtain multiple time period order data.

[0052] The time-slot ordering data analysis unit is used to analyze the ordering data of multiple time slots, determine the dishes of multiple users, and record the corresponding dish ordering data;

[0053] The factor determination unit is used to determine the intersecting factors of multiple dishes, including: the same dish name, the same price range, and the same interest recovery value range;

[0054] The table-intersecting dish selection unit is used to analyze the ordering data of multiple dishes based on the synchronously optimized menu and according to multiple dish intersection factors, and select multiple table-intersecting dishes.

[0055] As a further limitation of the technical solution of this embodiment of the invention, the dish association analysis module specifically includes:

[0056] Historical data acquisition unit, used to acquire historical data of restaurant orders;

[0057] The dish association analysis unit is used to perform dish association analysis on multiple dishes that overlap at the same table based on the historical data, identify multiple directly related dishes, and record the dish association data;

[0058] The matching and filtering unit is used to match and filter multiple directly related dishes based on the synchronized optimized menu;

[0059] The data optimization unit is used to optimize the dish association data according to multiple matching and associated dishes, and generate optimized association data;

[0060] The association comparison unit is used to compare multiple matched associated dishes based on the optimized association data and the preset association quantity, and select multiple table-related associated dishes.

[0061] As a further limitation of the technical solution of this embodiment of the invention, the menu arrangement optimization module specifically includes:

[0062] The intersecting submenu creation unit is used to create intersecting submenus based on multiple intersecting dishes at the same table;

[0063] The associated submenu creation unit is used to create associated submenus for the same table based on multiple dishes associated with the same table.

[0064] The other dish selection unit is used to select multiple other dishes from the synchronized optimized menu according to multiple dishes that intersect at the same table and multiple dishes that are related to each other at the same table;

[0065] The other sub-menu creation unit is used to create other sub-menus based on multiple of the other dishes;

[0066] The arrangement optimization unit is used to optimize the arrangement of the overlapping sub-menu, the related sub-menu, and the other sub-menu, and generate an optimized arrangement menu.

[0067] The order display unit is used to display the optimized menu to multiple customers at the same table.

[0068] Compared with the prior art, the beneficial effects of the present invention are:

[0069] (1) This invention can perform dish intersection analysis on the historical ordering data of multiple customers at the same table, select multiple dishes that intersect at the same table, select multiple dishes that are related to the same table, and then optimize the arrangement of the menu and display the order. It can deeply explore and analyze the dish preferences of customers at the same table, and then provide accurate and personalized menu recommendations and optimizations for customers at the same table, thereby realizing a customer-centric ordering experience.

[0070] (2) The present invention can perform dish intersection analysis on the historical ordering data of multiple customers at the same table based on multiple dish intersection factors such as the same dish name, the same price range, and the same interest recovery value range. From the synchronized optimized menu, multiple dishes that intersect at the same table can be selected, which can achieve accurate identification of the common interest dishes of multiple customers at the same table.

[0071] (3) Based on the determination of multiple dishes that overlap at the same table, the present invention can perform dish association analysis, dish matching and association comparison screening, select multiple dishes that overlap at the same table, and thus, in addition to recommending overlapping dishes, it can also recommend related dishes, thereby providing a basis for customers to quickly select dishes that they may be interested in.

[0072] (4) This invention can create intersecting sub-menus based on multiple dishes that are shared at the same table, create related sub-menus based on multiple related dishes that are shared at the same table, select multiple other dishes to create other sub-menus, and then optimize the arrangement of intersecting sub-menus, related sub-menus and other sub-menus to generate an optimized menu with a clear structure and clear priority, so as to achieve a perfect match between the menu and the customer's interests, and can promote the improvement of the ordering experience and satisfaction of customers ordering food at multiple tables. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0074] Figure 1 The diagram illustrates a process flow chart of an intelligent ordering method based on multi-party information synchronization in the kitchen, as provided in an embodiment of the present invention.

[0075] Figure 2 The following is an application architecture diagram of the intelligent ordering system based on multi-party information synchronization in the kitchen, provided by an embodiment of the present invention. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0077] Understandably, most existing applications of intelligent ordering technology are simply upgrades to traditional menu ordering. They can only make limited dynamic adjustments based on the merchant's marketing strategy and customer reviews of the dishes. In essence, they are still menu-centric service models, lacking in-depth analysis of the food preferences of other customers at the same table. They cannot provide accurate and personalized menu recommendations and optimizations for other customers at the same table, and thus cannot achieve a customer-centric ordering experience.

[0078] To address the aforementioned issues, this invention employs the following methods: First, it acquires a complete restaurant menu and simultaneously updates information from multiple kitchen stakeholders. Based on this information, it optimizes the complete restaurant menu to generate a synchronized optimized menu. Second, it identifies multiple customers ordering food at the same table, requests data access permissions for these customers, and acquires multiple historical order data sets after approval. Third, it performs dish intersection analysis on the historical order data based on the synchronized optimized menu, selecting multiple dishes from the synchronized optimized menu that intersect with the order at the same table. Fourth, it performs association analysis on the intersecting and associated dishes, selecting multiple related dishes from the synchronized optimized menu. Finally, it optimizes the arrangement of the synchronized optimized menu based on the intersecting and related dishes, generating an arranged optimized menu for ordering and display. It can perform cross-tabulation analysis of historical order data from multiple customers at the same table, select multiple dishes that overlap with each other at the same table, and select multiple related dishes at the same table. Then, it can optimize the arrangement and display of the menu and order information. It can deeply explore and analyze the food preferences of customers at the same table, and provide them with accurate and personalized menu recommendations and optimizations, thereby achieving a customer-centric ordering experience.

[0079] Figure 1 The diagram illustrates a process flow chart of an intelligent ordering method based on multi-party information synchronization in the kitchen, as provided in an embodiment of the present invention.

[0080] Specifically, in a preferred embodiment of the present invention, an intelligent ordering method based on multi-party information synchronization in the kitchen includes the following steps:

[0081] Step S101: Obtain the complete restaurant menu, synchronously update the kitchen information, and synchronously optimize the complete restaurant menu based on the kitchen information to generate a synchronously optimized menu.

[0082] In this embodiment of the invention, a complete restaurant menu is obtained and a synchronization period is determined. Then, according to the synchronization period, the kitchen material information and kitchen duty information are periodically updated synchronously. The kitchen material information and kitchen duty information are then merged to generate multi-party kitchen information. Based on the multi-party kitchen information, the complete restaurant menu is synchronously optimized, and dishes that cannot be made due to material or chef reasons are removed from the complete restaurant menu, generating a synchronously optimized menu.

[0083] It is understood that, in this embodiment of the invention, the synchronization period is 24 hours.

[0084] Understandably, kitchen material information, including ingredients, auxiliary materials, and seasonings, is determined according to a synchronization cycle. This allows us to determine which dishes on the complete restaurant menu can be made and which cannot during the current synchronization cycle. Kitchen duty information, which is the chef's duty information, is determined according to a synchronization cycle. This allows us to determine which chefs on duty are currently on duty and, consequently, which dishes on the complete restaurant menu can and cannot be made during the current synchronization cycle.

[0085] Specifically, in the preferred embodiment provided by the present invention, the steps of obtaining the complete restaurant menu, synchronously updating multiple kitchen information, and synchronously optimizing the complete restaurant menu based on the multiple kitchen information to generate a synchronously optimized menu specifically include the following steps:

[0086] Get the full restaurant menu;

[0087] Determine the synchronization period;

[0088] According to the aforementioned synchronization cycle, the kitchen material information is periodically updated synchronously.

[0089] According to the aforementioned synchronization cycle, the kitchen duty information is periodically updated synchronously.

[0090] By integrating the kitchen material information and the kitchen duty information, multi-party kitchen information is generated periodically;

[0091] Based on information from various sources in the kitchen, the complete restaurant menu is simultaneously optimized to generate a synchronized optimized menu.

[0092] Furthermore, the intelligent ordering method based on multi-party information synchronization in the kitchen also includes the following steps:

[0093] Step S102: Identify multiple customers who order food at the same table, apply for data access permissions for multiple customers who order food at the same table, and obtain multiple historical order data after the application is approved.

[0094] In this embodiment of the invention, after a customer enters the restaurant and is seated, they can scan the QR code on the table to order food online. At this time, the scanning data of other customers at the same table can be obtained. By identifying the scanning data of other customers at the same table, multiple customers who scanned the QR code to order food can be identified. At the same time, a data permission request is generated and sent to the mobile devices of multiple customers who scanned the QR code to order food. Multiple customers can view the data permission request on their mobile devices and give feedback by agreeing or rejecting it. In this way, feedback information from multiple customers' mobile devices can be received. If it is determined that multiple customers agree to the data permission request, the data access permissions of multiple customers can be obtained. Based on the data access permissions of multiple customers, multiple historical order data can be obtained through the mobile devices of multiple customers.

[0095] Understandably, historical ordering data refers to the data automatically recorded when customers at the same table order food at different restaurants, including information such as dish names, prices, and times.

[0096] It is understandable that multiple historical order data can be local data transmitted directly from multiple mobile devices of customers ordering at the same table, or cloud data obtained through the mobile devices of multiple customers ordering at the same table.

[0097] Specifically, in the preferred embodiment provided by the present invention, the steps of identifying multiple customers ordering food at the same table, requesting data access permissions for multiple customers ordering food at the same table, and obtaining multiple historical ordering data after the request is approved specifically include the following steps:

[0098] Obtain the scan data of your deskmate;

[0099] Based on the scan data of the same table, multiple customers who ordered food at the same table were identified;

[0100] Generate and send data permission requests to the mobile devices of multiple customers ordering food at the same table;

[0101] After the data permission request is approved, data access permissions are granted to multiple customers ordering food at the same table.

[0102] Based on data access permissions for multiple customers ordering food at the same table, multiple historical ordering data can be obtained through the mobile devices of multiple customers ordering food at the same table.

[0103] Furthermore, the intelligent ordering method based on multi-party information synchronization in the kitchen also includes the following steps:

[0104] Step S103: Based on the synchronized optimized menu, perform dish intersection analysis on multiple historical order data, and select multiple dishes that intersect at the same table from the synchronized optimized menu.

[0105] In this embodiment of the invention, multiple historical ordering data are extracted and processed according to a preset intersection time period to obtain ordering data for multiple time periods. Then, the ordering data for these multiple time periods is analyzed to identify multiple user dishes that each customer at the same table has an ordering record for within the intersection time period. The ordering data for these multiple user dishes is recorded. Next, multiple dish intersection factors, such as identical dish names, identical price ranges, and identical interest recovery value ranges, are determined. Based on the multiple dish ordering data, the interest recovery values ​​corresponding to the multiple user dishes are calculated. Then, based on these multiple dish intersection factors, the multiple dish ordering data and multiple interest recovery values ​​are analyzed. From the multiple user dishes, multiple dishes that satisfy the multiple dish intersection factors and belong to the synchronously optimized menu are selected as intersecting dishes for the same table. Specifically, the formula for calculating the interest recovery values ​​corresponding to the multiple user dishes is as follows:

[0106]

[0107] Where j represents customer j at the same table, d represents the dish d ordered by the user, t represents the current time, and I jd T represents the recovery value of customer j's interest in user's dish d at the same table. jd For the time when customer j at the same table last ordered dish d, f jd Let f be the frequency coefficient of ordering dish d by customer j at the same table. By analyzing ordering data for multiple dishes, the more frequently customer j orders dish d, the higher the frequency coefficient f. jd The smaller the value, the more k is a preset calculation factor.

[0108] It is understood that, in this embodiment of the invention, the intersection period can be set to 6 months.

[0109] Understandably, the food order data includes information such as the dish name, price, and time.

[0110] It is understandable that, through the formula for calculating interest recovery value, it can be determined that the interest recovery value of customers at the same table for a certain user's dish is related to factors such as the time interval between orders and the frequency of orders. Moreover, as time goes by, the growth slope of the interest recovery value for a certain user's dish gradually increases, indicating that the more interested customers at the same table are in a user's dish (the more frequently they order it and the smaller the order frequency coefficient), the faster the growth of the interest recovery value.

[0111] It is understandable that the dishes served at the same table are those that multiple customers at the same table are interested in, and that are part of the synchronized optimization menu.

[0112] Specifically, in the preferred embodiment provided by the present invention, the step of performing dish intersection analysis on multiple historical order data based on the synchronized optimized menu, and selecting multiple dishes that intersect at the same table from the synchronized optimized menu, specifically includes the following steps:

[0113] According to the preset intersecting time periods, the historical order data is extracted and processed to obtain order data for multiple time periods;

[0114] Analyze the ordering data from multiple time periods to identify the dishes ordered by multiple users and record the corresponding ordering data.

[0115] Identify the intersecting factors for multiple dishes, including: identical dish names, identical price ranges, and identical interest recovery value ranges;

[0116] Based on the synchronized optimized menu, the ordering data of multiple dishes are analyzed according to multiple intersecting factors, and multiple intersecting dishes at the same table are selected.

[0117] Furthermore, the intelligent ordering method based on multi-party information synchronization in the kitchen also includes the following steps:

[0118] Step S104: Perform association analysis based on the multiple dishes that overlap at the same table, and select multiple dishes that overlap at the same table from the synchronized optimization menu.

[0119] In this embodiment of the invention, historical ordering data from restaurants is acquired. Based on this historical data, a dish association analysis is performed on multiple dishes that overlap at the same table to identify multiple directly related dishes and record the dish association data. Then, based on the synchronized optimized menu, the multiple directly related dishes are matched. From the multiple directly related dishes, multiple matched related dishes belonging to the synchronized optimized menu are selected. The dish association data is then optimized according to the multiple matched related dishes, removing data that is irrelevant to the multiple matched related dishes to generate optimized association data. Then, based on the association frequency in the optimized association data, the multiple matched related dishes are arranged from high to low. According to a preset association number, the multiple table-related dishes that appear first in the list are selected.

[0120] It is understandable that "dish association" refers to the relationship where, when a customer selects one dish, they also select another dish at the same time.

[0121] Understandably, the dish association data records the number of times each directly associated dish is associated.

[0122] Specifically, in the preferred embodiment provided by the present invention, the step of performing correlation analysis based on multiple dishes that overlap at the same table, and selecting multiple related dishes from the synchronized optimized menu, specifically includes the following steps:

[0123] Obtain historical order data from restaurants;

[0124] Based on the historical data, a dish association analysis is performed on multiple dishes that overlap at the same table to identify multiple directly related dishes and record the dish association data;

[0125] Based on the synchronized optimized menu, multiple directly related dishes are matched and multiple matching related dishes are filtered.

[0126] Based on multiple matched and associated dishes, the dish association data is optimized to generate optimized association data;

[0127] Based on the optimized association data and the preset number of associations, multiple matched and associated dishes are compared and selected to be associated dishes at the same table.

[0128] Furthermore, the intelligent ordering method based on multi-party information synchronization in the kitchen also includes the following steps:

[0129] Step S105: Based on the multiple dishes that overlap at the same table and the multiple dishes that are related to each other at the same table, the synchronous optimization menu is arranged and optimized to generate an optimized menu and then displayed for ordering.

[0130] In this embodiment of the invention, a sub-menu for overlapping dishes at the same table is created, and a sub-menu for related dishes at the same table is created. From the synchronized optimized menu, multiple other dishes besides the overlapping and related dishes at the same table are selected, and other sub-menus are created based on these other dishes. Then, an optimized menu is generated with the overlapping sub-menu at the front, the related sub-menu in the middle, and the other sub-menus at the back. The optimized menu is then displayed to multiple customers at the same table for ordering.

[0131] Understandably, the specific dishes in the "Table-to-Table Interchange Submenu," "Table-to-Table Related Submenu," and other submenus are arranged in the corresponding submenu according to customer feedback ratings.

[0132] Specifically, in the preferred embodiment provided by the present invention, the step of arranging and optimizing the synchronized optimized menu according to the multiple overlapping dishes at the same table and the multiple related dishes at the same table, generating an optimized menu, and displaying it for ordering includes the following steps:

[0133] Create a sub-menu for dishes that appear at the same table, based on the multiple dishes mentioned above.

[0134] Create a table-related sub-menu based on multiple dishes associated with the same table;

[0135] Based on the multiple dishes that intersect at the same table and the multiple dishes that are related to each other at the same table, select multiple other dishes from the synchronized optimized menu;

[0136] Create additional sub-menus based on the other dishes mentioned above;

[0137] The arrangement of the overlapping sub-menu, the related sub-menu, and the other sub-menus is optimized to generate an optimized menu.

[0138] The optimized menu is displayed to multiple customers at the same table.

[0139] Furthermore, Figure 2 The following is an application architecture diagram of the intelligent ordering system based on multi-party information synchronization in the kitchen, provided by an embodiment of the present invention.

[0140] In another preferred embodiment of the present invention, an intelligent ordering system based on multi-party information synchronization in the kitchen includes:

[0141] The menu synchronization optimization module 101 is used to obtain the complete restaurant menu, synchronize and update the kitchen information, and optimize the complete restaurant menu based on the kitchen information to generate a synchronized optimized menu.

[0142] In this embodiment of the invention, the menu synchronization optimization module 101 obtains the complete restaurant menu and determines the synchronization period. Then, according to the synchronization period, it periodically updates the kitchen material information and kitchen duty information. The kitchen material information and kitchen duty information are then merged to generate kitchen multi-party information. Based on the kitchen multi-party information, the complete restaurant menu is synchronized and optimized, and dishes that cannot be made due to material or chef reasons are removed from the complete restaurant menu, thus generating a synchronized optimized menu.

[0143] The historical data acquisition module 102 is used to identify multiple customers who order food at the same table, apply for data access permissions for multiple customers who order food at the same table, and acquire multiple historical order data after the application is approved.

[0144] In this embodiment of the invention, after a customer enters the restaurant and is seated, they can scan the QR code on the table to order food online. At this time, the historical data acquisition module 102 can acquire the scanning data of other customers at the same table. By identifying the scanning data of other customers at the same table, multiple customers at the same table who scanned the QR code to order food can be identified. At the same time, a data permission application is generated and sent to the mobile devices of multiple customers at the same table who scanned the QR code to order food. Multiple customers at the same table can view the data permission application through their mobile devices and give feedback operations of agreeing or rejecting. In this way, feedback information from the mobile devices of multiple customers at the same table can be received. If it is determined that multiple customers at the same table agree to the data permission application, the data access permissions of multiple customers at the same table can be obtained. Then, based on the data access permissions of multiple customers at the same table, multiple historical ordering data can be obtained through the mobile devices of multiple customers at the same table.

[0145] The dish intersection analysis module 103 is used to perform dish intersection analysis on multiple historical order data based on the synchronized optimized menu, and select multiple dishes that intersect at the same table from the synchronized optimized menu.

[0146] In this embodiment of the invention, the dish intersection analysis module 103 processes multiple historical ordering data according to a preset intersection time period to obtain multiple time period ordering data. Then, it analyzes the ordering data for these multiple time periods to identify multiple user dishes that each customer at the same table has an ordering record within the intersection time period, and records the ordering data for these multiple user dishes. Next, it identifies multiple dish intersection factors, such as identical dish names, identical price ranges, and identical interest recovery value ranges. Based on the multiple dish ordering data, it calculates the interest recovery values ​​corresponding to the multiple user dishes. Then, it analyzes the multiple dish ordering data and multiple interest recovery values ​​according to the multiple dish intersection factors. From the multiple user dishes, it selects multiple dishes that satisfy the multiple dish intersection factors and belong to the synchronously optimized menu that are intersecting dishes at the same table. Specifically, the calculation formula for the interest recovery values ​​corresponding to the multiple user dishes is as follows:

[0147]

[0148] Where j represents customer j at the same table, d represents the dish d ordered by the user, t represents the current time, and I jd T represents the recovery value of customer j's interest in user's dish d at the same table. jd For the time when customer j at the same table last ordered dish d, f jd Let f be the frequency coefficient of ordering dish d by customer j at the same table. By analyzing ordering data for multiple dishes, the more frequently customer j orders dish d, the higher the frequency coefficient f. jd The smaller the value, the more k is a preset calculation factor.

[0149] Specifically, in the preferred embodiment provided by the present invention, the dish intersection analysis module 103 specifically includes:

[0150] The time period data extraction unit is used to extract and process multiple historical order data according to preset intersecting time periods to obtain multiple time period order data.

[0151] The time-slot ordering data analysis unit is used to analyze the ordering data of multiple time slots, determine the dishes of multiple users, and record the corresponding dish ordering data;

[0152] The factor determination unit is used to determine the intersecting factors of multiple dishes, including: the same dish name, the same price range, and the same interest recovery value range;

[0153] The table-intersecting dish selection unit is used to analyze the ordering data of multiple dishes based on the synchronously optimized menu and according to multiple dish intersection factors, and select multiple table-intersecting dishes.

[0154] Furthermore, the intelligent ordering system based on multi-party information synchronization in the kitchen also includes:

[0155] The dish association analysis module 104 is used to perform association analysis based on multiple dishes that overlap at the same table, and to select multiple dishes that overlap at the same table from the synchronized optimization menu.

[0156] In this embodiment of the invention, the dish association analysis module 104 acquires historical ordering data from the restaurant. Based on the historical data, it performs dish association analysis on multiple dishes that overlap at the same table, identifies multiple directly related dishes, and records the dish association data. Based on the synchronized optimized menu, it matches multiple directly related dishes, filters multiple matched related dishes belonging to the synchronized optimized menu from the multiple directly related dishes, and optimizes the dish association data according to the multiple matched related dishes, removing data that is irrelevant to the multiple matched related dishes, generating optimized association data. Then, according to the association frequency in the optimized association data, it arranges the multiple matched related dishes from high to low, and selects the multiple table-related dishes that are ranked first from the multiple matched related dishes according to the preset association quantity.

[0157] Specifically, in the preferred embodiment provided by the present invention, the dish association analysis module 104 specifically includes:

[0158] Historical data acquisition unit, used to acquire historical data of restaurant orders;

[0159] The dish association analysis unit is used to perform dish association analysis on multiple dishes that overlap at the same table based on the historical data, identify multiple directly related dishes, and record the dish association data;

[0160] The matching and filtering unit is used to match and filter multiple directly related dishes based on the synchronized optimized menu;

[0161] The data optimization unit is used to optimize the dish association data according to multiple matching and associated dishes, and generate optimized association data;

[0162] The association comparison unit is used to compare multiple matched associated dishes based on the optimized association data and the preset association quantity, and select multiple table-related associated dishes.

[0163] Furthermore, the intelligent ordering system based on multi-party information synchronization in the kitchen also includes:

[0164] The menu arrangement optimization module 105 is used to arrange and optimize the synchronous optimization menu based on multiple overlapping dishes at the same table and multiple related dishes at the same table, generate an optimized menu, and display it for ordering.

[0165] In this embodiment of the invention, the menu arrangement optimization module 105 creates overlapping sub-menus based on multiple overlapping dishes at the same table, and creates related sub-menus based on multiple related dishes at the same table. From the synchronized optimization menu, it selects multiple other dishes besides overlapping and related dishes at the same table, creates other sub-menus based on these other dishes, and generates an optimized menu in the order of overlapping sub-menus at the beginning, related sub-menus in the middle, and other sub-menus at the end. The optimized menu is then displayed to multiple customers at the same table for ordering.

[0166] Specifically, in the preferred embodiment provided by the present invention, the menu arrangement optimization module 105 specifically includes:

[0167] The intersecting submenu creation unit is used to create intersecting submenus based on multiple intersecting dishes at the same table;

[0168] The associated submenu creation unit is used to create associated submenus for the same table based on multiple dishes associated with the same table.

[0169] The other dish selection unit is used to select multiple other dishes from the synchronized optimized menu according to multiple dishes that intersect at the same table and multiple dishes that are related to each other at the same table;

[0170] The other sub-menu creation unit is used to create other sub-menus based on multiple of the other dishes;

[0171] The arrangement optimization unit is used to optimize the arrangement of the overlapping sub-menu, the related sub-menu, and the other sub-menu, and generate an optimized arrangement menu.

[0172] The order display unit is used to display the optimized menu to multiple customers at the same table.

[0173] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. An intelligent ordering method based on multi-party information synchronization in the kitchen, characterized in that, The method specifically includes the following steps: Obtain the complete restaurant menu, synchronously update information from multiple parties in the kitchen, and synchronously optimize the complete restaurant menu based on the information from multiple parties in the kitchen to generate a synchronously optimized menu; Identify multiple customers who order food at the same table, request data access permissions for these customers, and obtain multiple historical order data after the request is approved; Based on the synchronized optimized menu, perform dish intersection analysis on multiple historical order data, and select multiple dishes that intersect at the same table from the synchronized optimized menu; According to the preset intersection time period, multiple historical order data are extracted and processed to obtain order data for multiple time periods. Then, the order data for these multiple time periods is analyzed to identify multiple user dishes that each customer at the same table has an order record for within the intersection time period. The order data for these multiple user dishes is recorded. Next, the intersection factors for multiple dishes, including those with the same dish name, the same price range, and the same interest recovery value range, are determined. Based on the order data for these multiple dishes, the interest recovery values ​​corresponding to these multiple user dishes are calculated. Then, based on these intersection factors, the order data and interest recovery values ​​for these multiple dishes are analyzed. From the multiple user dishes, multiple dishes that satisfy both the multiple dish intersection factors and belong to the synchronized optimized menu are selected. The formula for calculating the interest recovery values ​​for these multiple user dishes is as follows: ; Where j represents customer j at the same table, d represents the dish d ordered by the user, and t represents the current time. Restore the interest value of customer j at the same table for user d's dish. The time when customer j at the same table last ordered dish d from user d. The frequency coefficient of ordering dish d by customer j at the same table is calculated by analyzing ordering data for multiple dishes. The more frequently customer j orders dish d, the higher the frequency coefficient. The smaller the value, the more likely k is to be a preset calculation factor; The interest recovery value of customers at the same table for a user's dishes is related to the time interval between orders and the frequency of orders. Moreover, as time goes by, the growth slope of the interest recovery value for a user's dishes gradually increases, indicating that the more interested customers are in a user's dishes, the faster the growth of the interest recovery value. In particular, the more interested a user is in a user's dishes, the more frequently they order them and the smaller the order frequency coefficient. Based on the multiple dishes that overlap at the same table, perform correlation analysis, and select multiple related dishes from the synchronized optimized menu; Based on the multiple dishes that overlap at the same table and the multiple dishes that are related at the same table, the synchronized optimized menu is arranged and optimized to generate an optimized menu, which is then displayed for ordering. Based on multiple dishes that overlap at the same table, create overlapping sub-menus. Based on multiple related dishes that overlap at the same table, create related sub-menus. From the synchronized optimized menu, select multiple other dishes besides overlapping and related dishes. Based on these other dishes, create other sub-menus. Then, generate an optimized menu with overlapping sub-menus at the beginning, related sub-menus in the middle, and other sub-menus at the end. Finally, display the optimized menu to customers at multiple tables who are ordering.

2. The intelligent ordering method based on multi-party information synchronization in the kitchen according to claim 1, characterized in that, The process of obtaining the complete restaurant menu, synchronously updating information from multiple kitchen sources, and synchronously optimizing the complete restaurant menu based on this kitchen information to generate a synchronously optimized menu specifically includes the following steps: Get the full restaurant menu; Determine the synchronization period; According to the aforementioned synchronization cycle, the kitchen material information is periodically updated synchronously. According to the aforementioned synchronization cycle, the kitchen duty information is periodically updated synchronously. By integrating the kitchen material information and the kitchen duty information, multi-party kitchen information is generated periodically; Based on information from various sources in the kitchen, the complete restaurant menu is simultaneously optimized to generate a synchronized optimized menu.

3. The intelligent ordering method based on multi-party information synchronization in the kitchen according to claim 1, characterized in that, The process of identifying multiple customers ordering food at the same table, requesting data access permissions for these customers, and obtaining multiple historical order data after the request is approved specifically includes the following steps: Obtain the scan data of your deskmate; Based on the scan data of the same table, multiple customers who ordered food at the same table were identified; Generate and send data permission requests to the mobile devices of multiple customers ordering food at the same table; After the data permission request is approved, data access permissions are obtained for multiple customers ordering food at the same table. Based on data access permissions for multiple customers ordering food at the same table, multiple historical ordering data can be obtained through the mobile devices of multiple customers ordering food at the same table.

4. The intelligent ordering method based on multi-party information synchronization in the kitchen according to claim 1, characterized in that, The step of performing correlation analysis on multiple dishes that overlap at the same table, and selecting multiple related dishes from the synchronized optimized menu, specifically includes the following steps: Obtain historical order data from restaurants; Based on the historical data, a dish association analysis is performed on multiple dishes that overlap at the same table to identify multiple directly related dishes and record the dish association data; Based on the synchronized optimized menu, multiple directly related dishes are matched and multiple matching related dishes are filtered. Based on multiple matched and associated dishes, the dish association data is optimized to generate optimized association data; Based on the optimized association data and the preset number of associations, multiple matched and associated dishes are compared and selected to be associated dishes at the same table.

5. An intelligent ordering system based on multi-party information synchronization in the kitchen, used to execute the intelligent ordering method based on multi-party information synchronization in the kitchen as described in any one of claims 1-4, characterized in that, The system specifically includes a menu synchronization optimization module, a historical data acquisition module, a dish intersection analysis module, a dish association analysis module, and a menu arrangement optimization module, wherein: The menu synchronization optimization module is used to obtain the complete restaurant menu, synchronize and update information from multiple parties in the kitchen, and optimize the complete restaurant menu based on the information from multiple parties in the kitchen to generate a synchronized optimized menu. The historical data acquisition module is used to identify multiple customers who order food at the same table, apply for data access permissions for multiple customers who order food at the same table, and acquire multiple historical order data after the application is approved; The dish intersection analysis module is used to perform dish intersection analysis on multiple historical order data based on the synchronized optimized menu, and select multiple intersecting dishes at the same table from the synchronized optimized menu; The dish association analysis module is used to perform association analysis based on multiple dishes that overlap at the same table, and to select multiple dishes that overlap at the same table from the synchronized optimized menu; The menu arrangement optimization module is used to arrange and optimize the synchronously optimized menu based on multiple overlapping dishes at the same table and multiple related dishes at the same table, generate an optimized menu, and display it for ordering.

6. The intelligent ordering system based on multi-party information synchronization in the kitchen according to claim 5, characterized in that, The dish intersection analysis module specifically includes: The time period data extraction unit is used to extract and process multiple historical order data according to preset intersecting time periods to obtain multiple time period order data. The time-slot ordering data analysis unit is used to analyze the ordering data of multiple time slots, determine the dishes of multiple users, and record the corresponding dish ordering data; The factor determination unit is used to determine the intersecting factors of multiple dishes, including: the same dish name, the same price range, and the same interest recovery value range; The table-intersecting dish selection unit is used to analyze the ordering data of multiple dishes based on the synchronized optimized menu and according to multiple dish intersection factors, and select multiple table-intersecting dishes.

7. The intelligent ordering system based on multi-party information synchronization in the kitchen according to claim 5, characterized in that, The dish association analysis module specifically includes: Historical data acquisition unit, used to acquire historical data of restaurant orders; The dish association analysis unit is used to perform dish association analysis on multiple dishes that overlap at the same table based on the historical data, identify multiple directly related dishes, and record the dish association data; The matching and filtering unit is used to match and filter multiple directly related dishes based on the synchronized optimized menu; The data optimization unit is used to optimize the dish association data according to multiple matching and associated dishes, and generate optimized association data; The association comparison unit is used to compare multiple matched associated dishes based on the optimized association data and the preset association quantity, and select multiple table-related associated dishes.

8. The intelligent ordering system based on multi-party information synchronization in the kitchen according to claim 5, characterized in that, The menu arrangement optimization module specifically includes: The intersecting submenu creation unit is used to create intersecting submenus based on multiple intersecting dishes at the same table; The associated submenu creation unit is used to create associated submenus for the same table based on multiple dishes associated with the same table. The other dish selection unit is used to select multiple other dishes from the synchronized optimized menu according to multiple dishes that intersect at the same table and multiple dishes that are related to each other at the same table; The other sub-menu creation unit is used to create other sub-menus based on multiple of the other dishes; The arrangement optimization unit is used to optimize the arrangement of the overlapping sub-menu, the related sub-menu, and the other sub-menu, and generate an optimized arrangement menu. The order display unit is used to display the optimized menu to multiple customers at the same table.

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