Fabric liquid amount detection and analysis system based on X-ray absorption spectrum

Abstract

The invention relates to the field of textile and printing and dyeing production, and discloses a fabric liquid amount detection and analysis system based on an X-ray absorption spectrum, which comprises an image and data acquisition module, a data storage and analysis module, a ray source and execution module and a client, the image and data acquisition module, the data storage and analysis module, the radiation source and execution module and the client are in communication connection through a network; the image and data acquisition module acquires parameters such as material type, thickness, density, fiber arrangement direction, spectral reflectivity, X-ray attenuation coefficient and the like of the fabric through equipment including but not limited to an industrial camera, a multispectral imaging sensor, a light sensor, an X-ray receiver, a temperature and humidity sensor, a thickness measuring instrument and the like. According to the invention, through the wavelength-adjustable X-ray source and the high-sensitivity detector, by using the X-ray absorption spectrum characteristic, the liquid amount change in the fabric is captured under the nanoscale resolution, and the maximum extraction of the liquid characteristic absorption signal is realized.

Description

Technical Field

[0001] The present invention relates to the fields of textile and printing production, and particularly to a fabric liquid content detection and analysis system based on X-ray absorption spectroscopy. Background Art

[0002] With the development of textile and printing technologies, the control of the liquid content of fabrics is particularly important in processes such as printing and post-finishing, and is one of the important links for achieving refined production and product quality control. Traditional fabric liquid content detection methods, such as mechanical compression method and infrared method, have the following technical defects:

[0003] The weighing method relies on the weight difference before and after the fabric absorbs liquid, and is easily affected by external vibrations, changes in environmental humidity, and uneven density distribution of the fabric itself, resulting in large fluctuations in the detection results; the conductivity method is based on the detection of the electrical conductivity of the liquid, but for fabrics with a low liquid content, the signal response is not significant, and when the fabric contains non-conductive liquids, the detection fails; although the infrared detection method can detect through the characteristic absorption band of the liquid, its detection depth is limited, it is difficult to penetrate a thick fabric layer, and it is sensitive to surface contaminants; different fabric materials have large differences in the liquid absorption capacity and adsorption behavior, and traditional methods cannot dynamically adjust the detection parameters and are difficult to be universal among various materials. Summary of the Invention

[0004] To make up for the above deficiencies, the present invention provides a fabric liquid content detection and analysis system based on X-ray absorption spectroscopy, aiming to improve the problem of "low efficiency of traditional methods" mentioned in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solution: A fabric liquid content detection and analysis system based on X-ray absorption spectroscopy, comprising:

[0006] An image and data acquisition module, a data storage and analysis module, a radiation source and execution module, and a client. The image and data acquisition module, the data storage and analysis module, the radiation source and execution module, and the client are connected through network communication. The image and data acquisition module obtains parameters such as the material type, thickness, density, fiber arrangement direction, spectral reflectance, and X-ray attenuation coefficient of the fabric through devices including but not limited to industrial cameras, multispectral imaging sensors, light sensors, X-ray receivers, temperature and humidity sensors, and thickness gauges, and transmits them to the data storage and analysis module in real time. The data storage and analysis module inputs the collected data into a pre-trained deep learning model, matches the best wavelength and power range according to the fabric characteristics and radiation absorption spectrum stored in the database, and generates a radiation control instruction. The radiation source and execution module adjusts the radiation wavelength according to the control instruction through the position and attitude of the filter, and dynamically adjusts the radiation power according to the fabric thickness, optimizes the signal quality, receives the signal through a high-sensitivity detector, extracts the key features of the absorption spectrum to calculate the liquid content of the fabric, establishes a fabric parameter database through a big data platform, and dynamically matches the fabric characteristics based on the fabric parameters and image information collected by high-precision sensors, and adjusts the radiation wavelength and power to achieve high-precision liquid content detection.

[0007] As a further description of the above technical solution:

[0008] The image and data acquisition module includes an industrial camera, a multispectral imaging sensor, a light sensor, an X-ray receiver, a temperature and humidity sensor, a thickness gauge, and a servo position sensor, and is used to collect in real time including but not limited to the surface texture, spectral reflectance, density, thickness, and environmental parameters of the fabric, and store the data in the database after denoising and standardizing the data through a signal processing module.

[0009] As a further description of the above technical solution:

[0010] The data storage and analysis module uses a deep learning model and a dynamic database management system, combines the material type, absorption spectrum characteristics, and X-ray attenuation coefficient of the fabric, matches the parameter set closest to the current fabric characteristics from the database, and generates a control instruction, including the target radiation wavelength range and emission power value.

[0011] As a further description of the above technical solution:

[0012] The ray source and execution module include but are not limited to synchrotron radiation light sources, filter plates, servo drive devices, optical focusing units, and high-sensitivity detectors. The filter plate adjusts its position through the servo drive device to select the target wavelength. The optical focusing unit adjusts the emission angle of the ray beam to accurately align with the target detection area. The detector receives the penetration signal and converts it into high-quality absorption spectrum data. The fabric parameters include but are not limited to material type, thickness, density, fiber arrangement direction, porosity, surface roughness, weaving structure, fiber diameter, liquid absorption rate, liquid retention capacity, spectral reflectance, spectral absorption characteristics, and X-ray attenuation coefficient.

[0013] As a further description of the above technical solution:

[0014] The ray wavelength regulation method monitors the output wavelength of the filter through a real-time wavelength monitor to form a closed-loop feedback mechanism, ensuring that the output wavelength strictly covers the absorption spectrum range recommended by the database, dynamically adjusting the ray power to adapt to different fabric thicknesses, and improving the detection accuracy and signal stability. The liquid volume detection method is to extract key feature points in the absorption spectrum through Fourier transform, compare and analyze them with the standard spectrum in the database, and accurately calculate the fabric liquid volume in combination with the ray absorption peak position and attenuation amplitude, and optimize the ray parameters through the feedback mechanism.

[0015] As a further description of the above technical solution:

[0016] The industrial camera captures the fiber arrangement and texture details of the fabric, inputs the obtained features into a pre-trained convolutional neural network, and through model inference, the system can quickly classify the fabric type, and the classification result is transmitted to the main control unit. The main control unit extracts the absorption characteristic parameters of this material from the database according to the fabric type.

[0017] As a further description of the above technical solution:

[0018] The data storage and analysis module enables the X-ray source to start the wavelength generation and regulation process according to the target wavelength range provided by the database. The synchrotron radiation light source first generates ray incident wavelength filter modules covering a wide spectrum. This module consists of multiple groups of high-precision filter plates and servo drive devices. By calculating and controlling the servo driver, the position and attitude of the filter plate are adjusted to ensure that the filter only allows the target wavelength range to pass through. The real-time wavelength monitor monitors the output wavelength of the filter and feeds back the actual wavelength to the main control unit to form a closed-loop regulation, ensuring that the output wavelength strictly covers the absorption spectrum range recommended by the database. The filtered ray is concentrated on the fabric detection area through the optical focusing unit, and at the same time, the ray emission angle is adjusted according to the position information provided by machine vision to ensure that the ray beam is accurately aligned with the target detection area.

[0019] As a further description of the above technical solution:

[0020] To adapt to the thickness and density of different fabrics, the ray source and the execution module will dynamically optimize the emission power of the X-ray. The main control unit combines the fabric thickness data estimated by the machine vision module, calculates the optimal emission power, and adjusts the input voltage of the ray source through a high-precision voltage controller. For thicker fabrics, the system will increase the ray power to ensure ray penetration, and for thinner fabrics, the power will be reduced to avoid signal oversaturation. The real-time power adjustment is combined with wavelength selection, enabling the system to maximize the absorption signal of the liquid while ensuring the stability and accuracy of the detection process. After the X-ray optimized in wavelength and power penetrates the fabric, absorption and attenuation occur in the liquid. The high-sensitivity detector receives the penetrated signal and converts it into an electrical signal. The signal processing module eliminates noise through dynamic filtering and amplification to obtain a high-quality absorption spectrum curve, and then uses Fourier transform to extract key feature points such as absorption peak positions and attenuation amplitudes in the spectrum, and compares and analyzes them with the standard absorption spectra stored in the database to accurately calculate the liquid volume in the fabric.

[0021] As a further description of the above technical solution:

[0022] The system also includes an intelligent feedback and self-calibration mechanism module. The intelligent feedback and self-calibration mechanism module is used to ensure the reliability of the detection results. When the real-time detection results deviate from the standard range, the system triggers a feedback mechanism to adjust the position of the filter or the ray power parameters, re-detect and correct the output. The environmental sensor monitors parameters including but not limited to temperature, humidity, and background radiation, and combines a compensation algorithm to optimize the detection accuracy. After each detection, the system will store the detection data in the database for dynamically optimizing the deep learning model to improve the system's adaptability to new fabrics or special liquids.

[0023] The present invention has the following beneficial effects:

[0024] 1. In the present invention, with a wavelength-tunable X-ray source and a high-sensitivity detector, by using the X-ray absorption spectrum characteristics, the change in the liquid volume in the fabric is captured at the nanoscale resolution, achieving the maximum extraction of the characteristic absorption signal of the liquid, and significantly improving the accuracy and application range of industrial detection.

[0025] 2. In the present invention, through the built-in temperature and humidity sensors and the background radiation monitoring module, the detection errors caused by high humidity or background light changes can be dynamically compensated. The system maintains stable performance in a variety of industrial environments and is suitable for complex application scenarios with high humidity and high dust such as textile and printing.

[0026] 3. In the present invention, methods such as real-time wavelength regulation, dynamic power optimization, signal filtering, and Fourier transform spectral analysis are adopted to improve the stability and reliability of signal acquisition; a dynamic filtering algorithm is used to perform real-time noise reduction processing on the acquired data to ensure the consistency and accuracy of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is the flowchart of dynamic wavelength adjustment in the present invention;

[0028] Figure 2 It is the schematic diagram of the system architecture in the present invention;

[0029] Figure 3 It is the working principle diagram of database update in the present invention;

[0030] Figure 4 It is the detailed schematic diagram of the system framework in the present invention.

[0031] Legend Explanation:

[0032] 1. Image and data acquisition module; 2. Data storage and analysis module; 3. Radiation source and execution module; 4. Intelligent feedback and self-calibration mechanism module; 5. System integration and optimization module; 6. User interface and interaction module; 7. Security and privacy protection module; 8. Remote monitoring and maintenance module; 9. System expansion and compatibility module. SPECIFIC EMBODIMENTS

[0033] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0034] Refer to Figures 1-4

[0035] Embodiment 1

[0036] This embodiment provides a fabric liquid volume detection and analysis system based on X-ray absorption spectroscopy, including: an image and data acquisition module 1, a data storage and analysis module 2, a radiation source and execution module 3, and a client. The image and data acquisition module 1, the data storage and analysis module 2, the radiation source and execution module 3, and the client are connected through network communication. The image and data acquisition module 1 obtains parameters such as the material type, thickness, density, fiber arrangement direction, spectral reflectance, and X-ray attenuation coefficient of the fabric through devices including but not limited to industrial cameras, multispectral imaging sensors, light sensors, X-ray receivers, temperature and humidity sensors, thickness gauges, etc., and transmits them to the data storage and analysis module in real time. Specifically, the image and data acquisition module 1 includes an industrial camera, a multispectral imaging sensor, a light sensor, an X-ray receiver, a temperature and humidity sensor, a thickness gauge, and a servo position sensor, which are used to collect the surface texture, spectral reflectance, density, thickness, and environmental parameters of the fabric in real time. After denoising and normalizing the data through a signal processing module, the data is stored in a database. The industrial camera captures the fiber arrangement and texture details of the fabric, and inputs the obtained features into a pre-trained convolutional neural network. Through model inference, the system can quickly classify the fabric type, and the classification result is transmitted to the main control unit. The main control unit extracts the absorption characteristic parameters of this material from the database according to the fabric type.

[0037] The data storage and analysis module 2 inputs the collected data into a pre-trained deep learning model, matches the best wavelength and power range according to the fabric characteristics and ray absorption spectrum stored in the database, and generates a ray regulation instruction. Specifically, the data storage and analysis module 2 uses a deep learning model and a dynamic database management system, combines the material type, absorption spectrum characteristics, and ray attenuation coefficient of the fabric, matches the parameter set closest to the current fabric characteristics from the database, and generates a regulation instruction, including the target ray wavelength range and emission power value. The data storage and analysis module 2 enables the X-ray source to start the wavelength generation and regulation process according to the target wavelength range provided by the database. The synchrotron radiation source first generates a ray incident wavelength filter module covering a wide spectrum. This module consists of multiple groups of high-precision filter plates and servo drive devices. By calculating and controlling the servo driver to adjust the position and attitude of the filter plate, it is ensured that the filter only allows the target wavelength range to pass. The real-time wavelength monitor monitors the output wavelength of the filter, and feeds the actual wavelength back to the main control unit to form a closed-loop regulation, ensuring that the output wavelength strictly covers the absorption spectrum range recommended by the database. The filtered ray is concentrated on the fabric detection area through an optical focusing unit, and at the same time, the ray emission angle is adjusted according to the position information provided by machine vision to ensure that the ray beam is accurately aligned with the target detection area.

[0038] The ray source and the execution module 3 adjust the ray wavelength according to the regulation instruction through the position and attitude of the filter, and dynamically adjust the ray power according to the fabric thickness at the same time, optimize the signal quality, and receive the signal through a high-sensitivity detector to extract the key features of the absorption spectrum to calculate the liquid volume of the fabric. Specifically, the ray source and the execution module 3 include a synchrotron radiation light source, a filter, a servo drive device, an optical focusing unit and a high-sensitivity detector; the filter adjusts the position through the servo drive device to select the target wavelength, and the optical focusing unit adjusts the emission angle of the ray beam to accurately align with the target detection area. The detector receives the penetration signal and converts it into high-quality absorption spectrum data. The fabric parameters include material type, thickness, density, fiber arrangement direction, porosity, surface roughness, weaving structure, fiber diameter, liquid absorption rate, liquid retention capacity, spectral reflectivity, spectral absorption characteristics, and X-ray attenuation coefficient. In addition, the ray wavelength regulation method monitors the output wavelength of the filter through a real-time wavelength monitor to form a closed-loop feedback mechanism to ensure that the output wavelength strictly covers the absorption spectrum range recommended by the database; dynamically adjusts the ray power to adapt to different fabric thicknesses, improves the detection accuracy and signal stability. The liquid volume detection method is to extract the key feature points in the absorption spectrum through Fourier transform, compare and analyze them with the standard spectrum in the database, and combine the ray absorption peak position and attenuation amplitude to accurately calculate the liquid volume of the fabric, and optimize the ray parameters through the feedback mechanism. Further, the ray source and the execution module 3 will dynamically optimize the emission power of the X-ray to adapt to the thickness and density of different fabrics. The main control unit combines the fabric thickness data estimated by the machine vision module, calculates the optimal emission power, and adjusts the input voltage of the ray source through a high-precision voltage controller. For thicker fabrics, the system will increase the ray power to ensure ray penetration. For thinner fabrics, the power will be reduced to avoid signal oversaturation. The real-time nature of power adjustment is combined with wavelength selection, enabling the system to maximize the absorption signal of the liquid while ensuring the stability and accuracy of the detection process. After the X-ray optimized in wavelength and power penetrates the fabric, the ray undergoes absorption and attenuation in the liquid. The high-sensitivity detector receives the penetrated signal and converts it into an electrical signal. The signal processing module eliminates noise through dynamic filtering and amplification to obtain a high-quality absorption spectrum curve, and then uses Fourier transform to extract the key feature points in the spectrum such as the absorption peak position and attenuation amplitude, and compares and analyzes them with the standard absorption spectrum stored in the database, so as to accurately calculate the liquid volume of the fabric.

[0039] A fabric parameter database is established through the big data platform. Based on the fabric parameters and image information collected by high-precision sensors, the fabric features are dynamically matched, and the ray wavelength and power are adjusted to achieve high-precision liquid volume detection.

[0040] The system also includes an intelligent feedback and self-calibration mechanism module 4, a system integration and optimization module 5, a user interface and interaction module 6, a security and privacy protection module 7, a remote monitoring and maintenance module 8, and a system expansion and compatibility module 9. The intelligent feedback and self-calibration mechanism module 4 is used to ensure the reliability of the detection results. When the real-time detection results deviate from the standard range, the system triggers a feedback mechanism to adjust the position of the filter or the ray power parameters, re-detect and correct the output. By monitoring parameters such as temperature, humidity, and background radiation through environmental sensors, and combining with a compensation algorithm to optimize the detection accuracy. After each detection, the system stores the detection data in a database for dynamically optimizing the deep learning model and enhancing the system's adaptability to new fabrics or special liquids. The system integration and optimization module 5 is responsible for the overall coordination and performance optimization of the system, including the scheduling of hardware devices, the allocation of software resources, and the monitoring of the system operation status. By monitoring the operation status of each module of the system in real time, this module can promptly discover and solve potential problems to ensure the continuous and stable operation of the system. The user interface and interaction module 6 interacts with the user through a graphical user interface (GUI), providing an intuitive operation interface and rich data display functions. The user can set detection parameters, start the detection process, view the detection results, and export the detection report through this interface. In addition, this module also supports multi-language interfaces to meet the needs of users in different regions. The security and privacy protection module 7 uses encryption technology to protect the security of data transmission and storage, ensuring that the detection data and user information are not accessed without authorization. At the same time, this module also provides an access control function to limit the access rights of different users to the system functions and prevent data leakage and system abuse. The remote monitoring and maintenance module 8 enables the system administrator to remotely monitor the operation status of the system, conduct fault diagnosis, and perform system maintenance. In addition, this module also supports remote software updates to ensure that the system always runs on the latest version, improving the security and performance of the system. The system expansion and compatibility module 9 is responsible for handling the system's expansion requirements and the compatibility issues of new devices. Through this module, the system can easily integrate new sensors, detection devices, or analysis algorithms to adapt to the changing detection requirements and technological developments.

[0041] Embodiment 2

[0042] The present invention also provides a high-precision fabric liquid volume detection and intelligent analysis method based on X-ray absorption spectroscopy. The specific steps include:

[0043] Step 1: Fabric image and parameter acquisition

[0044] Place the fabric to be tested on the conveyor belt, and the system first starts the image and data acquisition module. The industrial camera captures the high-definition texture image of the fabric surface, and uses a high-resolution lens to extract feature information such as the fiber arrangement direction and texture details. The multispectral imaging sensor is started synchronously, and the spectral reflection characteristics of the fabric are collected band by band, covering multiple bands from visible light to near-infrared, to record the reflectance of the fabric under different optical conditions. The uniform light source provides stable illumination, and its intensity is adjusted in real time by the light sensor to ensure that the acquisition quality of the image and spectral data is not affected by ambient light.

[0045] The collected image and spectral data are processed by the data processing unit. The image undergoes grayscale transformation, texture enhancement, and noise removal to form a two-dimensional texture feature matrix; the spectral data undergoes spectral decomposition to generate spectral feature vectors. These features are input into a pre-trained convolutional neural network model for inference. The model combines the classification rules during training to identify the fabric type, and the output fabric type data is transmitted to the main control unit. The temperature and humidity sensor, thickness gauge, etc. run synchronously to collect the ambient temperature and humidity of the detection area and the thickness data of the fabric respectively. These parameter data and the fabric type are transmitted to the main control unit together to provide a basis for subsequent detection parameter regulation.

[0046] Step 2: Detection parameter regulation and wavelength selection

[0047] Based on the identified fabric type, the main control unit extracts the absorption characteristic parameters of this type of fabric from the database, including the characteristic absorption wavelength range of the liquid and the optimal ray power value. The database records the standard absorption spectra and parameter recommended values of different material fabrics at various liquid contents. The extracted wavelength range is used as the target for subsequent ray regulation.

[0048] After the ray source is started, broadband X-rays are generated by the synchrotron radiation light source, and the rays enter the wavelength filter module for regulation. The wavelength filter consists of multiple groups of high-precision filter plates and servo drive devices. By calculating and controlling the servo driver, the position and angle of the filter plate are adjusted so that the filter only allows the rays within the target wavelength range to pass through. The real-time wavelength monitor detects the output wavelength of the filter and feeds the actual wavelength data back to the main control unit. The main control unit calculates the deviation and adjusts the servo driver if necessary to form a closed-loop regulation to ensure that the output wavelength accurately covers the recommended absorption spectrum range in the database. The filtered rays are concentrated on the fabric detection area after passing through the optical focusing unit.

[0049] Based on the thickness and density data of the fabric, the main control unit further optimizes the emission power of the rays. The thickness measuring instrument provides fabric thickness data for power calculation. The main control unit calls the power calculation formula in the database to determine the optimal ray power value. The voltage controller of the ray source adjusts the input voltage according to the calculation result to ensure that the ray power can meet the detection requirements. For thicker fabrics, the system increases the ray power to ensure ray penetration; for thinner fabrics, the power is reduced to avoid signal oversaturation. The rays after wavelength regulation and power optimization have the best signal-to-noise ratio.

[0050] Step 3: Signal acquisition and spectral analysis

[0051] The regulated X-rays penetrate the fabric and undergo characteristic absorption and attenuation with the liquid therein. The high-sensitivity detector receives the ray signal after penetration, converts it into an electrical signal, and transmits it to the signal processing module. The signal processing module performs dynamic filtering and signal amplification on the received signal, eliminates background noise, and enhances weak signals to generate a high-quality X-ray absorption spectral curve. Fourier transform technology is used to analyze the spectral curve and extract key feature points such as absorption peak positions and attenuation amplitudes. The extracted spectral feature data is compared and analyzed with the standard absorption spectra of the corresponding fabric types in the database. Through difference calculation, the system accurately estimates the liquid volume in the fabric.

[0052] Step 4: Intelligent feedback and self-calibration

[0053] When the real-time detection result deviates from the standard value in the database, the main control unit triggers a feedback mechanism, adjusts the position of the filter or the ray power parameters, and conducts a new detection. The temperature, humidity, and background radiation data collected by the environmental sensor are input into the compensation algorithm in real time to optimize the detection parameters and eliminate the interference of the external environment on the detection signal. When the detection device has a deviation due to long-term operation, the self-calibration module will detect the device status through sensors and correct the system parameters to ensure the normal working state of the device.

[0054] Step 5: Data storage and optimization

[0055] After each detection is completed, the system stores the detection results, corresponding environmental parameters, fabric characteristics, and absorption spectral data in the database to form a traceable historical record. The new detection data will be used to dynamically update the convolutional neural network model and parameter recommendation rules to enhance the system's adaptability to new fabrics and complex liquids. The detection results are presented on the client side through the data visualization module, including absorption spectral diagrams, liquid volume change trend diagrams, and historical data comparison charts. Users can choose to generate a standardized detection report, which includes detection parameters, result summaries, and optimization suggestions for facilitating production quality control and data archiving.

[0056] Step 6: System integration and optimization

[0057] In the system integration and optimization step, the system integration and optimization module 5 is responsible for coordinating the work of each module to ensure the efficiency of data transmission and processing. This module optimizes system performance by monitoring the usage of system resources in real time and dynamically adjusting the configuration of hardware and software. For example, when detecting heavy tasks, this module can automatically increase the computing resources of the data processing unit to ensure the smoothness of the detection process.

[0058] Step 7: User Interface and Interaction

[0059] In the user interface and interaction step, the user interface and interaction module 6 interacts with the user through the graphical user interface (GUI). The user can set detection parameters such as fabric type, detection area, detection accuracy, etc. through this interface and start the detection process. After the detection is completed, the user can view the detailed detection results through the interface, including absorption spectrogram, liquid volume change trend chart, and historical data comparison chart. In addition, the user can also choose to generate a standardized detection report, which includes detection parameters, result summary, and optimization suggestions, facilitating production quality control and data archiving.

[0060] Step 8: Security and Privacy Protection

[0061] In the security and privacy protection step, the security and privacy protection module 7 uses advanced encryption technology to protect the security of data transmission and storage. All detection data and user information are encrypted during transmission to ensure that the data is not accessed by unauthorized parties. At the same time, this module also provides access control functions to limit the access rights of different users to system functions, preventing data leakage and system abuse.

[0062] Step 9: Remote Monitoring and Maintenance

[0063] In the remote monitoring and maintenance step, the remote monitoring and maintenance module 8 allows system administrators to remotely monitor the operating status of the system. Through this module, administrators can perform fault diagnosis and system maintenance to ensure the continuous and stable operation of the system. In addition, this module also supports remote software updates to ensure that the system always runs on the latest version, improving the security and performance of the system.

[0064] Step 10: System Expansion and Compatibility

[0065] In the system expansion and compatibility step, the system expansion and compatibility module 9 is responsible for handling the expansion requirements of the system and the compatibility issues of new devices. Through this module, the system can easily integrate new sensors, detection devices, or analysis algorithms to adapt to changing detection requirements and technological developments. For example, when introducing new types of fabrics or liquids, this module can automatically update the database and analysis model to ensure that the system can accurately identify and detect new materials.

[0066] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A fabric liquid amount detection and analysis system based on X-ray absorption spectroscopy, comprising: An image and data acquisition module (1), a data storage and analysis module (2), a ray source and execution module (3) and a client, wherein the image and data acquisition module (1), the data storage and analysis module (2), the ray source and execution module (3) and the client are connected via network communication, characterized in that: the image and data acquisition module (1) obtains the material type, thickness, density, fiber arrangement direction, spectral reflectivity, and X-ray attenuation coefficient parameters of the fabric through equipment including but not limited to industrial cameras, multi-spectral imaging sensors, light sensors, X-ray receivers, temperature and humidity sensors, and thickness measuring instruments, and transmits them to the data storage and analysis module in real time, wherein the data storage The storage and analysis module (2) inputs the collected data into a pre-trained deep learning model, matches the optimal wavelength and power range according to the fabric characteristics and the radiation absorption spectrum stored in the database, and generates a radiation control instruction. The radiation source and execution module (3) adjusts the radiation wavelength through the position and posture of the filter according to the control instruction, and dynamically adjusts the radiation power according to the fabric thickness to optimize the signal quality, and receives the signal through a high-sensitivity detector, extracts the key features of the absorption spectrum to calculate the fabric liquid volume, establishes a fabric parameter database through a big data platform, dynamically matches the fabric characteristics based on the fabric parameters and image information collected by the high-precision sensor, and adjusts the radiation wavelength and power to achieve high-precision liquid volume detection.

2. The fabric liquid level detection and analysis system based on X-ray absorption spectroscopy according to claim 1, characterized in that: The image and data acquisition module (1) comprises an industrial camera, a multispectral imaging sensor, a light sensor, an X-ray receiver, a temperature and humidity sensor, a thickness measuring instrument, and a servo position sensor, and is used for real-time acquisition of data including but not limited to the surface texture, spectral reflectivity, density, thickness, and environmental parameters of the fabric, and performs denoising and standardization processing on the data through the signal processing module before storing the data in a database.

3. The fabric liquid level detection and analysis system based on X-ray absorption spectroscopy according to claim 1, characterized in that: The data storage and analysis module (2) uses a deep learning model and a dynamic database management system, combines the material type, absorption spectrum characteristics and ray attenuation coefficient of the fabric, matches the parameter set closest to the current fabric characteristics from the database, and generates a control instruction including a target ray wavelength range and an emission power value.

4. The fabric liquid level detection and analysis system based on X-ray absorption spectroscopy according to claim 1, characterized in that: The ray source and execution module (3) include but are not limited to a synchrotron radiation source, a filter, a servo drive device, an optical focusing unit and a high-sensitivity detector. The filter is adjusted in position by the servo drive device to select a target wavelength. The optical focusing unit adjusts the ray beam emission angle to accurately align the target detection area. The detector receives the penetration signal and converts it into high-quality absorption spectrum data. The fabric parameters include but are not limited to material type, thickness, density, fiber arrangement direction, porosity, surface roughness, weaving structure, fiber diameter, liquid absorption rate, liquid retention capacity, spectral reflectivity, spectral absorption characteristics, and X-ray attenuation coefficient.

5. The fabric liquid level detection and analysis system based on X-ray absorption spectroscopy according to claim 1, characterized in that: The ray wavelength control method monitors the filter output wavelength through a real-time wavelength monitor to form a closed-loop feedback mechanism, thereby ensuring that the output wavelength strictly covers the absorption spectrum range recommended by the database, dynamically adjusting the ray power to adapt to different fabric thicknesses, and improving detection accuracy and signal stability. The liquid volume detection method extracts key feature points in the absorption spectrum through Fourier transform, compares and analyzes it with the standard spectrum in the database, combines the ray absorption peak position and attenuation amplitude, accurately calculates the fabric liquid volume, and optimizes the ray parameters through the feedback mechanism.

6. The fabric liquid level detection and analysis system based on X-ray absorption spectroscopy according to claim 1, characterized in that: The industrial camera captures the fiber arrangement and texture details of the fabric, and inputs the obtained features into a pre-trained convolutional neural network. Through model reasoning, the system can quickly classify the fabric type. The classification results are transmitted to the main control unit, and the main control unit extracts the absorption characteristic parameters of the material from the database according to the fabric type.

7. The fabric liquid level detection and analysis system based on X-ray absorption spectroscopy according to claim 5, characterized in that: The data storage and analysis module (2) enables the X-ray source to start the wavelength generation and regulation process according to the target wavelength range provided by the database. The synchrotron radiation source first generates a ray incident wavelength filter module covering a wide spectrum. The module is composed of a plurality of groups of high-precision filters and a servo drive device. The servo drive is controlled by calculation to adjust the position and posture of the filter to ensure that the filter only allows the target wavelength range to pass. The real-time wavelength monitor monitors the output wavelength of the filter and feeds back the actual wavelength to the main control unit to form a closed-loop regulation to ensure that the output wavelength strictly covers the absorption spectrum range recommended by the database. The filtered rays are concentrated to the fabric detection area through the optical focusing unit. At the same time, the ray emission angle is adjusted according to the position information provided by the machine vision to ensure that the ray beam is accurately aligned with the target detection area.

8. The fabric liquid level detection and analysis system based on X-ray absorption spectroscopy according to claim 1, characterized in that: The ray source and the execution module (3) dynamically optimize the emission power of the X-rays to adapt to the thickness and density of different fabrics. The main control unit calculates the optimal emission power in combination with the fabric thickness data estimated by the machine vision module, and adjusts the input voltage of the ray source through a high-precision voltage controller. For thicker fabrics, the system increases the ray power to ensure ray penetration, and for thinner fabrics, the power is reduced to avoid signal oversaturation. The real-time power regulation is combined with the wavelength selection, so that the system can maximize the absorption signal of the liquid and ensure the stability and accuracy of the detection process. The X-rays after wavelength and power optimization penetrate the fabric, and the rays are absorbed and attenuated in the liquid. The high-sensitivity detector receives the signal after penetration and converts it into an electrical signal. The signal processing module removes noise through dynamic filtering and amplification to obtain a high-quality absorption spectrum curve, and then uses Fourier transform to extract key feature points in the spectrum such as absorption peak position and attenuation amplitude, and compares and analyzes them with the standard absorption spectrum stored in the database, so as to accurately calculate the amount of fabric liquid.

9. The fabric liquid level detection and analysis system based on X-ray absorption spectroscopy according to claim 6, characterized in that: The system also includes an intelligent feedback and self-calibration mechanism module (4), which is used to ensure the reliability of the detection results. When the real-time detection results deviate from the standard range, the system triggers the feedback mechanism, adjusts the filter position or the ray power parameter, re-detects and corrects the output, and monitors the environment including but not limited to temperature, humidity and background through environmental sensors. Radiation parameters are combined with compensation algorithms to optimize detection accuracy. After each detection, the system will store the detection data in the database. Used to dynamically optimize deep learning models and improve the system's adaptability to new fabrics or special liquids.

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