Etching pit evolution dynamic measurement system based on X-ray

Abstract

The invention discloses an X-ray-based etching pit evolution dynamic measurement system, which relates to the technical field of lunar exploration and comprises an X-ray generator module, a lunar soil container module, an imager module, a data processing and analysis module, an experiment control and synchronization module and a data display and report module. According to the erosion pit evolution dynamic measurement system based on the X-ray, the limitation of a traditional visible light imaging technology under dynamic interference of lunar dust is overcome, the X-ray can penetrate through the lunar dust and is not affected by shielding, high signal-to-noise ratio and accurate erosion pit data are ensured, and according to attenuation information of the X-ray after penetrating through a lunar soil container, the erosion pit evolution dynamic measurement can be realized. According to the method, the size, the depth and the evolution process of the corrosion pit are directly inverted, high-precision, simple and convenient dynamic measurement is provided, in addition, X-ray imaging can reconstruct the three-dimensional structure of the corrosion pit and track the morphological change of the corrosion pit in real time, the dynamic monitoring capability is enhanced, an X-ray system is more suitable for the complex lunar surface environment, and higher experiment feasibility and reliability are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of rice crust cutting devices, and in particular to an X-ray-based dynamic measurement system for etch pit evolution. Background Art

[0002] As human exploration of the moon deepens, lunar landers and probes face complex environmental challenges during the landing process. One of them is the problem of erosion pits formed by engine plumes on the lunar surface. The strong airflow generated by the engine during landing and takeoff causes strong erosion of the lunar surface material. The lunar dust is stirred up by the high-speed airflow and moves along the surface of the erosion pits, which in turn affects the stability of the lander and may even cause damage to the morphology of the lander's supporting surface. In addition, the reflection between the plume and the lunar surface will also complicate the thermal protection assessment. Therefore, it becomes particularly important to accurately and quickly monitor the lunar erosion process.

[0003] Existing lunar crater simulation experiments usually use visible light cameras to record the dynamic process of crater formation when the engine is working. However, this method based on visual images has many limitations. The lunar dust under the action of the engine plume will block part of the crater surface. Especially during the dynamic movement of the lunar dust, the line of sight is often blocked, making it difficult for the camera to clearly capture the image of the crater surface. In addition, the lunar surface itself lacks obvious texture features, which brings more challenges to visible light imaging. These factors make traditional imaging methods unreliable in real-time dynamic monitoring of the evolution of lunar craters. Therefore, there is an urgent need for a new technical means to overcome the limitations of visible light imaging technology, especially the difficulties in the problem of lunar dust obstruction, so as to achieve efficient and accurate monitoring of the lunar erosion process. Summary of the invention

[0004] The object of the present invention is to provide an X-ray based dynamic measurement system for etch pit evolution to solve the problems raised in the above background technology.

[0005] An X-ray-based dynamic measurement system for erosion pit evolution, comprising:

[0006] X-ray generator module: The X-ray generator module is used to generate an X-ray beam and emit X-rays to the lunar soil container;

[0007] Lunar soil container module: The lunar soil container module is electrically connected to the X-ray generator module. The lunar soil container module is used to simulate the lunar surface environment, place lunar soil, simulate lunar dust, and generate erosion pits under the action of the engine plume;

[0008] Imager module: The imager module is electrically connected to the lunar soil container module. The imager module is used to receive the image formed by X-rays passing through the lunar soil container and convert the image into an electronic signal.

[0009] Data processing and analysis module: The data processing and analysis module is electrically connected to the imager module, and is used to process and analyze the image data output by the imager based on the image processing algorithm, and calculate the size, depth and evolution process of the erosion pit;

[0010] Experiment control and synchronization module: The experiment control and synchronization module is electrically connected to the data processing and analysis module, the imager module, the lunar soil container module and the X-ray generator module. The experiment control and synchronization module is used to control the working state of the system, synchronously start the X-ray scanning, data acquisition and imaging operations, and ensure the timing and accuracy of the experimental data;

[0011] Data display and reporting module: The data display and reporting module is electrically connected to the experiment control and synchronization module, and is used to display the processed pit evolution data in a visual form and generate an experiment report.

[0012] Preferably, the X-ray generator module specifically comprises:

[0013] X-ray tube unit: The electron accelerator accelerates the electron flow through the vacuum tube to hit the anode target. The anode material collides with the electrons and releases X-rays;

[0014] High-voltage power supply unit: The high-voltage power supply system accelerates the current to the speed required by the electron flow to generate a high-energy electron beam;

[0015] X-ray beam adjustment unit: By selecting different filters, the energy spectrum of the X-ray beam is adjusted to control the emission direction, intensity and width of the X-rays to ensure that the X-ray beam accurately irradiates the lunar soil container;

[0016] Cooling unit: Based on water cooling system, heat is removed through circulating water flow;

[0017] X-ray intensity control and monitoring unit: Based on the X-ray intensity meter, the output intensity of X-rays is monitored in real time. According to the monitored intensity data, the high-voltage power supply is adjusted to maintain a constant output;

[0018] X-ray source shielding unit: Based on lead shielding to block X-ray radiation, the lead material prevents radiation diffusion by absorbing or scattering X-rays, ensuring a safe laboratory environment.

[0019] Preferably, the lunar soil container module specifically includes:

[0020] Lunar soil and dust simulation unit: simulates the soil and dust on the lunar surface, and reproduces the reaction of lunar dust under the action of engine plume;

[0021] Lunar soil conditioning unit: By controlling the pressure of the compaction device, the physical properties of the lunar soil are simulated, and its looseness and density are adjusted to simulate the conditions on the real lunar surface;

[0022] Vacuum chamber and environmental conditioning unit: Use a vacuum pump system to extract the air in the container, maintain a low pressure state, control the temperature in the container, simulate the temperature difference between day and night on the lunar surface, and monitor the temperature and humidity in the container in real time based on temperature and humidity monitoring equipment;

[0023] Jet simulation unit: Based on the experimental nozzle, the jet airflow is simulated to simulate the effect of the engine plume on the lunar surface. Gas is introduced into the nozzle and the airflow rate is adjusted by controlling the valve to simulate the plume intensity during the lunar landing process and control the jet direction.

[0024] Erosion pit formation and monitoring unit: Based on X-ray, sensor and laser scanning technology, it monitors and records the dynamic evolution of erosion pits in the lunar soil container in real time;

[0025] X-ray detector unit: connected to the X-ray generator, scans the erosion pits in the lunar soil container and records their morphological changes;

[0026] Sensor data collector unit: collects experimental parameters, including air velocity, lunar soil temperature, and lunar dust movement;

[0027] Computer analysis system: Receives data output by sensors, analyzes it through data processing software, and generates the morphology of erosion pits.

[0028] Preferably, the imager module specifically comprises:

[0029] X-ray receiving unit: receives X-rays that pass through the lunar soil container and converts X-ray signals into processable electronic signals;

[0030] Photodiode: converts the detector signal into an electrical signal;

[0031] Electronic amplifier: amplifies the signal from the detector to a suitable processing level;

[0032] Image data acquisition and processing unit: further processes and converts the electronic signals obtained from the detector and conversion unit into digital images, and performs noise removal and grayscale value adjustment;

[0033] Image display and storage unit: displays the processed digital image and stores the processed image data.

[0034] Preferably, the image data acquisition and processing unit specifically includes:

[0035] First, the signal obtained from the detector and the conversion unit is converted into a digital signal through an ADC;

[0036] The image is smoothed by calculating the average value of the neighborhood pixels to remove noise. The average value of the neighborhood pixels is calculated as follows:

[0037]

[0038] Where g(x,y) is the denoised image, M and N are the sizes of the filter windows, f(x+i,y+j) is the pixel value at position (x+i,y+j) in the input image, and i and J are variables used to traverse the neighborhood range in the convolution operation;

[0039] Based on histogram equalization, the grayscale value of the image is adjusted to improve the contrast of the image and enhance the details of the image. The calculation formula based on histogram equalization is:

[0040]

[0041] Where S(f(x,y)) is the cumulative distribution function of the gray value f(x,y) of the original image, S min and S max are the minimum and maximum values ​​of CDF, respectively, and L is the number of gray levels;

[0042] After denoising and grayscale value adjustment, the image data will be generated into the final X-ray image.

[0043] Preferably, the data processing and analysis module specifically includes:

[0044] Feature extraction unit: extracts the shape and size of the pit area based on the contour detection algorithm, calculates the area of ​​the pit, and calculates the depth of the pit through the grayscale value and color value of the image combined with the imaging depth information of the imager;

[0045] 3D reconstruction and depth analysis unit: reconstruct the 3D model of the pit based on image data from multiple different angles, calculate the relative depth of the pit based on the depth information of the image, perform surface fitting on the pit surface, calculate the surface normal and curvature parameters, and further infer its morphology and depth changes;

[0046] Pit evolution analysis unit: Analyzes image data at multiple time points, monitors the changes of pits at different times, calculates the changes of pits, including changes in size, depth and morphology, by comparing images at different time points, predicts the further evolution trend of pits based on regression analysis, and infers the rate of pit expansion and deepening.

[0047] Preferably, the feature extraction unit specifically includes:

[0048] Based on the contour detection algorithm, the pit boundary in the image is extracted, binarization is performed, a binary image is obtained, contours are found, a contour list is returned, and each contour and its related information are extracted. The area and boundary of the contour cannot be seen. The contour detection algorithm formula is:

[0049]

[0050] In the formula, C represents a set of points, (x, y) is the x and y is the coordinates of the point, S(x, y) represents the value of a function S at the coordinate point (x, y) is 1, S(x ` ,y ` )=0 means that at the coordinate point (x ` ,y ` ), the value of function S is 0;

[0051] The geometric features of the pits are calculated through contour information, and the output pit features include area, perimeter, shape description and depth.

[0052] Preferably, the three-dimensional reconstruction and depth analysis unit specifically includes:

[0053] Perform 3D reconstruction of images and depth data from different viewpoints, and use image data to extract 3D point cloud data through structured light and stereo vision;

[0054] The reconstructed 3D point cloud is filtered and denoised to retain key point cloud information. The sparse point cloud is converted into a continuous 3D surface model using a point cloud reconstruction algorithm to obtain a 3D mesh model that approximates the pit surface.

[0055] Based on the reconstructed 3D point cloud data, the relative depth of the pit in 3D space is calculated, and the surface of the pit is fitted to transform the discrete point cloud into a continuous smooth surface, thereby obtaining the precise geometric shape of the pit.

[0056] Based on the results of surface fitting, normal and curvature analysis, the morphological characteristics of the erosion pits are inferred, the surface is partitioned and analyzed, different morphological areas are identified, and the morphological differences are further analyzed.

[0057] Preferably, the etch pit evolution analysis unit specifically comprises:

[0058] Obtain the geometric characteristics of the pits and calculate the changes in pit morphology, size and depth by comparing the pit areas at different time points;

[0059] Combined with the depth map data, calculate the depth change of the pit at different time points, and evaluate the expansion and contraction of the pit by calculating the change of the area of ​​the pit at different time points, and evaluate the shape change of the pit to see whether there is expansion and deformation;

[0060] Using known change data, including the area, depth and shape of the pits at different time points, a regression model is constructed. The pit change data at multiple time points is used as input to train the regression model, learn the law of pit change, fit the model, and obtain the prediction function of the pit evolution process.

[0061] The trained regression model is used to predict the changing trend of the pits in the future, predict the future area, depth and morphological changes of the pits, further infer the rate of expansion and deepening of the pits, and obtain the long-term trend of the pit evolution.

[0062] Preferably, the regression model specifically includes:

[0063] The regression model expression is:

[0064] A(t)=β0+β1t+β2t 2 +…+β m t m

[0065] In the formula, A(t) is the target variable for prediction, t is the time variable, β0 is the intercept term in the model, β1 is the coefficient of the first-order term in the model, β2 is the coefficient of the second-order term, and β m are the coefficients of the higher-order terms in the model, and m is the order of the polynomial.

[0066] By adopting the above technical solution, dynamic measurement of the evolution of corrosion pits is achieved.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] The X-ray-based dynamic measurement system for crater evolution of the present invention overcomes the limitations of traditional visible light imaging technology under the dynamic interference of lunar dust. X-rays can penetrate lunar dust and are not affected by obstructions, ensuring a high signal-to-noise ratio and accurate crater data. The system directly inverts the size, depth and evolution process of craters through the attenuation information of X-rays after passing through the lunar soil container, providing high-precision and simple dynamic measurement. In addition, X-ray imaging can reconstruct the three-dimensional structure of craters, track the morphological changes of craters in real time, and enhance dynamic monitoring capabilities. Compared with traditional imaging methods, X-ray systems are more adaptable to the complex lunar environment, have higher experimental feasibility and reliability, and provide solid data support for lunar erosion research and lander design. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a system framework diagram of the present invention;

[0070] Figure 2 This is a system internal framework diagram of the X-ray generator module in the present invention;

[0071] Figure 3 This is a system internal framework diagram of the lunar soil container module in the present invention;

[0072] Figure 4 The figure is a system internal framework diagram of the imager module in the present invention;

[0073] Figure 5 This is a system internal framework diagram of the data processing and analysis module in the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] See also Figure 1-5 , the present invention provides a technical solution: an X-ray-based erosion pit evolution dynamic measurement system, comprising: an X-ray generator module: the X-ray generator module is used to generate an X-ray beam and emit X-rays to a lunar soil container;

[0076] Lunar soil container module: The lunar soil container module is electrically connected to the X-ray generator module. The lunar soil container module is used to simulate the lunar surface environment, place lunar soil, simulate lunar dust, and generate erosion pits under the action of the engine plume;

[0077] Imager module: The imager module is electrically connected to the lunar soil container module. The imager module is used to receive the image formed by X-rays passing through the lunar soil container and convert the image into an electronic signal.

[0078] Data processing and analysis module: The data processing and analysis module is electrically connected to the imager module, and is used to process and analyze the image data output by the imager based on the image processing algorithm, and calculate the size, depth and evolution process of the erosion pit;

[0079] Experiment control and synchronization module: The experiment control and synchronization module is electrically connected to the data processing and analysis module, the imager module, the lunar soil container module and the X-ray generator module. The experiment control and synchronization module is used to control the working state of the system, synchronously start the X-ray scanning, data acquisition and imaging operations, and ensure the timing and accuracy of the experimental data;

[0080] Data display and reporting module: The data display and reporting module is electrically connected to the experiment control and synchronization module, and is used to display the processed pit evolution data in a visual form and generate an experiment report.

[0081] Reference Figure 2 As shown, the X-ray generator module specifically includes:

[0082] X-ray tube unit: The electron accelerator accelerates the electron flow through the vacuum tube to hit the anode target. The anode material collides with the electrons and releases X-rays;

[0083] High-voltage power supply unit: The high-voltage power supply system accelerates the current to the speed required by the electron flow to generate a high-energy electron beam;

[0084] X-ray beam adjustment unit: By selecting different filters, the energy spectrum of the X-ray beam is adjusted to control the emission direction, intensity and width of the X-rays to ensure that the X-ray beam accurately irradiates the lunar soil container;

[0085] Cooling unit: Based on water cooling system, heat is removed through circulating water flow;

[0086] X-ray intensity control and monitoring unit: Based on the X-ray intensity meter, the output intensity of X-rays is monitored in real time. According to the monitored intensity data, the high-voltage power supply is adjusted to maintain a constant output;

[0087] X-ray source shielding unit: Based on lead shielding to block X-ray radiation, the lead material prevents radiation diffusion by absorbing or scattering X-rays, ensuring a safe laboratory environment.

[0088] It is worth mentioning that X-rays can penetrate lunar dust, overcoming the limitation of traditional visible light imaging methods that cannot clearly capture crater images under the interference of dynamic lunar dust. X-rays have high-resolution imaging capabilities and can capture subtle changes in the dynamic process of erosion.

[0089] Reference Figure 3 As shown, the lunar soil container module specifically includes:

[0090] Lunar soil and dust simulation unit: simulates the soil and dust on the lunar surface, and reproduces the reaction of lunar dust under the action of engine plume;

[0091] Lunar soil conditioning unit: By controlling the pressure of the compaction device, the physical properties of the lunar soil are simulated, and its looseness and density are adjusted to simulate the conditions on the real lunar surface;

[0092] Vacuum chamber and environmental conditioning unit: Use a vacuum pump system to extract the air in the container, maintain a low pressure state, control the temperature in the container, simulate the temperature difference between day and night on the lunar surface, and monitor the temperature and humidity in the container in real time based on temperature and humidity monitoring equipment;

[0093] Jet simulation unit: Based on the experimental nozzle, the jet airflow is simulated to simulate the effect of the engine plume on the lunar surface. Gas is introduced into the nozzle and the airflow rate is adjusted by controlling the valve to simulate the plume intensity during the lunar landing process and control the jet direction.

[0094] Erosion pit formation and monitoring unit: Based on X-ray, sensor and laser scanning technology, it monitors and records the dynamic evolution of erosion pits in the lunar soil container in real time;

[0095] X-ray detector unit: connected to the X-ray generator, scans the erosion pits in the lunar soil container and records their morphological changes;

[0096] Sensor data collector unit: collects experimental parameters, including air velocity, lunar soil temperature, and lunar dust movement;

[0097] Computer analysis system: receives the data output by the sensor, analyzes it through data processing software, and generates the morphology of the erosion pit.

[0098] It is worth mentioning that: the basic geometric characteristics of the diameter, depth and width of the erosion pit are calculated; based on the sensor data, the boundary information of the erosion pit is extracted and its specific size is calculated; through high-precision processing of the sensor data, the surface contour of the erosion pit is reconstructed, and its three-dimensional morphology at different scales is displayed, which helps to analyze the evolution process of the pit.

[0099] Reference Figure 4 As shown, the imager module specifically includes:

[0100] X-ray receiving unit: receives X-rays that pass through the lunar soil container and converts X-ray signals into processable electronic signals;

[0101] Photodiode: converts the detector signal into an electrical signal;

[0102] Electronic amplifier: amplifies the signal from the detector to a suitable processing level;

[0103] Image data acquisition and processing unit: further processes and converts the electronic signals obtained from the detector and conversion unit into digital images, and performs noise removal and grayscale value adjustment;

[0104] Image display and storage unit: displays the processed digital image and stores the processed image data;

[0105] First, the signal obtained from the detector and the conversion unit is converted into a digital signal through an ADC;

[0106] The image is smoothed by calculating the average value of the neighborhood pixels to remove noise. The average value of the neighborhood pixels is calculated as follows:

[0107]

[0108] Where g(x,y) is the denoised image, M and N are the sizes of the filter windows, f(x+i,y+j) is the pixel value at position (x+i,y+j) in the input image, and i and J are variables used to traverse the neighborhood range in the convolution operation;

[0109] Based on histogram equalization, the grayscale value of the image is adjusted to improve the contrast of the image and enhance the details of the image. The calculation formula based on histogram equalization is:

[0110]

[0111] Where S(f(x,y)) is the cumulative distribution function of the gray value f(x,y) of the original image, S min and S max are the minimum and maximum values ​​of CDF, respectively, and L is the number of gray levels;

[0112] After denoising and grayscale value adjustment, the image data will be generated into the final X-ray image.

[0113] Reference Figure 5 As shown, the data processing and analysis module specifically includes:

[0114] Feature extraction unit: extracts the shape and size of the pit area based on the contour detection algorithm, calculates the area of ​​the pit, and calculates the depth of the pit through the grayscale value and color value of the image combined with the imaging depth information of the imager;

[0115] Based on the contour detection algorithm, the pit boundary in the image is extracted, binarization is performed, a binary image is obtained, contours are found, a contour list is returned, and each contour and its related information are extracted. The area and boundary of the contour cannot be seen. The contour detection algorithm formula is:

[0116]

[0117] In the formula, C represents a set of points, (x, y) is the x and y is the coordinates of the point, S(x, y) represents the value of a function S at the coordinate point (x, y) is 1, S(x ` ,y ` )=0 means that at the coordinate point (x ` ,y ` ), the value of function S is 0;

[0118] The geometric features of the pits are calculated through contour information, and the output pit features include area, perimeter, shape description and depth.

[0119] 3D reconstruction and depth analysis unit: reconstruct the 3D model of the pit based on image data from multiple different angles, calculate the relative depth of the pit based on the depth information of the image, perform surface fitting on the pit surface, calculate the surface normal and curvature parameters, and further infer its morphology and depth changes;

[0120] Perform 3D reconstruction of images and depth data from different viewpoints, and use image data to extract 3D point cloud data through structured light and stereo vision;

[0121] The reconstructed 3D point cloud is filtered and denoised to retain key point cloud information. The sparse point cloud is converted into a continuous 3D surface model using a point cloud reconstruction algorithm to obtain a 3D mesh model that approximates the pit surface.

[0122] Based on the reconstructed 3D point cloud data, the relative depth of the pit in 3D space is calculated, and the surface of the pit is fitted to transform the discrete point cloud into a continuous smooth surface, thereby obtaining the precise geometric shape of the pit.

[0123] Based on the results of surface fitting, normal and curvature analysis, the morphological characteristics of the erosion pits are inferred, the surface is partitioned and analyzed, different morphological areas are identified, and the morphological differences are further analyzed.

[0124] Pit evolution analysis unit: Analyzes image data at multiple time points, monitors the change process of pits at different times, calculates the changes of pits, including changes in size, depth and morphology, by comparing images at different time points, predicts the further evolution trend of pits based on regression analysis, and infers the rate of pit expansion and deepening;

[0125] Obtain the geometric characteristics of the pits and calculate the changes in pit morphology, size and depth by comparing the pit areas at different time points;

[0126] Combined with the depth map data, calculate the depth change of the pit at different time points, and evaluate the expansion and contraction of the pit by calculating the change of the area of ​​the pit at different time points, and evaluate the shape change of the pit to see whether there is expansion and deformation;

[0127] Using known change data, including the area, depth and shape of the pits at different time points, a regression model is constructed. The pit change data at multiple time points is used as input to train the regression model, learn the law of pit change, fit the model, and obtain the prediction function of the pit evolution process.

[0128] Use the trained regression model to predict the changing trend of pits in the future, predict the future area, depth and morphological changes of pits, further infer the rate of pit expansion and deepening, and obtain the long-term trend of pit evolution;

[0129] The regression model expression is:

[0130] A(t)=β0+β1t+β2t 2 +…+β m t m

[0131] In the formula, A(t) is the target variable for prediction, t is the time variable, β0 is the intercept term in the model, β1 is the coefficient of the first-order term in the model, β2 is the coefficient of the second-order term, and β m are the coefficients of the higher-order terms in the model, and m is the order of the polynomial.

[0132] By adopting the above technical solution, dynamic measurement of the evolution of corrosion pits is achieved.

[0133] In summary, the advantages of the present invention are:

[0134] X-rays can penetrate lunar dust, overcoming the limitation of traditional visible light imaging methods that cannot clearly capture crater images under the interference of dynamic lunar dust. X-rays have high-resolution imaging capabilities and can capture tiny changes in the dynamic process of erosion, providing detailed three-dimensional structural information of lunar surface erosion. X-rays can work in complex optical environments, especially in high-intensity airflow environments generated by engine plumes, and can effectively respond to high-contrast changes. The intensity attenuation of X-rays after passing through the lunar soil container is directly related to the density, thickness and other factors of the lunar soil. Using this attenuation information, the measurement and calculation process of craters can be simplified, avoiding the complex image processing and feature extraction steps in traditional image methods;

[0135] Traditional visible light imaging technology is easily affected by the dynamic movement of lunar dust, resulting in low image signal-to-noise ratio and difficulty in accurately observing changes in erosion pits. X-rays can penetrate lunar dust and are not affected by its obstruction, ensuring the reliability and accuracy of the data.

[0136] The size and depth of the erosion pit can be directly inverted through the attenuation information of X-rays after passing through the lunar soil container. The calculation process is simple and is not easy to introduce additional errors.

[0137] X-ray imaging can provide accurate three-dimensional geometric information for erosion pits. By analyzing X-ray images at different time points, three-dimensional reconstruction of erosion pits can be performed, and their morphology and depth changes can be tracked, providing more accurate dynamic measurement data.

[0138] Compared with traditional imaging methods, X-ray imaging does not require complex optical lenses and image processing systems. Instead, it is based on simple detectors and sensors and can conduct experiments in a vacuum environment, which has higher experimental feasibility. The system has high experimental repeatability and can work stably under different experimental conditions, ensuring the reliability of experimental data and providing solid data support for engineering applications.

Claims

1. An X-ray based dynamic measurement system for erosion pit evolution, characterized in that: include: X-ray generator module: The X-ray generator module is used to generate an X-ray beam and emit X-rays to the lunar soil container; Lunar soil container module: The lunar soil container module is electrically connected to the X-ray generator module. The lunar soil container module is used to simulate the lunar surface environment, place lunar soil, simulate lunar dust, and generate erosion pits under the action of the engine plume; Imager module: The imager module is electrically connected to the lunar soil container module. The imager module is used to receive the image formed by X-rays passing through the lunar soil container and convert the image into an electronic signal. Data processing and analysis module: The data processing and analysis module is electrically connected to the imager module, and is used to process and analyze the image data output by the imager based on the image processing algorithm, and calculate the size, depth and evolution process of the erosion pit; Experiment control and synchronization module: The experiment control and synchronization module is electrically connected to the data processing and analysis module, the imager module, the lunar soil container module and the X-ray generator module. The experiment control and synchronization module is used to control the working state of the system, synchronously start the X-ray scanning, data acquisition and imaging operations, and ensure the timing and accuracy of the experimental data; Data display and reporting module: The data display and reporting module is electrically connected to the experiment control and synchronization module, and is used to display the processed pit evolution data in a visual form and generate an experiment report.

2. The X-ray based dynamic measurement system for etch pit evolution according to claim 1, characterized in that: The X-ray generator module specifically comprises: X-ray tube unit: The electron accelerator accelerates the electron flow through the vacuum tube to hit the anode target. The anode material collides with the electrons and releases X-rays; High-voltage power supply unit: The high-voltage power supply system accelerates the current to the speed required by the electron flow to generate a high-energy electron beam; X-ray beam adjustment unit: By selecting different filters, the energy spectrum of the X-ray beam is adjusted to control the emission direction, intensity and width of the X-rays to ensure that the X-ray beam accurately irradiates the lunar soil container; Cooling unit: Based on water cooling system, heat is removed through circulating water flow; X-ray intensity control and monitoring unit: Based on the X-ray intensity meter, the output intensity of X-rays is monitored in real time. According to the monitored intensity data, the high-voltage power supply is adjusted to maintain a constant output; X-ray source shielding unit: Based on lead shielding to block X-ray radiation, the lead material prevents radiation diffusion by absorbing or scattering X-rays, ensuring a safe laboratory environment.

3. The X-ray-based dynamic measurement system for etch pit evolution according to claim 2, characterized in that: The lunar soil container module specifically includes: Lunar soil and dust simulation unit: simulates the soil and dust on the lunar surface, and reproduces the reaction of lunar dust under the action of engine plume; Lunar soil conditioning unit: By controlling the pressure of the compaction device, the physical properties of the lunar soil are simulated, and its looseness and density are adjusted to simulate the conditions on the real lunar surface; Vacuum chamber and environmental conditioning unit: Use a vacuum pump system to extract the air in the container, maintain a low pressure state, control the temperature in the container, simulate the temperature difference between day and night on the lunar surface, and monitor the temperature and humidity in the container in real time based on temperature and humidity monitoring equipment; Jet simulation unit: Based on the experimental nozzle, the jet airflow is simulated to simulate the effect of the engine plume on the lunar surface. Gas is introduced into the nozzle and the airflow rate is adjusted by controlling the valve to simulate the plume intensity during the lunar landing process and control the jet direction. Erosion pit formation and monitoring unit: Based on X-ray, sensor and laser scanning technology, it monitors and records the dynamic evolution of erosion pits in the lunar soil container in real time; X-ray detector unit: connected to the X-ray generator, scans the erosion pits in the lunar soil container and records their morphological changes; Sensor data collector unit: collects experimental parameters, including air flow velocity, lunar soil temperature, and lunar dust movement.

4. The X-ray-based dynamic measurement system for etch pit evolution according to claim 3, characterized in that: The imager module specifically comprises: X-ray receiving unit: receives X-rays that pass through the lunar soil container and converts X-ray signals into processable electronic signals; Image data acquisition and processing unit: further processes and converts the electronic signals obtained from the detector and conversion unit into digital images, and performs noise removal and grayscale value adjustment; Image display and storage unit: displays the processed digital image and stores the processed image data.

5. The X-ray based dynamic measurement system for etch pit evolution according to claim 4, characterized in that: The image data acquisition and processing unit specifically includes: First, the signal obtained from the detector and the conversion unit is converted into a digital signal through an ADC; The image is smoothed by calculating the average value of the neighborhood pixels to remove noise. The average value of the neighborhood pixels is calculated as follows: Where g(x,y) is the denoised image, M and N are the sizes of the filter windows, f(x+i,y+j) is the pixel value at position (x+i,y+j) in the input image, and i and J are variables used to traverse the neighborhood range in the convolution operation; Based on histogram equalization, the gray value of the image is adjusted to improve the contrast of the image and enhance the details of the image. The calculation formula based on histogram equalization is: Where S(f(x,y)) is the cumulative distribution function of the gray value f(x,y) of the original image, S min and S max are the minimum and maximum values ​​of CDF, respectively, and L is the number of gray levels; After denoising and grayscale value adjustment, the image data will be generated into the final X-ray image.

6. The X-ray based dynamic measurement system for etch pit evolution according to claim 5, characterized in that: The data processing and analysis module specifically includes: Feature extraction unit: extracts the shape and size of the pit area based on the contour detection algorithm, calculates the area of ​​the pit, and calculates the depth of the pit through the grayscale value and color value of the image combined with the imaging depth information of the imager; 3D reconstruction and depth analysis unit: reconstruct the 3D model of the pit based on image data from multiple different angles, calculate the relative depth of the pit based on the depth information of the image, perform surface fitting on the pit surface, calculate the surface normal and curvature parameters, and further infer its morphology and depth changes; Pit evolution analysis unit: Analyzes image data at multiple time points, monitors the changes of pits at different times, calculates the changes of pits, including changes in size, depth and morphology, by comparing images at different time points, predicts the further evolution trend of pits based on regression analysis, and infers the rate of pit expansion and deepening.

7. The X-ray based dynamic measurement system for pit evolution according to claim 6, characterized in that: The feature extraction unit specifically includes: Based on the contour detection algorithm, the pit boundary in the image is extracted, binarization is performed, a binary image is obtained, contours are found, a contour list is returned, and each contour and its related information are extracted. The area and boundary of the contour cannot be seen. The contour detection algorithm formula is: In the formula, C represents a set of points, (x, y) is the x and y is the coordinates of the point, S(x, y) represents the value of a function S at the coordinate point (x, y) is 1, S(x ` ,y ` )=0 means that at the coordinate point (x ` ,y ` ), the value of function S is 0; The geometric features of the pits are calculated through contour information, and the output pit features include area, perimeter, shape description and depth.

8. The X-ray based dynamic measurement system for etch pit evolution according to claim 7, characterized in that: The three-dimensional reconstruction and depth analysis unit specifically includes: Perform 3D reconstruction of images and depth data from different viewpoints, and use image data to extract 3D point cloud data through structured light and stereo vision; The reconstructed 3D point cloud is filtered and denoised to retain key point cloud information. The sparse point cloud is converted into a continuous 3D surface model using a point cloud reconstruction algorithm to obtain a 3D mesh model that approximates the pit surface. Based on the reconstructed 3D point cloud data, the relative depth of the pit in 3D space is calculated, and the surface of the pit is fitted to transform the discrete point cloud into a continuous smooth surface, thereby obtaining the precise geometric shape of the pit. Based on the results of surface fitting, normal and curvature analysis, the morphological characteristics of the erosion pits are inferred, the surface is partitioned and analyzed, different morphological areas are identified, and the morphological differences are further analyzed.

9. The X-ray based dynamic measurement system for etch pit evolution according to claim 8, characterized in that: The etch pit evolution analysis unit specifically includes: Obtain the geometric characteristics of the pits and calculate the changes in pit morphology, size and depth by comparing the pit areas at different time points; Combined with the depth map data, calculate the depth change of the pit at different time points, and evaluate the expansion and contraction of the pit by calculating the change of the area of ​​the pit at different time points, and evaluate the shape change of the pit to see whether there is expansion and deformation; Using known change data, including the area, depth and shape of the pits at different time points, a regression model is constructed. The pit change data at multiple time points is used as input to train the regression model, learn the law of pit change, fit the model, and obtain the prediction function of the pit evolution process. The trained regression model is used to predict the changing trend of the pits in the future, predict the future area, depth and morphological changes of the pits, further infer the rate of expansion and deepening of the pits, and obtain the long-term trend of the pit evolution.

10. The X-ray based dynamic measurement system for etch pit evolution according to claim 9, characterized in that: The regression model specifically include: The regression model expression is: A(t)=β0+β1t+β2t 2 +…+b m t m In the formula, A(t) is the target variable for prediction, t is the time variable, β0 is the intercept term in the model, β1 is the coefficient of the first-order term in the model, β2 is the coefficient of the second-order term, and β m are the coefficients of the higher-order terms in the model, and m is the order of the polynomial.

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