A method, apparatus, and cooking equipment for determining cooking parameters

Abstract

This application provides a method, apparatus, and cooking equipment for determining cooking parameters. The method includes acquiring current environmental information and the current cooking mode for the current cooking task; determining multiple candidate cooking parameter groups adapted to the current environmental information and current cooking mode from multiple preset cooking parameter groups, each preset cooking parameter group being associated with corresponding historical environmental information, historical cooking modes, and a current parameter recommendation factor, the current parameter recommendation factor reflecting the correlation between the preset cooking parameter group and historically recommended parameter groups when actually used; determining the candidate cooking parameter group corresponding to the largest current parameter recommendation factor from the multiple candidate cooking parameter groups as the first target cooking parameter group, the first target cooking parameter group being recommended for the current cooking task. This application provides a more adaptive cooking parameter determination scheme.

Description

Technical Field

[0001] This application relates to the field of intelligent electrical appliance technology, and in particular to a method, apparatus and cooking equipment for determining cooking parameters. Background Technology

[0002] Cooking equipment can offer at least one cooking mode (such as baking or steaming) as a candidate for processing ingredients. In related technologies, a fixed set of cooking parameters is often set for a single cooking mode, such as a fixed cooking temperature and cooking time in baking mode. This fixed set of cooking parameters is then recommended for ingredient processing. However, the application of fixed cooking parameter sets has limitations; they do not closely match the user's actual cooking needs, thus affecting the user experience. Summary of the Invention

[0003] To address at least one of the aforementioned technical problems, this application provides a method, apparatus, and cooking equipment for determining cooking parameters: According to a first aspect of this application, a method for determining cooking parameters is provided, the method comprising: Obtain current environmental information and current cooking mode for the current cooking task; Multiple candidate cooking parameter groups that are adapted to the current environment information and the current cooking mode are determined from multiple preset cooking parameter groups. Each preset cooking parameter group is associated with corresponding historical environment information, historical cooking mode and current parameter recommendation factor. The current parameter recommendation factor is used to reflect the correlation between the preset cooking parameter group and the historical recommended parameter group when the preset cooking parameter group is actually used. The candidate cooking parameter group corresponding to the largest current parameter recommendation factor is determined from the plurality of candidate cooking parameter groups as the first target cooking parameter group, and the first target cooking parameter group is recommended to participate in the current cooking task.

[0004] According to a second aspect of this application, a cooking parameter determining apparatus is provided, the apparatus comprising: Task data acquisition module: used to acquire current environmental information and current cooking mode for the current cooking task; Candidate cooking parameter group determination module: used to determine multiple candidate cooking parameter groups that are adapted to the current environment information and the current cooking mode from multiple preset cooking parameter groups. Each preset cooking parameter group is associated with corresponding historical environment information, historical cooking mode and current parameter recommendation factor. The current parameter recommendation factor is used to reflect the correlation between the preset cooking parameter group and the historical recommended parameter group when the preset cooking parameter group is actually used. Target cooking parameter group determination module: used to determine the candidate cooking parameter group corresponding to the largest current parameter recommendation factor from the plurality of candidate cooking parameter groups as the first target cooking parameter group, and the first target cooking parameter group is recommended to participate in the current cooking task.

[0005] According to a third aspect of this application, a cooking apparatus is provided, including a cooking parameter determining device as described in the second aspect.

[0006] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this application.

[0007] Implementing this application will have the following beneficial effects: This application provides a more adaptive cooking parameter determination scheme. It acquires current environmental information and the current cooking mode for the current cooking task; then, it determines multiple candidate cooking parameter groups from multiple preset cooking parameter groups that are compatible with the current environmental information and the current cooking mode; furthermore, it determines the candidate cooking parameter group corresponding to the largest current parameter recommendation factor from the multiple candidate cooking parameter groups as the first target cooking parameter group. This application uses the current environmental information and the current cooking mode as the matching basis to determine multiple candidate cooking parameter groups. By introducing current environmental information to enrich the matching basis, it also enriches the candidate sources for the first target cooking parameter group. When determining the first target cooking parameter group from multiple candidate cooking parameter groups, it focuses on whether its corresponding current parameter recommendation factor is the largest. The current parameter recommendation factor reflects the correlation between the preset cooking parameter group and the historical recommended parameter group when actually used, and reflects the recommendation conversion ability of the historical recommended parameter group. This guides the determination of the first target cooking parameter group, using the candidate cooking parameter group as the optimal recommendation conversion result as the first target cooking parameter group, which helps to improve the fit between the determined first target cooking parameter group and the user's actual cooking needs, thereby improving the user experience.

[0008] Other features and aspects of this application will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0009] The objectives, technical solutions, and beneficial effects of the present invention described above can be clearly obtained through the following detailed description of specific embodiments that enable the implementation of the present invention, in conjunction with the accompanying drawings.

[0010] The same reference numerals and symbols in the accompanying drawings and the specification are used to represent the same or equivalent elements.

[0011] Figure 1 This is a flowchart illustrating a method for determining cooking parameters provided in this application; Figure 2 This is a schematic diagram of the process for determining multiple candidate cooking parameter groups provided in this application; Figure 3 This is a flowchart illustrating the process of generating the target parameter recommendation factor provided in this application; Figure 4 This is a flowchart illustrating the process of updating the data record set provided in this application; Figure 5 This is a block diagram of a cooking parameter determining device provided in this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0013] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0014] Various exemplary embodiments, features, and aspects of this application will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0015] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0016] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0017] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed description. Those skilled in the art should understand that this application can be implemented without certain specific details. In some instances, methods, means, components, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.

[0018] Figure 1 This diagram illustrates a flowchart of a method for determining cooking parameters according to an embodiment of this application. Figure 1 As shown, the method includes: S101: Obtain current environmental information and current cooking mode for the current cooking task; In this embodiment, the execution subject of the cooking parameter determination method provided in this application can be the cooking device itself or a server belonging to the same cooking system as the cooking device. The current environment information and current cooking mode obtained here are for the current cooking task. For the cooking task, it focuses on a purposeful food processing. The current environment information and current cooking mode can be obtained from the received cooking instructions as the data source. The cooking instructions can be generated by the user. For example, 1) the cooking system also includes a terminal, which can be directly or indirectly connected to the cooking device or the server via wired or wireless communication. The terminal provides a user interface, and the cooking instructions can be generated by the user by triggering the relevant controls provided by the user interface. Accordingly, the terminal sends the cooking instructions to the cooking device or the server. 2) The cooking device provides a user interface, and the cooking instructions can be generated by the user by triggering the relevant controls provided by the user interface. Accordingly, the cooking device obtains the cooking instructions. 3) The cooking device can be directly or indirectly connected to the server via wired or wireless communication. The cooking device provides a user interface, and the cooking instructions can be generated by the user by triggering the relevant controls provided by the user interface. Accordingly, the cooking device sends cooking instructions to the server. The current environmental information and current cooking mode can be selected by the user and carried in the cooking instructions. The current environmental information and current cooking mode can be obtained based on the cooking instructions.

[0019] Current environmental information can refer to information such as current time or current location. Current cooking mode can represent the expected processing method of the ingredients, such as roasting, steaming, baking, stewing, or frying. In practical applications, current time information can be represented by at least two sub-information items across different time dimensions. Candidate time dimensions can include season, month, and time period dimensions. Sub-information under the season dimension can indicate spring, summer, autumn, or winter; sub-information under the month dimension can indicate one of the months from January to December; and sub-information under the time period dimension can indicate morning, noon, or evening.

[0020] S102: Determine multiple candidate cooking parameter groups from multiple preset cooking parameter groups that are compatible with the current environment information and the current cooking mode. Each preset cooking parameter group is associated with corresponding historical environment information, historical cooking mode and current parameter recommendation factor. The current parameter recommendation factor is used to reflect the correlation between the preset cooking parameter group and the historical recommended parameter group when the preset cooking parameter group is actually used. In this embodiment, multiple candidate cooking parameter groups are determined based on the current environmental information and the current cooking mode. The current environmental information is matched with the historical environmental information associated with the preset cooking parameter group, and the current cooking mode is matched with the historical cooking mode associated with the preset cooking parameter group. For the multiple candidate cooking parameter groups, it should be understood that they are determined after matching according to preset matching rules. The preset matching rules can provide two types of matching execution order, such as the order of a single preset cooking parameter group dimension, or the order of multiple preset cooking parameter group dimensions; and can provide two types of matching judgment conditions, such as the judgment condition for matching environmental information, or the judgment condition for matching cooking modes. Generally, the judgment condition for matching cooking modes indicates that the current cooking mode is the same as the historical cooking mode. The judgment condition for matching environmental information indicates that the current environmental information is similar to or the same as the historical environmental information.

[0021] The preset cooking parameter set is associated with historical environment information, historical cooking modes, and a current parameter recommendation factor. The current parameter recommendation factor reflects the relevance between the preset cooking parameter set and the historically recommended parameter set when actually used. It can be understood that the historically recommended parameter set is parameter set X, and the preset cooking parameter set is parameter set Y. Historical cooking task i focuses on a purposeful food processing step, and parameter set X is recommended for historical cooking task i. During the execution of historical cooking task i, parameter set X is initially used, but the user may adjust the parameters according to the actual situation, thus parameter set X becomes parameter set Y. The current parameter recommendation factor associated with parameter set Y reflects the relevance between parameter set Y as the adjusted result and parameter set X as the initial recommendation. Generally, the smaller the adjustment, the higher the relevance and the larger the current parameter recommendation factor; conversely, the larger the adjustment, the lower the relevance and the smaller the current parameter recommendation factor. The current parameter recommendation factor reflects the recommendation conversion capability of the historically recommended parameter set, that is, the ability of historically recommended parameters as initial recommendations to be converted into actual usage results. The smaller the adjustment range, the stronger the ability to translate into actual adoption results; conversely, the larger the adjustment range, the weaker the ability to translate into actual adoption results. The indicator reflecting the recommendation conversion ability of historical recommended parameter sets is transplanted and correlated with preset cooking parameter sets, thus serving as the basis for guiding the determination of the first target cooking parameter set. Compared to using the recommendation conversion ability of historical recommended parameter sets themselves as an indicator for recommending historical parameters, this approach facilitates the dynamic updating of multiple preset cooking parameter sets as data sources (the data source may not include the target recommended parameters); it also allows multiple preset cooking parameter sets as data sources to guide the determination of the first target cooking parameter set based on positive feedback, resulting in greater accuracy and reliability.

[0022] As one possible implementation, such as Figure 2 As shown, determining multiple candidate cooking parameter sets that adapt to the current environment information and the current cooking mode from multiple preset cooking parameter sets includes: S201: Determine a plurality of first cooking parameter groups from the plurality of preset cooking parameter groups that match the historical cooking mode with the current cooking model; S202: If there are multiple second cooking parameter groups in the multiple first cooking parameter groups that match the historical environment information with the current environment information, determine the multiple second cooking parameter groups as the multiple candidate cooking parameter groups; S203: If there are no multiple second cooking parameter groups in the multiple first cooking parameter groups that match the historical environment information with the current environment information, then the multiple first cooking parameter groups are determined as the multiple candidate cooking parameter groups.

[0023] This section provides a matching business logic that first performs global matching at the cooking mode dimension, and then performs overall matching at the environmental information dimension based on the results of the global matching. Considering the influence of cooking mode and environmental information on the determination of cooking parameters, prioritizing global matching at the cooking mode dimension improves the efficiency of obtaining valid candidates. Building on this, overall matching at the environmental information dimension, on the one hand, improves matching speed for candidates with an appropriate amount of data; on the other hand, if the matching determines the existence of a cooking parameter set that meets the judgment criteria, it can further narrow down the number of valid candidates and improve the quality of the obtained valid candidates, thereby supporting the subsequent efficient and accurate determination of the first target cooking parameter set.

[0024] For example, there are M preset cooking parameter groups. After global matching at the cooking mode dimension, N preset cooking parameter groups meet the criteria for matching cooking modes, where N is less than or equal to M. Then, for these N preset cooking parameter groups, a global matching at the environmental information dimension is performed. If L preset cooking parameter groups meet the criteria for matching environmental information, then these L preset cooking parameter groups are multiple candidate cooking parameter groups, where L is less than or equal to N; if no preset cooking parameter group meets the criteria for matching environmental information, then these N preset cooking parameter groups are multiple candidate cooking parameter groups.

[0025] In practical applications, when the environmental information refers to time-related information, and this time-related information includes two sub-information items, a multi-level matching strategy can be used to determine multiple candidate cooking parameter groups. The two sub-information items correspond one-to-one with the two time dimensions. The first level of matching in the multi-level matching strategy requires that both sub-information items match all three dimensions of the cooking mode (e.g., month dimension + time period dimension + cooking mode dimension). The second level of matching requires that the more granular sub-information items within the two sub-information items match all two dimensions of the cooking mode (e.g., time period dimension + cooking mode dimension). The third level of matching requires that the cooking mode dimension itself match.

[0026] S103: Determine the candidate cooking parameter group corresponding to the largest current parameter recommendation factor from the plurality of candidate cooking parameter groups as the first target cooking parameter group, and the first target cooking parameter group is recommended to participate in the current cooking task.

[0027] In this embodiment, in conjunction with the recording of the current parameter recommendation factor in step S102 above, the current parameter recommendation factor serves as the basis for guiding the determination of the first target cooking parameter group. Multiple candidate cooking parameter groups are used as candidates to determine the first target cooking parameter group. During the execution of the current cooking task, the first target cooking parameter group, initially recommended, is adopted.

[0028] As one possible implementation, such as Figure 3 As shown, after determining the candidate cooking parameter group corresponding to the largest current parameter recommendation factor from the plurality of candidate cooking parameter groups as the first target cooking parameter group, the method further includes: S301: Obtain the actual characterization parameter set during the execution of the current cooking task; S302: Generate a target parameter recommendation factor for the actual characterization parameter group based on the difference between the actual characterization parameter group and the first target cooking parameter group, wherein the target parameter recommendation factor is negatively correlated with the difference.

[0029] Referring to the description of the current parameter recommendation factor in step S102 above, this section provides a method for generating the target parameter recommendation factor for the actual representation parameter group. The current cooking task focuses on a purposeful food processing step, and a first target cooking parameter group is recommended for the current cooking task. During the execution of the current cooking task, the first target cooking parameter group is initially used, but the user may adjust the parameters according to the actual situation, thus the first target cooking parameter group becomes the actual representation parameter group after adjustment. The target parameter recommendation factor for the actual representation parameter group can reflect the correlation between the actual representation parameter group as the result of the adjustment and the initial recommendation of the target parameter recommendation factor for the actual representation parameter group. Whether it is the current parameter recommendation factor associated with the preset cooking parameter group or the target parameter recommendation factor for the actual representation parameter group, their generation logic is the same. This supports the subsequent application of the actual representation parameter group as a preset cooking parameter group, thereby supporting the dynamic updating of multiple preset cooking parameter groups and facilitating the determination of the subsequent first target cooking parameter group.

[0030] During the execution of the current cooking task, the first target cooking parameter group is initially adopted, but the user may adjust the parameters according to the actual situation. The first target cooking parameter group can include target cooking parameters of duration and non-duration type. For non-duration type target cooking parameters, the corresponding actual cooking parameters can be determined based on the actual duration used. For example, if the target cooking parameter is the target cooking temperature, the temperature with the longest actual duration can be taken as the actual cooking temperature; alternatively, a weighted calculation can be performed based on the duration used for each temperature to determine the actual cooking temperature.

[0031] When determining the difference between the actual representation parameter set and the first target cooking parameter set, the parameter differences under the same category can be calculated separately to obtain at least one sub-difference. The sub-difference can be calculated using direct difference calculation, or a variation based on difference calculation can be used. The target parameter recommendation factor is calculated based on at least one sub-difference. For each sub-difference, a sub-factor negatively correlated with the sub-difference can be determined to obtain at least one sub-factor. The target parameter recommendation factor is generated based on at least one sub-factor. Alternatively, the representation difference can be determined based on at least one sub-factor, and then the target parameter recommendation factor negatively correlated with the representation difference can be determined.

[0032] Furthermore, the first target cooking parameter set includes a target cooking temperature and a target cooking time, and the actual characterization parameter set includes an actual cooking temperature and an actual cooking time. Generating a parameter recommendation factor for the actual characterization parameter set based on the difference between the actual characterization parameter set and the target cooking parameters may include the following steps: 1) determining a first difference between the target cooking temperature and the actual cooking temperature, and generating a first sub-factor based on the first difference; 2) determining a second difference between the target cooking time and the actual cooking time, and generating a second sub-factor based on the second difference; 3) obtaining the target parameter recommendation factor based on the first sub-factor and the second sub-factor.

[0033] The target cooking temperature is used as a non-duration-based target cooking parameter, while the target cooking duration is used as a duration-based target cooking parameter. This document provides the logic for generating recommendation factors for target parameters involving both non-duration-based and duration-based cooking parameters, supporting the orderliness and accuracy of target parameter recommendation factor calculation.

[0034] For example, MAX_TIME_DIFF represents the maximum cooking time deviation, and MAX_TEMP_DIFF represents the maximum temperature deviation. `time_reward` represents the first sub-factor, calculated as 100 - (|actual cooking time - target cooking time| × 100 / MAX_TIME_DIFF). `temp_reward` represents the second sub-factor, calculated as 100 - (|actual cooking temperature - target cooking temperature| × 100 / MAX_TEMP_DIFF). The average of the first and second sub-factors can be used as the recommended target parameter factor. In practical applications, if |actual cooking time - target cooking time| is greater than the maximum cooking time deviation, `time_reward` as the first sub-factor is set to 0; if |actual cooking temperature - target cooking temperature| is greater than the maximum temperature deviation, `temp_reward` as the second sub-factor is set to 0. 100 / MAX_TIME_DIFF can be set to 1.67, and 100 / MAX_TEMP_DIFF can be set to 2.

[0035] In addition, such as Figure 4 As shown, after generating parameter recommendation factors for the actual characterization parameter set based on the difference between the actual characterization parameter set and the first target cooking parameter set, the method further includes: S401: If no cooking parameter group matches the actual characterization parameter group among the plurality of preset cooking parameter groups, update the data record set based on the target data record. The data record set includes a plurality of preset data records for the plurality of preset cooking parameter groups. Each preset data record includes the preset cooking parameter group, the historical environment information, the historical cooking mode, and the current parameter recommendation factor corresponding to the preset cooking parameter group. The target data record includes the actual characterization parameter group, the current environment information, the current cooking mode, and the target parameter recommendation factor. S402: If there is a second target cooking parameter group that matches the actual characterization parameter group among the plurality of preset cooking parameter groups, determine a first candidate data record containing the second target cooking parameter group from the data record set, and update the current parameter recommendation factor contained in the first candidate data record based on the target parameter recommendation factor.

[0036] This section provides an application where actual characterization parameter sets are used as preset cooking parameter sets. Cooking parameter sets, environmental information, cooking modes, and parameter recommendation factors are maintained uniformly in the form of data records, which also supports matching across the aforementioned cooking mode and environmental information dimensions. This allows the dynamic updates of multiple preset cooking parameter sets to be reflected through the dynamic updates of the data record set, thus improving the timeliness of parameter recommendation factors.

[0037] For example, the current data record set includes J preset data records, where preset data record j is the j-th data record among the J preset data records, and the value of j ranges from 1 to J. Preset data record j includes a preset cooking parameter group j, as well as the historical environment information j, historical cooking mode j, and current parameter recommendation factor j corresponding to preset cooking parameter group j. Correspondingly, the J preset data records also involve J preset cooking parameter groups. Taking the target data record as data record w as an example, data record w includes the actual representation parameter group w, the current environment information w, the current cooking mode w, and the target parameter recommendation factor w. If there is no cooking parameter group among the J preset cooking parameter groups that matches the actual representation parameter group w, then the current data record set can be updated using data record w. If among the J preset cooking parameter groups, there exists a preset cooking parameter group e that matches the actual representative parameter group w (the value of e ranges from 1 to J), then first determine the preset data record e containing the preset cooking parameter group e, and then update the current parameter recommendation factor e based on the target parameter recommendation factor w. For example, the sum of the target parameter recommendation factor w and the current parameter recommendation factor e can be used as the new current parameter recommendation factor e in the preset data record e. It should be noted that, in addition to updating the current parameter recommendation factor contained in the first candidate data record based on the target parameter recommendation factor, if the current environmental information is similar to but not the same as the historical environmental information, the historical environmental information can also be updated using the current environmental information.

[0038] Furthermore, the step of updating the data record set based on the target data record may include the following steps: First, determining the total number of data records corresponding to the plurality of preset data records; then, when the total number of data records is equal to the preset number, determining the second candidate data record corresponding to the smallest current parameter recommendation factor from the data record set; and then, updating the data record set by removing the second candidate record and adding the target data record.

[0039] Considering the storage resource consumption of the maintained data record set, this section provides a management method that constrains the number of stored data records, which can save storage resources and improve matching efficiency. Referring to the previous example, when updating the current data record set using data record w, we can first determine the total number of data records P corresponding to the current data record set. If P is less than Q (e.g., 50), then we can update the data record set by adding data record w to the current data record set. In this case, the data record set includes data record w and J preset data records. Correspondingly, the actual representation parameter group w is used as the preset cooking parameter group, and the target parameter recommendation factor w is used as the current parameter recommendation factor associated with the preset cooking parameter group. If P equals Q (e.g., 50), then we can first determine the preset data record f corresponding to the smallest current parameter recommendation factor (f's value range is 1-J). By removing the preset data record f and adding data record w to the current data record set, we can update the data record set. In this case, the data record set includes data record w and J-1 preset data records.

[0040] In practical applications, information in data records can be stored primarily in integer form to avoid floating-point calculations as much as possible, as shown in Table 1 below: Table 1 The time complexity can be controlled to O(n), which is suitable for low-performance MCUs, such as the ARM Cortex-M series. This also results in low memory resource consumption.

[0041] In addition, the algorithm shown in the code below can be used to update the data record set and determine the first target cooking parameter set: At the end of the current cooking task, the actual set of representative parameters needs to be saved for the calculation of the target parameter recommendation factor: update_history(current_month, current_timeSlot,selected_mode, final_temp, final_time, suggested_temp, suggested_time); The current environment information is represented by current_month and current_timeSlot, selected_mode represents the current cooking mode, the actual characterization parameter group is represented by final_temp and final_time, and the first target cooking parameter group is represented by suggested_temp and suggested_time.

[0042] When a user submits their current environment information and current cooking mode, it is necessary to obtain the first set of recommended target cooking parameters: get_recommended(current_month,current_timeSlot,selected_mode,&suggested_temp,&suggested_time); On embedded devices, a single recommendation calculation takes less than 5ms, meeting real-time requirements.

[0043] As one possible implementation, taking into account some practical considerations, the determination of the first target cooking parameter set is further described here: (i) Each of the preset cooking parameter groups is also associated with a corresponding number of times it has been used. The step of determining the candidate cooking parameter group corresponding to the largest current parameter recommendation factor from the plurality of candidate cooking parameter groups as the first target cooking parameter group may include the following steps: First, determine at least two third cooking parameter groups corresponding to the largest current parameter recommendation factor from the plurality of candidate cooking parameter groups; then, determine the third cooking parameter group corresponding to the most times it has been used from the at least two third cooking parameter groups as the first target cooking parameter group.

[0044] Considering that among multiple candidate cooking parameter groups, at least two groups may have the same and maximum current parameter recommendation factor, the number of adoptions can be introduced as a screening metric. The cooking parameter group with the highest number of adoptions is prioritized as the first target cooking parameter group. It should be understood that the update logic for the number of adoptions is the same as the update logic for the current parameter recommendation factor. Using the number of adoptions as a screening metric allows for consideration of user preferences when determining the first target cooking parameter group, resulting in a more accurate determination.

[0045] (ii) Each of the preset cooking parameter groups is also associated with a corresponding previous adoption time. The step of determining the candidate cooking parameter group corresponding to the largest current parameter recommendation factor from the plurality of candidate cooking parameter groups as the first target cooking parameter group may include the following steps: First, determine at least two fourth cooking parameter groups corresponding to the larger current parameter recommendation factor from the plurality of candidate cooking parameter groups; then, obtain the first weight corresponding to the current parameter recommendation factor and the second weight corresponding to the previous adoption time; furthermore, for each of the fourth cooking parameters in the at least two fourth cooking parameter groups, generate a parameter evaluation factor based on the current parameter recommendation factor, the first weight, the previous adoption time, the second weight, and the current time; finally, determine the first target cooking parameter group from the at least two fourth cooking parameter groups based on the parameter evaluation factors corresponding to each of the at least two fourth cooking parameter groups.

[0046] Considering that user preferences change over time, and that there may be preset cooking parameter groups with poor timeliness among the multiple preset cooking parameter groups maintained by the data record set, this paper introduces the previous adoption time and the current parameter recommendation factor together as the basis for guiding the determination of the first target cooking parameter group. This can further improve the accuracy of determining the first target cooking parameter group and improve the timeliness of the determined first target cooking parameter group.

[0047] The number of at least two fourth cooking parameter groups can be constrained by a target number. The first and second weights can be preset and can be flexibly adjusted according to actual needs. The parameter evaluation factor can be expressed by the following formula: Parameter evaluation factor = First weight * Current parameter recommendation factor + Second weight * 1 / (Current time - Last adopted time). The fourth cooking parameter corresponding to the largest parameter evaluation factor can be selected as the first target cooking parameter group.

[0048] As can be seen from the technical solutions provided by the embodiments of this application above, the embodiments of this application provide a more adaptive cooking parameter determination scheme. The embodiments of this application acquire current environmental information and current cooking mode for the current cooking task; then, determine multiple candidate cooking parameter groups that are adapted to the current environmental information and current cooking mode from multiple preset cooking parameter groups; furthermore, determine the candidate cooking parameter group corresponding to the largest current parameter recommendation factor from the multiple candidate cooking parameter groups as the first target cooking parameter group. The embodiments of this application determine multiple candidate cooking parameter groups based on the current environmental information and current cooking mode. By introducing current environmental information to enrich the matching basis, the candidate sources for the first target cooking parameter group are also enriched. When determining the first target cooking parameter group from multiple candidate cooking parameter groups, attention is paid to whether its corresponding current parameter recommendation factor is the largest. The current parameter recommendation factor is used to reflect the correlation between the preset cooking parameter group and the historical recommended parameter group when actually adopted, and the current parameter recommendation factor reflects the recommendation conversion capability of the historical recommended parameter group. This guides the determination of the first target cooking parameter set. Using the candidate cooking parameter set that represents the optimal recommendation conversion result as the first target cooking parameter set helps to improve the fit between the determined first target cooking parameter set and the user's actual cooking needs, thereby improving the user experience.

[0049] This application also provides a cooking parameter determining device, such as... Figure 5 As shown, the cooking parameter determining device 50 includes: Task data acquisition module 501: used to acquire current environmental information and current cooking mode for the current cooking task; Candidate cooking parameter group determination module 502: used to determine multiple candidate cooking parameter groups that are adapted to the current environment information and the current cooking mode from multiple preset cooking parameter groups. Each preset cooking parameter group is associated with corresponding historical environment information, historical cooking mode and current parameter recommendation factor. The current parameter recommendation factor is used to reflect the correlation between the preset cooking parameter group and the historical recommended parameter group when the preset cooking parameter group is actually used. Target cooking parameter group determination module 503: used to determine the candidate cooking parameter group corresponding to the largest current parameter recommendation factor from the plurality of candidate cooking parameter groups as the first target cooking parameter group, and the first target cooking parameter group is recommended to participate in the current cooking task.

[0050] It should be noted that the apparatus and method embodiments described in the device embodiments are based on the same inventive concept.

[0051] This application also provides a cooking device, including the cooking parameter determining device described above.

[0052] In practical applications, cooking equipment can be steaming or baking equipment.

[0053] It should be noted that the devices and methods described in the device embodiments are based on the same inventive concept.

[0054] This application also provides a computer-readable storage medium storing at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the above-described method. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0055] This application also provides a computer program product, which includes at least one instruction or at least one program segment, wherein the at least one instruction or at least one program segment is loaded and executed by a processor to implement the above method.

[0056] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A cooking parameter determination method characterized by, The method comprises: obtaining current environment information and a current cooking mode for a current cooking task; determining a plurality of candidate cooking parameter groups from a plurality of preset cooking parameter groups, the candidate cooking parameter groups being adapted to the current environment information and the current cooking mode, each of the preset cooking parameter groups being associated with corresponding historical environment information, a historical cooking mode and a current parameter recommendation factor, the current parameter recommendation factor being used to reflect the relevance between the preset cooking parameter group and a historical recommended parameter group when the preset cooking parameter group is actually adopted; determining, from the plurality of candidate cooking parameter groups, a candidate cooking parameter group corresponding to a maximum current parameter recommendation factor as a first target cooking parameter group, the first target cooking parameter group being recommended to participate in the current cooking task.

2. The method of claim 1, wherein, The method further comprises: obtaining current environment information and a current cooking mode for a current cooking task; determining a plurality of candidate cooking parameter groups from a plurality of preset cooking parameter groups, the candidate cooking parameter groups being adapted to the current environment information and the current cooking mode, each of the preset cooking parameter groups being associated with corresponding historical environment information, a historical cooking mode and a current parameter recommendation factor, the current parameter recommendation factor being used to reflect the relevance between the preset cooking parameter group and a historical recommended parameter group when the preset cooking parameter group is actually adopted; determining, from the plurality of candidate cooking parameter groups, a candidate cooking parameter group corresponding to a maximum current parameter recommendation factor as a first target cooking parameter group, the first target cooking parameter group being recommended to participate in the current cooking task.

3. The method of claim 1, wherein, The method further comprises: obtaining actual characteristic parameter groups during execution of the current cooking task; generating a target parameter recommendation factor for the actual characteristic parameter groups based on the difference between the actual characteristic parameter groups and the first target cooking parameter group, the target parameter recommendation factor being negatively correlated with the difference.

4. The method of claim 3, wherein, The first target cooking parameter group comprises a target cooking temperature and a target cooking time length, and the actual characteristic parameter group comprises an actual cooking temperature and an actual cooking time length, and the generation of the parameter recommendation factor for the actual characteristic parameter groups based on the difference between the actual characteristic parameter groups and the target cooking parameter comprises: determining a first difference value between the target cooking temperature and the actual cooking temperature, and generating a first sub-factor based on the first difference value; determining a second difference value between the target cooking time length and the actual cooking time length, and generating a second sub-factor based on the second difference value; obtaining the target parameter recommendation factor based on the first sub-factor and the second sub-factor.

5. The method according to claim 3 or 4, characterized in that, The method further comprises: obtaining actual characteristic parameter groups during execution of the current cooking task; generating a target parameter recommendation factor for the actual characteristic parameter groups based on the difference between the actual characteristic parameter groups and the first target cooking parameter group, the target parameter recommendation factor being negatively correlated with the difference. in a case where there is no cooking parameter group in the plurality of preset cooking parameter groups that matches the actual characteristic parameter group, updating a data record set based on a target data record, the data record set comprising a plurality of preset data records for the plurality of preset cooking parameter groups, each preset data record comprising a preset cooking parameter group, and historical environment information, a historical cooking mode corresponding to the preset cooking parameter group, and a current parameter recommendation factor corresponding to the preset cooking parameter group, the target data record comprising the actual characteristic parameter group, the current environment information, the current cooking mode, and a target parameter recommendation factor; in a case where there is a second target cooking parameter group in the plurality of preset cooking parameter groups that matches the actual characteristic parameter group, determining a first candidate data record containing the second target cooking parameter group from the data record set, and updating the current parameter recommendation factor contained in the first candidate data record based on the target parameter recommendation factor.

6. The method of claim 5, wherein, The updating of the data record set based on the target data record comprises: determining a total number of data records corresponding to the plurality of preset data records; in a case where the total number of data records is equal to a preset number, determining a second candidate data record corresponding to a minimum current parameter recommendation factor from the data record set; updating the data record set by removing the second candidate record and adding the target data record.

7. The method according to claim 1 or 2, characterized in that, Each preset cooking parameter group is further associated with a corresponding number of times of adoption, and the candidate cooking parameter group corresponding to a maximum current parameter recommendation factor from the plurality of candidate cooking parameter groups is a first target cooking parameter group, which comprises: determining at least two third cooking parameter groups corresponding to a maximum current parameter recommendation factor from the plurality of candidate cooking parameter groups; determining the third cooking parameter group corresponding to a maximum number of times of adoption from the at least two third cooking parameter groups as the first target cooking parameter group.

8. The method of claim 1 or 2, wherein, Each preset cooking parameter group is further associated with a corresponding previous time of adoption, and the candidate cooking parameter group corresponding to a maximum current parameter recommendation factor from the plurality of candidate cooking parameter groups is a first target cooking parameter group, which comprises: determining at least two fourth cooking parameter groups corresponding to a larger current parameter recommendation factor from the plurality of candidate cooking parameter groups; obtaining a first weight corresponding to the current parameter recommendation factor and a second weight corresponding to the previous time of adoption; for each fourth cooking parameter in the at least two fourth cooking parameter groups, generating a parameter evaluation factor based on the current parameter recommendation factor, the first weight, the previous time of adoption, the second weight, and a current time; determining the first target cooking parameter group from the at least two fourth cooking parameter groups based on the parameter evaluation factor corresponding to each of the at least two fourth cooking parameter groups.

9. A cooking parameter determination apparatus characterized by comprising: The device comprises: a task data acquisition module configured to acquire current environment information and a current cooking mode for a current cooking task; a target data record updating module configured to update a data record set based on a target data record in a case where there is no cooking parameter group in the plurality of preset cooking parameter groups that matches the actual characteristic parameter group, the data record set comprising a plurality of preset data records for the plurality of preset cooking parameter groups, each preset data record comprising a preset cooking parameter group, and historical environment information, a historical cooking mode corresponding to the preset cooking parameter group, and a current parameter recommendation factor corresponding to the preset cooking parameter group, the target data record comprising the actual characteristic parameter group, the current environment information, the current cooking mode, and a target parameter recommendation factor; The candidate cooking parameter set determination module is configured to determine a plurality of candidate cooking parameter sets from a plurality of preset cooking parameter sets, the candidate cooking parameter sets being adapted to the current environment information and the current cooking mode, each of the preset cooking parameter sets being associated with corresponding historical environment information, a historical cooking mode, and a current parameter recommendation factor, the current parameter recommendation factor being used to reflect a correlation between the preset cooking parameter set and a historical recommended parameter set when the preset cooking parameter set is actually adopted; The target cooking parameter set determination module is configured to determine a candidate cooking parameter set corresponding to a maximum current parameter recommendation factor from the plurality of candidate cooking parameter sets as a first target cooking parameter set, the first target cooking parameter set being recommended to participate in the current cooking task.

10. A cooking apparatus, characterized by, The cooking parameter determination device according to claim 9.

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