Method, device and related equipment for predicting food weight based on cooking utensils

Abstract

The present application provides a method, apparatus, and related equipment for predicting food weight based on cooking utensils. The method includes: determining the target characteristics and / or food characteristics and temperature of the cooking utensil, where the temperature includes the outer wall temperature and / or inner wall temperature of the cooking utensil; and inputting the target characteristics and / or food characteristics and temperature of the cooking utensil as input data into a pre-trained prediction model, which outputs the weight of the food in the cooking utensil. There is no need to construct different linear relationship models between temperature and food weight based on different scenarios. Different target characteristics and temperatures can be used as model inputs. Based on the training of a universal prediction model, the prediction model can be used to predict the weight of different foods under different conditions. Predicting food weight using multiple target characteristics can improve the accuracy and stability of the prediction results.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device and related equipment for predicting food weight based on cooking utensils. Background Art

[0002] In the related art, in order to infer the weight of food in the cooking utensil by the outer wall temperature of the cooking utensil, different linear relationship models between the outer wall temperature and the food weight are usually constructed based on different scenarios.

[0003] However, the linear relationship model constructed using relevant technologies has low accuracy and poor stability in predicting the weight of food in the cooking appliance. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method, device and related equipment for predicting food weight based on cooking utensils.

[0005] Based on the above objectives, in a first aspect, the present application provides a method for predicting food weight based on a cooking utensil, comprising:

[0006] determining target characteristics and / or food properties and temperatures of the cooking vessel, including outer and / or inner wall temperatures of the cooking vessel; and

[0007] The target features of the cooking utensil and / or the food characteristics and the temperature are used as input data and input into a pre-trained prediction model, and the weight of the food in the cooking utensil is obtained as output.

[0008] In a possible implementation, the target feature includes: the heating capacity of the cooking appliance;

[0009] The determining of the target characteristic of the cooking appliance further comprises:

[0010] Obtaining real-time information about the pot material, pot thickness, contact area between the pot and the coil, and layout information of the coil;

[0011] determining a heating coefficient based on the layout information of the coil;

[0012] The heating capacity of the cooking appliance is determined according to the ratio of the pot body material information to the pot body thickness information, the contact area between the pot body and the coil, and the heating coefficient.

[0013] In a possible implementation, the target feature includes: an initial temperature of the cooking appliance, a current temperature of the cooking appliance, and a compensated temperature change rate;

[0014] The determining of the target characteristic of the cooking appliance further comprises:

[0015] Acquiring the initial temperature of the cooking utensil, the current temperature of the cooking utensil, and the initial state of the cooking utensil; wherein the initial state includes: a cold pot state and a hot pot state;

[0016] determining a first time corresponding to the initial temperature according to the initial temperature;

[0017] determining a second time corresponding to the current temperature according to the current temperature;

[0018] determining a compensation coefficient according to the initial state;

[0019] The compensated temperature change rate is determined according to a ratio of an absolute value of a difference between the initial temperature and the current temperature to an absolute value of a difference between the first time and the second time, and the compensation coefficient.

[0020] In a possible implementation, the target feature includes: a loss coefficient of the cooking appliance;

[0021] The determining of the target characteristic of the cooking appliance further comprises:

[0022] determining a peeling state of a coating on a cooking utensil and a usage time of the cooking utensil based on the cooking utensil;

[0023] The loss coefficient of the cooking utensil is determined according to the peeling state of the coating of the cooking utensil and the usage time of the cooking utensil.

[0024] In a possible implementation, the target feature further includes: the heating power of the cooking appliance.

[0025] In a possible implementation, the method further includes:

[0026] obtaining the moisture content and / or specific heat capacity of the food in the cooking utensil;

[0027] The food characteristic is determined according to the moisture content of the food in the cooking utensil and / or the specific heat capacity of the food.

[0028] In a possible implementation, the method further includes:

[0029] Acquire target feature sample data; wherein the target feature sample data includes: one or more of: heating capacity sample data of the cooking appliance, initial temperature sample data of the cooking appliance, current temperature sample data of the cooking appliance, compensated temperature change rate sample data, loss coefficient sample data of the cooking appliance, heating power sample data of the cooking appliance, and food characteristic sample data;

[0030] Determining the Gini coefficient of the target feature sample data;

[0031] Determining the optimal split point based on the Gini coefficient;

[0032] Determining a prediction model to be trained based on a decision tree model according to the optimal split point;

[0033] The prediction model to be trained is trained according to the target feature sample data to obtain a prediction model.

[0034] In a second aspect, the present application provides a device for predicting food weight based on a cooking utensil, comprising:

[0035] a determination module configured to determine a target characteristic and / or food characteristic and a temperature of the cooking utensil, wherein the temperature includes an outer wall temperature and / or an inner wall temperature of the cooking utensil;

[0036] The prediction module is configured to take the target features of the cooking utensil and / or the food characteristics and the temperature as input data, input them into a pre-trained prediction model, and output the weight of the food in the cooking utensil.

[0037] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for predicting food weight based on cooking utensils as described in the first aspect is implemented.

[0038] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method for predicting food weight based on cooking utensils as described in the first aspect.

[0039] As can be seen from the foregoing, the present application provides a method, apparatus, and related equipment for predicting food weight based on a cooking utensil. The method determines the target characteristics and / or food characteristics of the cooking utensil, as well as the temperature, including the outer and / or inner wall temperatures of the cooking utensil. The target characteristics and / or food characteristics, as well as the temperature of the cooking utensil, are used as input data into a pretrained prediction model, which outputs the weight of the food within the cooking utensil. This eliminates the need to construct different linear relationship models between temperature and food weight based on different scenarios. Instead, different target characteristics and temperatures can be used as model inputs. Based on the training of a universal prediction model, the model can be used to predict the weight of different foods under different conditions. Predicting food weight using multiple target characteristics can improve the accuracy and stability of the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 A schematic diagram of an exemplary flow chart of a method for predicting food weight based on cooking utensils provided in an embodiment of the present application is shown.

[0042] Figure 2 A schematic diagram shows the trend of temperature change over time at dry-burning rates for different pot materials, different pot thicknesses, and different coil layouts according to an embodiment of the present application.

[0043] Figure 3 A schematic diagram shows the trend of the temperature change of the outer wall of the pot body over time under different water volume and different power conditions according to an embodiment of the present application.

[0044] Figure 4 A schematic diagram of an exemplary structure of a device for predicting food weight based on cooking utensils provided in an embodiment of the present application is shown.

[0045] Figure 5 A schematic diagram of an exemplary structure of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0046] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0047] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meanings understood by people with ordinary skills in the field to which this application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0048] As described in the background technology section, in order to infer the weight of food in a cooking utensil from the outer wall temperature of the cooking utensil, different linear relationship models between the outer wall temperature and the food weight are usually constructed based on different scenarios.

[0049] In a related technology, the temperature / humidity / quality or volume of food is detected by a temperature / humidity / optical sensor, and compared with a pre-stored expected value / threshold value to determine whether the food is overloaded.

[0050] In another related technology, for different ingredients, containers, power, and temperature rise rates, temperature change rate and weight relationship curves are pre-stored, and the weight of the ingredients is determined based on real-time temperature change information.

[0051] In another related technology, the real-time heat absorption curve is compared with a preset heat absorption curve, and if the difference between the two is greater than a threshold, the cooking parameters are adjusted.

[0052] However, the applicant found through research that the related technology only uses the outer wall temperature and food weight to model the linear relationship, which is too simple. In addition, different linear relationship models of the outer wall temperature and food weight are constructed based on different scenarios, which makes the prediction process complicated. Different models need to be found to predict the food weight in different scenarios, which will not only cause a decrease in prediction accuracy, but also easily cause errors when matching different models. The wrong model is used to predict the food weight of the current scenario, resulting in a decrease in prediction stability.

[0053] For this reason, the present application provides a method, apparatus, and related equipment for predicting food weight based on a cooking utensil. The method determines the target characteristics and / or food characteristics of the cooking utensil, as well as the temperature, including the outer and / or inner wall temperatures of the cooking utensil. The target characteristics and / or food characteristics of the cooking utensil, as well as the temperature, are used as input data into a pre-trained prediction model, which outputs the weight of the food within the cooking utensil. This eliminates the need to construct different linear relationship models between temperature and food weight based on different scenarios. Instead, different target characteristics and temperatures can be used as model inputs. Based on the training of a universal prediction model, the model can be used to predict the weight of different foods under different conditions. Predicting food weight using multiple target characteristics can improve the accuracy and stability of the prediction results.

[0054] The following is a detailed description of the method for predicting food weight based on cooking utensils provided in the embodiments of the present application through specific examples.

[0055] Figure 1 A schematic diagram of an exemplary flow chart of a method for predicting food weight based on cooking utensils provided in an embodiment of the present application is shown.

[0056] refer to Figure 1 The method for predicting food weight based on cooking utensils provided in an embodiment of the present application specifically includes the following steps:

[0057] S102: Determine target characteristics and / or food characteristics and temperature of the cooking utensil, wherein the temperature includes an outer wall temperature and / or an inner wall temperature of the cooking utensil.

[0058] S104: The target characteristics of the cooking utensil and / or the food characteristics and the temperature are input into a pre-trained prediction model as input data, and the weight of the food in the cooking utensil is obtained as output.

[0059] In some optional embodiments, temperature-weight and other related feature data under various conditions can be collected offline and converted into training sample data. A prediction model can then be developed based on a decision tree model. The temperature sample data can be obtained by measuring the outer and / or inner wall temperatures of the cooking appliance using temperature sensors such as thermistors, thermocouples, thermal resistors, and infrared sensors. For example, an NTC thermistor can be used to measure the outer wall temperature, which can be used as the temperature sample data for training the prediction model.

[0060] In some optional embodiments, the obtained target feature sample data can be used as a training set for training the prediction model. The target feature sample data can include one or more of the following: cooking appliance heating capacity sample data, cooking appliance initial temperature sample data, cooking appliance current temperature sample data, compensated temperature change rate sample data, cooking appliance loss coefficient sample data, cooking appliance heating power sample data, and food characteristic sample data. Furthermore, a decision tree model is generated, and the decision tree model is used to identify the optimal node and the optimal branching method. The evaluation criterion can be the Gini coefficient. The Gini coefficient of each target feature sample data is calculated to select the optimal split point. Child nodes are then recursively generated from top to bottom based on the optimal split point and the Gini coefficients of all target feature sample data. The decision tree growth process is terminated until the training set becomes indivisible, thereby obtaining the prediction model to be trained. To avoid overfitting of the prediction model determined based on the decision tree model, the maximum length of the decision tree can be controlled, but the specific length is not limited. Furthermore, the target feature sample data can be used as a training set to train the prediction model to be trained, thereby obtaining a prediction model for predicting food weight.

[0061] It should be noted that the prediction model can also be trained based on a logistic regression model and / or a random forest model.

[0062] In some optional embodiments, the data input into the prediction model may include at least one target characteristic and / or food property, as well as temperature. The temperature may be the measured outer and / or inner wall temperature of the cooking vessel. The target characteristics of the cooking vessel may include the heating capacity of the cooking vessel, the initial temperature of the cooking vessel, the current temperature of the cooking vessel, the compensated temperature change rate, the loss coefficient of the cooking vessel, and the heating power of the cooking vessel.

[0063] It should be noted that the heating capacity of the cooking appliance can be determined by the cooking pot material information, pot thickness information, the contact area between the pot and the coil, and the coil layout information. Specifically, the heating capacity of the cooking appliance can be calculated using the following formula:

[0064]

[0065] Among them, C Heating Indicates the heating capacity of the cooking appliance, P material The material information of the cooking pot can be expressed by thermal conductivity, P thickness Indicates the thickness of the cooking pot, S coil represents the contact area between the pot and the coil, and k represents the heating coefficient, which can be determined based on the coil layout and can range from 0 to 1. To maintain dimensionality, the pot material, pot thickness, and contact area between the pot and the coil are normalized across different ranges.

[0066] Figure 2 A schematic diagram shows the trend of temperature change over time at dry-burning rates for different pot materials, different pot thicknesses, and different coil layouts according to an embodiment of the present application.

[0067] Regarding the heating capacity of cooking utensils, when the heat conduction efficiency of the cooking container is high, the heating speed is faster, when the thickness of the cooking container is large, the heating speed is slower, and when the contact area between the pot body and the coil is larger, the heating capacity is stronger. Figure 2 The pot body materials are divided into cast iron and composite materials, and the pot body thicknesses are 2.5mm, 3mm and 5mm respectively. The coil layout methods are divided into uniform and non-uniform. When the heating power is 3 levels, the cast iron cooking utensils with uniform coil layout and 5mm thickness have the highest heating rate, and the cast iron cooking utensils with uniform coil layout and 3mm thickness have the lowest heating rate. This determines the heating capacity of the cooking utensils under the conditions of different pot body materials, different pot body thicknesses and different coil layout methods.

[0068] In some optional embodiments, the temperature characteristics of the cooking appliance can be used as target features, specifically represented by the initial temperature of the cooking appliance, the current temperature of the cooking appliance, and the compensated temperature change rate. The initial temperature refers to the initial temperature of the cooking appliance when cooking begins. The initial temperature can be normalized to between 0 and 1 to unify the dimensions. The current temperature refers to the current real-time temperature of the cooking appliance. Similarly, to unify the dimensions, the current temperature can be normalized to between 0 and 1. The compensated temperature change rate can be calculated using the following formula:

[0069]

[0070] Among them, V temp Indicates the temperature change rate after compensation, T start Indicates the initial temperature of the cooking appliance, T current Indicates the current temperature of the cooking appliance, t start Indicates the first time corresponding to the initial temperature, t current represents the second time corresponding to the current temperature, and η represents the compensation coefficient, which is determined by the initial state of the cooking appliance and can be between 0 and 1. The initial state of the cooking appliance can include a cold pot state and a hot pot state. Considering that when the cooking appliance is just starting to cook, i.e., in the cold pot state, it takes a long time to heat up, the calculated temperature change rate is low. In this case, η is taken as a larger value. Conversely, when the pot is hot, the calculated temperature change rate is high, and η is taken as a smaller value.

[0071] It should be noted that the temperature change rate after compensation may also be determined by the change rate of the current temperature relative to the temperature at the previous moment.

[0072] In some optional embodiments, the loss coefficient of a cooking utensil can be determined by determining the flaking state of the coating on the cooking utensil and the age of the cooking utensil. With increasing use, the coating on a coated pot will become increasingly flaky, and its thermal conductivity will actually improve. Similarly, decreasing the thickness of the pot will also improve its thermal conductivity. Improved thermal conductivity directly affects the rate of temperature change. To maintain a consistent dimension, the loss coefficient of a cooking utensil can be set between 0 and 1.

[0073] In some optional embodiments, the heating power of the cooking appliance may be expressed as a percentage of the actual power or maximum power of the cooking appliance. To unify the dimensions, the value range of the heating power of the cooking appliance may be normalized to between 0 and 5.

[0074] Figure 3A schematic diagram shows the trend of the temperature change of the outer wall of the pot body over time under different water volume and different power conditions according to an embodiment of the present application.

[0075] refer to Figure 3 The heating power can be further illustrated by the temperature change trend over time under different water volumes and different power conditions. Among them, the cooking appliance with an actual power of 3200w is filled with 500g of water, and the outer wall of the pot heats up the fastest at this time; the cooking appliance with an actual power of 8000w is filled with 1500g of water, and the outer wall of the pot heats up the slowest at this time.

[0076] In some optional embodiments, the data input into the prediction model may also include food properties, and the weight of the food in the cooking vessel is predicted based on the food properties and the temperature of the cooking vessel. The food properties may include the moisture content of the food and / or the specific heat capacity of the food.

[0077] The technical effect of the present application can be further demonstrated by designing experiments, in which several groups of data with different powers and different water volumes were randomly selected for testing. The water volumes were 500g, 1000g, 1500g, 2000g, and 2500g, respectively. Among them, there were 4 groups of experimental samples with 500g of water volume, 10 groups of experimental samples with 1000g of water volume, 78 groups of experimental samples with 1500g of water volume, 8 groups of experimental samples with 2000g of water volume, and 24 groups of experimental samples with 2500g of water volume. In addition, the experimental samples corresponding to 1500g of water volume included different results with heating powers of 1600w, 3200w, 4800w, and 6400w. The experimental results show that the overall prediction accuracy of food weight is 99.19%, and the discrimination of the same water volume under different powers can reach 100%.

[0078] As can be seen from the foregoing, the present application provides a method, apparatus, and related equipment for predicting food weight based on a cooking utensil. The method determines the target characteristics and / or food characteristics of the cooking utensil, as well as the temperature, including the outer and / or inner wall temperatures of the cooking utensil. The target characteristics and / or food characteristics, as well as the temperature of the cooking utensil, are used as input data into a pretrained prediction model, which outputs the weight of the food within the cooking utensil. This eliminates the need to construct different linear relationship models between temperature and food weight based on different scenarios. Instead, different target characteristics and temperatures can be used as model inputs. Based on the training of a universal prediction model, the model can be used to predict the weight of different foods under different conditions. Predicting food weight using multiple target characteristics can improve the accuracy and stability of the prediction results.

[0079] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario and performed by multiple devices working together. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the method.

[0080] It should be noted that the above description is limited to some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] Figure 4 A schematic diagram of an exemplary structure of a device for predicting food weight based on cooking utensils provided in an embodiment of the present application is shown.

[0082] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a device for predicting food weight based on cooking utensils.

[0083] refer to Figure 4 The device for predicting food weight based on cooking utensils includes: a determination module and a prediction module; wherein,

[0084] a determination module configured to determine a target characteristic and / or food characteristic and a temperature of the cooking utensil, wherein the temperature includes an outer wall temperature and / or an inner wall temperature of the cooking utensil;

[0085] The prediction module is configured to take the target features of the cooking utensil and / or the food characteristics and the temperature as input data, input them into a pre-trained prediction model, and output the weight of the food in the cooking utensil.

[0086] In a possible implementation, the target feature includes: the heating capacity of the cooking appliance;

[0087] The determining module is further configured to:

[0088] Obtaining real-time information about the pot material, pot thickness, contact area between the pot and the coil, and layout information of the coil;

[0089] determining a heating coefficient based on the layout information of the coil;

[0090] The heating capacity of the cooking appliance is determined according to the ratio of the pot body material information to the pot body thickness information, the contact area between the pot body and the coil, and the heating coefficient.

[0091] In a possible implementation, the target feature includes: an initial temperature of the cooking appliance, a current temperature of the cooking appliance, and a compensated temperature change rate;

[0092] The determining module is further configured to:

[0093] Acquiring the initial temperature of the cooking utensil, the current temperature of the cooking utensil, and the initial state of the cooking utensil; wherein the initial state includes: a cold pot state and a hot pot state;

[0094] determining a first time corresponding to the initial temperature according to the initial temperature;

[0095] determining a second time corresponding to the current temperature according to the current temperature;

[0096] determining a compensation coefficient according to the initial state;

[0097] The compensated temperature change rate is determined according to a ratio of an absolute value of a difference between the initial temperature and the current temperature to an absolute value of a difference between the first time and the second time, and the compensation coefficient.

[0098] In a possible implementation, the target feature includes: a loss coefficient of the cooking appliance;

[0099] The determining module is further configured to:

[0100] determining a peeling state of a coating on a cooking utensil and a usage time of the cooking utensil based on the cooking utensil;

[0101] The loss coefficient of the cooking utensil is determined according to the peeling state of the coating of the cooking utensil and the usage time of the cooking utensil.

[0102] In a possible implementation, the determining module is further configured to:

[0103] obtaining the moisture content and / or specific heat capacity of the food in the cooking utensil;

[0104] The food characteristic is determined according to the moisture content of the food in the cooking utensil and / or the specific heat capacity of the food.

[0105] In a possible implementation, the apparatus further includes: a training module;

[0106] The training module is configured to:

[0107] Acquire target feature sample data; wherein the target feature sample data includes: one or more of: heating capacity sample data of the cooking appliance, initial temperature sample data of the cooking appliance, current temperature sample data of the cooking appliance, compensated temperature change rate sample data, loss coefficient sample data of the cooking appliance, heating power sample data of the cooking appliance, and food characteristic sample data;

[0108] Determining the Gini coefficient of the target feature sample data;

[0109] Determining the optimal split point based on the Gini coefficient;

[0110] Determining a prediction model to be trained based on a decision tree model according to the optimal split point;

[0111] The prediction model to be trained is trained according to the target feature sample data to obtain a prediction model.

[0112] For the convenience of description, the above system is described as being divided into various modules according to their functions. Of course, when implementing this application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0113] The system of the above embodiment is used to implement the corresponding method of predicting food weight based on cooking utensils in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0114] Figure 5 A schematic diagram of an exemplary structure of an electronic device provided in an embodiment of the present application is shown.

[0115] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the method for predicting food weight based on cooking utensils as described in any of the above-mentioned embodiments is implemented. Figure 5 A more specific hardware structure diagram of an electronic device provided in this embodiment is shown. The device may include: a processor 510, a memory 520, an input / output interface 530, a communication interface 540, and a bus 550. The processor 510, the memory 520, the input / output interface 530, and the communication interface 540 are connected to each other within the device via the bus 550.

[0116] The processor 510 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0117] The memory 520 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 520 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 520 and is called and executed by the processor 510.

[0118] The input / output interface 530 is used to connect an input / output module to implement information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0119] The communication interface 540 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WiFi, Bluetooth, etc.).

[0120] The bus 550 comprises a pathway for transmitting information between the various components of the device (eg, the processor 510 , the memory 520 , the input / output interface 530 , and the communication interface 540 ).

[0121] It should be noted that although the above device only shows the processor 510, memory 520, input / output interface 530, communication interface 540, and bus 550, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will understand that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figures.

[0122] The electronic device of the above embodiment is used to implement the corresponding method of predicting food weight based on cooking utensils in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0123] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method of predicting food weight based on cooking utensils as described in any of the above embodiments.

[0124] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0125] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the method of predicting food weight based on cooking utensils as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0126] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. Within the scope of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0127] In addition, for simplicity of description and discussion, and in order not to make the embodiment of the application difficult to understand, the known power supply / ground connection with integrated circuit (IC) chip and other components may or may not be shown in the accompanying drawings provided. In addition, the device can be shown in the form of a block diagram to avoid making the embodiment of the application difficult to understand, and this also takes into account the following fact, that is, the details of the embodiment of these block diagram devices are highly dependent on the platform to be implemented in the embodiment of the application (that is, these details should be fully within the scope of understanding of those skilled in the art). When specific details (for example, circuit) are set forth to describe exemplary embodiments of the application, it will be apparent to those skilled in the art that the embodiment of the application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0128] Although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize the embodiments discussed.

[0129] The embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of this application.

Claims

1. A method for predicting food weight based on cooking utensils, characterized in that: include: determining target characteristics, food characteristics, and temperature of the cooking vessel, the temperature including an outer wall temperature and / or an inner wall temperature of the cooking vessel; as well as The target features of the cooking utensil, the characteristics of the food, and the temperature are used as input data and input into a pre-trained prediction model, and the weight of the food in the cooking utensil is obtained as output; wherein, The target characteristics include a heating capacity of the cooking appliance, a compensated temperature change rate of the cooking appliance, and a heating power of the cooking appliance; The food characteristics are determined based on the moisture content of the food in the cooking appliance and the specific heat capacity of the food.

2. The method according to claim 1, characterized in that The target characteristics include: the heating capacity of the cooking appliance; The determining of the target characteristic of the cooking appliance further comprises: Obtaining real-time information about the pot material, pot thickness, contact area between the pot and the coil, and layout information of the coil; determining a heating coefficient based on the layout information of the coil; The heating capacity of the cooking appliance is determined according to the ratio of the pot body material information to the pot body thickness information, the contact area between the pot body and the coil, and the heating coefficient.

3. The method according to claim 1, characterized in that The target characteristics include: the initial temperature of the cooking appliance, the current temperature of the cooking appliance, and the compensated temperature change rate; The determining of the target characteristic of the cooking appliance further comprises: Acquiring the initial temperature of the cooking utensil, the current temperature of the cooking utensil, and the initial state of the cooking utensil; wherein the initial state includes: a cold pot state and a hot pot state; determining a first time corresponding to the initial temperature according to the initial temperature; determining a second time corresponding to the current temperature according to the current temperature; determining a compensation coefficient according to the initial state; The compensated temperature change rate is determined according to a ratio of an absolute value of a difference between the initial temperature and the current temperature to an absolute value of a difference between the first time and the second time, and the compensation coefficient.

4. The method according to claim 1, wherein The target characteristics include: the loss coefficient of the cooking appliance; The determining of the target characteristic of the cooking appliance further comprises: determining a peeling state of a coating on a cooking utensil and a usage time of the cooking utensil based on the cooking utensil; The loss coefficient of the cooking utensil is determined according to the peeling state of the coating of the cooking utensil and the usage time of the cooking utensil.

5. The method according to claim 1, wherein The target feature also includes: the heating power of the cooking appliance.

6. The method according to claim 1, characterized in that The method further comprises: obtaining the moisture content and / or specific heat capacity of the food in the cooking utensil; The food characteristic is determined according to the moisture content of the food in the cooking utensil and / or the specific heat capacity of the food.

7. The method according to claim 1, characterized in that The method further comprises: Acquire target feature sample data; wherein the target feature sample data includes: heating capacity sample data of the cooking appliance, initial temperature sample data of the cooking appliance, current temperature sample data of the cooking appliance, compensated temperature change rate sample data, loss coefficient sample data of the cooking appliance, heating power sample data of the cooking appliance, and food characteristic sample data; Determining the Gini coefficient of the target feature sample data; Determining the optimal split point based on the Gini coefficient; Determining a prediction model to be trained based on a decision tree model according to the optimal split point; The prediction model to be trained is trained according to the target feature sample data to obtain a prediction model.

8. A device for predicting food weight based on cooking utensils, characterized in that: include: a determination module configured to determine a target characteristic, a food characteristic, and a temperature of the cooking utensil, wherein the temperature includes an outer wall temperature and / or an inner wall temperature of the cooking utensil; The prediction module is configured to input the target characteristics of the cooking utensil, the food characteristics and the temperature as input data into a pre-trained prediction model, and output the weight of the food in the cooking utensil; wherein, The target characteristics include a heating capacity of the cooking appliance, a compensated temperature change rate of the cooking appliance, and a heating power of the cooking appliance; The food characteristics are determined based on the moisture content of the food in the cooking appliance and the specific heat capacity of the food.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable the computer to implement the method according to any one of claims 1 to 7.

Images