Multilevel Intelligent Overheat Protection System and Method Based on Gallium Nitride Module Antenna

Abstract

The present invention relates to the technical field of antennas, in particular to a multi-level intelligent overheat protection system and method based on a gallium nitride module antenna. The method analyzes the optimal heat load migration path based on a virtual heat capacity network model, synchronously tunes the input impedance matching network of the target unit according to the optimal heat load migration path to compensate for the beam phase distortion caused by heat migration; injects the boundary constraint conditions of the virtual heat capacity network model according to the characteristics of the real-time threat scenario, dynamically solves the game equilibrium solution of the heat load migration path and the beam parameter tuning amount, and generates a thermal-radio frequency joint control instruction set. It realizes the full-process closed-loop control from heat inertia feature analysis, heat network modeling to thermal-radio frequency collaborative optimization, enabling the gallium nitride antenna system to not only intelligently plan the optimal heat migration path but also compensate for the heat-induced performance distortion in real time in complex thermal environments and threat scenarios, ultimately achieving the dual optimization of heat dissipation efficiency and radio frequency indicators.

Description

Technical Field

[0001] The present invention relates to the technical field of antennas, and particularly to a multi-level intelligent overheat protection system and method based on a gallium nitride module antenna. Background Art

[0002] With the development of communication systems towards high frequency bands and high power, gallium nitride (GaN) materials have been gradually widely used in high-power and high-frequency radio frequency electronic devices, especially in fields such as radar, communication systems, and power amplifiers. However, the thermal accumulation effect generated under high radio frequency power loading easily causes the antenna unit to overheat, which in turn leads to beam distortion, efficiency decline, and even hardware damage. Traditional overheat protection methods mostly use temperature threshold triggers to reduce power or forced heat dissipation, but it is difficult to balance the real-time nature of thermal management and the stability of radio frequency performance. In the prior art, although phase change energy storage materials can relieve temperature rise by absorbing heat, the dynamic coupling mechanism between their phase change rates and radio frequency power has not been quantitatively modeled; and the thermal migration path planning based on a fixed thermal resistance network lacks the ability to synergistically compensate for beam phase distortion. In addition, electromagnetic interference and thermal radiation will further exacerbate the game conflict between thermal and radio frequency parameters, and existing systems are difficult to dynamically balance the contradiction between thermal load migration and beam pointing accuracy. Therefore, there is an urgent need for a multi-level intelligent protection method that integrates dynamic thermal inertia characteristic analysis, virtual heat capacity network modeling, and thermal-radio frequency joint tuning to achieve adaptive thermal optimization and radio frequency performance guarantee of GaN antennas under complex working conditions. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a multi-level intelligent overheat protection system and method based on a gallium nitride module antenna.

[0004] The technical solution adopted by the present invention to achieve the above object is as follows:

[0005] The first aspect of the present invention discloses a multi-level intelligent overheat protection method based on a gallium nitride module antenna, including the following steps:

[0006] Analyze in real time the coupling relationship between the phase change rate of the phase change energy storage material in the gallium nitride antenna unit and the radio frequency power loading time sequence to generate a dynamic thermal inertia characteristic map;

[0007] Construct a virtual heat capacity network model based on the dynamic thermal inertia characteristic map, and the virtual heat capacity network model includes the thermal conduction weight coefficients between units and the radio frequency power bearing margin parameters;

[0008] Analyze the optimal thermal load migration path based on the virtual heat capacity network model, and synchronously tune the input impedance matching network of the target unit according to the optimal thermal load migration path to compensate for the beam phase distortion caused by thermal migration;

[0009] Inject the boundary constraint conditions of the virtual heat capacity network model according to the characteristics of the real-time threat scenario, dynamically solve the game equilibrium solution of the heat load migration path and the beam parameter tuning amount, and generate a thermal-radio frequency joint control instruction set.

[0010] Preferably, the coupling relationship between the phase change rate of the phase change energy storage material in the gallium nitride antenna unit and the radio frequency power loading time sequence is analyzed in real time to generate a dynamic thermal inertia characteristic map, specifically:

[0011] Collect the phase change rate time sequence vectors and the corresponding radio frequency power loading time sequence pulse parameters at multiple preset monitoring points inside the gallium nitride antenna unit in real-time and synchronously. After aligning the two according to the time stamp, form a spatio-temporal correlation data set;

[0012] Segment the spatio-temporal correlation data set based on a sliding time window, and use the mutual information analysis method to calculate the correlation degree sequences of the phase change rate change gradient and the radio frequency power pulse amplitude and duty cycle within each time window, and extract the dynamic lag time coefficient between the two through the grey correlation degree algorithm;

[0013] Input the correlation degree sequence and the lag time coefficient into a preset three-dimensional feature fusion model, map them to a thermal inertia time delay factor, a phase change heat response intensity factor, and a radio frequency power cumulative effect index respectively in the time-frequency domain, and perform piecewise linear interpolation in combination with a preset phase change rate change rate threshold to generate a dynamic thermal inertia characteristic matrix including the temperature field evolution trend;

[0014] According to the spatio-temporal distribution of each factor in the dynamic thermal inertia characteristic matrix, visualize the temperature field evolution trend, the heat response intensity, and the power cumulative effect through a three-dimensional interpolation algorithm and an isosurface rendering technique, and generate a dynamic thermal inertia characteristic map including color gradients and contour lines.

[0015] Preferably, construct a virtual heat capacity network model based on the dynamic thermal inertia characteristic map, specifically:

[0016] Extract the thermal inertia time delay factor, the phase change heat response intensity factor, and the radio frequency power cumulative effect index from the dynamic thermal inertia characteristic map as the model input parameters;

[0017] Based on the correlation degree sequence and the lag time coefficient between each antenna unit within a sliding time window, construct a heat conduction weight coefficient using a graph theory adjacency matrix, where the correlation degree sequence is used as the edge weight after normalization, and the lag time coefficient is used to correct the weight distribution ratio of adjacent units after exponential smoothing;

[0018] Non-linearly superimpose the radio frequency power cumulative effect index and the phase change heat response intensity factor, and combine the gradient direction of the temperature field evolution trend to determine the radio frequency power bearing margin parameter of each unit;

[0019] Based on the real-time updated heat conduction weight coefficient and radio frequency power carrying margin parameter, the node connection structure of the virtual heat capacity network is optimized using the greedy algorithm, and the heat flow balance constraint condition is applied synchronously to ensure that the heat load migration path and the radio frequency power distribution satisfy the first law of thermodynamics;

[0020] The convergence of the network model is verified through Monte Carlo random perturbation tests, and the adjustment step of the weight coefficient and the update threshold of the margin parameter are corrected backward according to the statistical distribution characteristics of the perturbation response data, realizing the dynamic matching of the model parameters and the physical field changes.

[0021] Preferably, based on the virtual heat capacity network model, the optimal heat load migration path is analyzed, and the input impedance matching network of the target unit is synchronously tuned according to the optimal heat load migration path to compensate for the beam phase distortion caused by heat migration. Specifically:

[0022] Based on the heat conduction weight coefficient and the radio frequency power carrying margin parameter, the Dijkstra algorithm is used to determine the minimum thermal resistance path between each target unit, and a set of candidate heat load migration paths is generated;

[0023] Combined with the gradient direction of the temperature field evolution trend, by calculating the product of the temperature gradient change rate between adjacent nodes on each candidate migration path and the phase change heat response intensity factor and then performing weighted average, the path heat flow dynamic equilibrium factor is obtained;

[0024] The path heat flow dynamic equilibrium factor is compared with a preset heat flow equilibrium threshold, the candidate migration paths with factor values greater than the preset heat flow equilibrium threshold are eliminated, and the candidate migration paths with factor values not greater than the preset heat flow equilibrium threshold are retained to obtain the optimal heat load migration path set;

[0025] According to the heat flow distribution characteristics on the optimal migration path, the change in the heat-induced dielectric constant of the preset node is extracted, and the corresponding beam phase offset compensation value is determined based on the radio frequency power cumulative effect index;

[0026] The input impedance matching network of the target unit is dynamically adjusted using the heat-induced dielectric constant change and the phase offset compensation value to compensate for the beam phase distortion caused by heat migration.

[0027] Preferably, the input impedance matching network of the target unit is dynamically adjusted using the heat-induced dielectric constant change and the phase offset compensation value to compensate for the beam phase distortion caused by heat migration. Specifically:

[0028] According to the change in the heat-induced dielectric constant of each node on the optimal migration path, the relative deviation rate from the nominal dielectric constant of the antenna unit substrate is calculated to generate a dielectric constant perturbation coefficient matrix;

[0029] Decompose the phase offset compensation value into azimuth and elevation phase gradient components according to the preset beam pointing angle, and calculate the equivalent susceptance adjustment amount required for the matching network in combination with the dielectric constant perturbation coefficient matrix;

[0030] Dynamically adjust the capacitance value distribution of the adjustable capacitor bank according to the equivalent susceptance adjustment amount, so that the reflection phase at the input end of the matching network cancels out the thermally induced phase distortion component;

[0031] Verify the phase compensation effect by real-time monitoring of the antenna port standing wave ratio. If the residual phase error exceeds the preset error threshold, use the gradient descent method to iteratively optimize the mapping relationship between the dielectric constant perturbation coefficient and the susceptance adjustment amount;

[0032] Update the power carrying margin parameter in the virtual heat capacity network according to the optimized mapping relationship to form a closed-loop adaptive matching of thermal-electric-phase parameters.

[0033] Preferably, inject the boundary constraint conditions of the virtual heat capacity network model according to the real-time threat scenario characteristics, dynamically solve the game equilibrium solution of the thermal load migration path and the beam parameter tuning amount, and generate a thermal-radio frequency joint control instruction set, specifically:

[0034] Obtain the electromagnetic interference intensity distribution and thermal radiation value of the threat scenario in real time, and quantify them into a heat flux density constraint vector and a radio frequency power limit matrix as the dynamic boundary conditions of the virtual heat capacity network model;

[0035] Conduct a constrained processing on the heat conduction weight coefficient based on the boundary conditions to generate a constrained thermal resistance topology map, and determine the heat flux accommodation space of each node in combination with the radio frequency power carrying margin parameter;

[0036] Introduce the heat flux density constraint vector on the thermal resistance topology map for two-way search, eliminate the path branches that exceed the heat flux accommodation space, and obtain a set of feasible thermal load migration paths that meet the boundary conditions;

[0037] According to the radio frequency power limit matrix of each node on the set of feasible migration paths, determine the maximum allowable beam scanning angle and power tuning range corresponding to the path, and generate a path-beam coupling parameter table;

[0038] By solving the multi-objective optimization function of the minimum path thermal resistance and the maximum beam pointing stability, screen out the Pareto optimal solution set in the path-beam coupling parameter table;

[0039] Linearly weighted and fuse the thermal resistance gradient change rate and the beam phase tuning amount in the Pareto optimal solution set to generate a thermal-radio frequency joint control instruction set including power reallocation instructions, thermal flux migration timing, and phase compensation parameters.

[0040] The multi-level intelligent overheat protection method further includes the following steps:

[0041] Real-time acquisition of antenna unit temperature field distribution data and beam pointing accuracy deviation after the instruction set is executed, extraction of the offset between the actual heat flow migration path and the theoretical path, and generation of a heat conduction error vector;

[0042] The correlation between the heat conduction error vector and the RF power loading time sequence is analyzed to obtain the dynamic correction factor of the heat conduction weight coefficient, and the weight distribution ratio of adjacent nodes is adjusted in combination with the hysteresis time coefficient.

[0043] The actual impact of the thermally induced dielectric constant disturbance is inferred based on the beam pointing accuracy deviation value, and the attenuation gradient of the RF power carrying margin parameter is updated in combination with the phase change thermal response intensity factor.

[0044] The adjacency matrix of the virtual heat capacity network is reconstructed using the modified heat conduction weight coefficient and power carrying margin parameter, and the network convergence is verified by heat flow balance constraints.

[0045] Based on the residual distribution of the predicted and measured node temperatures of the reconstructed network, the update step size of the weight coefficients and margin parameters is adaptively adjusted.

[0046] A second aspect of the present invention discloses a multi-level intelligent overheating protection system based on a gallium nitride module antenna, which is applied to any of the above-mentioned multi-level intelligent overheating protection methods based on a gallium nitride module antenna, including:

[0047] The gallium nitride antenna array module uses a GaN-on-SiC broadband antenna array, consisting of at least eight radiating elements. Each element has a built-in temperature sensor and micro phase-change energy storage material. The operating frequency range covers 1.2-5.8 GHz, and the element spacing is 0.75 times the center frequency wavelength.

[0048] The thermal state dynamic monitoring module, which integrates the temperature sensor of the gallium nitride antenna unit, the phase change rate detection circuit of the phase change energy storage material, and the thermal inertia characteristic calculation unit, outputs in real time a thermal state vector set including the chip junction temperature gradient, substrate heat flux density, and the solid-liquid phase change ratio of the phase change material;

[0049] A virtual thermal capacitance network modeling module is used to dynamically calculate the heat conduction weight coefficient and RF power carrying margin parameter between adjacent antenna elements based on the thermal state vector set, and generate a virtual thermal capacitance network topology diagram that includes heat migration path priority and beam distortion compensation.

[0050] The RF-thermal coupling compensation module, which includes an adjustable capacitor array and distributed phase shifters, dynamically tunes the reflection coefficient of the input impedance matching network based on the thermal migration path data of the virtual thermal capacitance network topology to achieve beam pointing phase predistortion compensation within ±0.25dB.

[0051] The threat scenario adaptive decision-making module dynamically calculates the Pareto optimal solution of the thermal load migration path and beam synthesis parameters according to the modulation type and pulse width characteristics of the real-time threat signal, and outputs an interference effectiveness enhancement instruction set with a frequency band priority of 1.2 - 1.6 GHz.

[0052] The present invention solves the technical defects existing in the background art, and the present invention has the following beneficial effects: By establishing a dynamic regulation mechanism of thermal-electric coupling, the present invention realizes a full-process closed-loop control from thermal inertia feature analysis, thermal network modeling to thermal-radio frequency co-optimization, enabling the antenna system to not only intelligently plan the optimal thermal migration path in a complex thermal environment, but also compensate for thermal-induced performance distortion in real time, and finally achieve the dual optimization of heat dissipation efficiency and radio frequency indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0054] Figure 1 It is the overall method flowchart of this multi-level intelligent overheat protection method;

[0055] Figure 2 It is the partial method flowchart of this multi-level intelligent overheat protection method;

[0056] Figure 3 It is the system block diagram of this multi-level intelligent overheat protection system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] In order to more clearly understand the above objects, features and advantages of the present invention, the following will further describe the present invention in detail with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0058] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0059] As Figure 1 shown, the first aspect of the present invention discloses a multi-level intelligent overheat protection method based on a gallium nitride module antenna, including the following steps:

[0060] S102. Analyze the coupling relationship between the phase change rate of the phase change energy storage material in the gallium nitride antenna unit and the RF power loading timing in real time, and generate a dynamic thermal inertia characteristic map;

[0061] S104. Construct a virtual heat capacity network model based on the dynamic thermal inertia characteristic map. The virtual heat capacity network model includes the heat conduction weight coefficients between units and the RF power bearing margin parameters;

[0062] S106. Analyze the optimal heat load migration path based on the virtual heat capacity network model, and synchronously tune the input impedance matching network of the target unit according to the optimal heat load migration path to compensate for the beam phase distortion caused by heat migration;

[0063] S108. Inject the boundary constraint conditions of the virtual heat capacity network model according to the real-time threat scenario characteristics, dynamically solve the game equilibrium solution of the heat load migration path and the beam parameter tuning amount, and generate a thermal-RF joint control instruction set.

[0064] It should be noted that due to the difficulty of the traditional GaN antenna system to simultaneously consider effective heat dissipation and stable RF performance under high-temperature and high-power conditions, the thermal management efficiency is low and the beam quality deteriorates. The present invention realizes the full-process closed-loop control from thermal inertia characteristic analysis, thermal network modeling to thermal-RF collaborative optimization by establishing a dynamic regulation mechanism of thermal-electric coupling, enabling the antenna system to intelligently plan the optimal heat migration path and compensate for the heat-induced performance distortion in real time in a complex thermal environment, and finally achieving the dual optimization of heat dissipation efficiency and RF indicators.

[0065] Preferably, analyze the coupling relationship between the phase change rate of the phase change energy storage material in the gallium nitride antenna unit and the RF power loading timing in real time, and generate a dynamic thermal inertia characteristic map, as Figure 2 shown, specifically:

[0066] S202. Synchronously collect the phase change rate time series vectors and the corresponding RF power loading timing pulse parameters of multiple preset monitoring points inside the gallium nitride antenna unit in real time, and form a spatio-temporal correlation data set after aligning the two according to the time stamp;

[0067] Among them, the phase change rate time series vector refers to a dynamic data sequence composed of the change rates of the solid-liquid phase change ratios of the phase change material (such as paraffin) sampled and recorded at different time points; the RF power loading timing pulse parameters refer to the set of characteristic parameters such as the power amplitude, pulse width, and duty cycle of the RF signal in the time dimension.

[0068] S204. Segment the spatio-temporal correlation data set based on a sliding time window, calculate the correlation degree sequences between the phase change rate change gradients and the RF power pulse amplitude and duty cycle in each time window using the mutual information analysis method, and extract the dynamic lag time coefficient between the two through the grey correlation degree algorithm;

[0069] S206. Input the correlation degree sequence and the lag time coefficient into a preset three-dimensional feature fusion model, map them respectively as the thermal inertia time delay factor, the phase change heat response intensity factor and the radio frequency power cumulative effect index in the time-frequency domain, and perform piecewise linear interpolation in combination with a preset phase change rate change threshold to generate a dynamic thermal inertia feature matrix containing the evolution trend of the temperature field.

[0070] Among them, the preset three-dimensional feature fusion model is an intelligent analysis model based on multi-dimensional data fusion obtained through pre-training, and is used to perform collaborative mapping and feature extraction on the thermal inertia time delay factor, the phase change heat response intensity factor and the radio frequency power cumulative effect index in the time domain, frequency domain and spatial domain. This model uses wavelet transform to decompose time-frequency features, combines long short-term memory (LSTM) networks to capture dynamic lag effects, and enhances features of the spatial thermal field distribution through a convolutional neural network (CNN), and finally outputs a dynamic thermal inertia feature matrix containing the temperature field gradient, heat response intensity and power cumulative effect.

[0071] S208. According to the spatio-temporal distribution of each factor in the dynamic thermal inertia feature matrix, visualize the evolution trend of the temperature field, the heat response intensity and the power cumulative effect through a three-dimensional interpolation algorithm and an isosurface rendering technique, and generate a dynamic thermal inertia feature map containing color gradients and contour lines.

[0072] Taking a GaN antenna array with a working frequency band of 2.4 GHz and a unit peak power of 150 W as an example, in specific implementation: embed paraffin-based phase change materials (melting point 60 °C) and distributed temperature sensors in 4 radiation units respectively, synchronously collect the time series data of the liquid phase ratio of the phase change material (such as the phase change rate of unit A is 0.8% / s at t = 1.2 s) and the radio frequency power pulse parameters (pulse width 10 ms, duty cycle 30%) at a sampling rate of 100 Hz; segment the aligned spatio-temporal data set through a 5 ms sliding time window, and use the mutual information analysis method to obtain the correlation degree sequence between the phase change gradient and the power amplitude (such as the maximum mutual information value in the window is 0.75), and extract the lag time coefficient in combination with the grey correlation degree algorithm (such as 2.3 ms); input the above parameters into a three-dimensional feature fusion model (including a wavelet transform layer and an LSTM network), output the thermal inertia time delay factor (such as level 1.8), the phase change heat response intensity factor (such as level 2.4) and the power cumulative effect index (such as level 3.1), and generate a dynamic thermal inertia feature matrix (resolution 0.1 °C / pixel) after piecewise linear interpolation; finally, generate a three-dimensional thermal field map through an OpenGL rendering engine, and intuitively display the temperature gradient between units (such as the highest temperature on the surface of unit A is 62.5 °C) and the heat flow diffusion direction (such as the decreasing rate along the positive Y-axis is 1.2 °C / mm) with a red-blue gradient color scale.

[0073] In summary, the present invention accurately characterizes the evolution trend of the temperature field by real-time analyzing the spatio-temporal correlation between the phase change rate and the radio frequency power, and fusing the thermal inertia, thermal response and power accumulation effect, so as to generate a visual dynamic thermal inertia characteristic map, providing high-precision data support for subsequent intelligent thermal regulation.

[0074] Preferably, a virtual heat capacity network model is constructed based on the dynamic thermal inertia characteristic map, specifically as follows:

[0075] Extract the thermal inertia time delay factor, phase change thermal response intensity factor and radio frequency power accumulation effect index from the dynamic thermal inertia characteristic map as the model input parameters;

[0076] Based on the correlation sequence and lag time coefficient between antenna elements within a sliding time window, a heat conduction weight coefficient is constructed using a graph theory adjacency matrix, where the correlation sequence is used as the edge weight after normalization, and the lag time coefficient is used to correct the weight distribution ratio of adjacent elements after exponential smoothing;

[0077] It should be noted that when constructing the heat conduction weight coefficient, first normalize the thermal correlation degree values (such as the mutual information values between 0 and 1) between each antenna element to unify all values within the range of 0-1 as the initial weight of the edge connecting each element in the adjacency matrix. Then, perform exponential smoothing on the measured lag time coefficient (such as a 3.5 ms heat transfer delay between two elements) to obtain a more stable correction coefficient. Finally, use this correction coefficient to adjust the initial weight distribution ratio between adjacent elements. For example, the original weight from A to B is 0.8, and after lag time correction, it may be adjusted to 0.7, which can more accurately reflect the actual heat conduction situation. Through this method, a weight network model more in line with the actual heat transfer characteristics can be established.

[0078] Nonlinearly superimpose the radio frequency power accumulation effect index and the phase change thermal response intensity factor, and combine the gradient direction of the temperature field evolution trend to determine the radio frequency power bearing margin parameter of each unit;

[0079] It should be noted that when calculating the radio frequency power bearing margin parameter of each antenna element, the index representing the power accumulation degree (such as level 3.1) and the intensity factor reflecting the heat absorption capacity of the phase change material (such as level 2.4) are superimposed and calculated through a nonlinear function (such as an S-shaped curve) to obtain a comprehensive heat load index. Then, in combination with the diffusion direction of the real-time temperature field (such as a gradient change of 1.2 °C / mm from the high temperature area to the low temperature area), the comprehensive index is directionally corrected: when the heat flow direction is consistent with the power distribution, the margin is appropriately increased, and vice versa. Finally, the percentage of radio frequency power that each unit can safely withstand under the current working conditions is output (such as the bearing margin of unit A is 85%), providing a basis for subsequent power distribution.

[0080] Based on the real-time updated heat conduction weight coefficient and radio frequency power carrying margin parameter, the node connection structure of the virtual heat capacity network is optimized using the greedy algorithm, and the heat flow balance constraint condition is applied synchronously to ensure that the heat load migration path and the radio frequency power distribution satisfy the first law of thermodynamics;

[0081] The convergence of the network model is verified through Monte Carlo random perturbation tests, and the adjustment step of the weight coefficient and the update threshold of the margin parameter are corrected backward according to the statistical distribution characteristics of the perturbation response data, realizing the dynamic matching of the model parameters and the physical field changes.

[0082] Among them, the Monte Carlo random perturbation test refers to a method of verifying the model stability by inputting a series of randomly simulated interference factors (such as temperature fluctuations, power mutations, etc.) into the system; the perturbation response data refers to the recorded data set of the actual reactions (such as temperature change curves, thermal resistance fluctuations, etc.) of the system to these random interferences during the test.

[0083] Based on the dynamic thermal inertia characteristic map of the aforementioned GaN antenna array (operating frequency band 2.4 GHz) (including the thermal inertia time delay factor of 1.8 levels, the phase change heat response intensity factor of 2.4 levels, and the power accumulation effect index of 3.1 levels for unit A), in specific implementation: the correlation sequence (such as mutual information value 0.62) and the lag time coefficient (such as 3.5 ms) of adjacent units A - B are extracted with a 200 ms sliding time window, and a 5×5 graph theory adjacency matrix is constructed, where the normalized correlation (0.62 / maximum value 0.75 ≈ 0.83) is used as the edge weight, and the lag coefficient after exponential smoothing (α = 0.3) is used to adjust the A - B weight allocation to 0.72; the power accumulation effect index (3.1 levels) and the heat response intensity factor (2.4 levels) of unit A are nonlinearly superimposed through the Sigmoid function (output value 0.68), combined with the temperature field gradient direction (1.2℃ / mm in the positive Y - axis direction), to calculate its radio frequency power carrying margin parameter; the greedy algorithm is used to optimize the network structure, reducing the thermal resistance from unit A to unit C by 12%, and at the same time ensuring that the error between the total input power (50 W) and the heat dissipation power is < 3% through the heat flow balance constraint; finally, the convergence of the model is verified through Monte Carlo perturbation tests (such as 1000 samplings). For example, when the temperature prediction residual exceeds 0.5℃, the adjustment step of the weight coefficient is automatically reduced from 0.1 to 0.05 to achieve dynamic matching with the measured thermal field.

[0084] In summary, since the traditional thermal management model is difficult to dynamically characterize the coupling relationship between the heat conduction characteristics and power - carrying capacity among the units in the gallium nitride antenna array, it leads to a lack of accurate guidance for heat load distribution and radio - frequency performance optimization. The present invention constructs a virtual heat - capacity network model to dynamically quantify the heat conduction weight and power - carrying margin among the units, and uses an optimization algorithm to ensure heat - flow balance, thereby realizing the intelligent planning and dynamic adjustment of the heat - load migration path, effectively improving the heat - distribution uniformity and radio - frequency working stability of the antenna array.

[0085] Preferably, based on the virtual heat - capacity network model, analyze the optimal heat - load migration path, and synchronously tune the input impedance matching network of the target unit according to the optimal heat - load migration path to compensate for the beam - phase distortion caused by heat migration. Specifically:

[0086] Based on the heat - conduction weight coefficient and radio - frequency power - carrying margin parameters, use the Dijkstra algorithm to determine the minimum - thermal - resistance path between each target unit, and generate a set of candidate heat - load migration paths;

[0087] It should be noted that when planning the heat - load migration path, each unit in the antenna array is regarded as a network node, and the heat - conduction weight coefficient between the units (such as 0.72) is converted into the path thermal resistance value. Then, using the Dijkstra shortest - path algorithm, starting from the overheated unit (such as unit A at 62.5 °C), calculate the total thermal resistance values of all possible paths to other units (especially units with good heat - dissipation conditions) step by step. Then compare the accumulated thermal - resistance values of different paths, and find the path with the minimum thermal resistance from the heat - source unit to the target heat - dissipation unit (such as the total thermal resistance of the path from A to B to D is 1.8 K / W). Finally, output several (such as 3) candidate paths with relatively small thermal resistance for subsequent heat - flow balance evaluation and optimal - path selection.

[0088] Combined with the gradient direction of the temperature - field evolution trend, by calculating the weighted average of the product of the temperature - gradient change rate and the phase - change heat - response intensity factor between adjacent nodes on each candidate migration path, obtain the path heat - flow dynamic balance factor;

[0089] Compare the path heat - flow dynamic balance factor with a preset heat - flow balance threshold, eliminate the candidate migration paths with factor values greater than the preset heat - flow balance threshold, and retain the candidate migration paths with factor values not greater than the preset heat - flow balance threshold to obtain the set of optimal heat - load migration paths;

[0090] According to the heat - flow distribution characteristics on the optimal migration path, extract the change amount of the heat - induced dielectric constant of the preset node, and determine the corresponding beam - phase offset compensation value based on the radio - frequency power cumulative - effect index;

[0091] Among them, the heat flow distribution characteristics refer to the dynamic change characteristics such as the temperature gradient and heat flux density of the heat on the transmission path; the preset nodes are the key unit positions for monitoring and control in the pre-selected antenna array; the change in thermally induced dielectric constant represents the offset value of the dielectric characteristics (such as dielectric constant) of the antenna material caused by temperature change.

[0092] The input impedance matching network of the target unit is dynamically adjusted by using the change in thermally induced dielectric constant and the phase shift compensation value to compensate for the beam phase distortion caused by heat migration.

[0093] Among them, the target unit refers to a specific antenna unit (such as unit A mentioned above and unit B that needs to compensate for phase shift) in the gallium nitride antenna array that currently needs to perform heat load migration and impedance matching tuning.

[0094] Taking the aforementioned GaN antenna array (unit A temperature 62.5 °C, load margin 85%) as an example, specifically in implementation: Based on the virtual heat capacity network model (adjacent matrix edge weight 0.72), the Dijkstra algorithm is used to calculate the minimum thermal resistance path from unit A to the heat dissipation unit D (such as the total thermal resistance of the path from A to B to D is 1.8 K / W), and 3 candidate paths are generated; combined with the temperature field gradient direction (1.2 °C / mm on the Y axis), the thermal flow dynamic equilibrium factor of each path is calculated (such as the factor value of the path from A to B to D is 0.65, and the preset threshold is 0.7), and the optimal path A to B to D is selected; the change in thermally induced dielectric constant (Δε r = 0.3) of unit B on this path is extracted, and the phase shift compensation value (such as 5.2 °) is calculated according to its power accumulation effect index (level 2.8); the capacitance value of the matching network of unit B is adjusted from 3.5 pF to 3.2 pF through an adjustable capacitor array to achieve beam pointing deviation compensation (residual error < 0.5 °), while maintaining the power output of unit A stable at 48 W (load margin consumption 12%).

[0095] In summary, the present invention intelligently plans the optimal heat migration path through the virtual heat capacity network model and synchronously adjusts the impedance matching network to achieve the collaborative compensation of heat load migration and beam phase distortion, which not only ensures the efficient evacuation of heat but also maintains the stability of the radio frequency performance of the antenna array, effectively solving the problem of thermal-electric coupling interference.

[0096] Preferably, the input impedance matching network of the target unit is dynamically adjusted by using the change in thermally induced dielectric constant and the phase shift compensation value to compensate for the beam phase distortion caused by heat migration, specifically as follows:

[0097] According to the change in thermally induced dielectric constant of each node on the optimal migration path, calculate its relative deviation rate from the nominal dielectric constant of the antenna unit substrate to generate a dielectric constant perturbation coefficient matrix;

[0098] Decompose the phase offset compensation value into phase gradient components in the azimuth and elevation dimensions according to the preset beam pointing angle, and calculate the equivalent susceptance adjustment amount required for the matching network in combination with the dielectric constant perturbation coefficient matrix;

[0099] It should be noted that when calculating the equivalent susceptance adjustment amount of the matching network, convert the thermally induced dielectric constant change amount into the corresponding perturbation coefficient matrix (for example, the Δε of unit B r = 0.3 corresponds to the perturbation coefficient 0.85). Then decompose the phase offset value to be compensated (such as 5.2°) into phase components in the azimuth and elevation directions (such as azimuth 3.1° + elevation 2.1°). Next, match these phase components with the corresponding unit data in the perturbation coefficient matrix to obtain the susceptance value that needs to be adjusted for each unit's matching network (for example, unit B needs to increase the susceptance by 0.15 S). Finally, output the list of equivalent susceptance adjustment amounts required for each unit to guide the parameter setting of the subsequent adjustable capacitor bank.

[0100] Dynamically adjust the capacitance distribution of the adjustable capacitor bank according to the equivalent susceptance adjustment amount, so that the reflection phase at the input end of the matching network cancels out the thermally induced phase distortion component;

[0101] Verify the phase compensation effect by real-time monitoring of the antenna port standing wave ratio. If the residual phase error exceeds the preset error threshold, use the gradient descent method to iteratively optimize the mapping relationship between the dielectric constant perturbation coefficient and the susceptance adjustment amount;

[0102] Update the power carrying margin parameter in the virtual heat capacity network according to the optimized mapping relationship to form a closed-loop adaptive matching of thermal-electric-phase parameters.

[0103] For the thermally induced dielectric constant change amount Δε of unit B (temperature 58°C) in the aforementioned GaN antenna array r = 0.3 (nominal ε r = 9.8), determine the relative deviation rate of 3.06% and generate the perturbation coefficient matrix (coefficient of unit B is 0.97); decompose the 5.2° phase offset to be compensated into 3.1° in the azimuth dimension and 2.1° in the elevation dimension, and through electromagnetic field simulation inversion calculation in combination with the perturbation coefficient matrix, it is obtained that the matching network of unit B needs to increase the equivalent susceptance by 0.15 S; accordingly, adjust the capacitance of the adjustable capacitor bank of unit B from 3.5 pF to 3.8 pF (step accuracy 0.1 pF), so that the reflection phase at the input end cancels out the thermal distortion phase in the opposite direction, and the measured standing wave ratio drops from 1.8 to 1.2; when the residual phase error of 0.6° exceeds the threshold of 0.5°, use the gradient descent method to iterate 3 times to optimize the mapping relationship, and finally update the power carrying margin parameter of unit B from 82% to 79%, and at the same time update the thermal conduction weight coefficient from unit A to unit B in the virtual heat capacity network from 0.72 to 0.68 to form a closed-loop adjustment.

[0104] In summary, the present invention dynamically calculates the correspondence between the dielectric constant perturbation and the phase shift, and adjusts the impedance matching network parameters in real time, so that the reflection phase actively cancels the thermal-induced phase distortion. At the same time, the compensation accuracy is continuously corrected through closed-loop optimization, realizing the adaptive collaborative regulation of thermal deformation and electrical performance, and effectively ensuring the beam pointing stability of the antenna array under the change of thermal load.

[0105] Preferably, according to the characteristics of the real-time threat scenario, boundary constraint conditions are injected into the virtual heat capacity network model, and the game equilibrium solution of the thermal load migration path and the beam parameter tuning amount is dynamically solved to generate a thermal-radio frequency joint control instruction set, specifically:

[0106] The electromagnetic interference intensity distribution and the thermal radiation value of the threat scenario are obtained in real time, and they are quantified into a heat flux density constraint vector and a radio frequency power limit matrix as the dynamic boundary conditions of the virtual heat capacity network model;

[0107] Based on the boundary conditions, the thermal conduction weight coefficient is constrained to generate a constrained thermal resistance topology map, and the heat flux accommodation space of each node is determined in combination with the radio frequency power bearing margin parameter;

[0108] It should be noted that when generating the constrained thermal resistance topology map, the original thermal conduction weight coefficient is adjusted according to the limiting conditions of the threat scenario (such as the power upper limit of unit C is 30W and the heat flux upper limit of unit D is 50W). Specifically, when a certain unit is restricted by power or heat flux, the thermal conduction weight between it and the adjacent unit will be reduced (for example, the weight from unit C to D is adjusted from 0.65 to 0.58), which is equivalent to "setting up roadblocks" on these restricted paths. Then, the equivalent thermal resistance value between each unit is recalculated with the adjusted weight coefficient, and a new thermal resistance topology map is drawn. The constrained high thermal resistance paths on the thermal resistance topology map will be clearly marked (such as the path from unit C to D is shown in red), ensuring that these restricted areas are automatically avoided during subsequent path planning, just like a navigation system avoiding congested roads.

[0109] It should be noted that when determining the heat flux accommodation space of each node, the current radio frequency power bearing margin parameter of each antenna unit is obtained (such as the bearing margin of unit D is 75%). Then, according to the maximum theoretical heat dissipation capacity of the unit (such as 80W) and its current working state, the additional heat flux upper limit that the unit can withstand under the current margin is determined (such as 80W × 75% = 60W). Then, in combination with the thermal conduction weight coefficient between adjacent units, this heat flux upper limit is allocated to each possible heat transfer path (such as 42W is allocated to the path from unit C to D). Finally, by real-time monitoring the actual heat flux values of each path, this allocation scheme is dynamically adjusted to ensure that the calculated heat flux accommodation space will not be exceeded at any time, thus avoiding local overheating.

[0110] Introduce a heat flux density constraint vector on the thermal resistance topology map for two-way search, eliminate the path branches that exceed the heat flux accommodation space, and obtain a set of feasible migration paths of heat loads that meet the boundary conditions;

[0111] According to the radio frequency power limit matrix of each node on the set of feasible migration paths, determine the maximum allowable beam scanning angle and power tuning range corresponding to the path, and generate a path-beam coupling parameter table;

[0112] By solving the multi-objective optimization function of the minimum path thermal resistance and the maximum beam pointing stability, screen out the Pareto optimal solution set in the path-beam coupling parameter table;

[0113] It should be noted that when screening the optimal solution set, a coupling parameter table including all feasible paths (such as from A to C to D) and their corresponding thermal resistance values (such as 2.1 K / W) and beam stability parameters (such as pointing error 0.8°) is established. Then, two optimization objectives are set: to make the path thermal resistance as small as possible (high heat transfer efficiency), and at the same time make the beam pointing error as small as possible (stable radio frequency performance). Then, analyze these path schemes to find the compromise solutions (i.e., Pareto optimal solutions) that cannot be comprehensively surpassed by other solutions - for example, a certain solution has a non-minimal thermal resistance but the steadiest beam, or the smallest thermal resistance but a slightly worse beam. Finally, output the set of these optimal candidate solutions for the system to select the most appropriate balance solution according to real-time requirements (such as preferentially selecting a solution with a 15% reduction in thermal resistance and a beam error ≤ 0.8°).

[0114] Linearly weighted and fuse the thermal resistance gradient change rate and beam phase tuning amount in the Pareto optimal solution set to generate a thermal-radio frequency joint control instruction set including power reallocation instructions, heat flow migration time sequence, and phase compensation parameters.

[0115] Among them, the threat scenario includes a complex combat environment under the combined action of enemy electromagnetic interference (such as directional radio frequency suppression) and environmental thermal radiation (such as solar radiation or high-temperature heat sources).

[0116] For example, in a threat scenario where electromagnetic interference in the 1.5 GHz frequency band (field strength 20 V / m) and sunlight radiation (heat flux density 800 W / m²) from the enemy are encountered, first, the characteristics of the interference signal are quantified into a radio frequency power limit matrix (for example, the power upper limit of unit C in the 1.4 - 1.6 GHz frequency band is set to 30 W), and at the same time, the heat radiation value is converted into a heat flux density constraint vector (for example, the heat flux upper limit on the surface of unit D is set to 50 W). Based on these constraint conditions, the heat conduction weight coefficient from unit C to D in the virtual heat capacity network is corrected from 0.65 to 0.58, a constrained thermal resistance topology map is generated, and the current heat flux accommodation space of unit D is determined to be 42 W. After excluding 3 over-limit paths through two-way path search, the feasible path A to C to D (thermal resistance 2.1 K / W) is retained, and the maximum beam scanning angle allowed for this path is determined to be ±25° according to the power limit matrix. After multi-objective optimization, the Pareto optimal solution is selected: the thermal resistance is reduced by 15% while the beam pointing error ≤ 0.8°. Finally, an instruction set is generated - reduce the power of unit C from 35 W to 28 W, start the cooling fan of unit D with a 2 ms delay, and compensate the matching network of unit C with a 3.2° phase shift.

[0117] In summary, due to the difficulty of simultaneously meeting the thermal management requirements and optimizing the radio frequency performance of the GaN antenna array in a complex threat environment, the thermal load migration and beam parameter tuning restrict each other. By quantifying the characteristics of the threat scenario into network constraint conditions, the present invention intelligently balances the multi-objective requirements of minimizing thermal resistance and beam stability, and dynamically generates cooperative control instructions, enabling the antenna system to not only ensure efficient heat dissipation but also maintain the optimal radio frequency operating state under the threats of electromagnetic interference and thermal radiation, achieving the comprehensive optimization of thermal - electrical performance.

[0118] The multi-level intelligent overheat protection method further includes the following steps:

[0119] Real-time collect the temperature field distribution data of the antenna unit and the beam pointing accuracy deviation value after the execution of the instruction set, extract the offset between the actual heat flow migration path and the theoretical path, and generate a heat conduction error vector;

[0120] Conduct a correlation analysis on the heat conduction error vector and the radio frequency power loading time sequence, obtain the dynamic correction factor of the heat conduction weight coefficient, and adjust the weight distribution ratio of adjacent nodes in combination with the lag time coefficient;

[0121] Based on the beam pointing accuracy deviation value, inversely deduce the actual influence of the heat-induced dielectric constant perturbation amount, and update the attenuation gradient of the radio frequency power bearing margin parameter in combination with the phase change heat response intensity factor;

[0122] Reconstruct the adjacency matrix of the virtual heat capacity network using the corrected heat conduction weight coefficient and power bearing margin parameter, and verify the network convergence through the heat flow balance constraint;

[0123] Based on the residual distribution between the predicted value and the measured value of the node temperature of the reconstruction network, adaptively adjust the update step sizes of the weight coefficient and the margin parameter.

[0124] Taking the antenna array after executing the thermal-radio frequency joint control instruction set as an example (the measured temperature of unit D is 61.2 °C / the theoretical value is 60.5 °C, and the beam pointing deviation is 0.9 °), in specific implementation: collect the temperature field data through an infrared thermal imager, and it is found that the actual path of the heat flow from A to C to D deviates by 15% from the theoretical path (generating an error vector [0, 0.15, 0]); analyze that its correlation coefficient with the radio frequency power pulse (duty cycle 40%) of unit C reaches 0.78, and calculate the dynamic correction factor of 0.92 accordingly, and correct the heat conduction weight coefficient from unit C to D from 0.58 to 0.53; inversely deduce the actual change amount Δε of the dielectric constant of unit D according to the 0.9 ° beam deviation r = 0.35 (originally predicted 0.3), combined with its phase change heat response intensity factor of level 2.1, adjust the power bearing margin decay gradient from 12% / min to 15% / min; reconstruct the adjacency matrix with the corrected parameters, and after verifying that the heat flow balance error < 5%, adjust the update step size of the weight coefficient from 0.05 to 0.03 according to the temperature residual of 0.7 °C of unit D to improve the prediction accuracy in the next cycle.

[0125] It should be noted that to solve the problem that the traditional thermal management system cannot correct the error of the heat conduction model in real time under dynamic working conditions, resulting in a deviation between the theoretical thermal management strategy and the actual execution effect. The present invention dynamically corrects the heat conduction weight and the power margin parameter through real-time feedback of the temperature field and beam performance data, enables the virtual heat capacity network to continuously approach the actual physical system, forms a closed-loop thermal-electric collaborative control mechanism with self-learning ability, and effectively improves the real-time accuracy and environmental adaptability of the thermal management strategy.

[0126] In this embodiment, the multi-level intelligent overheat protection method further includes the following steps:

[0127] Collect the difference between the measured value and the theoretical value of the temperature gradient on the heat load migration path in real time, generate a heat flow path offset vector, and synchronously extract the residual error of the beam phase compensation to form a phase residual sequence; fuse the two into a bimodal error feature matrix;

[0128] Perform time-frequency decomposition on the bimodal error feature matrix, extract the spectral features of the heat flow offset and the time-domain correlation of the phase residual, and output the heat conduction weight correction factor and the dielectric constant perturbation sensitivity coefficient;

[0129] Based on the heat conduction weight correction factor, perform sliding window weighted adjustment on the original heat conduction weight coefficient in the virtual heat capacity network, and use the dielectric constant perturbation sensitivity coefficient to adjust the mapping relationship between the phase residual sequence and the dielectric constant perturbation to generate the updated heat conduction weight coefficient and the mapping relationship of the dielectric constant perturbation;

[0130] Substitute the updated mapping relationship between the heat conduction weight coefficient and the dielectric constant perturbation into the preset heat flow balance model for solution to obtain a new heat flow path offset vector and a phase residual sequence;

[0131] According to the newly generated heat flow path offset vector and phase residual sequence, dynamically adjust the sliding window size of the heat conduction weight correction factor and the scaling ratio of the dielectric constant perturbation sensitivity coefficient to form a closed-loop optimization.

[0132] Among them, the preset heat flow balance model refers to a mathematical model used to describe the dynamic balance relationship between heat flow input, conduction, and dissipation in an antenna system, ensuring that the heat accumulation and dissipation of each node during the heat load migration process always satisfy the law of conservation of energy.

[0133] It should be noted that to solve the problem that traditional thermal management solutions are difficult to correct the heat flow path offset and phase compensation residual error in real time under dynamic working conditions, resulting in the mismatch of thermal-electric parameters. In this embodiment, through the dual-mode feature analysis of fusing temperature gradient and phase error, the mapping relationship between heat conduction weight and dielectric parameters is dynamically adjusted to realize the closed-loop collaborative optimization of the heat flow migration path and beam compensation parameters, so that the system always maintains the optimal heat distribution and radio frequency performance under dynamic heat load changes.

[0134] In this embodiment, the multi-level intelligent overheat protection method further includes the following steps:

[0135] Based on the currently updated mapping relationship of dielectric constant perturbation, decompose the phase residual sequence according to frequency components to generate the initial capacitance value adjustment amount of the adjustable capacitor group of each antenna element;

[0136] According to the heat conduction weight coefficient of each node in the virtual heat capacity network, perform weighted correction on the initial capacitance value adjustment amount;

[0137] After performing capacitance adjustment, collect the scattering parameters of the antenna port in real time, extract the characteristic deviation from its mapping relationship with the dielectric constant perturbation, and generate a scattering parameter residual vector;

[0138] Use the heat conduction weight coefficient to construct the covariance matrix of the Mahalanobis distance, calculate the statistical distance between the scattering parameter residual vector and the phase residual sequence, and quantify the compensation effect of the thermoelectric performance distortion;

[0139] According to the distribution characteristics of the Mahalanobis distance and the spatial correlation of the heat conduction weight coefficient, output a self-consistency verification index including a path consistency factor and a dielectric compensation efficiency;

[0140] When the self-consistency index exceeds the limit, correct the sensitivity coefficient of the mapping relationship of the dielectric constant perturbation in the direction of the Mahalanobis distance gradient, and synchronously adjust the sliding window size of the heat conduction weight coefficient.

[0141] It should be noted that to address the mismatch between dielectric compensation efficiency and thermal management requirements, which is difficult to precisely coordinate capacitor tuning and thermal conductivity characteristics in traditional GaN antenna systems under dynamic thermal environments, this embodiment establishes a self-consistent verification mechanism for thermal-electric parameters to dynamically optimize the synergistic relationship between dielectric compensation and thermal conductivity. This allows capacitor tuning to effectively offset thermally induced phase distortion while maintaining spatial consistency with the heat flow path, achieving dual optimization of thermal management and electrical performance compensation.

[0142] like Figure 3 As shown, the second aspect of the present invention discloses a multi-level intelligent overheating protection system based on a gallium nitride module antenna, which is applied to any of the multi-level intelligent overheating protection methods based on a gallium nitride module antenna described above, including:

[0143] The GaN antenna array module 1011 uses a GaN-on-SiC broadband antenna array, consisting of at least eight radiating elements. Each element has a built-in temperature sensor and micro phase-change energy storage material. The operating frequency range covers 1.2-5.8 GHz, and the element spacing is 0.75 times the center frequency wavelength.

[0144] Thermal state dynamic monitoring module 1022, integrated with the temperature sensor of the gallium nitride antenna unit, the phase change rate detection circuit of the phase change energy storage material, and the thermal inertia characteristic calculation unit, outputs in real time a thermal state vector set including the chip junction temperature gradient, substrate heat flux density, and the solid-liquid phase change ratio of the phase change material;

[0145] A virtual heat capacity network modeling module 1033 is configured to dynamically calculate heat conduction weight coefficients and radio frequency power carrying margin parameters between adjacent antenna elements based on a thermal state vector set, and generate a virtual heat capacity network topology diagram including heat migration path priorities and beam distortion compensation amounts.

[0146] The RF-thermal coupling compensation module 1044 includes an adjustable capacitor array and a distributed phase shifter. Based on the thermal migration path data of the virtual thermal capacitance network topology, it dynamically tunes the reflection coefficient of the input impedance matching network to achieve beam pointing phase predistortion compensation within ±0.25dB.

[0147] The threat scenario adaptive decision module 1055 dynamically calculates the Pareto optimal solution of the thermal load migration path and beamforming parameters based on the modulation type and pulse width characteristics of the real-time threat signal, and outputs an interference effectiveness enhancement instruction set with a frequency band priority of 1.2-1.6GHz.

[0148] The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A multi-level intelligent overheat protection method based on a gallium nitride module antenna, characterized in that It includes the following steps: Real-time analyze the coupling relationship between the phase change rate of the phase change energy storage material in the gallium nitride antenna unit and the radio frequency power loading time sequence, and generate a dynamic thermal inertia characteristic map; Construct a virtual heat capacity network model based on the dynamic thermal inertia characteristic map. The virtual heat capacity network model includes the heat conduction weight coefficient between each unit and the radio frequency power bearing margin parameter; Analyze the optimal heat load migration path based on the virtual heat capacity network model, and synchronously tune the input impedance matching network of the target unit according to the optimal heat load migration path to compensate for the beam phase distortion caused by heat migration; Inject the boundary constraint conditions of the virtual heat capacity network model according to the real-time threat scenario characteristics, dynamically solve the game equilibrium solution of the heat load migration path and the beam parameter tuning amount, and generate a thermal-radio frequency joint control instruction set.

2. The multi-level intelligent overheat protection method based on a gallium nitride module antenna according to claim 1, characterized in that, Real-time analyze the coupling relationship between the phase change rate of the phase change energy storage material in the gallium nitride antenna unit and the radio frequency power loading time sequence, and generate a dynamic thermal inertia characteristic map. Specifically: Real-time synchronously collect the phase change rate time sequence vector of multiple preset monitoring points inside the gallium nitride antenna unit and the corresponding radio frequency power loading time sequence pulse parameters, and form a spatio-temporal correlation data set after aligning the two according to the time stamp; Segment the spatio-temporal correlation data set based on a sliding time window, use the mutual information analysis method to calculate the correlation degree sequence between the phase change rate change gradient and the radio frequency power pulse amplitude and duty cycle within each time window, and extract the dynamic lag time coefficient between the two through the grey correlation degree algorithm; Input the correlation degree sequence and the lag time coefficient into a preset three-dimensional feature fusion model, map them into a thermal inertia time delay factor, a phase change heat response intensity factor, and a radio frequency power cumulative effect index in the time-frequency domain respectively, and perform piecewise linear interpolation in combination with a preset phase change rate change rate threshold to generate a dynamic thermal inertia characteristic matrix including the temperature field evolution trend; According to the spatio-temporal distribution of each factor in the dynamic thermal inertia characteristic matrix, visualize the temperature field evolution trend, heat response intensity, and power cumulative effect through a three-dimensional interpolation algorithm and an isosurface rendering technique, and generate a dynamic thermal inertia characteristic map including color gradients and contour lines.

3. The multi-level intelligent overheat protection method based on a gallium nitride module antenna according to claim 1, wherein, Construct a virtual heat capacity network model based on the dynamic thermal inertia characteristic map. Specifically: Extract the thermal inertia time delay factor, the phase change heat response intensity factor, and the radio frequency power cumulative effect index from the dynamic thermal inertia characteristic map as the model input parameters; Based on the correlation degree sequence and the lag time coefficient between each antenna unit within a sliding time window, construct the heat conduction weight coefficient using a graph theory adjacency matrix, where the correlation degree sequence is used as the edge weight after normalization, and the lag time coefficient is used to correct the weight distribution ratio of adjacent units after exponential smoothing; Non-linearly superimpose the radio frequency power cumulative effect index and the phase change heat response intensity factor, and determine the radio frequency power bearing margin parameter of each unit in combination with the gradient direction of the temperature field evolution trend; Based on the real-time updated heat conduction weight coefficient and radio frequency power bearing margin parameter, use the greedy algorithm to optimize the node connection structure of the virtual heat capacity network, and synchronously apply the heat flow balance constraint condition to ensure that the heat load migration path and the radio frequency power distribution satisfy the first law of thermodynamics; Verify the convergence of the network model through Monte Carlo random perturbation testing, and inversely correct the adjustment step of the weight coefficient and the update threshold of the margin parameter according to the statistical distribution characteristics of the perturbation response data to achieve the dynamic matching between the model parameters and the physical field changes.

4. The multi-level intelligent overheat protection method based on a gallium nitride module antenna according to claim 1, wherein Analyze the optimal heat load migration path based on the virtual heat capacity network model, and synchronously tune the input impedance matching network of the target unit according to the optimal heat load migration path to compensate for the beam phase distortion caused by heat migration. Specifically: Based on the heat conduction weight coefficient and the radio frequency power bearing margin parameter, use the Dijkstra algorithm to determine the minimum thermal resistance path between each target unit, and generate a set of candidate heat load migration paths; Combined with the gradient direction of the temperature field evolution trend, calculate the weighted average after multiplying the temperature gradient change rate between adjacent nodes on each candidate migration path by the phase change heat response intensity factor to obtain the path heat flow dynamic equilibrium factor; Compare the path heat flow dynamic equilibrium factor with the preset heat flow equilibrium threshold, eliminate the candidate migration paths with factor values greater than the preset heat flow equilibrium threshold, and retain the candidate migration paths with factor values not greater than the preset heat flow equilibrium threshold to obtain the optimal heat load migration path set; Extract the heat-induced dielectric constant change amount of the preset node according to the heat flow distribution characteristics on the optimal migration path, and determine the corresponding beam phase offset compensation value based on the radio frequency power cumulative effect index; Dynamically adjust the input impedance matching network of the target unit using the heat-induced dielectric constant change amount and the phase offset compensation value to compensate for the beam phase distortion caused by heat migration.

5. The multi-level intelligent overheat protection method based on a gallium nitride module antenna according to claim 4, characterized in that Dynamically adjust the input impedance matching network of the target unit using the heat-induced dielectric constant change amount and the phase offset compensation value to compensate for the beam phase distortion caused by heat migration. Specifically: According to the heat-induced dielectric constant change amount of each node on the optimal migration path, calculate its relative deviation rate from the nominal dielectric constant of the antenna unit substrate to generate a dielectric constant perturbation coefficient matrix; Decompose the phase offset compensation value into the phase gradient components in the azimuth dimension and the elevation dimension according to the preset beam pointing angle, and calculate the equivalent susceptance adjustment amount required for the matching network in combination with the dielectric constant perturbation coefficient matrix; Dynamically adjust the capacitance value distribution of the adjustable capacitor bank according to the equivalent susceptance adjustment amount, so that the reflection phase at the input end of the matching network cancels out the heat-induced phase distortion component; Verify the phase compensation effect by real-time monitoring the antenna port standing wave ratio. If the residual phase error exceeds the preset error threshold, use the gradient descent method to iteratively optimize the mapping relationship between the dielectric constant perturbation coefficient and the susceptance adjustment amount; Update the power bearing margin parameter in the virtual heat capacity network according to the optimized mapping relationship to form a closed-loop adaptive matching of the heat-electric-phase parameters.

6. The multi-level intelligent overheat protection method based on a gallium nitride module antenna according to claim 1, characterized in that Inject the boundary constraint conditions of the virtual heat capacity network model according to the real-time threat scenario characteristics, and dynamically solve the game equilibrium solution of the heat load migration path and the beam parameter tuning amount to generate a heat-radio frequency joint control instruction set. Specifically: Obtain the electromagnetic interference intensity distribution and the heat radiation value of the threat scenario in real time, and quantify them into a heat flux density constraint vector and a radio frequency power limit matrix as the dynamic boundary conditions of the virtual heat capacity network model; The heat conduction weight coefficient is constrained based on boundary conditions to generate a constrained thermal resistance topology map, and the heat flow accommodation space of each node is determined in combination with the radio frequency power bearing margin parameter; A two-way search is introduced on the thermal resistance topology map with a heat flux density constraint vector to eliminate the path branches that exceed the heat flow accommodation space, and a set of feasible migration paths of the heat load that meet the boundary conditions is obtained; According to the radio frequency power limit matrix of each node on the set of feasible migration paths, the maximum allowable beam scanning angle and power tuning range corresponding to the path are determined, and a path-beam coupling parameter table is generated; By solving the multi-objective optimization function of the minimum value of the path thermal resistance and the maximum value of the beam pointing stability, a Pareto optimal solution set is selected from the path-beam coupling parameter table; The thermal resistance gradient change rate and the beam phase tuning amount in the Pareto optimal solution set are linearly weighted and fused to generate a thermal-radio frequency joint control instruction set including power reallocation instructions, heat flow migration time sequence, and phase compensation parameters.

7. The multi-level intelligent overheat protection method based on a gallium nitride module antenna according to claim 1, characterized in that, The multi-level intelligent overheat protection method further includes the following steps: The temperature field distribution data of the antenna unit and the beam pointing accuracy deviation value after the execution of the instruction set are collected in real time, the offset between the actual heat flow migration path and the theoretical path is extracted, and a heat conduction error vector is generated; The correlation analysis is performed on the heat conduction error vector and the radio frequency power loading time sequence to obtain the dynamic correction factor of the heat conduction weight coefficient, and the weight distribution ratio of adjacent nodes is adjusted in combination with the lag time coefficient; The actual influence of the heat-induced dielectric constant perturbation amount is deduced inversely according to the beam pointing accuracy deviation value, and the attenuation gradient of the radio frequency power bearing margin parameter is updated in combination with the phase change heat response intensity factor; The adjacency matrix of the virtual heat capacity network is reconstructed by using the corrected heat conduction weight coefficient and power bearing margin parameter, and the network convergence is verified through the heat flow balance constraint; Based on the residual distribution of the predicted value and the measured value of the node temperature of the reconstructed network, the update step sizes of the weight coefficient and the margin parameter are adaptively adjusted.

8. A multi-level intelligent overheat protection system based on a gallium nitride module antenna, which is applied to the steps of the multi-level intelligent overheat protection method based on a gallium nitride module antenna according to any one of claims 1 to 7, characterized in that including: A gallium nitride antenna array module, using a GaN-on-SiC broadband antenna array, including at least 8 radiation units, each unit is built-in with a temperature sensor and a micro-phase change energy storage material, the working frequency band covers 1.2 - 5.8 GHz, and the element spacing is 0.75 times the center frequency wavelength; A thermal state dynamic monitoring module, integrated with a temperature sensor of a gallium nitride antenna element, a phase change rate detection circuit of a phase change energy storage material, and a thermal inertia characteristic calculation unit, and real-time outputs a set of thermal state vectors including the chip junction temperature gradient, the substrate heat flux density, and the solid-liquid phase change ratio of the phase change material; A virtual heat capacity network modeling module, used to dynamically calculate the heat conduction weight coefficient and the radio frequency power bearing margin parameter between adjacent antenna units according to the set of thermal state vectors, and generate a virtual heat capacity network topology map including the heat migration path priority and the beam distortion compensation amount; A radio frequency-thermal coupling compensation module, including an adjustable capacitor array and a distributed phase shifter, based on the heat migration path data of the virtual heat capacity network topology map, dynamically tunes the reflection coefficient of the input impedance matching network, and realizes the beam pointing phase pre-distortion compensation within ±0.25 dB; The threat scenario adaptive decision-making module dynamically calculates the Pareto optimal solutions of the thermal load migration path and beam synthesis parameters according to the modulation type and pulse width characteristics of real-time threat signals, and outputs an interference effectiveness enhancement instruction set with a frequency band priority of 1.2 - 1.6 GHz.

Images