Boiling recognition method and device, electronic equipment and storage medium

Abstract

The application discloses a boiling recognition method and device, electronic equipment and storage medium. The method comprises the following steps: obtaining target temperature time sequence information and target liquid weight information of a pot during cooking; determining a first temperature difference feature, a second temperature difference feature and a temperature change feature corresponding to a first preset time length based on the target temperature time sequence information; determining a temperature weight coupling feature based on the target temperature time sequence information and the target liquid weight information corresponding to a second preset time length, the temperature weight coupling feature being used to represent the relationship between the temperature change information and the liquid weight information within the second preset time length; fusing the first temperature difference feature, the second temperature difference feature, the temperature change feature and the temperature weight coupling feature to obtain a target fusion feature, and inputting the target fusion feature into a target boiling recognition network to recognize the boiling time to obtain a target boiling time of the pot. The application can improve the accuracy and robustness of the boiling time recognition of the pot.

Description

Technical Field

[0001] This invention relates to the field of smart kitchen appliance technology, and in particular to a boiling recognition method, device, electronic device, and storage medium. Background Technology

[0002] With the advancement of human life, kitchen appliances are developing towards intelligence. During cooking, processes such as stewing and boiling use a large amount of water, and overflowing is a common problem. This can occur due to factors such as insufficient response during rapid heating, or prolonged cooking where the liquid has already boiled without the user noticing. Therefore, it is necessary to intelligently and accurately identify the boiling point of the liquid in the cookware to improve the user experience. Summary of the Invention

[0003] To address the aforementioned problems in the prior art, this invention discloses a boiling recognition method, apparatus, electronic device, and storage medium, which can improve the accuracy and robustness of boiling time recognition in cookware. The technical solution disclosed in this invention is as follows: According to one aspect of the disclosed embodiments of the present invention, a boiling identification method is provided, comprising: During the cooking process, acquire the target temperature timing information and target liquid weight information of the cookware; Based on the target temperature time series information, a first temperature difference feature, a second temperature difference feature, and a temperature change feature corresponding to a first preset duration are determined; the first temperature difference feature is used to characterize the degree of difference between the upper limit temperature information and the lower limit temperature information in the target temperature time series information within the first preset duration; the second temperature difference feature is used to characterize the degree of difference between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset duration; and the temperature change feature is used to characterize the trend of the temperature change rate within the first preset duration. Based on the target temperature time-series information and target liquid weight information corresponding to the second preset time period, a temperature-weight coupling feature is determined; the temperature-weight coupling feature is used to characterize the relationship between temperature change information and liquid weight information within the second preset time period. The first temperature difference feature, the second temperature difference feature, the temperature change feature, and the temperature-weight coupling feature are fused to obtain the target fused feature. The target fusion features are input into the target boiling recognition network to identify the boiling time, thereby obtaining the target boiling time of the pot.

[0004] Optionally, the step of fusing the first temperature difference feature, the second temperature difference feature, the temperature change feature, and the temperature-weight coupling feature to obtain the target fused feature includes: Obtain first weight information corresponding to the first temperature difference feature, the second temperature difference feature, and the temperature change feature, as well as second weight information corresponding to the temperature-weight coupling feature; Based on the first weight information, the first temperature difference information, the second temperature difference information, and the temperature change information are weighted respectively to obtain the first weighted temperature difference information, the second weighted temperature difference information, and the weighted temperature change information. Based on the second weight information, the temperature weight coupling feature is weighted to obtain the weighted temperature weight coupling feature. The first weighted temperature difference information, the second weighted temperature difference information, the weighted temperature change information, and the weighted temperature weight coupling feature are spliced ​​together to obtain the target fusion feature.

[0005] Optionally, obtaining the first weight information corresponding to the first temperature difference feature, the second temperature difference feature, and the temperature change feature, and the second weight information corresponding to the temperature-weight coupling feature includes: Determine the first information entropy of the temperature feature corresponding to the target temperature time series information, and the second information entropy corresponding to the temperature-weight coupling feature; Divide the first information entropy by the sum of the first information entropy and the second information entropy to obtain the first weight information; The difference between the preset information and the first weight information is used as the second weight information.

[0006] Optionally, determining the first temperature difference feature, the second temperature difference feature, and the temperature change feature corresponding to the first preset duration based on the target temperature time series information includes: In the target temperature time series information, the first temperature difference feature is determined based on the difference information between the target upper limit temperature information and the target lower limit temperature information corresponding to the first preset duration; Based on the difference information between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset time period, the second temperature difference feature is determined. Based on the change information of the temperature change rate corresponding to the target temperature time series information within the first preset time period, the temperature change characteristics are determined.

[0007] Optionally, determining the second temperature difference feature based on the difference information between each temperature information in the target temperature time series information and the corresponding average temperature information within the first preset time period includes: Based on the difference information between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset time period, the temperature standard deviation information is calculated. Based on the temperature standard deviation information, the second temperature difference characteristic is determined.

[0008] Optionally, determining the temperature-weight coupling feature based on the target temperature time-series information and target liquid weight information corresponding to the second preset duration includes: Based on the target temperature time series information, determine the target temperature change information within the second preset time period; The temperature-weight coupling feature is obtained by dividing the target temperature change information by the target liquid weight information.

[0009] Optionally, the target boiling recognition network is trained in the following manner: During the cooking process, the sample temperature time sequence information of the sample cookware and its corresponding sample boiling time label, as well as the sample liquid weight information, are obtained. Based on the sample temperature time series information, the first sample temperature difference feature, the second sample temperature difference feature, and the sample temperature change feature corresponding to the third preset duration are determined. Based on the sample temperature time-series information and sample liquid weight information corresponding to the fourth preset time period, the sample temperature-weight coupling feature is determined; the sample temperature-weight coupling feature is used to characterize the relationship between sample temperature change information and sample liquid weight information within the fourth preset time period. The temperature difference features of the first sample, the temperature difference features of the second sample, the temperature change features of the sample, and the temperature-weight coupling features of the sample are fused to obtain the sample fusion features. The sample fusion features are input into the boiling recognition network to be trained to predict the boiling time, and the predicted boiling time label of the sample pot is obtained. Based on the difference between the predicted boiling time label and the sample boiling time label, the boiling recognition network to be trained is trained to obtain the target boiling recognition network.

[0010] According to another aspect of the disclosed embodiments of the present invention, a boiling detection device is provided, comprising: The first acquisition module is used to acquire the target temperature timing information and target liquid weight information of the cookware during the cooking process; The first temperature feature determination module is used to determine, based on the target temperature time series information, a first temperature difference feature, a second temperature difference feature, and a temperature change feature corresponding to a first preset duration; the first temperature difference feature is used to characterize the degree of difference between the upper limit temperature information and the lower limit temperature information in the target temperature time series information within the first preset duration; the second temperature difference feature is used to characterize the degree of difference between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset duration; and the temperature change feature is used to characterize the trend of the temperature change rate within the first preset duration. The first coupling feature determination module is used to determine the temperature-weight coupling feature based on the target temperature time-series information and the target liquid weight information corresponding to the second preset time period; the temperature-weight coupling feature is used to characterize the relationship between temperature change information and liquid weight information within the second preset time period. The first fusion feature determination module is used to perform fusion processing on the first temperature difference feature, the second temperature difference feature, the temperature change feature and the temperature-weight coupling feature to obtain the target fusion feature; The target boiling time determination module is used to input the target fusion features into the target boiling time recognition network to identify the boiling time and obtain the target boiling time of the pot.

[0011] According to another aspect of the disclosed embodiments of the present invention, an electronic device for boiling identification is provided, including a processor and a memory, wherein the memory stores at least one instruction, the at least one instruction being loaded and executed by the processor to implement the boiling identification method described in any of the preceding claims.

[0012] According to another aspect of the embodiments disclosed in this invention, a computer-readable storage medium is provided, wherein at least one instruction is stored in the computer storage medium, the at least one instruction being loaded and executed by a processor to implement the boiling identification method described in any of the preceding claims.

[0013] According to another aspect of the disclosed embodiments of the present invention, a computer program product containing instructions is provided, which, when run on a computer, causes the computer to perform the boiling identification method described in any of the above embodiments of the present invention.

[0014] The boiling identification method provided by this invention has the following technical effects: This invention acquires target temperature time-series information and target liquid weight information of the cookware during the cooking process. Based on the target temperature time-series information, it determines a first temperature difference feature, a second temperature difference feature, and a temperature change feature corresponding to a first preset time period. The first temperature difference feature characterizes the degree of difference between the upper and lower temperature limits in the target temperature time-series information within the first preset time period. The second temperature difference feature characterizes the degree of difference between each temperature value in the target temperature time-series information and the corresponding average temperature value within the first preset time period. The temperature change feature characterizes the trend of the temperature change rate within the first preset time period. Based on the target temperature time-series information and target liquid weight information corresponding to the second preset time period, a temperature-weight coupling feature is determined. This feature characterizes the relationship between temperature change information and liquid weight information within the second preset time period. Furthermore, the first temperature difference feature, second temperature difference feature, temperature change feature, and temperature-weight coupling feature are fused to obtain a target fusion feature, which is then input into a target boiling recognition network for boiling time recognition to obtain the target boiling time of the cookware. This improves the accuracy and robustness of cookware boiling time recognition.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a boiling identification method according to an exemplary embodiment; Figure 2 This is a schematic flowchart illustrating a process for determining a first temperature difference feature, a second temperature difference feature, and a temperature change feature according to an exemplary embodiment. Figure 3 This is a schematic diagram illustrating a process for determining target fusion features according to an exemplary embodiment; Figure 4 This is a schematic diagram illustrating a process for determining first weight information and second weight information according to an exemplary embodiment; Figure 5 This is a block diagram illustrating a boiling detection device according to an exemplary embodiment; Figure 6This is a block diagram illustrating a terminal electronic device for boiling identification according to an exemplary embodiment; Figure 7 This is a block diagram illustrating a server electronic device for boiling identification according to an exemplary embodiment. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions disclosed in this invention, the technical solutions in the disclosed embodiments will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention disclosed herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0020] The following describes a boiling identification method according to this application. Please refer to [link / reference]. Figure 1 , Figure 1 This is a flowchart illustrating a boiling point identification method according to an exemplary embodiment. This specification provides the operational steps of the method as described in the embodiments or flowchart, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual system or server product execution, the method can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment) as shown in the embodiments or drawings. Specifically, as... Figure 1 As shown, the above method may include: S101: During the cooking process, acquire the target temperature timing information and target liquid weight information of the cookware.

[0021] In one specific embodiment, the target temperature time-series information can be used to determine the temperature change curve of the cookware over time. This target temperature time-series information can include cookware temperature information at multiple moments within a preset time period during liquid cooking. Specifically, the cookware temperature information can be the temperature of the bottom of the cookware, which can be collected by a temperature sensor located at the bottom of the cookware. Specifically, adjacent moments can have the same preset interval duration, which can be set according to actual application requirements. Specifically, the cookware temperature information at each moment can be the actual cookware temperature collected at that moment, or it can be the average value of the cookware temperature information over a corresponding time period. The target liquid weight information can be the weight of the liquid inside the cookware, which can be collected by a weight sensor.

[0022] S103: Based on the target temperature time series information, determine the first temperature difference feature, the second temperature difference feature, and the temperature change feature corresponding to the first preset duration.

[0023] In one specific embodiment, the first temperature difference feature can be used to characterize the degree of difference between the upper and lower temperature information in the target temperature time series information within a first preset time period. The second temperature difference feature can be used to characterize the degree of difference between each temperature information in the target temperature time series information and the corresponding average temperature information within the first preset time period. The temperature change feature can be used to characterize the trend of the temperature change rate within the first preset time period. Specifically, the first preset time period can be set according to actual application requirements.

[0024] In an optional embodiment, such as Figure 2 As shown, the determination of the first temperature difference feature, the second temperature difference feature, and the temperature change feature corresponding to the first preset duration based on the target temperature time series information may include: S201: In the target temperature time series information, based on the difference information between the target upper limit temperature information and the target lower limit temperature information corresponding to the first preset time, a first temperature difference feature is determined.

[0025] In one specific embodiment, the target upper limit temperature information can be the maximum temperature information in the target temperature time sequence information within a first preset time period, and the target lower limit temperature information can be the minimum temperature information in the target temperature time sequence information within the first preset time period.

[0026] Specifically, the above-mentioned differences can be determined using the following formula:

[0027] in, This indicates the above differences. This indicates the target upper limit temperature information mentioned above. This indicates the target lower limit temperature information mentioned above.

[0028] In practical applications, the first temperature difference feature can characterize temperature fluctuations based on the maximum temperature difference within the sliding window (i.e., the extreme difference in temperature within the window), and can be used to quantify the degree of drastic temperature changes within a certain period of time.

[0029] S203: Based on the difference information between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset time period, determine the second temperature difference feature.

[0030] Optionally, determining the second temperature difference feature based on the difference between each temperature information in the target temperature time series information and the corresponding average temperature information within the first preset time period may include: Based on the difference between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset time period, the temperature standard deviation information is calculated. Based on the temperature standard deviation information, the second temperature difference feature is determined.

[0031] In one specific embodiment, the temperature standard deviation information can be calculated according to the following formula:

[0032] in, This indicates the above temperature standard deviation information. This indicates the average temperature information mentioned above.

[0033] In practical applications, the temperature standard deviation information can be determined as the second temperature difference feature. The second temperature difference feature can measure the degree of deviation of the temperature data within the window from the mean, and can be used to reflect the overall dispersion of temperature fluctuations and reflect the uniformity of temperature changes.

[0034] S205: Based on the change information of the temperature change rate corresponding to the target temperature time sequence information within the first preset time period, determine the temperature change characteristics.

[0035] In one specific embodiment, the change information of the temperature change rate can be obtained by taking the average second derivative of the temperature information in the target temperature time series information within a first preset time period. Specifically, the change information of the temperature change rate can be determined as the temperature change feature.

[0036] Specifically, it can be calculated using the following formula:

[0037] in, This indicates the above-mentioned temperature change characteristics.

[0038] In practical applications, temperature change characteristics can be used to capture the trend of temperature change rate, that is, the inflection point from accelerated heating to decelerated heating and then to a stable point. This is important for identifying the key transition stage before liquid boiling (such as the slowdown of temperature change when approaching the boiling point).

[0039] S105: Determine the temperature-weight coupling characteristics based on the target temperature time sequence information and target liquid weight information corresponding to the second preset duration.

[0040] In one specific embodiment, the temperature-weight coupling feature can be used to characterize the relationship between temperature change information and liquid weight information within a second preset time period. The second preset time period can be set according to actual application requirements.

[0041] Optionally, determining the temperature-weight coupling feature based on the target temperature time-series information and target liquid weight information corresponding to the second preset duration may include: Based on the target temperature time series information, determine the target temperature change information within the second preset time period; The temperature-weight coupling feature is obtained by dividing the target temperature change information by the target liquid weight information.

[0042] In one specific embodiment, the target temperature change information can be the maximum change among multiple temperature information in the target temperature time series within a second preset time period. Specifically, it can be the difference between the upper and lower temperature information in the target temperature time series within the second preset time period. In practical applications, the target temperature change information can be the maximum temperature change within a sliding window, reflecting the absolute temperature rise within the second preset time period. The temperature-weight coupling feature can be used to characterize the heating rate per unit weight. By associating the temperature change with the weight of the liquid in the cookware (such as the amount of water), the interference of water volume differences on the heating rate is eliminated, more fundamentally reflecting the heat absorption and heating efficiency per unit mass of liquid.

[0043] Specifically, the temperature-weight coupling characteristics can be calculated using the following formula:

[0044] in, This indicates the aforementioned temperature-weight coupling characteristics. This indicates the aforementioned target temperature change information. This indicates the weight information of the target liquid mentioned above.

[0045] S107: The first temperature difference feature, the second temperature difference feature, the temperature change feature, and the temperature-weight coupling feature are fused to obtain the target fused feature.

[0046] In an optional embodiment, such as Figure 3 As shown, the target fused feature obtained by fusing the first temperature difference feature, the second temperature difference feature, the temperature change feature, and the temperature-weight coupling feature can include: S301: Obtain the first weight information corresponding to the first temperature difference feature, the second temperature difference feature, and the temperature change feature, as well as the second weight information corresponding to the temperature-weight coupling feature.

[0047] Optional, such as Figure 4 As shown, the acquisition of the first weight information corresponding to the first temperature difference feature, the second temperature difference feature, and the temperature change feature, as well as the second weight information corresponding to the temperature-weight coupling feature, may include: S401: Determine the first information entropy of the temperature feature corresponding to the target temperature time series information, and the second information entropy corresponding to the temperature-weight coupling feature.

[0048] In a specific embodiment, the first information entropy can be used to characterize the information richness of the temperature features corresponding to the target temperature time series information. Specifically, the temperature features corresponding to the target temperature time series information may include the aforementioned first temperature difference feature, second temperature difference feature, and temperature change feature, and may also include real-time temperature features, etc. The second information entropy can be used to characterize the information richness of the temperature-weight coupling features.

[0049] S403: Divide the first information entropy by the sum of the first information entropy and the second information entropy to obtain the first weight information.

[0050] Specifically, the first weight information can be calculated using the following formula:

[0051] in, This indicates the aforementioned first weight information. This represents the first information entropy mentioned above. This indicates the temperature characteristics corresponding to the aforementioned target temperature time-series information. This represents the second information entropy mentioned above. Wherein, .

[0052] S405: Use the difference between the preset information and the first weight information as the second weight information.

[0053] Specifically, the preset information can be the sum of the first weight information and the second weight information; specifically, the preset information can be 1. Specifically, the second weight information... .

[0054] S303: Based on the first weight information, the first temperature difference information, the second temperature difference information, and the temperature change information are weighted respectively to obtain the first weighted temperature difference information, the second weighted temperature difference information, and the weighted temperature change information. Based on the second weight information, the temperature weight coupling feature is weighted to obtain the weighted temperature weight coupling feature.

[0055] S305: The first weighted temperature difference information, the second weighted temperature difference information, the weighted temperature change information, and the weighted temperature weight coupling feature are spliced ​​together to obtain the target fusion feature.

[0056] Specifically, the target fusion features can be represented in the following form:

[0057] in, This indicates the above-mentioned target fusion characteristics. This indicates the above-mentioned first weighted temperature difference information. This indicates the second weighted temperature difference information mentioned above. This indicates the above weighted temperature change information. This indicates the weighted temperature-weight coupling characteristics described above.

[0058] S109: Input the target fusion features into the target boiling recognition network to identify the boiling time and obtain the target boiling time of the pot.

[0059] In one specific embodiment, the target boiling recognition network can be used to identify the boiling time of liquid in a pot. Specifically, the network structure of the target boiling recognition network can be configured according to actual application requirements. For example, the target boiling recognition network can be a support vector machine (SVM) network or a neural network, and the neural network model can include a general neural network or a deep neural network. Specifically, the aforementioned target fusion features are input into the support vector machine network, which can be used to map the received feature vectors to one of a series of predefined boiling times.

[0060] In an optional embodiment, the target boiling recognition network described above can be trained in the following manner: During the cooking process, the sample temperature time sequence information of the sample cookware and its corresponding sample boiling time label, as well as the sample liquid weight information, are obtained. Based on the sample temperature time series information, the first sample temperature difference characteristics, the second sample temperature difference characteristics, and the sample temperature change characteristics corresponding to the third preset time period are determined. Based on the sample temperature time-series information and sample liquid weight information corresponding to the fourth preset duration, the sample temperature-weight coupling characteristics are determined. The temperature difference features of the first sample, the temperature difference features of the second sample, the temperature change features of the sample, and the temperature-weight coupling features of the sample are fused to obtain the sample fusion features. The sample fusion features are input into the boiling recognition network to be trained to predict the boiling time, and the predicted boiling time label of the sample pot is obtained. Based on the difference between the predicted boiling time label and the sample boiling time label, the boiling recognition network to be trained is trained to obtain the target boiling recognition network.

[0061] In one specific embodiment, the first sample temperature difference feature can be used to characterize the degree of difference between the upper and lower limit temperature information in the sample temperature time series information within a third preset time period. The second sample temperature difference feature can be used to characterize the degree of difference between each temperature information in the sample temperature time series information and the corresponding mean temperature information within the third preset time period. The sample temperature change feature can be used to characterize the trend of the temperature change rate within the third preset time period. The sample temperature-weight coupling feature can be used to characterize the relationship between the sample temperature change information and the sample liquid weight information within a fourth preset time period. Specifically, the third and fourth preset time periods can be set according to actual application requirements.

[0062] Specifically, the process of obtaining the sample temperature time series information of the sample cookware and its corresponding sample boiling time label and sample liquid weight information to obtain the sample fusion features can be found in the detailed steps of obtaining the target temperature time series information and target liquid weight information of the cookware to obtain the target fusion features, which will not be repeated here.

[0063] Optionally, the above-mentioned method of training the boiling recognition network to be trained based on the difference between the predicted boiling time label and the sample boiling time label to obtain the target boiling recognition network may include: determining the loss value based on the difference between the predicted boiling time label and the sample boiling time label; and training the boiling recognition network to be trained based on the loss value to obtain the target boiling recognition network.

[0064] Specifically, training the boiling recognition network based on the loss value to obtain the target boiling recognition network may include updating the network parameters of the boiling recognition network based on the loss value, and repeating the above training iteration steps of inputting sample fusion features into the boiling recognition network for boiling time prediction, up to updating the network parameters of the boiling recognition network based on the loss value, until a preset training convergence condition is met. Specifically, meeting the preset training convergence condition may be that the loss value is less than or equal to a preset loss threshold, or that the number of training iterations reaches a preset number, etc. Specifically, the preset loss threshold and the preset number of iterations can be set according to the network accuracy and training speed requirements in the actual application.

[0065] As can be seen from the technical solutions provided in the embodiments of this specification above, during the cooking process, the target temperature time-series information and target liquid weight information of the cookware are acquired; based on the target temperature time-series information, a first temperature difference feature, a second temperature difference feature, and a temperature change feature corresponding to a first preset time period are determined. The first temperature difference feature characterizes the degree of difference between the upper and lower limit temperature information in the target temperature time-series information within the first preset time period; the second temperature difference feature characterizes the degree of difference between each temperature information in the target temperature time-series information and the corresponding average temperature information within the first preset time period. Temperature change features are used to characterize the trend of temperature change rate within a first preset time period. Based on the target temperature time series information and target liquid weight information corresponding to a second preset time period, temperature-weight coupling features are determined, whereby the temperature-weight coupling features are used to characterize the relationship between temperature change information and liquid weight information within the second preset time period. Furthermore, the first temperature difference feature, the second temperature difference feature, the temperature change feature, and the temperature-weight coupling feature are fused to obtain the target fusion feature, which is then input into the target boiling recognition network for boiling time recognition to obtain the target boiling time of the cookware, thereby improving the accuracy and robustness of cookware boiling time recognition.

[0066] This invention also provides a boiling identification device, such as... Figure 5 As shown, the device includes: The first acquisition module 510 is used to acquire the target temperature timing information and target liquid weight information of the cookware during the cooking process. The first temperature feature determination module 520 is used to determine, based on the target temperature time series information, a first temperature difference feature, a second temperature difference feature, and a temperature change feature corresponding to a first preset duration; the first temperature difference feature is used to characterize the degree of difference between the upper limit temperature information and the lower limit temperature information in the target temperature time series information within the first preset duration; the second temperature difference feature is used to characterize the degree of difference between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset duration; and the temperature change feature is used to characterize the trend of the temperature change rate within the first preset duration. The first coupling feature determination module 530 is used to determine temperature-weight coupling features based on the target temperature time sequence information and target liquid weight information corresponding to the second preset time period; the temperature-weight coupling features are used to characterize the relationship between temperature change information and liquid weight information within the second preset time period. The first fusion feature determination module 540 is used to perform fusion processing on the first temperature difference feature, the second temperature difference feature, the temperature change feature and the temperature-weight coupling feature to obtain the target fusion feature; The target boiling time determination module 550 is used to input the target fusion features into the target boiling time recognition network to identify the boiling time and obtain the target boiling time of the pot.

[0067] Optionally, the first fusion feature determination module 540 includes: The acquisition unit is used to acquire first weight information corresponding to the first temperature difference feature, the second temperature difference feature, and the temperature change feature, as well as second weight information corresponding to the temperature-weight coupling feature; The weighting unit is used to weight the first temperature difference information, the second temperature difference information, and the temperature change information based on the first weight information to obtain first weighted temperature difference information, second weighted temperature difference information, and weighted temperature change information, and to weight the temperature weight coupling feature based on the second weight information to obtain weighted temperature weight coupling feature. The splicing unit is used to splice the first weighted temperature difference information, the second weighted temperature difference information, the weighted temperature change information, and the weighted temperature weight coupling feature to obtain the target fusion feature.

[0068] Optionally, the acquisition unit includes: An information entropy determination unit is used to determine the first information entropy of the temperature feature corresponding to the target temperature time series information, and the second information entropy corresponding to the temperature-weight coupling feature; The first weight information determination unit is used to divide the first information entropy by the sum of the first information entropy and the second information entropy to obtain the first weight information. The second weight information determination unit is used to take the difference between the preset information and the first weight information as the second weight information.

[0069] Optionally, the first temperature feature determination module 520 includes: The first temperature difference feature determination unit is used to determine the first temperature difference feature based on the difference information between the target upper limit temperature information and the target lower limit temperature information corresponding to the first preset duration in the target temperature time series information. The second temperature difference feature determination unit is used to determine the second temperature difference feature based on the difference information between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset time period. The temperature change feature determination unit is used to determine the temperature change feature based on the change information of the temperature change rate corresponding to the target temperature time series information within the first preset time period.

[0070] Optionally, the second temperature difference feature determination unit includes: The temperature standard deviation information determination unit is used to calculate the temperature standard deviation information based on the difference information between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset time period. The third temperature difference feature determination unit is used to determine the second temperature difference feature based on the temperature standard deviation information.

[0071] Optionally, the first fusion feature determination module 540 includes: The target temperature change information determination unit is used to determine the target temperature change information within the second preset time period based on the target temperature time sequence information; The temperature-weight coupling feature determination unit is used to divide the target temperature change information and the target liquid weight information to obtain the temperature-weight coupling feature.

[0072] Optionally, the device further includes: The second acquisition module is used to acquire the sample temperature time sequence information of the sample cookware and its corresponding sample boiling time label, as well as the sample liquid weight information during the cooking process. The second temperature feature determination module is used to determine the first sample temperature difference feature, the second sample temperature difference feature, and the sample temperature change feature corresponding to the third preset duration based on the sample temperature time series information. The second coupling feature determination module is used to determine the sample temperature-weight coupling feature based on the sample temperature time series information and sample liquid weight information corresponding to the fourth preset time period; the sample temperature-weight coupling feature is used to characterize the relationship between the sample temperature change information and the sample liquid weight information within the fourth preset time period. The second fusion feature determination module is used to perform fusion processing on the first sample temperature difference feature, the second sample temperature difference feature, the sample temperature change feature and the sample temperature-weight coupling feature to obtain sample fusion features; The predicted boiling time label determination module is used to input the sample fusion features into the boiling recognition network to be trained to predict the boiling time and obtain the predicted boiling time label of the sample pot. The training module is used to train the boiling recognition network to be trained based on the difference between the predicted boiling time label and the sample boiling time label, so as to obtain the target boiling recognition network.

[0073] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0074] Figure 6 This is a block diagram illustrating a terminal electronic device for boiling point identification according to an exemplary embodiment. The electronic device can be a terminal, and its internal structure diagram can be as follows: Figure 6 As shown, the electronic device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a boiling recognition method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0075] Figure 7 This is a block diagram illustrating a server electronic device for boiling point identification according to an exemplary embodiment. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a boiling point identification method.

[0076] Those skilled in the art will understand that Figure 6 or Figure 7 The structures shown are merely block diagrams of some structures related to the disclosed solutions of this invention, and do not constitute a limitation on the electronic devices to which the disclosed solutions of this invention are applied. Specific electronic devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.

[0077] In an exemplary embodiment, an electronic device for boiling identification is also provided, including a processor and a memory, the memory storing at least one instruction, which is loaded and executed by the processor to implement the boiling identification method as disclosed in the embodiments of the present invention.

[0078] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one instruction, which is loaded and executed by a processor to implement the boiling identification method in the disclosed embodiments of the present invention.

[0079] In an exemplary embodiment, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the boiling identification method in the disclosed embodiments of the present invention.

[0080] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0081] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles disclosed herein and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0082] It should be understood that the present invention is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for identifying boiling, characterized in that, The method includes: During the cooking process, acquire the target temperature timing information and target liquid weight information of the cookware; Based on the target temperature time series information, a first temperature difference feature, a second temperature difference feature, and a temperature change feature corresponding to a first preset duration are determined; the first temperature difference feature is used to characterize the degree of difference between the upper limit temperature information and the lower limit temperature information in the target temperature time series information within the first preset duration; the second temperature difference feature is used to characterize the degree of difference between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset duration; and the temperature change feature is used to characterize the trend of the temperature change rate within the first preset duration. Based on the target temperature time-series information and target liquid weight information corresponding to the second preset time period, a temperature-weight coupling feature is determined; the temperature-weight coupling feature is used to characterize the relationship between temperature change information and liquid weight information within the second preset time period. The first temperature difference feature, the second temperature difference feature, the temperature change feature, and the temperature-weight coupling feature are fused to obtain the target fused feature; The target fusion features are input into the target boiling recognition network to identify the boiling time, thereby obtaining the target boiling time of the pot.

2. The method according to claim 1, characterized in that, The process of fusing the first temperature difference feature, the second temperature difference feature, the temperature change feature, and the temperature-weight coupling feature to obtain the target fused feature includes: Obtain first weight information corresponding to the first temperature difference feature, the second temperature difference feature, and the temperature change feature, as well as second weight information corresponding to the temperature-weight coupling feature; Based on the first weight information, the first temperature difference information, the second temperature difference information, and the temperature change information are weighted respectively to obtain the first weighted temperature difference information, the second weighted temperature difference information, and the weighted temperature change information. Based on the second weight information, the temperature weight coupling feature is weighted to obtain the weighted temperature weight coupling feature. The first weighted temperature difference information, the second weighted temperature difference information, the weighted temperature change information, and the weighted temperature weight coupling feature are spliced ​​together to obtain the target fusion feature.

3. The method according to claim 2, characterized in that, The step of obtaining the first weight information corresponding to the first temperature difference feature, the second temperature difference feature, and the temperature change feature, and the second weight information corresponding to the temperature-weight coupling feature includes: Determine the first information entropy of the temperature feature corresponding to the target temperature time series information, and the second information entropy corresponding to the temperature-weight coupling feature; The first information entropy is divided by the sum of the first information entropy and the second information entropy to obtain the first weight information. The difference between the preset information and the first weight information is used as the second weight information.

4. The method according to claim 1, characterized in that, The step of determining the first temperature difference feature, the second temperature difference feature, and the temperature change feature corresponding to the first preset duration based on the target temperature time series information includes: In the target temperature time series information, the first temperature difference feature is determined based on the difference information between the target upper limit temperature information and the target lower limit temperature information corresponding to the first preset duration; Based on the difference information between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset time period, the second temperature difference feature is determined. Based on the change information of the temperature change rate corresponding to the target temperature time series information within the first preset time period, the temperature change characteristics are determined.

5. The method according to claim 4, characterized in that, The step of determining the second temperature difference feature based on the difference information between each temperature information in the target temperature time series information and the corresponding average temperature information within the first preset time period includes: Based on the difference information between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset time period, the temperature standard deviation information is calculated. Based on the temperature standard deviation information, the second temperature difference characteristic is determined.

6. The method according to claim 1, characterized in that, The determination of the temperature-weight coupling feature based on the target temperature time-series information and target liquid weight information corresponding to the second preset duration includes: Based on the target temperature time series information, determine the target temperature change information within the second preset time period; The temperature-weight coupling feature is obtained by dividing the target temperature change information by the target liquid weight information.

7. The method according to any one of claims 1 to 6, characterized in that, The target boiling recognition network was trained in the following way: During the cooking process, the sample temperature time sequence information of the sample cookware and its corresponding sample boiling time label, as well as the sample liquid weight information, are obtained. Based on the sample temperature time series information, the first sample temperature difference feature, the second sample temperature difference feature, and the sample temperature change feature corresponding to the third preset duration are determined. Based on the sample temperature time-series information and sample liquid weight information corresponding to the fourth preset duration, the sample temperature-weight coupling characteristics are determined. The sample temperature-weight coupling feature is used to characterize the relationship between sample temperature change information and sample liquid weight information within the fourth preset time period. The temperature difference features of the first sample, the temperature difference features of the second sample, the temperature change features of the sample, and the temperature-weight coupling features of the sample are fused to obtain the sample fusion features. The sample fusion features are input into the boiling recognition network to be trained to predict the boiling time, and the predicted boiling time label of the sample pot is obtained. Based on the difference between the predicted boiling time label and the sample boiling time label, the boiling recognition network to be trained is trained to obtain the target boiling recognition network.

8. A boiling detection device, characterized in that, The device includes: The first acquisition module is used to acquire the target temperature timing information and target liquid weight information of the cookware during the cooking process; The first temperature feature determination module is used to determine, based on the target temperature time series information, a first temperature difference feature, a second temperature difference feature, and a temperature change feature corresponding to a first preset duration; the first temperature difference feature is used to characterize the degree of difference between the upper limit temperature information and the lower limit temperature information in the target temperature time series information within the first preset duration; the second temperature difference feature is used to characterize the degree of difference between each temperature information in the target temperature time series information and the temperature mean information corresponding to the target temperature time series information within the first preset duration; and the temperature change feature is used to characterize the trend of the temperature change rate within the first preset duration. The first coupling feature determination module is used to determine the temperature-weight coupling feature based on the target temperature time-series information and the target liquid weight information corresponding to the second preset time period; the temperature-weight coupling feature is used to characterize the relationship between temperature change information and liquid weight information within the second preset time period. The first fusion feature determination module is used to perform fusion processing on the first temperature difference feature, the second temperature difference feature, the temperature change feature and the temperature-weight coupling feature to obtain the target fusion feature; The target boiling time determination module is used to input the target fusion features into the target boiling time recognition network to identify the boiling time and obtain the target boiling time of the pot.

9. An electronic device for boiling point detection, characterized in that, The electronic device includes a processor and a memory, the memory storing at least one instruction, which is loaded and executed by the processor to implement the boiling identification method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores at least one instruction, which is loaded and executed by a processor to implement the boiling identification method as described in any one of claims 1 to 7.

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