Water boiling prediction method and device, electronic equipment and storage medium

Abstract

The invention provides a water boiling prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical temperature data of the cookware; the adjacent historical temperature data have the same time interval; numbering the historical temperature data according to a time sequence, and grouping the numbered historical temperature data; each group of historical temperature data comprises a plurality of continuous input data and one output data, and the output data and the last input data are spaced by a preset duration; training a neural network model based on the grouped historical temperature data; the current dry-burning-resistant probe temperature of the cooker is obtained; inputting the current temperature of the anti-dry-burning probe into the trained neural network model, and outputting the predicted water temperature in the pot after the preset duration; based on the predicted water temperature in the pot and the current water temperature in the pot, whether water in the pot boils or not after the preset duration is determined. Whether water in the cookware is boiling or not can be accurately and rapidly identified, water boiling judgment is not delayed, and therefore 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 water boiling prediction method, device, electronic device and storage medium. Background Art

[0002] Currently, boiling point identification is based on the temperature of the anti-dry boil probe. If the temperature rise slope falls below a limit within a certain period of time (e.g., 30 seconds), the water in the pot is considered boiling, and the stove then proceeds to the next steps. However, this existing boiling point identification solution requires determining the temperature rise slope within a certain period of time (e.g., 30 seconds). By the time the water in the pot is determined to be boiling, it has actually already been boiling for a certain period of time (e.g., 30 seconds). This delay in boiling determination results in a poor user experience. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a water boiling prediction method, device, electronic device and storage medium to accurately and quickly identify whether the water in the pot is boiling, without delay in water boiling judgment, thereby improving the user experience.

[0004] In a first aspect, an embodiment of the present invention provides a water boiling prediction method, the method comprising: obtaining historical temperature data of a cookware; wherein adjacent historical temperature data have the same time interval; the historical temperature data comprises: historical anti-dry-boiling probe temperature and historical water temperature in the pot; numbering the historical temperature data in chronological order, and grouping the numbered historical temperature data; wherein each group of historical temperature data comprises 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; training a neural network model based on the grouped historical temperature data; obtaining the current anti-dry-boiling probe temperature of the cookware; inputting the current anti-dry-boiling probe temperature into the trained neural network model, and outputting a predicted water temperature in the pot after a preset time length; and determining whether the water in the cookware boils after a preset time length based on the predicted water temperature in the pot and the current water temperature in the pot.

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

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

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

[0008] In an optional embodiment of the present application, the above-mentioned step of determining whether the water in the pot will boil after a preset time period based on the predicted water temperature in the pot and the current water temperature in the pot includes: determining the average temperature rise slope after a preset time period based on the predicted water temperature in the pot and the current water temperature in the pot; and determining whether the water in the pot will boil after a preset time period based on the average temperature rise slope.

[0009] In an optional embodiment of the present application, the step of determining the average temperature rise slope after a preset time period based on the predicted water temperature in the pot and the current water temperature in the pot includes: determining the difference between the predicted water temperature in the pot and the current water temperature in the pot, and taking the ratio of the difference to the preset time period as the average temperature rise slope after the preset time period; the step of determining whether the water in the pot is boiling after the preset time period based on the average temperature rise slope includes: if the average temperature rise slope is less than a preset slope threshold, determining that the water in the pot is boiling after the preset time period; if the average temperature rise slope is greater than or equal to the slope threshold, determining that the water in the pot is not boiling after the preset time period.

[0010] In an optional embodiment of the present application, the above method also includes: displaying a temperature curve of the predicted water temperature in the pot and / or the current water temperature in the pot.

[0011] In a second aspect, an embodiment of the present invention further provides a water boiling prediction device, which includes: a historical temperature data acquisition module for acquiring historical temperature data of the cookware; wherein adjacent historical temperature data have the same time interval; the historical temperature data include: historical anti-dry-burn probe temperature and historical water temperature in the pot; a historical temperature data numbering and grouping module for numbering the historical temperature data in chronological order and grouping the numbered historical temperature data; wherein each group of historical temperature data includes multiple continuous input data and 1 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 temperature data; a predicted water temperature in the pot determination module for acquiring the current anti-dry-burn probe temperature of the cookware; inputting the current anti-dry-burn probe temperature into the trained neural network model, and outputting the predicted water temperature in the pot after the preset time length; a water boiling prediction module for determining whether the water in the cookware boils after the preset time length based on the predicted water temperature in the pot and the predicted water temperature in the pot.

[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 water boiling prediction method, device, electronic device, and storage medium, which obtain historical temperature data of a cookware; wherein adjacent historical temperature data have the same time interval; the historical temperature data includes: historical anti-dry-boil probe temperatures and historical pot water temperatures; the historical temperature data are numbered in chronological order and grouped; wherein each group of historical temperature data 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 temperature data; the current anti-dry-boil probe temperature of the cookware is obtained; the current anti-dry-boil probe temperature is input into the trained neural network model, and a predicted pot water temperature after a preset time interval is output; and based on the predicted pot water temperature and the current pot water temperature, whether the water in the cookware is boiling after the preset time interval is determined. In this method, whether the water in the cookware is boiling can be accurately and quickly identified, and the water boiling determination is made without delay, 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 flow chart of a water boiling prediction method provided by an embodiment of the present invention; Figure 3 A flow chart of another water boiling prediction method provided by an embodiment of the present invention; Figure 4 A schematic structural diagram of a water boiling prediction device provided by an embodiment of the present invention; Figure 5 A 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] Existing smart cooking technologies typically predict water boiling on stoves using a recipe's water boiling prediction curve (gear level + temperature). However, for long cooking times, such as stewing (over 45 minutes with unknown water levels), varying water levels can lead to varying boiling times and, consequently, varying gear switching times. Furthermore, due to varying thermal conductivity in different pots, the longer the cooking time, the greater the difference in water evaporation, which can lead to cooking failures. Therefore, different water boiling prediction curves can be used to match pot materials to improve the success rate of water boiling prediction.

[0021] However, for stoves without water boiling prediction recipes (that is, they do not have a water boiling prediction function and cannot adjust the gear, but they have an anti-dry burn function), the probe's anti-dry burn protection temperature is generally 280°C (that is, when the probe temperature reaches 280°C, the anti-dry burn protection will be triggered and the flame will automatically turn off). When using a casserole pot to stew, the anti-dry burn probe temperature may exceed 280°C, but the casserole has not yet boiled. At this time, triggering the anti-dry burn protection will cause the stove to turn off, resulting in cooking failure and a poor user experience.

[0022] Smart gas stoves offer manual mode (users adjust the heat via a knob, just like with regular gas stoves), steaming, stewing, frying, and deep-frying. The stew function involves boiling food over high heat first, then simmering it over low heat. Therefore, stewing requires detecting the boiling point, then switching to low heat for a slow simmer.

[0023] See also Figure 1 The temperature diagram of a pot boiling water is shown. When the water boils, the temperature rise slope of the anti-dry-boil probe changes significantly. Currently, boiling point identification is based on the temperature of the anti-dry-boil probe. When the temperature rise slope falls below a limit within a certain period of time (for example, within 30 seconds), the water in the pot is considered boiling, and the stove proceeds to the subsequent steps. However, the above-mentioned existing boiling point identification scheme requires determining the temperature rise slope within a certain period of time (for example, within 30 seconds). When the water in the pot is judged to be boiling, it has actually been boiling for a certain period of time (for example, within 30 seconds). The boiling judgment is delayed, and the user experience is poor.

[0024] Based on this, the embodiments of the present invention provide a water boiling prediction method, device, electronic device and storage medium, which specifically provides a boiling point identification method based on machine learning or neural network. It 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 anti-dry-burn probe temperature and domestic water temperature curve data, and then perform machine learning / neural network learning on the anti-dry-burn probe temperature rise curve and domestic water temperature to predict the boiling point.

[0025] To facilitate understanding of this embodiment, a water boiling prediction method disclosed in an embodiment of the present invention is first introduced in detail.

[0026] Example 1: The embodiment of the present invention provides a method for predicting water boiling. Figure 2 A flow chart of a water boiling prediction method is shown, and the water boiling prediction method includes the following steps: Step S202 , obtaining historical temperature data of the cookware; wherein adjacent historical temperature data have the same time interval; the historical temperature data includes: historical anti-dry-boiling probe temperature and historical water temperature in the cookware.

[0027] In this embodiment, the historical temperature data of the pot's anti-dry-boil probe and the historical water temperature in the pot can be obtained. This historical temperature data can be used to train a neural network model. In this embodiment, adjacent historical temperature data are obtained at the same time interval. For example, if the time interval is 1 second, this embodiment can obtain a piece of historical temperature data every 1 second.

[0028] The temperature data of the cookware in this embodiment may include: historical anti-dry-boiling probe temperature and historical water temperature in the pot.

[0029] In some embodiments, the cookware is placed on a stove equipped with a dry-boil prevention probe and a temperature sensor installed inside the cookware. The dry-boil prevention probe can detect the temperature of the cookware's dry-boil prevention probe, while the temperature sensor can detect the water temperature inside the cookware. Specifically, in this embodiment, the dry-boil prevention probe can be installed on the stove, and the temperature sensor can be installed inside the cookware. The temperature detected by the dry-boil prevention probe and the water temperature inside the cookware detected by the temperature sensor are used as the cookware's temperature data.

[0030] Step S204: number the historical temperature data in chronological order, and group the numbered historical temperature data.

[0031] In this embodiment, the historical temperature data may be numbered in chronological order. For example, the historical temperature data may be numbered in chronological order at intervals of 1 second, and then the numbered historical temperature data may be grouped.

[0032] Each set of historical temperature data 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.

[0033] In this embodiment, when the numbered historical temperature data is grouped, each group of data can be ensured to include the same number of consecutive multiple input data and one output data. When training a neural network model, multiple input data can be used as input to the neural network model, and one output data can be used as output of the neural network model.

[0034] Step S206: training a neural network model based on the grouped historical temperature data.

[0035] This embodiment can train a neural network model based on the grouped historical temperature data.

[0036] Step S208, obtaining the current anti-dry-boiling probe temperature of the cookware; inputting the current anti-dry-boiling probe temperature into the trained neural network model, and outputting the predicted water temperature in the pot after a preset time.

[0037] After the neural network model training is completed, this embodiment can obtain the current anti-dry-boiling probe temperature of the cookware, and input the current anti-dry-boiling probe temperature into the trained neural network model. The neural network model can then output the predicted water temperature in the pot after a preset time.

[0038] Step S210 , determining whether the water in the pot boils after a preset time based on the predicted water temperature in the pot and the current water temperature in the pot.

[0039] After the neural network model outputs the predicted water temperature in the pot after the preset time, this embodiment can determine whether the water in the pot is boiling after the preset time based on the predicted water temperature in the pot and the current water temperature in the pot. It can accurately and quickly identify whether the water in the pot is boiling, and there is no delay in judging whether the water is boiling, thereby improving the user experience.

[0040] An embodiment of the present invention provides a water boiling prediction method, which obtains historical temperature data of a cookware; wherein adjacent historical temperature data have the same time interval; the historical temperature data includes: historical anti-dry-boil probe temperatures and historical pot water temperatures; the historical temperature data are numbered in chronological order and grouped; wherein each group of historical temperature data 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 temperature data; the current anti-dry-boil probe temperature of the cookware is obtained; the current anti-dry-boil probe temperature is input into the trained neural network model, and a predicted pot water temperature after a preset time interval is output; and based on the predicted pot water temperature and the current pot water temperature, whether the water in the cookware is boiling after the preset time interval is determined. In this method, whether the water in the cookware is boiling can be accurately and quickly identified, and the water boiling determination is made without delay, thereby improving the user experience.

[0041] Example 2: This embodiment provides another method for predicting water boiling, which is implemented on the basis of the above embodiment, focusing on the specific method of neural network model training. Figure 3 Flowchart of another water boiling prediction method shown, the water boiling prediction method comprises the following steps: Step S302, obtaining historical temperature data of the cookware; wherein adjacent historical temperature data have the same time interval; the historical temperature data includes: historical anti-dry-boiling probe temperature and historical water temperature in the pot.

[0042] Step S304: number the historical temperature data in chronological order, and group the numbered historical temperature data.

[0043] Each set of historical temperature data 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.

[0044] In some embodiments, the interval between adjacent historical temperature data is x, each group of historical temperature data includes n consecutive input data, and the preset time length is k×x; the ath historical anti-dry-burning probe temperature to the a+n-1th historical anti-dry-burning probe temperature can be determined as the input data of the ath group of historical temperature data; the a+n+k-1th historical water temperature in the pot is determined as the output data of the ath group of historical temperature data.

[0045] For example, assuming that the interval between adjacent historical temperature data is 1s (ie, x=1), when numbering the historical temperature data, the first historical temperature data is numbered as a1...the pth historical temperature data is numbered as ap.

[0046] Assume n = 60 and k = 30. Each set of historical 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 60 seconds ago.

[0047] Then, the input data of the first group of historical temperature data is the historical anti-dry-burn probe temperature numbered a1-a60, and the output data of the first group of historical temperature data is the historical pot water temperature numbered a90; the input data of the second group of historical temperature data is the historical anti-dry-burn probe temperature numbered a2-a61, and the output data of the second group of historical temperature data is the historical pot water temperature numbered a91, and so on.

[0048] Step S306, input the grouped historical temperature data into the neural network model in sequence; use the multiple input data of each group of historical temperature data as the input of the neural network model, and use the output data of each group of historical temperature data as the output of the neural network model to train the neural network model.

[0049] 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 water temperature in the pot 30 seconds later based on the current temperature of the anti-dry-boil probe.

[0050] 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 temperature data 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.

[0051] Step S308, obtaining the current anti-dry-boiling probe temperature of the cookware; inputting the current anti-dry-boiling probe temperature into the trained neural network model, and outputting the predicted water temperature in the pot after a preset time period.

[0052] Continuing with the example of x=1, n=60, and k=30, in this embodiment, the current anti-dry-boil probe temperature of the pot can be obtained based on the anti-dry-boil probe. The current anti-dry-boil probe temperature can be the temperature 60 seconds ago. By inputting the anti-dry-boil probe temperature 60 seconds ago into the trained neural network model, the neural network model can predict the pot water temperature 30 seconds later as the predicted pot water temperature.

[0053] Step S310 , determining whether the water in the pot boils after a preset time based on the predicted water temperature in the pot and the current water temperature in the pot.

[0054] Continuing with the example of x=1, n=60, and k=30, in this embodiment, it is possible to determine whether the water in the pot will boil after 30 seconds (i.e., the preset time length) based on the water temperature in the pot after 30 seconds (i.e., the predicted water temperature in the pot) and the water temperature in the pot 60 seconds ago (i.e., the current water temperature in the pot).

[0055] In some embodiments, the average temperature rise slope after a preset time period can be determined based on the predicted water temperature in the pot and the current water temperature in the pot; and whether the water in the pot is boiling after the preset time period can be determined based on the average temperature rise slope.

[0056] Continuing with x=1, n=60, k=30 as an example, in this embodiment, after the neural network model outputs the water temperature in the pot 30 seconds later (i.e., the predicted water temperature in the pot), the average temperature rise slope after 30 seconds (i.e., the preset time length) can be calculated.

[0057] In some embodiments, the difference between the predicted water temperature in the pot and the current water temperature in the pot can be determined, and the ratio of the difference to the preset time length is used as the average temperature rise slope after the preset time length.

[0058] Continuing with the example of x=1, n=60, and k=30, in this embodiment, the average temperature rise slope can be calculated using the following formula: average temperature rise slope = (predicted pot water temperature 30 seconds later - current pot water temperature) / 30.

[0059] It should be noted here that, in actual application, this embodiment is not necessarily limited to x=1, n=60, k=30. In actual application, this embodiment can set specific values ​​of x, n and k according to actual needs, which will not be repeated in this embodiment.

[0060] In some embodiments, if the average temperature rise slope is less than a preset slope threshold, it is determined that the water in the pot boils after the preset time; if the average temperature rise slope is greater than or equal to the slope threshold, it is determined that the water in the pot does not boil after the preset time.

[0061] Continuing with the example of x=1, n=60, and k=30, if the average temperature rise slope is less than the slope threshold, it can be determined that the water in the pot will boil after 30 seconds. The stove can immediately switch gears and execute the subsequent automatic boiling sequence. This can accurately and quickly identify whether the water in the pot is boiling, without delay in determining whether the water is boiling, thereby improving the user experience. If the average temperature rise slope is greater than or equal to the slope threshold, it can be determined that the water in the pot will not boil after 30 seconds. In this case, the stove does not need to switch gears or execute the subsequent automatic boiling sequence.

[0062] In some embodiments, a temperature curve of the predicted water temperature in the pot and / or the current water temperature in the pot may also be displayed.

[0063] After determining the predicted pot water temperature, in this embodiment, the predicted pot water temperature and / or the current pot water temperature can also be displayed through a digital tube. In this embodiment, a temperature curve can also be generated based on multiple predicted pot water temperatures and the current pot water temperature, and the temperature curve can be displayed for the user to view.

[0064] The method provided by the embodiments of the present invention can obtain historical temperature data for a cookware. After chronologically numbering and grouping the historical temperature data, a neural network model is trained based on the grouped historical temperature data. The trained neural network model can then determine whether the water in the cookware has boiled after a preset time period, thereby accurately and quickly identifying whether the water in the cookware has boiled, without delay, thereby improving the user experience.

[0065] Example 3: Corresponding to the above method embodiment, the present invention provides a water boiling prediction device, see Figure 4 The schematic diagram of the structure of a water boiling prediction device shown in FIG. 1 includes: The historical temperature data acquisition module 41 is used to acquire the historical temperature data of the cookware; wherein adjacent historical temperature data have the same time interval; the historical temperature data includes: the historical anti-dry-boiling probe temperature and the historical water temperature in the pot; The historical temperature data numbering and grouping module 42 is used to number the historical temperature data in chronological order and group the numbered historical temperature data; wherein each group of historical temperature data 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 temperature data; The predicted pot water temperature determination module 44 is used to obtain the current anti-dry-boil probe temperature of the pot; input the current anti-dry-boil probe temperature into the trained neural network model, and output the predicted pot water temperature after a preset time period; The water boiling prediction module 45 is used to determine whether the water in the pot will boil after a preset time period based on the predicted water temperature in the pot and the current water temperature in the pot.

[0066] An embodiment of the present invention provides a water boiling prediction device that obtains historical temperature data of a cookware; wherein adjacent historical temperature data have the same time interval; the historical temperature data includes: historical anti-dry-boil probe temperatures and historical pot water temperatures; the historical temperature data are numbered in chronological order and grouped; wherein each group of historical temperature data 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 temperature data; the current anti-dry-boil probe temperature of the cookware is obtained; the current anti-dry-boil probe temperature is input into the trained neural network model, and a predicted pot water temperature after a preset time interval is output; and based on the predicted pot water temperature and the current pot water temperature, whether the water in the cookware is boiling after the preset time interval is determined. In this method, whether the water in the cookware is boiling can be accurately and quickly identified, and the water boiling determination is made without delay, thereby improving the user experience.

[0067] The above-mentioned pot is placed on the stove, which is equipped with an anti-dry-burning probe, and a temperature sensor is provided inside the pot; the above-mentioned device also includes: a temperature data detection module, which is used to detect the anti-dry-burning probe temperature of the pot through the anti-dry-burning probe; and detect the water temperature in the pot through the temperature sensor.

[0068] The interval between the above-mentioned adjacent historical temperature data is x, and each group of historical temperature data includes n consecutive input data, with a preset duration of k×x; the above-mentioned historical temperature data numbering and grouping module is used to determine the a-th historical anti-dry-burning probe temperature to the a+n-1-th historical anti-dry-burning probe temperature as the input data of the a-th group of historical temperature data; and determine the a+n+k-1-th historical pot water temperature as the output data of the a-th group of historical temperature data.

[0069] The above-mentioned neural network model training module is used to input the grouped historical temperature data into the neural network model in sequence; use the multiple input data of each group of historical temperature data as the input of the neural network model, and use the output data of each group of historical temperature data as the output of the neural network model to train the neural network model.

[0070] The water boiling prediction module is used to determine the average temperature rise slope after a preset time based on the predicted water temperature in the pot and the current water temperature in the pot; and determine whether the water in the pot boils after the preset time based on the average temperature rise slope.

[0071] The water boiling prediction module is used to determine the difference between the predicted water temperature in the pot and the current water temperature in the pot, and use the ratio of the difference to the preset time as the average temperature rise slope after the preset time; the water boiling prediction module is used to determine that the water in the pot boils after the preset time if the average temperature rise slope is less than the preset slope threshold; if the average temperature rise slope is greater than or equal to the slope threshold, determine that the water in the pot does not boil after the preset time.

[0072] The above device also includes: a temperature curve display module, which is used to display the temperature curve of the predicted water temperature in the pot and / or the current water temperature in the pot.

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

[0074] Example 4: The embodiment of the present invention further provides an electronic device for executing the above-mentioned water boiling prediction method; Figure 5 A structural schematic diagram of an electronic device is shown, which 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 water boiling prediction method.

[0075] Furthermore, 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 .

[0076] 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.

[0077] 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.

[0078] An embodiment of the present invention also 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 water boiling prediction method. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0079] The computer program product of the water boiling prediction method, device, electronic device and storage medium 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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 water boiling, characterized in that: The method comprises: Obtaining historical temperature data of the cookware; wherein adjacent historical temperature data have the same time interval; the historical temperature data includes: historical anti-dry-boiling probe temperature and historical pot water temperature; The historical temperature data are numbered in chronological order, and the numbered historical temperature data are grouped; wherein each group of the historical temperature data 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 temperature data; Obtaining the current anti-dry-boil probe temperature of the cookware; inputting the current anti-dry-boil probe temperature into the trained neural network model, and outputting the predicted water temperature in the pot after the preset time period; It is determined based on the predicted water temperature in the pot and the current water temperature in the pot whether the water in the pot boils after the preset time period.

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

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

4. The method according to claim 1, wherein The step of training a neural network model based on the grouped historical temperature data includes: Inputting the grouped historical temperature data into the neural network model in sequence; The neural network model is trained by using the multiple input data of each group of the historical temperature data as the input of the neural network model and using the output data of each group of the historical temperature data as the output of the neural network model.

5. The method according to claim 1, wherein The step of determining whether the water in the pot boils after the preset time based on the predicted water temperature in the pot and the current water temperature in the pot comprises: Determining an average temperature rise slope after the preset time period based on the predicted water temperature in the pot and the current water temperature in the pot; Based on the average temperature rise slope, it is determined whether the water in the pot boils after the preset time period.

6. The method according to claim 5, characterized in that The step of determining the average temperature rise slope after the preset time period based on the predicted water temperature in the pot and the current water temperature in the pot comprises: determining a difference between the predicted water temperature in the pot and the current water temperature in the pot, and taking a ratio of the difference to the preset time period as the average temperature rise slope after the preset time period; The step of determining whether the water in the pot is boiling after the preset time period based on the average temperature rise slope includes: if the average temperature rise slope is less than a preset slope threshold, determining that the water in the pot is boiling after the preset time period; if the average temperature rise slope is greater than or equal to the slope threshold, determining that the water in the pot is not boiling after the preset time period.

7. The method according to claim 1, characterized in that The method further comprises: Display the temperature curve of the predicted water temperature in the pot and / or the current water temperature in the pot.

8. A water boiling prediction device, characterized in that: The device comprises: A historical temperature data acquisition module is used to acquire historical temperature data of the cookware; wherein adjacent historical temperature data have the same time interval; the historical temperature data includes: historical anti-dry-boiling probe temperature and historical water temperature in the pot; A historical temperature data numbering and grouping module is used to number the historical temperature data in chronological order and group the numbered historical temperature data; wherein each group of the historical temperature data 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 temperature data; A predicted pot water temperature determination module is used to obtain the current anti-dry-boil probe temperature of the pot; input the current anti-dry-boil probe temperature into the trained neural network model, and output the predicted pot water temperature after the preset time period; The water boiling prediction module is used to determine whether the water in the pot boils after the preset time period based on the predicted water temperature in the pot and the current water temperature in the pot.

9. An electronic device, characterized in that: The invention 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 water boiling prediction method 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 a processor, the computer-executable instructions prompt the processor to implement the water boiling prediction method according to any one of claims 1 to 7.

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