A cooking control method and device, electronic equipment and storage medium

Abstract

The application discloses a cooking control method and device, electronic equipment and storage medium, the method comprises the following steps: obtaining initial food material data of cooking food material, and determining initial nutrient content based on the initial food material data; based on the initial nutrient content and the preset stability matrix, determine the cooking control coefficient; the preset stability matrix defines the mapping relationship between the initial nutrient content, the respective loss rate coefficient, the upper limit of the tolerance temperature and the sensitive weight; the initial nutrient content, the cooking control coefficient and the preset nutrient retention rate target are input into the preset retention optimization model to determine the target cooking parameter; and based on the target cooking parameter, the cooking equipment is controlled to cook; the preset retention optimization model is a model between the nutrient retention rate and the cooking temperature and the cooking time based on the loss rate coefficient, the upper limit of the tolerance temperature and the sensitive weight, through the technical scheme, the accuracy and timeliness of the cooking control are improved, and the loss of nutrient components is reduced.

Description

Technical Field

[0001] This invention relates to the field of control technology, and more specifically, to a cooking control method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the popularization of healthy eating concepts, people have placed higher demands on the preservation and supplementation of nutrients in food during the cooking process. Traditional cooking pots mainly rely on the control of basic parameters such as temperature and time to heat and cook food, but their function is limited to changes in physical state and lacks the ability to monitor and regulate the dynamic changes of the nutritional components of food during the cooking process.

[0003] Current cooking techniques have significant limitations in quantifying nutritional content. Traditional cookware cannot accurately detect the initial nutritional content of ingredients before cooking, nor can it track the dynamic changes of nutrients in real time during cooking. Specifically: lacking precise means of detecting the initial nutritional content of ingredients, current technology cannot perform rapid and non-destructive quantitative analysis of key nutrients such as vitamins (e.g., vitamin C, B vitamins), minerals (e.g., calcium, iron, zinc), proteins, and fats, resulting in a lack of baseline nutritional data before cooking and a lack of scientific basis for subsequent nutritional adjustments; it also cannot monitor nutrient loss during cooking in real time. During heating, the nutritional components of ingredients change with parameters such as temperature, time, and pressure, especially heat-sensitive nutrients (e.g., vitamin C), which decompose rapidly at high temperatures. Current technologies lack the ability to accurately collect and correlate environmental parameters such as temperature and pressure inside the pot, making it impossible to establish a quantitative relationship between temperature, time, and nutrient retention rate, let alone dynamically predict and intervene in the nutrient loss process. Furthermore, they lack intelligent control mechanisms based on nutrient retention. Although some high-end cookware achieves precise temperature control, its control logic remains limited to cooking effects (such as taste and doneness), without considering nutrient retention rate as an optimization objective. Due to the lack of nutrient stability models and optimization algorithms, the system cannot dynamically adjust the heating strategy according to the characteristics of the ingredients, making it difficult to maximize nutrient retention while ensuring food taste.

[0004] In summary, existing cooking equipment suffers from significant technological gaps in the detection, monitoring, and control of nutritional components. There is an urgent need for a new cooking control method to address the serious loss of nutrients caused by the low precision and poor timeliness of cooking control in existing technologies. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a cooking control method, device, electronic device, and storage medium to solve the problems of low cooking control precision and poor timeliness in the prior art, which lead to serious loss of nutrients.

[0006] One aspect of the present invention provides a cooking control method, the method comprising the following steps: Obtain initial ingredient data for cooking ingredients, and determine the initial nutritional content of cooking ingredients based on the initial ingredient data; Based on the initial nutrient content of the cooking ingredients and a preset stability matrix, the cooking control coefficient of the cooking ingredients is determined; the preset stability matrix defines the mapping relationship between various initial nutrient contents and their respective loss rate coefficients, tolerance temperature limits, and sensitivity weights. The initial nutrient content, the cooking control coefficient, and the preset nutrient retention rate target are input into the preset retention optimization model to determine the target cooking parameters of the cooking ingredients; and the cooking equipment is controlled to cook based on the target cooking parameters; the preset retention optimization model is a mathematical relationship model between nutrient retention rate and cooking temperature and cooking time, constructed based on the loss rate coefficient, the upper limit of the tolerance temperature, and the sensitivity weight.

[0007] Another aspect of the present invention provides a cooking apparatus, the apparatus comprising: The acquisition module is used to acquire initial ingredient data of cooking ingredients and determine the initial nutritional content of cooking ingredients based on the initial ingredient data. The determination module is used to determine the cooking control coefficient of the cooking ingredients based on the initial nutrient content and the preset stability matrix; the preset stability matrix defines the mapping relationship between various initial nutrient contents and their respective loss rate coefficients, tolerance temperature limits and sensitivity weights. The cooking module is used to input the initial nutrient content, the cooking control coefficient, and the preset nutrient retention rate target into the preset retention optimization model to determine the target cooking parameters of the cooking ingredients; and to control the cooking equipment to cook based on the target cooking parameters; the preset retention optimization model is a mathematical relationship model between nutrient retention rate and cooking temperature and cooking time constructed based on the loss rate coefficient, the upper limit of the tolerance temperature, and the sensitivity weight.

[0008] Another aspect of the present invention provides an electronic device, comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the cooking control method described in any one of the above descriptions.

[0009] Another aspect of the present invention provides a computer-readable storage medium that, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform any of the above-described cooking control methods.

[0010] This invention provides a cooking control method, device, electronic device, and storage medium. It acquires initial ingredient data of the ingredients to be cooked and determines the initial nutrient content of the ingredients based on this data. Based on the initial nutrient content and a preset stability matrix, it determines the cooking control coefficients for the ingredients. The preset stability matrix defines the mapping relationships between various initial nutrient contents and their respective loss rate coefficients, tolerance temperature limits, and sensitivity weights. Furthermore, by acquiring and storing the initial nutrient content in the preset stability matrix, it differentiates the treatment of nutrients with different characteristics. The initial nutrient content, the cooking control coefficients, and the preset nutrient retention rate target are input into a preset retention optimization model to determine the target cooking parameters for the ingredients. The cooking equipment is then controlled to perform cooking based on these target cooking parameters. The preset retention optimization model is a mathematical relationship model between nutrient retention rate and cooking temperature and cooking time, constructed based on the loss rate coefficient, tolerance temperature limit, and sensitivity weights. This model achieves quantitative modeling and prediction of nutrient loss, generates personalized and optimized cooking parameters based on the model and algorithm, and provides scientific and precise control, thereby significantly reducing the nutrient loss rate. Attached Figure Description

[0011] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic flowchart of a cooking control method provided according to an exemplary embodiment; Figure 2 This is a schematic diagram of a process for determining the initial nutritional content of cooking ingredients according to an exemplary embodiment; Figure 3 This is a schematic diagram of a process for constructing a feature vector according to an exemplary embodiment; Figure 4 This is a schematic diagram of a process for generating control instructions based on current cooking parameters and target cooking parameters, according to an exemplary embodiment. Figure 5 This is a schematic diagram of a process for determining the amount of supplement required, according to an exemplary embodiment. Figure 6 This is a schematic diagram of a process for determining supplement requirements based on a nutritional deficit value and supplement concentration, according to an exemplary embodiment. Figure 7 This is a schematic diagram of the structure of a cooking apparatus according to an exemplary embodiment. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification and drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Figure 1 This is a flowchart illustrating a cooking control method according to an exemplary embodiment. This specification provides method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or server product execution, the method can be executed in the order shown in the embodiments or drawings, or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as shown... Figure 1 As shown, taking a cooking system as the executing entity, an embodiment of a cooking control method of this application is introduced. The method may include: S101: Obtain the initial ingredient data of the cooking ingredients, and determine the initial nutritional content of the cooking ingredients based on the initial ingredient data.

[0016] In one specific embodiment, the cooking ingredients can be the ingredients that the user wants to cook; the initial ingredient data can be the ingredient analysis data corresponding to the cooking ingredients in the initial state; the initial nutrient content can be the nutrient content contained in the cooking ingredients in the initial state; specifically, the user puts the ingredients to be cooked (such as vegetables or meat) into the detection area built into the cooking device. Correspondingly, when the cooking device detects the cooking ingredients, it will start to acquire the initial ingredient data of the cooking ingredients, and perform data analysis based on the initial ingredient data to determine the initial nutrient content corresponding to the cooking ingredients.

[0017] Figure 2 This is a schematic diagram of a process for determining the initial nutritional content of cooking ingredients according to an exemplary embodiment. In an optional embodiment, such as... Figure 2 As shown, the initial nutritional content of cooking ingredients, determined based on initial ingredient data, includes: S201: Acquire the food spectral data collected by the near-infrared spectral sensor; and use the food spectral data as the initial food data; S203: Process the spectral data of the ingredients to construct feature vectors; S205: Input the feature vector into the preset component analysis model to obtain the initial nutritional content of various nutrients in the cooking ingredients.

[0018] In one specific embodiment, when the cooking device detects the food being cooked, it activates a near-infrared spectral sensor to emit near-infrared light in the 900-1700nm band and receive reflected spectral signals to acquire the food's spectral data. The device then processes this spectral data to construct a feature vector, which is then input into a pre-trained preset component analysis model. This preset component analysis model can be a partial least squares regression model. Optionally, this model is trained based on the spectral data of thousands of standard food samples and their precise nutritional component data measured by chemical methods. It can output predicted content values ​​of various nutrients in the food, including vitamins A, C, D, E, and B complex; minerals such as calcium, iron, and zinc; and proteins and fats, with a prediction error controlled within 3%. These predicted values ​​are recorded as the initial nutritional content of the food being cooked. Specifically, after the feature vector is input into the preset component analysis model, the model performs component analysis to obtain the initial nutritional content of the food being cooked.

[0019] Figure 3 This is a schematic diagram illustrating a process for constructing a feature vector according to an exemplary embodiment. In an optional embodiment, such as... Figure 3 As shown, the above-mentioned processing of food spectral data to construct feature vectors includes: S301: Perform data preprocessing on the spectral data of the ingredients to obtain standardized spectral data; S303: Extract features from the standard spectral data to obtain key feature data; and construct feature vectors based on the key feature data.

[0020] In a specific embodiment, the standardized spectral data can be data that meets the feature extraction requirements; the key feature data can be data with key feature wavelengths; specifically, the system preprocesses the food spectral data, including smoothing and denoising using a Savitzky-Golay filter with a window size of 5 points, and then eliminating the scattering effects caused by the surface morphology and moisture of the food through multivariate scattering correction, to obtain standardized spectral data with a signal-to-noise ratio improved by about 30%. Furthermore, the system extracts a certain number of key feature wavelengths from the standardized spectral data, i.e., key feature data (for example, the absorption peak at 1200nm is strongly correlated with protein content, and the absorption peak at 1450nm is strongly correlated with moisture content), and constructs a feature vector of a preset dimension.

[0021] In the above embodiments, the spectral food data acquired by the near-infrared spectral sensor is preprocessed to obtain standardized spectral data, and features are extracted. Then, feature vectors are constructed based on the extracted key feature data, which improves the accuracy and convenience of determining target cooking parameters and thus enhances the user experience.

[0022] S103: Determine the cooking control coefficient of the cooking ingredients based on the initial nutrient content and preset stability matrix of the cooking ingredients.

[0023] In one specific embodiment, the preset stability matrix defines the mapping relationship between various initial nutrient contents and their respective loss rate coefficients, tolerance temperature limits, and sensitivity weights; the cooking control coefficients can be the coefficients for cooking equipment to control cooking; specifically, the system memory stores a preset nutrient stability matrix, i.e., the preset stability matrix, which optionally defines the key thermodynamic parameters of various nutrients. For example, vitamin C and B vitamins are defined as "highly sensitive," with a sensitivity weight ω of 0.6, a maximum tolerance temperature T of 60℃, and a loss rate coefficient K of 0.8% / ℃·min. This means that for every degree the cooking temperature exceeds 60℃, vitamin C will be lost by 0.8% per minute. Protein and calcium are defined as "medium sensitive," with a sensitivity weight ω of 0.3, a maximum tolerance temperature T of 100℃, and a loss rate coefficient K of 0.2% / ℃·min. Fat and iron are defined as "low sensitive," with a sensitivity weight ω of 0.1, a maximum tolerance temperature T of 180℃, and a loss rate coefficient K of 0.05% / ℃·min. The cooking control coefficient is not a single value, but refers to a set of key parameters used for subsequent optimization calculations, namely the set {ω_i, k_i, T_i} corresponding to each nutrient. This step is to match the corresponding cooking control coefficient from the stability matrix for each nutrient detected in the current cooking ingredients.

[0024] S105: Input the initial nutrient content, cooking control coefficient, and preset nutrient retention rate target into the preset retention optimization model to determine the target cooking parameters for the ingredients; and control the cooking equipment to cook based on the target cooking parameters.

[0025] In a specific embodiment, the preset retention optimization model is a mathematical relationship model between nutrient retention rate and cooking temperature and cooking time, constructed based on the loss rate coefficient, the upper limit of the tolerance temperature, and the sensitivity weight. The target cooking parameters can be the control parameters of the cooking equipment for cooking ingredients; optionally, the target cooking parameters can include the target cooking temperature and the target cooking time. The preset nutrient retention rate target is usually set to "maximize". The core of the preset retention optimization model is a nonlinear programming problem, and its objective function is: The goal is to find the (T, t) combination that maximizes the value of Y. Constraints include: the cooking temperature T must be higher than the safe lower limit for ensuring food is cooked and sterilized (e.g., 85℃ for leafy greens); the cooking time t must be within a reasonable range; and sometimes, user-defined texture preferences (e.g., longer cooking time for "soft and tender") must be considered. The system uses a particle swarm optimization (PSO) algorithm to solve this constrained optimization problem. The PSO algorithm simulates bird flock foraging behavior, initializing a swarm of particles in a two-dimensional solution space defined by temperature and time. Each particle represents a (T, t) solution. The algorithm iterates 50 times per second, updating particle velocity and position based on the individual and swarm optimal positions, ultimately converging quickly to the optimal solution that maximizes the overall nutrient retention rate Y – the target cooking parameters, namely the target cooking temperature and target cooking time. Further, the system controls the cooking equipment based on these target cooking parameters. Specifically, the system sends the solved target cooking temperature and target cooking time to the main controller of the cooking equipment, which then drives the heating and pressure control modules to begin cooking according to the set target parameters.

[0026] Figure 4 This is a schematic diagram illustrating a process for generating control instructions based on current cooking parameters and target cooking parameters, according to an exemplary embodiment. In an optional embodiment, such as... Figure 4 As shown, the above method may further include: S401: Collects current cooking parameters based on a preset cycle; the current cooking parameters include at least the current cooking temperature and the current cooking pressure; S403: Determine the parameter deviation based on the current cooking parameters and the target cooking parameters; S405: Based on parameter deviation, a control command is generated through a preset control algorithm, and the execution module of the cooking equipment is controlled to adjust the cooking process based on the control command so that the current cooking parameters are close to the target cooking parameters.

[0027] In one specific embodiment, the current cooking parameters can be real-time cooking parameters collected during the cooking process; the parameter deviation can be the parameter deviation value between the current cooking parameters and the target cooking parameters; specifically, during the cooking process according to the target cooking parameters, the system collects the current cooking temperature and current cooking pressure in the pot at a fixed preset period (e.g., every 10 seconds) using a high-precision temperature sensor and a pressure sensor, and the system calculates the parameter deviation between the current cooking parameters and the target cooking parameters: ΔT = T_curr - T_target, ΔP = P_curr - P_target, these deviations and their rates of change (e.g., ΔT / Δt) are fed into the fuzzy PID controller, i.e., the preset control algorithm. The fuzzification interface converts the precise deviation (e.g., "ΔT=+3.2℃") into fuzzy linguistic values ​​(e.g., "moderate deviation"), and the deviation rate of change (e.g., "ΔT / Δt=-0.5℃ / s") into fuzzy linguistic values ​​(e.g., "slow negative change"). The fuzzy inference engine makes decisions based on a preset fuzzy rule base. Optionally, the fuzzy rule base includes rules such as "if the deviation is moderate and the rate of change is slow negative, then the heating power should be slightly reduced." The inference result outputs fuzzy control quantities, such as "heating power adjustment amount" being "small negative." The defuzzification interface converts these fuzzy control quantities into precise values, such as "reduce IH heating power by 15%" or "increase pressure relief valve opening by 10%." The main controller drives the actuators (heating module, pressure regulating valve) to act according to these precise instructions, so that the current cooking parameters (T_curr, ... P_curr) quickly and smoothly approaches the target cooking parameters (T_target, P_target). Optionally, ΔT represents the temperature deviation value, and ΔP represents the pressure deviation value.

[0028] In the above embodiments, the fuzzy PID control algorithm is used for real-time compensation and regulation, which can effectively overcome interference factors such as the thermal inertia of cooking equipment and changes in the amount of ingredients, control the temperature fluctuations during the cooking process within a very small range, avoid the rapid loss of nutrients caused by sudden temperature changes, and ensure the accurate implementation of the optimization strategy.

[0029] Figure 5 This is a schematic diagram of a process for determining supplement requirements according to an exemplary embodiment. In an optional embodiment, such as Figure 5 As shown, the above method may further include: S501: Obtain user demand information and supplement concentration; S503: Based on user demand information and initial nutrient content, determine the nutrient gap and the corresponding nutrient gap value; S505: Determine the required amount of supplements based on the nutritional deficit value and the supplement concentration; and control the addition of nutritional supplements based on the required amount of supplements.

[0030] In one specific embodiment, user demand information is used to indicate the user's current nutritional needs. Optionally, user demand information may include the user's basic information (age, gender, weight) and health goals (such as "muscle gain", "fat loss", "vitamin D supplementation"). The supplement concentration may be the dilution level of the supplement used for nutritional supplementation. The nutritional gap may be the nutrient that the user lacks. The nutritional gap value may be the degree of deficiency of the nutrient that the user lacks. The supplement requirement may be the dosage of supplement that the user needs. Specifically, the system also obtains user demand information through the user terminal (such as a mobile APP). Based on the user demand information and the Dietary Reference Intakes (DRIs), the system calculates the user's daily recommended intake of various nutrients and, combined with the proportion of each meal, obtains the "demand value" for this meal. Then, the system compares the initial nutrient content (i.e., the "supply value") provided by the cooking ingredients with the user's "demand value" for this meal. For nutrients where the "supply value" is less than the "demand value", a nutritional gap is determined, and the difference is the nutritional gap value. For example, if a user needs to consume 400 IU of vitamin D in this meal, but the food itself can only provide 50 IU, then the vitamin D nutritional deficit is 350 IU. Furthermore, based on the nutritional deficit and the supplement concentration, the required amount of supplement is determined. Further, the system controls the micro-pump of the nutritional supplement delivery module to accurately extract the required amount of nutrient concentrate and spray it into the food, thus completing personalized nutritional supplementation.

[0031] Figure 6 This is a schematic diagram of a process for determining supplement requirements based on a nutritional deficit value and supplement concentration, according to an exemplary embodiment. In an optional embodiment, such as Figure 6 As shown, determining the supplement requirement based on the nutritional deficit value and supplement concentration can include: S601: Obtain the absorption efficiency coefficient; S603: Determine the initial requirement based on the nutritional deficit value and supplement concentration; S605: Determine the supplement requirement based on the initial demand and absorption efficiency coefficient.

[0032] In a specific embodiment, the absorption efficiency coefficient can be the absorption efficiency corresponding to the cooking method. Specifically, the system pre-stores the absorption efficiency coefficient corresponding to the current cooking method and the concentration information of the supplement in each storage cell of the nutritional supplement dispensing module of the cooking equipment (e.g., storage cell 1: vitamin D concentrate, concentration 1000 IU / mL). Accordingly, when determining which cooking method to use, the absorption efficiency coefficient corresponding to the current cooking method (e.g., "stewing") is obtained (e.g., the absorption efficiency coefficient of vitamin D during stewing is 0.8). Then, the initial requirement is calculated. Optionally, the nutritional deficit value divided by the supplement concentration is the initial requirement, and then the supplement requirement (i.e., the actual dosage) is calculated. Optionally, the initial requirement is divided by the absorption efficiency coefficient to obtain the supplement requirement, and then supplementation is performed based on the calculated supplement requirement.

[0033] In the above embodiments, by incorporating user demand information, the system can accurately calculate individualized nutritional gaps and, considering absorption efficiency, precisely control the dosage of nutritional supplements. This allows the same cooking equipment to provide customized nutritional meals for different family members, greatly enhancing the product's practical value and health benefits.

[0034] In an optional embodiment, the above method may further include: In the event of an anomaly in the acquisition of the ingredient data, the stored ingredient data is retrieved, and the initial nutritional content of the cooking ingredient is determined based on the stored ingredient data.

[0035] In one specific embodiment, if the near-infrared spectral sensor fails to acquire valid data (e.g., due to improper placement of the food or sensor malfunction), the system will trigger an exception handling procedure. The system first attempts to re-acquire data several times; if this still fails, it is determined that the "food data is abnormal." Subsequently, the system calls upon the stored food data stored locally or in the cloud. Optionally, the system has a database containing average nutritional data for more than 500 common foods. The system determines the type of food (e.g., "spinach") through user manual input or image recognition assistance, and then retrieves the standard nutritional data for "spinach" from the database as the initial estimate of the nutritional composition of the food. Based on the initial estimate of the nutritional composition, the system continues to determine the target cooking parameters.

[0036] In the above embodiments, the initial nutrient content is determined by using the stored ingredient data in the database, and the subsequent target cooking parameters are determined based on the currently determined initial nutrient content. This ensures the continuation of the cooking process and the realization of the basic nutrient regulation function, providing good system robustness and user experience.

[0037] This invention provides a cooking control method that acquires initial ingredient data of the ingredients to be cooked, and determines the initial nutrient content of the ingredients based on the initial ingredient data; determines the cooking control coefficient of the ingredients based on the initial nutrient content and a preset stability matrix; the preset stability matrix defines the mapping relationship between various initial nutrient contents and their respective loss rate coefficients, tolerance temperature limits, and sensitivity weights; furthermore, by acquiring the memory of the initial nutrient content and introducing it into the preset stability matrix, different characteristics of nutrients are treated differently; the initial nutrient content, the cooking control coefficients, and the preset nutrient retention rate target are input into a preset retention optimization model to determine the target cooking parameters of the ingredients; and the cooking equipment is controlled to cook based on the target cooking parameters; the preset retention optimization model is a mathematical relationship model between nutrient retention rate and cooking temperature and cooking time, constructed based on the loss rate coefficient, tolerance temperature limit, and sensitivity weight, thereby realizing the quantitative modeling and prediction of nutrient loss, generating personalized and optimized cooking parameters based on the model and algorithm, and controlling scientifically and precisely, further significantly reducing the nutrient loss rate.

[0038] Figure 7 This is a schematic diagram of a cooking apparatus according to an exemplary embodiment. The following describes an embodiment of a cooking apparatus according to this application, specifically, as follows: Figure 7 As shown, the device includes: The acquisition module 701 is used to acquire initial ingredient data of cooking ingredients and determine the initial nutritional content of cooking ingredients based on the initial ingredient data. The determination module 703 is used to determine the cooking control coefficient of the cooking ingredients based on the initial nutrient content and the preset stability matrix; the preset stability matrix defines the mapping relationship between various initial nutrient contents and their respective loss rate coefficients, tolerance temperature limits and sensitivity weights. The cooking module 705 is used to input the initial nutrient content, the cooking control coefficient, and the preset nutrient retention rate target into a preset retention optimization model to determine the target cooking parameters for the ingredients; and to control the cooking equipment to perform cooking based on the target cooking parameters; the preset retention optimization model is a mathematical relationship model between nutrient retention rate and cooking temperature and cooking time, constructed based on the loss rate coefficient, the upper limit of the tolerance temperature, and the sensitivity weight.

[0039] In an optional embodiment, the above-described apparatus further includes: Concentration acquisition unit, used to acquire user demand information and supplement concentration; The gap determination unit is used to determine the nutrient gap and the corresponding nutrient gap value based on the user demand information and the initial nutrient content. An addition unit is used to determine the required amount of supplement based on the nutritional deficit value and the supplement concentration; and to control the addition of nutritional supplements based on the required amount of supplements.

[0040] In an optional embodiment, the above-mentioned adding unit includes: The coefficient acquisition sub-unit is used to obtain the absorption efficiency coefficient; An initial determination subunit is used to determine the initial requirement based on the nutritional deficit value and the supplement concentration; The demand determination subunit is used to determine the demand for the supplement based on the initial demand and the absorption efficiency coefficient.

[0041] In an optional embodiment, the above-described apparatus further includes: A parameter acquisition unit is used to acquire current cooking parameters based on a preset period; the current cooking parameters include at least the current cooking temperature and the current cooking pressure. A deviation calculation unit is used to determine the parameter deviation based on the current cooking parameters and the target cooking parameters; The control unit is used to generate control instructions based on the parameter deviation using a preset control algorithm, and to control the execution module of the cooking device to perform cooking adjustments based on the control instructions, so that the current cooking parameters approach the target cooking parameters.

[0042] In an optional embodiment, the acquisition module 701 includes: A data acquisition unit is used to acquire food spectral data collected by a near-infrared spectral sensor and to use the food spectral data as the initial food data. A vector construction unit is used to process the spectral data of the food ingredients and construct feature vectors; The model analysis unit is used to input the feature vector into a preset component analysis model to obtain the initial nutrient content of various nutrients in the cooking ingredients.

[0043] In an optional embodiment, the vector construction unit described above includes: The preprocessing subunit is used to preprocess the spectral data of the food ingredients to obtain standardized spectral data. A feature extraction subunit is used to extract features from the standardized spectral data to obtain key feature data; and the feature vector is constructed based on the key feature data.

[0044] In an optional embodiment, the above-described apparatus further includes a calling unit for: In the event of an anomaly in the acquisition of the ingredient data, the stored ingredient data is retrieved, and the initial nutritional content of the cooking ingredient is determined based on the stored ingredient data.

[0045] In an exemplary embodiment, an electronic device is also provided, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the cooking control method as described in the embodiments of this disclosure.

[0046] In an exemplary embodiment, a computer-readable storage medium is also provided, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the cooking control method of the embodiments of this disclosure. In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the cooking control method provided in the various optional implementations described above.

[0047] It is understood that in the specific embodiments of the present invention, user-related data is involved. When the above embodiments of the present invention are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0048] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0049] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0050] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A cooking control method characterized by, The method includes: Obtain initial ingredient data for cooking ingredients, and determine the initial nutritional content of cooking ingredients based on the initial ingredient data; Based on the initial nutrient content of the cooking ingredients and a preset stability matrix, the cooking control coefficient of the cooking ingredients is determined; the preset stability matrix defines the mapping relationship between various initial nutrient contents and their respective loss rate coefficients, tolerance temperature limits, and sensitivity weights. The initial nutrient content, the cooking control coefficient, and the preset nutrient retention rate target are input into the preset retention optimization model to determine the target cooking parameters of the cooking ingredients; and the cooking equipment is controlled to cook based on the target cooking parameters; the preset retention optimization model is a mathematical relationship model between nutrient retention rate and cooking temperature and cooking time, constructed based on the loss rate coefficient, the upper limit of the tolerance temperature, and the sensitivity weight.

2. The method of claim 1, wherein, The method further includes: Obtain user needs information and supplement concentrations; Based on the user demand information and the initial nutritional content, determine the nutritional gap and the corresponding nutritional gap value; Based on the nutritional deficit value and the supplement concentration, the required amount of supplement is determined; and the addition of nutritional supplements is controlled based on the required amount of supplement.

3. The method of claim 2, wherein, Determining the supplement requirement based on the nutritional deficit value and the supplement concentration includes: Obtain the absorption efficiency coefficient; Based on the nutritional deficit value and the supplement concentration, the initial requirement is determined; The required amount of the supplement is determined based on the initial demand and the absorption efficiency coefficient.

4. The method of claim 1, wherein, The method further includes: Current cooking parameters are collected based on a preset cycle; the current cooking parameters include at least the current cooking temperature and the current cooking pressure. Based on the current cooking parameters and the target cooking parameters, determine the parameter deviation; Based on the parameter deviation, a control command is generated through a preset control algorithm, and the execution module of the cooking equipment is controlled to adjust the cooking process based on the control command, so that the current cooking parameters approach the target cooking parameters.

5. The method of claim 1, wherein, Determining the initial nutritional content of the cooking ingredients based on the initial ingredient data includes: Acquire food spectral data collected by a near-infrared spectral sensor; and use the food spectral data as the initial food data; The spectral data of the ingredients are processed to construct feature vectors; The feature vector is input into a preset component analysis model to obtain the initial nutrient content of various nutrients in the cooking ingredients.

6. The method of claim 5, wherein, The process of processing the spectral data of the food ingredients to construct the feature vector includes: The spectral data of the food ingredients are preprocessed to obtain standardized spectral data; Feature extraction is performed on the standardized spectral data to obtain key feature data; and the feature vector is constructed based on the key feature data.

7. The method of claim 1, wherein, The method further includes: In the event of an anomaly in the acquisition of the ingredient data, the stored ingredient data is retrieved, and the initial nutritional content of the cooking ingredient is determined based on the stored ingredient data.

8. A cooking apparatus characterized by, The device includes: The acquisition module is used to acquire the initial ingredient data of the cooking ingredients and determine the initial nutritional content of the cooking ingredients based on the initial ingredient data. The determination module is used to determine the cooking control coefficient of the cooking ingredients based on the initial nutrient content and the preset stability matrix; the preset stability matrix defines the mapping relationship between various initial nutrient contents and their respective loss rate coefficients, tolerance temperature limits and sensitivity weights. The cooking module is used to input the initial nutrient content, the cooking control coefficient, and the preset nutrient retention rate target into the preset retention optimization model to determine the target cooking parameters of the cooking ingredients; and to control the cooking equipment to cook based on the target cooking parameters; the preset retention optimization model is a mathematical relationship model between nutrient retention rate and cooking temperature and cooking time constructed based on the loss rate coefficient, the upper limit of the tolerance temperature, and the sensitivity weight.

9. An electronic device, comprising: include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the cooking control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the cooking control method as described in any one of claims 1 to 7.

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