Health data control method and system for intelligent dining table

Abstract

The invention discloses a health data control method and system for an intelligent dining table, and relates to the technical field of intelligent dining tables. Comprising the following steps: pre-collecting and initializing data; constructing a dynamic health archive library containing personnel nutrition tolerance and a database associated with dish information; carrying out real-time management and control in a meal; collecting weight by a weighing sensor of a weighing unit, reading an RFID tag at the bottom of a draining plate by a dish reading core plate to obtain a dish ID, and reading a user ID confirmed by the core plate by a chopstick rack. And the data of the user ID, the dish ID and the clamping weight are automatically recorded. According to the arrangement mode that the weighing unit is matched with the dish reading core plate and the chopstick rack reading core plate, the dish reading core plate synchronously reads the RFID tag at the bottom of the draining plate and automatically obtains the ID of the dish, after a user picks up the dish, the clamping weight is calculated through secondary weighing, meanwhile, the chopstick rack reading core plate confirms the ID of the user, and the user can conveniently pick up the dish. And thus, the automatic binding of the user ID, the dish ID and the clamping weight can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent dining table, in particular to a health data control method and system for intelligent dining table. BACKGROUND

[0002] Dining table generally refers to a table for dining, and there are various types of dining tables, including intelligent electric dining table. The intelligent electric dining table is usually a kind of dining furniture with an electric turntable installed on the surface of a large round table. The electric turntable is driven by a motor to rotate automatically at a low speed, which facilitates the diners to take food.

[0003] With the development of health, it is usually necessary to weigh the amount of food to control diet. The existing intelligent electric dining table is usually equipped with a weighing function to weigh the weight of food. The user controls the food according to the weight, but the weight data obtained by the intelligent electric dining table usually cannot be linked with food recognition and user identity confirmation, resulting in the inability to associate the user's health data and control. Therefore, it is necessary to use the health data control method to weigh the food while linking with food recognition and user identity confirmation, and associating the user's health data in real time. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a health data control method and system for intelligent dining table, which solves the problem that the food recognition and user identity confirmation cannot be linked, resulting in the inability to associate the user's health data and control.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a health data control method for intelligent dining table, comprising the following specific steps: Step 1: data pre-collection and initialization, constructing a dynamic health profile database containing personnel nutritional tolerance and a database associated with food information, and completing the preliminary binding of user ID and food ID through the main body of intelligent dining table; Step 2: real-time control during meal, the weighing sensor of the weighing unit collects the weight, the food reading core board reads the RFID tag at the bottom of the draining tray to obtain the food ID, the chopstick holder reads the user ID confirmed by the core board, and automatically records the data of user ID, food ID and clamping weight, and associates the data with personnel health data, calculates real-time nutritional intake, dynamically adjusts the intake suggestion and displays it through the interactive interface of the main body of intelligent dining table; Step 3: feedback after meal, summarize the data of the day to generate a nutrition report, synchronize to the user terminal and store it encrypted; Step 4: long-term optimization, based on historical weighing data, RFID identification records and health goals, optimize menu recommendation and system parameters to form a closed-loop management.

[0006] Preferably, the nutritional tolerance of the personnel in step one includes protein, fat, and cholesterol. The construction of the dynamic health record database in step one specifically includes generating a table of diners, which contains user ID, name, identity type, and various nutritional tolerance data. The various nutritional tolerance data are the daily intake of protein, fat, and cholesterol. A unique QR code is generated based on each row of user data. The QR code contains user ID and identity verification information. The database associated with the dish information in step one specifically includes entering the basic information of the dishes, which includes the name and unit nutritional components, and associating it with the RFID tag code at the bottom of the draining tray.

[0007] Preferably, the weighing sensor in step two specifically collects the weight, including the total weight of the weighing pan, the dish, the draining pan, and the dish in real time. The RFID tag in step two is used to pre-store the unique identification information of the dish. The calculation of real-time nutrient intake in step two specifically includes obtaining the unit nutrient composition of the corresponding dish from the dish database. The unit nutrient composition is the protein, fat, and cholesterol content per 100g. Combined with the gripping weight collected by the weighing unit, the actual grams of protein, fat, and cholesterol ingested are calculated to generate real-time nutrient intake data.

[0008] Preferably, the dish identification process in step two specifically includes: S1: When the plate is placed on the weighing pan, the water-blocking plate forms a closed weighing area, and the weighing sensor collects the initial total weight. S2: The food reading core board simultaneously reads the RFID tag at the bottom of the draining tray, obtains the food ID, and matches it with the food information in the database; S3: After the user picks up the food with chopsticks, the weighing sensor collects the remaining weight and calculates the weight picked up by combining the initial total weight and the remaining weight. S4: The chopstick holder reads the user ID associated with the chopstick holder, binds the clamping weight to the user ID, and records it.

[0009] Preferably, in step S4, the user ID associated with the chopstick holder specifically includes a binding QR code inside the chopstick holder and an RFID tag at the bottom. The user scans the QR code to bind the ID. When the chopstick holder is placed in the preset area on top of the main body of the smart dining table, the chopstick holder reading core board reads the RFID tag at the bottom of the chopstick holder and reads the bound user ID.

[0010] Preferably, the dynamic adjustment of intake recommendations in step two specifically includes uploading the clamped weight data from the weighing sensor and uploading the dish ID from the dish reading core board, then calling the unit nutrient composition of the dish in the dish database, calculating the total amount of nutrients currently ingested, and simultaneously retrieving the daily tolerance threshold from the personnel's health record to compare the current intake with the tolerance threshold. When the percentage is ≤60%, a prompt indicating that it can be consumed normally will be generated; When 60% < percentage ≤ 80%, a suggestion to consume an appropriate amount is generated; When 80% < percentage ≤ 95%, a warning is generated indicating that the intake is approaching the upper limit of tolerance and should be reduced. When the percentage is > 95%, a warning is generated indicating that the tolerance limit has been reached and intake should be stopped. All prompts and warnings are displayed through the human-computer interaction interface of the main body of the smart dining table (1) and accompanied by voice broadcast.

[0011] Preferably, the nutrition report generated in step three specifically includes: The daily intake table displays the actual total intake of protein, fat, and cholesterol for each user, categorized by user identity type. Nutritional comparative analysis calculates the difference between the daily intake and the corresponding nutrient tolerance level, and marks the status as adequate, insufficient, or excessive. The contribution percentage of each dish is calculated as the percentage of total daily intake of protein, fat, and cholesterol.

[0012] Preferably, the basis for the optimized menu recommendation in step four includes the average weight and frequency of different dishes picked up by users in historical data, the rate of users' nutritional intake compliance, and the compatibility of dish cooking data with users' health status.

[0013] Preferably, the long-term optimization in step four includes summarizing the historical clamp weight data accumulated by the weighing unit, analyzing users' preferences for different dishes, combining the RFID tag identification records of the dish reading core plate, statistically analyzing the types of dishes frequently selected by users, associating changes in nutritional tolerance in health records, and adjusting the dish combinations and portion suggestions of the recommended menu.

[0014] A health data control system for a smart dining table includes: The main body of the smart dining table; The hardware includes a chopstick rest, chopsticks, a serving plate, and a weighing unit. The weighing unit includes a weighing pan, a weighing pan support, a food reading core plate, a weighing sensor, a water-retaining tray, a support frame, screws, a four-pronged nut, a load-bearing plate, the chopstick rest reading core plate, and a draining tray. The chopstick rest is located at the top of the smart dining table body, the chopsticks are located at the bottom of the chopstick rest, the serving plate is located at the top of the smart dining table body, the draining tray is located at the bottom of the serving plate, an RFID tag is installed at the bottom of the draining tray, the weighing pan is located at the bottom of the draining tray, and the weighing pan support is located on the weighing pan. At the bottom, the weighing pan bracket is installed inside the support frame, the food reading core plate is installed inside the support frame, the weighing sensor is installed inside the support frame and the weighing sensor is located at the bottom of the food reading core plate, the water baffle is located at the top of the support frame, the support frame is installed on the main body of the smart dining table, the screw is located on the main body of the smart dining table, the screw is slidably inserted into the support frame, the four-pronged nut is threaded onto the end of the screw, the bearing plate is located at the bottom of the support frame, and the chopstick holder reading core plate is installed at the top of the bearing plate; The data pre-collection and initialization module is used to construct a dynamic health record database containing personnel nutritional tolerance and a database of associated dish information, and to perform preliminary binding between user IDs and dish IDs. The real-time control module is used to collect weight, read the RFID tag at the bottom of the draining tray to obtain the dish ID, read the user ID confirmed by the core board of the chopstick holder, and automatically record the user ID, dish ID and the weight of the chopsticks. The module also associates the data with the user's health data, calculates the real-time nutritional intake, dynamically adjusts the intake recommendations and displays them through the interactive interface of the smart dining table. The feedback module is used to summarize the data of the day to generate a nutrition report, synchronize it to the user terminal, and store it in encrypted form. The long-term optimization module is used to optimize menu recommendations and system parameters based on historical weighing data, RFID identification records, and health goals, forming a closed-loop management system. A human-computer interaction module, which is used to perform human-computer interaction operations.

[0015] This invention discloses a health data control method and system for smart dining tables, which has the following beneficial effects: (1) By using the weighing unit in conjunction with the food reading core plate and the chopsticks holder reading core plate, when the food plate is placed, the weighing plate triggers the weighing sensor to collect the initial weight, and at the same time, the food reading core plate reads the RFID tag at the bottom of the draining plate to automatically obtain the food ID. After the user picks up the food, the second weighing is performed to calculate the weight of the food, and at the same time, the chopsticks holder reading core plate confirms the user ID. This facilitates the automatic binding of the user ID with the food ID and the weight of the food, real-time association with the health data of the user, dynamic adjustment of intake recommendations, and convenient control of the user's health data. (2) The present invention embeds the weighing unit under the tabletop of the smart dining table, with only the weighing pan exposed. It is integrated with the overall design of the dining table and does not occupy extra space. At the same time, the functions such as water-blocking edging and chopstick holder recognition are integrated into the weighing unit, which helps to reduce the number of independent hardware and reduce installation and maintenance costs. It can be seamlessly adapted to scenarios such as family meals and self-service dining in restaurants, improving the system's practicality and user convenience. (3) By using a stainless steel water-blocking rim around the weighing pan, the present invention helps to prevent soup from leaking into the weighing sensor, avoids performance degradation caused by corrosion, and improves the safety of the weighing unit. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the health data control method of the present invention; Figure 2 This is a schematic diagram of the main structure of the smart dining table of the present invention; Figure 3 This is a schematic diagram of the structure of the serving dish of the present invention; Figure 4 This is a schematic diagram of the structure of the drain tray of the present invention; Figure 5 This is a schematic diagram of the structure of the weighing pan in this invention; Figure 6 This is a schematic diagram of the structure of the weighing pan support of the present invention; Figure 7 This is a schematic diagram of the structure of the weighing sensor in this invention.

[0018] In the diagram: 1. Smart dining table main body; 2. Chopstick holder; 3. Chopsticks; 4. Vegetable plate; 5. Weighing unit; 51. Scale pan; 52. Scale pan support; 53. Vegetable reading core board; 54. Weighing sensor; 55. Water-retaining tray; 56. Support frame; 57. Screw; 58. Four-pronged nut; 59. Bearing plate; 510. Chopstick holder reading core board; 511. Draining tray. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, 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.

[0020] This application provides a health data control method and system for smart dining tables, solving the problem that the inability to link with food identification and user identity verification leads to the inability to associate user health data and control. When a plate is placed, the weighing pan triggers the weighing sensor to collect the initial weight, and the food reading core plate simultaneously reads the RFID tag at the bottom of the draining tray to automatically obtain the food ID. After the user picks up the food, a second weighing is performed to calculate the picked weight, and the chopsticks holder reads the core plate to confirm the user ID. This facilitates the automatic binding of user ID, food ID, and picked weight, enabling real-time association with personal health data and dynamic adjustment of intake recommendations.

[0021] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0022] This invention discloses a health data control method and system for smart dining tables.

[0023] According to the appendix Figures 1-7 As shown, the specific steps include the following: Step 1: Data pre-collection and initialization, constructing a dynamic health record database containing personnel nutritional tolerance and a database of associated dish information, and completing the initial binding of user ID and dish ID through the smart dining table main body 1; Step Two: Real-time monitoring during the meal. The weighing sensor 54 of the weighing unit 5 collects the weight. The food reading core plate 53 reads the RFID tag at the bottom of the draining tray 511 to obtain the food ID. The chopstick rest reads the user ID confirmed by the core plate 510 and automatically records the user ID, food ID, and the weight of the food being picked up. This data is then linked to the user's health data to calculate real-time nutritional intake, dynamically adjust intake recommendations, and display the results through the interactive interface of the smart dining table main body 1. Tag information entry needs to be integrated with the smart dining table menu management process, employing a two-step operation of pre-entry and binding: First, a menu entry is created in the backend management system, entering core data such as menu ID, name, nutritional components per unit (protein, fat, and cholesterol content per 100g), and cooking parameters to generate a structured menu information package. Then, using an RFID writing device (supporting the 13.56MHz high-frequency band, suitable for dining table scenarios), the unique menu ID and basic information (such as the menu name abbreviation) from the menu entry are encrypted and written to the RFID tag at the bottom of the drain tray 511, completing the one-to-one binding of menu data and tags. Furthermore, secondary modifications are supported after writing (e.g., rewriting the tag information when menu parameters are updated), ensuring real-time synchronization between tag information and the backend database. Tag recognition is achieved through the food reading core board 53 of the smart dining table weighing unit. This food reading core board 53 integrates a ring-shaped radio frequency card reader antenna (its coverage area completely overlaps with the area where the draining tray 511 is placed) and is triggered in conjunction with the weighing unit 5: when the draining tray 511 with the RFID tag is placed on the weighing pan 51 along with the dish tray 4, the weight of the weighing pan 51 triggers the weighing sensor 54 to start. At the same time, the food reading core board 53 is automatically powered on and emits a high-frequency radio frequency signal. After receiving the signal, the RFID tag is activated and feeds back the food ID and other information stored inside to the food reading core board 53 through the radio frequency signal. The food reading core board 53 decodes and decrypts the feedback signal and uploads the food ID to the backend server. The server retrieves the corresponding nutritional components, cooking parameters and other data according to the food ID to complete the tag recognition and data association. The entire recognition process takes ≤0.5 seconds and supports the simultaneous recognition of multiple tags (suitable for multi-person dining scenarios). Step 3: Post-meal feedback, summarizing the day's data to generate a nutrition report, synchronizing it to the user's terminal and storing it in encrypted form; Step 4: Long-term optimization. Based on historical weighing data, RFID identification records, and health goals, optimize menu recommendations and system parameters to form a closed-loop management system.

[0024] The nutritional tolerance data for individuals in Step 1 includes protein, fat, and cholesterol. The construction of the dynamic health record database in Step 1 specifically includes generating a table of diners, containing user ID, name, identity type, and nutritional tolerance data for each nutrient level (the daily allowable intake of protein, fat, and cholesterol). A unique QR code is generated for each row of user data. The QR code generation rule uses the QR code encoding standard, combining the user ID, identity type identifier, and verification code in the specified format. After conversion using the UTF-8 character set, a 200×200 pixel QR code image is generated and placed within the table. The QR code contains the user ID and identity verification information. The database associated with the dish information in Step 1 specifically includes entering basic dish information, including name and unit nutritional components, and associating it with the RFID tag code on the bottom of the draining tray 511.

[0025] In step two, the weighing sensor 54 collects weights, specifically including real-time collection of the total weight of the weighing pan 51, the vegetable plate 4, the draining pan 511, and the vegetables. The RFID tag in step two is used to pre-store the unique identification information of the vegetables. The calculation of real-time nutrient intake in step two specifically includes obtaining the unit nutrient content of the corresponding vegetables in the vegetable database. The unit nutrient content is the protein, fat, and cholesterol content per 100g. Combined with the gripping weight collected by the weighing unit 5, the actual number of grams of protein, fat, and cholesterol ingested at the time is calculated. The calculation formula can be gripping weight (g) ÷ 100 × unit nutrient content, generating real-time nutrient intake data.

[0026] The specific steps of the dish identification process in step two include: S1: When the vegetable tray 4 is placed on the weighing pan 51, the water-blocking plate 55 forms a closed weighing area, and the weighing sensor 54 collects the initial total weight. S2: The food reading core board 53 synchronously reads the RFID tag at the bottom of the draining tray 511, obtains the food ID, and matches it with the food information in the database; S3: After the user uses chopsticks 3 to pick up the food, the weighing sensor 54 collects the remaining weight and calculates the weight picked up by combining the initial total weight and the remaining weight. S4: The chopstick holder reading core board 510 reads the user ID associated with the chopstick holder 2. The chopstick holder reading core board 510 integrates a ring radio frequency card reader antenna, which binds and records the clamping weight with the user ID.

[0027] In step S4, the user ID associated with the chopstick holder 2 specifically includes a binding QR code inside the chopstick holder 2 and an RFID tag at the bottom. The user scans the QR code to bind the ID. When the chopstick holder 2 is placed in the preset area on top of the main body 1 of the smart dining table, the chopstick holder reading core board 510 reads the RFID tag at the bottom of the chopstick holder 2 and reads the bound user ID. At the same time, it performs double verification with the user ID read from the RFID tag on the plate, which helps to ensure that the weight picked up is accurately attributed to the corresponding user and avoids data confusion.

[0028] The dynamic adjustment of intake recommendations in step two specifically includes the weighing sensor 54 uploading the clamped weight data and the dish reading core board 53 uploading the dish ID, then calling the unit nutrient composition of the dish in the dish database, calculating the total amount of nutrients currently ingested, and at the same time retrieving the daily tolerance threshold in the personnel's health record to compare the current intake with the tolerance threshold. When the percentage is ≤60%, a prompt indicating that it can be consumed normally will be generated; When 60% < percentage ≤ 80%, a suggestion to consume an appropriate amount is generated; When 80% < percentage ≤ 95%, a warning is generated indicating that the intake is approaching the upper limit of tolerance and should be reduced. When the percentage is > 95%, a warning is generated indicating that the tolerance limit has been reached and intake should be stopped. All prompts and warnings are displayed through the human-computer interaction interface of the smart dining table 1 and accompanied by voice broadcast.

[0029] The nutrition report generated in step three specifically includes: The daily intake table displays the actual total intake of protein, fat, and cholesterol for each user, categorized by user identity type. Nutritional comparative analysis calculates the difference between the daily intake and the corresponding nutrient tolerance level, and marks the status as adequate, insufficient, or excessive. The contribution percentage of each dish is calculated as the percentage of total daily intake of protein, fat, and cholesterol.

[0030] The optimized menu recommendations in step four are based on historical data, including the average weight and frequency of different dishes picked up by users, the rate of users meeting nutritional intake standards, and the compatibility of dish cooking data with users' health status.

[0031] The long-term optimization in step four includes summarizing the historical gripping weight data accumulated by the weighing unit 5, analyzing users' preferences for different dishes, combining the RFID tag identification records of the dish reading core plate 53, statistically analyzing the types of dishes frequently selected by users, associating changes in nutritional tolerance in health records, and adjusting the dish combinations and portion suggestions of the recommended menu.

[0032] A health data control system for a smart dining table includes: Smart dining table main body 1; The hardware includes a chopstick holder 2, chopsticks 3, a serving plate 4, and a weighing unit 5. The chopstick holder 2 facilitates the placement of the chopsticks 3 and is placed in a preset area, where it is recognized by the chopstick holder reading core board 510. The serving plate 4 facilitates the placement of the product. The weighing unit 5 includes a weighing pan 51, a weighing pan support 52, a food reading core board 53, a weighing sensor 54, a water-retaining plate 55, a support frame 56, screws 57, a four-pronged nut 58, a bearing plate 59, the chopstick holder reading core board 510, and a draining tray 511. The weighing pan 51 provides support for the draining tray 511, and the weighing pan support 52 facilitates integration with the smart dining table. The main body 1 is connected to facilitate the installation of the weighing pan 51. The food reading core plate 53 is designed to work with the RFID tag at the bottom of the draining tray 511 for easy identification of the food. The weighing sensor 54 is designed to collect the total weight of the weighing pan 51, the food tray 4, the draining tray 511, and the food in real time. The water-retaining plate 55 is circular, which helps to block soup or water and prevents contamination of the weighing sensor 54. The support frame 56 is designed to support the water-retaining plate 55 and facilitates its installation on the main body 1 of the smart dining table. The screws 57 are designed to connect the support frame 56 to the main body 1 of the smart dining table for easy connection of the weighing unit 5. The installation and disassembly are facilitated by the support plate 59, which supports the chopstick holder reading core plate 510. The chopstick holder reading core plate 510 facilitates the identification of the RFID tag at the bottom of the chopstick holder 2 to read the user ID. The draining tray 511 facilitates the draining of water from the dish 4. The chopstick holder 2 is located at the top of the smart dining table body 1, the chopsticks 3 are located at the bottom of the chopstick holder 2, the dish 4 is located at the top of the smart dining table body 1, the draining tray 511 is located at the bottom of the dish 4, and an RFID tag is installed at the bottom of the draining tray 511. The weighing tray 51 is located at the bottom of the draining tray 511, and the weighing tray support 52 is located at the bottom of the weighing tray 51. The weighing pan bracket 52 is installed inside the support frame 56, the food reading core plate 53 is installed inside the support frame 56, the weighing sensor 54 is installed inside the support frame 56 and is located at the bottom of the food reading core plate 53, the water baffle plate 55 is located at the top of the support frame 56, the support frame 56 is installed on the main body 1 of the smart dining table, the screw 57 is located on the main body 1 of the smart dining table and is slidably inserted into the support frame 56, the four-prong nut 58 is threaded onto the end of the screw 57, the bearing plate 59 is located at the bottom of the support frame 56, and the chopstick holder reading core plate 510 is installed on the top of the bearing plate 59; The data pre-collection and initialization module is used to build a dynamic health record database containing personnel nutritional tolerance and a database of associated dish information, and to perform preliminary binding between user IDs and dish IDs. The real-time control module is used to collect weight, read the RFID tag at the bottom of the draining tray 511 to obtain the dish ID, read the user ID confirmed by the core board 510 of the chopsticks holder, and automatically record the user ID, dish ID and the weight of the chopsticks. The module also associates the data with the health data of the personnel, calculates the real-time nutritional intake, dynamically adjusts the intake recommendations and displays them through the interactive interface of the main body of the smart dining table 1. The feedback module is used to summarize the data of the day, generate a nutrition report, synchronize it to the user terminal, and store it in encrypted form. The long-term optimization module is used to optimize menu recommendations and system parameters based on historical weighing data, RFID identification records, and health goals, forming a closed-loop management system. The human-computer interaction module is used for human-computer interaction operations.

[0033] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A health data control method for a smart dining table, characterized by, The method comprises the following specific steps: Step one: data pre-collection and initialization, constructing a dynamic health profile database containing the nutritional tolerance of personnel and a database associated with dish information, and completing the preliminary binding of user ID and dish ID through the intelligent table main body (1); Step two: real-time control during meals, the weighing sensor (54) of the weighing unit (5) collects the weight, the dish reading core board (53) reads the RFID tag at the bottom of the draining tray (511) to obtain the dish ID, the chopstick rack reading core board (510) confirms the user ID, and automatically records the user ID, dish ID and clamping weight data, and associates the data with personnel health data, calculates real-time nutritional intake, dynamically adjusts the intake recommendation and displays it through the interactive interface of the intelligent table main body (1); Step three: post-meal feedback, aggregate daily data to generate a nutrition report, synchronize to the user terminal and store in encrypted form; Step four: long-term optimization, based on historical weighing data, RFID identification records and health goals, optimize menu recommendations and system parameters to form a closed-loop management. 2.The health data control method for the smart dining table of claim 1, wherein, The nutritional tolerance of personnel in step one includes protein, fat and cholesterol, and the construction of the dynamic health profile database in step one specifically includes generating a table of meal personnel, which contains user ID, name, identity type and various nutritional tolerance data, and the daily intake of each nutritional tolerance data is protein, fat and cholesterol, and a unique corresponding two-dimensional code is generated based on each row of user data, which contains user ID and identity verification information, and the database associated with dish information in step one specifically includes entering dish basic information, including name and unit nutritional ingredients, and associating the RFID tag code at the bottom of the draining tray (511). 3.The health data control method for the smart dining table of claim 1, wherein, The weighing sensor (54) collects the weight in step two specifically includes real-time collection of the total weight of the scale tray (51), dish tray (4), draining tray (511) and dish, the RFID tag in step two is used for pre-storing unique dish identification information, and the calculation of real-time nutritional intake in step two specifically includes obtaining the unit nutritional ingredients of the corresponding dish in the dish database, the unit nutritional ingredients being the protein, fat and cholesterol content per 100g, combining the clamping weight collected by the weighing unit (5) to calculate the actual grams of protein, fat and cholesterol currently ingested, and generating real-time nutritional intake data. 4.The health data control method for the smart dining table of claim 1, wherein, The dish recognition process in step two specifically includes: S1: When the dish tray (4) is placed on the scale tray (51), the water blocking tray (55) forms a closed weighing area, and the weighing sensor (54) collects the initial total weight; S2: The dish reading core board (53) synchronously reads the RFID tag at the bottom of the draining tray (511) to obtain the dish ID and match the dish information in the database; S3: After the user clamps the dish with the chopsticks (3), the weighing sensor (54) collects the remaining weight, and the clamping weight is calculated by the initial total weight and the remaining weight; S4: The chopstick rack reading core board (510) reads the user ID associated with the chopstick rack (2), binds and records the clamping weight with the user ID. 5.The health data control method for the smart dining table of claim 4, wherein, The user ID associated with the chopstick rest (2) in step S4 specifically includes that the chopstick rest (2) is provided with a binding two-dimensional code and an RFID tag at the bottom, the user scans the two-dimensional code to bind the ID, when the chopstick rest (2) is placed on the top of the smart table main body (1) in the preset area, the chopstick rest reads the RFID tag at the bottom of the chopstick rest (2) through the core board (510), and the bound user ID is read. 6.The health data control method for the smart dining table of claim 1, wherein, The dynamic adjustment of the intake suggestion in step two specifically includes that the weighing sensor (54) uploads the clamping weight data and the dish reading core board (53) uploads the dish ID, then the unit nutrient composition of the dish in the dish database is called, the total amount of the currently ingested nutrients is calculated, the tolerance threshold in the personnel health record on the same day is called, and the proportion of the current intake amount to the tolerance threshold is compared; When the proportion is less than or equal to 60%, a normal intake prompt is generated; When 60%<the proportion is less than or equal to 80%, a suggestion of appropriate intake is generated; When 80%<the proportion is less than or equal to 95%, a warning of approaching the upper limit of tolerance and reducing intake is generated; When the proportion is greater than 95%, a warning of reaching the upper limit of tolerance and stopping intake is generated, all prompts and warnings are displayed through the man-machine interface of the smart table main body (1) and accompanied by voice broadcast. 7.The health data control method for the smart dining table of claim 1, wherein, The nutritional report generated in step three specifically includes: The daily intake table, which shows the actual total intake of protein, fat and cholesterol of each user according to the user identity type; Nutrition comparison and analysis, difference calculation between daily intake and corresponding nutritional tolerance, and marking of reaching, insufficient or exceeding the standard; The contribution proportion of dishes, which is the percentage of each dish to the total intake of protein, fat and cholesterol on the same day. 8.The health data control method for the smart dining table of claim 1, wherein, The basis for optimizing the menu recommendation in step four includes the average clamping weight and frequency of different dishes in the historical data, the user's nutritional intake compliance rate, and the adaptability of dish cooking data to the user's health status. 9.The health data control method for the smart dining table of claim 1, wherein, The long-term optimization in step four includes summarizing the historical clamping weight data accumulated by the weighing unit (5), analyzing the user's preference for different dishes, combining the RFID tag identification record of the dish reading core board (53), and counting the types of dishes frequently selected by the user, associating the change of nutritional tolerance in the health record, and adjusting the dish combination and portion suggestion of the recommended menu.

10. The health data control system for a smart dining table of claim 1, wherein, It includes: The smart table main body (1); Hardware, the hardware includes chopstick rack (2), chopsticks (3), dish (4) and weighing unit (5), the weighing unit (5) includes scale pan (51), scale pan support (52), dish reading core plate (53), weighing sensor (54), water baffle (55), support frame (56), screw (57), four claw nut (58), bearing plate (59), chopstick rack reading core plate (510) and drip tray (511), the chopstick rack (2) is located at the top of intelligent dining table main body (1), the chopsticks (3) are located at the bottom of chopstick rack (2), the dish (4) is located at the top of intelligent dining table main body (1), the drip tray (511) is located at the bottom of dish (4), the bottom of drip tray (511) is installed with RFID tag, the scale pan (51) is located at the bottom of drip tray (511), the scale pan support (52) is located at the bottom of scale pan (51), the scale pan support (52) is installed in the inside of support frame (56), the dish reading core plate (53) is installed in the inside of support frame (56), the weighing sensor (54) is installed in the inside of support frame (56), and the weighing sensor (54) is located at the bottom of dish reading core plate (53), the water baffle (55) is located at the top of support frame (56), the support frame (56) is installed on intelligent dining table main body (1), the screw (57) is located on intelligent dining table main body (1), the screw (57) is slidably connected on support frame (56), the four claw nut (58) is threadedly connected to the end of screw (57), the bearing plate (59) is located at the bottom of support frame (56), the chopstick rack reading core plate (510) is installed at the top of bearing plate (59); Data pre-acquisition and initialization module, the data pre-acquisition and initialization module is used for constructing dynamic health profile library containing personnel nutrition tolerance and database associated with dish information, and carrying out preliminary binding of user ID and dish ID; Real-time control module, the real-time control module is used for collecting weight, reading RFID tag at the bottom of drip tray (511) to obtain dish ID, user ID confirmed by chopstick rack reading core plate (510), and automatically recording user ID, dish ID and clamping weight data, and correlating the data with personnel health data, calculating real-time nutrition intake, dynamically adjusting intake suggestion and displaying through the interactive interface of intelligent dining table main body (1); Feedback module, the feedback module is used for generating nutrition report by summarizing daily data, synchronizing to user terminal and storing in encrypted form; Long-term optimization module, the long-term optimization module is used for optimizing menu recommendation and system parameters based on historical weighing data, RFID identification record and health target, forming closed-loop management; Man-machine interaction module, the man-machine interaction module is used for man-machine interaction operation.

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