Satellite chip thermal management optimization method and device

Abstract

The invention provides a satellite chip thermal management optimization method and device, and the method comprises the steps: carrying out the thermal load time sequence prediction of orbit elements and solar calendar data, obtaining the thermal load characteristics of an orbit, and calculating a heat dissipation control parameter according to a characteristic coefficient; according to the parameter matrix, fatigue accumulation characteristics of the heat dissipation part are analyzed, a health state evaluation result is obtained, a working mode is switched, and a time division multiplexing control strategy is obtained; performing matching optimization on chip thermal resistance and heat dissipation thermal resistance according to a control strategy, and coupling chip power consumption and a heat dissipation structure through a predictive control mechanism to obtain a heat balance control parameter; and analyzing thermal management performance according to the control parameters to obtain performance change trend data, predicting degradation of the heat dissipation structure, and obtaining a degradation prediction result and a compensation control instruction. According to the method, predictive thermal load analysis based on the track position and heat dissipation structure self-adaptive regulation and control are established, so that progressive failure of a thermal management system caused by asymmetric thermal circulation of the elliptical track is avoided.

Description

Technical Field

[0001] This invention relates to the field of thermal management technology, and in particular to a method and apparatus for optimizing thermal management of satellite chips. Background Technology

[0002] Currently, satellite chip thermal management technology mainly employs fixed thermal resistance configurations based on average orbital thermal loads and passive heat dissipation strategies. These methods address the thermal management needs of satellites during on-orbit operation by designing fixed heat dissipation structural parameters, such as heat pipe fan speed, heat sink deployment angle, and heat pipe circulation flow rate. Traditional thermal management systems are typically designed and optimized once based on the average thermal environment conditions of the mission orbit, maintaining a relatively stable operating state after satellite launch.

[0003] However, for elliptical orbit satellites, the solar radiation heat load experienced at perigee and apogee differs significantly due to orbital eccentricity, potentially by several times. Existing fixed thermal management strategies cannot effectively address this periodic but asymmetric change in heat load. This can lead to chip temperatures exceeding safe operating ranges at perigee, while at apogee, the thermal interface material experiences shrinkage stress due to a sudden temperature drop. Over long-term operation, this can cause gradual failure of the thermal management system, severely impacting the reliability and lifespan of the satellite chip. Summary of the Invention

[0004] The main objective of this invention is to solve the technical problem that existing satellite chip thermal management methods cannot effectively cope with the progressive failure of thermal management systems caused by asymmetric thermal load cycles in elliptical orbits. The first aspect of this invention provides a method for optimizing the thermal management of satellite chips, the method comprising: The orbital elements and solar ephemeris data of the satellite are subjected to thermal load time-series prediction processing to obtain orbital thermal load characteristics. Based on the orbital thermal load characteristics, the heat dissipation structure control parameters of the satellite chip are calculated collaboratively to obtain heat dissipation control parameters. The fatigue accumulation of the heat dissipation component is analyzed based on the heat dissipation control parameters to obtain the health status assessment result of the heat dissipation component. The working mode of the heat dissipation component is switched according to the health status assessment result to obtain the time-sharing multiplexing control strategy of the heat dissipation structure. Based on the time-division multiplexing control strategy, the chip thermal resistance and heat dissipation thermal resistance are matched and optimized to obtain thermal balance control parameters. The thermal management performance of the multi-track cycle is analyzed and processed based on the thermal balance control parameters to obtain performance change trend data. The degradation prediction of the heat dissipation structure is then performed based on the performance change trend data to obtain the corresponding degradation prediction results and compensation control commands.

[0005] Optionally, in a first implementation of the first aspect of the present invention, the step of performing thermal load timing prediction processing on the satellite's orbital elements and solar ephemeris data to obtain orbital thermal load characteristics, and then co-calculating the heat dissipation structure control parameters of the satellite chip based on the orbital thermal load characteristics to obtain heat dissipation control parameters, includes: The semi-major axis, eccentricity, and true anomaly angle parameters in the satellite's orbital elements are processed with solar ephemeris data to calculate the thermal load of the elliptical orbit. The orbital thermal load asymmetry index is obtained by calculating the variance of the ratio of perigee to apogee thermal load in a continuous orbital period. Phase compensation factor is obtained by combining the track thermal load asymmetry index with the chip packaging thermal time constant and track motion speed. The orbital thermal load asymmetry index and phase compensation factor are used as orbital thermal load characteristics to jointly calculate the heat dissipation structure control parameters of the satellite chip, thereby obtaining the heat dissipation control parameters.

[0006] Optionally, in a second implementation of the first aspect of the present invention, the step of analyzing the fatigue accumulation of the heat dissipation component based on the heat dissipation control parameters to obtain a health status assessment result of the heat dissipation component, and switching the working mode of the heat dissipation component based on the health status assessment result to obtain a time-division multiplexing control strategy for the heat dissipation structure includes: Based on the radiator heat conduction path switch control sequence in the heat dissipation control parameters, fatigue accumulation modeling is performed on the historical load data of the heat conduction column fan, heat sink rotation mechanism, and heat pipe circulation pump to obtain the remaining life prediction data of each heat dissipation component. Based on the remaining lifetime prediction data, the heat dissipation components are subjected to a health status classification assessment to obtain the health status assessment results. Based on the health status assessment results, the heat dissipation structure is divided into working groups for near-site high-power mode, far-site energy-saving mode, and transitional adjustment mode to obtain the time-sharing multiplexing control strategy for the heat dissipation structure.

[0007] Optionally, in a third implementation of the first aspect of the present invention, the step of dividing the heat dissipation structure into near-site high-power mode, far-site energy-saving mode, and transitional adjustment mode based on the health status assessment results to obtain a time-sharing multiplexing control strategy for the heat dissipation structure includes: Based on the health status assessment results, the heat conduction column fan, heat sink rotation mechanism, and heat pipe circulation pump are classified according to their health levels. Heat dissipation components with high health levels are assigned to the near-point high-power mode group, heat dissipation components with medium health levels are assigned to the transitional adjustment mode group, and heat dissipation components with low health levels are assigned to the far-point energy-saving mode group, thus obtaining a mode allocation scheme based on health status. Based on the health status-based mode allocation scheme and combined with the elliptical orbit phase information, the prestress adjustment calculation is performed on the start-up timing of each work group to obtain the prestress adjustment timing control parameters. Based on the prestressed adjustment timing control parameters, the switching thresholds and durations of the near-site high-power mode, the far-site energy-saving mode, and the transition adjustment mode are optimized and configured to obtain a time-division multiplexing control strategy for the heat dissipation structure.

[0008] Optionally, in a fourth implementation of the first aspect of the present invention, the step of performing matching optimization processing on the chip thermal resistance and heat dissipation thermal resistance according to the time-division multiplexing control strategy to obtain thermal balance control parameters includes: Based on the work group division information in the time-division multiplexing control strategy of the heat dissipation structure, a thermal resistance matching optimization objective function is generated for the thermal resistance of the chip structure and the thermal resistance of the heat dissipation environment. Based on the thermal resistance matching optimization objective function, the chip power consumption is dynamically allocated through multi-core load balancing and frequency adjustment algorithms, while the working intensity of the heat dissipation structure is adjusted in real time to obtain the coupled control parameters of chip power consumption and heat dissipation structure. Based on the coupling control parameters, predictive optimization is performed on the matching relationship between chip power consumption requirements and heat dissipation capabilities over multiple future orbital cycles to obtain thermal balance control parameters.

[0009] Optionally, in a fifth implementation of the first aspect of the present invention, the step of predictively optimizing the matching relationship between chip power consumption requirements and heat dissipation capabilities over multiple future orbital cycles based on the coupling control parameters to obtain thermal balance control parameters includes: Based on the power consumption dynamic allocation data in the coupling control parameters and the number of orbital elements, the orbital position and thermal load changes in multiple future orbital cycles are predicted and calculated to obtain orbital thermal environment prediction data. The chip power consumption requirements at each track position are calculated based on the predicted track thermal environment data. At the same time, the heat dissipation capacity of the heat dissipation structure at the corresponding track position is evaluated to obtain the matching relationship data between power consumption requirements and heat dissipation capacity. Based on the matching relationship data, a multi-objective optimization algorithm is used to collaboratively optimize the chip power consumption allocation ratio and the working intensity of the heat dissipation structure to obtain the power consumption-heat dissipation configuration scheme for each track position. Based on the power consumption-heat dissipation configuration scheme, the chip voltage frequency adjustment parameters and heat dissipation structure control parameters are time-optimized to obtain thermal balance control parameters.

[0010] Optionally, in a sixth implementation of the first aspect of the present invention, the step of analyzing and processing the multi-track cycle thermal management performance based on the thermal balance control parameters to obtain performance change trend data, and predicting the degradation of the heat dissipation structure based on the performance change trend data to obtain the corresponding degradation prediction result and compensation control command includes: Based on the thermal balance control parameters, statistical analysis and processing are performed on the chip temperature control accuracy, heat dissipation structure response time, and energy consumption efficiency data in the multi-track cycle to obtain performance change trend data. Based on the performance change trend data, the degradation characteristics of key components of the heat dissipation structure are identified using a health assessment model to obtain the degradation prediction results of the heat dissipation structure. Based on the degradation prediction results of the heat dissipation structure, the working intensity of the heat dissipation structure other than the degraded heat dissipation structure and the chip power consumption distribution are compensated and optimized to obtain compensation control instructions.

[0011] A second aspect of the present invention provides a satellite chip thermal management optimization device, the satellite chip thermal management optimization device comprising: The thermal load prediction module is used to perform thermal load time-series prediction processing on the satellite's orbital elements and solar ephemeris data to obtain orbital thermal load characteristics. Based on the orbital thermal load characteristics, the heat dissipation structure control parameters of the satellite chip are calculated collaboratively to obtain heat dissipation control parameters. The fatigue analysis module is used to analyze the fatigue accumulation of the heat dissipation component based on the heat dissipation control parameters, obtain the health status assessment result of the heat dissipation component, switch the working mode of the heat dissipation component based on the health status assessment result, and obtain the time-sharing multiplexing control strategy of the heat dissipation structure. The thermal resistance matching module is used to match and optimize the chip's thermal resistance and heat dissipation thermal resistance according to the time-division multiplexing control strategy to obtain thermal balance control parameters. The degradation prediction module is used to analyze and process the multi-track cycle thermal management performance based on the thermal balance control parameters, obtain performance change trend data, predict the degradation of the heat dissipation structure based on the performance change trend data, and obtain the corresponding degradation prediction results and compensation control commands.

[0012] The aforementioned satellite chip thermal management optimization method and apparatus obtain orbital thermal load characteristics by predicting the thermal load time series based on orbital elements and solar ephemeris data, and calculates heat dissipation control parameters based on characteristic coefficients; analyzes the fatigue accumulation characteristics of heat dissipation components based on the parameter matrix to obtain health status assessment results, switches operating modes, and obtains a time-division multiplexing control strategy; optimizes the matching between chip thermal resistance and heat dissipation thermal resistance based on the control strategy, and couples chip power consumption and heat dissipation structure through a predictive control mechanism to obtain thermal balance control parameters; analyzes thermal management performance based on control parameters to obtain performance change trend data, predicts heat dissipation structure degradation, and obtains degradation prediction results and compensation control commands. This invention avoids progressive failure of the thermal management system caused by asymmetric thermal cycling in elliptical orbits by establishing predictive thermal load analysis based on orbital position and adaptive control of the heat dissipation structure.

[0013] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the first embodiment of the satellite chip thermal management optimization method in this invention; Figure 2 This is a schematic diagram of one embodiment of the satellite chip thermal management optimization device in this invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this invention are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0018] To facilitate understanding of this embodiment, a detailed description of a satellite chip thermal management optimization method disclosed in this embodiment of the invention will be provided first. For example... Figure 1 As shown, this method includes the following steps: 101. Perform thermal load time-series prediction processing on the satellite's orbital elements and solar ephemeris data to obtain orbital thermal load characteristics. Based on the orbital thermal load characteristics, perform collaborative calculation on the heat dissipation structure control parameters of the satellite chip to obtain heat dissipation control parameters. In one embodiment of the present invention, the thermal load timing prediction processing of the satellite's orbital elements and ephemeris data to obtain orbital thermal load characteristics, and the collaborative calculation of the heat dissipation structure control parameters of the satellite chip based on the orbital thermal load characteristics to obtain heat dissipation control parameters, includes: performing elliptical orbit thermal load calculation processing on the semi-major axis, eccentricity, and true anomaly angle parameters in the satellite's orbital elements and ephemeris data; obtaining the orbital thermal load asymmetry index by calculating the variance of the ratio of perigee to apogee thermal load within a continuous orbital period; performing phase compensation calculation processing based on the orbital thermal load asymmetry index combined with the chip packaging thermal time constant and orbital motion velocity to obtain a phase compensation factor; and using the orbital thermal load asymmetry index and phase compensation factor as orbital thermal load characteristics to collaboratively calculate the heat dissipation structure control parameters of the satellite chip to obtain heat dissipation control parameters.

[0019] Specifically, the thermal load time-series prediction process is performed on the orbital elements and solar ephemeris data. This process begins by obtaining three key parameters from the satellite's orbital elements: the semi-major axis, eccentricity, and true anomaly. The semi-major axis determines the size of the orbit, the eccentricity characterizes the degree to which the orbit deviates from a circle, and the true anomaly describes the satellite's instantaneous position in the orbit. The solar ephemeris data contains celestial mechanical information about the sun's position, recording the sun's spatial coordinates and motion at different times. By combining these orbital parameters with the solar ephemeris data, the system can accurately calculate the solar radiation thermal load experienced by the satellite at different positions in its elliptical orbit. The calculation process needs to consider the variation of solar radiation intensity with the Earth-Sun distance, the influence of the satellite's attitude on the solar radiation receiving area, and the shading effect of the Earth's shadow on the thermal load. The special characteristic of elliptical orbits is that the thermal load differs significantly between perigee and apogee. At perigee, the satellite is closer to Earth and receives stronger albedo and infrared radiation, while at apogee, the influence of these heat sources is relatively weaker.

[0020] Specifically, after obtaining the thermal load distribution data of the elliptical orbit, the system calculates the change in the ratio of perigee to apogee thermal load over multiple consecutive orbital periods using statistical analysis methods. Specifically, the system first identifies the perigee and apogee locations in each orbital period, then extracts the corresponding thermal load values ​​and calculates the statistical variance of the ratio sequence. The orbital thermal load asymmetry index reflects the intensity of the periodic changes in the thermal environment of the elliptical orbit; a larger index value indicates a more significant difference in thermal load between perigee and apogee. Simultaneously, the system also needs to obtain the thermal time constant of the chip package, a parameter describing the response speed of the chip package structure to temperature changes, typically determined by the thermal capacity and thermal resistance characteristics of the packaging material. The orbital velocity is calculated through orbital mechanics, reflecting the speed of the satellite's motion in orbit. By performing mathematical operations on the orbital thermal load asymmetry index, the chip package thermal time constant, and the orbital velocity, the system can determine the phase relationship of the thermal load change's impact on the chip temperature.

[0021] Specifically, the calculation of the phase compensation factor involves a complex heat transfer analysis process. Due to the thermal inertia of the chip packaging structure, the change in internal chip temperature always lags behind the change in external thermal load. As the satellite moves from apogee to perigee, the external thermal load gradually increases, but there is a time delay in the rise of chip temperature. The role of the phase lead compensation factor is to quantify this time delay relationship, enabling the thermal management system to initiate heat dissipation measures in advance, ensuring that the chip temperature is effectively controlled when the thermal load peaks. The calculation process needs to comprehensively consider multiple factors such as orbital period, rate of change of thermal load, and thermal response characteristics of the chip packaging. The system solves the heat transfer differential equation using numerical integration methods to determine the optimal compensation time under different orbital positions. The orbital thermal load asymmetry index and the phase compensation factor together constitute the orbital thermal load characteristics, which fully describe the influence of the elliptical orbit thermal environment on the thermal management of satellite chips.

[0022] Specifically, based on the characteristics of orbital thermal loads, the system performs collaborative calculations on the control parameters of the satellite chip's heat dissipation structure. The heat dissipation structure includes multiple components such as heat-conducting column fans, heat sink rotation mechanisms, and heat pipe circulation pumps, each with corresponding control parameters that need optimization. The control parameters for the heat-conducting column fans include speed adjustment range, start-stop sequence, and power distribution ratio. The control parameters for the heat sink rotation mechanism involve deployment angle, rotation speed, and locking position. The control parameters for the heat pipe circulation pump include flow rate regulation, operating pressure, and circulation frequency. The collaborative calculation process employs a multi-objective optimization algorithm, aiming to minimize the chip's maximum temperature and temperature fluctuation range, while simultaneously constraining the power consumption and mechanical wear of each heat dissipation component. The calculation process requires establishing a mathematical model of the heat dissipation structure to describe the quantitative relationship between each control parameter and the heat dissipation effect. Through iterative optimization calculations, the system determines the optimal combination of control parameters for each orbital position, forming the heat dissipation control parameters. The rows of this matrix correspond to different orbital positions, the columns correspond to different control parameters, and the matrix elements represent the optimal setpoints for specific control parameters at specific orbital positions. The heat dissipation control parameters provide a complete parameter basis for subsequent thermal management control, ensuring that the heat dissipation system can automatically adjust its working status according to the satellite's real-time orbital position.

[0023] 102. Analyze and process the fatigue accumulation characteristics of the heat dissipation components based on the heat dissipation control parameters to obtain the health status assessment results of the heat dissipation components. Based on the health status assessment results, switch the working mode of the heat dissipation components to obtain the time-sharing multiplexing control strategy of the heat dissipation structure. In one embodiment of the present invention, the step of analyzing the fatigue accumulation of the heat dissipation components based on the heat dissipation control parameters to obtain the health status assessment result of the heat dissipation components, and switching the working mode of the heat dissipation components based on the health status assessment result to obtain the time-sharing multiplexing control strategy of the heat dissipation structure includes: performing fatigue accumulation modeling processing on the historical load data of the heat conduction column fan, the heat sink rotation mechanism, and the heat pipe circulation pump based on the heat dissipation control parameters to obtain the remaining life prediction data of each heat dissipation component; performing health status classification assessment processing on the heat dissipation components based on the remaining life prediction data to obtain the health status assessment result; and performing working group division processing on the heat dissipation structure according to the health status assessment result for near-point high power mode, far-point energy-saving mode, and transition adjustment mode to obtain the time-sharing multiplexing control strategy of the heat dissipation structure.

[0024] Specifically, the fatigue accumulation characteristic analysis of heat dissipation components begins with extracting the radiator heat conduction path switch control sequence from the heat dissipation control parameters. This sequence records the start-stop states and workload variation patterns of each heat dissipation component at different track positions. Plotting time on the horizontal axis and the control state of each heat dissipation component on the vertical axis, this sequence forms a complete workload time-series graph. The workload data for the heat pipe fan includes parameters such as speed change frequency, number of start-stop cycles, and high-load operating duration. These data directly reflect the mechanical stress state of the fan bearings and blades. The workload of the heat sink rotation mechanism is reflected in the number of angle adjustments, peak rotational acceleration, and duration of the locked state. Frequent angle adjustments can cause wear on the rotating bearings and drive motor. The workload data for the heat pipe circulation pump covers factors such as flow rate adjustment amplitude, pressure pulsation frequency, and start-stop transient impacts. These factors directly affect the fatigue life of the pump body seals and impeller.

[0025] Specifically, fatigue accumulation modeling employs the linear cumulative damage rule from materials fatigue theory. The system first establishes stress-life curves for key components of each heat dissipation unit, describing the fatigue life characteristics of these components under different stress levels. For the heat-conducting column fan, modeling focuses on contact fatigue of the bearing balls and vibration fatigue of the blades. Cumulative fatigue damage is calculated by statistically analyzing the number of stress cycles at different speeds. Bearing fatigue damage calculation needs to consider factors such as radial load, axial load, and lubrication status. The system extrapolates the actual working stress of the bearing based on historical fan speed data, then determines the corresponding fatigue life through table lookup or interpolation methods, and finally calculates the current degree of damage according to the damage accumulation formula. Fatigue modeling of the heat sink rotating mechanism focuses on contact fatigue of the gear transmission system and thermal fatigue of the motor windings. The system analyzes the history of torque changes, identifies the frequency and amplitude of high-stress events, and calculates cumulative damage by combining the material's fatigue characteristic parameters. Fatigue modeling of the heat pipe circulating pump focuses on aging fatigue of the sealing rings and hydrodynamic fatigue of the impeller. The accumulation process of fatigue damage is quantified by analyzing the stress cycles caused by pressure pulsations and flow rate changes to key components.

[0026] Specifically, through fatigue accumulation modeling, the system obtains the current fatigue damage values ​​of each heat dissipation component and then extrapolates the remaining life prediction data. The remaining life prediction uses an extrapolation method; the system predicts the time when the heat dissipation component will reach the fatigue failure threshold based on the current damage accumulation rate and expected future operating modes. The prediction process needs to consider the changing trends of the operating environment, including the impact of orbital decay on thermal load and the changes in the space environment due to the solar activity cycle. The remaining life of the heat pipe fan is mainly controlled by the bearing wear rate; the system predicts it based on the lubricant consumption rate and the generation pattern of wear particles. The remaining life of the heat sink rotating mechanism mainly depends on the wear degree of the gear pair and the aging state of the motor insulation material; the prediction algorithm needs to comprehensively consider the influence of both mechanical wear and electrical aging. The remaining life prediction of the heat pipe circulating pump focuses on the leakage risk of the sealing system and the fatigue crack propagation of the impeller, and life assessment is performed by monitoring the decay trend of the working fluid pressure and the changing characteristics of vibration signals.

[0027] Specifically, based on remaining lifetime prediction data, the system performs a health status grading assessment of heat dissipation components. The health status grading adopts a multi-level evaluation system, classifying the health status of heat dissipation components into five levels: excellent, good, fair, poor, and dangerous. The assessment process comprehensively considers multiple indicators such as the absolute value of remaining lifetime, the rate of damage accumulation, and the degree of degradation of key performance parameters. Components with a remaining lifetime exceeding 80% of their design lifetime are rated as excellent; those with a remaining lifetime between 60% and 80% are rated as good; those with a remaining lifetime between 40% and 60% are rated as fair; those with a remaining lifetime between 20% and 40% are rated as poor; and those with a remaining lifetime less than 20% of their design lifetime are rated as dangerous. Simultaneously, the system also monitors the changing trends of key performance indicators of heat dissipation components, including phenomena such as decreased heat dissipation efficiency, prolonged response time, and increased power consumption. Abnormal changes in these indicators will also affect the health status rating results.

[0028] Specifically, based on the health status assessment results, the system divides the heat dissipation structure into working groups. The pre-stress adjustment mechanism refers to applying preset mechanical or thermal stress before the heat dissipation components begin operation to reduce stress concentration during subsequent operation, thereby extending the fatigue life of the components. The perigee high-power mode corresponds to the section of the satellite orbit with the highest thermal load, where chip power consumption reaches its peak, requiring the heat dissipation system to operate at full capacity. In this mode, heat dissipation components with excellent or good health status are assigned to undertake the main heat dissipation tasks, and the system ensures the stability of these components under high load conditions through pre-stress adjustment. The apogee energy-saving mode corresponds to the section of the orbit with lower thermal load, where chip power consumption is relatively low and heat dissipation requirements are limited. In this mode, heat dissipation components with average or poor health status are assigned to perform basic heat dissipation tasks, delaying the accumulation of fatigue damage by reducing their workload. The transition adjustment mode corresponds to the section of the orbit with rapidly changing thermal load, requiring the heat dissipation system to have good dynamic response capabilities. In this mode, the system flexibly allocates resources according to the health status and response characteristics of the heat dissipation components, ensuring that the heat dissipation capacity matches the changes in thermal load. Through this time-sharing multiplexing control strategy, the system achieves optimized allocation of heat dissipation resources, ensuring both heat dissipation performance and extending the service life of heat dissipation components.

[0029] Furthermore, the step of dividing the heat dissipation structure into working groups based on the health status assessment results (near-site high-power mode, far-site energy-saving mode, and transitional adjustment mode) to obtain the time-sharing multiplexing control strategy for the heat dissipation structure includes: classifying the heat-conducting column fan, heat sink rotating mechanism, and heat pipe circulation pump according to the health status assessment results; assigning heat dissipation components with high health levels to the near-site high-power mode group, assigning heat dissipation components with medium health levels to the transitional adjustment mode group, and assigning heat dissipation components with low health levels to the far-site energy-saving mode group, thus obtaining a health status-based mode allocation scheme; performing pre-stress adjustment calculation processing on the start-up sequence of each working group based on the health status-based mode allocation scheme and elliptical orbit phase information, thus obtaining pre-stress adjustment timing control parameters; and optimizing the switching thresholds and durations of the near-site high-power mode, far-site energy-saving mode, and transitional adjustment mode according to the pre-stress adjustment timing control parameters, thus obtaining the time-sharing multiplexing control strategy for the heat dissipation structure.

[0030] Specifically, the heat dissipation structure working group division process begins with a detailed analysis of the health status assessment results. The system first classifies the three types of heat dissipation components—heat-conducting column fans, heat sink rotating mechanisms, and heat pipe circulation pumps—by their respective health levels. The health level classification of heat-conducting column fans is primarily based on key indicators such as bearing wear, blade balance, and motor insulation strength. The system identifies rolling element defects and cage wear in the bearings through vibration spectrum analysis, assesses the insulation aging of the motor windings through current waveform analysis, and checks the dynamic balance of the blades through speed stability testing. Heat-conducting column fans with high health levels are characterized by vibration amplitude below 10% of the design threshold, motor insulation resistance above 80% of the initial value, and speed fluctuation rate below 2% of the rated value. The health level classification of the heat sink rotating mechanism is based on parameters such as gear wear, bearing clearance, and changes in driving torque. The system monitors the attenuation of rotational accuracy using position sensors, detects the increasing trend of transmission resistance using torque sensors, and assesses abnormalities in frictional heating using temperature sensors. High-health-level heat sink rotating mechanisms possess characteristics such as rotational accuracy error less than 5% of the design tolerance, driving torque growth rate less than 15% of the initial value, and operating temperature rise controlled within the design range. The health-level classification of heat pipe circulating pumps is based on core indicators such as sealing performance, impeller efficiency, and working fluid purity. The system assesses leakage in the sealing system through pressure decay testing, analyzes impeller wear through flow-head characteristic curves, and determines internal corrosion and contamination levels through working fluid composition detection.

[0031] Specifically, based on the health level classification results, the system executes the mode allocation scheme. Heat dissipation components with high health levels are preferentially assigned to the perigee high-power mode group, which is responsible for peak heat dissipation during the satellite's perigee transit. The perigee phase is characterized by high heat load density, rapid change rate, and relatively short duration, placing extremely high demands on the response speed and operational stability of heat dissipation components. High-health-level heat-conducting column fans need to maintain maximum speed operation in this mode; blades need to withstand high-frequency vibration and aerodynamic loads; and bearings need to maintain precise radial and axial positioning at high speeds. High-health-level heat sink rotation mechanisms need to complete large-angle adjustments in a short time; gear transmission systems need to withstand significant dynamic impact loads; and drive motors need to provide peak torque output. High-health-level heat pipe circulation pumps need to maintain maximum flow circulation; the sealing system needs to withstand high pressure differentials; and the impeller needs to withstand significant centrifugal stress at high speeds. Heat dissipation components with medium health levels are assigned to the transition mode group, which is responsible for handling the heat dissipation needs of the orbital transition section. The transition mode is characterized by large heat load change gradients, frequent switching of operating states, and high requirements for dynamic response capabilities. Heat dissipation components with a medium health rating operate at moderate intensity parameters in this mode, ensuring necessary heat dissipation capacity while avoiding excessive fatigue damage accumulation. Heat dissipation components with a low health rating are assigned to the remote energy-saving mode group, which primarily undertakes basic heat dissipation tasks and system backup functions. The thermal load in remote energy-saving mode is relatively small and changes slowly, allowing heat dissipation components to operate at lower intensity, slowing the progression of fatigue damage by extending the duty cycle and reducing load intensity.

[0032] Specifically, the calculation process for the prestressing adjustment timing control parameters requires precise analysis in conjunction with the elliptical orbit phase information. Elliptical orbit phase information includes orbital elements such as true anomaly angle, off-anomaly angle, and mean anomaly angle. These parameters collectively describe the satellite's instantaneous position and motion state in orbit. The system determines the time intervals for the satellite to reach various key orbital nodes from its current position through orbital mechanics calculations, including the estimated times to reach perigee, apogee, and orbital midpoint. The prestressing adjustment calculation considers the start-up delay characteristics and thermal response time constant of each heat dissipation component to determine the optimal pre-start-up time window. The prestressing adjustment of the heat-conducting column fan involves the preheating of the bearing lubricant and the pre-rotation of the blades. The system starts running at low speed several minutes before the fan's official start-up to allow the lubricant in the bearing to reach its optimal operating temperature, while simultaneously reducing the impact load at the moment of start-up through a slow acceleration process. The pre-stress adjustment of the heat sink rotation mechanism includes pre-loading of the transmission system and pre-excitation of the drive motor. Before the heat sink adjustment action is executed, the system applies a small-amplitude reciprocating motion to the gear pair to eliminate transmission backlash and ensure uniform distribution of lubricant. At the same time, the drive motor is preheated to ensure the stability of the output torque. The pre-stress adjustment of the heat pipe circulation pump involves the pre-establishment of the working fluid circulation path and the pre-pressurization of the sealing system. Before the pump is put into operation, the system establishes a low-flow working fluid circulation to ensure uniform distribution of the working fluid in the pipeline and eliminate air bubbles. At the same time, a preset pressure is applied to the sealing system to verify the reliability of the sealing performance.

[0033] Specifically, the optimized configuration of the time-sharing multiplexing control strategy for the heat dissipation structure focuses on the switching thresholds and duration parameters of each operating mode. Setting the switching thresholds requires comprehensive consideration of multiple factors, including track position, thermal load intensity, heat dissipation requirements, and component health status. The activation threshold for the near-point high-power mode is set based on the rate of increase of thermal load density. When the system detects that the rate of change of thermal load density exceeds a preset value, this mode is automatically activated. The specific value of the switching threshold is determined through historical data statistics and simulation analysis. The activation threshold for the far-point energy-saving mode is set based on the absolute intensity of the thermal load. When the thermal load intensity decreases to a certain level, the system automatically switches to this mode. The threshold setting needs to ensure stable chip temperature control even under the lowest heat dissipation requirements. The activation conditions for the transitional adjustment mode are more complex, requiring simultaneous monitoring of the thermal load's changing trend and rate of change. This mode is activated when the system detects a rapidly changing thermal environment. The optimized configuration of the duration is determined based on track dynamics calculations and thermal response analysis. The duration of the perigee high-power mode is calculated based on the satellite's transit time through the perigee region and the characteristics of thermal load attenuation. The system predicts the satellite's dwell time in the high thermal load region using an orbital integration algorithm and determines the optimal duration of the mode by combining the hysteresis effect of the chip's thermal response. The duration of the apogee energy-saving mode is based on a balance between energy consumption optimization and component recovery requirements. The system calculates the fatigue recovery rate of the heat dissipation components under low load conditions and determines the shortest duration required to achieve the best recovery effect. The duration of the transition adjustment mode has strong dynamic adaptability. The system dynamically adjusts the mode duration according to real-time changes in thermal load and the response status of the heat dissipation components to ensure smooth mode switching and continuity of heat dissipation performance.

[0034] 103. Based on the time-division multiplexing control strategy of the heat dissipation structure, the thermal resistance of the chip and the thermal resistance of the heat dissipation are matched and optimized to obtain the thermal balance control parameters. In one embodiment of the present invention, the step of matching and optimizing the chip thermal resistance and heat dissipation thermal resistance according to the time-division multiplexing control strategy to obtain thermal balance control parameters includes: generating a thermal resistance matching optimization objective function for the chip structure thermal resistance and the heat dissipation environment thermal resistance based on the work group division information in the time-division multiplexing control strategy of the heat dissipation structure; dynamically allocating chip power consumption through multi-core load balancing and frequency adjustment algorithms according to the thermal resistance matching optimization objective function, while simultaneously performing real-time feedback adjustment of the heat dissipation structure's workload to obtain coupled control parameters for chip power consumption and heat dissipation structure; and performing predictive optimization on the matching relationship between chip power consumption demand and heat dissipation capacity in multiple future orbital cycles based on the coupled control parameters to obtain thermal balance control parameters.

[0035] Specifically, the matching and optimization of chip thermal resistance and heat dissipation thermal resistance begins with extracting work group partitioning information from the time-division multiplexing control strategy of the heat dissipation structure. The system constructs corresponding thermal resistance network models based on the configuration of heat dissipation components under different operating modes. Chip structural thermal resistance describes the thermal resistance characteristics of heat transfer from the chip's internal structure to the package shell. This thermal resistance value is mainly determined by factors such as the thermal conductivity of the chip material, its geometric dimensions, and the complexity of its internal structure. In multi-core chips, the operating states and power consumption distribution of different cores significantly affect the overall structural thermal resistance; the system needs to establish a dynamic thermal resistance model to describe this changing relationship. Environmental thermal resistance describes the thermal resistance characteristics of heat transfer from the chip package shell to the external environment. This thermal resistance value depends on the configuration and operating intensity of the heat dissipation structure. In near-point high-power mode, the heat pipe fan operates at high speed, the heat sink is fully deployed, and the heat pipe circulation pump operates at full speed; at this time, the shell-to-environment thermal resistance reaches its minimum. In far-point energy-saving mode, the heat dissipation components operate at lower intensity, and the shell-to-environment thermal resistance increases accordingly. In transitional adjustment mode, the heat dissipation structure is in a dynamic adjustment state, and the thermal resistance value exhibits complex changing characteristics over time.

[0036] Specifically, the series thermal resistance network modeling process employs a circuit analogy method for calculation. The system equates the internal heat transfer path of the chip to thermal resistance elements and the heat transfer process of the heat dissipation structure to external thermal resistance elements, forming a complete thermal resistance network through series connections. The modeling process needs to consider the nonlinear characteristics and temperature dependence of thermal resistance. The thermal resistance of the chip structure changes with the increase of chip temperature, and the thermal resistance of the heat dissipation environment fluctuates with changes in ambient temperature and heat dissipation conditions. The system calculates the values ​​of each thermal resistance element using the finite element method, establishing a functional relationship between thermal resistance and temperature, power consumption, and heat dissipation intensity. Under different working group configurations, the topology and parameter values ​​of the thermal resistance network will change accordingly, requiring the system to establish an independent thermal resistance model for each configuration. The construction of the thermal resistance matching optimization objective function comprehensively considers multiple optimization objectives such as chip temperature control, power efficiency, and heat dissipation energy consumption. The multi-objective problem is transformed into a single-objective optimization problem through methods such as weighted summation or Pareto optimality. The weighting coefficients of the objective function are dynamically adjusted according to mission requirements and constraints. In the orbital section with high thermal load, more emphasis is placed on temperature control, while in the section with low thermal load, more emphasis is placed on energy efficiency.

[0037] Specifically, the dynamic power consumption allocation process is implemented based on multi-core load balancing and frequency adjustment algorithms. The core idea of ​​the multi-core load balancing algorithm is to rationally distribute computing tasks among different cores, making the power consumption and temperature distribution of each core more uniform and avoiding local hotspots. The algorithm first analyzes the current task queue and the working state of each core, calculates the computational complexity and expected execution time of each task, and then makes task allocation decisions based on information such as the current temperature, historical load, and performance status of each core. The load balancing process uses dynamic programming to minimize the temperature difference between cores while meeting task execution time constraints. The frequency adjustment algorithm controls power consumption by adjusting the operating frequency of each core. The algorithm calculates the maximum allowable power consumption of each core based on the current thermal resistance network state and heat dissipation capacity, and then determines the corresponding optimal operating frequency. The frequency adjustment process needs to consider the processor's frequency switching latency and power switching characteristics, reducing the impact of frequency switching on system performance through predictive control. The real-time feedback adjustment of the heat dissipation structure's working intensity adopts a closed-loop control strategy. The system monitors the temperature distribution at various points on the chip and the working state of the heat dissipation structure in real time, adjusting the heat dissipation intensity based on temperature deviations and trends. The feedback control algorithm uses a PID controller or a model predictive controller to change the heat dissipation capacity by adjusting parameters such as the fan speed of the heat conduction column, the angle of the heat sink, and the circulation flow rate of the heat pipe.

[0038] Specifically, the calculation of coupling control parameters requires comprehensive consideration of the interplay between chip power consumption allocation and heat dissipation structure adjustment. Changes in chip power consumption directly affect the heat generation rate and temperature distribution, thus influencing heat dissipation requirements and the workload of the heat dissipation structure. Adjustments to the workload of the heat dissipation structure alter heat dissipation capacity and thermal resistance characteristics, which in turn affect the chip's temperature control and power consumption allocation strategies. The system describes this interaction by establishing a coupling mathematical model, which includes core elements such as the chip heat generation equation, heat conduction equation, and heat dissipation boundary conditions. The coupling control parameters are solved using an iterative optimization algorithm. The system repeatedly calculates the chip power consumption allocation and heat dissipation structure adjustment schemes in each control cycle until the two reach an optimal matching state. During the iteration process, convergence and stability need to be checked to ensure that the calculated results of the control parameters have physical meaning and engineering feasibility. The coupling control parameters include the power consumption allocation ratio of each core, the operating frequency setpoint, the workload command of the heat dissipation structure, and control timing parameters. These parameters together constitute a complete control scheme for the coordinated operation of the chip and the heat dissipation system.

[0039] Specifically, predictive optimization performs forward-looking analysis of the system behavior over multiple future orbital periods. The orbital mechanics prediction algorithm, based on Kepler orbital theory and perturbation theory, calculates the satellite's precise orbital position and motion state over future time periods. The prediction process considers factors such as the non-spherical effect of Earth's gravitational field, gravitational perturbations from the Sun and Moon, and atmospheric drag, solving the orbital motion equations using numerical integration methods. Based on the orbital prediction results, the system further calculates the thermal load distribution and chip power consumption requirements at various future moments. Chip power consumption requirement prediction is based on mission planning and resource scheduling information, considering the computational resource requirements of different types of tasks, such as communication, data processing, and attitude control. Heat dissipation capacity prediction is based on the health evolution and performance degradation trends of heat dissipation components; the system predicts the performance changes of each heat dissipation component over future time periods using a fatigue accumulation model. The optimization calculation of the matching relationship employs global optimization methods such as dynamic programming or genetic algorithms to find the optimal matching scheme between chip power consumption and heat dissipation capacity within the entire prediction time window. The optimization process needs to balance current performance and long-term reliability, meeting short-term heat dissipation requirements while avoiding excessive fatigue damage to heat dissipation components. The thermal balance control parameters are output as optimization results, providing parameter basis and control guidance for the long-term stable operation of the system.

[0040] Furthermore, the step of predictively optimizing the matching relationship between chip power consumption demand and heat dissipation capacity over multiple future orbital cycles based on the coupling control parameters to obtain thermal balance control parameters includes: predicting and calculating changes in orbital position and thermal load over multiple future orbital cycles based on the dynamic power consumption allocation data in the coupling control parameters combined with the number of orbital elements to obtain orbital thermal environment prediction data; calculating the chip power consumption demand at each orbital position based on the orbital thermal environment prediction data, and simultaneously evaluating the heat dissipation capacity of the corresponding heat dissipation structure at the corresponding orbital position to obtain matching relationship data between power consumption demand and heat dissipation capacity; using a multi-objective optimization algorithm to collaboratively optimize the chip power consumption allocation ratio and the working intensity of the heat dissipation structure based on the matching relationship data to obtain a power consumption-heat dissipation configuration scheme for each orbital position; and performing time-series optimization on the chip voltage frequency adjustment parameters and heat dissipation structure control parameters based on the power consumption-heat dissipation configuration scheme to obtain thermal balance control parameters.

[0041] Specifically, the generation of orbital thermal environment prediction data begins with the extraction of dynamic power consumption distribution data from the coupled control parameters. The system analyzes the power consumption distribution patterns and variation laws of each core within the current orbital cycle, identifying the orbital location and duration characteristics of power consumption peaks. Through statistical analysis of historical power consumption data, the system establishes a correlation model between power consumption distribution and orbital position. This model describes the variation law of chip power consumption with orbital position under different mission types and operating modes. Combining six orbital elements from the satellite orbital elements—semi-major axis, eccentricity, orbital inclination, right ascension of the ascending node, argument of perigee, and true anomaly—the system uses Kepler orbital theory to calculate the precise position sequence of the satellite within the next 2-3 orbital cycles. The orbital position calculation process first solves for the satellite's angular position on the orbit using the Kepler equation between the true anomaly and the deviated anomaly. Then, based on the orbital geometry, the three-dimensional position vector of the satellite in the inertial coordinate system is calculated. Considering the long-term influence of factors such as the non-spherical effect of the Earth's gravitational field, atmospheric drag perturbations, and gravitational perturbations from the Sun and Moon on the orbit, the system uses numerical integration methods to correct the orbital elements, ensuring that the prediction accuracy meets the requirements of thermal environment analysis.

[0042] Specifically, based on accurate orbital position predictions, the system performs detailed calculations of thermal load variations. Thermal load calculations involve a comprehensive analysis of three main heat sources: direct solar radiation, Earth's albedo radiation, and Earth's infrared radiation. Calculating direct solar radiation requires determining the angle between the solar vector and the satellite's surface normal vector, considering the time-varying characteristics of satellite attitude changes and the solar incidence angle, and calculating the solar radiation flux density at each moment using spherical trigonometry. Calculating Earth's albedo radiation is based on the Earth's surface reflection characteristics and the satellite's observation geometry. The system divides the Earth's surface into several grid cells, calculates the contribution of each grid cell to the satellite's reflected radiation, and then obtains the total albedo heat flux through integration and summation. Calculating Earth's infrared radiation considers the diurnal variation of Earth's surface temperature and latitudinal distribution characteristics, using the blackbody radiation law to calculate the infrared radiation intensity of various surface regions, and calculating the infrared heat flux received by the satellite using the field-of-view factor. Under elliptical orbit conditions, the periodic variation of the satellite's altitude leads to significant fluctuations in the intensity of each heat source. At perigee, the influence of Earth's radiation heat sources is significantly enhanced, while at apogee, direct solar radiation becomes the dominant factor. The system generates orbital thermal environment prediction data covering multiple future orbital periods by establishing a functional relationship between thermal load and orbital position. This data, with time as the horizontal axis and thermal load intensity as the vertical axis, fully describes the time-varying characteristics of the satellite's thermal environment.

[0043] Specifically, generating data on the matching relationship between chip power consumption requirements and heat dissipation capacity requires in-depth analysis and processing of orbital thermal environment prediction data. The calculation of chip power consumption requirements is based on the prediction results of task scheduling and resource allocation. The system determines the computational workload and communication data processing requirements for each orbital position according to the satellite mission plan. In the perigee region, satellites typically perform ground communication and data downlink tasks, where the power consumption requirements of communication processors and encoders are high. In the apogee region, satellites mainly perform deep space exploration and scientific experiment tasks, where the power consumption requirements of data processors and controllers are relatively high. The system establishes a quantitative relationship model between task load and chip power consumption by analyzing the processor utilization and computational complexity of different task types. The power consumption requirement calculation also needs to consider the feedback effect of chip temperature on power consumption. Increased leakage current in high-temperature environments leads to increased static power consumption; the system corrects the power consumption calculation results using a temperature coefficient. The evaluation of the heat dissipation capacity of the heat dissipation structure is based on the working state and performance degradation of the heat dissipation components. The evaluation of the heat dissipation capacity of the heat-conducting column fan considers factors such as speed adjustment range, blade efficiency, and bearing condition. The system calculates the heat dissipation power at each speed based on the fan characteristic curve and current health status. The heat dissipation capacity assessment of the heat sink rotation mechanism involves parameters such as deployment angle, surface emissivity, and geometric configuration. The system determines the radiative heat dissipation capacity under different angle configurations through radiative heat transfer calculations. The heat dissipation capacity assessment of the heat pipe circulation pump is based on factors such as working fluid flow rate, heat transfer coefficient, and piping configuration. The system uses heat transfer network analysis methods to calculate the heat transfer capacity of the heat pipe system.

[0044] Specifically, the collaborative optimization process of the multi-objective optimization algorithm employs a non-dominated sorting genetic algorithm. This algorithm can simultaneously handle multiple conflicting optimization objectives, such as chip temperature control, power efficiency, and heat dissipation component lifespan. Optimization variables include parameters such as the power allocation ratio of each core, operating frequency settings, and the workload of the heat dissipation structure. Constraints include chip maximum temperature limits, total power budget constraints, and heat dissipation component capacity limitations. The algorithm first generates an initial population, with each individual representing a power-heat dissipation configuration scheme. Then, through genetic operations such as fitness evaluation, selection, crossover, and mutation, a new generation of population is generated. The fitness evaluation process requires thermal simulation calculations for each configuration scheme to evaluate performance indicators such as chip temperature distribution, power efficiency, and heat dissipation component load. The non-dominated sorting algorithm hierarchically sorts the individuals in the population according to the Pareto optimality principle. Individuals within the same layer are non-dominated, while individuals between different layers have a dominant relationship. Through multiple rounds of evolutionary iteration, the algorithm gradually converges to the Pareto optimal front, obtaining a series of optimal configuration schemes under different objective weights. To address the unique characteristics of elliptical orbits, the optimization algorithm employs a piecewise optimization strategy, dividing the complete orbital period into several sub-intervals. Local optimization is performed within each sub-interval, and global optimality is ensured through boundary condition matching. The power consumption and heat dissipation configuration scheme uses the orbital position as an index, recording the optimal chip power consumption allocation and heat dissipation structure operating state for each orbital position, forming a complete optimization control sequence.

[0045] The core task of timing optimization is to transform the static optimal configuration into a dynamic sequence of control parameters, ensuring that the system can smoothly switch between different operating states. Timing optimization of chip voltage and frequency regulation parameters needs to consider the processor's dynamic power consumption characteristics and switching delay constraints. The voltage and frequency switching process includes multiple time constants such as the voltage regulator's response time, the phase-locked loop's locking time, and the processor pipeline's refresh time. The system describes these delay characteristics by establishing a switching timing model. The timing optimization algorithm uses dynamic programming to minimize performance loss and power consumption fluctuations during switching while satisfying switching time constraints. The optimization process needs to reserve sufficient switching buffer time to ensure that voltage and frequency regulation can be completed before thermal load changes. Timing optimization of heat dissipation structure control parameters needs to consider the startup characteristics and response delays of each heat dissipation component. The startup process of the heat-conducting column fan includes stages such as motor excitation, rotor acceleration, and airflow establishment. The system determines the optimal startup timing based on the fan's speed-time characteristic curve. The adjustment process of the heat sink rotation mechanism involves position sensor feedback, servo control, and mechanical motion. The system determines the optimal adjustment timing parameters through control theory analysis. The startup process of a heat pipe circulating pump needs to consider factors such as the establishment of working fluid circulation, heat transfer stabilization, and pressure balance. The system determines the startup sequence based on the heat transfer response characteristics of the heat pipe. The thermal balance control parameters, as the final output of the timing optimization, contain the complete control timing of the chip and the heat dissipation system throughout the entire orbital cycle, providing precise parameter guidance and control basis for the autonomous operation of the system.

[0046] 104. Analyze and process the multi-track cycle thermal management performance based on the thermal balance control parameters to obtain performance change trend data. Based on the performance change trend data, predict the degradation of the heat dissipation structure and obtain the corresponding degradation prediction results and compensation control commands.

[0047] In one embodiment of the present invention, the step of analyzing and processing the multi-track cycle thermal management performance based on the thermal balance control parameters to obtain performance change trend data, and predicting the degradation of the heat dissipation structure based on the performance change trend data to obtain corresponding degradation prediction results and compensation control instructions includes: performing statistical analysis and processing on the chip temperature control accuracy, heat dissipation structure response time, and energy consumption efficiency data within the multi-track cycle based on the thermal balance control parameters to obtain performance change trend data; performing degradation feature identification processing on key components of the heat dissipation structure using a health assessment model based on the performance change trend data to obtain degradation prediction results of the heat dissipation structure; and performing compensation optimization calculation processing on the working intensity of the heat dissipation structure other than the degraded heat dissipation structure and the chip power consumption distribution based on the degradation prediction results of the heat dissipation structure to obtain compensation control instructions.

[0048] Specifically, the system continuously collects chip temperature control accuracy data over 20-50 orbital cycles. Chip temperature control accuracy is quantified by calculating the deviation between the set temperature and the actual temperature. Within each orbital cycle, the system collects temperature data for each core at a rate of seconds, and statistically analyzes the mean, standard deviation, and maximum deviation of the temperature deviation. Deterioration in temperature control accuracy manifests as a drift in the mean deviation and an increase in the standard deviation; the system identifies these patterns of change using trend analysis algorithms. The acquisition of heat dissipation structure response time data involves the complete time series from the issuance of control commands to the achievement of steady-state heat dissipation. The response time of the heat pipe fan includes multiple stages such as motor start-up delay, speed establishment time, and airflow stabilization time. The system measures the duration of each stage by monitoring signal changes from speed and temperature sensors. The response time of the heat sink rotation mechanism includes command parsing time, motor drive time, mechanical movement time, and position stabilization time. The system accurately measures the response time using feedback signals from position encoders and torque sensors. The response time of the heat pipe circulation pump involves processes such as pump start-up time, flow establishment time, and heat transfer stabilization time. The system evaluates response performance through joint monitoring by flow and temperature sensors. The statistical analysis of energy efficiency data is based on a comprehensive evaluation of power consumption monitoring and heat dissipation effect. The system calculates the power consumption corresponding to a unit heat dissipation and identifies the degradation trend of system performance by comparing the changes in energy efficiency in different orbital cycles.

[0049] Specifically, statistical analysis employs time series analysis methods to deeply mine the collected performance data. The system first preprocesses the raw data, including outlier detection, data smoothing, and periodicity separation, to eliminate the impact of measurement noise and random interference on trend analysis. Outlier detection uses the 3-sigma criterion or quartile-based outlier identification methods to mark data points that significantly deviate from the normal range and process them specially. Data smoothing uses moving averages or low-pass filtering techniques to reduce the interference of high-frequency noise on long-term trend judgment. Periodicity separation uses Fourier transform or wavelet analysis to separate the periodic components of the data from the long-term trend components, focusing on long-term trend information reflecting system aging. Trend analysis uses mathematical methods such as linear regression, polynomial fitting, and exponential smoothing to establish mathematical models of performance indicators changing over time. Trend analysis of chip temperature control accuracy focuses on the long-term drift and fluctuation amplitude growth trend of control deviation; the system quantifies the rate and confidence of performance degradation by calculating the slope of the trend line and the correlation coefficient. The trend analysis of the heat dissipation structure response time focuses on the gradual increase in response delay and the decrease in response stability. The system assesses the degree of mechanical wear and performance aging by analyzing the statistical characteristics of the response time distribution. The trend analysis of energy efficiency focuses on the upward trend of power consumption per unit heat dissipation and the increase in efficiency fluctuations. The system predicts future performance changes by establishing an efficiency degradation model.

[0050] Specifically, after statistical analysis of performance change trend data, the system establishes a health assessment model to identify degradation characteristics of key components in the heat dissipation structure. The health assessment model is built based on machine learning algorithms, employing methods such as support vector machines, random forests, and neural networks to establish a mapping relationship between component health status and performance indicators. The model training process uses known fault cases and normal operation data from historical data as training samples, learning data characteristics under different health states through feature extraction and pattern recognition techniques. The degradation characteristic identification of the heat-conducting column fan focuses on phenomena such as increased vibration due to bearing wear, increased friction due to lubricant aging, and decreased efficiency due to blade imbalance. The system identifies these degradation characteristics by analyzing multi-dimensional data such as vibration spectrum, power consumption changes, and temperature distribution. Bearing wear is characterized by an increase in vibration amplitude and the appearance of harmonic components at specific frequencies; the system extracts these characteristic parameters through frequency domain analysis. Lubricant aging is characterized by increased startup power consumption and increased operating temperature; the system identifies changes in lubrication status by comparing power consumption-speed characteristic curves at different times. The degradation feature identification of the heat sink rotating mechanism focuses on issues such as gear wear, increased bearing clearance, and motor performance degradation. The system assesses the health of the mechanical system by monitoring parameters such as transmission accuracy, driving torque, and position repeatability. Gear wear is characterized by increased transmission clearance and decreased position accuracy; the system identifies the degree of wear by analyzing the noise level and repeatability of the position feedback signal. The degradation feature identification of the heat pipe circulating pump focuses on issues such as seal aging, impeller wear, and working fluid contamination. The system assesses the health of the pump system by monitoring phenomena such as pressure drop, flow rate decrease, and reduced heat transfer efficiency.

[0051] Specifically, based on the analysis results of the health assessment model, the system generates degradation prediction results for the heat dissipation structure and formulates corresponding compensation and control strategies. The degradation prediction uses a remaining useful life estimation method, combining the current health status and historical degradation rate to predict when the component will reach the failure threshold. The prediction process considers the randomness and uncertainty of the degradation process, and uses probabilistic statistical methods to provide confidence intervals for the prediction results. For heat dissipation structures identified as having degradation risk, the system formulates compensation optimization calculation strategies to maintain overall heat dissipation performance. The core idea of ​​the compensation strategy is to compensate for the performance loss of the degraded structure by increasing the workload of other normal heat dissipation structures, while adjusting the chip power consumption distribution to reduce the heat dissipation burden. When the heat pipe fan experiences performance degradation, the system compensates for the loss of heat dissipation capacity by increasing the deployment angle of the heat sink and increasing the workload of the heat pipe circulation pump, while reducing heat generation by lowering the operating frequency of the chip core in the relevant area. The compensation optimization calculation uses a constrained optimization algorithm to minimize the additional load on normal heat dissipation structures and chip performance loss while meeting chip temperature control requirements. The calculation process requires establishing a collaborative model between heat dissipation structures to describe the mutual influence and superposition effects of different heat dissipation methods. The compensation control instructions include specific parameter adjustment schemes and execution timing arrangements. The instructions cover aspects such as the increase in workload of the normal heat dissipation structure, the redistribution of power consumption of each core of the chip, and the dynamic adjustment of control timing. The execution of the compensation control instructions adopts a gradual adjustment strategy to avoid the impact of drastic parameter changes on system stability, and ensures the continuity and reliability of heat dissipation performance through a smooth transition.

[0052] In this embodiment, the thermal load characteristics of the orbit are obtained by predicting the thermal load time series based on orbital elements and solar ephemeris data, and heat dissipation control parameters are calculated based on the characteristic coefficients. The fatigue accumulation characteristics of the heat dissipation components are analyzed based on the parameter matrix to obtain a health status assessment result. The operating mode is switched to obtain a time-division multiplexing control strategy. The chip thermal resistance and heat dissipation thermal resistance are matched and optimized according to the control strategy. The chip power consumption and heat dissipation structure are coupled through a predictive control mechanism to obtain thermal balance control parameters. The thermal management performance is analyzed based on the control parameters to obtain performance change trend data, predict the degradation of the heat dissipation structure, and obtain degradation prediction results and compensation control commands. This invention avoids the progressive failure of the thermal management system caused by the asymmetric thermal cycle of elliptical orbits by establishing predictive thermal load analysis based on orbital position and adaptive regulation of the heat dissipation structure.

[0053] The satellite chip thermal management optimization method in the embodiments of the present invention has been described above. The satellite chip thermal management optimization device in the embodiments of the present invention will be described below. Please refer to [link / reference]. Figure 2 One embodiment of the satellite chip thermal management optimization device in this invention includes: The thermal load prediction module 201 is used to perform thermal load time-series prediction processing on the satellite's orbital elements and solar ephemeris data to obtain orbital thermal load characteristics, and to perform collaborative calculation on the heat dissipation structure control parameters of the satellite chip based on the orbital thermal load characteristics to obtain heat dissipation control parameters. The fatigue analysis module 202 is used to analyze the fatigue accumulation of the heat dissipation component according to the heat dissipation control parameters, obtain the health status assessment result of the heat dissipation component, switch the working mode of the heat dissipation component according to the health status assessment result, and obtain the time-sharing multiplexing control strategy of the heat dissipation structure. The thermal resistance matching module 203 is used to match and optimize the chip thermal resistance and heat dissipation thermal resistance according to the time-division multiplexing control strategy to obtain thermal balance control parameters. The degradation prediction module 204 is used to analyze and process the multi-track cycle thermal management performance according to the thermal balance control parameters, obtain performance change trend data, perform degradation prediction on the heat dissipation structure according to the performance change trend data, and obtain the corresponding degradation prediction results and compensation control commands.

[0054] In this embodiment of the invention, the satellite chip thermal management optimization device operates the aforementioned satellite chip thermal management optimization method. The device predicts the orbital thermal load characteristics by analyzing the orbital elements and solar ephemeris data, and calculates heat dissipation control parameters based on characteristic coefficients. It analyzes the fatigue accumulation characteristics of heat dissipation components based on the parameter matrix to obtain a health status assessment result, switches operating modes, and obtains a time-division multiplexing control strategy. Based on the control strategy, it optimizes the matching of chip thermal resistance and heat dissipation thermal resistance, and couples chip power consumption and heat dissipation structure through a predictive control mechanism to obtain thermal balance control parameters. Based on the control parameters, it analyzes thermal management performance to obtain performance change trend data, predicts heat dissipation structure degradation, and obtains degradation prediction results and compensation control commands. This invention avoids progressive failure of the thermal management system caused by asymmetric thermal cycling in elliptical orbits by establishing predictive thermal load analysis based on orbital position and adaptive control of the heat dissipation structure.

[0055] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0056] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0057] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing thermal management of satellite chips, characterized in that, The satellite chip thermal management optimization method includes: The orbital elements and solar ephemeris data of the satellite are subjected to thermal load time-series prediction processing to obtain orbital thermal load characteristics. Based on the orbital thermal load characteristics, the heat dissipation structure control parameters of the satellite chip are calculated collaboratively to obtain heat dissipation control parameters. The fatigue accumulation of the heat dissipation component is analyzed based on the heat dissipation control parameters to obtain the health status assessment result of the heat dissipation component. The working mode of the heat dissipation component is switched according to the health status assessment result to obtain the time-sharing multiplexing control strategy of the heat dissipation structure. Based on the time-division multiplexing control strategy, the chip thermal resistance and heat dissipation thermal resistance are matched and optimized to obtain thermal balance control parameters. The thermal management performance of the multi-track cycle is analyzed and processed based on the thermal balance control parameters to obtain performance change trend data. The degradation prediction of the heat dissipation structure is then performed based on the performance change trend data to obtain the corresponding degradation prediction results and compensation control commands.

2. The satellite chip thermal management optimization method according to claim 1, characterized in that, The thermal load timing prediction processing of the satellite's orbital elements and solar ephemeris data yields orbital thermal load characteristics. Based on these characteristics, the heat dissipation structure control parameters of the satellite chip are collaboratively calculated to obtain the following heat dissipation control parameters: The semi-major axis, eccentricity, and true anomaly angle parameters in the satellite's orbital elements are processed with solar ephemeris data to calculate the thermal load of the elliptical orbit. The orbital thermal load asymmetry index is obtained by calculating the variance of the ratio of perigee to apogee thermal load within a continuous orbital period. Phase compensation factor is obtained by combining the track thermal load asymmetry index with the chip packaging thermal time constant and track motion speed. The orbital thermal load asymmetry index and phase compensation factor are used as orbital thermal load characteristics to jointly calculate the heat dissipation structure control parameters of the satellite chip, thereby obtaining the heat dissipation control parameters.

3. The satellite chip thermal management optimization method according to claim 1, characterized in that, The process of analyzing the fatigue accumulation of the heat dissipation component based on the heat dissipation control parameters to obtain a health status assessment result of the heat dissipation component, and switching the working mode of the heat dissipation component based on the health status assessment result, to obtain a time-sharing multiplexing control strategy for the heat dissipation structure, includes: Based on the radiator heat conduction path switch control sequence in the heat dissipation control parameters, fatigue accumulation modeling is performed on the historical load data of the heat conduction column fan, heat sink rotation mechanism, and heat pipe circulation pump to obtain the remaining life prediction data of each heat dissipation component. Based on the remaining lifespan prediction data, the heat dissipation components are subjected to a health status classification assessment to obtain the health status assessment results. Based on the health status assessment results, the heat dissipation structure is divided into working groups for near-site high-power mode, far-site energy-saving mode, and transitional adjustment mode to obtain the time-sharing multiplexing control strategy for the heat dissipation structure.

4. The satellite chip thermal management optimization method according to claim 3, characterized in that, The process of dividing the heat dissipation structure into near-site high-power mode, far-site energy-saving mode, and transitional adjustment mode based on the health status assessment results, to obtain the time-sharing multiplexing control strategy for the heat dissipation structure, includes: Based on the health status assessment results, the heat conduction column fan, heat sink rotation mechanism, and heat pipe circulation pump are classified according to their health levels. Heat dissipation components with high health levels are assigned to the near-point high-power mode group, heat dissipation components with medium health levels are assigned to the transitional adjustment mode group, and heat dissipation components with low health levels are assigned to the far-point energy-saving mode group, thus obtaining a mode allocation scheme based on health status. Based on the health status-based mode allocation scheme and combined with the elliptical orbit phase information, the prestress adjustment calculation is performed on the start-up timing of each work group to obtain the prestress adjustment timing control parameters. Based on the prestressed adjustment timing control parameters, the switching thresholds and durations of the near-site high-power mode, the far-site energy-saving mode, and the transition adjustment mode are optimized and configured to obtain a time-division multiplexing control strategy for the heat dissipation structure.

5. The satellite chip thermal management optimization method according to claim 1, characterized in that, The process of matching and optimizing the chip thermal resistance and heat dissipation thermal resistance according to the time-division multiplexing control strategy to obtain thermal balance control parameters includes: Based on the work group division information in the time-division multiplexing control strategy of the heat dissipation structure, a thermal resistance matching optimization objective function is generated for the thermal resistance of the chip structure and the thermal resistance of the heat dissipation environment. Based on the thermal resistance matching optimization objective function, the chip power consumption is dynamically allocated through multi-core load balancing and frequency adjustment algorithms, while the working intensity of the heat dissipation structure is adjusted in real time to obtain the coupled control parameters of chip power consumption and heat dissipation structure. Based on the coupling control parameters, predictive optimization is performed on the matching relationship between chip power consumption requirements and heat dissipation capabilities over multiple future orbital cycles to obtain thermal balance control parameters.

6. The satellite chip thermal management optimization method according to claim 5, characterized in that, The step of predictively optimizing the matching relationship between chip power consumption requirements and heat dissipation capabilities over multiple future orbital cycles based on the coupling control parameters to obtain thermal balance control parameters includes: Based on the power consumption dynamic allocation data in the coupling control parameters and the number of orbital elements, the orbital position and thermal load changes in multiple future orbital cycles are predicted and calculated to obtain orbital thermal environment prediction data. The chip power consumption requirements at each track position are calculated based on the predicted track thermal environment data. At the same time, the heat dissipation capacity of the heat dissipation structure at the corresponding track position is evaluated to obtain the matching relationship data between power consumption requirements and heat dissipation capacity. Based on the matching relationship data, a multi-objective optimization algorithm is used to collaboratively optimize the chip power consumption allocation ratio and the working intensity of the heat dissipation structure to obtain the power consumption-heat dissipation configuration scheme for each track position. Based on the power consumption-heat dissipation configuration scheme, the chip voltage frequency adjustment parameters and heat dissipation structure control parameters are time-optimized to obtain thermal balance control parameters.

7. The satellite chip thermal management optimization method according to claim 1, characterized in that, The process of analyzing and processing the multi-track cycle thermal management performance based on the thermal balance control parameters to obtain performance change trend data, and predicting the degradation of the heat dissipation structure based on the performance change trend data to obtain the corresponding degradation prediction results and compensation control commands includes: Based on the thermal balance control parameters, statistical analysis and processing are performed on the chip temperature control accuracy, heat dissipation structure response time, and energy consumption efficiency data in the multi-track cycle to obtain performance change trend data. Based on the performance change trend data, the degradation characteristics of key components of the heat dissipation structure are identified using a health assessment model to obtain the degradation prediction results of the heat dissipation structure. Based on the degradation prediction results of the heat dissipation structure, the working intensity of the heat dissipation structure other than the degraded heat dissipation structure and the chip power consumption distribution are compensated and optimized to obtain compensation control instructions.

8. A satellite chip thermal management optimization device, characterized in that, The satellite chip thermal management optimization device includes: The thermal load prediction module is used to perform thermal load time-series prediction processing on the satellite's orbital elements and solar ephemeris data to obtain orbital thermal load characteristics. Based on the orbital thermal load characteristics, the heat dissipation structure control parameters of the satellite chip are calculated collaboratively to obtain heat dissipation control parameters. The fatigue analysis module is used to analyze the fatigue accumulation of the heat dissipation component based on the heat dissipation control parameters, obtain the health status assessment result of the heat dissipation component, switch the working mode of the heat dissipation component based on the health status assessment result, and obtain the time-sharing multiplexing control strategy of the heat dissipation structure. The thermal resistance matching module is used to match and optimize the chip's thermal resistance and heat dissipation thermal resistance according to the time-division multiplexing control strategy to obtain thermal balance control parameters. The degradation prediction module is used to analyze and process the multi-track cycle thermal management performance based on the thermal balance control parameters, obtain performance change trend data, predict the degradation of the heat dissipation structure based on the performance change trend data, and obtain the corresponding degradation prediction results and compensation control commands.

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