Intelligent cooking heating control method and system based on multi-dimensional user evaluation driving

Abstract

The application relates to the field of cooking heating control, and discloses an intelligent cooking heating control method and system based on multi-dimensional user evaluation driving, which comprises the following steps: acquiring a plurality of cooking heating parameters corresponding to each control dimension of a target cooking device, acquiring a priority control dimension and a non-priority control dimension determined by a user in the plurality of control dimensions, and acquiring user historical feedback data corresponding to each control dimension; determining a plurality of optimized cooking heating parameters corresponding to the priority control dimension of the target cooking device according to the user historical feedback data corresponding to the priority control dimension and the plurality of cooking heating parameters corresponding to the priority control dimension; and determining a plurality of adjusted cooking heating parameters corresponding to the non-priority control dimension according to the user historical feedback data corresponding to the non-priority control dimension, the plurality of optimized cooking heating parameters and the plurality of cooking heating parameters corresponding to the non-priority control dimension. The application can perform deep parameter optimization on the cooking effect index that is most concerned by the user.

Description

Technical Field

[0001] This application relates to the field of cooking heating control technology, and more specifically, to an intelligent cooking heating control method and system based on multi-dimensional user evaluation. Background Technology

[0002] Current intelligent cooking equipment often employs temperature control strategies based on preset programs or shallow parameter feedback mechanisms for cooking optimization, making it difficult to deeply optimize for core user needs. Taking Chinese stir-frying as an example, existing systems generally monitor the pot temperature using thermocouples and maintain constant heat using PID algorithms. However, they cannot establish a dynamic mapping relationship between oil temperature fluctuation curves and core indicators such as the Maillard reaction degree of ingredients and nutrient retention rate, nor can they optimize the heating process. Furthermore, when the equipment simultaneously receives dual-objective commands to "shorten cooking time" and "reduce oil fume emissions," the optimization algorithm will homogenize the trade-off between time cost and energy efficiency, resulting in a decrease in browning degree of the dish compared to single-objective optimization, and excessively high fluctuations in vitamin C retention rate, revealing the deficiency of insufficient depth in optimizing key indicators.

[0003] Traditional multi-objective optimization frameworks suffer from inaccurate coupling of objective functions in cooking and heating scenarios, particularly when user preference indicators and equipment physical parameters exhibit a non-linear relationship, leading to systematic biases in resource allocation strategies. Taking the preparation of Kung Pao Chicken as an example, existing systems decompose heat control into multiple parallel optimization dimensions such as temperature stability, gas consumption, and oil fume concentration, using fixed weight coefficients for joint solution. When user feedback requests optimization of the dish's crispness and tenderness, existing multi-objective optimization algorithms consume significant computational resources on secondary indicators with low user perception, while the crispness and tenderness indicator receives low optimization weight. Ultimately, the Pareto front of the multi-objective approach converges to a suboptimal solution domain, failing to meet users' demands for optimal core quality indicators. Summary of the Invention

[0004] The purpose of this application is to provide an intelligent cooking heating control method and system based on multi-dimensional user evaluation, which solves the technical problem of not being able to perform in-depth parameter optimization for the cooking effect indicators that users care about most, and achieves the technical effect of performing in-depth parameter optimization for the cooking effect indicators that users care about most.

[0005] This application provides an intelligent cooking heating control method based on multi-dimensional user evaluation. The method includes: acquiring multiple cooking heating parameters corresponding to each control dimension of the target cooking device; acquiring the priority control dimension and non-priority control dimension determined by the user among the multiple control dimensions; and acquiring user historical feedback data corresponding to each control dimension. The multiple cooking heating parameters include heat source distribution parameters, food humidity, heating power, heating temperature, and heating time for each cooking heating segment; the multiple control dimensions include health and taste dimensions; and there is one priority control dimension. A cooking heating optimization unit determines multiple optimized cooking heating parameters corresponding to the priority control dimension of the target cooking device based on the user historical feedback data corresponding to the priority control dimension and the multiple cooking heating parameters corresponding to the priority control dimension. A cooking heating adjustment unit determines multiple adjusted cooking heating parameters corresponding to the non-priority control dimension of the target cooking device based on the user historical feedback data corresponding to the non-priority control dimension, the multiple optimized cooking heating parameters, and the multiple cooking heating parameters corresponding to the non-priority control dimension.

[0006] In one possible implementation, the method further includes: acquiring user historical time-series feedback data corresponding to each control dimension; determining update weights for multiple cooking heating parameters corresponding to each control dimension based on the user historical time-series feedback data corresponding to each control dimension through a user preference migration identification unit; wherein the user historical time-series feedback data corresponding to each control dimension includes the user's rating value in the time sequence feedback for each control dimension; determining multiple optimized cooking heating parameters corresponding to the priority control dimension of the target cooking device based on the user historical time-series feedback data corresponding to the priority control dimension, the multiple cooking heating parameters corresponding to the priority control dimension, and the update weights of the multiple cooking heating parameters through a cooking heating optimization unit; and determining multiple adjusted cooking heating parameters corresponding to the non-priority control dimension of the target cooking device based on the user historical feedback data corresponding to the non-priority control dimension, the multiple optimized cooking heating parameters, the multiple cooking heating parameters corresponding to the non-priority control dimension, and the update weights of the cooking heating parameters through a cooking heating adjustment unit.

[0007] In another possible implementation, a user preference migration identification unit determines the update weights of multiple cooking heating parameters corresponding to each control dimension based on the user's historical time-series feedback data for each control dimension. This includes: using a feedback optimization unit based on a cooking knowledge graph, determining multiple conflicting historical time-series feedback data groups based on the user's historical time-series feedback data for each control dimension. Each conflicting historical time-series feedback data group includes two conflicting sets of user historical time-series feedback data for each control dimension. The update weights of the cooking heating parameters corresponding to the non-priority control dimensions in each conflicting historical time-series feedback data group are multiplied by a preset update weight adjustment ratio to adjust the update weights of the cooking heating parameters corresponding to the non-priority control dimensions.

[0008] In another possible implementation, the method further includes: determining multiple first cooking heating parameters and multiple second cooking heating parameters among multiple cooking heating parameters corresponding to the priority control dimension; the multiple first cooking heating parameters are the cooking heating parameters that are prioritized for optimization among the multiple cooking heating parameters corresponding to the priority control dimension; and the multiple second cooking heating parameters are the cooking heating parameters that are not prioritized for optimization among the multiple cooking heating parameters corresponding to the priority control dimension; through a cooking heating optimization unit, based on the user's historical time-series feedback data corresponding to the priority control dimension, the multiple first cooking heating parameters, and the multiple cooking heating parameter update weights, determining multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters, and obtaining the subsequent optimization parameters of the target cooking device. The system collects the user's first feedback data when cooking with the first cooking heating parameter. When the first feedback data is positive, the cooking heating optimization unit determines multiple optimized second cooking heating parameters corresponding to the multiple second cooking heating parameters of the target cooking device based on the user's historical time-series feedback data corresponding to the priority control dimension, multiple first cooking heating parameters, and the cooking heating parameter update weights corresponding to the multiple first cooking heating parameters. When the first feedback data is negative, the cooking heating optimization unit re-determines multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters of the target cooking device based on the first feedback data, multiple first cooking heating parameters, and the cooking heating parameter update weights corresponding to the multiple first cooking heating parameters.

[0009] In another possible implementation, the method further includes: obtaining the number of negative feedbacks corresponding to the first feedback data as negative feedback; when the number of negative feedbacks is greater than or equal to a preset number of negative feedbacks, obtaining the correlation coefficients between the multiple second cooking heating parameters and the multiple first cooking heating parameters, wherein the correlation coefficients characterize the degree of influence of the adjustment of the first cooking heating parameters on the second cooking heating parameters; determining a preset number of multiple target second cooking heating parameters among the multiple second cooking heating parameters in descending order of the correlation coefficients; and, through a cooking heating optimization unit, determining multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters and the multiple target second cooking heating parameters, based on the first feedback data, the multiple first cooking heating parameters, the cooking heating parameter update weights corresponding to the multiple first cooking heating parameters, the multiple target second cooking heating parameters, and the cooking heating parameter update weights corresponding to the multiple target second cooking heating parameters.

[0010] In another possible implementation, a cooking heating optimization unit redetermines multiple optimized first cooking heating parameters based on first feedback data, adjusted multiple first cooking heating parameters, and update weights of multiple cooking heating parameters corresponding to the multiple first cooking heating parameters. This includes: determining a preset number of target second cooking heating parameters as first cooking heating parameters from multiple second cooking heating parameters, and obtaining parameter adjustment thresholds corresponding to the multiple target second cooking heating parameters; within the parameter adjustment threshold range corresponding to the multiple target second cooking heating parameters, the cooking heating optimization unit determines multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters and multiple optimized target second cooking heating parameters corresponding to the multiple target second cooking heating parameters based on the first feedback data, multiple first cooking heating parameters, update weights of cooking heating parameters corresponding to the multiple first cooking heating parameters, multiple target second cooking heating parameters, and update weights of cooking heating parameters corresponding to the multiple target second cooking heating parameters; and obtaining the user's first feedback data when the target cooking device subsequently cooks using the multiple optimized first cooking heating parameters and multiple optimized target second cooking heating parameters.

[0011] In another possible implementation, the cooking heating adjustment unit determines multiple adjustable cooking heating parameters corresponding to the non-priority control dimension based on user historical feedback data corresponding to the non-priority control dimension, multiple optimized cooking heating parameters, and multiple cooking heating parameters corresponding to the non-priority control dimension. This includes: obtaining the lower limit values ​​of multiple cooking heating parameters corresponding to the non-priority control dimension of the target cooking device; and determining the multiple adjustable cooking heating parameters corresponding to the non-priority control dimension with the lowest energy consumption of the target cooking device based on user historical feedback data corresponding to the non-priority control dimension, multiple optimized cooking heating parameters, and the lower limit values ​​of multiple cooking heating parameters corresponding to the non-priority control dimension.

[0012] In another possible implementation, the method further includes: acquiring related cooking devices that have a similar relationship to the target cooking device, and acquiring the mapping relationship of cooking heating parameters between the related cooking devices and the target cooking device; determining multiple mapped cooking heating parameters of the related cooking devices based on multiple optimized cooking heating parameters and the mapping relationship of cooking heating parameters; and determining the remaining cooking heating parameters of the related cooking devices based on the multiple mapped cooking heating parameters of the related cooking devices through the cooking parameter adjustment model of the related cooking devices.

[0013] In another possible implementation, the method further includes: acquiring user-mapped cooking feedback data when the associated cooking device cooks using multiple mapped cooking heating parameters; when the mapped cooking feedback data is positive, determining the remaining cooking heating parameters of the associated cooking device based on the multiple mapped cooking heating parameters of the associated cooking device using the cooking parameter adjustment model; when the mapped cooking feedback data is negative, determining the negative score value of the mapped cooking feedback data; when the negative score value of the mapped cooking feedback data is greater than or equal to a preset negative score value, reverting the multiple mapped cooking heating parameters of the associated cooking device to the cooking heating parameters before optimization; when the negative score value of the mapped cooking feedback data is less than the preset negative score value, acquiring the parameter sensitivity corresponding to each of the multiple mapped cooking heating parameters of the associated cooking device; determining a preset number of suppressive mapped cooking heating parameters in descending order of parameter sensitivity among the multiple mapped cooking heating parameters; and multiplying the multiple suppressive mapped cooking heating parameters by a preset attenuation coefficient to weaken the adjustment of the multiple suppressive mapped cooking heating parameters.

[0014] This application also provides an intelligent cooking heating control system driven by multi-dimensional user evaluation, including a unit for performing the method described in any of the preceding claims.

[0015] The beneficial effects of the embodiments in this application compared with the prior art are:

[0016] This application provides an intelligent cooking heating control method based on multi-dimensional user evaluation. The method includes: acquiring multiple cooking heating parameters corresponding to each control dimension of the target cooking device; acquiring the priority control dimension and non-priority control dimensions determined by the user among the multiple control dimensions; and acquiring historical user feedback data corresponding to each control dimension. The multiple cooking heating parameters include heat source distribution parameters, food humidity, heating power, heating temperature, and heating time for each cooking heating segment; the multiple control dimensions include health and taste dimensions; and there is one priority control dimension. A cooking heating optimization unit determines multiple optimized cooking heating parameters corresponding to the priority control dimension of the target cooking device based on the historical user feedback data corresponding to the priority control dimension and the multiple cooking heating parameters corresponding to the priority control dimension. A cooking heating adjustment unit determines multiple adjusted cooking heating parameters corresponding to the non-priority control dimension of the target cooking device based on the historical user feedback data corresponding to the non-priority control dimension, the multiple optimized cooking heating parameters, and the multiple cooking heating parameters corresponding to the non-priority control dimension. The method in this application embodiment can perform in-depth parameter optimization on the cooking effect indicators that users care about most, avoiding the resource dispersion problem in the multi-objective optimization process. At the same time, it can make secondary adjustments by combining feedback data from non-priority dimensions, which can achieve a balanced improvement of multi-dimensional indicators while ensuring the core requirements, thereby improving the optimization effect of the cooking heating process. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the first intelligent cooking heating control method based on multi-dimensional user evaluation provided in this application embodiment;

[0019] Figure 2 A schematic diagram illustrating the workflow of the first intelligent cooking heating control method based on multi-dimensional user evaluation provided in this application embodiment;

[0020] Figure 3 A flowchart illustrating the second intelligent cooking heating control method based on multi-dimensional user evaluation provided in this application embodiment;

[0021] Figure 4 A flowchart illustrating the third intelligent cooking heating control method based on multi-dimensional user evaluation provided in this application embodiment;

[0022] Figure 5 This is a schematic diagram of the logical structure of an intelligent cooking heating control system driven by multi-dimensional user evaluation, provided in an embodiment of this application. Detailed Implementation

[0023] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0024] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0026] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0028] Existing automatic cooking equipment generally achieves basic cooking functions by preset temperature-time curves. The health of the dishes is simply limited by the amount of oil (<5mL) and the maximum temperature (≤180℃). The taste of the dishes is fixed by increasing the heat to above 220℃. This makes it difficult to reconcile the contradiction between nutrient loss and taste deterioration. It cannot dynamically balance the conflict threshold between vitamin retention rate (requiring low temperature and slow cooking) and Maillard reaction (requiring instantaneous high temperature). It is also not compatible with users' personalized needs and real-time environmental variables.

[0029] Based on the above reasons, this application provides an intelligent cooking heating control method driven by multi-dimensional user evaluation. The method includes: acquiring multiple cooking heating parameters corresponding to each control dimension of the target cooking device; acquiring the priority control dimension and non-priority control dimensions determined by the user among the multiple control dimensions; and acquiring historical user feedback data corresponding to each control dimension. The multiple cooking heating parameters include heat source distribution parameters, food humidity, heating power, heating temperature, and heating time for each cooking heating segment; the multiple control dimensions include health and taste dimensions; and there is one priority control dimension. Through a cooking heating optimization unit, multiple optimized cooking heating parameters corresponding to the priority control dimension of the target cooking device are determined based on the historical user feedback data corresponding to the priority control dimension and the multiple cooking heating parameters corresponding to the priority control dimension. Through a cooking heating adjustment unit, multiple adjusted cooking heating parameters corresponding to the non-priority control dimension of the target cooking device are determined based on the historical user feedback data corresponding to the non-priority control dimension, the multiple optimized cooking heating parameters, and the multiple cooking heating parameters corresponding to the non-priority control dimension. The method in this application embodiment can perform in-depth parameter optimization on the cooking effect indicators that users care about most, avoiding the resource dispersion problem in the multi-objective optimization process. At the same time, it can make secondary adjustments by combining feedback data from non-priority dimensions, which can achieve a balanced improvement of multi-dimensional indicators while ensuring the core requirements, thereby improving the optimization effect of the cooking heating process.

[0030] In some scenarios, the intelligent cooking heating control method based on multi-dimensional user evaluation driven by this application embodiment can be applied to the parameter optimization of stir-fry cooking systems, which can improve the parameter optimization effect of stir-fry cooking systems and improve the effect of in-depth parameter optimization of cooking effect indicators that users care about most.

[0031] The following describes in detail, with specific examples, an intelligent cooking heating control method based on multi-dimensional user evaluation driven by embodiments of this application.

[0032] Figure 1 A flowchart illustrating the first intelligent cooking heating control method based on multi-dimensional user evaluation provided in this application embodiment is shown below. Figure 1As shown, the intelligent cooking heating control method based on multi-dimensional user evaluation driven in the embodiments of this application includes S110 to S120, and S110 to S120 will be described in detail below.

[0033] S110. Obtain multiple cooking heating parameters corresponding to each control dimension of the target cooking device, obtain the priority control dimension and non-priority control dimension determined by the user among the multiple control dimensions, and obtain the user's historical feedback data corresponding to each control dimension. Among them, the multiple cooking heating parameters include the heat source distribution parameters of each cooking heating segment, food humidity, heating power, heating temperature and heating time, the multiple control dimensions include health dimension and taste dimension, and the number of priority control dimensions is 1.

[0034] In this implementation, in order to achieve intelligent control and optimization of cooking heating parameters, multiple cooking heating parameters corresponding to each control dimension can be obtained through the target cooking device. The multiple cooking heating parameters corresponding to each control dimension can be different from each other. These parameters can cover detailed control elements of different heating stages. These control elements can specifically include heat source distribution parameters, food humidity, heating power, heating temperature and heating time for each cooking heating segment.

[0035] During cooking control, the heat source distribution parameters of each cooking heating section can describe the energy output configuration mode of the heating element in space, the food humidity parameters can reflect the water content of the food monitored by the real-time humidity sensor, and the heating power and temperature parameters correspond to the operating parameter settings of the actuators of the cooking equipment.

[0036] For example, the multiple cooking heating parameters corresponding to each control dimension can be preset according to the characteristics of the cooking equipment.

[0037] For example, each control dimension can be preset, and multiple control dimensions can include a health dimension and a taste dimension. The health dimension represents the health status of the dish, and the taste dimension represents the taste status of the dish.

[0038] In actual operation, users can select a single priority control dimension from multiple preset control dimensions such as health and taste through the device's interactive interface. At the same time, the cooking optimization system can simultaneously obtain historical evaluation data corresponding to each dimension as the basis for optimization.

[0039] For example, when optimizing cooking, historical user feedback data for each control dimension can be collected and stored so that subsequent optimization can be performed based on the historical user feedback data for each control dimension in the historical data.

[0040] For example, the health dimension can include evaluation indicators such as nutrient retention rate and the amount of harmful substances generated, while the taste dimension can involve sensory indicators such as crispness and tenderness. When a user selects the health dimension as the priority control dimension, the system can retrieve the user's past rating records for indicators such as vitamin content and burnt residue in dishes. This historical feedback data can be stored locally on the device or in a cloud database and linked to specific recipes through timestamps.

[0041] S120. The cooking heating optimization unit determines multiple optimized cooking heating parameters corresponding to the priority control dimension of the target cooking device based on the user historical feedback data corresponding to the priority control dimension and multiple cooking heating parameters corresponding to the priority control dimension. The cooking heating adjustment unit determines multiple adjusted cooking heating parameters corresponding to the non-priority control dimension of the target cooking device based on the user historical feedback data corresponding to the non-priority control dimension, multiple optimized cooking heating parameters, and multiple cooking heating parameters corresponding to the non-priority control dimension.

[0042] Figure 2 A schematic diagram of the workflow of the first intelligent cooking heating control method based on multi-dimensional user evaluation provided in the embodiments of this application is shown below. Figure 2 As shown, during cooking optimization, the cooking heating optimization unit can perform in-depth analysis of heating parameters related to the priority control dimension. Specifically, the cooking heating optimization unit can be a neural network model based on deep learning, which can determine multiple optimized cooking heating parameters corresponding to the priority control dimension of the target cooking device based on the user's historical feedback data corresponding to the priority control dimension and multiple cooking heating parameters corresponding to the priority control dimension.

[0043] When optimizing cooking, the cooking heating optimization unit can dynamically adjust the combination of parameters such as heat source distribution parameters, food humidity, heating power, heating temperature and heating time to generate an optimized parameter set that meets both the requirements of the priority control dimension and the physical constraints of the equipment, thereby obtaining multiple optimized cooking heating parameters corresponding to the priority control dimension of the target cooking equipment.

[0044] For example, when optimizing cooking through the cooking heating optimization unit, if the priority control dimension is the health dimension, the duration of the high-temperature heating section can be shortened accordingly, while increasing the uniform heat source coverage of the medium-temperature stage.

[0045] like Figure 2As shown, after optimizing the parameters of the priority control dimension, the cooking heating adjustment unit can coordinately adjust the parameters of the non-priority control dimension. The cooking heating adjustment unit can optimize the multiple cooking heating parameters corresponding to the non-priority control dimension of the target cooking device based on the optimized multiple cooking heating parameters, while introducing the user historical feedback data corresponding to the non-priority dimension and the multiple cooking heating parameters corresponding to the non-priority control dimension, and obtain the multiple adjusted cooking heating parameters corresponding to the non-priority control dimension of the target cooking device.

[0046] For example, after optimizing multiple cooking heating parameters for the health dimension, user feedback data for the taste dimension can be used to fine-tune multiple cooking heating parameters corresponding to the non-priority control dimensions of the target cooking equipment. This allows the adjusted parameters to improve the crispness of the crust while maintaining the level of nutrient retention. This phased optimization mechanism can ensure that the control needs of different dimensions receive differentiated processing priorities.

[0047] The beneficial effect of the above implementation method is that by setting a filtering mechanism with a single priority control dimension, we can focus on the cooking effect indicators that users care about most for in-depth parameter optimization, avoid the problem of resource dispersion in the multi-objective optimization process, and at the same time, combine the feedback data of non-priority dimensions for secondary adjustment, so as to achieve a balanced improvement of multi-dimensional indicators while ensuring the core needs, thereby improving the optimization effect of the cooking heating process.

[0048] The beneficial effects of the above implementation method are that by establishing a dynamic correlation model between historical feedback data and heating parameters, personalized parameter adaptation can be carried out based on the user's preference characteristics formed over a long period of use; at the same time, the phased optimization-adjustment mechanism not only maintains the clarity of the main optimization direction, but also takes into account the collaborative optimization space of secondary parameters, forming an intelligent control scheme with user characteristics adaptability, which further improves the optimization effect of the cooking heating process.

[0049] In some implementations, the above method also includes S130 to S140, which are described in detail below.

[0050] S130. Obtain the user's historical time-series feedback data corresponding to each control dimension. Through the user preference migration recognition unit, determine the update weights of multiple cooking heating parameters corresponding to each control dimension based on the user's historical time-series feedback data. The user's historical time-series feedback data for each control dimension includes the user's rating value in the time sequence for each control dimension.

[0051] In this implementation, the user's historical time-series feedback data for different control dimensions can also be obtained in chronological order during the historical cooking process. The user's historical time-series feedback data for each control dimension records the evolution trajectory of the user's rating for specific cooking effect indicators in different time periods. For example, the dynamic rating sequence given by the user's health dimension indicator after five consecutive uses of the automatic cooking equipment. Then, the cooking can be optimized based on the user's historical time-series feedback data for each control dimension.

[0052] For example, user historical time-series feedback data can be structured and stored through the storage module built into the cooking device or a cloud database, and the correspondence between timestamps and the user's rating values ​​in the time sequence feedback for each control dimension can be preserved.

[0053] After obtaining the historical time-series feedback data of users corresponding to each control dimension, the fluctuation characteristics and trend of the scoring sequences of different control dimensions can be analyzed through the user preference migration identification unit. Based on the historical time-series feedback data of users corresponding to each control dimension, the update weights of multiple cooking heating parameters corresponding to each control dimension can be determined. Then, the cooking parameters can be optimized based on the update weights of multiple cooking heating parameters corresponding to each control dimension.

[0054] For example, regarding the crispness dimension, if it is detected that the user's three most recent feedback ratings are consistently lower than the historical average, the parameter update weight coefficient for this dimension can be automatically increased. This weight allocation mechanism can capture the migration patterns of user preferences as they change with the seasons or tastes. For example, in winter, users may be more inclined to increase the weight configuration of moisture retention.

[0055] S140. Through the cooking heating optimization unit, based on the user's historical time-series feedback data corresponding to the priority control dimension, multiple cooking heating parameters corresponding to the priority control dimension, and the update weights of multiple cooking heating parameters, the target cooking device is determined to have multiple optimized cooking heating parameters corresponding to the priority control dimension. Through the cooking heating adjustment unit, based on the user's historical feedback data corresponding to the non-priority control dimension, multiple optimized cooking heating parameters, multiple cooking heating parameters corresponding to the non-priority control dimension, and the update weights of cooking heating parameters, the target cooking device is determined to have multiple adjusted cooking heating parameters corresponding to the non-priority control dimension.

[0056] After obtaining the updated weights of multiple cooking heating parameters, the cooking heating optimization unit can determine multiple optimized cooking heating parameters corresponding to the priority control dimension of the target cooking device based on the user's historical time-series feedback data corresponding to the priority control dimension, the multiple cooking heating parameters corresponding to the priority control dimension, and the updated weights of the multiple cooking heating parameters, thereby achieving optimization of the multiple cooking heating parameters corresponding to the priority control dimension.

[0057] For example, when the priority control dimension is health, the duration parameter of the high-temperature stage can be adjusted, and the weights of multiple cooking heating parameters corresponding to the user can be updated to generate a parameter configuration scheme that can both improve the target dimension score and meet the equipment safety threshold.

[0058] After obtaining multiple optimized cooking heating parameters corresponding to the priority control dimension of the target cooking equipment, the cooking heating adjustment unit can determine multiple adjustable cooking heating parameters corresponding to the non-priority control dimension of the target cooking equipment based on user historical feedback data corresponding to the non-priority control dimension, multiple optimized cooking heating parameters, multiple cooking heating parameters corresponding to the non-priority control dimension, and cooking heating parameter update weights, thereby achieving optimization of multiple cooking heating parameters corresponding to the non-priority control dimension.

[0059] In this implementation, the cooking heating adjustment unit can perform supplementary optimization based on historical feedback data of non-priority dimensions after the priority dimension parameters are determined. This phased optimization mechanism can ensure that the adjustment of non-priority dimension parameters will not affect the optimized core indicators, thus guaranteeing the optimization effect of cooking parameters.

[0060] The beneficial effects of the above implementation method are that by establishing a dynamic weight allocation mechanism through time series data analysis, it is possible to accurately identify the migration patterns of user preferences, so that the parameter optimization process always adapts to the latest focus of users; by adopting a hierarchical optimization strategy, while ensuring that the priority control dimension reaches the optimal parameter configuration, the multi-indicator collaborative optimization is achieved through compensatory adjustments of non-priority dimensions, avoiding the deterioration of other indicators caused by single-dimensional optimization.

[0061] The beneficial effect of the above implementation method is that by combining historical data and real-time feedback, it not only maintains the continuity of personalized optimization, but also provides sufficient historical reference for adjusting equipment parameters.

[0062] In some implementations, in S130 above, the user preference migration identification unit determines the update weights of multiple cooking heating parameters corresponding to each control dimension based on the user's historical time-series feedback data corresponding to each control dimension, including S131 to S132. S131 to S132 will be explained in detail below.

[0063] S131. Through the feedback optimization unit based on cooking knowledge graph, multiple conflicting historical time-series feedback data groups are determined according to the user historical time-series feedback data corresponding to each control dimension. Each conflicting historical time-series feedback data group includes two sets of user historical time-series feedback data that conflict with each other in the user historical time-series feedback data corresponding to multiple control dimensions.

[0064] In user feedback, there are often conflicting feedback messages. In this implementation, in order to improve the accuracy of calculating the update weights of multiple cooking heating parameters corresponding to each control dimension, a feedback optimization unit can be constructed based on the cooking knowledge graph. Conflict relationship analysis can be performed on the user's historical time-series feedback data. By parsing the user's historical time-series feedback data of different control dimensions, feedback data combinations with negative correlations and contradictory relationships can be identified.

[0065] For example, in an automatic stir-fry cooking system, when a user's rating of the crispness of stir-fried green peppers with pork slices shows an upward trend, if it is accompanied by a corresponding decrease in the moisture retention rating, the system can determine that the feedback data of these two dimensions constitute a conflicting historical time-series feedback data group. This identification of conflicting relationships can be used to analyze two sets of conflicting user historical time-series feedback data.

[0066] S132. Multiply the update weight of the cooking heating parameters corresponding to the non-priority control dimension in each conflict history time-series feedback data group by a preset update weight adjustment ratio to adjust the update weight of the cooking heating parameters corresponding to the non-priority control dimension.

[0067] After obtaining the historical time-series feedback data set of conflicts, the parameter update weights of non-priority control dimensions can be dynamically adjusted through the feedback optimization unit. For example, in the optimization of the temperature control dimension of an automatic cooking pot, if the system identifies a scoring conflict between the taste dimension (priority dimension) and the health dimension (non-priority dimension), the parameter update weight of the health dimension is multiplied by a proportional coefficient of 0.6. This weight adjustment mechanism can effectively alleviate the parameter conflict problem in the multi-objective optimization process. At the same time, the conflict handling method based on domain knowledge allows the parameter weight adjustment to fit the cooking principles of specific ingredients. For example, when processing fish, the system will automatically strengthen the correlation analysis weight between the moisture retention dimension and the fishy smell control dimension.

[0068] For example, while maintaining the optimization of the core firepower parameters of the stir-fry mode, the constraint intensity on the temperature uniformity index of the pot body can be appropriately relaxed.

[0069] The beneficial effects of the above implementation method are that, through intelligent identification and dynamic weight adjustment of conflicting data groups, the parameter conflict problem between multi-dimensional optimization objectives can be effectively solved, ensuring that the core needs of the priority dimension are fully met; and by combining the domain knowledge of the cooking knowledge graph for weight adjustment, the parameter optimization process can meet both the user's personalized needs and follow scientific cooking principles.

[0070] The beneficial effect of the above implementation method is that, through the proportional decay mechanism of the weights of non-priority dimensions, reasonable optimization space is reserved for the parameter adjustment of non-priority dimensions while maintaining the stability of the main optimization direction.

[0071] Figure 3 A flowchart illustrating the second intelligent cooking heating control method based on multi-dimensional user evaluation provided in this application embodiment is shown below. Figure 3 As shown, the above method also includes S210 to S230, which will be explained in detail below.

[0072] S210. Determine multiple first cooking heating parameters and multiple second cooking heating parameters among multiple cooking heating parameters corresponding to the priority control dimension. The multiple first cooking heating parameters are the cooking heating parameters that are prioritized for optimization among the multiple cooking heating parameters corresponding to the priority control dimension, and the multiple second cooking heating parameters are the cooking heating parameters that are not prioritized for optimization among the multiple cooking heating parameters corresponding to the priority control dimension.

[0073] In this implementation, when optimizing the cooking heating parameters of the priority control dimension, the multiple cooking heating parameters corresponding to the priority control dimension can be divided into multiple first cooking heating parameters that need to be optimized first and multiple second cooking heating parameters that are not optimized first.

[0074] For example, when handling the stir-fry mode in an automatic cooking system, the priority control dimension is the heat intensity. The system can classify the gas valve opening and flame distribution uniformity as the first cooking heating parameter group, and the stir-fry frequency and pot tilt angle as the second cooking heating parameter group. This parameter classification mechanism can ensure the rapid optimization of the core cooking effect. For example, when the user pursues the effect of high-heat stir-fry, the gas supply parameters are adjusted first to achieve instantaneous high temperature output.

[0075] S220. Through the cooking heating optimization unit, based on the user's historical time-series feedback data corresponding to the priority control dimension, multiple first cooking heating parameters, and multiple cooking heating parameter update weights, determine multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters, and obtain the user's first feedback data when the target cooking device subsequently cooks using the multiple optimized first cooking heating parameters.

[0076] After obtaining multiple first cooking heating parameters and multiple second cooking heating parameters, based on the user's historical time-series feedback data corresponding to the priority control dimension, the multiple first cooking heating parameters, and the multiple cooking heating parameter update weights, multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters are determined, thereby realizing the first round of optimization of multiple first cooking heating parameters based on user historical feedback data.

[0077] It should be noted that the update weights of multiple cooking heating parameters include the update weights of multiple first cooking heating parameters.

[0078] For example, when the automatic cooking pot is processing Kung Pao Chicken, the system prioritizes adjusting the heating power parameters and preheating time parameters of the high-temperature section based on the user's five consecutive ratings of the dryness and aroma index. The optimized parameter combination can be immediately applied to the subsequent cooking process. The system also obtains the first feedback data by obtaining the user's real-time rating of the dish's caramel aroma and flavor, evaluates the first feedback data, and then determines whether the first feedback data is positive or negative feedback.

[0079] For example, when determining whether the first feedback data is positive or negative feedback, after obtaining multiple optimized first cooking heating parameters, the dry aroma score can be obtained after optimization through multiple optimized first cooking heating parameters. When the dry aroma score increases by 2 levels, it can be determined as positive feedback, and when the dry aroma score decreases, it can be determined as negative feedback.

[0080] S230. When the first feedback data is positive, the cooking heating optimization unit determines multiple optimized second cooking heating parameters corresponding to the multiple second cooking heating parameters of the target cooking device based on the user's historical time-series feedback data corresponding to the priority control dimension, multiple first cooking heating parameters, and the cooking heating parameter update weights corresponding to the multiple first cooking heating parameters. When the first feedback data is negative, the cooking heating optimization unit re-determines multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters of the target cooking device based on the first feedback data, multiple first cooking heating parameters, and the cooking heating parameter update weights corresponding to the multiple first cooking heating parameters.

[0081] When positive feedback is received, the optimization process for the second cooking heating parameters can be initiated. After the core heat parameters of the automatic cooking system have been successfully optimized, the second cooking heating parameters can be optimized based on historical user ratings of the dish's integrity. For example, the motion trajectory parameters of the stirring mechanism and the timing parameters of ingredient addition can be optimized in the second cooking heating parameters. This phased optimization strategy can effectively reduce the risk of parameter coupling; for example, while ensuring that the heat intensity meets the standard, the stirring angle parameter can be adjusted to make the food heated more evenly.

[0082] It should be noted that the update weights of multiple cooking heating parameters include the update weights of multiple second cooking heating parameters.

[0083] When negative feedback is detected, the re-optimization mechanism of the first cooking heating parameter can be automatically triggered. For example, after the stir-frying temperature parameter of the automatic wok is adjusted, the weights can be updated based on the first feedback data, multiple first cooking heating parameters, and the cooking heating parameters corresponding to the multiple first cooking heating parameters, and the multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters of the target cooking device can be re-determined. This closed-loop optimization mechanism can achieve dynamic calibration of parameters.

[0084] For example, if a user reports a decrease in the local charring score of the food, the matching relationship between the heat transfer coefficient and the temperature gradient parameter can be recalculated, and multiple optimized first cooking heating parameters corresponding to multiple first cooking heating parameters of the target cooking device can be re-determined.

[0085] The beneficial effects of the above implementation method are that, through the intelligent division of parameter optimization priorities and the step-by-step verification mechanism, it is possible to gradually improve the configuration of auxiliary parameters while ensuring the stable improvement of the core cooking effect; by establishing a feedback-driven iterative optimization process, the parameter adjustment process has self-correction capabilities, effectively avoiding the decline in user experience caused by single optimization errors.

[0086] The beneficial effect of the above implementation method is that by adopting a modular optimization strategy for parameter groups, the flexibility of system adjustment is maintained, while ensuring the collaborative operation relationship between different parameter groups is ensured.

[0087] In some implementations, the above method also includes S240 to S250, which will be described in detail below.

[0088] S240. Obtain the number of negative feedbacks corresponding to the first feedback data. When the number of negative feedbacks is greater than or equal to the preset number of negative feedbacks, obtain the correlation coefficients between multiple second cooking heating parameters and multiple first cooking heating parameters. The correlation coefficients characterize the degree of influence of adjusting the first cooking heating parameters on the second cooking heating parameters.

[0089] In this implementation, the number of negative feedbacks generated during the optimization of priority control dimensions can be continuously tracked, further optimizing the cooking parameters corresponding to the priority control dimensions. For example, when optimizing the stir-frying heat parameters in an automatic stir-fry cooking system, if the system receives three consecutive negative ratings from users regarding the degree of caramelization of ingredients, it will trigger a correlation parameter analysis mechanism. This mechanism optimizes the cooking parameters. This feedback threshold setting can effectively identify persistent problems that require systematic adjustment. For example, if a user's rating of the crispness of dry-fried cauliflower is lower than the expected threshold five times consecutively, a deep optimization process will be initiated.

[0090] When optimizing cooking, if the number of negative feedbacks is greater than or equal to the preset number of negative feedbacks, the interaction between different heating parameters can be analyzed through the parameter correlation coefficient calculation module. The correlation coefficients between multiple second cooking heating parameters and multiple first cooking heating parameters can be obtained. The correlation coefficients represent the degree of influence of adjusting the first cooking heating parameters on the second cooking heating parameters. Subsequently, the parameters can be optimized by using the correlation coefficients between multiple second cooking heating parameters and multiple first cooking heating parameters.

[0091] For example, in an automatic cooking wok, analysis of historical optimization data revealed a high correlation coefficient of 0.85 between the stir-frying frequency parameter and the oil temperature control parameter. This means that when adjusting the oil temperature parameter, the motion parameters of the stir-frying mechanism must be considered simultaneously.

[0092] S250. Based on the correlation coefficient from highest to lowest, determine a preset number of target second cooking heating parameters from among the multiple second cooking heating parameters. Through the cooking heating optimization unit, based on the first feedback data, the multiple first cooking heating parameters, the cooking heating parameter update weights corresponding to the multiple first cooking heating parameters, the multiple target second cooking heating parameters, and the cooking heating parameter update weights corresponding to the multiple target second cooking heating parameters, determine multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters and the multiple target second cooking heating parameters.

[0093] When optimizing cooking parameters, the parameters can be prioritized according to their influence intensity. A preset number of target second cooking heating parameters can be determined from multiple second cooking heating parameters, and then the cooking parameters can be optimized in order of their influence intensity from high to low.

[0094] For example, when an automatic cooking system processes the cooking task of Kung Pao Chicken, if the correlation coefficient of 0.92 is detected between the pot body temperature parameter (first cooking heating parameter) and the feeding sequence parameter (second cooking heating parameter), the feeding sequence parameter can be included in the current optimization batch. This correlation-driven parameter selection mechanism can effectively solve the problem of collaborative optimization of coupled parameter groups. For example, when optimizing the heating power parameter, the stirring blade speed parameter can be adjusted simultaneously to balance the heat transfer efficiency.

[0095] After obtaining a preset number of target second cooking heating parameters, the cooking heating optimization unit can determine multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters and the multiple target second cooking heating parameters based on the first feedback data, the multiple first cooking heating parameters, the cooking heating parameter update weights corresponding to the multiple first cooking heating parameters, the multiple target second cooking heating parameters, and the cooking heating parameter update weights corresponding to the multiple target second cooking heating parameters, thereby achieving synchronous optimization of the multiple optimized first cooking heating parameters.

[0096] For example, in the cooking scenario of shredded pork with garlic sauce in an automatic wok, when the heat intensity parameter (first cooking heating parameter) and the stir-fry trajectory parameter (target second cooking heating parameter) need to be jointly optimized, the cooking heating optimization unit can find a combination of parameters that can both improve the stir-frying effect and avoid food breakage.

[0097] The beneficial effects of the above implementation method are that, through the correlation parameter identification and joint optimization mechanism, the parameter coupling problem in complex cooking scenarios can be effectively solved, and the overall efficiency of system optimization can be improved; the triggering mechanism based on the number of feedbacks ensures that the deep optimization process is only started when necessary, which not only ensures the efficiency of conventional optimization but also retains the ability to handle complex problems.

[0098] The beneficial effect of the above implementation method is that, through the above comprehensive optimization method, we can overcome the limitations of simply optimizing the parameters of the priority control dimension in a single step, and achieve partial synchronous optimization of the cooking parameters of the priority control dimension and the cooking parameters of the non-priority control dimension, thereby improving the optimization effect of the cooking parameters.

[0099] In some implementations, in the above-mentioned S230, the cooking heating optimization unit re-determines the multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters based on the first feedback data, the adjusted multiple first cooking heating parameters, and the multiple cooking heating parameters corresponding to the multiple first cooking heating parameters, including S231 to S232. S231 to S232 will be explained in detail below.

[0100] S231. Among multiple second cooking heating parameters, a preset number of target second cooking heating parameters are determined as first cooking heating parameters, and parameter adjustment thresholds corresponding to the multiple target second cooking heating parameters are obtained.

[0101] In the above-mentioned S230, when the first feedback data is negative feedback, and it is necessary to redetermine the multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters of the target cooking device, the multiple second cooking heating parameters can be repositioned through dynamic reorganization of parameter groups and threshold constraints during the parameter optimization process. The cooking parameter optimization effect can be improved by synchronously adjusting the multiple first cooking heating parameters and the multiple second cooking heating parameters.

[0102] For example, when an automatic stir-fry cooking system is processing the task of stir-frying green beans, once the stirring mechanism speed parameter (second cooking heating parameter) is selected as the target second cooking heating parameter, the speed adjustment range can be automatically set to 800-1200 rpm (parameter adjustment threshold) according to the equipment performance specifications. This parameter threshold setting can ensure the safe operation of the equipment, such as preventing mechanical failures caused by motor overheating, while providing a feasible parameter search space for the optimization algorithm.

[0103] For example, the preset number of multiple target second cooking heating parameters can be a number preset based on empirical values.

[0104] In this implementation, a parameter group reorganization mechanism can be used to prioritize key auxiliary parameters for optimization. For example, when optimizing the cooking process of Kung Pao Chicken in an automatic wok, if a high correlation is detected between the pot temperature uniformity parameter (second cooking heating parameter) and the heat intensity parameter (first cooking heating parameter), the temperature uniformity parameter can be temporarily promoted to the first cooking heating parameter. This dynamic parameter reorganization strategy can overcome the limitations of traditional parameter classification. For instance, when handling stir-fried dishes that require precise temperature control, temperature stability parameters and heat intensity parameters can be optimized simultaneously.

[0105] S232. Within the parameter adjustment threshold range corresponding to multiple target second cooking heating parameters, the cooking heating optimization unit determines multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters and multiple optimized target second cooking heating parameters corresponding to the multiple target second cooking heating parameters, based on the first feedback data, multiple first cooking heating parameters, cooking heating parameter update weights corresponding to the multiple first cooking heating parameters, multiple target second cooking heating parameters, and cooking heating parameter update weights corresponding to the multiple target second cooking heating parameters. It also acquires the user's first feedback data when the target cooking device subsequently cooks using the multiple optimized first cooking heating parameters and the multiple optimized target second cooking heating parameters.

[0106] In the joint optimization of cooking parameters, a cooking heating optimization unit can determine multiple optimized first cooking heating parameters corresponding to the multiple first cooking heating parameters and multiple optimized target second cooking heating parameters corresponding to the multiple target second cooking heating parameters based on the first feedback data, multiple first cooking heating parameters, cooking heating parameter update weights corresponding to the multiple first cooking heating parameters, multiple target second cooking heating parameters, and cooking heating parameter update weights corresponding to the multiple target second cooking heating parameters.

[0107] For example, through the cooking heating optimization unit, the automatic stir-fry system can simultaneously optimize the induction cooker power parameters (first cooking heating parameters) and the ingredient feeding interval parameters (second cooking heating parameters) to find a combination of cooking parameters that can both ensure high-heat stir-frying and achieve precise ingredient feeding.

[0108] During cooking, the user's first feedback data can be obtained when the target cooking device cooks using multiple optimized first cooking heating parameters and multiple optimized target second cooking heating parameters, and the cooking parameters can be optimized using the methods in S240 to S250.

[0109] The beneficial effects of the above implementation method are that, through the dynamic reorganization of parameter groups and threshold constraint mechanism, it is possible to break through the limitations of traditional parameter classification and achieve precise control of key influencing parameters; by adopting a multi-dimensional collaborative optimization model, it effectively solves the multi-parameter coupling problem in complex cooking scenarios and improves the overall optimization efficiency of the system.

[0110] In some implementations, in S120 above, the cooking heating adjustment unit determines multiple adjusted cooking heating parameters corresponding to the non-priority control dimension based on the user's historical feedback data corresponding to the non-priority control dimension, multiple optimized cooking heating parameters, and multiple cooking heating parameters corresponding to the non-priority control dimension, including S121 to S122. S121 to S122 will be explained in detail below.

[0111] S121. Obtain the lower limit values ​​of multiple cooking heating parameters corresponding to the non-priority control dimension of the target cooking equipment.

[0112] In this implementation, an energy consumption optimization mechanism can be introduced when adjusting parameters of non-priority control dimensions. For example, when optimizing the oil temperature control parameters of the stir-fry mode in an automatic stir-fry cooking system, the minimum safe threshold of hot oil temperature can be obtained as 160℃. This lower limit setting can not only ensure the basic requirements of the cooking process, such as preventing the starch from being removed from the ingredients due to insufficient oil temperature, but also provide a benchmark constraint for energy consumption optimization.

[0113] S122. Through the cooking heating adjustment unit, based on the user historical feedback data corresponding to the non-priority control dimension, multiple optimized cooking heating parameters, and the lower limit values ​​of multiple cooking heating parameters corresponding to the non-priority control dimension, determine the multiple adjustable cooking heating parameters corresponding to the non-priority control dimension with the lowest energy consumption of the target cooking device.

[0114] When optimizing parameters through the cooking heating adjustment unit, multi-objective calculations can be performed by combining historical feedback data and equipment energy consumption characteristics to achieve the goal of determining multiple cooking heating parameters corresponding to the non-priority control dimension with the lowest energy consumption of the target cooking equipment.

[0115] For example, when an automatic wok is processing stir-fried shredded beef, it can optimize the hot oil holding time from 90 seconds to 75 seconds based on historical user ratings of the dish's dryness and aroma, while ensuring the oil temperature doesn't drop below 180℃. This adjustment method combines user preferences with the device's energy efficiency curve. By analyzing historical data, it finds that users have a high tolerance for a slight reduction in oiliness, automatically shortening the duration of the high-energy-consumption phase.

[0116] For example, when determining the adjustment parameters, a cooking heating adjustment unit based on an energy consumption-effect balance algorithm can be used for optimization. For instance, when optimizing the power parameters of the stir-frying mechanism in an automatic cooking system, the lower limit of the motor speed parameter can be set to 800 rpm based on the average rating level of users for the integrity of the ingredients.

[0117] In this implementation, a cooking heating adjustment unit based on rotation speed, energy consumption, and integrity is established to find the combination of rotation speed parameters that can maintain the integrity of the food while minimizing energy consumption. For example, Pareto optimality for energy consumption and effect is achieved at 850 rpm, thereby improving the optimization effect of cooking parameters.

[0118] The beneficial effects of the above implementation method are that, by setting the lower limit of the parameter, the energy-saving potential of the equipment can be maximized while ensuring the basic cooking effect; and by combining historical feedback data to optimize energy consumption, the parameter adjustment not only conforms to the implicit preferences formed by users over a long period of use, but also achieves the economic goal of improving energy efficiency.

[0119] Figure 4 A flowchart illustrating the third intelligent cooking heating control method based on multi-dimensional user evaluation provided in this application embodiment is shown below. Figure 4 As shown, the above method also includes S310 to S320, which will be described in detail below.

[0120] S310. Obtain related cooking devices that have a similar relationship with the target cooking device, and obtain the mapping relationship of cooking heating parameters between the related cooking devices and the target cooking device.

[0121] In this implementation, associated cooking devices that have a similar relationship to the target cooking device can be obtained, and the cooking heating parameter mapping relationship between the associated cooking devices and the target cooking device can be obtained. The parameters between multiple devices can be transferred through the cooking heating parameter mapping relationship to achieve cooking optimization of multiple cooking devices.

[0122] For example, in an automated cooking system, after the user optimizes the heating parameters of the built-in wok (target device), the system can automatically identify a countertop wok (associated device) with the same heating module as a similar device. This device association can be set through empirical values, such as establishing a mapping benchmark by analyzing the difference in maximum power and heat transfer efficiency between the two devices.

[0123] S320. Based on multiple optimized cooking heating parameters and their mapping relationships, determine multiple mapped cooking heating parameters for the associated cooking equipment. Using the cooking parameter adjustment model for the associated cooking equipment, determine the remaining cooking heating parameters for the associated cooking equipment based on these multiple mapped cooking heating parameters.

[0124] When optimizing cooking parameters, multiple mapped cooking heating parameters of the associated cooking equipment can be determined based on multiple optimized cooking heating parameters and the mapping relationship between cooking heating parameters, thereby achieving optimization of multiple mapped cooking heating parameters according to the mapping relationship between cooking heating parameters.

[0125] For example, when the optimized parameters of the built-in frying pan (target cooking device) include "preheating stage power parameter 65%", the mapping parameter can be automatically adjusted to "preheating stage power parameter 75%" based on the characteristic that the rated power of the countertop frying pan (associated cooking device) is 15% lower.

[0126] When optimizing cooking parameters, it is also possible to further optimize the remaining cooking heating parameters of the associated cooking equipment by adjusting the cooking parameter model of the associated cooking equipment and determining the remaining cooking heating parameters of the associated cooking equipment based on multiple mapped cooking heating parameters of the associated cooking equipment.

[0127] For example, once the core firepower parameters of the frying pan (associated with the cooking device) have been mapped and adapted, the system automatically calculates the air supply interval parameters and fan speed parameters based on the unique hot air circulation structure characteristics of the cooking device.

[0128] The beneficial effects of the above implementation method are that, through device parameter mapping and supplementary optimization mechanisms, personalized cooking solutions can be quickly migrated across devices, greatly improving the user's convenience in multi-device scenarios; and by using a device-specific parameter adjustment model for supplementary optimization, the problem of parameter adaptation distortion caused by hardware differences can be effectively solved, ensuring the consistency of optimization effects across different devices.

[0129] In some implementations, the above method also includes S330 to S350, which will be described in detail below.

[0130] S330: Obtain the user's mapped cooking feedback data when the associated cooking device is cooking with multiple mapped cooking heating parameters.

[0131] In this implementation, dynamic calibration of parameter migration can be further achieved by establishing a cross-device feedback verification mechanism. Specifically, the user's mapped cooking feedback data can be obtained when the associated cooking device cooks with multiple mapped cooking heating parameters, and then the cooking optimization effect can be evaluated based on the mapped cooking feedback data.

[0132] For example, in an automatic stir-fry cooking system, when the optimized stir-frying parameters of an embedded wok are transferred to a countertop wok, the system continuously collects user ratings of the dryness and aroma of the food cooked on the new equipment. This feedback data can be synchronized to a cloud analysis platform in real time through the device's network module. For example, after a user completes three Kung Pao Chicken cooking sessions, an evaluation dataset containing 12 sets of rating data is generated.

[0133] S340. When the mapped cooking feedback data is positive, adjust the cooking parameter model of the associated cooking equipment and determine the remaining cooking heating parameters of the associated cooking equipment based on multiple mapped cooking heating parameters of the associated cooking equipment. When the mapped cooking feedback data is negative, determine the negative score value of the mapped cooking feedback data.

[0134] After obtaining the mapped cooking feedback data, a differentiated parameter adjustment strategy can be implemented through a positive and negative feedback classification mechanism. When the mapped cooking feedback data is positive, the remaining cooking heating parameters of the associated cooking equipment can be determined through the cooking parameter adjustment model of the associated cooking equipment based on multiple mapped cooking heating parameters of the associated cooking equipment, thereby achieving targeted optimization of the cooking parameters.

[0135] For example, when a user of a countertop wok (associated with other cooking equipment) gives five or more consecutive positive reviews of the migrated parameters, the system automatically triggers the optimization process for the remaining parameters. For instance, based on the preheating time characteristics of the equipment, the system supplements and optimizes the temperature compensation parameters during the ingredient feeding window. This positive feedback mechanism accelerates parameter coordination between devices.

[0136] After obtaining the mapped cooking feedback data, when the mapped cooking feedback data is negative, the negative score value of the mapped cooking feedback data can be determined, and the cooking parameters can be quantitatively optimized based on the negative score value.

[0137] S350. When the negative score value of the mapped cooking feedback data is greater than or equal to a preset negative score value, the multiple mapped cooking heating parameters of the associated cooking equipment are reverted to their pre-optimization cooking heating parameters. When the negative score value of the mapped cooking feedback data is less than a preset negative score value, the parameter sensitivity corresponding to each of the multiple mapped cooking heating parameters of the associated cooking equipment is obtained. Among the multiple mapped cooking heating parameters, a preset number of suppressive mapped cooking heating parameters are determined in descending order of parameter sensitivity. A preset attenuation coefficient is multiplied by the multiple suppressive mapped cooking heating parameters to weaken their adjustment.

[0138] When dealing with negative feedback, if the negative score of the mapped cooking feedback data is greater than or equal to the preset negative score, it indicates that the user's negative feedback is too strong. In this case, multiple mapped cooking heating parameters of the associated cooking equipment can be reverted to the cooking heating parameters before optimization, so as to re-optimize the cooking process and thus ensure the cooking optimization effect.

[0139] When optimizing cooking, if the negative score of the mapped cooking feedback data is less than the preset negative score, it means that the user's negative feedback is relatively small. The parameter sensitivity corresponding to multiple mapped cooking heating parameters of the associated cooking device can be obtained, and the cooking process can be optimized based on the parameter sensitivity.

[0140] For example, when the system detects that a user of a Taiwanese wok has given a rating of food caramelization below the threshold three times in a row, the system can identify the heat transfer efficiency parameter (sensitivity 0.88) as the key influencing factor through the parameter influence factor analysis module.

[0141] After obtaining the parameter sensitivity, a preset number of suppression mapping cooking heating parameters can be determined from multiple mapping cooking heating parameters in descending order of parameter sensitivity. This allows for fine-tuning of the cooking process using multiple suppression mapping cooking heating parameters. Multiplying these multiple suppression mapping cooking heating parameters by a preset attenuation coefficient weakens the adjustment of these parameters, thereby improving the cooking optimization effect.

[0142] For example, in optimizing the mapping parameters of a Taiwanese wok, when the sensitivity of the oil temperature fluctuation parameter ranks first, the system applies a decay coefficient of 0.7 to that parameter. This dynamic adjustment strategy can effectively suppress over-optimization, for example, by reducing the magnitude of parameter adjustments while keeping the core optimization direction unchanged, thus avoiding oscillations in cooking results caused by aggressive adjustments to a single parameter.

[0143] The beneficial effects of the above implementation method are that by establishing a cross-device feedback closed-loop verification mechanism, it is possible to achieve intelligent migration and dynamic calibration of personalized parameters, ensuring reliable reproduction of optimization effects across different devices; by adopting a parameter sensitivity-driven attenuation adjustment strategy, it can correct obvious deviations while avoiding the complete rejection of existing optimization results when dealing with negative feedback.

[0144] The beneficial effects of the above implementation method are that when the negative score value of the mapped cooking feedback data is greater than or equal to the preset negative score value, the multiple mapped cooking heating parameters of the associated cooking equipment will be reverted to the cooking heating parameters before optimization; when the negative score value of the mapped cooking feedback data is less than the preset negative score value, a graded response strategy will be established through a preset threshold mechanism, so that the parameter adjustment process has both flexibility and stability.

[0145] This application also provides an intelligent cooking heating control system driven by multi-dimensional user evaluation, including a unit for performing the method described in any of the preceding claims.

[0146] Figure 5 A schematic diagram of the logical structure of an intelligent cooking heating control system driven by multi-dimensional user evaluation, provided in an embodiment of this application, is shown below. Figure 5As shown, the system 1 of this embodiment includes a processing unit 11, a storage unit 12, and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12, and the transceiver unit 13 cooperate with each other to implement the above-described method. The beneficial effects of the embodiments of this application have been described in the above-described method and will not be repeated here.

[0147] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0148] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0149] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0150] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0151] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0152] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0153] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0154] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A multi-dimension user evaluation driven intelligent cooking heating control method, characterized in that, The method comprises: obtaining a plurality of cooking heating parameters corresponding to each control dimension of a target cooking device, obtaining a priority control dimension and a non-priority control dimension determined by a user in the plurality of control dimensions, and obtaining user historical feedback data corresponding to each control dimension; wherein the plurality of cooking heating parameters comprise heat source distribution parameters, food material humidity, heating power, heating temperature and heating duration of each cooking heating section, the plurality of control dimensions comprise a health dimension and a taste dimension, and the number of priority control dimensions is 1; determining, by a cooking heating optimization unit, a plurality of optimized cooking heating parameters corresponding to the priority control dimension of the target cooking device according to the user historical feedback data corresponding to the priority control dimension and the plurality of cooking heating parameters corresponding to the priority control dimension; and determining, by a cooking heating adjustment unit, a plurality of adjusted cooking heating parameters corresponding to the non-priority control dimension of the target cooking device according to the user historical feedback data corresponding to the non-priority control dimension, the plurality of optimized cooking heating parameters and the plurality of cooking heating parameters corresponding to the non-priority control dimension; The method further comprises: obtaining user historical timing feedback data corresponding to each control dimension; and determining, by a user preference migration identification unit, a plurality of cooking heating parameter update weights corresponding to each control dimension according to the user historical timing feedback data corresponding to each control dimension; wherein the user historical timing feedback data corresponding to each control dimension comprises a score value fed back in time sequence by a user corresponding to each control dimension; determining, by a cooking heating optimization unit, a plurality of optimized cooking heating parameters corresponding to the priority control dimension of the target cooking device according to the user historical timing feedback data corresponding to the priority control dimension, the plurality of cooking heating parameters corresponding to the priority control dimension and the plurality of cooking heating parameter update weights; and determining, by a cooking heating adjustment unit, a plurality of adjusted cooking heating parameters corresponding to the non-priority control dimension of the target cooking device according to the user historical feedback data corresponding to the non-priority control dimension, the plurality of optimized cooking heating parameters, the plurality of cooking heating parameters corresponding to the non-priority control dimension and the cooking heating parameter update weights.

2. The method of claim 1, wherein, Determining, by a user preference migration identification unit, a plurality of cooking heating parameter update weights corresponding to each control dimension according to user historical timing feedback data corresponding to each control dimension comprises: determining, by a feedback optimization unit based on a cooking knowledge graph, a plurality of conflict historical timing feedback data groups according to the user historical timing feedback data corresponding to each control dimension, each conflict historical timing feedback data group comprising two groups of user historical timing feedback data that exist in conflict with each other in the user historical timing feedback data corresponding to the plurality of control dimensions; multiplying a preset update weight adjustment proportion value by the cooking heating parameter update weight corresponding to the non-priority control dimension in each conflict historical timing feedback data group to adjust the cooking heating parameter update weight corresponding to the non-priority control dimension.

3. The method of claim 2, wherein, The method further comprises: determine a plurality of first cooking heating parameters and a plurality of second cooking heating parameters corresponding to the plurality of cooking heating parameters of the priority control dimension, the plurality of first cooking heating parameters being cooking heating parameters that are prioritized for optimization, and the plurality of second cooking heating parameters being cooking heating parameters that are not prioritized for optimization; update the weights according to the user historical time sequence feedback data corresponding to the priority control dimension, the plurality of first cooking heating parameters, and the plurality of cooking heating parameters, determine a plurality of optimized first cooking heating parameters corresponding to the plurality of first cooking heating parameters, and obtain first feedback data of the user when the target cooking device subsequently performs cooking through the plurality of optimized first cooking heating parameters; when the first feedback data is positive feedback, update the weights according to the user historical time sequence feedback data corresponding to the priority control dimension, the plurality of first cooking heating parameters, and the plurality of first cooking heating parameters respectively corresponding to the plurality of cooking heating parameters, determine a plurality of optimized second cooking heating parameters corresponding to the plurality of second cooking heating parameters of the target cooking device, and when the first feedback data is negative feedback, update the weights according to the first feedback data, the plurality of first cooking heating parameters, and the plurality of first cooking heating parameters respectively corresponding to the plurality of cooking heating parameters, and re-determine a plurality of optimized first cooking heating parameters corresponding to the plurality of first cooking heating parameters of the target cooking device.

4. The method of claim 3, wherein, The method further comprises: obtain a negative feedback frequency corresponding to the negative feedback of the first feedback data, and when the negative feedback frequency is greater than or equal to a preset negative feedback frequency, obtain a correlation coefficient of the plurality of second cooking heating parameters and the plurality of first cooking heating parameters respectively, the correlation coefficient representing the degree of influence of the first cooking heating parameter adjustment on the second cooking heating parameter; determine a preset number of target second cooking heating parameters in the plurality of second cooking heating parameters in order from high to low according to the correlation coefficient, and update the weights according to the first feedback data, the plurality of first cooking heating parameters, the plurality of first cooking heating parameters respectively corresponding to the plurality of cooking heating parameters, the plurality of target second cooking heating parameters, and the plurality of target second cooking heating parameters respectively corresponding to the plurality of cooking heating parameters through the cooking heating optimization unit, to determine a plurality of optimized first cooking heating parameters corresponding to the plurality of first cooking heating parameters and the plurality of target second cooking heating parameters respectively.

5. The method of claim 4, wherein, re-determine a plurality of optimized first cooking heating parameters corresponding to the plurality of first cooking heating parameters through the cooking heating optimization unit according to the first feedback data, the adjusted plurality of first cooking heating parameters, and the plurality of cooking heating parameters respectively corresponding to the plurality of first cooking heating parameters, including: determine a preset number of target second cooking heating parameters in the plurality of second cooking heating parameters as the first cooking heating parameters, and obtain parameter adjustment thresholds corresponding to the plurality of target second cooking heating parameters; In a parameter adjustment threshold range corresponding to the plurality of target second cooking heating parameters, the cooking heating optimization unit determines a plurality of optimized first cooking heating parameters corresponding to the plurality of first cooking heating parameters and a plurality of optimized target second cooking heating parameters corresponding to the plurality of target second cooking heating parameters according to the first feedback data, the plurality of first cooking heating parameters, the cooking heating parameter update weights corresponding to the plurality of first cooking heating parameters respectively, the plurality of target second cooking heating parameters and the cooking heating parameter update weights corresponding to the plurality of target second cooking heating parameters respectively; and obtains the first feedback data of the user when the target cooking device cooks subsequently through the plurality of optimized first cooking heating parameters and the plurality of optimized target second cooking heating parameters.

6. The method of claim 5, wherein, The cooking heating adjustment unit determines the plurality of adjustment cooking heating parameters corresponding to the non-priority control dimension according to the user historical feedback data corresponding to the non-priority control dimension, the plurality of optimized cooking heating parameters and the plurality of cooking heating parameters corresponding to the non-priority control dimension, including: obtaining the lower limit value of the plurality of cooking heating parameters corresponding to the non-priority control dimension of the target cooking device; The cooking heating adjustment unit determines the plurality of adjustment cooking heating parameters corresponding to the non-priority control dimension with the lowest energy consumption of the target cooking device according to the user historical feedback data corresponding to the non-priority control dimension, the plurality of optimized cooking heating parameters, the plurality of cooking heating parameters corresponding to the non-priority control dimension and the lower limit value of the plurality of cooking heating parameters corresponding to the non-priority control dimension.

7. The method of claim 6, wherein, The method further comprises: obtaining an associated cooking device having a similar relationship with the target cooking device, and obtaining a cooking heating parameter mapping relationship between the associated cooking device and the target cooking device; determining a plurality of mapping cooking heating parameters of the associated cooking device according to the plurality of optimized cooking heating parameters and the cooking heating parameter mapping relationship; and determining the remaining cooking heating parameters of the associated cooking device according to the plurality of mapping cooking heating parameters of the associated cooking device by using the cooking parameter adjustment model of the associated cooking device.

8. The method of claim 7, wherein, The method further comprises: obtaining mapping cooking feedback data of the user when the associated cooking device cooks through the plurality of mapping cooking heating parameters; when the mapping cooking feedback data is positive feedback, determining the remaining cooking heating parameters of the associated cooking device according to the plurality of mapping cooking heating parameters of the associated cooking device by using the cooking parameter adjustment model of the associated cooking device; when the mapping cooking feedback data is negative feedback, determining a negative score value of the mapping cooking feedback data; when the negative score value of the mapping cooking feedback data is greater than or equal to a preset negative score value, reverting the plurality of mapping cooking heating parameters of the associated cooking device to the cooking heating parameters before optimization; when the negative score value of the mapping cooking feedback data is less than the preset negative score value, obtaining parameter sensitivity corresponding to the plurality of mapping cooking heating parameters of the associated cooking device respectively; in the plurality of mapping cooking heating parameters, determining a preset number of inhibited mapping cooking heating parameters in order from high to low according to the parameter sensitivity; and multiplying a preset attenuation coefficient on the plurality of inhibited mapping cooking heating parameters to weaken and adjust the plurality of inhibited mapping cooking heating parameters.

9. A multi-dimension user evaluation driven intelligent cooking heating control system, characterized in that, comprising means for performing the method of any one of claims 1 to 8.

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