A method for collecting temperature data of an induction heating device and related equipment

Abstract

The application provides an induction heating equipment temperature data acquisition method and related equipment, and relates to the technical field of electromagnetic heating equipment control. The method comprises the following steps: monitoring the running state information of the induction heating equipment and determining the current electromagnetic interference level; obtaining a phase reference signal and identifying the interference fluctuation period in the induction heating cycle; determining the sampling trigger time according to the interference fluctuation period; dynamically configuring the sampling parameters according to the electromagnetic interference level; starting the analog-to-digital conversion at the determined sampling trigger time based on the dynamically configured sampling parameters to obtain the electrical signal samples and extract the temperature characteristic value. The method aims to solve the problems in the prior art that the hardware filtering introduces signal delay, the fixed sampling strategy cannot avoid transient interference, and the conventional multiple sampling and averaging method has limited effect on systematic electromagnetic interference, and significantly improves the anti-interference ability and data reliability of the induction heating equipment temperature data acquisition.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic heating equipment control technology, and more specifically, to a method for acquiring temperature data of induction heating equipment and related equipment. Background Technology

[0002] In practical applications of electromagnetic heating equipment, temperature data acquisition systems face the challenge of complex electromagnetic interference environments. The high-frequency alternating magnetic field generated during the operation of induction heating equipment not only acts on the heating load but also creates a strong electromagnetic radiation field inside the equipment. This electromagnetic environment has multiple effects on temperature sensing circuits: First, the high-frequency magnetic field generated by the induction coil will couple common-mode noise into the signal transmission path of the temperature sensor, causing baseline drift in the sampling signal; Secondly, the transient electromagnetic pulses generated when power devices switch will be injected into the signal chain through spatial radiation and conduction, forming spike interference with amplitudes far exceeding those of normal temperature signals; Furthermore, the superposition effect of multi-source electromagnetic interference can distort the sampling waveform, and in severe cases, cause the analog-to-digital converter output data to be completely distorted.

[0003] Traditional solutions typically employ hardware filtering and fixed sampling strategies to combat interference. For example, adding an LC filter network at the signal input or using a metal shield to isolate electromagnetic radiation. However, these methods have significant limitations: hardware filtering introduces signal delay, affecting the real-time performance of temperature monitoring; and fixed sampling strategies cannot adapt to the dynamic changes in electromagnetic interference. When the device switches power, the original sampling point may happen to be in the phase range where interference is strongest. More importantly, existing technologies lack the ability to identify the timing characteristics of electromagnetic interference and cannot proactively avoid periods of high transient interference such as power device switching, resulting in periodic jumps in the acquired temperature data.

[0004] At the signal processing level, while conventional multiple sampling and averaging methods can suppress random noise, their effectiveness against systemic electromagnetic interference synchronized with the driving frequency is limited. Especially when the induction cooker operates in high-power mode, the induction coil current can reach tens of amperes, and the resulting electromagnetic noise can completely overwhelm the effective signal from the temperature sensor. Existing technologies that use higher sampling rates may actually collect more contaminated data points, increasing the processor load and potentially amplifying measurement errors.

[0005] Meanwhile, the wireless transmission process also faces significant challenges: the high-order harmonics generated by the internal switching power supply of the induction cooker extend into the 2.4GHz frequency band, causing overlapping interference with the Bluetooth communication band. This co-channel interference leads to a significant increase in the temperature data packet loss rate, and existing technologies lack adaptive transmission strategies for electromagnetic interference levels, maintaining a fixed transmission frequency even in environments with strong interference, resulting in wasted communication resources and increased energy consumption. Furthermore, traditional solutions lack a coordination mechanism between temperature acquisition and device drive signals; the sampling time and power switching actions compete with each other, further reducing the system's anti-interference capability.

[0006] There is currently no effective technical solution to the above problems. Summary of the Invention

[0007] The purpose of this invention is to provide a method and related equipment for acquiring temperature data of induction heating equipment, which aims to solve the shortcomings of existing technologies such as signal delay introduced by hardware filtering, inability of fixed sampling strategies to avoid transient interference, and limited effectiveness of conventional multiple sampling and averaging methods against systematic electromagnetic interference, thereby significantly improving the anti-interference capability and data reliability of temperature data acquisition for induction heating equipment.

[0008] In a first aspect, the present invention provides a method for acquiring temperature data of an induction heating device, comprising the following steps: S1. Monitor the operating status information of the induction heating equipment and determine the current electromagnetic interference level based on the operating status information; S2. Obtain a phase reference signal synchronized with the driving frequency of the induction heating device, and identify the interference fluctuation period in the induction heating cycle based on the phase reference signal; S3. Determine the sampling trigger time based on the interference fluctuation period, and ensure that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching; S4. Dynamically configure sampling parameters according to the electromagnetic interference level; the sampling parameters include the number of sample acquisitions in a single sampling period, and the number of sample acquisitions increases when the electromagnetic interference level increases; S5. Based on the dynamically configured sampling parameters, start analog-to-digital conversion at the determined sampling trigger time to obtain the first electrical signal sample corresponding to the measured temperature; S6. By performing numerical processing on the first electrical signal sample, temperature feature values ​​are extracted, and the temperature feature values ​​are output through a wireless communication link.

[0009] The temperature data acquisition method for induction heating equipment provided by this invention can achieve accurate acquisition of temperature data of induction heating equipment, effectively avoid electromagnetic interference, and dynamically adjust the sampling strategy according to the interference level, thereby improving the reliability and accuracy of temperature data.

[0010] In a second aspect, the present invention provides a temperature data acquisition device for an induction heating device, comprising: The monitoring and determination module is used to monitor the operating status information of the induction heating equipment and determine the current electromagnetic interference level based on the operating status information. The acquisition and identification module is used to acquire a phase reference signal synchronized with the driving frequency of the induction heating device, and to identify the interference fluctuation period in the induction heating cycle based on the phase reference signal. The trigger determination module is used to determine the sampling trigger time based on the interference fluctuation period, and to ensure that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching; A configuration module is used to dynamically configure sampling parameters according to the electromagnetic interference level; the sampling parameters include the number of sample acquisitions in a single sampling period, and the number of sample acquisitions increases when the electromagnetic interference level increases; The sample acquisition module is used to initiate analog-to-digital conversion at a determined sampling trigger time based on dynamically configured sampling parameters in order to acquire the first electrical signal sample corresponding to the measured temperature. The extraction module is used to extract temperature feature values ​​by performing numerical processing on the first electrical signal sample, and output the temperature feature values ​​through a wireless communication link.

[0011] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing computer-readable instructions, which, when executed by the processor, perform the steps of the induction heating device temperature data acquisition method provided in the first aspect above.

[0012] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of the induction heating device temperature data acquisition method provided in the first aspect above.

[0013] As can be seen from the above, the temperature data acquisition method for induction heating equipment provided by this invention determines the current electromagnetic interference level by monitoring the operating status information of the induction heating equipment and dynamically configures the sampling parameters according to the level, thus achieving adaptive response to electromagnetic interference. Simultaneously, by acquiring a phase reference signal synchronized with the driving frequency, the interference fluctuation period in the induction heating cycle is identified, and the sampling trigger time is determined accordingly. This ensures that the sampling time avoids the transient interference phase generated when the equipment switches drives, effectively solving the problem that traditional fixed sampling strategies cannot adapt to dynamic changes in electromagnetic interference and are susceptible to transient interference. Increasing the number of sample acquisitions when the electromagnetic interference level increases can effectively suppress random noise and systematic electromagnetic interference. Finally, at the determined sampling trigger time, analog-to-digital conversion is initiated to acquire the first electrical signal sample and extract the temperature feature value, which is then output through a wireless communication link, ensuring the real-time performance and accuracy of the temperature data. This method overcomes the shortcomings of existing technologies, such as signal delay introduced by hardware filtering, the inability of fixed sampling strategies to avoid transient interference, and the limited effectiveness of conventional multiple sampling and averaging methods against systematic electromagnetic interference. It significantly improves the anti-interference capability and data reliability of temperature data acquisition for induction heating equipment.

[0014] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method for acquiring temperature data of an induction heating device provided in an embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram of a temperature data acquisition device for an induction heating equipment provided in an embodiment of the present invention.

[0017] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0018] Label Explanation: 100. Monitoring and determination module; 200. Acquisition and identification module; 300. Trigger determination module; 400. Configuration module; 500. Sample acquisition module; 600. Extraction module; 13. Electronic equipment; 1301. Processor; 1302. Memory; 1303. Communication bus. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] In the temperature monitoring system of induction heating equipment, electromagnetic interference during high-power operation leads to inaccurate temperature data acquisition and interruption of wireless transmission. Specifically, the alternating magnetic field generated by the induction coil induces noise signals in the temperature acquisition circuit. These noise signals are superimposed on the output voltage of the temperature sensor, causing the analog-to-digital converter's sampled value to deviate from the voltage value corresponding to the true temperature. Simultaneously, electromagnetic interference is introduced into the wireless communication module, resulting in an increased data transmission error rate. Consequently, the reliability and continuity of temperature data transmission are compromised, affecting the temperature feedback-based cooking control process.

[0022] For example, during high-power frying operations on an induction cooker, the induction coil operates at a high-frequency drive frequency. The NTC thermistor is exposed to the electromagnetic field due to its physical location close to the heating panel. The voltage signal sampled by the analog-to-digital converter is affected by periodic interference fluctuations. When the user adjusts the heat to maintain a constant oil temperature, the control system's response is incorrectly executed due to the instantaneous deviation of the temperature data, causing temperature control failure.

[0023] If the above problems are not resolved, the temperature monitoring system will be unable to provide reliable real-time data, rendering temperature-feedback-based cooking control strategies ineffective; wireless communication link interruptions will occur, hindering user monitoring of the cooking process and increasing the likelihood of cooking failures. The overall reliability of the system will be reduced, and the practical performance of the equipment will be affected.

[0024] For this, please refer to Figure 1 , Figure 1 This is a flowchart of a method for acquiring temperature data from an induction heating device. The method includes the following steps: S1. Monitor the operating status information of the induction heating equipment and determine the current electromagnetic interference level based on the operating status information; S2. Acquire a phase reference signal synchronized with the driving frequency of the induction heating device, and identify the interference fluctuation period in the induction heating cycle based on the phase reference signal; S3. Determine the sampling trigger time based on the interference fluctuation period, and ensure that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching; S4. Dynamically configure sampling parameters according to the electromagnetic interference level; the sampling parameters include the number of sample acquisitions in a single sampling period, and the number of sample acquisitions increases when the electromagnetic interference level increases; S5. Based on the dynamically configured sampling parameters, start analog-to-digital conversion at the determined sampling trigger time to obtain the first electrical signal sample corresponding to the measured temperature; S6. By performing numerical processing on the first electrical signal sample, temperature feature values ​​are extracted, and the temperature feature values ​​are output through a wireless communication link.

[0025] For ease of understanding, the following explains some key terms in this embodiment: Induction heating equipment: refers to devices that use the principle of electromagnetic induction to heat objects, such as induction cookers and induction heating furnaces. These devices generate a strong electromagnetic field during operation, which may interfere with surrounding electronic equipment.

[0026] Operating status information: This refers to various data generated by the induction heating equipment during operation, such as power setting, heating mode, operating frequency, voltage, and current. This information reflects the current operating intensity of the equipment and the potential level of electromagnetic interference.

[0027] Electromagnetic Interference Level: This refers to an indicator that measures the intensity of electromagnetic interference generated when induction heating equipment is operating. This level can be classified based on factors such as the amplitude, frequency, and duration of the interference signal, and is used to guide subsequent sampling parameter configuration.

[0028] Phase reference signal: This refers to a periodic signal synchronized with the drive frequency of the induction heating device. This signal can be used to determine the start and end of the induction heating cycle, as well as to identify events in a specific phase of the cycle, such as drive switching moments.

[0029] Interference fluctuation period: refers to the periodic variation in electromagnetic interference intensity during the operation of induction heating equipment. This period is usually related to the equipment's driving frequency or its harmonics. Identifying this period helps to avoid sampling during periods of high interference.

[0030] It should be noted that identifying the start and end points of the "interference fluctuation cycle" is a crucial step in achieving accurate sampling triggering. These cycles are typically identified and defined using the drive synchronization signal within the induction heating device. Specifically: First, obtaining a phase reference signal synchronized with the driving frequency of the induction heating device is fundamental. The core of the induction heating device is its high-frequency drive circuit, which generates a high-frequency alternating magnetic field through switching operations using power transistors such as IGBTs. These switching operations are periodic, and their frequency (e.g., 20kHz to 50kHz) determines the periodicity of electromagnetic interference. A microcontroller unit (MCU) can utilize its internal high-precision timer module, configured, for example, in PWM input capture mode or external interrupt mode, to synchronously capture the drive signal of the main power transistor or its switching frequency synchronization signal. These synchronization signals, such as the rising and falling edges of the drive pulse, directly reflect the start and end of a complete operating cycle of the induction heating device.

[0031] Secondly, based on the acquired phase reference signal, the MCU can accurately calculate the duration of the interference fluctuation cycle in the induction heating cycle. For example, if the frequency of the synchronization signal is 25kHz, then a complete interference fluctuation cycle is 1 / 25kHz, or 40 microseconds. This 40-microsecond cycle defines the overall start and end points of the interference fluctuation cycle (the electromagnetic interference intensity reaches its peak at the moment of drive switching, and is lower during the stable on or off period of drive). By capturing specific events of the synchronization signal (such as rising or falling edges), the MCU can mark the start of each cycle, thereby determining the boundary of the entire interference fluctuation cycle.

[0032] Furthermore, after identifying the overall range of the interference fluctuation cycle, it is necessary to further identify the specific transient interference phases within that cycle. Transient interference phases typically occur at the instant of drive circuit switching, when current and voltage changes drastically, and the electromagnetic interference intensity reaches its peak. By analyzing the waveform characteristics of the synchronization signal, such as the width of the PWM pulse and the duration of its rising and falling edges, the MCU can identify these specific time windows of high interference. For example, in a 40-microsecond switching cycle, if it is known that drive switching generates transient interference at the beginning and middle of the cycle, then these transient interference phases may occupy the first 10 microseconds and the last 10 microseconds of the cycle. By accurately identifying these transient interference phases, it is indirectly confirmed which time periods within the interference fluctuation cycle are the start and end points of high interference, and which time periods are relatively "quiet."

[0033] In summary, identifying the start and end points of the interference fluctuation cycle is accomplished by acquiring a phase reference signal synchronized with the driving frequency of the induction heating equipment and precisely calculating the duration of each driving cycle using the MCU's timer function. This driving cycle is the interference fluctuation cycle. Based on this, by analyzing the waveform characteristics of the synchronization signal, the specific transient interference phase within the cycle is further identified, thus clarifying the precise start and end points of the high-interference period and providing a basis for determining the subsequent sampling trigger time.

[0034] Sampling trigger time: refers to the precise point in time when the analog-to-digital converter begins data sampling. By precisely controlling the sampling trigger time, sampling can be ensured during "quiet periods" with relatively low electromagnetic interference, thereby improving data accuracy.

[0035] Transient interference phase: refers to the brief, high-intensity electromagnetic interference generated by the drastic changes in current and voltage when the drive circuit of an induction heating device switches (e.g., the IGBT switch is turned on or off). These interferences are usually short in duration but large in amplitude.

[0036] Sampling parameters: These refer to the settings used to control sampling behavior during analog-to-digital conversion, such as sampling rate, resolution, and the number of samples acquired within a single sampling period. Dynamically configuring these parameters allows the sampling process to adapt to different electromagnetic interference environments.

[0037] It should be noted that the number of sample acquisitions within a single sampling period is dynamically configured based on the current electromagnetic interference (EMI) level. The system monitors the operating status of the induction heating equipment and determines the current EMI level (e.g., low, medium, high). When the EMI level increases, the system correspondingly increases the number of sample acquisitions within a single sampling period. This dynamic configuration is typically achieved through a preset sampling parameter configuration table, which associates different EMI levels with the corresponding number of sample acquisitions. For example, in a low-interference environment, the system may be configured to acquire fewer samples (e.g., 1 or 2 times) to save power consumption and processing time; while in a high-interference environment, the number of sample acquisitions will be increased (e.g., 4, 8, 16, or even more times). By performing numerical processing (e.g., averaging) on ​​these multiple acquisitions, random noise can be effectively suppressed, improving the accuracy of the sampling data.

[0038] The duration of a single sampling period depends on two main factors: the dynamically configured number of sample acquisitions and the sampling rate of the analog-to-digital converter (ADC) (i.e., the time required to acquire one sample at a time). The duration of a single sampling period = number of sample acquisitions * sampling rate. For example, if the ADC acquires 16 samples, then the duration of the entire "single sampling period" is the sum of the time required for those 16 samples. This process needs to be completed in a very short time to ensure that all samples are acquired within the selected "quiet period."

[0039] Number of sample acquisitions: This refers to the number of times the analog-to-digital converter performs digital conversion on the analog signal during a single sampling operation. By increasing the number of sample acquisitions and averaging multiple samples, the impact of random noise can be effectively reduced, and the signal-to-noise ratio of the sampled data can be improved.

[0040] The first electrical signal sample refers to the digital signal sequence corresponding to the measured temperature obtained by the analog-to-digital converter after optimized sampling. This sample is the basis for subsequent temperature feature value extraction.

[0041] Temperature characteristic value: refers to the numerical value extracted from the first electrical signal sample that accurately reflects the measured temperature. This value is usually the actual temperature value after calibration and conversion.

[0042] Wireless communication link: refers to a wireless connection used to transmit temperature characteristic values ​​between an induction heating device and an external receiving device, such as Bluetooth, Wi-Fi, etc.

[0043] This application proposes a method for acquiring temperature data in an induction heating device, aiming to solve the problems of inaccurate temperature data acquisition and wireless transmission interruption caused by electromagnetic interference during the operation of the induction heating device. The method includes the following steps: In step S1, the operating status information of the induction heating equipment is monitored, and the current electromagnetic interference level is determined based on this information. This step can be implemented in several ways. For example, a dedicated electromagnetic interference sensor can be set up to monitor the electromagnetic field strength around the equipment in real time, convert the monitored analog signals into digital signals, and then determine the electromagnetic interference level based on the amplitude or frequency characteristics of the digital signals. Another approach is to estimate the current electromagnetic interference level by reading parameters such as the power output and operating frequency inside the induction heating equipment and combining them with a preset mapping table. For example, when the equipment is operating in high-power mode, its electromagnetic interference level can be presumed to be high; when the equipment is in low-power or standby mode, its electromagnetic interference level can be presumed to be low. The electromagnetic interference level can also be indirectly assessed by analyzing current or voltage fluctuations on the power line.

[0044] In step S2, a phase reference signal synchronized with the drive frequency of the induction heating device is acquired, and the interference fluctuation period in the induction heating cycle is identified based on the phase reference signal. This step can be implemented, for example, by introducing a synchronization signal output port into the drive circuit of the induction heating device. The signal output from this port is synchronized with the switching frequency of the driving IGBT, for example, it can be the rising or falling edge of the drive pulse. The microcontroller unit can receive this synchronization signal as a phase reference. By analyzing this phase reference signal, the microcontroller unit can accurately identify the periodic fluctuation pattern of electromagnetic interference intensity that may occur in the induction heating device within a complete operating cycle. For example, the interference usually reaches its peak at the moment of drive switching, while the interference is relatively small during the stable on or off period of drive.

[0045] In step S3, the sampling trigger time is determined based on the interference fluctuation period, and the sampling trigger time is set to avoid the transient interference phase generated by the induction heating device during drive switching. This step can be implemented, for example, by analyzing the interference intensity distribution of different phases within the identified interference fluctuation period. Typically, high-intensity transient electromagnetic interference is generated at the moment of switching of the drive circuit. Therefore, a time window can be set that covers these transient interference phases. The sampling trigger time will be scheduled outside this time window, i.e., during a relatively quiet "quiet period" with less interference, for analog-to-digital conversion. For example, if drive switching occurs at the beginning and middle of each cycle, the sampling trigger time can be set between these points to avoid transient interference.

[0046] In step S4, sampling parameters are dynamically configured according to the electromagnetic interference level. These parameters include the number of sample acquisitions within a single sampling period, with the number of acquisitions increasing as the electromagnetic interference level rises. This step can be implemented, for example, by pre-setting a sampling parameter configuration table in the microcontroller unit. This table associates different electromagnetic interference levels with the corresponding number of sample acquisitions. When step S1 determines the current electromagnetic interference level to be "low," it can be configured to acquire fewer samples within a single sampling period, such as 1 or 2 times, to save power consumption and processing time. When the electromagnetic interference level rises to "medium" or "high," the microcontroller unit automatically increases the number of sample acquisitions according to the configuration table, for example, increasing it to 4, 8, or more times. After increasing the number of sample acquisitions, the microcontroller unit averages these samples, effectively suppressing random noise and improving the accuracy of the sampling data.

[0047] In step S5, based on the dynamically configured sampling parameters, an analog-to-digital converter (ADC) is initiated at a determined sampling trigger time to acquire a first electrical signal sample corresponding to the measured temperature. This step can be implemented, for example, by an ADC within a microcontroller unit. When the microcontroller unit receives the sampling trigger time signal determined in step S3, it immediately initiates the ADC for ADC conversion. The ADC acquires a specified number of samples within a single sampling period according to the sampling parameters dynamically configured in step S4. These samples are digital representations of the voltage signal output by an analog temperature sensor (such as an NTC thermistor). By performing multiple samplings and averaging during low-interference periods, a relatively pure and accurate first electrical signal sample can be obtained.

[0048] In step S6, the temperature feature value is extracted by numerical processing of the first electrical signal sample and output through a wireless communication link. This step can be implemented, for example, through firmware in a microcontroller unit. After receiving the first electrical signal sample, the microcontroller unit performs a series of numerical processing on it. For example, if the first electrical signal sample is the voltage value of an NTC thermistor voltage divider circuit, the microcontroller unit will convert the voltage value into the corresponding resistance value according to a pre-stored NTC resistance-temperature lookup table or an algorithm such as the Steinhart-Hart equation, and then convert the resistance value into the actual temperature value. This converted temperature value is the temperature feature value. Subsequently, the microcontroller unit will encapsulate this temperature feature value into a data packet through its integrated wireless communication module (e.g., a Bluetooth module) and send it out through the wireless communication link for external devices to receive and display.

[0049] The core of this technical solution lies in its approach: instead of simply attempting to combat the strong electromagnetic interference generated by an induction cooker operating at high power using hardware or fixed software methods, it first monitors the induction cooker's current cooking mode and determines the extent of surrounding interference. Then, based on this real-time information, it flexibly adjusts the sampling timing and density of the analog-to-digital converter (ADC). When interference is strong, it selects moments with relatively weak interference for rapid and multiple sampling, then processes the sampled information, removing inaccurate data. When interference is weak, it slows down the sampling speed and frequency to conserve power. This dynamically adjusted intelligent strategy ensures accurate temperature data acquisition while maximizing energy savings in the complex and ever-changing environment of an induction cooker.

[0050] The following example will provide a more detailed explanation of the above technical solution: Imagine a smart kitchen environment where an induction heating device is operating at high power to rapidly heat cookware. Traditional temperature acquisition methods might face significant electromagnetic interference issues in this situation, leading to unstable temperature readings or transmission interruptions.

[0051] To address this issue, this application proposes a method for acquiring temperature data from an induction heating device. First, in step S1, the control unit inside the induction heating device continuously monitors the device's operating status information, such as the current heating power being set to its maximum value. According to preset rules, when the heating power reaches its maximum value, the current electromagnetic interference level can be determined to be "high".

[0052] Next, in step S2, the control unit obtains a phase reference signal synchronized with the drive frequency from the drive circuit of the induction heating device. For example, this signal can be a synchronization pulse of the PWM waveform driving the IGBT. By analyzing this synchronization pulse, the control unit can identify the periodic fluctuation pattern of electromagnetic interference in the induction heating cycle; for example, the interference intensity will significantly increase at the moment of switching in each PWM cycle.

[0053] Subsequently, in step S3, the control unit precisely determines a sampling trigger time based on the identified interference fluctuation cycle. This time is carefully selected to avoid transient interference phases generated by the induction heating device during drive switching. For example, if the drive switching occurs at the 0-degree and 180-degree phases of the PWM cycle, the sampling trigger time can be set at the 90-degree or 270-degree phases, which are typically “quiet periods” with relatively low electromagnetic interference.

[0054] After determining the electromagnetic interference level and sampling trigger time, in step S4, the control unit dynamically configures the sampling parameters based on the "high" electromagnetic interference level determined in step S1. Specifically, it increases the number of sample acquisitions within a single sampling period, for example, from the default 4 times to 16 times, to enhance the suppression capability against high-intensity electromagnetic interference.

[0055] Then, in step S5, based on the dynamically configured sampling parameters (e.g., 16 sample acquisitions), the control unit precisely initiates the analog-to-digital conversion at the sampling trigger time determined in step S3. At this time, the analog-to-digital converter performs 16 rapid samples (multiple samplings are performed at even intervals) of the analog voltage signal output by the temperature sensor (e.g., an NTC thermistor) during a low-interference "quiet period." These sampled data are then averaged to obtain a highly accurate first electrical signal sample that is minimally affected by interference.

[0056] Finally, in step S6, the control unit performs numerical processing on this first electrical signal sample, such as converting it into an actual temperature value through table lookup or calculation, thereby extracting the temperature characteristic value. Subsequently, this accurate temperature characteristic value is transmitted via a wireless communication link (e.g., Bluetooth) for real-time display on an external smartphone or screen.

[0057] As can be seen from the above examples, the temperature data acquisition method for induction heating equipment proposed in this application effectively solves the problems of inaccurate temperature data acquisition and unreliable wireless transmission in environments with strong electromagnetic interference, such as high-power operation of induction heating equipment, by comprehensively utilizing technologies such as dynamic interference level assessment, synchronous phase reference identification, sampling trigger time optimization, and adaptive sampling parameter configuration.

[0058] Compared with traditional temperature acquisition schemes, the solution presented in this application has significant technical contributions. Traditional schemes often employ fixed sampling strategies, which cannot adapt to dynamic changes in electromagnetic interference intensity. For example, in the aforementioned high-power heating scenario, if fixed sampling is used, the acquired temperature data will be filled with noise, causing readings to jump and making it difficult for users to accurately determine the temperature of the cookware. This application, however, dynamically monitors the electromagnetic interference level in step S1 and dynamically configures the sampling parameters in step S4, enabling the system to adjust the sampling strategy according to the actual interference situation. For example, it increases the number of sample acquisitions when interference increases, thereby effectively suppressing noise and ensuring the accuracy of data under different interference environments.

[0059] Furthermore, traditional solutions typically fail to consider the periodic interference characteristics within the induction heating device, potentially sampling at the peak of interference, leading to severe data distortion. This application obtains a phase reference signal synchronized with the driving frequency in step S2 and identifies the interference fluctuation period. Then, step S3 determines the sampling trigger time to avoid transient interference phases. This ensures that sampling always occurs within a relatively "quiet" window of the electromagnetic environment. This precise timing control significantly improves the purity of the first electrical signal sample, avoiding the direct impact of transient high-energy pulses on the sampling. For example, extremely strong transient interference occurs at the moment of drive switching in the induction heating device. Traditional solutions might sample precisely at this moment, resulting in data anomalies. This application effectively avoids these high-interference moments, ensuring the reliability of the sampled data.

[0060] In summary, the overall technical concept of this application lies in constructing a system capable of stably and accurately acquiring and transmitting temperature data in complex electromagnetic environments by real-time sensing, periodic analysis, and intelligent avoidance of electromagnetic interference, combined with an adaptive sampling strategy. This comprehensive solution significantly improves the robustness and reliability of temperature data acquisition in induction heating equipment, providing users with a more accurate and reliable cooking experience, and possesses high technological advancement and practical value.

[0061] In some embodiments, step S1, determining the current electromagnetic interference level based on the operating status information, includes: S11. Based on the operating status information, obtain the second electrical signal sample corresponding to the measured temperature; S12. By performing anomaly detection on each data point in the second electrical signal sample, abnormal data points are identified; the specific steps include: comparing the deviation of the current data point with other data points in the second electrical signal sample, determining whether the deviation exceeds a preset pulse transition threshold, and identifying the corresponding data points whose deviation exceeds the pulse transition threshold as abnormal data points. S13. When the number of abnormal data points exceeds the preset abnormal point tolerance, it is determined that the second electrical signal sample is affected by occasional high-energy electromagnetic pulse interference, and resampling is triggered to obtain a new electrical signal sample as a new second electrical signal sample in the next silent period, until it is determined that the second electrical signal sample is not interfered with or exceeds the preset limit. S14. Use the second electrical signal sample that is not interfered with as the target electrical signal sample for evaluating the level of electromagnetic interference, or when the preset limit is exceeded, use the third electrical signal sample that has been confirmed to be uninterrupted in the historical data as the target electrical signal sample for evaluating the level of electromagnetic interference. S15. Determine the current electromagnetic interference level based on the target electrical signal sample.

[0062] Specifically, when acquiring a second electrical signal sample corresponding to the measured temperature ("corresponding to the measured temperature" means that the acquired second electrical signal sample has a definite and quantifiable functional relationship between its electrical characteristics (e.g., voltage or current value) and the actual physical temperature of the heating area of ​​the induction heating device), this correspondence is the foundation of temperature measurement technology, allowing the measured temperature to be indirectly derived by measuring the electrical signal. This correspondence is usually defined and established in the following ways: First, it depends on the physical characteristics of the temperature sensor. For example, the NTC thermistor mentioned in the background art has a resistance value that changes non-linearly with temperature. This inherent physical characteristic is the fundamental source of "corresponding to the measured temperature." When the measured temperature rises or falls, the resistance of the NTC thermistor will decrease or increase accordingly. Second, the change in physical quantity is converted into a measurable electrical signal through the cooperation of the sensor and external circuitry. The NTC thermistor is usually configured with a fixed resistor to form a voltage divider circuit. When the resistance of the NTC thermistor changes with temperature, The output voltage of the voltage divider circuit will also change accordingly. Therefore, this output voltage becomes an electrical signal directly related to the measured temperature. Finally, this correspondence needs to be precisely quantified through calibration and standardization. Before the equipment is manufactured or deployed, a series of experiments are conducted to measure the corresponding electrical signal output at a known precise temperature. These measurement data are used to establish a "resistance-temperature lookup table" or "resistance-temperature conversion algorithm" (e.g., the Steinhart-Hart equation). This lookup table or algorithm precisely describes the resistance value that the sensor should output at a specific temperature, and then calculates the corresponding voltage value through the voltage divider circuit. Therefore, when the system obtains an electrical signal sample of a certain voltage value, it can convert it in reverse to the corresponding resistance value according to this preset lookup table or algorithm, and then convert it to the actual temperature value. Operating status information can refer to parameters such as the current working mode, power setting, and heating time of the induction heating equipment. This information can be directly provided by the controller inside the equipment, for example, by reading register values ​​or receiving sensor feedback. Acquiring the second electrical signal sample refers to digitizing the analog voltage signal output by the sensor (such as a thermistor or thermocouple) used to measure temperature in the induction heating device through an analog-to-digital converter (ADC), forming a series of discrete digital values. This can be continuous sampling within a preset time window, or single or multiple sampling under specific triggering conditions. Anomaly detection for each data point in the second electrical signal sample aims to identify instantaneous, abnormal data points caused by intermittent high-energy electromagnetic pulse interference. This can be achieved through various statistical or signal processing methods; for example, a moving average method can be used, comparing the current data point with the average of the previous N data points; or a median filtering method can be used, comparing the current data point with the median of its neighboring data points.The preset pulse transition threshold is a pre-defined value used to quantify the standard for judging whether a data point is abnormal. This threshold can be empirically set or calibrated experimentally based on the actual working environment of the induction heating equipment, sensor characteristics, and desired anti-interference capability. When the number of abnormal data points exceeds the preset abnormality tolerance (a threshold representing the maximum number or proportion of abnormal data points allowed in a second electrical signal sample), the system considers the sample to be severely interfered with. Triggering resampling means that when severe interference is detected in the current sample, the system discards the current sample and restarts the data acquisition process. This can be done by sending a resampling command to the ADC controller or by re-initializing the sampling process through software logic. The next quiet period refers to the time period in the induction heating equipment's operating cycle when electromagnetic interference is relatively weak or non-existent. This can be predicted by analyzing the equipment's drive frequency and phase reference signal, for example, during the intervals of induction coil drive switching or when the equipment is in low-power or standby mode. Exceeding the preset limit means that the number of resampling attempts or the total resampling waiting time has reached the preset maximum value. An undisturbed second electrical signal sample is used as the target electrical signal sample for assessing the electromagnetic interference (EMI) level. Alternatively, if a preset limit is exceeded, a third electrical signal sample confirmed to be undisturbed from historical data is used as the target electrical signal sample for assessing the EMI level. The undisturbed second electrical signal sample refers to the second electrical signal sample whose number of abnormal data points does not exceed a preset anomaly tolerance after anomaly detection. The third electrical signal sample confirmed to be undisturbed from historical data refers to the electrical signal sample that the system successfully acquired and confirmed to be undisturbed before the current resampling cycle. Finally, based on the target electrical signal sample, the current EMI level is determined. This involves quantifying the EMI intensity of the current environment based on the characteristics of the target electrical signal sample (e.g., noise level, fluctuation amplitude, number of residual anomalies, etc.). This can be achieved by calculating the sample's standard deviation, peak-to-peak value, signal-to-noise ratio, etc., and comparing them with preset level classification standards.

[0063] This solution effectively filters out the instantaneous impact of occasional high-energy electromagnetic pulse interference on electrical signal samples by introducing anomaly detection and resampling mechanisms, ensuring the purity and reliability of sample data used to assess electromagnetic interference levels. When the induction heating equipment is operating, the microcontroller first continuously samples the output voltage of the voltage divider circuit connected to the temperature sensor using its built-in analog-to-digital converter, based on the equipment's current power setting, heating mode, and other operating status information, to obtain a second electrical signal sample containing multiple data points. Subsequently, the microcontroller performs anomaly detection on these data points. Specifically, for each data point in the sample, the microcontroller calculates its absolute deviation from the average of all other data points in the sample and compares this absolute deviation with a preset pulse transition threshold. If the absolute deviation exceeds the threshold, the data point is marked as an anomaly. After completing anomaly detection for the entire second electrical signal sample, the microcontroller counts the number of anomaly data points. If the count shows that the number of anomaly data points exceeds a preset anomaly tolerance, the microcontroller determines that the current second electrical signal sample has been subjected to occasional high-energy electromagnetic pulse interference. At this point, the microcontroller triggers a resampling operation, waits for the quiet period in the next induction heating cycle, and then restarts the analog-to-digital converter to sample again, acquiring a new second electrical signal sample. This resampling process repeats until the number of abnormal data points in the acquired samples does not exceed a preset tolerance, or the number of resampling attempts reaches a preset upper limit. If an undisturbed second electrical signal sample is successfully acquired, this sample is selected as the target electrical signal sample for assessing the electromagnetic interference level. If the number of resampling attempts reaches the upper limit and still no undisturbed sample is acquired, the system will backtrack and select a third electrical signal sample from historical data that has been confirmed to be undisturbed as the target electrical signal sample. Finally, based on this target electrical signal sample, the microcontroller calculates its standard deviation or peak-to-peak value and compares it with a preset electromagnetic interference level classification standard to determine the current electromagnetic interference level.

[0064] The following is a concrete example. During the startup or operation of an induction heating device, the microcontroller, based on the device's current power setting, heating mode, and other operating status information, continuously samples the output voltage of the voltage divider circuit connected to the temperature sensor using its built-in analog-to-digital converter, acquiring a second electrical signal sample containing 100 data points. Subsequently, the microcontroller performs anomaly detection on these 100 data points. Specifically, for each data point in the sample, the microcontroller calculates its absolute deviation from the average of all other data points in that sample. For example, if the current data point is V_i and the sample average is V_avg, then |V_i - V_avg| is calculated. The microcontroller then compares this absolute deviation with a preset pulse transition threshold (e.g., set to 50mV). If the absolute deviation exceeds 50mV, the data point is marked as an anomaly. After completing the anomaly detection for the entire second electrical signal sample, the microcontroller counts the number of anomaly data points. Assume the preset anomaly tolerance is 5 data points. If the statistical results show that the number of abnormal data points exceeds 5, the microcontroller determines that the current second electrical signal sample is subject to intermittent high-energy electromagnetic pulse interference. At this point, the microcontroller triggers a resampling operation. It waits for a quiet period in the next induction heating cycle (e.g., via the drive signal of the synchronous induction coil, during a very short interval of drive switching) and then restarts the analog-to-digital converter to sample again, acquiring a new second electrical signal sample. This resampling process repeats until the number of abnormal data points in the acquired sample does not exceed 5, or the number of resampling attempts reaches a preset upper limit (e.g., a maximum of 3 resampling attempts). If an undisturbed second electrical signal sample is successfully acquired, this sample is selected as the target electrical signal sample for assessing the electromagnetic interference level. If the number of resampling attempts reaches the upper limit and still fails to acquire an undisturbed sample, the system will backtrack and select a third electrical signal sample from historical data that has been confirmed to be undisturbed as the target electrical signal sample. Finally, the microcontroller calculates the standard deviation or peak-to-peak value of this target electrical signal sample and compares it with a preset electromagnetic interference level classification standard. For example, if the sample standard deviation is less than 10mV, it is classified as "low" interference; if it is between 10mV and 50mV, it is classified as "medium" interference; and if it is greater than 50mV, it is classified as "high" interference. This determined electromagnetic interference level will be used for subsequent dynamic configuration of sampling parameters.

[0065] Through the above technical solution, this application effectively solves the problem that in the strong electromagnetic interference environment of induction heating equipment, sporadic high-energy electromagnetic pulse interference causes distortion of temperature data acquisition samples, thus affecting the accurate assessment of electromagnetic interference levels. By introducing an anomaly detection and resampling mechanism for the second electrical signal sample, this solution can identify and avoid the contamination of samples by transient high-energy interference, ensuring the purity and reliability of the sample data used to assess the electromagnetic interference level. This enables the system to more accurately determine the current electromagnetic interference intensity, thus providing a solid foundation for subsequent dynamic configuration of sampling parameters (e.g., increasing the number of sample acquisitions when the electromagnetic interference level increases). Therefore, in the entire temperature data acquisition method for induction heating equipment, this solution significantly improves the accuracy of electromagnetic interference level assessment, thereby ensuring the accuracy of the final temperature characteristic value, and obtaining stable and reliable temperature data even in harsh electromagnetic environments.

[0066] In some embodiments, the specific steps in step S3 include: S31. Acquire the drive synchronization signals of multiple periodic electromagnetic interference sources inside the induction heating equipment; S32. Based on the interference fluctuation period and the drive synchronization signal, identify the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to each periodic electromagnetic interference source; S33. By analyzing the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to multiple periodic electromagnetic interference sources, a common time window without transient interference is determined. S34. When there is a common time window without transient interference, the sampling trigger time is set within the common time window without transient interference, so that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to all periodic electromagnetic interference sources. S35. When there is no common time window without transient interference, based on the transient interference phase generated by the induction heating device during drive switching and the intensity of the transient interference phase corresponding to each periodic electromagnetic interference source, the transient interference phase with the highest intensity is taken as the target phase, and the sampling trigger time is set to avoid the target phase.

[0067] In the step of acquiring the drive synchronization signals of multiple periodic electromagnetic interference sources within the induction heating equipment, this step aims to comprehensively identify all components within the induction heating equipment, excluding the main induction heating drive circuit, that may generate periodic electromagnetic interference. These interference sources may include cooling fan motors, switching power supply modules, auxiliary heating elements, or communication modules, etc. (Specifically, determining which interference sources exist in the induction heating equipment and acquiring their drive synchronization signals can be done in two ways: The first method is to directly read or capture the level changes on the control signal lines of these interference sources in the control unit of the induction heating equipment. For example, a microcontroller (MCU) can monitor the control signal lines connected to components such as cooling fan motors, switching power supply modules, or LED display backlights in real time through its general purpose input / output (GPIO) ports. When the level on these control signal lines changes, the microcontroller can identify the start-up, switching, or operating state of the corresponding interference source, thereby determining the presence of the interference source and its operating cycle. The second method is to deploy [equipment] near each interference source.) Miniature current or voltage sensors are used. These sensors can detect periodic changes in current or voltage generated when an interference source is operating. For example, placing a miniature current sensor near a cooling fan motor will detect periodic current fluctuations when the fan rotates. These analog signals are then converted into digital synchronization signals for processing by a microcontroller. In this way, even without direct access to the control signal lines, the drive synchronization information of the interference source can be indirectly obtained, thus determining its presence and operating status. Through any or a combination of the above methods, the system can clearly identify which components inside the induction heating equipment are operating and generating periodic electromagnetic interference, thus laying the foundation for subsequent identification of the transient interference phases corresponding to these interference sources, ensuring that all major electromagnetic interference sources are comprehensively considered and avoided during temperature data acquisition. The purpose of acquiring their drive synchronization signals is to accurately grasp the periodic operating states of these interference sources, such as startup, switching, or commutation, thereby enabling the prediction of the specific timing of their transient electromagnetic interference. Specifically, this can be achieved in the following ways: One way is to directly read or capture the level changes on the drive control signal lines of these interference sources in the control unit of the induction heating equipment, for example, through real-time monitoring via the general purpose input / output (GPIO) ports of a microcontroller (MCU). Another way is to deploy miniature current or voltage sensors near each interference source to sense the periodic changes in their operating current or voltage and convert these analog signals into digital synchronization signals.

[0068] In the step of identifying the transient interference phase generated by the induction heating equipment during drive switching and the transient interference phase corresponding to each periodic electromagnetic interference source based on the interference fluctuation period and drive synchronization signal, the core of this step lies in accurately locating the specific time period during which the main drive circuit of the induction heating equipment and other periodic interference sources generate transient interference within their respective operating cycles, i.e., the "transient interference phase". The transient interference phase refers to the brief but high-intensity electromagnetic noise generated at the moment of drive circuit switching, power conversion, or motor commutation due to drastic changes in current or voltage. Specifically, this can be achieved in the following ways: One method is to conduct electromagnetic compatibility (EMC) testing on the induction heating equipment beforehand. Under different operating modes, an oscilloscope or spectrum analyzer is used to monitor the electromagnetic radiation or conducted noise of each interference source, and combined with its drive synchronization signal, the waveform and time window of the transient interference are plotted and stored as a lookup table or model. Another method is to dynamically calculate the duration and phase of the transient interference by monitoring the rising or falling edge of the drive synchronization signal in real time and combining it with known characteristics of the interference source (e.g., a fan motor generates transient interference for a specific period after startup).

[0069] In the step of determining a common transient-free time window by analyzing the transient interference phases generated during drive switching of the induction heating equipment and the transient interference phases corresponding to multiple periodic electromagnetic interference sources, the goal is to find one or more time periods among all identified transient interference phases where all interference sources are in a "silent" state, i.e., time windows where no transient interference occurs. This requires temporal superposition and analysis of the transient interference phases of all interference sources. Specifically, this can be achieved in the following ways: One approach is to mark all identified transient interference phases on a time axis, and then use a logical "AND" operation to find all continuous time periods outside the marked areas; these time periods are the common transient-free time windows. Another approach is to establish a time window management algorithm, representing the transient interference phase of each interference source as a time interval, then calculating the complements of these intervals, and finding the intersection of all complements to obtain the common transient-free time window.

[0070] When a common transient-free time window exists, the sampling trigger time is set within this window. This ensures that the sampling trigger time avoids the transient interference phases generated during drive switching of the induction heating device and the transient interference phases corresponding to all periodic electromagnetic interference sources. If the system successfully finds a time window where all interference sources are silent, the ideal sampling strategy is to perform analog-to-digital conversion within this window to minimize the impact of transient interference on temperature data acquisition. Specifically, this can be achieved in the following ways: One approach is to select the center point of the common transient-free time window as the sampling trigger time to provide the maximum margin. Another approach is to select the start or end point of the window, or determine the specific sampling trigger time based on other optimization objectives (such as alignment with the next sampling period).

[0071] When a common window free of transient interference does not exist, the transient interference phase generated by the induction heating device during drive switching and the intensity of the transient interference phases corresponding to each periodic electromagnetic interference source are used to select the transient interference phase with the highest intensity as the target phase. The sampling trigger time is then set to avoid this target phase. In some complex operating scenarios, the transient interference phases of all interference sources may overlap, making it impossible to find a completely interference-free window. In this case, a compromise strategy is needed: prioritizing the avoidance of the most influential interference source. Specifically, this can be achieved in the following ways: One approach is to assess the intensity of each transient interference phase by pre-measuring or measuring its peak voltage, energy, or duration, and then selecting the one with the highest intensity as the target phase to avoid. Another approach is to use machine learning algorithms to dynamically assess the "intensity" of each interference source based on historical data and the degree of influence of interference on the sampling results, and then select the one with the highest priority for avoidance.

[0072] This application employs a comprehensive analysis and decision-making mechanism to ensure optimized selection of the sampling trigger time even in complex multi-source interference environments, thereby improving the accuracy of temperature data acquisition. In the temperature data acquisition method for induction heating equipment, to more accurately determine the sampling trigger time, this application first acquires the drive synchronization signals of multiple periodic electromagnetic interference sources within the induction heating equipment. These synchronization signals provide precise time references for the working cycles of each interference source. Based on these drive synchronization signals and the interference fluctuation cycle in the induction heating cycle, the system can identify the transient interference phase generated by the induction heating equipment during drive switching, as well as the corresponding transient interference phase of each periodic electromagnetic interference source. This identification process ensures that all potential transient interference periods are accurately captured and located. Subsequently, the system performs comprehensive analysis on all identified transient interference phases to determine whether a common transient-free time window exists. This window represents the time period during which all interference sources are in a relatively quiet state. If such a common transient-free time window is successfully found, the system sets the sampling trigger time within that window. In this way, the start-up time of analog-to-digital conversion can effectively avoid transient interference generated during the main drive switching of the induction heating device, as well as transient interference corresponding to all other periodic electromagnetic interference sources, thus acquiring temperature signals in the "cleanest" electromagnetic environment. However, under certain extreme or complex operating conditions, all transient interference phases may overlap, making it impossible to find a completely interference-free common time window. In this case, the solution of this application adopts an intelligent trade-off strategy: based on the intensity of the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to each periodic electromagnetic interference source, the transient interference phase with the highest intensity is taken as the target phase. Subsequently, the system sets the sampling trigger time to avoid this target phase with the highest intensity. This strategy ensures that even if all interference cannot be completely avoided, the interference source with the greatest impact on temperature data acquisition can be prioritized, thereby minimizing the negative impact of interference on the sampling results. Through the synergistic effect of the above steps, the solution of this application significantly enhances the intelligence and adaptability of the sampling time setting, making the acquisition of temperature data more accurate and reliable in the strong electromagnetic interference environment of induction heating devices. This further improves the step of determining the sampling trigger time in the temperature data acquisition method for induction heating equipment, enabling it to effectively cope with the challenges of multi-source interference.

[0073] The following is a concrete example. In an induction heating device, besides the main induction coil drive circuit, there is a DC brushless fan motor for cooling and a switching power supply module for powering the control circuit. Both of these generate periodic transient electromagnetic interference (EMI). First, the system acquires the PWM synchronization signal of the main induction coil drive circuit, the commutation synchronization signal of the fan motor, and the switching synchronization signal of the switching power supply module. For example, the main drive circuit may generate transient interference at the rising and falling edges of each PWM cycle, the fan motor may generate transient interference during each commutation, and the switching power supply module may generate transient interference during each switching action. Next, based on these synchronization signals and a preset interference characteristic model, the system identifies the specific phase of these transient interferences. For example, the transient interference from the main drive circuit may last approximately 1 microsecond, the transient interference from the fan motor may last approximately 2 milliseconds, and the transient interference from the switching power supply module may last approximately 500 nanoseconds. The system then marks these transient interference phases on the time axis of the induction heating cycle. The system then analyzes the phases of these marked transient interferences, attempting to find a common time window where all interference sources are free from transient interference. For example, if the main drive interference occurs at 0-1 microseconds and 10-11 microseconds, the fan interference at 3-5 microseconds, and the switching power supply interference at 7-7.5 microseconds, the system might identify time periods such as 2-2.5 microseconds or 8-9 microseconds as a common transient-free time window. When such a common transient-free time window exists, the system sets the sampling trigger time of the analog-to-digital conversion within that window, for example, by selecting the center point of the window. This effectively avoids all known transient electromagnetic interferences when sampling temperature signals. However, if all interference phases overlap, for example, if the main drive interference occurs at 0-1 microseconds, the fan interference at 0.5-1.5 microseconds, and the switching power supply interference at 0.8-1.2 microseconds, a completely interference-free common time window does not exist. In this case, the system determines which interference source has the highest transient interference intensity based on pre-assessed or real-time monitored interference strength. For example, if the transient interference intensity of the main drive circuit is the highest, the system will take it as the target phase and set the sampling trigger time to avoid the target phase, such as starting sampling at 1.6 microseconds. Even if this may slightly overlap with the tail interference of weaker interference sources (such as fans), the strongest interference is avoided first.

[0074] Through the above technical solution, this application effectively solves the technical problem of difficulty in selecting a single sampling trigger moment to simultaneously avoid the transient interference phases of all interference sources when multiple periodic electromagnetic interference sources exist inside the induction heating equipment. The solution of this application systematically acquires and identifies the driving synchronization signals of all periodic electromagnetic interference sources and their corresponding transient interference phases, enabling a comprehensive understanding of the electromagnetic interference situation inside the equipment. Based on this, intelligent analysis determines a common transient-free time window, allowing temperature data acquisition to be performed in the "cleanest" electromagnetic environment, thereby minimizing the impact of multi-source transient interference on sampling accuracy. Even in complex situations where a completely interference-free window cannot be found, the solution of this application can prioritize interference based on its intensity, prioritizing the avoidance of the strongest transient interference, thus achieving the optimal sampling strategy under limited conditions. This significantly improves the accuracy and reliability of temperature data acquisition in induction heating equipment, enabling stable and accurate temperature data to be obtained even in environments with strong electromagnetic interference, thereby enhancing the intelligent control level and user experience of the entire induction heating equipment.

[0075] In some embodiments, the specific steps in step S6 include: S61. Determine the temperature range of the current temperature based on the voltage value of the first electrical signal sample; S62. Based on the determined temperature range, select the set of conversion parameters corresponding to the temperature range from the pre-stored set of conversion parameters; S63. Using the selected set of conversion parameters, convert the first electrical signal sample into temperature characteristic values.

[0076] Determining the current temperature range involves identifying the temperature range represented by the voltage value of the first electrical signal sample. This step aims to provide a preliminary, rough classification of temperatures, offering a location for subsequent precise conversion. Specifically, a series of voltage thresholds can be preset, dividing the entire voltage measurement range into multiple discrete sub-intervals, each corresponding to a specific temperature range. When the voltage value of the first electrical signal sample falls within a certain sub-interval, the system determines that the current temperature is within the temperature range represented by that sub-interval. Alternatively, a pre-established mapping table between voltage values ​​and temperature ranges can be consulted to directly obtain the corresponding temperature range based on the voltage value of the first electrical signal sample.

[0077] A pre-stored set of conversion parameters refers to one or more sets of mathematical model parameters or lookup table data used to convert voltage values ​​to temperature, pre-calibrated or calculated and stored to address the nonlinear characteristics of temperature sensors (such as NTC thermistors). These parameters may include polynomial coefficients fitted for different temperature ranges (e.g., coefficients of the Steinhart-Hart equation), or lookup tables (LUTs) built for different temperature ranges, containing denser voltage-temperature correspondences. These parameter sets are typically stored in the device's non-volatile memory, such as the flash memory of a microcontroller (MCU) or an external electrically erasable programmable read-only memory (EEPROM).

[0078] Selecting the set of conversion parameters corresponding to the temperature range refers to choosing the most suitable set of parameters for temperature conversion from multiple pre-stored sets of conversion parameters, based on the current temperature range determined in step S61. This step ensures that more suitable conversion parameters are used within a specific temperature range, rather than a single global parameter, thereby enhancing the adaptability and accuracy of the conversion. In specific implementation, the corresponding parameter set can be loaded directly from memory using the temperature range identifier (ID) as an index. Alternatively, conditional judgment logic can be used to select and execute the corresponding parameter loading or activation operation based on the range of the temperature range.

[0079] Converting the first electrical signal sample into a temperature characteristic value using the selected set of transformation parameters involves performing precise mathematical operations or table lookup interpolation on the voltage value of the first electrical signal sample using the specific parameter set selected in step S62, thereby obtaining the final temperature value. If polynomial coefficients are selected, the voltage value of the first electrical signal sample is substituted into the corresponding polynomial equation for calculation. If a lookup table is selected, interpolation operations (such as linear interpolation or spline interpolation) are performed in the corresponding lookup table to convert the voltage value into a temperature characteristic value. This step is crucial for achieving high-precision temperature measurement, ensuring that the output temperature characteristic value accurately reflects the measured temperature.

[0080] The proposed solution first determines the temperature range of the current temperature by analyzing the voltage value of the first electrical signal sample. This initial determination is crucial because it provides a rough temperature range for subsequent precise conversion, effectively avoiding the accuracy loss caused by using a single conversion model over a wide temperature range. Subsequently, based on the determined temperature range, the system intelligently selects the parameter set that best matches the temperature range from multiple pre-stored conversion parameter sets. These parameter sets are optimized and calibrated for specific temperature ranges, enabling a more accurate description of the sensor's nonlinear characteristics within that range. Finally, using the selected conversion parameter set, the first electrical signal sample undergoes precise numerical processing, converting it into the final temperature feature value. This interval-based, adaptive conversion strategy ensures that the temperature conversion algorithm consistently maintains a high degree of matching with the current actual temperature range, thereby significantly improving conversion accuracy across the entire temperature range, especially in extremely high or low temperature regions where sensor nonlinearity is more pronounced.

[0081] This solution works in conjunction with other steps in the aforementioned method for acquiring temperature data for induction heating equipment. Particularly after acquiring the first electrical signal sample, which has undergone electromagnetic interference suppression, this solution further ensures that the sample can be accurately converted into temperature characteristic values. The aforementioned method aims to acquire a first electrical signal sample that is as pure and interference-free as possible by monitoring the electromagnetic interference level, synchronizing the phase reference signal, determining the sampling trigger time to avoid transient interference phases, and dynamically configuring sampling parameters. Based on this, this solution solves the conversion accuracy problem caused by sensor nonlinearity through interval adaptive conversion. The combination of these two approaches optimizes the entire chain from the original electrical signal to the final temperature characteristic value, ensuring not only the reliability of the acquired electrical signal but also the accuracy of the converted temperature data, thus providing a solid foundation for precise temperature control of induction heating equipment.

[0082] As a specific implementation method, the following approach can be adopted: Assume that the microcontroller (MCU) in the induction heating device has a built-in 12-bit analog-to-digital converter (ADC) used to convert the output voltage of the NTC thermistor voltage divider circuit into digital values ​​from 0 to 4095. In step S61, the MCU firmware can preset three voltage thresholds. For example, the ADC reading 0-500 is defined as the low-temperature zone (e.g., 0-50°C), 501-2000 as the medium-temperature zone (e.g., 51-150°C), and 2001-4095 as the high-temperature zone (e.g., 151-250°C). After the ADC completes the analog-to-digital conversion and outputs the digital value of the first electrical signal sample, the MCU will determine which preset temperature range it falls into based on the digital value. In step S62, the MCU's flash memory pre-stores three sets of coefficients (A, B, C) of the Steinhart-Hart equations. Each set of coefficients is precisely calibrated and corresponds to the low-temperature zone, medium-temperature zone, and high-temperature zone, respectively. For example, if step S61 determines that the current temperature is in the medium temperature range, the MCU will select and load the Steinhart-Hart coefficients (A, B, C) corresponding to the medium temperature range from the flash memory. In step S63, the MCU processor substitutes the loaded coefficients and the digital value of the first electrical signal sample (the resistance R of the NTC thermistor is obtained through calculation) into the Steinhart-Hart equation: 1 / T=A+B*ln(R)+C*(ln(R))^3, calculates the temperature T in Kelvin, and then converts it to Celsius as the final temperature characteristic value.

[0083] Through the above technical solution, this application effectively solves the problem of decreased temperature data accuracy caused by the nonlinear characteristics of temperature sensors and the limitations of conversion algorithms in traditional methods. By segmenting and judging, selecting a matching set of conversion parameters, and using these parameters for precise conversion, this solution ensures high-precision temperature characteristic values ​​can be obtained over a wide temperature range of the induction heating device, especially at extremely high or low temperatures. This significantly improves the accuracy and reliability of temperature data acquisition, enabling the induction heating device to provide more accurate temperature feedback, thereby achieving more precise cooking control and more stable equipment operation.

[0084] Please refer to Figure 2 , Figure 2 This invention provides a temperature data acquisition device for an induction heating device, which is integrated into a back-end control device in the form of a computer program. The device includes: The monitoring and determination module 100 is used to monitor the operating status information of the induction heating equipment and determine the current electromagnetic interference level based on the operating status information. The acquisition and identification module 200 is used to acquire a phase reference signal that is synchronized with the driving frequency of the induction heating device, and to identify the interference fluctuation period in the induction heating cycle based on the phase reference signal. The trigger determination module 300 is used to determine the sampling trigger time based on the interference fluctuation period, and to ensure that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching. The configuration module 400 is used to dynamically configure sampling parameters according to the electromagnetic interference level; the sampling parameters include the number of sample acquisitions in a single sampling period, and the number of sample acquisitions increases when the electromagnetic interference level increases; The sample acquisition module 500 is used to initiate analog-to-digital conversion at a determined sampling trigger time based on dynamically configured sampling parameters in order to acquire the first electrical signal sample corresponding to the measured temperature. The extraction module 600 is used to extract temperature feature values ​​by performing numerical processing on the first electrical signal sample, and output the temperature feature values ​​through a wireless communication link.

[0085] In some embodiments, the monitoring and determination module 100 performs the following when monitoring the operating status information of the induction heating device and determining the current electromagnetic interference level based on the operating status information: S11. Based on the operating status information, obtain the second electrical signal sample corresponding to the measured temperature; S12. Identify abnormal data points by performing anomaly detection on each data point in the second electrical signal sample; S13. When the number of abnormal data points exceeds the preset abnormal point tolerance, it is determined that the second electrical signal sample is affected by occasional high-energy electromagnetic pulse interference, and resampling is triggered to obtain a new electrical signal sample as a new second electrical signal sample in the next silent period, until it is determined that the second electrical signal sample is not interfered with or exceeds the preset limit. S14. Use the second electrical signal sample that is not interfered with as the target electrical signal sample for evaluating the level of electromagnetic interference, or when the preset limit is exceeded, use the third electrical signal sample that has been confirmed to be uninterrupted in the historical data as the target electrical signal sample for evaluating the level of electromagnetic interference. S15. Determine the current electromagnetic interference level based on the target electrical signal sample.

[0086] In some embodiments, the trigger determination module 300 is executed when determining the sampling trigger time based on the interference fluctuation period and ensuring that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching: S31. Acquire the drive synchronization signals of multiple periodic electromagnetic interference sources inside the induction heating equipment; S32. Based on the interference fluctuation period and the drive synchronization signal, identify the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to each periodic electromagnetic interference source; S33. By analyzing the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to multiple periodic electromagnetic interference sources, a common time window without transient interference is determined. S34. When there is a common time window without transient interference, the sampling trigger time is set within the common time window without transient interference, so that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to all periodic electromagnetic interference sources. S35. When there is no common time window without transient interference, based on the transient interference phase generated by the induction heating device during drive switching and the intensity of the transient interference phase corresponding to each periodic electromagnetic interference source, the transient interference phase with the highest intensity is taken as the target phase, and the sampling trigger time is set to avoid the target phase.

[0087] In some embodiments, the extraction module 600 performs the following operations when it is used to extract temperature feature values ​​by numerically processing the first electrical signal sample and outputting the temperature feature values ​​through a wireless communication link: S61. Determine the temperature range of the current temperature based on the voltage value of the first electrical signal sample; S62. Based on the determined temperature range, select the set of conversion parameters corresponding to the temperature range from the pre-stored set of conversion parameters; S63. Using the selected set of conversion parameters, convert the first electrical signal sample into temperature characteristic values.

[0088] Please refer to Figure 3 , Figure 3This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The present invention provides an electronic device 13, including: a processor 1301 and a memory 1302. The processor 1301 and the memory 1302 are interconnected and communicate with each other via a communication bus 1303 and / or other forms of connection mechanism (not shown). The memory 1302 stores computer-readable instructions executable by the processor 1301. When the electronic device is running, the processor 1301 executes the computer-readable instructions to perform the method in any optional implementation of the above embodiments, thereby achieving the following functions: monitoring the operating status information of the induction heating device and determining the current electromagnetic interference level based on the operating status information; acquiring information related to the induction heating device... The system synchronizes the phase reference signal with the drive frequency and identifies the interference fluctuation period in the induction heating cycle based on the phase reference signal. Based on the interference fluctuation period, it determines the sampling trigger time and ensures that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching. The sampling parameters are dynamically configured according to the electromagnetic interference level. These parameters include the number of sample acquisitions within a single sampling period, and the number of sample acquisitions increases as the electromagnetic interference level increases. Based on the dynamically configured sampling parameters, analog-to-digital conversion is initiated at the determined sampling trigger time to acquire a first electrical signal sample corresponding to the measured temperature. Temperature feature values ​​are extracted by numerically processing the first electrical signal sample and output through a wireless communication link.

[0089] This invention provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it performs the method in any optional implementation of the above embodiments to achieve the following functions: monitoring the operating status information of an induction heating device and determining the current electromagnetic interference level based on the operating status information; acquiring a phase reference signal synchronized with the driving frequency of the induction heating device and identifying the interference fluctuation period in the induction heating cycle based on the phase reference signal; determining the sampling trigger time based on the interference fluctuation period and ensuring that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching; dynamically configuring sampling parameters according to the electromagnetic interference level; the sampling parameters include the number of sample acquisitions in a single sampling period, and the number of sample acquisitions increases when the electromagnetic interference level increases; based on the dynamically configured sampling parameters, initiating analog-to-digital conversion at the determined sampling trigger time to acquire a first electrical signal sample corresponding to the measured temperature; extracting temperature feature values ​​by numerically processing the first electrical signal sample and outputting the temperature feature values ​​through a wireless communication link.

[0090] The computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0091] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0092] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0093] Furthermore, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0094] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0095] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for acquiring temperature data of an induction heating device, characterized in that, Includes the following steps: S1. Monitor the operating status information of the induction heating equipment and determine the current electromagnetic interference level based on the operating status information; S2. Obtain a phase reference signal synchronized with the driving frequency of the induction heating device, and identify the interference fluctuation period in the induction heating cycle based on the phase reference signal; S3. Determine the sampling trigger time based on the interference fluctuation period, and ensure that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching; S4. Dynamically configure sampling parameters according to the electromagnetic interference level; the sampling parameters include the number of sample acquisitions in a single sampling period, and the number of sample acquisitions increases when the electromagnetic interference level increases; S5. Based on the dynamically configured sampling parameters, start analog-to-digital conversion at the determined sampling trigger time to obtain the first electrical signal sample corresponding to the measured temperature; S6. By performing numerical processing on the first electrical signal sample, temperature feature values ​​are extracted, and the temperature feature values ​​are output through a wireless communication link; Step S1, the step of determining the current electromagnetic interference level based on the operating status information, includes: S11. Based on the operating status information, obtain a second electrical signal sample corresponding to the measured temperature; S12. Abnormal data points are determined by performing anomaly detection on each data point in the second electrical signal sample; S13. When the number of abnormal data points exceeds the preset abnormal point tolerance, it is determined that the second electrical signal sample is subject to occasional high-energy electromagnetic pulse interference, and resampling is triggered to obtain a new electrical signal sample as a new second electrical signal sample in the next silent period, until it is determined that the second electrical signal sample is not interfered with or exceeds the preset limit. S14. Use the second electrical signal sample that is not interfered with as the target electrical signal sample for evaluating the level of electromagnetic interference, or when the preset limit is exceeded, use the third electrical signal sample that has been confirmed to be uninterrupted in the historical data as the target electrical signal sample for evaluating the level of electromagnetic interference. S15. Determine the current electromagnetic interference level based on the target electrical signal sample; The specific steps in step S3 include: S31. Obtain the drive synchronization signal of multiple periodic electromagnetic interference sources inside the induction heating device; S32. Based on the interference fluctuation period and the drive synchronization signal, identify the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to each of the periodic electromagnetic interference sources; S33. By analyzing the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to the multiple periodic electromagnetic interference sources, a common time window without transient interference is determined; S34. When there is a common time window without transient interference, the sampling trigger time is set within the common time window without transient interference, so that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to all the periodic electromagnetic interference sources. S35. When there is no common time window without transient interference, based on the transient interference phase generated by the induction heating device during drive switching and the intensity of the transient interference phase corresponding to each periodic electromagnetic interference source, the transient interference phase with the highest intensity is taken as the target phase, and the sampling trigger time is set to avoid the target phase.

2. The method for acquiring temperature data of an induction heating device according to claim 1, characterized in that, The specific steps in step S12 include: The deviation between the current data point and other data points in the second electrical signal sample is compared, and it is determined whether the deviation exceeds a preset pulse transition threshold. The corresponding data points whose deviation exceeds the pulse transition threshold are identified as abnormal data points.

3. The method for acquiring temperature data of an induction heating device according to claim 1, characterized in that, The specific steps in step S6 include: S61. Determine the temperature range of the current temperature based on the voltage value of the first electrical signal sample; S62. Based on the determined temperature range, select the set of conversion parameters corresponding to the temperature range from the pre-stored set of conversion parameters; S63. Using the selected set of conversion parameters, the first electrical signal sample is converted into a temperature feature value.

4. A temperature data acquisition device for an induction heating equipment, characterized in that, include: The monitoring and determination module is used to monitor the operating status information of the induction heating equipment and determine the current electromagnetic interference level based on the operating status information. The acquisition and identification module is used to acquire a phase reference signal synchronized with the driving frequency of the induction heating device, and to identify the interference fluctuation period in the induction heating cycle based on the phase reference signal. The trigger determination module is used to determine the sampling trigger time based on the interference fluctuation period, and to ensure that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching; A configuration module is used to dynamically configure sampling parameters according to the electromagnetic interference level; the sampling parameters include the number of sample acquisitions in a single sampling period, and the number of sample acquisitions increases when the electromagnetic interference level increases; The sample acquisition module is used to initiate analog-to-digital conversion at a determined sampling trigger time based on dynamically configured sampling parameters in order to acquire the first electrical signal sample corresponding to the measured temperature. The extraction module is used to extract temperature feature values ​​by performing numerical processing on the first electrical signal sample, and output the temperature feature values ​​through a wireless communication link. The monitoring and determination module performs the following when monitoring the operating status information of the induction heating equipment and determining the current electromagnetic interference level based on the operating status information: S11. Based on the operating status information, obtain a second electrical signal sample corresponding to the measured temperature; S12. Abnormal data points are determined by performing anomaly detection on each data point in the second electrical signal sample; S13. When the number of abnormal data points exceeds the preset abnormal point tolerance, it is determined that the second electrical signal sample is subject to occasional high-energy electromagnetic pulse interference, and resampling is triggered to obtain a new electrical signal sample as a new second electrical signal sample in the next silent period, until it is determined that the second electrical signal sample is not interfered with or exceeds the preset limit. S14. Use the second electrical signal sample that is not interfered with as the target electrical signal sample for evaluating the level of electromagnetic interference, or when the preset limit is exceeded, use the third electrical signal sample that has been confirmed to be uninterrupted in the historical data as the target electrical signal sample for evaluating the level of electromagnetic interference. S15. Determine the current electromagnetic interference level based on the target electrical signal sample; The trigger determination module performs the following steps when determining the sampling trigger time based on the interference fluctuation period, and ensuring that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching: S31. Obtain the drive synchronization signal of multiple periodic electromagnetic interference sources inside the induction heating device; S32. Based on the interference fluctuation period and the drive synchronization signal, identify the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to each of the periodic electromagnetic interference sources; S33. By analyzing the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to the multiple periodic electromagnetic interference sources, a common time window without transient interference is determined; S34. When there is a common time window without transient interference, the sampling trigger time is set within the common time window without transient interference, so that the sampling trigger time avoids the transient interference phase generated by the induction heating device during drive switching and the transient interference phase corresponding to all the periodic electromagnetic interference sources. S35. When there is no common time window without transient interference, based on the transient interference phase generated by the induction heating device during drive switching and the intensity of the transient interference phase corresponding to each periodic electromagnetic interference source, the transient interference phase with the highest intensity is taken as the target phase, and the sampling trigger time is set to avoid the target phase.

5. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions, which, when executed by the processor, perform the steps of the temperature data acquisition method for the induction heating device as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it performs the steps in the temperature data acquisition method for the induction heating device as described in any one of claims 1-3.

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