Method and device for predicting current temperature of cookware, electronic equipment and storage medium

Abstract

The invention provides a current temperature prediction method and device of cookware, electronic equipment and a storage medium. The method comprises the following steps: acquiring a historical anti-dry-burning probe temperature of the cookware; wherein the adjacent historical dry-burning-resistant probe temperatures have the same time interval; the historical anti-dry-burning probe temperatures are numbered according to the time sequence, and the numbered historical anti-dry-burning probe temperatures are grouped; training a neural network model based on the grouped historical anti-dry-burning probe temperatures; obtaining an anti-dry-burning probe temperature of the cooker before a preset time length; inputting the temperature of the anti-dry-burning probe before a preset time length into the trained neural network model, and outputting the predicted current temperature of the anti-dry-burning probe of the cookware; and determining whether water in the cookware is boiled or not based on the current temperature of the anti-dry-burning probe. In the mode, the current temperature of the cooker can be accurately and quickly predicted, whether the water in the cooker is boiling or not can be identified, and temperature prediction and water boiling judgment are not delayed, so that the experience feeling of a user is improved.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and in particular to a method, device, electronic device and storage medium for predicting the current temperature of a cookware. Background Art

[0002] Currently, the pot's current temperature is typically predicted after obtaining the anti-dry-boil probe's temperature. However, this existing pot temperature prediction solution requires obtaining the anti-dry-boil probe's temperature before predicting the pot's current temperature, which introduces a certain delay and a poor user experience. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, device, electronic device and storage medium for predicting the current temperature of a cookware, so as to accurately and quickly predict the current temperature of the cookware and identify whether the water in the cookware is boiling. There is no delay in temperature prediction and water boiling judgment, thereby improving the user experience.

[0004] In a first aspect, an embodiment of the present invention provides a method for predicting the current temperature of a cookware, the method comprising: obtaining historical anti-dry-boil probe temperatures of the cookware; wherein adjacent historical anti-dry-boil probe temperatures have the same time interval; numbering the historical anti-dry-boil probe temperatures in chronological order, and grouping the numbered historical anti-dry-boil probe temperatures; wherein each group of historical anti-dry-boil probe temperatures includes multiple consecutive input data and one output data, and the output data is separated from the last input data by a preset time length; training a neural network model based on the grouped historical anti-dry-boil probe temperatures; obtaining the anti-dry-boil probe temperature of the cookware before a preset time length; inputting the anti-dry-boil probe temperature before the preset time length into the trained neural network model, and outputting the predicted current anti-dry-boil probe temperature of the cookware; and determining whether the water in the cookware is boiling based on the current anti-dry-boil probe temperature.

[0005] In an optional embodiment of the present application, the above-mentioned pot is placed on a stove, and an anti-dry-burning probe is provided on the stove; the above-mentioned method also includes: detecting the anti-dry-burning probe temperature of the pot by using the anti-dry-burning probe.

[0006] In an optional embodiment of the present application, the interval between the adjacent historical anti-dry-heat probe temperatures is x, each group of historical anti-dry-heat probe temperatures includes n consecutive input data, and the preset duration is k×x; the step of grouping the numbered historical anti-dry-heat probe temperatures includes: determining the ath historical anti-dry-heat probe temperature to the a+n-1th historical anti-dry-heat probe temperature as the input data of the ath group of historical anti-dry-heat probe temperatures; and determining the a+n+k-1th historical anti-dry-heat probe temperature as the output data of the ath group of historical anti-dry-heat probe temperatures.

[0007] In an optional embodiment of the present application, the step of training the neural network model based on the grouped historical anti-dry-heat probe temperatures includes: inputting the grouped historical anti-dry-heat probe temperatures into the neural network model in sequence; using multiple input data of each group of historical anti-dry-heat probe temperatures as input of the neural network model, and using the output data of each group of historical anti-dry-heat probe temperatures as output of the neural network model to train the neural network model.

[0008] In an optional embodiment of the present application, the above method also includes: if it is predicted that the current anti-dry-burn probe temperature is greater than or equal to a preset temperature threshold, stopping inputting the anti-dry-burn probe temperature before the preset time into the trained neural network model.

[0009] In an optional embodiment of the present application, the above method further includes: determining the predicted current water temperature of the cookware based on the predicted current anti-dry-boiling probe temperature of the cookware.

[0010] In an optional embodiment of the present application, the above method further includes: displaying the predicted current temperature of the pot on the panel of the stove.

[0011] In a second aspect, an embodiment of the present invention further provides a device for predicting the current temperature of a cookware, the device comprising: a historical anti-dry-burn probe temperature acquisition module, for acquiring the historical anti-dry-burn probe temperature of the cookware; wherein adjacent historical anti-dry-burn probe temperatures have the same time interval; a historical anti-dry-burn probe temperature numbering and grouping module, for numbering the historical anti-dry-burn probe temperatures in chronological order and grouping the numbered historical anti-dry-burn probe temperatures; wherein each group of historical anti-dry-burn probe temperatures includes a plurality of continuous input data and one output data, and the output data is separated from the last input data by a preset time length; a neural network model training module, for training a neural network model based on the grouped historical anti-dry-burn probe temperatures; a current anti-dry-burn probe temperature prediction module, for acquiring the anti-dry-burn probe temperature of the cookware before a preset time length; inputting the anti-dry-burn probe temperature before the preset time length into the trained neural network model, and outputting the predicted current anti-dry-burn probe temperature of the cookware; and determining whether the water in the cookware is boiling based on the current anti-dry-burn probe temperature.

[0012] In a third aspect, an embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the above-mentioned automatic cooking method.

[0013] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned automatic cooking method.

[0014] The embodiments of the present invention bring the following beneficial effects: Embodiments of the present invention provide a method, device, electronic device, and storage medium for predicting the current temperature of a cookware. These methods include obtaining the historical anti-dry-boil probe temperatures of the cookware, wherein adjacent historical anti-dry-boil probe temperatures have the same time interval. The historical anti-dry-boil probe temperatures are chronologically numbered and grouped. Each group of historical anti-dry-boil probe temperatures includes multiple consecutive input data and one output data, with the output data separated from the last input data by a preset time interval. A neural network model is trained based on the grouped historical anti-dry-boil probe temperatures. The anti-dry-boil probe temperature of the cookware before a preset time interval is obtained. The anti-dry-boil probe temperature before the preset time interval is input into the trained neural network model, which outputs the predicted current anti-dry-boil probe temperature of the cookware. The method then determines whether the water in the cookware is boiling based on the current anti-dry-boil probe temperature. This method accurately and quickly predicts the current temperature of the cookware and identifies whether the water in the cookware is boiling. There is no delay between temperature prediction and water boiling determination, thereby improving the user experience.

[0015] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by practicing the above-mentioned technology of the present disclosure.

[0016] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of the temperature for boiling water in a pot provided by an embodiment of the present invention; Figure 2 A flowchart of a method for predicting the current temperature of a cookware provided by an embodiment of the present invention; Figure 3 A flowchart of another method for predicting the current temperature of a cookware provided by an embodiment of the present invention; Figure 4 A schematic structural diagram of a device for predicting the current temperature of a cookware provided by an embodiment of the present invention; Figure 5A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] See also Figure 1 The diagram of a pot boiling water temperature shows a certain correspondence between the anti-dry-boil probe temperature and the water temperature inside the pot. Currently, the pot's current temperature is typically predicted after obtaining the anti-dry-boil probe temperature. However, the existing pot temperature prediction scheme requires obtaining the anti-dry-boil probe temperature before predicting the pot's current temperature, which results in a certain delay and a poor user experience.

[0021] Based on this, the embodiments of the present invention provide a method, device, electronic device and storage medium for predicting the current temperature of a cookware, and specifically provide a method for displaying the pot temperature based on machine learning or neural networks. The method can be based on the same stove and repeated tests in different environments (different pots, different water volumes, different ambient temperatures, etc.) to obtain the temperature curve data of the anti-dry-burn probe, and then perform machine learning / neural network learning on the temperature rise curve of the anti-dry-burn probe, so as to directly predict the water temperature based on the temperature of the anti-dry-burn probe.

[0022] To facilitate understanding of this embodiment, a method for predicting the current temperature of a cookware disclosed in an embodiment of the present invention is first introduced in detail.

[0023] Example 1: The present invention provides a method for predicting the current temperature of a cookware. Figure 2 The flowchart of a method for predicting the current temperature of a cookware is shown, and the method for predicting the current temperature of the cookware includes the following steps: Step S202 , obtaining the historical temperature of the cookware's anti-dry-boiling probe; wherein adjacent historical temperatures of the anti-dry-boiling probe have the same time interval.

[0024] In this embodiment, the historical anti-dry-boil probe temperature of the cookware can be obtained and used to train the neural network model. In this embodiment, adjacent historical anti-dry-boil probe temperatures are obtained at the same time interval. For example, if the time interval is 1 second, this embodiment can obtain a historical anti-dry-boil probe temperature every 1 second.

[0025] The anti-dry-boiling probe temperature of the cookware in this embodiment may be the water temperature in the cookware, or may be the anti-dry-boiling probe temperature detected by the anti-dry-boiling probe of the cookware.

[0026] In some embodiments, the cookware is placed on a stove equipped with a dry-boil prevention probe. The dry-boil prevention probe can detect the temperature of the cookware. That is, in this embodiment, the dry-boil prevention probe can be installed on the stove, and the temperature detected by the dry-boil prevention probe can be used as the dry-boil prevention probe temperature of the cookware.

[0027] Step S204: number the historical anti-dry-boiling probe temperatures in chronological order, and group the numbered historical anti-dry-boiling probe temperatures.

[0028] In this embodiment, the historical anti-dry-heat probe temperatures may be numbered in chronological order. For example, the historical anti-dry-heat probe temperatures may be numbered in chronological order every 1 second, and then the numbered historical anti-dry-heat probe temperatures may be grouped.

[0029] Each set of historical anti-dry-burn probe temperatures includes multiple consecutive input data and one output data, and the output data is separated from the last input data by a preset time interval.

[0030] In this embodiment, when the numbered historical anti-dry-heat probe temperatures are grouped, each group of data can be guaranteed to include the same number of consecutive input data and one output data. When training a neural network model, the multiple input data can be used as the input of the neural network model, and the one output data can be used as the output of the neural network model.

[0031] Step S206: training a neural network model based on the grouped historical anti-dry-burn probe temperatures.

[0032] In this embodiment, a neural network model may be trained based on the grouped historical anti-dry-boiling probe temperatures.

[0033] Step S208, obtaining the anti-dry-boil probe temperature of the cookware before a preset time; inputting the anti-dry-boil probe temperature before the preset time into the trained neural network model, and outputting the predicted current anti-dry-boil probe temperature of the cookware.

[0034] The neural network model in this embodiment first obtains the anti-dry boil probe temperature of the cookware, i.e., the temperature of the water boiling prevention probe before a preset time period. This temperature is then input into the trained neural network model, which then outputs the predicted current anti-dry boil probe temperature. Therefore, the method provided in this embodiment eliminates the need to first obtain the anti-dry boil probe temperature before predicting the current temperature of the cookware. This method can accurately and quickly predict the current temperature of the cookware and identify whether the water in the cookware is boiling. There is no delay between temperature prediction and water boiling determination, thereby improving the user experience.

[0035] In some embodiments, the predicted current temperature of the pot may also be displayed on the panel of the cooktop.

[0036] After obtaining the predicted current anti-dry-burn probe temperature, this embodiment can also display the predicted current anti-dry-burn probe temperature on the stove panel. The user can view the predicted current anti-dry-burn probe temperature through the stove panel, which is very convenient and can also improve the user experience.

[0037] Step S210: determining whether the water in the pot is boiling based on the current temperature of the anti-dry-boiling probe.

[0038] After predicting the current temperature of the anti-dry boil probe, this embodiment may determine whether the water in the pot is boiling based on the predicted current temperature of the anti-dry boil probe.

[0039] In some embodiments, if the current anti-dry-boil probe temperature is predicted to be greater than or equal to a preset temperature threshold, the anti-dry-boil probe temperature before the preset time is stopped from being input into the trained neural network model.

[0040] In this embodiment, the neural network model can continuously predict the current anti-dry boil probe temperature. If the predicted current anti-dry boil probe temperature is greater than or equal to a preset temperature threshold, it means that the water has already boiled and the neural network model no longer needs to predict the current anti-dry boil probe temperature. At this point, the input of the anti-dry boil probe temperature before the preset time period into the trained neural network model can be stopped.

[0041] In some embodiments, the predicted current water temperature of the cookware may also be determined based on the predicted current anti-dry boil probe temperature of the cookware.

[0042] If the temperature detected by the anti-dry boil probe is used as the anti-dry boil probe temperature of the cookware, in this embodiment, the predicted current water temperature of the cookware can be determined based on the previously acquired temperature detected by the anti-dry boil probe. There is no need to obtain the temperature detected by the anti-dry boil probe when predicting the water temperature of the cookware. The current temperature of the cookware can be accurately and quickly predicted and whether the water in the cookware is boiling can be determined. There is no delay between temperature prediction and water boiling determination, thereby improving the user experience.

[0043] An embodiment of the present invention provides a method for predicting the current temperature of a cookware. The method comprises obtaining the historical anti-dry-boil probe temperatures of the cookware, wherein adjacent historical anti-dry-boil probe temperatures have the same time interval. The historical anti-dry-boil probe temperatures are numbered in chronological order and grouped. Each group of historical anti-dry-boil probe temperatures includes multiple consecutive input data and one output data, with the output data separated from the last input data by a preset time interval. A neural network model is trained based on the grouped historical anti-dry-boil probe temperatures. The anti-dry-boil probe temperature of the cookware before a preset time interval is obtained. The anti-dry-boil probe temperature before the preset time interval is input into the trained neural network model, which outputs the predicted current anti-dry-boil probe temperature of the cookware. The method then determines whether the water in the cookware is boiling based on the current anti-dry-boil probe temperature. This method accurately and quickly predicts the current temperature of the cookware and identifies whether the water in the cookware is boiling. There is no delay between temperature prediction and water boiling determination, thereby improving the user experience.

[0044] Example 2: This embodiment provides another method for predicting the current temperature of a cookware. This method is implemented based on the above embodiment and focuses on describing the specific method of training the neural network model. Figure 3 The flowchart of another method for predicting the current temperature of a cookware is shown, and the method for predicting the current temperature of the cookware includes the following steps: Step S302 , obtaining the historical temperature of the cookware's anti-dry-boiling probe; wherein adjacent historical temperatures of the anti-dry-boiling probe have the same time interval.

[0045] Step S304: number the historical anti-dry-heat probe temperatures in chronological order and group the numbered historical anti-dry-heat probe temperatures; wherein each group of historical anti-dry-heat probe temperatures includes a plurality of consecutive input data and one output data, and the output data is separated from the last input data by a preset time length.

[0046] In some embodiments, the interval between adjacent historical anti-dry-heat probe temperatures is x, each group of historical anti-dry-heat probe temperatures includes n consecutive input data, and the preset time length is k×x; the ath historical anti-dry-heat probe temperature to the a+n-1th historical anti-dry-heat probe temperature can be determined as the input data of the ath group of historical anti-dry-heat probe temperatures; and the a+n+k-1th historical anti-dry-heat probe temperature is determined as the output data of the ath group of historical anti-dry-heat probe temperatures.

[0047] For example, assuming that the interval between adjacent historical anti-dry-heat probe temperatures is 1s (i.e., x=1), when numbering the historical anti-dry-heat probe temperatures, the first historical anti-dry-heat probe temperature is numbered as a1...the pth historical anti-dry-heat probe temperature is numbered as ap.

[0048] Assume n = 60 and k = 30. Each set of historical anti-dry-heat probe temperature data includes 60 consecutive input data points, and the preset interval between the output data and the last input data point is 30 × 1 = 30 seconds. This means the neural network model is used to predict the output data 30 seconds later based on the input data from 60 seconds ago.

[0049] Then, the input data of the first group of historical anti-dry-heat probe temperatures are the historical anti-dry-heat probe temperatures numbered a1-a60, and the output data of the first group of historical anti-dry-heat probe temperatures are the historical anti-dry-heat probe temperatures numbered a90; the input data of the second group of historical anti-dry-heat probe temperatures are the historical anti-dry-heat probe temperatures numbered a2-a61, and the output data of the second group of historical anti-dry-heat probe temperatures are the historical anti-dry-heat probe temperatures numbered a91, and so on.

[0050] Step S306, the grouped historical anti-dry-heat probe temperatures are sequentially input into the neural network model; multiple input data of each group of historical anti-dry-heat probe temperatures are used as inputs of the neural network model, and the output data of each group of historical anti-dry-heat probe temperatures are used as outputs of the neural network model to train the neural network model.

[0051] Continuing with the example of x=1, n=60, and k=30, the neural network model is used to predict output data 30 seconds later based on input data from 60 seconds ago. Therefore, in this embodiment, there is no delay in determining whether the water is boiling. The neural network model can predict the anti-dry boil probe temperature 30 seconds later based on the current anti-dry boil probe temperature.

[0052] The neural network model in this embodiment can be deployed locally (for example, on the stove) or in the cloud. For example, a chip with computing power can be used locally, and the neural network model can be deployed on the stove. Alternatively, depending on the performance of the stove chip, the stove can communicate with the range hood via Bluetooth, and the range hood can communicate with the cloud. The stove can upload historical dry-boil probe temperatures to the cloud through the range hood, and the cloud can perform the calculations, or the range hood has computing power to perform the calculations. The neural network model can be deployed in the range hood or in the cloud.

[0053] Step S308, obtaining the anti-dry-boil probe temperature of the cookware before a preset time; inputting the anti-dry-boil probe temperature before the preset time into the trained neural network model, and outputting the predicted current anti-dry-boil probe temperature of the cookware.

[0054] Continuing with x=1, n=60, and k=30 as an example, the neural network model in this embodiment can predict the anti-dry-burn probe temperature 30 seconds later based on the anti-dry-burn probe temperature 60 seconds ago, that is, predict the anti-dry-burn probe temperature 90 seconds after the input data.

[0055] Therefore, this embodiment can also first obtain the anti-dry-burn probe temperature of the cookware 90s (i.e., the preset time length) before, input the anti-dry-burn probe temperature 90s (i.e., the preset time length) before into the trained neural network model, and output the predicted current anti-dry-burn probe temperature.

[0056] Step S310: determining whether the water in the pot is boiling based on the current temperature of the anti-dry-boiling probe.

[0057] The method provided by the embodiments of the present invention can obtain the historical boil-dry probe temperatures of a cookware. After chronologically numbering and grouping these historical boil-dry probe temperatures, a neural network model is trained based on the grouped historical boil-dry probe temperatures. Based on the trained neural network model, the predicted current boil-dry probe temperature of the cookware can be determined and displayed. Based on the current boil-dry probe temperature, whether the water in the cookware is boiling can be determined. This method can accurately and quickly predict the current temperature of the cookware and identify whether the water in the cookware is boiling, without delay between temperature prediction and water boiling determination, thereby improving the user experience.

[0058] Example 3: Corresponding to the above method embodiment, the present invention provides a device for predicting the current temperature of a cookware, see Figure 4 The schematic diagram of the structure of a current temperature prediction device for a cookware is shown, and the current temperature prediction device for the cookware includes: A historical anti-dry-boiling probe temperature acquisition module 41 is used to acquire the historical anti-dry-boiling probe temperature of the cookware; wherein adjacent historical anti-dry-boiling probe temperatures have the same time interval; The historical anti-dry-heat probe temperature numbering and grouping module 42 is used to number the historical anti-dry-heat probe temperatures in chronological order and group the numbered historical anti-dry-heat probe temperatures; wherein each group of historical anti-dry-heat probe temperatures includes a plurality of consecutive input data and one output data, and the output data is separated from the last input data by a preset time length; A neural network model training module 43 is used to train a neural network model based on the grouped historical anti-dry-burn probe temperatures; The current anti-dry-boil probe temperature prediction module 44 is used to obtain the anti-dry-boil probe temperature of the cookware before a preset time; input the anti-dry-boil probe temperature before the preset time into the trained neural network model, and output the predicted current anti-dry-boil probe temperature of the cookware; The water boiling prediction module 45 is used to determine whether the water in the pot is boiling based on the current temperature of the anti-dry boil probe.

[0059] An embodiment of the present invention provides a device for predicting the current temperature of a cookware. The device obtains the historical anti-dry-boil probe temperatures of the cookware, wherein adjacent historical anti-dry-boil probe temperatures have the same time interval. The historical anti-dry-boil probe temperatures are numbered in chronological order and grouped. Each group of historical anti-dry-boil probe temperatures includes multiple consecutive input data and one output data, with the output data separated from the last input data by a preset time interval. A neural network model is trained based on the grouped historical anti-dry-boil probe temperatures. The anti-dry-boil probe temperature of the cookware before a preset time interval is obtained. The anti-dry-boil probe temperature before the preset time interval is input into the trained neural network model, which outputs the predicted current anti-dry-boil probe temperature of the cookware. The device then determines whether the water in the cookware is boiling based on the current anti-dry-boil probe temperature. This method accurately and quickly predicts the current temperature of the cookware and identifies whether the water in the cookware is boiling. There is no delay between temperature prediction and water boiling determination, thereby improving the user experience.

[0060] The above-mentioned pot is placed on the stove, and an anti-dry-burning probe is provided on the stove; the above-mentioned device also includes: an anti-dry-burning probe temperature detection module, which is used to detect the anti-dry-burning probe temperature of the pot through the anti-dry-burning probe.

[0061] The interval between the adjacent historical anti-dry-heat probe temperatures is x, and each group of historical anti-dry-heat probe temperatures includes n consecutive input data, with a preset duration of k×x; the historical anti-dry-heat probe temperature numbering and grouping module is used to determine the ath historical anti-dry-heat probe temperature to the a+n-1th historical anti-dry-heat probe temperature as the input data of the ath group of historical anti-dry-heat probe temperatures; and determine the a+n+k-1th historical anti-dry-heat probe temperature as the output data of the ath group of historical anti-dry-heat probe temperatures.

[0062] The above-mentioned neural network model training module is used to input the grouped historical anti-dry-burning probe temperatures into the neural network model in sequence; use multiple input data of each group of historical anti-dry-burning probe temperatures as the input of the neural network model, and use the output data of each group of historical anti-dry-burning probe temperatures as the output of the neural network model to train the neural network model.

[0063] The above-mentioned device includes: a neural network model stopping training module, which is used to stop inputting the anti-dry-burning probe temperature before a preset time into the trained neural network model if it is predicted that the current anti-dry-burning probe temperature is greater than or equal to a preset temperature threshold.

[0064] The above-mentioned device includes: a current water temperature prediction module of the cookware, which is used to determine the predicted current water temperature of the cookware based on the predicted current anti-dry-boiling probe temperature of the cookware.

[0065] The device comprises: a predicted current temperature display module, which is used to display the predicted current temperature of the pot on the panel of the cooker.

[0066] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the current temperature prediction device of the cookware described above can refer to the corresponding process in the embodiment of the aforementioned current temperature prediction method of the cookware, and will not be repeated here.

[0067] Example 4: The embodiment of the present invention further provides an electronic device for executing the above-mentioned method for predicting the current temperature of a cookware; Figure 5 The structure diagram of an electronic device shown in FIG. 1 includes a memory 100 and a processor 101, wherein the memory 100 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 101 to implement the above-mentioned current temperature prediction method of the cookware.

[0068] Further, Figure 5 The electronic device shown further includes a bus 102 and a communication interface 103 , and the processor 101 , the communication interface 103 and the memory 100 are connected via the bus 102 .

[0069] The memory 100 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is achieved through at least one communication interface 103 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 102 may be an ISA bus, a PCI bus, or an EISA bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0070] The processor 101 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 101 or software instructions. The above processor 101 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as a random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or register. The storage medium is located in the memory 100, and the processor 101 reads the information in the memory 100 and, in conjunction with its hardware, completes the steps of the method of the aforementioned embodiment.

[0071] An embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned method for predicting the current temperature of the cookware. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0072] The computer program product of the method, device, electronic device and storage medium for predicting the current temperature of a cookware provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method in the previous method embodiment. The specific implementation can be found in the method embodiment and will not be repeated here.

[0073] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working process of the system and / or device described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0074] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0075] If a function 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, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0076] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0077] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for predicting the current temperature of a cookware, characterized in that: The method comprises: Obtaining the historical anti-dry-boiling probe temperature of the cookware; wherein adjacent historical anti-dry-boiling probe temperatures have the same time interval; The historical anti-dry-heat probe temperatures are numbered in chronological order, and the numbered historical anti-dry-heat probe temperatures are grouped; wherein each group of the historical anti-dry-heat probe temperatures includes a plurality of consecutive input data and one output data, and the output data is separated from the last input data by a preset time length; Training a neural network model based on the grouped historical anti-dry-burn probe temperatures; Obtaining the anti-dry-boil probe temperature of the cookware before the preset time; inputting the anti-dry-boil probe temperature before the preset time into the trained neural network model, and outputting the predicted current anti-dry-boil probe temperature of the cookware; Determine whether the water in the pot is boiling based on the current anti-dry boil probe temperature.

2. The method according to claim 1, characterized in that The pot is placed on a stove, and an anti-dry-burning probe is provided on the stove; the method further includes: The anti-dry-boiling probe temperature of the cookware is detected by the anti-dry-boiling probe.

3. The method according to claim 1, characterized in that The interval between adjacent historical anti-dry-heat probe temperatures is x, each set of historical anti-dry-heat probe temperatures includes n consecutive input data, and the preset time length is k×x; The step of grouping the numbered historical anti-dry-burn probe temperatures includes: Determine the ath historical anti-dry-heat probe temperature to the a+n-1th historical anti-dry-heat probe temperature as input data of the ath group of historical anti-dry-heat probe temperatures; Determine the a+n+k-1th historical anti-dry-heat probe temperature as the output data of the ath group of historical anti-dry-heat probe temperatures.

4. The method according to claim 1, wherein The step of training a neural network model based on the grouped historical anti-dry-burn probe temperatures includes: The grouped historical anti-dry-burn probe temperatures are sequentially input into a neural network model; The neural network model is trained by using the multiple input data of each set of the historical anti-dry-heat probe temperature as the input of the neural network model, and using the output data of each set of the historical anti-dry-heat probe temperature as the output of the neural network model.

5. The method according to claim 1, wherein The method further comprises: If the predicted current anti-dry-boiling probe temperature is greater than or equal to a preset temperature threshold, stop inputting the anti-dry-boiling probe temperature before the preset time period into the trained neural network model.

6. The method according to claim 2, characterized in that The method further comprises: The predicted current water temperature of the cookware is determined based on the predicted current anti-dry boil probe temperature of the cookware.

7. The method according to claim 1, characterized in that The method further comprises: The predicted current temperature of the pot is displayed on the panel of the cooker.

8. A device for predicting the current temperature of a cookware, characterized in that: The device comprises: A historical anti-dry-boiling probe temperature acquisition module is used to obtain the historical anti-dry-boiling probe temperature of the cookware; wherein adjacent historical anti-dry-boiling probe temperatures have the same time interval; A historical anti-dry-heat probe temperature numbering and grouping module is used to number the historical anti-dry-heat probe temperatures in chronological order and group the numbered historical anti-dry-heat probe temperatures; wherein each group of the historical anti-dry-heat probe temperatures includes a plurality of consecutive input data and one output data, and the output data is separated from the last input data by a preset time length; A neural network model training module, configured to train a neural network model based on the grouped historical anti-dry-burn probe temperatures; a current anti-dry-boil probe temperature prediction module, configured to obtain the anti-dry-boil probe temperature of the cookware before the preset time period; input the anti-dry-boil probe temperature before the preset time period into the trained neural network model, and output the predicted current anti-dry-boil probe temperature of the cookware; The water boiling prediction module is used to determine whether the water in the pot is boiling based on the current temperature of the anti-dry boil probe.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the current temperature prediction method of the cookware according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the method for predicting the current temperature of a cookware according to any one of claims 1 to 7.

Images