Cooker energy gathering ring detection method, system and equipment, medium and program product

Abstract

The invention provides an energy-gathering ring detection method, system and device of a kitchen range, a medium and a program product, the energy-gathering ring detection method comprises the following steps: acquiring collection data of the kitchen range, the collection data comprising real-time reflected light intensity of the surface of an energy-gathering ring; the obtained reflected light intensity of the energy gathering ring in the clean state serves as the reference reflected light intensity; and detecting the smudginess degree and / or smudginess type of the energy gathering ring based on a comparison result of the real-time reflected light intensity and the reference reflected light intensity or a judgment model. According to the smudginess detection method and device, the real-time reflected light intensity of the surface of the energy gathering ring is collected, the smudginess degree and / or the smudginess type of the energy gathering ring are / is detected based on the comparison result of the real-time reflected light intensity and the reference reflected light intensity or the judgment model, non-contact and high-precision smudginess detection is achieved, and the detection efficiency, the detection accuracy and the user experience degree are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of energy-gathering ring detection for cookers, and in particular to a method, system, device, medium, and program product for detecting an energy-gathering ring for a cooker. Background Art

[0002] The energy-gathering ring of a stove is an important component of kitchen cooking equipment. Its main function is to gather heat, improve combustion efficiency and cooking results. However, the energy-gathering ring is prone to accumulate dirt such as oil and carbon scale during long-term use. These dirt will reduce the energy-gathering effect and even affect the safety and service life of the stove. Traditional dirt detection methods rely on manual observation by users, which has problems such as low detection efficiency, inaccurate judgment, and poor user experience. Summary of the Invention

[0003] The technical problem to be solved by the present disclosure is to overcome the existing method of detecting the energy-gathering ring of a stove by manual observation, which has the defects of low detection efficiency, inaccurate judgment and poor user experience, and to provide a method, system, equipment, medium and program product for detecting the energy-gathering ring of a stove.

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

[0005] A first aspect of the present disclosure provides a method for detecting an energy-gathering ring of a cooker, the method comprising:

[0006] Acquiring collected data of the cooker, wherein the collected data includes the real-time reflected light intensity of the surface of the energy-gathering ring;

[0007] The obtained reflected light intensity of the energy focusing ring in a clean state is used as the reference reflected light intensity;

[0008] Based on the comparison result of the real-time reflected light intensity and the reference reflected light intensity, or the judgment model, the degree of dirtiness and / or the type of dirtiness of the energy focusing ring is detected.

[0009] Preferably, the step of detecting the degree of dirtiness of the energy focusing ring based on the comparison result of the real-time reflected light intensity and the reference reflected light intensity includes:

[0010] Obtaining a difference between the real-time reflected light intensity and the reference reflected light intensity;

[0011] The degree of contamination of the energy focusing ring is determined based on a comparison result of the difference and a preset threshold.

[0012] Preferably, in the case where the judgment model includes a dirt judgment model, the step of detecting the dirt degree and / or dirt type of the energy focusing ring based on the judgment model includes:

[0013] Acquire a training data set, wherein the training data set includes the reflected light intensity of the energy focusing ring in a clean state and the reflected light intensity of the energy focusing ring in a dirty state;

[0014] training a dirt judgment model based on the training data set to obtain a trained dirt judgment model;

[0015] The real-time reflected light intensity is input into the trained dirt judgment model to obtain the dirt type and dirt degree of the energy focusing ring.

[0016] Preferably, the collected data further includes the combustion temperature and usage time of the stove, and the energy-gathering ring detection method further includes:

[0017] The degree of dirtiness of the energy focusing ring is detected based on the real-time reflected light intensity of the surface of the energy focusing ring, the combustion temperature of the cooker and / or the usage time.

[0018] Preferably, the energy ring detection method further includes:

[0019] Collect the temperature value and ambient light intensity of the energy-gathering circle;

[0020] Adjusting the preset threshold based on the temperature value of the energy focusing ring and the ambient light intensity;

[0021] and / or,

[0022] The step of determining the degree of contamination of the energy gathering ring based on the comparison result of the difference and the preset threshold value includes:

[0023] In response to the difference being greater than a first preset threshold and less than a second preset threshold, determining that the degree of dirtiness of the energy focusing ring is lightly soiled; or, in response to the difference being greater than the second preset threshold and less than a third preset threshold, determining that the degree of dirtiness of the energy focusing ring is moderately soiled; or, in response to the difference being greater than the third preset threshold, determining that the degree of dirtiness of the energy focusing ring is heavily soiled;

[0024] The first preset threshold is smaller than the second preset threshold, and the second preset threshold is smaller than the third preset threshold.

[0025] Preferably, the energy ring detection method further includes:

[0026] outputting cleaning reminder information based on the degree of dirtiness of the energy-gathering ring;

[0027] and / or,

[0028] The energy-gathering ring detection method further comprises:

[0029] performing denoising processing on the real-time reflected light intensity to obtain denoised real-time reflected light intensity;

[0030] Normalizing the real-time reflected light intensity after denoising;

[0031] and / or,

[0032] The energy-gathering ring detection method further comprises:

[0033] Collect multiple real-time reflected light intensities at multiple locations on the surface of the energy focusing ring;

[0034] Obtaining an average value of the multiple real-time reflected light intensities;

[0035] The degree of contamination of the energy focusing ring is detected based on a comparison result of the average value and the reference reflected light intensity.

[0036] A second aspect of the present disclosure provides an energy-gathering ring detection system for a cooker, the energy-gathering ring detection system comprising:

[0037] A first acquisition module is used to acquire data collected by the cooker, wherein the collected data includes the real-time reflected light intensity of the surface of the energy-gathering ring;

[0038] A second acquisition module is used to use the acquired reflected light intensity of the energy focusing ring in a clean state as a reference reflected light intensity;

[0039] The first detection module is used to detect the degree of dirtiness and / or the type of dirtiness of the energy focusing ring based on the comparison result of the real-time reflected light intensity and the reference reflected light intensity, or based on the judgment model.

[0040] Preferably, the first detection module includes:

[0041] A first acquiring unit, configured to acquire a difference between the real-time reflected light intensity and the reference reflected light intensity;

[0042] The determining unit is configured to determine the degree of contamination of the energy focusing ring based on a comparison result between the difference and a preset threshold.

[0043] Preferably, when the judgment model includes a dirt judgment model, the first detection module further includes:

[0044] A second acquisition unit is used to acquire a training data set, wherein the training data set includes the reflected light intensity of the energy focusing ring in a clean state and the reflected light intensity of the energy focusing ring in a dirty state;

[0045] A training unit, configured to train a dirt judgment model based on the training data set to obtain a trained dirt judgment model;

[0046] The third acquisition unit is used to input the real-time reflected light intensity into the trained dirt judgment model to obtain the dirt type and dirt degree of the energy focusing ring.

[0047] Preferably, the collected data also includes the combustion temperature and usage time of the stove, and the energy-gathering ring detection system also includes:

[0048] The second detection module is used to detect the degree of dirtiness of the energy focusing ring based on the real-time reflected light intensity of the surface of the energy focusing ring and the combustion temperature of the cooker and / or the usage time.

[0049] Preferably, the energy ring detection system further includes:

[0050] The first acquisition module is used to collect the temperature value of the energy-gathering ring and the ambient light intensity;

[0051] An adjustment module, configured to adjust the preset threshold based on the temperature value of the energy focusing ring and the ambient light intensity;

[0052] and / or,

[0053] The determining unit is configured to determine, in response to the difference being greater than a first preset threshold and less than a second preset threshold, that the degree of dirtiness of the energy focusing ring is lightly soiled; or, in response to the difference being greater than the second preset threshold and less than a third preset threshold, that the degree of dirtiness of the energy focusing ring is moderately soiled; or, in response to the difference being greater than the third preset threshold, that the degree of dirtiness of the energy focusing ring is heavily soiled;

[0054] The first preset threshold is smaller than the second preset threshold, and the second preset threshold is smaller than the third preset threshold.

[0055] Preferably, the energy ring detection system further includes:

[0056] An output module, configured to output cleaning prompt information based on the degree of dirtiness of the energy-gathering ring;

[0057] and / or,

[0058] The energy-gathering ring detection system further comprises:

[0059] A first processing module is used to perform denoising on the real-time reflected light intensity to obtain the denoised real-time reflected light intensity;

[0060] A second processing module is used to normalize the real-time reflected light intensity after denoising;

[0061] and / or,

[0062] The energy-gathering ring detection system further comprises:

[0063] The second acquisition module is used to collect multiple real-time reflected light intensities at multiple locations on the surface of the energy focusing ring;

[0064] A third acquisition module is used to obtain an average value of the multiple real-time reflected light intensities;

[0065] The third detection module is used to detect the degree of dirtiness of the energy focusing ring based on the comparison result of the average value and the reference reflected light intensity.

[0066] A third aspect of the present disclosure provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and configured to run on the processor. When the processor executes the computer program, the method for detecting an energy-gathering ring of a cooker according to the first aspect is implemented.

[0067] A fourth aspect of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for detecting an energy-gathering ring of a cooker according to the first aspect is implemented.

[0068] A fifth aspect of the present disclosure provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method for detecting an energy gathering ring of a cooker as described in the first aspect.

[0069] On the basis of conforming to the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present disclosure.

[0070] The positive progress of this disclosure is:

[0071] The present disclosure realizes non-contact, high-precision dirt detection by collecting the real-time reflected light intensity of the surface of the energy focusing ring, and based on the comparison result of the real-time reflected light intensity and the benchmark reflected light intensity, or by using a judgment model to detect the degree of dirtiness and / or type of dirtiness of the energy focusing ring, thereby improving the detection efficiency, detection accuracy and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a first structural schematic diagram of the stove provided in the disclosed embodiments 1 and 2.

[0073] Figure 2 This is a second structural schematic diagram of the stove provided in the disclosed embodiments 1 and 2.

[0074] Figure 3 This is a flow chart of the method for detecting the energy-gathering ring of a cooker provided in Example 1.

[0075] Figure 4 This is a module schematic diagram of the energy-gathering ring detection system for a cooker provided in Example 2 of the present disclosure.

[0076] Figure 5 This is a structural diagram of an electronic device for implementing a method for detecting an energy-gathering ring of a cooker according to embodiment 3 of the present disclosure. DETAILED DESCRIPTION

[0077] The present disclosure will be further illustrated by the following examples, but the present disclosure is not limited to the scope of the examples.

[0078] The prefix words such as "first", "second" in the embodiments of the present disclosure are only used to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. The use of ordinal words such as "first" in the embodiments of the present disclosure does not constitute a limitation on the described objects, and the description of the described objects should be referred to the description of the context in the claims or embodiments, and should not constitute an unnecessary limitation because of the use of such prefix words. In addition, in the description of the embodiments, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0079] In the embodiments of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of personal information of the users involved comply with the relevant laws and regulations, and do not violate public order and good customs.

[0080] Embodiment 1

[0081] Figure 1 A flow chart of a focused ring detection method of a stove provided in Embodiment 1 of the present disclosure is shown in Figure 1-2 The stove includes a focused ring 1, a light source module 2, a sensor module 3 and a control module 4. In the present embodiment, the focused ring 1 is an annular structure around the burner of the stove, which is used to concentrate heat; the light source module 2 is used to emit a light beam of a specific wavelength (such as visible light or infrared light); the sensor module 3 is used to measure the reflected light intensity of the light beam on the surface of the focused ring; and the control module 4 is used to receive the signal of the sensor module 3 and judge the degree of dirt according to the change of the reflected light intensity, and then control the start or optimization of the cleaning function, as shown in Figure 3 The focused ring detection method includes:

[0082] S1, acquiring the collection data of the stove, the collection data including the real-time reflected light intensity on the surface of the focused ring;

[0083] In the present embodiment, during the use of the stove, the real-time reflected light intensity data on the surface of the focused ring is collected regularly or irregularly, and the frequency of data collection can be adjusted according to the use scenario of the stove:

[0084] High-frequency collection: during cooking, the reflected light intensity is collected once every certain time (such as 1 minute).

[0085] Low-frequency collection: when the stove is not in use, the reflected light intensity is collected once every certain time (such as 1 hour).

[0086] S2, taking the reflected light intensity of the focused ring in the cleaning state as the reference reflected light intensity;

[0087] In this embodiment, the reflective light intensity of the focusing cup in a clean state is measured as the baseline reflective light intensity (or reference reflective light intensity). The baseline reflective light intensity can be established in the following ways:

[0088] · Factory calibration: When the stove is factory-calibrated, the baseline reflective light intensity is measured by a standard clean focusing cup surface.

[0089] · User self-cleaning calibration: After the user cleans the focusing cup, the system automatically records the current reflective light intensity as the new baseline reflective light intensity.

[0090] The baseline reflective light intensity needs to be stored in the control module for comparison in subsequent dirt detection.

[0091] It should be noted that the reflective light intensity of the focusing cup can also be measured multiple times in a clean state, and the average value can be calculated as the baseline reflective light intensity.

[0092] S3, based on the comparison result of real-time reflective light intensity and baseline reflective light intensity, or the judgment model detects the degree of dirt and / or dirt type of the focusing cup.

[0093] In this embodiment, the dirt (such as oil stains, carbon deposits, etc.) on the surface of the focusing cup will affect the reflection characteristics of light. The main effects of dirt on reflective light intensity are as follows:

[0094] · Oil stains: Oil stains can make the surface of the focusing cup smooth, but also absorb part of the light, resulting in a decrease in reflective light intensity.

[0095] · Carbon deposits: Carbon deposits can make the surface of the focusing cup rough, which may change the direction of reflected light, resulting in a decrease or scattering of reflective light intensity.

[0096] · Mixed dirt: The mixed dirt of oil stains and carbon deposits may further reduce the reflective light intensity, and even change the reflection characteristics of light.

[0097] By measuring the change in reflective light intensity, the degree of dirt on the surface of the focusing cup can be determined. The lower the reflective light intensity, the higher the degree of dirt.

[0098] In this embodiment, the energy-gathering ring is activated and tested every few days to determine whether the stove is in the off state (specifically, the judgment component and communication module of the stove detect the current in the solenoid valve of the stove. If it is less than a preset current value, the stove is determined to be in the off state; if it is not less than the preset current value, the stove is determined to be in the on state). If not, a prompt is given to turn off the fire. If so, a determination is made as to whether there is an obstruction on the energy-gathering ring on the stove (specifically, a picture of the stove is taken by an imaging system on the stove and compared with a state where there is no pot). If so, a prompt is given to remove the obstruction (for example, a pot); if not, a light source is emitted to the energy-gathering ring to collect the intensity of the reflected light from the surface of the energy-gathering ring.

[0099] This embodiment collects the real-time reflected light intensity of the surface of the energy focusing ring, and detects the degree of dirtiness and / or type of dirtiness of the energy focusing ring based on the comparison result of the real-time reflected light intensity and the reference reflected light intensity, or judges the model, thereby realizing non-contact, high-precision dirtiness detection, improving the detection efficiency, detection accuracy and user experience.

[0100] In an optional embodiment, S3 includes:

[0101] S31, obtaining the difference between the real-time reflected light intensity and the reference reflected light intensity;

[0102] S32. Determine the degree of dirtiness of the energy gathering ring based on a comparison result between the difference and a preset threshold.

[0103] In this embodiment, the degree of contamination of the energy focusing ring is detected by a simple threshold method. Specifically, a fixed contamination judgment threshold (eg, a preset threshold) is set to compare the difference between the real-time reflected light intensity and the reference reflected light intensity.

[0104] The difference ΔI between the real-time reflected light intensity and the reference reflected light intensity is calculated.

[0105] If the difference ΔI exceeds a preset threshold (eg, decreases by 20%), it is determined that the surface of the energy gathering ring is dirty.

[0106] The degree of dirtiness (such as light dirtiness, moderate dirtiness, and heavy dirtiness) is further determined based on the size of the difference ΔI.

[0107] The simple threshold method for detecting the dirtiness of the energy-gathering ring has the following advantages and disadvantages: Advantages: Simple implementation and fast calculation speed. Disadvantages: Sensitive to factors such as changes in ambient light and differences in the energy-gathering ring material, which can easily lead to misjudgment.

[0108] In an optional embodiment, when the judgment model includes a dirt judgment model, S3 includes:

[0109] Obtaining a training data set, the training data set includes the reflected light intensity of the energy focusing ring in a clean state and the reflected light intensity of the energy focusing ring in a dirty state;

[0110] Training a dirt judgment model based on a training data set to obtain a trained dirt judgment model;

[0111] The real-time reflected light intensity is input into the trained dirt judgment model to obtain the dirt type and dirt degree of the energy focusing circle.

[0112] In this embodiment, the contamination type and degree of the energy-gathering ring are detected using a model recognition method. Specifically, machine learning or pattern recognition algorithms are used to analyze the changing patterns of reflected light intensity to determine the contamination type and degree. Specifically, data on the reflected light intensity of the energy-gathering ring in both a dirty and unstained state is collected to establish a training dataset. A contamination determination model is trained using a machine learning algorithm (such as a support vector machine or neural network). The reflected light intensity is collected in real time and input into the trained contamination determination model for classification. Based on the output of the contamination determination model, the contamination type (e.g., oil, carbon) and degree are determined.

[0113] Using the model recognition method to detect the degree of contamination of the energy-gathering ring has the following advantages and disadvantages: Advantages: It can distinguish different types of contamination and has high judgment accuracy. Disadvantages: It requires complex algorithm implementation and large computing resources.

[0114] In an optional embodiment, the collected data also includes the combustion temperature and usage time of the stove, and S3 includes:

[0115] The dirtiness of the energy-gathering ring is detected based on the real-time reflected light intensity of the surface of the energy-gathering ring and the combustion temperature and / or usage time of the cooker.

[0116] In this embodiment, the degree of dirtiness of the energy-gathering ring is detected by a multi-factor comprehensive judgment method, that is, the degree of dirtiness is comprehensively judged by combining the change value of the reflected light intensity and multiple factors such as the usage time and / or combustion temperature of the stove. Specifically, multi-factor data such as the real-time reflected light intensity on the surface of the energy-gathering ring, the usage time of the stove, and the combustion temperature are collected. A multi-factor comprehensive judgment model is established. For example: if the usage time of the stove exceeds a certain threshold (such as 3 months) and the real-time reflected light intensity drops by more than 15%, it is judged to be severely dirty. If the combustion temperature of the stove is high (such as high-temperature cooking) and the real-time reflected light intensity drops by more than 10%, it is judged to be moderately dirty. According to the results output by the multi-factor comprehensive judgment model, the corresponding cleaning reminder or cleaning function is triggered.

[0117] The advantages and disadvantages of using a multi-factor comprehensive judgment method to detect the degree of dirtiness of the energy-gathering ring are: Advantages: The judgment logic is more comprehensive and can be combined with the actual usage of the stove. Disadvantages: It requires more sensors and data processing logic.

[0118] In an optional embodiment, the energy ring detection method further includes:

[0119] Collect the temperature value and ambient light intensity of the energy-gathering circle;

[0120] Adjust the preset threshold based on the temperature value of the energy-gathering circle and the ambient light intensity;

[0121] In this embodiment, the degree of contamination of the energy-concentrating ring is detected using a dynamic threshold method. Specifically, the contamination determination threshold (e.g., a preset threshold) is dynamically adjusted based on the operating environment of the energy-concentrating ring (e.g., temperature, ambient light, etc.). Parameters such as ambient light intensity and the temperature of the energy-concentrating ring are collected. Based on these parameters, the preset threshold is dynamically adjusted. For example, in a high-temperature environment, the reflected light intensity may vary due to thermal expansion and contraction of the material, requiring a lowering of the preset threshold. Strong ambient light may interfere with the measurement of reflected light intensity, requiring a lowering of the preset threshold.

[0122] The difference ΔI between the real-time reflected light intensity and the reference reflected light intensity is calculated and compared with the dynamically adjusted preset threshold.

[0123] If the difference ΔI exceeds the dynamic threshold, it is determined that the surface of the energy gathering ring is dirty.

[0124] Advantages and disadvantages of using the dynamic threshold method to detect the dirtiness of the energy-gathering ring: Advantages: Strong adaptability and the ability to effectively eliminate environmental interference. Disadvantages: Requires complex algorithms and more sensor support.

[0125] It should be noted that the baseline value is updated and maintained as follows: Automatic update: After the user cleans the energy-gathering ring, the system can automatically update the baseline reflected light intensity; if the user does not clean it, the system can dynamically adjust the baseline reflected light intensity based on the detected degree of dirtiness. Manual update: The user can manually trigger the baseline reflected light intensity update function through the cooktop display or mobile phone app.

[0126] In an optional embodiment, S32 includes:

[0127] In response to the difference being greater than a first preset threshold and less than a second preset threshold, determining that the degree of dirtiness of the energy focusing ring is lightly soiled; or, in response to the difference being greater than the second preset threshold and less than a third preset threshold, determining that the degree of dirtiness of the energy focusing ring is moderately soiled; or, in response to the difference being greater than the third preset threshold, determining that the degree of dirtiness of the energy focusing ring is heavily soiled;

[0128] The first preset threshold is smaller than the second preset threshold, and the second preset threshold is smaller than the third preset threshold.

[0129] In this embodiment, the first preset threshold, the second preset threshold, and the third preset threshold are all set according to actual conditions and are not specifically limited here.

[0130] In an optional embodiment, the energy ring detection method further includes:

[0131] The dirty degree output cleaning prompt information based on the energy ring;

[0132] In this embodiment, the dirty degree can be divided into the following categories:

[0133] · Mildly dirty: the reflected light intensity decreases by 5%-10%, prompting the user to "suggest cleaning".

[0134] · Moderate dirty: the reflected light intensity decreases by 10%-20%, prompting the user to "clean".

[0135] · Severe dirty: the reflected light intensity decreases by more than 20%, prompting the user to "clean immediately" or starting the automatic cleaning function.

[0136] Further, the cleaning prompt and the triggering of the automatic cleaning function,

[0137] · Cleaning prompt: send a cleaning prompt to the user through the stove display screen or the mobile phone APP. The prompt content can include the dirty degree, the recommended cleaning time, etc.

[0138] · Automatic cleaning function: if the dirty degree reaches "severe", the system can automatically start the cleaning function (such as high-temperature self-cleaning, wind cleaning, etc.). The start of the cleaning function needs to consider the current state of the stove (such as whether it is off) and the safety of the user.

[0139] In an optional embodiment, the energy ring detection method further comprises:

[0140] Denoising the real-time reflected light intensity to obtain the denoised real-time reflected light intensity;

[0141] Normalizing the denoised real-time reflected light intensity;

[0142] In this embodiment, data acquisition: the sensor module needs to collect the real-time reflected light intensity on the surface of the energy ring, which can use high-precision light sensors (such as CMOS sensors or photodiodes). Data acquisition needs to consider whether the emission intensity of the light source module is stable, which can be calibrated by the output of the light source module to ensure the accuracy of the data. Data preprocessing: denoising the collected real-time reflected light intensity data to eliminate environmental light interference. Then normalize the denoised real-time reflected light intensity to ensure the comparability of the measurement results of different sensor modules.

[0143] In an optional embodiment, the energy ring detection method further comprises:

[0144] Collecting multiple real-time reflected light intensities at multiple places on the surface of the energy ring;

[0145] Obtaining the average value of the multiple real-time reflected light intensities;

[0146] The degree of contamination of the focus cup is detected based on the comparison result of the average value and the reference reflected light intensity.

[0147] In this embodiment, multiple light source modules and sensor modules are arranged around the focus cup, multiple real-time reflected light intensities at multiple points on the surface of the focus cup are collected, the average value of the multiple real-time reflected light intensities is calculated, and the comprehensiveness and accuracy of detection are improved. Multiple real-time reflected light intensities at multiple points are collected. The average value of these real-time reflected light intensities is calculated as the final reflected light intensity value. If the difference between the average value and the reference reflected light intensity exceeds the preset threshold value, it is determined that the state is contaminated.

[0148] In the specific implementation process, for ambient light compensation, specifically, ambient light (such as kitchen light, natural light, etc.) will affect the measurement of reflected light intensity, and a compensation mechanism is needed to eliminate interference. An ambient light detection function is added to the sensor module, and the emission intensity of the light source module and the sensitivity of the sensor module are dynamically adjusted according to the ambient light intensity.

[0149] For temperature compensation, specifically, the focus cup may expand and contract due to high temperature, affecting its reflection characteristics, and a temperature sensor is added to the stove to monitor the temperature of the focus cup in real time. The contamination judgment threshold is dynamically adjusted according to the temperature change.

[0150] Further, the degree and / or type of contamination of the focus cup can also be detected by multispectral detection. Specifically, different types of contamination have different effects on the reflection characteristics of light of different wavelengths, and multispectral detection can further improve the judgment accuracy. A multispectral light source module (such as red, green, blue, etc.) is used to measure the reflected light intensity on the surface of the focus cup. The type and degree of contamination are determined according to the change in reflected intensity of light of different wavelengths.

[0151] Further, a machine learning algorithm is used to train a relationship model between reflected light intensity and contamination degree, improving the intelligent level of judgment. Specifically, a large number of reflected light intensities under clean and contaminated conditions are collected to establish a training data set, a deep learning algorithm (such as a convolutional neural network) is used to train a contamination judgment model, and reflected light intensity is collected in real time and input into the contamination judgment model for classification and judgment.

[0152] Further, remote monitoring and maintenance: through Internet of Things technology, contamination detection data is uploaded to the cloud to realize remote monitoring and maintenance. Specifically, a wireless communication module (such as Wi-Fi, Bluetooth, etc.) is integrated into the stove. Contamination detection data is uploaded to the cloud, and users can view the cleaning status of the focus cup through a mobile phone APP or web page. The cloud can provide personalized cleaning suggestions or automatic cleaning solutions based on the detection data.

[0153] The core of this embodiment's energy-concentrating ring dirtiness detection technology, which uses reflected light intensity to determine the degree and type of dirtiness, is the cooker's energy-concentrating ring dirtiness detection technology. This technology must be designed based on the light reflection characteristics, the cooker's operating environment, and user needs. By establishing a baseline reflected light intensity, collecting reflected light intensity in real time, dynamically adjusting preset thresholds, and incorporating a multi-factor comprehensive judgment method, high-precision dirtiness detection can be achieved. Further expansion into multispectral detection, machine learning models, and remote monitoring could further enhance the system's intelligence and user experience.

[0154] Example 2

[0155] Corresponding to the aforementioned embodiment of a method for detecting an energy-gathering ring of a cooker, the present disclosure further provides an embodiment of a system for detecting an energy-gathering ring of a cooker.

[0156] Figure 4 This is a module diagram of a stove energy-gathering ring detection system provided in Example 2 of the present disclosure, as shown in FIG. Figure 1-2 As shown, the cooker includes an energy-gathering ring 1, a light source module 2, a sensor module 3, and a control module 4. In this embodiment, the energy-gathering ring 1 is an annular structure around the cooker burner for gathering heat; the light source module 2 is used to emit a light beam of a specific wavelength (such as visible light or infrared light); the sensor module 3 is used to measure the intensity of the light beam reflected from the surface of the energy-gathering ring; the control module 4 is used to receive the signal from the sensor module 3, and judge the degree of dirtiness according to the change in the intensity of the reflected light, thereby controlling the start-up or optimization operation of the cleaning function, as shown in FIG. Figure 3 As shown, the energy ring detection system includes:

[0157] The first acquisition module 21 is used to acquire data collected by the cooker, including the real-time reflected light intensity of the energy-gathering ring surface;

[0158] In this embodiment, during the use of the cooker, real-time reflected light intensity data from the surface of the energy-gathering ring is collected regularly or irregularly. The frequency of data collection can be adjusted according to the use scenario of the cooker:

[0159] High-frequency acquisition: During the cooking process, the reflected light intensity is collected at regular intervals (e.g., 1 minute).

[0160] Low-frequency acquisition: When the cooker is not in use, the reflected light intensity is collected every certain period of time (e.g., 1 hour).

[0161] A second acquisition module 22 is configured to use the acquired reflected light intensity of the energy focusing ring in a clean state as a reference reflected light intensity;

[0162] In this embodiment, when the energy focusing ring is in a clean state, its reflected light intensity is measured as the baseline reflected light intensity (or reference reflected light intensity). The baseline reflected light intensity can be established in the following manner:

[0163] Factory calibration: When the cooktop leaves the factory, the reference reflected light intensity is measured through the surface of a standard clean energy-gathering ring.

[0164] Calibration after user self-cleaning: After the user cleans the energy-gathering ring, the system automatically records the current reflected light intensity as the new baseline reflected light intensity.

[0165] The reference reflected light intensity needs to be stored in the control module for comparison during subsequent dirt detection.

[0166] It should be noted that when the energy focusing ring is in a clean state, the reflected light intensity of the energy focusing ring can also be measured multiple times, and the average value can be calculated as the reference reflected light intensity.

[0167] The first detection module 23 is used to detect the degree of dirtiness and / or the type of dirtiness of the energy focusing ring based on the comparison result of the real-time reflected light intensity and the reference reflected light intensity, or based on the judgment model.

[0168] In this embodiment, dirt (such as oil, carbon, etc.) on the surface of the energy-gathering ring will affect the light reflection characteristics. The following are the main effects of dirt on the intensity of reflected light:

[0169] Oil stains: Oil stains will make the surface of the energy-gathering ring smooth, but at the same time they will also absorb some light, resulting in a decrease in the intensity of reflected light.

[0170] Carbon scale: Carbon scale will make the surface of the energy focusing ring rough, which may change the direction of the reflected light, causing the intensity of the reflected light to be weakened or scattered.

[0171] Mixed dirt: Mixed dirt of oil and carbon may further reduce the intensity of reflected light and even change the reflection characteristics of light.

[0172] By measuring the change in the intensity of the reflected light, the degree of dirtiness on the surface of the energy focusing ring can be determined. The lower the intensity of the reflected light, the higher the degree of dirtiness.

[0173] In this embodiment, the energy-gathering ring is activated and tested every few days to determine whether the stove is in the off state (specifically, the judgment component and communication module of the stove detect the current in the solenoid valve of the stove. If it is less than a preset current value, the stove is determined to be in the off state; if it is not less than the preset current value, the stove is determined to be in the on state). If not, a prompt is given to turn off the fire. If so, a determination is made as to whether there is an obstruction on the energy-gathering ring on the stove (specifically, a picture of the stove is taken by an imaging system on the stove and compared with a state where there is no pot). If so, a prompt is given to remove the obstruction (for example, a pot); if not, a light source is emitted to the energy-gathering ring to collect the intensity of the reflected light from the surface of the energy-gathering ring.

[0174] This embodiment collects the real-time reflected light intensity of the surface of the energy focusing ring, and detects the degree of dirtiness and / or type of dirtiness of the energy focusing ring based on the comparison result of the real-time reflected light intensity and the reference reflected light intensity, or judges the model, thereby realizing non-contact, high-precision dirtiness detection, improving the detection efficiency, detection accuracy and user experience.

[0175] In an optional embodiment, the first detection module includes:

[0176] A first acquiring unit, configured to acquire a difference between the real-time reflected light intensity and the reference reflected light intensity;

[0177] The determining unit is used to determine the degree of dirtiness of the energy gathering ring based on a comparison result between the difference value and a preset threshold value.

[0178] In this embodiment, the degree of contamination of the energy focusing ring is detected by a simple threshold method. Specifically, a fixed contamination judgment threshold (eg, a preset threshold) is set to compare the difference between the real-time reflected light intensity and the reference reflected light intensity.

[0179] The difference ΔI between the real-time reflected light intensity and the reference reflected light intensity is calculated.

[0180] If the difference ΔI exceeds a preset threshold (eg, decreases by 20%), it is determined that the surface of the energy gathering ring is dirty.

[0181] The degree of dirtiness (such as light dirtiness, moderate dirtiness, and heavy dirtiness) is further determined based on the size of the difference ΔI.

[0182] The simple threshold method for detecting the dirtiness of the energy-gathering ring has the following advantages and disadvantages: Advantages: Simple implementation and fast calculation speed. Disadvantages: Sensitive to factors such as changes in ambient light and differences in the energy-gathering ring material, which can easily lead to misjudgment.

[0183] In an optional embodiment, when the judgment model includes a dirt judgment model, the first detection module further includes:

[0184] A second acquisition unit is used to acquire a training data set, where the training data set includes the reflected light intensity of the energy focusing ring in a clean state and the reflected light intensity of the energy focusing ring in a dirty state;

[0185] A training unit, configured to train a dirt judgment model based on a training data set to obtain a trained dirt judgment model;

[0186] The third acquisition unit is used to input the real-time reflected light intensity into the trained dirt judgment model to obtain the dirt type and dirt degree of the energy focusing ring.

[0187] In this embodiment, the contamination type and degree of the energy-gathering ring are detected using a model recognition method. Specifically, machine learning or pattern recognition algorithms are used to analyze the changing patterns of reflected light intensity to determine the contamination type and degree. Specifically, data on the reflected light intensity of the energy-gathering ring in both a dirty and unstained state is collected to establish a training dataset. A contamination determination model is trained using a machine learning algorithm (such as a support vector machine or neural network). The reflected light intensity is collected in real time and input into the trained contamination determination model for classification. Based on the output of the contamination determination model, the contamination type (e.g., oil, carbon) and degree are determined.

[0188] Using the model recognition method to detect the degree of contamination of the energy-gathering ring has the following advantages and disadvantages: Advantages: It can distinguish different types of contamination and has high judgment accuracy. Disadvantages: It requires complex algorithm implementation and large computing resources.

[0189] In an optional embodiment, the collected data also includes the combustion temperature and usage time of the stove, and the energy ring detection system further includes:

[0190] The second detection module is used to detect the dirtiness of the energy-gathering ring based on the real-time reflected light intensity of the surface of the energy-gathering ring and the combustion temperature and / or usage time of the stove.

[0191] In this embodiment, the degree of dirtiness of the energy-gathering ring is detected by a multi-factor comprehensive judgment method, that is, the degree of dirtiness is comprehensively judged by combining the change value of the reflected light intensity and multiple factors such as the usage time and / or combustion temperature of the stove. Specifically, multi-factor data such as the real-time reflected light intensity on the surface of the energy-gathering ring, the usage time of the stove, and the combustion temperature are collected. A multi-factor comprehensive judgment model is established. For example: if the usage time of the stove exceeds a certain threshold (such as 3 months) and the real-time reflected light intensity drops by more than 15%, it is judged to be severely dirty. If the combustion temperature of the stove is high (such as high-temperature cooking) and the real-time reflected light intensity drops by more than 10%, it is judged to be moderately dirty. According to the results output by the multi-factor comprehensive judgment model, the corresponding cleaning reminder or cleaning function is triggered.

[0192] The advantages and disadvantages of using a multi-factor comprehensive judgment method to detect the degree of dirtiness of the energy-gathering ring are: Advantages: The judgment logic is more comprehensive and can be combined with the actual usage of the stove. Disadvantages: It requires more sensors and data processing logic.

[0193] In an optional embodiment, the energy ring detection system further includes:

[0194] The first acquisition module is used to collect the temperature value of the energy-gathering ring and the ambient light intensity;

[0195] An adjustment module, configured to adjust the preset threshold based on the temperature value of the energy focusing ring and the ambient light intensity;

[0196] In this embodiment, the degree of dirt on the focusing ring is detected by a dynamic threshold method. Specifically, the dirt judgment threshold (e.g., a preset threshold) is dynamically adjusted according to the use environment of the focusing ring (such as temperature, ambient light, etc.). The ambient light intensity, focusing ring temperature and other parameters are collected. According to these parameters, the preset threshold is dynamically adjusted. For example: in a high temperature environment, the reflection intensity of light may change due to thermal expansion and contraction of materials, so the preset threshold needs to be lowered; when the ambient light is strong, it may interfere with the measurement of the reflection intensity, so the preset threshold needs to be lowered.

[0197] The difference ΔI between the real-time reflection light intensity and the reference reflection light intensity is calculated and compared with the dynamically adjusted preset threshold.

[0198] If the difference ΔI exceeds the dynamic threshold, it is determined that the surface of the focusing ring is dirty.

[0199] The advantages and disadvantages of detecting the degree of dirt on the focusing ring by the dynamic threshold method are as follows: the advantages are strong adaptability and effective elimination of environmental interference; the disadvantages are the need for complex algorithms and more sensor support.

[0200] It should be noted that the updating and maintenance of the reference value: automatic updating: after the user cleans the focusing ring, the system can automatically update the reference reflection light intensity; if the user does not clean, the system can dynamically adjust the reference reflection light intensity according to the detected degree of dirt. Manual update: the user can manually trigger the reference reflection light intensity update function through the display screen of the stove or the mobile phone APP.

[0201] In an optional embodiment, the determining unit is configured to determine that the degree of dirt on the focusing ring is light dirt in response to the difference being greater than a first preset threshold and less than a second preset threshold; or determine that the degree of dirt on the focusing ring is moderate dirt in response to the difference being greater than the second preset threshold and less than a third preset threshold; or determine that the degree of dirt on the focusing ring is heavy dirt in response to the difference being greater than the third preset threshold.

[0202] The first preset threshold is less than the second preset threshold, and the second preset threshold is less than the third preset threshold.

[0203] In this embodiment, the first preset threshold, the second preset threshold and the third preset threshold are set according to actual conditions, which are not limited here.

[0204] In an optional embodiment, the focusing ring detection system further comprises:

[0205] The output module is configured to output a cleaning prompt information based on the degree of dirt on the focusing ring.

[0206] In this embodiment, the degree of dirt can be classified into the following categories:

[0207] Lightly soiled: The reflected light intensity decreases by 5%-10%, and the user is prompted with "Cleaning Recommended".

[0208] Moderately dirty: The reflected light intensity decreases by 10%-20%, prompting the user to "cleaning required".

[0209] Severe soiling: If the reflected light intensity drops by more than 20%, the user will be prompted to "clean now" or activate the automatic cleaning function.

[0210] Furthermore, the cleaning reminder and the triggering of the automatic cleaning function,

[0211] Cleaning Reminder: The cooktop will send cleaning reminders to users via the cooktop display or mobile app. The reminders may include the degree of dirtiness and recommended cleaning time.

[0212] Automatic cleaning function: If the degree of dirtiness reaches "heavy", the system can automatically activate the cleaning function (such as high-temperature self-cleaning, wind cleaning, etc.). The activation of the cleaning function needs to take into account the current status of the stove (such as whether it is turned off) and the safety of the user.

[0213] In an optional embodiment, the energy ring detection system further includes:

[0214] The first processing module is used to perform denoising on the real-time reflected light intensity to obtain the denoised real-time reflected light intensity;

[0215] The second processing module is used to normalize the real-time reflected light intensity after denoising;

[0216] In this embodiment, data acquisition: The sensor module needs to collect the real-time reflected light intensity from the surface of the energy-gathering ring. A high-precision optical sensor (such as a CMOS sensor or a photodiode) can be used. Data acquisition needs to consider whether the emission intensity of the light source module is stable. The accuracy of the data can be ensured by calibrating the output of the light source module. Data preprocessing: The collected real-time reflected light intensity data is denoised to eliminate ambient light interference. The denoised real-time reflected light intensity is then normalized to ensure comparability of measurement results from different sensor modules.

[0217] In an optional embodiment, the energy ring detection system further includes:

[0218] The second acquisition module is used to collect multiple real-time reflected light intensities at multiple locations on the surface of the energy focusing ring;

[0219] A third acquisition module is used to obtain an average value of multiple real-time reflected light intensities;

[0220] The third detection module is used to detect the degree of dirtiness of the energy focusing ring based on the comparison result of the average value and the reference reflected light intensity.

[0221] In this embodiment, multiple light source modules and sensor modules are positioned around the energy-concentrating ring to collect multiple real-time reflected light intensities at multiple points on the ring's surface and calculate the average of these intensities, improving the comprehensiveness and accuracy of detection. Multiple real-time reflected light intensities are collected at multiple points. The average of these real-time reflected light intensities is calculated as the final reflected light intensity value. If the difference between the average and the baseline reflected light intensity exceeds a preset threshold, a contamination condition is determined.

[0222] In the specific implementation process, ambient light compensation is required. Specifically, ambient light (such as kitchen lights and natural light) can affect the measurement of reflected light intensity, and a compensation mechanism is needed to eliminate interference. Ambient light detection function is added to the sensor module to dynamically adjust the emission intensity of the light source module and the sensitivity of the sensor module based on the ambient light intensity.

[0223] Regarding temperature compensation, specifically, the energy-gathering ring may expand and contract in high-temperature environments, affecting its reflective properties. Therefore, a temperature sensor is added to the cooktop to monitor the temperature of the energy-gathering ring in real time. Based on this temperature change, the contamination threshold is dynamically adjusted.

[0224] Furthermore, multispectral detection can be used to detect the degree and / or type of dirt on the energy-concentrating ring. Specifically, different types of dirt have different effects on the reflection characteristics of light of different wavelengths, which can further improve the accuracy of judgment. Using multispectral light source modules (such as red, green, and blue light), the intensity of reflected light from the surface of the energy-concentrating ring is measured separately. The type and degree of dirt can be determined based on the changes in the reflected intensity of light of different wavelengths.

[0225] Furthermore, through machine learning algorithms, a model is trained to model the relationship between reflected light intensity and soiling level, improving the intelligent level of judgment. Specifically, a large amount of reflected light intensity in clean and dirty states is collected to establish a training data set. A deep learning algorithm (such as a convolutional neural network) is used to train the soiling judgment model. The reflected light intensity is collected in real time and input into the soiling judgment model for classification and judgment.

[0226] Furthermore, remote monitoring and maintenance: Through IoT technology, dirt detection data is uploaded to the cloud for remote monitoring and maintenance. Specifically, a wireless communication module (such as Wi-Fi, Bluetooth, etc.) is integrated into the cooktop. By uploading dirt detection data to the cloud, users can check the cleaning status of the energy ring through a mobile app or website. The cloud can provide personalized cleaning recommendations or automatic cleaning solutions based on the detection data.

[0227] The core of this embodiment's energy-concentrating ring dirtiness detection technology, which uses reflected light intensity to determine the degree and type of dirtiness, is the cooker's energy-concentrating ring dirtiness detection technology. This technology must be designed based on the light reflection characteristics, the cooker's operating environment, and user needs. By establishing a baseline reflected light intensity, collecting reflected light intensity in real time, dynamically adjusting preset thresholds, and incorporating a multi-factor comprehensive judgment method, high-precision dirtiness detection can be achieved. Further expansion into multispectral detection, machine learning models, and remote monitoring could further enhance the system's intelligence and user experience.

[0228] Since the system embodiments generally correspond to the method embodiments, reference will be made to the description of the method embodiments for relevant details. The system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components of the units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the disclosed solution.

[0229] Example 3

[0230] Figure 5 This is a structural diagram of an electronic device shown in Example 3 of the present disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, it implements the energy ring detection method described in any of the above embodiments. Figure 5 The electronic device 90 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0231] like Figure 5 As shown, the electronic device 90 may be a general-purpose computing device, such as a server device. Components of the electronic device 90 may include, but are not limited to, the at least one processor 91, the at least one memory 92, and a bus 93 connecting different system components (including the memory 92 and the processor 91).

[0232] The bus 93 includes a data bus, an address bus, and a control bus.

[0233] The memory 92 may include a volatile memory, such as a random access memory (RAM) 921 and / or a cache memory 922 , and may further include a read-only memory (ROM) 923 .

[0234] The memory 92 may also include a program tool 925 (or utility) having a set (at least one) of program modules 924, such program modules 924 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0235] The processor 91 executes various functional applications and data processing by running the computer program stored in the memory 92, such as the energy ring detection method provided in any of the above embodiments.

[0236] The electronic device 90 can also communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication can be performed through an input / output (I / O) interface 95. In addition, the electronic device 90 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 96. Figure 5 As shown, the network adapter 96 communicates with other modules of the electronic device 90 via the bus 93. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 90, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0237] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0238] Example 4

[0239] Embodiment 4 of the present disclosure further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the energy ring detection method provided by any of the above embodiments is implemented.

[0240] The readable storage medium may include, but is not limited to, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0241] Example 5

[0242] Embodiment 5 of the present disclosure further provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described energy ring detection methods.

[0243] The program code for executing the computer program product of the present disclosure may be written in any combination of one or more programming languages, and the program code may be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on the remote device.

[0244] While specific embodiments of the present disclosure have been described above, those skilled in the art will appreciate that these are merely illustrative and that the scope of protection of the present disclosure is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, and such changes and modifications are intended to fall within the scope of protection of the present disclosure.

Claims

1. A method for detecting an energy-gathering ring of a cooker, characterized in that: The energy-gathering ring detection method comprises: Acquiring collected data of the cooker, wherein the collected data includes the real-time reflected light intensity of the surface of the energy-gathering ring; The obtained reflected light intensity of the energy focusing ring in a clean state is used as the reference reflected light intensity; Based on the comparison result of the real-time reflected light intensity and the reference reflected light intensity, or the judgment model, the degree of dirtiness and / or the type of dirtiness of the energy focusing ring is detected.

2. The method for detecting the energy-gathering ring of a cooker according to claim 1, wherein: The step of detecting the degree of dirtiness of the energy focusing ring based on the comparison result of the real-time reflected light intensity and the reference reflected light intensity comprises: Obtaining a difference between the real-time reflected light intensity and the reference reflected light intensity; The degree of contamination of the energy focusing ring is determined based on a comparison result of the difference and a preset threshold.

3. The method for detecting the energy-gathering ring of a cooker according to claim 1, wherein: In the case where the judgment model includes a dirt judgment model, the step of detecting the dirt degree and / or dirt type of the energy focusing ring based on the judgment model includes: Acquire a training data set, wherein the training data set includes the reflected light intensity of the energy focusing ring in a clean state and the reflected light intensity of the energy focusing ring in a dirty state; training a dirt judgment model based on the training data set to obtain a trained dirt judgment model; The real-time reflected light intensity is input into the trained dirt judgment model to obtain the dirt type and dirt degree of the energy focusing ring.

4. The method for detecting the energy-gathering ring of a cooker according to claim 1, wherein: The collected data also includes the combustion temperature and usage time of the stove. The step of detecting the degree of dirtiness and / or the type of dirtiness of the energy gathering ring based on the judgment model includes: The degree of dirtiness of the energy focusing ring is detected based on the real-time reflected light intensity of the surface of the energy focusing ring, the combustion temperature of the cooker and / or the usage time.

5. The method for detecting the energy-gathering ring of a cooker according to claim 2, wherein: The energy-gathering ring detection method further comprises: Collect the temperature value and ambient light intensity of the energy-gathering circle; Adjusting the preset threshold based on the temperature value of the energy focusing ring and the ambient light intensity; and / or, The step of determining the degree of contamination of the energy gathering ring based on the comparison result of the difference and the preset threshold value includes: In response to the difference being greater than a first preset threshold and less than a second preset threshold, determining that the degree of dirtiness of the energy focusing ring is lightly soiled; or, in response to the difference being greater than the second preset threshold and less than a third preset threshold, determining that the degree of dirtiness of the energy focusing ring is moderately soiled; or, in response to the difference being greater than the third preset threshold, determining that the degree of dirtiness of the energy focusing ring is heavily soiled; The first preset threshold is smaller than the second preset threshold, and the second preset threshold is smaller than the third preset threshold.

6. The method for detecting the energy-gathering ring of a cooker according to claim 1, wherein: The energy-gathering ring detection method further comprises: outputting cleaning reminder information based on the degree of dirtiness of the energy-gathering ring; and / or, The energy-gathering ring detection method further comprises: performing denoising processing on the real-time reflected light intensity to obtain denoised real-time reflected light intensity; Normalizing the real-time reflected light intensity after denoising; and / or, The energy-gathering ring detection method further comprises: Collect multiple real-time reflected light intensities at multiple locations on the surface of the energy focusing ring; Obtaining an average value of the multiple real-time reflected light intensities; The degree of contamination of the energy focusing ring is detected based on a comparison result of the average value and the reference reflected light intensity.

7. A cooking stove energy-gathering ring detection system, characterized in that: The energy-gathering ring detection system comprises: A first acquisition module is used to acquire data collected by the cooker, wherein the collected data includes the real-time reflected light intensity of the surface of the energy-gathering ring; A second acquisition module is used to use the acquired reflected light intensity of the energy focusing ring in a clean state as a reference reflected light intensity; The detection module is used to detect the degree of dirtiness and / or the type of dirtiness of the energy focusing ring based on the comparison result of the real-time reflected light intensity and the reference reflected light intensity, or the judgment model.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and configured to run on the processor, wherein: When the processor executes the computer program, the method for detecting an energy-gathering ring of a cooker according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for detecting an energy gathering ring of a cooker according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for detecting an energy gathering ring of a cooker according to any one of claims 1 to 6 is implemented.

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