Method, device and equipment for determining temperature of food and cooking robot

Abstract

The invention discloses a method, a device and equipment for determining the temperature of food and a cooking robot. The method comprises the following steps: measuring the measured food placed in a measurement area by using an infrared temperature measurement lens, obtaining first temperature data of the measured food, measuring the measured food by using a thermocouple, and obtaining second temperature data of the measured food; establishing a corresponding relation between the first temperature data and the second temperature data; establishing a function model, and performing regression calculation according to the first temperature data and the second temperature data to obtain a fitting formula; and compensating the first temperature data according to a fitting formula to obtain the temperature of the food. The function model is established, the regression calculation is performed according to the first temperature data and the second temperature data to obtain the fitting formula, and the accurate temperature of the food can be obtained only by obtaining the first temperature measured by the infrared lens through the fitting formula; and meanwhile, the food sanitation and safety risk caused by using a thermocouple to measure the food temperature is also avoided.

Description

Technical Field

[0001] The present application relates to the field of cooking technology, and in particular to a method, device, equipment and cooking robot for determining the temperature of food. Background Art

[0002] In cooking scenarios, food temperature measurement is an important means to ensure food safety. For food safety considerations, the temperature of food that is not in the cooking process also needs to be detected. Non-contact infrared temperature measurement is widely used because of its convenience and safety. However, infrared thermometers often preset the emissivity of the object being measured during use, and food has different surface emissivities due to its own properties, which causes the infrared thermometer reading to deviate from the actual temperature value. When using thermocouples to measure food temperature, the obtained temperature is more accurate, but because the thermocouple head needs to contact the surface of the food or be shallowly buried in the surface of the food, there is a risk of food hygiene and safety. Summary of the invention

[0003] The technical problem to be solved by the present application is to provide a method, device, equipment and cooking robot for determining the temperature of food in view of the deficiencies in the prior art.

[0004] According to a first aspect of the present application, the present application provides a method for determining the temperature of food, comprising:

[0005] Using an infrared temperature measuring lens to measure the food to be measured placed in the measuring area to obtain first temperature data of the food to be measured, and using a thermocouple to measure the food to be measured to obtain second temperature data of the food to be measured;

[0006] Establishing a correspondence between the first temperature data and the second temperature data;

[0007] Establishing a function model, and performing regression calculation based on the first temperature data and the second temperature data to obtain a fitting formula;

[0008] The first temperature data is compensated according to the fitting formula to obtain the temperature of the food.

[0009] In the above method, the step of establishing a function model and performing regression calculation based on the first temperature data and the second temperature data to obtain a fitting formula includes:

[0010] A linear function model is established with the first temperature data as an independent variable and the second temperature data as a dependent variable. The formula of the linear function model is:

[0011] y=kx+b (1)

[0012] Wherein, y represents the second temperature data, x represents the first temperature data, k represents the change multiplier, and b is the scalar coefficient;

[0013] Performing regression calculation on the linear function according to the first temperature data and the second temperature data to obtain a change multiplier k and a scalar coefficient b;

[0014] Substitute the change multiplier k and the scalar coefficient b into the formula of the linear function model to obtain a fitting formula.

[0015] The above method, before establishing the function model and performing regression calculation based on the first temperature data and the second temperature data to obtain the fitting formula, also includes:

[0016] The first temperature data and the second temperature data are preprocessed to clean up erroneous data.

[0017] The above method, before establishing the function model and performing regression calculation based on the first temperature data and the second temperature data to obtain the fitting formula, also includes:

[0018] Preprocessing the first temperature data and the second temperature data to clean up erroneous data;

[0019] In the above method, the preprocessing of the first temperature data and the second temperature data to remove erroneous data includes:

[0020] Subtract the first temperature data from the second temperature data to obtain a new set of data d i :

[0021] d i =TC i -IR i (2)

[0022] Among them, TC i is the temperature of the thermocouple at time stamp i, IR i is the temperature of the infrared temperature measuring lens at timestamp i;

[0023] Calculate d i If the absolute value of the z score of each data is greater than a preset value, the data corresponding to the timestamp i is discarded from the first temperature data and the second temperature data.

[0024] According to a second aspect of the present application, the present application provides a device for determining the temperature of food, comprising:

[0025] The measuring module is used to measure the food to be measured placed in the measuring area using an infrared temperature measuring lens to obtain first temperature data of the food to be measured, and to measure the food to be measured using a thermocouple to obtain second temperature data of the food to be measured;

[0026] An index module, used to establish a corresponding relationship between the first temperature data and the second temperature data;

[0027] A processing module, used for establishing a function model, and performing regression calculation according to the first temperature data and the second temperature data to obtain a fitting formula;

[0028] The temperature compensation module is used to compensate the first temperature data according to the fitting formula to obtain the temperature of the food.

[0029] The above device, the processing module, includes:

[0030] A modeling unit is used to establish a linear function model with the first temperature data as an independent variable and the second temperature data as a dependent variable, wherein the formula of the linear function model is:

[0031] y=kx+b (1)

[0032] Wherein, y represents the second temperature data, x represents the first temperature data, k represents the change multiplier, and b is the scalar coefficient;

[0033] A regression unit, configured to perform regression calculation on the linear function according to the first temperature data and the second temperature data to obtain a change multiplier k and a scalar coefficient b;

[0034] A processing unit is used to substitute the change multiplier k and the scalar coefficient b into the formula of the linear function model to obtain a fitting formula.

[0035] The above device also includes: a cleaning module, which is used to pre-process the first temperature data and the second temperature data and clean up erroneous data.

[0036] The above device, the cleaning module, comprises:

[0037] A calculation unit, configured to obtain a set of new data d by subtracting the first temperature data from the second temperature data i :

[0038] d i =TC i -IR i (2)

[0039] Among them, TC i is the temperature of the thermocouple at time stamp i, IR i is the temperature of the infrared temperature measuring lens at timestamp i;

[0040] Cleaning unit, used to calculate d i If the absolute value of the z score of each data is greater than a preset value, the data corresponding to the timestamp i is discarded from the first temperature data and the second temperature data.

[0041] According to a third aspect of the present application, the present application provides a device for determining the temperature of food, comprising:

[0042] Memory, used to store programs,

[0043] The processor is used to execute the program stored in the memory to implement the above method.

[0044] According to a fourth aspect of the present application, the present application provides a cooking robot, comprising:

[0045] Memory, used to store programs,

[0046] The processor is used to execute the program stored in the memory to implement the above method.

[0047] According to a fifth aspect of the present application, the present application provides a computer-readable storage medium, comprising a program, wherein the program can be executed by a processor to implement the above method.

[0048] Due to the adoption of the above technical solution, the beneficial effects of this application are:

[0049] The method for determining the temperature of food provided in the embodiment of the present application includes: using an infrared temperature measuring lens to measure the food to be measured placed in the measurement area to obtain the first temperature data of the food to be measured, using a thermocouple to measure the food to be measured to obtain the second temperature data of the food to be measured; establishing a corresponding relationship between the first temperature data and the second temperature data; establishing a function model, and performing regression calculation based on the first temperature data and the second temperature data to obtain a fitting formula; compensating the first temperature data according to the fitting formula to obtain the temperature of the food. Since the present application establishes a function model, performs regression calculation based on the first temperature data and the second temperature data to obtain a fitting formula, through the fitting formula, only the first temperature measured by the infrared lens needs to be obtained to obtain a more accurate temperature of the food, and at the same time, the food hygiene and safety risks caused by using thermocouples to measure the temperature of food are avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of the method in Example 1 of the present application in one implementation manner;

[0051] Figure 2 A flow chart of obtaining a fitting formula in one implementation of the method in Example 1 of the present application;

[0052] Figure 3 This is a flow chart of another implementation of the method in Example 1 of the present application;

[0053] Figure 4 A flowchart of cleaning erroneous data in one implementation of the method in Example 1 of the present application;

[0054] Figure 5 A schematic diagram of a program module of the device in Example 2 of the present application in one implementation manner;

[0055] Figure 6 This is a schematic diagram of a program module of the device in Example 2 of the present application in another implementation manner;

[0056] Figure 7 This is a schematic diagram of a program module of the device in Embodiment 2 of the present application in another implementation manner;

[0057] Figure 8 Temperature data recorded by thermocouples and infrared temperature measuring cameras within ten minutes after the deep-fried prawns were cooked;

[0058] Fig. 9 Schematic diagram of the temperature measured by the thermocouple and the food temperature fitted by this application. DETAILED DESCRIPTION

[0059] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0060] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0061] The serial numbers assigned to the components in this article, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning.

[0062] In addition, the technical features and technical solutions described herein may be combined in any suitable manner in one or more embodiments. It is easy for a person skilled in the art to understand that the steps or operation sequence of the methods related to the embodiments provided herein may also be changed. Therefore, any sequence in the drawings and embodiments is only for illustrative purposes and does not imply a requirement to follow a certain sequence unless it is explicitly stated that a certain sequence is required.

[0063] Embodiment 1:

[0064] like Figure 1 As shown, the method for determining the temperature of food provided in Example 1 of the present application, in one implementation manner, may include the following steps:

[0065] Step 102: Use an infrared temperature measuring lens to measure the food to be measured placed in the measuring area to obtain first temperature data of the food to be measured, and use a thermocouple to measure the food to be measured to obtain second temperature data of the food to be measured.

[0066] Pre-plan a food measurement area on the horizontal plane, and fix the infrared temperature measuring lens with a bracket so that the measured food is within the visible range of the infrared temperature measuring lens. In one embodiment, the infrared temperature measuring lens can be placed directly above the food measurement area, and the optical axis of the infrared lens is 90 degrees to the horizontal direction. Adjust the preset emissivity to 0.95, read the first temperature data with timestamp in the area near the thermocouple buried point, and the reading frequency can be set as needed. In this embodiment, the reading frequency can be 1Hz. Stop recording data when the reading drops to room temperature.

[0067] Place a thermocouple near the food testing area so that the thermocouple wire can contact the food. After cooking is completed, quickly move the food to the food testing area before it cools down. Place the thermocouple head in contact with the food surface or shallowly bury it in the food surface, and use computer software to read the second temperature data with a time stamp. The reading frequency can be set as needed. In this embodiment, the reading frequency can be 1Hz. Stop recording data when the reading drops to room temperature.

[0068] Step 104: Establishing a corresponding relationship between the first temperature data and the second temperature data.

[0069] The first test data measured by the infrared temperature measuring lens and the second temperature data measured by the thermocouple within the same time period are selected, and a one-to-one correspondence between the two sets of data is obtained according to the timestamp.

[0070] Step 106: Establish a function model, and perform regression calculation based on the first temperature data and the second temperature data to obtain a fitting formula.

[0071] Step 108: Compensate the first temperature data according to the fitting formula to obtain the temperature of the food.

[0072] like Figure 2 As shown, step 104 may include the following steps:

[0073] Step 1041: Using the first temperature data as the independent variable and the second temperature data as the dependent variable, a linear function model is established. The formula of the linear function model is:

[0074] y=kx+b (1)

[0075] Wherein, y represents the second temperature data, x represents the first temperature data, k represents the change multiplier, and b is the scalar coefficient.

[0076] Step 1042: Perform regression calculation on the linear function according to the first temperature data and the second temperature data to obtain the change multiplier k and the scalar coefficient b.

[0077] Step 1043: Substitute the change multiplier k and the scalar coefficient b into the formula of the linear function model to obtain a fitting formula.

[0078] There are many methods for regression calculation. In one implementation, scikitlearn software can be used for regression calculation, with thermocouple temperature measurement data as the dependent variable y and infrared temperature measurement data as the independent variable x. After confirming the coefficients k and b of the formula model and obtaining the fitting formula, the accurate food temperature can be obtained by correcting the infrared temperature.

[0079] In one embodiment, fried prawns are used as the food to be tested, and the surface temperature data of the prawns within ten minutes after cooking is measured using a thermocouple and an infrared temperature measuring lens. The two sets of data are matched and cleaned according to time, and the first temperature data and the second temperature data obtained are as follows: Figure 8 As shown, A is the first temperature data and B is the second temperature data.

[0080] like Fig. 9 As shown, linear regression is used to estimate the change multiplier k and the variable coefficient b, so as to fit a straight line. The fitting formula is:

[0081] y=0.66x+15.92 (3)

[0082] Fig. 9 In the figure, C is the temperature fitted according to the first temperature data, and D is the second temperature data. The average absolute error of the fitting using this formula is 0.34, and the maximum absolute error is 0.81.

[0083] In one embodiment, Figure 3 As shown, step 105 may be further included between step 104 and step 106:

[0084] Step 105: pre-process the first temperature data and the second temperature data to clean up erroneous data.

[0085] Because there is a small probability that the infrared temperature measuring lens will give an erroneous reading, this part of the data needs to be discarded.

[0086] like Figure 4 As shown, step 105 may include the following steps:

[0087] Step 1051: Use the second temperature data to subtract the first temperature data to obtain a set of new data d i :

[0088] d i =TC i -IR i (2)

[0089] Subtracting the first temperature data from the second temperature data with the same timestamp can produce a new set of data d i In the formula, TC i is the temperature of the thermocouple at time stamp i, IR i is the temperature of the infrared temperature measuring lens at timestamp i.

[0090] Step 1052: Calculate d i If the absolute value of the z score of each data is greater than a preset value, the data corresponding to the timestamp i is discarded from the first temperature data and the second temperature data.

[0091] The preset value may be an empirical value, and in this embodiment, the preset value may be 3. When the preset value is greater than 3, the temperature measured by the infrared temperature measuring lens corresponding to timestamp i is discarded from the first temperature data, and the temperature measured by the thermocouple corresponding to timestamp i is discarded from the second temperature data.

[0092] Embodiment 2:

[0093] like Figure 5 As shown, the device 500 for determining the temperature of food provided in the second embodiment of the present application, in one implementation, may include a measurement module 510 , an index module 520 , a processing module 530 and a temperature compensation module 540 .

[0094] The measuring module 510 is used to use an infrared temperature measuring lens to measure the food to be measured placed in the measuring area to obtain first temperature data of the food to be measured, and use a thermocouple to measure the food to be measured to obtain second temperature data of the food to be measured.

[0095] The index module 520 is used to establish a corresponding relationship between the first temperature data and the second temperature data.

[0096] The processing module 530 is used to establish a function model and perform regression calculation according to the first temperature data and the second temperature data to obtain a fitting formula.

[0097] The temperature compensation module 540 is used to compensate the first temperature data according to the fitting formula to obtain the temperature of the food.

[0098] The device for determining the temperature of food provided in the second embodiment of the present application is as follows: Figure 6 As shown, another implementation thereof may include a measurement module 610 , an index module 620 , a cleaning module 630 , a processing module 640 and a temperature compensation module 650 .

[0099] The measuring module 610 is used to use an infrared temperature measuring lens to measure the food to be measured placed in the measuring area to obtain first temperature data of the food to be measured, and use a thermocouple to measure the food to be measured to obtain second temperature data of the food to be measured.

[0100] Pre-plan a food measurement area on the horizontal plane, and fix the infrared temperature measuring lens with a bracket so that the measured food is within the visible range of the infrared temperature measuring lens. In one embodiment, the infrared temperature measuring lens can be placed directly above the food measurement area, and the optical axis of the infrared lens is 90 degrees to the horizontal direction. Adjust the preset emissivity to 0.95, read the first temperature data with timestamp in the area near the thermocouple buried point, and the reading frequency can be set as needed. In this embodiment, the reading frequency can be 1Hz. Stop recording data when the reading drops to room temperature.

[0101] Place a thermocouple near the food testing area so that the thermocouple wire can contact the food. After cooking is completed, quickly move the food to the food testing area before it cools down. Place the thermocouple head in contact with the food surface or shallowly bury it in the food surface, and use computer software to read the second temperature data with a time stamp. The reading frequency can be set as needed. In this embodiment, the reading frequency can be 1Hz. Stop recording data when the reading drops to room temperature.

[0102] The index module 620 is used to establish a corresponding relationship between the first temperature data and the second temperature data.

[0103] The first test data measured by the infrared temperature measuring lens and the second temperature data measured by the thermocouple within the same time period are selected, and a one-to-one correspondence between the two sets of data is obtained according to the timestamp.

[0104] The cleaning module 630 is used to pre-process the first temperature data and the second temperature data and clean up erroneous data.

[0105] Because there is a small probability that the infrared temperature measuring lens will give an erroneous reading, this part of the data needs to be discarded.

[0106] The processing module 640 is used to establish a function model and perform regression calculation according to the first temperature data and the second temperature data to obtain a fitting formula.

[0107] The temperature compensation module 650 is used to compensate the first temperature data according to the fitting formula to obtain the temperature of the food.

[0108] In one implementation, the processing module 640 may include:

[0109] The modeling unit 641 is used to establish a linear function model with the first temperature data as an independent variable and the second temperature data as a dependent variable. The formula of the linear function model is:

[0110] y=kx+b (1)

[0111] Wherein, y represents the second temperature data, x represents the first temperature data, k represents the change multiplier, and b is the scalar coefficient.

[0112] The regression unit 642 is used to perform regression calculation on the linear function according to the first temperature data and the second temperature data to obtain the change multiplier k and the scalar coefficient b.

[0113] There are many methods for regression calculation. In one implementation, scikitlearn software can be used for regression calculation, with thermocouple temperature measurement data as the dependent variable y and infrared temperature measurement data as the independent variable x. After confirming the coefficients k and b of the formula model and obtaining the fitting formula, the accurate food temperature can be obtained by correcting the infrared temperature.

[0114] The processing unit 643 is used to substitute the change multiplier k and the scalar coefficient b into the formula of the linear function model to obtain a fitting formula.

[0115] In one embodiment, fried prawns are used as the food to be tested, and the surface temperature data of the prawns within ten minutes after cooking is measured using a thermocouple and an infrared temperature measuring lens. The two sets of data are matched and cleaned according to time, and the first temperature data and the second temperature data obtained are as follows: Figure 8 As shown, A is the first temperature data and B is the second temperature data.

[0116] like Fig. 9 As shown, linear regression is used to estimate the change multiplier k and the variable coefficient b, so as to fit a straight line. The fitting formula is:

[0117] y=0.66x+15.92 (3)

[0118] Fig. 9 In the figure, C is the temperature fitted according to the first temperature data, and D is the second temperature data. The average absolute error of the fitting using this formula is 0.34, and the maximum absolute error is 0.81.

[0119] In one embodiment, the cleaning module 630 may include:

[0120] The calculation unit 631 is used to use the second temperature data to subtract the first temperature data to obtain a set of new data d i :

[0121] d i =TC i -IR i (2)

[0122] Among them, TC i is the temperature of the thermocouple at time stamp i, IR i is the temperature of the infrared temperature measuring lens at timestamp i;

[0123] Cleaning unit 632, used to calculate d i If the absolute value of the z score of each data is greater than a preset value, the data corresponding to the timestamp i is discarded from the first temperature data and the second temperature data.

[0124] The preset value may be an empirical value, and in this embodiment, the preset value may be 3. When the preset value is greater than 3, the temperature measured by the infrared temperature measuring lens corresponding to timestamp i is discarded from the first temperature data, and the temperature measured by the thermocouple corresponding to timestamp i is discarded from the second temperature data.

[0125] Embodiment three:

[0126] An apparatus for determining the temperature of food provided in Example 3 of the present application, in one implementation manner, includes a memory and a processor.

[0127] Memory, used to store programs;

[0128] The processor is used to implement the method in the first embodiment by executing the program stored in the memory.

[0129] Embodiment 4:

[0130] The cooking robot provided in Example 4 of the present application, in one implementation form, includes a memory and a processor.

[0131] Memory, used to store programs;

[0132] The processor is used to implement the method in the first embodiment by executing the program stored in the memory.

[0133] Embodiment five:

[0134] A computer-readable storage medium includes a program, and the program can be executed by a processor to implement the method in embodiment 1.

[0135] Those skilled in the art will appreciate that all or part of the steps of the various methods in the above embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium can include: a read-only memory, a random access memory, a disk or an optical disk, etc. The above content is a further detailed description of the present application in combination with specific implementation methods, and it cannot be determined that the specific implementation of the present application is limited to these descriptions. For ordinary technicians in the technical field to which the present application belongs, several simple deductions or substitutions can be made without departing from the concept of the present application.

Claims

1. A method for determining the temperature of food, characterized in that include: Using an infrared temperature measuring lens to measure the food to be measured placed in the measuring area to obtain first temperature data of the food to be measured, and using a thermocouple to measure the food to be measured to obtain second temperature data of the food to be measured; Establishing a correspondence between the first temperature data and the second temperature data; Establishing a function model, and performing regression calculation based on the first temperature data and the second temperature data to obtain a fitting formula; The first temperature data is compensated according to the fitting formula to obtain the temperature of the food.

2. The method according to claim 1, characterized in that The establishing of the function model and performing regression calculation according to the first temperature data and the second temperature data to obtain a fitting formula includes: A linear function model is established with the first temperature data as an independent variable and the second temperature data as a dependent variable. The formula of the linear function model is: y=kx+b (1) Wherein, y represents the second temperature data, x represents the first temperature data, k represents the change multiplier, and b is the scalar coefficient; Performing regression calculation on the linear function according to the first temperature data and the second temperature data to obtain a change multiplier k and a scalar coefficient b; Substitute the change multiplier k and the scalar coefficient b into the formula of the linear function model to obtain a fitting formula.

3. The method according to claim 1, characterized in that The method of establishing a function model and performing regression calculation according to the first temperature data and the second temperature data to obtain a fitting formula further includes: The first temperature data and the second temperature data are preprocessed to clean up erroneous data.

4. The method according to claim 3, characterized in that The preprocessing of the first temperature data and the second temperature data to remove erroneous data includes: Subtract the first temperature data from the second temperature data to obtain a new set of data d i : d i =TC i -IR i (2) Among them, TC i is the temperature of the thermocouple at time stamp i, IR i is the temperature of the infrared temperature measuring lens at timestamp i; Calculate d i If the absolute value of the z score of each data is greater than a preset value, the data corresponding to the timestamp i is discarded from the first temperature data and the second temperature data.

5. A device for determining the temperature of food, characterized in that include: The measuring module is used to measure the food to be measured placed in the measuring area using an infrared temperature measuring lens to obtain first temperature data of the food to be measured, and to measure the food to be measured using a thermocouple to obtain second temperature data of the food to be measured; An index module, used to establish a corresponding relationship between the first temperature data and the second temperature data; A processing module, used for establishing a function model, and performing regression calculation according to the first temperature data and the second temperature data to obtain a fitting formula; The temperature compensation module is used to compensate the first temperature data according to the fitting formula to obtain the temperature of the food.

6. The device according to claim 5, characterized in that The processing module comprises: A modeling unit is used to establish a linear function model with the first temperature data as an independent variable and the second temperature data as a dependent variable, wherein the formula of the linear function model is: y=kx+b (1) Wherein, y represents the second temperature data, x represents the first temperature data, k represents the change multiplier, and b is the scalar coefficient; A regression unit, used for performing regression calculation on the linear function according to the first temperature data and the second temperature data to obtain a change multiplier k and a scalar coefficient b; A processing unit is used to substitute the change multiplier k and the scalar coefficient b into the formula of the linear function model to obtain a fitting formula.

7. The device according to claim 6, characterized in that Also includes: A cleaning module is used to pre-process the first temperature data and the second temperature data to clean up erroneous data.

8. The device according to claim 7, characterized in that The cleaning module comprises: A calculation unit, configured to obtain a set of new data d by subtracting the first temperature data from the second temperature data i : d i =TC i -IR i (2) Among them, TC i is the temperature of the thermocouple at time stamp i, IR i is the temperature of the infrared temperature measuring lens at timestamp i; Cleaning unit, used to calculate d i If the absolute value of the z score of each data is greater than a preset value, the data corresponding to the timestamp i is discarded from the first temperature data and the second temperature data.

9. A device for determining the temperature of food, characterized in that include: Memory, used to store programs, A processor, configured to execute the program stored in the memory to implement the method according to any one of claims 1 to 4.

10. A cooking robot, characterized in that: include: Memory, used to store programs, A processor, configured to execute the program stored in the memory to implement the method according to any one of claims 1 to 4.

11. A computer-readable storage medium, characterized in that: The method comprises a program which can be executed by a processor to implement the method according to any one of claims 1 to 4.

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