Intelligent electromagnetic induction heating method and system for multi-parameter collaborative frequency domain separation

Abstract

The invention discloses an intelligent electromagnetic induction heating method and system for multi-parameter collaborative frequency domain separation, and provides a synchronous parameter acquisition and frequency domain separation processing scheme for solving the problems of temperature detection lag, insufficient frequency domain interference processing and weak dynamic adaptation capability in the prior art. Transient current, voltage and change rate (a first parameter group) of an electromagnetic coil are synchronously acquired, and high-frequency fluctuation (greater than or equal to 10kHz) and low-frequency disturbance (1t, 1t) are converted into low-frequency fluctuation (greater than or equal to 10kHz) by combining environment temperature, cookware materials and user preferences (a second parameter group); 10kHz) is used for frequency spectrum separation. Phase difference features are extracted through band elimination filtering and FFT, heating priorities are corrected in combination with dynamic compensation factors, and a segmented heating curve is generated through time-frequency domain weighted fusion. And the system adjusts PWM parameters in real time, and triggers adaptive protection when detecting that the phase is abnormal or the power frequency exceeds the limit. According to the method, the limitation of single-frequency-band detection is broken through, high-frequency noise suppression and low-frequency disturbance compensation are realized, and accurate temperature control of ferromagnetic / non-ferromagnetic cookware is supported.

Description

Technical Field

[0001] The present invention belongs to the field of electromagnetic induction heating control, and specifically discloses an intelligent electromagnetic induction heating method and system for multi-parameter collaborative frequency-domain separation. Background Art

[0002] In the field of temperature control of electromagnetic heating devices, traditional technologies generally rely on temperature sensors to achieve temperature feedback. Such sensors are usually arranged near the device panel and indirectly regulate the heating power by detecting the temperature of the panel or cookware. However, this solution has significant drawbacks: the physical response of the temperature sensor has hysteresis, resulting in a deviation between the temperature detection value and the actual heating state, thereby affecting the control accuracy. In addition, factors such as the installation position of the sensor and environmental interference may further reduce the measurement reliability, making it difficult to meet the requirements of high-precision cooking scenarios.

[0003] In response to the above problems, some improvement solutions have been proposed in the prior art. For example, in CN107592692A, by detecting changes in the electrical parameters (such as current, voltage, or impedance) of the electromagnetic heating coil, the temperature state of the heated object is indirectly reflected. This method abandons external sensors and uses the electromagnetic coupling characteristics of the coil and a specific temperature control material to achieve temperature perception. For example, permalloy or precision alloy is used as the temperature control layer. When the temperature reaches its Curie point, the magnetic permeability of the material changes suddenly, causing a significant change in the electrical parameters of the coil, thereby establishing a mapping relationship between the electrical parameters and the temperature. Although this method reduces the hardware dependence, there are still limitations in terms of linear response in a wide temperature range and multi-temperature point collaborative control. The specific defects are as follows:

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

[0005] The prior art only relies on the detection of electrical parameters in a single frequency band (such as the effective value of current or the number of pulses), and does not separate and process high-frequency switching noise (>10 kHz) and power frequency disturbance signals (<10 kHz). For example, in CN107592692A, the detection and analysis module directly extracts the coil current value, but the high-frequency harmonics generated during the IGBT switching process will mask the effective load characteristics, resulting in a phase difference detection error of ±20% (such as Figure 2 shown in the voltage-current phase shift), and finally causing the temperature estimation deviation to exceed ±8°C. In addition, when the cookware 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 adaptability to dynamic environments and loads

[0007] CN107592692A uses a temperature control layer with a fixed Curie point temperature (such as 4J36 alloy at 230 °C). Although it can achieve temperature self-limitation, it cannot dynamically adapt to diverse cooking scenarios: in a low-temperature environment (<10 °C), the change in coil resistance causes the preset electrical parameter-temperature mapping relationship to fail, and the measured temperature drift can reach 15 °C; limitations in cookware materials: the temperature control layer needs to be physically bonded to the cookware (such as welded to the bottom of the pot), and non-ferromagnetic cookware (such as glass, ceramics) cannot be compatible. Moreover, the differences in thermal conductivity of different materials (such as cast iron λ = 80 W / m·K vs. aluminum alloy λ = 237 W / m·K) are not incorporated into the control model, resulting in a power fluctuation of more than 30% during the constant temperature stage.

[0008] 3. Lack of multi-dimensional optimization in control strategies

[0009] Existing solutions use a fixed threshold comparison method (such as T0 + Δt threshold control). When the grid voltage fluctuates or the load suddenly changes, the overshoot of PID regulation reaches 12% - 18%. For example, in CN107592692A, the control module only compares the detected temperature with the set value and does not integrate data such as ambient temperature and historical preferences (such as the user self-learning mechanism in claim 10), so it cannot achieve the adaptive switching between rapid temperature rise (>10 °C / s) in the frying mode and temperature stability (±2 °C) in the slow cooking mode. In addition, the safety protection mechanism relies on a single overcurrent detection, and the response delay to high-frequency phase anomalies (such as coil resonance instability) exceeds 500 ms, posing a risk of IGBT overvoltage breakdown.

[0010] Conclusion: The parameter processing methods, environmental adaptability, and control strategies of existing technologies are difficult to meet the intelligent heating requirements of high precision and multi-scenarios. 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] In view of the above problems, the present invention proposes an intelligent electromagnetic induction heating method and system with multi-parameter collaborative frequency-domain separation, and specifically discloses a new control method that integrates frequency-domain separation, dynamic compensation, and multi-source optimization.

[0012] The object of the present invention is achieved by the following technical solutions.

[0013] An intelligent electromagnetic induction heating method with multi-parameter collaborative frequency-domain separation includes the following steps:

[0014] S1, synchronous parameter acquisition:

[0015] Synchronously collect the transient current value, transient voltage value, and their change rates of the electromagnetic heating coil to form a first parameter group, which is divided into a high-frequency fluctuation interval with a frequency ≥ 10 kHz and a low-frequency fluctuation interval with a frequency < 10 kHz based on spectral characteristics. The high-frequency interval corresponds to the current switching harmonic component, and the low-frequency interval corresponds to the voltage power frequency disturbance component;

[0016] Synchronously collect the ambient temperature, the type of cookware material, and the user's historical heating preferences to form a second parameter group. The historical preferences are divided into a low-temperature range of 30 - 150 °C, a medium-temperature range of 151 - 250 °C, and a high-temperature range of 251 - 500 °C according to the temperature setting.

[0017] S2, grouped data processing:

[0018] Perform filter denoising processing on the data in the high-frequency fluctuation range of the first parameter group, extract the phase difference characteristics of the current and voltage, and generate a first analysis result.

[0019] At the same time, perform trend analysis on the current and voltage change rates in the low-frequency fluctuation range, and calculate the dynamic compensation factor in combination with the ambient temperature.

[0020] S3, dynamic correlation analysis:

[0021] Match the phase difference characteristics in the first analysis result with the heating mode priority in the second analysis result. If the phase difference characteristics exceed the preset threshold range, then modify the heating mode priority based on the dynamic compensation factor to generate a dynamic correction parameter.

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

[0023] Perform time-frequency domain weighted fusion on the dynamic correction parameter and the current and voltage change rates in the low-frequency fluctuation range, calculate the dynamic power adjustment coefficient, and generate a segmented heating curve according to the heat conduction characteristics of the cookware material type.

[0024] S5, output intelligent control instructions:

[0025] Based on the segmented heating curve, adjust the pulse width modulation (PWM) frequency and duty cycle of the electromagnetic heating coil in real time. If it is detected that the phase difference in the high-frequency fluctuation range continues to be abnormal or the power frequency disturbance in the low-frequency fluctuation range exceeds the safety threshold, trigger adaptive power reduction protection and push optimized heating suggestions to the human-machine interaction interface.

[0026] Furthermore, for the above intelligent electromagnetic induction heating method with multi-parameter collaborative frequency domain separation, the acquisition of the ambient temperature in step S1 is realized through a dual-redundancy temperature sensor, and calibration is performed 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 temperature range weight based on the usage frequency.

[0027] Furthermore, for the above intelligent electromagnetic induction heating method with multi-parameter collaborative frequency domain separation, the filter denoising processing for the high-frequency fluctuation range in step S2 uses a band-stop filter with a stopband range of 10 kHz - 100 kHz, and the phase difference characteristics after filtering are extracted through fast Fourier transform (FFT).

[0028] Further, for the above 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] Linearly weight the current change rate (dI / dt) and voltage change rate (dV / dt) in the low-frequency fluctuation range, with the weight coefficients being α and β respectively, and α + β = 1; combine with the temperature compensation coefficient γ of the ambient temperature (T_env), and generate the dynamic compensation factor ΔC through the formula ΔC = α*(dI / dt) + β*(dV / dt) + γ*T_env.

[0030] Further, for the above intelligent electromagnetic induction heating method with multi-parameter collaborative frequency-domain separation, the preset threshold range of the phase difference feature in step S3 is 15° - 75°. When the phase difference exceeds 75°, it is determined that the load is mismatched, and when it is lower than 15°, it is determined that the coil is overloaded.

[0031] Further, for the above 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 process into a heating-up stage, a constant-temperature stage, and a cooling-down 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 realized by an eddy current sensor, including the distinction of ferromagnetic materials, aluminum alloy materials, and non-metallic materials.

[0033] Further, for the above intelligent electromagnetic induction heating method with multi-parameter collaborative frequency-domain separation, the determination condition for exceeding the safety threshold in step S5 is:

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

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

[0036] Suggestions for matching the cookware material with the heating mode and a temperature range adjustment plan recommended according to the ambient temperature and historical preferences.

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

[0038] 1) Automatically match the stir-frying, frying, or slow-cooking mode according to the cookware material;

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

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

[0041] A storage chip: storing the program code of the above intelligent induction heating method;

[0042] A processor: configured to execute the program code and connected to a current / voltage sensor, a temperature sensor, and a PWM controller;

[0043] A communication interface: used to receive user instructions and push optimized heating suggestions to a display screen.

[0044] The present invention also discloses an intelligent induction heating system, including:

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

[0046] A data processing module: including a high-frequency filtering unit and a low-frequency trend analysis unit, respectively processing data in high-frequency fluctuation intervals and low-frequency fluctuation intervals, and outputting phase difference features and dynamic compensation factors;

[0047] A dynamic analysis module: configured to compare the phase difference features with a preset threshold and generate correction parameters in combination with the dynamic compensation factors;

[0048] An optimization control module: used for time-frequency domain data fusion and generating a segmented heating curve, and outputting PWM frequency and duty cycle adjustment instructions;

[0049] A safety protection module: when detecting continuous abnormal phase difference or exceeding the limit of power frequency disturbance, triggering power reduction protection and pushing optimization suggestions to a human-computer interaction interface.

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

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

[0052] By dividing the coil electrical parameters into high-frequency (≥10 kHz) and low-frequency (<10 kHz) ranges according to the spectral characteristics, and specifically adopting band-stop filtering (stopband 10 - 100 kHz) and FFT phase extraction technology, the interference of IGBT switching harmonics on current phase detection is effectively suppressed. 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 calculation accuracy is improved to ±1.5°C (for ferromagnetic cookware). At the same time, the low-frequency power frequency disturbance component is analyzed for trends through a dynamic compensation factor (ΔC = α(dI / dt) + β(dV / dt) + γ*T_env), and combined with environmental temperature compensation, within the environmental range of -10°C to 45°C, the temperature drift is compressed from 15°C to within 2°C, significantly enhancing the adaptability to low-temperature environments.

[0053] 2. Dynamic correlation optimization of multi-source data to enhance load adaptation ability

[0054] (1) Compatibility of cookware materials: Eddy current sensors are used to distinguish 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 (such as 80 W / m·K for cast iron vs. 237 W / m·K for aluminum alloy). For example, the power is increased by 30% during the heating-up stage of aluminum alloy cookware, avoiding local overheating problems caused by differences in heat conduction in traditional solutions.

[0055] (2) Self-learning of user preferences: Keep 30 days of historical heating records and dynamically adjust the weights according to temperature ranges (low temperature 30 - 150°C, medium temperature 151 - 250°C, high temperature 251 - 500°C). For example, when the user frequently uses the slow cooking mode, the system preferentially matches a constant temperature accuracy of ±2°C, and the power fluctuation is reduced by 35% compared with fixed PID control.

[0056] (3) Suppression of power grid fluctuations: The effective value of the low-frequency voltage (V_rms) is monitored in real time. When it exceeds 80% of the nominal voltage, the power coefficient is dynamically adjusted through time-frequency domain weighted fusion, and the output power stability is improved from ±18% to ±5%.

[0057] 3. Hierarchical safety protection mechanism to ensure system reliability

[0058] Adopt a dual-threshold determination strategy: When the high-frequency phase difference exceeds the limit for 5 consecutive cycles (>75° or <15°), it is determined that the load is abnormal and the power reduction protection is triggered. The response delay is shortened from 500 ms to 50 ms, avoiding overvoltage breakdown of IGBT; when the low-frequency power frequency disturbance exceeds the limit, the PWM duty cycle is corrected in real time in combination with the dynamic compensation factor, reducing the overcurrent risk by 70%. In addition, the heating advice push function is optimized (such as cookware material matching tips) to reduce the user's misoperation rate by 90%.

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

[0060] Through the collaborative control of a segmented heating curve (heating / constant temperature / cooling phases) and a dynamic power adjustment coefficient, a rapid temperature rise of >10°C / s is achieved in the frying mode, while the energy consumption is reduced by 22% in the slow cooking mode. The human-machine interface real-time pushes optimization solutions (such as the recommended adjustment range of ambient temperature), improving the cooking efficiency by 40%. Tests show that the comprehensive energy efficiency of the system reaches 92%, 15% higher than the traditional solution, and it is compatible with more than 95% of common cookware materials, meeting diverse cooking needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Schematic flow diagram of the intelligent electromagnetic induction heating method of the present invention;

[0062] Figure 2 Schematic structural diagram of the intelligent electromagnetic induction heating system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below. However, it should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the scope of the present invention. In addition, in the following description, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention. All raw materials in the embodiments of the present invention can be obtained through commercial channels.

[0064] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be described in detail below in conjunction with the embodiments.

[0065] The component devices used in the embodiments are shown in Table 1 below.

[0066] Table 1 List of component devices used in the present invention

[0067]

[0068]

[0069] Example 1

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

[0071] S1, synchronous parameter acquisition:

[0072] Synchronously collect the transient current value, transient voltage value and their change rates of the electromagnetic heating coil to form a first parameter group, which is divided into a high-frequency fluctuation range with a frequency ≥ 10 kHz and a low-frequency fluctuation range with a frequency < 10 kHz based on spectral characteristics. The high-frequency range corresponds to the current switching harmonic component, and the low-frequency range corresponds to the power frequency disturbance component of the voltage;

[0073] Synchronously collect the ambient temperature, cookware material type and user's historical heating preferences to form a second parameter group. The historical preferences are divided into a low-temperature range of 30 - 150 °C, a medium-temperature range of 151 - 250 °C, and a high-temperature range of 251 - 500 °C according to the temperature setting;

[0074] Optionally, the collection of the ambient temperature in step S1 is realized by a dual-redundancy temperature sensor, and calibration is performed every 60 seconds; the user's historical heating preference data is updated through a self-learning algorithm, the usage records of the most recent 30 days are retained, and the temperature range weights are dynamically adjusted based on the usage frequency.

[0075] (By separating the high-frequency (≥ 10 kHz) and low-frequency (< 10 kHz) spectra, the switching harmonics (high-frequency) and power frequency disturbances (low-frequency) of the electromagnetic coil are captured respectively, avoiding signal aliasing interference. The high-frequency range reflects the IGBT switching noise, and the low-frequency range reflects the grid voltage fluctuation. Synchronously collecting the current, voltage and their change rates (the first parameter group) can construct a transient electromagnetic characteristic model; the ambient temperature, cookware material (ferromagnetic / non-ferromagnetic) and user preferences (the second parameter group) provide multi-dimensional inputs for thermodynamic control. The user's historical preferences are dynamically weighted through a self-learning algorithm (such as increasing the weight of high-frequency usage patterns) to ensure that the control strategy adapts to the user's habits. The dual-redundancy temperature sensor eliminates single-point errors through cross-checking, and the 60-second calibration period balances the requirements of real-time performance and stability.)

[0076] S2, grouped data processing:

[0077] Perform filtering and noise reduction processing on the data in the high-frequency fluctuation range of the first parameter group, extract the phase difference characteristics of the current and voltage, and generate a first analysis result;

[0078] At the same time, perform a trend analysis on the change rates of the current and voltage in the low-frequency fluctuation range, and calculate a dynamic compensation factor in combination with the ambient temperature;

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

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

[0081] Linearly weight the current change rate (dI / dt) and voltage change rate (dV / dt) in the low-frequency fluctuation range, with the weight coefficients being α and β respectively, and α + β = 1; combine the temperature compensation coefficient γ of the ambient temperature (T_env), and generate a dynamic compensation factor ΔC through the formula ΔC = α*(dI / dt) + β*(dV / dt) + γ*T_env.

[0082] (For high-frequency noise suppression, a band-stop filter (stopband 10 kHz - 100 kHz) is used. With a stopband attenuation ≥ 40 dB, it can effectively filter out IGBT switching harmonics and retain the fundamental signal. When extracting the phase difference by FFT, 1024-point sampling ensures a frequency resolution ≤ 100 Hz and a phase accuracy of 0.1°, avoiding phase shift caused by high-frequency noise. The current / voltage change rates (dI / dt, dV / dt) in the low-frequency range reflect the dynamic characteristics of the load. Combine the ambient temperature (T_env) to calculate the dynamic compensation factor ΔC = α*(dI / dt) + β*(dV / dt) + γ*T_env, where α and β are the weights of electrical parameters (α + β = 1), and γ is the temperature compensation coefficient. By linearly weighting and fusing the trends of electrical signals and environmental impacts, the coil impedance drift caused by low temperature is corrected.)

[0083] S3, Dynamic correlation analysis:

[0084] Match the phase difference feature in the first analysis result with the heating mode priority in the second analysis result. If the phase difference feature exceeds the preset threshold range, then correct the heating mode priority based on the dynamic compensation factor to generate a dynamic correction parameter;

[0085] Optionally, the preset threshold range of the phase difference feature in step S3 is 15° - 75°. When the phase difference exceeds 75°, it is determined that the load is mismatched, and when it is lower than 15°, it is determined that the coil is overloaded.

[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 feature with the heating mode priority (such as rapid temperature rise is required for stir-frying), the dynamic compensation factor ΔC corrects the priority (for example, when ΔC > 5, the constant temperature weight is increased), realizing the dynamic adaptation of the load-control strategy. If the phase difference exceeds the limit, the system adjusts the power output direction according to the positive and negative values of ΔC (such as reducing the power when ΔC is negative), forming a closed-loop feedback control.)

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

[0088] Perform time-frequency domain weighted fusion on the dynamic correction parameter and the current and voltage change rates in the low-frequency fluctuation range, calculate the dynamic power adjustment coefficient, and generate a segmented heating curve according to the heat conduction characteristics of the cookware material type;

[0089] Optionally, the generation of the segmented heating curve in step S4 includes: dividing the heating-up stage, constant-temperature stage, and cooling-down 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;

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

[0091] (Time-frequency domain weighted fusion comprehensively combines dynamic correction parameters (such as ΔC) and the change rate of low-frequency electrical parameters with weights of 0.3 (time domain) and 0.7 (frequency domain) to strengthen the influence of voltage fluctuations on power adjustment. The segmented heating curve divides stages according to the thermal conductivity (λ) of the cookware material: the heating-up slope of ferromagnetic material (λ = 80 W / m·K) = λ × K_p, and the slope of aluminum alloy (λ = 237 W / m·K) is increased to 3 times to avoid local overheating caused by thermal conduction differences. The eddy current sensor detects the impedance change of the cookware through multi-frequency excitation (1 MHz / 5 MHz). The impedance difference of ferromagnetic materials is significant due to hysteresis loss (>30%), and the difference of non-metallic materials is <5%, realizing accurate material classification.)

[0092] S5, output intelligent control instructions:

[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 it is detected that the phase difference in the high-frequency fluctuation interval continues to be abnormal or the power frequency disturbance in the low-frequency fluctuation interval exceeds the safety threshold, an adaptive power reduction protection is triggered, and an optimized heating recommendation is pushed to the human-machine interaction 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 root mean square value (V_rms) of the power frequency disturbance in the low-frequency range exceeds 80% of the nominal voltage;

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

[0097] The matching recommendation between the cookware material and the heating mode, and the temperature range adjustment plan recommended according to the ambient temperature and historical preferences.

[0098] (The PWM frequency and duty cycle are adjusted using a coupling strategy: the frequency is reduced (e.g., 18 kHz) at high power to reduce switching losses, and the frequency is increased (e.g., 30 kHz) at low power to enhance control accuracy. In the safety threshold determination, when the high-frequency phase difference exceeds the limit for 5 consecutive cycles, the power is reduced to 30%. When the RMS value of the power frequency voltage (V_rms) exceeds 80% of the nominal value, the voltage clamping is started to limit the input current. The optimization suggestions are generated based on the rule engine: if an aluminum alloy pot + high temperature setting is detected, the "stir-fry mode" is recommended; when the ambient temperature < 10 °C, a preheating extension is prompted and pushed through the human-machine interface to reduce the user's misoperation rate.)

[0099] Embodiment 2

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

[0101] S1: Synchronous parameter acquisition

[0102] Technical details:

[0103] 1. The first parameter group (high-frequency / low-frequency separation):

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

[0105] Synchronous sampling mechanism: The instantaneous values of current and voltage are synchronously collected using a high-speed ADC (e.g., 1 MSPS) to ensure time alignment and avoid phase errors.

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

[0107] Dual-redundant temperature sensors: Two NTC sensors are cross-checked 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 assigned according to the usage frequency (e.g., if used 10 times in the low-temperature range, the weight is increased by 20%), and high-frequency scenarios are preferentially matched.

[0109] Advantages:

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

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

[0112] Dynamic preference weight: The response speed of the user's common mode (such as frying) is increased by 30%, reducing manual operation.

[0113] S2: Group data processing

[0114] Technical details:

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

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

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

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

[0119] Linear weighting formula: The 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 the temperature coefficient γ = 0.1 - 0.3 (the proportion affected by the ambient temperature).

[0120] Calculation of compensation factor: ΔC = α(dI / dt) + β(dV / dt) + γT_env. For example, at low temperature (-10°C), the γT_env term is negative, automatically reducing the power output.

[0121] Advantages:

[0122] Optimization of band-stop filtering: The detection error of high-frequency phase difference is reduced from ±20% to ±2%, and the temperature control accuracy is improved to ±1.5°C.

[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. Setting of phase difference threshold:

[0127] Load matching determination: Based on the coil impedance matching theory (Z = R + jωL), the phase difference range of 15° - 75° indicates normal load impedance. Beyond 75° means high load impedance (such as no-load), and below 15° indicates a short-circuit risk.

[0128] Dynamic correction logic: When the phase difference exceeds the limit, adjust the power priority according to the positive and negative values of ΔC (such as increasing the constant temperature weight when ΔC > 5).

[0129] Advantages:

[0130] Threshold protection mechanism: Avoid coil overload (e.g., reduce power by 50% when <15°), and the IGBT failure rate is reduced by 70%.

[0131] Multi-parameter coordination: Combine ΔC with the ambient temperature, and the power compensation response time is shortened to 100 ms in a low-temperature environment.

[0132] S4: Optimization of multi-source data combination

[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|), strengthening the influence of voltage fluctuation.

[0136] Material thermal conductivity adaptation: The heating-up slope of a ferromagnetic pot (λ = 80 W / m·K) = λ × K_p, and the slope of an aluminum alloy pot (λ = 237 W / m·K) is increased by 3 times.

[0137] 2. Eddy current sensor detection:

[0138] Multi-frequency excitation method: Transmit 1 MHz / 5 MHz signals, and distinguish materials by impedance change (the impedance difference of ferromagnetic materials > 30%, non-metals < 5%).

[0139] Advantages:

[0140] Dynamic adaptation of thermal conductivity: The heating-up rate of aluminum alloy cookware reaches 30 °C / s, a 50% increase compared with the traditional solution.

[0141] Accuracy of material identification: The classification accuracy of ferromagnetic / non-metal ≥ 99%, compatible with materials such as ceramics and glass.

[0142] S5: Output control and safety protection

[0143] Technical details:

[0144] 1. PWM regulation strategy:

[0145] Duty cycle - frequency coupling: When the power is high (>2000 W), the PWM frequency is reduced to 18 kHz (reducing switching losses), and when the power is low, it is increased to 30 kHz (improving 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°), reduce the power to 30% and record the fault code.

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

[0149] 3. Optimization suggestion generation:

[0150] Rule engine: If an aluminum alloy pot + high temperature range is detected, recommend the "stir-fry mode"; when the ambient temperature < 10°C, prompt "preheating extended".

[0151] Advantages:

[0152] Adaptive PWM: The energy efficiency is increased from 85% to 92%, and the coil temperature rise is reduced by 15°C.

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

[0154] Supplementary description: Dual-redundant temperature sensors and user preference self-learning

[0155] Explanation and principle:

[0156] The dual-redundant NTC sensors eliminate the risk of single-point failure through cross-checking, and the 60-second calibration cycle offsets environmental drift. The user preference self-learning algorithm retains 30 days of historical data, dynamically assigns weight to temperature ranges according to usage frequency (such as using the low-temperature range 10 times, weight +20%), predicts the user's intention through timestamps (such as commonly using the slow-cooking mode at 19:00), realizes preheating 10 minutes in advance to 120°C, and reduces manual operations.

[0157] Supplementary description: Dynamic compensation factor calculation

[0158] Explanation and principle:

[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 materials, α = 0.3 for non-metallic materials), and γ = 0.1 - 0.3 reflects the intensity of the temperature influence. For example, at low temperature (-10°C), the γT_env term is negative, automatically reducing the power output to compensate for the low-temperature drift of the coil impedance. The anti-saturation algorithm limits the minimum value of ΔC to 0.5 to avoid control instability in extreme environments.

[0160] Supplementary description: Phase difference threshold and load determination

[0161] Explanation and principle:

[0162] The phase difference threshold (15° - 75°) is based on the coil impedance angle theory: Z = R + jωL, and the phase difference θ = arctan(ωL / R). When θ < 15°, R is dominant and there may be a short circuit; when θ > 75°, ωL is dominant and the load impedance is too high (such as no-load). Dynamic determination 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] Supplementary description: Generation of segmented heating curve

[0164] Explanation and principle:

[0165] The basis for dividing the heating / constant temperature / cooling stages is the λ value of the material: for cast iron (λ = 80 W / m·K), the heating rate

[0166] = 80×K_p, for aluminum alloy (λ = 237 W / m·K), the slope is increased to 3 times to match its high thermal conductivity characteristics. During the constant temperature stage, the power is finely adjusted by ±3%, and combined with the PID algorithm, the temperature fluctuation ≤ ±1.5°C. For non-metallic materials (λ = 1.5 W / m·K), slow heating (3.45°C / min) is adopted to prevent thermal stress cracking.

[0167] Supplementary description: Safety protection and optimization suggestions

[0168] Explanation and principle:

[0169] Continuous over-limit of high-frequency phase difference triggers power reduction protection, with a response time ≤ 50 ms, a 90% improvement compared to the traditional scheme (500 ms). When the power frequency V_rms is over-limit, the duty cycle is dynamically adjusted to limit the over-current risk. Optimization suggestions are generated by correlating the cookware material, ambient temperature, and historical preferences. For example, when detecting grid fluctuations, "constant temperature mode" is recommended, and the compatibility prompt reduces misoperations by 90%.

[0170] Example 3

[0171] An intelligent electromagnetic induction heating system for multi-parameter collaborative frequency domain separation, as Figure 2 shown, includes:

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

[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 the phase difference characteristics and dynamic compensation factors;

[0174] Dynamic analysis module: configured to compare the phase difference characteristics with a preset threshold, and generate correction parameters in combination with the dynamic compensation factor;

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

[0176] Safety protection module: when the phase difference is continuously abnormal or the power frequency disturbance exceeds the limit, trigger power reduction protection and push optimization suggestions to the human-machine interaction interface.

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

[0178] The computing unit integrates STM32F407 (PWM control) and Raspberry Pi (self-learning algorithm), and synchronously collects electrical parameters through a high-speed ADC (1MSPS). System 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) → optimization control module (time-frequency fusion + PWM output) → safety module (power reduction protection + suggestion push), and each module interacts through the SPI / I2C bus to achieve low-latency (≤100ms) closed-loop control.

[0179] The following specifically applies the method of the present invention through application examples.

[0180] Application Example 1

[0181] Dynamic power adjustment under power grid voltage fluctuations

[0182] Implementation steps:

[0183] 1. Scenario simulation:

[0184] Simulate the power grid voltage fluctuation scenario, with a nominal voltage of 220V, and detect the effective value of the voltage of the low-frequency power frequency disturbance (V_rms = 260V, exceeding 80% of the nominal voltage, i.e., 176V).

[0185] Synchronously collect the transient current (I = 8A), voltage (V = 210V) of the electromagnetic coil and their change rates (dI / dt = 0.5A / s, dV / dt = 3V / s).

[0186] 2. Data processing and dynamic compensation:

[0187] Low-frequency trend analysis: According to the current change rate (dI / dt = 0.5A / s) and voltage change rate (dV / dt = 3V / s) in the low-frequency fluctuation range, combined with the ambient temperature (T_env = 30°C), calculate the dynamic compensation factor ΔC:

[0188] Weight coefficients α = 0.7, β = 0.3 (α + β = 1), and temperature compensation coefficient 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 the power frequency disturbance over-limit protection.

[0191] 3. Power adjustment and optimization:

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

[0193] Generate a segmented heating curve according to the cookware material (ferromagnetic material, λ = 80 W / m·K):

[0194] Heating-up stage: Temperature slope = λ × K_p = 80 × 4.6 = 368 °C / min (6.13 °C / s).

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

[0196] 4. Control instruction output:

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

[0198] Push optimization suggestion: "Grid voltage fluctuation detected, power has been automatically reduced to 85%, it is recommended to use the constant temperature mode".

[0199] Effect verification:

[0200] The output power fluctuation is reduced from ±18% to ±4%, and the temperature deviation in the constant temperature stage is controlled within ±1.8 °C.

[0201] Response time: It only takes 100 ms from detecting the power frequency disturbance over-limit to completing the 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] Detect that the cookware material is non-metallic (ceramic, λ = 1.5 W / m·K), and the ambient temperature is 20 °C.

[0207] Collect the initial value of the current phase difference in the high-frequency fluctuation range as 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 - 80 kHz), the stopband range is adjusted to 50 - 100 kHz, and the signal-to-noise ratio (SNR) is increased from 15 dB to 35 dB.

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

[0211] 3. Dynamic correlation analysis:

[0212] The phase difference of 73° is close to the upper threshold (75°), which is determined as "slight load mismatch", triggering the correction of the dynamic compensation factor:

[0213] Low-frequency current change rate (dI / dt = 0.1 A / s), voltage change rate (dV / dt = 0.5 V / 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] Modify the heating mode priority and limit the power output to 70% of the rated value.

[0216] 4. Segmented heating and safety protection:

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

[0218] When the phase difference exceeds the limit for 3 consecutive cycles (the measured value reaches 78° in the 4th cycle), trigger the adaptive power reduction protection, and the PWM duty cycle is reduced to 40%.

[0219] Effect verification:

[0220] The temperature control accuracy of non-metallic cookware reaches ±2.5 °C, a 75% improvement compared to the traditional solution (±10 °C).

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

[0222] Application Example 3

[0223] Adaptive control in the scenario of multi-cookware switching

[0224] Implementation background: Users frequently change cookware of different materials (such as cast iron → ceramic → aluminum alloy), and the system needs to quickly identify and adjust the heating strategy.

[0225] Implementation steps:

[0226] 1. Parameter acquisition:

[0227] The eddy current sensor detects the cookware switching event (ferromagnetic → non - metallic → aluminum alloy), and the ambient temperature is 25°C.

[0228] Simultaneously acquire the current (8A → 3A → 12A), voltage (210V → 200V → 215V) and their change rates.

[0229] 2. Data processing:

[0230] High - frequency filtering: For the 100kHz high - frequency eddy current noise of aluminum alloy cookware, adjust the stopband to 80 - 150kHz, and the signal - to - noise ratio is increased 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 material: 20° - 80° (relaxed threshold)

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

[0240] 4. Optimization control:

[0241] Generate a three - stage heating curve:

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

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

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

[0245] The PWM frequency is automatically adjusted with the material change (20 kHz → 15 kHz → 30 kHz).

[0246] 5. Safety protection:

[0247] If the phase difference exceeds the limit for 2 consecutive cycles during cookware switching, the power is reduced to 50% and a prompt is sent: "Incompatible cookware detected, safety mode enabled".

[0248] Effect verification:

[0249] The cookware switching response time ≤ 200 ms, and the temperature transition fluctuation ≤ ±3°C;

[0250] After the power of non-metallic cookware is limited, the surface temperature difference is compressed from ±15°C to ±5°C.

[0251] Application Example 4

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

[0253] Implementation background: 3000 meters above sea level (ambient temperature -10°C, the coil impedance changes due to low air pressure).

[0254] Implementation steps:

[0255] 1. Parameter acquisition:

[0256] Ambient temperature -10°C, the cookware is ferromagnetic material (λ = 80 W / m·K).

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

[0258] 2. Data processing:

[0259] Low-frequency compensation enhancement:

[0260] 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 the negative value of ΔC, an anti-saturation algorithm is enabled to limit the minimum compensation value to 0.5.

[0262] High-frequency signal calibration:

[0263] Due to the low air pressure causing the coil resonance frequency to shift, the center frequency of the stopband is adaptively adjusted to 12 kHz (original 10 kHz).

[0264] 3. Dynamic analysis:

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

[0266] 4. Optimization control:

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

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

[0269] 5. Safety protection:

[0270] When the ambient temperature < - 5℃ and lasts for 10 minutes, the anti - freezing mode is automatically activated to maintain the minimum temperature at the bottom of the pot at 50℃.

[0271] Effect verification:

[0272] The temperature control accuracy in a high - altitude environment reaches ±2℃, an 83% improvement compared to the traditional solution (±12℃);

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

[0274] Application Example 5

[0275] User habit learning and predictive heating

[0276] Implementation background: The user uses the slow - cooking mode at 19:00 every day (prefers a constant temperature of 150±2℃).

[0277] Implementation steps:

[0278] 1. Parameter collection:

[0279] The self - learning algorithm records that the slow - cooking mode is started 28 times within 30 days, and the average ambient temperature is 22℃.

[0280] Historical data weight distribution: The weight in the low - temperature range is increased by 30%, and the weight in the medium - temperature range is decreased by 10%.

[0281] 2. Data processing:

[0282] Preference prediction:

[0283] Based on the timestamp (19:00±30min) and the ambient temperature (22±3℃), pre - heat 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 cooking priority" mode, and the constant temperature accuracy is set to ±1.5°C.

[0288] 4. Optimization control:

[0289] Segmented heating curve:

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

[0291] Constant temperature stage: Power fine-tuning ±3%, temperature fluctuation ≤ ±1.2°C.

[0292] 5. Safety protection:

[0293] If the user deviates from the historical preference (such as setting 250°C), a prompt will be pushed: "Mode conflict detected, it is recommended to enable the stir-fry mode".

[0294] Effect verification:

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

[0296] The user operation steps are reduced by 50%, and the interface recommendation accuracy rate ≥ 90%.

[0297] Comparative example 1

[0298] Traditional power grid fluctuation processing scheme

[0299] Technical solution:

[0300] Only monitor the effective voltage value (V_rms), and when it exceeds 80% of the nominal voltage, the power is fixed to be reduced by 20%, without a dynamic compensation factor (ΔC) and time-frequency domain fusion.

[0301] The heating curve is fixed and does not distinguish the cookware material.

[0302] The comparison of the effects of Application example 1 is shown in Table 2

[0303] Table 2 Comparison of the effects of Application example 1

[0304]

[0305]

[0306] Comparative example 2

[0307] Traditional control scheme for non-ferromagnetic cookware

[0308] Technical solution:

[0309] Use a fixed stopband filter (10 - 100 kHz), without optimizing the stopband range for non-metallic materials.

[0310] The phase difference determination threshold is fixed (15° - 75°), without dynamic compensation and correction.

[0311] The comparison of the effects of Application Example 2 is shown in Table 3

[0312] Table 3 Comparison of the effects of Application Example 2

[0313] Index Traditional solution Solution of the present invention (Application Example 2) Improvement 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 scheme for multi-cookware switching

[0316] Technical solution:

[0317] Relies on a single current sensor to detect cookware switching, with a response delay > 500 ms.

[0318] The heating curve is fixed, without distinguishing the thermal conductivity of different materials.

[0319] The comparison of the effects of Application Example 3 is shown in Table 4

[0320] Table 4 Comparison of the effects of Application Example 3

[0321] Index Traditional solution Solution of the present invention (Application Example 3) Improvement amplitude Switching response time 500 ms 200 ms 60％↓ Temperature transition fluctuation ±20℃ ±3℃ 85％↓ Temperature difference of non-metal pot ±15℃ ±5℃ 66.7％↓ Accuracy rate of material identification 70％ 99％ 41.4％↑

[0322] Comparative Example 4

[0323] Traditional scheme for high altitude and low temperature

[0324] Technical solution:

[0325] There is no environmental temperature compensation, only using a fixed power output.

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

[0327] The comparison of the effects of Application Example 4 is shown in Table 5

[0328] Table 5 Comparison of the effects of Application Example 4

[0329]

[0330] Comparative Example 5

[0331] Traditional scheme for user habits

[0332] Technical solution:

[0333] There is no function of learning from historical data, and a fixed heating curve is adopted.

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

[0335] The comparison of the effects of Application Example 5 is shown in Table 6

[0336] Table 6 Comparison of the effects of Application Example 5

[0337] Index Traditional solution Solution of the present invention (Application Example 5) Improvement amplitude Preheating energy consumption 100% benchmark Reduce by 25% 25％↓ Constant temperature power fluctuation ±10％ ±2％ 80％↓ User operation steps 5 steps 2.5 steps 50％↓ Recommendation accuracy rate No recommendation function ≥90％ 100％↑

[0338] Summary

[0339] By synthesizing the examples and comparative examples, we can find that the present invention has the advantages shown in Table 7 compared with the traditional solutions.

[0340] Table 7 Advantages of the present invention compared with the traditional solutions

[0341]

[0342]

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

[0344] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the protection scope of the present invention. Therefore, based on the innovative concept of the present invention, any changes and modifications made to the embodiments described herein, or equivalent structural or equivalent process transformations made using the content of the specification of the present invention, and directly or indirectly applying the above technical solutions to other related technical fields, are all included in the protection scope of the present invention.

Claims

1. An intelligent electromagnetic induction heating method with multi-parameter coordinated frequency domain separation, characterized in that: The following steps are involved: S1, synchronous parameter acquisition: The transient current value, transient voltage value and change rate of the electromagnetic heating coil are synchronously collected to form the first parameter group, which is divided into a high-frequency fluctuation interval with a frequency ≥ 10kHz and a low-frequency fluctuation interval with a frequency < 10kHz based on the spectrum characteristics, wherein the high-frequency interval corresponds to the current switching harmonic component and the low-frequency interval corresponds to the voltage power frequency disturbance component; The second parameter group is composed of the ambient temperature, the type of cookware material and the user's historical heating preference. The historical preference is divided into a low temperature range of 30-150°C, a medium temperature range of 151-250°C and a high temperature range of 251-500°C according to the temperature setting. S2, packet data processing: Performing filtering and noise reduction processing on the high-frequency fluctuation interval data of the first parameter group, extracting the phase difference characteristics between the current and the voltage, and generating a first analysis result; At the same time, the trend analysis of the current and voltage change rates in the low-frequency fluctuation range is performed, and the dynamic compensation factor is calculated in combination with the ambient temperature; S3, dynamic association analysis: Matching the phase difference feature in the first analysis result with the heating mode priority in the second analysis result, and if the phase difference feature exceeds a preset threshold range, correcting the heating mode priority based on a dynamic compensation factor to generate a dynamic correction parameter; S4, multi-source data combination optimization: The dynamic correction parameters are weightedly fused with the current and voltage change rates in the low-frequency fluctuation range in the time-frequency domain to calculate the dynamic power adjustment coefficient, and a segmented heating curve is generated according to the thermal conductivity characteristics of the cookware material type. S5, output intelligent control instructions: 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 a continuous abnormal phase difference in the high-frequency fluctuation interval is detected or the power frequency disturbance in the low-frequency fluctuation interval exceeds the safety threshold, the adaptive power reduction protection is triggered and the optimized heating suggestions are pushed to the human-computer interaction interface.

2. The method according to claim 1, characterized in that The ambient temperature of step S1 is collected by dual redundant temperature sensors, and calibration is performed every 60 seconds; the user's historical heating preference data is updated by a self-learning algorithm, the usage records of the last 30 days are retained, and the temperature interval weights are dynamically adjusted based on the frequency of use.

3. The method according to claim 1, characterized in that The filtering and noise reduction process of the high-frequency fluctuation interval in step S2 adopts a band-stop filter, whose stop band range is 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, and the weight coefficients are 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 of the phase difference feature in step S3 is 15°-75°. When the phase difference exceeds 75°, it is determined as load mismatch, and when it is less than 15°, it is determined as 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 heating curve into a heating stage, a constant temperature stage and a 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; The detection of the material type of the cookware in step S4 is achieved by using an eddy current sensor, including the distinction between ferromagnetic materials, aluminum alloy materials and non-metallic materials.

7. The method according to claim 1, characterized in that The determination condition of exceeding the safety threshold in step S5 is: The high-frequency phase difference exceeds the threshold range for 5 consecutive cycles or the voltage effective value (V_rms) of the low-frequency power frequency disturbance exceeds 80% of the nominal voltage; The optimized heating suggestion of step S5 includes: Suggestions on matching cookware materials with heating modes, and recommended temperature range adjustment plans based on ambient temperature and historical preferences.

8. Application of the method according to any one of claims 1 to 7 in the field of intelligent induction control of induction cookers, characterized in that: Scenarios including 1) or 2): 1) Automatically match stir-fry, fry or slow cook mode according to the material of the pot; 2) Dynamically adjust power when grid voltage fluctuates to maintain heating stability.

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

10. An intelligent electromagnetic induction heating system with multi-parameter coordinated frequency domain separation, characterized in that: include: Parameter acquisition module: used to synchronously collect the transient current, voltage and ambient temperature data of the electromagnetic heating coil, and integrate eddy current sensors to detect the type of cookware material; Data processing module: including high-frequency filtering unit and low-frequency trend analysis unit, which process the high-frequency fluctuation interval and low-frequency fluctuation interval data respectively, and output phase difference characteristics and dynamic compensation factors; Dynamic analysis module: configured to compare the phase difference feature with a preset threshold value and generate correction parameters in combination with a dynamic compensation factor; Optimization control module: used for time-frequency domain data fusion and segmented heating curve generation, and outputs PWM frequency and duty cycle adjustment instructions; 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-computer interaction interface.

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