Auxiliary robot control system based on big data

Abstract

The invention discloses an auxiliary robot control system based on big data, and the system comprises a data collection module which is used for collecting user information; the control module is used for acquiring a heat range of food material selection and preparation; the output module is used for controlling the robot to adjust the dish putting process, the specific amount of food of the food grade ingested by the current user is divided and screened, when the food grade ingested by the user is too small, the food of the food grade is filtered out, and the main calorie ingestion of the user is effectively monitored; the method can predict the calorie needed to be ingested by the user every day on the premise of guaranteeing the health of the user, adjusts the actual food material selection calorie range based on the exercise increment, calculates the seasoning dosage putting time of dishes, improves the accuracy of the control system, improves the user experience, and is beneficial to maintaining the health of the user for a long time. The system has the characteristics of high humanization degree and high dish adding control precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the kitchen cooking auxiliary control technical field, in particular to an auxiliary robot control system based on big data. BACKGROUND

[0002] Nowadays, many young people either have no time or rent a house that is not convenient for frying, frying, cooking and frying, and some people do not know how to cook at all. Therefore, the cooking type auxiliary robot can help users cook daily and assist users in selecting and seasoning food materials, but the auxiliary robot has various cooking functions, but still has some problems.

[0003] In the prior art, the auxiliary robot can undertake a high proportion of food making, which meets the general needs of most user groups, but this means that the food made by the auxiliary robot will also deviate from the actual needs of the user, especially the eating habits of most users will change; when the user himself undertakes food making, although the user can freely choose the food making method and seasoning, but when the user himself makes food, there will be a certain deviation, especially when the user is tired, the actual quality of the food will decrease, and the health of the food selection will also have unreasonable possibilities. Therefore, it is necessary to design an auxiliary robot control system based on big data with high humanization and high food addition control precision. SUMMARY

[0004] The purpose of the present application is to provide an auxiliary robot control system based on big data to solve the problems raised in the background art.

[0005] In order to solve the above technical problems, the present application provides the following technical scheme: an auxiliary robot control system based on big data, comprising:

[0006] A data acquisition module is configured to acquire user information, wherein the user information includes authorization information in a user-bound mobile terminal and setting data of the auxiliary robot;

[0007] A control module is configured to monitor user behavior according to the user information, and based on the determination result obtained by monitoring the user behavior, to obtain the proportion of food selection and making controlled by the auxiliary robot, and based on the proportion of food selection and making controlled by the auxiliary robot, to obtain the heat range of the food selection;

[0008] An output module is configured to analyze and process the sensing data by using an intelligent algorithm, and calculate the seasoning dosage and feeding time of the dish according to the heat range of the food material, so as to control the robot to adjust the feeding process of the dish, which includes the proportion of the selection and preparation of the dish, the heat range of the food material and the seasoning dosage and feeding time of the dish.

[0009] According to the technical scheme, the data acquisition module comprises:

[0010] A user data acquisition module is configured to acquire authorization information in a user-bound mobile terminal, which includes daily step information recorded in a user exercise software, daily food expense consumption limit information of the user and preference factors of the user, the preference factors of the user including favorite food materials, favorite cooking methods and favorite tastes of the user, the preference factors of the user are input, a dish model to be prepared is searched and input in a database based on the preference factors of the user, the dish model including food materials needed for the dish and consumption levels corresponding to the food materials needed;

[0011] A setting data acquisition module is configured to divide food materials in different use areas in the kitchen of the user based on the consumption levels, and the auxiliary robot can control the food materials divided in different areas by using multiple mechanical arms to assist the user in use at the time of dish preparation.

[0012] According to the technical scheme, the control module comprises:

[0013] An intervention analysis module is configured to start a timer when it is detected that the user takes out the food material, the user taking out the food material is acquired by acquiring the characteristics of the food material by a camera and detecting the time of the food material staying on a tray provided by the auxiliary robot, and the proportion of the selection and preparation of the dish controlled by the auxiliary robot wherein, δ is a unit conversion coefficient, A is the daily step information recorded in the user exercise software, B is the time of the food material staying on the tray, C is the time of the user staying at home on the day, the time of the food material staying on the tray and the time of the user staying at home on the day are in hours, μ1 is the weight of the time of the food material staying on the tray, μ2 is the weight of the time of the user staying at home on the day, and a is a proportion series;

[0014] A food material selection module is configured to acquire the heat range of the food material selection based on the exercise increment of the user per day as wherein Z is a preset minimum daily calorie intake value; a dosage delivery detection module, the dosage delivery detection module being configured to install sensors and collect the sensor data in real time, the sensors including: a weight sensor for measuring the weight of ingredients; a temperature sensor for monitoring temperature data during the cooking process; and a humidity sensor for sensing ambient humidity data; analyzing and processing the sensor data using an intelligent algorithm including a neural network and fuzzy logic, establishing a recipe database, and constructing a seasoning model in the recipe database based on the analyzed data obtained from the analysis and processing, the seasoning model being configured to determine the timing for delivering seasonings to the dishes under a standard seasoning recipe; finding corresponding dimension values ​​according to the seasoning model based on the weight data of the ingredients, the temperature data monitored during the cooking process, and the sensed ambient humidity data, and calculating the dish quality according to a preset dish quality detection formula for the standard seasoning recipe, the dish quality detection formula being established based on the weight data of the ingredients, the temperature data monitored during the cooking process, and the sensed ambient humidity data; and recording the time point corresponding to the optimal point for calculating dish quality as the seasoning delivery timing for the dish.

[0015] According to the above technical solution, the food selection module includes:

[0016] An incremental parameter detection submodule is used to record the consumption level Y, and divide the consumption level Y into five food levels, namely, cereals, fruits and vegetables, milk and dairy products, animal foods, and oil, salt, sugar and condiments; by using the corresponding keywords of the food levels in the purchase records that can be checked in the food expenditure records, the user's consumption ratio of each food level in daily expenses is divided, and the consumption ratios η1, η2, η3, η4, and η5 corresponding to the five food levels are obtained, where η1+η2+η3+η4+η5=100%, then the user's daily exercise intake increment θ=λ1η1+λ2η2+λ3η3+λ4η4+λ5η5, where λ1, λ2, λ3, λ4, and λ5 are the calorie increment influence factors corresponding to cereals, fruits and vegetables, milk and dairy products, animal foods, and condiments, respectively, and the specific values ​​of the influence factors are 0 or 1;

[0017] The influence factor acquisition module is used to record the daily consumption amount of the user's daily food expenses as X and the user's consumption level reference coefficient based on the user's daily food expenses. Where δ is the user's reference consumption rating obtained by the system through the user's consumption record and searching the relevant consumption level reference information in the big database;

[0018] when If the user's consumption level reference coefficient is judged to be lower than the normal level, the calorie increment impact factor corresponding to the current food level is set to 0; It is judged that the reference coefficient of the user's consumption level is higher than the normal level, and the calorie increment impact factor corresponding to the current food level is set to 1.

[0019] According to the above technical solution, a big data-based auxiliary robot control method includes:

[0020] Collecting user information, the user information including authorization information bound to the user's mobile terminal and setting data of the assistive robot;

[0021] Monitoring user behavior according to the user information, and obtaining a ratio of dish selection and preparation controlled by the auxiliary robot based on a determination result obtained from the monitoring of the user behavior;

[0022] Based on the auxiliary robot controlling the selection and preparation ratio of dishes, the calorie range of the selected ingredients is obtained;

[0023] An intelligent algorithm is used to analyze and process the sensor data, and the timing of adding seasonings to the dishes is calculated based on the calorie range of the selected ingredients, and the robot is controlled to adjust the dish addition process. The dish addition process includes the auxiliary robot controlling the selection and production ratio of the dishes, the calorie range of the selected ingredients, and the timing of adding seasonings to the dishes.

[0024] According to the above technical solution, the collecting of user information includes:

[0025] Obtaining authorization information from a user-bound mobile terminal, the authorization information from the user-bound mobile terminal including: daily step count information recorded in the user's sports app, daily food spending information, and user preference factors;

[0026] In the user's kitchen, ingredients are divided into different usage areas based on the consumption level, and the assistive robot uses multiple robotic arms to perform corresponding control operations on the ingredients divided into different areas to assist the user in using them during cooking;

[0027] The user's preference factors include: the user's favorite ingredients, the user's favorite cooking methods, and the user's favorite flavors. The user's preference factors are entered;

[0028] Based on the user's preference factors, a dish model to be prepared is retrieved and entered into a database, where the dish model includes ingredients required for the dish and consumption levels corresponding to the ingredients required.

[0029] According to the above technical solution, the user behavior is monitored according to the user information, and based on the determination result obtained from the user behavior monitoring, the proportion of the dish selection and preparation controlled by the auxiliary robot is obtained, including:

[0030] When it is detected that the user takes out the ingredients, the timer starts the timer. The user takes out the ingredients by obtaining the characteristics of the ingredients through the camera and detecting the time the ingredients stay on the tray set by the auxiliary robot. The auxiliary robot then controls the selection and preparation of dishes. Wherein, δ is the unit conversion coefficient, A is the daily step count information recorded in the user's sports software, B is the time the food stays on the tray, C is the time the user is at home that day, the time the food stays on the tray and the time the user is at home that day are expressed in hours, μ1 is the weight of the time the food stays on the tray, μ2 is the weight of the time the user is at home that day, and a is the proportional series.

[0031] According to the above technical solution, obtaining the calorie range of the selected ingredients includes:

[0032] Recording consumption level Y, dividing consumption level Y into five food levels, namely, cereals, vegetables and fruits, milk and dairy products, animal foods, and oil, salt, sugar and condiments;

[0033] By using the corresponding keywords of the food grades in the purchase records that can be checked in the food expenditure records, the user's consumption ratio of each food grade in daily expenses is divided, and the consumption ratios η1, η2, η3, η4, and η5 corresponding to the five food grades are obtained, where η1+η2+η3+η4+η5=100%. Then the user's daily exercise intake increment θ=λ1η1+λ2η2+λ3η3+λ4η4+λ5η5, where λ1, λ2, λ3, λ4, and λ5 are the calorie increment influence factors corresponding to cereals, vegetables and fruits, milk and dairy products, animal foods, and condiments, respectively, and the specific values ​​of the influence factors are 0 or 1;

[0034] Based on the daily exercise increment of the user, the calorie range of the food selection is obtained as follows: Where Z is the preset minimum daily calorie intake.

[0035] According to the above technical solution, the method for calculating the calorie increment impact factor corresponding to each food grade based on the user's daily consumption amount includes:

[0036] Based on the user's daily food consumption information, the user's daily food consumption is recorded as X, and the user's consumption level reference coefficient is Where δ is the user's reference consumption rating obtained by the system through the user's consumption record and searching the relevant consumption level reference information in the big database;

[0037] when If the user's consumption level reference coefficient is judged to be lower than the normal level, the calorie increment impact factor corresponding to the current food level is set to 0; It is judged that the reference coefficient of the user's consumption level is higher than the normal level, and the calorie increment impact factor corresponding to the current food level is set to 1.

[0038] According to the above technical solution, the intelligent algorithm is used to analyze and process the sensor data, and the timing of adding seasoning to the dish is calculated according to the calorie range of the selected ingredients, including:

[0039] Install sensors to collect the sensor data in real time, the sensors including:

[0040] A weight sensor, used to measure the weight of food;

[0041] A temperature sensor, wherein the temperature sensor is used to monitor temperature data during cooking;

[0042] A humidity sensor is used to sense ambient humidity data;

[0043] Analyzing and processing the sensor data using an intelligent algorithm, the intelligent algorithm including a neural network and fuzzy logic, establishing a recipe database, and constructing a seasoning model in the recipe database based on the analysis data obtained by the analysis and processing, the seasoning model being used to determine the timing of seasoning dosage for the dish under a standard seasoning recipe;

[0044] Based on the weight data of the ingredients, the temperature data monitored during the cooking process and the perceived environmental humidity data, the corresponding dimension values ​​are found according to the seasoning model, and the dish quality is calculated according to the dish quality detection formula of the preset standard seasoning recipe. The dish quality detection formula is established based on the weight data of the ingredients, the temperature data monitored during the cooking process and the perceived environmental humidity data. The time node found at the optimal point for calculating the dish quality is recorded as the timing for adding the seasoning to the dish.

[0045] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, based on the auxiliary robot controlling the selection and production proportions of dishes, obtains the calorie range of the selected ingredients, divides and screens the specific amount of the food grade food currently consumed by the user, and when the amount of food grade consumed by the user is too little, the food grade food will be filtered out, effectively monitoring the user's main calorie intake; it can predict the daily calorie intake of the user while ensuring the user's health, and adjust the actual calorie range of the selected ingredients based on the exercise increment, calculate the timing of adding the seasoning amount to the dish, and control the robot to adjust the dish delivery process, thereby improving the accuracy of the control system, improving the user experience, and being conducive to maintaining the user's health for a long time. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0047] Figure 1 1 is a schematic diagram of a big data-based auxiliary robot control system module provided by an embodiment of the present invention;

[0048] Figure 2 This is a flow chart of a big data-based auxiliary robot control method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] See also Figure 1 , which is a schematic diagram of a big data-based auxiliary robot control system module provided by an embodiment of the present invention, such as Figure 1 It can be seen that the auxiliary robot control system based on big data includes:

[0051] A data acquisition module, the data acquisition module is used to collect user information, the user information including the authorization information bound to the user's mobile terminal and the setting data of the auxiliary robot;

[0052] a control module configured to monitor user behavior according to the user information, and obtain a ratio of the dish selected and prepared by the auxiliary robot control based on a determination result obtained from the user behavior monitoring, and obtain a calorie range of the selected ingredients based on the ratio of the dish selected and prepared by the auxiliary robot control;

[0053] The output module is used to analyze and process the sensor data using an intelligent algorithm, and calculate the timing of adding seasonings to the dishes based on the calorie range of the selected ingredients, and control the robot to adjust the dish addition process. The dish addition process includes the auxiliary robot controlling the selection and production ratio of the dishes, the calorie range of the selected ingredients, and the timing of adding seasonings to the dishes.

[0054] The embodiments of the present invention control the selection and preparation ratio of dishes based on the auxiliary robot, obtain the calorie range of the selected ingredients, divide and screen the specific amount of the food grade food currently consumed by the user, and when the amount of food grade consumed by the user is too small, the food grade food will be filtered out, effectively monitoring the user's main calorie intake; it can predict the daily calorie intake of the user while ensuring the user's health, and adjust the actual calorie range of the selected ingredients based on the exercise increment, calculate the timing of adding seasoning to the dish, and control the robot to adjust the dish delivery process, thereby improving the accuracy of the control system, improving the user experience, and helping to maintain the user's health for a long time.

[0055] In some preferred embodiments, the data acquisition module includes:

[0056] A user data collection module, which is used to obtain authorization information from a user-bound mobile terminal. The authorization information in the user-bound mobile terminal includes: daily step count information recorded in the user's sports software, daily food expenditure information, and user preferences; the user's preferences include: the user's favorite ingredients, the user's favorite cooking methods, and the user's favorite flavors. The user's preferences are entered, and based on the user's preferences, a dish model to be prepared is retrieved and entered into a database. The dish model includes the ingredients required for the dish and the corresponding consumption level of the required ingredients;

[0057] A data acquisition module is set up, and the data acquisition module is used to divide the ingredients into different usage areas in the user's kitchen based on the consumption level. The auxiliary robot uses multiple robotic arms to perform corresponding control operations on the ingredients divided into different areas, so as to assist the user in using them when cooking.

[0058] In some preferred embodiments, the control module includes:

[0059] The intervention analysis module is used to start the timer when it detects that the user takes out the ingredients. The user takes out the ingredients by obtaining the characteristics of the ingredients through the camera and detecting the time the ingredients stay on the tray set by the auxiliary robot. The proportion of the auxiliary robot controlling the selection and production of dishes Wherein, δ is the unit conversion coefficient, A is the daily step count information recorded in the user's sports software, B is the time the food stays on the tray, C is the time the user is at home that day, the time the food stays on the tray and the time the user is at home that day are expressed in hours, μ1 is the weight of the time the food stays on the tray, μ2 is the weight of the time the user is at home that day, and a is the proportional series;

[0060] The food selection module is used to obtain the calorie range of the food selection based on the daily exercise increment of the user wherein Z is a preset minimum daily calorie intake value; a dosage delivery detection module, the dosage delivery detection module being configured to install sensors for collecting the sensor data in real time, the sensors including: a weight sensor for measuring the weight of ingredients; a temperature sensor for monitoring temperature data during the cooking process; and a humidity sensor for sensing ambient humidity data; analyzing and processing the sensor data using an intelligent algorithm including a neural network and fuzzy logic, establishing a recipe database, and constructing a seasoning model in the recipe database based on the analyzed data obtained from the analysis and processing, the seasoning model being configured to determine the timing for delivering seasonings to the dishes under a standard seasoning recipe; finding corresponding dimension values ​​according to the seasoning model based on the weight data of the ingredients, the temperature data monitored during the cooking process, and the sensed ambient humidity data, and calculating the dish quality according to a preset dish quality detection formula for the standard seasoning recipe, the dish quality detection formula being established based on the weight data of the ingredients, the temperature data monitored during the cooking process, and the sensed ambient humidity data; and recording the time point corresponding to the optimal point for calculating the dish quality as the seasoning delivery timing for the dish.

[0061] In some preferred embodiments, the food selection module includes:

[0062] An incremental parameter detection submodule is used to record the consumption level Y, and divide the consumption level Y into five food levels, namely, cereals, fruits and vegetables, milk and dairy products, animal foods, and oil, salt, sugar and condiments; by using the corresponding keywords of the food levels in the purchase records that can be checked in the food expenditure records, the user's consumption ratio of each food level in daily expenses is divided, and the consumption ratios η1, η2, η3, η4, and η5 corresponding to the five food levels are obtained, where η1+η2+η3+η4+η5=100%, then the user's daily exercise intake increment θ=λ1η1+λ2η2+λ3η3+λ4η4+λ5η5, where λ1, λ2, λ3, λ4, and λ5 are the calorie increment influence factors corresponding to cereals, fruits and vegetables, milk and dairy products, animal foods, and condiments, respectively, and the specific values ​​of the influence factors are 0 or 1;

[0063] The influence factor acquisition module is used to record the daily consumption amount of the user's daily food expenses as X and the user's consumption level reference coefficient based on the user's daily food expenses. Where δ is the user's reference consumption rating obtained by the system through the user's consumption record and searching the relevant consumption level reference information in the big database;

[0064] when If the user's consumption level reference coefficient is judged to be lower than the normal level, the calorie increment impact factor corresponding to the current food level is set to 0; It is judged that the reference coefficient of the user's consumption level is higher than the normal level, and the calorie increment impact factor corresponding to the current food level is set to 1.

[0065] See also Figure 2 Based on the same concept of the present invention, the present invention also provides an auxiliary robot control method based on big data, including:

[0066] Step S1: collecting user information, wherein the user information includes the authorization information bound to the user's mobile terminal and the setting data of the auxiliary robot;

[0067] Step S2: monitoring the user behavior according to the user information, and obtaining the ratio of the auxiliary robot controlling the selection and preparation of dishes based on the determination result obtained from the monitoring of the user behavior, wherein the ratio of the auxiliary robot controlling the selection and preparation of dishes is the control of the calorie level of the dishes during the selection and preparation of dishes, specifically, when the user places ingredients, whether the robot controls the amount of ingredients placed by the user. The higher the ratio, the more ingredients placed by the user will be controlled by the robot; in other words, the lower the ratio, the less ingredients placed by the user will be controlled by the robot, and the user can independently select the amount of ingredients he or she likes.

[0068] Step S3: Based on the auxiliary robot controlling the selection and preparation ratio of dishes, the calorie range of the selected ingredients is obtained;

[0069] Step S4: Use an intelligent algorithm to analyze and process the sensor data, and calculate the timing of adding seasonings to the dishes based on the calorie range of the selected ingredients, and control the robot to adjust the dish addition process. The dish addition process includes the auxiliary robot controlling the selection and production ratio of the dishes, the calorie range of the selected ingredients, and the timing of adding seasonings to the dishes.

[0070] In the existing technology, cooking robots can store dish data and automatically execute cooking instructions when the user needs them. However, there will be some discrepancies between the dishes made by the cooking robots and the user's own needs. Moreover, cooking robots that can cook completely independently and adjust to user preferences are too expensive and cannot be supported by most ordinary consumer groups. Therefore, an auxiliary robot is designed to assist users in matching ingredients and using ingredients, which can take into account user needs while ensuring a low price.

[0071] In this embodiment, collecting user information includes:

[0072] Step S11: Obtaining authorization information in the user-bound mobile terminal, wherein the authorization information in the user-bound mobile terminal includes: daily step count information recorded in the user's sports software, daily food expenditure information, and user preference factors;

[0073] Step S12: In the user's kitchen, ingredients are divided into different usage areas based on the consumption levels. The auxiliary robot uses multiple robotic arms to perform corresponding control operations on the ingredients divided into different areas to assist the user in using them during cooking.

[0074] The user's preference factors include: the user's favorite ingredients, the user's favorite cooking methods, and the user's favorite flavors. The user's preference factors are entered;

[0075] Step S13: Based on the user's preference factors, the dish model to be prepared is retrieved and entered into the database, where the dish model includes the ingredients needed for the dish and the consumption level corresponding to the ingredients needed.

[0076] In this embodiment, the monitoring of user behavior according to the user information and obtaining the proportion of the dish selection and preparation controlled by the auxiliary robot based on the determination result obtained from the monitoring of the user behavior include:

[0077] Step S21: When it is detected that the user takes out the ingredients, the timer starts the timer. The user takes out the ingredients by obtaining the characteristics of the ingredients through the camera and detecting the time the ingredients stay on the tray set by the auxiliary robot. The auxiliary robot then controls the selection and preparation of the dishes. Wherein, δ is the unit conversion coefficient, A is the daily step count information recorded in the user's sports software, B is the time the food stays on the tray, C is the time the user is at home that day, the time the food stays on the tray and the time the user is at home that day are expressed in hours, μ1 is the weight of the time the food stays on the tray, μ2 is the weight of the time the user is at home that day, and a is the proportional series. a can control the value of δA divided by the value of a(μ1B+μ2C) to be in the range of (0, 1), so that the controlled proportional result is between (0, 100%).

[0078] The time it takes to prepare the ingredients, the number of steps the user takes that day, and the time the user spends at home can be used to reflect the user's fatigue level. The higher the user's fatigue level, the weaker the user's willingness to cook well. Conversely, the lower the user's fatigue level, the stronger the user's willingness to cook well.

[0079] The assistive robot only provides suggestions for food selection to the user through the display, and only controls the amount and timing of seasonings during the cooking process;

[0080] The user's consumption, the expiration date of the ingredients purchased by the user, the user's current preferences, and the calorie content of the dishes the user consumes.

[0081] After consumers consume food continuously for several days, the amount of stored food will increase. However, since the stored food can remain fresh for different periods of time, there are two levels of time hierarchy: storage time and freshness time.

[0082] In this embodiment, obtaining the calorie range of the selected ingredients includes:

[0083] Step S31: Recording the consumption level Y, dividing the consumption level Y into five food levels, namely, cereals, vegetables and fruits, milk and dairy products, animal foods, and oil, salt, sugar and condiments;

[0084] Step S32: By recording the purchase records in the food expense record, the consumption proportion of each food grade in the user's daily expenses is divided by corresponding keywords in the searchable records, and the consumption proportions η1, η2, η3, η4, and η5 corresponding to the five food grades are obtained, where η1+η2+η3+η4+η5=100%, and the daily intake of the user's exercise increment θ=λ1η1+λ2η2+λ3η3+λ4η4+λ5η5, where λ1, λ2, λ3, λ4, and λ5 are the heat increment influence factors corresponding to cereal food, vegetables and fruits, milk and dairy products, animal food, and condiments, respectively. The specific values of the influence factors are 0 or 1. The influence factors can divide and filter the specific amount of food grade food that the user consumes. When the user consumes too little food grade food, the food grade food is filtered out, effectively monitoring the user's main heat intake. Similarly, the exercise increment is based on the user's expenses and daily exercise, and can predict the daily heat intake of the user under the premise of ensuring the user's health, and adjust the heat range of the actual food selection based on the exercise increment, improving the accuracy of the control system and improving the user experience, which is beneficial to long-term maintenance of the user's health.

[0085] Step S33: Based on the daily intake of the exercise increment of the user, the heat range of the food selection is where Z is a preset minimum daily heat intake.

[0086] The greater the proportion of the auxiliary robot controlling the selection and preparation of dishes, the more accurate the range of food selection; otherwise, the smaller the proportion of the auxiliary robot controlling the selection and preparation of dishes, the less accurate the range of food selection.

[0087] The five divisions of food grades correspond to the "pyramid" of food categories, where the first layer of the "pyramid" is the most important cereal food; the second layer of the "pyramid" is vegetables and fruits, which are an important source of vitamin intake; the third layer of the "pyramid" is milk and dairy products to supplement high-quality protein and calcium; the fourth layer of the "pyramid" is animal food, which mainly provides protein, fat, B vitamins, and inorganic salts; and the fifth layer of the "pyramid" is a moderate amount of oil, salt, and sugar.

[0088] User dietary health is a long-term and periodic process, so when selecting food for the user, as long as the selection of each food grade is within a certain range and the selection of each food grade is within a certain range of healthy intake, the user's dietary health can be guaranteed.

[0089] This technical solution solves the problem of difficulty in developing customized food selection plans tailored to a user's dietary needs when different ingredients have varying calorie counts. By recording the proportion of each grade of food consumed by a user within food expenditure records and using fixed healthy food selection data, the system can control daily food selection based on the user's actual needs, improving the comprehensiveness and stability of the assistive robot control system.

[0090] In this embodiment, the method for calculating the calorie increment impact factor corresponding to each food grade based on the user's daily consumption quota includes:

[0091] Step S321: Based on the daily consumption quota information of the user's daily food expenses, record the daily consumption quota of the user's daily food expenses as X, and the user's consumption level reference coefficient Where δ is the user's reference consumption rating obtained by the system through the user's consumption record and searching the relevant consumption level reference information in the big database;

[0092] Step S322: When If the user's consumption level reference coefficient is judged to be lower than the normal level, the calorie increment impact factor corresponding to the current food level is set to 0; It is judged that the reference coefficient of the user's consumption level is higher than the normal level, and the calorie increment impact factor corresponding to the current food level is set to 1.

[0093] In this embodiment, the use of intelligent algorithms to analyze and process sensor data and calculate the timing of adding seasonings to dishes based on the calorie range of the selected ingredients includes:

[0094] Step S41: Install sensors to collect sensor data in real time to provide basic information for intelligent seasoning control. The sensors include:

[0095] A weight sensor, used to measure the weight of food;

[0096] A temperature sensor, wherein the temperature sensor is used to monitor temperature data during cooking;

[0097] A humidity sensor is used to sense ambient humidity data;

[0098] Step S42: Analyzing and processing the sensor data using an intelligent algorithm, including a neural network and fuzzy logic, to establish a recipe database. A seasoning model is constructed in the recipe database based on the analytical data obtained from the analysis. The seasoning model is used to determine the timing of seasoning dosage for the dish under a standard seasoning recipe to adapt to variations in different ingredients and tastes.

[0099] Step S43: Based on the weight data of the ingredients, the temperature data monitored during the cooking process and the perceived environmental humidity data, the corresponding dimension values ​​are found according to the seasoning model, and the dish quality is calculated according to the dish quality detection formula of the preset standard seasoning recipe. The dish quality detection formula is established based on the weight data of the ingredients, the temperature data monitored during the cooking process and the perceived environmental humidity data. The time node found at the optimal point for calculating the dish quality is recorded as the timing for adding the seasoning to the dish.

[0100] The seasoning model is a three-dimensional model, and the weight data of the ingredients, the temperature data monitored during the cooking process, and the perceived environmental humidity data are respectively the three axis data of the three-dimensional model. Calculations based on the weight data of the ingredients, the temperature data monitored during the cooking process, and the perceived environmental humidity data can be used to obtain the quality of the dish, and the optimal time node for adding seasoning to the dish can be obtained based on the optimal point of the dish quality.

[0101] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0102] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An auxiliary robot control system based on big data, characterized by: include: A data acquisition module, the data acquisition module is used to collect user information, the user information including the authorization information bound to the user's mobile terminal and the setting data of the auxiliary robot; a control module configured to monitor user behavior according to the user information, and obtain a ratio of the dish selected and prepared by the auxiliary robot control based on a determination result obtained from the user behavior monitoring, and obtain a calorie range of the selected ingredients based on the ratio of the dish selected and prepared by the auxiliary robot control; The output module is used to analyze and process the sensor data using an intelligent algorithm, and calculate the timing of adding seasonings to the dishes based on the calorie range of the selected ingredients, and control the robot to adjust the dish addition process. The dish addition process includes the auxiliary robot controlling the selection and production ratio of the dishes, the calorie range of the selected ingredients, and the timing of adding seasonings to the dishes.

2. The big data-based auxiliary robot control system according to claim 1, characterized in that: The data acquisition module includes: A user data collection module, which is used to obtain authorization information from a user-bound mobile terminal. The authorization information in the user-bound mobile terminal includes: daily step count information recorded in the user's sports software, daily food expenditure information, and user preferences; the user's preferences include: the user's favorite ingredients, the user's favorite cooking methods, and the user's favorite flavors. The user's preferences are entered, and based on the user's preferences, a dish model to be prepared is retrieved and entered into a database. The dish model includes the ingredients required for the dish and the corresponding consumption level of the required ingredients; A data acquisition module is set up, and the data acquisition module is used to divide the ingredients into different usage areas in the user's kitchen based on the consumption level. The auxiliary robot uses multiple robotic arms to perform corresponding control operations on the ingredients divided into different areas, so as to assist the user in using them when cooking.

3. The big data-based auxiliary robot control system according to claim 2, characterized in that: The control module includes: The intervention analysis module is used to start the timer when it detects that the user takes out the ingredients. The user takes out the ingredients by obtaining the characteristics of the ingredients through the camera and detecting the time the ingredients stay on the tray set by the auxiliary robot. The proportion of the auxiliary robot controlling the selection and production of dishes Wherein, δ is the unit conversion coefficient, A is the daily step count information recorded in the user's sports software, B is the time the food stays on the tray, C is the time the user is at home that day, the time the food stays on the tray and the time the user is at home that day are expressed in hours, μ1 is the weight of the time the food stays on the tray, μ2 is the weight of the time the user is at home that day, and a is the proportional series; The food selection module is used to obtain the calorie range of the food selection based on the daily exercise increment of the user wherein Z is a preset minimum daily calorie intake value; a dosage delivery detection module, the dosage delivery detection module being configured to install sensors and collect the sensor data in real time, the sensors including: a weight sensor for measuring the weight of ingredients; a temperature sensor for monitoring temperature data during the cooking process; and a humidity sensor for sensing ambient humidity data; analyzing and processing the sensor data using an intelligent algorithm including a neural network and fuzzy logic, establishing a recipe database, and constructing a seasoning model in the recipe database based on the analyzed data obtained from the analysis and processing, the seasoning model being configured to determine the timing for delivering seasonings to the dishes under a standard seasoning recipe; finding corresponding dimension values ​​according to the seasoning model based on the weight data of the ingredients, the temperature data monitored during the cooking process, and the sensed ambient humidity data, and calculating the dish quality according to a preset dish quality detection formula for the standard seasoning recipe, the dish quality detection formula being established based on the weight data of the ingredients, the temperature data monitored during the cooking process, and the sensed ambient humidity data; and recording the time point corresponding to the optimal point for calculating dish quality as the seasoning delivery timing for the dish.

4. The big data-based auxiliary robot control system according to claim 3, characterized in that: The food selection module includes: An incremental parameter detection submodule is used to record the consumption level Y, and divide the consumption level Y into five food levels, namely, cereals, fruits and vegetables, milk and dairy products, animal foods, and oil, salt, sugar and condiments; by using the corresponding keywords of the food levels in the purchase records that can be checked in the food expenditure records, the user's consumption ratio of each food level in daily expenses is divided, and the consumption ratios η1, η2, η3, η4, and η5 corresponding to the five food levels are obtained, where η1+η2+η3+η4+η5=100%, then the user's daily exercise intake increment θ=λ1η1+λ2η2+λ3η3+λ4η4+λ5η5, where λ1, λ2, λ3, λ4, and λ5 are the calorie increment influence factors corresponding to cereals, fruits and vegetables, milk and dairy products, animal foods, and condiments, respectively, and the specific values ​​of the influence factors are 0 or 1; The influence factor acquisition module is used to record the daily consumption amount of the user's daily food expenses as X and the user's consumption level reference coefficient based on the user's daily food expenses. Where δ is the user's reference consumption rating obtained by the system through the user's consumption record and searching the relevant consumption level reference information in the big database; when If the user's consumption level reference coefficient is judged to be lower than the normal level, the calorie increment impact factor corresponding to the current food level is set to 0; It is judged that the reference coefficient of the user's consumption level is higher than the normal level, and the calorie increment impact factor corresponding to the current food level is set to 1.

5. A big data-based auxiliary robot control method, characterized by: include: Collecting user information, the user information including authorization information bound to the user's mobile terminal and setting data of the assistive robot; Monitoring user behavior according to the user information, and obtaining a ratio of dish selection and preparation controlled by the auxiliary robot based on a determination result obtained from the monitoring of the user behavior; Based on the auxiliary robot controlling the selection and preparation ratio of dishes, the calorie range of the selected ingredients is obtained; An intelligent algorithm is used to analyze and process the sensor data, and the timing of adding seasonings to the dishes is calculated based on the calorie range of the selected ingredients, and the robot is controlled to adjust the dish addition process. The dish addition process includes the auxiliary robot controlling the selection and production ratio of the dishes, the calorie range of the selected ingredients, and the timing of adding seasonings to the dishes.

6. The big data-based assistive robot control method according to claim 5, characterized in that: The collecting of user information includes: Obtaining authorization information from a user-bound mobile terminal, the authorization information from the user-bound mobile terminal including: daily step count information recorded in the user's sports app, daily food spending information, and user preference factors; In the user's kitchen, ingredients are divided into different usage areas based on the consumption level, and the assistive robot uses multiple robotic arms to perform corresponding control operations on the ingredients divided into different areas to assist the user in using them during cooking; The user's preference factors include: the user's favorite ingredients, the user's favorite cooking methods, and the user's favorite flavors. The user's preference factors are entered; Based on the user's preference factors, a dish model to be prepared is retrieved and entered into a database, where the dish model includes ingredients required for the dish and consumption levels corresponding to the ingredients required.

7. The big data-based assistive robot control method according to claim 6, characterized in that: The monitoring of user behavior according to the user information and obtaining the proportion of the dish selection and preparation controlled by the auxiliary robot based on the determination result obtained from the monitoring of the user behavior include: When it is detected that the user takes out the ingredients, the timer starts the timer. The user takes out the ingredients by obtaining the characteristics of the ingredients through the camera and detecting the time the ingredients stay on the tray set by the auxiliary robot. The auxiliary robot then controls the selection and preparation of dishes. Wherein, δ is the unit conversion coefficient, A is the daily step count information recorded in the user's sports software, B is the time the food stays on the tray, C is the time the user is at home that day, the time the food stays on the tray and the time the user is at home that day are expressed in hours, μ1 is the weight of the time the food stays on the tray, μ2 is the weight of the time the user is at home that day, and a is the proportional series.

8. The big data-based assistive robot control method according to claim 7, characterized in that: The step of obtaining the calorie range of the selected ingredients includes: Recording consumption level Y, dividing consumption level Y into five food levels, namely, cereals, vegetables and fruits, milk and dairy products, animal foods, and oil, salt, sugar and condiments; By using the corresponding keywords of the food grades in the purchase records that can be checked in the food expenditure records, the user's consumption ratio of each food grade in daily expenses is divided, and the consumption ratios η1, η2, η3, η4, and η5 corresponding to the five food grades are obtained, where η1+η2+η3+η4+η5=100%. Then the user's daily exercise intake increment θ=λ1η1+λ2η2+λ3η3+λ4η4+λ5η5, where λ1, λ2, λ3, λ4, and λ5 are the calorie increment influence factors corresponding to cereals, vegetables and fruits, milk and dairy products, animal foods, and condiments, respectively, and the specific values ​​of the influence factors are 0 or 1; Based on the daily exercise increment of the user, the calorie range of the food selection is obtained as follows: Where Z is the preset minimum daily calorie intake.

9. The big data-based assistive robot control method according to claim 8, characterized in that: The method for calculating the calorie increment impact factor corresponding to each food grade based on the user's daily consumption quota includes: Based on the user's daily food consumption information, the user's daily food consumption is recorded as X, and the user's consumption level reference coefficient is Where δ is the user's reference consumption rating obtained by the system through the user's consumption record and searching the relevant consumption level reference information in the big database; when If the user's consumption level reference coefficient is judged to be lower than the normal level, the calorie increment impact factor corresponding to the current food level is set to 0; It is judged that the reference coefficient of the user's consumption level is higher than the normal level, and the calorie increment impact factor corresponding to the current food level is set to 1.

10. The big data-based assistive robot control method according to claim 9, characterized in that: The intelligent algorithm is used to analyze and process the sensor data, and calculate the timing of adding seasoning to the dish according to the calorie range of the selected ingredients, including: Install sensors to collect the sensor data in real time, and the sensors include: A weight sensor, used to measure the weight of food; A temperature sensor, wherein the temperature sensor is used to monitor temperature data during cooking; A humidity sensor is used to sense ambient humidity data; Analyzing and processing the sensor data using an intelligent algorithm, the intelligent algorithm including a neural network and fuzzy logic, establishing a recipe database, and constructing a seasoning model in the recipe database based on the analysis data obtained by the analysis and processing, the seasoning model being used to determine the timing of seasoning dosage for the dish under a standard seasoning recipe; Based on the weight data of the ingredients, the temperature data monitored during the cooking process and the perceived environmental humidity data, the corresponding dimension values ​​are found according to the seasoning model, and the dish quality is calculated according to the dish quality detection formula of the preset standard seasoning recipe. The dish quality detection formula is established based on the weight data of the ingredients, the temperature data monitored during the cooking process and the perceived environmental humidity data. The time node found at the optimal point for calculating the dish quality is recorded as the timing for adding the seasoning to the dish.

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