Anti-stalling adaptive control method and system for food processor based on vibration characteristics

Abstract

This application relates to the field of food processor control and discloses a method and system for adaptive electronic control of food processors to prevent stalling based on vibration characteristics. The method includes: determining historical high-frequency operating parameters of a preset number of similar ingredients used for the current food processing corresponding to the user's identity information, as multiple candidate historical operating parameters; determining the stall safety matching degree corresponding to the multiple candidate historical operating parameters based on the ingredient configuration parameters; determining the similarity between historical user processing demand characteristics and user processing demand characteristics, as the user processing demand matching degree; determining the comprehensive matching degree of the multiple candidate historical operating parameters based on the stall safety matching degree, user processing demand matching degree, and historical vibration index; and recommending multiple candidate historical operating parameters to the user in descending order of comprehensive matching degree. This application uses the vibration index combined with the stall safety matching degree to quantitatively assess operating risks and avoid stalling problems during food processing.

Description

Technical Field

[0001] This application relates to the field of food processor control technology, and more specifically, to an adaptive electronic control method and system for preventing food processor stalling based on vibration characteristics. Background Technology

[0002] Food processors, commonly used in households, are adaptable to various food processing and cooking modes. They can handle different types of ingredients such as grains, fruits and vegetables, meats, and beans, encompassing multiple cooking modes including blending, grinding, juicing, steaming, and heating, thus meeting diverse food processing and cooking needs. These food processors typically consist of a main unit, a blending cup, a drive motor, a blade assembly, and a control module. The control module adjusts the drive motor's speed and running time, driving the blade assembly to cut, crush, and blend the ingredients. Some high-end models also feature additional functions such as food pretreatment and temperature control, significantly improving food processing efficiency and ease of operation. They are widely used in daily food preparation and small-scale food processing scenarios.

[0003] Existing food processors are not very effective at preventing motor stalling. Stalling occurs when the processor is handling hard, viscous, or excessive amounts of food, or when foreign objects are mixed into the blending chamber. In these situations, the blade assembly becomes stuck and cannot operate normally, causing the drive motor to stall. Because existing food processors lack effective stall prevention mechanisms, this not only leads to excessive load on the drive motor and severe overheating, shortening the lifespan of the motor and the entire machine, but may even cause electrical failures due to motor overheating. Furthermore, stalling interrupts food processing, affecting processing efficiency and user experience. In severe cases, it can even damage components such as the blades and blending cup, failing to meet users' needs for stable, safe, and efficient operation of the food processor. Summary of the Invention

[0004] The purpose of this application is to provide an adaptive electronic control method and system for preventing food jamming in a food processor based on vibration characteristics. This method solves the technical problem of poor anti-jamming performance in food processors and achieves the technical effect of quantitatively assessing operational risks and avoiding food processing jamming problems by combining vibration index with jamming safety matching metric.

[0005] In a first aspect, embodiments of this application provide an adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics. The method includes: acquiring user identity information, food configuration parameters, and user processing requirements information corresponding to the food processor's cooking process for the current ingredients; determining a preset number of historical high-frequency operating parameters, arranged from high to low frequency of use of similar ingredients corresponding to the current ingredients, as multiple candidate historical operating parameters, based on a user operation habit rule base and according to the user identity information; wherein the food configuration parameters include food type, food hardness, food particle size, and food weight; and the multiple candidate historical operating parameters include historical food configuration parameters, historical user processing requirements information, and historical vibration index, whereby the historical vibration index characterizes the vibration amplitude of the food processor; and based on the food configuration parameters... The system calculates the stall safety matching degree for multiple candidate historical operating parameters; determines the historical user processing demand characteristics based on historical user processing demand information for multiple candidate historical operating parameters; determines the user processing demand characteristics based on user processing demand information; determines the similarity between historical user processing demand characteristics and user processing demand characteristics as the user processing demand matching degree; determines the comprehensive matching degree for multiple candidate historical operating parameters based on the stall safety matching degree, user processing demand matching degree, and historical vibration index; recommends multiple candidate historical operating parameters to the user in descending order of comprehensive matching degree; obtains the target historical operating parameter from the multiple candidate historical operating parameters selected by the user, and controls the food processor to cook the current ingredients according to the target historical operating parameter.

[0006] In one possible implementation, based on the ingredient configuration parameters, the stall safety matching degree corresponding to multiple candidate historical operating parameters is determined, including: obtaining the blade information of the food processor; obtaining the stall safety index of the ingredient type, the stall safety index of the ingredient hardness, the stall safety index of the ingredient particle size, and the stall safety index of the ingredient weight corresponding to the blade information and the ingredient configuration parameters; and determining the sum of the stall safety index of the ingredient type, the stall safety index of the ingredient hardness, the stall safety index of the ingredient particle size, and the stall safety index of the ingredient weight corresponding to the multiple candidate historical operating parameters as the stall safety matching degree corresponding to the multiple candidate historical operating parameters.

[0007] In another possible implementation, determining the stall safety matching degree corresponding to multiple candidate historical operating parameters based on the ingredient configuration parameters further includes: obtaining the ingredient type weight, ingredient hardness weight, ingredient particle size weight, and ingredient weight weight corresponding to the historical vibration index of the candidate historical operating parameters; and determining the sum of the products of the ingredient type stall safety index and ingredient type weight, the ingredient hardness stall safety index and ingredient hardness weight, the ingredient particle size stall safety index and ingredient particle size weight, and the ingredient weight stall safety index and ingredient weight weight corresponding to the multiple candidate historical operating parameters as the stall safety matching degree corresponding to the multiple candidate historical operating parameters.

[0008] In another possible implementation, the comprehensive matching degree of multiple candidate historical operating parameters is determined based on the stall safety matching degree, user processing demand matching degree, and historical vibration index of multiple candidate historical operating parameters. This includes: obtaining the rated power of the food processor and the food processor cooking mode corresponding to the multiple candidate historical operating parameters; obtaining the stall safety weight, user processing demand weight, and historical vibration weight corresponding to the rated power of the food processor and the food processor cooking mode corresponding to the multiple candidate historical operating parameters; and determining the sum of the products of the stall safety matching degree and stall safety weight of the multiple candidate historical operating parameters, the product of the user processing demand matching degree and user processing demand weight, and the product of the historical vibration index and historical vibration weight as the comprehensive matching degree of the multiple candidate historical operating parameters.

[0009] In another possible implementation, the method further includes: obtaining the historical non-blocking rate corresponding to multiple candidate historical operating parameters; obtaining the rated power of the food processor and the historical non-blocking weight corresponding to the food processor cooking mode corresponding to the multiple candidate historical operating parameters; and determining the sum of the products of the blockage safety matching degree and the blockage safety weight of the multiple candidate historical operating parameters, the product of the user processing demand matching degree and the user processing demand weight, the product of the historical vibration index and the historical vibration weight, and the product of the historical non-blocking rate and the historical non-blocking weight, as the comprehensive matching degree of the multiple candidate historical operating parameters.

[0010] In another possible implementation, the method further includes: clustering multiple historical operating parameters to obtain multiple historical operating parameter groups, each including multiple historical operating parameters; determining the average historical vibration index corresponding to the multiple historical operating parameters of each historical operating parameter group; determining the parameter group vibration weight corresponding to each historical operating parameter group based on the proportion of food processor cooking modes corresponding to the multiple historical operating parameters of each historical operating parameter group; obtaining the average historical vibration index and parameter group vibration weight of the historical operating parameter groups to which the multiple candidate historical operating parameters belong; and determining the sum of the product of the stall safety matching degree and stall safety weight of the multiple candidate historical operating parameters, the product of the user processing demand matching degree and the user processing demand weight, and the product of the average historical vibration index and the parameter group vibration weight, as the comprehensive matching degree of the multiple candidate historical operating parameters.

[0011] In another possible implementation, the method further includes: determining the average historical non-blocking rate corresponding to multiple historical operating parameters of each historical operating parameter group; determining the non-blocking weight of each historical operating parameter group based on the proportion of food processor cooking modes corresponding to multiple historical operating parameters of each historical operating parameter group; obtaining the average historical non-blocking rate and the non-blocking weight of each parameter group to which multiple candidate historical operating parameters belong; and determining the sum of the products of the blockage safety matching degree and the blockage safety weight of multiple candidate historical operating parameters, the product of the user processing demand matching degree and the user processing demand weight, the product of the average historical vibration index and the parameter group vibration weight, and the product of the average historical non-blocking rate and the parameter group non-blocking weight, as the comprehensive matching degree of multiple candidate historical operating parameters.

[0012] In another possible implementation, the method further includes: when there are multiple target candidate historical operating parameters belonging to the same historical operating parameter group among multiple candidate historical operating parameters, retaining the target candidate historical operating parameter with the highest comprehensive matching degree belonging to the same historical operating parameter group among the multiple target candidate historical operating parameters, so as to determine a preset number of historical high-frequency operating parameters that do not belong to the same historical operating parameter group and are arranged from high to low frequency of use of the same type of food for the current food, as multiple candidate historical operating parameters.

[0013] In another possible implementation, the method further includes: obtaining information on the first type of food cooked by the user multiple times within a preset first historical time period, and information on the second type of food cooked multiple times within a preset second historical time period; determining the first food type feature corresponding to the first food type information, and the second food type feature corresponding to the second food type information; determining the similarity between the first food type feature and the second food type feature as the food type variability; wherein the preset first historical time period is the preceding historical time period of the preset second historical time period and has the same duration; when the food type variability is greater than the preset food type variability, re-clustering the multiple historical operating parameters to obtain multiple historical operating parameter groups, each including multiple historical operating parameters.

[0014] Secondly, embodiments of this application provide an adaptive electronic control system for preventing food stalling in a food processor based on vibration characteristics, including units for implementing the above-described method.

[0015] The beneficial effects of the embodiments in this application compared with the prior art are: This application provides an adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics. The method includes: determining multiple historical high-frequency operating parameters corresponding to the user's identity information as multiple candidate historical operating parameters based on a user operation habit rule base and user identity information; determining the stall safety matching degree corresponding to the multiple candidate historical operating parameters based on ingredient configuration parameters; determining historical user processing demand characteristics based on historical user processing demand information of the multiple candidate historical operating parameters; determining user processing demand characteristics based on user processing demand information; determining the similarity between historical user processing demand characteristics and user processing demand characteristics as user processing demand matching degree; determining the comprehensive matching degree of the multiple candidate historical operating parameters based on the stall safety matching degree, user processing demand matching degree, and historical vibration index; and recommending multiple candidate historical operating parameters to the user in descending order of comprehensive matching degree. In this application embodiment, the comprehensive matching degree is calculated by combining the stall safety matching degree, user processing demand matching degree, and historical vibration index. After recommending parameters in order, the user selects the target historical operating parameter to control the operation, which takes into account both safety and user needs, and can also match the attributes of ingredients to ensure that the processing effect meets expectations. Attached Figure Description

[0016] 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.

[0017] Figure 1A schematic flowchart of the first adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics provided in this application embodiment; Figure 2 A schematic diagram of the workflow of the first adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics provided in the embodiments of this application; Figure 3 A schematic flowchart of the second adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics provided in this application embodiment; Figure 4 A schematic diagram illustrating the workflow of the second adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics, provided in an embodiment of this application; Figure 5 A schematic flowchart of the third adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics provided in this application embodiment; Figure 6 A schematic flowchart of the fourth adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics provided in this application embodiment; Figure 7 A schematic diagram of the workflow of the fourth adaptive electronic control method for preventing food stalling based on vibration characteristics provided in the embodiments of this application; Figure 8 A flowchart illustrating the fifth adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics, provided in this application embodiment; Figure 9 A schematic flowchart of the sixth adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics, provided in the embodiments of this application; Figure 10 A schematic diagram of the workflow of the sixth adaptive electronic control method for preventing food stalling based on vibration characteristics provided in the embodiments of this application; Figure 11 A flowchart illustrating the seventh adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics, provided in this application embodiment; Figure 12 A flowchart illustrating the eighth adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics, provided in this application embodiment; Figure 13 This is a schematic diagram of the logic structure of an adaptive electronic control system for preventing food stalling based on vibration characteristics, provided in an embodiment of this application. Detailed Implementation

[0018] 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.

[0019] 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.

[0020] 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]."

[0021] 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.

[0022] 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.

[0023] Because existing food processors lack effective stall prevention mechanisms, they cannot meet users' needs for stable, safe, and efficient operation.

[0024] Based on the above reasons, this application provides an adaptive electronic control method for preventing stalling in a food processor based on vibration characteristics. The method includes: acquiring user identity information, food configuration parameters, and user processing requirements information corresponding to the food processor's cooking process for the current ingredients; determining a preset number of historical high-frequency operating parameters, arranged from high to low frequency of use of similar ingredients corresponding to the current ingredients, as multiple candidate historical operating parameters, based on a user operation habit rule base and the user identity information; determining the stall safety matching degree corresponding to the multiple candidate historical operating parameters based on the food configuration parameters; determining historical user processing requirement characteristics based on the historical user processing requirement information of the multiple candidate historical operating parameters; determining user processing requirement characteristics based on the user processing requirement information; determining the similarity between the historical user processing requirement characteristics and the user processing requirement characteristics, as the user processing requirement matching degree; determining the comprehensive matching degree of the multiple candidate historical operating parameters based on the stall safety matching degree, user processing requirement matching degree, and historical vibration index; recommending multiple candidate historical operating parameters to the user in descending order of comprehensive matching degree; acquiring the target historical operating parameter among the multiple candidate historical operating parameters selected by the user, and controlling the food processor to cook the current ingredients according to the target historical operating parameter. In this embodiment, the overall matching degree is calculated by combining the stall safety matching degree, the user's processing needs matching degree, and the historical vibration index. After recommending parameters in sequence, the user selects the target historical operating parameters to control the operation. This takes into account both safety and user needs, and can also match the attributes of the ingredients to ensure that the processing effect meets expectations.

[0025] In some scenarios, the adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics, as described in this application, can be applied to the cooking control of a household food processor. It can prevent food stalling by controlling the food processor based on the vibration characteristics of the food processor during use, thereby improving the control effect of the food processor.

[0026] The following describes in detail, with specific examples, an adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics, provided in the embodiments of this application.

[0027] Figure 1 A schematic flowchart of the first adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics provided in this application embodiment is shown below. Figure 1 As shown in the embodiment of this application, an adaptive electronic control method for preventing food processor stalling based on vibration characteristics is provided. The method includes steps S110 to S130, which are described in detail below.

[0028] S110. Obtain the user identity information, ingredient configuration parameters, and user processing requirements information corresponding to the food processor's cooking process for the current ingredients. Using a user operation habit rule base, determine a preset number of historical high-frequency operating parameters, ranked from highest to lowest frequency of use, for the current ingredients corresponding to the user identity information, as multiple candidate historical operating parameters. The ingredient configuration parameters include ingredient type, ingredient hardness, ingredient particle size, and ingredient weight. The multiple candidate historical operating parameters include historical ingredient configuration parameters, historical user processing requirements information, and historical vibration index, whereby the historical vibration index characterizes the vibration amplitude of the food processor.

[0029] Figure 2 A schematic diagram illustrating the workflow of the first adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics, as provided in this application embodiment, is shown below. Figure 2 As shown in the figure, this implementation can collect three types of core basic information corresponding to the current cooking operation of the food processor: the user identity information that triggered the current cooking operation, the relevant attribute parameters of the ingredients to be processed, and the user's effect requirements for the current processing, so as to provide data support for subsequent parameter matching.

[0030] It should be noted that the above three types of information are all core input items for matching and adapting operating parameters. They can be obtained through different collection channels. User identity information can be obtained through the account information bound to the food processor. Ingredient configuration parameters and user processing requirements information can be obtained by the user manually entering them on the kitchen appliance control app. User processing requirements information can also be obtained by entering them through the food processor's interactive interface.

[0031] For example, the current ingredient can be set to soybeans, the user identity information can be set to the bound family master user account information, the ingredient configuration parameters can be set to the various attribute parameters of the corresponding soybeans, and the user processing requirements information can be set to make the finest level of residue-free soy milk.

[0032] In this implementation, a pre-stored user operation habit rule library can be called. The collected user identity information is used as the search tag to filter the historical operation data of the corresponding user. The relevant operation records of ingredients of the same type as the current ingredients are extracted from the data. After sorting the data by usage frequency from high to low, a preset number of parameter groups are selected as candidate historical operation parameters for subsequent matching and filtering.

[0033] In this implementation, the food configuration parameters collected cover four core attributes: food type, food hardness, food particle size, and food weight. The candidate historical operating parameters include historical food configuration parameters, historical user processing requirements information, and historical vibration index. The historical vibration index is used to characterize the vibration amplitude level of the food processor during the corresponding operation.

[0034] It should be noted that the user operation habit rule base will synchronize all historical operation data of the corresponding user in real time, store them according to the type of ingredients, and prioritize the selection of frequently used parameters of the same type of ingredients during each search to ensure the compatibility of candidate parameters with user habits.

[0035] For example, if the current ingredient is soybeans, which belong to the grain category, the historical operation parameters of all grain processing for the corresponding user can be retrieved first. The number of times each parameter group is used can be counted. After sorting the parameters from high to low frequency of use, a preset number of parameter groups can be selected to obtain multiple candidate historical operation parameters.

[0036] It should be noted that the particle size and weight of the ingredients can be obtained through user input. The particle size can be the size of the particles that the ingredients are cut into, and the weight can be obtained and input through weighing.

[0037] It should be noted that the historical vibration index can be the percentage of time during the cooking stage when the vibration amplitude is greater than or equal to the preset vibration amplitude, or the historical vibration index can be the percentage of time during the cooking stage when the vibration frequency is greater than or equal to the preset vibration frequency.

[0038] It should be noted that all four attributes of the ingredient configuration parameters are quantified values ​​that can be directly used for subsequent matching degree calculations. The three items of the historical operation parameters are also stored as quantified data that can be directly accessed.

[0039] For example, the type of food can be set to a quantitative label value for grains, the hardness of food can be set to a quantitative value within the corresponding grade range, the particle size of food can be set to a quantitative value within the corresponding particle size range, and the weight of food can be set to a quantitative value obtained by weighing.

[0040] For example, the historical ingredient configuration parameters can be set to the attribute values ​​of the grains corresponding to a certain historical processing session, the historical user processing demand information can be set to the soy milk fineness requirement value corresponding to the processing session, and the historical vibration index can be set to the quantitative value of the vibration amplitude detected during the processing session.

[0041] S120. Based on the ingredient configuration parameters, determine the stall safety matching degree corresponding to multiple candidate historical operating parameters. Based on the historical user processing demand information of multiple candidate historical operating parameters, determine the historical user processing demand characteristics. Based on the user processing demand information, determine the user processing demand characteristics. Determine the similarity between the historical user processing demand characteristics and the user processing demand characteristics, as the user processing demand matching degree.

[0042] In this implementation, the currently collected ingredient configuration parameters can be compared with the historical ingredient configuration parameters corresponding to each candidate historical operating parameter. Combined with the food processor's stall risk judgment rules, the stall safety matching degree corresponding to each candidate historical operating parameter can be calculated. The higher the matching degree, the lower the probability of stall failure when using the parameter.

[0043] It should be noted that the stall safety matching degree is a quantitative value in the range of 0 to 1. The closer the value is to 1, the lower the stall risk and the higher the operational safety.

[0044] For example, if the historical food attributes corresponding to a certain candidate historical operating parameter have a high degree of compatibility with the current food attributes, the risk of stalling is extremely low when processing with this parameter, and the corresponding stalling safety matching degree can be set to a high quantitative value within the range.

[0045] In this implementation, features can be extracted from the historical user processing requirements information corresponding to each candidate historical operating parameter to obtain the corresponding historical user processing requirement features. Then, features can be extracted from the user processing requirement information collected this time to obtain the current user processing requirement features. The similarity between the two types of features can be calculated, and the similarity value can be used as the matching degree of the user processing requirements corresponding to the candidate historical operating parameter.

[0046] It should be noted that feature extraction can be achieved through vector transformation, which converts the textual or graded information into computable vector data, and then uses a vector similarity algorithm to calculate the degree of matching between two features.

[0047] For example, each requirement dimension in the historical user processing requirement information can be converted into a feature vector of fixed length as the historical user processing requirement feature, and each requirement dimension in the current user processing requirement information can be converted into a feature vector of the same dimension as the user processing requirement feature.

[0048] For example, the cosine similarity between the historical user processing demand feature vector and the user processing demand feature vector can be calculated, and the cosine similarity value can be used as the matching degree of the user processing demand corresponding to the candidate historical running parameter.

[0049] S130. Based on the stall safety matching degree, user processing demand matching degree, and historical vibration index of multiple candidate historical operating parameters, determine the comprehensive matching degree of multiple candidate historical operating parameters. Recommend multiple candidate historical operating parameters to the user in descending order of comprehensive matching degree. Obtain the target historical operating parameter from the multiple candidate historical operating parameters selected by the user, and control the food processor to cook the current ingredients according to the target historical operating parameter.

[0050] In this implementation, corresponding weight coefficients can be configured for the stall safety matching degree, user processing demand matching degree, and historical vibration index corresponding to each candidate historical operating parameter. The comprehensive matching degree of each candidate historical operating parameter is obtained by weighted calculation. The higher the comprehensive matching degree, the better the overall adaptability of the parameter group.

[0051] It should be noted that the overall matching degree is a quantitative value in the range of 0 to 1. The closer the value is to 1, the better the overall performance of the parameter group in the three dimensions of security, adaptability to user needs, and operational stability.

[0052] For example, if the stall safety matching degree and user processing demand matching degree of a certain candidate historical operating parameter are both at a high level, the corresponding historical vibration index is low and the operating stability is good, its comprehensive matching degree can be set to a high quantitative value within the range.

[0053] In this implementation, all candidate historical running parameters can be sorted in descending order according to the comprehensive matching degree value, and the sorted parameters can be displayed to the user through the food processor's interactive interface for the user to select.

[0054] It should be noted that the recommended content can also be labeled with auxiliary information such as the historical usage count and processing effect tags for each parameter group, making it easier for users to quickly identify the parameters that meet their needs.

[0055] For example, if there are multiple sets of candidate historical running parameters, they can be arranged in order of comprehensive matching degree from high to low in the parameter recommendation bar of the interactive interface, and the processing effect, running time and other information corresponding to each set of parameters can be displayed in turn.

[0056] In this implementation, the parameter set selected by the user in the recommended parameter list can be collected as the target historical operating parameters. The operating parameters such as motor speed, heating temperature, and processing time corresponding to the target historical operating parameters are written into the control module of the food processor, and the food processor is controlled to complete the processing of the current ingredients according to the set of parameters.

[0057] It should be noted that once the parameters are selected, the food processor can automatically enter the operating process without requiring the user to set any additional operating parameters, thus reducing the user's operating costs.

[0058] For example, when a user selects a set of parameters from the recommended sets as the target historical operating parameters, the food processor can call up the corresponding data such as motor speed curve, heating power curve, and total running time to start the processing flow and complete the cooking of the current ingredients.

[0059] This implementation method obtains user identity information and user processing requirements related to cooking, retrieves candidate historical running parameters of the corresponding user through the user operation habit rule base, calculates the matching degree of user processing requirements and incorporates it into the comprehensive matching degree calculation, which can fully meet user operation habits and processing needs, and reduce the operation cost of users debugging parameters.

[0060] By using this method, when selecting candidate historical operating parameters, the current food configuration parameters are combined to calculate the stall safety matching degree. This degree, along with the historical vibration index that characterizes the vibration amplitude, is included in the comprehensive matching degree calculation, which can effectively reduce the probability of food processor stall failure and improve the stability of equipment operation.

[0061] This implementation method calculates the overall matching degree by comprehensively considering the stall safety matching degree, the user's processing needs matching degree, and the historical vibration index. After recommending parameters in sequence, the user selects the target historical operating parameters to control the operation. This method takes into account both safety and user needs, and can also match the properties of the ingredients to ensure that the processing effect meets expectations.

[0062] Figure 3 A schematic flowchart of the second vibration-based adaptive electronic control method for preventing food stalling in a food processor, as provided in this application embodiment, is shown below. Figure 3 As shown, in some implementations, in the above-mentioned S120, the stall safety matching degree corresponding to multiple candidate historical operating parameters is determined according to the ingredient configuration parameters, including S121 to S122. S121 to S122 will be explained in detail below.

[0063] S121. Obtain the blade information of the food processor. Obtain the blade information and the corresponding food configuration parameters, including the food type, food hardness, food particle size, and food weight, as well as their corresponding jamming safety index.

[0064] Figure 4 A schematic diagram illustrating the workflow of the second vibration-based adaptive electronic control method for preventing food stalling in a food processor, as provided in this application embodiment, is shown below. Figure 4 As shown, in this implementation, when the calculation process for stall safety matching degree is started, the corresponding attribute parameters of the cutting component currently loaded in the food processor, i.e., the blade information, can be collected first, so as to provide a hardware-level benchmark reference for subsequent risk index matching.

[0065] It should be noted that the blade information can be for various food processor blade models that are compatible with the food processor. The risk of jamming can be determined based on experience.

[0066] S122. Determine the sum of the food type stall safety index, food hardness stall safety index, food particle size stall safety index, and food weight stall safety index corresponding to multiple candidate historical operating parameters, and use it as the stall safety matching degree corresponding to multiple candidate historical operating parameters.

[0067] In this implementation, an index matching database that has been pre-built through actual measurement and calibration can be called. Using the currently collected blade information and food configuration parameters as search conditions, the food type stall safety index, food hardness stall safety index, food particle size stall safety index, and food weight stall safety index can be obtained in sequence.

[0068] It should be noted that the four dimensions of the stall safety index correspond to the stall risk level when different ingredients are used and the blade is matched. The higher the value, the lower the stall risk under the corresponding dimension.

[0069] For example, for the combination of a 4-blade stainless steel blade and dried soybeans, the following safety indices can be obtained: food type stall safety index, food hardness stall safety index, food particle size stall safety index, and food weight stall safety index, which are 24, 21, 19, and 26, respectively.

[0070] In this implementation, the four dimensions of the stall safety index corresponding to a single set of candidate historical operating parameters can be accumulated and calculated. The sum of the values ​​is used as the stall safety matching degree corresponding to the set of candidate historical operating parameters, thereby realizing the quantitative assessment of stall risk.

[0071] It should be noted that the value of stall safety matching degree is positively correlated with operational safety; the higher the value, the lower the probability of stall failure during the operation of this set of parameters.

[0072] For example, if the four dimensions of stall safety index corresponding to a single set of candidate historical operating parameters are 24, 21, 19, and 26 respectively, the sum of these values, resulting in 90, can be used as the stall safety matching degree corresponding to that set of candidate historical operating parameters.

[0073] This implementation method obtains the blade information of the food processor when calculating the stall safety matching degree. It matches the blade information with the food configuration parameters to obtain the stall safety index for food type, food hardness, food particle size, and food weight. The four indices are summed to obtain the final stall safety matching degree, which closely matches the actual cutting ability of the blade. This can significantly improve the accuracy of stall risk assessment and reduce the possibility of misjudging stall risk.

[0074] This implementation method uses the precisely quantified stall safety matching degree as one of the core evaluation dimensions when screening candidate historical operating parameters. It combines the matching degree of user processing needs and the historical vibration index to calculate the comprehensive matching degree. This can directly screen out unqualified candidate historical operating parameters with excessive stall risk, effectively improve the operating safety of candidate historical operating parameters recommended to users, and reduce the probability of stall when the food processor is working.

[0075] Figure 5 A flowchart illustrating the third adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics provided in this application embodiment is shown below. Figure 5 As shown, in some implementations, S120 above, which determines the stall safety matching degree corresponding to multiple candidate historical operating parameters based on the ingredient configuration parameters, also includes S123 to S124. S123 to S124 will be explained in detail below.

[0076] S123. Obtain the weights of food type, food hardness, food particle size, and food weight corresponding to the historical vibration index of the candidate historical operating parameters.

[0077] In this implementation, the historical vibration index corresponding to each candidate historical operating parameter can be retrieved, and the pre-constructed correlation mapping relationship between the vibration index and the weight of each food attribute can be matched to obtain the food type weight, food hardness weight, food particle size weight and food weight corresponding to the candidate historical operating parameter. Different weight allocation rules are corresponding to the historical vibration index in different intervals.

[0078] It should be noted that the correlation between the historical vibration index and the weights of each ingredient attribute is set according to the degree of influence of each ingredient attribute on the risk of stalling under different vibration states during historical operation, and can be iteratively updated as historical operation data is accumulated.

[0079] For example, when the historical vibration index of a candidate historical operating parameter is in a preset high vibration range, the weight of the food attribute that has a more significant impact on the stall can be set to a preset value corresponding to that range, so as to meet the stall risk assessment needs in high vibration scenarios.

[0080] S124. Determine the sum of the products of food type stall safety index and food type weight, food hardness stall safety index and food hardness weight, food particle size stall safety index and food particle size weight, and food weight stall safety index and food weight weight corresponding to multiple candidate historical operating parameters, and use them as the stall safety matching degree corresponding to multiple candidate historical operating parameters.

[0081] In this implementation, the product of the food type stall safety index and the food type weight, the product of the food hardness stall safety index and the food hardness weight, the product of the food particle size stall safety index and the food particle size weight, and the product of the food weight stall safety index and the food weight weight can be calculated separately. Then, the four product results are summed, and the summed value is used as the stall safety matching degree of the corresponding candidate historical operating parameters.

[0082] It should be noted that this weighted summation calculation method can incorporate the differences in the actual impact of different food attributes on the risk of blockage into the assessment. Compared with the method of directly summing the four types of blockage safety indices, it is more in line with actual operating conditions and improves the calculation accuracy of the blockage safety matching degree.

[0083] For example, when calculating the stall safety matching degree of a candidate historical operating parameter, the four types of stall safety indices and corresponding weights corresponding to the parameter can be directly substituted into the parameter. After completing the product calculation and summation operation, the result can be used for the subsequent calculation of the comprehensive matching degree.

[0084] This implementation method calculates the stall safety matching degree by obtaining the weights of food type, food hardness, food particle size, and food weight corresponding to the historical vibration index of the candidate historical operating parameters. Then, the results are obtained by multiplying each stall safety index and its corresponding weight and summing them. Compared with the original method of direct summation, this method significantly improves the accuracy of stall safety matching degree calculation.

[0085] This implementation introduces a dynamic weighting method associated with historical vibration indices to calculate the stall safety matching degree. This allows the actual impact of different food attributes on stalling to be included in the evaluation. The comprehensive matching degree calculated subsequently is more in line with actual working conditions, effectively improving the rationality of the recommended candidate historical operating parameters. The weighted calculation logic also takes into account the inherent stalling risk of food and past vibration feedback data. The selected target historical operating parameters can reduce the vibration amplitude of the food processor while matching user needs, thereby improving operational stability and safety.

[0086] Figure 6 A flowchart illustrating the fourth adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics provided in this application embodiment is shown below. Figure 6 As shown, in some implementations, in the above-mentioned S130, the comprehensive matching degree of multiple candidate historical operating parameters is determined based on the stall safety matching degree, user processing demand matching degree, and historical vibration index of multiple candidate historical operating parameters, including S131 to S132. S131 to S132 will be explained in detail below.

[0087] S131. Obtain the rated power of the food processor and the corresponding cooking modes of the food processor based on multiple candidate historical operating parameters. Obtain the stall safety weight, user processing demand weight, and historical vibration weight corresponding to the rated power of the food processor and the corresponding cooking modes of the food processor based on multiple candidate historical operating parameters.

[0088] Figure 7 A schematic diagram illustrating the workflow of the fourth vibration-based adaptive electronic control method for preventing food stalling in a food processor, as provided in this application embodiment, is shown below. Figure 7 As shown, in this implementation, the inherent rated power parameters pre-stored at the factory of the food processor can be collected, and the corresponding cooking mode information associated with the tags of multiple candidate historical operating parameters can be retrieved. These two types of parameters serve as the basis for subsequent weight matching, ensuring that the weight coefficients obtained later are adapted to the current equipment attributes and processing scenarios.

[0089] For example, the rated power of the food processor can be read directly from the built-in storage module of the device, and the food processor cooking modes corresponding to multiple candidate historical operating parameters can include different operating modes such as soy milk mode, smoothie mode, soup mode, and meat grinding mode.

[0090] In this implementation, the weight coefficients corresponding to the three evaluation dimensions can be matched based on the read rated power and the cooking mode corresponding to each candidate historical operating parameter. The three weights correspond to the priorities of the three evaluation dimensions of stall safety, user processing needs, and operational stability, respectively, to adapt to the evaluation emphasis of different devices and scenarios.

[0091] It should be noted that the matching and acquisition of the three types of weights can be completed through a pre-stored empirical value table. The empirical value table pre-stores the stall safety weight, user processing demand weight, and historical vibration weight calibrated under different rated power ranges and different cooking modes. These can be directly called without real-time calculation, thus improving the efficiency of parameter acquisition.

[0092] For example, when a food processor in the corresponding rated power range operates in the corresponding cooking mode, the matched stall safety weight, user processing demand weight, and historical vibration weight can be directly taken from the corresponding calibration values ​​stored in the empirical value table.

[0093] S132. Determine the sum of the products of stall safety matching degree and stall safety weight, user processing demand matching degree and user processing demand weight, and historical vibration index and historical vibration weight of multiple candidate historical operating parameters, as the comprehensive matching degree of multiple candidate historical operating parameters.

[0094] In this implementation, the stall safety matching degree, user processing demand matching degree, and historical vibration index corresponding to each candidate historical operating parameter can be multiplied by their respective weights to obtain three product terms. The three product terms are then summed to obtain the final result, which is the comprehensive matching degree of the candidate historical operating parameter. The higher the comprehensive matching degree, the better the parameter is adapted to the current scenario.

[0095] For example, the stall safety matching degree, user processing demand matching degree, and historical vibration index of a candidate historical operating parameter are respectively the calibration values ​​of the corresponding dimensions. After multiplying them by their corresponding weights and summing them, the comprehensive matching degree value of the candidate historical operating parameter can be obtained.

[0096] By using this implementation method, when calculating the comprehensive matching degree of multiple candidate historical operating parameters, the rated power of the food processor and the food processor cooking mode corresponding to multiple candidate historical operating parameters are obtained. The corresponding stall safety weight, user processing demand weight and historical vibration weight are matched and then the comprehensive matching degree is obtained by weighted summation, which effectively improves the adaptability of weight configuration and operating scenario.

[0097] This implementation directly binds the stall safety weight to the food processor's rated power and cooking mode. When calculating the overall matching degree, the proportion of stall safety matching degree can be adaptively adjusted, prioritizing parameters that match the equipment's load capacity and significantly reducing the probability of stalling during food processor operation. The overall matching degree calculation also incorporates the user's processing needs weight for the corresponding scenario, and combines parameters from the stall safety dimension and historical vibration index dimension for comprehensive evaluation. This ensures operational safety while matching the user's actual processing preferences, thereby improving user satisfaction.

[0098] Figure 8 A flowchart illustrating the fifth adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics, as provided in this application embodiment, is shown below. Figure 8 As shown, in some implementations, the above method also includes S210 to S220, which will be described in detail below.

[0099] S210. Obtain the historical non-blocking rate corresponding to multiple candidate historical operating parameters. Obtain the rated power of the food processor and the historical non-blocking weight corresponding to the food processor's cooking mode for multiple candidate historical operating parameters.

[0100] In this implementation, the historical operation records corresponding to each candidate historical operation parameter can be retrieved, and the ratio of the number of times the parameter did not experience a stall failure during the past cooking of the same type of ingredients to the total number of operations can be calculated to obtain the corresponding historical non-stall failure rate, providing reference data for historical operation verification for subsequent comprehensive matching degree calculation.

[0101] For example, the historical non-stall rate corresponding to each candidate historical operating parameter can be obtained by statistical calculation of the stall failure records in the historical operating database of each parameter stored in the background.

[0102] In this implementation, the rated power parameters of the food processor currently in use, as well as the cooking mode attributes corresponding to each candidate historical operating parameter, can be combined to obtain the historical non-blocking weight that is suitable for the current operating scenario, ensuring that the weight meets the anti-blocking priority requirements of the equipment load capacity and the cooking mode.

[0103] It should be noted that the historical non-blocking weight can be obtained by relying on a pre-stored experience value table. The experience value table stores the corresponding weight configuration rules under different rated power ranges and different cooking mode types, and the matching results can be directly called according to the input parameters.

[0104] For example, the experience value table can be pre-built based on actual running test data. Different running scenarios correspond to different historical non-blocking weights, and the weight value of the corresponding scenario can be directly called during matching.

[0105] S220. Determine the sum of the products of the stall safety matching degree and stall safety weight of multiple candidate historical operating parameters, the product of the user processing demand matching degree and the user processing demand weight, the product of the historical vibration index and the historical vibration weight, and the product of the historical non-stall rate and the historical non-stall weight, as the comprehensive matching degree of multiple candidate historical operating parameters.

[0106] In this implementation, the evaluation parameters and corresponding weights of the four dimensions are multiplied and summed to obtain the comprehensive matching degree of each candidate historical operating parameter. This calculation method also incorporates the evaluation indicators of stall safety dimension, user demand dimension, historical vibration dimension and historical operation verification dimension to ensure the comprehensiveness of the comprehensive matching degree evaluation.

[0107] For example, when calculating the overall matching degree, the following can be calculated separately: the product of the stall safety matching degree and the stall safety weight, the product of the user processing demand matching degree and the user processing demand weight, the product of the historical vibration index and the historical vibration weight, and the product of the historical non-stall rate and the historical non-stall weight. The final overall matching degree can then be obtained by adding the four products together.

[0108] This implementation method simultaneously obtains the rated power of the food processor and the historical non-blocking weights corresponding to the cooking modes of the food processor corresponding to multiple candidate historical operating parameters through the historical non-blocking rate dimension. The product of the historical non-blocking rate and the historical non-blocking weights is included in the summation term of the comprehensive matching degree, which can effectively fill the coverage gap of the original calculation dimensions and improve the rationality of the comprehensive matching degree calculation results.

[0109] This implementation method introduces the historical non-blocking rate, which has been verified in actual operation, as one of the core reference indicators. Combined with the historical non-blocking weight parameters adapted to the corresponding scenario and the comprehensive matching degree calculation, the selected high-matching operating parameters can better meet the actual anti-blocking needs, significantly reducing the probability of blockage when the food processor is running according to the recommended parameters, and improving the effectiveness of anti-blocking control. In addition, the reference of the blockage performance in actual historical operation can improve the adaptability of the recommended parameters to the current equipment and the current usage scenario, and improve the user experience.

[0110] Figure 9 A flowchart illustrating the sixth adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics, as provided in this application embodiment, is shown below. Figure 9 As shown, in some implementations, the above method also includes S310 to S320, which will be described in detail below.

[0111] S310. Cluster multiple historical operating parameters to obtain multiple historical operating parameter groups, each including multiple historical operating parameters. Determine the mean historical vibration index corresponding to the multiple historical operating parameters of each historical operating parameter group. Determine the parameter group vibration weight corresponding to each historical operating parameter group based on the proportion of food processor cooking modes corresponding to the multiple historical operating parameters of each historical operating parameter group.

[0112] Figure 10 A schematic diagram illustrating the workflow of the sixth vibration-based adaptive electronic control method for preventing food stalling in a food processor, as provided in this application embodiment, is shown below. Figure 10 As shown, in this implementation, clustering operations can be performed on all stored historical running parameters. According to the preset clustering dimension, historical running parameters that meet the attribute similarity requirements are grouped into the same group, resulting in several historical running parameter groups, each containing multiple historical running parameters.

[0113] It should be noted that the clustering process can use historical food configuration parameters, historical user processing needs information, and historical food processor cooking modes as clustering dimensions to ensure that the operating scenarios of historical operating parameters within the same historical operating parameter group have a high degree of similarity, and to avoid parameters with too large differences in scenarios being grouped into the same group.

[0114] For example, during clustering, all historical operating parameters that use grain ingredients and the corresponding soy milk making mode can be grouped into one historical operating parameter group, and all historical operating parameters that use fruit and vegetable ingredients and the corresponding juicing mode can be grouped into another historical operating parameter group.

[0115] In this implementation, the historical vibration index corresponding to each of the historical operating parameters in the same historical operating parameter group can be retrieved, and the mean value of all indices can be calculated to obtain the mean value of the historical vibration index corresponding to the historical operating parameter group.

[0116] It should be noted that the historical average vibration index can reflect the average vibration amplitude of the food processor under the same type of scenario. Compared with the historical vibration index of a single historical operating parameter, it can reduce the interference of abnormal data from a single operation on the vibration assessment and improve the stability of the vibration assessment.

[0117] For example, a certain historical operating parameter group contains 5 historical operating parameters, with corresponding historical vibration indices of 12, 11, 13, 12 and 12, respectively. After averaging, the average historical vibration index of the historical operating parameter group is 12.

[0118] S320. Obtain the average historical vibration index and vibration weight of the historical operating parameter group to which the multiple candidate historical operating parameters belong. Determine the sum of the products of the stall safety matching degree and stall safety weight of the multiple candidate historical operating parameters, the products of the user processing demand matching degree and user processing demand weight, and the products of the average historical vibration index and the vibration weight of the parameter group, as the comprehensive matching degree of the multiple candidate historical operating parameters.

[0119] In this implementation, the distribution ratio of the food processor cooking modes corresponding to all historical operating parameters within each historical operating parameter group can be statistically analyzed. Combined with the preset weight correspondence rules, the vibration weight of the parameter group corresponding to that historical operating parameter group can be calculated.

[0120] It should be noted that different food processor cooking modes have different tolerances to vibration. Therefore, the vibration weight of the parameter group needs to be dynamically adjusted according to the proportion of the food processor cooking modes in the group to ensure that the weight configuration is adapted to the main operating scenarios of the group.

[0121] For example, the vibration weight of the parameter group can be matched by a pre-stored empirical value table. If the proportion of the cell wall breaking mode in a certain historical operating parameter group is higher than 80%, the vibration weight of the corresponding parameter group is matched to be 0.3; if the proportion of the complementary food preparation mode in the group is higher than 70%, the vibration weight of the corresponding parameter group is matched to be 0.2.

[0122] In this implementation, after determining multiple candidate historical operating parameters, the historical operating parameter group corresponding to each candidate historical operating parameter can be matched one by one, and the average historical vibration index and vibration weight of the parameter group can be retrieved for subsequent comprehensive matching degree calculation.

[0123] It should be noted that the retrieval process can directly read the corresponding parameters of each group that are pre-stored in the storage area after the clustering is completed, without repeating the calculation, which can improve the calculation efficiency of the comprehensive matching degree.

[0124] For example, parameters can be retrieved through a pre-stored table of empirical values. If a candidate historical operating parameter corresponds to a group of historical operating parameters for grains and soy milk, the pre-stored historical vibration index average of 12 and the parameter group vibration weight of 0.25 can be retrieved directly.

[0125] For example, different historical operating parameter groups can correspond to different parameter values. The historical vibration index of the historical operating parameter group for fruit and vegetable juicing is 8, and the vibration weight of the parameter group is 0.2; the historical vibration index of the historical operating parameter group for crushed ice is 18, and the vibration weight of the parameter group is 0.35.

[0126] In this implementation, the values ​​of the three product terms can be calculated separately, and then the values ​​of the three product terms can be summed. The final summation result is the comprehensive matching degree corresponding to the candidate historical running parameter.

[0127] It should be noted that this calculation logic combines multi-dimensional evaluations of stall safety, user needs, and average vibration in the same scenario, which can further improve the rationality of the overall matching degree and ensure that the recommended operating parameters simultaneously meet the requirements of safety, meeting needs, and stable operation.

[0128] For example, the stall safety matching degree corresponding to a certain candidate historical operating parameter is 90, and the stall safety weight is 0.4; the user processing demand matching degree is 85, and the user processing demand weight is 0.35; the historical vibration index average is 12, and the parameter group vibration weight is 0.25; after calculating the products of the three terms, they are 36, 29.75, and 3, respectively. After summing, the comprehensive matching degree of the candidate historical operating parameter is 68.75.

[0129] This implementation method clusters multiple historical operating parameters to obtain multiple historical operating parameter groups before calculating the comprehensive matching degree of candidate historical operating parameters. It calculates the average historical vibration index of each group and then determines the vibration weight of the corresponding parameter group based on the proportion of food processor cooking modes in each group. The above two parameters of the group to which the candidate historical operating parameter belongs are introduced into the calculation, which can avoid the random error of a single historical vibration index and improve the accuracy of the comprehensive matching degree calculation.

[0130] This implementation method eliminates the direct use of historical vibration indices for individual candidate historical operating parameters when calculating the overall matching degree. Instead, it replaces this with the product of the average historical vibration index of the historical operating parameter group to which the candidate historical operating parameter belongs and the vibration weight of the parameter group. This covers the common vibration characteristics of the same cooking mode within the same group, reduces the interference of abnormal vibration data on parameter selection, and improves the reliability of the food processor's anti-stall control. By dynamically configuring the vibration weight of the corresponding parameter group based on the proportion of each food processor's cooking mode, the overall matching degree calculation is completed by directly calling the corresponding parameter of the group when matching candidate historical operating parameters. This not only conforms to the vibration characteristics of different food processor cooking modes but also effectively improves the rationality of recommending operating parameters to users.

[0131] Figure 11 A flowchart illustrating the seventh adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics, as provided in this application embodiment, is shown below. Figure 11 As shown, in some implementations, the above method also includes S330 to S340, which will be described in detail below.

[0132] S330. Determine the average historical non-blocking rate corresponding to multiple historical operating parameters for each historical operating parameter group. Based on the proportion of food processor cooking modes corresponding to multiple historical operating parameters for each historical operating parameter group, determine the non-blocking weight of each parameter group.

[0133] In this implementation, for each historical operating parameter group that has been clustered, the historical non-blocking rate data corresponding to all historical operating parameters in the group can be retrieved, and the average value of multiple groups of data can be calculated to obtain the average historical non-blocking rate corresponding to each historical operating parameter group. The average historical non-blocking rate can reflect the overall anti-blocking performance of the group of operating parameters in the historical operation process, avoiding the random deviation of a single data point.

[0134] For example, a certain historical operating parameter group contains multiple historical operating parameters related to the same type of cooking mode, and the corresponding historical non-blocking rates are values ​​within different preset ranges. The average historical non-blocking rate of the group is obtained after averaging.

[0135] In this implementation, we can first count all the food processor cooking modes corresponding to the historical operating parameters contained in each historical operating parameter group, calculate the proportion of each type of food processor cooking mode in the group, and then match the non-blocking weight of the parameter group corresponding to the historical operating parameter group based on the proportion results. The non-blocking weight of the parameter group can reflect the degree of correlation of the blocking risk of the cooking mode corresponding to the operating parameters in that group.

[0136] It should be noted that the matching of the non-blocking weight of the parameter group can be completed based on a pre-built empirical value table, which stores the standard non-blocking weight values ​​of the parameter group corresponding to the proportion range of different food processor cooking modes.

[0137] For example, in the proportion of cooking modes of a food processor in a certain historical operating parameter group, the proportion of high-load cooking mode is in a preset high proportion range, and the non-blocking weight of the corresponding parameter group can be obtained directly by matching the empirical value table.

[0138] S340. Obtain the average historical non-blocking rate and the non-blocking weight of the parameter group to which the multiple candidate historical operating parameters belong. Determine the sum of the products of the blocking safety matching degree and blocking safety weight of the multiple candidate historical operating parameters, the product of the user processing demand matching degree and user processing demand weight, the product of the average historical vibration index and the vibration weight of the parameter group, and the product of the average historical non-blocking rate and the non-blocking weight of the parameter group, as the comprehensive matching degree of the multiple candidate historical operating parameters.

[0139] In this implementation, the historical operating parameter group to which each candidate historical operating parameter belongs can be identified first. Then, the average historical non-blocking rate and the non-blocking weight of the parameter group, which are pre-calculated for the corresponding historical operating parameter group, can be retrieved and used as one of the reference dimensions for the comprehensive matching degree calculation. Specifically, the pre-stored group-level parameters can be directly called, without having to perform repeated calculations on individual parameters, thus improving calculation efficiency.

[0140] For example, a candidate historical operating parameter belongs to the historical operating parameter group corresponding to the mixed grain paste cooking mode. The average historical non-blocking rate and the non-blocking weight of the parameter group, which are stored in advance in the group, can be directly retrieved for subsequent calculations.

[0141] In this implementation, the product terms of the parameters in the four dimensions can be calculated separately, and then the four product terms are summed. The summation result is used as the comprehensive matching degree of the corresponding candidate historical operating parameters. At the same time, it covers four core dimensions: stall safety, user needs, vibration performance, and historical non-stall performance, ensuring the comprehensiveness of the comprehensive matching degree evaluation.

[0142] It should be noted that the parameters for all four dimensions are pre-calculated or retrieved quantitative values. The calculation process can be completed automatically through preset operation logic without human intervention.

[0143] For example, for a candidate historical operating parameter, the comprehensive matching degree corresponding to the candidate historical operating parameter can be obtained by calculating the product of the stall safety matching degree and the stall safety weight, the product of the user processing demand matching degree and the user processing demand weight, the product of the historical vibration index mean and the parameter group vibration weight, and the product of the historical non-stall rate mean and the parameter group non-stall weight.

[0144] This implementation method calculates the average historical non-blocking rate for multiple historical operating parameters in each historical operating parameter group, determines the non-blocking weight of the corresponding parameter group based on the proportion of food processor cooking modes in each historical operating parameter group, and incorporates the product of the two into the comprehensive matching degree calculation item, effectively improving the comprehensiveness of blockage risk prediction and reducing the blockage probability of recommended parameters.

[0145] This implementation changes the calculation logic that only considers the historical non-blocking rate of a single candidate historical operating parameter. It introduces the collective non-blocking data of the historical operating parameter group to which the candidate historical operating parameter belongs as a reference dimension, which can avoid the interference of random errors of a single historical operating parameter and significantly improve the reliability of the candidate historical operating parameter recommendation results. The addition of a product calculation term of the average historical non-blocking rate and the non-blocking weight of the parameter group makes the final selected target historical operating parameters suitable for both user processing needs and food processor safety operation requirements, thereby improving the operational stability of the cooking process.

[0146] In some implementations, the above method further includes: when there are multiple target candidate historical operating parameters belonging to the same historical operating parameter group among multiple candidate historical operating parameters, retain the target candidate historical operating parameter with the highest comprehensive matching degree belonging to the same historical operating parameter group among the multiple target candidate historical operating parameters, so as to determine a preset number of historical high-frequency operating parameters that do not belong to the same historical operating parameter group and are arranged from high to low frequency of use of the same type of food for the current food, as multiple candidate historical operating parameters.

[0147] In this implementation, the historical operating parameter group affiliation verification can be performed on the multiple candidate historical operating parameters initially retrieved, and the scenario where there are multiple target candidate historical operating parameters under the same historical operating parameter group can be identified. All target candidate historical operating parameters in the same group are sorted according to the comprehensive matching degree, and only the target candidate historical operating parameter with the highest comprehensive matching degree in the group is retained to complete the deduplication of parameters in the same group.

[0148] It should be noted that parameters within the same historical operating parameter group correspond to similar food processor operating characteristics and ingredient compatibility effects. If multiple groups of parameters are recommended at the same time, there will be homogeneous and redundant options, which will not only increase the user's selection cost, but also waste subsequent computing resources.

[0149] For example, among the candidate historical operating parameters initially retrieved, four parameters belong to the same historical operating parameter group corresponding to the fruit and vegetable juicing mode. The comprehensive matching degree of the four parameters is 0.94, 0.89, 0.83, and 0.78, respectively. Therefore, only the parameter with a comprehensive matching degree of 0.94 is retained, and the other three parameters in the same group are not included in the subsequent screening process.

[0150] In this implementation, all historical operating parameters after deduplication can be sorted in descending order according to the frequency of use of the same type of food in the current food, and a preset number of parameters at the top of the sorted sequence can be extracted. The historical operating parameter groups of these parameters are not repeated, and finally multiple candidate historical operating parameters are obtained for subsequent recommendations.

[0151] It should be noted that this filtering logic takes into account both users' high-frequency usage habits and the scenario coverage of parameters. It can match users' common operation preferences and provide users with different running parameter options to meet diverse usage needs.

[0152] For example, the preset quantity is set to 4. After deduplication of the same group, there are a total of 9 historical operating parameters. After sorting them from high to low according to the frequency of use of the same type of ingredients, the first 4 parameters are selected, and the 4 parameters belong to 4 different historical operating parameter groups. These 4 parameters can be determined as the final multiple candidate historical operating parameters.

[0153] By using this implementation method, when filtering multiple candidate historical operating parameters, if multiple target candidate historical operating parameters belonging to the same historical operating parameter group are identified, only the target candidate historical operating parameter with the highest comprehensive matching degree within the group is retained, and the final output is multiple candidate historical operating parameters that do not belong to the same historical operating parameter group. This can avoid homogeneous redundancy in the recommended parameters, reduce invalid options when users select, and improve the accuracy of parameter recommendations and the efficiency of user selection.

[0154] This implementation method completes the deduplication of parameters within the same group during the initial screening stage of candidate historical operating parameters. Subsequently, only candidate historical operating parameters from different historical operating parameter groups after deduplication are subjected to comprehensive matching degree calculation, sorting, and recommendation processes. This reduces the ineffective computing power consumption in subsequent calculation stages, lowers the operating load of the food processor's electronic control module, and shortens the response time of parameter recommendation. It not only ensures the adaptation accuracy of a single recommended parameter but also covers more diverse operating scenarios, reducing the probability of the food processor stalling due to users selecting poorly adapted parameters.

[0155] Figure 12 A flowchart illustrating the ninth adaptive electronic control method for preventing food stalling in a food processor based on vibration characteristics, as provided in this application embodiment, is shown below. Figure 12 As shown, in some implementations, the above method also includes S410 to S420, which will be described in detail below.

[0156] S410. Obtain information on the first type of ingredient cooked multiple times by the user within a preset first historical time period, and information on the second type of ingredient cooked multiple times within a preset second historical time period. Determine the first ingredient type feature corresponding to the first ingredient type information, and the second ingredient type feature corresponding to the second ingredient type information. Determine the similarity between the first ingredient type feature and the second ingredient type feature as the ingredient type variability. The preset first historical time period is the preceding historical time period of the preset second historical time period and has the same duration.

[0157] In this implementation, the user's historical cooking records stored in the food processor can be retrieved, and the types of ingredients corresponding to all cooking tasks within two specified historical time periods can be extracted and summarized to obtain the sets of ingredients corresponding to the two time periods.

[0158] For example, the preset first historical time period can be set to a time range of 30 to 60 days from the current time. The first type of food ingredients that the user cooks multiple times during this time period may include three types of grain ingredients: soybeans, black beans, and red beans.

[0159] For example, the preset second historical time period can be set to a time interval of 0 to 30 days from the current time. The second ingredient type information that the user cooks multiple times during this time period may include three root vegetables: carrots, purple sweet potatoes, and pumpkins.

[0160] In this implementation, a feature vector transformation method can be used to encode the types of ingredients in each time period, convert the attribute dimensions of the ingredients into standardized vector data, and obtain the ingredient type features corresponding to the two time periods respectively.

[0161] In this implementation, the vector similarity of the features of two food types can be calculated, and the similarity result can be used as the quantitative value of the change in food type. The time range of the two time periods is set to be the same length of preceding and following intervals to ensure that the benchmark for comparing food usage habits is consistent.

[0162] It should be noted that the preset first historical time period is the preceding historical time period of the preset second historical time period and has the same duration. This can eliminate the interference of time length differences on the statistical results of food types and ensure the reference value of the calculation results of the degree of change of food types.

[0163] For example, the attribute dimensions of ingredients can be divided into four dimensions: grains, root vegetables, meats, and fruits and vegetables. Values ​​are assigned according to the frequency of occurrence of each type of ingredient in the first ingredient category information to obtain the corresponding first ingredient category feature vector. The same encoding rules are used to process the second ingredient category information to obtain the corresponding second ingredient category feature vector.

[0164] For example, the cosine similarity algorithm can be used to calculate the cosine distance between the feature vectors of the first and second food categories. The calculated cosine similarity value is used as the degree of change of food categories. The lower the value, the greater the difference in food categories between the two time periods.

[0165] S420. When the variability of food types is greater than the preset variability of food types, multiple historical operating parameters are re-clustered to obtain multiple historical operating parameter groups, each including multiple historical operating parameters.

[0166] In this implementation, the calculated degree of change in food types can be compared with a pre-set threshold. When the degree of change exceeds the threshold, it is determined that the user's food consumption habits have deviated significantly, triggering a re-clustering process of historical operating parameters and updating the division results of historical operating parameter groups.

[0167] For example, the preset ingredient variety variation can be set to a fixed threshold. When the calculated ingredient variety variation exceeds the threshold, a clustering algorithm is called to re-divide all stored historical operating parameters according to parameter attributes, resulting in multiple updated historical operating parameter groups for subsequent candidate historical operating parameter screening processes.

[0168] This implementation method obtains information on the first type of food that the user has cooked multiple times within a preset first historical time period and information on the second type of food that the user has cooked multiple times within a preset second historical time period. It then calculates the similarity between the features of the first and second types of food to obtain the degree of change in food type. When the degree of change in food type is greater than the preset degree of change in food type, it triggers the re-clustering of historical operating parameters. This method can adapt to changes in the user's food usage habits in a timely manner and avoid the clustering results of historical operating parameter groups becoming outdated.

[0169] This implementation uses the difference in food type characteristics between two consecutive historical time periods of the same duration as the clustering trigger condition. There is no need to set a fixed clustering period. Clustering is only performed when there is a significant shift in the user's food usage preferences. This can reduce the unnecessary computing power consumption of the food processor, improve the computing efficiency and response speed of the electronic control system, effectively improve the comprehensive matching degree of candidate historical operating parameters, reduce the probability of stalling during food processor operation, and better meet the user's processing needs.

[0170] This application also provides an adaptive electronic control system for preventing food stalling in a food processor based on vibration characteristics, including a unit for implementing the method described above.

[0171] Figure 13 This application provides a schematic diagram of the logic structure of an adaptive electronic control system for preventing food stalling in a food processor based on vibration characteristics, as shown in the embodiment of this application. Figure 13As 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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 vibration feature-based anti-stall adaptive control method for a food processor, characterized in that, The method includes: The system acquires user identity information, ingredient configuration parameters, and user processing requirements related to the cooking process of the food processor for the current ingredients. Based on the user's operating habit rule base, and according to the user identity information, it determines a preset number of historical high-frequency operating parameters, arranged from highest to lowest frequency of use for the same type of ingredients corresponding to the current ingredients, as multiple candidate historical operating parameters. The ingredient configuration parameters include ingredient type, ingredient hardness, ingredient particle size, and ingredient weight. The multiple candidate historical operating parameters include historical ingredient configuration parameters, historical user processing requirements, and historical vibration index, with the historical vibration index representing the magnitude of the food processor's vibration. Based on the ingredient configuration parameters, determine the stall safety matching degree corresponding to multiple candidate historical operating parameters; based on the historical user processing demand information of multiple candidate historical operating parameters, determine the historical user processing demand characteristics; based on the user processing demand information, determine the user processing demand characteristics; determine the similarity between the historical user processing demand characteristics and the user processing demand characteristics, as the user processing demand matching degree. Based on the stall safety matching degree, user processing demand matching degree, and historical vibration index of multiple candidate historical operating parameters, the comprehensive matching degree of multiple candidate historical operating parameters is determined; multiple candidate historical operating parameters are recommended to the user in order of comprehensive matching degree from high to low; the target historical operating parameter is obtained from the multiple candidate historical operating parameters selected by the user, and the food processor is controlled to cook the current ingredients according to the target historical operating parameter.

2. The method according to claim 1, characterized in that, Based on the ingredient configuration parameters, determine the stall safety matching degree corresponding to multiple candidate historical operating parameters, including: Obtain the blade information of the food processor; obtain the food type stall safety index, food hardness stall safety index, food particle size stall safety index, and food weight stall safety index corresponding to the blade information and food configuration parameters; The sum of the stall safety index for food type, stall safety index for food hardness, stall safety index for food particle size, and stall safety index for food weight corresponding to multiple candidate historical operating parameters is determined as the stall safety matching degree corresponding to multiple candidate historical operating parameters.

3. The method according to claim 2, characterized in that, Based on the ingredient configuration parameters, the stall safety matching degree corresponding to multiple candidate historical operating parameters is determined, including: Obtain the weights of food type, food hardness, food particle size, and food weight corresponding to the historical vibration index of the candidate historical operating parameters; The sum of the products of food type stall safety index and food type weight, food hardness stall safety index and food hardness weight, food particle size stall safety index and food particle size weight, and food weight stall safety index and food weight weight corresponding to multiple candidate historical operating parameters is used as the stall safety matching degree corresponding to multiple candidate historical operating parameters.

4. The method according to claim 3, characterized in that, Based on the stall safety matching degree, user processing demand matching degree, and historical vibration index of multiple candidate historical operating parameters, the comprehensive matching degree of multiple candidate historical operating parameters is determined, including: Obtain the rated power of the food processor and the corresponding cooking modes of the food processor with multiple candidate historical operating parameters; obtain the stall safety weight, user processing demand weight, and historical vibration weight of the food processor with the rated power of the food processor and the corresponding cooking modes of the food processor with multiple candidate historical operating parameters. The sum of the products of stall safety matching degree and stall safety weight, user processing demand matching degree and user processing demand weight, and historical vibration index and historical vibration weight of multiple candidate historical operating parameters is determined as the comprehensive matching degree of multiple candidate historical operating parameters.

5. The method according to claim 4, characterized in that, The method further includes: Obtain the historical non-blocking rate corresponding to multiple candidate historical operating parameters; obtain the rated power of the food processor and the historical non-blocking weight corresponding to the food processor cooking mode corresponding to multiple candidate historical operating parameters; The sum of the products of the stall safety matching degree and stall safety weight, the user processing demand matching degree and user processing demand weight, the historical vibration index and historical vibration weight, and the historical non-stall rate and historical non-stall weight of multiple candidate historical operating parameters is determined as the comprehensive matching degree of multiple candidate historical operating parameters.

6. The method according to claim 5, characterized in that, The method further includes: Clustering multiple historical operating parameters yields multiple historical operating parameter groups, each containing multiple historical operating parameters; the mean historical vibration index corresponding to the multiple historical operating parameters of each historical operating parameter group is determined; and the vibration weight of the parameter group corresponding to each historical operating parameter group is determined based on the proportion of food processor cooking modes corresponding to the multiple historical operating parameters of each historical operating parameter group. Obtain the average historical vibration index and vibration weight of the parameter group to which the multiple candidate historical operating parameters belong respectively; determine the sum of the product of the stall safety matching degree and stall safety weight of the multiple candidate historical operating parameters, the product of the user processing demand matching degree and the user processing demand weight, and the product of the average historical vibration index and the vibration weight of the parameter group, as the comprehensive matching degree of the multiple candidate historical operating parameters.

7. The method according to claim 6, characterized in that, The method further includes: Determine the average historical non-blocking rate corresponding to multiple historical operating parameters for each historical operating parameter group; determine the non-blocking weight of each historical operating parameter group based on the proportion of food processor cooking modes corresponding to multiple historical operating parameters for each historical operating parameter group. Obtain the average historical non-blocking rate and the non-blocking weight of the parameter group to which the multiple candidate historical operating parameters belong respectively; determine the sum of the products of the blocking safety matching degree and the blocking safety weight of the multiple candidate historical operating parameters, the product of the user processing demand matching degree and the user processing demand weight, the product of the average historical vibration index and the vibration weight of the parameter group, and the product of the average historical non-blocking rate and the non-blocking weight of the parameter group, as the comprehensive matching degree of the multiple candidate historical operating parameters.

8. The method according to claim 7, characterized in that, The method further includes: When multiple candidate historical operating parameters include multiple target candidate historical operating parameters belonging to the same historical operating parameter group, the target candidate historical operating parameter with the highest comprehensive matching degree among the multiple target candidate historical operating parameters belonging to the same historical operating parameter group is retained. In order to determine the preset number of historical high-frequency operating parameters that do not belong to the same historical operating parameter group and are arranged from high to low frequency of use of the same type of food for the current food, these are used as multiple candidate historical operating parameters.

9. The method according to claim 8, characterized in that, The method further includes: The system obtains information on the first type of food that the user has cooked multiple times within a preset first historical time period and information on the second type of food that the user has cooked multiple times within a preset second historical time period; it determines the first type of food feature corresponding to the first type of food feature and the second type of food feature corresponding to the second type of food feature; it determines the similarity between the first type of food feature and the second type of food feature as the degree of change of food type; wherein, the preset first historical time period is the preceding historical time period of the preset second historical time period and has the same duration. When the variability of food types exceeds the preset variability of food types, multiple historical operating parameters are re-clustered to obtain multiple historical operating parameter groups, each including multiple historical operating parameters.

10. A vibration-based adaptive electronic control system for preventing food stalling in a food processor, characterized in that: Includes units for implementing the method of any one of claims 1 to 9.

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