Composite material signal acquisition and damage monitoring system and implementation method thereof

Abstract

The application discloses a composite material signal acquisition and damage monitoring system and an implementation method thereof, comprising a DMC identification and data acquisition configuration module, an automatic operation environment deployment module, a differential pressure threshold setting and signal monitoring module, a damage positioning and imaging module and a result file export and management module. The DMC identification and data acquisition configuration module is used for identifying and connecting a DMC data acquisition card. The automatic operation environment deployment module is used for automatically installing and configuring Python and dependent libraries through a built-in virtual environment, so that a user does not need to manually build an environment. The damage positioning and imaging module is used for automatically converting data and calling a reconstruction algorithm to generate a damage image when the differential pressure is over the limit. The result file export and management module is used for automatically exporting damage image files, voltage data files and the like, and providing a cache arrangement and cleaning tool, so as to facilitate a user to archive, search and release a disk space. The application is aimed at the impact damage monitoring requirement of the composite material, and realizes the integration of signal acquisition, analysis and visualization.

Description

Technical Field

[0001] This invention relates to a composite material signal acquisition and damage monitoring system and its implementation method, belonging to the field of material signal acquisition and damage monitoring technology. Background Technology

[0002] Composite materials are widely used in critical fields such as aerospace, automotive manufacturing, and building materials, and their structural integrity is directly related to product safety and service life. Traditional composite material damage monitoring relies on manual intervention, which suffers from problems such as delayed signal acquisition, inaccurate damage location, and cumbersome data analysis. In addition, it requires additional complex operating environments, resulting in low deployment efficiency and making it difficult to meet the needs of rapid and accurate monitoring in engineering scenarios. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings and deficiencies of the existing technologies by proposing a composite material signal acquisition and damage monitoring system and its implementation method. This system, designed for impact damage monitoring of composite materials, integrates signal acquisition, analysis, and visualization, filling the gap in the application of efficient and accurate monitoring tools. The system features a built-in virtual environment, requiring no additional Python dependencies and can be started with a single click. It supports real-time device acquisition and test data generation, automatically completing damage localization, parameter calculation (center coordinates, area, impact energy, etc.), and result export.

[0004] The technical solution adopted by the present invention to solve its technical problem is: a composite material signal acquisition and damage monitoring system, which includes a DMC identification and data acquisition configuration module, an automatic deployment module for the operating environment, a differential pressure threshold setting and signal monitoring module, a damage location and imaging module, and a result file export and management module.

[0005] The DMC identification and data acquisition configuration module is designed to identify and connect to the DMC data acquisition card, configure acquisition tasks and channel parameters, and acquire 16 voltage signals in real time to provide data input for subsequent monitoring.

[0006] The automatic deployment module for the runtime environment automatically installs and configures Python and its dependent libraries (such as numpy, matplotlib, etc.) through a built-in virtual environment, eliminating the need for users to manually build the environment and allowing them to start the system with a single click.

[0007] The differential pressure threshold setting and signal monitoring module allows users to set differential pressure thresholds, and the system monitors voltage changes in real time to determine whether damage analysis is triggered; it also automatically generates test data when no equipment is available, ensuring the process can run.

[0008] The damage localization and imaging module automatically converts data and calls the reconstruction algorithm when the differential pressure exceeds the limit, generates damage images, and displays parameters such as the coordinates of the impact center, area, and energy, presenting the damage status intuitively.

[0009] The results file export and management module automatically exports damage images, voltage data, and other files, and provides cache organization and cleanup tools to facilitate users in archiving, retrieving, and freeing up disk space.

[0010] The present invention also provides a method for implementing a composite material signal acquisition and damage monitoring system, the method comprising the following steps:

[0011] Step 1: Download and install the required data acquisition card program DMC (Device Configuration Management Tool) and signal acquisition and damage monitoring system file package.

[0012] Step 2: After assembling the USB-3151 acquisition card, A68D terminal block, power supply, and Windows-enabled computer in sequence, access the Device Configuration Management tool (DMC). Create an analog input voltage signal acquisition task in the ArtDAQ task manager, and select the required channel and sampling frequency according to your measurement needs.

[0013] Step 3: After setting the prerequisites, open the folder and double-click "start_eit.bat" to launch the program. The program will automatically detect whether the computer has the required Python environment configured. If the configuration is successful, the message "[OK] Dependencies installed successfully" will appear. If the required environment is already present, the program will start running after checking that the files are complete.

[0014] Step 4: After the program runs, the system prompts "Please enter the differential pressure threshold. Press Enter to use the default value of 5.0V:". Users need to adjust according to different situations. After connecting the sample, test the sample voltage signal in DMC and set the differential pressure threshold according to the sample electrical signal and requirements. After the system detects that the differential pressure exceeds the set threshold, the system will execute the image reconstruction algorithm. No manual intervention from the user is required. After the analysis is completed, the damage imaging interface will automatically pop up, displaying the electrode layout, voltage distribution, impact center location, and key parameters.

[0015] The core imaging engine of this invention is based on an improved Tikhonov regularization algorithm and incorporates an original adaptive attenuation correction model based on impact energy prediction. This algorithm uses 16 voltage signals acquired by sensors to invert the energy distribution of the composite material after impact, achieving accurate quantitative identification of the damaged area.

[0016] The image reconstruction algorithm of this invention is implemented as follows:

[0017] (1) Correction of anisotropic sensitivity matrix

[0018] When calculating the sensitivity matrix S, considering the signal transmission differences caused by the fiber orientation of the composite material, the code constructs an anisotropic space mapping model by introducing an orientation factor η:

[0019] For the first Each electrode pair at position The sensitivity value at point k, where k is the electrode pair index. Here are the coordinates of the measurement center of the k-th electrode pair, in mm. This represents the actual distance between the electrodes, in mm. As a random orientation factor, it follows , which are normally distributed random numbers with a mean of 1 and a standard deviation of 0.2, used to simulate fiber randomness. To detect noise weights, follow the rules. , which is a uniformly distributed random number ranging from [0.8, 1.0], used to introduce small perturbations. The value 1.2 is the anisotropic fundamental amplification factor, used to adjust the distance attenuation scale. exp is an exponential function with the natural constant e as its base.

[0020] (2) Adaptive attenuation correction based on energy feedback

[0021] To bring the area error of the reconstructed image to within 5%, the algorithm calculates the predicted damage area. By dynamically adjusting the image field intensity, the following adaptive correction formula is derived: (x, y) represents the intensity value of the corrected image at pixel (x, y). The image is the initial reconstructed image obtained through SVD decomposition and Tikhonov filtering. This represents the physical distance from the pixel to the impact centroid, in mm. The decay rate is obtained through multi-stage optimization search. The distance index is used to predict the damage area. Adaptive value selection: If ,but ;otherwise .

[0022] The original voltage V of the 16 channels is converted into energy value by combining it with the PVDF sensitivity (192.2mV / J): The unit is J. After normalization, the measurement vector g is obtained: Singular value decomposition (SVD) is performed on the sensitivity matrix S, and adaptive regularization parameters are used. , These are the singular values ​​after S decomposition. Tikhonov filtering is performed to initially reconstruct the damage field, and then the weighted centroid method is used to determine the impact center. The calculation formula is: Among them, weight The grayscale value of the pixel that exceeds the threshold.

[0023] Using experimental formulas The constants 26.168 and -116.621 are coefficients obtained by linear regression fitting from a large amount of composite material impact test data, with the area unit being mm². The system predicts the target area. The system enters a three-stage process of "coarse search - fine search - iterative optimization," continuously adjusting the attenuation rate. Until the error between the reconstructed area and the predicted area at the 0.4 threshold is ≤5%.

[0024] Finally, the major axis, area, and center coordinates of the damaged area are automatically calculated, and a damage distribution map is generated.

[0025] Step 5: Run "Cache Sorting.py", the system will organize all generated files into folders named according to timestamps; run "Cache Cleanup.py", the system will automatically delete outdated cache folders and free up disk space.

[0026] Beneficial effects:

[0027] 1. This invention can automatically capture voltage signals and locate damage without manual intervention, thereby improving monitoring efficiency and data accuracy.

[0028] 2. This invention eliminates the need to install Python dependencies, is compatible with various Windows systems, and can be deployed with a single click, lowering the barrier to entry.

[0029] 3. This invention supports real-time device acquisition and test data generation.

[0030] 4. This invention integrates regularized data processing algorithms and visualization technology to achieve accurate calculation and intuitive presentation of damage parameters. Attached Figure Description

[0031] Figure 1 This is a system architecture diagram of the present invention.

[0032] Figure 2 This is an example image of damage localization in the system of the present invention.

[0033] Figure 3 This is a schematic diagram of the result file of the system of the present invention.

[0034] Figure 4 This is a flowchart of the method of the present invention. Detailed Implementation

[0035] The invention will now be described in further detail with reference to the accompanying drawings.

[0036] like Figure 1 As shown, the present invention provides a composite material signal acquisition and damage monitoring system, which includes a DMC identification and data acquisition configuration module, an automatic deployment module for the operating environment, a differential pressure threshold setting and signal monitoring module, a damage localization and imaging module, and a result file export and management module.

[0037] The DMC identification and data acquisition configuration module requires the installation of the official DMC device configuration management tool before the system is first activated. Tasks are created according to requirements, and the necessary channels and sampling frequencies are selected. This invention's system seamlessly integrates with the official DMC software to complete hardware device identification, physical connection verification, and acquisition parameter configuration, enabling real-time monitoring of material voltage signals. After the acquisition task configuration is complete, the system automatically establishes a data transmission link with the DMC software, and the DMC interface displays the voltage data for each channel. The system synchronously reads the 16 voltage signals acquired by the DMC, and upon the occurrence of impact damage, the data acquisition status is reported on the command prompt main interface, achieving real-time monitoring of voltage signals.

[0038] The automatic deployment module for the runtime environment is based on the built-in virtual environment (venv folder). It automatically deploys the Python runtime environment and its dependencies without requiring users to install Python or related components separately. First, it ensures all system files are in the same directory. Double-clicking the "start_eit.bat" batch file starts the system and environment deployment process. This invention automatically detects the virtual environment status, eliminating the need for manual configuration of the Python path or installation of dependencies. Finally, wait for the deployment to complete until the command prompt window displays "[OK] Dependencies installed successfully" and "Starting EIT Workflow...". This indicates that the system has successfully activated the virtual environment, automatically calling the built-in Python and its dependencies (including numpy, scipy, matplotlib, pandas, etc.), adapting to various Windows operating systems, and ensuring stable program operation.

[0039] The differential pressure threshold setting and signal monitoring module allows users to test the sample voltage signal in the DMC and adjust the differential pressure threshold according to the sample electrical signal and requirements. After setting the differential pressure threshold, the system acquires 16 voltage signals through the configured acquisition tasks, synchronously monitors differential pressure changes, and determines whether damage analysis is triggered. When no device is connected, it automatically generates test data to ensure the process progresses.

[0040] After environment deployment is complete, the system prompts "Please enter the differential pressure threshold, press Enter to use the default value 5.0V:". The user enters the target threshold (e.g., 0.8V) and presses Enter to confirm. After the system displays "Set differential pressure threshold: 0.8V" and completes the Artdaq library import, the "Integrated Data Processing Program" automatically starts. The system will prioritize real-time device data acquisition, and the DMC tool interface will synchronously display the voltage data for each channel. If no device connection is detected, the system automatically switches to test data generation mode and continues with subsequent processes. With successful threshold setting, the system achieves real-time voltage signal acquisition or test data generation, with zero delay in differential pressure monitoring, ensuring no abnormal signals are missed, and allowing verification of functional integrity even without devices.

[0041] The damage localization and imaging module automatically triggers "Data Conversion.py" when the system detects a differential pressure exceeding a set threshold, converting the collected data into an EIT reconstruction format (supporting CSV and Excel). Then, it calls "EIT_FINAL_auto.py" to execute the image reconstruction algorithm. After analysis, no manual user intervention is required, and the damage imaging interface automatically pops up, displaying the electrode layout, voltage distribution, impact center location, and key parameters. For example... Figure 2 As shown, the generated imaging results include:

[0042] Left side image: Electrode positions (marked with different colors) and corresponding voltage values ​​(represented by color gradient);

[0043] The right-hand figure shows parameters such as the coordinates of the impact center (e.g., (45.0, 36.0) mm), the damage area (e.g., 26.3 mm²), the impact energy (e.g., 5.4500 J), and the maximum voltage (e.g., 4.9000 V). The normalized intensity distribution intuitively reflects the damage area. The data is accurate and the visualization effect is clear, and it can be directly used for the assessment of the damage status of laminates.

[0044] The results file export and management module allows the system to automatically export damage analysis-related files and provides cache organization and cleanup tools to facilitate data archiving, free up disk space, and improve file management efficiency.

[0045] After damage imaging is completed, the system automatically exports three types of files to the working directory: damage visualization image (name format: EIT_result_workflow_damage_time.png); differential pressure exceedance data table (name format: differential pressure exceedance data_damage_time_xth acquisition.xlsx); voltage signal data table (name format: workflow_output_damage_time_16ch.xlsx). Running "Cache Cleanup.py" will organize all generated files into folders named according to timestamps; running "Cache Cleanup.py" will automatically delete outdated cache folders and free up disk space.

[0046] The exported files are complete and have standardized naming conventions, making it easy for users to search by time or type. After cache cleanup, the file structure is clear, and the cleanup tool can quickly free up storage space, adapting to the file management needs of long-term testing scenarios.

[0047] like Figure 3 As shown, the present invention provides a method for implementing a composite material signal acquisition and damage monitoring system, the method comprising the following steps:

[0048] Step 1: Download and install the required data acquisition card program DMC (Device Configuration Management Tool) and signal acquisition and damage monitoring system file package.

[0049] Step 2: After assembling the USB-3151 acquisition card, A68D terminal block, power supply, and Windows-enabled computer in sequence, access the Device Configuration Management tool (DMC). Create an analog input voltage signal acquisition task in the ArtDAQ task manager, and select the required channel and sampling frequency according to your measurement needs.

[0050] Step 3: After setting the prerequisites, open the folder and double-click "start_eit.bat" to launch the program. The program will automatically detect whether the computer has the required Python environment configured. If the configuration is successful, the message "[OK] Dependencies installed successfully" will appear. If the required environment is already present, the program will start running after checking that the files are complete.

[0051] Step 4: After the program runs, the system prompts "Please enter the differential pressure threshold. Press Enter to use the default value of 5.0V:". Users need to adjust according to different situations. After connecting the sample, test the sample voltage signal in the DMC and set the differential pressure threshold according to the sample electrical signal and requirements. After the system detects that the differential pressure exceeds the set threshold, the system will execute the image reconstruction algorithm. After the analysis is completed, the damage imaging interface will pop up, showing the electrode layout, voltage distribution, impact center location, and key parameters.

[0052] The core imaging engine of this invention is based on an improved Tikhonov regularization algorithm and incorporates an original adaptive attenuation correction model based on impact energy prediction. This algorithm uses 16 voltage signals acquired by sensors to invert the energy distribution of the composite material after impact, achieving accurate quantitative identification of the damaged area.

[0053] The image reconstruction algorithm of this invention is implemented as follows:

[0054] (1) Correction of anisotropic sensitivity matrix

[0055] When calculating the sensitivity matrix S, considering the signal transmission differences caused by the fiber orientation of the composite material, the code constructs an anisotropic space mapping model by introducing an orientation factor η:

[0056] For the first Each electrode pair at position The sensitivity value at point k, where k is the electrode pair index. Here are the coordinates of the measurement center of the k-th electrode pair, in mm. This represents the actual distance between the electrodes, in mm. As a random orientation factor, it follows , which are normally distributed random numbers with a mean of 1 and a standard deviation of 0.2, used to simulate fiber randomness. To detect noise weights, follow the rules. , which is a uniformly distributed random number ranging from [0.8, 1.0], used to introduce small perturbations. The value 1.2 is the anisotropic fundamental amplification factor, used to adjust the distance attenuation scale. exp is an exponential function with the natural constant e as its base.

[0057] (2) Adaptive attenuation correction based on energy feedback

[0058] To bring the area error of the reconstructed image to within 5%, the algorithm calculates the predicted damage area. By dynamically adjusting the image field intensity, the following adaptive correction formula is derived: (x, y) represents the intensity value of the corrected image at pixel (x, y). The image is the initial reconstructed image obtained through SVD decomposition and Tikhonov filtering. This represents the physical distance from the pixel to the impact centroid, in mm. The decay rate is obtained through multi-stage optimization search. The distance index is used to predict the damage area. Adaptive value selection: If ,but ;otherwise .

[0059] This invention converts the 16 original voltages V into energy values ​​based on the PVDF sensitivity (192.2mV / J): The unit is J. After normalization, the measurement vector g is obtained: Singular value decomposition (SVD) is performed on the sensitivity matrix S, and adaptive regularization parameters are used. , These are the singular values ​​after S decomposition. Tikhonov filtering is performed to initially reconstruct the damage field, and then the weighted centroid method is used to determine the impact center. The calculation formula is: Among them, weight The grayscale value of the pixel that exceeds the threshold.

[0060] Using experimental formulas The constants 26.168 and -116.621 are coefficients obtained by linear regression fitting from a large amount of composite material impact test data, with the area unit being mm². The system predicts the target area. The system enters a three-stage process of "coarse search - fine search - iterative optimization," continuously adjusting the attenuation rate. Until the error between the reconstructed area and the predicted area at the 0.4 threshold is ≤5%.

[0061] Finally, the major axis, area, and center coordinates of the damaged area are automatically calculated, and a damage distribution map is generated.

[0062] Step 5: Run "Cache Sorting.py", the system will organize all generated files into folders named according to timestamps; run "Cache Cleanup.py", the system will automatically delete outdated cache folders and free up disk space.

[0063] The regularized data processing algorithm integrated in this invention specifically includes:

[0064] First, the inherent problems of electrical tomography (EIT) are addressed based on the Tikhonov regularization framework: singular value decomposition (SVD) is used to process the anisotropic sensitivity matrix S, and an adaptive regularization parameter is introduced. Sixteen normalized voltage signals are filtered to initially reconstruct an image reflecting the energy distribution on a preset grid. Subsequently, the algorithm achieves precise correction through cross-domain data fusion. The system then establishes a mapping relationship between induced voltage and damage area (prediction model) based on experiments. The predicted damage area is used as the feedback target value, and an original adaptive attenuation formula is introduced. In this integration process, the attenuation rate Based on the deviation between the initial reconstructed area and the predicted area, the algorithm automatically optimizes through three stages: coarse search, fine search, and iterative optimization, ensuring that the error between the reconstructed area and the predicted value converges to within 5% under a fixed threshold of 0.4. Finally, the algorithm converts the calculated weighted centroid coordinates, damage area, impact energy, and other quantitative parameters into 2D cloud maps and response distribution surfaces in real time using visualization technology.

[0065] It should be noted that the above content merely illustrates the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. For those skilled in the art, various improvements and modifications can be made without departing from the principle of the present invention, and all such improvements and modifications fall within the scope of protection of the claims of the present invention.

Claims

1. A composite signal acquisition and damage monitoring system, comprising: The system includes a DMC identification and data acquisition configuration module, an automatic deployment module for the operating environment, a differential pressure threshold setting and signal monitoring module, a damage localization and imaging module, and a result file export and management module. The function of the DMC identification and data acquisition configuration module is to identify and connect the DMC data acquisition card, configure the acquisition task and channel parameters, and acquire 16 voltage signals in real time to provide data input for subsequent monitoring. The automatic deployment module for the runtime environment automatically installs and configures Python and its dependent libraries, namely numpy and matplotlib, through a built-in virtual environment. Users do not need to manually set up the environment and can start the system with one click. The differential pressure threshold setting and signal monitoring module allows users to set differential pressure thresholds, and the system monitors voltage changes in real time to determine whether damage analysis is triggered; it also automatically generates test data when there is no equipment, ensuring that the process can run. The damage localization and imaging module is designed to automatically convert data and call reconstruction algorithms when the differential pressure exceeds the limit, generate damage images, and display parameters such as the coordinates of the impact center, area, and energy, thus presenting the damage status intuitively. The results file export and management module automatically exports damage images, voltage data, and other files, and provides cache organization and cleanup tools to facilitate users in archiving, retrieving, and freeing up disk space.

2. The composite signal acquisition and damage monitoring system of claim 1, wherein, The DMC identification and data acquisition configuration module requires the installation of the official DMC device configuration management tool before the system is first activated. Tasks are created according to requirements, and the necessary channels and sampling frequencies are selected. The system seamlessly integrates with the official DMC software to complete hardware device identification, physical connection verification, and acquisition parameter configuration. It performs real-time monitoring of material voltage signals. After the acquisition task configuration is complete, the system automatically establishes a data transmission link with the DMC software, and the DMC interface displays the voltage data for each channel. The system synchronously reads the 16 voltage signals acquired by the DMC. Upon the occurrence of impact damage, the data acquisition status is reported on the command prompt main interface, achieving real-time monitoring of voltage signals.

3. The composite signal acquisition and damage monitoring system of claim 1, wherein, The automatic deployment module for the runtime environment is based on the built-in virtual environment (venv folder). It automatically deploys the Python runtime environment and its dependent libraries without requiring users to install Python or related components. First, it ensures that all system files are in the same directory. Double-clicking the "start_eit.bat" batch file starts the system and environment deployment process. The system automatically detects the virtual environment status, eliminating the need for manual configuration of the Python path or installation of dependent libraries. Finally, wait for the deployment to complete until the command prompt window displays "[OK] Dependencies installed successfully" and "Starting EITWorkflow...". This indicates that the system has successfully activated the virtual environment and automatically calls the built-in Python and its dependent libraries (including numpy, scipy, matplotlib, pandas, etc.), adapting to various Windows operating systems and ensuring stable program operation.

4. The composite signal acquisition and damage monitoring system of claim 1, wherein, The differential pressure threshold setting and signal monitoring module allows users to test the sample voltage signal in the DMC, adjust the differential pressure threshold according to the sample electrical signal and requirements, and after setting the differential pressure threshold, the system acquires 16 voltage signals through the configured acquisition task, synchronously monitors the differential pressure change, and determines whether damage analysis is triggered; when no device is connected, it automatically generates test data to ensure the process progresses.

5. The composite signal acquisition and damage monitoring system of claim 4, wherein, After the environment deployment is complete, the system prompts "Please enter the differential pressure threshold, press Enter to use the default value 5.0V:". The user enters the target threshold (e.g., 0.8V) and presses Enter to confirm. After the system displays "Set differential pressure threshold: 0.8V" and completes the Artdaq library import, the "Integrated Data Processing Program" is automatically started. The system will prioritize real-time device data acquisition, and the DMC tool interface will synchronously display the voltage data of each channel. If no device connection is detected, the system will automatically switch to test data generation mode and continue to execute the subsequent process. Once the threshold is successfully set, the system realizes real-time acquisition of voltage signals or generation of test data. Differential pressure monitoring has no delay, ensuring that abnormal signals are not missed. The completeness of the function can also be verified even when there is no device.

6. The composite signal acquisition and damage monitoring system of claim 1, wherein, The damage localization and imaging module functions by automatically triggering "data conversion.py" after the system detects that the differential pressure exceeds a set threshold, converting the collected data into an EIT reconstruction format (supporting CSV and Excel); then calling "EIT_FINAL_auto.py" to execute the image reconstruction algorithm. After the analysis is completed without user intervention, the damage imaging interface automatically pops up, displaying the electrode layout, voltage distribution, impact center location, and key parameters.

7. The composite signal acquisition and damage monitoring system of claim 1, wherein, The result file export and management module automatically exports damage analysis-related files and provides cache organization and cleanup tools. After damage imaging is completed, the system automatically exports three types of files to the working directory: damage visualization image (name format: EIT_result_workflow_damage_time.png); differential pressure exceedance data table (name format: differential pressure exceedance data_damage_time_xth acquisition.xlsx); and voltage signal data table (name format: workflow_output_damage_time_16ch.xlsx). Running "Cache Organization.py" organizes all generated files into folders named according to timestamps; running "Cache Cleanup.py" automatically deletes outdated cache folders, freeing up disk space.

8. A method of implementing a composite signal acquisition and damage monitoring system, comprising: The method includes the following steps: Step 1: Download and install the required data acquisition card program DMC, namely the device configuration management tool and the signal acquisition and damage monitoring system file package; Step 2: After assembling the USB-3151 acquisition card, A68D terminal board, power supply and computer with Windows system in sequence, enter the device configuration management tool DMC, create an analog input voltage acquisition signal task in ArtDAQ task, and select the required channel and sampling frequency according to the measurement requirements. Step 3: After setting the prerequisites, open the folder and double-click "start_eit.bat" to enter the program. The program will automatically detect whether the computer has configured the required Python environment. If the configuration is successful, the message "[OK] Dependencies installed successfully" will appear. If the required environment is already in place, the program will start running after checking that the files are complete. Step 4: After the program runs, the system prompts "Please enter the differential pressure threshold, press Enter to use the default value 5.0V:" Users need to adjust according to different situations. After connecting the sample, test the sample voltage signal in DMC. Set the differential pressure threshold according to the sample electrical signal and requirements. After the system detects that the differential pressure exceeds the set threshold, the system will execute the image reconstruction algorithm. After the analysis is completed, the damage imaging interface will pop up, showing the electrode layout, voltage distribution, impact center position and key parameters. Step 5: Run "Cache Sorting.py", the system will organize all generated files into folders named according to timestamps; run "Cache Cleanup.py", the system will automatically delete outdated cache folders and free up disk space.

9. The method for implementing a composite material signal acquisition and damage monitoring system according to claim 8, characterized in that, Step 4 includes: (1) Correction of anisotropic sensitivity matrix; When calculating the sensitivity matrix S, considering the signal transmission differences caused by the fiber orientation of the composite material, the code constructs an anisotropic space mapping model by introducing an orientation factor η: For the first Each electrode pair at position The sensitivity value at point k, where k is the electrode pair index. Here are the coordinates of the measurement center of the k-th electrode pair, in mm. The actual distance between the electrodes, in mm. As a random orientation factor, it follows These are normally distributed random numbers with a mean of 1 and a standard deviation of 0.2, used to simulate the randomness of fibers. To detect noise weights, follow the rules. , which is a uniformly distributed random number in the range [0.8, 1.0], used to introduce small perturbations; the value of 1.2 is the anisotropic fundamental amplification factor used to adjust the distance attenuation scale; exp is an exponential function with the natural constant e as the base. (2) Adaptive attenuation correction based on energy feedback; To bring the area error of the reconstructed image to within 5%, the algorithm calculates the predicted damage area. By dynamically adjusting the image field intensity, the following adaptive correction formula is derived: (x, y) represents the intensity value of the corrected image at pixel (x, y). The image is the initial reconstructed image obtained through SVD decomposition and Tikhonov filtering. The distance from the pixel to the impact centroid, in mm. The decay rate is obtained through a multi-stage optimization search. The distance index is used to predict the damage area. Adaptive value selection: If ,but ;otherwise .

10. The method for implementing a composite material signal acquisition and damage monitoring system according to claim 9, characterized in that, The method converts the 16 original voltages V into energy values ​​based on the PVDF sensitivity (192.2mV / J): The unit is J, and after normalization, the measurement vector g is obtained: Singular value decomposition (SVD) is performed on the sensitivity matrix S, and adaptive regularization parameters are used. , The singular values ​​after S decomposition are used to perform Tikhonov filtering to initially reconstruct the damage field, and then the weighted centroid method is used to determine the impact center. The calculation formula is: where the weight is the pixel gray value that exceeds the threshold value; Using experimental formulas The constants 26.168 and -116.621 are coefficients obtained by linear regression fitting from a large amount of composite material impact test data. The area unit is mm². The system predicts the target area and enters three stages: "coarse search - fine search - iterative optimization", continuously adjusting the attenuation rate. Until the error between the reconstructed area and the predicted area at the 0.4 threshold is ≤5%; Finally, the major axis, area, and center coordinates of the damaged area are automatically calculated, and a damage distribution map is generated.

Images