Intelligent electromagnetic induction heating method and system with multi-parameter coordinated frequency domain separation

Abstract

The application discloses a kind of multi-parameter collaborative frequency domain separation intelligent electromagnetic induction heating method and system, for the temperature detection lag in the prior art, frequency domain interference processing is insufficient and the problem of weak dynamic adaptation capability, synchronous parameter acquisition and frequency domain separation processing scheme are proposed.Synchronously collect the transient current, voltage and its rate of change of electromagnetic coil (first parameter group), combined with environmental temperature, utensil material and user preference (second parameter group), high-frequency fluctuation (>=10kHz) and low-frequency disturbance (<10kHz) are separated in frequency spectrum.Using band-stop filtering and FFT to extract phase difference characteristics, combined with dynamic compensation factor to correct heating priority, and through time-frequency domain weighted fusion to generate segmented heating curve.The system adjusts PWM parameters in real time, and triggers adaptive protection when phase anomaly or power frequency exceeds limit.The method breaks through the limitation of single frequency band detection, realizes high-frequency noise suppression and low-frequency disturbance compensation, and supports precise temperature control of ferromagnetic / non-ferromagnetic utensils.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of electromagnetic induction heating control, and specifically discloses an intelligent electromagnetic induction heating method and system with multi-parameter coordinated frequency domain separation. BACKGROUND

[0002] In the field of temperature control of electromagnetic heating equipment, traditional technologies generally rely on temperature sensors to realize temperature feedback. Such sensors are usually arranged near the panel of the equipment to indirectly regulate the heating power by detecting the temperature of the panel or the pot. However, this scheme has significant defects: the physical response of the temperature sensor has a lag, resulting in a deviation between the temperature detection value and the actual heating state, which further affects the control accuracy. In addition, factors such as the installation position of the sensor and environmental interference can further reduce the measurement reliability, making it difficult to meet the needs of high-precision cooking scenarios.

[0003] To address the above problems, existing technologies have proposed some improvement schemes. For example, CN107592692A detects changes in electrical parameters (such as current, voltage, or impedance) of an electromagnetic heating coil to indirectly reflect the temperature state of the heated object. This method discards external sensors and uses the electromagnetic coupling characteristics of the coil and a specific temperature control material to realize temperature sensing. For example, permalloy or precision alloy is used as a temperature control layer, which has a sudden change in magnetic permeability when the temperature reaches its Curie point, causing a significant change in the electrical parameters of the coil, thereby establishing a mapping relationship between electrical parameters and temperature. Although this method reduces hardware dependence, it still has limitations in linear response in a wide temperature range and coordinated control of multiple temperature points, which are manifested in the following defects:

[0004] 1. Insufficient frequency domain separation of parameter acquisition and processing

[0005] Existing technologies only rely on single-band electrical parameter detection (such as current effective value or pulse number), without separating and processing high-frequency switching noise (>10 kHz) and power frequency disturbance signals (<10 kHz). For example, the detection and analysis module in CN107592692A directly extracts the coil current value, but the high-frequency harmonics generated during the IGBT switching process can mask the effective load characteristics, resulting in a phase difference detection error of ±20% (such as the voltage-current phase offset shown in Figure 2 ), ultimately causing a temperature calculation deviation of more than ±8℃. In addition, when the pot material is a low magnetic permeability material (such as aluminum alloy), the noise caused by high-frequency eddy current loss further deteriorates the signal quality.

[0006] 2. Weak dynamic environment and load adaptation capability

[0007] CN107592692A uses a temperature control layer with a fixed Curie point temperature (such as 4J36 alloy at 230℃). Although it can achieve temperature self-limitation, it cannot dynamically adapt to diverse cooking scenarios: in low-temperature environments (<10℃), changes in coil resistance cause the preset electrical parameter-temperature mapping relationship to fail, with measured temperature drift reaching 15℃; the cookware material is a limitation: the temperature control layer needs to be physically bonded to the cookware (such as welded to the bottom of the pot), which is not compatible with non-ferromagnetic cookware (such as glass and ceramic), and the difference in thermal conductivity between different materials (such as cast iron λ=80W / m·K vs. aluminum alloy λ=237W / m·K) is not included in the control model, resulting in power fluctuations exceeding 30% during the constant temperature stage.

[0008] 3. The control strategy lacks multi-dimensional optimization.

[0009] Existing solutions employ a fixed threshold comparison method (such as T0+Δt threshold control), resulting in PID overshoot of 12%-18% when the grid voltage fluctuates or the load changes abruptly. For example, in CN107592692A, the control module only compares the detected temperature with the setpoint, without incorporating data such as ambient temperature and historical preferences (as claimed in the user self-learning mechanism of claim 10). This prevents the adaptive switching between rapid heating (>10℃ / s) in frying mode and stable temperature (±2℃) in slow cooking mode. Furthermore, the safety protection mechanism relies on a single overcurrent detection, resulting in a response delay of over 500ms to high-frequency phase anomalies (such as coil resonance instability), posing a risk of IGBT overvoltage breakdown.

[0010] Conclusion: Existing parameter processing methods, environmental adaptability, and control strategies are no longer sufficient to meet the demands of high-precision, multi-scenario intelligent heating. There is an urgent need for a new control method that integrates frequency domain separation, dynamic compensation, and multi-source optimization. Summary of the Invention

[0011] To address the aforementioned problems, this invention proposes a multi-parameter collaborative frequency domain separation intelligent electromagnetic induction heating method and system, specifically disclosing a novel control method that integrates frequency domain separation, dynamic compensation, and multi-source optimization.

[0012] The objective of this invention is achieved through the following technical solution.

[0013] A multi-parameter cooperative frequency domain separation intelligent electromagnetic induction heating method includes the following steps:

[0014] S1, Synchronous parameter acquisition:

[0015] The transient current value, transient voltage value and their rate of change of the electromagnetic heating coil are collected synchronously to form the first parameter group. Based on the spectral characteristics, it is divided into a high-frequency fluctuation range (frequency ≥ 10kHz) and a low-frequency fluctuation range (frequency < 10kHz). The high-frequency range corresponds to the current switching harmonic component, and the low-frequency range corresponds to the voltage power frequency disturbance component.

[0016] The second parameter group is composed of synchronously collected ambient temperature, cookware material type and user's historical heating preferences. The historical preferences are divided into three temperature ranges: 30-150℃ low temperature range, 151-250℃ medium temperature range and 251-500℃ high temperature range.

[0017] S2, Grouped Data Processing:

[0018] The high-frequency fluctuation range data of the first parameter group is filtered and denoised to extract the phase difference features of current and voltage, and the first analysis result is generated.

[0019] Simultaneously, trend analysis was performed on the rate of change of current and voltage in the low-frequency fluctuation range, and dynamic compensation factors were calculated in conjunction with ambient temperature.

[0020] S3, Dynamic Correlation Analysis:

[0021] The phase difference feature in the first analysis result is matched with the heating mode priority in the second analysis result. If the phase difference feature exceeds the preset threshold range, the heating mode priority is corrected based on the dynamic compensation factor to generate dynamic correction parameters.

[0022] S4, Multi-source data combination optimization:

[0023] The dynamic correction parameters are fused with the current and voltage change rates in the low-frequency fluctuation range in the time and frequency domain to calculate the dynamic power adjustment coefficient, and a segmented heating curve is generated based on the thermal conductivity characteristics of the cookware material.

[0024] S5 outputs intelligent control commands:

[0025] Based on the segmented heating curve, the pulse width modulation (PWM) frequency and duty cycle of the electromagnetic heating coil are adjusted in real time. If the phase difference in the high-frequency fluctuation range is continuously abnormal or the power frequency disturbance in the low-frequency fluctuation range exceeds the safety threshold, adaptive power reduction protection is triggered, and optimized heating suggestions are pushed to the human-machine interface.

[0026] Furthermore, in the above-mentioned intelligent electromagnetic induction heating method with multi-parameter collaborative frequency domain separation, the ambient temperature in step S1 is acquired through dual redundant temperature sensors and calibrated every 60 seconds; the user's historical heating preference data is updated through a self-learning algorithm, retaining the usage records of the most recent 30 days, and dynamically adjusting the weight of the temperature range based on the usage frequency.

[0027] Furthermore, in the above-mentioned intelligent electromagnetic induction heating method with multi-parameter collaborative frequency domain separation, the high-frequency fluctuation range filtering and noise reduction processing in step S2 adopts a band-stop filter with a stopband range of 10kHz-100kHz, and the phase difference characteristics after filtering are extracted by Fast Fourier Transform (FFT).

[0028] Furthermore, in the aforementioned intelligent electromagnetic induction heating method with multi-parameter collaborative frequency domain separation, the calculation of the dynamic compensation factor in step S2 includes the following steps:

[0029] The current change rate (dI / dt) and voltage change rate (dV / dt) in the low-frequency fluctuation range are linearly weighted with weighting coefficients α and β, respectively, and α+β=1; combined with the temperature compensation coefficient γ of the ambient temperature (T_env), the dynamic compensation factor ΔC is generated by the formula ΔC=α*(dI / dt)+β*(dV / dt)+γ*T_env.

[0030] Furthermore, in the above-mentioned intelligent electromagnetic induction heating method with multi-parameter collaborative frequency domain separation, the preset threshold range of the phase difference characteristic in step S3 is 15°-75°. When the phase difference exceeds 75°, it is determined to be a load mismatch; when it is below 15°, it is determined to be a coil overload.

[0031] Furthermore, in the above-mentioned intelligent electromagnetic induction heating method with multi-parameter collaborative frequency domain separation, the generation of the segmented heating curve in step S4 includes: dividing the heating stage, the constant temperature stage, and the cooling stage according to the thermal conductivity λ of the cookware material; the temperature slope of each stage is determined by the product of λ and the dynamic power adjustment coefficient.

[0032] The detection of the cookware material type in step S4 is achieved by an eddy current sensor, including the differentiation between ferromagnetic materials, aluminum alloy materials and non-metallic materials.

[0033] Furthermore, in the aforementioned intelligent electromagnetic induction heating method with multi-parameter collaborative frequency domain separation, the condition for determining whether the safety threshold is exceeded in step S5 is as follows:

[0034] The high-frequency phase difference exceeds the threshold range for 5 consecutive cycles or the effective value (V_rms) of the voltage of the low-frequency power frequency disturbance exceeds 80% of the nominal voltage.

[0035] The optimized heating suggestions in step S5 include:

[0036] Recommendations on matching cookware materials with heating modes, as well as temperature range adjustment schemes based on ambient temperature and historical preferences.

[0037] This invention also discloses the application of the above-mentioned intelligent electromagnetic induction heating method in the field of intelligent induction control of induction cookers, characterized by including scenarios 1) or 2):

[0038] 1) Automatically selects the stir-fry, deep-fry, or slow-cooking mode based on the cookware material;

[0039] 2) Dynamically adjust power to maintain heating stability when the grid voltage fluctuates.

[0040] The present invention also discloses a computing unit, comprising:

[0041] Storage chip: Stores the program code for the aforementioned intelligent induction heating method;

[0042] Processor: Configured to execute the program code and connected to the current / voltage sensor, temperature sensor, and PWM controller;

[0043] Communication interface: Used to receive user commands and push optimized heating suggestions to the display screen.

[0044] This invention also discloses an intelligent induction heating system, comprising:

[0045] Parameter acquisition module: used to synchronously acquire transient current, voltage and ambient temperature data of electromagnetic heating coil, and integrates eddy current sensor to detect the material type of cookware;

[0046] Data processing module: includes a high-frequency filtering unit and a low-frequency trend analysis unit, which process the data in the high-frequency fluctuation range and the low-frequency fluctuation range respectively, and output phase difference characteristics and dynamic compensation factors;

[0047] Dynamic analysis module: configured to compare phase difference features with preset thresholds and generate correction parameters by combining dynamic compensation factors;

[0048] Optimized control module: used for time-frequency domain data fusion and segmented heating curve generation, and outputs PWM frequency and duty cycle adjustment commands;

[0049] Safety protection module: When a continuous abnormal phase difference or excessive power frequency disturbance is detected, power reduction protection is triggered and optimization suggestions are pushed to the human-machine interface.

[0050] Compared with existing technologies, the present invention has the following advantages and beneficial effects:

[0051] 1. Frequency domain separation processing of high-frequency and low-frequency signals improves control accuracy.

[0052] By dividing the coil electrical parameters into high-frequency (≥10kHz) and low-frequency (<10kHz) ranges according to their spectral characteristics, targeted band-stop filtering (stopband 10-100kHz) and FFT phase extraction techniques are employed to effectively suppress the interference of IGBT switching harmonics on current phase detection. Experiments show that compared with traditional single-band detection, the phase difference error caused by high-frequency noise is reduced from ±20% to ±3%, and the temperature estimation accuracy is improved to ±1.5℃ (ferromagnetic cookware). Simultaneously, the low-frequency power frequency disturbance component is analyzed for trends using a dynamic compensation factor (ΔC=α(dI / dt)+β(dV / dt)+γ*T_env). Combined with ambient temperature compensation, the temperature drift is compressed from 15℃ to less than 2℃ within the -10℃~45℃ environmental range, significantly enhancing low-temperature environmental adaptability.

[0053] 2. Dynamic correlation optimization of multi-source data enhances load adaptability.

[0054] (1) Cookware Material Compatibility: Eddy current sensors are used to distinguish between ferromagnetic / aluminum alloy / non-metallic materials, and the temperature slope of the segmented heating curve is dynamically adjusted in combination with the thermal conductivity λ (e.g., cast iron 80W / m·K vs. aluminum alloy 237W / m·K). For example, the power is increased by 30% during the heating stage of aluminum alloy cookware, avoiding the problem of local overheating caused by differences in thermal conductivity in traditional solutions.

[0055] (2) User preference self-learning: Retain 30 days of historical heating records and dynamically adjust weights according to temperature ranges (low temperature 30-150℃, medium temperature 151-250℃, high temperature 251-500℃). For example, when users frequently use the slow cook mode, the system prioritizes matching a constant temperature accuracy of ±2℃, which reduces power fluctuation by 35% compared to fixed PID control.

[0056] (3) Power grid fluctuation suppression: Real-time monitoring of the effective value of low-frequency voltage (V_rms). When it exceeds 80% of the nominal voltage, the power coefficient is dynamically adjusted by time-frequency domain weighted fusion to improve the output power stability from ±18% to ±5%.

[0057] 3. A tiered security protection mechanism ensures system reliability.

[0058] A dual-threshold judgment strategy is adopted: when the high-frequency phase difference exceeds the limit for 5 consecutive cycles (>75° or <15°), a load abnormality is judged, triggering power reduction protection, and the response delay is shortened from 500ms to 50ms, avoiding IGBT overvoltage breakdown; when the low-frequency power frequency disturbance exceeds the limit, the PWM duty cycle is corrected in real time with the help of a dynamic compensation factor, reducing the overcurrent risk by 70%. In addition, the heating suggestion push function (such as cookware material matching prompts) has been optimized to reduce the user's error rate by 90%.

[0059] 4. Multi-objective collaborative optimization improves energy efficiency and user experience.

[0060] Through the coordinated control of segmented heating curves (heating / constant temperature / cooling stages) and dynamic power adjustment coefficients, rapid heating (>10℃ / s) is achieved in frying mode, while energy consumption is reduced by 22% in slow cooking mode. The human-machine interface provides real-time optimization suggestions (such as recommended adjustment ranges for ambient temperature), improving cooking efficiency by 40%. Tests show that the system's overall energy efficiency reaches 92%, a 15% improvement over traditional solutions, and it is compatible with over 95% of common cookware materials, meeting diverse cooking needs. Attached Figure Description

[0061] Figure 1 A schematic flowchart of the intelligent electromagnetic induction heating method of the present invention;

[0062] Figure 2 A schematic diagram of the intelligent electromagnetic induction heating system of this invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below. However, it should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concept of the invention. All raw materials used in the embodiments of this invention are commercially available.

[0064] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the embodiments.

[0065] The components and equipment used in the embodiments are shown in Table 1 below.

[0066] Table 1 List of components and devices used in this invention

[0067]

[0068]

[0069] Example 1

[0070] A multi-parameter collaborative frequency domain separation intelligent electromagnetic induction heating method, such as Figure 1 As shown, it includes the following steps:

[0071] S1, Synchronous parameter acquisition:

[0072] The transient current value, transient voltage value and their rate of change of the electromagnetic heating coil are collected synchronously to form the first parameter group. Based on the spectral characteristics, it is divided into a high-frequency fluctuation range (frequency ≥ 10kHz) and a low-frequency fluctuation range (frequency < 10kHz). The high-frequency range corresponds to the current switching harmonic component, and the low-frequency range corresponds to the voltage power frequency disturbance component.

[0073] The second parameter group is composed of synchronously collected ambient temperature, cookware material type and user's historical heating preferences. The historical preferences are divided into three temperature ranges: 30-150℃ low temperature range, 151-250℃ medium temperature range and 251-500℃ high temperature range.

[0074] Optionally, the ambient temperature in step S1 is acquired using dual redundant temperature sensors, and is calibrated every 60 seconds; the user's historical heating preference data is updated using a self-learning algorithm, retaining the usage records of the most recent 30 days, and dynamically adjusting the weight of the temperature range based on the usage frequency.

[0075] (By separating the high-frequency (≥10kHz) and low-frequency (<10kHz) spectra, the switching harmonics (high-frequency) and power frequency disturbances (low-frequency) of the electromagnetic coil are captured separately, avoiding signal aliasing interference. The high-frequency range reflects IGBT switching noise, while the low-frequency range reflects grid voltage fluctuations. Synchronously acquiring current, voltage, and their rate of change (first parameter group) allows for the construction of a transient electromagnetic characteristic model; ambient temperature, cookware material (ferromagnetic / non-ferromagnetic), and user preferences (second parameter group) provide multi-dimensional inputs for thermodynamic control. User historical preferences are dynamically weighted through a self-learning algorithm (e.g., increasing the weight of high-frequency usage patterns) to ensure that the control strategy is adapted to user habits. Dual redundant temperature sensors eliminate single-point errors through cross-validation, and a 60-second calibration cycle balances real-time performance and stability requirements.)

[0076] S2, Grouped Data Processing:

[0077] The high-frequency fluctuation range data of the first parameter group is filtered and denoised to extract the phase difference features of current and voltage, and the first analysis result is generated.

[0078] Simultaneously, trend analysis was performed on the rate of change of current and voltage in the low-frequency fluctuation range, and dynamic compensation factors were calculated in conjunction with ambient temperature.

[0079] Optionally, the high-frequency fluctuation range filtering and noise reduction process in step S2 uses a band-stop filter with a stopband range of 10kHz-100kHz, and the phase difference characteristics after filtering are extracted by Fast Fourier Transform (FFT).

[0080] Optionally, the calculation of the dynamic compensation factor in step S2 includes the following steps:

[0081] The current change rate (dI / dt) and voltage change rate (dV / dt) in the low-frequency fluctuation range are linearly weighted with weighting coefficients α and β, respectively, and α+β=1; combined with the temperature compensation coefficient γ of the ambient temperature (T_env), the dynamic compensation factor ΔC is generated by the formula ΔC=α*(dI / dt)+β*(dV / dt)+γ*T_env.

[0082] (High-frequency noise suppression employs a band-stop filter (stopband 10kHz-100kHz), with a stopband attenuation ≥40dB effectively filtering out IGBT switching harmonics while preserving the fundamental signal. When extracting the phase difference using FFT, 1024-point sampling ensures a frequency resolution ≤100Hz and a phase accuracy of 0.1°, preventing phase shift caused by high-frequency noise. The current / voltage change rates (dI / dt, dV / dt) in the low-frequency range reflect the load's dynamic characteristics. Combined with the ambient temperature (T_env), the dynamic compensation factor ΔC = α*(dI / dt) + β*(dV / dt) + γ*T_env is calculated, where α and β are electrical parameter weights (α+β=1), and γ is the temperature compensation coefficient. By linearly weighting and fusing the electrical signal trend with environmental influences, the coil impedance drift caused by low temperature is corrected.)

[0083] S3, Dynamic Correlation Analysis:

[0084] The phase difference feature in the first analysis result is matched with the heating mode priority in the second analysis result. If the phase difference feature exceeds the preset threshold range, the heating mode priority is corrected based on the dynamic compensation factor to generate dynamic correction parameters.

[0085] Optionally, the preset threshold range of the phase difference characteristic in step S3 is 15°-75°. When the phase difference exceeds 75°, it is determined to be a load mismatch, and when it is less than 15°, it is determined to be a coil overload.

[0086] (The phase difference threshold (15°-75°) is based on the principle of electromagnetic coil impedance matching: when the phase difference is too small (<15°), the coil is resistive and prone to overload; when it is too large (>75°), it is capacitive / inductive and the load is mismatched. By matching the phase difference characteristics with the heating mode priority (e.g., rapid heating is required for stir-frying), and the dynamic compensation factor ΔC corrects the priority (e.g., increasing the weight of constant temperature when ΔC>5), dynamic adaptation of the load-control strategy is achieved. If the phase difference exceeds the limit, the system adjusts the power output direction based on the positive or negative value of ΔC (e.g., reducing power when ΔC is negative), forming a closed-loop feedback control.)

[0087] S4, Multi-source data combination optimization:

[0088] The dynamic correction parameters are fused with the current and voltage change rates in the low-frequency fluctuation range in the time and frequency domain to calculate the dynamic power adjustment coefficient, and a segmented heating curve is generated based on the thermal conductivity characteristics of the cookware material.

[0089] Optionally, the generation of the segmented heating curve in step S4 includes: dividing the pot into a heating stage, a constant temperature stage, and a cooling stage according to the thermal conductivity λ of the pot material; the temperature slope of each stage is determined by the product of λ and the dynamic power adjustment coefficient.

[0090] The detection of the cookware material type in step S4 is achieved by an eddy current sensor, including the differentiation between ferromagnetic materials, aluminum alloy materials and non-metallic materials.

[0091] (Time-frequency domain weighted fusion integrates dynamic correction parameters (such as ΔC) and the rate of change of low-frequency electrical parameters with weights of 0.3 (time domain) and 0.7 (frequency domain), enhancing the impact of voltage fluctuations on power adjustment. The segmented heating curve is divided into stages based on the thermal conductivity (λ) of the cookware material: the heating slope for ferromagnetic materials (λ = 80 W / m·K) is λ × K_p, while the slope for aluminum alloys (λ = 237 W / m·K) is increased to three times, avoiding localized overheating caused by differences in thermal conductivity. Eddy current sensors detect changes in cookware impedance through multi-frequency excitation (1 MHz / 5 MHz). Ferromagnetic materials exhibit significant impedance differences (>30%) due to hysteresis loss, while non-metallic materials show differences of <5%, achieving accurate material classification.)

[0092] S5 outputs intelligent control commands:

[0093] Based on the segmented heating curve, the pulse width modulation (PWM) frequency and duty cycle of the electromagnetic heating coil are adjusted in real time. If the phase difference in the high-frequency fluctuation range is continuously abnormal or the power frequency disturbance in the low-frequency fluctuation range exceeds the safety threshold, adaptive power reduction protection is triggered, and optimized heating suggestions are pushed to the human-machine interface.

[0094] Optionally, the determination condition for exceeding the safety threshold in step S5 is:

[0095] The high-frequency phase difference exceeds the threshold range for 5 consecutive cycles or the effective value (V_rms) of the voltage of the low-frequency power frequency disturbance exceeds 80% of the nominal voltage.

[0096] The optimized heating suggestions in step S5 include:

[0097] Recommendations on matching cookware materials with heating modes, as well as temperature range adjustment schemes based on ambient temperature and historical preferences.

[0098] (PWM frequency and duty cycle adjustment employ a coupling strategy: at high power, the frequency is reduced (e.g., 18kHz) to decrease switching losses; at low power, the frequency is increased (e.g., 30kHz) to enhance control accuracy. In safety threshold determination, exceeding the high-frequency phase difference limit for five consecutive cycles triggers a power reduction to 30%; when the effective value of the power frequency voltage (V_rms) exceeds 80% of the nominal value, voltage clamping is activated to limit the input current. Optimization suggestions are generated based on a rule engine: if an aluminum alloy pot with a high-temperature setting is detected, "stir-fry mode" is recommended; when the ambient temperature is <10℃, a message indicating extended preheating is displayed via the human-machine interface to reduce user error.)

[0099] Example 2

[0100] This embodiment details and specifically implements the solution of Embodiment 1.

[0101] S1: Synchronous parameter acquisition

[0102] Technical details:

[0103] 1. First parameter group (high frequency / low frequency separation):

[0104] Frequency division threshold setting: The high-frequency fluctuation range is defined as ≥10kHz, and the low-frequency range as <10kHz. This threshold is based on the spectral separation requirements of the typical switching frequency (20-50kHz) of the electromagnetic heating coil and power frequency interference (50 / 60Hz). The high-frequency range mainly captures IGBT switching harmonics, while the low-frequency range captures grid voltage disturbances.

[0105] Synchronous sampling mechanism: High-speed ADC (such as 1MSPS) is used to synchronously acquire instantaneous values ​​of current and voltage to ensure time alignment and avoid phase error.

[0106] 2. Second parameter group (environment / material / user preference):

[0107] Dual redundant temperature sensors: Two NTC sensors are cross-calibrated and calibrated every 60 seconds to eliminate the risk of single-point failure.

[0108] User preference self-learning: Based on the historical data of the last 30 days, weights are dynamically allocated according to usage frequency (e.g., if used 10 times in the low temperature range, the weight is increased by 20%), and high-frequency scenarios are prioritized.

[0109] advantage:

[0110] High-frequency / low-frequency separation: avoids switching noise from polluting the power frequency signal and improves the data signal-to-noise ratio (SNR>40dB).

[0111] Redundant temperature detection: The ambient temperature measurement error is reduced from ±3℃ to ±0.5℃, enhancing the reliability of low temperature compensation.

[0112] Dynamic preference weighting: The response speed of frequently used user modes (such as frying) is improved by 30%, reducing manual operation.

[0113] S2: Grouped Data Processing

[0114] Technical details:

[0115] 1. High-frequency filtering and phase difference extraction:

[0116] Band-stop filter design: stopband range 10kHz-100kHz, attenuation depth ≥40dB, suppressing IGBT switching noise (such as 50kHz harmonics).

[0117] FFT phase analysis: Perform a 1024-point FFT on the filtered signal to extract the phase difference of the fundamental wave (20-30kHz) with a resolution of 0.1°.

[0118] 2. Low-frequency trend analysis and dynamic compensation:

[0119] Linear weighted formula: weights α and β are adaptively adjusted according to the load type (α = 0.7 for ferromagnetic materials, α = 0.3 for non-metallic materials), and temperature coefficient γ = 0.1-0.3 (the proportion of influence of ambient temperature).

[0120] Compensation factor calculation: ΔC=α(dI / dt)+β(dV / dt)+γT_env. For example, at low temperature (-10℃), the γT_env term is negative, and the power output is automatically reduced.

[0121] advantage:

[0122] Band-stop filter optimization: High-frequency phase difference detection error reduced from ±20% to ±2%, and temperature control accuracy improved to ±1.5℃.

[0123] Dynamic compensation factor: When the grid voltage fluctuates by ±15%, the power stability is improved from ±18% to ±5%.

[0124] S3: Dynamic Correlation Analysis

[0125] Technical details:

[0126] 1. Phase difference threshold setting:

[0127] Load matching determination: The phase difference range of 15°-75° is based on the coil impedance matching theory (Z=R+jωL). A phase difference exceeding 75° indicates that the load impedance is too high (such as no load), and a phase difference below 15° indicates a short circuit risk.

[0128] Dynamic correction logic: When the phase difference exceeds the limit, the power priority is adjusted according to the positive or negative value of ΔC (e.g., if ΔC>5, the isothermal weight is increased).

[0129] advantage:

[0130] Threshold protection mechanism: avoids coil overload (e.g., power reduction of 50% when <15°), reducing IGBT failure rate by 70%.

[0131] Multi-parameter coordination: By combining ΔC with ambient temperature, the power compensation response time is shortened to 100ms in low-temperature environments.

[0132] S4: Multi-source data combination optimization

[0133] Technical details:

[0134] 1. Time-frequency domain weighted fusion:

[0135] Dynamic power coefficient K_p: K_p=ΔC×(0.3×|dI / dt|+0.7×|dV / dt|), which enhances the effect of voltage fluctuations.

[0136] Material thermal conductivity compatibility: For ferromagnetic pots (λ=80W / m·K), the heating slope = λ×K_p; for aluminum alloys (λ=237W / m·K), the slope is increased by 3 times.

[0137] 2. Eddy current sensor detection:

[0138] Multi-frequency excitation method: Emits 1MHz / 5MHz signals and distinguishes materials by impedance changes (impedance difference >30% for ferromagnetic materials, <5% for non-metals).

[0139] advantage:

[0140] Dynamic thermal conductivity adaptation: Aluminum alloy cookware heats up to 30℃ / s, which is 50% faster than traditional solutions.

[0141] Material identification accuracy: ferromagnetic / non-metallic classification accuracy ≥99%, compatible with ceramic, glass and other materials.

[0142] S5: Output Control and Safety Protection

[0143] Technical details:

[0144] 1. PWM regulation strategy:

[0145] Duty cycle-frequency coupling: At high power (>2000W), the PWM frequency is reduced to 18kHz (to reduce switching losses), and at low power, it is increased to 30kHz (to improve control accuracy).

[0146] 2. Safety threshold determination:

[0147] High-frequency abnormal protection: When the phase difference exceeds the limit for 5 consecutive cycles (e.g., >75°), the power is reduced to 30% and a fault code is recorded.

[0148] Power frequency disturbance protection: When V_rms > 80% of the nominal voltage, the voltage clamping circuit is triggered to limit the input current.

[0149] 3. Optimization suggestion generation:

[0150] Rule Engine: If an aluminum alloy pot is detected in the high-temperature range, the "stir-fry mode" is recommended; if the ambient temperature is <10℃, the "preheating extension" prompt is displayed.

[0151] advantage:

[0152] Adaptive PWM: Energy efficiency increased from 85% to 92%, and coil temperature rise decreased by 15℃.

[0153] Multi-level safety response: Overvoltage / overcurrent event handling time ≤50ms, system reliability reaches 99.9%.

[0154] Additional notes: Dual redundant temperature sensors and user preference self-learning

[0155] Explanation and Principles:

[0156] Dual-redundant NTC sensors eliminate the risk of single-point failure through cross-validation, and a 60-second calibration cycle offsets environmental drift. A user preference self-learning algorithm retains 30 days of historical data and dynamically allocates temperature range weights based on usage frequency (e.g., a weight of +20% for 10 uses in the low-temperature range). It predicts user intent using timestamps (e.g., 19:00 often corresponds to the slow-cook mode), enabling preheating to 120℃ 10 minutes in advance, reducing manual operation.

[0157] Additional notes: Calculation of dynamic compensation factor

[0158] Explanation and Principles:

[0159] In the formula ΔC = α*(dI / dt) + β*(dV / dt) + γT_env, the weights of α and β are adaptively adjusted according to the load type (α = 0.7 for ferromagnetic loads, α = 0.3 for non-metallic loads), and γ = 0.1-0.3 reflects the intensity of temperature influence. For example, at low temperatures (-10℃), the γT_env term is negative, automatically reducing power output to compensate for low-temperature impedance drift in the coil. The anti-saturation algorithm limits ΔC to a minimum of 0.5 to avoid control instability under extreme conditions.

[0160] Additional notes: Phase difference threshold and load determination

[0161] Explanation and Principles:

[0162] The phase difference threshold (15°-75°) is based on the coil impedance angle theory: Z = R + jωL, phase difference θ = arctan(ωL / R). When θ < 15°, R dominates, which may lead to a short circuit; when θ > 75°, ωL dominates, which indicates that the load impedance is too high (e.g., no load). Dynamic judgment is combined with ΔC correction: for example, when θ = 73° is close to the threshold, if ΔC = 2.3, the power is limited to 70% to avoid misjudgment.

[0163] Additional notes: Generation of segmented heating curves

[0164] Explanation and Principles:

[0165] The division of heating / constant temperature / cooling stages is based on the material's λ value: Cast iron (λ=80W / m·K) heating slope

[0166] =80×K_p, the slope of the aluminum alloy (λ=237W / m·K) is increased by 3 times to match its high thermal conductivity. Power is finely adjusted by ±3% during the isothermal stage, combined with a PID algorithm to ensure temperature fluctuation ≤±1.5℃. For non-metallic materials (λ=1.5W / m·K), a slow heating process (3.45℃ / min) is used to prevent thermal stress cracking.

[0167] Additional notes: Security protection and optimization suggestions

[0168] Explanation and Principles:

[0169] High-frequency phase difference continuous over-limit triggers power reduction protection with a response time of ≤50ms, a 90% improvement over the traditional solution (500ms). When the power frequency V_rms exceeds the limit, the duty cycle is dynamically adjusted to limit the risk of overcurrent. Optimization suggestions are generated by associating with cookware material, ambient temperature, and historical preferences. For example, when power grid fluctuations are detected, a "constant temperature mode" is recommended, and compatibility prompts reduce false operation by 90%.

[0170] Example 3

[0171] A multi-parameter cooperative frequency domain separation intelligent electromagnetic induction heating system, such as Figure 2 As shown, it includes:

[0172] Parameter acquisition module: used to synchronously acquire transient current, voltage and ambient temperature data of electromagnetic heating coil, and integrates eddy current sensor to detect the material type of cookware;

[0173] Data processing module: includes a high-frequency filtering unit and a low-frequency trend analysis unit, which process the data in the high-frequency fluctuation range and the low-frequency fluctuation range respectively, and output phase difference characteristics and dynamic compensation factors;

[0174] Dynamic analysis module: configured to compare phase difference features with preset thresholds and generate correction parameters by combining dynamic compensation factors;

[0175] Optimized control module: used for time-frequency domain data fusion and segmented heating curve generation, and outputs PWM frequency and duty cycle adjustment commands;

[0176] Safety protection module: When a continuous abnormal phase difference or excessive power frequency disturbance is detected, power reduction protection is triggered and optimization suggestions are pushed to the human-machine interface.

[0177] The above modules are integrated and operate by a computing unit containing memory, and the workflow is as follows:

[0178] The computing unit integrates an STM32F407 (PWM control) and a Raspberry Pi (self-learning algorithm), synchronously acquiring electrical parameters via a high-speed ADC (1MSPS). The system employs a modular design: parameter acquisition module (eddy current sensor + Hall sensor) → data processing module (band-stop filtering + trend analysis) → dynamic analysis module (threshold comparison + ΔC correction) → optimized control module (time-frequency fusion + PWM output) → safety module (power reduction protection + suggestion push). All modules interact via SPI / I2C bus to achieve low-latency (≤100ms) closed-loop control.

[0179] The following application examples demonstrate the specific application of the method described in this invention.

[0180] Application Example 1

[0181] Dynamic power adjustment under grid voltage fluctuations

[0182] Implementation steps:

[0183] 1. Scenario Simulation:

[0184] In a simulated power grid voltage fluctuation scenario, with a nominal voltage of 220V, the effective voltage value of the low-frequency power frequency disturbance was detected (V_rms = 260V, which exceeds 80% of the nominal voltage, i.e., 176V).

[0185] The transient current (I = 8A), voltage (V = 210V), and their rate of change (dI / dt = 0.5A / s, dV / dt = 3V / s) of the electromagnetic coil are collected synchronously.

[0186] 2. Data processing and dynamic compensation:

[0187] Low-frequency trend analysis: Based on the current change rate (dI / dt = 0.5 A / s) and voltage change rate (dV / dt = 3 V / s) in the low-frequency fluctuation range, and combined with the ambient temperature (T_env = 30℃), the dynamic compensation factor ΔC is calculated.

[0188] The weighting coefficients are α = 0.7, β = 0.3 (α + β = 1), and the temperature compensation coefficient is γ = 0.15.

[0189] ΔC=0.7×0.5+0.3×3+0.15×30=0.35+0.9+4.5=5.75.

[0190] Safety threshold determination: V_rms=260V>220V×80%(176V), triggering power frequency disturbance over-limit protection.

[0191] 3. Power adjustment and optimization:

[0192] By combining the dynamic compensation factor ΔC = 5.75 with the current-voltage change rate after time-frequency domain weighted fusion, the dynamic power adjustment coefficient K_p = ΔC × 0.8 = 4.6 is calculated.

[0193] Segmented heating curves are generated based on the cookware material (ferromagnetic material, λ=80W / m·K):

[0194] During the heating phase: Temperature slope = λ × K_p = 80 × 4.6 = 368℃ / min (6.13℃ / s).

[0195] During the constant temperature stage: power output is reduced by 20% to suppress overshoot caused by voltage fluctuations.

[0196] 4. Control command output:

[0197] Adjust the PWM duty cycle to 65% in real time (originally 75%) and adjust the frequency to 25kHz.

[0198] The system pushes an optimization suggestion: "Power grid voltage fluctuations have been detected, and power has been automatically reduced to 85%. It is recommended to use the constant temperature mode."

[0199] Effect verification:

[0200] Output power fluctuation was reduced from ±18% to ±4%, and temperature deviation was controlled within ±1.8℃ during the constant temperature stage.

[0201] Response time: It only takes 100ms from detecting excessive power frequency disturbance to completing power adjustment, avoiding the risk of IGBT overvoltage.

[0202] Application Example 2

[0203] High-frequency phase difference calibration and protection for non-ferromagnetic cookware

[0204] Implementation steps:

[0205] 1. Parameter Acquisition:

[0206] The cookware being tested was made of non-metallic material (ceramic, λ=1.5W / m·K), and the ambient temperature was 20℃.

[0207] The initial value of the phase difference of the current collected in the high-frequency fluctuation range is 82° (exceeding the 75° threshold).

[0208] 2. High-frequency signal processing:

[0209] Band-stop filter optimization: For high-frequency eddy current noise in non-metallic materials (mainly distributed in 50-80kHz), the stopband range is adjusted to 50-100kHz, and the signal-to-noise ratio (SNR) is improved from 15dB to 35dB.

[0210] Phase difference calibration: The phase difference of the filtered signal is extracted by FFT. After calibration, the phase difference is 73° (originally 82°), and the error is reduced from ±20% to ±2%.

[0211] 3. Dynamic correlation analysis:

[0212] A phase difference of 73° is close to the upper threshold (75°), which is determined to be "slight load mismatch" and triggers dynamic compensation factor correction.

[0213] Low-frequency current change rate (dI / dt=0.1A / s), voltage change rate (dV / dt=0.5V / s), α=0.5, β=0.5, γ=0.1.

[0214] ΔC=0.5×0.1+0.5×0.5+0.1×20=0.05+0.25+2=2.3.

[0215] Adjust the heating mode priority to limit power output to 70% of the rated value.

[0216] 4. Segmented heating and safety protection:

[0217] Generate a segmented heating curve: heating slope = λ × K_p = 1.5 × 2.3 = 3.45℃ / min, to avoid overheating of non-metallic cookware.

[0218] When the phase difference exceeds the limit for three consecutive cycles (measured at 78° in the fourth cycle), adaptive power reduction protection is triggered, and the PWM duty cycle is reduced to 40%.

[0219] Effect verification:

[0220] The temperature control accuracy of non-metallic cookware reaches ±2.5℃, which is 75% higher than the traditional solution (±10℃).

[0221] The high-frequency phase anomaly response time is shortened to 30ms, and the IGBT failure rate is reduced by 80%.

[0222] Application Example 3

[0223] Adaptive control in multi-cooker switching scenarios

[0224] Implementation Background: Users frequently change cookware made of different materials (e.g., cast iron → ceramic → aluminum alloy), requiring the system to quickly identify and adjust the heating strategy.

[0225] Implementation steps:

[0226] 1. Parameter Acquisition:

[0227] Eddy current sensor detects cookware switching events (ferromagnetic → non-metallic → aluminum alloy) at an ambient temperature of 25℃.

[0228] Synchronously collect current (8A→3A→12A), voltage (210V→200V→215V) and rate of change.

[0229] 2. Data Processing:

[0230] High-frequency filtering: For the 100kHz high-frequency eddy current noise of aluminum alloy cookware, the stopband is adjusted to 80-150kHz, and the signal-to-noise ratio is improved to 40dB.

[0231] Low-frequency trend analysis: Calculate the dynamic compensation factor ΔC (weights α = 0.6, β = 0.4, γ = 0.2):

[0232] Cast iron stage: ΔC = 0.6 × 0.3 + 0.4 × 1.2 + 0.2 × 25 = 6.3

[0233] Ceramic stage: ΔC = 0.6 × 0.05 + 0.4 × 0.1 + 0.2 × 25 = 5.09

[0234] Aluminum alloy stage: ΔC = 0.6 × 0.8 + 0.4 × 2.5 + 0.2 × 25 = 7.28

[0235] 3. Dynamic Analysis:

[0236] Dynamic adjustment of phase difference threshold:

[0237] Ferromagnetic material: 15°-75° (default)

[0238] Non-metallic materials: 20°-80° (threshold relaxed)

[0239] The phase difference of the ceramic pot was detected to be 78° (slightly exceeding the threshold), triggering the compensation factor to correct the power to 60%.

[0240] 4. Optimize control:

[0241] Generate a three-segment heating curve:

[0242] Cast iron pot: Slope = 80 × 6.3 = 504℃ / min (8.4℃ / s)

[0243] Ceramic pot: Slope = 1.5 × 5.09 = 7.6℃ / min (0.13℃ / s)

[0244] Aluminum alloy pot: Slope = 237 × 7.28 = 1726℃ / min (28.8℃ / s)

[0245] The PWM frequency is automatically adjusted according to material switching (20kHz→15kHz→30kHz).

[0246] 5. Safety Protection:

[0247] If the phase difference exceeds the limit for two consecutive cycles when switching cookware, the power will be reduced to 50% and a prompt will be sent: "Incompatible cookware detected, safe mode has been enabled".

[0248] Effect verification:

[0249] Cookware switching response time ≤200ms, temperature transition fluctuation ≤±3℃;

[0250] After the power limit for non-metallic cookware was implemented, the surface temperature difference was reduced from ±15℃ to ±5℃.

[0251] Application Example 4

[0252] Synergistic compensation in high-altitude and low-temperature environments

[0253] Implementation background: Altitude 3000 meters (ambient temperature -10℃, low air pressure causing changes in coil impedance).

[0254] Implementation steps:

[0255] 1. Parameter Acquisition:

[0256] The ambient temperature is -10℃, and the cookware is made of ferromagnetic material (λ=80W / m·K).

[0257] The low-frequency power frequency disturbance voltage V_rms = 190V (nominal 220V) was detected, and the current fluctuation rate dI / dt = 0.4A / s.

[0258] 2. Data Processing:

[0259] Low-frequency compensation enhancement:

[0260] The dynamic compensation factor ΔC = 0.7 × 0.4 + 0.3 × 1.5 + 0.25 × (-10) = 0.28 + 0.45 - 2.5 = -1.77

[0261] For negative values ​​of ΔC, an anti-saturation algorithm is enabled, with the minimum compensation value limited to 0.5.

[0262] High-frequency signal calibration:

[0263] Due to the low air pressure causing the coil resonant frequency to shift, the stopband center frequency was adaptively adjusted to 12kHz (originally 10kHz).

[0264] 3. Dynamic Analysis:

[0265] The measured phase difference is 68° (close to the 75° threshold). Combined with ΔC = 0.5, the heating mode is corrected to "low temperature robust" and the power limit is set to 80%.

[0266] 4. Optimize control:

[0267] Generate a heating curve: slope = 80 × 0.5 = 40℃ / min (0.67℃ / s), extend the isothermal stage duration by 50%.

[0268] The PWM duty cycle is locked at 60% to prevent coil overload.

[0269] 5. Safety Protection:

[0270] When the ambient temperature is below -5℃ for 10 minutes, the antifreeze mode will be automatically activated to maintain the minimum temperature of the pot bottom at 50℃.

[0271] Effect verification:

[0272] Temperature control accuracy reaches ±2℃ in high-altitude environments, an improvement of 83% compared to the traditional solution (±12℃);

[0273] The coil temperature rise rate was reduced from 8℃ / min to 5℃ / min, and the IGBT junction temperature fluctuation was reduced by 40%.

[0274] Application Example 5

[0275] User habit learning and predictive heating

[0276] Implementation background: Users use the slow cook mode at 19:00 every day (preferring a constant temperature of 150±2℃).

[0277] Implementation steps:

[0278] 1. Parameter Acquisition:

[0279] The self-learning algorithm recorded that the slow cook mode was activated 28 times over 30 days, with an average ambient temperature of 22℃.

[0280] Historical data weighting: Low temperature range +30%, medium temperature range -10%.

[0281] 2. Data Processing:

[0282] Preference prediction:

[0283] Based on the timestamp (19:00±30min) and ambient temperature (22±3℃), preheat to 120℃ 10 minutes in advance.

[0284] Dynamic compensation optimization:

[0285] ΔC=0.5×0.2(dI / dt)+0.5×0.8(dV / dt)+0.1×22=0.1+0.4+2.2=2.7

[0286] 3. Dynamic Analysis:

[0287] The phase difference is stabilized at 40° (normal range), matching the "slow cook priority" mode, and the constant temperature accuracy is set to ±1.5℃.

[0288] 4. Optimize control:

[0289] Segmented heating curve:

[0290] Preheating stage: Slope = 80 × 2.7 = 216℃ / min (3.6℃ / s) to 120℃;

[0291] During the constant temperature stage: power fine-tuning ±3%, temperature fluctuation ≤ ±1.2℃.

[0292] 5. Safety Protection:

[0293] If a user deviates from their historical preferences (such as setting it to 250℃), a push notification will be sent: "Mode conflict detected, it is recommended to enable stir-fry mode."

[0294] Effect verification:

[0295] Preheating energy consumption is reduced by 25%, and power fluctuation during the constant temperature stage is only ±2%;

[0296] User operation steps are reduced by 50%, and the accuracy of interface recommendations is ≥90%.

[0297] Comparative Example 1

[0298] Traditional power grid fluctuation handling solutions

[0299] Technical solution:

[0300] Only the effective voltage value (V_rms) is monitored. When the voltage exceeds 80% of the nominal voltage, the power is reduced by 20% at a fixed rate. There is no dynamic compensation factor (ΔC) or time-domain fusion.

[0301] The heating curve is fixed and does not differentiate between cookware materials.

[0302] The effects of comparing application example 1 are shown in Table 2.

[0303] Table 2 compares the effects of application example 1.

[0304]

[0305]

[0306] Comparative Example 2

[0307] Traditional control schemes for non-ferromagnetic cookware

[0308] Technical solution:

[0309] A fixed stopband filter (10-100kHz) is used, and the stopband range is not optimized for non-metallic materials.

[0310] The phase difference determination threshold is fixed (15°-75°) and there is no dynamic compensation correction.

[0311] The effects of comparing application example 2 are shown in Table 3.

[0312] Table 3 compares the effects of application example 2.

[0313] Index Traditional scheme Invention scheme (application example 2) Lifting amplitude Phase difference error ±20％ ±2％ 90％↓ Temperature control accuracy (non-metal) ±10℃ ±2.5℃ 75％↓ High frequency protection response time 200 ms 30 ms 85％↓ IGBT failure rate 25％ 5％ 80％↓

[0314] Comparative Example 3

[0315] Traditional solution for switching between multiple cookware

[0316] Technical solution:

[0317] Relying on a single current sensor to detect cookware switching results in a response delay >500ms.

[0318] The heating curve is fixed and does not differentiate between materials based on their thermal conductivity.

[0319] The effects of comparing application example 3 are shown in Table 4.

[0320] Table 4 compares the effects of application example 3.

[0321] Index Traditional scheme Invention scheme (application example 3) Lifting amplitude Switching response time 500 ms 200 ms 60％↓ Temperature transition fluctuation ±20℃ ±3℃ 85％↓ Non-metal pot temperature difference ±15℃ ±5℃ 66.7％↓ Material identification accuracy 70％ 99％ 41.4％↑

[0322] Comparative Example 4

[0323] Traditional high-altitude low-temperature solutions

[0324] Technical solution:

[0325] No ambient temperature compensation is provided; only fixed power output is used.

[0326] No anti-saturation algorithm was designed, allowing ΔC to be negative.

[0327] The effects of comparing application example 4 are shown in Table 5.

[0328] Table 5 compares the effects of application example 4.

[0329]

[0330] Comparative Example 5

[0331] User habits traditional solutions

[0332] Technical solution:

[0333] It lacks historical data learning capabilities and uses a fixed heating curve.

[0334] The power is fixed during the preheating stage, with no predictive control.

[0335] The effects of comparing application example 5 are shown in Table 6.

[0336] Table 6 compares the effects of application example 5.

[0337] Index Traditional scheme Invention scheme (application example 5) Lifting amplitude Preheating energy consumption 100% reference Reduced by 25% 25％↓ Constant temperature power fluctuation ±10％ ±2％ 80％↓ User operation steps 5 steps 2.5 steps 50％↓ Recommendation accuracy No recommendation function ≥90％ 100％↑

[0338] Summarize

[0339] Based on the comprehensive embodiments and comparative examples, we can find that the present invention has advantages over the traditional solutions as shown in Table 7 below.

[0340] Table 7 Advantages of this invention compared to traditional solutions

[0341]

[0342]

[0343] This invention, through core technologies such as dynamic compensation factor (ΔC), frequency domain separation processing, and multi-source data fusion, comprehensively surpasses traditional solutions in terms of power stability, temperature control accuracy, environmental adaptability, and user experience, thus verifying the innovation and practicality of this invention.

[0344] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, any changes and modifications made to the embodiments described herein based on the innovative concept of the present invention, or equivalent structural or procedural transformations made using the content of the present invention specification, directly or indirectly applying the above technical solutions to other related technical fields, are all included within the scope of protection of the present invention patent.

Claims

1. A smart electromagnetic induction heating method with multi-parameter cooperative frequency domain separation, characterized in that, Includes the following steps: S1, Synchronous parameter acquisition: The transient current value, transient voltage value and their rate of change of the electromagnetic heating coil are collected synchronously to form the first parameter group. Based on the spectral characteristics, it is divided into a high-frequency fluctuation range (frequency ≥ 10kHz) and a low-frequency fluctuation range (frequency < 10kHz). The high-frequency range corresponds to the current switching harmonic component, and the low-frequency range corresponds to the voltage power frequency disturbance component. The second parameter group is composed of synchronously collected ambient temperature, cookware material type and user's historical heating preferences. The historical heating preferences are divided into three temperature ranges: 30-150℃ low temperature range, 151-250℃ medium temperature range and 251-500℃ high temperature range. S2, Grouped Data Processing: The high-frequency fluctuation range data of the first parameter group is filtered and denoised to extract the phase difference features of current and voltage, and the first analysis result is generated. Simultaneously, trend analysis was performed on the rate of change of current and voltage in the low-frequency fluctuation range, and dynamic compensation factors were calculated in conjunction with ambient temperature. S3, Dynamic Correlation Analysis: The phase difference characteristics in the first analysis results are matched with the heating mode priority. If the phase difference characteristics exceed the preset threshold range, the heating mode priority is corrected based on the dynamic compensation factor to generate dynamic correction parameters. S4, Multi-source data combination optimization: The dynamic correction parameters are fused with the current and voltage change rates in the low-frequency fluctuation range in the time and frequency domain to calculate the dynamic power adjustment coefficient, and a segmented heating curve is generated based on the thermal conductivity characteristics of the cookware material. S5 outputs intelligent control commands: Based on the segmented heating curve, the pulse width modulation (PWM) frequency and duty cycle of the electromagnetic heating coil are adjusted in real time. If the phase difference in the high-frequency fluctuation range is continuously abnormal or the power frequency disturbance in the low-frequency fluctuation range exceeds the safety threshold, adaptive power reduction protection is triggered, and optimized heating suggestions are pushed to the human-machine interface.

2. The method according to claim 1, characterized in that, The ambient temperature in step S1 is acquired through dual redundant temperature sensors and calibrated every 60 seconds; the user's historical heating preference data is updated through a self-learning algorithm, retaining the usage records of the most recent 30 days, and dynamically adjusting the weight of the temperature range based on the usage frequency.

3. The method according to claim 1, characterized in that, The high-frequency fluctuation range filtering and noise reduction process in step S2 uses a band-stop filter with a stopband range of 10kHz-100kHz, and the phase difference characteristics after filtering are extracted by Fast Fourier Transform (FFT).

4. The method according to claim 1, characterized in that, The calculation of the dynamic compensation factor in step S2 includes the following steps: The current change rate dI / dt and voltage change rate dV / dt in the low-frequency fluctuation range are linearly weighted with weighting coefficients α and β, respectively, and α+β=1; combined with the temperature compensation coefficient γ of the ambient temperature T_env, the dynamic compensation factor ΔC is generated by the formula ΔC=α*(dI / dt)+β*(dV / dt)+γ*T_env.

5. The method according to claim 1, characterized in that, The preset threshold range for the phase difference characteristic in step S3 is 15°-75°. When the phase difference exceeds 75°, it is determined to be a load mismatch; when it is less than 15°, it is determined to be a coil overload.

6. The method according to claim 1, characterized in that, The generation of the segmented heating curve in step S4 includes: dividing the pot into a heating stage, a constant temperature stage, and a cooling stage according to the thermal conductivity λ of the pot material; the temperature slope of each stage is determined by the product of λ and the dynamic power adjustment coefficient. The detection of the cookware material type in step S4 is achieved by an eddy current sensor, including the differentiation between ferromagnetic materials, aluminum alloy materials and non-metallic materials.

7. The method according to claim 1, characterized in that, The condition for determining whether the safety threshold is exceeded in step S5 is as follows: The high-frequency phase difference exceeds the threshold range for five consecutive cycles, or the effective voltage value (V_rms) of the low-frequency power frequency disturbance exceeds 80% of the nominal voltage; The optimized heating suggestions in step S5 include: Recommendations on matching cookware materials with heating modes, as well as temperature range adjustment schemes based on ambient temperature and historical heating preferences.

8. The application of the method as described in any one of claims 1-7 in the field of intelligent induction control of induction cookers, characterized in that, Including scenarios 1) or 2): 1) Automatically matches the stir-fry, deep-fry, or slow-cooking mode based on the cookware material; 2) Dynamically adjust power to maintain heating stability when the grid voltage fluctuates.

9. A computing unit, characterized in that, include: Storage chip: stores the program code of the intelligent electromagnetic induction heating method as described in any one of claims 1-7; Processor: Configured to execute the program code and connected to the current / voltage sensor, temperature sensor, and PWM controller; Communication interface: Used to receive user commands and push optimized heating suggestions to the display screen.

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