Sampling control method and device of cooking equipment, equipment and storage medium
By dynamically adjusting the sampling frequency in the cooking equipment, the cooking status is determined based on the deviation between the current temperature and the set temperature. This solves the problem of inaccurate temperature control caused by the fixed sampling frequency in traditional cooking equipment, and achieves efficient and rapid temperature detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NINGBO FOTILE KITCHEN WARE CO LTD
- Filing Date
- 2026-01-21
- Publication Date
- 2026-05-15
AI Technical Summary
Traditional cooking equipment uses a fixed sampling frequency for its temperature control system, which cannot be dynamically adjusted according to the cooking stage. This results in the inability to detect the accurate temperature of food during periods of rapid temperature change, leading to low energy efficiency, slow response speed, and insufficient temperature control accuracy.
By acquiring current temperature data and set temperature data, the current sampling mode is determined, and the sensor error is dynamically adjusted based on the sensor noise model and the current sampling frequency to achieve high-precision adaptive sampling adjustment.
It achieves high-precision adaptive sampling and adjustment with fast response, solves the problem of ambiguity in cooking status, and improves the accuracy and efficiency of temperature control.
Smart Images
Figure CN122043981A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of cooking equipment testing technology, and in particular to a sampling control method, device, equipment and storage medium for cooking equipment. Background Technology
[0002] With the improvement of people's living standards and the promotion and popularization of technologies such as the Internet, big data, artificial intelligence, and voice interaction, more and more traditional lifestyles are gradually changing, and the use of kitchen appliances is gradually moving towards intelligence. In order to accurately monitor the surface temperature distribution of food and avoid local overheating or undercooking, infrared sensors are usually integrated into the cooking equipment to collect infrared data in real time during the cooking process and automatically adjust cooking parameters according to the type, weight, and initial temperature of the food.
[0003] Traditional cooking equipment typically uses a fixed sampling frequency for its temperature control system, which cannot be dynamically adjusted according to the cooking stage. However, the heating rate of food is often not uniform. A fixed sampling frequency will result in the inability to detect the accurate temperature of food during rapid temperature changes, leading to problems such as low energy efficiency, slow response speed, and insufficient temperature control accuracy. Summary of the Invention
[0004] To address the existing technical problems, this invention provides a sampling control method, device, equipment, and storage medium for cooking equipment. By accurately determining the sampling mode corresponding to the cooking state through the deviation between the current temperature and the set temperature, the ambiguity of the cooking state is resolved. By calculating the error corresponding to the sampling mode and dynamically adjusting the sampling frequency, a high-precision adaptive sampling adjustment with fast response is ultimately achieved.
[0005] In a first aspect, embodiments of this application provide a sampling control method for a cooking device, the method comprising: Get the current temperature data and the set temperature data; The current sampling mode is determined based on the current temperature data and the set temperature data; Get the current sampling frequency; The current sensor error is determined based on the current sampling mode, current sampling frequency, and sensor noise model; the sensor noise model is used to represent the relationship between sensor error and sampling frequency. The sampling frequency is adjusted based on the current sampling mode and the current sensor error. Sampling is performed by adjusting the sampling frequency.
[0006] In one possible implementation, determining the current sampling mode based on current temperature data and set temperature data includes: Determine temperature difference data and temperature gradient data based on current temperature data and set temperature data; If the temperature difference data is less than or equal to the temperature difference threshold, and the temperature gradient data is less than or equal to the first gradient threshold, the steady-state mode is determined as the current sampling mode; or, if the temperature difference data is greater than the temperature difference threshold, and the temperature gradient data is greater than the temperature gradient threshold, the transient mode is determined as the current sampling mode.
[0007] In one possible implementation, determining the current sampling mode based on current temperature data and set temperature data includes: Determine temperature difference data and temperature gradient data based on current temperature data and set temperature data; Obtain historical sampling patterns; If the historical sampling mode is a stateless mode, the temperature difference data is less than or equal to the temperature difference threshold, and the temperature gradient data is less than or equal to the first gradient threshold, then the steady-state mode is determined as the current sampling mode; or; If the historical sampling mode is a stateless mode, the temperature difference data is greater than the temperature difference threshold, and the temperature gradient data is greater than the first gradient threshold, then the transient mode is determined to be the current sampling mode; or; If the historical sampling mode is a steady-state mode, and the temperature difference data and temperature gradient data satisfy the first hysteresis condition, then the transient mode is determined as the current sampling mode; the first hysteresis condition is that the temperature difference data and the temperature gradient data are greater than the second gradient threshold; the second gradient threshold is greater than the first gradient threshold; or; If the historical sampling mode is a steady-state mode, but the temperature difference data and temperature gradient data do not meet the first hysteresis condition, determine the steady-state mode as the current sampling mode; or; If the historical sampling mode is a transient mode, and the temperature difference data and temperature gradient data satisfy the second hysteresis condition, the steady-state mode is determined as the current sampling mode; the first hysteresis condition is that the temperature gradient data is less than or equal to the third gradient threshold; the third gradient threshold is less than the first gradient threshold; or; If the historical sampling mode is a transient mode, but the temperature difference data and temperature gradient data do not meet the second hysteresis condition, the transient mode is determined to be the current sampling mode.
[0008] In one possible implementation, the current sampling mode includes a steady-state mode and a transient mode; The current sensor error is determined based on the current sampling mode, current sampling frequency, and sensor noise model, including: Determine the current noise error based on the current sampling frequency and sensor noise model; If the current sampling mode is steady-state mode, determine the current sensor error based on the current noise error; or; If the current sampling mode is transient, the current dynamic error is determined based on the current sampling frequency, and the current sensor error is determined based on the current noise error and the current dynamic error.
[0009] In one possible implementation, determining the adjustment of the sampling frequency based on the current sampling mode and the current sensor error includes: Determine error constraints based on the current sampling mode; Establish an error constraint model based on the current sensor error and error constraints; The sampling frequency is adjusted by solving the error constraint model.
[0010] In one possible implementation, before determining the sensor error based on the current sampling mode, sampling frequency, and sensor noise model, the method further includes: Multiple calibration current temperatures are acquired at multiple calibration sampling frequencies under multiple calibration set temperatures; Multiple calibration sensor errors are determined based on multiple calibration set temperatures and multiple calibration current temperatures; A sensor noise model is established based on multiple calibration sampling frequencies and multiple calibration sensor errors.
[0011] In one possible implementation, it also includes: If the current sampling mode is transient, obtain the smoothing factor; The exponentially weighted moving average is determined based on the smoothing factor and current temperature data; Noise suppression is based on an exponentially weighted moving average.
[0012] Secondly, embodiments of this application provide a sampling control device for a cooking apparatus, the device comprising: The first acquisition module is used to acquire current temperature data and set temperature data; The first determining module is used to determine the current sampling mode based on the current temperature data and the set temperature data; The second acquisition module is used to acquire the current sampling frequency; The second determining module is used to determine the current sensor error based on the current sampling mode, the current sampling frequency, and the sensor noise model; the sensor noise model is used to represent the relationship between the sensor error and the sampling frequency. The third determining module is used to determine and adjust the sampling frequency based on the current sampling mode and the current sensor error; The sampling control module is used to perform sampling based on adjusting the sampling frequency.
[0013] Thirdly, embodiments of this application provide an intelligent kitchen appliance, which includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. The processor loads and executes the at least one instruction, at least one program, code set, or instruction set to implement the sampling control method of the cooking appliance of the first aspect.
[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing at least one instruction or at least one program, wherein the at least one instruction or at least one program is loaded and executed by a processor to implement the sampling control method of the cooking device of the first aspect.
[0015] Fifthly, embodiments of this application provide a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the sampling control method for the cooking apparatus of the first aspect.
[0016] The sampling control method, apparatus, device, and storage medium for cooking equipment provided in this application have the following technical effects: The process involves acquiring current and set temperature data; determining the current sampling mode based on the current and set temperature data; acquiring the current sampling frequency; determining the current sensor error based on the current sampling mode, current sampling frequency, and sensor noise model; the sensor noise model representing the relationship between sensor error and sampling frequency; determining and adjusting the sampling frequency based on the current sampling mode and current sensor error; and performing sampling based on the adjusted sampling frequency. In this embodiment, the sampling mode corresponding to the cooking state is accurately determined by the deviation between the current and set temperatures, resolving the ambiguity of the cooking state. By calculating the error corresponding to the sampling mode and dynamically adjusting the sampling frequency, a high-precision adaptive sampling adjustment with rapid response is ultimately achieved. Attached Figure Description
[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a schematic diagram of an application environment provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a sampling control method for a cooking device provided in an embodiment of this application. Figure 1 ; Figure 3 This is a flowchart illustrating a sampling control method for a cooking device provided in an embodiment of this application. Figure 2 ; Figure 4This is a flowchart illustrating a method for determining the current sampling mode provided in an embodiment of this application. Figure 1 ; Figure 5 This is a flowchart illustrating a method for determining the current sampling mode provided in an embodiment of this application. Figure 2 ; Figure 6 This is a flowchart illustrating a method for determining the current sensor error provided in an embodiment of this application; Figure 7 This is a flowchart illustrating a transient mode noise suppression method provided in an embodiment of this application; Figure 8 This is a flowchart illustrating a method for establishing a sensor noise model according to an embodiment of this application; Figure 9 This is a schematic diagram of the structure of a sampling control device for a cooking apparatus provided in an embodiment of this application; Figure 10 This is a hardware structure block diagram of a server for a sampling control method of a cooking device provided in an embodiment of this application. Detailed Implementation
[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.
[0021] Please see Figure 1 , Figure 1 This is a schematic diagram of an application environment provided in an embodiment of this application. The steam oven 100 includes an infrared sensor 101, a control module 102, and a heating module 103.
[0022] In one possible embodiment, the cooking device may be a steam oven 100, or other cooking device that requires temperature detection.
[0023] In one possible embodiment, the infrared sensor 101 is installed inside the cavity to be detected in the steam oven 100, and can collect infrared temperature data at a set acquisition frequency and send the infrared temperature data to the control module 102.
[0024] In one possible embodiment, the heating module 103 is part of the steam oven 100 and can heat the cavity to be tested to a set temperature according to the set temperature.
[0025] In one possible embodiment, the control module 102 acquires current temperature data and set temperature data; determines the current sampling mode based on the current temperature data and set temperature data; acquires the current sampling frequency; determines the current sensor error based on the current sampling mode, current sampling frequency and sensor noise model; the sensor noise model is used to represent the relationship between sensor error and sampling frequency; determines to adjust the sampling frequency based on the current sampling mode and current sensor error; and performs sampling based on the adjusted sampling frequency.
[0026] In this embodiment, the sampling mode corresponding to the cooking state is accurately determined by the deviation between the current temperature and the set temperature, which solves the problem of ambiguity in the cooking state. By calculating the error corresponding to the sampling mode and dynamically adjusting the sampling frequency, a high-precision adaptive sampling adjustment with fast response is finally achieved.
[0027] The following describes a specific embodiment of a sampling control method for a cooking device according to this application. Figure 2 This is a flowchart illustrating a sampling control method for a cooking device provided in an embodiment of this application. Figure 1 This specification provides method operation steps as shown in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operation steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual system or server products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as shown in the embodiments or drawings... Figure 2 As shown, it may include: S201: Obtain current temperature data and set temperature data.
[0028] S202: Determine the current sampling mode based on the current temperature data and the set temperature data.
[0029] S203: Get the current sampling frequency.
[0030] S204: Determine the current sensor error based on the current sampling mode, current sampling frequency, and sensor noise model; the sensor noise model is used to represent the relationship between sensor error and sampling frequency.
[0031] S205: Determine and adjust the sampling frequency based on the current sampling mode and the current sensor error.
[0032] S206: Sampling is performed based on adjusting the sampling frequency.
[0033] Figure 3 This is a flowchart illustrating a sampling control method for a cooking device provided in an embodiment of this application. Figure 2 The method may include: S301: Obtain current temperature data and set temperature data.
[0034] In this embodiment, current temperature data is collected using an infrared sensor. and set temperature data .
[0035] S302: Determine the current sampling mode based on the current temperature data and the set temperature data.
[0036] In one possible implementation, using current temperature data and set temperature data The deviation between them can accurately determine the current sampling mode.
[0037] Figure 4 This is a flowchart illustrating a method for determining the current sampling mode provided in an embodiment of this application. Figure 1 In one possible implementation, determining the current sampling mode based on current temperature data and set temperature data includes: S3021: Determine temperature difference data and temperature gradient data based on current temperature data and set temperature data.
[0038] In this embodiment of the application, based on the current temperature data and set temperature data Determine temperature difference data and temperature gradient data .
[0039] S3022: Determine whether the steady-state condition is met. If yes, execute S3023; otherwise, execute S3024.
[0040] In this embodiment, the steady-state condition is that the temperature difference data is less than or equal to the temperature difference threshold. And the temperature gradient data is less than or equal to the first gradient threshold. .
[0041] S3023: Determine the steady-state mode as the current sampling mode.
[0042] S3024: Determine whether the transient condition is met. If yes, execute S3025; otherwise, execute S3026.
[0043] In this embodiment of the application, the transient condition is that the temperature difference data is greater than the temperature difference threshold. And the temperature gradient data is greater than the temperature gradient threshold. .
[0044] S3025: Determine the transient mode as the current sampling mode.
[0045] S3026: Maintain the current status and continue testing.
[0046] Figure 5 This is a flowchart illustrating a method for determining the current sampling mode provided in an embodiment of this application. Figure 2 In another possible implementation, the current sampling mode is determined based on the current temperature data and the set temperature data, including: S3021: Determine temperature difference data and temperature gradient data based on current temperature data and set temperature data.
[0047] In this embodiment of the application, based on the current temperature data and set temperature data Determine temperature difference data and temperature gradient data .
[0048] S3022: Obtain historical sampling patterns.
[0049] In the embodiments of this application, the historical sampling mode is actually the sampling mode of the previous state. That is, the sampling mode needs to be switched from the historical sampling mode to the current sampling mode, including stateless mode, steady state mode and transient mode.
[0050] S3023: Determine the historical sampling mode. If the historical sampling mode is a stateless mode, execute S3024; if the historical sampling mode is a steady-state mode, execute S3029; if the historical sampling mode is a transient mode, execute S30212.
[0051] S3024: Determine whether the steady-state condition is met. If yes, execute S3025; otherwise, execute S3026.
[0052] In the embodiments of this application, the steady-state condition is that the temperature difference data is less than or equal to the temperature difference threshold, and the temperature gradient data is less than or equal to the first gradient threshold.
[0053] S3025: Determine the steady-state mode as the current sampling mode.
[0054] S3026: Determine whether the transient condition is met. If yes, execute S3027; otherwise, execute S3028.
[0055] In this embodiment of the application, the transient temperature difference data is greater than the temperature difference threshold, and the temperature gradient data is greater than the first gradient threshold.
[0056] S3027: Determine the transient mode as the current sampling mode.
[0057] S3028: Maintain the current state and continue testing.
[0058] S3029: Determine whether the first hysteresis condition is met. If yes, execute S30210; otherwise, execute S30211.
[0059] In this embodiment, the first hysteresis condition is that the temperature difference data and the temperature gradient data are greater than the second gradient threshold, and the second gradient threshold is greater than the first gradient threshold.
[0060] S30210: Determine the transient mode as the current sampling mode.
[0061] S30211: Determine the steady-state mode as the current sampling mode.
[0062] S30212: Determine whether the second hysteresis condition is met. If yes, execute S30213; otherwise, execute S30214.
[0063] In this embodiment, the first hysteresis condition is that the temperature gradient data is less than or equal to the third gradient threshold, and the third gradient threshold is less than the first gradient threshold.
[0064] S30213: Determine the steady-state mode as the current sampling mode. S30214: Determine the transient mode as the current sampling mode.
[0065] In one possible implementation, if the historical sampling mode is a stateless mode, ,and The steady-state mode is determined as the current sampling mode.
[0066] In another possible embodiment, if the historical sampling mode is a stateless mode, ,and The transient mode is determined as the current sampling mode.
[0067] In another possible embodiment, if the historical sampling mode is a steady-state mode, and The transient mode is determined as the current sampling mode. These are empirical values obtained from experiments.
[0068] In another possible embodiment, if the historical sampling mode is a steady-state mode, but the temperature difference data and temperature gradient data do not meet the first hysteresis condition, the steady-state mode is determined to be the current sampling mode.
[0069] In another possible embodiment, if the historical sampling mode is a transient mode, and The steady-state mode is determined as the current sampling mode.
[0070] In another possible embodiment, if the historical sampling mode is a transient mode, but the temperature difference data and temperature gradient data do not meet the second hysteresis condition, the transient mode is determined to be the current sampling mode.
[0071] S303: Get the current sampling frequency.
[0072] In one possible embodiment, the current sampling frequency f of the infrared sensor is obtained.
[0073] S304: Determine the current sensor error based on the current sampling mode, current sampling frequency, and sensor noise model.
[0074] In this embodiment, the sensor noise model is used to represent the relationship between sensor error and sampling frequency.
[0075] In one possible implementation, the current sampling mode includes a steady-state mode and a transient mode.
[0076] Figure 6 This is a flowchart illustrating a method for determining the current sensor error according to an embodiment of this application. In one possible implementation, the current sensor error is determined based on the current sampling mode, the current sampling frequency, and the sensor noise model, including: S3041: Determine the current noise error based on the current sampling frequency and sensor noise model.
[0077] In this embodiment of the application, the sensor noise model is as follows: Current noise error 3 It covers 99.7% of the noise range.
[0078] S3042: Determine whether the current sampling mode is a steady-state mode. If yes, execute S3043; otherwise, execute S3044.
[0079] S3043: Determine the current sensor error based on the current noise error.
[0080] If the current sampling mode is steady-state mode, the current noise error is the current sensor error. .
[0081] S3044: Determine the current dynamic error based on the current sampling frequency.
[0082] In the embodiments of this application, dynamic error C is the system dynamic constant.
[0083] S3045: Determine the current sensor error based on the current noise error and the current dynamic error.
[0084] If the current sampling mode is transient mode, the current sensor error consists of the current noise error and the current dynamic error. .
[0085] S305: Determine and adjust the sampling frequency based on the current sampling mode and the current sensor error.
[0086] In one possible implementation, determining the adjustment of the sampling frequency based on the current sampling mode and the current sensor error includes: S3051: Determine error constraints based on the current sampling mode.
[0087] In this embodiment, the error constraint condition for the steady-state mode is the maximum steady-state error. The error constraint condition for the transient mode is the maximum transient error. .
[0088] S3052: Establish an error constraint model based on the current sensor error and error constraints.
[0089] For steady-state modes, according to Solve and adjust the sampling accuracy.
[0090] For transient modes, according to This allows for the determination and adjustment of sampling accuracy.
[0091] S3053: The sampling frequency is adjusted by solving the error constraint model.
[0092] S306: Sampling is performed based on adjusting the sampling frequency.
[0093] Figure 7 This is a flowchart illustrating a transient mode noise suppression method provided in an embodiment of this application. In one possible implementation, it includes: S307: If the current sampling mode is transient mode, obtain the smoothing factor.
[0094] S308: Determine the exponentially weighted moving average based on the smoothing factor and current temperature data.
[0095] S309: Noise suppression based on exponentially weighted moving average.
[0096] In this embodiment, the exponentially weighted moving average is determined based on the smoothing factor and the current temperature data. Among them, smoothing factor (0< <1) is an empirical value. The observed value at time t, which is the current temperature data. Let be the exponentially weighted moving average at time t. The exponentially weighted moving average is used to suppress noise during the transient phase.
[0097] Figure 8 This is a flowchart illustrating a method for establishing a sensor noise model according to an embodiment of this application. Before determining the sensor error based on the current sampling mode, sampling frequency, and sensor noise model, the method further includes: S401: Acquire multiple current calibration temperatures at multiple calibration sampling frequencies under multiple calibration set temperatures.
[0098] S402: Determine multiple calibration sensor errors based on multiple calibration set temperatures and multiple calibration current temperatures.
[0099] S403: Establish a sensor noise model based on multiple calibration sampling frequencies and multiple calibration sensor errors.
[0100] At multiple calibrated set temperatures [ Below, according to the calibrated sampling frequency [ Obtain multiple calibration temperatures and calculate the corresponding variance. This establishes the relationship between sensor variance and sampling frequency, i.e., the sensor noise model. .
[0101] This application also provides a sampling control device for cooking equipment. Figure 9 This is a schematic diagram of the structure of a sampling control device for a cooking apparatus provided in an embodiment of this application, as shown below. Figure 9 As shown, the device 500 includes: The first acquisition module 501 is used to acquire current temperature data and set temperature data; The first determining module 502 is used to determine the current sampling mode based on the current temperature data and the set temperature data; The second acquisition module 503 is used to acquire the current sampling frequency; The second determining module 504 is used to determine the current sensor error based on the current sampling mode, the current sampling frequency, and the sensor noise model; the sensor noise model is used to represent the relationship between the sensor error and the sampling frequency. The third determining module 505 is used to determine and adjust the sampling frequency based on the current sampling mode and the current sensor error; The sampling control module 506 is used for sampling based on adjusting the sampling frequency.
[0102] In one possible implementation, it also includes: The fourth determination module is used to determine the temperature difference data and temperature gradient data based on the current temperature data and the set temperature data; The fifth determination module is used to determine the steady-state mode as the current sampling mode if the temperature difference data is less than or equal to the temperature difference threshold and the temperature gradient data is less than or equal to the first gradient threshold; or if the temperature difference data is greater than the temperature difference threshold and the temperature gradient data is greater than the temperature gradient threshold, determine the transient mode as the current sampling mode.
[0103] In one possible implementation, it also includes: The sixth determination module is used to determine the temperature difference data and temperature gradient data based on the current temperature data and the set temperature data; The third acquisition module is used to acquire historical sampling patterns; The seventh determination module is used to determine the steady-state mode as the current sampling mode if the historical sampling mode is a stateless mode, the temperature difference data is less than or equal to the temperature difference threshold, and the temperature gradient data is less than or equal to the first gradient threshold; or; The eighth determination module is used to determine the transient mode as the current sampling mode if the historical sampling mode is a stateless mode, the temperature difference data is greater than the temperature difference threshold, and the temperature gradient data is greater than the first gradient threshold; or; The ninth determining module is used to determine the transient mode as the current sampling mode if the historical sampling mode is a steady-state mode and the temperature difference data and temperature gradient data satisfy the first hysteresis condition; the first hysteresis condition is that the temperature difference data and the temperature gradient data are greater than the second gradient threshold; the second gradient threshold is greater than the first gradient threshold; or; The tenth determination module is used to determine the steady-state mode as the current sampling mode if the historical sampling mode is a steady-state mode, but the temperature difference data and temperature gradient data do not meet the first hysteresis condition; or; The eleventh determination module is used to determine the steady-state mode as the current sampling mode if the historical sampling mode is a transient mode and the temperature difference data and temperature gradient data satisfy the second hysteresis condition; the first hysteresis condition is that the temperature gradient data is less than or equal to the third gradient threshold; the third gradient threshold is less than the first gradient threshold; or; The twelfth determination module is used to determine the transient mode as the current sampling mode if the historical sampling mode is a transient mode, but the temperature difference data and temperature gradient data do not meet the second hysteresis condition.
[0104] In one possible implementation, the current sampling mode includes a steady-state mode and a transient mode; it also includes: The thirteenth determination module is used to determine the current noise error based on the current sampling frequency and the sensor noise model; The fourteenth determining module is used to determine the current sensor error based on the current noise error if the current sampling mode is a steady-state mode; or; The fifteenth determination module is used to determine the current dynamic error based on the current sampling frequency and the current sensor error based on the current noise error and the current dynamic error if the current sampling mode is transient mode.
[0105] In one possible implementation, it also includes: The sixteenth determination module is used to determine error constraints based on the current sampling mode; The first model building module is used to build an error constraint model based on the current sensor error and error constraint conditions. The model solving module is used to solve the error constraint model to obtain the adjusted sampling frequency.
[0106] In one possible implementation, it also includes: The fourth acquisition module is used to acquire multiple current calibration temperatures at multiple calibration sampling frequencies under multiple calibration setting temperatures; The seventeenth determination module is used to determine the errors of multiple calibration sensors based on multiple calibration set temperatures and multiple calibration current temperatures; The second model building module is used to build a sensor noise model based on multiple calibration sampling frequencies and multiple calibration sensor errors.
[0107] In one possible implementation, it also includes: The fifth acquisition module is used to acquire the smoothing factor if the current sampling mode is transient. The eighteenth determination module is used to determine the exponentially weighted moving average based on the smoothing factor and the current temperature data; The noise suppression module is used for noise suppression based on the exponentially weighted moving average.
[0108] The apparatus and method embodiments in this application are based on the same application concept.
[0109] The methods and embodiments provided in this application can be executed on a computer terminal, server, or similar computing device. Taking running on a server as an example, Figure 10 This is a hardware structure block diagram of a server for a sampling control method of a cooking device provided in an embodiment of this application. For example... Figure 10As shown, the server 600 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 610 (CPUs 610 may include, but are not limited to, microprocessors such as MCUs or programmable logic devices such as FPGAs), a memory 630 for storing data, and one or more storage media 620 (e.g., one or more mass storage devices) for storing application programs 623 or data 622. The memory 630 and storage media 620 may be temporary or persistent storage. The program stored in the storage media 620 may include one or more modules, each module may include a series of instruction operations on the server. Furthermore, the CPU 610 may be configured to communicate with the storage media 620 and execute the series of instruction operations stored in the storage media 620 on the server 600. Server 600 may also include one or more power supplies 660, one or more wired or wireless network interfaces 650, one or more input / output interfaces 640, and / or one or more operating systems 621, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.
[0110] The input / output interface 640 can be used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of server 600. In one example, input / output interface 640 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, input / output interface 640 may be a radio frequency (RF) module used for wireless communication with the Internet.
[0111] Those skilled in the art will understand that Figure 10 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, server 600 may also include... Figure 10 The more or fewer components shown, or having the same Figure 10 The different configurations shown.
[0112] This application provides an intelligent kitchen appliance, which includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. The processor loads and executes the at least one instruction, at least one program, code set, or instruction set to implement the above-described data processing method.
[0113] Embodiments of this application also provide a computer-readable storage medium, which can be disposed in a server to store at least one instruction, at least one program, code set, or instruction set related to implementing a sampling control method for a cooking device in the method embodiment. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the sampling control method for the cooking device.
[0114] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.
[0115] As can be seen from the embodiments of the sampling control method, apparatus, device, or storage medium for cooking equipment provided in this application, the present application acquires current temperature data and set temperature data; determines the current sampling mode based on the current temperature data and set temperature data; acquires the current sampling frequency; determines the current sensor error based on the current sampling mode, current sampling frequency, and sensor noise model; the sensor noise model is used to represent the relationship between sensor error and sampling frequency; determines the adjustment sampling frequency based on the current sampling mode and current sensor error; and performs sampling based on the adjusted sampling frequency. In the embodiments of this application, the sampling mode corresponding to the cooking state is accurately determined by the deviation between the current temperature and the set temperature, solving the problem of ambiguity in the cooking state. By calculating the error corresponding to the sampling mode and dynamically adjusting the sampling frequency, a high-precision adaptive sampling adjustment with fast response is ultimately achieved.
[0116] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0117] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0118] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.
[0119] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A sampling control method for a cooking device, characterized in that, include: Get the current temperature data and the set temperature data; The current sampling mode is determined based on the current temperature data and the set temperature data; Get the current sampling frequency; The current sensor error is determined based on the current sampling mode, the current sampling frequency, and the sensor noise model. The sensor noise model is used to represent the relationship between sensor error and sampling frequency; The sampling frequency is adjusted based on the current sampling mode and the current sensor error. Sampling is performed based on the adjusted sampling frequency.
2. The sampling control method for a cooking device according to claim 1, characterized in that, Determining the current sampling mode based on the current temperature data and the set temperature data includes: Determine temperature difference data and temperature gradient data based on the current temperature data and the set temperature data; If the temperature difference data is less than or equal to the temperature difference threshold, and the temperature gradient data is less than or equal to the first gradient threshold, the steady-state mode is determined to be the current sampling mode; or, if the temperature difference data is greater than the temperature difference threshold, and the temperature gradient data is greater than the temperature gradient threshold, the transient mode is determined to be the current sampling mode.
3. The sampling control method for a cooking device according to claim 1, characterized in that, Determining the current sampling mode based on the current temperature data and the set temperature data includes: Determine temperature difference data and temperature gradient data based on the current temperature data and the set temperature data; Obtain historical sampling patterns; If the historical sampling mode is a stateless mode, the temperature difference data is less than or equal to the temperature difference threshold, and the temperature gradient data is less than or equal to the first gradient threshold, then the steady-state mode is determined to be the current sampling mode; or; If the historical sampling mode is the stateless mode, the temperature difference data is greater than the temperature difference threshold, and the temperature gradient data is greater than the first gradient threshold, then the transient mode is determined to be the current sampling mode; or; If the historical sampling mode is the steady-state mode, and the temperature difference data and the temperature gradient data satisfy the first hysteresis condition, then the transient mode is determined to be the current sampling mode; the first hysteresis condition is that the temperature difference data and the temperature gradient data are greater than the second gradient threshold; the second gradient threshold is greater than the first gradient threshold; or; If the historical sampling mode is the steady-state mode, but the temperature difference data and the temperature gradient data do not satisfy the first hysteresis condition, then the steady-state mode is determined to be the current sampling mode; or; If the historical sampling mode is the transient mode, and the temperature difference data and the temperature gradient data satisfy the second hysteresis condition, the steady-state mode is determined to be the current sampling mode; the first hysteresis condition is that the temperature gradient data is less than or equal to the third gradient threshold; the third gradient threshold is less than the first gradient threshold; or; If the historical sampling mode is the transient mode, but the temperature difference data and the temperature gradient data do not satisfy the second hysteresis condition, then the transient mode is determined to be the current sampling mode.
4. The sampling control method for a cooking device according to claim 1, characterized in that, The current sampling mode includes steady-state mode and transient mode; Determining the current sensor error based on the current sampling mode, the current sampling frequency, and the sensor noise model includes: The current noise error is determined based on the current sampling frequency and the sensor noise model; If the current sampling mode is the steady-state mode, determine the current sensor error based on the current noise error; or; If the current sampling mode is the transient mode, the current dynamic error is determined based on the current sampling frequency, and the current sensor error is determined based on the current noise error and the current dynamic error.
5. The sampling control method for a cooking device according to claim 4, characterized in that, The step of determining and adjusting the sampling frequency based on the current sampling mode and the current sensor error includes: Determine error constraints based on the current sampling mode; An error constraint model is established based on the current sensor error and the error constraint conditions. The adjusted sampling frequency is obtained by solving the error constraint model.
6. The sampling control method for a cooking device according to claim 1, characterized in that, Before determining the sensor error based on the current sampling mode, the sampling frequency, and the sensor noise model, the method further includes: Multiple calibration current temperatures are acquired at multiple calibration sampling frequencies under multiple calibration set temperatures; Multiple calibration sensor errors are determined based on the multiple calibration set temperatures and the multiple calibration current temperatures; The sensor noise model is established based on the multiple calibration sampling frequencies and the multiple calibration sensor errors.
7. The sampling control method for a cooking device according to claim 1, characterized in that, Also includes: If the current sampling mode is a transient mode, obtain the smoothing factor; Determine the exponentially weighted moving average based on the smoothing factor and the current temperature data; Noise suppression is performed based on the exponentially weighted moving average.
8. A sampling control device for a cooking apparatus, characterized in that, include: The first acquisition module is used to acquire current temperature data and set temperature data; The first determining module is used to determine the current sampling mode based on the current temperature data and the set temperature data; The second acquisition module is used to acquire the current sampling frequency; The second determining module is used to determine the current sensor error based on the current sampling mode, the current sampling frequency, and the sensor noise model. The sensor noise model is used to represent the relationship between sensor error and sampling frequency; The third determining module is used to determine the adjustment of the sampling frequency based on the current sampling mode and the current sensor error; A sampling control module is used to perform sampling based on the adjusted sampling frequency.
9. A smart kitchen appliance, characterized in that, The intelligent kitchen appliance includes a processor and a memory. The memory stores at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the sampling control method of the cooking appliance as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the sampling control method of the cooking device as described in any one of claims 1-7.