Satellite thermal management intelligent regulation method and system based on thermal bus

Abstract

This invention provides a satellite thermal management intelligent control method and system based on a thermal bus, comprising: acquiring on-orbit operation data of the satellite; identifying thermal characteristic parameters of the satellite thermal management system based on the operation data to establish time-varying performance patterns; using a whole-satellite temperature field prediction model and data fusion technology to invert the whole-satellite temperature field from satellite temperature measurement data for temperature field prediction and fault diagnosis; generating a hierarchical autonomous management strategy for the thermal control subsystem based on the whole-satellite energy status, external orbital heat flux, payload operating mode, and whole-satellite temperature field, and controlling the actuators according to the strategy to achieve online control of the thermal control system. This invention, while using the current temperature as a judgment criterion, adds the temperature distribution at future times as a constraint condition. Compared to existing technologies that only adjust based on the current temperature level, this effectively suppresses temperature fluctuations, thereby significantly reducing system energy consumption while ensuring temperature control accuracy.

Description

Technical Field

[0001] This invention relates to the field of satellite intelligent thermal management technology, specifically to a satellite thermal management intelligent control method and system based on a thermal bus. Background Technology

[0002] Conventional satellite thermal control systems typically employ a reactive design philosophy, supplemented by proactive measures. While simple and reliable, this approach has several drawbacks. First, the mission-oriented design means the thermal control system can only achieve its intended performance under stable external thermal environments and predetermined internal operating modes. This places stringent demands on the satellite's orbit, attitude, and operating modes, hindering platform scalability and multi-mission flexibility. Second, specific mission requirements necessitate a highly autonomous thermal management system that matches autonomous flight management capabilities. This means the system must be able to autonomously adjust spacecraft temperature based on changes in orbit, attitude, and onboard equipment operating modes. It must also possess strong environmental identification, temperature prediction, and thermal control mission coordination capabilities, enabling reliable operation in complex, unpredictable, and varied thermal environments. This allows the system to adapt to different orbits and mission profiles, meeting the requirements of multi-mission and multi-type spacecraft development.

[0003] To address the needs of spacecraft thermal control subsystems to adapt to complex thermal environments and complex on-orbit operating modes, research will be conducted on spacecraft thermal environment sensing technology, intelligent thermal control technology, and autonomous thermal management technology. Key core technologies such as on-orbit whole-satellite temperature field prediction and hierarchical thermal management system will be mastered. Complex thermal environment parameter identification model and whole-satellite temperature field prediction model will be established, and a hierarchical autonomous thermal management system based on intelligent agents will be constructed to realize the adaptive dynamic adjustment of the thermal control subsystem to mission and environmental changes, thereby improving the timeliness and responsiveness of the whole-satellite mission.

[0004] The paper "Intelligent Autonomous Thermal Control Method for Spacecraft Based on Space Environment Prediction" uses parameters such as the spacecraft's attitude adjustment and planned orbit over a future period as inputs to analyze the external heat flow absorbed by the outer surface at various future moments. Based on this, it performs feedforward control by adjusting heaters, adjustable radiators, and fluid loops, thereby enhancing the spacecraft's adaptability and reducing control time lag. However, this method only considers the external heat flow input and lacks a comprehensive consideration of changes in payload operating modes and the overall satellite energy status.

[0005] The paper "Intelligent Control Technology in Autonomous Thermal Management of Spacecraft" analyzes the intelligent control technology for spacecraft thermal management and its development trend, and proposes ideas for the intelligent control technology for autonomous thermal management to become more precise and intelligent, but lacks specific implementation methods.

[0006] The Chinese patent document with publication number CN119202452A discloses a multidimensional nonlinear collaborative intelligent thermal management method and system for satellite platforms and payloads. Under the constraint of a predetermined working mode of the payload, the method performs collaborative optimization and adjustment of the payload heat consumption and the thermal control heater of the entire satellite in the time domain. The power-on sequence and power-on duration of the individual satellite payload are used as adjustment variables for the thermal management of the entire satellite. However, this method does not consider the direct influence of instantaneous external heat flow on temperature.

[0007] The patent with publication number CN120122753A discloses a Chinese patent document on a satellite autonomous temperature control heating system based on orbit information. It can obtain the corresponding heating strategy from the storage unit according to the current time and orbit information, and autonomously control the operation of the heating execution unit. However, this method cannot update and correct the external heat flow on the satellite in real time, and its generalization ability is not strong.

[0008] Chinese patent literature with publication number CN111086655A discloses a method and system for saving power through thermal control compensation during the shadow period of a non-monitoring arc. After determining the heater status during the satellite's illumination and shadow periods through thermal simulation, the heater is controlled during the shadow period to reduce the risk of battery over-discharge. However, this method can further save energy by rationally distributing heat through a thermal bus.

[0009] Chinese patent publication CN112528488A discloses a method and system for saving power consumption during satellite shadow periods based on differences in thermal capacity. After calibrating the thermal capacity of each individual unit, it accurately calculates the heater's on-time during shadow periods, thereby saving costs and ensuring electrical safety. However, this solution is essentially an open-loop control, which presents certain environmental adaptability issues.

[0010] Chinese patent document CN120327826A discloses a thermal control fluid loop, thermal control system, and temperature control method applicable to modular satellites, designing a fluid loop and its temperature control method. However, this temperature control method only relies on the current temperature to adjust the pump power, lacking the ability to predict future temperature changes, which may result in a long response time.

[0011] In summary, given the problems of the existing technologies, researching a satellite thermal management intelligent control method and system based on a thermal bus has become a critical task that urgently needs to be addressed. Summary of the Invention

[0012] In view of the deficiencies in the existing technology, the purpose of this invention is to provide a satellite thermal management intelligent control method and system based on a thermal bus.

[0013] A satellite thermal management intelligent control method based on a thermal bus, according to the present invention, includes the following steps:

[0014] Step S1: Acquire the satellite's on-orbit operation data, identify the thermal characteristic parameters of the satellite's thermal management system based on the operation data, and establish the time-varying performance law; Step S2: Based on the whole satellite temperature field prediction model and data fusion technology, use satellite temperature measurement data to invert the whole satellite temperature field, and perform temperature field prediction and fault diagnosis; Step S3: Based on the whole satellite energy status, orbital heat flow, payload operating mode, and the whole satellite temperature field obtained in Step S2, generate a hierarchical autonomous management strategy for the thermal control subsystem, and control the actuators according to the strategy to realize online control of the thermal control system.

[0015] Preferably, the satellite thermal management intelligent control method based on thermal bus adopts a hierarchical autonomous management intelligent thermal control architecture, including: a decision layer, as the top-level management unit, configured to receive whole-satellite energy data, orbital heat flow data, and payload operating mode data, and according to the autonomous management mode of the satellite thermal management system; an optimization management layer, as an intermediate computing unit, configured to perform optimization calculations in a multi-dimensional parameter space based on intelligent optimization algorithms to generate control parameters that minimize the energy demand of the satellite thermal management system under the autonomous management mode; and an execution layer, as the bottom-level control unit, configured to execute intelligent thermal control of multi-parameter coupled temperature fields, drive the actuator based on the control parameters, and identify the comprehensive thermal effects under complex environments using partial observation data.

[0016] Preferably, step S1 includes: establishing an inverse mapping relationship from temperature monitoring data to material degradation characteristics based on the on-orbit operation data; introducing regularization methods and Bayesian optimization algorithms to process data sparsity and noise interference; and generating a material degradation threshold by combining the heat dissipation area limitation and temperature margin reservation requirements of the satellite thermal control design, thereby identifying the time-varying performance of the thermal control coating and heat transfer components.

[0017] Preferably, the whole-satellite temperature field prediction model in step S2 includes an external heat flow prediction model, a heat loss correction model, and a surrogate model for generating temperature field predictions.

[0018] Preferably, the external heat flow prediction model is used to predict external heat flow based on satellite orbit and attitude parameters using an analytical model of external space heat flow; the heat consumption correction model is used to correct the heat consumption value of a single device under various operating modes based on the heat capacity and temperature rise data of the single device.

[0019] Preferably, the method for constructing the surrogate model for generating temperature field prediction includes: integrating satellite on-orbit temperature data, ground accelerated aging test data, and simulation data; constructing a data-driven or physical information neural network, with input features including material properties, external orbital heat flow parameters, and spatial location parameters, and outputting a temperature field distribution; embedding heat conduction equations and radiation boundary conditions as physical constraints during the training process of the neural network; and using the difference between the predicted value of the surrogate model and the temperature at the on-orbit measurement point as a loss function for iterative training to obtain a high-fidelity surrogate model.

[0020] Preferably, the inversion method in step S2 includes: using the surrogate model, combined with the material degradation characteristics identified in step S1, to calculate and output the temperature field distribution data of the entire satellite based on the temperature values ​​of some sparse on-orbit measurement points.

[0021] Preferably, step S3 includes: assessing the heat and cooling load required in each region based on the whole satellite temperature field data; identifying regions that require heat replenishment or whose heat dissipation demand is increasing based on temperature deviation, thermal environment change trends, equipment operating status, and heat flow input; coordinating and regulating the heat dissipation path and power distribution method according to the identification results, wherein the control actuator actions include at least one of the following: starting and stopping heaters in key areas, adjusting the circulation flow rate and on / off ratio of the fluid loop, switching on and off the intelligent coating, switching on and off the electrically controlled heat insulation screen, and driving the deployment degree and attitude direction of the deployable radiator.

[0022] Preferably, in step S3, generating a hierarchical autonomous management strategy for the thermal control subsystem further includes: analyzing the energy consumption characteristics of the control methods of each actuator; and prioritizing energy scheduling by selecting a combination scheme with low energy consumption and high temperature control efficiency, with the goal of minimizing energy consumption, while ensuring the temperature control safety of the entire satellite and key equipment.

[0023] This invention also provides a satellite thermal management intelligent control system based on a thermal bus. This system can be implemented by executing the steps of the satellite thermal management intelligent control method based on a thermal bus. That is, those skilled in the art can understand the satellite thermal management intelligent control method based on a thermal bus as a preferred embodiment of the satellite thermal management intelligent control system based on a thermal bus. The system includes: The acquisition module obtains the satellite's on-orbit operation data, identifies the thermal characteristic parameters of the satellite's thermal management system based on the operation data, and establishes the time-varying performance law. The inversion module, based on the whole satellite temperature field prediction model and data fusion technology, uses satellite temperature measurement data to invert the whole satellite temperature field for temperature field prediction and fault diagnosis. The control module generates a hierarchical autonomous management strategy for the thermal control subsystem based on the overall satellite energy status, external orbital heat flow, payload operating mode, and the overall satellite temperature field obtained by the inversion module. It then controls the actuators to perform actions according to the strategy, thereby achieving online regulation of the thermal control system.

[0024] Compared with the prior art, the present invention has the following beneficial effects: 1. Based on the current temperature as the basis for judgment, this invention adds the temperature distribution at future times as a constraint condition. This predictive control strategy can respond to changes in the thermal environment in advance. Compared with the existing technology that only adjusts based on the current temperature level, it effectively suppresses temperature fluctuations and avoids "over-adjustment" or "under-adjustment" caused by response lag, thereby significantly reducing system energy consumption while ensuring temperature control accuracy.

[0025] 2. This invention breaks through the single-dimensional control mode. Based on the heat flow requirements, energy status, external orbit heat flow and payload working mode of the multi-module and multi-region of the whole satellite platform, it proposes a system autonomous management mode. With the goal of minimizing the heating power consumption of thermal control compensation, the whole satellite thermal management can be completed faster and with lower power consumption.

[0026] 3. This invention proposes a thermal control technology based on a thermal bus. Compared to existing technologies that rely solely on heaters to regulate local areas, this invention comprehensively considers the entire thermal control system. By transferring waste heat from high-temperature zones to low-temperature zones, it achieves spatial heat transfer and reuse. This mechanism significantly reduces reliance on electric heaters, thereby substantially lowering heater power requirements and on-orbit energy consumption.

[0027] 4. This invention uses a data-driven proxy model to replace traditional numerical simulation software with huge computational load. While ensuring prediction accuracy, this method reduces the computational load by orders of magnitude and reduces the requirements for the hardware performance of the integrated electronic system, making it possible to perform real-time full-satellite temperature field inversion and prediction at the satellite in orbit, and providing strong data support for autonomous thermal control. Attached Figure Description

[0028] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating a satellite thermal management intelligent control method based on a thermal bus according to the present invention. Detailed Implementation

[0029] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0030] Figure 1 A flowchart illustrating a satellite thermal management intelligent control method based on a thermal bus, as provided in an embodiment of the present invention, is shown below. Figure 1 As shown, the intelligent thermal control technology adopts a "layered autonomous management" approach, which specifically includes a decision-making layer, an optimization management layer, and an execution layer.

[0031] Specifically, the decision-making level conducts top-level planning based on the satellite's real-time status, acquiring real-time information on the satellite's overall energy status (e.g., fully charged batteries during sunlight periods or limited power supply during Earth's shadow periods), external orbital heat flux (e.g., deep space cold background or direct sunlight), and payload operating modes (e.g., high-power operation or standby). Based on this input information, the decision-making level uses logical judgment to determine the appropriate autonomous management mode for the current thermal control subsystem, including energy-saving insulation mode, powerful heat dissipation mode, or temperature balance mode, and then distributes constraints to lower levels.

[0032] To address the high energy consumption and slow response issues inherent in existing fixed-threshold passive thermal control modes, the optimization management layer introduces intelligent optimization technology. Under the constraints of the management mode determined by the decision-making layer, the optimization management layer performs global optimization across a multi-dimensional control parameter space (such as heating power, fluid flow rate, and coating on / off status). Its core objective is to minimize the energy demand of the thermal control system while maintaining temperature control accuracy, thereby calculating the optimal combination of control parameters.

[0033] The primary task of the execution layer is to improve the performance of the thermal control subsystem. It directly drives hardware components (such as heaters, fluid pumps, and louvers) by receiving control parameters from the optimization management layer. Specifically, addressing the multi-parameter coupling characteristics of the satellite thermal bus system, the execution layer employs intelligent thermal control technology with multi-parameter coupled temperature fields for decoupling control. Simultaneously, the execution layer integrates an environmental identification algorithm, enabling online identification of the comprehensive thermal effects of complex on-orbit environments using sensor data. The identification results are then fed back to the upper layer, forming a closed-loop control system, thereby ensuring the system's robustness under uncertain environments.

[0034] In this embodiment, the intelligent control method for satellite thermal management based on a thermal bus will be implemented through a technical approach that combines intelligent sensing of thermal characteristics, enhanced intelligent feature cognition, and intelligent decision-making in thermal management. By coupling the sensing, cognition, and actions of the thermal control subsystem, the adaptability of the spacecraft's thermal control subsystem design and its autonomous management capabilities in dealing with external thermal environments and complex missions on orbit will be improved. Specifically, the method includes the following steps: Step 1: By mining the massive operational data of satellites in orbit, we can identify the spatial influencing factors and time-varying performance patterns of thermal characteristic parameters such as thermal control coatings and heat transfer components.

[0035] Specifically, based on massive operational data from in-orbit satellites, an inverse mapping relationship is established to retrieve material degradation characteristics from temperature monitoring data. Regularization methods and Bayesian optimization are introduced to address data sparsity and noise interference issues in the inverse problem. Combined with satellite thermal control design requirements (such as heat dissipation area limitations and temperature margin reservations), a reliable material degradation threshold is generated.

[0036] Step 2: Based on the whole satellite temperature field prediction model and data fusion technology, the whole satellite temperature field is inverted using limited on-board temperature measurement data, which is used for temperature field prediction and fault diagnosis of on-orbit satellites.

[0037] The whole-satellite temperature field prediction model includes an external heat flow prediction model, a heat dissipation correction model, and a surrogate model for generating temperature field prediction. The specific construction principles and functions of each model are as follows: The variation of extraterrestrial heat flux is mainly influenced by the dynamics of satellite orbital parameters and attitude information. This embodiment does not rely on time-consuming traditional numerical integration; instead, it constructs an analytical model of extraterrestrial heat flux for rapid prediction. Using the satellite's real-time orbital elements (or position vector), attitude quaternions, solar vector, and Earth's infrared emissivity as input parameters, and through reasonable geometric analysis and mathematical simplification, an analytical algorithm is used to calculate the direct solar heat flux, Earth's albedo heat flux, and Earth's infrared heat flux on each surface of the satellite. This model can output current and future extraterrestrial heat flux distribution data at millisecond speeds, providing real-time environmental boundary conditions for subsequent temperature field calculations.

[0038] To address the issue that discrepancies often exist between the actual on-orbit heat dissipation of individual devices and the nominal heat dissipation provided by the device manufacturer, leading to significant differences between simulated and real temperatures, this embodiment introduces a heat dissipation correction mechanism. The heat dissipation correction model, based on the physical properties of the device (such as heat capacity) and measured temperature rise rate data, combined with the law of conservation of energy, reverse-corrects the heat dissipation values ​​of the device under different operating modes. By establishing a heat dissipation correction equation, the corrected heat dissipation value is input as an internal heat source boundary condition into the subsequent temperature field model. This effectively eliminates temperature prediction deviations caused by input source errors, improving the overall prediction accuracy of the system.

[0039] To address the limitation of onboard computing resources preventing the use of traditional finite element simulation software, this embodiment constructs a high-fidelity, low-computational-cost proxy model based on a Physical Information Neural Network (PINN) to achieve rapid mapping from material properties to the overall satellite temperature field. The model's construction and training involve the following steps: integrating satellite on-orbit temperature data, ground-based accelerated aging test data (proton irradiation, vacuum thermal cycling), and simulation data; embedding thermodynamic equations (such as Fourier's law of heat conduction) as physical constraints in the modeling to ensure the model conforms to the thermodynamic behavior of materials. Through limited experimental or real on-orbit data, a high-fidelity, low-computational-cost proxy model is established to achieve rapid mapping from material properties (such as thermal conductivity, solar absorptivity, and emissivity) to temperature output. The specific method for constructing the proxy model is as follows: constructing a data-driven or Physical Information Neural Network (PINN), with inputs including material properties, extraorbital heat flux (such as solar radiation intensity and Earth's albedo), and spatiotemporal location parameters; and outputting the temperature field distribution. Embedding heat conduction equations (such as Fourier's law) and radiation boundary conditions into the model ensures the proxy model conforms to fundamental thermodynamic laws, improving prediction reliability. This surrogate model integrates temperature data from on-orbit measurement points, using the difference between the surrogate model's predictions and the on-orbit measurement point temperatures as a loss function. This continuously enhances the accuracy of the surrogate model's predictions during training. After obtaining the surrogate model, the temperature field of the entire satellite is inverted based on the material degradation characteristics obtained in step 1, using partially sparse on-orbit measurement point temperature values. This result will be used for temperature field prediction and fault diagnosis of the on-orbit satellite.

[0040] Step 3: Construct a hierarchical autonomous management system for the thermal control subsystem, and propose an autonomous management model for the thermal control subsystem based on the overall satellite energy status, external orbital heat flow, and payload operating modes.

[0041] Specifically, the thermal control subsystem uses the overall satellite temperature field data obtained in step 2 within the integrated electrical software to analyze and assess the heat and cooling load required by each region, and dynamically responds to energy demands accordingly. Based on temperature deviations and thermal environment change trends, combined with equipment operating status, heat flow input, and known thermal models, it assesses which parts require additional heat and which areas or components are experiencing increased heat dissipation demands. On this basis, the thermal control system coordinates and regulates heat dissipation paths and power distribution methods, including but not limited to: starting and stopping heaters in critical areas, adjusting the circulation flow and on / off ratio of fluid loops, intelligent coating switches, electrically controlled thermal shield switches, and driving the deployment degree and attitude direction of deployable radiators. Through this multi-level, interconnected control method, the thermal environment of different functional modules of the spacecraft can be precisely adjusted and protected.

[0042] Meanwhile, to maximize energy efficiency and reduce the impact of thermal control operations on the overall power load, the energy consumption characteristics of various control methods are analyzed regularly. Priority is given to combinations of low energy consumption and high temperature control efficiency. While ensuring the temperature control safety of the entire satellite and even critical equipment, energy scheduling strategies are optimized, thermal equipment is switched on and off in an orderly manner, and control modes are rationally set to reduce the additional energy consumption caused by redundant control. This control process not only helps achieve the dual goals of minimizing energy consumption and maintaining temperature stability, but also possesses robustness against uncertainties, which is of great significance for extending equipment life and improving system reliability.

[0043] Through the above steps, a hierarchical autonomous management system is constructed using integrated electrical software. Taking into account the overall satellite energy, external orbital heat flow, and payload modes, it achieves global intelligent coordination of multiple modules and regions of complex large satellites. Combined with multi-parameter optimal online identification and self-tuning optimization technology, it breaks through the customized design barrier of traditional thermal control systems with hard coupling, realizes the "software definition" and rapid reconfiguration of thermal control strategies, and significantly improves the system's versatility, on-orbit adaptability, and intelligence level.

[0044] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0045] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A satellite thermal management intelligent control method based on a thermal bus, characterized in that, include: Step S1: Obtain the satellite's on-orbit operation data, identify the thermal characteristic parameters of the satellite's thermal management system based on the operation data, and establish the time-varying performance law; Step S2: Based on the whole satellite temperature field prediction model and data fusion technology, the whole satellite temperature field is retrieved using satellite temperature measurement data to predict the temperature field and diagnose faults. Step S3: Based on the overall satellite energy status, orbital heat flow, payload operating mode, and the overall satellite temperature field obtained in step S2, a hierarchical autonomous management strategy for the thermal control subsystem is generated, and the actuators are controlled according to the strategy to achieve online regulation of the thermal control system.

2. The intelligent control method for satellite thermal management based on a thermal bus according to claim 1, characterized in that, The satellite thermal management intelligent control method based on a thermal bus adopts a hierarchical autonomous management intelligent thermal control architecture, including: The decision-making layer, as the top-level management unit, is configured to receive whole-satellite energy data, orbital heat flow data, and payload operating mode data, and to manage the satellite according to the autonomous management mode of the satellite thermal management system. The management layer is optimized and configured to perform optimization calculations in a multi-dimensional parameter space based on intelligent optimization algorithms to generate control parameters that minimize the energy demand of the satellite thermal management system in the autonomous management mode. The execution layer is configured to perform intelligent thermal control of a multi-parameter coupled temperature field, drive the actuator to act based on the control parameters, and identify the comprehensive thermal effects in complex environments using some observation data.

3. The intelligent control method for satellite thermal management based on a thermal bus according to claim 1, characterized in that, Step S1 includes: Based on the aforementioned on-orbit operation data, an inverse mapping relationship is established from temperature monitoring data to material degradation characteristics; Regularization methods and Bayesian optimization algorithms are introduced to address data sparsity and noise interference. By combining the heat dissipation area limitations and temperature margin requirements of satellite thermal control design, a material degradation threshold is generated, thereby identifying the time-varying performance of thermal control coatings and heat transfer components.

4. The intelligent control method for satellite thermal management based on a thermal bus according to claim 1, characterized in that, The whole-satellite temperature field prediction model in step S2 includes an external heat flow prediction model, a heat dissipation correction model, and a surrogate model for generating temperature field prediction.

5. The intelligent control method for satellite thermal management based on a thermal bus according to claim 4, characterized in that, The external heat flow prediction model is used to predict external heat flow based on satellite orbit and attitude parameters using an analytical model of external space heat flow. The heat loss correction model is used to correct the heat loss value of a single machine under various operating modes based on the heat capacity and temperature rise data of the single machine.

6. The intelligent control method for satellite thermal management based on a thermal bus according to claim 4, characterized in that, The method for constructing the surrogate model for generating temperature field prediction includes: Integrate satellite on-orbit temperature data, ground-based accelerated aging test data, and simulation data; Construct a data-driven or physical information neural network, with input features including material properties, orbital external heat flux parameters, and spatial location parameters, and output as temperature field distribution; During the training process of the neural network, heat conduction equations and radiation boundary conditions are embedded as physical constraints. The difference between the predicted value of the surrogate model and the temperature at the on-orbit measurement point is used as the loss function for iterative training to obtain a high-fidelity surrogate model.

7. The intelligent control method for satellite thermal management based on a thermal bus according to claim 1, characterized in that, The inversion method in step S2 includes: Using the surrogate model and the material degradation characteristics identified in step S1, the temperature field distribution data of the entire satellite is calculated and output based on the temperature values ​​of some sparse on-orbit measurement points.

8. The intelligent control method for satellite thermal management based on a thermal bus according to claim 1, characterized in that, Step S3 includes: Based on the whole-satellite temperature field data, assess the heat and cooling load required for each region; Based on temperature deviation, thermal environment change trends, equipment operating status and heat flow input, identify areas that require heat replenishment or areas where heat dissipation demand increases. Based on the identification results, the heat dissipation path and power distribution method are coordinated and adjusted. The actions of the control actuator include at least one of the following: starting and stopping the heater in the key area, adjusting the circulation flow and on / off ratio of the fluid circuit, switching the intelligent coating, switching the electrically controlled heat insulation screen, and driving the deployment degree and orientation of the deployable radiator.

9. The intelligent control method for satellite thermal management based on a thermal bus according to claim 1, characterized in that, In step S3, generating the hierarchical autonomous management strategy for the thermal control subsystem also includes: Analyze the energy consumption characteristics of the control measures of various implementing agencies; Under the premise of ensuring the temperature control safety of the entire satellite and key equipment, with the goal of minimizing energy consumption, priority is given to selecting a combination scheme with low energy consumption and high temperature control efficiency for energy dispatch.

10. A satellite thermal management intelligent control system based on a thermal bus, characterized in that, include: The acquisition module obtains the satellite's on-orbit operation data, identifies the thermal characteristic parameters of the satellite's thermal management system based on the operation data, and establishes the time-varying performance law. The inversion module, based on the whole satellite temperature field prediction model and data fusion technology, uses satellite temperature measurement data to invert the whole satellite temperature field for temperature field prediction and fault diagnosis. The control module generates a hierarchical autonomous management strategy for the thermal control subsystem based on the overall satellite energy status, external orbital heat flow, payload operating mode, and the overall satellite temperature field obtained by the inversion module. It then controls the actuators to perform actions according to the strategy, thereby achieving online regulation of the thermal control system.

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