Field emission thruster extraction pole indium deposition cleaning temperature control method and system

Abstract

The application provides a field emission thruster extraction pole indium deposition cleaning temperature control method and system, relates to the technical field of space electric propulsion, and the method comprises the following steps: obtaining multi-source temperature data and electric field intensity data of a target area, the two kinds of data are processed through space-time fusion to generate target characteristic information reflecting the deposition state of indium material; based on the characteristic information, a fuzzy PID control algorithm is used to generate a power control signal, and then a heating unit is adjusted; the thermal impedance parameters are obtained by monitoring the electric heating characteristics, and then the heat flow distribution data are generated; then the state observer is used to analyze the heat flow data to predict the growth trend of indium deposition, obtain the deposition rate information, finally determine the cleaning control strategy according to the growth rate, and control the temperature change in the non-uniform heating mode, so that the indium material reaches the phase change state. The application improves the reliability and maintenance efficiency of the field emission thruster in long-term operation on the orbit.

Description

Technical Field

[0001] This application relates to the field of space electric propulsion technology, and in particular to a method and system for controlling the temperature of indium deposition cleaning in a field launch thruster. Background Technology

[0002] Field-launched thrusters face the challenge of indium deposition on the extractor surface during long-term space missions. The accumulation of deposits can lead to electric field distortion and decreased thrust stability. Furthermore, the microgravity characteristics of the space environment cause the deposit distribution to be non-uniform. Therefore, it is necessary to develop an autonomous cleaning technology that can adapt to on-orbit conditions and target the non-uniform deposition characteristics in order to maintain the stable operating life of the thruster.

[0003] One current technical solution to this problem is a timed, uniform heating and cleaning method. This involves starting the heating program at preset fixed time intervals to uniformly heat the entire extraction electrode surface to a set temperature, using the thermal effect to induce a phase transition and detachment of the indium deposition layer. This solution designs the cleaning cycle based on the time pattern of deposition accumulation and monitors the overall heating process using a temperature sensor.

[0004] However, uniform heating has limited effectiveness in treating non-uniformly distributed sediment layers, potentially leading to insufficient cleaning or overheating in localized areas. The selection of cleaning times based on fixed cycles deviates somewhat from the actual sedimentary growth state, potentially resulting in premature or delayed cleaning. This approach is insufficiently responsive to dynamic changes in the sedimentary state, and the energy utilization efficiency during the cleaning process needs improvement. Summary of the Invention

[0005] This application provides a method and system for controlling the temperature of indium deposition cleaning in field launch thrusters, in order to solve the problems of poor reliability and low maintenance efficiency of field launch thrusters during long-term on-orbit operation in the prior art.

[0006] To solve the above-mentioned technical problems, in a first aspect, this application provides a method for controlling the temperature of the indium deposition cleaning process in a field launch thruster, comprising:

[0007] Acquire multi-source temperature and electric field intensity data of the target region of the field launch thruster;

[0008] The multi-source temperature data and the electric field intensity data are spatiotemporally fused to generate target feature information reflecting the indium deposition state.

[0009] Based on the target feature information, a power control signal is generated through a fuzzy PID control algorithm, and the heating unit embedded in the target area is adjusted according to the power control signal.

[0010] The electrothermal characteristics of the heating unit are monitored to obtain thermal impedance parameters, and heat flow distribution data is generated based on the thermal impedance parameters.

[0011] Based on the heat flow distribution data, a state observer is used to predict the growth trend of indium material in order to generate deposition rate information.

[0012] Based on the deposition rate information, a cleaning control strategy is determined, and the temperature change of the heating unit is controlled according to the cleaning control strategy so that the indium material reaches a phase change state in the target area.

[0013] Optionally, the step of using a state observer to predict the growth trend of indium based on the heat flow distribution data to generate deposition rate information includes:

[0014] The heat flow distribution data is input into the state observer, and the parameter sequence is extracted through the state observer;

[0015] Based on the parameter sequence, and combined with the preset correlation model between deposition thickness and heat conduction, the change sequence is calculated;

[0016] Time series analysis is performed on the change sequence to generate thickness change trend data, and curve fitting is performed on the thickness change trend data based on particle swarm optimization algorithm to generate growth trend curve;

[0017] The thickness increase per unit time is extracted from the growth trend curve, and deposition rate information is generated based on the thickness increase.

[0018] Optionally, the step of performing time series analysis on the change sequence to generate thickness change trend data, and performing curve fitting on the thickness change trend data based on the particle swarm optimization algorithm to generate a growth trend curve, includes:

[0019] The change sequence is processed by sliding windows at different time scales to obtain a first smoothed sequence and a second smoothed sequence;

[0020] Separate the periodic feature component from the first smoothed sequence and extract the trend feature component from the second smoothed sequence;

[0021] The periodic feature component and the trend feature component are fused to obtain thickness change trend data, and the parameter optimization range of the exponential growth curve is set based on the thickness change trend data.

[0022] The optimal curve parameters are searched within the parameter optimization range using the particle swarm optimization algorithm, and a growth trend curve is constructed based on the optimal curve parameters.

[0023] Optionally, the spatiotemporal fusion processing of the multi-source temperature data and the electric field intensity data to generate target feature information reflecting the indium deposition state includes:

[0024] A temperature distribution map is established based on the location information and collection timestamp of each monitoring point in the multi-source temperature data.

[0025] Based on the location information and acquisition timestamp of each measurement point in the electric field intensity data, an electric field distribution map is established;

[0026] The temperature distribution map and the electric field distribution map are overlaid and analyzed to identify the spatial overlap between the temperature anomaly region and the electric field distortion region.

[0027] Based on the spatial overlap, the potential distribution area of ​​indium deposition is determined, and the temperature change characteristics and electric field distortion characteristics within the potential distribution area are extracted.

[0028] The temperature change features and the electric field distortion features are weighted and fused to generate target feature information.

[0029] Secondly, this application provides a temperature control system for cleaning the indium deposition electrode in a field launch thruster, comprising:

[0030] The acquisition module is used to acquire multi-source temperature data and electric field intensity data of the target area of ​​the field launch thruster;

[0031] The fusion module is used to perform spatiotemporal fusion processing on the multi-source temperature data and the electric field intensity data to generate target feature information reflecting the indium deposition state.

[0032] The adjustment module is used to generate a power control signal based on the target feature information using a fuzzy PID control algorithm, and adjust the heating unit embedded in the target area according to the power control signal.

[0033] The monitoring module is used to monitor the electrothermal characteristics of the heating unit to obtain thermal impedance parameters, and generate heat flow distribution data based on the thermal impedance parameters.

[0034] The generation module is used to predict the growth trend of indium material based on the heat flow distribution data using a state observer, so as to generate deposition rate information;

[0035] The determination module is used to determine a cleaning control strategy based on the deposition rate information, and to control the temperature change of the heating unit according to the cleaning control strategy so that the indium material reaches a phase change state in the target area.

[0036] Thirdly, this application provides an electronic device, comprising:

[0037] Memory, used to store computer programs;

[0038] A processor, configured to execute the computer program to implement the steps of the field launch thruster extraction indium deposition cleaning temperature control method as described in the first aspect above.

[0039] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the field launch thruster extraction indium deposition cleaning temperature control method described in the first aspect above.

[0040] The technical solution provided in this application has the following beneficial effects:

[0041] This application establishes a comprehensive understanding of the sedimentation state, providing data support for subsequent analysis; it combines monitoring data of different physical quantities to form an accurate description of sedimentation distribution characteristics, thereby enabling targeted heating based on these characteristics and improving the accuracy of energy utilization; it then monitors the heat transfer in the extraction electrode in real time, providing a basis for growth trend analysis; it also provides forward-looking guidance for cleaning decisions by understanding the dynamics of sedimentation development; and finally, it formulates the optimal cleaning scheme based on the sedimentation development status, ensuring the integrity and effectiveness of the cleaning process.

[0042] Furthermore, this application also analyzes heat flow distribution data and derives the deposition thickness variation by combining heat conduction characteristics, and then uses an optimization algorithm to fit the growth trend curve to finally obtain quantitative information on the deposition growth rate.

[0043] Furthermore, this process enables accurate prediction of sediment growth dynamics, providing data support for determining the optimal cleaning time and avoiding the adverse effects of cleaning too early or too late.

[0044] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A flowchart of a method for controlling the temperature of indium deposition cleaning in a field launch thruster, provided in an embodiment of this application;

[0047] Figure 2 This is a schematic diagram illustrating a specific implementation of a method for controlling the temperature of indium deposition cleaning in a field launch thruster, as provided in an embodiment of this application.

[0048] Figure 3 This is a schematic diagram of a temperature control system for the extraction and cleaning of indium deposition in a field launch thruster, provided as an embodiment of this application. Detailed Implementation

[0049] To address the problems of existing technologies, this application proposes a method for controlling the cleaning temperature of indium-deposited material in field-launched thrusters. This method constructs deposition state characteristics through collaborative analysis of temperature and electric field data, and employs an intelligent control algorithm to generate a non-uniform heating mode that matches the deposition distribution. Furthermore, this scheme innovatively introduces a heat flow monitoring and growth prediction mechanism to track the evolution trend of the deposits in real time and dynamically optimize the cleaning strategy. Therefore, this method overcomes the limitations of traditional fixed-cycle cleaning, achieving precise targeted removal of non-uniform deposits. This improves cleaning efficiency while avoiding overheating, effectively solving problems such as inaccurate cleaning time and unreasonable energy utilization in existing technologies, and providing a reliable guarantee for the long-term stable operation of the thruster in orbit.

[0050] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] The core of this application is to provide a method for controlling the temperature of the indium deposition cleaning process in a field launch thruster, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0052] Step 101: Acquire multi-source temperature data and electric field intensity data of the target area of ​​the field launch thruster.

[0053] In step 101, multi-source temperature data refers to the collection of temperature readings from multiple temperature sensors distributed at different locations of the extraction electrode. These sensors are arranged in a grid, and each monitoring point records the real-time temperature value at its location. Electric field strength data is the electric field strength value measured by an electric field probe installed around the extraction electrode, reflecting the electric field distribution at different locations. These two types of data together constitute the basic information for monitoring the deposition state. The field emission thruster target area refers to the key electrode components in the field emission thruster responsible for extracting and accelerating ions and the surrounding space. The performance of this area is affected by indium deposition during operation, and the deposition needs to be cleaned through heating control.

[0054] For example, in a microgravity environment in space, a certain type of field launch thruster has eight temperature sensors and four electric field probes arranged on the extraction pole surface. The temperature sensors record data such as 25℃, 28℃, and 31℃, while the electric field probes measure data such as 3.2kV / mm, 3.5kV / mm, and 3.8kV / mm. All data are labeled with the acquisition time and sensor number, forming the original monitoring dataset.

[0055] Step 102: Perform spatiotemporal fusion processing on the multi-source temperature data and the electric field intensity data to generate target feature information reflecting the indium deposition state.

[0056] In step 102, the target feature information is a comprehensive feature parameter generated by fusing temperature and electric field data, which includes deposition thickness value and distribution density value. The thickness value reflects the thickness of the deposition layer at different locations, and the distribution density value describes the degree of aggregation of the sediment in space.

[0057] In this embodiment, firstly, temperature data is used to construct a temperature distribution map based on sensor locations, and electric field data is used to construct an electric field distribution map based on probe locations. Then, the temperature distribution map and electric field distribution map are superimposed using coordinate mapping to identify regions with high temperatures and distorted electric fields. Next, the spatial overlap between these regions with high temperatures and distorted electric fields is calculated, and temperature gradient parameters and electric field gradient parameters are extracted based on this spatial overlap. Finally, the temperature gradient parameters and electric field gradient parameters are fused using a weighted fusion method to generate feature information containing thickness and distribution density values.

[0058] For example, firstly, based on the data obtained in step 101, a temperature distribution map is established, showing that the temperature at coordinate (1,1) is 31℃ and the temperature at coordinate (1,2) is 28℃. Simultaneously, an electric field distribution map is established, showing that the electric field strength at coordinate (1,1) is 3.2 kV / mm and the electric field strength at coordinate (1,2) is 3.5 kV / mm. Then, by overlaying the temperature distribution map and the electric field distribution map, it is found that the temperature in the coordinate (1,1) region exceeds 30℃ and the electric field strength is below 3.4 kV / mm. Next, the temperature gradient parameter for the coordinate (1,1) region is calculated to be 0.8℃ / mm and the electric field gradient parameter for the same region is 0.3 kV / mm². Finally, by weighted fusion of the temperature gradient parameter and the electric field gradient parameter, a thickness value of 0.3 mm and a distribution density value of 0.9 are obtained.

[0059] Step 103: Based on the target feature information, a power control signal is generated through a fuzzy PID control algorithm, and the heating unit embedded in the target area is adjusted according to the power control signal.

[0060] In step 103, the power control signal is an electrical signal used to adjust the power of the heating unit, and the non-uniform heating mode refers to a heating method in which different power is applied to each heating unit according to the deposition distribution.

[0061] In this embodiment, firstly, the thickness value and distribution density value are parsed from the target feature information; then, the deposition severity level is divided according to the magnitude of the thickness value and the distribution density value, and the base power coefficient of each heating unit is set based on the deposition severity level; next, the power correction amount of each heating unit is calculated by fuzzy rules, and the base power coefficient of each heating unit is combined with the power correction amount of each heating unit to obtain the real-time power setting value of each heating unit; finally, the real-time power setting value of each heating unit is converted into a pulse width modulation signal to drive each heating unit, ultimately forming a differentiated heating mode corresponding to the indium deposition distribution.

[0062] For example, based on the thickness value of 0.3 mm and the distribution density value of 0.9 obtained in step 102, the area is determined to be severely deposited. With the base power coefficient set at 1.0, a power correction amount of 0.1 is obtained through fuzzy calculation. After combination, the real-time power setting value is 1.1, which is converted into a pulse width modulation signal with a duty cycle of 75% to drive the heating unit to generate 110W of power.

[0063] Step 104: Monitor the electrothermal characteristics of the heating unit to obtain thermal impedance parameters, and generate heat flow distribution data based on the thermal impedance parameters.

[0064] In step 104, the thermal impedance parameter is a physical quantity that characterizes the relationship between the heat generated by the heating unit and the temperature rise.

[0065] The heat flow distribution data is a derived physical quantity obtained through calculation. It describes the path, direction, and intensity of heat transfer in the extraction electrode material, reflecting the dynamic process of energy transfer. Its physical meaning is the distribution of heat passing through a unit area per unit time in space.

[0066] In this embodiment, firstly, the voltage and current of each heating unit are measured during the non-uniform heating process to calculate the instantaneous power of each heating unit, and the temperature rise rate of the corresponding region of each heating unit is recorded simultaneously; then, the thermal impedance of each heating unit is calculated by the ratio of the instantaneous power of each heating unit to the temperature rise rate of the corresponding region of each heating unit; next, spatial interpolation is performed based on the position coordinates of each heating unit to construct a spatial distribution field, and the gradient direction in the spatial distribution field is analyzed to determine the main heat flow path; finally, heat flow distribution data is generated by combining the numerical distribution of the spatial distribution field.

[0067] For example, based on the operating voltage of the heating unit (12V) and current (9.2A), the instantaneous power of 110.4W is calculated. The corresponding area temperature rises from 25℃ to 85℃ in 60 seconds, with a temperature rise rate of 1℃ / s. The thermal resistance is 110.4W / 1℃ / s = 110.4℃ / W. The thermal resistance of the adjacent area is obtained by interpolation, which is 107℃ / W. Based on the gradient direction, the heat flow is determined to be transferred from coordinates (0,0) to (0,1), generating distribution data with a heat flux density of 850W / m².

[0068] Step 105: Based on the heat flow distribution data, a state observer is used to predict the growth trend of indium material to generate deposition rate information.

[0069] In step 105, the deposition rate information is a parameter describing the increase in deposition thickness per unit time, reflecting the deposition development trend.

[0070] In this embodiment, firstly, heat flow distribution data is input into a state observer to extract parameter sequences; then, the parameter sequences are used to calculate change sequences by combining a deposition thickness and heat conduction correlation model; next, time series analysis is performed on the change sequences to obtain the deposition thickness change trend, and an optimization algorithm is used to fit the indium growth curve; finally, the thickness increase per unit time is extracted from the indium growth curve to generate deposition rate information.

[0071] For example, with parameter sequences of 0.8, 0.75, and 0.7, and using a correlation model to calculate thickness changes of 0.02 mm / h, 0.025 mm / h, and 0.03 mm / h, the trend data is analyzed, and a growth curve is then fitted. ,in Indicates the thickness of the deposition. The time variable is used, and based on this growth curve, the thickness increase per unit time of 0.022 mm / h can be extracted as the deposition rate information.

[0072] Step 106: Based on the deposition rate information, determine the cleaning control strategy, and control the temperature change of the heating unit according to the cleaning control strategy so that the indium material reaches the phase change state in the target area.

[0073] In step 106, the cleaning control strategy includes a combination of cleaning time and temperature control parameters, and the phase change state refers to the physical state change process of indium material from solid to liquid.

[0074] In this embodiment, firstly, the time required for the indium material to reach the critical thickness is calculated based on the deposition rate information, thereby determining the cleaning time; then, a combination of temperature control parameters, including heating rate parameters, target temperature parameters, and holding time parameters, is formulated based on the indium phase transformation characteristics; finally, in a non-uniform heating mode, the temperature change of each heating unit is controlled according to the cleaning control parameters. Specifically, the temperature is first raised to the softening temperature of indium, and then raised to the phase transformation temperature range of indium and held for a sufficient time to complete the cleaning of the indium material.

[0075] For example, firstly, the cleaning time is calculated based on the deposition growth rate of 0.022 mm / hour and the critical thickness of 0.5 mm. Then, the heating rate is set to 5 degrees Celsius per minute, the target temperature is set to 156 degrees Celsius, and the holding time is set to 6 minutes. The heating unit is controlled to operate in a three-stage mode: first, the area with severe deposition is heated to 145 degrees Celsius and held for 2 minutes, then all heating units are heated to 156 degrees Celsius and held for 6 minutes. Finally, the indium material cleaning is confirmed to be complete by monitoring the electric field strength to recover to 3.8 kV / mm.

[0076] This method can accurately perceive the deposition state through multi-source data fusion, then use intelligent control to achieve precise heating, and combine growth prediction to optimize cleaning decisions, forming a complete closed loop of deposition monitoring and cleaning control, which improves cleaning efficiency and system reliability, and thus ensures the long-term stable operation of the thruster in orbit.

[0077] To address the issue of insufficient accuracy in predicting deposition growth trends, in some embodiments, step 105 involves using a state observer based on the heat flow distribution data to predict the growth trend of the indium material, thereby generating deposition rate information, such as... Figure 2 As shown, it includes:

[0078] Step 201: Input the heat flow distribution data into the state observer and extract the parameter sequence through the state observer.

[0079] In step 201, the parameter sequence is a sequence of parameters reflecting heat transfer efficiency extracted by the state observer from the heat flow distribution data. Each data point represents the degree of effectiveness of heat energy conduction in the deposition layer within a specific time period.

[0080] In this embodiment of the application, firstly, the heat flow distribution data is input into the state observer, and the data processing mechanism inside the state observer is used to analyze the pattern of heat flow change over time; then, the values ​​of heat flow conduction efficiency within a continuous time period are extracted, thereby forming an ordered heat flow conduction efficiency sequence.

[0081] Step 202: Based on the parameter sequence, and combined with the preset correlation model between deposition thickness and heat conduction, calculate the change sequence.

[0082] In step 202, the correlation model between deposition thickness and heat conduction is a physical model based on the principle of thermodynamic conduction. Its structure is similar to the thermal resistance network model. The mathematical relationship between thickness and heat conduction efficiency is constructed by treating the deposition layer as an additional thermal resistance layer. The parameters of the model are determined by experimentally measuring the actual heat conduction data under different deposition thicknesses and performing curve fitting.

[0083] The change sequence is a numerical sequence of the increase or decrease in deposition thickness over different time periods, calculated using this model.

[0084] In this embodiment of the application, the heat transfer efficiency sequence is input into the deposition thickness and heat transfer correlation model. Based on the correspondence between deposition thickness and heat transfer efficiency in the deposition thickness and heat transfer correlation model, the change in indium thickness in each time unit is calculated, thereby generating a sequence of indium thickness changes arranged in chronological order.

[0085] Step 203: Perform time series analysis on the change sequence to generate thickness change trend data, and perform curve fitting on the thickness change trend data based on particle swarm optimization algorithm to generate a growth trend curve.

[0086] In step 203, the thickness change trend data is characteristic data reflecting the overall direction of thickness change obtained after analyzing the change sequence, and the growth trend curve is a mathematical curve describing the growth law of deposition thickness over time, which is fitted by an optimization algorithm.

[0087] In this embodiment of the application, time series analysis is performed on the change sequence to identify the long-term trend characteristics of thickness change and generate thickness change trend data. Then, particle swarm optimization algorithm is used to perform curve fitting on the thickness change trend data. Through multiple iterations to optimize the curve parameters, a growth trend curve that can reflect the actual growth law is generated.

[0088] Step 204: Extract the thickness increase per unit time from the growth trend curve, and generate deposition rate information based on the thickness increase.

[0089] In step 204, the thickness increase is the increase in deposition thickness per unit time calculated from the growth trend curve.

[0090] In this embodiment, firstly, the thickness difference between adjacent time points is selected from the indium growth curve; then, the thickness increase per unit time is calculated based on the thickness difference; finally, deposition rate information is generated based on the magnitude and variation characteristics of the thickness increase, wherein the deposition rate information can reflect the actual growth rate of the indium.

[0091] Here is a specific example:

[0092] This embodiment further processes the heat flow distribution data obtained from the aforementioned implementation process. The heat flow distribution data with a heat flow density of 850 watts per square meter is input into the state observer. The state observer extracts the parameter sequence of three consecutive time units [0.80, 0.75, 0.70] by analyzing the characteristics of heat flow changes over time. This sequence represents the decreasing trend of heat conduction efficiency in the deposition layer.

[0093] Then, based on this parameter sequence and a pre-defined correlation model between deposition thickness and thermal conduction, the expression for this model can be: ,in This indicates the change in deposition thickness, expressed in millimeters. This represents the heat transfer efficiency, which is a dimensionless parameter. The model coefficients are 0.1 and 0.01 respectively. Substituting the efficiency sequence into the model, the change sequence is [0.02, 0.025, 0.03], in millimeters per hour.

[0094] Next, time series analysis was performed on the thickness change sequence, and the moving average method was used to generate thickness change trend data [0.021, 0.024, 0.028], in millimeters per hour. Subsequently, the trend data was fitted using the particle swarm optimization algorithm to obtain the expression of the growth trend curve. ;

[0095] Finally, the thickness increase per unit time is extracted from the curve. The thickness increase is calculated to be 0.022 mm per hour by calculating the derivative of the curve at t=10 hours. Based on this thickness increase, the deposition rate information of 0.022 mm per hour is generated.

[0096] In the embodiments of this application, this step scheme enables accurate prediction and quantitative description of the indium deposition growth trend, providing a reliable basis for cleaning time decision-making and effectively improving the timeliness and accuracy of deposition cleaning.

[0097] To further improve the accuracy of sediment growth trend prediction, in some embodiments, step 202: performing time series analysis on the change sequence to generate thickness change trend data, and performing curve fitting on the thickness change trend data based on particle swarm optimization algorithm to generate a growth trend curve, includes:

[0098] Step 301: Perform sliding window processing on the change sequence at different time scales to obtain a first smoothed sequence and a second smoothed sequence.

[0099] In step 301, two time scales can be used in this embodiment: a short-term scale of 3 hours and a long-term scale of 6 hours. The 3-hour scale is used to capture the periodic fluctuations of sedimentation changes, and the 6-hour scale is used to extract long-term change trends.

[0100] The first smoothed sequence is a smoothed data sequence obtained by processing the change sequence with a sliding window of a shorter time scale. The second smoothed sequence is a smoothed data sequence obtained by processing the same sequence with a sliding window of a longer time scale. The sliding window time scale corresponding to the second smoothed sequence is greater than the preset threshold, that is, the sequence processed by the 6-hour window. Because it covers a longer time range, it can better filter out short-term fluctuations and retain trend information.

[0101] Step 302: Separate the periodic feature component from the first smoothed sequence and extract the trend feature component from the second smoothed sequence.

[0102] In step 302, the periodic feature component is a data component reflecting the periodic fluctuation pattern separated from the first smoothed sequence, and the trend feature component is a data component reflecting the long-term change direction extracted from the second smoothed sequence.

[0103] In this embodiment, a periodic analysis is performed on the first smoothed sequence to identify the cyclical fluctuation pattern and separate the periodic feature component. At the same time, a trend analysis is performed on the second smoothed sequence to extract the long-term change pattern and obtain the trend feature component. Although the two smoothed sequences are of the same data type, they contain different information features due to the different time scales used in the processing. That is, the short-term window sequence retains more details of the periodic fluctuation and is suitable for separating the periodic component; the long-term window sequence smooths the periodic fluctuation and highlights the overall change direction, and is suitable for extracting the trend component.

[0104] Step 303: Perform feature fusion on the periodic feature component and the trend feature component to obtain thickness change trend data, and set the parameter optimization range of the exponential growth curve based on the thickness change trend data.

[0105] In step 303, the exponential growth curve refers to a mathematical curve model with an exponential function form; for example, its expression is: ,in The output value of the exponential growth curve, such as deposition thickness, , , For parameters to be optimized, The independent variable representing the exponential growth curve, such as time;

[0106] The growth trend curve refers to a specific curve instance generated by substituting the optimized exponential growth curve parameters into the model. The parameter optimization range is the numerical search interval set for each parameter of the exponential growth curve.

[0107] In this embodiment of the application, the periodic feature component and the trend feature component are weighted according to a preset weight to obtain the thickness change trend data, and the reasonable value range of each parameter in the exponential growth curve is set according to the statistical characteristics of the thickness change trend data.

[0108] The changes in deposition thickness are decomposed into two components: periodic fluctuations and trend growth. The fusion process can be carried out by weighted summation. For example, the specific formula is: thickness change trend data = periodic feature component × 0.3 + trend feature component × 0.7. The weight allocation is based on the fact that trend growth is dominant in the deposition growth process, while periodic fluctuations are secondary factors. The complete thickness change trend is reconstructed by the linear superposition of the primary and secondary components.

[0109] Step 304: Using the particle swarm optimization algorithm, search for the optimal curve parameters within the parameter optimization range, and construct a growth trend curve based on the optimal curve parameters.

[0110] In step 304, the optimal curve parameters are the combination of parameters that achieves the best curve fitting effect, obtained by searching within the parameter optimization range using the particle swarm optimization algorithm.

[0111] In this embodiment of the application, the particle swarm is initialized within the set parameter optimization range, and the parameter combination that best matches the exponential growth curve with the thickness change trend data is found through iterative calculation. The growth trend curve is then constructed based on the obtained optimal curve parameters.

[0112] Here is a specific example:

[0113] This embodiment continues to process the change sequence [0.02, 0.025, 0.03] obtained in the previous embodiment. First, a sliding window with a time scale of 3 hours is used to smooth the sequence to obtain a first smoothed sequence [0.023, 0.026, 0.028] millimeters per hour. Each value is obtained by calculating the arithmetic mean of 3 consecutive changes. For example, 0.023 is equal to the sum of 0.02, 0.025, and 0.03 divided by 3. At the same time, a sliding window with a time scale of 6 hours is used to smooth the same sequence to obtain a second smoothed sequence [0.024, 0.027] millimeters per hour. Each value is obtained by calculating the arithmetic mean of 6 consecutive changes.

[0114] Then, the amplitude of the periodic feature component is separated from the first smoothed sequence by Fourier transform, and the slope of the trend feature component is extracted from the second smoothed sequence by linear regression, which is 0.002 mm / h squared.

[0115] Then use the weighted calculation formula The periodic feature components and trend feature components are fused together. This data represents the trend of thickness change after fusion, in millimeters per hour. Represents the periodic characteristic component, with units of millimeters per hour. This represents the trend characteristic component, expressed in millimeters per hour squared. The weighting coefficients are 0.3 and 0.7 respectively. Substituting these values ​​into the numerical calculations yields the thickness variation trend data sequence [0.024, 0.027, 0.029], in millimeters per hour. For example... An exponential growth curve was set based on this thickness variation trend data. The parameter optimization range, where This indicates the deposition thickness, in millimeters. This represents the initial thickness parameter, in millimeters. This represents the growth rate parameter, expressed in reciprocals per hour. Indicates time, in hours. It is a natural constant, set based on the statistical characteristics of thickness variation trend data. The parameter range is [0.015, 0.035]. The parameter range is [0.05, 0.15];

[0116] The optimal curve parameters were searched within the parameter optimization range using a particle swarm optimization algorithm. With a population size of 50 particles, the optimal parameters were obtained after 100 iterations. =0.022 and =0.12, at which point the curve fit reaches 95%. A growth trend curve is then constructed based on the optimal curve parameters. This curve forms a complete connection with the sedimentation growth trend prediction step in the aforementioned embodiments.

[0117] In the embodiments of this application, this step scheme achieves accurate modeling of sediment growth trends through multi-scale analysis and optimized fitting, providing a more reliable predictive basis for cleaning decisions.

[0118] To further improve the accuracy of deposition state identification, in some embodiments, step 102: performing spatiotemporal fusion processing on the multi-source temperature data and the electric field intensity data to generate target feature information reflecting the indium deposition state includes:

[0119] Step 401: Based on the location information and collection timestamp of each monitoring point in the multi-source temperature data, establish a temperature distribution map.

[0120] In step 401, the temperature distribution map is a distribution map that records the temperature values ​​of each temperature monitoring point at different time points and their spatial location relationships. In the map, each node represents a monitoring point location, the lines between nodes represent spatial adjacency, and the node attributes contain the time series temperature data of that location.

[0121] In this embodiment, the spatial coordinates of each monitoring point are determined based on the location of the temperature sensors in the network, and then a temperature distribution map containing spatial location information and time change information is constructed by combining the temperature readings collected at each monitoring point at different times.

[0122] Step 402: Based on the location information and acquisition timestamp of each measurement point in the electric field intensity data, establish an electric field distribution map.

[0123] In step 402, the electric field distribution map is a distribution map that records the electric field intensity values ​​and spatial position relationships of each electric field measurement point at different time points. In the map, each node represents a measurement point location, the lines between nodes represent spatial adjacency, and the node attributes contain the time series electric field intensity data of that location.

[0124] In this embodiment, the spatial coordinates of each measurement point are determined based on the arrangement of the electric field probes in the network. Then, an electric field distribution map containing spatial location information and time change information is constructed by combining the electric field intensity readings collected at each measurement point at different times.

[0125] Step 403: Overlay the temperature distribution map with the electric field distribution map to identify the spatial overlap between the temperature anomaly region and the electric field distortion region.

[0126] In step 403, spatial overlap is a quantitative indicator of the degree of spatial overlap between the temperature anomaly region and the electric field distortion region. The temperature anomaly region is obtained by analyzing the temperature distribution map. Specifically, it is determined by identifying continuous spatial regions in the temperature monitoring points that are consistently higher than the reference temperature threshold and whose temperature change rate exceeds the normal range. These regions indicate the presence of abnormal heat accumulation. "Constantly" can be understood as exceeding the reference temperature threshold more than a preset number of times. The preset number of times is any value greater than 1.

[0127] The electric field distortion region is obtained by analyzing the electric field distribution map. Specifically, it is determined by identifying continuous spatial regions in the electric field measurement points where the electric field intensity value deviates from the expected distribution pattern and the electric field gradient changes abruptly. These regions indicate that the electric field distribution has been locally disturbed.

[0128] In this embodiment of the application, spatially superimposing the temperature distribution map and the electric field distribution map can identify the spatial range of overlap between the temperature anomaly region and the electric field distortion region, and then calculate the proportion of the overlapping part of the two regions to the area of ​​their respective regions, thereby obtaining the spatial overlap value.

[0129] Step 404: Based on the spatial overlap, determine the potential distribution area of ​​indium deposition, and extract the temperature change characteristics and electric field distortion characteristics within the potential distribution area.

[0130] In step 404, the potential distribution area of ​​indium deposition is the spatial range in which indium deposition may exist, determined based on the degree of spatial overlap. The temperature change characteristics are quantitative indicators describing the temperature change characteristics within the potential distribution area, and the electric field distortion characteristics are quantitative indicators describing the electric field distortion characteristics within the potential distribution area.

[0131] In this embodiment, potential deposition areas are screened based on the degree of spatial overlap. Within these potential distribution areas, the gradient rate of change and fluctuation amplitude of temperature data are extracted as temperature change features, and the gradient rate of change and fluctuation amplitude of electric field data are extracted as electric field distortion features. Then, temperature change features and electric field distortion features are extracted from the original monitoring data corresponding to the identified potential indium deposition distribution areas. Specifically, parameters such as temperature gradient and temperature change rate of each monitoring point within the potential distribution area are extracted from the temperature distribution map, and parameters such as electric field gradient and field strength fluctuation coefficient of each measurement point within the same potential distribution area are extracted from the electric field distribution map.

[0132] Step 405: The temperature change feature and the electric field distortion feature are weighted and fused to generate target feature information.

[0133] In this embodiment, temperature change features and electric field distortion features are weighted according to preset weights, and target feature information containing deposition thickness and distribution density values ​​is generated through feature fusion formula.

[0134] In this embodiment of the application, the step scheme achieves accurate characterization of the indium deposition state through multi-source data fusion and feature extraction, providing a reliable decision basis for subsequent cleaning control.

[0135] To further improve the accuracy of heating control, in some embodiments, step 103: generating a power control signal based on the target feature information using a fuzzy PID control algorithm, and adjusting the heating unit embedded in the target area according to the power control signal, includes:

[0136] Step 501: Extract the deposition thickness value and distribution density value from the target feature information, and determine the deposition severity level of each heating unit location based on the deposition thickness value and the distribution density value.

[0137] In step 501, the deposition severity level is a classification level used to characterize the severity of deposition based on the deposition thickness value and distribution density value, wherein different levels correspond to different treatment priorities.

[0138] In this embodiment of the application, firstly, the deposition thickness value and distribution density value are extracted from the target feature information; then, the deposition situation at the location of each heating unit is divided into different severity levels according to the preset classification criteria.

[0139] Step 502: Set the base power coefficient of each heating unit according to the severity level of the deposition.

[0140] In step 502, the base power coefficient is a proportional coefficient used to calculate the base power, which is set according to the severity level of deposition, and the magnitude of the coefficient is positively correlated with the severity of deposition.

[0141] In this embodiment, the base power coefficient of each heating unit is set according to the deposition severity level corresponding to each heating unit and the mapping relationship between the level and the coefficient.

[0142] Step 503: Based on the attention mechanism prediction model, predict the changing trend of the deposition thickness value and generate deposition evolution trend data.

[0143] In step 503, the attention-based prediction model can be constructed using a temporal prediction network with an encoder-decoder architecture. The encoder part extracts the temporal features of the historical deposition thickness sequence through a multi-head attention layer, and the decoder part focuses on the deposition change pattern at key time points through a temporal attention mechanism. Before applying the model, the model is trained. During the model training process, the model uses historical deposition thickness data and the corresponding actual evolution results as training samples, and completes the model parameter training by minimizing the error between the predicted value and the true value.

[0144] Sedimentary evolution trend data is sedimentary development data for a future time period obtained by predicting the trend of changes in sedimentary thickness values ​​using predictive models.

[0145] In this embodiment of the application, the historical sequence of deposition thickness values ​​is input into a prediction model based on an attention mechanism. The model analyzes the thickness variation pattern and predicts future trends to generate deposition evolution trend data.

[0146] Step 504: Calculate the power correction amount of each heating unit based on the spatial distribution characteristics of the deposition evolution trend data and the distribution density value using the fuzzy rules of the fuzzy PID control algorithm.

[0147] In step 504, the power correction amount is a correction value used to adjust the base power, which is calculated by fuzzy rules and its magnitude depends on the sedimentary evolution trend and distribution density characteristics.

[0148] In this embodiment, firstly, the spatial distribution characteristics of deposition evolution trend data and distribution density values ​​are processed by fuzzy rules of the fuzzy PID control algorithm; then, the power correction amount required for each heating unit is calculated based on the fuzzy inference results.

[0149] Step 505: Combine the basic power coefficient and the power correction amount to generate the real-time power setting value of each heating unit, and generate a power control signal based on the real-time power setting value.

[0150] In step 505, the real-time power setpoint is the final power control value obtained by combining the base power coefficient and the power correction amount.

[0151] In this embodiment, the basic power coefficient and the power correction amount are weighted and combined to generate the real-time power setting value of each heating unit, and the value is converted into the corresponding pulse width modulation signal as the power control signal.

[0152] Step 506: Drive each heating unit to generate differentiated heating power through the power control signal.

[0153] In step 506, the differentiated heating power refers to the different heating power generated by each heating unit according to the power control signal; the indium deposition distribution refers to the spatial arrangement of physical quantities such as the spatial position, thickness and density of the indium material on the extraction electrode surface, which is derived from the spatial analysis results of the deposition thickness value and distribution density value in the target feature information.

[0154] The indium deposition state includes both static spatial information of indium deposition distribution and dynamic characteristics such as deposition thickness variation trend, with indium deposition distribution being the core spatial element constituting the indium deposition state.

[0155] In the embodiments of this application, each heating unit is driven by a power control signal to generate heating power that is adapted to the deposition conditions at its location, thereby forming a non-uniform heating mode on the extraction electrode surface that corresponds to the indium deposition distribution.

[0156] In this embodiment of the application, the step scheme achieves directional heating treatment of non-uniform deposition through intelligent prediction and precise control, thereby effectively improving the targeting and energy efficiency of the cleaning process.

[0157] To further improve the accuracy of heat flow monitoring, in some embodiments, step 104: the monitoring of the electrothermal characteristics of the heating unit to obtain thermal impedance parameters, and based on the thermal impedance parameters, generating heat flow distribution data, including:

[0158] Step 601: Measure the operating voltage and operating current of each heating unit, and calculate the instantaneous heating power of each heating unit based on the operating voltage and operating current.

[0159] In step 601, the instantaneous heating power is the actual heat power generated by the heating unit at a specific moment, which is calculated by measuring the operating voltage and operating current.

[0160] In this embodiment of the application, under the non-uniform heating mode, the working voltage at both ends of each heating unit and the working current flowing through each heating unit are first collected in real time; then, based on the above working voltage and working current, the instantaneous heating power of each heating unit is calculated using the electric power calculation formula.

[0161] Step 602: Obtain the temperature rise rate of the corresponding area of ​​each heating unit, and calculate the thermal impedance parameter of each heating unit by the ratio of the instantaneous heating power to the temperature rise rate.

[0162] In this embodiment, the temperature change of the corresponding area of ​​each heating unit is monitored synchronously, and the temperature rise rate per unit time is calculated. At the same time, the instantaneous heating power is divided by the temperature rise rate to obtain the thermal resistance parameter.

[0163] Step 603: Based on the spatial coordinates of each heating unit, the thermal impedance parameters are spatially interpolated to construct a spatial distribution field.

[0164] In step 603, the spatial position coordinates of each heating unit refer to the installation position data of each heating unit in the three-dimensional structure of the extraction electrode. These coordinates are derived from the engineering design drawings and actual installation positioning records of the extraction electrode. The spatial distribution field is a continuous spatial distribution field constructed by spatial interpolation of discrete thermal impedance parameters.

[0165] In this embodiment, based on the spatial position coordinates of each heating unit on the extraction electrode surface, an interpolation algorithm is used to process the thermal impedance parameters measured at the aforementioned spatial position coordinates to generate a spatial distribution field covering the entire target area.

[0166] Step 604: Determine the main conduction path of heat in the target area based on the gradient change direction of the spatial distribution field.

[0167] In step 604, the gradient change direction of the spatial distribution field refers to the direction in which the thermal impedance value changes the fastest in space, and it can be the vector direction obtained by performing mathematical gradient calculations on the constructed spatial distribution field.

[0168] The primary conduction path refers to the route along which heat is preferentially transferred in the extraction electrode material. It can be determined by identifying regions in the spatial distribution field where the gradient direction is continuous and the thermal resistance value is less than a preset threshold. These regions indicate that heat is more easily conducted along this path.

[0169] In this embodiment of the application, the gradient vector of each point in the spatial distribution field is calculated, and then the directional distribution characteristics of the gradient vector are analyzed to identify the main direction of heat transfer, which can be used as the main conduction path.

[0170] Step 605: Combine the numerical distribution of the spatial distribution field and the main conduction path to generate heat flow distribution data.

[0171] In step 605, the numerical distribution of the spatial distribution field refers to the continuous numerical variation of the thermal impedance parameter in the entire three-dimensional space of the extraction electrode. This is a continuous field distribution generated by spatial interpolation calculation of the thermal impedance parameters measured by each heating unit based on its spatial position coordinates.

[0172] In this embodiment of the application, by combining the numerical distribution of the spatial distribution field and the directional characteristics of the main conduction path, the heat flux density value and the transfer direction at each location can be calculated to generate heat flux distribution data;

[0173] The specific implementation process is as follows: First, calculate the heat flux density at each location based on the numerical distribution of the spatial distribution field, and use Fourier's law formula, such as heat flux density equals temperature gradient divided by thermal resistance value. Then, combine the main conduction path to determine the preferred direction of heat flow. Finally, integrate the heat flux density values ​​according to spatial location and direction of conduction to form complete heat flow distribution data.

[0174] For example, if the temperature gradient at a point on the main conduction path is 50 degrees Celsius per millimeter and the thermal resistance is 100 degrees Celsius per watt, the calculated heat flux density at that point is 500 watts per square meter. At the same time, based on the direction of the conduction path, it is determined that the heat flux is transferred from coordinate 0-0 to coordinate 0-1. After integrating this information, the generated heat flux distribution data shows that the heat flux density on the path is 500 watts per square meter and the direction is towards coordinate 0-1.

[0175] In the embodiments of this application, this step scheme enables accurate monitoring and quantitative description of the heat transfer process, providing a reliable thermodynamic basis for deposition growth analysis.

[0176] To further improve the accuracy of the cleaning process control, in some embodiments, step 106: determining a cleaning control strategy based on the deposition rate information, and controlling the temperature change of the heating unit according to the cleaning control strategy, includes:

[0177] Step 701: Based on the deposition rate information, calculate the time required for the current deposition thickness to reach the preset critical deposition thickness, and obtain the cleaning time.

[0178] In step 701, the preset critical deposition thickness refers to the maximum allowable indium deposition thickness value preset according to the safety operation requirements of the field launch thruster. This thickness value is determined by experimentally measuring the relationship between the deposition thickness and the degree of electric field distortion.

[0179] The cleaning time is calculated based on the deposition growth rate and the current deposition thickness.

[0180] In this embodiment, the cleaning time is obtained by calculating the time required for the indium material to reach the critical deposition thickness value based on the deposition rate information and the current deposition thickness data, combined with the preset critical deposition thickness value, using a time calculation formula.

[0181] Step 702: Based on the phase transformation characteristics of indium material, formulate a combination of temperature control parameters including heating rate parameter, target temperature parameter, and holding time parameter.

[0182] In step 702, the phase transformation characteristics of indium material refer to the temperature characteristic parameters during the process of indium metal changing from solid to liquid. These parameters are derived from the melting point and phase transformation temperature range of indium metal recorded in the material handbook. The temperature control parameter combination is a set of temperature rise process control parameters and target control parameters formulated based on the phase transformation characteristics of indium material.

[0183] In the embodiments of this application, based on the temperature conditions and time characteristics required for indium material to change from solid to liquid, a parameter combination including heating rate control parameters, target temperature control parameters, and holding time control parameters is formulated.

[0184] Step 703: Combine and integrate the cleaning time and the temperature control parameters to form a cleaning control strategy.

[0185] In this embodiment of the application, the determined cleaning time is systematically integrated with the established temperature control parameters to form a complete cleaning control strategy that includes time scheduling and temperature control requirements.

[0186] Step 704: Adjust the temperature change process of each heating unit according to the parameters in the cleaning control strategy to control the temperature of the heating unit to reach the phase transition temperature range of indium. The heating rate parameter in the cleaning control strategy is used to control the power rise curve of each heating unit. The target temperature parameter in the cleaning control strategy is used to heat the target area to the phase transition temperature range of indium. The holding time parameter in the cleaning control strategy is used to maintain the phase transition temperature until the cleaning process is completed.

[0187] In step 704, the power rise curve of each heating unit refers to the control trajectory of the power of each heating unit changing with time during the cleaning process. This curve is calculated based on the heating rate parameter in the cleaning control strategy and the thermal inertia characteristics of the heating unit.

[0188] The temperature change process is a complete operation process of adjusting and controlling the power and temperature of the heating unit according to the cleaning control strategy. The phase transition temperature range of indium refers to the temperature range required for indium metal to complete the solid-liquid transition. This range can be determined by measuring the actual phase transition temperature of indium metal at different heating rates through material experiments.

[0189] In the embodiments of this application, firstly, in the non-uniform heating mode, the working state of each heating unit is controlled according to the parameters in the cleaning control strategy; then, the heating process of each heating unit is controlled by adjusting the power rise curve, and the indium material is made to reach the phase change temperature range according to the target temperature parameters in the cleaning control strategy; finally, the phase change state is maintained according to the maintenance time parameters in the cleaning control strategy, thereby completing the cleaning of the indium material.

[0190] In this embodiment of the application, the step scheme achieves the best match between the cleaning process and the deposition state through precise time judgment and temperature control, ensuring the cleaning effect and system safety.

[0191] Figure 3 A schematic diagram of a temperature control system for cleaning indium deposition electrodes in a field launch thruster, provided in an embodiment of this application, is shown. The system includes:

[0192] The acquisition module 31 is used to acquire multi-source temperature data and electric field intensity data of the target area of ​​the field launch thruster.

[0193] The fusion module 32 is used to perform spatiotemporal fusion processing on the multi-source temperature data and the electric field intensity data to generate target feature information reflecting the indium deposition state.

[0194] The adjustment module 33 is used to generate a power control signal based on the target feature information using a fuzzy PID control algorithm, and adjust the heating unit embedded in the target area according to the power control signal.

[0195] The monitoring module 34 is used to monitor the electrothermal characteristics of the heating unit to obtain thermal impedance parameters, and generate heat flow distribution data based on the thermal impedance parameters.

[0196] The generation module 35 is used to predict the growth trend of indium material based on the heat flow distribution data using a state observer, so as to generate deposition rate information.

[0197] The determination module 36 is used to determine a cleaning control strategy based on the deposition rate information, and to control the temperature change of the heating unit according to the cleaning control strategy so that the indium material reaches a phase change state in the target area.

[0198] The field launch thruster extraction indium deposition cleaning temperature control system of this application embodiment is used to implement the aforementioned field launch thruster extraction indium deposition cleaning temperature control method. Therefore, the specific implementation of the field launch thruster extraction indium deposition cleaning temperature control system can be found in the embodiment section of the field launch thruster extraction indium deposition cleaning temperature control method above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0199] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the field launch thruster extraction indium deposition cleaning temperature control method described above.

[0200] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described field launch thruster extraction indium deposition cleaning temperature control method.

[0201] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0202] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the field launch thruster extraction indium deposition cleaning temperature control method.

[0203] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0204] The above provides a detailed description of the method and system for controlling the cleaning temperature of indium deposition in a field launch thruster, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for controlling the temperature of indium deposition cleaning in a field launch thruster, characterized in that, include: Acquire multi-source temperature and electric field intensity data of the target region of the field launch thruster; The multi-source temperature data and the electric field intensity data are spatiotemporally fused to generate target feature information reflecting the indium deposition state. Based on the target feature information, a power control signal is generated through a fuzzy PID control algorithm, and the heating unit embedded in the target area is adjusted according to the power control signal. The electrothermal characteristics of the heating unit are monitored to obtain thermal impedance parameters, and heat flow distribution data is generated based on the thermal impedance parameters. Based on the heat flow distribution data, a state observer is used to predict the growth trend of indium material in order to generate deposition rate information. Based on the deposition rate information, a cleaning control strategy is determined, and the temperature change of the heating unit is controlled according to the cleaning control strategy so that the indium material reaches a phase change state in the target area. The step of generating a power control signal based on the target feature information using a fuzzy PID control algorithm, and adjusting the heating unit embedded in the target area according to the power control signal, includes: The deposition thickness and distribution density values ​​are extracted from the target feature information. Based on the deposition thickness and distribution density values, the deposition severity level of each heating unit location is determined. The base power coefficient of each heating unit is set according to the severity level of the deposition. A prediction model based on an attention mechanism is used to predict the changing trend of the deposition thickness value and generate deposition evolution trend data. Based on the fuzzy rules of the fuzzy PID control algorithm, and the spatial distribution characteristics of the deposition evolution trend data and the distribution density value, the power correction amount of each heating unit is calculated. The base power coefficient and the power correction amount are combined to generate the real-time power setting value for each heating unit, and a power control signal is generated based on the real-time power setting value. The power control signal drives each heating unit to generate differentiated heating power.

2. The method according to claim 1, characterized in that, The step of using a state observer to predict the growth trend of indium based on the heat flow distribution data to generate deposition rate information includes: The heat flow distribution data is input into the state observer, and the parameter sequence is extracted through the state observer; Based on the parameter sequence, and combined with the preset correlation model between deposition thickness and heat conduction, the change sequence is calculated; Time series analysis is performed on the change sequence to generate thickness change trend data, and curve fitting is performed on the thickness change trend data based on particle swarm optimization algorithm to generate growth trend curve; The thickness increase per unit time is extracted from the growth trend curve, and deposition rate information is generated based on the thickness increase.

3. The method according to claim 2, characterized in that, The step of performing time series analysis on the change sequence to generate thickness change trend data, and then performing curve fitting on the thickness change trend data based on the particle swarm optimization algorithm to generate a growth trend curve, includes: The change sequence is processed by sliding windows at different time scales to obtain a first smoothed sequence and a second smoothed sequence; Separate the periodic feature component from the first smoothed sequence and extract the trend feature component from the second smoothed sequence; The periodic feature component and the trend feature component are fused to obtain thickness change trend data, and the parameter optimization range of the exponential growth curve is set based on the thickness change trend data. The optimal curve parameters are searched within the parameter optimization range using the particle swarm optimization algorithm, and a growth trend curve is constructed based on the optimal curve parameters.

4. The method according to claim 1, characterized in that, The spatiotemporal fusion processing of the multi-source temperature data and the electric field intensity data to generate target feature information reflecting the indium deposition state includes: A temperature distribution map is established based on the location information and collection timestamp of each monitoring point in the multi-source temperature data. Based on the location information and acquisition timestamp of each measurement point in the electric field intensity data, an electric field distribution map is established; The temperature distribution map and the electric field distribution map are overlaid and analyzed to identify the spatial overlap between the temperature anomaly region and the electric field distortion region. Based on the spatial overlap, the potential distribution area of ​​indium deposition is determined, and the temperature change characteristics and electric field distortion characteristics within the potential distribution area are extracted. The temperature change features and the electric field distortion features are weighted and fused to generate target feature information.

5. The method according to claim 1, characterized in that, The monitoring unit's electrothermal characteristics are used to obtain thermal impedance parameters, and based on these parameters, heat flow distribution data is generated, including: Measure the operating voltage and operating current of each heating unit, and calculate the instantaneous heating power of each heating unit based on the operating voltage and operating current; The temperature rise rate of the corresponding area of ​​each heating unit is obtained, and the thermal impedance parameter of each heating unit is obtained by calculating the ratio of the instantaneous heating power to the temperature rise rate. Based on the spatial coordinates of each heating unit, the thermal impedance parameters are spatially interpolated to construct a spatial distribution field. Based on the gradient change direction of the spatial distribution field, the main heat conduction path in the target area is determined; Heat flow distribution data is generated by combining the numerical distribution of the spatial distribution field and the main conduction path.

6. The method according to claim 1, characterized in that, The step of determining a cleaning control strategy based on the deposition rate information and controlling the temperature change of the heating unit according to the cleaning control strategy includes: Based on the deposition rate information, the time required to reach the preset critical deposition thickness from the current deposition thickness is calculated, and the cleaning time is obtained. Based on the phase transition characteristics of indium materials, a combination of temperature control parameters including heating rate parameter, target temperature parameter, and holding time parameter is formulated. The cleaning time and the temperature control parameters are combined and integrated to form a cleaning control strategy; According to the parameters in the cleaning control strategy, the temperature change process of each heating unit is adjusted to control the temperature of the heating unit to reach the phase transition temperature range of indium. The heating rate parameter in the cleaning control strategy is used to control the power rise curve of each heating unit. The target temperature parameter in the cleaning control strategy is used to heat the target area to the phase transition temperature range of indium. The holding time parameter in the cleaning control strategy is used to maintain the phase transition temperature until the cleaning process is completed.

7. A temperature control system for cleaning indium deposition electrodes in a field launch thruster, characterized in that, include: The acquisition module is used to acquire multi-source temperature data and electric field intensity data of the target area of ​​the field launch thruster; The fusion module is used to perform spatiotemporal fusion processing on the multi-source temperature data and the electric field intensity data to generate target feature information reflecting the indium deposition state. The adjustment module is used to generate a power control signal based on the target feature information using a fuzzy PID control algorithm, and adjust the heating unit embedded in the target area according to the power control signal. The monitoring module is used to monitor the electrothermal characteristics of the heating unit to obtain thermal impedance parameters, and generate heat flow distribution data based on the thermal impedance parameters. The generation module is used to predict the growth trend of indium material based on the heat flow distribution data using a state observer, so as to generate deposition rate information; The determination module is used to determine a cleaning control strategy based on the deposition rate information, and control the temperature change of the heating unit according to the cleaning control strategy so that the indium material reaches a phase change state in the target area. The step of generating a power control signal based on the target feature information using a fuzzy PID control algorithm, and adjusting the heating unit embedded in the target area according to the power control signal, includes: The deposition thickness and distribution density values ​​are extracted from the target feature information. Based on the deposition thickness and distribution density values, the deposition severity level of each heating unit location is determined. The base power coefficient of each heating unit is set according to the severity level of the deposition. A prediction model based on an attention mechanism is used to predict the changing trend of the deposition thickness value and generate deposition evolution trend data. Based on the fuzzy rules of the fuzzy PID control algorithm, and the spatial distribution characteristics of the deposition evolution trend data and the distribution density value, the power correction amount of each heating unit is calculated. The base power coefficient and the power correction amount are combined to generate the real-time power setting value for each heating unit, and a power control signal is generated based on the real-time power setting value. The power control signal drives each heating unit to generate differentiated heating power.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the field launch thruster extraction indium deposition cleaning temperature control method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the field launch thruster extraction indium deposition cleaning temperature control method as described in any one of claims 1 to 6.

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