Redundant thermal isolation unlocking control method and device for satellite-borne deployable mechanism

Abstract

The invention relates to the technical field of communication data processing, in particular to a redundant thermal isolation unlocking control method and device for a satellite-borne deployable mechanism, and the method comprises the steps that a satellite platform collects an unlocking instruction data packet and multi-modal sensor data, and generates a standardized data frame after analysis; generating a system state estimation value and a heat knife assembly health degree score; predicting the fusing time and the thermal damage risk of the rope by using digital twinborn simulation; self-adaptive control is carried out based on a simulation result, and heating power parameters are dynamically adjusted; fault probability distribution is generated through fault diagnosis, and early warning is triggered; and performing redundancy decision by combining the fault probability and the simulation result, dynamically switching to a standby hot knife assembly to complete unlocking, and feeding back state data to form closed-loop control. The corresponding device comprises a data acquisition and analysis module, a data fusion processing module, a digital twin simulation module, an adaptive control module, a fault diagnosis module and a redundancy decision and execution virtual module. According to the invention, the fault-tolerant capability and task success rate of the system in a complex space environment are improved.

Description

Technical Field

[0001] This invention relates to the field of communication data processing technology, and in particular to a method and apparatus for controlling the redundancy thermal isolation unlocking of spaceborne deployable mechanisms. Background Technology

[0002] Existing single-point hot knife unlocking control technology is a method of releasing onboard mechanisms by melting a polymer restraint tether using an electrically heated element. Its working principle is that the satellite platform sends a command to activate a single hot knife module, causing the heating element to heat up until it melts the tether, releasing the restraint on the clamping arm and thus allowing the deployment of mechanisms such as solar panels or antennas. Because the system relies on only a single heating element and its control circuit, it inherently carries the risk of single-point failure. If the power supply line or heating element of the hot knife module fails in orbit, the unlocking command will not be effectively executed, resulting in the tether failing to melt.

[0003] Existing single-point hot knife unlocking control technology has the following technical pain points. Specifically, because the control circuit adopts a single-point design and lacks a redundancy backup mechanism, once the heating element or power supply circuit in the circuit fails, the unlocking command cannot be transmitted or executed, causing the on-board deployable mechanism, such as the solar array or antenna, to fail to release in orbit. For example, when the satellite receives the deployment command after entering orbit, the control circuit fails due to component aging or short circuit. The hot knife module cannot start the heating process, the restraint rope remains taut, the clamping arm cannot be unlocked, and the deployable mechanism cannot be deployed, ultimately affecting the satellite's functionality and mission success rate. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a redundant thermal isolation unlocking control method and device for spaceborne deployable mechanisms. This invention solves the technical problem of spaceborne unlocking command execution failure caused by a single point of failure in the control circuit.

[0005] To solve the above-mentioned technical problems, the specific contents of the present invention are as follows:

[0006] In a first aspect, the present invention provides a method for controlling the redundant thermal isolation unlocking of a spaceborne deployable mechanism, comprising:

[0007] Step 1: The satellite platform collects unlocking command data packets and multimodal sensor data of the onboard deployable mechanism. The multimodal sensor data includes temperature data, current data and vibration data. The unlocking command data packets are parsed to extract the command type and target mechanism identifier, and a standardized data frame is generated.

[0008] Step 2: Perform data fusion processing on the standardized data frame generated in Step 1. Use a filtering algorithm to fuse the multimodal sensor data to generate system state estimates, including heating element temperature trends and power stability index. Calculate the health score of the hot knife component based on the system state estimates.

[0009] Step 3: Use the health score and system state estimate generated in Step 2 for digital twin simulation. Predict the rope melting time and thermal damage risk of the hot knife component through physical modeling and real-time simulation, and output the simulation prediction results.

[0010] Step 4: Use the simulation prediction results output in Step 3 for adaptive control, dynamically adjust the heating power output curve of the hot knife component, and compensate for ambient temperature fluctuations through power regulation to generate real-time power parameters.

[0011] Step 5: Use the real-time power parameters generated in Step 4 for fault diagnosis. Detect current or temperature anomalies using a pattern recognition algorithm, generate a fault probability distribution, and trigger an early warning signal when the fault probability exceeds a threshold.

[0012] Step 6: Based on the failure probability distribution generated in Step 5 and the simulation prediction results output in Step 3, redundancy decision is made, the reliability of the main and backup hot knife components is evaluated, the switching timing is dynamically selected, and the backup hot knife component is activated to melt the restraint rope when the main hot knife component fails, thereby unlocking the onboard deployable mechanism. At the same time, the unlocking status data is fed back to the satellite platform and digital twin simulation to form a closed-loop control.

[0013] Furthermore, in the spaceborne deployable mechanism redundant thermal isolation unlocking control method of the present invention, step 1 includes:

[0014] The satellite receives unlock command data packets sent by the ground control center via the onboard communication bus, and simultaneously collects readings from temperature sensors, current sensors, and vibration sensors to obtain multimodal sensor data.

[0015] The integrity of the unlock command data packet is verified, and the command operation code and the organization address code are extracted according to the space data system protocol.

[0016] Based on the extracted instruction opcode and organization address code, a standardized data frame with a timestamp is generated for data fusion processing in step 2.

[0017] Furthermore, in the spaceborne deployable mechanism redundant thermal isolation unlocking control method of the present invention, step 2 includes:

[0018] The temperature data, current data, and vibration data in the standardized data frame generated in step 1 are asynchronously fused using the Kalman filter algorithm.

[0019] The temperature signal is filtered by moving average to eliminate noise, a thermodynamic state-space model is established, and the optimal temperature estimate of the heating element is calculated iteratively using the current reading as the input variable and the temperature reading as the observation variable.

[0020] Compare the residuals between the optimal temperature estimate and the actual measurement, calculate the health score of the hot knife component, normalize the health score into a percentage form, and use it for the digital twin simulation in step 3.

[0021] Furthermore, in the spaceborne deployable mechanism redundant thermal isolation unlocking control method of the present invention, step 3 includes:

[0022] A three-dimensional heat conduction simulation of the hot knife component was constructed based on the finite element method. Material thermal property parameters and orbital heat flow data were imported, and the transient heat conduction equation was solved by numerical solution.

[0023] The health score generated in step 2 is used as the material degradation coefficient to adjust the simulation parameters, and the system state estimate generated in step 2 is used as the initial condition for simulation. The probability distribution of rope melting time and heat-affected zone data are output.

[0024] The probability distribution and thermally affected zone data are used as simulation prediction results for adaptive control in step 4.

[0025] Furthermore, in the spaceborne deployable mechanism redundant thermal isolation unlocking control method of the present invention, step 5 includes:

[0026] A support vector machine classifier is used to extract features from the real-time power parameters generated in step 4. The first derivative and approximate entropy of the temperature curve are calculated as input vectors and compared with historical normal operating condition data.

[0027] By matching the simulation prediction results output in step 3 with the actual sensor readings through a Bayesian inference network, a fault confidence index is generated. When the fault confidence exceeds a preset threshold, an early warning signal is triggered.

[0028] The fault confidence index is used as the fault probability distribution for redundant decision-making in step 6.

[0029] Furthermore, in the redundant thermal isolation unlocking control method for spaceborne deployable mechanisms described in this invention, step 6 includes:

[0030] The fuzzy inference system processes the health score generated in step 2, the fault probability distribution generated in step 5, and the simulation prediction results output in step 3, and outputs a quantitative value for the switching timing.

[0031] Based on the switching timing quantization value, when the main hot knife component fails, a switching command is sent to start the backup hot knife component and adjust the parameters of the digital twin simulation.

[0032] After the backup hot knife assembly melts the restraint rope, it collects status signals and updates the weight parameters for fault diagnosis, and reports the unlocking status data to the satellite platform.

[0033] Secondly, the spaceborne deployable mechanism redundant thermal isolation unlocking control device provided by the present invention is applied to the aforementioned spaceborne deployable mechanism redundant thermal isolation unlocking control method, including:

[0034] The data acquisition and parsing virtual module is used to acquire unlocking command data packets and multimodal sensor data of the spaceborne deployable mechanism, parse the unlocking command data packets to extract the command type and target mechanism identifier, and generate standardized data frames;

[0035] The data fusion processing virtual module is used to perform data fusion processing on standardized data frames. It fuses multimodal sensor data through filtering algorithms to generate system state estimates, including heating element temperature trends and power stability indices, and calculates the health score of the hot knife component based on the system state estimates.

[0036] The digital twin simulation virtual module is used to apply health scores and system state estimates to digital twin simulations. It predicts the rope melting time and thermal damage risk of the hot knife component through physical modeling and real-time simulation, and outputs simulation prediction results.

[0037] The adaptive control virtual module is used to apply simulation prediction results to adaptive control, dynamically adjust the heating power output curve of the hot knife component, and compensate for ambient temperature fluctuations through power regulation to generate real-time power parameters.

[0038] The fault diagnosis virtual module is used to use real-time power parameters for fault diagnosis, detect current or temperature anomalies through pattern recognition algorithms, generate a fault probability distribution, and trigger an early warning signal when the fault probability exceeds a threshold.

[0039] The redundant decision-making and execution virtual module is used to make redundant decisions based on the failure probability distribution and simulation prediction results, evaluate the reliability of the main and backup hot knife components, dynamically select the switching timing, and activate the backup hot knife component to melt the restraint rope when the main hot knife component fails, thereby unlocking the onboard deployable mechanism. At the same time, the unlocking status data is fed back to the satellite platform and digital twin simulation to form a closed-loop control.

[0040] Beneficial effects of this invention:

[0041] This invention effectively solves the problem of onboard unlocking command execution failure caused by single-point failure in the control circuit by employing a redundant thermal isolation unlocking control method for onboard deployable mechanisms. Its beneficial effects are reflected in the following aspects: Standardized data frames are generated through multi-modal sensor data acquisition and analysis, providing a reliable data foundation for subsequent processing; system state estimates and health scores are generated using data fusion processing, improving the accuracy of state monitoring; digital twin simulation, based on physical modeling and real-time simulation, predicts rope melting time and thermal damage risk, providing an optimization basis for control; adaptive control dynamically adjusts the heating power output curve to compensate for environmental fluctuations and enhance system stability; fault diagnosis detects anomalies and generates fault probability distributions through pattern recognition algorithms, achieving early warning; redundancy decision-making assesses the reliability of primary and backup components based on multi-source information, dynamically switching to the backup hot knife component to avoid single-point failure; finally, closed-loop control feeds unlocking status data back to the satellite platform and digital twin simulation, continuously optimizing system parameters, thereby significantly improving the fault tolerance and mission success rate of onboard deployable mechanisms in complex space environments. Attached Figure Description

[0042] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating the redundant thermal isolation unlocking control method for spaceborne deployable mechanisms provided in an embodiment of the present invention. Detailed Implementation

[0044] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. 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. The present invention provided by various embodiments will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0045] Firstly, please refer to Figure 1 The present invention provides a method for controlling the redundant thermal isolation unlocking of a spaceborne deployable mechanism, comprising:

[0046] Step 1: The satellite platform collects unlocking command data packets and multimodal sensor data of the onboard deployable mechanism. The multimodal sensor data includes temperature data, current data and vibration data. The unlocking command data packets are parsed to extract the command type and target mechanism identifier, and a standardized data frame is generated.

[0047] Step 2: Perform data fusion processing on the standardized data frame generated in Step 1. Use a filtering algorithm to fuse the multimodal sensor data to generate system state estimates, including heating element temperature trends and power stability index. Calculate the health score of the hot knife component based on the system state estimates.

[0048] Step 3: Use the health score and system state estimate generated in Step 2 for digital twin simulation. Predict the rope melting time and thermal damage risk of the hot knife component through physical modeling and real-time simulation, and output the simulation prediction results.

[0049] Step 4: Use the simulation prediction results output in Step 3 for adaptive control, dynamically adjust the heating power output curve of the hot knife component, and compensate for ambient temperature fluctuations through power regulation to generate real-time power parameters.

[0050] Step 5: Use the real-time power parameters generated in Step 4 for fault diagnosis. Detect current or temperature anomalies using a pattern recognition algorithm, generate a fault probability distribution, and trigger an early warning signal when the fault probability exceeds a threshold.

[0051] Step 6: Based on the failure probability distribution generated in Step 5 and the simulation prediction results output in Step 3, redundancy decision is made, the reliability of the main and backup hot knife components is evaluated, the switching timing is dynamically selected, and the backup hot knife component is activated to melt the restraint rope when the main hot knife component fails, thereby unlocking the onboard deployable mechanism. At the same time, the unlocking status data is fed back to the satellite platform and digital twin simulation to form a closed-loop control.

[0052] The redundant thermal isolation unlocking control method for spaceborne deployable mechanisms provided by this invention is implemented as follows:

[0053] The satellite platform receives unlock command data packets from the ground control center via the onboard communication bus, while simultaneously acquiring readings from temperature, current, and vibration sensors to obtain multimodal sensor data including temperature, current, and vibration data. Cyclic redundancy checks are performed on the unlock command data packets to verify data integrity, and the data packets are parsed according to the Space Data Systems Advisory Committee protocol to extract the command opcode and target agency address code, generating a standardized data frame with a timestamp.

[0054] Standardized data frames are fused using a Kalman filter algorithm to asynchronously fuse temperature, current, and vibration data. First, a moving average filter is applied to the temperature signal to eliminate high-frequency noise. Then, a thermodynamic state-space model is established, using current readings as input variables and temperature readings as observed variables, to iteratively calculate the optimal temperature estimate for the heating element. By comparing the residuals between the optimal temperature estimate and the actual measured values, a health score for the hot knife assembly is calculated and normalized to a percentage.

[0055] Health scores and system state estimates are used in digital twin simulations. A three-dimensional heat conduction model of the hot knife component is constructed based on the finite element method. Material thermophysical parameters and track heat flow data are imported, and the transient heat conduction equation is solved numerically. The health score is used as a material degradation coefficient to adjust the simulation parameters, and the system state estimate is used as the initial condition for the simulation. The probability distribution of rope melting time and heat-affected zone data are output.

[0056] The simulation prediction results are used for adaptive control. A model predictive control algorithm is employed, using the simulated fusing time curve as a reference trajectory to dynamically adjust the heating power output curve of the hot knife assembly. Simultaneously, a fuzzy logic controller is introduced to compensate for power output in real time based on ambient temperature fluctuations, generating real-time power parameters.

[0057] Real-time power parameters are used for fault diagnosis. A support vector machine classifier is employed to extract features from the power parameters, and the first derivative and approximate entropy of the temperature curve are calculated as feature vectors, which are then compared with a historical database of normal operating conditions. A Bayesian inference network is used to match the predicted behavior of the digital twin model with actual sensor readings to generate a fault confidence index. When this index exceeds a preset threshold, an early warning signal is triggered.

[0058] Redundancy decisions are made based on fault probability distribution and simulation prediction results. A fuzzy inference system processes health scores, fault confidence indices, and remaining life predictions, outputting a quantitative value for switching timing. When the fault flag of the main hot knife component is activated, a switching command is sent through the redundant communication channel to activate the backup hot knife component. After the backup hot knife component heats and melts the constraint rope, a microswitch on the clamping arm generates a status signal, which is denoised by a signal conditioning circuit and then uploaded to the onboard computer. The onboard computer uses a neural network algorithm to analyze the execution results, updates the weight parameters of the fault diagnosis model, and encapsulates the successful unlocking status data into a protocol frame conforming to the Space Data Systems Advisory Committee standard. This frame is transmitted to the satellite platform's main control system via the onboard bus, while feedback data flows back to the digital twin model and control parameter library, forming a closed-loop optimized control.

[0059] Specifically, the redundant thermal isolation unlocking control method for spaceborne deployable mechanisms described in this invention includes step 1 as follows:

[0060] The satellite receives unlock command data packets sent by the ground control center via the onboard communication bus, and simultaneously collects readings from temperature sensors, current sensors, and vibration sensors to obtain multimodal sensor data.

[0061] The integrity of the unlock command data packet is verified, and the command operation code and the organization address code are extracted according to the space data system protocol.

[0062] Based on the extracted instruction opcode and organization address code, a standardized data frame with a timestamp is generated for data fusion processing in step 2.

[0063] The satellite platform of this invention receives unlocking command data packets sent by the ground control center via the onboard communication bus, and simultaneously collects readings from temperature, current, and vibration sensors to obtain multimodal sensor data. The unlocking command data packets are verified for integrity, parsed according to the space data system protocol, and the command operation code and mechanism address code are extracted. Based on the extracted command operation code and mechanism address code, a standardized data frame with a timestamp is generated for data fusion processing in step 2. This step completes data acquisition and preliminary parsing, ensuring reliable data sources and uniform format for subsequent processing, and providing a foundation for data fusion.

[0064] Specifically, in the spaceborne deployable mechanism redundant thermal isolation unlocking control method of the present invention, step 2 includes:

[0065] The temperature data, current data, and vibration data in the standardized data frame generated in step 1 are asynchronously fused using the Kalman filter algorithm.

[0066] The temperature signal is filtered by moving average to eliminate noise, a thermodynamic state-space model is established, and the optimal temperature estimate of the heating element is calculated iteratively using the current reading as the input variable and the temperature reading as the observation variable.

[0067] Compare the residuals between the optimal temperature estimate and the actual measurement, calculate the health score of the hot knife component, normalize the health score into a percentage form, and use it for the digital twin simulation in step 3.

[0068] This invention employs a Kalman filter algorithm to asynchronously fuse temperature, current, and vibration data from the standardized data frame generated in step 1. Noise is eliminated by moving average filtering of the temperature signal, and a thermodynamic state-space model is established. Using current readings as input variables and temperature readings as observed variables, the optimal temperature estimate of the heating element is iteratively calculated. The residual between the optimal temperature estimate and the actual measured value is compared to calculate the health score of the hot knife assembly. This health score is normalized to a percentage form and used in the digital twin simulation in step 3. This step improves the accuracy of system state assessment through data fusion and state estimation, with the health score serving as a key parameter input to the simulation stage.

[0069] Specifically, in the spaceborne deployable mechanism redundant thermal isolation unlocking control method of the present invention, step 3 includes:

[0070] A three-dimensional heat conduction simulation of the hot knife component was constructed based on the finite element method. Material thermal property parameters and orbital heat flow data were imported, and the transient heat conduction equation was solved by numerical solution.

[0071] The health score generated in step 2 is used as the material degradation coefficient to adjust the simulation parameters, and the system state estimate generated in step 2 is used as the initial condition for simulation. The probability distribution of rope melting time and heat-affected zone data are output.

[0072] The probability distribution and thermally affected zone data are used as simulation prediction results for adaptive control in step 4.

[0073] This invention constructs a three-dimensional heat conduction simulation of a hot knife component based on the finite element method. It imports material thermal properties and track heat flow data, and solves the transient heat conduction equation using numerical methods. The health score generated in step 2 is used as a material degradation coefficient to adjust the simulation parameters, and the system state estimate generated in step 2 is used as the initial simulation condition. The probability distribution of rope melting time and the heat-affected zone data are output. The probability distribution and heat-affected zone data are used as simulation prediction results for adaptive control in step 4. This step achieves high-precision prediction of the hot knife behavior through digital twin simulation, providing an optimization basis for adaptive control.

[0074] Specifically, in the spaceborne deployable mechanism redundant thermal isolation unlocking control method of the present invention, step 5 includes:

[0075] A support vector machine classifier is used to extract features from the real-time power parameters generated in step 4. The first derivative and approximate entropy of the temperature curve are calculated as input vectors and compared with historical normal operating condition data.

[0076] By matching the simulation prediction results output in step 3 with the actual sensor readings through a Bayesian inference network, a fault confidence index is generated. When the fault confidence exceeds a preset threshold, an early warning signal is triggered.

[0077] The fault confidence index is used as the fault probability distribution for redundant decision-making in step 6.

[0078] This invention employs a support vector machine classifier to extract features from the real-time power parameters generated in step 4, calculating the first derivative and approximate entropy of the temperature curve as input vectors, and comparing them with historical normal operating condition data. A Bayesian inference network is used to match the simulation prediction results output in step 3 with actual sensor readings to generate a fault confidence index. When the fault confidence index exceeds a preset threshold, an early warning signal is triggered. This fault confidence index is used as a fault probability distribution for redundant decision-making in step 6. This step enhances the timeliness and reliability of fault detection by using an intelligent diagnostic algorithm to monitor system anomalies in real time.

[0079] Specifically, in the spaceborne deployable mechanism redundant thermal isolation unlocking control method of the present invention, step 6 includes:

[0080] The fuzzy inference system processes the health score generated in step 2, the fault probability distribution generated in step 5, and the simulation prediction results output in step 3, and outputs a quantitative value for the switching timing.

[0081] Based on the switching timing quantization value, when the main hot knife component fails, a switching command is sent to start the backup hot knife component and adjust the parameters of the digital twin simulation.

[0082] After the backup hot knife assembly melts the restraint rope, it collects status signals and updates the weight parameters for fault diagnosis, and reports the unlocking status data to the satellite platform.

[0083] This invention uses a fuzzy inference system to process the health score generated in step 2, the fault probability distribution generated in step 5, and the simulation prediction results output in step 3, outputting a quantified value for the switching timing. Based on this quantified value, when the main hot knife component fails, a switching command is sent to activate the backup hot knife component, and the parameters of the digital twin simulation are adjusted. After the backup hot knife component melts the constraint rope, it collects status signals and updates the weight parameters for fault diagnosis, reporting the unlocking status data to the satellite platform. This step achieves intelligent decision-making and execution for redundant switching, improving the system's fault tolerance through closed-loop feedback.

[0084] Secondly, the spaceborne deployable mechanism redundant thermal isolation unlocking control device provided by the present invention is applied to the aforementioned spaceborne deployable mechanism redundant thermal isolation unlocking control method, including:

[0085] The data acquisition and parsing virtual module is used to acquire unlocking command data packets and multimodal sensor data of the spaceborne deployable mechanism, parse the unlocking command data packets to extract the command type and target mechanism identifier, and generate standardized data frames;

[0086] The data fusion processing virtual module is used to perform data fusion processing on standardized data frames. It fuses multimodal sensor data through filtering algorithms to generate system state estimates, including heating element temperature trends and power stability indices, and calculates the health score of the hot knife component based on the system state estimates.

[0087] The digital twin simulation virtual module is used to apply health scores and system state estimates to digital twin simulations. It predicts the rope melting time and thermal damage risk of the hot knife component through physical modeling and real-time simulation, and outputs simulation prediction results.

[0088] The adaptive control virtual module is used to apply simulation prediction results to adaptive control, dynamically adjust the heating power output curve of the hot knife component, and compensate for ambient temperature fluctuations through power regulation to generate real-time power parameters.

[0089] The fault diagnosis virtual module is used to use real-time power parameters for fault diagnosis, detect current or temperature anomalies through pattern recognition algorithms, generate a fault probability distribution, and trigger an early warning signal when the fault probability exceeds a threshold.

[0090] The redundant decision-making and execution virtual module is used to make redundant decisions based on the failure probability distribution and simulation prediction results, evaluate the reliability of the main and backup hot knife components, dynamically select the switching timing, and activate the backup hot knife component to melt the restraint rope when the main hot knife component fails, thereby unlocking the onboard deployable mechanism. At the same time, the unlocking status data is fed back to the satellite platform and digital twin simulation to form a closed-loop control.

[0091] This invention addresses the problem of single-point failure in control circuits leading to the failure of onboard unlocking commands by employing a redundant thermal isolation unlocking control method for onboard deployable mechanisms. The satellite platform collects unlocking command data packets and multimodal sensor data from the onboard deployable mechanism, including temperature, current, and vibration data. The unlocking command data packets are parsed to extract the command type and target mechanism identifier, generating standardized data frames. Data fusion processing is performed on the standardized data frames, fusing multimodal sensor data using a filtering algorithm to generate system state estimates, including heating element temperature trends and power stability indices. Based on these system state estimates, a health score for the hot knife assembly is calculated. The health score and system state estimates are used for digital twin simulation, predicting the rope melting time and thermal damage risk of the hot knife assembly through physical modeling and real-time simulation, outputting simulation prediction results. The simulation prediction results are used for adaptive control, dynamically adjusting the heating power output curve of the hot knife assembly and compensating for ambient temperature fluctuations through power regulation, generating real-time power parameters. These real-time power parameters are used for fault diagnosis, detecting current or temperature anomalies using pattern recognition algorithms, generating fault probability distributions, and triggering warning signals when the fault probability exceeds a threshold. Based on the failure probability distribution and simulation prediction results, redundancy decisions are made to assess the reliability of the primary and backup hot knife components and dynamically select the switching timing. When the primary hot knife component fails, the backup hot knife component is activated to melt the restraint ropes, completing the unlocking of the onboard deployable mechanism. Simultaneously, the unlocking status data is fed back to the satellite platform and digital twin simulation, forming a closed-loop control system. Through continuous optimization, the system's fault tolerance to single-point failures is improved.

Claims

1. A method for controlling redundant thermal isolation unlocking of a spaceborne deployable mechanism, characterized in that, include: Step 1: The satellite platform collects unlocking command data packets and multimodal sensor data of the onboard deployable mechanism. The multimodal sensor data includes temperature data, current data and vibration data. The unlocking command data packets are parsed to extract the command type and target mechanism identifier, and a standardized data frame is generated. Step 2: Perform data fusion processing on the standardized data frame generated in Step 1. Use a filtering algorithm to fuse the multimodal sensor data to generate system state estimates, including heating element temperature trends and power stability index. Calculate the health score of the hot knife component based on the system state estimates. Step 3: Use the health score and system state estimate generated in Step 2 for digital twin simulation. Predict the rope melting time and thermal damage risk of the hot knife component through physical modeling and real-time simulation, and output the simulation prediction results. Step 4: Use the simulation prediction results output in Step 3 for adaptive control, dynamically adjust the heating power output curve of the hot knife component, and compensate for ambient temperature fluctuations through power regulation to generate real-time power parameters. Step 5: Use the real-time power parameters generated in Step 4 for fault diagnosis. Detect current or temperature anomalies using a pattern recognition algorithm, generate a fault probability distribution, and trigger an early warning signal when the fault probability exceeds a threshold. Step 6: Based on the failure probability distribution generated in Step 5 and the simulation prediction results output in Step 3, redundancy decision is made, the reliability of the main and backup hot knife components is evaluated, the switching timing is dynamically selected, and the backup hot knife component is activated to melt the restraint rope when the main hot knife component fails, thereby unlocking the onboard deployable mechanism. At the same time, the unlocking status data is fed back to the satellite platform and digital twin simulation to form a closed-loop control.

2. The redundant thermal isolation unlocking control method for spaceborne deployable mechanisms according to claim 1, characterized in that, Step 1 includes: The satellite receives unlock command data packets sent by the ground control center via the onboard communication bus, and simultaneously collects readings from temperature sensors, current sensors, and vibration sensors to obtain multimodal sensor data. The integrity of the unlock command data packet is verified, and the command operation code and the organization address code are extracted according to the space data system protocol. Based on the extracted instruction opcode and organization address code, a standardized data frame with a timestamp is generated for data fusion processing in step 2.

3. The redundancy thermal isolation unlocking control method for spaceborne deployable mechanisms according to claim 1, characterized in that, Step 2 includes: The temperature data, current data, and vibration data in the standardized data frame generated in step 1 are asynchronously fused using the Kalman filter algorithm. The temperature signal is filtered by moving average to eliminate noise, a thermodynamic state-space model is established, and the optimal temperature estimate of the heating element is calculated iteratively using the current reading as the input variable and the temperature reading as the observation variable. Compare the residuals between the optimal temperature estimate and the actual measurement, calculate the health score of the hot knife component, normalize the health score into a percentage form, and use it for the digital twin simulation in step 3.

4. The redundancy thermal isolation unlocking control method for spaceborne deployable mechanisms according to claim 1, characterized in that, Step 3 includes: A three-dimensional heat conduction simulation of the hot knife component was constructed based on the finite element method. Material thermal property parameters and orbital heat flow data were imported, and the transient heat conduction equation was solved by numerical solution. The health score generated in step 2 is used as the material degradation coefficient to adjust the simulation parameters, and the system state estimate generated in step 2 is used as the initial condition for simulation. The probability distribution of rope melting time and heat-affected zone data are output. The probability distribution and thermally affected zone data are used as simulation prediction results for adaptive control in step 4.

5. The redundancy thermal isolation unlocking control method for spaceborne deployable mechanisms according to claim 1, characterized in that, Step 5 includes: A support vector machine classifier is used to extract features from the real-time power parameters generated in step 4. The first derivative and approximate entropy of the temperature curve are calculated as input vectors and compared with historical normal operating condition data. By matching the simulation prediction results output in step 3 with the actual sensor readings through a Bayesian inference network, a fault confidence index is generated. When the fault confidence exceeds a preset threshold, an early warning signal is triggered. The fault confidence index is used as the fault probability distribution for redundant decision-making in step 6.

6. The redundancy thermal isolation unlocking control method for spaceborne deployable mechanisms according to claim 1, characterized in that, Step 6 includes: The fuzzy inference system processes the health score generated in step 2, the fault probability distribution generated in step 5, and the simulation prediction results output in step 3, and outputs a quantitative value for the switching timing. Based on the switching timing quantization value, when the main hot knife component fails, a switching command is sent to start the backup hot knife component and adjust the parameters of the digital twin simulation. After the backup hot knife assembly melts the restraint rope, it collects status signals and updates the weight parameters for fault diagnosis, and reports the unlocking status data to the satellite platform.

7. A redundant thermal isolation unlocking control device for a spaceborne deployable mechanism, applied to the redundant thermal isolation unlocking control method for a spaceborne deployable mechanism as described in any one of claims 1 to 6, characterized in that, include: The data acquisition and parsing virtual module is used to acquire unlocking command data packets and multimodal sensor data of the spaceborne deployable mechanism, parse the unlocking command data packets to extract the command type and target mechanism identifier, and generate standardized data frames; The data fusion processing virtual module is used to perform data fusion processing on standardized data frames. It fuses multimodal sensor data through filtering algorithms to generate system state estimates, including heating element temperature trends and power stability indices, and calculates the health score of the hot knife component based on the system state estimates. The digital twin simulation virtual module is used to apply health scores and system state estimates to digital twin simulations. It predicts the rope melting time and thermal damage risk of the hot knife component through physical modeling and real-time simulation, and outputs simulation prediction results. The adaptive control virtual module is used to apply simulation prediction results to adaptive control, dynamically adjust the heating power output curve of the hot knife component, and compensate for ambient temperature fluctuations through power regulation to generate real-time power parameters. The fault diagnosis virtual module is used to use real-time power parameters for fault diagnosis, detect current or temperature anomalies through pattern recognition algorithms, generate a fault probability distribution, and trigger an early warning signal when the fault probability exceeds a threshold. The redundant decision-making and execution virtual module is used to make redundant decisions based on the failure probability distribution and simulation prediction results, evaluate the reliability of the main and backup hot knife components, dynamically select the switching timing, and activate the backup hot knife component to melt the restraint rope when the main hot knife component fails, thereby unlocking the onboard deployable mechanism. At the same time, the unlocking status data is fed back to the satellite platform and digital twin simulation to form a closed-loop control.

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