Cooking utensil state detection method, intelligent extractor hood and computer device

Abstract

The application relates to a cooking utensil state detection method, an intelligent range hood and a computer device, wherein the method comprises the following steps: performing infrared thermal imaging on a target region above a heated cooking utensil to obtain a real-time temperature matrix corresponding to the target region; the real-time temperature matrix comprises a plurality of temperature values, and each temperature value corresponds to a corresponding position in the target region; gradient analysis is performed on each temperature value in the real-time temperature matrix to obtain a corresponding gradient amplitude matrix; the gradient amplitude matrix comprises a temperature gradient amplitude corresponding to each temperature value; based on each temperature value in the real-time temperature matrix and the gradient amplitude matrix, a heat diffusion coefficient of the cooking utensil is determined; and based on the heat diffusion coefficient, the current state of the cooking utensil is identified. Through the application, the problem that the actual state of the cooking utensil cannot be accurately detected is solved, and the state of the cooking utensil is accurately detected, so that the user can timely avoid the safety and health risks caused by the change of the state of the cooking utensil.

Description

Technical Field

[0001] This application relates to the field of smart home appliances, and in particular to methods for detecting the status of cooking appliances, smart range hoods, and computer equipment. Background Technology

[0002] Over long-term use, cooking utensils gradually experience coating wear, peeling, and body aging. This deterioration process is typically slow and insidious. Current technology lacks effective methods for assessing the condition of cooking utensils, relying solely on visual observation or subjective cooking experience for rough judgments, making it impossible to accurately determine the actual condition of the utensils.

[0003] There is currently no effective solution to the problem that related technologies cannot accurately detect the actual condition of cooking utensils. Summary of the Invention

[0004] This embodiment provides a cooking appliance status detection method, a smart range hood, and a computer device to solve the problem in related technologies that cannot accurately detect the actual status of cooking appliances.

[0005] Firstly, this embodiment provides a method for detecting the state of a cooking utensil, including:

[0006] Infrared thermal imaging is performed on the target area above the heated cooking appliance to obtain a real-time temperature matrix corresponding to the target area; the real-time temperature matrix includes multiple temperature values, each temperature value corresponding to a corresponding position in the target area;

[0007] Gradient analysis is performed on each temperature value in the real-time temperature matrix to obtain the corresponding gradient magnitude matrix; the gradient magnitude matrix includes the temperature gradient magnitude corresponding to each temperature value;

[0008] The thermal diffusivity of the cooking appliance is determined based on the temperature values ​​in the real-time temperature matrix and the gradient amplitude matrix.

[0009] The current state of the cooking appliance is identified based on the thermal diffusivity.

[0010] In some embodiments, performing infrared thermal imaging on a target area above the heated cooking appliance to obtain a real-time temperature matrix corresponding to the target area includes:

[0011] By using an infrared sensor with a preset pixel array, the target area above the heated cooking appliance is sampled to obtain the output voltage corresponding to each pixel position in the preset pixel array;

[0012] Based on preset calibration coefficients, the output voltage corresponding to each pixel position is linearized and calibrated to generate the corresponding real-time temperature matrix.

[0013] In some embodiments, performing gradient analysis on each temperature value in the real-time temperature matrix to obtain the corresponding gradient magnitude matrix includes:

[0014] Determine the horizontal gradient component corresponding to each temperature value in the real-time temperature matrix; the horizontal gradient component is the difference between the temperature value to the right of the temperature value and the temperature value to the left of the temperature value.

[0015] Determine the vertical gradient component corresponding to each temperature value in the real-time temperature matrix; the vertical gradient component is the difference between the temperature value adjacent to the temperature value below and the temperature value adjacent to the temperature value above.

[0016] Based on the horizontal and vertical gradient components corresponding to each temperature value, the temperature gradient amplitude corresponding to each temperature value is determined to form the gradient amplitude matrix.

[0017] In some embodiments, determining the thermal diffusivity of the cooking appliance based on the temperature values ​​in the real-time temperature matrix and the gradient magnitude matrix includes:

[0018] The temperature values ​​in the real-time temperature matrix are sorted, and the maximum and minimum temperature values ​​among the temperature values ​​are determined based on the sorting results.

[0019] The mean value of the temperature gradient magnitude corresponding to each temperature value in the gradient magnitude matrix is ​​calculated to obtain the mean temperature gradient value of each temperature value.

[0020] The thermal diffusivity of the cooking appliance is determined based on the maximum temperature value, the minimum temperature value, and the average temperature gradient.

[0021] In some embodiments, identifying the current state of the cooking appliance based on the thermal diffusivity includes:

[0022] Determine the preset threshold range to which the thermal diffusivity belongs; the preset threshold range corresponds to the thermal conductivity of the cooking appliance;

[0023] The current state of the cooking appliance is identified based on the preset threshold range to which the thermal diffusivity belongs.

[0024] In some embodiments, the current state of the cooking appliance includes one or more combinations of coating peeling, aging, and remaining service life.

[0025] Secondly, this embodiment provides an intelligent range hood, which includes an infrared sensor and a controller;

[0026] The infrared sensor is used to perform infrared thermal imaging on the target area above the cooking appliance formed by heating, so as to obtain a real-time temperature matrix corresponding to the target area; the real-time temperature matrix includes multiple temperature values, and each temperature value corresponds to a corresponding position in the target area;

[0027] The controller is used to perform gradient analysis on each temperature value in the real-time temperature matrix to obtain a corresponding gradient magnitude matrix; the gradient magnitude matrix includes the temperature gradient magnitude corresponding to each temperature value;

[0028] The controller is further configured to determine the thermal diffusivity of the cooking appliance based on the temperature values ​​in the real-time temperature matrix and the gradient amplitude matrix.

[0029] The controller is also used to identify the current state of the cooking appliance based on the thermal diffusivity.

[0030] In some embodiments, the wavelength range of the infrared sensor matches the wavelength characteristics of the thermal radiation signal emitted by the cooking appliance.

[0031] Thirdly, this embodiment provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cooking appliance status detection method described in the first aspect above.

[0032] Fourthly, this embodiment provides a storage medium storing a computer program that, when executed by a processor, implements the cooking appliance status detection method described in the first aspect above.

[0033] Compared with related technologies, the cooking appliance status detection method, intelligent range hood, and computer equipment provided in this embodiment obtain a real-time temperature matrix corresponding to the target area by performing infrared thermal imaging on the target area above the heated cooking appliance. The real-time temperature matrix includes multiple temperature values, each corresponding to a specific location in the target area. Gradient analysis is performed on each temperature value in the real-time temperature matrix to obtain the corresponding gradient amplitude matrix. The gradient amplitude matrix includes the temperature gradient amplitude corresponding to each temperature value. Based on each temperature value in the real-time temperature matrix and the gradient amplitude matrix, the thermal diffusivity of the cooking appliance is determined. Based on the thermal diffusivity, the current state of the cooking appliance is identified. This solves the problem of not being able to accurately detect the actual state of the cooking appliance, and achieves accurate detection of the cooking appliance state so that users can avoid safety and health risks caused by changes in the state of the cooking appliance in a timely manner.

[0034] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0035] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0036] Figure 1 This is a structural block diagram of an intelligent range hood provided in one embodiment of this application;

[0037] Figure 2 This is a flowchart of a cooking appliance status detection method provided in an embodiment of this application;

[0038] Figure 3 This is a flowchart of a method for obtaining a real-time temperature matrix according to an embodiment of this application;

[0039] Figure 4 This is a flowchart of a gradient magnitude matrix calculation method provided in an embodiment of this application;

[0040] Figure 5 This is a flowchart of a method for calculating the thermal diffusivity of a cooking appliance according to an embodiment of this application;

[0041] Figure 6 This is a flowchart of a method for identifying the current state of a cooking appliance according to an embodiment of this application;

[0042] Figure 7 This is a schematic flowchart of a cooking appliance status detection method provided in an embodiment of this application.

[0043] In the diagram: 100, Smart range hood; 101, Controller; 102, Infrared sensor; 103, Memory; 104, Light module; 105, Switch module; 106, Communication module. Detailed Implementation

[0044] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0045] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these” used in this application do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to these processes, methods, products, or devices. Words such as “connected,” “linked,” and “coupled” used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. Normally, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific order of objects.

[0046] The method embodiment provided in this example can be executed by the intelligent range hood 100. Figure 1 This is a structural block diagram of the intelligent range hood 100 in this embodiment, as shown below. Figure 1 As shown, the intelligent range hood 100 includes a controller 101, an infrared sensor 102, a memory 103, a light module 104, a switch module 105, and a communication module 106. The controller 101 can be a microprocessor (MCU) or a field-programmable gate array (FPGA) device. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the smart range hood 100 described above. For example, the smart range hood 100 may also include components that are more...Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.

[0047] Infrared sensor 102 can be installed at the bottom of the smoke collection hood or the bottom of the smoke baffle of the smart range hood 100, usually facing the center of the cooking appliance, to perform infrared thermal imaging of the target area above the cooking appliance to obtain the real-time temperature matrix corresponding to the target area. Specifically, for each cooking appliance, infrared sensor 102 has sufficient pixel coverage, such as using a 16×16 pixel array; the field of view (FOV) of infrared sensor 102 at least covers the placement area of ​​the cooking appliance (including single-burner area, double-burner area, etc.), for example, a FOV of 110° to cover the double-burner area, and the installation position is about 30~60 cm away from the cooking appliance; the wavelength range of infrared sensor 102 matches the wavelength characteristics of the thermal radiation signal emitted by the cooking appliance (e.g., 8~14). The temperature resolution of the infrared sensor 102 meets the requirements for detecting minute changes in the thermal field (e.g., 0.1℃).

[0048] The memory 103 is used to store computer programs, such as the computer program corresponding to the cooking appliance status detection method in this embodiment. The controller 101 executes various functional applications and data processing by running the computer program stored in the memory 103, thereby realizing the above-described method. The memory 103 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.

[0049] The lighting module 104 provides illumination for the cooking area, typically using LED light sources. This module receives control signals from the controller 101, enabling the lights to be turned on and off, and their brightness adjusted to suit different ambient light intensities and cooking needs. The switch module 105, as the core component for human-machine interaction, receives manual operation commands from the user (such as power on / off, gear switching, and lighting control), and converts these commands into electrical signals that are transmitted to the controller 101.

[0050] The communication module 106 is used to realize information interaction between the smart range hood 100 and external devices or networks, and supports intelligent functions such as remote control and data uploading. It can support one or more communication protocols such as Wi-Fi, Bluetooth, and ZigBee.

[0051] This embodiment provides a method for detecting the state of cooking appliances. Figure 2 This is a flowchart of the cooking appliance status detection method in this embodiment, as follows: Figure 2 As shown, the process includes the following steps:

[0052] Step S210: Perform infrared thermal imaging on the target area above the heated cooking appliance to obtain a real-time temperature matrix corresponding to the target area; the real-time temperature matrix includes multiple temperature values, each temperature value corresponding to a corresponding position in the target area;

[0053] Step S220: Perform gradient analysis on each temperature value in the real-time temperature matrix to obtain the corresponding gradient magnitude matrix; the gradient magnitude matrix includes the temperature gradient magnitude corresponding to each temperature value.

[0054] Step S230: Determine the thermal diffusivity of the cooking appliance based on the temperature values ​​and gradient amplitude matrix in the real-time temperature matrix.

[0055] Step S240: Identify the current state of the cooking appliance based on the thermal diffusivity.

[0056] In this embodiment, the temperature distribution of a target area above the cooking appliance is monitored in real time. The target area is the steam plume formed above the cooking appliance. Specifically, an infrared sensor or infrared camera (with sufficient pixel resolution) integrated on the smart range hood is used to collect signals from the target area above the cooking appliance, obtaining the output voltage corresponding to each pixel. Based on a preset calibration coefficient, the output voltage corresponding to each pixel is linearized and calibrated to generate a corresponding real-time temperature matrix.

[0057] The system automatically acquires signals at preset time intervals (e.g., 0.5 seconds), outputting a real-time temperature matrix corresponding to the pixel resolution for each acquisition to continuously and dynamically reflect the temperature changes in the target area. This real-time temperature matrix includes the temperature values ​​of multiple pixels.

[0058] Furthermore, based on the temperature values ​​of each pixel in the real-time temperature matrix, a corresponding gradient magnitude matrix is ​​calculated. This matrix includes the temperature gradient magnitude for each pixel, reflecting the severity of temperature changes. Subsequently, based on the temperature values ​​in the real-time temperature matrix and the gradient magnitude matrix, the thermal diffusivity of the cooking appliance is calculated. Specifically, this calculation can be achieved by: determining the maximum and minimum temperature values ​​in the real-time temperature matrix and calculating the mean of the temperature gradient magnitudes corresponding to each temperature value in the gradient magnitude matrix; and then calculating the thermal diffusivity of the cooking appliance based on the maximum, minimum, and mean temperature gradient values. Alternatively, the effective temperature measurement area of ​​the cooking appliance can be pre-selected from the real-time temperature matrix, and the median gradient magnitude of the corresponding effective area in the gradient magnitude matrix can be extracted. Then, the thermal diffusivity of the cooking appliance can be calculated based on the maximum, minimum, and median gradient magnitudes within the effective temperature measurement area.

[0059] Understandably, the thermal diffusivity of cooking appliances is used to quantify their thermal conductivity. Continuous monitoring and analysis of the thermal diffusivity can identify the current state of the appliance, enabling intelligent assessment of its health. Specifically, when the non-stick or protective coating on the surface of a cooking appliance peels off locally, the thermal conductivity of the exposed substrate material differs significantly from that of the coated area. This results in uneven temperature distribution between the intact and peeled areas under uniform heating conditions, leading to abnormal heat diffusion. Therefore, monitoring the thermal diffusivity allows for identification of coating peeling. Furthermore, over long-term use, changes in the microstructure of cooking appliances due to material fatigue, oxidation, or carbon buildup reduce overall thermal conductivity. Monitoring the thermal diffusivity allows for assessment of the appliance's aging level. In addition, by continuously tracking the decay curve of the thermal diffusivity and setting a safety threshold, predictive maintenance reminders for the remaining lifespan of the appliance can be provided.

[0060] For example, in the case of a coated frying pan, the coating is initially intact, with a high and uniformly distributed thermal diffusivity α value. After a period of use, if the coating in the center of the pan bottom peels off due to scratches, the system will detect an abnormal increase in the temperature gradient amplitude in that local area, and the calculated thermal diffusivity will also show an anomaly, thus triggering a coating damage alarm. Over time, the system may detect a slow decrease in the thermal diffusivity α value, at which point it will prompt the user to check for decreased thermal conductivity or output the predicted remaining safe service life so that the user can replace the pan in a timely manner.

[0061] Over long-term use, cooking utensils gradually experience coating wear, peeling, and body aging. This deterioration process is typically slow and insidious. Current technology lacks effective methods for assessing the condition of cooking utensils, relying solely on visual observation or subjective cooking experience for rough judgments, making it impossible to accurately determine the actual condition of the utensils.

[0062] Compared to existing technologies, this application performs infrared thermal imaging on a target area above a heated cooking appliance to obtain a real-time temperature matrix corresponding to the target area. The real-time temperature matrix includes multiple temperature values, each corresponding to a specific location within the target area. Gradient analysis is performed on each temperature value in the real-time temperature matrix to obtain a corresponding gradient amplitude matrix. This gradient amplitude matrix includes the temperature gradient amplitude corresponding to each temperature value. Based on the temperature values ​​in the real-time temperature matrix and the gradient amplitude matrix, the thermal diffusivity of the cooking appliance is determined. Based on this thermal diffusivity, the current state of the cooking appliance is identified. By analyzing the spatiotemporal evolution characteristics of the temperature field and converting them into a thermal diffusivity coefficient characterizing the intrinsic thermal conductivity of the material, the state of the cooking appliance can be accurately identified using the thermal diffusivity coefficient. This solves the problem of inaccurate detection of the actual state of the cooking appliance, enabling accurate detection of the appliance's state so that users can promptly avoid safety and health risks caused by changes in the appliance's state.

[0063] In some of these embodiments, such as Figure 3 As shown, step S210, which involves performing infrared thermal imaging on the target area above the heated cooking appliance to obtain the real-time temperature matrix corresponding to the target area, includes the following steps:

[0064] Step S211: By using an infrared sensor with a preset pixel array, the target area above the heated cooking appliance is sampled to obtain the output voltage corresponding to each pixel position in the preset pixel array.

[0065] Step S212: Based on preset calibration coefficients, the output voltage corresponding to each pixel position is linearized and calibrated to generate the corresponding real-time temperature matrix.

[0066] In this embodiment, an infrared sensor is installed at the bottom of the smoke collection hood or the bottom of the smoke baffle of the smart range hood, directly facing the center of the cooking appliance. This allows the infrared sensor to monitor the temperature distribution in a target area above the cooking appliance in real time. The target area is the plume of steam formed by boiling inside the cooking appliance.

[0067] Specifically, an infrared sensor with a preset pixel array is used to collect signals from the target area above the cooking appliance, obtaining the output voltage corresponding to each pixel. Based on preset calibration coefficients, the output voltage of each pixel is linearized and calibrated to generate a corresponding real-time temperature matrix. The specific expression for linearization calibration is as follows:

[0068] (1)

[0069] In equation (1), represents the temperature value of the pixel in the i-th row and j-th column of the real-time temperature matrix, in °C; k and b are preset calibration coefficients. This represents the output voltage corresponding to the pixel.

[0070] For example, when the infrared sensor uses a 16×16 pixel array, the corresponding real-time temperature matrix is ​​a 16×16 temperature matrix, the specific expression of which is as follows:

[0071] (2)

[0072] In equation (2), This is a real-time temperature matrix; This represents the temperature value of the pixel in the i-th row and j-th column of the real-time temperature matrix, in °C.

[0073] This embodiment utilizes an infrared sensor to achieve real-time measurement of the temperature field in the cooking area, providing a reliable data foundation for subsequent assessment of the cooking appliance's condition.

[0074] In some of these embodiments, such as Figure 4 As shown, step S220 involves performing gradient analysis on each temperature value in the real-time temperature matrix to obtain the corresponding gradient magnitude matrix, including the following steps:

[0075] Step S221: Determine the horizontal gradient component corresponding to each temperature value in the real-time temperature matrix; the horizontal gradient component is the difference between the temperature value to the right of the temperature value and the temperature value to the left of the temperature value.

[0076] Step S222: Determine the vertical gradient component corresponding to each temperature value in the real-time temperature matrix; the vertical gradient component is the difference between the temperature value below and above the temperature value.

[0077] Step S223: Based on the horizontal and vertical gradient components corresponding to each temperature value, determine the temperature gradient magnitude corresponding to each temperature value to form a gradient magnitude matrix.

[0078] In this embodiment, based on each temperature value in the real-time temperature matrix, the corresponding gradient magnitude matrix is ​​calculated. The gradient magnitude matrix includes the temperature gradient magnitude corresponding to each temperature value, and the temperature gradient magnitude is used to reflect the degree of temperature change.

[0079] Specifically, the horizontal and vertical gradient components corresponding to each temperature value in the real-time temperature matrix are calculated. The horizontal gradient component is the difference between the temperature value to the right and the temperature value to the left of the current temperature value, and the vertical gradient component is the difference between the temperature value below and the temperature value above the current temperature value. The formula for calculating the magnitude of the temperature gradient corresponding to each temperature value is as follows:

[0080] (3)

[0081] In equation (3), This represents the magnitude of the temperature gradient corresponding to each temperature value; horizontal gradient component. Vertical gradient component .

[0082] This embodiment accurately calculates the temperature gradient amplitude corresponding to each temperature value to obtain the corresponding gradient amplitude matrix, laying a solid foundation for the reliable estimation of the subsequent thermal diffusivity.

[0083] In some of these embodiments, such as Figure 5 As shown, step S230, which determines the thermal diffusivity of the cooking appliance based on the temperature values ​​and gradient magnitude matrix in the real-time temperature matrix, includes the following steps:

[0084] Step S231: Sort the temperature values ​​in the real-time temperature matrix and determine the maximum and minimum temperature values ​​based on the sorting results.

[0085] Step S232: Perform mean calculation on the temperature gradient magnitude corresponding to each temperature value in the gradient magnitude matrix to obtain the mean temperature gradient of each temperature value.

[0086] Step S233: Determine the thermal diffusivity of the cooking appliance based on the maximum temperature value, the minimum temperature value, and the average temperature gradient.

[0087] Specifically, all temperature values ​​in the real-time temperature matrix are sorted, and the maximum temperature value T among them is determined. max With minimum temperature value T min Meanwhile, the arithmetic mean of all values ​​in the calculated gradient magnitude matrix is ​​taken to obtain the mean temperature gradient.

[0088] Furthermore, based on the two core characteristic quantities mentioned above, the thermal diffusivity of the cooking appliance is calculated using a pre-defined physical model. Its specific expression is as follows:

[0089] (4)

[0090] In equation (4), This represents the maximum temperature value in the real-time temperature matrix. This represents the minimum temperature value in the real-time temperature matrix. This represents the average temperature gradient, expressed in °C per pixel.

[0091] This embodiment accurately calculates the thermal diffusivity of cooking appliances, laying a key foundation for the automated assessment of the condition of cooking appliances.

[0092] In some of these embodiments, such as Figure 6As shown, step S240, which identifies the current state of the cooking appliance based on the thermal diffusivity, includes the following steps:

[0093] Step S241: Determine the preset threshold range to which the thermal diffusivity belongs; the preset threshold range corresponds to the thermal conductivity of the cooking appliance.

[0094] Step S242: Identify the current state of the cooking appliance based on the preset threshold range to which the thermal diffusivity belongs.

[0095] Specifically, the thermal diffusivity is established in advance. A quantitative correspondence between the thermal conductivity and the condition of the appliance. For example, by measuring and analyzing a large number of samples in known conditions (such as appliances with intact coatings, appliances with varying degrees of coating peeling, and appliances with a known service life), the typical range of thermal diffusivity values ​​corresponding to different conditions is statistically summarized, thereby setting corresponding preset threshold ranges. Each threshold range corresponds to a specific thermal conductivity level or physical condition.

[0096] In real-time monitoring, the calculated thermal diffusivity is compared with a preset threshold range. Based on the specific range it falls within, a qualitative judgment on the current state of the cooking appliance is automatically output. For example, if the thermal diffusivity is detected to be less than 10%, the cooking appliance is judged to have poor thermal conductivity, and the user is advised to replace the appliance; if the thermal diffusivity is detected to be less than 15%, the thermal conductivity of the cooking appliance is judged to have decreased; in addition, the thermal diffusivity detected each time is recorded.

[0097] In this embodiment, a stable classification decision boundary is constructed by presetting a threshold range corresponding to the physical state, thereby achieving rapid and accurate identification of the state of cooking utensils.

[0098] In some embodiments, the current state of the cooking appliance includes one or more combinations of coating peeling, aging, and remaining service life.

[0099] In this embodiment, the identified cooking appliance condition includes coating peeling status, aging degree, and remaining service life. This condition assessment covers the entire process of the cooking appliance from its immediate physical condition to its long-term reliability. Coating peeling status reflects the local integrity of the cooking appliance's inner surface, aging degree reflects the overall material performance degradation of the cooking appliance, and remaining service life can be predicted based on the aforementioned performance degradation trend.

[0100] Understandably, by monitoring the actual changes in the thermal diffusivity, each state dimension can be graded and evaluated or combined according to actual needs. For example, coating peeling status can be subdivided into levels such as intact, slight wear, and localized peeling; aging degree can be quantified into stages such as slight, moderate, and severe; and remaining service life can be calculated based on the aging rate to determine the specific time range. More importantly, this embodiment supports cross-correlation analysis of each state dimension (such as identifying composite states where localized coating peeling accelerates overall aging), thereby providing more comprehensive and accurate state diagnosis results.

[0101] This embodiment constructs a multi-dimensional status assessment system covering local, overall, and long-term trends, enabling a systematic assessment of the health of cooking appliances and providing complete data support for users' maintenance decisions and safe use.

[0102] The present embodiment will be described and explained below through specific examples.

[0103] Figure 7 This is a flowchart illustrating the cooking appliance status detection method of this embodiment, as shown below. Figure 7 As shown, the cooking utensil status detection method includes the following steps:

[0104] The temperature distribution in a target area above the cooking appliance is monitored in real time. The target area is the steam plume S701 formed above the cooking appliance. Specifically, an infrared sensor with a preset pixel array is used to collect signals from the target area above the cooking appliance, obtain the output voltage corresponding to each pixel, and linearize and calibrate the output voltage corresponding to each pixel based on a preset calibration coefficient to generate the corresponding real-time temperature matrix S702.

[0105] The gradient magnitude matrix is ​​calculated based on the real-time temperature matrix, which includes the temperature gradient magnitude S703 for each pixel. The maximum and minimum temperature values ​​in the real-time temperature matrix are determined, and the mean value of the temperature gradient magnitude corresponding to each temperature value in the gradient magnitude matrix is ​​calculated. Based on the maximum temperature value, the minimum temperature value, and the mean value of the temperature gradient, the thermal diffusivity S704 of the cooking appliance is calculated.

[0106] Further, determine whether the thermal diffusivity of the cooking appliance is less than a first threshold (e.g., 10%) S705. If the thermal diffusivity of the cooking appliance is less than the first threshold, it is determined that the cooking appliance has poor thermal conductivity, and it is recommended that the user replace the appliance S706; if the thermal diffusivity of the cooking appliance is greater than or equal to the first threshold, determine whether the thermal diffusivity of the cooking appliance is less than a second threshold (e.g., 15%) S707. When the thermal diffusivity is detected to be less than the second threshold, it is determined that the thermal conductivity of the cooking appliance has decreased S708; otherwise, record the monitoring data for this time S709.

[0107] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0108] This embodiment also provides a computer device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0109] Optionally, the computer device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0110] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0111] S1, perform infrared thermal imaging on the target area above the heated cooking appliance to obtain the real-time temperature matrix corresponding to the target area; the real-time temperature matrix includes multiple temperature values, each temperature value corresponding to a corresponding position in the target area;

[0112] S2, perform gradient analysis on each temperature value in the real-time temperature matrix to obtain the corresponding gradient magnitude matrix; the gradient magnitude matrix includes the temperature gradient magnitude corresponding to each temperature value;

[0113] S3, based on the temperature values ​​and gradient amplitude matrix in the real-time temperature matrix, determines the thermal diffusivity of the cooking appliance;

[0114] S4 identifies the current state of cooking appliances based on the thermal diffusivity coefficient.

[0115] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0116] Furthermore, in conjunction with the cooking appliance status detection method provided in the above embodiments, this embodiment can also provide a storage medium for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any of the cooking appliance status detection methods described in the above embodiments.

[0117] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0118] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0119] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0120] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A method for detecting the state of cooking utensils, characterized in that, include: Infrared thermal imaging is performed on the target area above the heated cooking appliance to obtain the real-time temperature matrix corresponding to the target area. The real-time temperature matrix includes multiple temperature values, each of which corresponds to a specific location in the target area; Gradient analysis is performed on each temperature value in the real-time temperature matrix to obtain the corresponding gradient magnitude matrix; The gradient magnitude matrix includes the temperature gradient magnitude corresponding to each temperature value; The thermal diffusivity of the cooking appliance is determined based on the temperature values ​​in the real-time temperature matrix and the gradient amplitude matrix. The current state of the cooking appliance is identified based on the thermal diffusivity.

2. The cooking utensil status detection method according to claim 1, characterized in that, The step of performing infrared thermal imaging on the target area above the heated cooking appliance to obtain the real-time temperature matrix corresponding to the target area includes: By using an infrared sensor with a preset pixel array, the target area above the heated cooking appliance is sampled to obtain the output voltage corresponding to each pixel position in the preset pixel array; Based on preset calibration coefficients, the output voltage corresponding to each pixel position is linearized and calibrated to generate the corresponding real-time temperature matrix.

3. The cooking utensil status detection method according to claim 1, characterized in that, The step of performing gradient analysis on each temperature value in the real-time temperature matrix to obtain the corresponding gradient magnitude matrix includes: Determine the horizontal gradient component corresponding to each temperature value in the real-time temperature matrix; the horizontal gradient component is the difference between the temperature value to the right of the temperature value and the temperature value to the left of the temperature value. Determine the vertical gradient component corresponding to each temperature value in the real-time temperature matrix; the vertical gradient component is the difference between the temperature value adjacent to the temperature value below and the temperature value adjacent to the temperature value above. Based on the horizontal and vertical gradient components corresponding to each temperature value, the temperature gradient amplitude corresponding to each temperature value is determined to form the gradient amplitude matrix.

4. The cooking utensil status detection method according to claim 1, characterized in that, Determining the thermal diffusivity of the cooking appliance based on the temperature values ​​in the real-time temperature matrix and the gradient magnitude matrix includes: The temperature values ​​in the real-time temperature matrix are sorted, and the maximum and minimum temperature values ​​among the temperature values ​​are determined based on the sorting results. The mean value of the temperature gradient magnitude corresponding to each temperature value in the gradient magnitude matrix is ​​calculated to obtain the mean temperature gradient value of each temperature value. The thermal diffusivity of the cooking appliance is determined based on the maximum temperature value, the minimum temperature value, and the average temperature gradient.

5. The cooking utensil status detection method according to claim 1, characterized in that, The step of identifying the current state of the cooking appliance based on the thermal diffusivity includes: Determine the preset threshold range to which the thermal diffusivity belongs; the preset threshold range corresponds to the thermal conductivity of the cooking appliance; The current state of the cooking appliance is identified based on the preset threshold range to which the thermal diffusivity belongs.

6. The cooking utensil condition detection method according to claim 1 or 5, characterized in that, The current condition of the cooking appliance includes one or more of the following: coating peeling, degree of aging, and remaining service life.

7. A smart range hood, characterized in that, The intelligent range hood includes an infrared sensor and a controller; The infrared sensor is used to perform infrared thermal imaging on the target area above the cooking appliance formed by heating, so as to obtain a real-time temperature matrix corresponding to the target area; the real-time temperature matrix includes multiple temperature values, and each temperature value corresponds to a corresponding position in the target area; The controller is used to perform gradient analysis on each temperature value in the real-time temperature matrix to obtain a corresponding gradient magnitude matrix; the gradient magnitude matrix includes the temperature gradient magnitude corresponding to each temperature value; The controller is further configured to determine the thermal diffusivity of the cooking appliance based on the temperature values ​​in the real-time temperature matrix and the gradient amplitude matrix. The controller is also used to identify the current state of the cooking appliance based on the thermal diffusivity.

8. The intelligent range hood according to claim 7, characterized in that, The wavelength range of the infrared sensor matches the wavelength characteristics of the thermal radiation signal emitted by the cooking appliance.

9. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the steps of the cooking appliance status detection method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the cooking appliance status detection method according to any one of claims 1 to 6.

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