A qualitative online analysis system and method for textile fiber components

Abstract

The present invention relates to the technical field of textile fiber component analysis, and discloses a system and method for qualitative online analysis of textile fiber components. The system comprises: a spectrum acquisition module, a data processing terminal, and a component identification center, wherein the component identification center includes a spectrum preprocessing unit, a feature matching unit, and a component determination unit. The spectrum acquisition module acquires multi-band spectrum data of textile samples in real time, and the data processing terminal receives instructions to convert the spectrum data format. The spectrum preprocessing unit determines the interference section by recording the spectrum distortion and baseline correction interval, and controls the processing terminal to generate a spectrum feature matrix to calibrate the sample surface component distribution; the feature matching unit matches the fiber type based on this and generates a component report; and the component determination unit controls re-measurement in the interference section when an abnormal spectrum is detected. The system realizes online real-time qualitative analysis of textile fiber components, enhances anti-interference capability, and can be widely used in the field of textile fiber component analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of textile fiber component analysis, and in particular to a system and method for qualitative online analysis of textile fiber components. Background Art

[0002] Accurate analysis of textile fiber composition is crucial to the development of the textile industry. It is not only a key component in ensuring textile quality but also a fundamental foundation for textile product development, production, and quality control. As the textile industry continues to advance, market demands for textiles are increasing, posing numerous challenges to textile fiber composition analysis technology.

[0003] Traditional methods for analyzing textile fiber composition have significant limitations. For example, some methods require destructive processing of textile samples, which undoubtedly damages the samples and cannot meet the requirements of some testing scenarios that require sample integrity. Moreover, most of these traditional methods require offline analysis in a laboratory environment, which is cumbersome and time-consuming, making it difficult to achieve real-time monitoring and quality control of the textile production process. In today's rapidly developing textile production, production efficiency is constantly improving. The lag of traditional offline analysis methods seriously hinders timely adjustment and optimization of the production process, and cannot meet the modern textile industry's demand for efficient, real-time testing.

[0004] Furthermore, some existing online analysis technologies present numerous challenges. Some technologies suffer from low detection accuracy, making it difficult to accurately identify fibers with complex compositions or low content. Furthermore, these technologies are less robust against interference in the complex textile production environment, making them susceptible to external factors and resulting in inaccurate results. For example, the textile production process can be subject to fluctuations in environmental factors such as temperature, humidity, and light, as well as interference from vibrations caused by operating production equipment. These factors can adversely affect the results of online analysis technologies.

[0005] Furthermore, existing textile fiber composition analysis systems also have shortcomings in data processing and analysis. Data processing efficiency is low, making it difficult to process and analyze large amounts of test data in a timely manner, making it difficult to quickly generate accurate composition analysis reports. Furthermore, the existing system lacks an effective mechanism for handling anomalies that arise during testing, preventing timely review and correction, further impacting the reliability of test results.

[0006] With the continuous innovation and diversification of textile materials, new textile fibers are constantly emerging, which places higher demands on textile fiber composition analysis technology. Traditional analysis methods and technologies are no longer able to accurately analyze the composition of new textile fibers. It is necessary to develop more advanced, efficient, and accurate online analysis systems and methods to meet the development needs of the textile industry. Summary of the Invention

[0007] The object of the present invention is to provide a system and method for qualitative online analysis of textile fiber components to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solutions: a system and method for qualitative online analysis of textile fiber composition, the system comprising:

[0009] A spectrum acquisition module, a data processing terminal and a component identification center, wherein the spectrum acquisition module and the data processing terminal are respectively connected to the component identification center for communication, and the component identification center includes a spectrum preprocessing unit, a feature matching unit and a component determination unit;

[0010] The spectrum acquisition module is used to obtain multi-band spectrum data of textile samples in real time;

[0011] The data processing terminal is used to receive processing instructions from the component identification center to convert the spectral data format;

[0012] The spectrum preprocessing unit is used to record the spectrum distortion interval and baseline correction interval during processing by the data processing terminal, determine the interference segment according to the spectrum distortion interval and the baseline correction interval, control the data processing terminal to maintain a preset filter threshold for the interference spectrum and record the correction parameters, generate a spectrum feature matrix according to the correction parameters, and calibrate the component distribution of the sample surface;

[0013] The feature matching unit is used to perform fiber type matching on the sample surface that has completed component distribution calibration and generate a component report;

[0014] The component determination unit includes a conventional analysis unit and an abnormality review unit. The conventional analysis unit is used to control the data processing terminal to execute a baseline component mode when the data processing terminal is in a standard state; the abnormality review unit is used to control the data processing terminal to perform a re-measurement operation in an interference section according to a component report when an abnormal spectrum is detected.

[0015] Preferably, the spectrum acquisition module includes an optical dark box and a multi-channel sensor, a spectrum analysis device, a quality assessment module and a communication relay module arranged in the dark box;

[0016] The multi-channel sensor is used to synchronously acquire spectral signals of the near-infrared band and the mid-infrared band;

[0017] The spectrum analysis device is used to extract the absorption peak characteristics of the spectrum data;

[0018] The quality assessment module is used to calculate the similarity between the current absorption peak characteristics and the standard spectrum library based on the support vector machine, determine the data signal-to-noise ratio level, and trigger a retest signal according to the calculation result;

[0019] The communication relay module is used to upload spectral data to the component identification center through the optical fiber channel.

[0020] Preferably, the data processing terminal includes a cabinet housing and a signal conversion module, a baseline calibration module, an instruction execution module and a protocol adaptation module arranged in the housing.

[0021] Preferably, the spectral distortion interval and baseline correction interval during processing by the recording data processing terminal include:

[0022] When the data processing terminal detects spectral distortion, it records the wavelength data of the distortion starting point, continuously monitors the correction end point through the baseline calibration module, and records the channel number where the correction is completed.

[0023] Preferably, determining the interference section according to the spectral distortion interval and the baseline correction interval includes:

[0024] Generate distortion coordinates and correction coordinates based on wavelength data and channel numbers;

[0025] The distortion coordinates, the correction coordinates and the position of the spectrum acquisition module are spatially correlated to form a continuous segment, and the continuous segment is marked as an interference segment.

[0026] Preferably, the control data processing terminal maintains a preset filtering threshold value for the interference spectrum and records correction parameters, generates a spectral feature matrix according to the correction parameters, and calibrates the component distribution of the sample surface, including the following steps:

[0027] Controlling the data processing terminal to adjust the interference section filtering parameters and obtain the intensity value in real time through the baseline calibration module;

[0028] A preset filter threshold range is set. If the intensity value exceeds the preset filter threshold range, the data processing terminal is controlled to maintain the preset filter threshold processing;

[0029] Continuously record the spectrum correction value of the data processing terminal to form a correction parameter set;

[0030] According to the set of correction parameters, the wavelet transform algorithm is used to generate the spectral feature matrix;

[0031] Extracting a number of eigenvectors at equal wavelength intervals from the spectral feature matrix, wherein the number of the eigenvectors is proportional to the matrix dimension;

[0032] The composition distribution of the sample surface is calibrated based on the extracted feature vectors.

[0033] Preferably, the fiber type matching on the sample surface after component distribution calibration includes logically shielding a preset detection path on the sample surface that overlaps with a high-interference section after component distribution calibration on the sample surface.

[0034] Preferably, when the data processing terminal detects an abnormal spectrum, controlling the data processing terminal to perform a re-measurement operation in the interference section according to the component report includes:

[0035] S1. Select the starting position of the interference section with the shortest processing flow according to the next detection node to be scanned;

[0036] S2, controlling the data processing terminal to switch to the starting position and perform parameter initialization;

[0037] S3, controlling the data processing terminal to enter the interference section from the starting position and perform spectrum re-acquisition according to the detection path in the component report;

[0038] S4, acquiring the output spectrum of the data processing terminal in real time, and performing baseline fitting on the output spectrum using the least squares method;

[0039] S5. After each continuous scanning operation is completed in the interference section, the data processing terminal is controlled to suspend acquisition and exit the interference section, and S1 is re-executed.

[0040] Preferably, said S1 comprises the following steps:

[0041] The feasible starting position of each interference segment is calculated using the Floyd algorithm, and a comprehensive evaluation is performed based on the processing flow and wavelength spacing parameters;

[0042] Based on the comprehensive evaluation results, the starting position of the interference section with the shortest processing flow and the smallest wavelength interval is selected;

[0043] The comprehensive evaluation based on the processing flow and wavelength interval parameters includes:

[0044] A two-dimensional evaluation matrix containing the number of processing steps and wavelength interval values ​​was established;

[0045] After normalizing the two-dimensional evaluation matrix, factor analysis is used to extract the first principal factor as a comprehensive evaluation indicator;

[0046] The component determination unit is used to select the starting position of the interference section with the largest comprehensive evaluation index value.

[0047] Preferably, the present invention further includes a method for qualitative online analysis of textile fiber components, which is applied to the above-mentioned system for qualitative online analysis of textile fiber components, and the method comprises the following steps:

[0048] Step 1: acquiring multi-band spectral data of textile samples in real time through a spectral acquisition module;

[0049] Step 2: The spectral preprocessing unit records the spectral distortion interval and baseline correction interval during processing by the data processing terminal, determines the interference segment based on the spectral distortion interval and the baseline correction interval, controls the data processing terminal to maintain a preset filter threshold for the interference spectrum and records the correction parameters, generates a spectral feature matrix based on the correction parameters, and calibrates the component distribution of the sample surface;

[0050] Step 3: The feature matching unit performs fiber type matching on the sample surface after component distribution calibration and generates a component report;

[0051] In step 4, the component determination unit determines the status of the data processing terminal. If it is in the standard state, the conventional analysis unit controls it to execute the baseline component mode. If an abnormal spectrum is detected, the abnormal review unit controls the data processing terminal to perform a re-measurement operation within the interference section according to the component report. At the same time, the data processing terminal receives processing instructions from the component identification center to convert the spectral data format.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The qualitative online analysis system and method for textile fiber components provided by the present invention have significant advantages in many aspects. In the spectral acquisition link, the spectral acquisition module includes components such as an optical darkroom and a multi-channel sensor. The multi-channel sensor can simultaneously acquire spectral signals in the near-infrared and mid-infrared bands, extract absorption peak features in combination with a spectral analysis device, and then perform similarity calculations and signal-to-noise ratio level judgments based on a support vector machine through a quality assessment module, thereby ensuring the accuracy and reliability of the collected data and laying a good foundation for subsequent analysis. Components such as the signal conversion module of the data processing terminal work in coordination with the various units of the component identification center, determine the interference segment by recording the spectral distortion interval and the baseline correction interval, perform preset filtering threshold processing on the interference spectrum and record the correction parameters, generate a spectral feature matrix and complete component distribution calibration. This precise preprocessing method effectively improves data quality and reduces the impact of interference factors on the analysis results.

[0054] After completing the component distribution calibration, the feature matching unit logically shields the preset detection paths that overlap with the high-interference section, avoiding the adverse effects of the interference section on fiber type matching, improving the accuracy of fiber type matching, and generating more reliable component reports. The component determination unit has a clear division of labor between the routine analysis unit and the abnormality review unit. The routine analysis unit executes the baseline component mode under standard conditions to ensure analysis efficiency under normal conditions. When the abnormality review unit detects an abnormal spectrum, it performs a re-measurement operation within the interference section through a series of steps, such as using the Floyd algorithm to select the optimal starting position, initializing parameters, and adopting the least squares baseline fitting method. This ensures accurate handling of abnormal situations and improves the reliability and stability of the system.

[0055] This system enables online analysis of textile fiber composition without requiring destructive sample processing. It acquires and analyzes spectral data in real time, significantly improving analysis efficiency and meeting the needs of real-time monitoring and quality control during textile production. In terms of data processing, the wavelet transform algorithm generates a spectral feature matrix, along with a series of parameter processing and analysis methods, improving data processing efficiency and accuracy, enabling the rapid generation of accurate composition analysis reports. Furthermore, the system's effective handling mechanism for abnormal situations further ensures the reliability of test results. Furthermore, the system is adaptable to complex textile production environments, exhibits strong anti-interference capabilities, and operates stably under a variety of environmental conditions. It can also accurately analyze the composition of novel textile fibers, demonstrating broad applicability and promising development prospects, providing strong technical support for the development of the textile industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a diagram showing the working principle of the online qualitative analysis system for textile fiber components according to the present invention;

[0057] Figure 2 Schematic diagram of the spectrum acquisition module;

[0058] Figure 3 This is the design diagram of the spectrum preprocessing process;

[0059] Figure 4 This is a design diagram of the spectrum preprocessing and interference segment processing process. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0061] See also Figures 1-4 The present invention relates to a qualitative online analysis system for textile fiber components, which includes: a spectrum acquisition module, a data processing terminal and a component identification center. The spectrum acquisition module and the data processing terminal are respectively connected to the component identification center for communication. The component identification center includes a spectrum preprocessing unit, a feature matching unit and a component determination unit.

[0062] The spectrum acquisition module is used to acquire multi-band spectral data of textile samples in real time. The data processing terminal is used to receive processing instructions from the component identification center and convert the spectral data format. The spectrum preprocessing unit is used to record the spectral distortion interval and baseline correction interval during processing by the data processing terminal, determine the interference segment based on the spectral distortion interval and baseline correction interval, control the data processing terminal to maintain the preset filter threshold processing on the interference spectrum and record the correction parameters, generate the spectral feature matrix based on the correction parameters, and calibrate the component distribution of the sample surface. The feature matching unit is used to match the fiber type of the sample surface after the component distribution calibration is completed and generate a component report. The component determination unit includes a routine analysis unit and an abnormality review unit. The routine analysis unit is used to control the data processing terminal to execute the baseline component mode when it is in the standard state; the abnormality review unit is used to control the data processing terminal to perform a re-measurement operation within the interference segment based on the component report when an abnormal spectrum is detected.

[0063] Example 1:

[0064] In this implementation, the spectral acquisition module serves as the front-end data acquisition unit of the entire system. Its structural design and functional implementation directly impact the accuracy and real-time performance of textile fiber composition analysis. This module comprises an optical darkroom, a multi-channel sensor housed within it, a spectral analysis device, a quality assessment module, and a communication relay module. These components are precisely integrated and work together to efficiently collect and initially process multi-band spectral data from textile samples.

[0065] The optical darkroom provides a stable measurement environment for the optical components within. Its enclosure is constructed of opaque material and features a light-absorbing coating on the inner walls, effectively preventing interference from ambient light on the spectral signal. The darkroom's dimensions are designed to meet the testing requirements of common textile samples, with ample internal space to accommodate the sample placement platform and various functional components while ensuring the stability of the optical path. A sample entrance is located on one side of the darkroom, equipped with an automatically closing shade. The shade opens when a sample is placed or replaced, and automatically closes upon completion, maintaining the optically sealed environment within the darkroom.

[0066] The multi-channel sensor is mounted on a fixed bracket inside the darkroom, with its detection end facing the sample placement platform, and is used to simultaneously acquire spectral signals in the near-infrared and mid-infrared bands. The sensor uses an array detector design. The near-infrared channel covers the wavelength range of 780nm-2500nm, and the mid-infrared channel covers the wavelength range of 2.5μm-25μm. The sampling frequency of the two channels is consistent, ensuring that the synchronously collected spectral data is consistent in the time dimension. The optical lens group of the multi-channel sensor is precisely calibrated and can accurately focus the light signal reflected or transmitted by the sample surface onto the detector chip. The detector chip converts the light signal into an electrical signal, which is then converted into a digital spectral signal through the internal analog-to-digital conversion circuit.

[0067] The spectral analysis device is connected to the multi-channel sensor via a high-speed data bus and is used to extract the absorption peak characteristics of the spectral data. The device has a built-in digital signal processing chip and uses a preset algorithm model to preprocess the raw spectral data, including smoothing, denoising, baseline correction, and other operations to eliminate the effects of high-frequency noise and baseline drift. During the absorption peak feature extraction process, the spectral analysis device first performs peak detection on the preprocessed spectral data, identifying the locations of all significant absorption peaks, and then calculates the characteristic parameters of each absorption peak, such as peak wavelength, peak height, peak area, and half-peak width. These characteristic parameters constitute the key feature vectors of the spectral data, providing basic data for subsequent fiber composition analysis.

[0068] The quality assessment module calculates the similarity between the current absorption peak characteristics and the standard spectral library based on the support vector machine algorithm, determines the data signal-to-noise ratio level, and triggers a retest signal based on the calculation results. The standard spectral library stores standard spectral data and corresponding absorption peak characteristic parameters for various common textile fibers. The quality assessment module compares the absorption peak characteristics collected in real time with the data in the standard spectral library and calculates the similarity value between the two using the support vector machine algorithm. At the same time, the module also evaluates the noise level of the spectral data and calculates the signal-to-noise ratio index. When the similarity value is lower than the preset threshold or the signal-to-noise ratio is low, the quality assessment module determines that the quality of the currently collected data does not meet the analysis requirements and sends a retest signal to the multi-channel sensor, triggering the operation of re-collecting the spectral data.

[0069] The communication relay module establishes a high-speed data transmission link with the component identification center via a fiber optic channel for uploading spectral data. This module utilizes a fiber optic transceiver as its core component, packaging the absorption peak characteristic data processed by the spectral analysis device and the evaluation results of the quality assessment module, and transmitting them to the component identification center via the fiber optic channel. Fiber optic transmission offers advantages such as strong anti-interference capabilities, high transmission rates, and long transmission distances. This ensures that spectral data is not subject to electromagnetic interference during transmission, ensuring data integrity and accuracy. The communication relay module also features a data caching function. If the fiber optic channel is temporarily congested, the data to be transmitted can be temporarily stored in the buffer and transmitted again after the channel returns to normal, thus preventing data loss.

[0070] During operation, textile samples are placed on a sample platform within a darkroom. A multi-channel sensor simultaneously collects the sample's spectral signals in the near-infrared and mid-infrared bands and transmits them to a spectral analysis device for feature extraction. The absorption peak characteristic data processed by the spectral analysis device and the evaluation results of the quality assessment module are uploaded to the component identification center via a communication relay module. If the quality assessment module determines that the data quality is poor, the multi-channel sensor is triggered to re-acquire spectral data until a spectral signal that meets quality requirements is obtained.

[0071] Example 2:

[0072] In the online qualitative analysis system for textile fiber composition, the data processing terminal undertakes the critical tasks of spectral data format conversion, baseline calibration, and executing instructions from the component identification center. Its structural design and functional implementation are directly related to the accuracy and reliability of subsequent component analysis. In this embodiment, the data processing terminal comprises a cabinet housing and internally housed signal conversion, baseline calibration, instruction execution, and protocol adapter modules. These components work together to ensure efficient processing of spectral data.

[0073] The cabinet housing is constructed of high-strength metal, offering excellent electromagnetic shielding and heat dissipation, providing a stable operating environment for internal components. The front panel features a display and operation buttons for displaying system operating status and configuring parameters. The rear panel is equipped with various interfaces, including power and data input / output, for easy connection to other devices.

[0074] The signal conversion module, a core component of the data processing terminal, is primarily responsible for converting the format of received spectral data to meet subsequent processing requirements. Utilizing a high-performance digital signal processor and dedicated conversion algorithms, this module quickly and accurately converts raw spectral data collected by multi-channel sensors into a unified data format. During the conversion process, the module adjusts the sampling rate and performs bit depth conversion to ensure data consistency and compatibility. The module also features data compression, reducing data transmission and storage requirements without compromising data quality.

[0075] The baseline calibration module is used to calibrate the baseline of spectral data and eliminate the effects of baseline drift on spectral analysis. Baseline drift is a common problem in spectral measurements, causing an overall shift in the spectral signal, thereby affecting the accurate extraction of absorption peak features. The baseline calibration module utilizes an adaptive filtering algorithm that monitors baseline changes in spectral data in real time and dynamically adjusts based on the monitoring results. In practice, the module first analyzes the raw spectral data to identify the baseline region. It then generates a smooth baseline curve using a fitting algorithm. Finally, the baseline curve is subtracted from the raw spectral data to obtain the corrected spectral data. This dynamic calibration method effectively addresses baseline drift issues in different samples and measurement environments, improving the quality of spectral data.

[0076] The instruction execution module serves as a bridge between the data processing terminal and the component identification center. It is responsible for receiving processing instructions from the component identification center and executing the corresponding operations. This module uses an embedded microcontroller as its core processor, ensuring high real-time performance and reliability. The instruction execution module maintains a connection to the component identification center via a dedicated communication interface, enabling it to receive and interpret instructions from the center in real time. Upon receiving an instruction, the instruction execution module invokes the appropriate functional module for processing based on the instruction type and content. For example, if a spectral data acquisition instruction is received, the instruction execution module controls the multi-channel sensor for data acquisition. If a data processing instruction is received, the instruction execution module invokes the signal conversion module and baseline calibration module for the corresponding data processing operations.

[0077] The protocol adapter module is used to adapt the communication protocol between the data processing terminal and other devices, ensuring smooth communication between the various parts of the system. In the online qualitative analysis system for textile fiber composition, different devices may use different communication protocols, which requires the protocol adapter module to perform protocol conversion and adaptation. This module supports a variety of common industrial communication protocols, such as Modbus, Profibus, CANopen, etc., and can be flexibly configured according to actual needs. The protocol adapter module uses a built-in protocol conversion engine to convert instructions from the component identification center into a format that conforms to the communication protocol of the target device, and at the same time converts the data returned by the target device into a format that can be understood by the component identification center. This protocol adaptation mechanism enables the data processing terminal to seamlessly connect with various types of devices, improving the compatibility and scalability of the system.

[0078] In actual operation, when the data processing terminal detects spectral distortion, the exception handling process is immediately initiated. First, the baseline calibration module records the wavelength data of the distortion starting point, while continuously monitoring the correction end point and recording the channel number where the correction is completed. This data is very important for subsequent analysis and processing, as it can help the system accurately locate the location and range of the distortion. Based on the recorded wavelength data and channel number, the system generates distortion coordinates and correction coordinates, which represent the location where the distortion occurs and the location where the correction is completed, respectively. The system then spatially associates the distortion coordinates, correction coordinates, and spectral acquisition module position to form a continuous segment and marks this continuous segment as an interference segment. This spatial association and labeling method can help the system quickly identify and process interference segments during subsequent processing, improving analysis efficiency and accuracy.

[0079] Protected by a cabinet enclosure, the various components of the data processing terminal work together through precise circuit connections and software control, enabling efficient processing of spectral data and timely response to anomalies. The signal conversion module ensures data format consistency and compatibility, the baseline calibration module improves spectral data quality, the command execution module ensures coordination between system components, and the protocol adapter module enhances system compatibility and scalability. When faced with anomalies such as spectral distortion, the data processing terminal provides strong support for subsequent analysis and processing by performing a series of operations, including recording key data, generating coordinates, spatially correlating, and marking interfering segments.

[0080] Example 3:

[0081] In online qualitative analysis systems for textile fiber composition, spectral processing and component distribution calibration for interference regions are key steps in ensuring analytical accuracy. In this implementation, the spectral preprocessing unit controls the data processing terminal to filter the interference spectra, record correction parameters, and generate a spectral feature matrix and component distribution calibration based on these parameters. Furthermore, when matching fiber types, it logically blocks overlapping paths in high-interference regions, forming a complete processing flow.

[0082] After the system determines the interference segment through the data processing terminal, the spectral preprocessing unit first issues an instruction to control the data processing terminal to adjust the filtering parameters of the interference segment. The adjustment of the filtering parameters is based on the spectral characteristics of the interference segment. For example, bandpass filtering or low-pass filtering is selected for the noise type in a specific wavelength range. The baseline calibration module of the data processing terminal will obtain the spectral intensity value of the area in real time. During this process, the baseline calibration module continuously monitors the baseline offset of the spectral signal to ensure the accuracy of the intensity value acquisition. The preset filter threshold range is set according to the normal spectral noise level of similar fibers in the standard spectral library. For example, for the near-infrared band, the preset threshold may be set to 2-3 times the standard deviation of the noise intensity. If the intensity value collected in real time exceeds the preset range, it means that the current filtering effect has failed to effectively suppress the interference. The system will control the data processing terminal to maintain the preset filter threshold processing to avoid spectral distortion due to frequent parameter adjustments.

[0083] During the filtering process, the data processing terminal continuously records the spectral correction values ​​for each wavelength. These correction values ​​include the intensity adjustment after filtering and the baseline offset correction. These correction values ​​are organized in order of wavelength to form a correction parameter set. This set is stored in an array, with each element corresponding to the correction parameter for a specific wavelength. For example, in the mid-infrared band from 4000 cm⁻¹ to 400 cm⁻¹, correction parameters are recorded at intervals of 1 cm⁻¹, forming an array containing 3601 elements. Based on the correction parameter set, the system uses a wavelet transform algorithm to perform a multi-scale decomposition of the spectral data. The wavelet transform decomposes the spectral signal into different frequency components, preserving useful characteristic information while removing high-frequency noise. By selecting appropriate wavelet basis functions (such as Daubechies wavelets or Symlet wavelets), the corrected spectral data is subjected to a multi-layer decomposition. The coefficients of each layer are extracted and reconstructed to form a spectral feature matrix. The rows of the matrix correspond to different wavelength intervals, and the columns correspond to the decomposed characteristic coefficients. For example, for the near-infrared band of 2500nm-780nm, it is divided into 35 wavelength intervals at intervals of 50nm. Each interval generates 10 characteristic coefficients, forming a 35×10 characteristic matrix.

[0084] From the spectral feature matrix, several eigenvectors are extracted at equal wavelength intervals. The extraction interval is determined by the spectral resolution. For example, for spectral data with a resolution of 10 nm, eigenvectors are extracted at 20 nm intervals to ensure that the eigenvectors reflect the overall spectral characteristics while avoiding redundancy. The number of eigenvectors is proportional to the matrix dimension. For example, from the aforementioned 35×10 matrix, 15 eigenvectors can be extracted, each containing a combination of characteristic coefficients for the corresponding wavelength interval. These eigenvectors are further processed using dimensionality reduction algorithms such as principal component analysis (PCA) to remove highly correlated dimensions and retain features that best represent fiber composition. Based on the extracted eigenvectors, the system calibrates the sample surface for component distribution. This calibration process uses a spatial mapping algorithm to associate eigenvectors with physical locations on the sample surface. For example, the sample platform is divided into a 100×100 grid of points, each corresponding to a spectral acquisition location. The eigenvectors are interpolated to each grid point, forming a two-dimensional heat map of the component distribution. The color depth of the heat map indicates the relative concentration of different fiber components.

[0085] After completing the component distribution calibration on the sample surface, the feature matching unit performs fiber type matching on the sample surface. At this point, the system logically blocks any pre-set detection paths that overlap with high-interference sections on the sample surface. A pre-set detection path is a typical scanning route set based on the general requirements of fiber composition analysis, such as a zigzag or grid-shaped path across the sample surface. High-interference sections are areas where the interference level exceeds a preset threshold. For example, in component distribution calibration, areas with abnormal thermal map colors and large fluctuations in correction parameters are observed. Logical blocking is achieved by modifying the coordinate points of the detection path, removing path points in overlapping areas from the scan sequence and adjusting the connection method of adjacent path points to ensure the continuity of the scan path. For example, if the original detection path includes coordinate points A(10,10), B(20,20), and C(30,30), and point B is located in a high-interference section, after blocking point B, the path is adjusted so that A(10,10) directly connects to C(30,30), and a new intermediate point is inserted between A and C to ensure scan density.

[0086] During the entire processing process, the data processing terminal maintains real-time communication with the spectral preprocessing unit. The adjustment of filter parameters, the acquisition of intensity values, and the recording of correction parameters are all achieved through bidirectional data transmission to ensure the dynamic response of the processing process. The generation of the spectral feature matrix and the extraction of the feature vector are completed in the calculation unit of the component identification center, using high-performance processors for parallel calculation to improve processing efficiency. The spatial mapping algorithm for component distribution calibration combines the mechanical coordinate system of the sample platform and obtains sample position information in real time through the encoder to ensure the spatial accuracy of the calibration results. The logical shielding operation does not affect the normal scanning of the non-interference area. While improving the analysis efficiency, it avoids the misjudgment of the fiber type matching results in the interference section.

[0087] Example 4:

[0088] In a qualitative online analysis system for textile fiber composition, when a data processing terminal detects an abnormal spectrum, the abnormality review unit needs to control it to perform a re-measurement operation within the interference section to ensure the accuracy of the spectral data. The following describes the working process of this embodiment in detail with reference to specific examples.

[0089] Suppose that during online analysis of a batch of cotton and linen blended fabric, the system acquires multi-band spectral data from the fabric through the spectrum acquisition module. During data processing, the data processing terminal detects an anomaly in a certain segment of the spectral data. Analysis determines that the interference segment corresponding to the anomaly is in the wavelength range of 1500nm-1700nm and channels 3-5. At this point, the anomaly review unit initiates the retest process, which involves the following steps:

[0090] First, execute step S1 to select the starting position of the interference segment with the shortest processing flow based on the next detection node to be scanned. Assume that the current detection process has completed scanning most of the sample surface area, and the next detection node to be scanned is coordinate point (50, 60). At this point, the system needs to determine the starting position in the interference segment 1500nm-1700nm and channels 3-5 to re-measure the shortest processing flow.

[0091] The system calculates the feasible starting positions of each interference segment using the Floyd algorithm. The Floyd algorithm is used here to analyze the path lengths and processing steps from different starting positions within the interference segment to the next detection node. For this interference segment, possible starting positions include the channel positions corresponding to wavelengths of 1500nm, 1550nm, 1600nm, 1650nm, and 1700nm. The system calculates the number of processing steps involved in the path from each starting position to the next detection node (50, 60), such as the number of filter parameters that need to be adjusted starting from 1500nm, the number of spectral points that need to be collected, etc., while taking into account the wavelength interval parameter, that is, the degree of proximity of the starting position to the wavelength range required by the next detection node.

[0092] Next, a comprehensive evaluation is performed based on the process flow and wavelength interval parameters. The system creates a two-dimensional evaluation matrix that includes the number of process flow steps and wavelength interval values. For example, for a starting position of 1500 nm, the number of process flow steps is 10, and the wavelength interval value is 200 nm; for a starting position of 1550 nm, the number of process flow steps is 8, and the wavelength interval value is 150 nm; for a starting position of 1600 nm, the number of process flow steps is 6, and the wavelength interval value is 100 nm; for a starting position of 1650 nm, the number of process flow steps is 7, and the wavelength interval value is 50 nm; and for a starting position of 1700 nm, the number of process flow steps is 9, and the wavelength interval value is 0 nm.

[0093] The two-dimensional evaluation matrix was then normalized, converting the number of process steps and wavelength interval values ​​to a unified dimension. For example, dividing the number of process steps by the maximum number of steps, 10, yielded normalized values ​​of 1, 0.8, 0.6, 0.7, and 0.9, respectively; dividing the wavelength interval values ​​by the maximum interval value, 200 nm, yielded normalized values ​​of 1, 0.75, 0.5, 0.25, and 0, respectively. Factor analysis was then used to extract the first principal factor as the comprehensive evaluation indicator. Factor analysis analyzes the correlation between the number of process steps and wavelength interval values ​​and combines them into a single principal factor that best reflects the information from both variables. Calculations revealed that the starting position, 1600 nm, had the highest value for the comprehensive evaluation indicator, so the component determination unit selected this position as the starting position for retesting.

[0094] After selecting the starting position, execute step S2, controlling the data processing terminal to switch to the starting position of 1600 nm and initialize the parameters. Upon receiving the command, the data processing terminal adjusts the internal wavelength control module to set the starting wavelength of spectrum acquisition to 1600 nm. It also initializes the sensor parameters for channels 3-5, including gain and integration time, to ensure optimal sensor operation.

[0095] Next, step S3 is executed, controlling the data processing terminal to enter the interference region from the starting position of 1600nm and perform spectral recollection according to the detection path specified in the composition report. The composition report records the detection path for the sample, such as a grid-like path across the sample surface. The data processing terminal follows this path, starting from the starting position of 1600nm and collecting spectra in the interference region of 1500nm-1700nm, strictly adhering to the preset acquisition parameters, such as the sampling interval and number of scans.

[0096] During the spectral reacquisition process, step S4 is executed to acquire the output spectrum of the data processing terminal in real time and perform baseline fitting on the output spectrum using the least squares method. The least squares method seeks an optimal baseline curve that minimizes the sum of squared errors between the original spectral data and the baseline curve. For example, for the reacquisition spectral data, the system uses wavelength as the x-axis and spectral intensity as the y-axis to fit a smooth baseline curve using the least squares method. This baseline curve is then subtracted from the original spectral data to obtain the corrected spectral data, eliminating the effects of baseline drift.

[0097] After completing a continuous scan, execute step S5, controlling the data processing terminal to pause acquisition and exit the interference segment, then re-execute step S1. Assuming this scan completes spectral re-acquisition of the interference segment 1500nm-1700nm, the system controls the data processing terminal to stop acquisition and adjust the wavelength to outside the interference segment, exiting that area. The system then reselects the starting position of the interference segment with the shortest processing flow based on the next detection node to be scanned, and repeats steps S1 to S5 until all interference segments requiring re-measurement are complete.

[0098] In this specific example, the system uses the Floyd algorithm and factor analysis to select the optimal starting position from multiple possible starting positions, reducing processing steps and time consumption during the re-measurement process. Parameter initialization ensures that the data processing terminal is in an accurate working state during the re-measurement, improving the quality of the re-acquired spectral data. Least squares baseline fitting further optimizes the re-acquired spectral data, ensuring that it more accurately reflects the true spectral characteristics of the sample.

[0099] Throughout the retest process, the system dynamically adjusts the starting position based on the detection nodes and interference segments, ensuring efficient and accurate retesting. Furthermore, by logically shielding pre-set detection paths that overlap with high-interference segments, the system prevents the impact of anomalous spectra on component analysis results, ensuring the reliability of the final component report. This retest mechanism effectively addresses anomalous spectra that may arise during textile fiber composition analysis, improving the system's overall analytical performance and stability.

[0100] Example 5:

[0101] In the online qualitative analysis system for textile fiber composition, the analysis method of Example 5 achieves online qualitative analysis of textile sample composition through the coordinated operation of various modules and units. The implementation process of this method is described in detail below with reference to specific examples.

[0102] Taking a batch of polyester and cotton blended fabrics produced by a garment factory as an example, the system performs a qualitative composition analysis on the fabrics. First, step 1 is performed, where the spectrum acquisition module acquires multi-band spectral data of the textile sample in real time. The blended fabric is placed on a sample placement platform within the spectrum acquisition module's optical darkroom. A multi-channel sensor simultaneously acquires spectral signals in the near-infrared and mid-infrared bands. The near-infrared channel covers the wavelength range of 780nm-2500nm, and the mid-infrared channel covers the wavelength range of 2.5μm-25μm. Both channels collect light signals reflected from the fabric surface at the same sampling frequency. After converting the light signals into electrical signals, they are converted into digital spectral signals via an analog-to-digital conversion circuit. The spectrum analysis device then extracts absorption peak characteristics, such as peak wavelength, peak height, and peak area. Finally, the communication relay module uploads this spectral data to the component identification center via a fiber optic channel.

[0103] Then proceed to step 2, where the spectrum preprocessing unit determines the interference segment by recording the spectrum distortion interval and baseline correction interval during processing by the data processing terminal, and processes the interference spectrum and calibrates the component distribution. Assume that during the data processing process, the data processing terminal detects that the spectrum is distorted near a wavelength of 1730nm. The baseline calibration module records the wavelength data of the distortion starting point as 1730nm, and continuously monitors the correction end point. When the baseline correction is completed at a wavelength of 1750nm, the channel number for the correction is recorded as 4. Based on the wavelength data 1730nm-1750nm and channel number 4, the distortion coordinates and correction coordinates are generated, and then the distortion coordinates, correction coordinates, and the position of the spectrum acquisition module are spatially associated to form a continuous interference segment, that is, the fabric surface area corresponding to the wavelength 1730nm-1750nm and channel 4.

[0104] After determining the interference segment, the spectral preprocessing unit controls the data processing terminal to adjust the filtering parameters for that interference segment, and the baseline calibration module acquires intensity values ​​in real time. The preset filter threshold range is 2-3 standard deviations of the normal spectral noise intensity. If the real-time intensity value exceeds this range, the system controls the data processing terminal to maintain the preset filter threshold. Simultaneously, the spectral correction values ​​of the data processing terminal are continuously recorded to form a set of correction parameters. Based on this set, a wavelet transform algorithm is used to generate a spectral feature matrix. For example, spectral data in the mid-infrared band is decomposed into different frequency components, and the matrix is ​​reconstructed after retaining the characteristic information. Within the spectral feature matrix, eigenvectors are extracted at equal wavelength intervals, such as one eigenvector every 5 nm. The number of eigenvectors is proportional to the matrix dimension. Finally, based on these eigenvectors, the sample surface is calibrated for component distribution. The fabric surface is divided into multiple grid points, each corresponding to an eigenvector. An interpolation algorithm is used to generate a component distribution heat map. Different colors in the heat map represent the relative distribution of different fiber components.

[0105] After completing step 2, execute step 3, and the feature matching unit performs fiber type matching on the sample surface that has completed the component distribution calibration and generates a component report. Based on the component distribution calibration, the feature matching unit compares the spectral feature vector of the sample surface with the standard features of various types of fibers in the standard spectral library, for example, using the standard spectral features of polyester fiber and cotton as a reference. For the preset detection path on the sample surface that overlaps with the high-interference segment (i.e., the wavelength 1730nm-1750nm, the area corresponding to channel 4), if a scanning path originally planned in a grid shape passes through this area, this part of the path is logically shielded, the overlapping path points are eliminated, and the connection between adjacent path points is adjusted to avoid the influence of the interference segment on the matching result. Through matching, the component distribution of polyester fiber and cotton in the blended fabric is determined, and a component report is generated, which contains information such as the distribution area and relative content of each fiber component.

[0106] Finally, step 4 begins. The component determination unit determines the status of the data processing terminal and performs corresponding operations. Simultaneously, the data processing terminal receives processing instructions and converts the spectral data format. Assuming that the data processing terminal is in a standard state during initial processing, the routine analysis unit controls it to execute a baseline component mode, processing and analyzing the spectral data according to a pre-set standard process to generate preliminary component analysis results. If, during subsequent processing, the data processing terminal detects an anomaly in a particular segment of spectral data, such as a low signal-to-noise ratio in another wavelength region, the anomaly review unit, based on the component report, controls the data processing terminal to re-measure within the corresponding interference segment.

[0107] For example, assuming a spectral anomaly is detected in the region between 800nm ​​and 850nm, corresponding to channel 2, the anomaly review unit first calculates feasible starting positions for this interference segment, such as 800nm, 820nm, and 850nm, based on the next detection node to be scanned (e.g., coordinate point (30, 40)). Using the Floyd algorithm, it then establishes a two-dimensional evaluation matrix based on the number of processing steps and wavelength interval parameters. Normalization and factor analysis are then used to extract the first principal factor, and the starting position with the highest comprehensive evaluation index value (e.g., 820nm) is selected. The data processing terminal is then controlled to switch to the 820nm starting position and initialize its parameters. From this position, the interference segment is entered, and spectral resampling is performed according to the detection path in the component report. The output spectrum is acquired in real time and baseline fitting is performed using the least squares method. After each continuous scan, acquisition is paused, the interference segment is exited, and a new starting position is selected until the resampling is complete. Throughout this process, the data processing terminal continuously receives processing instructions from the component identification center and performs format conversion on the spectral data to ensure data compatibility and transmission accuracy between modules.

[0108] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0109] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A qualitative online analysis system for textile fiber components, characterized in that: include: A spectrum acquisition module, a data processing terminal and a component identification center, wherein the spectrum acquisition module and the data processing terminal are respectively connected to the component identification center for communication, and the component identification center includes a spectrum preprocessing unit, a feature matching unit and a component determination unit; The spectrum acquisition module is used to obtain multi-band spectrum data of textile samples in real time; The data processing terminal is used to receive processing instructions from the component identification center to convert the spectral data format; The spectrum preprocessing unit is used to record the spectrum distortion interval and baseline correction interval during processing by the data processing terminal, determine the interference segment according to the spectrum distortion interval and the baseline correction interval, control the data processing terminal to maintain a preset filter threshold for the interference spectrum and record the correction parameters, generate a spectrum feature matrix according to the correction parameters, and calibrate the component distribution of the sample surface; The feature matching unit is used to perform fiber type matching on the sample surface that has completed component distribution calibration and generate a component report; The component determination unit includes a regular analysis unit and an abnormality review unit. The regular analysis unit is used to control the data processing terminal to execute a reference component mode when the data processing terminal is in a standard state; the abnormality review unit is used to control the data processing terminal to execute a re-measurement operation in the interference section according to the component report when an abnormal spectrum is detected; The spectral distortion interval and baseline correction interval during processing by the recording data processing terminal include: When the data processing terminal detects spectral distortion, it records the wavelength data of the distortion starting point, continuously monitors the correction end point through the baseline calibration module, and records the channel number where the correction is completed; Determining the interference section according to the spectrum distortion section and the baseline correction section includes: Generate distortion coordinates and correction coordinates based on wavelength data and channel numbers; Perform spatial correlation on the distorted coordinates, the corrected coordinates and the position of the spectrum acquisition module to form a continuous segment, and mark the continuous segment as an interference segment; The control data processing terminal maintains a preset filtering threshold for the interference spectrum and records the correction parameters, generates a spectral feature matrix according to the correction parameters, and calibrates the component distribution of the sample surface, including the following steps: Controlling the data processing terminal to adjust the interference section filtering parameters and obtain the intensity value in real time through the baseline calibration module; A preset filter threshold range is set. If the intensity value exceeds the preset filter threshold range, the data processing terminal is controlled to maintain the preset filter threshold processing; Continuously record the spectrum correction value of the data processing terminal to form a correction parameter set; According to the set of correction parameters, the wavelet transform algorithm is used to generate the spectral feature matrix; Extracting a number of eigenvectors at equal wavelength intervals from the spectral feature matrix, wherein the number of the eigenvectors is proportional to the matrix dimension; The composition distribution of the sample surface is calibrated based on the extracted feature vectors.

2. A textile fiber composition qualitative online analysis system according to claim 1, characterized in that: The spectrum acquisition module includes an optical dark box and a multi-channel sensor arranged in the dark box, a spectrum analysis device, a quality assessment module and a communication relay module; The multi-channel sensor is used to synchronously acquire spectral signals of the near-infrared band and the mid-infrared band; The spectrum analysis device is used to extract the absorption peak characteristics of the spectrum data; The quality assessment module is used to calculate the similarity between the current absorption peak characteristics and the standard spectrum library based on the support vector machine, determine the data signal-to-noise ratio level, and trigger a retest signal according to the calculation result; The communication relay module is used to upload spectral data to the component identification center through the optical fiber channel.

3. A textile fiber composition qualitative online analysis system according to claim 1, characterized in that: The data processing terminal includes a cabinet shell and a signal conversion module, a baseline calibration module, an instruction execution module and a protocol adaptation module arranged in the shell.

4. A textile fiber composition qualitative online analysis system according to claim 1, characterized in that: The fiber type matching on the sample surface after component distribution calibration includes logically shielding a preset detection path on the sample surface that overlaps with a high-interference section after component distribution calibration on the sample surface.

5. A textile fiber composition qualitative online analysis system according to claim 1, characterized in that: When the data processing terminal detects an abnormal spectrum, controlling the data processing terminal to perform a re-measurement operation in the interference section according to the component report includes: S1. Select the starting position of the interference section with the shortest processing flow according to the next detection node to be scanned; S2, controlling the data processing terminal to switch to the starting position and perform parameter initialization; S3, controlling the data processing terminal to enter the interference section from the starting position and perform spectrum re-acquisition according to the detection path in the component report; S4, acquiring the output spectrum of the data processing terminal in real time, and performing baseline fitting on the output spectrum using the least squares method; S5. After each continuous scanning operation is completed in the interference section, the data processing terminal is controlled to suspend acquisition and exit the interference section, and S1 is re-executed.

6. A textile fiber composition qualitative online analysis system according to claim 5, characterized in that: Said S1 comprises the following steps: The feasible starting position of each interference segment is calculated using the Floyd algorithm, and a comprehensive evaluation is performed based on the processing flow and wavelength spacing parameters; Based on the comprehensive evaluation results, the starting position of the interference section with the shortest processing flow and the smallest wavelength interval is selected; The comprehensive evaluation based on the processing flow and wavelength interval parameters includes: A two-dimensional evaluation matrix containing the number of processing steps and wavelength interval values ​​was established; After normalizing the two-dimensional evaluation matrix, factor analysis is used to extract the first principal factor as a comprehensive evaluation indicator; The component determination unit is used to select the starting position of the interference section with the largest comprehensive evaluation index value.

7. A method for qualitative online analysis of textile fiber components, applied to a system for qualitative online analysis of textile fiber components as claimed in any one of claims 1 to 6, characterized in that: The steps include: Step 1: acquiring multi-band spectral data of textile samples in real time through a spectral acquisition module; Step 2: The spectral preprocessing unit records the spectral distortion interval and baseline correction interval during processing by the data processing terminal, determines the interference segment based on the spectral distortion interval and the baseline correction interval, controls the data processing terminal to maintain a preset filter threshold for the interference spectrum and records the correction parameters, generates a spectral feature matrix based on the correction parameters, and calibrates the component distribution of the sample surface; Step 3: The feature matching unit performs fiber type matching on the sample surface after component distribution calibration and generates a component report; In step 4, the component determination unit determines the status of the data processing terminal. If it is in the standard state, the conventional analysis unit controls it to execute the baseline component mode. If an abnormal spectrum is detected, the abnormal review unit controls the data processing terminal to perform a re-measurement operation within the interference section according to the component report. At the same time, the data processing terminal receives processing instructions from the component identification center to convert the spectral data format.

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