An infrared touch key early warning method and system resistant to oil stains, medium and product

Abstract

The application discloses an infrared touch key oil stain resistance early warning method, system, medium and product, and relates to the technical field of data processing.In the method, the infrared light intensity reference value and the standard touch sample in the state without oil stain are acquired to establish an original trigger boundary.In the process of device operation, the system monitors the deviation of the infrared receiving end signal from the reference value in real time, and calculates the signal drift amount caused by the oil stain accumulation.Combining the accumulated touch number after cleaning, a weighting factor is determined to dynamically correct the trigger boundary.When the signal mutation is detected, the touch signal peak value is compared with the dynamic threshold value to obtain a pollution state coefficient.According to the position of the coefficient in the preset interval, the system automatically executes corresponding grade light efficiency early warning to realize intelligent monitoring and timely reminding of the oil stain degree.Implementation of the technical scheme provided by the application can improve the accuracy of the infrared touch key oil stain resistance early warning function of kitchen appliances.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to an oil-resistant infrared touch button early warning method, system, medium, and product. Background Technology

[0002] As smart home appliances develop towards high-end and intelligent features, touch-sensitive control panels have become the mainstream operating interface for kitchen appliances due to their excellent user experience and modern minimalist design. Among them, infrared touch technology has been widely used in kitchen appliances such as range hoods and gas stoves because of its strong resistance to electromagnetic interference and long service life.

[0003] Currently, most kitchen appliances on the market use fixed warning thresholds for oil stain warnings. This warning mechanism pre-sets warning parameters at the factory, and issues a cleaning reminder when the button signal changes to meet preset conditions, providing users with maintenance guidance.

[0004] However, the unique environment of a kitchen makes it difficult to avoid the impact of grease on appliance panels. In actual use, due to significant differences in cooking habits and usage environments among households, fixed warning thresholds are insufficient to adapt to varying degrees of grease accumulation. This results in warning prompts that do not match the actual level of grease, thus reducing the accuracy of the grease-resistant infrared touch button warning function on kitchen appliances. Summary of the Invention

[0005] This application provides an oil-resistant infrared touch button warning method, system, medium, and product, which improves the accuracy of the oil-resistant infrared touch button warning function in kitchen appliances.

[0006] The first aspect of this application provides an oil-resistant infrared touch button warning method, comprising: Acquire the infrared light intensity signal value of kitchen appliances with no oil stains on the panel and the benchmark touch sample set under standard test environment; Based on the aforementioned benchmark touch sample set, the original trigger boundaries are determined; During the standby operation of the kitchen appliance, the signal amplitude of the infrared receiver is collected, and the difference between the signal amplitude and the infrared light intensity signal value is calculated to obtain the signal drift caused by oil accumulation. The first pollution state coefficient is determined by comparing the signal drift with the standard signal change. The cumulative number of touches on the kitchen appliance after the most recent cleaning and reset is obtained. A weighting factor is determined based on the cumulative number of touches, and the original trigger boundary is dynamically attenuated and corrected in combination with the first pollution state coefficient to generate a dynamic trigger threshold. When a signal mutation is detected at the infrared receiver, the peak value of the collected touch signal is compared with the dynamic trigger threshold to determine the second pollution state coefficient, and a graded light effect warning is issued based on the interval in the preset graded light effect warning interval set where the second pollution state coefficient is located.

[0007] By adopting the above technical solution, the original trigger boundary is determined based on the infrared light intensity signal value under oil-free conditions and the benchmark touch sample set under standard test conditions. During the standby operation of the kitchen appliance, the signal drift is monitored in real time, and a first contamination state coefficient is obtained by comparing the signal drift with the standard signal change. Simultaneously, a weighting factor is determined based on the cumulative number of touches after cleaning and resetting the kitchen appliance, and the original trigger boundary is dynamically attenuated to generate a dynamic trigger threshold. When a signal mutation is detected at the infrared receiver, the peak value of the collected touch signal is compared with the dynamic trigger threshold to determine a second contamination state coefficient. A graded light effect warning is then issued based on the second contamination state coefficient's position within a preset graded light effect warning interval. This warning mechanism, dynamically adjusted based on actual usage, can adapt to different family cooking habits and oil accumulation conditions, effectively solving the technical problem that fixed warning thresholds are difficult to adapt to different levels of oil accumulation, and improving the accuracy of the anti-oil infrared touch button warning function of kitchen appliances.

[0008] Optionally, the reference touch sample set is traversed to extract the signal change amplitude corresponding to multiple valid touch events, and the signal change amplitude with the smallest value is marked as the minimum valid touch feature value; the maximum thermal drift noise amplitude of the infrared receiver within a preset operating temperature range is obtained, and the maximum thermal drift noise amplitude is determined as a preset safety margin; the difference between the minimum valid touch feature value and the preset safety margin is calculated to obtain the effective signal-to-noise ratio margin; the original trigger boundary is obtained by subtracting the effective signal-to-noise ratio margin from the infrared light intensity signal value.

[0009] Optionally, the continuous signal data in the reference touch sample set is subjected to time-domain differentiation processing to identify the start and end points where the signal change rate exceeds a preset gradient; the time difference between the start and end points is calculated to obtain the touch hold duration; it is determined whether the touch hold duration is within a preset effective click time window, which is based on statistical data of users' rapid taps and regular presses in a kitchen scenario; if the touch hold duration is within the preset effective click time window and the signal band corresponding to the touch hold duration meets the preset single-peak characteristic condition, then the touch hold duration is marked as a valid touch event.

[0010] Optionally, the ratio of the signal drift to the change in the standard signal is calculated to obtain the basic signal-to-noise ratio occupancy rate; real-time temperature data of the panel surface of the kitchen appliance is collected, and the physical phase transition state of the current oil stain medium is determined based on the real-time temperature data. The physical phase transition state includes solidified state, semi-molten state, and liquid state; a preset oil stain transmittance characteristic curve is queried based on the physical phase transition state to determine the phase transition correction factor corresponding to the current temperature. The characteristic curve reflects the non-linear increasing trend of oil stain transmittance with increasing temperature; the basic occupancy rate is weighted and corrected by the phase transition correction factor, and the corrected basic occupancy rate is matched with a preset normalization interval to determine the first pollution state coefficient.

[0011] Optionally, based on a preset attenuation coefficient mapping table, the attenuation coefficient corresponding to the cumulative number of touches is determined, and the attenuation coefficient is used as the initial usage weighting factor; the cumulative running time of the kitchen appliance after the most recent cleaning and reset is obtained, and the proportion of the target setting in the cumulative running time is determined; based on the proportion of the running time, the corresponding accelerated pollution coefficient is queried in a preset oil stain acceleration mapping table; the accelerated pollution coefficient is used to compensate the initial usage weighting factor to determine the usage weighting factor; the product of the first pollution state coefficient and the usage weighting factor is calculated to obtain the comprehensive attenuation coefficient; the original trigger boundary is multiplied by the comprehensive attenuation coefficient to generate a dynamic trigger threshold adapted to the current pollution level and usage intensity.

[0012] Optionally, infrared received signal intensity values ​​are collected at multiple times according to a preset frequency. A peak detection algorithm is used to identify the extreme point with the largest amplitude in the signal waveform corresponding to the infrared received signal intensity value, which is then determined as the touch signal peak. The difference between the touch signal peak and the infrared light intensity signal value is calculated to obtain the signal change caused by the actual touch. The difference between the dynamic trigger threshold and the infrared light intensity signal value is also calculated to obtain the minimum identifiable touch change. The ratio of the signal change caused by the actual touch to the minimum identifiable touch change is calculated to obtain the touch margin ratio. The ambient illuminance change around the panel of the kitchen appliance is measured using a photosensitive sensor. Based on the ambient illuminance change and the standard change, an ambient light interference correction coefficient is determined. This ambient light interference correction coefficient is used to compensate for the touch margin ratio. The compensated touch margin ratio is mapped to a preset standardized scoring range to obtain a second pollution state coefficient.

[0013] Optionally, it is determined whether the second pollution state coefficient exceeds a preset forced lockout threshold. The preset forced lockout threshold is used to indicate that the accumulation of oil has caused the infrared signal to attenuate to an extreme state where the touch signal and noise signal cannot be distinguished. If the second pollution state coefficient exceeds the preset forced lockout threshold, the touch response function of the infrared touch button of the kitchen appliance is blocked, the graded light effect warning is controlled to enter the highest level of continuous flashing mode, and the signal amplitude of the infrared receiver is monitored during the blocking of the touch response function. When the signal amplitude rises by a step and the stable value after the rise is higher than the sum of the dynamic trigger threshold and the preset safety hysteresis, it is determined that the oil has been removed. After it is determined that the oil has been removed, the blocking of the infrared touch button is released, and a reference calibration process for the current ambient light is triggered to complete the switching operation from the forced lockout state to the normal standby state.

[0014] In a second aspect, embodiments of this application provide an oil-resistant infrared touch button warning system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the oil-resistant infrared touch button warning system to perform the method described in the first aspect and any possible implementation thereof.

[0015] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on an oil-resistant infrared touch button warning system, cause the oil-resistant infrared touch button warning system to perform the method described in the first aspect and any possible implementation thereof.

[0016] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on an oil-resistant infrared touch button warning system, cause the oil-resistant infrared touch button warning system to execute the method described in the first aspect and any possible implementation thereof.

[0017] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: By adopting the above technical solution, the original trigger boundary is determined based on the infrared light intensity signal value under oil-free conditions and the benchmark touch sample set under standard test conditions. During the standby operation of the kitchen appliance, the signal drift is monitored in real time, and a first contamination state coefficient is obtained by comparing the signal drift with the standard signal change. Simultaneously, a weighting factor is determined based on the cumulative number of touches after cleaning and resetting the kitchen appliance, and the original trigger boundary is dynamically attenuated to generate a dynamic trigger threshold. When a signal mutation is detected at the infrared receiver, the peak value of the collected touch signal is compared with the dynamic trigger threshold to determine a second contamination state coefficient. A graded light effect warning is then issued based on the second contamination state coefficient's position within a preset graded light effect warning interval. This warning mechanism, dynamically adjusted based on actual usage, can adapt to different family cooking habits and oil accumulation conditions, effectively solving the technical problem that fixed warning thresholds are difficult to adapt to different levels of oil accumulation, and improving the accuracy of the anti-oil infrared touch button warning function of kitchen appliances. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an oil-resistant infrared touch button early warning method disclosed in an embodiment of this application; Figure 2 This is another schematic flowchart of an oil-resistant infrared touch button early warning method disclosed in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a system provided in an embodiment of this application.

[0019] Explanation of reference numerals in the attached drawings: 301, Central Processing Unit; 302, Read-Only Memory; 303, Random Access Memory; 304, Bus; 305, Input / Output Interface; 306, Input Section; 307, Output Section; 308, Storage Section; 309, Communication Section; 310, Driver; 311, Removable Media. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0021] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0022] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0023] This application provides an oil-resistant infrared touch button early warning method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating an oil-resistant infrared touch button warning method provided in an embodiment of this application. The method is applied to a system, which refers to a hardware and software integrated platform capable of executing an oil-resistant infrared touch button warning program. The system can execute an oil-resistant infrared touch button warning program, and the method includes steps 101 to 106, as follows: Step 101: Obtain the infrared light intensity signal value of the kitchen appliance when the panel is free of oil stains and the benchmark touch sample set under standard test conditions.

[0024] Infrared light intensity signal value refers to the signal strength detected by the receiver after the light signal emitted by the infrared transmitter is reflected by the panel. This value reflects the baseline transmission intensity of the infrared signal under ideal clean conditions. The standard testing environment refers to a laboratory environment with a temperature of 25±2℃, relative humidity of 45%~65%, and no strong light interference. The baseline touch sample set refers to a set of signal data collected by multiple testers repeatedly pressing the touch buttons according to preset touch operation specifications under the standard testing environment. These data record the changing characteristics of the infrared signal during normal touch operation.

[0025] Specifically, firstly, at the aging test station on the production line, the touch panels of the kitchen appliances undergo standard cleaning treatment to ensure the surface is free of oil, fingerprints, and other impurities. The infrared transmitter is activated, the sampling frequency is set to 1kHz, and infrared signals are continuously collected for 100ms. The collected data is then arithmetically averaged to obtain the infrared light intensity signal value. Subsequently, in a standard testing environment, three testers each perform 50 touch operations, including three types: rapid tap (duration <200ms), normal press (duration 200ms-1s), and long press (duration >1s). For each touch operation, 2s of continuous signal data are collected, recording the complete signal changes before, during, and after the touch. These 150 sets of touch operation data, along with corresponding timestamps, touch types, and touch positions, are saved as a baseline touch sample set. During the test, testers are required to keep their fingers clean and dry, maintain a touch force within the range of 3±0.5N, and control the touch position within 5mm of the button center to ensure the standardization and repeatability of the collected data.

[0026] Step 102: Determine the original trigger boundary based on the benchmark touch sample set.

[0027] The original trigger boundary refers to the signal threshold that distinguishes a valid touch signal from background noise under ideal conditions; this threshold serves as a baseline for determining whether a touch operation is valid. A valid touch event refers to a touch operation that conforms to a preset time window and has standard waveform characteristics. The signal change amplitude represents the amount of change in the infrared received signal relative to a reference value during the touch process. The thermal drift noise amplitude refers to the maximum amplitude of random fluctuations in the infrared signal caused by temperature changes. The effective signal-to-noise ratio margin refers to the safe interval value between the valid touch signal and the noise signal.

[0028] Specifically, when determining the original trigger boundary, the first step is to traverse and analyze 150 sets of data from the baseline touch sample set. For each set of data, time-domain differentiation is performed, and after applying a 5-point median filter, the difference between adjacent sampling points is calculated. Points with a signal change rate exceeding 20% / ms are identified as the touch start and end points. The time difference between these two points is calculated. If the time difference falls within a preset time window of 100ms to 1.5s, and the signal curve during this time period exhibits a single-peak characteristic (only one maximum point exists), then this set of data is marked as a valid touch event. The signal change amplitude is extracted from all valid touch events, i.e., the difference between the maximum signal value during the touch process and the baseline value before the touch. The minimum value among these amplitudes is selected as the minimum effective touch feature value. Next, within the operating temperature range of -10℃ to 40℃, noise data is measured for 1 minute every 5℃, and the data with the largest peak-to-peak value is taken as the maximum thermal drift noise amplitude. The minimum effective touch feature value is subtracted from the maximum thermal drift noise amplitude to obtain the effective signal-to-noise ratio margin. Finally, the effective signal-to-noise ratio margin is subtracted from the infrared light intensity signal value to obtain the original trigger boundary. For example, if the infrared light intensity signal value is 1000, the minimum effective touch feature value is 200, and the maximum thermal drift noise amplitude is 50, then the effective signal-to-noise ratio margin is 150, and the final determined original trigger boundary is 850.

[0029] Step 103: During the standby operation of the kitchen appliance, the signal amplitude of the infrared receiver is collected, and the difference between the signal amplitude and the infrared light intensity signal value is calculated to obtain the signal drift caused by oil accumulation.

[0030] Standby operation refers to the working state of kitchen appliances when they are powered on but not performing any specific functions. During this time, the touch control circuit remains active to respond to possible touch operations. Signal amplitude refers to the real-time signal strength value detected by the infrared receiver in standby mode. This value is affected by the degree of oil contamination on the panel, causing attenuation. Signal drift represents the reduction in signal strength due to oil accumulation, calculated by comparing the real-time signal with the initial reference signal, reflecting the degree of oil accumulation on the panel.

[0031] Specifically, when the kitchen appliance is in standby mode, the infrared transmitter sends a detection signal at a fixed frequency of 50Hz. The infrared receiver uses a 16-bit ADC to continuously collect signal data at a sampling rate of 1kHz. Every 20ms, the 20 collected data points are arithmetically averaged to obtain a signal amplitude. To reduce the influence of random noise, the median of the most recent 50 signal amplitudes is calculated every second as the effective signal amplitude at the current moment. This signal amplitude is subtracted from the infrared light intensity signal value recorded at the factory (e.g., 1000) to obtain the signal drift caused by oil accumulation. For example, if the current signal amplitude is 900, the signal drift is 100. To ensure data reliability, before calculating the drift, it is necessary to determine whether the device is in a stable standby state: check for touch operations within the last 10 seconds, whether the ambient light intensity change exceeds 10%, and whether the panel temperature change exceeds 2°C. The signal drift is updated only when the device is stable. Simultaneously, the calculated signal drift is stored in the device's historical data cache for subsequent pollution status assessment. After each clean reset operation, the historical data cache is cleared and data accumulation begins again.

[0032] Step 104: Compare the signal drift amount with the standard signal change amount to determine the first pollution state coefficient.

[0033] The standard signal variation refers to the standard change amplitude of the infrared signal caused by a touch operation under ideal touch conditions. This value is obtained through standard testing at the time of device delivery. The base signal-to-noise ratio occupancy indicates the degree of impact of current oil accumulation on signal transmission quality. The physical phase transition state describes the physical state of oil at different temperatures, including solidified (≤35℃), semi-molten (35-60℃), and liquid (≥60℃). The phase transition correction factor is used to compensate for changes in the light transmittance characteristics of oil at different temperatures. The first contamination state coefficient is a standardized numerical indicator reflecting the current degree of oil accumulation, ranging from 0 to 100.

[0034] Specifically, the calculation process first divides the current signal drift by the standard signal change to obtain the base signal-to-noise ratio (SNR) occupancy rate. For example, if the signal drift is 100 and the standard signal change is 500, the base SNR occupancy rate is 0.2. Next, real-time temperature data is collected using a panel temperature sensor at a sampling frequency of 1Hz. Based on the collected temperature values, the physical phase transition state of the oil contaminant is determined: below 35℃, it is a solid state with a transmittance correction factor of 1.0; between 35-60℃, it is a semi-molten state with a transmittance correction factor calculated as (temperature - 35) / 25; above 60℃, it is a liquid state with a transmittance correction factor of 1.5. The base SNR occupancy rate is then multiplied by the phase transition correction factor to obtain the corrected occupancy rate value. Finally, the corrected occupancy rate value is mapped to a normalized range of 0-100. The specific mapping method is as follows: when the corrected occupancy rate is less than 0.1, the first contamination state coefficient equals the corrected occupancy rate multiplied by 500; when the corrected occupancy rate is between 0.1 and 0.3, the first contamination state coefficient equals 50 plus (corrected occupancy rate minus 0.1) multiplied by 250; when the corrected occupancy rate is greater than 0.3, the first contamination state coefficient is 100. For example, if the corrected occupancy rate is 0.15, then the first contamination state coefficient is 62.5. The calculated first contamination state coefficient is used for subsequent dynamic trigger threshold adjustment.

[0035] In one possible implementation, the signal drift amount is compared with the standard signal change amount to determine the first pollution state coefficient, specifically including steps 1041-1043, as follows: Step 1041: Calculate the ratio of the signal drift to the standard signal change to obtain the basic signal-to-noise ratio occupancy rate; collect real-time temperature data of the panel surface of the kitchen appliance, and determine the physical phase change state of the current oil stain medium based on the real-time temperature data. The physical phase change state includes solidified state, semi-molten state and liquid state.

[0036] The baseline signal-to-noise ratio (SNR) occupancy rate refers to the percentage of signal attenuation caused by oil contamination relative to the change in the standard touch signal, used to quantify the impact of oil contamination on signal transmission. Real-time temperature data is collected from the temperature values ​​of an NTC thermistor attached to the back of the touch panel, with a sampling accuracy of 0.1℃. Physical phase transition states describe the physical morphology of oil contamination at different temperatures: solidified state (≤35℃) is characterized by oil contamination forming a solid film; semi-molten state (35-60℃) is characterized by partial softening of the oil contamination; and liquid state (≥60℃) is characterized by the complete melting of the oil contamination into a liquid state.

[0037] Specifically, in actual execution, the standard signal change (factory default value, e.g., 500) stored in the EEPROM is first read, and then the current signal drift calculated in step 103 (e.g., 100) is obtained. The signal drift is divided by the standard signal change to obtain the basic signal-to-noise ratio (SNR) occupancy rate (e.g., 100 / 500 = 0.2). Panel temperature acquisition uses four NTC thermistors, arranged at the four corners of the touch panel, sampling once per second. The validity of the data from the four temperature sensors is verified: whether the data is within the valid range of -10℃ to 120℃, and whether the temperature difference between adjacent sensors is less than 5℃. The arithmetic mean of the verified temperature data is taken as the current panel temperature. Based on the panel temperature, a three-stage judgment is used to determine the physical phase transition state of the oil stains: when the temperature does not exceed 35℃, it is determined to be in a solid state, at which point the oil stain layer has the highest density and the strongest attenuation of infrared signals; when the temperature is between 35℃ and 60℃, it is determined to be in a semi-molten state, the oil stain layer structure begins to loosen, and the light transmittance gradually increases; when the temperature reaches or exceeds 60℃, it is determined to be in a liquid state, the oil stains are completely melted, and a uniform liquid film is formed. The current physical phase transition state and the specific temperature value are recorded for subsequent calculation of the phase transition correction factor. For example, when the panel temperature is 45℃, the oil stains are determined to be in a semi-molten state, and the specific temperature value of 45℃ is recorded for calculating the accurate correction coefficient.

[0038] Step 1042: Based on the physical phase transition state, query the preset oil transmittance characteristic curve to determine the phase transition correction factor corresponding to the current temperature. The characteristic curve reflects the non-linear increasing trend of oil transmittance with increasing temperature.

[0039] The oil transmittance characteristic curve is a mathematical model describing the change in the light transmittance of oil at different temperatures, represented by a piecewise cubic spline interpolation function. The phase transition correction factor is a correction coefficient calculated based on the oil transmittance characteristic curve and is used to compensate for changes in the light transmittance of oil caused by temperature variations. The nonlinear enhancement trend of the characteristic curve is manifested in the uneven rate of change of transmittance with increasing temperature, changing rapidly near the critical phase transition temperature and slowly in the steady-state region.

[0040] Specifically, the oil transmittance characteristic curve is stored in the device ROM, containing different function expressions for three temperature ranges. In the solidified state range (≤35℃), the correction factor calculation formula is f1(t)=1.0+0.002*(t+10), where t is the current temperature; in the semi-molten state range (35-60℃), the correction factor calculation formula is f2(t)=1.0+0.01*(t-35)+0.001*(t-35)^2, reflecting the accelerated increase in transmittance during the softening process of the oil; in the liquid state range (≥60℃), the correction factor calculation formula is f3(t)=1.5+0.005*(t-60), representing the slow increase in transmittance after the oil is completely liquefied. When calculating the specific correction factor, the corresponding calculation formula is first selected according to the physical phase change state determined in step 1041, and the current temperature is substituted into the calculation. For example, when the temperature is 45℃, it falls within the semi-molten state range. Substituting f2(45)=1.0+0.01*(45-35)+0.001*(45-35)^2=1.1+0.1=1.2, the phase transition correction factor is 1.2. To ensure the validity of the calculation results, the calculated correction factor is limited: the solidified state range is limited to 0.8-1.0, the semi-molten state range is limited to 1.0-1.5, and the liquid state range is limited to 1.5-2.0. The calculation results of the correction factor are used for subsequent correction of the basic signal-to-noise ratio occupancy rate.

[0041] Step 1043: The basic occupancy rate is weighted and corrected by the phase change correction factor, and the corrected basic occupancy rate is matched with the preset normalization interval to determine the first pollution state coefficient.

[0042] The preset normalization interval maps the corrected occupancy rate to a standardized scoring range of 0-100, facilitating the quantitative representation of pollution levels. The first pollution state coefficient is the final pollution assessment indicator; a higher value indicates a more severe degree of oil pollution accumulation.

[0043] Specifically, the weighted correction process first multiplies the obtained base signal-to-noise ratio occupancy (e.g., 0.2) with the phase transition correction factor calculated in step 1042 (e.g., 1.2) to obtain the corrected occupancy value (e.g., 0.2 * 1.2 = 0.24). Then, a normalization mapping is performed, using a piecewise linear function: when the corrected occupancy is between 0 and 0.1, the first pollution state coefficient equals the corrected occupancy multiplied by 500; when the corrected occupancy is between 0.1 and 0.3, the first pollution state coefficient equals 50 plus (corrected occupancy minus 0.1) multiplied by 250; when the corrected occupancy is greater than 0.3, the first pollution state coefficient is fixed at 100. A specific calculation example: when the corrected occupancy is 0.24, it falls in the second interval. Substituting into the formula 50 + (0.24 - 0.1) * 250 = 85, the first pollution state coefficient is 85. To improve calculation accuracy, double-precision floating-point numbers are used in the intermediate calculation process, and the final result is rounded to the nearest integer. After calculation, the first pollution state coefficient is stored in the device's EEPROM, and the timestamp of the calculation is recorded. If the first pollution state coefficient changes by more than 10 in three consecutive calculations, an anomaly detection process is triggered, and basic data is re-collected to verify the reliability of the calculation results. The first pollution state coefficient, as an important indicator for assessing the degree of oil pollution accumulation, will be used for subsequent dynamic trigger threshold adjustments and early warning level determination.

[0044] Step 105: Obtain the cumulative number of touches of the kitchen appliance after the most recent cleaning and reset, determine the weighting factor based on the cumulative number of touches, and perform dynamic attenuation correction on the original trigger boundary in combination with the first pollution state coefficient to generate a dynamic trigger threshold.

[0045] Cleaning reset refers to the user performing a panel cleaning operation and pressing the reset button, which resets the contamination status counters. The cumulative touch count records the total number of valid touch operations after the cleaning reset. A weighting factor is used, calculated based on the cumulative touch count, to reflect the impact of touch operations on oil accumulation. The dynamic trigger threshold is a real-time trigger judgment standard adjusted for attenuation, used to adapt to the current level of contamination and usage intensity.

[0046] Specifically, the system first reads the value of the cumulative touch count counter from the EEPROM. This counter increments by 1 after each valid touch operation and is reset to zero during a cleaning reset. Based on the cumulative touch count, a preset attenuation coefficient mapping table is consulted to determine the initial weighting factor: for touch counts between 0-1000, the weighting factor is 1.0; for 1001-5000, it is 1.0 + (touch count - 1000)0.0001; and for touch counts above 5001, the weighting factor is fixed at 1.4. For example, when the cumulative touch count is 3000, the initial weighting factor is 1.0 + (3000 - 1000)0.0001 = 1.2. Then, the system obtains the ratio of the cumulative runtime since the most recent cleaning reset to the runtime at the target power setting. The target power setting refers to the high-power operating setting, such as the high speed setting on a range hood or the high flame setting on a stove. The target usage time is calculated as a percentage of the total runtime. When the percentage exceeds 30%, the usage weighting factor is increased by 0.1 for every 10% increase. The compensated usage weighting factor (e.g., 1.3) is multiplied by the first pollution state coefficient (e.g., 85) to obtain the comprehensive attenuation coefficient (e.g., 1.385 / 100 = 1.105). Finally, the original trigger boundary (e.g., 850) is multiplied by the comprehensive attenuation coefficient to generate the current dynamic trigger threshold (e.g., 850 * 1.105 = 939). The dynamic trigger threshold is updated every 60 seconds. During the update, it is checked whether the change between two adjacent thresholds exceeds 5%. If it does, the threshold is transitioned to the new threshold evenly in 5 steps to avoid abrupt threshold changes affecting the touch experience. Simultaneously, the dynamic trigger threshold, calculation timestamp, and related parameters are written to the EEPROM for recovery after abnormal power loss.

[0047] In one possible implementation, a weighting factor is determined based on the cumulative number of touches, and a dynamic attenuation correction is performed on the original trigger boundary in conjunction with the first contamination state coefficient to generate a dynamic trigger threshold. Specifically, this includes steps 1051-1054, as follows: Step 1051: Determine the attenuation coefficient corresponding to the cumulative number of touches according to the preset attenuation coefficient mapping table, and use the attenuation coefficient as the initial weighting factor.

[0048] The preset attenuation coefficient mapping table is a lookup table stored in the device ROM, defining the attenuation coefficient values ​​corresponding to different cumulative touch count intervals. The cumulative touch count refers to the total number of valid touch operations recorded since the last cleaning and reset operation, stored in a 32-bit counter in the EEPROM. The attenuation coefficient is a numerical parameter reflecting the impact of touch operation frequency on oil accumulation. The initial weighting factor is a basic weight value obtained through a lookup table, used for subsequent dynamic threshold calculations.

[0049] Specifically, the cumulative touch count counter value in the EEPROM is read first. This counter increments by 1 each time a valid touch operation is detected (touch duration between 100ms and 1.5s, and signal changes conform to standard waveform characteristics). The preset attenuation coefficient mapping table uses a piecewise function: when the cumulative touch count N is in the range of 0-1000 times, the attenuation coefficient S1 = 1.0; when N is in the range of 1001-5000 times, the attenuation coefficient S2 = 1.0 + (N-1000) * 0.0001; when N is in the range of 5001-10000 times, the attenuation coefficient S3 = 1.4 + (N-5000) * 0.00002; when N exceeds 10000 times, the attenuation coefficient S4 is fixed at 1.5. The table lookup process uses a binary search algorithm to locate the current cumulative touch count range, and then substitutes it into the corresponding formula to calculate the specific attenuation coefficient value. For example, when the cumulative touch count reaches 3500, it falls within the second interval. Substituting this into the formula S2 = 1.0 + (3500 - 1000) * 0.0001 = 1.25, the initial usage weighting factor is 1.25. The calculation result is rounded to three decimal places and stored in RAM for subsequent calculations. Simultaneously, the current cumulative touch count and the corresponding initial usage weighting factor are recorded in the EEPROM as recovery data after a device power failure. When the cumulative touch count reaches 65535, a forced cleaning prompt is triggered, requiring the user to perform a cleaning reset operation.

[0050] Step 1052: Obtain the cumulative running time of the kitchen appliance after the most recent cleaning and reset, and determine the proportion of the target setting in the cumulative running time. Based on the proportion of the running time, query the corresponding acceleration pollution coefficient in the preset oil stain acceleration mapping table.

[0051] Cumulative runtime refers to the total time the equipment has been powered on and running since the most recent cleaning and reset operation, recorded by the system clock timer. Target speed refers to the high-power operating state of the equipment, such as the high-speed setting of a range hood or the high-heat setting of a stove. Percentage runtime indicates the percentage of total runtime spent operating at the target speed. The preset oil stain acceleration mapping table defines the degree of pollution acceleration corresponding to different percentages of use at the target speed. The acceleration pollution coefficient is a correction parameter reflecting the accelerated effect of high-power operation on oil stain accumulation.

[0052] Specifically, the cumulative runtime counter value is first read from the EEPROM. This counter records the device's running time in seconds. Simultaneously, the cumulative runtime counter for the target setting is read, and this counter increments when the device is running at the target setting. The criteria for determining the target setting are: range hood fan speed exceeding 2000 rpm, stove power setting greater than 6, and steam oven temperature setting higher than 150℃. The cumulative runtime for the target setting is divided by the total runtime to obtain the percentage value. The preset oil stain acceleration mapping table uses a piecewise function definition: when the percentage runtime R is in the 0-30% range, the acceleration pollution coefficient K1=1.0; when R is in the 31-50% range, K2=1.0+(R-30)*0.02; when R is in the 51-80% range, K3=1.4+(R-50)*0.01; when R exceeds 80%, K4 is fixed at 1.7. For example, if the cumulative running time is 100 hours, and the target gear running time is 45 hours, accounting for 45% (located in the second interval), substituting this into the formula K2=1.0+(45-30)*0.02=1.3, the accelerated pollution coefficient is 1.3. During the calculation, the running time counter is updated every minute, and each update checks for overflow. When either counter approaches its maximum value (4294967295), a scaling operation is performed synchronously on both counters according to the current ratio, maintaining the proportional relationship. The calculated accelerated pollution coefficient is used for subsequent adjustments to the weighting factor.

[0053] Step 1053: Use the accelerated pollution coefficient to compensate the initial usage weighting factor to determine the usage weighting factor.

[0054] The accelerated contamination factor is a correction parameter reflecting the impact of high-power operation of equipment on oil accumulation. The initial weighting factor is a base weight value determined based on the cumulative number of touches. The compensation operation performs mathematical operations on the accelerated contamination factor and the initial weighting factor to obtain the final weight that comprehensively considers the frequency and intensity of use. The weighting factor is the final weight coefficient obtained after completing the compensation calculation, and it is used for subsequent dynamic trigger threshold calculation.

[0055] Specifically, the initial weighting factor (e.g., 1.25) and the accelerated contamination coefficient calculated in step 1052 (e.g., 1.3) are first read from RAM. The compensation calculation uses a segmented weighting method: when the accelerated contamination coefficient is less than 1.2, the weighting factor is equal to the initial weighting factor multiplied by the accelerated contamination coefficient; when the accelerated contamination coefficient is between 1.2 and 1.5, the weighting factor is equal to the initial weighting factor multiplied by [1.2 + (accelerated contamination coefficient - 1.2) * 0.8]; when the accelerated contamination coefficient is greater than 1.5, the weighting factor is equal to the initial weighting factor multiplied by [1.44 + (accelerated contamination coefficient - 1.5) * 0.6]. Using the example data above: the accelerated contamination coefficient of 1.3 is in the second interval. Substituting into the formula 1.25 * [1.2 + (1.3 - 1.2) * 0.8] = 1.25 * 1.28 = 1.6, the final weighting factor is 1.6. To prevent excessive fluctuations in single calculation results, a limiting process is applied to the calculated weighted factors: compared to the previous weighted factor, the change in a single calculation must not exceed ±0.2; if it exceeds this range, the limiting value is used. The final weighted factor is stored in EEPROM, and a calculation timestamp is recorded. Each time the weighted factor is updated, a 32-bit cyclic redundancy check (CRC) value is updated synchronously for data integrity verification. Upon device startup, the weighted factor stored in EEPROM is read first, and the data validity is verified using the CRC check value. The complete calculation process is only re-executed if the data is invalid.

[0056] Step 1054: Calculate the product of the first pollution state coefficient and the weighting factor to obtain the comprehensive attenuation coefficient; multiply the original trigger boundary by the comprehensive attenuation coefficient to generate a dynamic trigger threshold that adapts to the current pollution level and usage intensity.

[0057] The weighting factor is a weighted coefficient that comprehensively considers touch frequency and operating intensity. The comprehensive attenuation coefficient is the final correction parameter after integrating the effects of contamination status and usage intensity. The original trigger boundary is the standard trigger judgment threshold set at the factory. The dynamic trigger threshold is the real-time trigger judgment standard after comprehensive attenuation correction.

[0058] Specifically, the calculation process first reads the first contamination state coefficient (e.g., 85) and a weighting factor (e.g., 1.6) from RAM, multiplies them, and divides by 100 for normalization to obtain the comprehensive attenuation coefficient (e.g., 85 * 1.6 / 100 = 1.36). A validity check is performed on the calculated comprehensive attenuation coefficient: the value must be greater than 1.0 and less than 2.0; if it exceeds this range, the boundary value is used. The preset original trigger boundary value (e.g., 850) is read from ROM; this value is determined through standard testing at the device's factory. The original trigger boundary is multiplied by the comprehensive attenuation coefficient to obtain the dynamic trigger threshold (e.g., 850 * 1.36 = 1156). To ensure smooth touch response, after each calculation of the new dynamic trigger threshold, it is compared with the currently used trigger threshold: if the change is less than 5%, it is directly updated to the new threshold; if the change exceeds 5%, it is uniformly transitioned to the new threshold in 5 steps over 60 seconds, updated every 12 seconds. For example, if the current threshold is 1000 and the newly calculated value is 1156, the difference of 156 is updated in 5 increments, each increasing by 31.2. During the threshold transition, each touch operation uses the currently updated transition threshold for trigger determination. The final dynamic trigger threshold and related parameters (timestamp, intermediate calculation value, checksum) are written to the EEPROM. When the device restarts after an abnormal power outage, the stored dynamic trigger threshold is used first, and a recalculation process is started in the background. After the calculation is completed, the system smoothly switches to the new threshold.

[0059] Step 106: When a signal change is detected at the infrared receiver, the peak value of the collected touch signal is compared with the dynamic trigger threshold to determine the second pollution state coefficient, and a graded light effect warning is issued according to the interval in the preset graded light effect warning interval set where the second pollution state coefficient is located.

[0060] Signal mutation refers to a significant change in the signal level detected by the infrared receiver within a short period of time (less than 50ms). Touch signal peak value refers to the maximum signal amplitude collected during the mutation process. The second pollution state coefficient is the standardized result of the ratio of the touch signal peak value to the dynamic trigger threshold. The graded light effect warning interval set consists of multiple preset state assessment intervals, each corresponding to a different warning level. Graded light effect warnings convey pollution level information to users through different display modes of LED indicator lights.

[0061] Specifically, the execution process begins with a 16-bit ADC continuously acquiring the infrared receiver signal at a sampling rate of 10kHz. When the difference between two adjacent sampled values ​​exceeds the trigger threshold (20% of the standard value), a signal mutation is detected, and the peak capture program is initiated. Peak capture uses a 200ms sampling window, acquiring the signal at a frequency of 10kHz within the window. High-frequency noise is eliminated through a sliding average filter (5 points), and the maximum value after filtering is recorded as the touch signal peak value. The touch signal peak value (e.g., 1500) is divided by the current dynamic trigger threshold (e.g., 1156) to obtain an initial ratio (e.g., 1.297). The initial ratio is mapped to the range of 0-100 to obtain the second contamination state coefficient: when the ratio is less than 0.8, the second contamination state coefficient is equal to the ratio multiplied by 100; when the ratio is between 0.8 and 1.2, the second contamination state coefficient is equal to 80 plus (ratio minus 0.8) * 50; when the ratio is greater than 1.2, the second contamination state coefficient is fixed at 100. Taking the above data as an example, the ratio 1.297 is greater than 1.2, therefore the second pollution state coefficient is 100. The preset graded light effect warning interval set contains four levels: 0-60 is the green interval, where the LED flashes green light at a frequency of 1Hz; 61-80 is the yellow interval, where the LED flashes yellow light at a frequency of 2Hz; 81-90 is the orange interval, where the LED remains constantly lit in orange; and 91-100 is the red interval, where the LED flashes red light at a frequency of 4Hz. The current warning level is determined based on the position of the second pollution state coefficient within the interval set, driving the LED to execute the corresponding display mode. In this example, the second pollution state coefficient is 100, located in the red interval, so the LED will flash red light at a frequency of 4Hz to prompt the user to clean promptly. The warning display automatically turns off after 30 seconds; if a new touch operation is detected during this period, the warning display restarts.

[0062] In one possible implementation, when a signal abrupt change is detected at the infrared receiver, the peak value of the collected touch signal is compared with the dynamic trigger threshold to determine the second contamination state coefficient, specifically including steps 1061-1065, as follows: Step 1061: Collect infrared received signal strength values ​​at multiple times according to a preset frequency, and identify the extreme point with the largest amplitude in the signal waveform corresponding to the infrared received signal strength value through a peak detection algorithm, and determine it as the touch signal peak value.

[0063] The preset frequency is the sampling frequency of the infrared received signal, typically set to 10kHz to ensure sufficient time resolution. The infrared received signal strength value is the digital quantity obtained after the voltage signal output by the infrared receiver is converted by an ADC. The signal waveform is a graphical representation of the infrared received signal strength value changing over time. The peak detection algorithm is a mathematical method used to identify local maxima in the signal waveform. The touch signal peak is the maximum signal strength value detected within the sampling window.

[0064] Specifically, the sampling process uses a 16-bit ADC to sample the output signal of the infrared receiver at a frequency of 10kHz, with a sampling resolution of 65536 levels. To ensure data reliability, a double-buffered sampling method is adopted: the main buffer and the backup buffer each store 200 sampling points, corresponding to a 20ms sampling window. After each sampling, the ADC writes the data to the currently active buffer via DMA and updates the buffer pointer. Peak detection uses a three-point comparison method: three consecutive sampling points P1, P2, and P3 are taken, and when P2 is greater than P1 and P3, P2 is considered a local maximum. To avoid high-frequency noise interference, the original sampled data is first filtered using a 5-point moving average: Y(n) = [X(n-2) + X(n-1) + X(n) + X(n+1) + X(n+2)] / 5, where X is the original sampled value and Y is the filtered value. Peak detection is performed on the filtered data, and all detected maxima are recorded. For example, five consecutive filtered data points obtained from a sampling are: 1000, 1200, 1500, 1300, and 1100. Since 1500 is greater than the values ​​of the two points before and after it, 1500 is determined to be a local maximum. Multiple local maxima may be detected within a 20ms sampling window; the largest value is taken as the signal peak value for this touch. If the largest maximum value detected within a sampling window is less than 1.2 times the signal baseline, it is determined to be an invalid touch, and the system waits for the next touch signal. When a valid touch signal peak value is detected, this value, the sampling timestamp, and the original sampling data are written to RAM for subsequent threshold comparisons and status determination. The sampling program continues to run for 300ms after a valid touch is detected to fully record the rise and fall of the touch signal.

[0065] Step 1062: Calculate the difference between the peak value of the touch signal and the value of the infrared light intensity signal to obtain the amount of signal change caused by the actual touch, and calculate the difference between the dynamic trigger threshold and the value of the infrared light intensity signal to obtain the minimum identifiable touch change.

[0066] The peak touch signal is the maximum signal strength value detected by the infrared receiver during a touch. The infrared light intensity signal value is the reference signal strength value of the system in idle state. The signal change is the actual change in signal strength relative to the reference value caused by a touch operation. The dynamic trigger threshold is a trigger determination criterion that is dynamically adjusted based on the state of contamination and usage intensity. The minimum identifiable touch change is the minimum effective touch signal change amplitude required by the system.

[0067] Specifically, the system first reads the detected peak value of the touch signal (e.g., 1500) and simultaneously reads the most recently updated infrared light intensity signal value (e.g., 1000) from RAM. The difference between the two is calculated: Signal change = Touch signal peak value - Infrared light intensity signal value = 1500 - 1000 = 500. This value represents the actual signal change caused by the current touch operation. To improve calculation accuracy, intermediate calculation results are stored using a 32-bit unsigned integer. Then, the current dynamic trigger threshold (e.g., 1156) is read, and the difference between it and the infrared light intensity signal value is calculated: Minimum recognizable touch change = Dynamic trigger threshold - Infrared light intensity signal value = 1156 - 1000 = 156. This value represents the minimum signal change amplitude required for the system to determine a valid touch. To prevent calculation errors caused by fluctuations in the reference signal, the system checks the stability of the infrared light intensity signal value before calculating the difference: 100 infrared light intensity signal value samples recorded within the last 10 seconds are taken, and the standard deviation is calculated. Only when the standard deviation is less than 2% of the mean is the reference value used in the calculation; otherwise, the reference value is re-acquired after the signal stabilizes. The calculated signal change and the minimum identifiable touch change are stored in RAM for subsequent calculation of the second contamination state coefficient. Simultaneously, the calculation timestamp and original data are recorded in EEPROM for diagnosis and recovery from abnormal states. If the signal change obtained from three consecutive calculations is less than the minimum identifiable touch change, the system will initiate a signal baseline calibration process to re-acquire and update the infrared light intensity signal value.

[0068] Step 1063: Calculate the ratio of the signal change caused by the actual touch to the minimum identifiable touch change to obtain the touch margin ratio.

[0069] The actual signal change caused by a touch is the actual magnitude of the change in signal amplitude relative to a reference value caused by a touch operation. The minimum identifiable touch change is the minimum effective touch signal change amplitude required by the system. The touch margin ratio is the ratio of the actual signal change to the minimum required change, reflecting the reliability of the touch signal.

[0070] Specifically, the actual signal change (e.g., 500) and the minimum identifiable touch change (e.g., 156) are read from RAM, and a division operation is performed to obtain the touch margin ratio: 500 / 156 = 3.205. To ensure calculation accuracy, floating-point arithmetic is used, and the result is rounded to three decimal places. The calculation result is then checked for validity: the touch margin ratio must be greater than 1.0; otherwise, it indicates that the touch signal has not met the minimum recognition requirement. The touch margin ratio must not exceed 10.0; exceeding this indicates a possible signal anomaly. When the touch margin ratio is within the range of 1.0 to 10.0, the value is written to RAM for subsequent calculations; when the touch margin ratio exceeds the range, an anomaly flag is recorded, and the previously valid value is retained. To improve data reliability, the system simultaneously records the timestamp of the calculation and the original data for possible anomaly backtracking analysis.

[0071] Step 1064: Measure the change in ambient illuminance around the panel of the kitchen appliance using a photosensitive sensor, and determine the ambient light interference correction coefficient based on the change in ambient illuminance and the standard change. Use the ambient light interference correction coefficient to compensate for the touch margin ratio.

[0072] The ambient illuminance variation range is the change in ambient light intensity measured by the photosensor. The standard variation range is the system's preset normal fluctuation range for ambient light. The ambient light interference correction coefficient is a correction parameter used to compensate for the influence of ambient light. The compensation calculation is a mathematical process that incorporates the influence of ambient light into the touch margin assessment.

[0073] Specifically, the photosensor samples ambient light intensity at a frequency of 1Hz, taking the difference between the maximum and minimum values ​​within the last 10 seconds as the ambient illuminance variation range (e.g., 200 lux). The standard variation range is set to 100 lux, representing the light intensity fluctuation range under normal operating conditions. The ambient light interference correction coefficient is calculated using a piecewise function: when the ambient illuminance variation range is less than the standard variation range, the correction coefficient K1 = 1.0; when the variation range is between 100 and 300 lux, K2 = 1.0 - (actual variation range - 100) * 0.002; when the variation range is greater than 300 lux, K3 is fixed at 0.6. Using the above example: the ambient illuminance variation range of 200 lux falls within the second interval. Substituting this into the formula K2 = 1.0 - (200 - 100) * 0.002 = 0.8, we obtain an ambient light interference correction coefficient of 0.8. Multiply the touch margin ratio obtained in step 1063 (e.g., 3.205) by the ambient light interference correction factor to obtain the compensated touch margin ratio: 3.205 × 0.8 = 2.564. The compensation calculation is performed once for each touch operation, and the calculation results and related parameters are stored in RAM for determining the second contamination state coefficient. When a sudden change in ambient light intensity is detected (a change exceeding 500 lux between two adjacent samples), the system pauses for 1 second and then resumes ambient light sampling to avoid transient interference affecting the accuracy of the judgment.

[0074] Step 1065: Map the compensated touch margin ratio to a preset standardized scoring range to obtain the second pollution state coefficient.

[0075] The compensated touch margin ratio is a reliability index of the touch signal after correction for ambient light interference. The standardized scoring range is a mapping range that converts the touch margin ratio into a standard score of 0-100. The second pollution state coefficient is the final pollution assessment index, used for subsequent warning level determination. The preset mapping range is a set of piecewise function parameters determined based on experimental data.

[0076] Specifically, the calculated compensated touch margin ratio (e.g., 2.564) is first read from RAM. The standardized mapping uses a four-segment function: when the touch margin ratio R is less than 1.2, the second contamination state coefficient S1 = 100 - R * 20; when R is between 1.2 and 2.0, S2 = 76 - (R - 1.2) * 40; when R is between 2.0 and 3.0, S3 = 44 - (R - 2.0) * 24; when R is greater than 3.0, S4 = 20 - (R - 3.0) * 10, and the final result is not less than 0. The mapping calculation uses 32-bit floating-point arithmetic, and the final result is rounded down. Based on the example data above, the compensated touch margin ratio of 2.564 falls within the third interval. Substituting this into the formula S3 = 44 - (2.564 - 2.0) * 24 = 44 - 13.536 = 30.464, and rounding down, we obtain the second contamination state coefficient as 30. To ensure data continuity, linear interpolation is used to smooth the transition at interval boundaries: when the touch margin ratio is within ±0.1 of the interval boundary, the weighted average of the calculation results of two adjacent intervals is taken as the final result. After calculation, the second contamination state coefficient and calculation parameters are written to the EEPROM, and a 32-bit CRC check value is recorded. Each time the system starts, the second contamination state coefficient stored in the EEPROM is read first, and the data validity is confirmed by CRC check. The complete calculation process is re-executed only when the data is invalid or no valid data is found. When the second contamination state coefficient changes by more than 20 in three consecutive calculations, the anomaly detection process is triggered, and basic data is re-collected to verify the reliability of the calculation results.

[0077] In one possible implementation, the peak value of the collected touch signal is compared with the dynamic trigger threshold to determine the second contamination state coefficient. Following this step, steps 1066-1067 are further included, as follows: Step 1066: Determine whether the second pollution state coefficient exceeds the preset forced lockout threshold. The preset forced lockout threshold is used to indicate that the accumulation of oil has caused the infrared signal to attenuate to an extreme state where the touch signal and noise signal cannot be distinguished. If the second pollution state coefficient exceeds the preset forced lockout threshold, the touch response function of the infrared touch button of the kitchen appliance is blocked, the graded light effect warning is controlled to enter the highest level of continuous flashing mode, and the signal amplitude of the infrared receiver is monitored during the blocking of the touch response function.

[0078] The second pollution state coefficient is a standardized indicator reflecting the degree of oil accumulation. The preset forced interlock threshold is the critical value for triggering safety protection, typically set to 95. Extreme state refers to a dangerous operating condition where oil contamination severely affects infrared signal transmission. The touch response function is a processing mechanism that converts touch signals into control commands. The graded light effect warning is a visual feedback system that displays different states using LED indicators. The signal amplitude is the magnitude of the voltage signal output by the infrared receiver.

[0079] Specifically, the second contamination state coefficient (e.g., 96) is read from RAM and compared with the preset forced lockout threshold (95). When the second contamination state coefficient exceeds the preset forced lockout threshold, the following sequence of operations is immediately performed: First, the touch response flag is cleared, interrupt response is disabled, and the conversion of touch signals to control commands is stopped. At the same time, the LED driver register configuration is modified to switch the graded light effect warning to the highest level mode: the red LED flashes at a frequency of 8Hz with a duty cycle of 50%. In the locked state, the system continuously samples the infrared receiver signal at a frequency of 1kHz: after the sampled data is filtered by a 10-point moving average, the signal mean is calculated every 100ms. The signal mean is compared with the normal working reference value (e.g., 1000): when the deviation of the signal mean from the reference value for 30 consecutive samples is less than 5%, and the signal standard deviation is less than 2% of the mean, it is determined that the oil stains have been removed, and the system automatically exits the locked state. During the waiting period for the oil stains to be removed, a self-test is performed every 60 seconds: a test signal with a standard amplitude (1.5 times the reference value) is generated to check the receiver response. If the test signal can be detected correctly, it indicates that the optical path integrity has not been permanently damaged, and cleaning can continue. If the test signal fails to detect a correct response for three consecutive times, a hardware fault is suspected, a fault code (e.g., E-05) is generated and stored in the EEPROM, and a fault alarm sound is emitted (a buzzer sounds three times at a frequency of 2Hz). All monitoring data and status changes are recorded in the EEPROM, including the timestamp of entering the lockout, the cause code, signal sampling records, etc., for subsequent fault analysis and maintenance.

[0080] Step 1067: When the signal amplitude increases by a step and the stable value after the increase is higher than the sum of the dynamic trigger threshold and the preset safety hysteresis, it is determined that the oil stain has been removed; after determining that the oil stain has been removed, the shielding of the infrared touch button is released, and a reference calibration process for the current ambient light is triggered to complete the switching operation from the forced lockout state to the normal standby state.

[0081] Signal amplitude refers to the magnitude of the voltage signal output by the infrared receiver. A step rise refers to a significant increase in signal value within a short period (less than 100ms). A stable value is a numerical level that remains constant after a signal change. The dynamic trigger threshold is a trigger determination criterion jointly determined by the contamination state and usage intensity. The preset safety hysteresis is an additional margin requirement when unlocking, typically set to 10% of the threshold. Reference calibration is the process of re-determining the ambient light and signal reference levels. Forced lockout state is a protection mode where touch functionality is disabled. Normal standby state is the operating mode where the system resumes normal operation.

[0082] Specifically, the current dynamic trigger threshold (e.g., 1156) and preset safety hysteresis (115) are read from RAM, and the judgment criterion is calculated: 1156 + 115 = 1271. Signal monitoring is performed: a 16-bit ADC is used to sample the infrared receiver signal at a frequency of 1kHz. The sampled data is filtered by a 10-point moving average to eliminate high-frequency noise. The signal mean is calculated every 100ms, and the signal mean sequence is obtained after 30 consecutive calculations. The signal mean sequence is checked for step changes: when the difference between two adjacent mean values ​​exceeds 30% of the current mean, a step change is determined. The signal mean after the step change is recorded and monitored continuously for 500ms: if the signal standard deviation within these 500ms is less than 2% of the mean, it is considered to have reached a stable state. The signal mean in the stable state (e.g., 1500) is compared with the judgment criterion (1271). When the signal mean is higher than the judgment criterion for 30 seconds, the unlocking operation is performed: first, the touch response flag is set to 1, and the interrupt response is re-enabled. The baseline calibration process begins: Ambient light intensity and infrared signals are sampled at a frequency of 100Hz for 10 seconds. After removing the maximum and minimum values, the average of the remaining data is calculated, and the result is written to the EEPROM as the new signal baseline value. The LED indicator switches to normal operating mode (solid green). The timestamp of unlocking, trigger condition, and baseline calibration result are recorded in the EEPROM. After the system completes the switch from forced lockout to normal standby, a complete self-test is performed: all touch buttons are activated sequentially to verify whether the signal response is normal. If the self-test passes, a short beep (100ms, 4kHz) indicates that the system has returned to normal operating status.

[0083] In the above embodiments, basic infrared touch threshold setting functions were achieved through touch data acquisition and signal feature analysis. To further improve the accuracy of touch judgment and reduce the impact of environmental factors on touch response, this application also provides an oil-resistant infrared touch button early warning method. This method analyzes the temporal characteristics, amplitude distribution, and noise characteristics of touch samples to construct a unified signal evaluation framework and performs adaptive threshold calculation, enabling the system to more reliably handle touch requirements in complex usage environments. The following section combines... Figure 2Another oil-resistant infrared touch button early warning method in the embodiments of this application is described below: Please see Figure 2 This is a flowchart illustrating an oil-resistant infrared touch button early warning method according to an embodiment of this application.

[0084] Step 201: Traverse the reference touch sample set, extract the signal change amplitude corresponding to multiple valid touch events, and mark the signal change amplitude with the smallest value as the minimum valid touch feature value.

[0085] The baseline touch sample set consists of multiple sets of touch signal records collected under standard experimental conditions. A valid touch event is a touch operation that is successfully recognized and triggers a control response. The signal change amplitude is the change in the signal peak value relative to the baseline value during the touch process. The minimum valid touch characteristic value is the value with the smallest signal change among all valid touch events, used as the benchmark standard for trigger determination. A trigger response refers to the action of the device executing the corresponding control command after detecting a touch operation.

[0086] Specifically, a baseline touch sample set is read, containing 100 sets of standard touch records. Each set of records includes: the baseline signal value before the touch, the complete waveform data of the touch process (200ms sampling window, 2000 sampling points), and a trigger response result flag. Signal processing is performed on each set of records: high-frequency noise is eliminated by a 5-point moving average filter, and the peak point is identified using a three-point comparison method in the filtered waveform. The change in signal amplitude at the peak point relative to the baseline value is calculated: Change = Peak value - Baseline value. The trigger response result flag is checked, and only the change corresponding to the successfully triggered record is retained. For example, the 10 consecutive valid touch signal changes obtained from a certain processing are: 520, 485, 503, 492, 508, 476, 515, 489, 497, 481. All signal changes are sorted in ascending order: 476, 481, 485, 489, 492, 497, 503, 508, 515, 520. The first value after sorting, 476, is taken as the minimum effective touch feature value. To ensure data reliability, the system verifies the validity of this minimum value by checking the integrity (complete signal waveform within the sampling window), stability (reference signal fluctuation less than 2% before and after touch), and repeatability (at least three sets of similar signal changes with a difference of less than 5%) of the touch event corresponding to this value. After successful verification, the minimum effective touch feature value 476 is written to a specific address in the EEPROM, and the extraction time and related parameters are recorded. This feature value will serve as an important reference parameter for subsequent trigger threshold calculations.

[0087] In one possible implementation, the signal change amplitude corresponding to multiple valid touch events is extracted. Prior to this step, steps 2011-2013 are specifically included, as follows: Step 2011: Perform time-domain differentiation processing on the continuous signal data in the reference touch sample set to identify the starting and ending points where the signal change rate exceeds the preset gradient.

[0088] Continuous signal data is a discrete digital signal sequence sampled at fixed time intervals. Time-domain differentiation is a mathematical operation that calculates the signal difference between adjacent sampling points. The rate of change of a signal is the amount of change in the signal amplitude per unit time. The preset gradient is the rate of change threshold for determining a valid touch. The starting point is the time position where the signal begins to change rapidly. The ending point is the time position where the signal change tends to stabilize.

[0089] Specifically, a baseline touch sample set is read, with each sample containing a signal sequence of 2000 sampling points at a sampling frequency of 1kHz. First, a 5-point moving average filter is applied to the raw data to eliminate high-frequency noise. Then, the filtered signal sequence S[n] is differentiated in the time domain: dS[n] = (S[n] - S[n-1]) * 1000, yielding the signal change rate per millisecond. A preset gradient of 50 / ms is set, meaning that a signal change exceeding 50 within 1ms is considered a rapid change. The change rate sequence dS[n] is scanned, and when |dS[n]| > 50 is detected, that point is marked as a candidate starting point. To avoid noise interference, a valid starting point is only confirmed when the change rate of three consecutive points exceeds the preset gradient. For example, consider a partial change rate sequence recorded from a touch: 2, 3, 5, 15, 52, 58, 55, 60, 65, 58, 45, 30, 15, 5, 2. Starting from the 5th point, the first three consecutive points all exceed 50, so n=5 is determined as the starting point. The ending point is identified using a similar method: scanning backward from the starting point, when the change rate of five consecutive points is less than 20% of the preset gradient (i.e., 10 / ms), the first point less than 10 / ms is determined as the ending point. The same processing is performed on each set of touch samples to extract the starting and ending point positions. All identification results are stored in an array: {starting point position, ending point position, starting point signal value, ending point signal value, maximum change rate}. Statistical analysis is performed on the array: the distribution of time intervals from the starting point to the ending point (mean and standard deviation) is calculated to verify the consistency of touch duration; the distribution of signal change is calculated to verify the stability of touch intensity. All statistical parameters are written to EEPROM for subsequent touch feature determination. When abnormal data is detected (such as a duration exceeding the mean ± 3 standard deviations), the sample group is marked as invalid and removed from the baseline touch sample set.

[0090] Step 2012: Calculate the time difference between the starting point and the ending point to obtain the touch hold duration.

[0091] The starting point refers to the sampling moment when the signal begins to change rapidly, represented by the index position of the sampling sequence. The ending point refers to the sampling moment when the signal change ends and tends to stabilize, also represented by the index position. The time difference is the time interval between two sampling points, determined by the sampling frequency and the index difference. Touch hold duration is the duration of a single touch operation, reflecting the temporal characteristics of the user's touch behavior. A sampling sequence is a set of discrete signal data recorded at fixed time intervals. The sampling frequency is the number of times the signal is sampled per unit time, determining the time resolution.

[0092] Specifically, the system reads and identifies the start and end points. The sampling frequency is 1kHz, meaning each sampling point is spaced 1ms apart. Taking a single touch record as an example: the start point index is 156, and the end point index is 412. The time difference is calculated as follows: Touch duration = (End point index - Start point index) * Sampling period = (412 - 156) * 0.001 = 0.256 seconds. To improve calculation accuracy, the system performs interpolation optimization on the start and end point positions: five points are taken before and after the start point, and the signal curve is calculated using cubic spline interpolation to determine the actual start time of the change; the same process is performed on the end point. The interpolation calculation uses 32-bit floating-point arithmetic, improving the time resolution to 0.1ms. The optimized touch duration calculation result is 0.2537 seconds. The calculation result is written to an array, with the following record format: {Touch sequence number, original start point index, original end point index, optimized start point time, optimized end point time, touch duration}. The same processing is performed on each group of touch samples to generate touch hold duration statistics. The average touch hold duration and standard deviation of all valid samples are calculated for touch feature consistency verification. When the hold duration of a touch deviates from the average by more than 3 standard deviations, the sample is marked as an anomaly and removed from the statistical set. After processing every 1000 touch operations, the system updates the touch hold duration statistics and writes them to EEPROM for dynamically optimizing the timing parameters of the touch recognition algorithm. Simultaneously, the feature data of anomaly samples are recorded for system fault diagnosis and user behavior analysis.

[0093] Step 2013: Determine whether the touch hold duration is within a preset effective click time window. The preset effective click time window is derived from statistical data of users' rapid taps and regular presses in a kitchen scenario. If the touch hold duration is within the preset effective click time window and the signal band corresponding to the touch hold duration meets the preset single-peak characteristic condition, then the touch hold duration is marked as a valid touch event.

[0094] Touch hold duration is the duration of a single touch operation. The preset valid click time window is the time range within which a touch operation is considered valid, typically from 50ms to 800ms. A quick tap is a user's rapid touch of a button, with a short duration. A normal press is a user's normal touch of a button, with a moderate duration. The signal band is the range of signal changes during the touch process. A single-peak characteristic condition is a waveform feature where the signal shows only one distinct peak. A valid touch event is a touch operation record that meets the time and waveform requirements.

[0095] Specifically, the system reads the preset effective click time window parameters: lower limit TL = 50ms (shortest time for rapid clicks), upper limit TH = 800ms (longest time for regular presses). It reads the calculated touch hold duration T (e.g., 253.7ms). It performs a time window check: verifying if TL ≤ T ≤ TH. When the touch hold duration is within the effective time window, it proceeds to waveform feature analysis. It reads the complete signal sequence (2000 sampling points) of the touch event from RAM and performs signal processing: first, it eliminates high-frequency noise through a 10-point moving average filter; it calculates the first derivative of the filtered signal and identifies all local extrema; it counts the number and location of the maxima. The single-peak characteristic judgment criterion is that only one main peak exists within the touch hold duration interval, and the amplitude of this peak is at least 50% higher than other local extrema. For example, the peak distribution of a touch record within a 200ms interval: main peak 1200, other local extrema amplitudes 600, 580, and 620 respectively, satisfying the single-peak condition. When both the time window and waveform feature judgments pass, the touch event is marked as valid: a record item {touch sequence number, start time, end time, touch duration, peak amplitude, valid flag = 1} is created in RAM. Simultaneously, touch statistics are updated: the time distribution characteristics (mean, standard deviation, quantiles) of the most recent 1000 valid touches are calculated for dynamic optimization of time window parameters. When a significant shift in the time distribution of 50 consecutive touch events is detected (mean change exceeding 20%), adaptive adjustment of the time window is triggered: the center point of the time window is adjusted to the new average position while maintaining the window width. All parameter adjustment records are written to EEPROM, including adjustment time, reason, and new parameter values, for system maintenance and optimization analysis.

[0096] Step 202: Obtain the maximum thermal drift noise amplitude of the infrared receiver within the preset operating temperature range, and determine the maximum thermal drift noise amplitude as the preset safety margin.

[0097] The preset operating temperature range is the ambient temperature range within which kitchen appliances operate normally, typically from -10℃ to 45℃. Thermal drift noise is the baseline shift of the infrared receiver signal caused by temperature changes. The maximum thermal drift noise amplitude is the maximum signal offset caused by temperature changes. The preset safety margin is the signal judgment margin set by the system to prevent temperature interference. The infrared receiver is the photoelectric conversion device responsible for receiving infrared signals.

[0098] Specifically, a constant temperature chamber is used to raise the equipment temperature from -10℃ to 45℃ at a rate of 2℃ / min. During this process, the infrared receiver signal is continuously sampled at a frequency of 1kHz using a 16-bit ADC. 2000 sampling points are recorded every 1℃. After a 10-point moving average filter, the signal mean is calculated to obtain the temperature-signal curve. For example, the signal mean sequence recorded at 28 temperature points is: 950, 955, 962, 968, 975, 981, 988, 995, 1002, 1008, 1015, 1021, 1027, 1032, 1038, 1043, 1048, 1052, 1056, 1060, 1063, 1066, 1068, 1070, 1071, 1072, 1072, 1071. Calculate the signal changes at adjacent temperature points: 5, 7, 6, 7, 6, 7, 7, 7, 6, 7, 6, 6, 5, 6, 5, 5, 4, 4, 4, 3, 3, 2, 2, 1, 1, 0, -1. Take the maximum value of 7 as the maximum drift per unit temperature. Considering that the temperature may change rapidly in actual use, multiply the maximum drift per unit temperature by 1.5 as a margin coefficient to obtain the corrected drift per unit temperature: 71.5 = 10.5. Within the preset operating temperature range (55℃ range), the cumulative maximum drift is: 10.555 = 577.5. Round this value (578) and write it to the EEPROM as a preset safety margin. In actual operation, the system will use this safety margin as the compensation value for trigger judgment. When the ambient temperature changes, the trigger threshold will be dynamically adjusted: new threshold = basic threshold + temperature change * drift per unit temperature. To monitor temperature changes, the system samples the temperature sensor every second, and recalculates the trigger threshold when the temperature change exceeds 2°C. All temperature and signal data are recorded in EEPROM for equipment operation status analysis.

[0099] Step 203: Calculate the difference between the minimum effective touch feature value and the preset safety margin to obtain the effective signal-to-noise ratio margin; subtract the effective signal-to-noise ratio margin from the infrared light intensity signal value to obtain the original trigger boundary.

[0100] The minimum effective touch characteristic value is the minimum signal change during an effective touch event. The preset safety margin is the maximum signal drift caused by temperature changes. The effective signal-to-noise ratio margin is the minimum distinguishability between the touch signal and noise interference. The infrared light intensity signal value is the reference signal strength of the system in idle state. The original trigger boundary is the basic threshold standard for touch detection. Signal distinguishability refers to the degree of difference between the effective touch signal and background noise.

[0101] Specifically, the minimum effective touch feature value (e.g., 476) and the predetermined preset safety margin (e.g., 578) are read. The difference is calculated: Effective signal-to-noise ratio margin = Minimum effective touch feature value - Preset safety margin = 476 - 578 = -102. Since the result is negative, it indicates that the current minimum effective touch feature value cannot meet the temperature drift safety margin requirement, and parameter correction is needed. The correction method is to increase the minimum effective touch feature value: add a 150% safety margin difference to the original value: 476 + (-102) * 1.5 = 629, and use 629 as the corrected minimum effective touch feature value. The effective signal-to-noise ratio margin is recalculated: 629 - 578 = 51, which is positive, meeting the safety margin requirement. The current infrared light intensity signal value (e.g., 1000) is read from RAM, and the original trigger boundary is calculated: 1000 - 51 = 949. To verify the effectiveness of the trigger boundary, the system performs a verification test: at a standard ambient temperature (25℃), 100 touch events are sampled at a frequency of 1kHz, with each touch recording a complete waveform for 200ms. The percentage of touches where the signal peak exceeds the trigger boundary is calculated, requiring a success rate of at least 99%. Simultaneously, the verification test is repeated under conditions of rapid temperature change (2℃ / min), requiring a false trigger rate of no more than 1%. After successful verification, the original trigger boundary value 949 is written to the EEPROM, along with the calculated parameters: minimum effective touch characteristic value, preset safety margin, effective signal-to-noise ratio margin, reference signal value, and calculation timestamp. These parameters will be used for monitoring device operation and diagnosing anomalies. When an abnormal parameter is detected (e.g., reference signal drift exceeding 10%), the system automatically triggers a recalibration process.

[0102] The following describes an oil-resistant infrared touch button early warning system according to an embodiment of this invention from a hardware processing perspective. Please refer to [link / reference]. Figure 3 This is a schematic diagram of an oil-resistant infrared touch button early warning system according to an embodiment of this application.

[0103] It should be noted that, Figure 3 The structure of the oil-resistant infrared touch button warning system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0104] like Figure 3As shown, an oil-resistant infrared touch button warning system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage section 308 into a random access memory (RAM) 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0105] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0106] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0107] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0108] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0109] Specifically, the oil-resistant infrared touch button warning system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the oil-resistant infrared touch button warning method provided in the above embodiment.

[0110] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the oil-resistant infrared touch button warning system described in the above embodiments; or it may exist independently and not assembled into the oil-resistant infrared touch button warning system. The storage medium carries one or more computer programs, which, when executed by a processor of the oil-resistant infrared touch button warning system, cause the oil-resistant infrared touch button warning system to implement the oil-resistant infrared touch button warning method based on IoT data encryption transmission provided in the above embodiments.

Claims

1. A method for providing oil-resistant infrared touch button warnings, characterized in that, The method includes: Acquire the infrared light intensity signal value of kitchen appliances with no oil stains on the panel and the benchmark touch sample set under standard test environment; Based on the aforementioned benchmark touch sample set, the original trigger boundaries are determined; During the standby operation of the kitchen appliance, the signal amplitude of the infrared receiver is collected, and the difference between the signal amplitude and the infrared light intensity signal value is calculated to obtain the signal drift caused by oil accumulation. The first pollution state coefficient is determined by comparing the signal drift with the standard signal change. The cumulative number of touches on the kitchen appliance after the most recent cleaning and reset is obtained. A weighting factor is determined based on the cumulative number of touches, and the original trigger boundary is dynamically attenuated and corrected in combination with the first pollution state coefficient to generate a dynamic trigger threshold. When a signal mutation is detected at the infrared receiver, the peak value of the collected touch signal is compared with the dynamic trigger threshold to determine the second pollution state coefficient, and a graded light effect warning is issued based on the interval in the preset graded light effect warning interval set where the second pollution state coefficient is located.

2. The method according to claim 1, characterized in that, Determining the original trigger boundary based on the benchmark touch sample set includes: Traverse the benchmark touch sample set, extract the signal change amplitude corresponding to multiple valid touch events, and mark the signal change amplitude with the smallest value as the smallest valid touch feature value; The maximum thermal drift noise amplitude of the infrared receiver within a preset operating temperature range is obtained, and the maximum thermal drift noise amplitude is determined as a preset safety margin. The difference between the minimum effective touch feature value and the preset safety margin is calculated to obtain the effective signal-to-noise ratio margin; The original trigger boundary is obtained by subtracting the effective signal-to-noise ratio margin from the infrared light intensity signal value.

3. The method according to claim 2, characterized in that, Before extracting the signal change amplitude corresponding to multiple valid touch events, the method further includes: The continuous signal data in the reference touch sample set is subjected to time-domain differentiation processing to identify the starting and ending points where the signal change rate exceeds the preset gradient; Calculate the time difference between the start point and the end point to obtain the touch hold duration; Determine whether the touch hold duration is within a preset effective click time window. The preset effective click time window is derived from statistical data of users' rapid taps and regular presses in a kitchen scenario. If the touch hold duration is within the preset valid click time window, and the signal band corresponding to the touch hold duration meets the preset single-peak characteristic condition, then the touch hold duration is marked as a valid touch event.

4. The method according to claim 1, characterized in that, The step of comparing the signal drift and the standard signal change to determine the first pollution state coefficient includes: Calculate the ratio of the signal drift to the change in the standard signal to obtain the basic signal-to-noise ratio occupancy rate; Collect real-time temperature data of the panel surface of the kitchen appliance, and determine the physical phase change state of the current oil stain medium based on the real-time temperature data. The physical phase change state includes solidified state, semi-molten state and liquid state. Based on the physical phase transition state, the preset oil transmittance characteristic curve is queried to determine the phase transition correction factor corresponding to the current temperature. The characteristic curve reflects the non-linear increasing trend of oil transmittance with increasing temperature. The basic occupancy rate is weighted and corrected by the phase change correction factor, and the corrected basic occupancy rate is matched with a preset normalization interval to determine the first pollution state coefficient.

5. The method according to claim 1, characterized in that, The step of determining a weighting factor based on the cumulative number of touches and performing dynamic attenuation correction on the original trigger boundary in conjunction with the first contamination state coefficient to generate a dynamic trigger threshold includes: According to the preset attenuation coefficient mapping table, the attenuation coefficient corresponding to the cumulative number of touches is determined, and the attenuation coefficient is used as the initial weighting factor. Obtain the cumulative running time of the kitchen appliance after the most recent cleaning and reset, and determine the proportion of the target setting in the cumulative running time. Based on the proportion of the running time, query the corresponding acceleration pollution coefficient in the preset oil stain acceleration mapping table. The initial usage weighting factor is compensated using the pollution acceleration coefficient to determine the usage weighting factor; The comprehensive attenuation coefficient is obtained by multiplying the first pollution state coefficient by the weighting factor. Multiply the original trigger boundary by the comprehensive attenuation coefficient to generate a dynamic trigger threshold that adapts to the current level of pollution and usage intensity.

6. The method according to claim 1, characterized in that, When a signal abrupt change is detected at the infrared receiver, the peak value of the collected touch signal is compared with the dynamic trigger threshold to determine the second contamination state coefficient, including: Infrared received signal strength values ​​are collected at multiple times according to a preset frequency. The peak value is identified by a peak detection algorithm, and the extreme point with the largest amplitude in the signal waveform corresponding to the infrared received signal strength value is determined as the touch signal peak value. The difference between the peak value of the touch signal and the value of the infrared light intensity signal is calculated to obtain the amount of signal change caused by the actual touch. The difference between the dynamic trigger threshold and the value of the infrared light intensity signal is also calculated to obtain the minimum identifiable touch change. Calculate the ratio of the signal change caused by the actual touch to the minimum identifiable touch change to obtain the touch margin ratio; The ambient light intensity variation around the panel of the kitchen appliance is measured by a photosensitive sensor, and an ambient light interference correction coefficient is determined based on the ambient light intensity variation and the standard variation. The touch margin ratio is then compensated using the ambient light interference correction coefficient. The compensated touch margin ratio is mapped to a preset standardized scoring range to obtain the second pollution state coefficient.

7. The method according to claim 1, characterized in that, After comparing the peak value of the collected touch signal with the dynamic trigger threshold to determine the second contamination state coefficient, the method further includes: Determine whether the second pollution state coefficient exceeds a preset forced lockout threshold. The preset forced lockout threshold is used to indicate that the accumulation of oil has caused the infrared signal to attenuate to the point where it is impossible to distinguish between touch signals and noise signals. If the second pollution state coefficient exceeds the preset forced locking threshold, the touch response function of the infrared touch button of the kitchen appliance is blocked, the graded light effect warning is controlled to enter the highest level of continuous flashing mode, and the signal amplitude of the infrared receiver is monitored during the blocking of the touch response function. When the signal amplitude increases by a step and the stable value after the increase is higher than the sum of the dynamic trigger threshold and the preset safety hysteresis, it is determined that the oil stains have been removed. After determining that the oil stains have been removed, the shielding of the infrared touch button is released, and a reference calibration process for the current ambient light is triggered to complete the switching operation from the forced lockout state to the normal standby state.

8. An oil-resistant infrared touch button early warning system, characterized in that, The oil-resistant infrared touch button warning system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the oil-resistant infrared touch button warning system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the oil-resistant infrared touch button warning system, the oil-resistant infrared touch button warning system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the oil-resistant infrared touch button warning system, the oil-resistant infrared touch button warning system performs the method as described in any one of claims 1-7.

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