Intelligent food temperature monitoring method, device, equipment and storage medium

Abstract

This application relates to the technical field of food temperature monitoring, and more particularly to an intelligent food temperature monitoring method, apparatus, device, and storage medium. The method includes collecting temperature data via a temperature sensor of a wireless barbecue thermometer, and determining whether the wireless barbecue thermometer is in use based on the temperature data; if the wireless barbecue thermometer is in use, collecting food temperature data based on the temperature data collected by the temperature sensor according to a preset time period to construct a food temperature dataset, and constructing a food temperature fluctuation curve based on the food temperature dataset; fitting and optimizing the food temperature fluctuation curve to determine a target remaining cooking time; obtaining a new food temperature fluctuation curve, and updating the target remaining cooking time based on the new food temperature fluctuation curve. This application improves the accuracy of food temperature monitoring and the accuracy of food cooking time prediction, thereby enabling dynamic updating of food cooking time.

Description

Technical Field

[0001] The present application relates to the technical field of food temperature monitoring, and in particular to an intelligent food temperature monitoring method, device, equipment and storage medium. Background Art

[0002] As an essential component of modern cooking tools, wireless barbecue thermometers have seen rapid growth in recent years. They not only monitor food's internal temperature in real time but also transmit data to the user's smartphone via wireless Bluetooth technology, enhancing cooking experience and efficiency.

[0003] Currently, various methods are commonly used to effectively monitor food temperature and predict remaining cooking time. A common approach involves detecting specific conditions, such as the type of meat and the target temperature, and then recording the average rate of temperature increase over a period of time to estimate the time required to reach the target temperature. However, in real-world cooking environments, changing the heat level midway can lead to significant errors in the predicted time using this method, leaving room for improvement. Summary of the Invention

[0004] The present application provides an intelligent food temperature monitoring method, device, equipment and storage medium, which can improve the accuracy of food temperature monitoring and the accuracy of food cooking time prediction, and realize dynamic updating of food cooking time.

[0005] In a first aspect, the present application provides an intelligent food temperature monitoring method for a wireless barbecue thermometer, the intelligent food temperature monitoring method comprising:

[0006] S1. collecting temperature data through a temperature sensor of the wireless barbecue thermometer, and determining whether the wireless barbecue thermometer is in use according to the temperature data;

[0007] S2. If the wireless barbecue thermometer is in use, collect food temperature data based on the temperature sensor according to a preset time period to construct a food temperature data set, and construct a food temperature fluctuation curve based on the food temperature data set;

[0008] S3. Fitting and optimizing the food temperature fluctuation curve to determine the target remaining cooking time;

[0009] S4. Obtain a new food temperature fluctuation curve, and update the target remaining cooking time according to the new food temperature fluctuation curve.

[0010] By adopting the above technical solution, when it is determined that the wireless barbecue thermometer is in use, food temperature data is further collected and a temperature fluctuation curve is constructed according to a preset time period, which can provide a continuous temperature change trend, thereby improving the accuracy of temperature monitoring. By fitting the food temperature curve and calculating the target remaining cooking time, the error is reduced and the time prediction is made more accurate. By dynamically updating the remaining cooking time and adapting to temperature changes, the real-time and accuracy of the remaining cooking time calculation are improved, and the dynamic update of food cooking time is realized.

[0011] In conjunction with some embodiments of the first aspect, in some embodiments, determining whether the wireless barbecue thermometer is in use based on the temperature data specifically includes:

[0012] S11. Determine a rate of change of the temperature data based on the temperature data;

[0013] S12: If the change rate of the temperature data is greater than a preset temperature change rate, it is determined that the wireless barbecue thermometer is in use.

[0014] By adopting the above technical solution, by judging the rate of change of temperature data, it is determined whether the wireless barbecue thermometer is in use, thereby reducing the triggering probability of the wireless barbecue thermometer, and also reducing the power consumption of the wireless barbecue thermometer, improving the applicability of temperature data for prediction, and making temperature monitoring more in line with actual barbecue scenarios.

[0015] In conjunction with some embodiments of the first aspect, in some embodiments, performing fitting optimization on the food temperature fluctuation curve to determine target grilling temperature data specifically includes:

[0016] S31, obtaining a set food type and a corresponding food target temperature;

[0017] S32. Fit the food temperature fluctuation curve based on the least square method, and determine the target remaining cooking time according to the food target temperature.

[0018] By adopting the above technical solution, by obtaining the set food type and the corresponding food target temperature, the food temperature fluctuation curve is further fitted by the least squares method, and the target remaining cooking time is determined according to the food target temperature, thereby reducing random fluctuations in temperature data and improving the accuracy of temperature prediction, thereby making the remaining time calculation more accurate, ensuring that the food can achieve the ideal cooking effect within the optimal time, and avoiding the problem of undercooking or overcooking of certain foods due to the use of a unified standard.

[0019] In conjunction with some embodiments of the first aspect, in some embodiments, fitting the food temperature fluctuation curve based on the least squares method and determining the target remaining cooking time according to the food target temperature specifically includes:

[0020] S321, determining a first parameter and a second parameter according to the food temperature fluctuation curve;

[0021] S322: constructing a calculation formula for a target remaining cooking time based on the first parameter, the second parameter, and the target food temperature;

[0022] S323: Calculate the target remaining cooking time according to the calculation formula.

[0023] By adopting the above technical solution, the first parameter and the second parameter are determined through the food temperature fluctuation curve, and the key parameters affecting the food temperature change are extracted, so that subsequent calculations are based on reliable data features, and the accuracy of the remaining cooking time calculation is improved. The calculation formula of the target remaining cooking time is constructed through the first parameter, the second parameter and the target food temperature. The target remaining cooking time is further calculated through the calculation formula to make the relationship between temperature and time clearer, thereby improving the accuracy of the prediction of the remaining cooking time, making the time estimation result closer to the actual barbecue scene, and avoiding food cooking failure due to estimation errors.

[0024] In conjunction with some embodiments of the first aspect, in some embodiments, the wireless barbecue thermometer further includes a timer, and the timer is triggered after the target remaining cooking time is obtained;

[0025] When the timer is triggered, the current food temperature fluctuation curve is obtained, and step S3 is repeated to update the target remaining cooking time.

[0026] By adopting the above technical solution, by setting a timer in the wireless barbecue thermometer and triggering the timer after obtaining the target remaining cooking time, it is ensured that the temperature data is collected and updated within the set time interval, thereby improving the real-time performance of temperature monitoring. Furthermore, by fitting the temperature curve of new food, the time prediction error can be continuously corrected, making the estimation of the remaining cooking time more accurate and avoiding the problem of inaccurate cooking time estimation due to temperature fluctuations.

[0027] In conjunction with some embodiments of the first aspect, in some embodiments, after updating the target remaining cooking time, the intelligent food temperature monitoring method further includes:

[0028] S3011. Determine, based on the food temperature fluctuation curve, whether a time period in the food temperature fluctuation curve exceeds a preset time period;

[0029] S3012: If the time period exceeds the preset time period, take the food temperature fluctuation curve of the preset time period as a new food temperature fluctuation curve.

[0030] By adopting the above technical solution, the food temperature fluctuation curve is updated by judging whether it exceeds the preset time period. When the temperature fluctuation exceeds the preset range, the calculation data can be dynamically adjusted, thereby avoiding the interference of previous food temperature data on the cooking time prediction and improving the stability of the remaining cooking time estimation.

[0031] In conjunction with some embodiments of the first aspect, in some embodiments, obtaining a new food temperature fluctuation curve and updating the target remaining cooking time according to the new food temperature fluctuation curve specifically includes:

[0032] S41. Repeat step S3 according to the new food temperature fluctuation curve to update the target remaining cooking time.

[0033] By adopting the above technical solution and updating the remaining cooking time through the new food temperature fluctuation curve, the time prediction can be adjusted as the temperature changes, and the accurate prediction of the cooking time can be optimized, thereby ensuring the real-time and accuracy of the remaining cooking time estimation, so that the final doneness of the food will not be affected by the deviation of time calculation.

[0034] In a second aspect, the present application provides an intelligent food temperature monitoring device, comprising:

[0035] a state detection module, configured to collect temperature data through a temperature sensor of the wireless barbecue thermometer and determine whether the wireless barbecue thermometer is in use according to the temperature data;

[0036] a data acquisition module, configured to collect food temperature data based on the temperature sensor at a preset time period when the wireless barbecue thermometer is in use, to construct a food temperature data set, and to construct a food temperature fluctuation curve based on the food temperature data set;

[0037] A time prediction module is used to perform fitting optimization on the food temperature fluctuation curve to determine the target remaining cooking time;

[0038] The time updating module is used to obtain a new food temperature fluctuation curve and update the target remaining cooking time according to the new food temperature fluctuation curve.

[0039] By adopting the above technical solution, when it is determined that the wireless barbecue thermometer is in use, food temperature data is further collected and a temperature fluctuation curve is constructed according to a preset time period, which can provide a continuous temperature change trend, thereby improving the accuracy of temperature monitoring. By fitting the food temperature curve and calculating the target remaining cooking time, the error is reduced and the time prediction is made more accurate. By dynamically updating the remaining cooking time and adapting to temperature changes, the real-time and accuracy of the remaining cooking time calculation are improved, and the dynamic update of food cooking time is realized.

[0040] In a third aspect, the present application provides a wireless barbecue thermometer, comprising a temperature sensor, a timer, a display screen, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the intelligent food temperature monitoring method provided in the first aspect are implemented.

[0041] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the intelligent food temperature monitoring method provided in the first aspect.

[0042] In summary, this application includes at least one of the following beneficial technical effects:

[0043] 1. When the wireless barbecue thermometer is determined to be in use, it further collects food temperature data and constructs a temperature fluctuation curve according to a preset time period. This can provide a continuous temperature change trend, thereby improving the accuracy of temperature monitoring. By fitting the food temperature curve and calculating the target remaining cooking time, errors are reduced and time prediction is made more accurate. By dynamically updating the remaining cooking time to adapt to temperature changes, the real-time and accuracy of the remaining cooking time calculation are improved, and the dynamic update of food cooking time is achieved;

[0044] 2. By setting a timer in the wireless barbecue thermometer and triggering the timer after obtaining the target remaining cooking time, the collection and update of temperature data are ensured within the set time interval, thereby improving the real-time performance of temperature monitoring. Furthermore, by fitting the temperature curve of new food, the time prediction error can be continuously corrected, making the estimation of the remaining cooking time more accurate and avoiding the problem of inaccurate cooking time estimation due to temperature fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flowchart of an implementation method of an intelligent food temperature monitoring method in one embodiment of the present application;

[0046] Figure 2 This is a flowchart for implementing step S1 in the intelligent food temperature monitoring method in one embodiment of the present application;

[0047] Figure 3 This is a flowchart for implementing step S3 in the intelligent food temperature monitoring method in one embodiment of the present application;

[0048] Figure 4 This is a flowchart for implementing step S32 in the intelligent food temperature monitoring method in one embodiment of the present application;

[0049] Figure 5 This is another implementation flow chart of the intelligent food temperature monitoring method in one embodiment of the present application;

[0050] Figure 6 This is a flowchart for implementing step S4 in the intelligent food temperature monitoring method in one embodiment of the present application;

[0051] Figure 7 This is a principle block diagram of an intelligent food temperature monitoring device in one embodiment of the present application. DETAILED DESCRIPTION

[0052] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular expressions "a", "an", "said", "above", "the", and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0053] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0054] The present application is further described in detail below with reference to the accompanying drawings.

[0055] The embodiment of the present application provides a wireless barbecue thermometer, which includes a temperature sensor, a timer, a display screen, and a controller.

[0056] The temperature sensor is located within the hollow probe, which is used to contact the food to collect the food's temperature. The temperature sensor uses a thermocouple or thermistor to detect the internal temperature of the food. After the probe is inserted into the food, the sensor collects temperature data in real time and transmits the signal to the controller. The sensor's location and hollow structure reduce external interference with the temperature data, improving measurement accuracy.

[0057] A timer is used to control the update of the target remaining cooking time. When the food temperature rises 5°F above the initial recorded point, the timer triggers the calculation process at a preset interval, such as every 30 or 60 seconds. When the timer triggers, the controller obtains the current temperature data and analyzes it based on the temperature fluctuation curve to calculate the updated target remaining cooking time. This can avoid the controller from performing high-frequency real-time calculations, thereby reducing unnecessary computing resource consumption;

[0058] The display is used to display the target remaining cooking time. The display can be an LCD display and display information such as the current food temperature and the remaining cooking time in real time. The displayed data will be updated after each time the timer is triggered to ensure that the user obtains the most up-to-date cooking time forecast information. The user can check the grilling status at any time through the display and determine whether adjustments are needed based on the remaining time, such as increasing or decreasing the heating time or adjusting the oven power.

[0059] The controller is connected to the temperature sensor, timer, and display screen, and is used to implement the intelligent food temperature monitoring method. The controller receives temperature data from the temperature sensor and, based on the data, determines whether the thermometer is in use. After confirming that the device is in use, the controller collects temperature data at preset time intervals and constructs a food temperature fluctuation curve. The controller fits the temperature fluctuation curve using the least squares method and calculates the target remaining cooking time. When the timer is triggered, the controller obtains the latest temperature data and updates the temperature fluctuation curve to calculate the new target remaining cooking time. The controller then transmits the calculated remaining cooking time data to the display screen, allowing the user to intuitively view the remaining cooking time.

[0060] In one embodiment, if Figure 1 As shown, the present application discloses an intelligent food temperature monitoring method, which specifically includes the following steps:

[0061] S1. Collect temperature data through the temperature sensor of the wireless barbecue thermometer, and determine whether the wireless barbecue thermometer is in use based on the temperature data.

[0062] Specifically, the wireless barbecue thermometer includes a temperature sensor. By inserting the wireless barbecue thermometer into food, the temperature sensor can collect the temperature of the food. The food temperature unit is Fahrenheit (°F). Then, based on the trend of the food temperature change, it can be determined whether the wireless barbecue thermometer is in use. For example, if the temperature continues to rise and reaches a certain rate of change, it can be determined that the wireless barbecue thermometer is in use. Therefore, it can be determined whether the wireless barbecue thermometer is in use.

[0063] S2. If the wireless barbecue thermometer is in use, food temperature data is collected based on the temperature sensor according to a preset time period, a food temperature data set is constructed, and a food temperature fluctuation curve is constructed based on the food temperature data set.

[0064] Specifically, when the wireless barbecue thermometer is in use, it regularly collects temperature data. According to a preset time period, the temperature data is collected at regular intervals to generate a temperature dataset. Time-temperature data points are extracted from the constructed food temperature dataset, and a food temperature fluctuation curve reflecting food temperature changes is constructed based on these data points. The food temperature fluctuation curve can better observe the temperature change trend of the food. The preset time period can be every second or every 5 seconds, thereby regularly collecting temperature data.

[0065] S3. Fit and optimize the food temperature fluctuation curve to determine the target remaining cooking time.

[0066] Specifically, the constructed food temperature fluctuation curve is fitted, and the fitting method can be a linear regression method. The trend of temperature change is analyzed through the fitting process to determine the distance between the food and the target temperature, and the remaining time required to reach the temperature is calculated to obtain the target remaining cooking time.

[0067] S4. Obtain a new food temperature fluctuation curve, and update the target remaining cooking time according to the new food temperature fluctuation curve.

[0068] Specifically, food temperature data is collected at preset intervals. After each temperature data collection, a new food temperature fluctuation curve is generated. The collected food temperature data is added to the existing food temperature fluctuation curve. This new food temperature fluctuation curve may deviate from the previous data, especially when the heat level fluctuates significantly during the cooking process. Therefore, based on the new temperature data, the fluctuation curve is reconstructed and fitted, thereby updating the target remaining cooking time. This more accurately reflects the actual cooking progress of the food and ensures that the remaining cooking time prediction remains real-time and accurate.

[0069] In one embodiment, if Figure 2 As shown, in step S1, judging whether the wireless barbecue thermometer is in use based on the temperature data specifically includes:

[0070] S11. Determine the rate of change of the temperature data based on the temperature data.

[0071] Specifically, by performing differential calculation on the continuously collected temperature data, the speed of temperature data change is obtained. If the temperature change rate is faster at the beginning, it means that the cooking process is proceeding rapidly, so it can be determined whether the wireless barbecue thermometer is in working condition.

[0072] S12: If the rate of change of the temperature data is greater than a preset temperature change rate, it is determined that the wireless barbecue thermometer is in use.

[0073] Specifically, by setting a preset temperature change rate threshold, when the calculated temperature data change rate exceeds the preset value, it can be inferred that the wireless barbecue thermometer is in contact with food and the food is in the process of heating and cooking, thereby judging that the wireless barbecue thermometer is in a use state; if the temperature change rate is less than the preset value, it means that the temperature change is not sufficient to indicate that the wireless barbecue thermometer is in use, and the wireless barbecue thermometer is judged to be in a non-use state.

[0074] In one embodiment, if Figure 3 As shown, in step S3, the food temperature fluctuation curve is fitted and optimized to determine the target grilling temperature data, which specifically includes:

[0075] S31. Obtain the set food type and the corresponding food target temperature.

[0076] Specifically, before starting cooking, users can set the food type and the corresponding target temperature through an app connected to the wireless grill thermometer. Different foods have different temperature rise trends under the same conditions, so the estimated time remaining to the temperature point can be calculated based on this data. Among them, the food type can be steak, chicken, fish, etc., and the corresponding target temperature can be 145°F for steak. The remaining cooking time is then calculated based on the set value to ensure that the food achieves the desired cooking effect.

[0077] S32. Fitting the food temperature fluctuation curve based on the least squares method, and determining the target remaining cooking time according to the target food temperature.

[0078] Specifically, based on the constructed food temperature fluctuation curve, the least squares method is used to fit the food temperature fluctuation curve, and the optimal fitting curve is found between the temperature data points so that the error between the data points and the fitting curve is minimized. A mathematical model is obtained, which can accurately predict the trend of temperature change over time. Combined with the set food target temperature, the target remaining cooking time is determined by calculating the difference between the model and the target temperature, so as to provide an accurate cooking time estimate.

[0079] In one embodiment, if Figure 4As shown, in step S32, the food temperature fluctuation curve is fitted based on the least square method, and the target remaining cooking time is determined according to the target food temperature, which specifically includes:

[0080] S321. Determine a first parameter and a second parameter according to a food temperature fluctuation curve.

[0081] Specifically, the least squares method was used to fit the food temperature fluctuation curve. This method is a mathematical method commonly used in data analysis. Its basic principle is to find an optimal regression curve that minimizes the sum of the squared distances from all data points to the curve, thereby reducing data errors and obtaining a mathematical model that accurately reflects temperature variation trends. To analyze the food temperature fluctuation curve, key parameters related to temperature variation were first extracted, such as the slope of the curve (the rate of temperature increase or decrease) as the first parameter, and the intercept of the curve (the initial temperature of the food) as the second parameter. These parameters better reflect the temperature variation trend and provide data support for calculating the remaining time.

[0082] S322: Construct a calculation formula for the target remaining cooking time based on the first parameter, the second parameter, and the target temperature of the food.

[0083] Specifically, based on the least squares method, a linear equation is constructed based on the first parameter, the second parameter, and the target food temperature: Y = a2 + a1X, where Y represents temperature, X represents time, a1 is the first parameter, and a2 is the second parameter. To solve a1 and a2, it is necessary to calculate the statistical parameters in the data set, including the sum of the temperature data, the sum of the time data, the sum of the products of the time-temperature data points, and the sum of the squares of the time data. Therefore, a1 and a2 are calculated according to the following formula:

[0084]

[0085] Among them, ∑(X i Y i ) is the product of all time and temperature data points, ∑(X i ) is the sum of all time data, ∑(Y i ) is the sum of all temperature data, ∑(X i 2 ) is the sum of squares of all time data, n is the number of data points;

[0086] Furthermore, based on the above calculation method, the target food temperature Y_target is substituted into the linear equation to infer the target remaining cooking time X_target:

[0087]

[0088] The above formula calculates the time required for the food to reach the target temperature from the current temperature, taking into account the speed of temperature change and the current state, making the calculation of the remaining time more accurate.

[0089] S323: Calculate the target remaining cooking time according to the calculation formula.

[0090] Specifically, using the above calculation formula, substitute the target temperature value into the formula to calculate the remaining cooking time, ensuring that the accurate target cooking remaining time is calculated through the temperature change rate and the initial temperature value. This time will be displayed on the display screen of the wireless barbecue thermometer to help users control the barbecue process.

[0091] In one embodiment, the wireless barbecue thermometer further includes a timer, which is triggered after the target remaining cooking time is obtained;

[0092] When the timer is triggered, the current food temperature fluctuation curve is obtained, and step S3 is repeated to update the target remaining cooking time.

[0093] Specifically, after calculating the target remaining cooking time, a timer is triggered. Since calculating the remaining cooking time takes time, continuing to calculate it during this period would overconsume the wireless barbecue thermometer's computing resources. Therefore, the timer is triggered at a set interval (for example, 30 seconds). When triggered, the system first obtains new food temperature data and constructs a new food temperature fluctuation curve. The remaining cooking time is then calculated and updated based on this new food temperature fluctuation curve.

[0094] Furthermore, when the timer is triggered, new food temperature data is automatically obtained and a new temperature fluctuation curve is constructed. These new data are used to update the remaining time prediction through the above calculation method, that is, repeating step S3. The role of the timer is to ensure that the temperature status of the food is re-evaluated at regular intervals to ensure that the target remaining cooking time is updated according to the preset time period to adapt to possible changes in the barbecue process.

[0095] In one embodiment, if Figure 5 As shown, after step S3, the intelligent food temperature monitoring method further includes:

[0096] S3011. Determine, based on the food temperature fluctuation curve, whether a time period in the food temperature fluctuation curve exceeds a preset time period.

[0097] Specifically, since the temperature sensor detects and saves the temperature data of the food in real time, taking into account the influence of the food temperature data in the initial stage of grilling, it is judged whether the time period in these data exceeds the preset maximum time period based on the time period in the current food temperature fluctuation curve. If the current time period of the collected food temperature data exceeds the preset maximum time period, the preset time period refers to the time period closest to the current time, which can be set to 7 minutes.

[0098] S3012: If the time period exceeds the preset time period, take the food temperature fluctuation curve with the preset time period as a new food temperature fluctuation curve.

[0099] Specifically, if the time period in the recorded food temperature fluctuation curve exceeds the preset time period, the time period in the food temperature fluctuation curve is selected as the preset time period, and the food temperature fluctuation curve is updated as a new food temperature fluctuation curve, so as to predict the remaining cooking time based on the new food temperature fluctuation curve, thereby ensuring that the new data reflects the actual temperature changes in the current barbecue process, and avoiding inaccurate remaining time prediction due to time mismatch.

[0100] In one embodiment, if Figure 6 As shown, in step S4, i.e., obtaining a new food temperature fluctuation curve, updating the target remaining cooking time according to the new food temperature fluctuation curve, specifically includes:

[0101] S41. Repeat step S3 according to the new food temperature fluctuation curve to update the target remaining cooking time.

[0102] Specifically, based on the new food temperature fluctuation curve, the food temperature fluctuation curve is fitted again, and based on the above calculation method, the target remaining time is recalculated through the updated curve, and step S3 is repeated to calculate the new remaining cooking time, ensuring that the time prediction during the barbecue process always matches the current temperature change, providing a more accurate remaining cooking time.

[0103] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0104] In one embodiment, an intelligent food temperature monitoring device is provided, which corresponds to the intelligent food temperature monitoring method in the above embodiment. Figure 7 As shown, the intelligent food temperature monitoring device includes a state detection module, a data acquisition module, a time prediction module, and a time update module. The functional modules are described in detail as follows:

[0105] A status detection module is used to collect temperature data through the temperature sensor of the wireless barbecue thermometer and determine whether the wireless barbecue thermometer is in use based on the temperature data;

[0106] A data acquisition module is used to collect food temperature data based on the temperature sensor according to a preset time period when the wireless barbecue thermometer is in use, to construct a food temperature data set, and to construct a food temperature fluctuation curve based on the food temperature data set;

[0107] The time prediction module is used to fit and optimize the food temperature fluctuation curve to determine the target remaining cooking time;

[0108] The time update module is used to obtain a new food temperature fluctuation curve and update the target remaining cooking time according to the new food temperature fluctuation curve.

[0109] Optionally, the status detection module specifically includes:

[0110] The rate calculation submodule is used to determine the rate of change of the temperature data based on the temperature data;

[0111] The state determination submodule is used to determine that the wireless barbecue thermometer is in use if the change rate of the temperature data is greater than a preset temperature change rate.

[0112] Optionally, the time prediction module specifically includes:

[0113] The target temperature setting submodule is used to obtain the set food type and the corresponding food target temperature;

[0114] The curve fitting submodule is used to fit the food temperature fluctuation curve based on the least squares method and determine the target remaining cooking time according to the food target temperature.

[0115] Optionally, the curve fitting submodule specifically includes:

[0116] a parameter extraction unit, configured to determine a first parameter and a second parameter according to a food temperature fluctuation curve;

[0117] a time calculation unit, configured to construct a calculation formula for a target remaining cooking time based on the first parameter, the second parameter, and the target temperature of the food;

[0118] The time prediction unit is used to calculate the target remaining cooking time according to a calculation formula.

[0119] Optionally, the intelligent food temperature monitoring device also includes:

[0120] A time period determination module is used to determine whether a time period in the food temperature fluctuation curve exceeds a preset time period based on the food temperature fluctuation curve;

[0121] The curve updating module is used to take the food temperature fluctuation curve with the preset time period as the new food temperature fluctuation curve if the time period exceeds the preset time period.

[0122] Optionally, the time update module specifically includes:

[0123] The time iteration submodule is used to repeat the steps of the curve fitting submodule according to the new food temperature fluctuation curve to update the target remaining cooking time.

[0124] The specific limitations of the intelligent food temperature monitoring device can be found in the limitations of the intelligent food temperature monitoring method described above and will not be further elaborated here. Each module in the intelligent food temperature monitoring device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the modules described above can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so that the processor can call and execute the operations corresponding to each of the modules described above.

[0125] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0126] The temperature sensor of the wireless barbecue thermometer collects temperature data, and determines whether the wireless barbecue thermometer is in use according to the temperature data;

[0127] If the wireless barbecue thermometer is in use, the temperature sensor collects food temperature data according to a preset time period, constructs a food temperature data set, and constructs a food temperature fluctuation curve based on the food temperature data set;

[0128] Fit and optimize the food temperature fluctuation curve to determine the target remaining cooking time;

[0129] A new food temperature fluctuation curve is obtained, and the target remaining cooking time is updated according to the new food temperature fluctuation curve.

[0130] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0131] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0132] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0133] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. An intelligent food temperature monitoring method, characterized in that: For wireless barbecue thermometers, the intelligent food temperature monitoring method includes: S1. collecting temperature data through a temperature sensor of the wireless barbecue thermometer, and determining whether the wireless barbecue thermometer is in use according to the temperature data; S2. If the wireless barbecue thermometer is in use, collect food temperature data based on the temperature sensor according to a preset time period to construct a food temperature data set, and construct a food temperature fluctuation curve based on the food temperature data set; S3. Fitting and optimizing the food temperature fluctuation curve to determine the target remaining cooking time; S4. Obtain a new food temperature fluctuation curve, and update the target remaining cooking time according to the new food temperature fluctuation curve; The step of fitting and optimizing the food temperature fluctuation curve to determine target grilling temperature data specifically includes: S31, obtaining a set food type and a corresponding food target temperature; S32. Fitting the food temperature fluctuation curve based on the least squares method, and determining the target remaining cooking time according to the food target temperature; The step of fitting the food temperature fluctuation curve based on the least squares method and determining the target remaining cooking time according to the food target temperature specifically includes: S321, determining a first parameter and a second parameter based on the food temperature fluctuation curve; S322, constructing a calculation formula for a target remaining cooking time based on the first parameter, the second parameter, and the target food temperature; Among them, based on the least squares method, according to the first parameter, the second parameter and the target temperature of the food, a linear equation is constructed as Y=a2+a1X, where Y represents temperature, X represents time, a1 is the first parameter, and a2 is the second parameter. In order to solve a1 and a2, it is necessary to calculate the statistical parameters in the data set, including the sum of temperature data, the sum of time data, the product sum of time-temperature data points, and the sum of squares of time data. Therefore, a1 and a2 are calculated according to the following formula: ； ； Among them, ∑(X i Y i ) is the product of all time and temperature data points, ∑(X i ) is the sum of all time data, ∑(Y i ) is the sum of all temperature data, ∑(X i 2 ) is the sum of squares of all time data, n is the number of data points; S323, calculating the target remaining cooking time according to the calculation formula; The wireless barbecue thermometer further includes a timer, which is triggered after the target remaining cooking time is obtained; When the timer is triggered, the current food temperature fluctuation curve is obtained, and step S3 is repeated to update the target remaining cooking time; After updating the target remaining cooking time, the intelligent food temperature monitoring method further includes: S3011. Determine, based on the food temperature fluctuation curve, whether a time period in the food temperature fluctuation curve exceeds a preset time period; S3012: If the time period exceeds the preset time period, take the food temperature fluctuation curve of the preset time period as a new food temperature fluctuation curve.

2. The intelligent food temperature monitoring method according to claim 1, characterized in that: The determining whether the wireless barbecue thermometer is in use according to the temperature data specifically includes: S11. Determine a rate of change of the temperature data based on the temperature data; S12: If the change rate of the temperature data is greater than a preset temperature change rate, it is determined that the wireless barbecue thermometer is in use.

3. The intelligent food temperature monitoring method according to claim 1, characterized in that: The acquiring of the new food temperature fluctuation curve and updating the target remaining cooking time according to the new food temperature fluctuation curve specifically include: S41. Repeat step S3 according to the new food temperature fluctuation curve to update the target remaining cooking time.

4. An intelligent food temperature monitoring device, characterized in that: The intelligent food temperature monitoring device comprises: a state detection module, configured to collect temperature data through a temperature sensor of the wireless barbecue thermometer and determine whether the wireless barbecue thermometer is in use according to the temperature data; a data acquisition module, configured to collect food temperature data based on the temperature sensor at a preset time period when the wireless barbecue thermometer is in use, to construct a food temperature data set, and to construct a food temperature fluctuation curve based on the food temperature data set; A time prediction module is used to perform fitting optimization on the food temperature fluctuation curve to determine the target remaining cooking time; a time updating module, configured to obtain a new food temperature fluctuation curve and update the target remaining cooking time according to the new food temperature fluctuation curve; The time prediction module specifically includes: The target temperature setting submodule is used to obtain the set food type and the corresponding food target temperature; The curve fitting submodule is used to fit the food temperature fluctuation curve based on the least squares method and determine the target remaining cooking time according to the food target temperature; The curve fitting submodule specifically includes: a parameter extraction unit, configured to determine a first parameter and a second parameter according to a food temperature fluctuation curve; a time calculation unit, configured to construct a calculation formula for a target remaining cooking time based on the first parameter, the second parameter, and the target temperature of the food; Among them, based on the least squares method, according to the first parameter, the second parameter and the target temperature of the food, a linear equation is constructed as Y=a2+a1X, where Y represents temperature, X represents time, a1 is the first parameter, and a2 is the second parameter. In order to solve a1 and a2, it is necessary to calculate the statistical parameters in the data set, including the sum of temperature data, the sum of time data, the product sum of time-temperature data points, and the sum of squares of time data. Therefore, a1 and a2 are calculated according to the following formula: ； ； Among them, ∑(X i Y i ) is the product of all time and temperature data points, ∑(X i ) is the sum of all time data, ∑(Y i ) is the sum of all temperature data, ∑(X i 2 ) is the sum of squares of all time data, n is the number of data points; A time prediction unit, configured to calculate a target remaining cooking time according to a calculation formula; The intelligent food temperature monitoring device also includes: A time period determination module is used to determine whether a time period in the food temperature fluctuation curve exceeds a preset time period based on the food temperature fluctuation curve; The curve updating module is used to take the food temperature fluctuation curve with the preset time period as the new food temperature fluctuation curve if the time period exceeds the preset time period.

5. A wireless barbecue thermometer comprising a temperature sensor, a timer, a display screen, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the intelligent food temperature monitoring method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the intelligent food temperature monitoring method according to any one of claims 1 to 3 are implemented.

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