Dry burning detection method, system, stove, electronic device and storage medium

Abstract

The present invention discloses a dry-burn detection method, system, stove, electronic device and storage medium. The present invention does not determine whether dry-burn occurs by simply comparing the detected pot temperature with a pre-set dry-burn threshold, but calculates a confidence score based on at least two temperature features extracted from the pot temperature data and their corresponding confidence coefficients, and then dynamically determines whether the pot has dry-burned based on at least two dimensions of information. Therefore, the present invention takes into account the dynamic changes in the reference importance of different temperature features for dry-burn detection, and can be applied to different cooking scenarios, thereby helping to improve the accuracy of dry-burn detection of pots. On the other hand, the technical solution of the present invention can significantly shorten the time for dry-burn judgment and enhance the user experience.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a dry-burn detection method, system, stove, electronic device and storage medium. Background Art

[0002] When the pot is in a long-term cooking state and the person is not present, it is easy for the water in the pot to dry up or the oil temperature in the pot to be too high, which can easily cause the pot to burn through or even cause a fire. Therefore, it is necessary to prevent such dangers from occurring. In order to prevent such dangers from occurring, it is necessary to detect the dry burning of the pot. The dry burning detection schemes currently on the market are relatively simple. Specifically, they determine whether dry burning has occurred by comparing the detected temperature of the bottom of the pot with a pre-set dry burning threshold. However, the logic of this dry burning detection scheme is simple and the accuracy of the detection results is low. For example, the stove may be mistakenly turned off due to a dry burning misjudgment in a stir-frying scenario. Some dry burning detection schemes use one or several parameters, and the judgment process is complicated, resulting in the water in the pot being dried up for a long time before feedback is received, and dry burning cannot be detected in time, resulting in a decline in user experience. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the defects of the dry burning detection scheme in the prior art, such as low detection accuracy, long dry burning detection judgment time, and untimely feedback, and to provide a dry burning detection method, system, stove, electronic device and storage medium.

[0004] The present invention solves the above technical problems through the following technical solutions:

[0005] A method for detecting dry burning, comprising:

[0006] Get the temperature data of the pot;

[0007] extracting a first temperature feature and a second temperature feature from the temperature data;

[0008] Calculating a confidence score based on a first temperature feature and a first confidence coefficient corresponding to the first temperature feature, and a second temperature feature and a second confidence coefficient corresponding to the second temperature feature, wherein a sum of the first confidence coefficient and the second confidence coefficient is 1;

[0009] Determine whether the cookware has dry-boiled according to the confidence score.

[0010] Preferably, the first temperature characteristic includes a temperature value characteristic, and the second temperature characteristic includes a temperature rise rate characteristic;

[0011] and / or,

[0012] The first confidence coefficient and the second confidence coefficient are determined according to a confidence function or a sigmoid function.

[0013] Preferably, the confidence score is calculated according to the following formula:

[0014] score=param_a*(aA)+param_b*(bB)

[0015] Among them, score represents the confidence score, param_a represents the first confidence coefficient, a represents the first temperature feature, A represents the first temperature feature threshold, param_b represents the second confidence coefficient, b represents the second temperature feature, and B represents the second temperature feature threshold.

[0016] Preferably, before the step of calculating the confidence score, the detection method further comprises:

[0017] The first temperature characteristic threshold and the second temperature characteristic threshold are determined respectively according to the fire level of the stove.

[0018] Preferably, after the step of extracting the first temperature feature and the second temperature feature from the temperature data, the method further includes:

[0019] Whether to perform the step of calculating the confidence score is determined according to the first temperature feature and / or the second temperature feature.

[0020] Preferably, after the step of determining whether the cookware has dry-burned according to the confidence score, the method further includes:

[0021] When the cookware is determined to be dry-burned for a preset number of consecutive times, a detection result of the cookware being dry-burned is output.

[0022] Preferably, after the step of outputting the detection result of the cookware being dry-burned, the method further includes:

[0023] The stove is controlled to shut down according to the detection result.

[0024] A dry burning detection system, comprising:

[0025] Acquisition module, used to obtain the temperature data of the pot;

[0026] An extraction module, configured to extract a first temperature feature and a second temperature feature from the temperature data;

[0027] a calculation module, configured to calculate a confidence score based on a first temperature feature and a first confidence coefficient corresponding to the first temperature feature, and a second temperature feature and a second confidence coefficient corresponding to the second temperature feature, wherein the sum of the first confidence coefficient and the second confidence coefficient is 1;

[0028] A dry-boiling determination module is used to determine whether the cookware has dry-boiling according to the confidence score.

[0029] Preferably, the first temperature characteristic includes a temperature value characteristic, and the second temperature characteristic includes a temperature rise rate characteristic;

[0030] and / or,

[0031] The first confidence coefficient and the second confidence coefficient are determined according to a confidence function or a sigmoid function.

[0032] Preferably, the confidence score is calculated according to the following formula:

[0033] score=param_a*(aA)+param_b*(bB)

[0034] Among them, score represents the confidence score, param_a represents the first confidence coefficient, a represents the first temperature feature, A represents the first temperature feature threshold, param_b represents the second confidence coefficient, b represents the second temperature feature, and B represents the second temperature feature threshold.

[0035] Preferably, the detection system further comprises:

[0036] The threshold determination module is used to determine the first temperature characteristic threshold and the second temperature characteristic threshold according to the fire level of the stove.

[0037] Preferably, the detection system further comprises:

[0038] An execution determination module is used to determine whether to execute the step of calculating the confidence score according to the first temperature feature and / or the second temperature feature.

[0039] Preferably, the detection system further comprises:

[0040] The output module is used to output a detection result of the cookware being dry-burned when the cookware is determined to be dry-burned for a preset number of consecutive times.

[0041] Preferably, the detection system further comprises:

[0042] The control module is used to control the stove to turn off the fire according to the detection result.

[0043] A stove comprises the dry-burning detection system described above.

[0044] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-mentioned dry burning detection methods when executing the computer program.

[0045] A computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of any one of the dry-burn detection methods described above.

[0046] The positive progress of the present invention lies in that it does not determine whether a dry-burn has occurred by simply comparing the detected pot temperature with a pre-set dry-burn threshold. Instead, it calculates a confidence score based on at least two temperature features extracted from the pot temperature data and their corresponding confidence coefficients, and then dynamically determines whether the pot has dry-burned based on at least two dimensions of information. As a result, the present invention takes into account the dynamic changes in the reference importance of different temperature features for dry-burn detection and can be applied to different cooking scenarios, thereby improving the accuracy of dry-burn detection of pots, avoiding misjudgments, and enhancing the user experience. On the other hand, the present invention greatly shortens the duration of dry-burn detection, can provide timely feedback on dry-burn detection results, and is more secure. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1a Flowchart of a method for detecting dry burning according to Example 1 of the present invention.

[0048] Figure 1b Schematic diagram of experimental data of a dry burning detection method used in Example 1 of the present invention.

[0049] Figure 1c Schematic diagram of experimental data of another dry burning detection method used in Example 1 of the present invention.

[0050] Figure 2 Schematic diagram of the modules of the dry burning detection system according to Example 2 of the present invention.

[0051] Figure 3 Schematic diagram of the structure of an electronic device according to embodiment 4 of the present invention. DETAILED DESCRIPTION

[0052] The present invention is further described below by way of examples, but the present invention is not limited to the scope of the examples.

[0053] Example 1

[0054] This embodiment provides a dry-burn detection method, which can be used to detect whether a cookware has dry-burned after the cooker is turned on. Figure 1a1 , the detection method of this embodiment includes:

[0055] S101: Obtain temperature data of the cookware.

[0056] In this embodiment, the period for obtaining temperature data can be customized according to the actual application. For example, the period can be set to 1s. The part of the pot for obtaining temperature data can also be selected according to the actual application. For example, the temperature data of the bottom of the pot can be obtained. In addition, the electronic device for obtaining temperature data can also be selected according to the actual application. This embodiment is not intended to limit these.

[0057] S102: Extract a first temperature feature and a second temperature feature from the temperature data.

[0058] In this embodiment, the temperature characteristics used to detect whether the cookware has dry-boiled can be determined based on actual applications. For example, the temperature characteristics extracted from the temperature data may include a temperature value characteristic, which is used to characterize the current temperature value of the cookware; a temperature rise characteristic, which is used to characterize the difference between the current temperature value of the cookware and the previous temperature value; or a temperature rise rate characteristic, which is used to characterize the rate at which the cookware changes from the previous temperature value to the current temperature value. It should be understood that the temperature data used to determine the temperature rise characteristic or the temperature rise rate characteristic is not limited to two adjacent temperature data, but can be selected based on actual applications.

[0059] S103 : Calculate a confidence score according to the first temperature feature and a first confidence coefficient corresponding to the first temperature feature, and the second temperature feature and a second confidence coefficient corresponding to the second temperature feature.

[0060] In this embodiment, the sum of the first confidence coefficient and the second confidence coefficient is 1, and how to determine the first confidence coefficient and the second confidence coefficient can be selected according to actual applications. For example, the first confidence coefficient and the second confidence coefficient can be determined based on a confidence function or a sigmoid function.

[0061] In this embodiment, the dynamic changes in the reference importance of different temperature features for dry-burn detection are taken into account, and the confidence coefficients corresponding to different temperature features are different. For example, when the temperature feature includes a temperature value feature and a temperature rise rate feature, for the low-temperature area, although the value of the temperature value feature is not high, if the pot is dry-burned, the corresponding value of the temperature rise rate feature is large. Therefore, the reference value of the temperature rise rate feature for dry-burn detection will be greater than that of the temperature value feature; for the high-temperature area, considering that the larger the value of the temperature value feature, the faster the heat diffuses, the smaller the value of the temperature rise rate feature. Therefore, the reference value of the temperature value feature for dry-burn detection is greater than that of the temperature rise rate feature.

[0062] Based on this, for low temperature areas, it is preferred to give a higher confidence coefficient to the temperature rise rate feature, and for high temperature areas, it is preferred to give a higher confidence coefficient to the temperature value feature.

[0063] When the confidence coefficient is determined based on the confidence function, the confidence function is as follows:

[0064]

[0065] For the temperature feature, the confidence coefficient is param_a = f((aA) / (Max_Temperature - A)). Max_Temperature represents the maximum temperature the cookware can withstand, determined by the cookware material. For example, it could be 350°C. A represents the temperature feature threshold, which can be determined based on the cookware's current power level. Based on this, for the temperature rise rate feature, the confidence coefficient is param_b = 1 - param_a.

[0066] When the confidence coefficient is determined according to the sigmoid function, the sigmoid function is as follows:

[0067]

[0068] For the temperature value feature, the confidence coefficient is param_a = S((aA) / (Max_Temperature-A)). Based on this, for the temperature rise rate feature, the confidence coefficient is param_b = 1-param_a.

[0069] Furthermore, in this embodiment, the confidence score can be calculated according to the following formula:

[0070] score=param_a*(aA)+param_b*(bB)

[0071] Where score represents the confidence score, param_a represents the first confidence coefficient, a represents the first temperature feature, A represents the first temperature feature threshold, param_b represents the second confidence coefficient, b represents the second temperature feature, and B represents the second temperature feature threshold.

[0072] It should be noted that before calculating the confidence score, a, A, b, and B need to be normalized, and the confidence score is calculated based on the normalized a, A, b, and B.

[0073] In addition, different fire levels of the stove may correspond to different first temperature characteristic thresholds and different second temperature characteristic thresholds, respectively. Accordingly, before step S103, the detection method of this embodiment may further include the steps of respectively determining the first temperature characteristic threshold and the second temperature characteristic threshold according to the fire level of the stove, so as to obtain the first temperature characteristic threshold and the second temperature characteristic threshold corresponding to the current fire level of the stove.

[0074] For example, in this embodiment, when the first temperature feature includes a temperature value feature, the corresponding relationship between the fire level of the stove and the first temperature feature threshold value - the temperature value feature threshold value is shown in the following table:

[0075] Firepower level Temperature characteristic threshold A Gear 1 260℃ Gear 2 270℃ Gear 3 280℃ Gear 4 290℃ Gear 5 300℃

[0076] When the second temperature characteristic includes a temperature rise rate characteristic, the corresponding relationship between the fire level of the stove and the second temperature characteristic threshold value - the temperature rise rate characteristic threshold value is shown in the following table:

[0077]

[0078] Furthermore, after step S102, the detection method of this embodiment may also include a step of determining whether to perform calculation of the confidence score based on the first temperature feature and / or the second temperature feature. For example, when the first temperature feature includes a temperature value feature, if a temperature value feature is greater than a preset temperature value, step S103 is performed; if a temperature value feature is not greater than the preset temperature value, step S103 and subsequent steps are not performed, thereby achieving effective utilization of resources.

[0079] S104: Determine whether the cookware is dry-boiled based on the confidence score.

[0080] In this embodiment, an association between the confidence score and whether the cookware has dry-burned can be pre-established based on actual applications. For example, when the confidence score is greater than a preset score threshold, it is determined that the cookware has dry-burned, and when the confidence score is not greater than the preset score threshold, it is determined that the cookware has not dry-burned.

[0081] Reference Figure 1a The detection method of this embodiment may further include, after step S104:

[0082] S105: When the cookware is determined to be dry-burned for a preset number of consecutive times, output a detection result that the cookware is dry-burned.

[0083] In this embodiment, the value of the preset number of times can be customized according to the actual application, and when the dry burning of the cookware is determined to have occurred after the preset number of consecutive times, the accuracy of the detection result of the dry burning of the cookware is enhanced. The detection result based on this output that characterizes the dry burning of the cookware can be used to implement other applications related to dry burning control. For example, the stove can be controlled to be turned off according to the detection result to avoid further occurrence of dry burning. The range hood can also be controlled to be turned off at the same time as the stove is controlled to be turned off or after a period of time to avoid unnecessary consumption of electricity by the range hood.

[0084] This embodiment does not determine whether dry-burning has occurred by simply comparing the detected pot temperature with a pre-set dry-burning threshold. Instead, it calculates a confidence score based on at least two temperature features extracted from the pot temperature data and their corresponding confidence coefficients, dynamically determining whether the pot has dry-burned based on at least two dimensions of information. Therefore, this embodiment takes into account the dynamic changes in the reference importance of different temperature features for dry-burning detection, and can be applied to different cooking scenarios, thereby improving the accuracy of dry-burning detection of pots, avoiding misjudgments, and enhancing the user experience.

[0085] The following is a further explanation of the dry burning detection process through experimental data:

[0086] Figure 1b The figure shows the temperature change trend of a cookware over time. At point A in the figure, the stove is at power level 3, the temperature of the cookware (first temperature characteristic) is 220°C, and the temperature rise rate (second temperature characteristic) is 8°C / s. From the table, we can see that A = 280°C and B = 5°C / s. According to the above confidence function, we can get:

[0087] param_a=0, param_b=1;

[0088] The normalized a, A, b, and B are:

[0089] a=220 / 280=0.786, A=1, b=8 / 5=1.6, B=1;

[0090] score=param_a*(aA)+param_b*(bB)=0.6;

[0091] When the preset value of score is 0.5, the situation at point A satisfies the score greater than the preset value, which also satisfies the condition of dry burning and turning off the fire.

[0092] Figure 1cThe figure shows the temperature change trend of the pot over time. At point B in the figure, the stove is at power level 4, the pot temperature (first temperature characteristic) is 260°C, and the temperature rise rate (second temperature characteristic) is 0.5°C / s. From the table, we can see that A = 290°C and B = 4°C / s. According to the above confidence function, we can get:

[0093] param_a=0.25, param_b=0.75;

[0094] The normalized a, A, b, and B are:

[0095] a=260 / 290=0.897, A=1, b=0.125, B=1;

[0096] score=param_a*(aA)+param_b*(bB)=-0.682;

[0097] When the preset value of score is 0.5, the situation at point B does not meet the score greater than the preset value, that is, it does not meet the condition of dry burning and turning off the fire.

[0098] See also Figure 1c At point C in the figure, the stove is at power level 4, the pot temperature (first temperature characteristic) is 290°C, and the temperature rise rate (second temperature characteristic) is 4°C / s. From the table, we can see that A = 290°C, B = 3°C / s. According to the above confidence function, we can get:

[0099] param_a=0.5, param_b=0.5;

[0100] The normalized a, A, b, and B are:

[0101] a=290 / 290=1, A=1, b=1.333, B=1;

[0102] score=param_a*(aA)+param_b*(bB) / B=0.667;

[0103] When the preset value of score is 0.5, the situation at point C satisfies the score greater than the preset value, which also satisfies the condition of dry burning and turning off the fire.

[0104] Table 1 shows the comparison results of experimental data for dry burn detection using the dry burn detection method provided in this embodiment and dry burn detection methods in the prior art. As can be seen from Table 1, the dry burn detection method provided in this embodiment can accurately detect dry burn within 30 seconds of the occurrence of dry burn, while the dry burn detection methods in the prior art generally take more than 60 seconds to detect, and even take 90 seconds. Through this comparison, it can be seen that the dry burn detection method provided in this embodiment can greatly shorten the dry burn detection time, solve the problem of dry burn detection taking a long time in the prior art, and is safer.

[0105]

[0106]

[0107] Example 2

[0108] This embodiment provides a dry-burn detection system that can be used to detect whether a cookware has dry-burned after the cooker is turned on. Figure 2 Schematic diagram of the modules of the detection system of this embodiment is shown. Figure 2 , the detection system of this embodiment includes:

[0109] The acquisition module 1 is used to obtain the temperature data of the cookware.

[0110] In this embodiment, the period for obtaining temperature data can be customized according to the actual application. For example, the period can be set to 1s. The part of the pot for obtaining temperature data can also be selected according to the actual application. For example, the temperature data of the bottom of the pot can be obtained. In addition, the electronic device for obtaining temperature data can also be selected according to the actual application. This embodiment is not intended to limit these.

[0111] The extraction module 2 is used to extract the first temperature feature and the second temperature feature from the temperature data.

[0112] In this embodiment, the temperature characteristics used to detect whether the cookware has dry-boiled can be determined based on actual applications. For example, the temperature characteristics extracted from the temperature data may include a temperature value characteristic, which is used to characterize the current temperature value of the cookware; a temperature rise characteristic, which is used to characterize the difference between the current temperature value of the cookware and the previous temperature value; or a temperature rise rate characteristic, which is used to characterize the rate at which the cookware changes from the previous temperature value to the current temperature value. It should be understood that the temperature data used to determine the temperature rise characteristic or the temperature rise rate characteristic is not limited to two adjacent temperature data, but can be selected based on actual applications.

[0113] The calculation module 3 is configured to calculate a confidence score according to the first temperature feature and a first confidence coefficient corresponding to the first temperature feature, the second temperature feature and a second confidence coefficient corresponding to the second temperature feature.

[0114] In this embodiment, the sum of the first confidence coefficient and the second confidence coefficient is 1, and how to determine the first confidence coefficient and the second confidence coefficient can be selected according to actual applications. For example, the first confidence coefficient and the second confidence coefficient can be determined based on a confidence function or a sigmoid function.

[0115] In this embodiment, the dynamic changes in the reference importance of different temperature features for dry-burn detection are taken into account, and the confidence coefficients corresponding to different temperature features are different. For example, when the temperature feature includes a temperature value feature and a temperature rise rate feature, for the low-temperature area, although the value of the temperature value feature is not high, if the pot is dry-burned, the corresponding value of the temperature rise rate feature is large. Therefore, the reference value of the temperature rise rate feature for dry-burn detection will be greater than that of the temperature value feature; for the high-temperature area, considering that the larger the value of the temperature value feature, the faster the heat diffuses, the smaller the value of the temperature rise rate feature. Therefore, the reference value of the temperature value feature for dry-burn detection is greater than that of the temperature rise rate feature.

[0116] Based on this, for low temperature areas, it is preferred to give a higher confidence coefficient to the temperature rise rate feature, and for high temperature areas, it is preferred to give a higher confidence coefficient to the temperature value feature.

[0117] When the confidence coefficient is determined based on the confidence function, the confidence function is as follows:

[0118]

[0119] For the temperature feature, the confidence factor is param_a = f((aA) / (Max_Temperature - A)). Max_Temperature represents the maximum temperature the cookware can withstand, determined by the cookware material. For example, it could be 350°C. A represents the temperature feature threshold, which can be determined based on the cookware's current power level. Based on this, the confidence factor for the temperature rise rate feature is param_b = 1 - param_a.

[0120] When the confidence coefficient is determined according to the sigmoid function, the sigmoid function is as follows:

[0121]

[0122] For the temperature value feature, the confidence coefficient is param_a = S((aA) / (Max_Temperature-A)). Based on this, for the temperature rise rate feature, the confidence coefficient is param_b = 1-param_a.

[0123] Furthermore, in this embodiment, the confidence score can be calculated according to the following formula:

[0124] score=param_a*(aA)+param_b*(bB)

[0125] Where score represents the confidence score, param_a represents the first confidence coefficient, a represents the first temperature feature, A represents the first temperature feature threshold, param_b represents the second confidence coefficient, b represents the second temperature feature, and B represents the second temperature feature threshold.

[0126] It should be noted that before calculating the confidence score, a, A, b, and B need to be normalized, and the confidence score is calculated based on the normalized a, A, b, and B.

[0127] In addition, different fire levels of the stove can correspond to different first temperature characteristic thresholds and different second temperature characteristic thresholds, respectively. Accordingly, the detection system of this embodiment can also include a threshold determination module for determining the first temperature characteristic threshold and the second temperature characteristic threshold according to the fire level of the stove, so as to obtain the first temperature characteristic threshold and the second temperature characteristic threshold corresponding to the current fire level of the stove.

[0128] For example, in this embodiment, when the first temperature feature includes a temperature value feature, the corresponding relationship between the fire level of the stove and the first temperature feature threshold value - the temperature value feature threshold value is shown in the following table:

[0129] Firepower level Temperature characteristic threshold A Gear 1 260℃ Gear 2 270℃ Gear 3 280℃ Gear 4 290℃ Gear 5 300℃

[0130] When the second temperature characteristic includes a temperature rise rate characteristic, the corresponding relationship between the fire level of the stove and the second temperature characteristic threshold value - the temperature rise rate characteristic threshold value is shown in the following table:

[0131]

[0132] Furthermore, the detection system of this embodiment may also include an execution determination module for determining whether to execute the calculation of the confidence score based on the first temperature feature and / or the second temperature feature. For example, when the first temperature feature includes a temperature value feature, if a temperature value feature is greater than a preset temperature value, the calculation module 3 is called; if a temperature value feature is not greater than the preset temperature value, the calculation module 3 and subsequent modules are not called, thereby achieving effective utilization of resources.

[0133] The dry-boiling determination module 4 is used to determine whether the cookware has dry-boiling according to the confidence score.

[0134] In this embodiment, an association between the confidence score and whether the cookware has dry-burned can be pre-established based on actual applications. For example, when the confidence score is greater than a preset score threshold, it is determined that the cookware has dry-burned, and when the confidence score is not greater than the preset score threshold, it is determined that the cookware has not dry-burned.

[0135] Reference Figure 2 , the detection system of this embodiment may further include:

[0136] The output module 5 is used to output the detection result of the cookware being dry-burned when the cookware is determined to be dry-burned for a preset number of consecutive times.

[0137] In this embodiment, the value of the preset number of times can be customized according to actual application, and when the dry burning of the cookware is determined to have occurred after the preset number of consecutive times, the accuracy of the detection result of the dry burning of the cookware is enhanced. The detection result based on this output that characterizes the dry burning of the cookware can be used to implement other applications related to dry burning control of the control module also included in the detection system. For example, the control module can control the stove to turn off the fire according to the detection result to avoid further occurrence of dry burning. The control module can also control the range hood to turn off at the same time as controlling the stove to turn off the fire or after a period of time, so as to avoid unnecessary consumption of electricity by the range hood.

[0138] This embodiment does not determine whether dry-burning has occurred by simply comparing the detected pot temperature with a pre-set dry-burning threshold. Instead, it calculates a confidence score based on at least two temperature features extracted from the pot temperature data and their corresponding confidence coefficients, dynamically determining whether the pot has dry-burned based on at least two dimensions of information. Therefore, this embodiment takes into account the dynamic changes in the reference importance of different temperature features for dry-burning detection, and can be applied to different cooking scenarios, thereby improving the accuracy of dry-burning detection of pots, avoiding misjudgments, and enhancing the user experience.

[0139] Example 3

[0140] This embodiment provides a cooker including the boil-dry detection system provided in Example 2. Building on Example 2, this embodiment calculates a confidence score based on at least two temperature features extracted from pot temperature data and their corresponding confidence coefficients, dynamically determining whether the pot has boiled dry based on at least two dimensions of information. This embodiment thus takes into account the dynamic changes in the reference importance of different temperature features for boil-dry detection and is applicable to different cooking scenarios, thereby improving the accuracy of boil-dry detection, avoiding misjudgments, and enhancing the user experience.

[0141] Example 4

[0142] This embodiment provides an electronic device, which can be expressed in the form of a computing device (for example, a server device), including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the dry burning detection method provided in Example 1 can be implemented when the processor executes the computer program.

[0143] Figure 3 The hardware structure diagram of this embodiment is shown in FIG. Figure 3 As shown, the electronic device 9 specifically includes:

[0144] At least one processor 91, at least one memory 92, and a bus 93 for connecting different system components (including the processor 91 and the memory 92), wherein:

[0145] The bus 93 includes a data bus, an address bus, and a control bus.

[0146] The memory 92 includes a volatile memory, such as a random access memory (RAM) 921 and / or a cache memory 922 , and may further include a read-only memory (ROM) 923 .

[0147] Memory 92 also includes a program / utility 925 having a set (at least one) of program modules 924, such program modules 924 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0148] The processor 91 executes various functional applications and data processing by running computer programs stored in the memory 92, such as the dry-burn detection method provided in Example 1 of the present invention.

[0149] The electronic device 9 can further communicate with one or more external devices 94 (e.g., a keyboard, pointing device, etc.). Such communication can be performed via an input / output (I / O) interface 95. Furthermore, the electronic device 9 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 via a bus 93. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device 9, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0150] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, depending on the embodiment of the present application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0151] Example 5

[0152] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the dry burn detection method provided in Example 1 are implemented.

[0153] The readable storage medium may include, but is not limited to, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0154] In a possible implementation manner, the present invention can also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of the dry burning detection method described in Example 1.

[0155] The program code for executing the present invention may be written in any combination of one or more programming languages, and may be executed entirely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or entirely on the remote device.

[0156] Although specific embodiments of the present invention have been described above, those skilled in the art will appreciate that these are merely illustrative and that the scope of the present invention is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, and such changes and modifications are intended to fall within the scope of the present invention.

Claims

1. A method for detecting dry burning, characterized in that: include: Get the temperature data of the pot; extracting a first temperature feature and a second temperature feature from the temperature data; Calculating a confidence score based on a first temperature feature and a first confidence coefficient corresponding to the first temperature feature, and a second temperature feature and a second confidence coefficient corresponding to the second temperature feature, wherein a sum of the first confidence coefficient and the second confidence coefficient is 1; determining whether the cookware is dry-burned according to the confidence score; The first temperature characteristic includes a temperature value characteristic, and the second temperature characteristic includes a temperature rise rate characteristic; The first confidence coefficient and the second confidence coefficient are determined according to a confidence function or a sigmoid function; After the step of extracting the first temperature feature and the second temperature feature from the temperature data, the method further includes: Whether to perform the step of calculating the confidence score is determined according to the first temperature feature and / or the second temperature feature.

2. The dry burning detection method according to claim 1, wherein: The confidence score is calculated according to the following formula: score=param_a*(aA)+param_b*(bB) Among them, score represents the confidence score, param_a represents the first confidence coefficient, a represents the first temperature feature, A represents the first temperature feature threshold, param_b represents the second confidence coefficient, b represents the second temperature feature, and B represents the second temperature feature threshold.

3. The dry burning detection method according to claim 2, wherein: Before the step of calculating the confidence score, the detection method further includes: The first temperature characteristic threshold and the second temperature characteristic threshold are determined respectively according to the fire level of the stove.

4. The dry burning detection method according to claim 1, wherein: After the step of determining whether the cookware is dry-boiled according to the confidence score, the method further includes: When the cookware is determined to be dry-burned for a preset number of consecutive times, a detection result of the cookware being dry-burned is output.

5. The dry burning detection method according to claim 4, wherein: After the step of outputting the detection result of the cookware being dry-burned, the method further includes: The stove is controlled to shut down according to the detection result.

6. A dry burning detection system, characterized in that: include: Acquisition module, used to obtain the temperature data of the pot; An extraction module, configured to extract a first temperature feature and a second temperature feature from the temperature data; a calculation module, configured to calculate a confidence score based on a first temperature feature and a first confidence coefficient corresponding to the first temperature feature, and a second temperature feature and a second confidence coefficient corresponding to the second temperature feature, wherein the sum of the first confidence coefficient and the second confidence coefficient is 1; a dry-burning determination module, configured to determine whether the cookware has dry-burned according to the confidence score; The first temperature characteristic includes a temperature value characteristic, and the second temperature characteristic includes a temperature rise rate characteristic; The first confidence coefficient and the second confidence coefficient are determined according to a confidence function or a sigmoid function; The detection system further includes an execution determination module, configured to determine whether to execute the step of calculating the confidence score according to the first temperature feature and / or the second temperature feature.

7. A stove, characterized in that: The dry burning detection system comprises the dry burning detection system as claimed in claim 6.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the dry burning detection method according to any one of claims 1 to 5 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the dry burning detection method according to any one of claims 1 to 5 are implemented.

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