Cooking device control method and apparatus, and cooking device

Abstract

The present application relates to the technical field of electric appliances, and discloses a cooking equipment control method and device and cooking equipment. Food material information of a food material to be cooked and health meal requirements of a meal person are acquired. Based on the food material information, original cooking parameters of the food material to be cooked are determined, and the food material to be cooked is cooked based on the original cooking parameters. In response to a cooking adjustment operation of a user in a cooking process of the cooking equipment, when it is determined that the cooking adjustment operation is a food material addition operation or a health meal requirement switching operation, target cooking parameters are determined based on operation content of the cooking adjustment operation and the original cooking parameters. The cooking equipment is controlled to cook based on the target cooking parameters. The cooking equipment is suitable for diversified home cooking scenes, closely links the cooking process of the food material to the actual health requirements of the user, and meets the personalized health management requirements of the user.

Description

Technical Field

[0001] This invention relates to the field of electrical appliance technology, specifically to cooking equipment control methods, devices, and cooking equipment. Background Technology

[0002] When users cook with smart kitchen appliances, they usually use sensors to identify the type of food or monitor the temperature. However, after the food is identified, the cooking process still requires the user to manually set the cooking parameters. Each menu corresponds to a fixed set of cooking parameters, which lacks accurate identification of the actual state of the food. It is unknown whether different foods can meet the user's health needs under the current cooking parameters, resulting in a lack of correlation between the cooking process of the food and the user's actual health needs. Summary of the Invention

[0003] This invention provides a cooking equipment control method, apparatus, and cooking equipment to address the problem of a lack of correlation between the cooking process and the user's actual health needs.

[0004] In a first aspect, the present invention provides a method for controlling a cooking device, the method comprising: Obtain information on the ingredients to be cooked and the healthy dining needs of the diners; Based on the ingredient information, determine the original cooking parameters of the ingredients to be cooked, and cook the ingredients based on the original cooking parameters; In response to the user's cooking adjustment operation during the cooking process of the cooking equipment, when it is determined that the cooking adjustment operation is a new ingredient operation or a switch operation for healthy dining needs, the target cooking parameters are determined based on the original cooking parameters and the operation content of the new ingredient operation or the switch operation for healthy dining needs. Cooking is carried out by controlling the cooking equipment based on target cooking parameters.

[0005] This invention's cooking device acquires information about the ingredients to be cooked and the health needs of the diners. Based on the ingredient information, it determines and executes the original cooking parameters. Simultaneously, it continuously monitors user adjustments during the cooking process. When an adjustment is detected—adding ingredients or switching health needs—a new target cooking parameter is calculated based on the operation and the original cooking parameters. This target parameter is then used to control the device to continue cooking. This achieves automated initial settings based on ingredient characteristics, significantly reducing the user's operational threshold and fully integrating health needs, making the cooking plan more targeted and nutritionally sound. The dynamic response mechanism gives users the initiative to intervene mid-cooking, preventing the device from mechanically executing fixed programs and allowing it to flexibly adapt to changes in personal taste or immediate conditions. The combination of cooking adjustments and the original cooking parameters ensures that the adjusted parameters still have a basis for adjustment, enabling them to adaptively optimize and adjust according to the user's cooking adjustments. This achieves continuous, uninterrupted intelligent cooking decisions, significantly improving the cooking success rate. The device is suitable for diverse home cooking scenarios, closely linking the cooking process with the user's actual health needs and meeting personalized health management requirements.

[0006] In one optional implementation, the target cooking parameters are determined based on the original cooking parameters and the operation content of adding ingredients or switching to healthy dining needs, including: When the cooking adjustment operation is determined to be an ingredient addition operation, the new ingredient is identified, and based on the original cooking parameters of the new ingredient and the ingredient to be cooked, the target cooking parameters of the cooking equipment when resuming cooking are determined. When a cooking adjustment operation is determined to be a healthy dining requirement switching operation, the target healthy dining requirement after the switch is determined, and the current cooking progress of the ingredients to be cooked is determined based on the original cooking parameters. Based on the current cooking progress and the target healthy dining requirement, the target cooking parameters are determined.

[0007] When the cooking adjustment operation involves adding ingredients, the device first identifies the specific new ingredient and uses both the new ingredient and the original cooking parameters of the ingredients to be cooked as input to recalculate the target cooking parameters that the device should use when resuming the cooking process. When the cooking adjustment operation involves switching to a health-conscious dining need, the device first determines the new health-conscious dining need target after the switch, then evaluates the current cooking progress based on the original cooking parameters, and then combines the current progress with the new health need to deduce the subsequent target cooking parameters. This allows for precise differentiation of different operation intentions and the adoption of targeted parameter adjustment strategies. It avoids the imbalance in doneness or insufficient heating caused by simply using the original parameters when adding ingredients, and also prevents the waste of time and damage to taste caused by ignoring the current progress and being forced to recalculate globally when switching health needs. At the same time, by introducing the current cooking progress as a constraint, the change in health needs is not a rigid application of the initial plan, but rather adapted to the heating state that the ingredients have already undergone, thereby achieving a dynamic balance between nutritional goals and the actual properties of the food, significantly improving the adaptability of the device and enhancing the intelligent cooking experience.

[0008] In one optional implementation, when the cooking adjustment operation is determined to be an ingredient addition operation, determining the added ingredient includes: Obtain the real-time weight of the ingredients to be cooked during the cooking process; In response to the user's cooking adjustment operation on the cooking equipment, determine the target weight of the food to be cooked after the cooking adjustment operation; Calculate the weight increment based on the real-time weight and the target weight; When the weight increment is greater than or equal to the preset weight threshold, the cooking adjustment operation is determined to be an ingredient addition operation; The cooking equipment is used to identify ingredients and obtain new ingredients.

[0009] When a user performs a cooking adjustment operation, this invention determines the target weight of the adjusted ingredients. By calculating the difference between the target weight and the real-time weight, the weight increment is obtained. If this increment reaches or exceeds a preset weight threshold, the device recognizes this operation as an ingredient addition operation and automatically activates the ingredient recognition function of the cooking device to ultimately determine the specific type of the added ingredient. This significantly improves the recognition accuracy and system robustness. On this basis, the automatically triggered ingredient recognition ensures that the information of the added ingredient can be entered into the cooking decision chain in a timely and accurate manner, providing a reliable data foundation for subsequent parameter reconstruction. This allows the device to proactively adapt to temporary ingredient addition scenarios, avoiding the risk of missing ingredients or overcooking, improving the smoothness of the cooking process and the consistency of the final product's taste, and optimizing the user experience and cooking success rate.

[0010] In one optional implementation, based on the original cooking parameters of the added ingredients and the ingredients to be cooked, the target cooking parameters for the cooking equipment during the resumption of cooking are determined, including: Based on the original cooking parameters and the current cooking time, determine the remaining amount of heat required for the ingredients to be cooked; Determine the target cooking parameters based on the remaining required heating amount and the added ingredients.

[0011] This invention calculates the remaining heat required by the original ingredients in subsequent stages based on their original cooking parameters and the current cooking time. Using this remaining heat as a benchmark, and combining it with the characteristics of any new ingredients, the target cooking parameters for resuming cooking are calculated. This precisely quantifies the unmet heat requirements of the original ingredients, thus avoiding overcooking or excessive nutrient loss due to reheating after adding ingredients midway. Furthermore, the introduction of new ingredients is not handled independently but integrated with the remaining heat, allowing both types of ingredients to reach their optimal cooking state within the same remaining time frame. This ensures that the original ingredients do not age while waiting and that the new ingredients are fully heated and safe to eat. It significantly improves cooking success rate, consistency of food texture, and nutrient retention, making the entire cooking process both flexible and precise, and widely applicable to the practical needs of cooking multiple ingredients in stages in daily household cooking.

[0012] In one alternative implementation, target cooking parameters are determined based on the current cooking progress and the target healthy eating requirements, including: Once it is determined that the cooking progress has not exceeded the preset cooking progress, the current temperature of the ingredients is determined; Based on the current temperature of the ingredients and the target healthy dining requirements, target cooking parameters are generated.

[0013] This invention determines whether the current cooking progress has exceeded a preset progress threshold. After confirming that the condition is met, it measures the actual temperature of the current ingredients and then combines this measured temperature with the target healthy eating requirements after the switch to generate new target cooking parameters. By limiting the progress threshold, it avoids making significant parameter adjustments when the ingredients are nearly cooked or overcooked, which could lead to deterioration in taste or loss of nutrients. This ensures that the switch to health requirements is only performed within an effective intervention window. At the same time, by introducing the current ingredient temperature instead of relying on theoretically calculated time, it can truly reflect the internal heating state of the ingredients, compensating for the deviation caused by simply estimating the progress based on time. The dual input of measured temperature and health requirements allows the adjusted cooking strategy to not only meet the new nutritional goals and maintain the simplicity of operation, but also significantly improve the device's adaptability to different healthy eating patterns. This significantly enhances the practical effectiveness of intelligent cooking in personalized health management and improves user satisfaction.

[0014] In one optional implementation, obtaining the ingredient information of the ingredients to be cooked includes: Obtain a pre-constructed ingredient information matrix; wherein, the ingredient information matrix includes multiple types of ingredients, the cooking mode of each ingredient in the multiple types of ingredients, and the nutrient retention rate of each ingredient; By retrieving the ingredient information matrix based on the ingredients to be cooked, the types of ingredients, cooking modes, and nutrient retention rates that match the ingredients to be cooked are determined, thus obtaining the ingredient information of the ingredients to be cooked.

[0015] This invention acquires a pre-constructed ingredient information matrix, which covers multiple ingredient categories, the corresponding cooking modes for each ingredient, and the nutrient retention rate of each ingredient during cooking. Then, using the ingredient to be cooked as the query key, a search and matching process is performed within the matrix to ultimately determine the matching ingredient type, the appropriate cooking mode, and the expected nutrient retention rate, thus obtaining complete information about the ingredient. The pre-constructed matrix enables standardized and digital management of ingredient attributes, allowing the device to quickly obtain accurate parameters without relying on external networks or manual input, significantly improving response speed and reliability. Simultaneously, the matrix-based data structure supports the coverage of a large number of ingredient types, possessing good scalability and adaptability to meet the needs of different regions and dietary habits. The correspondence between ingredient types and cooking modes ensures that the heating method matches the ingredient characteristics, while the nutrient retention rate, as a quantitative indicator, can be directly used by subsequent algorithms. This achieves end-to-end automation from ingredient identification to parameter generation, ensuring the predictability and consistency of nutritional balance in each meal, thereby comprehensively enhancing the practical value of intelligent cooking equipment and the user's healthy eating experience.

[0016] In one optional implementation, the ingredient information includes the type and quantity of ingredients to be cooked; based on the ingredient information, the original cooking parameters of the ingredients to be cooked are determined, including: When the number of cooking ingredients is greater than or equal to a preset quantity threshold and the preset cooking modes of each ingredient are inconsistent, a benchmark cooking mode is selected from the preset cooking modes of each ingredient. Based on the degree of nutrient loss of each ingredient under the benchmark cooking mode and the preset cooking mode of each ingredient, the original cooking parameters are obtained. When the number of cooking ingredients is greater than or equal to a preset threshold and the preset cooking modes of each ingredient are consistent, or when the number of cooking ingredients is less than a preset threshold, the original cooking parameters are obtained based on the preset cooking mode of any ingredient.

[0017] This invention determines the branching based on whether the number of ingredients and the preset cooking mode are consistent. When there are many ingredients and different cooking modes, a baseline cooking mode is first selected. Then, the original cooking parameters are derived by combining the degree of nutrient loss of each ingredient under the baseline and its own preset cooking mode. When there are many ingredients but the modes are consistent, or when there are few ingredients, the preset mode of any one ingredient is directly used as the original cooking parameters. When the preset cooking modes of multiple ingredients conflict, the invention effectively coordinates the nutrients of each ingredient and the overall cooking parameters, so as to take into account the differences in nutrient retention of different ingredients before cooking starts. This provides a more reasonable starting control benchmark for subsequent temperature-changing strategies and dynamic interventions, thereby improving the nutrient retention capacity in multi-ingredient cooking scenarios and the adaptability to users' healthy eating needs.

[0018] In one alternative implementation, the preset cooking mode is determined through the following steps: The ingredient type of each ingredient to be cooked is located from the pre-constructed ingredient information matrix, and the nutrient retention rate of each ingredient to be cooked in each cooking mode is determined based on the ingredient type. For each ingredient to be cooked, a comprehensive retention rate score is calculated based on the nutrient retention rate in each cooking mode. The ingredients are then sorted from largest to smallest based on their comprehensive retention rate scores to determine the candidate cooking modes with the highest comprehensive retention rate scores. For each ingredient to be cooked, the user-defined desired cooking parameters are determined, and candidate cooking modes are filtered based on these parameters to determine the preset cooking mode for the ingredient.

[0019] This invention locates the ingredient category corresponding to each ingredient to be cooked from a pre-constructed ingredient information matrix, and determines the nutrient retention rate of each ingredient in each available cooking mode based on the category. Then, for each ingredient, a comprehensive retention rate score is calculated based on these retention rates in each mode. The scores are then sorted from high to low, and several candidate cooking modes with the highest rankings are selected. Finally, the user's pre-set desired cooking parameters are obtained, and the candidate cooking modes are further filtered based on these parameters, thereby finally determining the preset cooking mode for the ingredient. By sorting the candidate modes by the comprehensive retention rate score in advance, the set of candidates with excellent nutritional performance is pre-selected, which greatly reduces the computational complexity of subsequent matching and improves the system response efficiency. At the same time, the introduction of user-desired parameters allows the final mode to fit individual cooking habits, thereby improving user satisfaction.

[0020] In one optional implementation, a baseline cooking mode is selected from the preset cooking modes of each ingredient to be cooked. Based on the degree of nutrient loss of each ingredient under the baseline cooking mode and the preset cooking mode of each ingredient, original cooking parameters are obtained, including: The ingredient with the largest weight among all the ingredients to be cooked is designated as the main ingredient, and the other ingredients are designated as auxiliary ingredients. The preset cooking mode corresponding to the main ingredient is set as the baseline cooking mode. The nutrient loss rate of each auxiliary ingredient under the baseline cooking mode is calculated, and the basic nutrient loss rate of each auxiliary ingredient is determined. Based on the nutrient loss rate and the basic nutrient loss rate, the relative nutrient loss rate of each supplementary ingredient is calculated. When the relative nutrient loss rate of any auxiliary ingredient is determined to be greater than or equal to the preset loss rate threshold, a temperature control curve is generated based on the preset cooking modes of the main ingredient and all auxiliary ingredients. When the expected nutrient retention rates of the main ingredient and auxiliary ingredients are determined to be no less than the basic nutrient retention rates of their respective preset cooking modes, the original cooking parameters are obtained based on the temperature control curve. When the relative nutrient loss rate of the auxiliary ingredients is determined to be less than the preset loss rate threshold, the cooking parameters corresponding to the benchmark cooking mode are adjusted to obtain the original cooking parameters.

[0021] This invention identifies the main and auxiliary ingredients based on their weight, and uses the preset cooking mode of the main ingredient as the baseline cooking mode. Then, it calculates the nutrient loss rate of each auxiliary ingredient under this baseline cooking mode. If the loss rate of an auxiliary ingredient reaches or exceeds a preset threshold, a temperature control curve is generated based on the preset cooking modes of the main ingredient and all auxiliary ingredients, serving as the original cooking parameters. If the loss rates of all auxiliary ingredients are below the threshold, only the cooking parameters under the baseline cooking mode are adjusted to obtain the original cooking parameters. The invention prioritizes the quality of the main ingredient by using the ingredient with the largest weight as the core decision-making factor. Simultaneously, it categorizes auxiliary ingredients by quantifying their loss rates. Temperature control curves are actively generated for high-loss combinations of multiple ingredients, while parameters are fine-tuned for low-loss combinations. This effectively reduces excessive nutrient loss of auxiliary ingredients during mixed cooking while controlling algorithm complexity and cooking time.

[0022] In one alternative implementation, the method further includes: In response to the completion of the cooking process of the ingredients to be cooked, based on the ingredient information, the nutrient intake of the diners after the cooking of the ingredients to be cooked is determined, and cooking management is carried out based on the nutrient intake.

[0023] This invention, after monitoring the completion of the cooking process of the ingredients to be cooked, calculates the actual nutrient content ingested by the diner based on the previously acquired ingredient information, and further uses this quantitative result as input to drive subsequent cooking management actions; it can obtain accurate nutrient intake based on the actual cooked food state, which significantly improves accuracy and reference value compared to the method of estimating only based on the initial raw weight; at the same time, the device actively intervenes in subsequent management based on this intake, enhancing the practicality of the device in daily diet management, and achieving the effect of the cooking process conforming to the user's healthy eating needs.

[0024] In one optional implementation, in response to the completion of the cooking process of the food to be cooked, based on the food information, the nutrient intake of the diner after the food to be cooked is determined, including: Obtain the nutrient retention rate of the ingredients to be cooked after cooking, and determine the initial nutrient content and nutrient usage of each ingredient. Based on the initial nutrient content, nutrient usage, and nutrient retention rate, the individual nutrient intake for each diner is obtained. The individual nutrient intake of each diner is added together with the current cumulative nutrient intake to obtain the nutrient intake of each diner after the food to be cooked is finished.

[0025] This invention first obtains the nutrient retention rate of each ingredient after cooking, and determines its initial nutrient content and actual usage. Then, by calculating the weight ratio between the initial content, usage, and retention rate, it determines the amount of each nutrient ingested by each diner. Finally, it adds this amount to the cumulative intake for the day to obtain the latest cumulative intake for each diner after the meal. By uniformly incorporating the actual nutrient retention after cooking, the amount of ingredients used, and the individual allocation weights into the calculation, it ensures that nutrient attribution is quantifiable and cumulative, providing an accurate data foundation for subsequent overdose warnings and health goal management.

[0026] In one alternative implementation, cooking management based on nutrient intake includes: Obtain the target nutrient intake for each diner; For each diner, an early warning will be issued if the diner's nutrient intake exceeds the corresponding target nutrient intake.

[0027] This invention obtains the single-item target nutrient intake of each diner, and then compares each person's actual nutrient intake for this meal with the corresponding target value. If it exceeds the target value, an early warning of nutrient excess is issued. This enables real-time compliance checks on nutrient intake after the meal, and the early warning helps users understand the nutritional risks of their members in a timely manner, providing a basis for adjusting subsequent meal plans and forming a closed-loop feedback from accounting to management.

[0028] In one alternative implementation, the method further includes: After the ingredients to be cooked have finished cooking, obtain the remaining weight after cooking; Calculate the current weight loss rate of the ingredients to be cooked based on their initial weight and remaining weight before cooking begins. Based on the relationship between the current weight loss rate of the ingredients to be cooked and the baseline weight loss rate, the weight loss rate deviation is determined, and when the weight loss rate deviation exceeds a preset deviation threshold, the baseline weight loss rate is corrected based on the current weight loss rate. The actual nutrient intake of diners is obtained based on the corrected baseline weight loss rate, initial weight, initial nutrient values ​​of each ingredient to be cooked, and nutrient retention rate of each ingredient to be cooked.

[0029] This invention obtains the remaining weight after cooking and calculates the current weight loss rate by combining it with the initial weight before cooking. The current weight loss rate is compared with a preset baseline weight loss rate to obtain the weight loss rate deviation. When this deviation exceeds a preset threshold, the baseline loss rate is corrected using the current weight loss rate. Based on the corrected baseline loss rate, initial weight, initial nutrient values, and nutrient retention rate, the diner's actual nutrient intake is calculated. By dynamically correcting theoretical loss parameters through real-time weighing, this invention effectively compensates for deviations caused by unpredictable factors such as individual differences in ingredients, environmental humidity, or fluctuations in heat, resulting in a more accurate estimation of final intake that closely reflects actual consumption. This significantly improves the accuracy and reliability of nutrient monitoring. Simultaneously, the deviation threshold setting avoids meaningless corrections triggered by minor fluctuations, ensuring system stability and computational efficiency. This allows the intelligent cooking equipment to continuously optimize its model based on real feedback from each cooking session, gradually approaching the goal of personalized precision nutrition over long-term use, thus enhancing the practical value and user satisfaction of intelligent kitchen equipment.

[0030] In a second aspect, the present invention provides a cooking equipment control device, the device comprising: The acquisition module is used to acquire information about the ingredients to be cooked and the healthy dining needs of the diners. The determination module is used to determine the original cooking parameters of the ingredients to be cooked based on the ingredient information, and to cook the ingredients based on the original cooking parameters; The adjustment module is used to respond to the cooking adjustment operation performed by the user during the cooking process of the cooking equipment. When it is determined that the cooking adjustment operation is a new ingredient operation or a switch operation for healthy eating needs, the target cooking parameters are determined based on the original cooking parameters and the operation content of the new ingredient operation or the switch operation for healthy eating needs. The control module is used to control the cooking equipment to perform cooking based on the target cooking parameters.

[0031] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0032] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0033] Figure 1 This is a structural block diagram of a cooking apparatus according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of a first method for controlling a cooking device according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a second process for controlling a cooking device according to an embodiment of the present invention; Figure 4 This is a general functional architecture diagram of a cooking equipment control method according to an embodiment of the present invention; Figure 5 This is a first working process diagram of a cooking apparatus according to an embodiment of the present invention; Figure 6 This is a second working process diagram of a cooking apparatus according to an embodiment of the present invention; Figure 7 This is a flowchart illustrating the workflow of a cooking device according to an embodiment of the present invention in an application scenario. Figure 8 This is a first timing logic diagram of a cooking device according to an embodiment of the present invention; Figure 9 This is a second timing logic diagram of a cooking device according to an embodiment of the present invention; Figure 10 This is a structural block diagram of a cooking equipment control device according to an embodiment of the present invention; Figure 11This is a schematic diagram of the hardware structure of the cooking equipment controller according to an embodiment of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0035] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0036] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0037] As an optional application scenario of this invention, such as Figure 1 As shown, a cooking device includes a controller 101, which is used to execute a cooking device control method. The overall process of the controller 101 executing the cooking device control method is detailed in the relevant description of the method embodiment below, and will not be repeated here.

[0038] The basic principle of a steam oven is to use an electric heating element, a steam generator, and a magnetron as heat sources. When powered on, it converts electrical energy into heat energy, latent heat of steam, and microwave energy inside a sealed cavity, thereby achieving the purpose of steaming, baking, microwaving, or combined heating of food.

[0039] Steam oven technology utilizes steam condensation, hot air convection, and microwave dielectric heating for cooking, offering advantages such as rapid heating, uniform temperature, and excellent preservation of food texture. In recent years, some high-end steam ovens have begun to incorporate cameras or temperature probes, providing them with some food identification or temperature monitoring capabilities.

[0040] Currently, although a few steam ovens have attempted to incorporate a single sensor (such as an internal camera), the following problems exist: First, there is a lack of systematic mapping logic between sensor data and cooking decisions. That is, after the camera captures a picture of salmon, the steam oven still executes the menu manually set by the user, without knowing what temperature profile or cooking time is appropriate to maximize nutrient retention for that salmon given the user's current health goals. Second, the menu system of traditional steam ovens is fixed; each menu corresponds to a fixed set of temperature and time parameters, making it impossible to dynamically generate cooking parameters based on the actual weight and freshness of the ingredients and the user's personalized health needs. Third, when users add new ingredients midway through cooking, or when family members have different health goals, traditional steam ovens completely lack the ability to dynamically re-determine decisions. Fourth, after a meal is cooked, the steam oven cannot record the actual nutritional intake of the meal and provide feedback to the user's long-term health management, resulting in a disconnect between cooking and health management.

[0041] Addressing the lack of a systematic mapping between ingredient perception and cooking parameters, even steam ovens in related technologies, while capable of identifying ingredient types, cannot calculate the optimal cooking temperature curve based on the ingredient's weight, freshness, and the user's health goals. For example, with the same piece of salmon, a user aiming to lose fat needs to first use high temperature to render the oil and then bake at a low temperature to cook it thoroughly; a user aiming to gain muscle needs to maintain the protein structure at a low temperature throughout the cooking process; and a user aiming for a balanced diet requires a compromise curve that balances taste and nutrition. Current steam oven menu systems cannot generate such personalized and differentiated cooking parameters.

[0042] To address the lack of conflict resolution strategies when cooking multiple ingredients together: When users bake fish and vegetables in one cavity at the same time, the optimal cooking temperature and time for different ingredients are contradictory (fish needs to be tenderly baked at 160 degrees for 12 minutes, and vegetables need to be quickly baked at 200 degrees for 8 minutes). Traditional steam ovens can only execute a single menu for this, resulting in poor cooking quality and excessive nutrient loss of at least one ingredient.

[0043] Addressing the issue of nutrient intake not being attributed to individuals when multiple family members eat together: Since the whole family shares a meal, current technologies cannot individually record the nutrients consumed by each member. Furthermore, each person has different health goals (e.g., father losing fat, son gaining muscle, elderly controlling blood sugar), making it impossible to provide targeted information to each member about their daily protein, carbohydrate, and fat intake, how much they are lacking, and which nutrient is about to exceed their recommended intake.

[0044] To address the lack of continuous decision-making capability in scenarios where changes are made midway through cooking: When users open the cavity to add ingredients or change their health goals during cooking, the original cooking parameters immediately become invalid. It is necessary to recalculate the temperature control curve for the remaining time based on the cooked state of the ingredients, rather than simply starting from scratch or making users guess how much time to add.

[0045] To address the above issues, a nutrition and cooking mapping engine control method based on multimodal perception is proposed. By receiving multimodal food perception data (food type, weight, freshness, and three-dimensional shape) and user health record data, the nutrition and cooking mapping decision algorithm is run to automatically generate a personalized optimal cooking temperature control curve. After cooking, the actual nutrient intake is estimated and written into the user's health log, thus upgrading the cooking process from passive execution to proactive health decision-making.

[0046] According to an embodiment of the present invention, a method for controlling a cooking device is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0047] This embodiment provides a cooking equipment control method, which can be used in the controller of the aforementioned cooking equipment. Figure 2 This is a schematic flowchart of a first method for controlling a cooking device according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain information on the ingredients to be cooked and the healthy dining needs of the diners.

[0048] It should be noted that the ingredient information refers to the various attributes of all ingredients to be put into the pot that need to be collected before cooking begins, including but not limited to the type of ingredient, its weight before cooking, the baseline content of major nutrients, and the most suitable cooking mode and corresponding nutrient retention rate for each ingredient. Among them, the ingredients to be cooked must include at least one type of ingredient. The ingredients to be cooked can be determined according to the cooking recipe, which can be a cooking recipe set by the user, or a recipe recommended by the system based on the user's healthy eating needs, or a recipe generated based on existing ingredients.

[0049] The food information includes, but is not limited to, food type label (string), food net weight W (grams, floating point), freshness rating F (1 to 5 integers), and food maximum thickness H (millimeters, floating point).

[0050] User health meal requirements include, but are not limited to, user ID, age, gender, weight, health goal label G (enumerated values: fat loss / sugar control / muscle gain / balanced diet), allergen list, chronic disease label list, recommended daily intake (RDI) values ​​for each nutrient, and may also include data set by the user in real time, including number of diners N (integer), expected completion time Tmax (minutes), and taste preference label P (enumerated values: light / moderate / rich).

[0051] For example, an independent nutrition profile is created and maintained for each family member to constitute the healthy eating needs of the diners. Each profile contains the following data fields: a unique member identifier, a health goal label G (enumerated values: fat loss / blood sugar control / muscle gain / balanced diet), a recommended daily intake (RDI) array for each nutrient (covering at least 15 nutrients such as protein (g), fat (g), carbohydrates (g), dietary fiber (g), and vitamin C (mg), etc.), and a cumulative intake array for the day (the cumulative value for each nutrient for the day, automatically reset to zero at midnight each day).

[0052] In this context, "dining personnel" refers to each family member with an independent nutrition record, which is used to determine their healthy eating needs. This record includes their health goal label, recommended intake of each nutrient, and the cumulative intake for the day, serving as the basic unit for nutrient attribution and early warning assessment.

[0053] For example, before cooking begins, a list of members participating in the meal is determined, M = {m1, m2, ..., mp}. All family members participate by default, but users can manually add or remove members. Methods for confirming diners may also include: associating diners with family members' meal schedules and automatically filtering diners by mealtime; integrating with a smart home system to determine diners by using human presence sensors; or allowing users to confirm diners with a single click via an app or device interface before cooking.

[0054] Step S202: Based on the ingredient information, determine the original cooking parameters of the ingredients to be cooked, and cook the ingredients based on the original cooking parameters.

[0055] The original cooking parameters can be a temperature control curve or other parameters that can achieve temperature control, such as the heating power of the equipment and the working mode of the equipment.

[0056] Step S203: In response to the user's cooking adjustment operation during the cooking process of the cooking equipment, when it is determined that the cooking adjustment operation is a food addition operation or a healthy dining requirement switching operation, the target cooking parameters are determined based on the original cooking parameters and the operation content of the food addition operation or the healthy dining requirement switching operation.

[0057] It should be noted that if a new ingredient addition operation is received during the cooking process based on the original cooking parameters, the new ingredient will be added to the existing ingredients to be cooked, based on the content of the new ingredient addition operation. This will determine all the ingredients required for cooking, and the target cooking parameters will be determined in conjunction with the original cooking parameters. If a healthy eating requirement switching operation is received, the user's healthy eating requirements will be updated, and the updated healthy eating requirements will be used as the cooking target during the cooking process. The target cooking parameters will be determined in conjunction with the original cooking parameters. The cooking target can be a new nutrient retention rate requirement or a new fat control requirement determined based on the updated healthy eating requirements.

[0058] Cooking adjustment operations refer to user-initiated interventions during the cooking process, including but not limited to adding ingredients or changing health-conscious eating preferences. Original cooking parameters refer to the operating parameters of the cooking equipment before the cooking adjustment operation is performed, including but not limited to basic data such as cooking time and temperature. Target cooking parameters are new cooking parameters derived through logical processing by combining the user's adjustment intentions with the original cooking parameters.

[0059] It should be noted that the above cooking adjustment operation is an adjustment of cooking ingredients or healthy dining needs based on the user's intention during the cooking process. Based on the addition of ingredients or the switching of healthy dining needs, the cooking equipment automatically completes the adaptation and adjustment of operating parameters to obtain the target cooking parameters.

[0060] In some optional embodiments of this example, the cooking adjustment operation can also be an adjustment of the operating parameters during the operation of the cooking equipment, including but not limited to adjusting the heat, switching cooking modes, or adjusting the remaining cooking time. For example, the user can manually adjust the heat according to the coloring of the food, and then determine the target cooking parameters based on the adjusted heat level, or switch between modes such as frying, deep-frying, steaming, and baking, and then determine the target cooking parameters based on the cooking parameters of the new mode. Alternatively, the user can actively adjust the remaining cooking time based on their own dietary habits, and the cooking equipment determines the target cooking parameters based on the adjusted remaining cooking time, thereby meeting the user's personalized needs.

[0061] Step S204: Control the cooking equipment to cook based on the target cooking parameters.

[0062] Specifically, the target cooking parameters are converted into control commands that the cooking equipment can recognize and sent to the execution unit of the cooking equipment, so that the cooking equipment operates according to the target cooking parameters, thereby completing the cooking of the ingredients to be cooked. The target cooking parameters may include cooking temperature and cooking time obtained by adjusting the original cooking parameters.

[0063] The cooking equipment control method provided in this embodiment allows the cooking equipment to acquire information about the ingredients to be cooked and the health needs of the diners. Based on the ingredient information, it determines and executes the original cooking parameters for cooking. Simultaneously, it continuously monitors user adjustments during the cooking process. When a new ingredient addition or a change in health needs is detected, the method calculates new target cooking parameters based on the operation and the original cooking parameters, and uses these parameters to control the equipment to continue cooking. This achieves automated initial settings based on ingredient characteristics, significantly reducing the user's operational threshold and fully integrating health needs, making the cooking plan more targeted and nutritionally sound. The dynamic response mechanism gives users the initiative to intervene midway, preventing the equipment from mechanically executing fixed programs and allowing it to flexibly adapt to changes in personal taste or immediate conditions. The combination of cooking adjustments and original cooking parameters ensures that the adjusted cooking parameters still have a basis for adjustment, enabling the cooking parameters to adaptively optimize and adjust according to the user's cooking adjustments. This achieves continuous, uninterrupted intelligent cooking decisions, significantly improving the cooking success rate, making the cooking equipment suitable for diverse home cooking scenarios, increasing user satisfaction, and meeting users' personalized health management needs.

[0064] In this embodiment, the system is implemented collaboratively by functional modules including input aggregation and normalization processing, management of the 3D mapping matrix of nutrients × cooking method × retention rate, mapping decision reasoning, post-cooking nutrient estimation, user health log management, and cloud synchronization communication. Among these functional modules, input aggregation and normalization processing, mapping decision reasoning, and post-cooking nutrient estimation run on an edge inference processor; the 3D mapping matrix is ​​stored in non-volatile memory; and user health records and health logs are stored in local persistent storage. The functional modules communicate with each other via an internal data bus and incrementally synchronize with the cloud via a wireless network using the MQTT protocol.

[0065] This embodiment provides a cooking equipment control method, which can be used in the controller of the aforementioned cooking equipment. Figure 3 This is a schematic diagram of a second process for controlling a cooking device according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps: Step S301: Obtain information on the ingredients to be cooked and the healthy dining needs of the diners.

[0066] Specifically, step S301 includes: Step S3011: Obtain a pre-constructed ingredient information matrix; wherein, the ingredient information matrix includes multiple types of ingredients, the cooking mode of each type of ingredient, and the nutrient retention rate of each ingredient.

[0067] Specifically, the multiple food categories include approximately 200 common food categories, organized as a category tree, with leaf nodes representing specific food items and parent nodes representing general category values; when there is no actual measured data for a specific food item, the general category value is inherited upwards.

[0068] The cooking modes for each type of ingredient include approximately 10 modes, including: pure steaming at 100 degrees Celsius, low-temperature steaming at 60 to 80 degrees Celsius, tender roasting at 160 degrees Celsius, medium-temperature roasting at 200 degrees Celsius, high-temperature roasting at 230 degrees Celsius, segmented temperature control A (high temperature followed by low temperature), segmented temperature control B (low temperature followed by high temperature), segmented temperature control C (alternating steaming and roasting), first microwave-assisted combination, and second microwave-assisted combination.

[0069] The nutrient retention rate of each food ingredient includes approximately 15 indicators, including: protein retention rate, fat retention rate, change in the ratio of saturated fatty acids to unsaturated fatty acids, vitamin C retention rate, vitamin B1 retention rate, vitamin B2 retention rate, vitamin A retention rate, vitamin E retention rate, dietary fiber retention rate, mineral retention rate, change in glycemic index (GI), water loss rate, weight loss rate, Maillard reaction product grade, and total antioxidant capacity retention rate.

[0070] Each cell stores the corresponding retention rate value (a floating-point number from 0.0 to 1.0), confidence score (0 to 5 stars, indicating the reliability of the data), and applicable freshness range (e.g., F≥3).

[0071] The matrix is ​​pre-loaded with basic data (from laboratory nutritional testing) at the factory, and subsequently receives regular updates (from user cooking feedback data and new laboratory test data) through cloud synchronization. When the freshness F of the ingredients output by the input aggregation stage is less than 3, the retention rate value is automatically multiplied by the freshness decay coefficient (e.g., multiplied by 0.85 when F=2, multiplied by 0.70 when F=1) to compensate for the decrease in the nutritional baseline value of stale ingredients.

[0072] Step S3012: Based on the ingredients to be cooked, the ingredient information matrix is ​​searched to determine the types of ingredients, cooking modes, and nutrient retention rates that match the ingredients to be cooked, thereby obtaining the ingredient information of the ingredients to be cooked.

[0073] For example, the cooking device uses the ingredients to be cooked as the retrieval basis to perform a matching query in a pre-built ingredient information matrix, finds the corresponding ingredient category, suitable cooking mode, and the expected retention ratio of various nutrients under the mode, and thus comprehensively forms complete ingredient information for subsequent parameter setting.

[0074] The cooking equipment control method provided in this embodiment acquires a pre-constructed ingredient information matrix. This matrix covers multiple ingredient categories, the corresponding cooking modes for each ingredient, and the nutrient retention rate of each ingredient during cooking. Then, using the ingredient to be cooked as the query key, a search and matching operation is performed within the matrix to ultimately determine the ingredient type, the appropriate cooking mode, and the expected nutrient retention rate, thus obtaining complete information about the ingredient. The pre-constructed matrix enables standardized and digital management of ingredient attributes, allowing the equipment to quickly obtain accurate parameters without relying on external networks or manual input, significantly improving response speed and reliability. Simultaneously, the matrix-based data structure supports the coverage of a large number of ingredient types, possessing good scalability and adaptability to meet the needs of different regions and dietary habits. The correspondence between ingredient types and cooking modes ensures that the heating method matches the ingredient characteristics, while the nutrient retention rate, as a quantitative indicator, can be directly used by subsequent algorithms. This achieves end-to-end automation from ingredient identification to parameter generation, ensuring the predictability and consistency of nutritional balance in each meal, thereby comprehensively enhancing the practical value of intelligent cooking equipment and the user's healthy eating experience.

[0075] Step S302: Based on the ingredient information, determine the original cooking parameters of the ingredients to be cooked, and cook the ingredients based on the original cooking parameters.

[0076] The ingredient information includes the types of ingredients and the quantity of each type of ingredient used in cooking.

[0077] Specifically, step S302 includes: Step S3021: When it is determined that the number of cooking ingredients is greater than or equal to a preset quantity threshold and the preset cooking modes of each ingredient are inconsistent, a benchmark cooking mode is selected from the preset cooking modes of each ingredient. Based on the degree of nutrient loss of each ingredient under the benchmark cooking mode and the preset cooking mode of each ingredient, the original cooking parameters are obtained.

[0078] The preset quantity threshold can be between 2 and 4; for example, the preset quantity threshold is 2. After the food ingredients are identified, the number of cooking food types, n, is obtained. If n ≥ 2, and the preset cooking modes for each food ingredient are inconsistent, the following steps are performed to determine the original cooking parameters.

[0079] Obtain the list of ingredients to be cooked in this batch, F = {f1, f2, ..., fn}, where f represents the ingredient to be cooked, and the subscript indicates the ingredient number. Each ingredient includes: ingredient type, net weight W (grams), preset cooking mode M_opt (including temperature T_opt℃, duration t_opt minutes, and heating method mode), and the expected retention rate vector R_opt for each nutrient under the preset cooking mode (the proportion of each nutrient remaining after optimal cooking). The preset cooking mode is selected from the preset nutrient × cooking method × retention rate matrix, choosing the one with the highest overall score.

[0080] The preset nutrients refer to the various nutrient types covered in the matrix (i.e., at least 15 nutrients such as protein, fat, carbohydrates, dietary fiber, vitamin C, vitamin B1, vitamin B2, calcium, iron, and zinc). These are the row labels (nutrient dimension) of the matrix, forming a complete matrix together with the nutrient retention rate values ​​of each ingredient under this cooking method. It does not refer to the nutrient values ​​of this batch of ingredients, nor to the intake requirements set by the user, but rather to the various nutrient types covered in the dataset. The cooking methods include at least 10 modes (including pure steaming at 100℃, low-temperature steaming at 60-80℃, tender roasting at 160℃, medium-temperature roasting at 200℃, high-temperature roasting at 230℃, segmented temperature control A (high then low), segmented temperature control B (low then high), segmented temperature control C (alternating steaming and roasting), microwave-assisted combination 1, and microwave-assisted combination 2), which are the column labels (cooking method dimension) of the matrix. The preset nutrient type list, cooking method list, and retention rate matrix data are all pre-stored data, existing in the system storage before cooking begins, and are directly retrieved from the table during cooking.

[0081] The comprehensive score refers to the weighted comprehensive score calculated for the nutrient retention rate of each cooking method (column): For cooking method k, the comprehensive score Score_k = Σ_j(w_j × R_kj), where w_j is the weight coefficient of the j-th nutrient (graded according to heat sensitivity: heat-sensitive nutrients such as vitamin C and vitamin B1 have a weight of 3; macronutrients such as protein and fat have a weight of 1; and heat-stable nutrients such as minerals have a weight of 0.5), and R_kj is the retention rate value of nutrient j under cooking method k. After calculating Score_k for each of the 10 cooking methods, the one with the largest Score_k is taken as the preset cooking mode for that ingredient, i.e., the optimal cooking mode.

[0082] In some optional implementations, step S3021 above includes: Step a01: Determine the main ingredient as the food with the largest weight among all the ingredients to be cooked, and determine the other ingredients as auxiliary ingredients.

[0083] In this process, all ingredients to be cooked are sorted in descending order of weight W, and the ingredient with the largest weight W is selected as the main ingredient f_main. Here, weight refers to the net weight of the ingredient to be cooked. The rest are secondary ingredients, and a secondary ingredient set F_sub is created.

[0084] Step a02: Determine the preset cooking mode corresponding to the main ingredient as the baseline cooking mode, calculate the nutrient loss rate of each auxiliary ingredient under the baseline cooking mode, and determine the basic nutrient loss rate of each auxiliary ingredient.

[0085] It should be noted that the preset cooking mode M_main(T_main, t_main, mode_main) of the main ingredient f_main is used as the baseline cooking mode. Here, T_main represents the optimal cooking temperature (°C) of the main ingredient; t_main represents the optimal cooking time (minutes) of the main ingredient; and mode_main represents the optimal heating method of the main ingredient (an enumerated value, such as the heating method corresponding to 160°C for tender roasting is the "tender roasting" mode).

[0086] Specifically, for each supplementary ingredient f_k in the supplementary ingredient set F_sub, the following evaluation is performed: find the expected nutrient retention rate R_main_k of f_k under the baseline cooking mode from the retention rate matrix.

[0087] The expected retention rate of each nutrient is obtained by looking up a table. Specifically, it is obtained by using the "nutrient × cooking method × retention rate" matrix in the pre-stored matrix, with "auxiliary ingredient f_k" as the row search condition and "cooking method of the baseline cooking mode" as the column search condition, and reading the retention rate value of the corresponding cell. This value is the expected retention rate vector R_main_k of each nutrient of auxiliary ingredient f_k in the baseline cooking mode.

[0088] Determine the nutrient loss rate L_k between the expected retention rate R_opt_k of the supplementary food f_k in its optimal mode and the expected retention rate R_main_k of each nutrient. L_k is the weighted average of the deviations in the retention rates of each nutrient, and the weights are sorted according to the heat sensitivity of the nutrients (heat-sensitive nutrients such as vitamin C and vitamin B1 have a weight of 3; macronutrients such as protein and fat have a weight of 1; and heat-stable nutrients such as minerals have a weight of 0.5).

[0089] The formula for calculating the nutrient loss rate is as follows: L_k=Σw_j×max(0,R_opt_kj-R_main_kj) / Σw_j; Where R_opt_kj represents the retention rate of nutrient j of auxiliary ingredient f_k in its optimal mode (looked up from the matrix); R_main_kj represents the retention rate of nutrient j of auxiliary ingredient f_k in the baseline cooking mode (looked up from the matrix); max(0,R_opt_kj-R_main_kj) means that nutrient loss is only counted when the retention rate in the baseline cooking mode is lower than that in its optimal mode (i.e., nutrient loss has occurred). If the retention rate in the baseline cooking mode is higher, the nutrient is not counted; w_j represents the weight of nutrient j (graded according to heat sensitivity, vitamin C / B1, etc. = 3, protein / fat, etc. = 1, minerals, etc. = 0.5); Σw_j represents the sum of all weights (normalized denominator). The result of L_k represents the weighted average loss of each nutrient of auxiliary ingredient f_k in the baseline cooking mode relative to its optimal mode. L_k=0 means no loss, and the larger the value of L_k, the more severe the loss. The weights of w_j are set according to the heat sensitivity of nutrients: micronutrients with high heat sensitivity (such as vitamin C and vitamin B1) have a weight of 3, indicating that their nutrient loss has the greatest impact on the overall score; macronutrients (such as protein, fat, and carbohydrates) have a weight of 1; and heat-stable nutrients (such as calcium and iron) have a weight of 0.5, indicating that their differences under different cooking methods are small and their impact is limited.

[0090] Step a03: Calculate the relative nutrient loss rate of each supplementary ingredient based on the nutrient loss rate and the basic nutrient loss rate.

[0091] Specifically, calculate the relative loss rate L = (benchmark retention rate) Mmain retention rate / baseline retention rate.

[0092] The baseline retention rate is obtained by querying a pre-constructed ingredient information matrix; the retention rate under Main represents the nutrient retention rate of each auxiliary ingredient under the preset cooking mode of the main ingredient.

[0093] Step a04: When the relative nutrient loss rate of any auxiliary ingredient is determined to be greater than or equal to the preset loss rate threshold, a temperature control curve is generated based on the preset cooking modes of the main ingredient and all auxiliary ingredients. When the expected nutrient retention rates of the main ingredient and auxiliary ingredients are determined to be no less than the basic nutrient retention rates of their respective preset cooking modes, the original cooking parameters are obtained based on the temperature control curve. Step a041: Calculate the first temperature by weighted averaging the standard temperatures and weights of all auxiliary ingredients, and determine the first time based on the maximum standard cooking time among the standard cooking times of each auxiliary ingredient. Based on the first temperature and the first time, obtain the first temperature control curve.

[0094] Specifically, the generation method of time-division parameters includes: auxiliary ingredient priority stage: the temperature T1 is the weighted average by weight of the optimal temperatures of all auxiliary ingredients, T1=Σ(f_k∈F_sub)(T_opt_k×W_k) / ΣW_k; the duration t1 is 70% of the maximum value of the optimal durations of all auxiliary ingredients; the heating mode mode1 is selected from the optimal heating modes with the highest occurrence frequency among the auxiliary ingredients. For example, if auxiliary ingredient A is 200g of broccoli with an optimal temperature of 100°C, and auxiliary ingredient B is 150g of egg liquid with an optimal temperature of 85°C, then T1=(100×200+85×150) / (200+150)=93.6°C. Weighting is performed based on actual weight, with heavier ingredients prioritized. This is physically reasonable because the greater the weight of an ingredient, the larger its heat capacity, and the greater its impact on the overall thermal state of the cooking cavity.

[0095] Taking 70% of the maximum value of the auxiliary ingredients' own optimal duration is because the first stage operates near the optimal temperature for auxiliary ingredients, and the heat transfer efficiency is higher than the temperature condition of the reference cooking mode, so the actual heating rate of auxiliary ingredients is faster than that under their optimal conditions. Full execution according to their optimal duration will cause overcooking of auxiliary ingredients; 70% is an empirical reduction coefficient, and the same 0.70 coefficient is also used in the subsequent single-stage fine-tuning scheme to maintain consistency; this coefficient is not absolutely fixed and can be adjusted according to ingredient categories in system settings. The selection of 70% is intended to ensure that auxiliary ingredients obtain a sufficient nutrient retention window in the first stage without excessively prolonging the total cooking time.

[0096] Step a042, changing the first temperature to the standard temperature of the main ingredient at a preset temperature change rate to obtain a second temperature control curve.

[0097] Transitional temperature change stage: linearly changing the temperature from T1 to T_main, with the temperature change rate not exceeding 30°C / min, and the duration t_trans=|T_main-T1| / 30. The temperature change direction between the linear temperature change from T1 to T_main is related to the combination of various ingredients in the food to be cooked. For example, if the main ingredient is steak (T_main=200°C, medium-temperature roasting) and the auxiliary ingredient is vegetables (T1=95°C), then T1<T_main, and the transition stage is temperature rising; for another example, if the main ingredient is salmon (T_main=160°C, tender roasting) and the auxiliary ingredient is potato cubes (T1=220°C, high-temperature roasting), then T1>T_main, and the transition stage is temperature decreasing. Therefore, the absolute value is used in the temperature change rate formula for the transition stage: t_trans=|T_main-T1| / 30, which is applicable to both temperature rising and temperature decreasing.

[0098] t_trans represents the duration (in minutes) of the transition temperature range, that is, the time required for the temperature to change from the initial temperature T1 at a constant (linear) rate not exceeding 30℃ / min to the reference cooking mode temperature T_main. For example, if T1=100℃, T_main=190℃, and the temperature difference=90℃, then t_trans=90 / 30=3 minutes.

[0099] Step a043: Adjust the standard cooking time of the main ingredient to obtain the second time, and based on the standard temperature of the main ingredient and the second time, obtain the third temperature control curve.

[0100] Priority segment for main ingredients: temperature T_main, duration t2 is 90% to 100% of t_main, specifically determined according to the principle that the total duration does not exceed 1.2×t_main.

[0101] Step a044: Based on the first temperature control curve, the second temperature control curve, and the third temperature control curve, generate a temperature control curve to obtain the original cooking parameters.

[0102] The total duration of the temperature control curve is t_total = t1 + t_trans + t2.

[0103] Step a05: When the relative nutrient loss rate of the auxiliary ingredients is determined to be less than the preset loss rate threshold, adjust the cooking parameters corresponding to the benchmark cooking mode to obtain the original cooking parameters.

[0104] It should be noted that if the nutrient loss rate L_k of all auxiliary ingredients does not exceed the preset loss rate threshold T, where T can be between 0.1 and 1 (the default value for T is 0.40), it means that all auxiliary ingredients are acceptable under the baseline cooking mode. In this case, the heating time of the auxiliary ingredients is fine-tuned based on the baseline cooking mode. The heating time corresponding to each auxiliary ingredient is multiplied by a reduction coefficient α, which can be between 0.5 and 1 (the default value for T is 0.70) to reduce excessive nutrient loss from the auxiliary ingredients. The fine-tuned uniform temperature control parameters are then output to obtain the original cooking parameters.

[0105] The cooking equipment control method provided in this embodiment determines the main ingredient and auxiliary ingredients from each ingredient based on their weight. It uses the preset cooking mode of the main ingredient as the baseline cooking mode and then calculates the nutrient loss rate of each auxiliary ingredient under this baseline cooking mode. If the loss rate of an auxiliary ingredient reaches or exceeds a preset threshold, a temperature control curve is generated based on the preset cooking modes of the main ingredient and all auxiliary ingredients, serving as the original cooking parameters. If the loss rates of all auxiliary ingredients are below the threshold, only the cooking parameters under the baseline cooking mode are adjusted to obtain the original cooking parameters. The method prioritizes the quality of the main ingredient by using the ingredient with the largest weight as the core decision-making factor. Simultaneously, it quantifies the loss rate of auxiliary ingredients for tiered processing. Temperature control curves are actively generated for high-loss combinations of multiple ingredients, while parameters are fine-tuned for low-loss combinations. This effectively reduces excessive nutrient loss of auxiliary ingredients during mixed cooking while controlling algorithm complexity and cooking time increases.

[0106] Step S3022: When the number of cooking ingredients is greater than or equal to a preset quantity threshold and the preset cooking modes of each ingredient are consistent, or when the number of cooking ingredients is less than a preset quantity threshold, the original cooking parameters are obtained based on the preset cooking mode of any ingredient.

[0107] For example, if the number of cooking ingredients is greater than or equal to a preset threshold and the preset cooking modes of all ingredients are consistent, then the temperature control parameters are output using this unified mode; or if the number of cooking ingredients is n=1, then the standard single-ingredient cooking parameter generation method is executed. The temperature control curve output at this judgment point serves as the basis for subsequent cooking execution.

[0108] The cooking equipment control method provided in this embodiment determines the branch based on whether the number of ingredients and the preset cooking mode are consistent. When there are many ingredients and different cooking modes, a benchmark cooking mode is first selected, and then the original cooking parameters are derived by combining the degree of nutrient loss of each ingredient under the benchmark and its own preset cooking mode. When there are many ingredients but the modes are consistent, or when there are few ingredients, the preset mode of any one ingredient is directly used as the original cooking parameters. When the preset cooking modes of multiple ingredients conflict, the method effectively coordinates the nutrients between the ingredients and the overall cooking parameters, so as to take into account the differences in nutrient retention of different ingredients before cooking starts. This provides a more reasonable starting control benchmark for subsequent temperature-changing strategies and dynamic interventions, thereby improving the nutrient retention capacity in multi-ingredient cooking scenarios and the adaptability to users' healthy dining needs.

[0109] This embodiment also includes: evaluating whether the expected nutrient retention rate of the main and auxiliary ingredients under the time-segmentation strategy is not less than 80% of their respective optimal modes. If the condition is met, the time-segmentation temperature control curve is output; if not, feedback is given to the user and batch cooking is recommended.

[0110] For example, the expected nutrient retention rate under the time-segmented strategy is calculated and compared with the baseline nutrient retention rate of each ingredient to be cooked: Based on the actual heating segment parameters corresponding to each ingredient in the generated time-segmented temperature control curve (the first time segment parameter corresponds to the auxiliary ingredients, and the second time segment parameter corresponds to the main ingredients), the expected retention rate R_actual of each nutrient under the condition of that segment is obtained by looking up the table. For each nutrient j of each ingredient, R_actual_j / R_opt_j is calculated. If the ratio is ≥0.80, then it satisfies "not less than 80% of its respective optimal mode".

[0111] For example, salmon has a protein retention rate of 0.90% in its optimal cooking mode (tender grilling at 160℃ for 15 minutes). In the second time slot of the segmented cooking strategy (medium-temperature grilling at 200℃ for 10 minutes), the expected protein retention rate is 0.75%. Therefore, 0.75 / 0.90 = 83.3% > 80%, which meets the requirement. If this ratio for a certain nutrient (such as vitamin C) drops to 0.60 (i.e., 60% < 80%), the requirement is not met, and the system will provide feedback to the user and recommend cooking in batches.

[0112] In some optional implementations, the preset cooking mode is determined through the following steps: Step b01: Locate the type of each ingredient to be cooked from the pre-constructed ingredient information matrix, and determine the nutrient retention rate of each ingredient in each cooking mode based on the ingredient type.

[0113] For example, based on the ingredient category label, the data is located in the ingredient dimension of a pre-constructed ingredient information matrix, and the row vector of nutrient retention rates for all 10 cooking methods for that ingredient is obtained. Here, the ingredient category label is the name string of the specific ingredient (e.g., "salmon," "broccoli," "chicken breast," "rice"). The data is organized as an ingredient category tree, with leaf nodes representing specific ingredients and parent nodes representing general category values. Specifically: leaf nodes (specific ingredients): approximately 200 types such as salmon, chicken breast, broccoli, and potatoes, each with an independent ID and measured retention rate data; parent nodes (ingredient categories): fish, poultry, leafy vegetables, root vegetables, etc., serving as general default values ​​inherited upwards when no measured data is available. Therefore, the "ingredient category label" matches the leaf node entry of the specific ingredient during location, and only inherits the general retention rate value of the parent node (category) when no measured data is available for that specific ingredient.

[0114] Step b02: For each ingredient to be cooked, calculate the comprehensive retention rate score of the ingredient to be cooked in each cooking mode based on the nutrient retention rate, and sort them from largest to smallest according to the comprehensive retention rate score to determine the candidate cooking mode with the comprehensive retention rate score in the top preset position.

[0115] The calculation of the retention rate of each cooking method is based on a weighted average of the retention rates of various nutrients. The weights are determined by the user's health goal G. For example, for the goal of fat loss, the weight of fat retention rate is negative, meaning the lower the better, while the weight of protein retention rate is positive. For the goal of blood sugar control, the weight of GI change is negative, with the absolute value being the largest. For the goal of muscle gain, the weight of protein retention rate is the highest. For the goal of balance, all weights are equal.

[0116] Step b03: For each ingredient to be cooked, determine the user-defined desired cooking parameters, and filter the candidate cooking modes based on the desired cooking parameters to determine the preset cooking mode for the ingredient to be cooked.

[0117] The desired cooking parameters include, but are not limited to, desired completion time and taste preferences.

[0118] For example, the top 3 cooking methods with the highest overall scores are selected as candidate cooking modes.

[0119] The cooking equipment control method provided in this embodiment locates the type of each ingredient to be cooked from a pre-constructed ingredient information matrix, determines the nutrient retention rate of each ingredient in each available cooking mode based on the type, calculates a comprehensive retention rate score for each ingredient in each mode based on these retention rates, sorts the scores from high to low, and selects several candidate cooking modes with the highest rankings. Finally, it obtains the user's pre-set desired cooking parameters and uses these parameters as conditions to further filter the candidate cooking modes, thereby finally determining the preset cooking mode for the ingredient. By sorting the comprehensive retention rate scores in advance to select a set of candidate modes with excellent nutritional performance, the computational complexity of subsequent matching is greatly reduced, and the system response efficiency is improved. At the same time, the introduction of user-desired parameters allows the final mode to fit individual cooking habits, thereby improving user satisfaction.

[0120] Step S303: In response to the user's cooking adjustment operation during the cooking process of the cooking equipment, when it is determined that the cooking adjustment operation is a food addition operation or a healthy dining requirement switching operation, the target cooking parameters are determined based on the original cooking parameters and the operation content of the food addition operation or the healthy dining requirement switching operation.

[0121] Specifically, step S303 includes: Step S3031: When the cooking adjustment operation is determined to be an ingredient addition operation, the new ingredient is determined, and the target cooking parameters of the cooking equipment when resuming cooking are determined based on the original cooking parameters of the new ingredient and the ingredient to be cooked.

[0122] Specifically, the addition of new ingredients is determined by detecting the action event of the cavity door opening and then closing again.

[0123] In some optional implementations, step S3031 above includes: Step a1: Obtain the real-time weight of the ingredients to be cooked during the cooking process.

[0124] It should be noted that the real-time weight can be obtained through a weight detection sensor, which can acquire the real-time weight during the cooking process at certain time intervals.

[0125] Step a2: In response to the user's cooking adjustment operation on the cooking equipment, determine the target weight of the food to be cooked after the cooking adjustment operation.

[0126] For example, after a user adds an ingredient, the target weight is determined based on the real-time weight data detected after the ingredient is added.

[0127] Step a3: Calculate the weight increment based on the real-time weight and the target weight.

[0128] Specifically, the weight increment is obtained based on the difference between the target weight and the real-time weight.

[0129] Step a4: When it is determined that the weight increment is greater than or equal to the preset weight threshold, the cooking adjustment operation is determined to be an ingredient addition operation.

[0130] If the weighing detection shows that the weight increase is greater than a preset weight threshold, the cooking adjustment operation is determined to be an addition of ingredients. For example, the preset weight threshold can be between 10g and 20g; in this embodiment, 10g is used as an example.

[0131] Step a5: Identify ingredients in the cooking equipment to obtain the newly added ingredients.

[0132] Specifically, the cooking equipment automatically detects the ingredients placed in the container using a built-in recognition module, thereby obtaining a complete list of ingredients and identifying any new ingredients that are not previously recorded. For these new ingredients, the equipment no longer relies on the initial recognition results but initiates a secondary, refined detection process. This includes re-performing image recognition to capture the ingredient's shape, color distribution, and surface texture features, while simultaneously performing a spectral scan to obtain optical response data such as its internal molecular structure and moisture content. This allows for a multi-dimensional and accurate characterization of the new ingredients, providing an accurate basis for subsequent cooking parameter optimization and nutritional calculations.

[0133] The cooking equipment control method provided in this embodiment determines the target weight of the ingredients after the user performs a cooking adjustment operation. The weight increment is obtained by calculating the difference between the target weight and the real-time weight. If the increment reaches or exceeds a preset weight threshold, the equipment recognizes this operation as an ingredient addition operation and automatically activates the ingredient recognition function of the cooking equipment to ultimately determine the specific type of the added ingredient. This significantly improves the recognition accuracy and system robustness. On this basis, the automatically triggered ingredient recognition ensures that the information of the added ingredient can enter the cooking decision chain in a timely and accurate manner, providing a reliable data foundation for subsequent parameter reconstruction. This allows the equipment to proactively adapt to temporary ingredient addition scenarios, avoiding the risk of missing or overcooking ingredients, improving the smoothness of the cooking process and the consistency of the final product's taste, and optimizing the user experience and cooking success rate.

[0134] Step a6: Based on the original cooking parameters and the current cooking time, determine the remaining amount of heat required for the ingredients to be cooked.

[0135] In the process of inputting and aggregating new ingredient data, the remaining heating amount required for the existing ingredients under the current cooking time is first calculated. That is, the remaining amount is estimated based on the proportion of the completed portion in the original temperature control curve to the total heating amount.

[0136] Step a7: Determine the target cooking parameters based on the remaining required heating amount and the added ingredients.

[0137] Based on the remaining required heating capacity and the added ingredients, the cooking equipment uses both as new input conditions to trigger a complete decision-making logic calculation, instead of using the original remaining cooking plan. It comprehensively considers the existing heat reserve in the cookware, the initial temperature, mass, heat capacity, and nutritional characteristics of the added ingredients, and plans the entire heating strategy from the current moment to the predetermined end time, generating a new temperature control curve covering the remaining period. This curve refines the target temperature, heating rate, and power distribution at each time point, serving as the final target cooking parameters.

[0138] The cooking equipment control method provided in this embodiment calculates the remaining heating amount required by the original ingredients in subsequent stages based on the original cooking parameters and the current cooking time already executed. Using this remaining heating amount as a benchmark, and combined with the characteristics of the newly added ingredients, the target cooking parameters to be used by the equipment when resuming cooking are calculated. This method accurately quantifies the unfinished heat requirements of the original ingredients, thereby avoiding overcooking or excessive nutrient loss caused by reheating due to adding ingredients midway. At the same time, the introduction of new ingredients is not handled independently, but is integrated with the remaining heating amount, so that both types of ingredients can reach their optimal state of maturity within the same remaining time period. This ensures that the original ingredients do not age while waiting, and that the newly added ingredients are fully heated and safe to eat. This method significantly improves the cooking success rate, the consistency of food taste, and the level of nutrient retention, making the entire cooking process both flexible and precise. It is widely applicable to the actual needs of cooking multiple ingredients in stages in daily family life.

[0139] Step S3032: When the cooking adjustment operation is determined to be a healthy dining requirement switching operation, the target healthy dining requirement after the switch is determined, and the current cooking progress of the ingredients to be cooked is determined based on the original cooking parameters. Based on the current cooking progress and the target healthy dining requirement, the target cooking parameters are determined.

[0140] Among them, the system determines the switching operation of healthy eating needs when the user modifies the health goal G through the interactive interface during cooking, such as switching from fat loss to muscle gain.

[0141] In some optional implementations, step S3032 above includes: Step b1: If the cooking progress has not exceeded the preset cooking progress, determine the current temperature of the ingredients.

[0142] For example, the current food center temperature Tc is first read by temperature detection, and the cooking progress is judged by combining the cooking time t1 and the original temperature control curve to determine the current food temperature, i.e., t1 / original total time T0.

[0143] For example, the preset cooking progress can be 55% to 70%. In this embodiment, 60% is used as an example. If the progress exceeds 60%, it indicates that the current ingredients are basically cooked and switching is not very meaningful, but it can still be switched. If the progress does not exceed 60%, the mapping decision is rerun with Tc as the new initial temperature and the weight vector corresponding to the new health target, generating a temperature control curve that only contains the remaining time period, covering the remaining part of the original curve.

[0144] Step b2: Based on the current food temperature and the target healthy dining requirements, generate the target cooking parameters.

[0145] For example, if you switch from "balanced" to "fat reduction": the new target's fat retention rate weight is negative (the lower the better), and the algorithm may tend to choose a mode with higher temperature and shorter time (such as high-temperature baking at 230℃ instead of tender baking at 160℃) to accelerate the removal of fat - at this time the temperature increases and the time may be shortened; If you switch from "fat loss" to "muscle gain": the retention rate of the new target protein has the highest weight, and the algorithm tends to use a low-temperature, long-time mode to protect the protein structure. At this time, the temperature is reduced and the time may be extended. If you switch from "fat loss" to "balanced": all weights are equal, the algorithm tends to compromise, and both temperature and time are adjusted to the middle value.

[0146] The determination method involves using the current food core temperature Tc as the new initial condition and the weight vector corresponding to the new health target Gnew to recalculate the top-ranked candidate pattern and its corresponding temperature control curve for the remaining time period. The system does not manually determine "how much the temperature should increase or decrease," but rather automatically solves the problem through an algorithm, outputting the optimal solution under the new constraints.

[0147] The time only applies to the remaining time period: the temperature control curve output by the algorithm only covers the remaining time period, that is, the total time minus the completed part, and the cooking time that has been executed is not affected.

[0148] The cooking equipment control method provided in this embodiment determines whether the current cooking progress has not exceeded a preset progress threshold. After confirming that the condition is met, it measures the actual temperature of the current ingredients and then combines the measured temperature with the target healthy eating requirements after the switch to generate new target cooking parameters. By limiting the progress threshold, it avoids making significant parameter adjustments when the ingredients are close to being cooked or overcooked, which could lead to deterioration in taste or loss of nutrients. This ensures that the switch to health requirements is only performed within a time window that can be effectively intervened. At the same time, by introducing the current temperature of the ingredients instead of relying on theoretically calculated time, it can truly reflect the internal heating state of the ingredients and make up for the deviation caused by simply estimating the progress based on the time. The dual input of measured temperature and health requirements allows the adjusted cooking strategy to not only meet the new nutritional goals and maintain the simplicity of operation, but also greatly improve the adaptability of the equipment to different healthy eating patterns. This significantly enhances the practical effectiveness of intelligent cooking in personalized health management and improves user satisfaction.

[0149] The cooking equipment control method provided in this embodiment, when the cooking adjustment operation is to add ingredients, the equipment first identifies the specific added ingredients, and uses the original cooking parameters of the added ingredients and the original ingredients to be cooked as inputs to recalculate the target cooking parameters that the equipment should use when resuming the cooking process. When the cooking adjustment operation is to switch to a healthy eating requirement, the equipment first determines the new healthy eating requirement target after the switch, then evaluates the current cooking progress based on the original cooking parameters, and then combines the current progress with the new health requirement to deduce the subsequent target cooking parameters. It can accurately distinguish different operation intentions and take targeted parameter adjustment strategies accordingly. This avoids the imbalance of maturity or insufficient heating caused by simply using the original parameters when adding ingredients, and also prevents the waste of time and damage to taste caused by ignoring the current progress and being forced to recalculate the whole when switching health requirements. At the same time, by introducing the current cooking progress as a constraint, the change of health requirements is not a rigid application of the initial plan, but rather adapted to the heating state that the ingredients have already undergone, thereby achieving a dynamic balance between nutritional goals and the actual properties of food, significantly improving the adaptability of the equipment and enhancing the intelligent cooking experience.

[0150] Step S304: Control the cooking equipment to perform cooking based on the target cooking parameters. For details, please refer to [link / reference]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0151] In step S305, in response to the completion of the cooking process of the food to be cooked, based on the food information, the nutrient intake of the diner after the food to be cooked is determined, and cooking management is carried out based on the nutrient intake.

[0152] Specifically, step S305 includes: Step c1: Obtain the nutrient retention rate of the ingredients to be cooked after cooking, and determine the initial nutrient content and nutrient usage of each ingredient.

[0153] For example, after cooking, the total output vector N_total of all nutrients in the meal is calculated. The j-th component of N_total, N_j = Σ(initial nutrient content of ingredient k_kg,j × amount of ingredient k used × retention rate of nutrient j after cooking_kj), where the retention rate_kj is obtained from a table in the fresh food cooking retention rate dataset (organized by cooking method × nutrient dimension) based on the actual heat mode executed in this cooking.

[0154] Step c2: Based on the initial nutrient content, nutrient usage, and nutrient retention rate, obtain the individual nutrient intake for each diner.

[0155] Based on the recommended intake weighting, the total output of each nutrient is allocated to each member. The amount of nutrient j allocated to member m_i is: N_i_j = N_j × (RDI_i_j / Σ_{m∈M}RDI_m_j), which is the proportion of member i's recommended intake of this nutrient in the total recommended intake of all participating members.

[0156] Wherein, N_i_j: the amount of nutrient j allocated to member m_i, i.e., the allocation result value; N_j: the total output of nutrient j in this meal (calculated by step S103); RDI_i_j: the daily recommended intake of nutrient j for member m_i (from the member's nutrition profile); Σ_{m∈M}RDI_m_j: the sum of the recommended intakes of nutrient j for all members participating in this meal; M: the set of members participating in this meal; i: member index; j: nutrient type index.

[0157] Step c3: Add the individual nutrient intake of each diner to the current cumulative nutrient intake to obtain the nutrient intake of each diner after the food to be cooked is finished.

[0158] The split results are added to the daily cumulative intake of each member: Intake_i_j (updated cumulative intake) = Intake_i_j (current cumulative intake) + N_i_j (the intake of nutrient j of the i-th member in this meal).

[0159] For each nutrient j in each member m_i, check if Intake_i_j > RDI_i_j × 1.10. Here, RDI_i_j represents the recommended daily intake of nutrient j for member i, which is the j-th component in the preset recommended daily intake array RDI for each nutrient for that member. The subscript i identifies the member, and the subscript j identifies the nutrient type. 1.10 represents a 10% exceedance of the recommended value as the warning threshold. The reason for choosing 10% instead of 100% (i.e., exactly equal to the recommended value) is that the estimation of nutrient content from actual cooking has a certain margin of error. If the threshold were set to 1.00, it might lead to a large number of invalid warnings for slight exceedances, affecting the user experience. The 10% buffer achieves a reasonable balance between "not missing serious exceedances" and "not issuing excessive warnings." For example, if the cumulative amount exceeds RDI × 1.10: it means that the member's cumulative daily intake of that nutrient has significantly exceeded the recommended intake (more than 10%), and the system determines it is necessary to notify the user and generate a warning message. If the cumulative amount does not exceed RDI×1.10, it indicates that it is still within the acceptable range (not exceeding the limit at all or slightly exceeding the limit within the tolerance range), and no warning will be triggered, allowing the user to dine normally. This coefficient is not a fixed value and can be adjusted by the user in the system settings.

[0160] The cooking equipment control method provided in this embodiment first obtains the nutrient retention rate of each ingredient after cooking, and determines its initial nutrient content and actual usage. Then, by calculating the weight ratio between the initial content, usage, and retention rate, the amount of each diner ingested for this meal is obtained. Finally, this amount is added to the cumulative intake for the day to obtain the latest cumulative intake for each diner after this meal. The actual nutrient retention after cooking, the amount of ingredients used, and the individual allocation weight are all included in the calculation to ensure that nutrient attribution is quantifiable and cumulative, providing an accurate data basis for subsequent overdose warnings and health goal management.

[0161] The cooking equipment control method provided in this embodiment calculates the actual nutrient content ingested by the diner based on previously acquired ingredient information after the cooking process of the food to be cooked has ended. This quantitative result is then used as input to drive subsequent cooking management actions. This method can obtain accurate nutrient intake based on the actual cooked food state, which significantly improves accuracy and reference value compared to methods that only estimate based on the initial raw weight. At the same time, the equipment actively intervenes in subsequent management based on this intake, enhancing the practicality of the equipment in daily diet management and achieving the effect of cooking processes that meet the user's healthy dining needs.

[0162] Step c4: Obtain the target nutrient intake for each diner.

[0163] It should be noted that the target nutrient intake for each diner is determined based on the user's healthy eating needs.

[0164] Step c5: For each diner, when it is determined that the diner's nutrient intake exceeds the corresponding target nutrient intake, issue a nutrient excess warning.

[0165] For example, an alert message for exceeding the recommended intake of a particular member and nutrient is generated, in the following format: Dining member, healthy eating requirement is fat control, today's intake / g, recommended intake / g, excess / % . The alert message is a non-mandatory reminder and does not affect the user's ability to continue eating. The daily allocation results and alert records are written to the user's nutrition log for long-term trend analysis and member health management.

[0166] The cooking equipment control method provided in this embodiment obtains the single target nutrient intake of each diner, and then compares each person's actual nutrient intake with the corresponding target value. If it exceeds the target value, a nutrient excess warning is issued. This enables real-time nutritional intake compliance checks after the meal, and the warnings help users understand the nutritional risks of their members in a timely manner, providing a basis for adjusting subsequent meal plans and forming a closed-loop feedback from accounting to management.

[0167] In some optional implementations, the above method further includes: Step d1: After the cooking of the ingredients is completed, obtain the remaining weight after cooking.

[0168] For example, after confirming that the entire cooking process has been completed, the cooking device will activate the weighing module to perform a final weight measurement of the food in the pot and read the total weight of the ingredients after cooking, including the weight of the broth.

[0169] Step d2: Calculate the current weight loss rate of the ingredients to be cooked based on their initial weight and remaining weight before cooking begins.

[0170] Calculate the weightlessness rate RW = (Wstart) Wend) / Wstart; Where Wend represents the remaining weight after cooking; Wstart represents the net weight of the ingredients before cooking.

[0171] Step d3: Based on the relationship between the current weight loss rate of the food to be cooked and the baseline weight loss rate, determine the weight loss rate deviation, and when the weight loss rate deviation exceeds the preset deviation threshold, correct the baseline weight loss rate based on the current weight loss rate.

[0172] Specifically, based on the weight loss rate benchmark value Rref of the selected cooking mode (from the weight loss rate cell of the ingredient under this cooking method in the matrix), RW is compared with Rref. If the deviation exceeds ±15%, the actual RW is used to replace Rref to improve the estimation accuracy.

[0173] It should be noted that the main reasons for the weight loss of ingredients during cooking are moisture evaporation and oil loss. The actual weight loss rate RW directly reflects the actual degree of heating in this cooking process. Due to individual differences in the initial moisture content, fat distribution, and actual thickness of the ingredients used by each user, the pre-stored baseline value of weight loss rate Rref in the matrix is ​​an average value under laboratory standard conditions and may deviate from the actual weight loss in this cooking process.

[0174] Correcting the baseline weight loss rate involves comparing the actual measured RW with the Rref in the matrix. If the deviation exceeds ±15%, it indicates a significant difference between the actual heating level of this cooking and the standard mode (e.g., the fish purchased by the user was too thin and dehydrated more, or the freshness was too low, leading to faster water loss). In this case, the measured RW is used to replace Rref to recalibrate the subsequent estimation of nutrient intake, making the estimation result closer to the actual nutrient retention of this cooking. In short, the weight loss rate is the calibration bridge connecting "standard data" and "actual situation".

[0175] Step d4: Based on the corrected baseline weight loss rate, initial weight, initial nutrient values ​​of each ingredient to be cooked, and nutrient retention rate of each ingredient to be cooked, the actual nutrient intake of the diners is obtained.

[0176] For example, the intake of each nutrient is calculated as follows: Corrected baseline weight loss rate × Wstart × Original nutrient density of the food (from the food nutrient database) × Retention rate of the corresponding nutrient (from the three-dimensional mapping matrix). Each nutrient is calculated independently and then summarized into a single-meal nutrient intake list. This list is output to the user health log management system, written to member logs, and the daily cumulative value is updated. Incremental data is synchronized to the cloud via wireless network for long-term trend analysis. A daily nutrient intake report for each member is generated in the cloud every 24 hours and displayed on the user interface or mobile app.

[0177] The cooking equipment control method provided in this embodiment obtains the remaining weight after cooking, and calculates the current weight loss rate by combining it with the initial weight before cooking. The current weight loss rate is compared with a preset benchmark weight loss rate to obtain the weight loss rate deviation. When the deviation exceeds a preset threshold, the benchmark loss rate is corrected using the current weight loss rate. Based on the corrected benchmark loss rate, initial weight, initial nutrient value, and nutrient retention rate, the actual nutrient intake of the diner is calculated. By dynamically correcting the theoretical loss parameters through real-time weighing, the method effectively compensates for deviations in water evaporation caused by unpredictable factors such as individual differences in ingredients, environmental humidity, or fluctuations in heat. This makes the estimated intake result closer to the actual consumption state, significantly improving the accuracy and reliability of nutrient monitoring. At the same time, the setting of the deviation threshold avoids meaningless corrections triggered by minor fluctuations, ensuring the stability and computational efficiency of the system. This allows the intelligent cooking equipment to continuously optimize its model based on the real feedback from each cooking session, thereby gradually approaching the goal of personalized precision nutrition in long-term use, enhancing the practical value and user satisfaction of the intelligent kitchen equipment.

[0178] Combination Figures 4 to 9 This describes an optional application embodiment of a cooking equipment control method.

[0179] Figure 4This is the overall functional architecture diagram of the cooking equipment control method, showing the logical relationships and data flow between various functional modules. Specifically, the multimodal sensing data input stage receives food sensing data (food type, weight, freshness, and three-dimensional morphology); the user health record storage stage maintains the health record information of each family member; the input aggregation and normalization processing stage unifies and normalizes food data, user record data, and real-time constraints into structured data; the nutrient × cooking method × retention rate three-dimensional mapping matrix storage stage serves as the core data foundation for mapping decisions; the mapping decision reasoning stage runs on an edge inference processor, matching and calculating the optimal temperature control curve in the mapping matrix based on the normalized input data; the optimal temperature control curve output stage sends the curve parameters to the cooking execution end; the post-cooking nutrient estimation stage calculates the actual nutrient intake based on the weight loss rate; the user health log writing stage writes the nutrient allocation results for this meal into each member's log and updates the cumulative value for the day; the cloud synchronization communication stage synchronizes incremental data to the cloud via a wireless network for long-term trend analysis; the user interaction stage is used for parameter confirmation and status display; and the cavity status detection signal is used to trigger dynamic re-decision during the process.

[0180] Figure 5 This is the first type of workflow diagram for cooking equipment, showing the complete process of the first step of input aggregation (ingredient data + user profile + constraint normalization), the second step of mapping decision (matrix matching, candidate pattern generation, health goal priority ranking, and optimal temperature control curve output), and the third step of execution and recording (parameter issuance and execution, weight loss rate calculation, nutrient intake estimation, and health log writing), as well as the data flow between each step.

[0181] This embodiment also provides a data structure diagram of a three-dimensional mapping matrix of nutrients × cooking method × retention rate. The first dimension is the type of ingredients (about 200 common ingredients, each assigned a unique ID), the second dimension is the cooking method (pure steaming, tender roasting at 160 degrees, medium roasting at 200 degrees, high roasting at 230 degrees, segmented temperature control mode A to E, etc., about 10 types), and the third dimension is the type of nutrients (protein retention rate, fat retention rate, vitamin C retention rate, B vitamins retention rate, dietary fiber retention rate, GI glycemic index change, etc., about 15 indicators). Each cell in the matrix stores the retention rate percentage (0 to 100%) of the corresponding nutrient under the corresponding cooking method, and also records the confidence level and applicable freshness range of the data.

[0182] Figure 6This is the second type of workflow diagram for cooking equipment, showing: inputting a list of multiple ingredients and the optimal cooking mode for each ingredient; determining whether the modes are consistent; if consistent, executing directly; if inconsistent, taking the main ingredient mode as the baseline; calculating the nutrient loss rate of auxiliary ingredients under the baseline mode; determining whether it exceeds the tolerance threshold (default 40%); if it exceeds, generating a time-segmented temperature variation strategy (the temperature in the first time segment is taken near the optimal temperature of the auxiliary ingredients, and the temperature in the second time segment is taken at the optimal temperature of the main ingredient); outputting the time-segmented temperature control curve; and after cooking, estimating the nutrient retention rate of each ingredient by looking up a table. This is the complete decision-making path.

[0183] Figure 7 This is a workflow diagram of cooking equipment in an application scenario, specifically a schematic diagram of a family multi-person nutrition accounting strategy. The diagram shows a family member profile table (each person includes ID, name, age, health goal label, and daily recommended intake (RDI) value for each nutrient), as well as the total nutrition accounting algorithm for this meal: after cooking, the total nutrient output of the whole meal is estimated first, and then it is divided according to the RDI weight ratio of each member. The division results are added to the daily intake accumulator of each member; the values ​​of each accumulator are compared with the RDI in real time, and an alert is triggered if the daily intake of any nutrient exceeds 110% of the RDI.

[0184] Figure 8 This is the first type of timing logic diagram for cooking equipment. Figure 8 The control logic for adding ingredients during the cooking process is shown, including: cooking in progress, cavity door opening signal trigger, weighing detection of weight increment, secondary identification of the added ingredients by the camera and spectral module, fusion of the remaining required heating amount of the existing ingredients with the new ingredients, rerunning the mapping decision to generate a new temperature control curve for the remaining time period, and continuing to execute the complete sequence of events covering the original curve.

[0185] Figure 9 This is the second type of timing logic diagram for cooking equipment. Figure 9 The timeline logic diagram of dynamic re-decision (switching health goals) during cooking is shown, including: the user switches health goals (such as from balanced to fat loss) through an interactive interface during cooking, obtains the current food core temperature Tc and the cooking time t1, calculates the percentage of cooking progress, allows switching if t1 is less than 60% of the original total time, and recalculates the temperature control curve for the remaining time period with Tc as the new initial temperature and new mode matrix parameters.

[0186] This embodiment also provides a cooking equipment control device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0187] This embodiment provides a cooking equipment control device, such as... Figure 10 As shown, it includes: The acquisition module 1001 is used to acquire information about the ingredients to be cooked and the healthy dining needs of the diners. The determination module 1002 is used to determine the original cooking parameters of the ingredients to be cooked based on the ingredient information, and to cook the ingredients based on the original cooking parameters. The adjustment module 1003 is used to respond to the cooking adjustment operation performed by the user during the cooking process of the cooking equipment. When it is determined that the cooking adjustment operation is a food addition operation or a health-conscious dining requirement switching operation, the target cooking parameters are determined based on the original cooking parameters and the operation content of the food addition operation or the health-conscious dining requirement switching operation. The control module 1004 is used to control the cooking equipment to cook based on the target cooking parameters.

[0188] In some optional implementations, the acquisition module 1001 includes: The acquisition unit is used to acquire a pre-constructed ingredient information matrix; wherein, the ingredient information matrix includes multiple types of ingredients, the cooking mode of each type of ingredient, and the nutrient retention rate of each ingredient. The determination unit is used to retrieve the ingredient information matrix based on the ingredients to be cooked, determine the types of ingredients, cooking modes, and nutrient retention rates that match the ingredients to be cooked, and obtain the ingredient information of the ingredients to be cooked.

[0189] In some alternative implementations, the determining module 1002 includes: The first cooking parameter determination unit is used to select a benchmark cooking mode from the preset cooking modes of each ingredient when the number of types of ingredients to be cooked is greater than or equal to a preset quantity threshold and the preset cooking modes of each ingredient to be cooked are inconsistent. Based on the degree of nutrient loss of each ingredient to be cooked under the benchmark cooking mode and the preset cooking mode of each ingredient to be cooked, the original cooking parameters are obtained.

[0190] In some optional implementations, the first cooking parameter determining unit includes: The main ingredient determination subunit is used to determine the main ingredient as the ingredient with the largest weight among all ingredients to be cooked, and to determine the other ingredients as auxiliary ingredients. The calculation subunit is used to determine the preset cooking mode corresponding to the main ingredient as the benchmark cooking mode, calculate the nutrient loss rate of each auxiliary ingredient under the benchmark cooking mode, and determine the basic nutrient loss rate of each auxiliary ingredient. The loss rate calculation subunit is used to calculate the relative nutrient loss rate of each auxiliary ingredient based on the nutrient loss rate and the basic nutrient loss rate. The temperature control subunit is used to generate a temperature control curve based on the preset cooking modes of the main ingredient and all auxiliary ingredients when the relative nutrient loss rate of any auxiliary ingredient is greater than or equal to the preset loss rate threshold. It also obtains the original cooking parameters based on the temperature control curve when the expected nutrient retention rates of the main ingredient and auxiliary ingredients are not lower than the basic nutrient retention rates of their respective preset cooking modes. The cooking parameter determination subunit is used to adjust the cooking parameters corresponding to the benchmark cooking mode to obtain the original cooking parameters when the relative nutrient loss rate of the auxiliary ingredients is less than the preset loss rate threshold.

[0191] The second cooking parameter determination unit is used to obtain the original cooking parameters based on the preset cooking mode of any ingredient when the number of cooking ingredients is greater than or equal to a preset quantity threshold and the preset cooking modes of each ingredient are consistent, or when the number of cooking ingredients is less than a preset quantity threshold.

[0192] In some optional implementations, the preset cooking mode is determined through the following steps: The retention rate calculation unit is used to locate the type of each ingredient to be cooked from the pre-constructed ingredient information matrix, and determine the nutrient retention rate of each ingredient to be cooked in each cooking mode based on the ingredient type. The candidate mode determination unit is used to calculate the comprehensive retention rate score of each ingredient to be cooked under each cooking mode based on the nutrient retention rate, and sort them from largest to smallest according to the comprehensive retention rate score to determine the candidate cooking modes with the comprehensive retention rate score in the top preset position. The filtering unit is used to determine the user-defined desired cooking parameters for each ingredient to be cooked, and to filter candidate cooking modes based on the desired cooking parameters to determine the preset cooking mode for the ingredient to be cooked.

[0193] In some alternative implementations, the adjustment module 1003 includes: A new determination unit is added to determine the new ingredients when the cooking adjustment operation is determined to be an ingredient addition operation, and to determine the target cooking parameters of the cooking equipment when resuming cooking based on the original cooking parameters of the new ingredients and the ingredients to be cooked.

[0194] In some optional implementations, the additional determining unit includes: The weight sub-unit is used to obtain the real-time weight of the food to be cooked during the cooking process; The target weight sub-unit is used to determine the target weight of the food to be cooked after the cooking adjustment operation in response to the user's cooking adjustment operation on the cooking equipment. The increment calculation subunit is used to calculate the weight increment based on the real-time weight and the target weight; The newly identified operation subunit is used to determine the cooking adjustment operation as a new ingredient operation when the weight increment is greater than or equal to a preset weight threshold. The identification subunit is used to identify ingredients in the cooking equipment and obtain the new ingredients.

[0195] The heat determination subunit is used to determine the remaining heat required for the ingredients to be cooked based on the original cooking parameters and the current cooking time. The parameter determination subunit is used to determine the target cooking parameters based on the remaining required heating amount and the added ingredients.

[0196] The target cooking parameter determination unit is used to determine the target healthy dining requirements after the cooking adjustment operation is determined to be a healthy dining requirement switching operation, and to determine the current cooking progress of the ingredients to be cooked based on the original cooking parameters, and to determine the target cooking parameters based on the current cooking progress and the target healthy dining requirements.

[0197] In some optional implementations, the target cooking parameter determination unit includes: The temperature determination subunit is used to determine the current temperature of the ingredients when the cooking progress has not exceeded the preset cooking progress. The generation subunit is used to generate target cooking parameters based on the current food temperature and the target healthy dining requirements.

[0198] The cooking module is used to control the cooking equipment to cook based on target cooking parameters.

[0199] The management module is used to respond to the completion of the cooking process of the ingredients to be cooked, determine the nutrient intake of the diners after the ingredients have been cooked based on the ingredient information, and manage the cooking based on the nutrient intake.

[0200] Specifically, the management module includes: The first management unit is used to obtain the nutrient retention rate of the ingredients to be cooked after cooking, and to determine the initial nutrient content and nutrient usage of each ingredient. The second management unit is used to obtain the individual nutrient intake of each diner based on the initial nutrient content, nutrient usage, and nutrient retention rate. The third management unit is used to add up the individual nutrient intake of each diner with the current cumulative nutrient intake to obtain the nutrient intake of each diner after the food to be cooked is finished.

[0201] Intake acquisition unit is used to acquire the target nutrient intake for each diner; The alert unit is used to issue a nutrient excess warning for each diner when it is determined that the diner's nutrient intake exceeds the corresponding target nutrient intake.

[0202] In some optional implementations, the above method further includes: The remaining weight acquisition module is used to acquire the remaining weight after cooking is completed, once the cooking of the ingredients to be cooked is determined to be finished. The weight loss rate calculation module is used to calculate the current weight loss rate of the food to be cooked based on the initial weight and remaining weight of the food before cooking begins. The correction module is used to determine the weight loss rate deviation based on the relationship between the current weight loss rate of the food to be cooked and the baseline weight loss rate, and to correct the baseline weight loss rate based on the current weight loss rate when the weight loss rate deviation exceeds a preset deviation threshold. The actual intake calculation module is used to obtain the actual nutrient intake of diners based on the corrected baseline weight loss rate, initial weight, initial nutrient values ​​of each ingredient to be cooked, and nutrient retention rate of each ingredient to be cooked.

[0203] The cooking equipment control device provided in this embodiment of the invention can execute the cooking equipment control method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0204] Figure 11 This is a schematic diagram of the structure of a controller provided in an embodiment of the present invention.

[0205] The following is a detailed reference. Figure 11 The diagram illustrates a structural schematic suitable for implementing a controller according to an embodiment of the present invention. The controller may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 1101, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 1102 or a program loaded from memory 1108 into random access memory (RAM) 1103. The RAM 1103 also stores various programs and data required for controller operation. The processor 1101, ROM 1102, and RAM 1103 are interconnected via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0206] Typically, the following devices can be connected to I / O interface 1105: input devices 1106 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 1107 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; memory 1108 including, for example, magnetic tape, hard disk, etc.; and communication devices 1109. Communication device 1109 allows the controller to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 11 A controller with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown, and may alternatively implement or have more or fewer devices.

[0207] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 1109, or installed from a memory 1108, or installed from a ROM 1102. When the computer program is executed by the processor 1101, it performs the functions defined in the cooking equipment control method of the embodiments of the present invention.

[0208] Figure 11 The controller shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0209] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the cooking equipment control method shown in the above embodiments is implemented.

[0210] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0211] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for controlling a cooking device, characterized in that, The method includes: Obtain information on the ingredients to be cooked and the healthy dining needs of the diners; Based on the ingredient information, determine the original cooking parameters of the ingredients to be cooked, and cook the ingredients based on the original cooking parameters; In response to a user's cooking adjustment operation during the cooking process of the cooking device, when it is determined that the cooking adjustment operation is an ingredient addition operation or a healthy dining requirement switching operation, a target cooking parameter is determined based on the original cooking parameters and the operation content of the ingredient addition operation or healthy dining requirement switching operation. The determination of target cooking parameters based on the original cooking parameters and the operation content of adding ingredients or switching to healthy dining needs includes: When the cooking adjustment operation is determined to be an ingredient addition operation, the added ingredient is determined, and based on the original cooking parameters of the added ingredient and the ingredient to be cooked, the target cooking parameters of the cooking device when resuming cooking are determined; The cooking equipment is controlled to perform cooking based on the target cooking parameters.

2. The method according to claim 1, characterized in that, The process of determining the target cooking parameters based on the original cooking parameters and the operations of adding ingredients or switching to healthy dining needs also includes: When the cooking adjustment operation is determined to be a healthy dining requirement switching operation, the target healthy dining requirement after the switch is determined, and the current cooking progress of the ingredients to be cooked is determined based on the original cooking parameters. Based on the current cooking progress and the target healthy dining requirement, the target cooking parameters are determined.

3. The method according to claim 1, characterized in that, When determining that the cooking adjustment operation is a new ingredient operation, determining the new ingredient includes: Obtain the real-time weight of the food to be cooked during the cooking process; In response to a user's cooking adjustment operation on the cooking equipment, the target weight of the food to be cooked after the cooking adjustment operation is determined; Calculate the weight increment based on the real-time weight and the target weight; When it is determined that the weight increment is greater than or equal to a preset weight threshold, the cooking adjustment operation is determined to be an ingredient addition operation; The cooking equipment is used to identify ingredients to obtain new ingredients.

4. The method according to claim 1, characterized in that, The process of determining the target cooking parameters for the cooking equipment during the resumption of cooking, based on the original cooking parameters of the newly added ingredients and the ingredients to be cooked, includes: Based on the original cooking parameters and the current cooking time, determine the remaining required heating amount for the ingredients to be cooked; Based on the remaining required heating amount and the added ingredients, the target cooking parameters are determined.

5. The method according to claim 2, characterized in that, The determination of target cooking parameters based on the current cooking progress and the target healthy eating requirements includes: When it is determined that the cooking progress has not exceeded the preset cooking progress, the current food temperature is determined; Based on the current temperature of the ingredients and the target healthy dining requirements, target cooking parameters are generated.

6. The method according to claim 1, characterized in that, Obtain the ingredient information of the ingredients to be cooked, including: Obtain a pre-constructed ingredient information matrix; wherein, the ingredient information matrix includes multiple types of ingredients, the cooking mode of each ingredient in the multiple types of ingredients, and the nutrient retention rate of each ingredient; Based on the ingredients to be cooked, the ingredient information matrix is ​​searched to determine the types of ingredients, cooking modes, and nutrient retention rates that match the ingredients to be cooked, thereby obtaining the ingredient information of the ingredients to be cooked.

7. The method according to claim 1, characterized in that, The ingredient information includes the types and quantities of ingredients to be cooked; determining the original cooking parameters of the ingredients to be cooked based on the ingredient information includes: When it is determined that the number of types of cooking ingredients is greater than or equal to a preset quantity threshold and the preset cooking modes of each ingredient to be cooked are inconsistent, a benchmark cooking mode is selected from the preset cooking modes of each ingredient to be cooked. Based on the degree of nutrient loss of each ingredient to be cooked under the benchmark cooking mode and the preset cooking mode of each ingredient to be cooked, the original cooking parameters are obtained. When the number of cooking ingredients is greater than or equal to a preset quantity threshold and the preset cooking modes of each ingredient are consistent, or when the number of cooking ingredients is less than the preset quantity threshold, the original cooking parameters are obtained based on the preset cooking mode of any ingredient.

8. The method according to claim 7, characterized in that, The preset cooking mode is determined through the following steps: Locate the type of each ingredient to be cooked from the pre-constructed ingredient information matrix, and determine the nutrient retention rate of each ingredient in each cooking mode based on the ingredient type. For each ingredient to be cooked, based on the nutrient retention rate, calculate the comprehensive retention rate score of the ingredient to be cooked in each cooking mode, and sort the comprehensive retention rate scores from largest to smallest to determine the candidate cooking modes with the comprehensive retention rate scores in the top preset positions. For each ingredient to be cooked, the user-defined desired cooking parameters are determined, and the candidate cooking modes are filtered based on the desired cooking parameters to determine the preset cooking mode for the ingredient to be cooked.

9. The method according to claim 7, characterized in that, The step of selecting a benchmark cooking mode from the preset cooking modes of each ingredient to be cooked, and obtaining the original cooking parameters based on the degree of nutrient loss of each ingredient under the benchmark cooking mode and the preset cooking mode of each ingredient to be cooked, includes: The ingredient with the largest weight among all the ingredients to be cooked is determined as the main ingredient, and the other ingredients to be cooked are determined as auxiliary ingredients. The preset cooking mode corresponding to the main ingredient is determined as the benchmark cooking mode. The nutrient loss rate of each auxiliary ingredient under the benchmark cooking mode is calculated, and the basic nutrient loss rate of each auxiliary ingredient is determined. Based on the stated nutrient loss rate and the stated basic nutrient loss rate, calculate the relative nutrient loss rate of each supplementary ingredient; When the relative nutrient loss rate of any auxiliary ingredient is determined to be greater than or equal to a preset loss rate threshold, a temperature control curve is generated based on the preset cooking modes of the main ingredient and all auxiliary ingredients. When the expected nutrient retention rates of the main ingredient and auxiliary ingredients are determined to be no less than the basic nutrient retention rates of their respective preset cooking modes, the original cooking parameters are obtained based on the temperature control curve. When the relative nutrient loss rate of the auxiliary ingredients is determined to be less than the preset loss rate threshold, the cooking parameters corresponding to the benchmark cooking mode are adjusted to obtain the original cooking parameters.

10. The method according to any one of claims 1 to 9, characterized in that, The method further includes: In response to the completion of the cooking process of the ingredients to be cooked, based on the ingredient information, the nutrient intake of the diner after the cooking of the ingredients to be cooked is determined, and cooking management is carried out based on the nutrient intake.

11. The method according to claim 10, characterized in that, In response to the completion of the cooking process of the food to be cooked, based on the food information, the nutrient intake of the diner after the food to be cooked is determined, including: Obtain the nutrient retention rate of the ingredients to be cooked after cooking, and determine the initial nutrient content and nutrient usage of each ingredient. Based on the initial nutrient content, the amount of nutrient used, and the nutrient retention rate, the individual nutrient intake for each diner is obtained. The individual nutrient intake of each diner is added together with the current cumulative nutrient intake to obtain the nutrient intake of each diner after the food to be cooked is finished.

12. The method according to claim 10, characterized in that, The cooking management based on the nutrient intake includes: Obtain the target nutrient intake for each diner; For each diner, an early warning of nutrient overdose will be issued when it is determined that the diner's nutrient intake exceeds the corresponding target nutrient intake.

13. The method according to claim 1, characterized in that, The method further includes: After the cooking of the ingredients is completed, obtain the remaining weight after cooking; Based on the initial weight of the food to be cooked before cooking begins and the remaining weight, calculate the current weight loss rate of the food to be cooked. Based on the relationship between the current weight loss rate and the baseline weight loss rate of the food to be cooked, the weight loss rate deviation is determined, and when the weight loss rate deviation exceeds a preset deviation threshold, the baseline weight loss rate is corrected based on the current weight loss rate. The actual nutrient intake of the diners is obtained based on the corrected baseline weight loss rate, the initial weight, the initial nutrient values ​​of each ingredient to be cooked, and the nutrient retention rate of each ingredient to be cooked.

14. A cooking equipment control device, characterized in that, The device includes: The acquisition module is used to acquire information about the ingredients to be cooked and the healthy dining needs of the diners. The determining module is used to determine the original cooking parameters of the ingredients to be cooked based on the ingredient information, and to cook the ingredients to be cooked based on the original cooking parameters; The adjustment module is used to respond to the cooking adjustment operation performed by the user during the cooking process of the cooking device. When it is determined that the cooking adjustment operation is a food addition operation or a health-conscious dining requirement switching operation, the target cooking parameters are determined based on the original cooking parameters and the operation content of the food addition operation or the health-conscious dining requirement switching operation. The adjustment module includes: A new determining unit is added, which is used to determine the added ingredient when the cooking adjustment operation is determined to be an ingredient addition operation, and to determine the target cooking parameters of the cooking device when resuming cooking based on the original cooking parameters of the added ingredient and the ingredient to be cooked; A control module is used to control the cooking equipment to perform cooking based on the target cooking parameters.

15. A cooking device, characterized in that, The cooking device includes a controller, the controller comprising: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 13.

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