Gallium nitride-based E-type effect amplifier electromagnetic heating method and system

Abstract

The invention relates to the technical field of chip encryption, in particular to an E-type effect amplifier electromagnetic heating method and system based on gallium nitride. The real-time resistance correction value is calculated by acquiring the real-time resistance data and the real-time temperature data and combining the working characteristic information and the ideal parameter information of the gallium nitride device, so that the temperature change of the gallium nitride device can be quickly responded, and then the resistance division subset and the temperature division subset are respectively acquired; a plurality of temperature-resistance correlation groups are obtained after permutation and combination, a data basis is provided for establishing a working voltage prediction model, then the working voltage prediction model is trained through historical data, and finally a real-time resistance correction value and a real-time temperature value are input into the corresponding working voltage prediction model to obtain a working voltage simulation value. And a circuit where the gallium nitride device is located is adjusted according to the working voltage analog value, so that the working voltage and the working current of the gallium nitride device are close to ideal values, and stable power output of the device is ensured.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, and particularly to an electromagnetic heating method and system for a class-E power amplifier based on gallium nitride. Background Art

[0002] Gallium nitride is a wide-bandgap semiconductor material with excellent properties such as high electron mobility, high breakdown electric field, and high thermal conductivity. A class-E power amplifier based on gallium nitride is a specific type of high-power amplifier made of gallium nitride material. Through special circuit design and working mode, the transistor can quickly switch between the on and off states, enabling high-efficiency power conversion at a relatively high frequency, and its efficiency can usually reach more than 70%-90%. Among them, the gallium nitride power device is the core component, which is responsible for converting the input electrical energy into high-frequency alternating current to provide the energy required for electromagnetic heating.

[0003] During the operation of the gallium nitride device, the temperature of the gallium nitride device will change. The temperature fluctuation will cause the on-resistance of the gallium nitride device to change, which means that under the same operating current, the voltage of the gallium nitride device will also change, and then the power consumption of the gallium nitride device will change. The change in power consumption will further affect the physical characteristics and working state of the gallium nitride device. Higher power consumption will make physical processes such as electron migration inside the gallium nitride device become complex, generating additional thermal effects, etc., thereby affecting the electric field distribution inside the gallium nitride device. This will make the response of the device unstable when it is turned on and off under the action of the control signal, making the precise control of the class-E power amplifier unstable for signals, and then it is difficult to achieve stable power output. Since the system power output is unstable, the heat received by the object to be heated also fluctuates accordingly. This unstable heat fluctuation will cause the electromagnetic heating system to fail to achieve the expected heating effect. Summary of the Invention

[0004] The main object of the present invention is to provide an electromagnetic heating method and system for a class-E power amplifier based on gallium nitride, aiming to solve the technical problems in the prior art.

[0005] The present invention proposes an electromagnetic heating method for a class-E power amplifier based on gallium nitride, including:

[0006] Obtaining the device characteristic information of the gallium nitride device, and obtaining the working characteristic information and ideal parameter information according to the device characteristic information;

[0007] Obtaining the electrical parameter data of the circuit where the gallium nitride device is located, and obtaining the real-time resistance data of the gallium nitride device according to the working characteristic information and the electrical parameter data, where the real-time resistance data includes a real-time resistance value and a plurality of historical resistance values;

[0008] Obtain the real-time temperature data of the gallium nitride device according to the temperature sensor, where the real-time temperature data includes the real-time temperature value and multiple historical temperature values, and obtain the real-time resistance correction value according to the working characteristic information, ideal parameter information, real-time temperature value and real-time resistance value;

[0009] Obtain the resistance subset and temperature subset according to the working characteristic information, and obtain the temperature-resistance association group according to the resistance subset and temperature subset;

[0010] Construct a corresponding working voltage prediction model according to the temperature-resistance association group, real-time resistance data and real-time temperature data, and input the real-time resistance correction value and real-time temperature value into the corresponding working voltage prediction model to obtain the working voltage simulation value;

[0011] Adjust the circuit where the gallium nitride device is located according to the working voltage simulation value to obtain the compensated adjustment electrical parameters. Among them, the compensated adjustment electrical parameters include the compensated adjustment current value and the compensated adjustment voltage value, and obtain the real-time output power of the gallium nitride device according to the compensated adjustment current value and the compensated adjustment voltage value;

[0012] Judge whether the real-time output power is greater than the preset power threshold;

[0013] If the real-time output power is greater than the preset power threshold, it is determined that the electromagnetic heating effect meets the expectation;

[0014] If the real-time output power is not greater than the preset power threshold, it is determined that the electromagnetic heating effect does not meet the expectation, and then return to the step of constructing the corresponding working voltage prediction model according to the temperature-resistance association group, real-time resistance data and real-time temperature data until the electromagnetic heating effect meets the expectation.

[0015] Preferably, the step of obtaining the real-time resistance data of the gallium nitride device according to the working characteristic information and electrical parameter data includes:

[0016] Obtain the signal change frequency and signal stability value of the gallium nitride device according to the working characteristic information;

[0017] Obtain the interference frequency and interference intensity of the electromagnetic device, and obtain the mean sample number according to the interference frequency, interference intensity, signal change frequency and signal stability value;

[0018] Obtain multiple fluctuating voltage values and multiple fluctuating current values corresponding to each detection moment according to the electrical parameter data;

[0019] Obtain the corresponding real-time voltage data according to the multiple fluctuating voltage values and the mean sample number, and obtain the corresponding real-time current data according to the multiple fluctuating current values and the mean sample number;

[0020] Obtain corresponding real-time resistance data based on the said real-time voltage data and corresponding real-time current data.

[0021] Preferably, the step of comparing the standard temperature-resistance curve and the temperature-voltage change curve to obtain the resistance drift value of the gallium nitride device at the current temperature includes:

[0022] The step of obtaining a real-time resistance correction value according to the working characteristic information, ideal parameter information, real-time temperature value, and real-time resistance value includes:

[0023] Obtain the material characteristic coefficient of the gallium nitride device according to the working characteristic information;

[0024] Obtain the ideal temperature value and standard resistance value of the gallium nitride device according to the ideal parameter information, and obtain the resistance drift value according to the real-time resistance value and the standard resistance value;

[0025] Calculate the resistance compensation value of the gallium nitride device according to the material characteristic coefficient, resistance drift value, real-time temperature value, and ideal temperature value, where the calculation formula is:

[0026]

[0027] Among them, R c represents the resistance compensation value, R i represents the resistance drift value, α represents the material characteristic coefficient, T represents the real-time temperature value, and T 0 represents the ideal temperature value;

[0028] Obtain the real-time resistance correction value according to the resistance compensation value and the real-time resistance value.

[0029] Preferably, the step of obtaining a resistance subset and a temperature subset according to the working characteristic information, and obtaining a temperature-resistance association group according to the resistance subset and the temperature subset includes:

[0030] Obtain temperature influence data and resistance influence data according to the working characteristic information, and obtain a first temperature influence threshold and a second temperature influence threshold according to the temperature influence data;

[0031] Obtain the temperature subset according to the first temperature influence threshold and the second temperature influence threshold;

[0032] Obtain a first resistance influence threshold and a second resistance influence threshold according to the resistance influence data, and obtain the resistance subset according to the first resistance influence threshold and the second resistance influence threshold;

[0033] Arrange and combine the temperature subset and the resistance subset to obtain a plurality of temperature-resistance association groups.

[0034] Preferably, the step of constructing a corresponding working voltage prediction model according to the temperature-resistance correlation group, real-time resistance data and real-time temperature data, and inputting the real-time resistance correction value and real-time temperature value into the corresponding working voltage prediction model to obtain a working voltage simulation value includes:

[0035] Pair the real-time resistance data and real-time temperature data with each other to obtain a plurality of temperature-resistance pairing groups;

[0036] Obtain the ideal voltage value and ideal power value of the gallium nitride device according to the ideal parameter information, and obtain the corresponding working voltage simulation value according to the ideal voltage value, ideal power value and a plurality of temperature-resistance pairing groups;

[0037] Construct a working voltage prediction model for the corresponding temperature-resistance correlation group according to the temperature-resistance pairing group and the working voltage simulation value;

[0038] Sequentially input the corresponding historical resistance value and historical temperature value in each temperature-resistance pairing group into the working voltage prediction model for training, and use the remaining plurality of historical resistance values and historical temperature values as a validation set for validation to obtain a plurality of prediction training results;

[0039] Adjust the model parameters of the working voltage prediction model according to the plurality of prediction training results to obtain a working voltage prediction model;

[0040] Input the real-time resistance correction value and real-time temperature value into the working voltage prediction model of the corresponding temperature-resistance correlation group to obtain a working voltage simulation value.

[0041] Preferably, the step of adjusting the circuit where the gallium nitride device is located according to the working voltage simulation value to obtain a compensated adjustment electrical parameter includes:

[0042] Obtain a voltage error value according to the working voltage simulation value and the real-time voltage value;

[0043] Obtain a voltage regulation signal according to the voltage error value, and adjust a voltage regulation element according to the voltage regulation signal to obtain a compensated regulation voltage value;

[0044] Obtain the voltage dividing resistor information of the circuit where the gallium nitride device is located according to the electrical parameter data, and adjust the voltage dividing resistor according to the voltage dividing resistor information and the real-time resistance correction value to obtain a compensated regulation resistor value;

[0045] Obtain a compensated regulation current value according to the compensated regulation voltage value and the compensated regulation resistor value.

[0046] The present application also provides a class-E power amplifier electromagnetic heating system based on gallium nitride, including:

[0047] A first acquisition module, configured to acquire device characteristic information of a gallium nitride device, and acquire operating characteristic information and ideal parameter information according to the device characteristic information;

[0048] A second acquisition module, configured to acquire electrical parameter data of a circuit where the gallium nitride device is located, and acquire real-time resistance data of the gallium nitride device according to the operating characteristic information and the electrical parameter data, where the real-time resistance data includes a real-time resistance value and a plurality of historical resistance values;

[0049] A third acquisition module, configured to acquire real-time temperature data of the gallium nitride device according to a temperature sensor, where the real-time temperature data includes a real-time temperature value and a plurality of historical temperature values, and acquire a real-time resistance correction value according to the operating characteristic information, the ideal parameter information, the real-time temperature value, and the real-time resistance value;

[0050] A division module, configured to acquire a resistance division subset and a temperature division subset according to the operating characteristic information, and acquire a temperature-resistance association group according to the resistance division subset and the temperature division subset;

[0051] A prediction module, configured to construct a corresponding operating voltage prediction model according to the temperature-resistance association group, the real-time resistance data, and the real-time temperature data, and input the real-time resistance correction value and the real-time temperature value into the corresponding operating voltage prediction model to obtain an operating voltage simulation value;

[0052] An adjustment module, configured to adjust the circuit where the gallium nitride device is located according to the operating voltage simulation value to obtain compensated adjustment electrical parameters, where the compensated adjustment electrical parameters include a compensated adjustment current value and a compensated adjustment voltage value, and acquire the real-time output power of the gallium nitride device according to the compensated adjustment current value and the compensated adjustment voltage value;

[0053] A judgment module, configured to judge whether the real-time output power is greater than a preset power threshold;

[0054] If the real-time output power is greater than the preset power threshold, it is determined that the electromagnetic heating effect reaches the expectation;

[0055] If the real-time output power is not greater than the preset power threshold, it is determined that the electromagnetic heating effect does not reach the expectation, and then return to the step of constructing a corresponding operating voltage prediction model according to the temperature-resistance association group, the real-time resistance data, and the real-time temperature data until the electromagnetic heating effect reaches the expectation;

[0056] Preferably, the division module includes:

[0057] A first acquisition unit, configured to acquire temperature influence data and resistance influence data according to the working characteristic information, and acquire a first temperature influence threshold and a second temperature influence threshold according to the temperature influence data;

[0058] A second acquisition unit, configured to acquire a temperature partition subset according to the first temperature influence threshold and the second temperature influence threshold;

[0059] A third acquisition unit, configured to acquire a first resistance influence threshold and a second resistance influence threshold according to the resistance influence data, and acquire a resistance partition subset according to the first resistance influence threshold and the second resistance influence threshold;

[0060] A permutation and combination unit, configured to permute and combine the temperature partition subset and the resistance partition subset to obtain a plurality of temperature-resistance association groups.

[0061] The present invention further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned electromagnetic heating method for a GaN-based class E power amplifier are implemented.

[0062] The present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned electromagnetic heating method for a GaN-based class E power amplifier are implemented.

[0063] The beneficial effects of the present invention are as follows: By analyzing the working characteristic information and ideal parameter information of the gallium nitride device in the present invention, subsequent operations can be carried out starting from the essential characteristics of the gallium nitride device and closely around the actual situation of the gallium nitride device. Then, according to the electrical parameter data of the circuit where the gallium nitride device is located and the working characteristic information of the gallium nitride device, the real-time resistance data of the gallium nitride device is obtained. Since temperature is the main factor affecting the resistance change of the gallium nitride device, obtaining the real-time temperature data of the gallium nitride device can reflect the heat generation situation of the gallium nitride device in the past and at the current moment. Then, the real-time resistance correction value is calculated through the obtained working characteristic information, ideal parameter information, real-time temperature value, and real-time resistance value. This compensation mechanism based on real-time data can quickly respond to temperature changes, thereby improving the adaptive ability of the circuit. Then, the resistance subset and temperature subset are respectively obtained, and multiple temperature-resistance correlation groups are obtained after arranging and combining the resistance subset and temperature subset. Different temperature-resistance correlation groups can reflect the internal relationship between different temperature change ranges and different resistance change ranges, providing a data basis for establishing a working voltage prediction model. Then, a corresponding working voltage prediction model is constructed according to the temperature-resistance correlation groups, real-time resistance data, and real-time temperature data. Then, the real-time resistance correction value and real-time temperature value are input into the corresponding working voltage prediction model. The working voltage prediction model can predict the working voltage required by the gallium nitride device, that is, the working voltage simulation value, under the current real-time resistance correction value and real-time temperature value according to the relationship between temperature and resistance. Immediately afterwards, the circuit where the gallium nitride device is located is adjusted according to the working voltage simulation value to obtain the compensated adjustment electrical parameters, and the circuit where the gallium nitride device is located is adjusted through the compensated adjustment electrical parameters, so that the working voltage and working current of the gallium nitride device can approach the ideal values to ensure the stable performance of the device. Finally, the real-time output power of the gallium nitride device is calculated through the compensated adjustment current value and compensated adjustment voltage value. The real-time output power can intuitively reflect the energy conversion efficiency of the device in the current working state, and comparing the real-time output power with the preset power threshold can intuitively judge whether the electromagnetic heating effect meets the expectation. When the heating effect does not meet the expectation, return to reconstruct the working voltage prediction model and make adjustments to form a closed-loop feedback control system, and continuously optimize the circuit parameters until the expected heating effect is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present application.

[0065] Figure 2 It is a schematic structural diagram of the system according to an embodiment of the present application.

[0066] Figure 3 It is a schematic internal structure diagram of a computer device according to an embodiment of the present application.

[0067] The realization, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0068] It should be understood that the specific embodiments described herein are merely used to explain the present invention and are not used to limit the present invention.

[0069] As Figures 1 - 3 shown, the present application provides a GaN-based Class-E power amplifier electromagnetic heating method, including:

[0070] S1. Obtain the device characteristic information of the GaN device, and obtain the working characteristic information and ideal parameter information according to the device characteristic information;

[0071] S2. Obtain the electrical parameter data of the circuit where the GaN device is located, and obtain the real-time resistance data of the GaN device according to the working characteristic information and the electrical parameter data, where the real-time resistance data includes a real-time resistance value and multiple historical resistance values;

[0072] S3. Obtain the real-time temperature data of the GaN device according to the temperature sensor, where the real-time temperature data includes a real-time temperature value and multiple historical temperature values, and obtain a real-time resistance correction value according to the working characteristic information, ideal parameter information, real-time temperature value, and real-time resistance value;

[0073] S4. Obtain a resistance subset and a temperature subset according to the working characteristic information, and obtain a temperature-resistance association group according to the resistance subset and the temperature subset;

[0074] S5. Construct a corresponding working voltage prediction model according to the temperature-resistance association group, real-time resistance data, and real-time temperature data, and input the real-time resistance correction value and the real-time temperature value into the corresponding working voltage prediction model to obtain a working voltage simulation value;

[0075] S6. Adjust the circuit where the GaN device is located according to the working voltage simulation value to obtain compensated adjustment electrical parameters, where the compensated adjustment electrical parameters include a compensated adjustment current value and a compensated adjustment voltage value, and obtain the real-time output power of the GaN device according to the compensated adjustment current value and the compensated adjustment voltage value;

[0076] S7. Determine whether the real-time output power is greater than a preset power threshold;

[0077] If the real-time output power is greater than the preset power threshold, it is determined that the electromagnetic heating effect meets the expectation;

[0078] If the real-time output power is not greater than the preset power threshold, it is determined that the electromagnetic heating effect does not meet the expectation, and then return to the step of constructing the corresponding working voltage prediction model according to the temperature-resistance correlation group, real-time resistance data and real-time temperature data until the electromagnetic heating effect meets the expectation.

[0079] As described in the above steps S1 - S7, gallium nitride is a wide - bandgap semiconductor material with excellent properties such as high electron mobility, high breakdown electric field, and high thermal conductivity. The class - E power amplifier based on gallium nitride is a specific type of high - power amplifier made of gallium nitride material. Through special circuit design and working mode, it enables the transistor to quickly switch between the on and off states, and can achieve high - efficiency power conversion at relatively high frequencies, with its efficiency usually reaching more than 70% - 90%. Among them, the gallium nitride power device is the core component, responsible for converting the input electrical energy into high - frequency alternating current to provide the required energy for electromagnetic heating. During the operation of the gallium nitride device, the temperature of the gallium nitride device will change, and the temperature fluctuation will cause the on - resistance of the gallium nitride device to change. This means that under the same operating current, the voltage of the gallium nitride device will also change, and further cause the power consumption of the gallium nitride device to change. The change in power consumption will further affect the physical characteristics and operating state of the gallium nitride device. Higher power consumption will make physical processes such as electron migration inside the gallium nitride device become complex, generating additional thermal effects, etc., thus affecting the electric field distribution inside the gallium nitride device. This will make the response of the device when it is turned on and off under the action of the control signal unstable, making the precise control of the class - E power amplifier for signals unstable, and then it is difficult to achieve stable power output. Since the system power output is unstable, the heat received by the object to be heated also fluctuates. This unstable heat fluctuation will cause the electromagnetic heating system to fail to achieve the expected heating effect. In the present invention, the operating characteristic information and ideal parameter information are obtained according to the device characteristic information of the gallium nitride device. Among them, the device characteristic information refers to the information related to various physical and electrical attributes inherent in the gallium nitride device itself, such as material characteristics, manufacturing process - related parameters, etc. By analyzing this information, the extracted operating characteristic information refers to the performance and behavior law information of the gallium nitride device in the actual working circuit, such as the signal change frequency and signal stability value during the operation of the gallium nitride device, etc. The ideal parameter information refers to the various parameter values that the gallium nitride device should have under the most ideal working conditions, such as the ideal operating temperature, ideal resistance value, ideal output power, etc. Starting from the essential characteristics of the gallium nitride device, the subsequent operations can be closely carried out around the actual situation of the gallium nitride device, improving the pertinence and accuracy of the subsequent adjustment method. Then, according to the electrical parameter data of the circuit where the gallium nitride device is located and the operating characteristic information of the gallium nitride device, the real - time resistance data of the gallium nitride device is obtained. Among them, the electrical parameter data refers to the specific values of various electrical - related parameters in the circuit where the gallium nitride device is located, such as voltage, current, resistance, power, frequency, phase, etc. The real - time resistance data refers to a series of data related to resistance obtained in real - time during the operation of the gallium nitride device, which includes the real - time resistance value and multiple historical resistance values. Among them, the real - time resistance value refers to the resistance value of the gallium nitride device obtained at the current moment,The historical resistance value refers to the resistance values of gallium nitride devices obtained at different times in the past. Since temperature is the main factor affecting the resistance change of gallium nitride devices, obtaining the real-time temperature data of gallium nitride devices can reflect the heat generation situation of gallium nitride devices in the historical and current moments. Among them, the real-time temperature data refers to a series of temperature-related data collected in real time during the operation of gallium nitride devices, including the real-time temperature value and multiple historical temperature values. Among them, the real-time temperature value refers to the actual temperature value of the gallium nitride device measured by a temperature sensor at the current moment, and the historical temperature value refers to the temperature records of the gallium nitride device measured at different past time points. Then, the real-time resistance correction value is calculated by obtaining the working characteristic information, ideal parameter information, real-time temperature value, and real-time resistance value. Among them, the real-time resistance correction value is a value obtained through calculation for compensating the real-time resistance. This compensation mechanism based on real-time data can quickly respond to temperature changes, thereby improving the adaptive ability of the circuit. Then, the resistance subset and the temperature subset are obtained respectively. Among them, the resistance subset refers to the resistance range divided according to the same influence degree of gallium nitride devices by different resistance magnitudes, and the temperature subset refers to the temperature range divided according to the same influence degree of gallium nitride devices by different temperature magnitudes. After arranging and combining the resistance subset and the temperature subset, multiple temperature-resistance correlation groups are obtained. Among them, the temperature-resistance correlation group refers to multiple groups of relationships obtained by mutually matching different resistance subsets and different temperature subsets. Different temperature-resistance correlation groups can reflect the internal relationship between different temperature change ranges and different resistance change ranges, providing a data basis for establishing a working voltage prediction model. Then, a corresponding working voltage prediction model is constructed according to the temperature-resistance correlation group, real-time resistance data, and real-time temperature data. Then, the real-time resistance correction value and the real-time temperature value are input into the corresponding working voltage prediction model. The working voltage prediction model can predict the working voltage required by the gallium nitride device under the current real-time resistance correction value and real-time temperature value according to the relationship between temperature and resistance, that is, the working voltage simulation value. Among them, the working voltage simulation value is the simulated estimated value of the working voltage that the gallium nitride device should have in the current state obtained through model calculation. Immediately afterwards, the circuit where the gallium nitride device is located is adjusted according to the working voltage simulation value to obtain the compensated adjustment electrical parameters. Among them, the compensated adjustment electrical parameters refer to the electrical-related parameters obtained after adjusting the circuit where the gallium nitride device is located, mainly including the compensated adjustment current value and the compensated adjustment voltage value, so as to enable the working voltage and working current of the gallium nitride device to approach the ideal values, thereby ensuring the stable performance of the device. Finally, the real-time output power of the gallium nitride device is calculated by the compensated adjustment current value and the compensated adjustment voltage value. Among them, the real-time output power refers to the power value actually output to the load by the gallium nitride device after a series of parameter adjustments and circuit controls.The real-time output power can intuitively reflect the energy conversion efficiency of the device under the current working state. By comparing the real-time output power with the preset power threshold, it is possible to intuitively determine whether the electromagnetic heating effect meets the expectation. When the heating effect does not meet the expectation, return to reconstruct the working voltage prediction model and make adjustments to form a closed-loop feedback control system, continuously optimizing the circuit parameters until the expected heating effect is achieved.

[0080] In one embodiment, step S2 of obtaining the real-time resistance data of the gallium nitride device according to the working characteristic information and the electrical parameter data includes:

[0081] S21. Obtain the signal change frequency and signal stability value of the gallium nitride device according to the working characteristic information;

[0082] S22. Obtain the interference frequency and interference intensity of the electromagnetic device, and obtain the mean sample number according to the interference frequency, interference intensity, signal change frequency and signal stability value. The calculation formula is:

[0083]

[0084] where N represents the mean sample number, a represents the proportional constant, f d represents the signal change frequency, ω 1 represents the first coefficient, C represents the signal stability value, ω 2 represents the second coefficient, f n represents the interference frequency, ω 3 represents the third coefficient, I represents the interference intensity, ω 4 represents the fourth coefficient, and b represents the base sample number offset;

[0085] S23. Obtain a plurality of fluctuating voltage values and a plurality of fluctuating current values corresponding to each detection moment according to the electrical parameter data;

[0086] S24. Obtain the corresponding real-time voltage data according to the plurality of fluctuating voltage values and the mean sample number, and obtain the corresponding real-time current data according to the plurality of fluctuating current values and the mean sample number;

[0087] S25. Obtain the corresponding real-time resistance data according to the real-time voltage data and the corresponding real-time current data.

[0088] As described in the above steps S21 - S25, in the present invention, the signal change frequency and signal stability value of the gallium nitride device are obtained. Among them, the signal change frequency refers to the number of times the electrical signal of the gallium nitride device changes periodically per unit time during operation, and the signal stability value refers to a relatively fixed value that the electrical signal approaches and maintains when the gallium nitride device is in a stable operating state. And the interference frequency and interference intensity of the electromagnetic device are obtained. Among them, the interference frequency refers to the frequency of the interference signal generated by the electromagnetic device in the electromagnetic environment where the gallium nitride device is located, and the interference intensity refers to the value used to measure the degree of influence of electromagnetic interference on the gallium nitride device. In the circuit environment where the gallium nitride device is located, the electromagnetic device will generate electromagnetic interference, which affects the measurement accuracy of the voltage and current of the gallium nitride device. And the interference frequency and interference intensity can describe the detailed characteristics of this interference. And the mean sample number is calculated according to the obtained values by a specific formula. Among them, the mean sample number refers to the number of samples selected for calculating the average value when calculating the real-time voltage data and real-time current data. By using the mean sample number, when obtaining the real-time voltage and real-time current, the influence of electromagnetic interference can be effectively suppressed, and the accuracy and reliability of the data can be improved. When calculating using the above formula, it can be known that the higher the signal change frequency, the more times the signal changes per unit time. In order to accurately capture the change characteristics of the signal, more samples are required for mean calculation. And the first coefficient represents the sensitivity of the sample number to the frequency change. The larger the signal stability value, the more stable the signal. For a stable signal, relatively fewer samples are required. And the second coefficient represents the degree to which the sample number is affected by the signal stability. The ratio of the interference frequency to the signal change frequency reflects their relative relationship. When the interference frequency is much higher than the signal change frequency, the change of the interference signal within the signal period is relatively more complex. In order to accurately distinguish the signal and the interference, more sample numbers are required for mean calculation to filter out the influence of the interference. The third coefficient represents the degree to which the sample number is affected by the relative relationship between the interference frequency and the signal change frequency. Similarly, the greater the interference intensity, the more serious the influence of the interference on the signal. In order to accurately extract the signal characteristics in a strong interference environment, more sample numbers are required for mean calculation. The fourth coefficient represents the degree to which the sample number is affected by the interference intensity. In summary, this formula establishes a model that can reasonably calculate the mean sample number by comprehensively considering the logical relationship between each parameter and the mean sample number, so as to meet the requirements for processing the electrical data of the gallium nitride device under different working conditions. The high-speed sampling chip has the ability of fast sampling and can sample the same voltage signal multiple times in an extremely short time. For example, the sampling frequency of the high-speed ADC chip can reach the GHz level and can sample the input voltage signal and current signal multiple times within an extremely short time interval, such as a few nanoseconds or even shorter. The voltage data and current data collected within this extremely short time are regarded as multiple voltage data and multiple current data at the same moment.The fluctuating voltage value and the fluctuating current value are obtained. Among them, the fluctuating voltage value refers to the value collected at the same moment for calculating the voltage at a specific moment, and the fluctuating current value refers to the value collected at the same moment for calculating the current at a specific moment. For example, the multiple fluctuating voltage values obtained at a certain moment are 4.0V, 4.1V, 4.0V, 4.2V, 4.1V, 4.1V, 4.0V respectively, and the calculated mean sample number is 5. Then, take 5 fluctuating voltage values at the middle position from the multiple fluctuating voltage values and calculate the mean to obtain the real-time voltage value at this moment. The calculation result is: (4.1V + 4.0V + 4.2V + 4.1V + 4.1V) / 5 = 4.1V. Similarly, the real-time current value at this moment can be calculated. Through the above calculation method, relatively stable and accurate real-time voltage data and real-time current data can be obtained. This can eliminate the fluctuations of voltage and current caused by interference and other factors, and obtain the electrical parameter values that can better reflect the actual working state of the device. Finally, according to Ohm's law, the real-time resistance value at this moment is calculated through the real-time voltage value and the real-time current value. The resistance values corresponding to multiple historical moments and the resistance value corresponding to the current moment together constitute the real-time resistance data.

[0089] In one embodiment, the step S3 of obtaining the real-time resistance correction value according to the working characteristic information, the ideal parameter information, the real-time temperature value and the real-time resistance value includes:

[0090] S31. Obtain the material characteristic coefficient of the gallium nitride device according to the working characteristic information;

[0091] S32. Obtain the ideal temperature value, the standard resistance value and the ideal voltage value of the gallium nitride device according to the ideal parameter information, and obtain the resistance drift value according to the real-time resistance value and the standard resistance value;

[0092] S33. Calculate the resistance compensation value of the gallium nitride device according to the material characteristic coefficient, the resistance drift value, the real-time temperature value and the ideal temperature value. Among them, the calculation formula is:

[0093]

[0094] where, R c represents the resistance compensation value, R i represents the resistance drift value, α represents the material characteristic coefficient, T represents the real-time temperature value, and T 0 represents the ideal temperature value;

[0095] S34. Obtain the real-time resistance correction value according to the resistance compensation value and the real-time resistance value.

[0096] As described in the above steps S31 - S34, in the present invention, by obtaining the material characteristic coefficient of the gallium nitride device, where the material characteristic coefficient is a parameter reflecting the influence law of the physical characteristics of the gallium nitride material itself on the resistance, different material characteristic coefficients will result in different degrees of resistance variation with temperature. Then, the ideal temperature value and the standard resistance value of the gallium nitride device are obtained. The ideal temperature value refers to the most suitable and standard operating temperature of the gallium nitride device, and the standard resistance value refers to the resistance value of the gallium nitride device under specific standard conditions (such as standard temperature, standard voltage, etc.). Immediately afterwards, the resistance drift value is obtained through the difference between the real - time resistance value and the standard resistance value. The resistance drift value refers to the difference between the real - time resistance value and the standard resistance value of the gallium nitride device, which reflects the change of the resistance value of the gallium nitride device relative to the resistance value under the standard state due to temperature change during the actual working process. Its magnitude and direction can help judge the change trend and degree of the resistance. Then, according to the obtained values, the resistance compensation value is calculated according to a specific formula. The resistance compensation value is a correction amount calculated to compensate for the resistance deviation caused by temperature and is used to adjust the real - time resistance value. Finally, the sum of the resistance compensation value and the real - time resistance value is obtained to get the real - time resistance correction value. The real - time resistance correction value refers to the value obtained after correcting the real - time resistance value, thereby realizing the correction of the real - time resistance of the gallium nitride device to make it closer to the real resistance value that should be under ideal conditions.

[0097] In one embodiment, step S4 of obtaining the resistance sub - set and the temperature sub - set according to the working characteristic information and obtaining the temperature - resistance association group according to the resistance sub - set and the temperature sub - set includes:

[0098] S41. Obtain the temperature influence data and the resistance influence data according to the working characteristic information, and obtain the first temperature influence threshold and the second temperature influence threshold according to the temperature influence data;

[0099] S42. Obtain the temperature sub - set according to the first temperature influence threshold and the second temperature influence threshold;

[0100] S43. Obtain the first resistance influence threshold and the second resistance influence threshold according to the resistance influence data, and obtain the resistance sub - set according to the first resistance influence threshold and the second resistance influence threshold;

[0101] S44. Arrange and combine the temperature sub - set and the resistance sub - set to obtain a plurality of temperature - resistance association groups.

[0102] As described in the above steps S41 - S44, in the present invention, temperature influence data and resistance influence data are obtained. Among them, the temperature influence data refers to various data that include the influence of temperature on the working characteristics of the gallium nitride device, and the resistance influence data refers to various data that include the influence of resistance on the working characteristics of the gallium nitride device. The temperature influence data and the resistance influence data can help understand the relevant laws and action mechanisms of temperature and resistance in the working characteristics of the gallium nitride device. Then, the first temperature influence threshold and the second temperature influence threshold are obtained through the temperature influence data. Among them, the first temperature influence threshold and the second temperature influence threshold respectively refer to the lower limit values for distinguishing the influence of high and low temperatures on the gallium nitride device to a certain extent. For example, if the temperature is lower than the first temperature influence threshold, it can be determined that the influence of temperature on the gallium nitride device is small; the temperature between the first temperature influence threshold and the second temperature influence threshold has a medium influence on the gallium nitride device; and the temperature higher than the second temperature influence threshold has a large influence on the gallium nitride device. Similarly, the first resistance influence threshold and the second resistance influence threshold are obtained through the resistance influence data. Among them, the first resistance influence threshold and the second resistance influence threshold refer to the lower limit values for distinguishing the influence of the magnitude of resistance on the gallium nitride device to a certain extent. Then, multiple historical temperature values are divided according to the set first temperature influence threshold and second temperature influence threshold to obtain a temperature partition subset, and multiple historical resistance values are divided according to the set first resistance influence threshold and second resistance influence threshold to obtain a resistance partition subset, thereby realizing the preliminary classification and sorting of temperature data and resistance data, and classifying temperature values and resistance values with similar influence degrees into one category. For example, assuming the first temperature influence threshold is 18 °C and the second temperature influence threshold is 28 °C, then the obtained temperature partition subset includes a low - influence temperature set, a medium - influence temperature set, and a high - influence temperature set. Among them, the low - influence temperature set is for temperature values ≤ 18 °C, the medium - influence temperature set is for 18 °C < temperature values < 28 °C, and the high - influence temperature set is for temperature values ≥ 28 °C. Assuming the first resistance influence threshold is 10 Ω and the second resistance influence threshold is 15 Ω, then the obtained resistance partition subset includes a low - influence resistance set, a medium - influence resistance set, and a high - influence resistance set. Among them, the low - influence resistance set is for resistance values ≤ 10 Ω, the medium - influence resistance set is for 10 Ω < resistance values < 15 Ω, and the high - influence resistance set is for resistance values ≥ 15 Ω. Then, the temperature partition subset and the resistance partition subset are respectively arranged and combined to obtain temperature - resistance correlation groups. For example: low - influence temperature - low - influence resistance group, low - influence temperature - medium - influence resistance group, low - influence temperature - high - influence resistance group, medium - influence temperature - low - influence resistance group, medium - influence temperature - medium - influence resistance group, medium - influence temperature - high - influence resistance group, high - influence temperature - low - influence resistance group, high - influence temperature - medium - influence resistance group, high - influence temperature - high - influence resistance group. Through the above - mentioned arrangement and combination method, all possible correlations between different temperature ranges and different resistance ranges can be comprehensively considered, forming multiple temperature - resistance correlation groups.Provides a complete data structure for further studying the relationship between temperature and resistance.

[0103] In one embodiment, step S5 of constructing a corresponding working voltage prediction model according to the temperature-resistance association group, real-time resistance data, and real-time temperature data, and inputting the real-time resistance correction value and real-time temperature value into the corresponding working voltage prediction model to obtain a working voltage simulation value includes:

[0104] S51. Pair the real-time resistance data and real-time temperature data with each other to obtain a plurality of temperature-resistance pairing groups;

[0105] S52. Obtain the ideal voltage value and ideal power value of the gallium nitride device according to the ideal parameter information, and obtain the corresponding working voltage simulation value according to the ideal voltage value, ideal power value, and a plurality of temperature-resistance pairing groups;

[0106] S53. Construct a working voltage prediction model for the corresponding temperature-resistance association group according to the temperature-resistance pairing group and the working voltage simulation value;

[0107] S54. Sequentially input the corresponding historical resistance value and historical temperature value in each temperature-resistance pairing group into the working voltage prediction model for training, and use the remaining plurality of historical resistance values and historical temperature values as a validation set for validation to obtain a plurality of prediction training results;

[0108] S55. Adjust the model parameters of the working voltage prediction model according to the plurality of prediction training results to obtain the working voltage prediction model;

[0109] S56. Input the real-time resistance correction value and real-time temperature value into the working voltage prediction model of the corresponding temperature-resistance association group to obtain a working voltage simulation value.

[0110] As described in steps S51-S56 above, in the present invention, the resistance data and temperature data obtained in real time are paired to form a temperature-resistance pairing group. Among them, the temperature-resistance pairing group means that the purpose of doing this is to associate the temperature and resistance information at the same moment so as to analyze their relationship with the working voltage subsequently. Then, combining the ideal voltage value, ideal power value, and temperature-resistance pairing group, the working voltage simulation value under different temperature-resistance combinations can be calculated. The calculation formula is: These simulation values provide an initial data basis for constructing the working voltage prediction model subsequently, helping the model understand the possible value range of the working voltage under different conditions. Suppose the working voltage prediction model for the low-impact temperature-low-impact resistance group is: V = β 0 +β a *T + β b*R+∈, this model aims to reveal the intrinsic mathematical relationship between temperature, resistance and working voltage. By learning and analyzing the existing data, a calculation framework is provided for the subsequent prediction of working voltage. Then, half of the historical resistance values, historical temperature values ​​and working voltage simulation values ​​corresponding to the low-impact temperature-low-impact resistance group mode in the temperature-resistance pairing group are input into the working voltage prediction model as a training set for training, and the remaining half of the historical resistance values ​​and historical temperature values ​​are used as a verification set for verification, and multiple prediction training results are obtained. Then, the model parameters of the working voltage prediction model are adjusted according to the multiple prediction training results, and the obtained working voltage prediction model is: V=β 0 +β c *T+β d *R+∈, by continuously adjusting the model parameters, the model can perform well on both the training set and the validation set, providing a basis for model optimization. Finally, the real-time resistance correction value and the real-time temperature value are input into the working voltage prediction model of the corresponding temperature-resistance association group. The model can calculate the working voltage simulation value under the current conditions according to the learned rules, providing a reference for actual circuit control and performance evaluation.

[0111] In one embodiment, the step S6 of adjusting the circuit where the gallium nitride device is located according to the operating voltage analog value to obtain the compensated adjustment electrical parameter includes:

[0112] S61, obtaining a voltage error value according to the working voltage simulation value and the real-time voltage value;

[0113] S62, obtaining a voltage regulation signal according to the voltage error value, and regulating a voltage regulation element according to the voltage regulation signal to obtain a compensation regulation voltage value;

[0114] S63, acquiring voltage-dividing resistance information of the circuit where the gallium nitride device is located according to the electrical parameter data, and adjusting the voltage-dividing resistance according to the voltage-dividing resistance information and the real-time resistance correction value to obtain a compensation adjustment resistance value;

[0115] S64. Obtain a compensation adjustment current value according to the compensation adjustment voltage value and the compensation adjustment resistance value.

[0116] As described in the above steps S61 - S64, the present invention obtains a voltage error value by comparing the simulated working voltage value and the real - time voltage value. Among them, the voltage error value refers to the difference between the simulated working voltage value and the real - time voltage value. The voltage error value can clarify the deviation degree between the actual voltage of the circuit and the expected simulated voltage. Then, a voltage adjustment signal is generated according to the voltage error value. Among them, the voltage adjustment signal is a control signal generated according to the voltage error value, and its function is to control the voltage adjustment element to realize the adjustment of the circuit voltage, and then obtain a compensated adjustment voltage value. Among them, the compensated adjustment voltage value refers to the voltage value across the gallium nitride device obtained after adjusting the voltage adjustment element. Immediately afterwards, the voltage - dividing resistor information of the circuit where the gallium nitride device is located is obtained according to the electrical parameter data. Among them, the voltage - dividing resistor information refers to various parameter information related to the voltage - dividing resistor in the circuit where the gallium nitride device is located, such as the resistance value, power, etc. of the voltage - dividing resistor. Then, the voltage - dividing resistor is adjusted in combination with the real - time resistor correction value to obtain a compensated adjustment resistor value. Among them, the compensated adjustment resistor value refers to the adjusted voltage - dividing resistor value that needs to be adjusted considering the real - time resistor correction value of the gallium nitride device and the voltage - dividing requirements of the circuit. By adjusting the voltage - dividing resistor, the voltage - dividing situation in the circuit is adjusted, making the voltage distribution of each part more reasonable and meeting the working requirements of the circuit. Finally, the compensated adjustment current value is calculated according to the compensated adjustment voltage value and the compensated adjustment resistor value, obtaining the current situation of the entire circuit after voltage and resistor adjustment, so that the current in the circuit can also reach an appropriate state.

[0117] This application also provides a class - E power amplifier electromagnetic heating system based on gallium nitride, including:

[0118] A first acquisition module, configured to acquire the device characteristic information of the gallium nitride device, and acquire the working characteristic information and ideal parameter information according to the device characteristic information;

[0119] A second acquisition module, configured to acquire the electrical parameter data of the circuit where the gallium nitride device is located, and acquire the real - time resistance data of the gallium nitride device according to the working characteristic information and the electrical parameter data, where the real - time resistance data includes a real - time resistance value and a plurality of historical resistance values;

[0120] A third acquisition module, configured to acquire the real - time temperature data of the gallium nitride device according to a temperature sensor, where the real - time temperature data includes a real - time temperature value and a plurality of historical temperature values, and acquire a real - time resistor correction value according to the working characteristic information, ideal parameter information, real - time temperature value, and real - time resistance value;

[0121] A division module, configured to acquire a resistor division subset and a temperature division subset according to the working characteristic information, and acquire a temperature - resistance association group according to the resistor division subset and the temperature division subset;

[0122] A prediction module, configured to construct a corresponding working voltage prediction model according to the temperature-resistance correlation group, real-time resistance data, and real-time temperature data, and input the real-time resistance correction value and real-time temperature value into the corresponding working voltage prediction model to obtain a simulated working voltage value;

[0123] An adjustment module, configured to adjust the circuit where the gallium nitride device is located according to the simulated working voltage value to obtain compensated adjustment electrical parameters, where the compensated adjustment electrical parameters include a compensated adjustment current value and a compensated adjustment voltage value, and obtain the real-time output power of the gallium nitride device according to the compensated adjustment current value and the compensated adjustment voltage value;

[0124] A judgment module, configured to judge whether the real-time output power is greater than a preset power threshold;

[0125] If the real-time output power is greater than the preset power threshold, it is determined that the electromagnetic heating effect meets the expectation;

[0126] If the real-time output power is not greater than the preset power threshold, it is determined that the electromagnetic heating effect does not meet the expectation, and then return to the step of constructing a corresponding working voltage prediction model according to the temperature-resistance correlation group, real-time resistance data, and real-time temperature data until the electromagnetic heating effect meets the expectation.

[0127] In one embodiment, the partitioning module includes:

[0128] A first acquisition unit, configured to acquire temperature influence data and resistance influence data according to the working characteristic information, and acquire a first temperature influence threshold and a second temperature influence threshold according to the temperature influence data;

[0129] A second acquisition unit, configured to acquire temperature subsets according to the first temperature influence threshold and the second temperature influence threshold;

[0130] A third acquisition unit, configured to acquire a first resistance influence threshold and a second resistance influence threshold according to the resistance influence data, and acquire resistance subsets according to the first resistance influence threshold and the second resistance influence threshold;

[0131] A permutation and combination unit, configured to permute and combine the temperature subsets and the resistance subsets to obtain a plurality of temperature-resistance correlation groups.

[0132] The present invention also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned electromagnetic heating method based on a gallium nitride class-E power amplifier are implemented.

[0133] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned electromagnetic heating method of the class-E power amplifier based on gallium nitride are implemented.

[0134] Those of ordinary skill in the art can understand that all or part of the processes in the above-described embodiment methods can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-described method embodiments. Among them, any reference to a memory, storage, database, or other medium provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0135] It should be noted that in this article, the terms "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, device, article, or method including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such a process, device, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, device, article, or method including that element.

[0136] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for electromagnetic heating of a class E power amplifier based on gallium nitride, characterized in that: include: Acquire device characteristic information of the gallium nitride device, and acquire operating characteristic information and ideal parameter information according to the device characteristic information; Acquire electrical parameter data of a circuit where the gallium nitride device is located, and acquire real-time resistance data of the gallium nitride device according to the working characteristic information and the electrical parameter data, wherein the real-time resistance data includes a real-time resistance value and a plurality of historical resistance values; Acquire real-time temperature data of the gallium nitride device according to the temperature sensor, wherein the real-time temperature data includes a real-time temperature value and a plurality of historical temperature values, and acquire a real-time resistance correction value according to the working characteristic information, the ideal parameter information, the real-time temperature value and the real-time resistance value; Acquire a resistance division subset and a temperature division subset according to the working characteristic information, and acquire a temperature-resistance association group according to the resistance division subset and the temperature division subset; Constructing a corresponding working voltage prediction model according to the temperature-resistance association group, the real-time resistance data and the real-time temperature data, and inputting the real-time resistance correction value and the real-time temperature value into the corresponding working voltage prediction model to obtain a working voltage simulation value; The circuit where the gallium nitride device is located is adjusted according to the analog value of the working voltage to obtain a compensation adjustment electrical parameter, wherein the compensation adjustment electrical parameter includes a compensation adjustment current value and a compensation adjustment voltage value, and the real-time output power of the gallium nitride device is obtained according to the compensation adjustment current value and the compensation adjustment voltage value; Determining whether the real-time output power is greater than a preset power threshold; If the real-time output power is greater than the preset power threshold, it is determined that the electromagnetic heating effect has reached the expected level; If the real-time output power is not greater than the preset power threshold, it is determined that the electromagnetic heating effect has not met expectations, and the process returns to the step of constructing a corresponding working voltage prediction model based on the temperature-resistance association group, real-time resistance data, and real-time temperature data until the electromagnetic heating effect meets expectations.

2. The electromagnetic heating method of the gallium nitride-based class E power amplifier according to claim 1, characterized in that: The step of acquiring real-time resistance data of the gallium nitride device according to the operating characteristic information and the electrical parameter data comprises: Acquire the signal change frequency and signal stability value of the gallium nitride device according to the working characteristic information; Obtaining the interference frequency and interference intensity of the electromagnetic device, and obtaining the mean sample number according to the interference frequency, interference intensity, signal change frequency and signal stability value; Acquire multiple fluctuating voltage values ​​and multiple fluctuating current values ​​corresponding to each detection moment according to the electrical parameter data; Acquire corresponding real-time voltage data according to the plurality of said fluctuating voltage values ​​and the average sample number, and acquire corresponding real-time current data according to the plurality of said fluctuating current values ​​and the average sample number; The corresponding real-time resistance data is acquired according to the real-time voltage data and the corresponding real-time current data.

3. The electromagnetic heating method of the gallium nitride-based class E power amplifier according to claim 1, characterized in that: The step of obtaining the real-time resistance correction value according to the working characteristic information, the ideal parameter information, the real-time temperature value and the real-time resistance value comprises: Acquire a material characteristic coefficient of the gallium nitride device according to the working characteristic information; Acquire an ideal temperature value and a standard resistance value of the gallium nitride device according to the ideal parameter information, and acquire a resistance drift value according to the real-time resistance value and the standard resistance value; Obtaining a resistance compensation value of the gallium nitride device according to the material characteristic coefficient, the resistance drift value, the real-time temperature value and the ideal temperature value; A real-time resistance correction value is obtained according to the resistance compensation value and the real-time resistance value.

4. The electromagnetic heating method of the gallium nitride-based class E power amplifier according to claim 1, characterized in that: The step of acquiring a resistance division subset and a temperature division subset according to the operating characteristic information, and acquiring a temperature-resistance association group according to the resistance division subset and the temperature division subset comprises: Acquire temperature influence data and resistance influence data according to the operating characteristic information, and acquire a first temperature influence threshold and a second temperature influence threshold according to the temperature influence data; Acquire a temperature partition subset according to the first temperature impact threshold and the second temperature impact threshold; Acquire a first resistance influence threshold and a second resistance influence threshold according to the resistance influence data, and acquire a resistance division subset according to the first resistance influence threshold and the second resistance influence threshold; The temperature division subsets and the resistance division subsets are arranged and combined to obtain a plurality of temperature-resistance association groups.

5. The electromagnetic heating method of the gallium nitride-based class E power amplifier according to claim 1, characterized in that: The step of constructing a corresponding working voltage prediction model according to the temperature-resistance association group, the real-time resistance data and the real-time temperature data, and inputting the real-time resistance correction value and the real-time temperature value into the corresponding working voltage prediction model to obtain a working voltage simulation value includes: Pairing the real-time resistance data and the real-time temperature data to obtain a plurality of temperature-resistance pairing groups; Acquire an ideal voltage value and an ideal power value of the gallium nitride device according to the ideal parameter information, and acquire a corresponding operating voltage simulation value according to the ideal voltage value, the ideal power value and a plurality of temperature-resistance pairing groups; Constructing a working voltage prediction model for the corresponding temperature-resistance association group according to the temperature-resistance pairing group and the working voltage simulation value; The historical resistance value and the historical temperature value corresponding to each temperature-resistance pairing group are sequentially input into the working voltage prediction model as a training set for training, and the remaining multiple historical resistance values ​​and historical temperature values ​​are used as a verification set for verification to obtain multiple prediction training results; Adjusting the model parameters of the working voltage prediction model according to the plurality of prediction training results to obtain the working voltage prediction model; The real-time resistance correction value and the real-time temperature value are input into the working voltage prediction model of the corresponding temperature-resistance association group to obtain the working voltage simulation value.

6. The electromagnetic heating method of the gallium nitride-based class E power amplifier according to claim 1, characterized in that: The step of adjusting the circuit where the gallium nitride device is located according to the operating voltage analog value to obtain a compensation adjustment electrical parameter includes: Acquire a voltage error value according to the working voltage simulation value and the real-time voltage value; Acquire a voltage regulation signal according to the voltage error value, and adjust the voltage regulation element according to the voltage regulation signal to obtain a compensation regulation voltage value; Acquire voltage-dividing resistance information of a circuit in which the gallium nitride device is located according to the electrical parameter data, and adjust the voltage-dividing resistance according to the voltage-dividing resistance information and the real-time resistance correction value to obtain a compensation adjustment resistance value; The compensation adjustment current value is obtained according to the compensation adjustment voltage value and the compensation adjustment resistance value.

7. A class E power amplifier electromagnetic heating system based on gallium nitride, characterized in that: include: A first acquisition module is used to acquire device characteristic information of the gallium nitride device, and acquire operating characteristic information and ideal parameter information according to the device characteristic information; A second acquisition module is used to acquire electrical parameter data of a circuit where the gallium nitride device is located, and acquire real-time resistance data of the gallium nitride device according to the working characteristic information and the electrical parameter data, wherein the real-time resistance data includes a real-time resistance value and a plurality of historical resistance values; a third acquisition module, configured to acquire real-time temperature data of the gallium nitride device according to the temperature sensor, wherein the real-time temperature data includes a real-time temperature value and a plurality of historical temperature values, and acquire a real-time resistance correction value according to the working characteristic information, the ideal parameter information, the real-time temperature value and the real-time resistance value; A partitioning module, used to obtain a resistance partitioning subset and a temperature partitioning subset according to the working characteristic information, and to obtain a temperature-resistance association group according to the resistance partitioning subset and the temperature partitioning subset; A prediction module, used for constructing a corresponding working voltage prediction model according to the temperature-resistance association group, the real-time resistance data and the real-time temperature data, and inputting the real-time resistance correction value and the real-time temperature value into the corresponding working voltage prediction model to obtain a working voltage simulation value; An adjustment module, configured to adjust the circuit where the gallium nitride device is located according to the analog value of the working voltage to obtain a compensation adjustment electrical parameter, wherein the compensation adjustment electrical parameter includes a compensation adjustment current value and a compensation adjustment voltage value, and obtain the real-time output power of the gallium nitride device according to the compensation adjustment current value and the compensation adjustment voltage value; A judging module, used to judge whether the real-time output power is greater than a preset power threshold; If the real-time output power is greater than the preset power threshold, it is determined that the electromagnetic heating effect has reached the expected level; If the real-time output power is not greater than the preset power threshold, it is determined that the electromagnetic heating effect has not met expectations, and the process returns to the step of constructing a corresponding working voltage prediction model based on the temperature-resistance association group, real-time resistance data, and real-time temperature data until the electromagnetic heating effect meets expectations.

8. The gallium nitride-based class E power amplifier electromagnetic heating system according to claim 7, characterized in that: The division module comprises: A first acquisition unit, configured to acquire temperature influence data and resistance influence data according to the operating characteristic information, and acquire a first temperature influence threshold and a second temperature influence threshold according to the temperature influence data; A second acquisition unit, configured to acquire a temperature partition subset according to the first temperature impact threshold and the second temperature impact threshold; a third acquisition unit, configured to acquire a first resistance influence threshold and a second resistance influence threshold according to the resistance influence data, and acquire a resistance division subset according to the first resistance influence threshold and the second resistance influence threshold; The permutation and combination unit is used to permutate and combine the temperature division subsets and the resistance division subsets to obtain a plurality of temperature-resistance association groups.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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