Oven fault detection method, device and equipment and intelligent kitchen appliance

Abstract

The invention discloses an oven fault detection method, device and equipment and an intelligent kitchen appliance, and the method comprises the steps: obtaining the real-time working voltage of an oven, the real-time temperature of a cavity of the oven and the accumulated working time of the oven in the operation process of the oven; according to the real-time working voltage and the rated power of a heating device of the oven, the real-time working power of the heating device is determined; the temperature of the cavity is predicted according to the real-time working power and the accumulated working duration, and the predicted temperature of the cavity is obtained; when it is detected that the temperature difference value between the predicted temperature and the real-time temperature is larger than the preset temperature difference value, a temperature change curve of the oven in the preset time period is obtained; and determining the fault type of the oven according to the temperature change curve. Whether the oven has a fault or not can be judged in the operation process of the oven, the fault type of the oven can be determined, and corresponding maintenance can be conveniently carried out in time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of household appliances, in particular to a fault detection method and device of an oven, an oven equipment and an intelligent kitchen appliance. BACKGROUND

[0002] As an indispensable household appliance in modern kitchens, the performance and reliability of an oven have a direct impact on the cooking effect. However, various faults may occur during the use of the oven, such as heating abnormalities, temperature control failures, and electrical leakage.

[0003] The existing device diagnosis systems on the market often use a self-checking mode, which needs to enter through a special path and needs to be manually inspected with the help of other devices, and it is difficult to achieve comprehensive self-checking of the device in the user's home. The existing oven products often cannot reliably locate the fault cause during factory inspection and when an abnormality occurs in the user's home, and the degree of intelligent detection is low. SUMMARY

[0004] The present application provides a fault detection method and device of an oven, an oven equipment and an intelligent kitchen appliance, which can determine whether the oven has a fault and determine the fault type of the oven during the operation of the oven, facilitating timely maintenance.

[0005] In one aspect, the present application provides a fault detection method of an oven, the method comprising: During the operation of the oven, obtaining a real-time working voltage of the oven, a real-time temperature of a cavity of the oven, and a cumulative working duration of the oven; According to the real-time working voltage and the rated power of the heating device of the oven, determining a real-time working power of the heating device; According to the real-time working power and the cumulative working duration, predicting the temperature of the cavity to obtain a predicted temperature of the cavity; In the case where the temperature difference between the predicted temperature and the real-time temperature is greater than a preset temperature difference, obtaining a temperature change curve of the oven within a preset time period; the preset time period represents a time period from the start of the operation of the oven to a preset time; the preset time is the time when the temperature difference between the predicted temperature and the real-time temperature is greater than the preset temperature difference; According to the temperature change curve, determining the fault type of the oven.

[0006] In an exemplary embodiment, the method further comprises: During the operation of the sample oven, collecting sample temperature change curves of the sample oven under a plurality of sample faults; the sample temperature change curves are labeled with sample fault types; characteristics of the sample temperature curve are extracted from the sample temperature change curve; The sample fault detection result is obtained by detecting the sample temperature curve characteristics based on a preset model. The preset model is trained based on the difference between the sample fault detection result and the sample fault type, and a fault detection model is obtained. The fault type of the oven is determined according to the temperature change curve, comprising: The temperature change curve is input into the fault detection model to obtain the fault type of the oven.

[0007] In an exemplary embodiment, the characteristics of the sample temperature curve are extracted from the sample temperature change curve, comprising: The sample central tendency characteristics are extracted from the sample temperature change curve, and the sample central tendency characteristics represent the aggregation degree of the sample temperature data in the sample temperature change curve. The sample dispersion degree characteristics are extracted from the sample temperature change curve, and the sample dispersion degree characteristics represent the fluctuation degree of the sample temperature data. The sample distribution shape characteristics are extracted from the sample temperature change curve. The sample temperature curve characteristics are determined according to the sample central tendency characteristics, the sample dispersion degree characteristics and the sample distribution shape characteristics.

[0008] In an exemplary embodiment, the sample central tendency characteristics are extracted from the sample temperature change curve, comprising: The sample mean, sample median and sample mode of the sample temperature data in the sample temperature change curve are obtained. The sample central tendency characteristics are determined according to the sample mean, sample median and sample mode.

[0009] In an exemplary embodiment, the temperature of the cavity is predicted according to the real-time working power and the cumulative working time, and the predicted temperature of the cavity is obtained, comprising: The attribute information of the oven is obtained. The target calibration coefficient of the oven is determined by querying a calibration coefficient database according to the attribute information; the calibration coefficient database comprises a corresponding relationship between a preset attribute and a preset calibration coefficient. The predicted temperature is determined according to the cumulative working time, real-time working power and target calibration coefficient.

[0010] In an example embodiment, the determining the predicted temperature according to the cumulative working time length, the real-time working power and the target calibration coefficient comprises: In a case where it is detected that the oven is in operation, an initial temperature of the cavity is acquired; A calibration temperature of the cavity is determined according to the cumulative working time length, the real-time working power and the target calibration coefficient; The predicted temperature of the cavity is determined according to the initial temperature and the calibration temperature.

[0011] In an example embodiment, after the determining the fault type of the oven according to the real-time temperature change curve, the method comprises: If the fault type is a first preset fault type, a first fault prompt information is generated; the first preset fault type represents a fault of the heating device; If the fault type is a second preset fault type, a second fault prompt information is generated; the second preset fault type represents a fault of a temperature detection device of the oven; the temperature detection device is used to detect a real-time temperature of the cavity.

[0012] Another aspect provides a fault detection device of an oven, the device comprising: An acquisition module is configured to acquire a real-time working voltage of the oven, a real-time temperature of a cavity of the oven and a cumulative working time length of the oven during operation of the oven; A real-time working power determination module is configured to determine a real-time working power of a heating device of the oven according to the real-time working voltage and a rated power of the heating device; A prediction module is configured to predict a temperature of the cavity according to the real-time working power and the cumulative working time length, to obtain a predicted temperature of the cavity; A temperature change curve acquisition module is configured to acquire a temperature change curve of the oven within a preset time period in a case where a temperature difference between the predicted temperature and the real-time temperature is greater than a preset temperature difference; the preset time period represents a time period from the start of the operation of the oven to a preset time point; the preset time point is a time point at which the temperature difference between the predicted temperature and the real-time temperature is greater than the preset temperature difference; A fault type determination module is configured to determine a fault type of the oven according to the temperature change curve.

[0013] Another aspect provides an electronic device comprising a processor and a memory, the processor being configured to store the memory with processor-executable instructions; wherein the processor is configured to execute the instructions to implement the fault detection method of the oven as described above.

[0014] Another aspect provides a smart kitchen appliance employing the oven fault detection method as described above.

[0015] Another aspect provides a computer-readable storage medium having at least one instruction or at least one program stored therein, which is loaded and executed by a processor to implement the oven fault detection method as described above.

[0016] The oven fault detection method, device, equipment and smart kitchen appliance provided by the present application have the following technical effects: In the process of running the oven, the present application acquires the real-time working voltage of the oven, the real-time temperature of the cavity of the oven and the cumulative working duration of the oven; according to the real-time working voltage and the rated power of the heating device of the oven, the real-time working power of the heating device is determined; according to the real-time working power and the cumulative working duration, the temperature of the cavity is predicted to obtain the predicted temperature of the cavity; in the case that the temperature difference between the predicted temperature and the real-time temperature is greater than the preset temperature difference, the temperature change curve of the oven within a preset time period is acquired; the preset time period represents the time period between the start of the oven and a preset time point, and the preset time point is the time point at which the temperature difference between the predicted temperature and the real-time temperature is greater than the preset temperature difference; according to the temperature change curve, the fault type of the oven is determined. By acquiring the real-time working voltage of the oven, the present application can calculate the real-time working power of the heating device of the oven, thereby predicting the temperature of the cavity of the oven; when the difference between the predicted temperature and the measured temperature detected by the temperature detection device in the oven is large, it can be determined that the oven has a fault; by pre-establishing a fault detection model, the fault type of the oven can be quickly and accurately determined, thereby effectively improving the detection efficiency of fault detection; by directly determining the fault type of the oven according to the temperature change curve of the oven, timely fault prompt can be performed, which facilitates the user to directly lock the fault position of the oven, greatly reduces the fault troubleshooting time and improves the maintenance efficiency of the oven.

[0017] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions and advantages of the embodiments or prior art in the specification, the drawings needed in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0019] Figure 1is a flowchart of a fault detection method of an oven provided by an embodiment of the present specification; Figure 2 is a flowchart of obtaining a predicted temperature of an oven cavity provided by an embodiment of the present specification; Figure 3 is a flowchart of determining a predicted temperature according to an initial temperature and a calibration temperature provided by an embodiment of the present specification; Figure 4 is a flowchart of obtaining a fault detection model provided by an embodiment of the present specification; Figure 5 is a flowchart of obtaining a sample temperature curve feature provided by an embodiment of the present specification; Figure 6 is a flowchart of generating fault prompt information provided by an embodiment of the present specification; Figure 7 is a schematic diagram of a fault detection device of an oven provided by an embodiment of the present specification; Figure 8 is a structural schematic diagram of a server for a fault detection method of an oven provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present specification will be described clearly and completely below with reference to the drawings in the embodiments of the present specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0022] The following describes a fault detection method of an oven, Figure 1is a flowchart of a fault detection method of an oven provided by an embodiment of the present specification. The present specification provides method operation steps as described in the embodiments or flowcharts, but more or fewer operation steps can be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is only one of the many step execution orders, and does not represent the only execution order. In actual system or server product execution, the method order shown in the embodiments or the drawings can be executed in sequence or in parallel (for example, in a parallel processor or multi-thread processing environment). Specifically as shown in Figure 1 The method can include: S1: During the operation of the oven, the real-time working voltage of the oven, the real-time temperature of the cavity of the oven, and the cumulative working time length of the oven are obtained; S2: According to the real-time working voltage and the rated power of the heating device of the oven, the real-time working power of the heating device is determined; S3: According to the real-time working power and the cumulative working time length, the temperature of the cavity is predicted to obtain the predicted temperature of the cavity; S4: In the case where the temperature difference between the predicted temperature and the real-time temperature is greater than a preset temperature difference, the temperature change curve of the oven within a preset time period is obtained; the preset time period represents the period from the start of the oven operation to a preset time; the preset time is the time when the temperature difference between the predicted temperature and the real-time temperature is greater than the preset temperature difference; S5: According to the temperature change curve, the fault type of the oven is determined.

[0023] In the embodiments of the present application, a temperature detection device is arranged in the oven to monitor the temperature of the cavity in real time. The temperature detection device can be a temperature sensor, a temperature detection probe, or other devices that can detect temperature in real time. In addition, a voltage detection circuit can be provided to obtain the actual working voltage of the oven, i.e., the real-time working voltage of the oven.

[0024] In the embodiments of the present application, during the operation of the oven, the real-time working voltage of the oven, the real-time temperature of the cavity of the oven, and the cumulative working time length of the oven are obtained, and according to the obtained real-time working voltage of the oven and the rated power of the heating device of the oven, the real-time working power of the heating device during actual heating is calculated, and then the temperature of the cavity of the oven is predicted according to the real-time working power of the heating device and the cumulative working time length to obtain the predicted temperature, i.e., the temperature that the cavity of the oven can reach after heating under normal heating conditions.

[0025] In the embodiment of the present application, in the case that the temperature difference between the predicted temperature and the real-time temperature detected by the temperature detection device is greater than the preset temperature difference, it can be determined that the oven has a fault, the temperature change curve of the oven in a preset time period is obtained, and the fault type of the oven is determined according to the temperature change curve. The temperature change curve is the temperature curve in the period from when the oven is started to run to when the temperature difference between the predicted temperature and the real-time temperature is greater than the preset temperature difference.

[0026] In the embodiment of the present application, in order to make the temperature inside the oven cavity more uniform, the heating device of the oven can be a top heating pipe, a back heating pipe, or a bottom heating pipe. Any of the above faults will cause the real-time temperature detected by the temperature detection device to be less than the predicted temperature.

[0027] In the embodiment of the present application, if the temperature difference between the predicted temperature and the real-time temperature detected by the temperature detection device is too large, it can also be determined whether the temperature detection device is abnormal.

[0028] The embodiment of the present application can calculate the real-time working power of the oven heating device by obtaining the real-time working voltage of the oven, thereby predicting the temperature of the oven cavity. When the difference between the predicted temperature and the measured temperature measured by the temperature detection device in the oven is large, it can be determined that the oven has a fault. By directly determining the fault type of the oven according to the temperature change curve of the oven, timely fault prompt can be performed, which facilitates the user to directly lock the fault part of the oven and timely perform corresponding maintenance, thereby avoiding the influence of the cooking effect of food caused by the oven fault, greatly reducing the fault troubleshooting time, and improving the maintenance efficiency of the oven.

[0029] In an example embodiment, as shown in FIG. 1, the oven 100 can include a cavity 101, a heating device 102, a temperature detection device 103, and a processor 104. Figure 2 The processor 104 can be configured to: S31: Obtain attribute information of the oven; S32: Query a calibration coefficient database according to the attribute information to determine a target calibration coefficient of the oven; the calibration coefficient database includes a corresponding relationship between a preset attribute and a preset calibration coefficient; S33: Determine the predicted temperature according to the cumulative working time length, the real-time working power, and the target calibration coefficient.

[0030] In the embodiment of the present application, in order to obtain a more accurate predicted temperature of the oven cavity, it is necessary to determine the calibration coefficient corresponding to the device according to the attribute information of the oven, so as to calculate the calibration temperature of the oven cavity. After obtaining the attribute information of the oven, the target calibration coefficient corresponding to the current device is determined by searching the calibration coefficient database according to the attribute information of the current device, and the predicted temperature of the oven cavity is determined according to the cumulative working time of the oven, the real-time working power of the heating device and the target calibration coefficient. The calibration coefficient database includes the corresponding relationship between the preset attribute and the preset calibration coefficient, and each oven corresponds to a calibration coefficient. Therefore, the calibration coefficient database includes the corresponding relationship between a plurality of preset attributes and a plurality of preset calibration coefficients. After determining the attribute information of the oven, the calibration coefficient corresponding to the oven, i.e. the target calibration coefficient, can be quickly and accurately determined by searching the calibration coefficient database, thereby effectively improving the determination efficiency of the target calibration coefficient. The attribute information of the oven can include the model, capacity, power, size, heating mode and other information of the oven.

[0031] According to the specific attribute information of the oven, the calibration coefficient corresponding to the current device can be determined in the embodiment of the present application, so as to obtain a more accurate predicted temperature and improve the accuracy of fault judgment.

[0032] In an exemplary embodiment, as shown in Figure 3 The determination of the predicted temperature according to the cumulative working time, the real-time working power and the target calibration coefficient can include: S331: In the case of detecting that the oven is running, the initial temperature of the cavity is obtained; S332: The calibration temperature of the cavity is determined according to the cumulative working time, the real-time working power and the target calibration coefficient; S333: The predicted temperature of the cavity is determined according to the initial temperature and the calibration temperature.

[0033] In the embodiment of the present application, in the case of detecting that the oven is running, the initial temperature T1 of the cavity of the oven is obtained, and the calibration temperature T2 of the cavity of the oven is determined according to the cumulative working time Tw of the oven, the real-time working power Pr of the heating device and the target calibration coefficient a of the oven determined above. The calculation formula can be as follows:

[0034] After obtaining the calibration temperature T2, the sum of the initial temperature T1 and the calibration temperature T2 of the cavity of the oven is calculated, so as to obtain the predicted temperature Tp of the cavity. The calculation formula can be as follows:

[0035] After the predicted temperature of the cavity is obtained, the predicted temperature of the cavity is compared with the measured temperature of the cavity, so as to determine whether the oven has a fault.

[0036] The embodiment of the present application can predict the temperature during the operation of the oven according to the initial temperature of the oven and the actual heating power of the heating device, obtain a more accurate predicted temperature, obtain a more accurate and reliable temperature comparison result, and thus determine whether the oven has a fault, thereby improving the detection accuracy of fault detection.

[0037] In an example embodiment, as shown in Figure 4 The method can further include: S051: During the operation of the sample oven, sample temperature change curves of the sample oven under a plurality of sample faults are collected; the sample temperature change curves are labeled with sample fault types; S052: Feature extraction is performed on the sample temperature change curves to obtain sample temperature curve features; S053: The sample temperature curve features are detected based on a preset model to obtain a sample fault detection result; S054: The preset model is trained based on the difference between the sample fault detection result and the sample fault type to obtain a fault detection model; The determination of the fault type of the oven according to the temperature change curve includes: The temperature change curve is input into the fault detection model to obtain the fault type of the oven.

[0038] In the embodiment of the present application, the fault detection model can be trained in advance, and after the temperature change curve is obtained, the specific fault type of the oven can be quickly determined, and the accuracy of fault detection can be improved. For the fault detection model, sample temperature change curves of the sample oven under a plurality of different sample faults can be collected during the operation of the sample oven, and sample temperature change curves of the sample oven under normal operation without faults also need to be collected synchronously to obtain a training data set for model training, wherein each sample temperature change curve is labeled with a corresponding label, including whether the sample oven has a fault and the sample fault type of the sample oven when the sample oven has a fault. Collecting a large number of sample temperature change curves can enhance the generalization ability of the model. In addition, the labels in the training data set can reflect the true properties of the data (whether the sample oven has a fault and the sample fault type of the sample oven), and by using the data set with labels for model training, the obtained model can better adapt to the real application scenario, thereby improving the reliability of the fault detection model in actual application.

[0039] In the embodiment of the present application, the sample temperature change curve needs to be feature extracted to obtain a sample temperature curve feature, and then the sample temperature curve feature is detected based on a preset model to obtain a sample fault detection result. The preset model is trained according to the difference between the sample fault detection result and a sample fault type, and the model at the end of the training is taken as a fault detection model. Feature extraction on the sample temperature change curve can convert the original temperature data into a more representative and distinguishable sample temperature curve feature, so that the preset model can more easily learn the rules in the training data set, and the accuracy of the model is improved. The preset model can be a preset AI model. Compared with ordinary models, the AI model does not need human intervention, can automatically adjust parameters according to the training data set, has stronger adaptability, and can quickly adapt to different application scenarios.

[0040] The embodiment of the present application can quickly determine the specific fault type of the oven according to the temperature change curve in the oven running process by pre-constructing a fault detection model, while ensuring the accuracy of the oven fault detection, and improving the detection efficiency of the oven fault detection.

[0041] In an example embodiment, as shown in Figure 5 The feature extraction on the sample temperature change curve to obtain a sample temperature curve feature can include: S0521: The sample temperature change curve is subjected to extraction processing of a centralized trend feature to obtain a sample centralized trend feature. The sample centralized trend feature represents the aggregation degree of the sample temperature data in the sample temperature change curve. S0522: The sample temperature change curve is subjected to extraction processing of a dispersion degree feature to obtain a sample dispersion degree feature. The sample dispersion degree feature represents the fluctuation degree of the sample temperature data. S0523: The sample temperature change curve is subjected to extraction processing of a distribution shape feature to obtain a sample distribution shape feature. S0524: The sample temperature curve feature is determined according to the sample centralized trend feature, the sample dispersion degree feature, and the sample distribution shape feature.

[0042] In the embodiment of the present application, the sample temperature change curve is extracted for feature extraction, and the sample temperature curve features of the sample oven under different types of faults can be obtained, wherein the sample temperature curve features mainly include sample central tendency features, sample dispersion degree features and sample distribution shape features. Through these features, the temperature change features of the sample oven under different fault types can be better understood, and the accuracy of the model can be improved. The sample central tendency feature refers to the degree to which the sample temperature data in the sample temperature change curve is close to a certain central value in the overall distribution, that is, it represents the aggregation degree of the sample temperature data in the sample temperature change curve, and can reflect the average level or typical value of the sample temperature data. Through the sample central tendency feature, the overall situation of temperature change can be quickly understood.

[0043] In the embodiment of the present application, the sample dispersion degree feature refers to the dispersion or fluctuation degree of the sample temperature data. The greater the dispersion degree, the more unstable the temperature change; the smaller the dispersion degree, the more stable the temperature change. The sample dispersion degree feature can be obtained by obtaining the range, average difference, variance, standard deviation and coefficient of variation of the sample temperature data. The range is the difference between the maximum and minimum values of the sample temperature data in the sample temperature change curve, and the calculation method is simple. The average difference is the average of the absolute difference between each temperature data point and the mean value, which can consider the deviation degree of all temperature data points from the mean value. The variance is the average of the square of the difference between each temperature data point and the mean value. The greater the variance, the higher the dispersion degree of the sample temperature data. The standard deviation is the square root of the variance. The greater the standard deviation, the higher the dispersion degree of the sample temperature data. The coefficient of variation is the ratio of the standard deviation to the mean value, which can be expressed as a percentage and can be used to compare the dispersion degrees of different temperature data.

[0044] In the embodiment of the present application, the sample distribution shape feature refers to the overall distribution form feature of the sample temperature data, which can be obtained by skewness and kurtosis to obtain the distribution form feature of the temperature curve of the sample oven under different fault types.

[0045] The embodiment of the present application extracts the sample central tendency feature, the sample dispersion degree feature and the sample distribution shape feature of the sample temperature change curve, and can obtain the temperature curve features of the sample oven under different types of faults, improve the accuracy during model training, and improve the efficiency of fault detection.

[0046] In an exemplary embodiment, the extraction processing of the sample temperature change curve for the central tendency feature obtains the sample central tendency feature, which can include: obtaining the sample mean, sample median and sample mode of the sample temperature data in the sample temperature change curve; determining the sample central tendency feature according to the sample mean, sample median and sample mode.

[0047] In this embodiment, to extract the central tendency features of the sample temperature change curve, the sample mean, median, and mode of the sample temperature data in the sample temperature change curve can be obtained. The mean provides an overall average level of the temperature data, suitable for describing the overall trend of temperature change. The median is the value in the middle after sorting all temperature values ​​from smallest to largest. If the number of temperature data points is even, the median is the average of the two middle values. When there are anomalies in the temperature data, the median better reflects the typical level of the temperature data. The mode is the temperature value that appears most frequently. If multiple values ​​appear with the same and highest frequency, these values ​​are all modes. Obtaining and analyzing the sample mean, median, and mode in the sample temperature data allows for better extraction of the central tendency features in the sample temperature change curve, thus obtaining more accurate sample temperature curve features.

[0048] In this embodiment of the application, in addition to the sample mean, sample median and sample mode mentioned above, the sample weighted mean of the sample temperature data can also be obtained. The sample weighted mean can be used to analyze the situation where some temperature values ​​are more important than others (e.g., the temperature of certain more critical time periods).

[0049] This application embodiment, by acquiring and analyzing the sample mean, sample median, and sample mode in the sample temperature data, can better extract the sample central tendency features in the sample temperature change curve, obtain more accurate sample temperature curve features, and improve the accuracy of fault detection.

[0050] In one exemplary embodiment, such as Figure 6 As shown, after determining the fault type of the oven based on the real-time temperature change curve, the method may include: S511: If the fault type is a first preset fault type, generate a first fault prompt message; the first preset fault type represents a fault in the heating device. S512: If the fault type is a second preset fault type, generate a second fault prompt message; the second preset fault type indicates a fault in the temperature detection device of the oven; the temperature detection device is used to detect the real-time temperature of the cavity.

[0051] In the embodiment of the present application, the fault types of the oven mainly include the heating device fault and the temperature detection device fault. After determining the corresponding fault type of the oven according to the temperature change curve, the corresponding fault prompt information is generated in time to give a warning, prompting the user to maintain and repair in time according to the fault type, so as to avoid the oven from being unable to be normally used due to the fault. If the obtained fault type is the first preset fault type, i.e., the heating device fault, the first fault prompt information can be generated to prompt the user that the heating device of the oven has a fault and needs to be replaced or repaired in time. If the obtained fault type is the second preset fault type, i.e., the temperature detection device fault, the second fault prompt information can be generated to prompt the user that the temperature detection device of the oven has a fault and needs to be maintained in time, so as to avoid the temperature detection device fault from causing the temperature of the oven cavity to be unable to be accurately obtained and the food to be unable to be accurately cooked according to the temperature set by the user, thereby affecting the cooking effect of the food.

[0052] In the embodiment of the present application, since the heating device of the oven can include the top heating pipe, the back heating pipe and the bottom heating pipe, the heating device fault of the oven can include the top heating pipe fault, the back heating pipe fault and the bottom heating pipe fault. For example, if the fault type of the oven is determined to be the top heating pipe fault according to the temperature change curve, the first fault prompt information generated can include the "top heating pipe fault" or other prompt information that can prompt the specific fault, so as to facilitate the user to directly maintain the top heating pipe with the fault and reduce the troubleshooting time of other heating pipes, thereby effectively improving the maintenance efficiency of the oven. For example, if the fault type of the oven is determined to be the back heating pipe fault according to the temperature change curve, the first fault prompt information generated can include the "back heating pipe fault" or other prompt information that can prompt the specific fault.

[0053] In the embodiment of the present application, the fault prompt can be in the form of voice broadcast, and the warning can be in the form of sound prompt. For different types of faults, different volumes and different sound prompt frequencies can be set, and the corresponding fault prompt information can also be displayed on the display panel of the oven, so as to facilitate the user to view the specific fault type and perform corresponding maintenance.

[0054] In the embodiment of the present application, the corresponding fault prompt information is generated according to different fault types, so as to facilitate the user to directly lock the fault position of the oven according to the fault prompt information and maintain and repair in time, thereby greatly improving the maintenance efficiency of the oven.

[0055] The embodiments of the present application can calculate the real-time working power of the heating device of the oven by acquiring the real-time working voltage of the oven, thereby predicting the temperature of the oven cavity, and determining that the oven has a fault when the difference between the predicted temperature and the measured temperature detected by the temperature detection device in the oven is large. The fault detection model is established in advance, so that the fault type of the oven can be quickly and accurately determined, thereby effectively improving the detection efficiency of fault detection. The fault type of the oven is directly determined according to the temperature change curve of the oven, so that the fault prompt can be timely given, the fault part of the oven can be directly locked by the user, the time for troubleshooting is greatly reduced, and the maintenance efficiency of the oven is improved.

[0056] The embodiments of the present application also provide a fault detection device of an oven, as shown in the accompanying drawings, the device can comprise: Figure 7 The acquisition module 710 is configured to acquire the real-time working voltage of the oven, the real-time temperature of the cavity of the oven and the cumulative working time length of the oven during the operation of the oven. The real-time working power determination module 720 is configured to determine the real-time working power of the heating device according to the real-time working voltage and the rated power of the heating device of the oven. The prediction module 730 is configured to predict the temperature of the cavity according to the real-time working power and the cumulative working time length, and obtain the predicted temperature of the cavity. The temperature change curve acquisition module 740 is configured to acquire the temperature change curve of the oven within a preset time period when it is detected that the temperature difference between the predicted temperature and the real-time temperature is greater than a preset temperature difference value. The preset time period represents the time period from the start of the operation of the oven to a preset time point. The preset time point is the time point when it is detected that the temperature difference between the predicted temperature and the real-time temperature is greater than the preset temperature difference value. The fault type determination module 750 is configured to determine the fault type of the oven according to the temperature change curve.

[0057] In an exemplary embodiment, the device can further comprise: The sample temperature change curve acquisition module is configured to acquire the sample temperature change curves of the sample oven under a plurality of sample faults during the operation of the sample oven. The sample temperature change curves are labeled with sample fault types. The sample temperature curve feature acquisition module is configured to perform feature extraction on the sample temperature change curves to obtain sample temperature curve features. The sample fault prediction result acquisition module is configured to detect the sample temperature curve features based on a preset model to obtain a sample fault detection result. ​The fault detection model acquisition module is configured to train the preset model based on a difference between the sample fault detection result and the sample fault type, and obtain a fault detection model; and the fault type of the oven is determined according to the temperature change curve, including: inputting the temperature change curve into the fault detection model to obtain the fault type of the oven.

[0058] In an example embodiment, the sample temperature curve feature acquisition module can include: A sample central tendency feature acquisition unit is configured to perform a central tendency feature extraction process on the sample temperature change curve, and obtain a sample central tendency feature; the sample central tendency feature represents a degree of aggregation of sample temperature data in the sample temperature change curve. A sample dispersion degree feature acquisition unit is configured to perform a dispersion degree feature extraction process on the sample temperature change curve, and obtain a sample dispersion degree feature; the sample dispersion degree feature represents a fluctuation degree of the sample temperature data. A sample distribution shape feature acquisition unit is configured to perform a distribution shape feature extraction process on the sample temperature change curve, and obtain a sample distribution shape feature. A sample temperature curve feature determination unit is configured to determine the sample temperature curve feature according to the sample central tendency feature, the sample dispersion degree feature, and the sample distribution shape feature.

[0059] In an example embodiment, the sample central tendency feature acquisition unit can include: A sample data acquisition sub-unit is configured to acquire a sample mean, a sample median, and a sample mode of the sample temperature data in the sample temperature change curve. A sample central tendency feature determination sub-unit is configured to determine the sample central tendency feature according to the sample mean, the sample median, and the sample mode.

[0060] In an example embodiment, the prediction module 730 can include: An attribute information acquisition unit is configured to acquire attribute information of the oven. A target calibration coefficient acquisition unit is configured to query a calibration coefficient database according to the attribute information, and determine a target calibration coefficient of the oven; the calibration coefficient database includes a corresponding relationship between a preset attribute and a preset calibration coefficient. A predicted temperature determination unit is configured to determine the predicted temperature according to the cumulative working time length, the real-time working power, and the target calibration coefficient.

[0061] In an example embodiment, the predicted temperature determination unit can include: An initial temperature acquisition subunit is configured to acquire an initial temperature of the cavity when it is detected that the oven is in operation. A calibration temperature determination subunit is configured to determine a calibration temperature of the cavity according to the cumulative working time length, the real-time working power, and the target calibration coefficient. A predicted temperature determination subunit is configured to determine the predicted temperature of the cavity according to the initial temperature and the calibration temperature.

[0062] In an exemplary embodiment, the apparatus can further include: A first fault prompt information generation module is configured to generate first fault prompt information if the fault type is a first preset fault type, the first preset fault type representing a fault of the heating device. A second fault prompt information generation module is configured to generate second fault prompt information if the fault type is a second preset fault type, the second preset fault type representing a fault of a temperature detection device of the oven, the temperature detection device being configured to detect a real-time temperature of the cavity.

[0063] The apparatus and the method in the above-described apparatus embodiment are based on the same inventive concept.

[0064] The electronic device provided by the embodiments of the present disclosure includes a processor and a memory, the memory stores at least one instruction or at least one program, the at least one instruction or at least one program is loaded and executed by the processor to implement the fault detection method of the oven provided by the above method embodiments.

[0065] In another aspect, an intelligent kitchen appliance is provided, which adopts the fault detection method of the oven as described above.

[0066] The embodiments of the present disclosure further provide a computer-readable storage medium, which can be arranged in a terminal to save at least one instruction or at least one program for implementing the fault detection method of the oven in the method embodiments, and the at least one instruction or at least one program is loaded and executed by the processor to implement the fault detection method of the oven provided by the above method embodiments.

[0067] The embodiments of the present disclosure further provide a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to enable the computer device to perform the fault detection method of the oven provided by the above method embodiments.

[0068] Optionally, in the embodiments of the present specification, the storage medium can be located in at least one of the plurality of network servers of the computer network. Optionally, in the embodiments, the storage medium can include, but is not limited to, a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media capable of storing program codes.

[0069] The memory in the embodiments of the present specification can be used to store software programs and modules, and the processor executes various function applications and data processing by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required for functions, etc.; and the data storage area can store data created according to the use of the device, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the memory can also include a memory controller to provide the processor with access to the memory.

[0070] The fault detection method for the oven provided by the embodiments of the present specification can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Taking the case of running on a server, Figure 8 is a hardware structure block diagram of a server for a fault detection method for an oven provided by the embodiments of the present specification. As shown in Figure 8As shown, the server 800 can vary greatly in configuration and performance, and can include one or more Central Processing Units (CPU) 810 (the CPU 810 can include, but is not limited to, a processing device such as a microprocessor, a programmable logic device, a field programmable gate array (FPGA), etc.), a memory 830 for storing data, one or more storage media 820 (such as one or more mass storage devices) for storing applications 823 or data 822. The memory 830 and the storage media 820 can be of any type generally known or used in the art including, but not limited to, volatile memory, non-volatile memory, removable storage media, etc. The applications 823 stored in the storage media 820 can include one or more modules, each of which can include a series of instructions for operating the server. Further, the CPU 810 can be configured to communicate with the storage media 820 to execute the series of instructions in the storage media 820 on the server 800. The server 800 can also include one or more power supplies 860, one or more wired or wireless network interfaces 850, one or more input / output interfaces 840, and / or one or more operating systems 821, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0071] The input / output interface 840 can be used to receive or transmit data via a network. Examples of the network can include a wireless network provided by a communication provider of the server 800. In one example, the input / output interface 840 includes a network interface controller (NIC) that can connect to other network devices through a base station to communicate with the Internet. In one example, the input / output interface 840 can be a radio frequency (RF) module that is used to communicate with the Internet through a wireless manner.

[0072] Those of ordinary skill in the art can understand that, Figure 8 The structure shown is merely illustrative and does not limit the structure of the electronic device described above. For example, the server 800 can include more or fewer components than those shown, or have a different configuration than that shown. Figure 8 For example, the server 800 can include more or fewer components than those shown, or have a different configuration than that shown. Figure 8 For example, the server 800 can include more or fewer components than those shown, or have a different configuration than that shown.

[0073] From the above, it can be seen from the embodiments of the oven fault detection method and device provided in the present application that the real-time working voltage of the oven, the real-time temperature of the cavity of the oven and the cumulative working time length of the oven are obtained during the operation of the oven; the real-time working power of the heating device is determined according to the real-time working voltage and the rated power of the heating device of the oven; the temperature of the cavity is predicted according to the real-time working power and the cumulative working time length to obtain the predicted temperature of the cavity; in the case that the temperature difference between the predicted temperature and the real-time temperature is greater than the preset temperature difference, the temperature change curve of the oven within a preset time period is obtained; the preset time period represents the time period from the start of the operation of the oven to a preset time point, and the preset time point is the time point at which the temperature difference between the predicted temperature and the real-time temperature is greater than the preset temperature difference; and the fault type of the oven is determined according to the temperature change curve. By obtaining the real-time working voltage of the oven, the real-time working power of the heating device of the oven can be calculated, so that the temperature of the cavity of the oven is predicted, and when the difference between the predicted temperature and the measured temperature detected by the temperature detection device in the oven is large, it can be determined that the oven has a fault; by pre-establishing a fault detection model, the fault type of the oven can be quickly and accurately determined, and the detection efficiency of fault detection is effectively improved; by directly determining the fault type of the oven according to the temperature change curve of the oven, timely fault prompt can be performed, the fault position of the oven can be directly locked by the user, the fault troubleshooting time is greatly reduced, and the maintenance efficiency of the oven is improved.

[0074] It should be noted that the above-mentioned embodiments of the present application are in the order of description only, and do not represent the advantages and disadvantages of the embodiments. The above-mentioned embodiments of the present application are described. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0075] Each embodiment in the present specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the device, equipment and storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts are referred to the part of the method embodiment.

[0076] A person of ordinary skill in the art can understand that all or part of the steps of the above-mentioned embodiments can be completed by hardware, or by program to instruct relevant hardware, and the program can be stored in a computer storage medium. The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk.

[0077] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for detecting faults in an oven, characterized in that, The method includes: During the operation of the oven, the real-time operating voltage of the oven, the real-time temperature of the oven cavity, and the cumulative operating time of the oven are acquired. The real-time operating power of the heating element is determined based on the real-time operating voltage and the rated power of the heating element of the oven. The temperature of the cavity is predicted based on the real-time operating power and the cumulative operating time, thus obtaining the predicted temperature of the cavity; If the temperature difference between the predicted temperature and the real-time temperature is detected to be greater than a preset temperature difference, the temperature change curve of the oven within a preset time period is obtained; the preset time period represents the period from when the oven is turned on to when it is set to a preset time, and the preset time is the moment when the temperature difference between the predicted temperature and the real-time temperature is detected to be greater than the preset temperature difference. The type of malfunction of the oven is determined based on the temperature change curve.

2. The method according to claim 1, characterized in that, The method further includes: During the operation of the sample oven, sample temperature change curves were collected under various sample malfunctions; the sample temperature change curves were labeled with the sample malfunction types. Feature extraction is performed on the sample temperature change curve to obtain the sample temperature curve features; Based on a preset model, the characteristics of the sample temperature curve are detected to obtain the sample fault detection result; The preset model is trained based on the difference between the sample fault detection results and the sample fault types to obtain a fault detection model; Determining the oven's malfunction type based on the temperature change curve includes: The temperature change curve is input into the fault detection model to obtain the fault type of the oven.

3. The method according to claim 2, characterized in that, The step of extracting features from the sample temperature change curve to obtain sample temperature curve features includes: The central tendency features of the sample temperature change curves are extracted to obtain sample central tendency features; the sample central tendency features characterize the degree of clustering of sample temperature data in the sample temperature change curves. The sample temperature change curve is processed to extract the dispersion feature, which represents the degree of fluctuation of the sample temperature data. The temperature change curve of the sample is processed to extract the distribution shape features, thereby obtaining the sample distribution shape features; The temperature curve characteristics of the sample are determined based on the central tendency characteristics of the sample, the dispersion characteristics of the sample, and the shape characteristics of the sample distribution.

4. The method according to claim 3, characterized in that, The process of extracting the central tendency features from the temperature change curve of the sample to obtain the central tendency features of the sample includes: Obtain the sample mean, sample median, and sample mode of the sample temperature data in the sample temperature change curve; The central tendency characteristics of the sample are determined based on the sample mean, the sample median, and the sample mode.

5. The method according to claim 1, characterized in that, The step of predicting the temperature of the cavity based on the real-time operating power and the cumulative operating time to obtain the predicted temperature of the cavity includes: Obtain the attribute information of the oven; The target calibration coefficient for the oven is determined by querying the calibration coefficient database based on the attribute information; the calibration coefficient database includes the correspondence between preset attributes and preset calibration coefficients. The predicted temperature is determined based on the cumulative working time, the real-time working power, and the target calibration coefficient.

6. The method according to claim 5, characterized in that, The step of determining the predicted temperature based on the cumulative working time, the real-time working power, and the target calibration coefficient includes: When the oven is detected to be running, the initial temperature of the cavity is obtained; The calibration temperature of the cavity is determined based on the cumulative working time, the real-time working power, and the target calibration coefficient. The predicted temperature of the cavity is determined based on the initial temperature and the calibration temperature.

7. The method according to claim 1, characterized in that, After determining the fault type of the oven based on the real-time temperature change curve, the method includes: If the fault type is a first preset fault type, a first fault prompt message is generated; the first preset fault type indicates a fault in the heating device. If the fault type is a second preset fault type, a second fault prompt message is generated; the second preset fault type indicates a fault in the oven's temperature detection device; the temperature detection device is used to detect the real-time temperature of the cavity.

8. A fault detection device for an oven, characterized in that, The device includes: The acquisition module is used to acquire the real-time operating voltage of the oven, the real-time temperature of the oven cavity, and the cumulative operating time of the oven during the operation of the oven. A real-time operating power determination module is used to determine the real-time operating power of the heating element based on the real-time operating voltage and the rated power of the heating element of the oven. The prediction module is used to predict the temperature of the cavity based on the real-time operating power and the cumulative operating time, and to obtain the predicted temperature of the cavity. The temperature change curve acquisition module is used to acquire the temperature change curve of the oven within a preset time period when the temperature difference between the predicted temperature and the real-time temperature is detected to be greater than a preset temperature difference. The preset time period represents the period from when the oven is turned on to when the preset time is when the temperature difference between the predicted temperature and the real-time temperature is detected to be greater than the preset temperature difference. The fault type determination module is used to determine the fault type of the oven based on the temperature change curve.

9. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the oven fault detection method as described in any one of claims 1-7.

10. A smart kitchen appliance, characterized in that, The intelligent kitchen appliance adopts the oven fault detection method as described in any one of claims 1-7.

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