Accelerated aging test method for antenna performance in sand and dust environment

Abstract

The invention discloses an antenna performance sand-dust environment accelerated aging test method, which relates to the technical field of antenna performance aging tests, and comprises the following steps: on the premise of establishing a unified electromagnetic field observation baseline, carrying out high-resolution electric field distribution mapping on an antenna radiation area, extracting a potential dust permeation path in a radiation gap, and calculating a dust permeation path; quantifiable field gradient priori is generated based on the electric field distribution data; under the constraint of field gradient prior, controllable vibration is applied to trigger simulation, so that dust particles generate dynamic migration under high-frequency electromagnetic excitation, and a critical accumulation area possibly forming dielectric bridging is identified according to a migration track. According to the invention, by establishing a multi-stage accelerated aging test process driven by electromagnetic field distribution, visual analysis of dust migration and dielectric bridging is realized, and active correction and steady-state regulation and control of electric field distribution are realized through a multi-scale phase traction and time reversal gating technology, so that the stability and reliability of the antenna in a wind and sand environment are improved.

Description

Technical Field

[0001] This invention relates to the field of antenna performance aging test technology, specifically to a method for accelerated aging test of antenna performance in a dusty environment. Background Technology

[0002] Accelerated aging tests for antenna performance in dusty environments simulate the erosion and wear effects of high-dust environments such as deserts and sandstorms on antennas during long-term service under controlled laboratory conditions. By accelerating the aging process of antennas in real-world environments (several years or even decades), the durability and performance degradation characteristics of antennas can be evaluated within a limited timeframe. This test is typically conducted in a sealed test chamber equipped with a sandstorm circulation system. By controlling dust particle concentration, wind speed, temperature and humidity parameters, and impact frequency, the antenna surface is subjected to continuous impact, abrasion, and deposition over a short period. The test focuses on examining changes in the antenna's electrical performance (such as VSWR, gain, and radiation pattern), structural stability (such as radiator wear and interface sealing), and material weather resistance (such as coating peeling and dielectric aging). Accelerated aging tests allow for the early identification of potential performance degradation patterns and failure modes of antennas under extreme environments, providing crucial information for antenna design optimization, material selection, reliability assessment, and service life prediction.

[0003] The existing technology has the following shortcomings: In existing technologies, antenna performance degradation in arid and dusty environments is typically attributed to surface abrasion and dust deposition. However, under strong wind and sand pulse conditions, a more insidious but extremely dangerous failure risk exists: fine dust particles, drawn by the electromagnetic field distribution, gradually infiltrate the antenna's radiating gaps and accumulate locally under high-frequency excitation. With the continuous action of vibrational energy, these dust particles gradually evolve into bridging structures with dielectric properties, thereby inducing non-uniform electric field distortion within the radiation region. This distortion not only disrupts the integrity of the original radiation pattern but may also lead to main lobe energy transfer and abnormal amplification of side lobe amplitude, resulting in a significant decrease in link gain or even communication failure. Due to the insidious and sudden nature of this problem, conventional surface deposition assessment methods struggle to identify it in a timely manner, thus posing a serious threat to the performance stability of antennas in accelerated aging tests under sand and dust conditions.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for accelerated aging test of antenna performance in a dusty environment, so as to solve the problems in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for accelerated aging test of antenna performance in a sand and dust environment, comprising the following steps: Step 1: Under the premise of establishing a unified electromagnetic field observation baseline, perform high-resolution electric field distribution mapping on the antenna radiation area, extract potential dust infiltration paths within the radiation gaps, and generate quantifiable field gradient priors based on the electric field distribution data for subsequent dust dynamic evolution monitoring. Step 2: Under the constraint of the field gradient prior, a controllable vibration-triggered simulation is applied to make the dust particles dynamically migrate under high-frequency electromagnetic excitation. The critical accumulation region that may form dielectric bridging is identified according to the migration trajectory, and an evolution risk factor matrix is ​​generated based on the characteristics of the region. Step 3: With the support of the evolutionary risk factor matrix, execute the counterfactual replay chain to replay the evolutionary process of dust accumulation in time, remove external random disturbance factors, solidify the real dust bridging pattern, and locate the potential root cause of the pattern distortion caused by the bridging pattern. Step 4: After the real dust bridging mode is solidified, run multi-scale phase traction calculation to inject micro-scale electric field disturbance into the critical gap region in order to dynamically deconstruct the dielectric continuity of the dust accumulation structure and form a suppression instruction set for subsequent closed-loop intervention execution. Step 5: Under the constraint of the suppression instruction set, the time-reversal phase gating mechanism is activated. Combined with the dynamic calibration matrix write-back module, the electromagnetic distribution is corrected in real time to achieve closed-loop steady-state control of the radiation pattern distortion, thereby suppressing performance failure caused by dust dielectric bridging.

[0007] Preferably, the steps for high-resolution electric field distribution mapping of the antenna radiation area, under the premise of establishing a unified electromagnetic field observation baseline, include: Multi-point synchronous electric field measurements were performed on the antenna radiation area under standard cleanroom conditions to obtain information on electric field intensity, phase distribution and vector direction at different spatial coordinates. The original electric field distribution matrix was formed by time-domain averaging and spectral denoising to establish a unified observation baseline for subsequent electromagnetic response comparison. Based on the observation baseline, a continuous electric field distribution mapping volume is constructed by spatial grid refinement sampling and multi-band synchronous scanning, and gradient direction analysis is performed on the mapping results to identify the electric field concentration areas at radiation gaps and interfaces. Based on the electric field distribution mapping data, the characteristics of electric field intensity gradient and direction change rate are extracted to identify potential dust infiltration paths and perform visual reconstruction in three-dimensional space. The gradient magnitude and direction information are calculated based on the electric field distribution data to generate quantifiable field gradient priors, which serve as a benchmark for monitoring the dynamic evolution of dust.

[0008] Preferably, in the process of generating the field gradient prior, the region in the electric field distribution mapping volume whose gradient magnitude exceeds three times the standard deviation of the average gradient is taken as the key calculation region. The gradient intensity matrix of this region is normalized and weighted by region to extract the average gradient index of dust infiltration into the key area, which is used to quantify the response sensitivity of the electric field to dust accumulation.

[0009] Preferably, the step of applying controllable vibration to trigger the simulation under the constraint of the field gradient prior includes: Under the constraint of the field gradient prior, a vibration triggering signal is applied to reproduce the mechanical excitation conditions in the external wind and sand environment. The vibration triggering signal is differentially modulated according to the electric field sensitivity of each spatial location in the field gradient prior, so that the high field strength gradient region can obtain high frequency and high amplitude vibration input, so as to achieve the synergistic superposition of electromagnetic and mechanical effects. During the continuous application of vibration triggering, the dynamic migration process of dust particles under high-frequency electromagnetic excitation is observed in real time by using high-resolution electric field distribution mapping, and the migration trajectory is weighted and mapped according to the field gradient prior to form the electromagnetic driving force field distribution. Cluster analysis was performed based on the spatial aggregation and energy density similarity of dust migration trajectories to identify dielectric bridging critical stacking regions with long residence times and continuous dielectric constant growth trends. An evolutionary risk factor matrix is ​​generated based on the characteristics of the critical stacking region of dielectric bridging to characterize the comprehensive probability of forming a stable dielectric bridge in each spatial sub-region, which is used for subsequent analysis of the root causes of electric field distortion and stability regulation.

[0010] Preferably, in the process of generating the evolution risk factor matrix, the field gradient prior, the rate of change of electric field intensity, the dust trajectory density, the growth rate of dielectric constant and the vibration response amplitude are used as input parameters for weighted normalization. The matrix results under different vibration periods are then superimposed over time and corrected for correlation to obtain a dynamic risk factor field that reflects the evolution trend of dust accumulation, which is used to assess the stability probability of dielectric bridging formation.

[0011] Preferably, the steps of executing the counterfactual replay chain, supported by the evolutionary risk factor matrix, include: After generating the evolution risk factor matrix, the spatial regions corresponding to the high-risk factor intervals are selected to establish the original time series sample set of dust accumulation evolution. The sample data are then sampled with time weighting based on the risk factors to achieve high-precision reconstruction of the dust accumulation evolution process. Based on the time-series sample set, the forward replay of the counterfactual replay chain is performed to calculate the dynamic evolution path of dust accumulation hour by hour. By removing external random disturbance factors through counterfactual reasoning, the pure evolution trajectory of dust accumulation under interference-free conditions is obtained. Based on the pure evolution trajectory, the stable convergence characteristics of dust accumulation in terms of spatial distribution, electric field response and dielectric properties are calculated to solidify the real dust bridging mode. Based on the difference between the dust bridging mode and the baseline electric field distribution, the potential root cause of the radiation pattern distortion caused by the dust bridging mode is located.

[0012] Preferably, during the execution of the counterfactual replay chain, by comparing the time deviation between the actual observed data of dust accumulation and the calculation results based on the evolutionary risk factor matrix, when the deviation of multiple consecutive time nodes exceeds a preset threshold, the deviation is determined to be an external random disturbance, and the environmental parameters within that time period are corrected by substitution regression to obtain the ideal evolutionary state of dust accumulation.

[0013] Preferably, the steps for running multi-scale phase traction calculations after solidification of the real dust bridging mode include: After the real dust bridging mode is solidified, the electromagnetic parameters of the mode are recalibrated, and a hierarchical analysis is performed based on the field gradient prior to establish the correspondence between electric field distortion intensity, dielectric continuity and spatial scale, so as to construct a multi-scale electric field response model. Based on the electric field response model, a spatiotemporally resolved phase perturbation function was constructed, and microscale electric field perturbation was injected into the critical gap region to induce energy redistribution of the dust bridge and cause micro-fractures in the dielectric interconnect structure. By continuously applying a gradual electric field perturbation and monitoring the phase response changes in real time, the dielectric continuity of the dust accumulation structure is dynamically deconstructed in a layer-by-layer peeling manner, restoring the uniformity of the electric field inside the radiation region. Based on phase traction calculations and dynamic deconstruction data, a set of suppression instructions is generated, which includes phase control commands, disturbance intensity parameters, and electric field recovery thresholds, for subsequent closed-loop intervention execution.

[0014] Preferably, the phase change range of the microscale electric field disturbance injected into the critical gap region is limited to within ±π / 4, and the disturbance frequency adopts a combination of microsecond-level modulation period and nanometer-level field strength gradient change to ensure that the electric field uniformity remains stable during the energy redistribution of the dust bridge and to avoid additional reflection effects on the overall radiation structure of the antenna.

[0015] Preferably, the steps of initiating the time-reversal phase-gating mechanism and combining it with dynamic calibration matrix write-back under the constraint of the suppression instruction set include: The initialization of the time-reversal phase gating mechanism is initiated under the constraints of the suppression instruction set. By reading the time-seriesd phase offset and disturbance intensity parameters in the suppression instruction set, an inversion window is established in the time domain to trace back the historical state of the electric field distribution and realize the reverse resolution of electric field distortion. By combining the coordinate information of the critical gap region obtained from multi-scale phase traction calculation, a phase modulation signal matching the suppression command set is applied to compensate for and restore the electric field uniformity of the critical region in real time, so that the direction of electric field energy flow gradually returns to the ideal distribution. Based on the time-reversal phase-gated correction results, the dynamic calibration matrix write-back process is initiated to provide real-time feedback and weight correction for the electric field error vector at each spatial location, so as to achieve accurate steady-state maintenance of the electromagnetic distribution. The steady-state control effect was verified based on the offset rate between the corrected electric field distribution and the observation baseline, and a reusable steady-state calibration model was established for subsequent automated correction.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention achieves visualization, quantification, and controllability of the performance degradation process of antennas under arid and sandy conditions by constructing a multi-stage accelerated aging test process based on electromagnetic field distribution. By generating a high-resolution electric field distribution map under a unified electromagnetic field observation baseline and combining field gradient priors and vibration-triggered simulations, the dynamic migration behavior of dust particles under high-frequency excitation can be reproduced under controlled conditions, thereby revealing the true evolution law of dust accumulation and dielectric bridging formation inside the antenna's radiating gaps. This process transforms the electromagnetic field distribution of the antenna affected by dust from unobservable to resolvable, significantly improving the physical realism and data accuracy of the sandstorm accelerated aging test.

[0017] This invention achieves active correction and closed-loop steady-state control of electromagnetic distribution under dust bridging conditions by introducing multi-scale phase-pull calculation and time-reversal phase gating technology. By injecting microscale electric field disturbances into the critical gap region and dynamically deconstructing the dielectric continuity of dust accumulation, a suppression instruction set is generated and combined with dynamic calibration matrix write-back, achieving real-time repair of radiation pattern distortion and adaptive balance of energy distribution. This scheme enables the antenna to possess self-calibration and self-suppression capabilities during wind and sand aging, effectively preventing link attenuation and radiation instability caused by dust bridging, extending antenna service life, and improving communication reliability in extreme environments. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a flowchart of a method for accelerating the aging test of antenna performance in a dusty environment according to the present invention. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] This invention provides, for example Figure 1 The method for accelerated aging test of antenna performance in a dusty environment, as shown, includes the following steps: Step 1: Under the premise of establishing a unified electromagnetic field observation baseline, perform high-resolution electric field distribution mapping on the antenna radiation area, extract potential dust infiltration paths within the radiation gaps, and generate quantifiable field gradient priors based on the electric field distribution data for subsequent dust dynamic evolution monitoring. To achieve controllable and quantifiable accelerated aging simulation based on the mechanisms of real-world aeolian sandstorm environments, a unified electromagnetic field observation baseline must first be established at the initial stage of the experiment. The entire process includes the following sub-steps: First, when establishing a unified electromagnetic field observation baseline, the radiation area of ​​the target antenna under standard dust-free conditions is selected as the reference object. A multi-point synchronous measurement array is deployed in three-dimensional space to acquire information on the electric field intensity, phase distribution, and vector direction at different spatial coordinates. This observation process is conducted in a closed environment to avoid interference from external random electromagnetic noise. After time-domain averaging and spectral denoising, the measurement data forms the original electric field distribution matrix. This matrix is ​​defined as the benchmark dataset for subsequent electromagnetic responses, used to characterize the inherent radiation characteristics of the antenna under ideal conditions. By establishing this observation baseline, an absolute reference can be provided for any subsequent electric field changes caused by dust disturbances, ensuring that all subsequent offset calculations have a unified coordinate framework and numerical comparability.

[0022] After establishing the electromagnetic field observation baseline, a high-resolution electric field distribution mapping was performed on the antenna radiation area. This process involved refining the sampling on a spatial grid, controlling the spacing between each observation point within one-hundredth to one-thousandth of the electromagnetic wavelength to ensure the capture of minute electric field gradient changes near the radiation gaps and feed channels. Using multi-band synchronous scanning technology, the field strength vectors at different operating frequencies were overlapped and fitted to construct a continuous electric field distribution mapping volume. This mapping volume, with three-dimensional spatial coordinates as independent variables and electric field amplitude and phase as dependent variables, forms a spatially continuous and differentiable function model. This model not only reproduces the antenna's energy distribution in the spatial domain but also reveals the transition characteristics between local electric field concentration areas and field strength attenuation areas within the radiation region. By analyzing the gradient direction of the mapping results, the concentration trend of electric field lines at gaps, interfaces, and corners can be intuitively identified, providing a basis for the electric field energy driving force for subsequent dust migration path analysis.

[0023] After obtaining the high-resolution electric field distribution mapping, gradient feature extraction is performed on the mapped data to identify potential dust infiltration paths within the radiative gaps. This process is based on the calculation of the electric field intensity gradient and the rate of change of direction, selecting regions where the gradient amplitude exceeds three standard deviations of the average gradient as the key analysis area. By calculating the electric field linear density and field vector continuity in these regions, several possible path channels guiding dust particle movement can be obtained. Since dust is subjected to both dielectric and Coulomb forces in high-frequency electromagnetic fields, the path channels typically extend along regions with high electric field linear density and stable gradient direction. Therefore, the analysis considers not only the electric field amplitude but also the phase gradient and energy flux density parameters for comprehensive judgment to ensure that the identified paths accurately reflect the migration trend of dust under strong field conditions. Subsequently, these paths are visualized and reconstructed in three-dimensional space to obtain a complete dust infiltration channel distribution map, which serves as the input condition for dust dynamics simulation, providing precise spatial constraint boundaries for subsequent dust dynamic evolution monitoring.

[0024] After extracting the potential dust infiltration paths within the radiating slits, a quantifiable field gradient prior is generated based on the obtained electric field distribution data, serving as a benchmark for subsequent monitoring of dust dynamic evolution. The generation method for this field gradient prior includes three consecutive calculation stages: First, the gradient magnitude and direction information at each point in the electric field distribution are calculated using gradient operators, and then differen from the original electric field data in the observation baseline to quantify the sensitivity to local electric field distortion potentially caused by dust infiltration; second, the difference results are normalized to form a gradient intensity matrix corresponding one-to-one with spatial location, thus establishing a ternary correlation system of gradient magnitude, spatial coordinates, and electric field disturbance sensitivity; third, based on the geometric characteristics of the antenna radiation area, the above gradient intensity matrix is ​​regionally weighted and averaged to extract the average gradient index representing the key area of ​​dust intrusion, which is defined as the field gradient prior. This field gradient prior not only reflects the response characteristics of the electric field to dust accumulation at different spatial locations but also serves as a quantitative input basis for subsequent dynamic monitoring and risk assessment. By continuously comparing the offset between the real-time measured electric field distribution and the prior in subsequent steps, the trend of dust penetration and dielectric bridging can be determined in real time, thereby establishing a dynamic and traceable monitoring closed loop in accelerated aging tests.

[0025] By implementing the above steps, not only was a precise characterization of the electromagnetic properties of the antenna radiation region achieved in all dimensions, but also an electric field reference system that can evolve and be updated over time was established, enabling the dust infiltration behavior to be quantitatively characterized and predictable at the electromagnetic level.

[0026] Step 2: Under the constraint of the field gradient prior, a controllable vibration-triggered simulation is applied to make the dust particles dynamically migrate under high-frequency electromagnetic excitation. The critical accumulation region that may form dielectric bridging is identified according to the migration trajectory, and an evolution risk factor matrix is ​​generated based on the characteristics of the region. After establishing the field gradient prior, in order to further reveal the dynamic migration mechanism of dust under the action of high-frequency electromagnetic fields and simulate its evolution under long-term service conditions, it is necessary to apply controllable vibration-triggered simulation under the constraint of the field gradient prior to achieve a realistic reproduction of the migration of dust particles under high-frequency electromagnetic excitation. Furthermore, by analyzing the migration trajectory, critical accumulation regions that may form dielectric bridging can be identified, thus providing a data foundation for the subsequent construction of the evolution risk factor matrix. The entire process includes the following sub-steps: Under the constraint of the field gradient prior, the mechanical excitation conditions experienced by the antenna structure in an external windy and sandy environment are reproduced by applying a vibration trigger signal. This vibration trigger signal is differentially modulated according to the electric field sensitivity at different spatial locations in the field gradient prior, so that the high field gradient region receives a higher frequency and amplitude vibration input, while the low gradient region receives a lower intensity excitation, maintaining the consistency of electromagnetic and mechanical interactions. The vibration frequency is typically selected from 10 Hz to 5 kHz to simultaneously simulate different physical disturbance modes such as micro-vibrations, wind impacts, and high-frequency flutter. During this process, the vibration direction is switched between perpendicular and parallel to the field gradient direction to detect the influence of vibrations in different directions on the dust movement path. By controlling the vibration duration and the number of periodic repetitions, the cumulative effect of dust vibration migration over several years of natural service can be reproduced more quickly within a finite time. The key to this stage is to create a spatially consistent superposition effect between the mechanical disturbance and the electromagnetic field gradient, so that the migration dynamics of dust particles include both dielectric force and inertial impact components, achieving composite field coupling excitation.

[0027] While the controlled vibration triggering effect is continuously applied, the dynamic migration process of dust particles under high-frequency electromagnetic excitation is observed in real time using the high-resolution electric field distribution mapping established in the previous stage. By deploying multi-point electric field sensing arrays and optical tracking units in the radiation gap and key field gradient regions, the spatial coordinates, charge polarity, and velocity vector information of dust particles at different time points are collected. The collected particle trajectory data are used to form a continuous migration curve through temporal fitting and spatial interpolation. Since the electric field sensitivity index for different regions has been given in the field gradient prior, the migration curve can be weighted and mapped, superimposing the dust motion trajectory with the electric field response characteristics to obtain the electromagnetic driving force field distribution of dust migration. This distribution reflects the force direction and energy accumulation point of dust particles of different sizes under electric field constraints, thus identifying where dust is prone to stagnation, accumulation, or rotational aggregation, among other microscopic dynamic behaviors. By continuously observing the vibration triggering response over multiple cycles, the motion statistical characteristics of the dust group in the time and frequency domains can be obtained, providing multi-dimensional data support for subsequent identification of dielectric bridging formation regions.

[0028] After acquiring the dynamic migration trajectories of dust particles, cluster analysis is performed on the trajectories to identify critical accumulation regions that may form dielectric bridging. Specifically, the migration curves are clustered based on spatial similarity and energy density similarity. When the residence time of multiple dust trajectories in the same spatial region exceeds twice the standard deviation of the average and the energy decay rate is lower than a preset threshold, the region is identified as a potential accumulation region. Subsequently, the rate of change of the equivalent dielectric constant of the dust particles in this region is calculated to determine whether the conditions for forming a stable bridging structure are met. If the calculation results show a continuous trend in dielectric constant growth and spatial connectivity, the region is defined as a dielectric bridging critical region. This process relies not only on the spatial aggregation of dust trajectories but also on the sensitivity to local electric field distortion in the field gradient prior to ensure that the identification results are highly consistent with changes in the electromagnetic environment. Through repeated verification using multiple vibration-triggered experiments, the spatial distribution statistical characteristics of the dielectric bridging critical region can be obtained, indicating that this region has high dust accumulation stability under different excitation conditions.

[0029] After identifying the critical accumulation regions for dielectric bridging, an evolutionary risk factor matrix is ​​generated based on the characteristics of these regions. This matrix uses field gradient priors, electric field intensity change rate, dust trajectory density, dielectric constant growth rate, and vibration response amplitude as input parameters, and forms a multidimensional risk characterization through weighted normalization. Each matrix unit corresponds to a spatial sub-region, and its value reflects the comprehensive probability of stable dielectric bridging forming in that sub-region. To ensure the temporal consistency of the risk factor matrix, the matrix results obtained under different vibration periods are time-stacked and correlation-corrected, thus obtaining a dynamic risk factor field reflecting the dust accumulation evolution trend. This evolutionary risk factor matrix contains both the static sensitivity of spatial location to the electric field response and the temporal evolution information during dust migration and accumulation, which can be directly called in subsequent counterfactual playback steps to trace the generation path of electric field distortion and the formation mechanism of dust bridging. By generating the evolutionary risk factor matrix, the entire vibration-triggered simulation stage not only achieves visualization and quantification of dust migration behavior but also constructs a risk prediction model under electromagnetic-mechanical coupling conditions, providing a scientific basis for subsequent electric field stability control.

[0030] Through the coordinated implementation of the above steps, a precise simulation of the entire process of dust dynamic migration under the prior constraint of field gradient was achieved. This process, for the first time, unifies electromagnetic field gradient, mechanical vibration response, and dust accumulation evolution within the same experimental framework, enabling quantitative characterization and repeatability of dust at the three levels of force, motion, and accumulation.

[0031] Step 3: With the support of the evolutionary risk factor matrix, execute the counterfactual replay chain to replay the evolutionary process of dust accumulation in time, remove external random disturbance factors, solidify the real dust bridging pattern, and locate the potential root cause of the pattern distortion caused by the bridging pattern. After obtaining the evolutionary risk factor matrix, to further reveal the dynamic evolutionary patterns in the dust accumulation process and eliminate the interference of environmental noise and experimental disturbances on the data, a counterfactual replay chain needs to be executed with the support of the evolutionary risk factor matrix. This replays the evolutionary process of dust accumulation in time, strips away external random disturbances, solidifies the true dust bridging pattern, and identifies the potential root causes of pattern distortion caused by this bridging pattern. The entire process includes the following sub-steps: After generating the evolutionary risk factor matrix, an original time-series sample set of dust accumulation evolution is established by selecting the spatial regions corresponding to high-risk factor intervals in the matrix. This sample set originates from continuous observation data collected during the vibration-triggered simulation phase, including dust particle position sequences, electric field intensity sequences, dielectric constant change sequences, and radiation pattern shift sequences. To ensure the accuracy of time-series replay, all observation data are synchronously rearranged according to sampling time, ensuring a one-to-one spatial correspondence between the data at each time point. Subsequently, using the values ​​of the evolutionary risk factor matrix as time-weighted parameters, the dust accumulation state at different times in the sample set is reconstructed on a time scale, giving higher temporal resolution to the evolutionary behavior of high-risk regions, while the changes in low-risk regions are appropriately smoothed. Through this risk factor-based weighted sampling, a high-precision reconstruction of the entire accumulation evolution process can be achieved within a limited time window, providing a reliable time-series foundation for the construction of the counterfactual replay chain.

[0032] After establishing the original time-series sample set, a forward replay stage of the counterfactual replay chain is executed. The goal of this stage is to recreate the dynamic evolution path of dust accumulation under specific conditions based on existing data, in order to analyze the impact of environmental disturbances on the formation of accumulation patterns. Specifically, using the dust accumulation state from time t0 to t1 as a reference sequence, the migration, collision, aggregation, and desorption behaviors of dust particles are calculated recursively time-by-time, and the accumulation state at each time point is cross-mapped with the corresponding parameters in the evolutionary risk factor matrix. When the deviation between the actual observed data and the results calculated based on risk extrapolation exceeds a preset threshold at multiple consecutive time points, it is determined that the deviation originates from external random disturbances rather than the intrinsic mechanism of the accumulation process. At this time, the environmental parameters (such as temperature and humidity fluctuations, airflow velocity changes, or vibration noise) within this period are replaced and regressed using counterfactual reasoning methods to obtain the ideal state of dust accumulation under conditions without external disturbances. This inversion calculation is executed cyclically within multiple time windows until the disturbance effects in the entire time-series chain are completely eliminated. This process yields a pure evolutionary trajectory of dust accumulation that is unaffected by random noise, laying the foundation for the solidification of the true bridging mode.

[0033] After completing the forward replay and perturbation removal of the counterfactual replay chain, the process enters the solidification stage of the real dust bridging mode. This stage uses the clean evolution trajectory after perturbation removal as input to calculate the stable convergence characteristics of dust accumulation in terms of spatial distribution, electric field response, and dielectric properties. Specifically, this includes three aspects: first, determining the geometric similarity of the dust accumulation morphology across multiple time steps through continuous frame difference calculations; second, evaluating the local field strength gain and phase drift of the accumulation region through an electric field reconstruction algorithm; and third, detecting whether the dielectric connectivity of the dust accumulation region reaches the bridging critical threshold through dielectric constant curve fitting. When all three indicators simultaneously meet the steady-state condition, the spatial region is determined to be a real dust bridging mode. This mode has a repeatable spatial structure and stable electromagnetic response characteristics, accurately reflecting the energy accumulation path of dust accumulation under a strong electric field. By solidifying this mode, the originally time-varying and uncertain dust accumulation behavior can be transformed into a describable and verifiable steady-state structure, thus providing a solid basis for quantitative analysis of the root causes of pattern distortion.

[0034] After the actual dust bridging pattern is solidified, the potential root causes of antenna pattern distortion are located. This step is based on differential analysis between the solidified dust bridging pattern and the baseline electric field distribution to calculate the impact of the bridging region on the antenna radiation characteristics. By comparing the pattern differences with and without dust bridging, the main lobe energy shift, sidelobe amplitude amplification, and beam pointing drift angle are identified. Subsequently, these pattern deviation parameters are inversely mapped to the evolution risk factor matrix to determine the key bridging regions and their evolution stages that lead to specific pattern distortions. Furthermore, by replaying the accumulation trajectory of this region at different time points, the specific time window of distortion formation and the characteristics of the dominant dust population can be traced, thereby revealing the physical root cause of pattern distortion. This process not only locates the failure location but also clarifies the evolution mechanism of distortion, providing precise target inputs for subsequent phase-pull calculations and time-reversal control.

[0035] Through the implementation of the above steps, supported by the evolutionary risk factor matrix, the time-series replay and causal inversion of the entire dust accumulation process were achieved. This process overcomes the limitations of traditional accelerated aging tests, which can only observe static deposition morphology, and achieves a leap from recording phenomena to tracing mechanisms. The introduction of the counterfactual replay chain allows for the reconstruction of the time-series information of the dust accumulation process, and through perturbation stripping and pattern solidification, the purification and stabilization analysis of the electromagnetic response is achieved. This method establishes a direct causal link between antenna radiation characteristic degradation and microscopic dust bridging morphology, enabling the root causes of radiation pattern distortion to be visualized and verifiable.

[0036] Step 4: After the real dust bridging mode is solidified, run multi-scale phase traction calculation to inject micro-scale electric field disturbance into the critical gap region in order to dynamically deconstruct the dielectric continuity of the dust accumulation structure and form a suppression instruction set for subsequent closed-loop intervention execution. After executing the counterfactual replay chain based on the evolutionary risk factor matrix, a realistic dust bridging mode consistent with actual sandstorm environments has been obtained. To further verify and control the impact of this dust bridging mode on the electromagnetic field distribution, multi-scale phase traction calculations need to be run. By injecting microscale electric field perturbations into the critical gap region, the dielectric continuity of the dust accumulation structure is dynamically deconstructed, thereby forming an executable suppression instruction set, providing a direct basis for subsequent closed-loop intervention. The entire process includes the following sub-steps: First, after the actual dust bridging pattern is solidified, preparations are made for multi-scale phase-traction calculations. This stage begins with recalibrating the electromagnetic parameters of the solidified dust bridging pattern, measuring and calculating its equivalent dielectric constant, equivalent conductivity, and spatial distribution gradient. By performing a layered analysis of the dust bridging structure at different scales, the overall bridging body is divided into three regions: a macroscopic stacking layer, a dielectric transition layer, and a particle contact layer. The electromagnetic response characteristics of each layer are cross-compared with prior field gradients to determine its dominant contribution to electric field line distortion. By establishing the correspondence between electric field distortion intensity, dielectric continuity, and spatial scale, a multi-scale electric field response model can be constructed, providing hierarchical input parameters for subsequent phase-traction calculations. This step ensures that the applied electric field perturbation accurately acts on the region with the largest contribution to electromagnetic distortion without causing additional reflection effects in non-target regions.

[0037] In the multi-scale phase traction calculation, based on the aforementioned multi-scale electric field response model, a phase perturbation function with spatiotemporal resolution is constructed and applied as an external excitation source to the critical gap region. This phase perturbation function achieves continuous injection of microscale electric field perturbations by setting a microsecond-level modulation period in the time dimension and applying a nanometer-level field strength gradient change in the spatial dimension. The phase change range of the perturbation is typically controlled within ±π / 4 to avoid disrupting the stability of the overall radiation structure. Through iterative traction of different phase combinations, the electric field line distribution can be locally adjusted without changing the macroscopic radiation direction, causing a perturbation split in the dielectric continuity within the dust bridge. The key to this stage is utilizing the temporal correlation of the multi-scale phase traction calculation to create a continuously and gradually changing phase traction field in space, thereby inducing a local energy redistribution within the dust bridge. This energy redistribution disrupts the electrostatic attraction balance between particles within the accumulation, leading to minute fractures in the dielectric connectivity structure of the dust bridge, providing a physical basis for subsequent dynamic deconstruction.

[0038] After multi-scale phase-traction calculations are completed, the dielectric continuity of the dust accumulation structure within the critical gap region is dynamically deconstructed. This process involves continuously applying progressively small-amplitude electric field perturbations and monitoring changes in the electric field phase response in real time. Specifically, the breakdown process of the dielectric channel in the dust bridge is determined by measuring the instantaneous phase shift, energy attenuation rate, and current density changes at different locations within the critical gap region. When the monitoring results show a periodic reversal in the phase response or a sudden increase in the local energy release rate, it indicates that the dielectric bridge structure has been deconstructed. At this point, by fine-tuning the perturbation frequency and amplitude, the deconstruction process is carried out layer by layer, gradually progressing from the external non-critical areas to the internal high-dielectric-connection areas. Throughout the entire dynamic deconstruction process, the field gradient prior is always used as a constraint to prevent the deconstruction process from disrupting the overall electric field distribution balance. Finally, through continuous multiple rounds of dynamic cycles of deconstruction and restoration, the dielectric connectivity of the dust bridge can be completely destroyed without damaging the main antenna structure, thereby restoring the uniformity of the electric field within the radiation region.

[0039] After the dielectric continuity of the dust accumulation structure is dynamically deconstructed, a suppression instruction set for subsequent closed-loop intervention is generated based on the full-process data of phase traction calculation and electric field deconstruction. This suppression instruction set consists of a set of time-series-based phase control instructions, spatial disturbance intensity parameters, and electric field recovery thresholds. The generation process includes three stages: first, extracting the key disturbance modes that trigger dielectric deconstruction based on the multi-scale phase traction calculation results; second, performing optimal energy combination on these disturbance modes to achieve maximum deconstruction efficiency with minimum input power; and third, determining the final phase traction cutoff condition and recovery boundary based on the electric field recovery trend after dielectric deconstruction. This suppression instruction set records the phase offset and disturbance intensity at each moment in digital instruction form and guides the real-time adjustment of the electric field distribution in subsequent experiments through a closed-loop write-back mechanism. By applying this instruction set, real-time suppression of dust bridging formation can be repeatedly achieved in future accelerated aging experiments in dusty environments, thus realizing complete closed-loop control from identification to intervention.

[0040] Through the implementation of the above steps, active electromagnetic field deconstruction and precise phase-level control were achieved based on a real dust bridging mode. This process introduces multi-scale phase traction into the field of electromagnetic deconstruction of dust accumulation, enabling the experimental method to have active intervention capabilities. This implementation method achieves non-contact deconstruction of dielectric bridging through micro-scale electric field perturbation, providing a new technical path for adaptive repair and performance maintenance of antennas in extreme environments.

[0041] Step 5: Under the constraint of the suppression instruction set, the time-reversal phase gating mechanism is activated. Combined with the dynamic calibration matrix write-back module, the electromagnetic distribution is corrected in real time to achieve closed-loop steady-state control of the radiation pattern distortion, thereby suppressing the performance failure caused by dust dielectric bridging. After a suppression instruction set is formed through multi-scale phase-pull calculations, in order to achieve closed-loop steady-state control of the electric field distortion caused by dust dielectric bridging, a time-reversal phase gating mechanism needs to be activated under the constraints of the suppression instruction set, and combined with dynamic calibration matrix write-back to correct the electromagnetic distribution in real time. The core objective of this stage is to transform the local intervention capability obtained from the aforementioned phase-pull calculations into a global steady-state control capability, thereby maintaining the structural integrity and radiation uniformity of the antenna pattern under dynamic dust conditions and preventing gain drift and link failure caused by dust bridging. The entire process includes the following sub-steps: Under the constraints of the suppression instruction set, the initialization of the time-reversal phase gating mechanism is initiated. This mechanism uses the previously formed suppression instruction set as input, and establishes a reversal window in the time domain by reading the time-seriesd phase offset and disturbance intensity parameters. This reversal window is used to trace back the historical state of the electric field distribution at a specific moment to identify key time nodes leading to non-uniform electric field distribution. The basic principle of time-reversal phase gating is to compare the offset between the current measured phase response of the electric field and the target phase trajectory, and to gradually adjust the phase of the input signal in a time-reversal manner, so that the electric field distribution approaches the original ideal state along the reverse evolution path. In this stage, a high-precision time synchronization mechanism limits each reversal period to the nanosecond level to ensure the accuracy of capturing transient changes in the electric field. Through this process, reverse resolution of electric field distortion can be achieved in the time dimension, thus laying the foundation for subsequent steady-state correction in the spatial dimension.

[0042] After the time-reversal phase-gating mechanism has stabilized, the critical gap region coordinate information obtained from the aforementioned multi-scale phase-traction calculations is used to apply a phase modulation signal matching the suppression command set to this region. This modulation signal contains phase correction commands dynamically generated by the time-reversal window, aiming to restore the electric field uniformity of the critical gap region through real-time phase compensation. Specifically, the phase change rate of the modulation signal is determined by the disturbance intensity parameters recorded in the suppression command set, while its phase modulation amplitude is dynamically adjusted based on the real-time electric field offset. During this process, the energy flow direction of the electric field in space gradually recovers to that of ideal radiation. Figure 1 The distribution state is consistent. Since the dust bridge is in a discontinuous state after the dielectric deconstruction in the previous stage, the electric field reconstruction at this time can gradually repair the pattern distortion without inducing new bridging. Through continuous phase gating adjustment, the electric field forms a periodic recurring fluctuation in space, thereby achieving a dynamic balance of electromagnetic distribution.

[0043] After multiple rounds of phase compensation are completed by the time-reversal phase-gated mechanism, a dynamic calibration matrix write-back process is initiated to achieve precise correction of the electromagnetic distribution. This process uses the electric field phase correction data obtained in the time-reversal stage as input to construct a dynamic calibration matrix, which describes the electric field error vector at different time points for each spatial location. This matrix feeds the correction parameters back to the electric field observation baseline through a step-by-step write-back method to update the baseline state and ensure consistency with the current corrected electric field distribution. The core of the dynamic calibration matrix write-back lies in its real-time performance and adaptability; that is, it compares the difference in electric field amplitude before and after correction within each inversion cycle and automatically adjusts the write-back weights to gradually approach the steady-state target value. This process not only ensures the spatial consistency of the electric field distribution but also effectively suppresses local reflections or energy retention caused by residual dielectric particles in dust. Through continuous matrix write-back and recalculation, the spatiotemporal evolution of the entire electromagnetic distribution tends to stabilize, thereby achieving steady-state maintenance of the radiation pattern under dynamic dust interference.

[0044] After the dynamic calibration matrix is ​​written back and the electromagnetic distribution stabilizes, the closed-loop steady-state control effect on pattern distortion is verified and consolidated. This stage first calculates the recovery degree of the main lobe and sidelobe energy distribution based on the offset rate between the observed baseline and the current electric field distribution. When the main lobe power recovery rate exceeds 95% and the sidelobe amplitude deviation is less than 0.5 dB, steady-state control is considered successful. Subsequently, a steady-state calibration model is established by integrating the execution results of the suppression instruction set, the phase compensation curve of the time-reversal gating, and the electric field differential data after the dynamic calibration matrix is ​​written back, forming a reusable closed-loop control strategy. In subsequent accelerated aging tests, this steady-state calibration model can be directly invoked to achieve automated correction of pattern distortion under different dust bridging configurations. Through the implementation of this final stage, the antenna possesses a comprehensive capability of real-time self-calibration, dynamic repair, and steady-state maintenance under accelerated aging conditions caused by dust dielectric bridging.

[0045] Through the coordinated implementation of the above steps, the time-reversal phase gating mechanism and dynamic calibration matrix write-back achieve real-time correction and closed-loop steady-state control of the electromagnetic distribution. This process not only transforms electromagnetic disturbances from a passive response to active control, but also introduces the concept of time reversal in accelerated aging tests, realizing the reversible evolution of the electric field distribution and the self-recovery of the radiation pattern. This implementation method allows the antenna's radiation performance under extreme dust conditions to no longer rely on passive protection, but achieves active compensation and dynamic stability through inherent electromagnetic feedback. Through this time-space coupled closed-loop control mechanism, long-term stable electrical performance can be guaranteed for space communication antennas, vehicle-mounted communication antennas, and extreme climate monitoring antennas in practical applications.

[0046] This invention achieves visualization, quantification, and controllability of the performance degradation process of antennas under arid and sandy conditions by constructing a multi-stage accelerated aging test process based on electromagnetic field distribution. By generating a high-resolution electric field distribution map under a unified electromagnetic field observation baseline and combining field gradient priors and vibration-triggered simulations, the dynamic migration behavior of dust particles under high-frequency excitation can be reproduced under controlled conditions, thereby revealing the true evolution law of dust accumulation and dielectric bridging formation inside the antenna's radiating gaps. This process transforms the electromagnetic field distribution of the antenna affected by dust from unobservable to resolvable, significantly improving the physical realism and data accuracy of the sandstorm accelerated aging test.

[0047] This invention achieves active correction and closed-loop steady-state control of electromagnetic distribution under dust bridging conditions by introducing multi-scale phase-pull calculation and time-reversal phase gating technology. By injecting microscale electric field disturbances into the critical gap region and dynamically deconstructing the dielectric continuity of dust accumulation, a suppression instruction set is generated and combined with dynamic calibration matrix write-back, achieving real-time repair of radiation pattern distortion and adaptive balance of energy distribution. This scheme enables the antenna to possess self-calibration and self-suppression capabilities during wind and sand aging, effectively preventing link attenuation and radiation instability caused by dust bridging, extending antenna service life, and improving communication reliability in extreme environments.

[0048] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An antenna performance sand and dust environment accelerated aging test method, characterized in that, The method comprises the following steps: Step 1: Under the premise of establishing a unified electromagnetic field observation baseline, high-resolution electric field distribution mapping is performed on the antenna radiation area, potential dust infiltration paths in the radiation gap are extracted, and a quantifiable field gradient prior is generated based on the electric field distribution data; Step 2: Under the constraint of the field gradient prior, a controllable vibration trigger simulation is applied, so that dust particles produce dynamic migration under high-frequency electromagnetic excitation, the critical accumulation area that may form dielectric bridging is identified according to the migration trajectory, and an evolution risk factor matrix is generated based on the area characteristics; Step 3: Under the support of the evolution risk factor matrix, a counterfactual playback chain is executed, the evolution process of dust accumulation is played back in time sequence, external random disturbance factors are stripped, the real dust bridging mode is solidified, and the potential root cause of the directional diagram distortion caused by the bridging mode is located; Step 4: After the real dust bridging mode is solidified, a multi-scale phase traction algorithm is run, micro-scale electric field disturbance is injected into the critical gap area, the dielectric continuity of the dust accumulation structure is dynamically deconstructed, and an inhibition instruction set is formed; Step 5: Under the constraint of the inhibition instruction set, a time reversal phase gating mechanism is started, the dynamic calibration matrix write-back module is combined, the electromagnetic distribution is corrected in real time, and closed-loop stable regulation and control of the directional diagram distortion are realized.

2. The method according to claim 1, wherein, The step of performing high-resolution electric field distribution mapping on the antenna radiation area under the premise of establishing a unified electromagnetic field observation baseline comprises: Under the standard dust-free state, multi-point synchronous electric field measurement is performed on the antenna radiation area, the electric field intensity, phase distribution and vector direction information at different spatial coordinates are obtained, and time domain averaging and spectral denoising processing are performed to form an original electric field distribution matrix, so as to establish a unified observation baseline for subsequent electromagnetic response comparison; On the basis of the observation baseline, a continuous electric field distribution mapping body is constructed through spatial grid refinement sampling and multi-band synchronous scanning, and gradient direction analysis is performed on the mapping results to identify the electric field concentration area at the radiation gap and the interface; According to the electric field distribution mapping data, the electric field intensity gradient and direction change rate characteristics are extracted, the potential dust infiltration path is identified, and visual reconstruction is performed in three-dimensional space; According to the electric field distribution data, the gradient amplitude and direction information are calculated to generate a quantifiable field gradient prior, which serves as a benchmark reference for dust dynamic evolution monitoring.

3. The method according to claim 2, wherein In the process of generating the field gradient prior, the area with a gradient amplitude exceeding three times the average gradient standard deviation in the electric field distribution mapping body is taken as the key calculation area, the gradient intensity matrix of the area is normalized and regionally weighted averaged to extract the average gradient index of the dust infiltration key area.

4. The method of claim 1, wherein the sand and dust environment is a sandstorm environment. The step of applying controllable vibration trigger simulation under the constraint of the field gradient prior comprises: Under the constraint of the field gradient prior, the vibration trigger signal is loaded to reproduce the mechanical excitation conditions in the external sand environment, the vibration trigger signal is differentially modulated according to the electric field sensitivity of each spatial position in the field gradient prior, so that the high-field strength gradient area obtains high-frequency high-amplitude vibration input; During the vibration trigger continuous application, the dynamic migration process of dust particles under high-frequency electromagnetic excitation is observed in real time by using high-resolution electric field distribution mapping, and the migration trajectory is weighted and mapped according to the field gradient prior to form an electromagnetic driving force field distribution; According to the spatial aggregation and energy density similarity of the dust migration trajectory, clustering analysis is performed to identify the dielectric bridging critical accumulation region with long residence time and continuous dielectric constant growth trend; Based on the characteristics of the dielectric bridging critical accumulation region, an evolution risk factor matrix is generated to represent the comprehensive probability of each spatial sub-region forming a stable dielectric bridge.

5. The method according to claim 4, wherein the sandstorm environment is a sandstorm environment in which the wind speed is 15 m / s or more and the sand particle size is 0.1 μm or less. During the generation of the evolution risk factor matrix, the field gradient prior, electric field intensity change rate, dust trajectory density, dielectric constant growth rate and vibration response amplitude are weighted and normalized as input parameters, and the matrix results under different vibration periods are time superimposed and correlation corrected to obtain a dynamic risk factor field reflecting the dust accumulation evolution trend.

6. The method of claim 1, wherein the sand and dust environment accelerated aging test method of antenna performance is characterized by, The steps of executing the counterfactual playback chain under the support of the evolution risk factor matrix include: After generating the evolution risk factor matrix, the spatial region corresponding to the high risk factor interval is selected to establish the original time sequence sample set of dust accumulation evolution, and the sample data is time weighted and resampled according to the risk factor; Based on the time sequence sample set, the forward playback of the counterfactual playback chain is executed, the dynamic evolution path of the dust accumulation is calculated recursively, and the external random disturbance factors are removed through counterfactual reasoning to obtain the pure evolution trajectory of the dust accumulation under the condition of no interference; Based on the pure evolution trajectory, the stable convergence characteristics of the dust accumulation in the spatial distribution, electric field response and dielectric properties are calculated, and the real dust bridging mode is solidified; According to the difference between the dust bridging mode and the baseline electric field distribution, the potential root cause of the directional diagram distortion caused by the dust bridging mode is located.

7. The method according to claim 6, wherein During the execution process of the counterfactual playback chain, by comparing the time deviation between the actual observation data of the dust accumulation and the calculation results based on the evolution risk factor matrix, when the deviation of continuous multiple time nodes exceeds the preset threshold, it is determined that the deviation is an external random disturbance, and the environmental parameters in this time period are corrected through replacement regression to obtain the ideal evolution state of the dust accumulation.

8. The method of claim 1, wherein the sand and dust environment is a desert environment. The steps of running the multi-scale phase traction algorithm after the real dust bridging mode is solidified include: After the real dust bridging mode is solidified, the electromagnetic parameters of the mode are recalibrated, and hierarchical analysis is performed according to the field gradient prior to establish the correspondence between the electric field distortion intensity, dielectric continuity and spatial scale, and to construct a multi-scale electric field response model; According to the electric field response model, a phase disturbance function with spatial and temporal resolution is constructed, and a micro-scale electric field disturbance is injected into the critical gap region to induce energy redistribution of the dust bridging body and promote the fine fracture of the dielectric connected structure; Gradual electric field disturbance is continuously applied and the phase response change is monitored in real time to dynamically deconstruct the dielectric continuity of the dust accumulation structure in a layer-by-layer manner to restore the internal electric field uniformity of the radiation region; According to the phase traction algorithm and the dynamic deconstruction data, an inhibition instruction set containing phase control instructions, disturbance intensity parameters and electric field recovery thresholds is generated.

9. The method according to claim 8, wherein the sandstorm environment is a sandstorm environment in which the wind speed is 15 m / s or more and the sand particle size is 0.1 μm or less. The phase variation range of the micro-scale electric field disturbance injected into the critical gap region is limited within ±π / 4, and the disturbance frequency adopts a combination of microsecond-level modulation period and nanometer-level field strength gradient variation.

10. The method of claim 1, wherein the sand and dust environment accelerated aging test method is characterized by, The step of starting the time reversal phase gating mechanism under the constraint of the inhibition instruction set and combining the dynamic calibration matrix write-back includes: The initialization of the time reversal phase gating mechanism under the constraint of the inhibition instruction set is performed by reading the time-sequenced phase offset and disturbance intensity parameters in the inhibition instruction set, establishing an inversion window in the time domain to backtrack the electric field distribution history state, and realizing the reverse elimination of the electric field distortion; Based on the critical gap region coordinate information obtained by the multi-scale phase traction algorithm, a phase modulation signal matched with the inhibition instruction set is applied to compensate and restore the electric field uniformity of the critical region in real time, so that the electric field energy flow direction gradually returns to the ideal distribution; Based on the time reversal phase gating correction result, a dynamic calibration matrix write-back process is started to perform real-time feedback and weight correction on the electric field error vector of each spatial position; The steady-state regulation effect is verified according to the offset rate of the corrected electric field distribution and the observation baseline, and a reusable steady-state calibration model is established for subsequent automatic correction.

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