Three-partition intelligent dinner plate with automatic weighing and voice feedback functions

Abstract

The invention provides a three-subarea intelligent dinner plate with automatic weighing and voice feedback, and relates to the technical field of intelligent dinner plates, the three-subarea intelligent dinner plate comprises a dinner plate body, the dinner plate body is provided with three independent subareas, each subarea is used for containing different kinds of food, and a high-precision pressure sensor is arranged below each independent subarea and used for monitoring the weight of the food in the subarea in real time. And the data processing module is used for receiving signals from the pressure sensors, calculating the food weight of each subarea based on a machine learning algorithm and generating calorie estimation. According to the three-partition intelligent dinner plate with the automatic weighing and voice feedback functions, the food weight of each partition can be calculated in real time based on data from the pressure sensor, and accurate heat estimation is generated through a machine learning algorithm. The system not only can calculate the calorie according to the weight of food, but also can dynamically adjust a calorie calculation model, considers factors such as food types and temperature, and ensures the precision of calorie calculation.

Description

Technical Field

[0001] This invention relates to the field of smart plate technology, specifically a three-zone smart plate with automatic weighing and voice feedback. Background Technology

[0002] A smart plate is a high-tech tableware that integrates sensors, wireless communication technology, and data processing. Its basic structure typically includes: a sensor module, an embedded processor, a wireless communication module, a power module, and the plate itself. The sensor module usually integrates temperature, weight, and pressure sensors, enabling real-time monitoring of food weight, temperature, and other data. The embedded processor collects and processes the data from the sensors and transmits the results to a smartphone or other device via the wireless communication module. The power module provides stable power to the entire system. The smart plate works by using sensors to acquire information about the food on the plate and transmitting the data wirelessly to the cloud or user device. This helps users understand the nutritional composition, calorie content, and portion control of the food, thus assisting in health management.

[0003] Despite the many advanced features of smart plates, they still have some shortcomings, such as the inability to simultaneously achieve high-precision food weighing, calorie control, and voice feedback. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a three-zone smart plate with automatic weighing and voice feedback. The technical problem this invention aims to solve is to achieve high-precision estimation of food weight and calories by integrating pressure sensors, temperature sensors, machine learning algorithms, and an optimized power management system.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a three-zone smart plate with automatic weighing and voice feedback, comprising: The plate itself has three independent sections, each for holding different types of food; Each of the independent compartments is equipped with a high-precision pressure sensor for real-time monitoring of the weight of the food within that compartment. The data processing module receives signals from the pressure sensor, calculates the weight of food in each compartment based on machine learning algorithms, and generates a calorie estimate. The calorie calculation module dynamically calculates the total calories of the food on the plate based on the weight and type of food in each section, as well as the user's health needs. The voice feedback module outputs voice prompts through a speaker, reminding users of dietary calories, nutritional status, and health advice.

[0006] Preferably, the pressure sensor is a piezoelectric sensor with an error range of ±0.2 grams, ensuring accurate measurement of the food weight in each section of the plate.

[0007] Preferably, the data processing module includes a microprocessor and an optimization algorithm. The optimization algorithm is based on big data and machine learning and can adjust the calorie model of food types in real time to ensure accurate calculation of calories for each meal.

[0008] Preferably, the calorie calculation module adopts a hybrid model, which includes calorie calculation formulas and food attribute data, and combines the weight and type of food in different zones to accurately estimate the calorie content of the meal.

[0009] Preferably, the calorie calculation module uses the following formula to estimate the calorie content of the meal: ; in, The total calories of all food on the plate; For the first The weight of food within each section; For the first Calories per unit of food in each section ( This value is dynamically adjusted based on the type and attributes of the food. This formula takes into account the weight and calories of the food in each section and calculates the total calories of the food on the plate.

[0010] Preferably, the specific function expression for the voice prompt is designed as follows: ; in: , where is the weighting coefficient, representing the importance of each factor to the final voice feedback content.

[0011] Preferably, the plate body is made of antibacterial material and has a self-cleaning function to ensure the health and safety of users. Each section of the plate body adopts an independent power system to improve battery efficiency and system energy management capabilities.

[0012] Preferably, a temperature sensor is installed under the plate body to monitor the temperature of the food, so as to dynamically adjust the calorie calculation model and ensure the accuracy of the calculation results.

[0013] This invention provides a three-zone smart plate with automatic weighing and voice feedback. It offers the following advantages: This three-zone smart plate with automatic weighing and voice feedback can calculate the weight of food in each zone in real time based on data from pressure sensors and generate accurate calorie estimates through machine learning algorithms. The system not only calculates calories based on food weight but also dynamically adjusts the calorie calculation model, taking into account factors such as food type and temperature to ensure accuracy. Furthermore, the optimization algorithm, based on big data and machine learning technology, continuously learns and optimizes the calorie density model, providing personalized dietary suggestions based on users' eating habits and health needs. This helps users better manage their calorie intake, avoiding excessive or insufficient calorie intake and achieving the goal of healthy eating.

[0014] Each partition is powered independently as needed, thereby improving battery efficiency and the system's overall energy management capabilities. Furthermore, the system's temperature sensors and machine learning algorithms work together to achieve dynamic calorie calculation and real-time adjustments, automatically optimizing the calculation model based on the user's actual diet and food temperature changes. This intelligent adjustment capability not only enhances the system's accuracy but also ensures energy efficiency, avoiding unnecessary power waste. Users can also obtain real-time feedback and dietary suggestions through an app or voice assistant, further enhancing the smart plate's interactivity and user experience. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the structure for realizing the invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] like Figure 1As shown, this embodiment of the invention provides a three-zone smart plate with automatic weighing and voice feedback. It includes a plate body with three independent zones, each for holding different types of food. The plate body is made of antibacterial material and has a self-cleaning function to ensure user health and safety. Each zone of the plate body uses an independent power system to improve battery efficiency and system energy management capabilities. A temperature sensor is located under the plate body to monitor the temperature of the food and dynamically adjust the calorie calculation model to ensure the accuracy of the calculation results. The plate surface uses an antibacterial coating with added silver ions, providing long-lasting antibacterial properties. Silver ions can effectively kill most bacteria and microorganisms, preventing bacterial growth on the plate surface. The antibacterial material also has stain-resistant properties, reducing the adhesion of food residue and oil. Each zone is equipped with an independent battery and power management module. The power system of each zone includes a built-in battery, a charging interface, and a battery management unit. The battery is charged via a wireless charging module, supporting wireless charging technology (such as the Qi standard). Users simply place the plate on the charging base to charge. The battery in each zone operates independently, and the power of each zone can be switched on and off as needed. The calorie calculation model on the plate is dynamically adjusted based on data provided by the temperature sensor. Since the calorie estimates for hot and cold food differ, temperature changes detected by the sensor will affect the calculation. When the plate detects a high food temperature, the system will use the corresponding calorie calculation formula, taking into account the impact of temperature on the food's nutritional components.

[0018] Each individual compartment is equipped with a high-precision pressure sensor to monitor the weight of the food in that compartment in real time. The pressure sensor uses a piezoelectric sensor with an error range of ±0.2 grams to ensure accurate measurement of the weight of the food in each compartment of the plate. The data processing module receives signals from pressure sensors, calculates the weight of food in each compartment based on machine learning algorithms, and generates calorie estimates. The module includes a microprocessor and optimization algorithms. These optimization algorithms, based on big data and machine learning, can adjust the calorie model for each food type in real time, ensuring accurate calorie calculations for each meal. The microprocessor is the core computing unit of the data processing module, responsible for real-time processing and computation of the sensor input data. The microprocessor, combined with optimization algorithms, operates through the following steps: Data preprocessing: The microprocessor first preprocesses the sensor signals from each partition to remove noise and unstable signals, and then calibrates the sensor data to ensure that the weight data of each partition is accurate.

[0019] Food weight calculation: Based on real-time signals from pressure sensors, the microprocessor calculates the food weight for each compartment. This calculation process takes into account sensor accuracy, error correction, and environmental factors (such as temperature and humidity).

[0020] ; in: The weight of the food within the partition (in grams).

[0021] This refers to the signal voltage output by the sensor.

[0022] This is the baseline voltage of the sensor (i.e., the voltage when there is no food).

[0023] The sensitivity of the sensor (weight per volt).

[0024] Machine Learning Algorithm: After obtaining the food weight, the microprocessor further utilizes a machine learning-based optimization algorithm to calculate the calories in each food section. This algorithm combines information such as food type, weight, and temperature to generate a more accurate calorie estimate. The system uses big data and trained machine learning models to predict the calorie density of different types of food and generates the final calorie estimate based on the input data.

[0025] The calorie calculation module dynamically calculates the total calories of the food on the plate based on the weight and type of food in each section, as well as the user's health needs. The module employs a hybrid model, which includes calorie calculation formulas and food attribute data. By combining the weight and type of food in each section, it accurately estimates the calorie content of the meal. The calorie calculation module uses the following formula to estimate the calorie content of the meal: ; in, The total calories of all food on the plate; For the first The weight of food within each section; For the first Calories per unit of food in each section ( This value is dynamically adjusted based on the type and attributes of the food. This formula takes into account the weight and calories of the food in each section and calculates the total calories of the food on the plate. The voice feedback module outputs voice prompts through a speaker, reminding users of dietary calories, nutritional status, and health advice. The specific function expression design for the voice prompts is as follows: ; in: , where is the weighting coefficient, representing the importance of each factor to the final voice feedback content. This represents a calculation function based on the user's adjusted calorie intake and health profile, used to generate calorie-related voice feedback (e.g., calorie overdose alerts or goal completion alerts).

[0026]

[0027] This function represents a calculation based on the nutritional composition of the food on the plate, providing suggestions related to nutritional components. For example, if protein intake is insufficient, corresponding voice feedback is generated.

[0028]

[0029] This function indicates how to adjust the tone or content based on the time of day. For example, the tone and content at breakfast might differ from those at dinner.

[0030]

[0031] This refers to a function generated based on personalized data such as age, gender, and health goals from a user's health profile. For example, if a user's goal is to lose weight, the voice feedback style might be more encouraging; while if the user's goal is to gain muscle, the voice feedback style might be more motivating.

[0032]

[0033] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A three-zone smart plate with automatic weighing and voice feedback, characterized in that: include: The plate itself has three independent sections, each for holding different types of food; Each of the independent compartments is equipped with a high-precision pressure sensor for real-time monitoring of the weight of the food within that compartment. The data processing module receives signals from the pressure sensor, calculates the weight of food in each compartment based on machine learning algorithms, and generates a calorie estimate. The calorie calculation module dynamically calculates the total calories of the food on the plate based on the weight and type of food in each section, as well as the user's health needs. The voice feedback module outputs voice prompts through a speaker, reminding users of dietary calories, nutritional status, and health advice.

2. The three-zone smart plate with automatic weighing and voice feedback as described in claim 1, characterized in that: The pressure sensor is a piezoelectric sensor with an error range of ±0.2 grams.

3. The three-zone smart plate with automatic weighing and voice feedback as described in claim 1, characterized in that: The data processing module includes a microprocessor and an optimization algorithm, which is based on big data and machine learning.

4. The three-zone smart plate with automatic weighing and voice feedback according to claim 1, characterized in that: The calorie calculation module uses a hybrid model, which includes calorie calculation formulas and food attribute data. It combines the weight and type of food in different zones to accurately estimate the calorie content of meals.

5. The three-zone smart plate with automatic weighing and voice feedback according to claim 1, characterized in that: The calorie calculation module uses the following formula to estimate the calories in the meal: ； in, The total calories of all food on the plate; For the first The weight of food within each section; For the first Calories per unit of food in each section ( This value is dynamically adjusted based on the food type and attributes.

6. The three-zone smart plate with automatic weighing and voice feedback according to claim 1, characterized in that: The specific function expression design for the voice prompt: ； in: , where is the weighting coefficient, representing the importance of each factor to the final voice feedback content.

7. The three-zone smart plate with automatic weighing and voice feedback according to claim 1, characterized in that: The plate body is made of antibacterial material and has a self-cleaning function to ensure the health and safety of users. Each section of the plate body uses an independent power system.

8. The three-zone smart plate with automatic weighing and voice feedback according to claim 1, characterized in that: A temperature sensor is installed under the plate to monitor the temperature of the food, so as to dynamically adjust the calorie calculation model and ensure the accuracy of the calculation results.

Images