An intelligent management system and method for efficient utilization of straw feed resources

Abstract

The present invention discloses an intelligent management system and method for efficient utilization of straw feed resources, specifically relating to the technical field of production management, and is used to solve the problem of the lack of precise identification and dynamic control of local structural abnormal phenomena in the existing straw pretreatment process. It generates fiber component distribution information through spectral component analysis, combines a multiple linear regression algorithm to identify areas prone to solidification risk in the local structure, collects and extracts microscopic image features to identify potential solidification feature areas, generates a pretreatment risk adjustment area based on the two, and evaluates the abnormal degree of the risk area by analyzing the deviation amplitude of the fiber direction arrangement and the asymmetry coupling strength of the interface interaction. Finally, it determines whether parameter optimization processing is required according to the evaluation result, realizes the dynamic monitoring and precise control of the straw pretreatment process, and improves the stability of the pretreatment process and the utilization efficiency of straw feed resources.

Description

Technical Field

[0001] The present invention relates to the technical field of production management. More specifically, the present invention relates to an intelligent management system and method for efficient utilization of straw feed resources. Background Art

[0002] With the development of straw resource utilization technology, straw as a feed raw material has gradually become an important link in agriculture and animal husbandry. However, during the pretreatment and fermentation of straw, affected by the high-temperature and high-humidity environment and complex process conditions, the structural characteristics of cellulose and hemicellulose in straw may change. Pretreatment refers to a series of preliminary treatment processes carried out on straw raw materials in the process of straw feed resource utilization to improve the nutritional value and processing performance of straw; within a local area, the microscopic structure of fibers will show an uneven arrangement state. In addition, the interfacial interaction between cellulose and hemicellulose shows a non-linear response under different process conditions, resulting in instability in the reaction performance of local areas. These phenomena will have a significant impact on the resource utilization efficiency of straw feed.

[0003] In the prior art, there is a lack of precise evaluation and control means for the local structure abnormal phenomena during the straw pretreatment process, resulting in the easy solidification of the microscopic structure of cellulose and hemicellulose in specific areas, thus significantly reducing the efficiency of subsequent enzymatic hydrolysis treatment. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an intelligent management system and method for efficient utilization of straw feed resources to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An intelligent management method for efficient utilization of straw feed resources, comprising the following steps:

[0007] Perform spectral component analysis on straw raw materials to generate fiber component distribution information, and fit and process the fiber component distribution information based on the multiple linear regression algorithm to determine the areas with high risk of local structure solidification;

[0008] Collect microscopic images of straw raw materials to generate local microstructure maps, and identify potential solidification characteristic areas based on feature extraction and classification;

[0009] Generate a pretreatment risk adjustment area based on the areas with high risk of local structure solidification and potential solidification characteristic areas;

[0010] By analyzing the change in the gradient of the fiber alignment direction in the preprocessed risk-adjusted area, evaluate whether the deviation amplitude of the fiber direction alignment is normal; by analyzing the interface interaction between cellulose and hemicellulose in the preprocessed risk-adjusted area, evaluate whether the asymmetric coupling strength of the fiber component interface interaction is reasonable;

[0011] Based on the evaluation results of the deviation amplitude of the fiber direction alignment and the asymmetric coupling strength of the fiber component interface interaction, determine whether the preprocessed risk-adjusted area needs to be immediately optimized for parameters.

[0012] In a preferred embodiment, perform spectral component analysis on the straw raw material to generate fiber component distribution information, and perform fitting processing on the fiber component distribution information based on the multiple linear regression algorithm to determine the locally structured easy-to-cure risk area, specifically including:

[0013] Collect data on the spectral characteristics of the straw raw material through a near-infrared spectroscopy device to obtain spectral reflection data including cellulose and hemicellulose;

[0014] Extract the distribution characteristics of cellulose and hemicellulose based on the spectral reflection data to generate fiber component distribution information;

[0015] Use the multiple linear regression algorithm to perform fitting processing on the fiber component distribution information, and based on the regression parameter weights obtained by fitting, determine the local area where the fiber components are prone to curing risk and mark it as the locally structured easy-to-cure risk area.

[0016] In a preferred embodiment, collect microscopic images of the straw raw material to generate a local microstructure map, and mark potential curing characteristic areas based on feature extraction and classification recognition, specifically including:

[0017] Collect microscopic images of the straw raw material using a microscopic imaging device, and perform denoising and contrast enhancement processing on the microscopic images of the straw raw material through an image preprocessing method;

[0018] Extract the edge features of the fiber microstructure in the microscopic image of the straw raw material through an edge detection algorithm;

[0019] Use the support vector machine classification algorithm to classify and recognize the edge features of the fiber microstructure, and mark the potential curing characteristic areas in the microscopic image of the straw raw material according to the classification results.

[0020] In a preferred embodiment, generate a preprocessed risk-adjusted area based on the locally structured easy-to-cure risk area and the potential curing characteristic area, specifically including:

[0021] Extract the spatial position data of the locally structured easy-to-cure risk area and the potential curing characteristic area;

[0022] Perform a union calculation on the spatial position data of the areas prone to curing risk in the local structure and the potential curing feature areas, determine the minimum bounding box covering the two area ranges, and generate a preprocessed risk adjustment area.

[0023] In a preferred embodiment, by analyzing the gradient change of the fiber arrangement direction in the preprocessed risk adjustment area, evaluate whether the deviation amplitude of the fiber direction arrangement is normal, specifically including:

[0024] Extract the fiber arrangement direction data in the preprocessed risk adjustment area, and generate a two-dimensional distribution map of the fiber arrangement direction based on the fiber arrangement direction data;

[0025] Perform gradient analysis on the two-dimensional distribution map of the fiber arrangement direction, and calculate the gradient change of the fiber arrangement direction at different spatial positions;

[0026] Calculate the deviation amplitude of the fiber direction arrangement in the preprocessed risk adjustment area based on the gradient change of the fiber arrangement direction at different spatial positions. The deviation amplitude includes the average gradient change amplitude and the discreteness of the deviation amplitude;

[0027] Compare the deviation amplitude of the fiber direction arrangement with the preset normal range of the deviation amplitude to evaluate whether the deviation amplitude of the fiber direction arrangement in the preprocessed risk adjustment area is normal.

[0028] In a preferred embodiment, the specific method for obtaining the average gradient change amplitude and the discreteness of the deviation amplitude is as follows:

[0029] Average gradient change amplitude: ; where is the average gradient change amplitude in the preprocessed risk adjustment area, is the total number of grids, represents the row number of the grid in the two-dimensional distribution map, represents the column number of the grid in the two-dimensional distribution map, represents the gradient change amplitude of the fiber arrangement direction in the grid located in the th row and the th column of the two-dimensional distribution map;

[0030] Discreteness of the deviation amplitude: ; where represents the discreteness of the deviation amplitude in the preprocessed risk adjustment area.

[0031] In a preferred embodiment, by analyzing the interface interaction between cellulose and hemicellulose in the preprocessed risk adjustment area, evaluate whether the asymmetric coupling strength of the fiber component interface interaction is reasonable, specifically including:

[0032] Extract the spectral data of cellulose and hemicellulose in the preprocessing risk adjustment area, and generate the interfacial characterization information of fiber components by combining molecular structure detection means;

[0033] Preprocess the interfacial characterization information, and extract the interfacial interaction variables by combining chemical feature analysis methods;

[0034] Construct a high-dimensional feature matrix of the interfacial interaction variables, and decompose the high-dimensional feature matrix by the coupling degree calculation method to obtain the coupling coefficient distribution of the interface interaction between cellulose and hemicellulose. The coupling coefficient distribution includes the correlation strength between the spatial position and the feature dimension, and the correlation strength between the feature dimension and the interfacial interaction variables;

[0035] Based on the positive and negative differences in the coupling coefficient distribution, calculate the asymmetric coupling strength of the interface interaction of fiber components;

[0036] Compare the asymmetric coupling strength with the reasonable range of the coupling strength to judge whether the asymmetric coupling strength of the interface interaction of fiber components in the preprocessing risk adjustment area is reasonable.

[0037] In a preferred embodiment, based on the offset amplitude of the fiber direction arrangement and the evaluation result of the asymmetric coupling strength of the interface interaction of fiber components, judge whether the preprocessing risk adjustment area needs to be immediately optimized for parameters, specifically including:

[0038] When the offset amplitude of the fiber direction arrangement in the preprocessing risk adjustment area is normal and the asymmetric coupling strength of the interface interaction of fiber components is reasonable, it is determined that the preprocessing risk adjustment area does not need to be immediately optimized for parameters; otherwise, it is determined that the preprocessing risk adjustment area needs to be immediately optimized for parameters.

[0039] On the other hand, the present invention provides an intelligent management system for efficient utilization of straw feed resources, including a spectral component analysis module, a microscopic image recognition module, an adjustment area generation module, a fiber offset evaluation module, an interface interaction evaluation module, and a parameter optimization judgment module;

[0040] Spectral component analysis module: Perform spectral component analysis on the straw raw material to generate fiber component distribution information, and fit and process the fiber component distribution information based on the multiple linear regression algorithm to determine the local structure easy-to-cure risk area;

[0041] Microscopic image recognition module: Collect microscopic images of the straw raw material to generate local microstructure maps, and mark potential curing feature areas based on feature extraction and classification recognition;

[0042] Adjustment area generation module: Generate a preprocessing risk adjustment area based on the local structure easy-to-cure risk area and the potential curing feature area;

[0043] Fiber deviation evaluation module: By analyzing the gradient change of the fiber arrangement direction in the preprocessed risk-adjusted area, evaluate whether the deviation amplitude of the fiber direction arrangement is normal;

[0044] Interface interaction evaluation module: By analyzing the interface interaction between cellulose and hemicellulose in the preprocessed risk-adjusted area, evaluate whether the asymmetric coupling strength of the fiber component interface interaction is reasonable;

[0045] Parameter optimization judgment module: Based on the evaluation results of the deviation amplitude of the fiber direction arrangement and the asymmetric coupling strength of the fiber component interface interaction, judge whether the preprocessed risk-adjusted area needs to be immediately subjected to parameter optimization processing.

[0046] Technical effects and advantages of the intelligent management system and method for efficient utilization of straw feed resources of the present invention:

[0047] 1. By obtaining the distribution information of cellulose and hemicellulose through spectral component analysis, and combining the multiple linear regression algorithm to identify the risk areas where the local structure is prone to solidification, the problem of inaccurate identification of local structure anomalies in the prior art is solved. At the same time, through microscopic image acquisition and feature extraction, potential solidification feature areas are further identified, and the two are analyzed to generate a preprocessed risk-adjusted area, providing a basis for subsequent targeted parameter optimization.

[0048] 2. By analyzing the deviation amplitude of the fiber direction arrangement and the asymmetric coupling strength of the interface interaction between cellulose and hemicellulose in the preprocessed risk-adjusted area, the risk factors of microstructure solidification are comprehensively evaluated, making up for the deficiency in the prior art of lacking quantitative evaluation of the abnormal state of the local microstructure. Based on the evaluation results, it is judged whether parameter optimization processing needs to be immediately carried out, realizing the dynamic control of the preprocessing process, improving the preprocessing effect of cellulose and hemicellulose, reducing the probability of microstructure solidification, and ensuring the efficiency of the subsequent enzymatic hydrolysis process and the efficient utilization of straw feed resources. Brief Description of the Drawings

[0049] Figure 1 It is a schematic diagram of an intelligent management method for efficient utilization of straw feed resources of the present invention;

[0050] Figure 2 It is a schematic diagram of the structure of an intelligent management system for efficient utilization of straw feed resources of the present invention. Detailed Embodiments

[0051] 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 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.

[0052] Embodiment 1: Figure 1 An intelligent management method for efficient utilization of straw feed resources of the present invention is given, which includes the following steps:

[0053] Perform spectral component analysis on the straw raw material to generate fiber component distribution information, and perform fitting processing on the fiber component distribution information based on the multiple linear regression algorithm to determine the locally structured easy-to-cure risk area.

[0054] Collect microscopic images of the straw raw material to generate a local microstructure map, and identify potential curing characteristic areas based on feature extraction and classification.

[0055] Generate a preprocessing risk adjustment area based on the locally structured easy-to-cure risk area and the potential curing characteristic area.

[0056] By analyzing the gradient change of the fiber arrangement direction in the preprocessing risk adjustment area, evaluate whether the deviation amplitude of the fiber direction arrangement is normal; by analyzing the interface interaction between cellulose and hemicellulose in the preprocessing risk adjustment area, evaluate whether the asymmetric coupling strength of the fiber component interface interaction is reasonable.

[0057] Based on the evaluation results of the deviation amplitude of the fiber direction arrangement and the asymmetric coupling strength of the fiber component interface interaction, determine whether the preprocessing risk adjustment area needs to be immediately optimized for parameters.

[0058] Perform spectral component analysis on the straw raw material to generate fiber component distribution information, and perform fitting processing on the fiber component distribution information based on the multiple linear regression algorithm to determine the locally structured easy-to-cure risk area, specifically including:

[0059] Collect data on the spectral characteristics of the straw raw material through a near-infrared spectroscopy device to obtain spectral reflection data including cellulose and hemicellulose:

[0060] Use a near-infrared spectroscopy device (such as a Fourier transform near-infrared spectrometer with a wavelength range of 800–2500 nm) to collect spectral characteristic data of the straw raw material. Specifically, the straw raw material is placed at the detection window of the spectroscopy device under normal temperature and natural humidity conditions, and the effective area of the detection window is 5 cm² to ensure uniform coverage of the collected spectral data.

[0061] Among them, the collected straw samples are crushed, and the particle size is controlled below 0.2 mm to ensure the uniformity of spectral detection; each sample is scanned 32 times repeatedly, and the average value is taken to reduce noise interference; the original data output by the near-infrared spectroscopy equipment is a spectral reflectance curve (a function of reflectance varying with wavelength), and the spectral response characteristics of cellulose and hemicellulose at different wavelengths are included in the curve.

[0062] Feature extraction is performed on the distribution characteristics of cellulose and hemicellulose based on the spectral reflectance data to generate fiber component distribution information:

[0063] The spectral reflectance is normalized between 0 and 1 to eliminate the influence of intensity differences; the Savitzky-Golay smoothing filtering method is used for denoising, the filtering window is set to 11 points, and the signal details are retained; the spectral change rate is extracted to enhance the signal characteristics of cellulose and hemicellulose and suppress background interference.

[0064] The fiber component distribution information includes the following cellulose distribution characteristics and hemicellulose distribution characteristics.

[0065] The cellulose distribution characteristics include: characteristic bands: 1200 nm, 1450 nm (cellulose characteristic bands); characteristic variables: reflectance peak, reflection area, peak position, etc. of cellulose in the characteristic bands; distribution law: through the statistical analysis of multiple sample data, the distribution pattern of cellulose in space is formed.

[0066] The hemicellulose distribution characteristics include: characteristic bands: 1700 nm, 1930 nm (hemicellulose characteristic bands); characteristic variables: reflectance peak, reflection area, etc. of hemicellulose in the characteristic bands; distribution law: similarly, the spatial distribution law of hemicellulose is formed based on the reflection characteristics.

[0067] The fiber component distribution information is fitted using the multiple linear regression algorithm, and based on the regression parameter weights obtained from the fitting, the local regions with a risk of easy solidification of the fiber components are determined and marked as local structure easy solidification risk regions:

[0068] A regression model is established, with the cellulose distribution characteristics and the distribution characteristic variables of hemicellulose as inputs to predict their influence on the solidification risk: ; where represents the solidification risk score of the local region, represents the cellulose characteristic variable, represents the hemicellulose characteristic variable, and are the regression coefficients, represents the random error.

[0069] The local area refers to a specific small - range area divided by spatial position calibration during the spectral acquisition of the straw sample, which is used to accurately represent the physical position corresponding to the spectral data; and are the regression coefficients in the multiple linear regression model, which respectively represent the relative influence degrees of the cellulose characteristic variables and the hemicellulose characteristic variables on the curing risk score, and their values are determined by fitting experimental data.

[0070] The cellulose characteristic variables are important parameters reflecting the distribution characteristics of cellulose extracted from spectral reflection data, including the peak reflectance, peak area, and peak position of cellulose in specific bands (such as 1200 nm and 1450 nm); the hemicellulose characteristic variables are important parameters reflecting the distribution characteristics of hemicellulose extracted from spectral reflection data, including the peak reflectance, peak area, and peak position of hemicellulose in specific bands (such as 1700 nm and 1930 nm).

[0071] Through the regression calculation of multiple local areas, the regression parameter weights (such as the values of and ) are obtained, and the contribution degrees of cellulose and hemicellulose characteristics to the curing risk are analyzed.

[0072] According to the regression model, the curing risk score is calculated for each local area, providing a basis for subsequent area risk judgment.

[0073] When the curing risk score of the local area is greater than the local curing risk threshold, the local area is marked as a local structure easily - cured risk area, indicating that the distribution characteristics of fiber components within this area may lead to an increased risk of microstructure curing. It is necessary to confirm through subsequent analysis whether optimization treatment is required to avoid adverse effects on the pretreatment process.

[0074] The local curing risk threshold is a scoring critical value determined through experimental data and statistical analysis, which is used to determine whether there is an easily - cured risk in the local area and is usually dynamically set according to different straw varieties and process conditions.

[0075] Microscopic images of the straw raw material are collected to generate local microstructure maps, and potential curing characteristic areas are marked based on feature extraction and classification recognition, specifically including:

[0076] Use a microscopic imaging device to collect microscopic images of the straw raw material, and perform denoising and contrast enhancement processing on the microscopic images of the straw raw material through image pre - processing methods:

[0077] Use a microscopic imaging device to image the cross - section of the straw raw material, and the specific operation is as follows:

[0078] The straw raw material was cut into sample blocks with dimensions of 5 mm × 5 mm, and a representative cross-section was selected for imaging; the surface of the sample was treated with a metal spraying layer (thickness about 10 nm) to improve the imaging effect; multi-angle imaging was performed on each sample cross-section to ensure the integrity of microstructural details in different directions.

[0079] The Gaussian filtering algorithm was used to denoise the image. The window size of the Gaussian filter was set to 3×3 to effectively remove the imaging noise while retaining the edge details of the fiber microstructure.

[0080] The histogram equalization method was used to enhance the image contrast, making the fiber edges clearer.

[0081] Through denoising and contrast enhancement processing, clear and high-quality microscopic images were generated, providing a basis for subsequent edge feature extraction.

[0082] The edge features of the fiber microstructure in the microscopic image of the straw raw material were extracted through an edge detection algorithm:

[0083] The Canny edge detection algorithm was used. This algorithm has low sensitivity to noise and is suitable for processing fine structures in microscopic images.

[0084] First, Gaussian smoothing was performed on the preprocessed image, and the smoothing parameter was the same as that in the noise removal step.

[0085] The gradient magnitude of the smoothed image was calculated: ; where and are the horizontal and vertical gradient values of the pixel, and is the gradient magnitude.

[0086] The gradient direction was calculated: ; where is the gradient direction.

[0087] Non-maximum suppression was performed on the gradient magnitude to retain the edge pixels of the local maximum of the gradient.

[0088] The double-threshold segmentation method was used to determine the edge pixel points. The pixels with edge intensity higher than the upper threshold were marked as edges, the pixels with edge intensity lower than the lower threshold were marked as non-edges, and the pixels with intensity between the two were judged through adjacent edge connectivity.

[0089] The result output by the Canny algorithm is a binary image, where the white pixels represent the edge positions of the fiber microstructure and the black pixels represent the non-edge regions.

[0090] The support vector machine classification algorithm was used to classify and identify the edge features of the fiber microstructure, and the potential curing feature regions in the microscopic image of the straw raw material were marked according to the classification results:

[0091] Extract the following fiber edge features from the binary edge image:

[0092] Edge density: The number of edge pixels per unit area;

[0093] Edge length: The cumulative length of the fiber edge;

[0094] Edge directionality: The distribution statistics of the edge gradient direction.

[0095] Combine the fiber edge features into a multi-dimensional feature vector as the input of the classification algorithm.

[0096] Use the labeled dataset to train the support vector machine classification algorithm. The positive samples are the known cured areas, and the negative samples are the normal areas.

[0097] The training process uses the radial basis kernel function (RBF) to achieve non-linear classification: Through this kernel function, the fiber edge features are mapped from a low-dimensional space to a high-dimensional space to enhance the discrimination between features, thereby achieving effective classification of potential cured feature areas. The kernel function parameters are optimized through cross-validation to ensure the accuracy and robustness of the classification model.

[0098] Input the fiber edge feature vector into the trained support vector machine classification algorithm to classify each region in the microscopic image.

[0099] Mark the regions with positive sample classification results as potential cured feature areas and generate an annotation map.

[0100] Based on the locally structured easily cured risk areas and potential cured feature areas, generate a preprocessing risk adjustment area, specifically including:

[0101] Extract the spatial location data of the locally structured easily cured risk areas and potential cured feature areas:

[0102] The data of the locally structured easily cured risk areas comes from the analysis results of the fiber component distribution information. This result determines the high-risk areas through the multiple linear regression algorithm and stores their spatial location data.

[0103] The data of the potential cured feature areas comes from the microscopic image classification and recognition results, and the spatial location coordinates of each feature area have been marked in the classification results.

[0104] The spatial location data of both types of areas are stored in the form of three-dimensional coordinates, specifically including the number, starting coordinates, and ending coordinates of each area.

[0105] Extract the spatial location data of the two types of regions from the corresponding data storage structures respectively; uniformly standardize the coordinate data and map all coordinate values into the three-dimensional space layout range of the preprocessing device to ensure data consistency.

[0106] Perform a union calculation on the spatial location data of the regions with high risk of local structure solidification and the regions with potential solidification features, determine the minimum bounding box covering the two regions, and generate the preprocessing risk adjustment region:

[0107] Merge the spatial locations of the regions with high risk of local structure solidification and the regions with potential solidification features to form a comprehensive region containing all high-risk positions, that is, the preprocessing risk adjustment region.

[0108] The union calculation is based on the three-dimensional coordinate bounding box of the region, and each bounding box is defined by the starting coordinates and the ending coordinates.

[0109] By analyzing the change in the fiber arrangement direction gradient in the preprocessing risk adjustment region, evaluate whether the deviation amplitude of the fiber direction arrangement is normal, specifically including:

[0110] Extract the fiber arrangement direction data in the preprocessing risk adjustment region and generate a two-dimensional distribution map of the fiber arrangement direction based on the fiber arrangement direction data:

[0111] The fiber arrangement direction data is derived from the microscopic image analysis in the preprocessing risk adjustment region. After edge detection and feature extraction of the microscopic image, the relevant data of the fiber arrangement direction is generated and stored in vector form. Each vector includes three items: the spatial coordinates of the data point, the arrangement direction angle of the fiber, and the region number where it is located.

[0112] Map the fiber arrangement direction data in the three-dimensional space onto a two-dimensional plane. Specifically, extract the horizontal and vertical spatial coordinate information from each data point, ignore the height coordinate, and convert it into a planar position.

[0113] The fiber direction angle data is directly retained to represent the specific arrangement direction of the fiber on this plane.

[0114] Divide the plane into several small regions (rasterize) on a two-dimensional coordinate plane. The fiber arrangement direction within each grid is represented by an arrow. The orientation of the arrow corresponds to the fiber arrangement direction angle, and the color or thickness of the arrow represents the distribution intensity of the fiber direction data. The finally generated two-dimensional distribution map visually presents the spatial distribution of the fiber direction for subsequent gradient change analysis.

[0115] Perform gradient analysis on the two-dimensional distribution map of the fiber arrangement direction and calculate the gradient change of the fiber arrangement direction at different spatial positions:

[0116] The purpose of gradient analysis is to calculate the rate of change of the fiber alignment direction in a two-dimensional plane, which is used to measure the consistency of the fiber directions in adjacent regions. The analysis method determines the magnitude and direction of the direction change by comparing the difference in the fiber direction angles in each grid with those in its adjacent grids. The gradient change in the horizontal direction represents the difference in the fiber alignment directions in the horizontal direction between adjacent grids, and the gradient change in the vertical direction represents the difference in the fiber alignment directions in the vertical direction between adjacent grids.

[0117] For each grid in the two-dimensional distribution map, calculate the difference values of the fiber alignment directions in the horizontal and vertical directions one by one; the total gradient change of each grid can be obtained by combining the horizontal direction difference and the vertical direction difference to generate a comprehensive change value for the grid.

[0118] Store the gradient change results (the comprehensive change values of the grids) of all grids in the form of a matrix, where each matrix element corresponds to the gradient change value of a grid; for example, each row of the matrix represents a row of grids in the two-dimensional distribution map, and each column represents the column position of the grid.

[0119] Calculate the offset amplitude of the fiber direction alignment in the preprocessing risk adjustment area based on the gradient change of the fiber alignment direction at different spatial positions:

[0120] The offset amplitude is an important indicator to measure the consistency of the fiber alignment direction, which represents the overall change trend of the fiber direction in spatial positions. By statistically analyzing the data of the gradient change matrix, calculate the offset amplitude within the area.

[0121] Calculate the overall gradient change by taking the mean of all elements in the gradient change matrix: ; where is the average gradient change amplitude within the preprocessing risk adjustment area, is the total number of grids, represents the row number of the grid in the two-dimensional distribution map, represents the column number of the grid in the two-dimensional distribution map, represents the gradient change amplitude of the fiber alignment direction within the grid located in the th row and th column in the two-dimensional distribution map.

[0122] The offset amplitude is quantified in the form of standard deviation: ; where represents the discreteness of the offset amplitude within the preprocessing risk adjustment area.

[0123] The offset amplitude includes the average gradient change amplitude and the discreteness of the offset amplitude.

[0124] Compare the deviation amplitude of the fiber direction arrangement with the normal range of the preset deviation amplitude to evaluate whether the deviation amplitude of the fiber direction arrangement in the preprocessing risk adjustment area is normal:

[0125] Compare the average gradient change amplitude in the preprocessing risk adjustment area with its corresponding normal range of the preset deviation amplitude; compare the discreteness of the deviation amplitude in the preprocessing risk adjustment area with its corresponding normal range of the preset deviation amplitude.

[0126] When both the average gradient change amplitude and the discreteness of the deviation amplitude in the preprocessing risk adjustment area are within their corresponding normal ranges of the preset deviation amplitude, it is determined that the deviation amplitude of the fiber direction arrangement in the preprocessing risk adjustment area is normal; otherwise, it is determined that the deviation amplitude of the fiber direction arrangement in the preprocessing risk adjustment area is abnormal.

[0127] Among them, the normal range of the preset deviation amplitude corresponding to the average gradient change amplitude is a numerical interval set through experiments and statistical analyses, which is used to determine whether the overall change level of the fiber direction arrangement is within the process standard; the normal range of the preset deviation amplitude corresponding to the discreteness of the deviation amplitude is a standard for measuring the uniformity of the change distribution of the fiber direction arrangement, which is used to determine whether the local gradient fluctuation meets the stability index requirements of the process.

[0128] By analyzing the interfacial interaction between cellulose and hemicellulose in the preprocessing risk adjustment area, evaluate whether the asymmetric coupling strength of the fiber component interfacial interaction is reasonable, specifically including:

[0129] Extract the spectral data of cellulose and hemicellulose in the preprocessing risk adjustment area, and generate interfacial characterization information of the fiber components in combination with molecular structure detection means:

[0130] Use a high-resolution near-infrared spectroscopy analysis device to collect spectra of the fiber components (cellulose and hemicellulose) in the preprocessing risk adjustment area. The spectral data includes the reflection intensities in different wavelength ranges, and these wavelengths correspond to the characteristic absorption peaks of cellulose and hemicellulose.

[0131] Utilize the molecular vibration frequency analysis method to deduce the intermolecular interaction characteristics between cellulose and hemicellulose at the interface through the spectral data. The obtained interfacial characteristic parameters include hydrogen bond strength, intermolecular force change value, and chemical shift.

[0132] Convert the spectral data into interfacial characterization information through molecular structure detection. The interfacial characterization information includes intermolecular interaction force, adsorption energy distribution, and chemical environment change parameters.

[0133] Preprocess the interfacial characterization information, and extract interfacial interaction variables in combination with chemical characteristic analysis methods:

[0134] Normalize the interface characterization information to convert the values from different sources into the same dimensional range.

[0135] Extract the following interface interaction variables from the normalized interface characterization information:

[0136] Intermolecular interaction strength variable: Measure the interaction strength between cellulose and hemicellulose at the interface;

[0137] Chemical shift variable: Reflect the change in the intermolecular chemical environment at the interface;

[0138] Molecular adsorption variable: Represent the adsorption ability of interface molecules.

[0139] Construct a high-dimensional feature matrix of the interface interaction variables, and decompose the high-dimensional feature matrix through a coupling degree calculation method to obtain the coupling coefficient distribution of the cellulose-hemicellulose interface interaction:

[0140] Construct the high-dimensional feature matrix: Organize the interface interaction variables into a matrix structure, where the rows of the matrix represent different spatial positions and the columns represent different variable types.

[0141] The elements of the high-dimensional feature matrix are defined as: ; where represents the element in the th row and th column of the high-dimensional feature matrix, represents the rd spatial position corresponding to the value of the th interface interaction variable.

[0142] Use the sparse regularization decomposition method to decompose the high-dimensional feature matrix, extract the coupling coefficient distribution of the cellulose-hemicellulose interface interaction, and the decomposed high-dimensional feature matrix is expressed as: ; where represents the correlation strength between the th spatial position and the th feature dimension (used to reflect the contribution degree of the interface interaction variable at this position in a specific dimension), represents the correlation strength between the th feature dimension and the th interface interaction variable (used to reflect the influence weight of this feature dimension among different interaction variables).

[0143] The coupling coefficient distribution is the result obtained after decomposing the high-dimensional feature matrix, specifically including the correlation strength between the spatial position and the feature dimension and the correlation strength between the feature dimension and the interface interaction variable.

[0144] Based on the positive and negative differences in the coupling coefficient distribution, calculate the asymmetric coupling strength of the fiber component interface interaction:

[0145] Extract the difference value of the forward and reverse interaction intensities from the coupling coefficient distribution, and define the forward and reverse difference as: ; where represents the difference value of the forward and reverse interaction intensities, represents the correlation intensity of the th spatial position in the preprocessed risk adjustment region on the th forward interaction dimension, represents the correlation intensity of the th spatial position in the preprocessed risk adjustment region on the th reverse interaction dimension, represents the total number of spatial positions, represents the number of the forward interaction dimension, represents the number of the reverse interaction dimension.

[0146] The asymmetric coupling intensity represents the overall difference between the forward and reverse interactions in the entire region, that is, the asymmetric coupling intensity is the ratio of the sum of the difference values of the forward and reverse interaction intensities of all interface interaction variables to the number of interface interaction variables.

[0147] Compare the asymmetric coupling intensity with the reasonable range of the coupling intensity to determine whether the asymmetric coupling intensity of the fiber component interface interaction in the preprocessed risk adjustment region is reasonable:

[0148] Set the reasonable range of the coupling intensity. The reasonable range of the coupling intensity is a numerical interval determined by experimental data statistics and process analysis, which is used to judge the balance and stability of the fiber component interface interaction. The upper and lower limits of the reasonable range correspond to the minimum stable value and the maximum allowable deviation of the interface interaction respectively, ensuring that the interface function meets the requirements of the preprocessing process.

[0149] Judge whether the asymmetric coupling intensity is within the reasonable range of the coupling intensity:

[0150] When the asymmetric coupling intensity is not within the reasonable range of the coupling intensity, it is determined that the asymmetric coupling intensity of the fiber component interface interaction in the preprocessed risk adjustment region is unreasonable; it indicates that there are significant forward and reverse differences between cellulose and hemicellulose in the interface interaction, which may lead to functional imbalance or reduced reaction efficiency of the interface interaction.

[0151] When the asymmetric coupling intensity is within the reasonable range of the coupling intensity, it is determined that the asymmetric coupling intensity of the fiber component interface interaction in the preprocessed risk adjustment region is reasonable; it indicates that the interface interaction is stable and meets the process requirements of the preprocessed risk adjustment region.

[0152] Based on the evaluation results of the offset amplitude of the fiber direction arrangement and the asymmetry coupling strength of the fiber component interface interaction, determine whether the pretreatment risk adjustment area requires immediate parameter optimization, specifically including:

[0153] An abnormal offset amplitude indicates abnormal distribution of the fiber direction in the local area, which may lead to fiber stress concentration or structural looseness in the processing area, thus triggering the curing effect or affecting the efficiency and uniformity of subsequent processing (such as enzymatic hydrolysis); an unreasonable asymmetry coupling strength of the interface interaction means that there is a functional imbalance in the interaction between cellulose and hemicellulose at the local interface, which may cause uneven response of the fiber components to chemical reagents or the thermal and humid environment during pretreatment, further exacerbating the risk of local structure curing.

[0154] When the offset amplitude of the fiber direction arrangement in the pretreatment risk adjustment area is normal and the asymmetry coupling strength of the fiber component interface interaction is reasonable, it is determined that the pretreatment risk adjustment area does not require immediate parameter optimization, indicating that the arrangement stability of cellulose and hemicellulose in the local structure and the interface interaction performance both meet the requirements of the pretreatment process, and parameter optimization is not required immediately under the current process conditions.

[0155] When the offset amplitude of the fiber direction arrangement in the pretreatment risk adjustment area is abnormal, or the asymmetry coupling strength of the fiber component interface interaction is unreasonable, it is determined that the pretreatment risk adjustment area requires immediate parameter optimization, indicating that there is an abnormal arrangement of the fiber microstructure in this area, or the interface interaction performance between cellulose and hemicellulose does not reach the expected equilibrium state, which may lead to local curing or a decrease in the pretreatment effect. Therefore, it is determined that this area requires immediate parameter optimization.

[0156] Parameter optimization is aimed at the abnormal arrangement of the fiber direction or the unreasonable interface interaction of the fiber components in the pretreatment risk adjustment area. By adjusting the process parameters, the processing effect of the local area is restored to ensure the stable and efficient overall pretreatment process. Parameter optimization includes:

[0157] Adjust the humidity parameter: increase or decrease the humidity level in the pretreatment risk adjustment area to make the softening degree of the structures of cellulose and hemicellulose uniform; install a precise humidification device in the area with abnormal humidity to perform local spray humidification on the area with insufficient humidity; for the area with excessive humidity, increase the wind speed of the ventilation equipment to reduce the local humidity.

[0158] Adjust the temperature parameter: by regulating the temperature distribution in the pretreatment risk adjustment area, promote the uniformity of the interface reaction between cellulose and hemicellulose, and reduce the risk of local structure curing; in the area with insufficient temperature, use local heating equipment such as steam injection or heating rods to increase the area temperature; in the area with excessive temperature, reduce the heat source output or increase the wind speed of the cooling fan to balance the area temperature.

[0159] Adjust the concentration of chemical reagents: Optimize the local concentration distribution of chemical reagents in the pre-treatment risk adjustment area to ensure stable reaction conditions at the interface between cellulose and hemicellulose; in areas where the reagent concentration is insufficient, increase the injection volume of local chemical reagents and adjust the injection angle to cover the target area; in areas where the reagent concentration is too high, dilute the chemical reagents or reduce the injection time to improve local uniformity.

[0160] Optimize the reaction time: By extending or shortening the pre-treatment time, improve the stability of fiber orientation arrangement and restore a reasonable state of interface interaction; for areas where the reaction time needs to be extended, increase the residence time or cycle times of the equipment; for areas where the reaction time needs to be shortened, reduce the operating speed of the equipment or the material handling time to avoid fiber damage caused by over-treatment.

[0161] Optimize the mechanical pressure: Adjust the mechanical pressure distribution to improve the uniformity of fiber orientation arrangement and prevent local abnormalities caused by excessive or insufficient pressure; in areas where the pressure is insufficient, increase the local pressure of the compression equipment; in areas where the pressure is too high, reduce the compression strength or adjust the material distribution to ensure uniform mechanical action.

[0162] Example 2: The difference between Example 2 and Example 1 of the present invention is that this example introduces an intelligent management system for efficient utilization of straw feed resources.

[0163] Figure 2 The structural schematic diagram of an intelligent management system for efficient utilization of straw feed resources of the present invention is given. An intelligent management system for efficient utilization of straw feed resources includes a spectral component analysis module, a microscopic image recognition module, an adjustment area generation module, a fiber offset evaluation module, an interface interaction evaluation module, and a parameter optimization judgment module.

[0164] Spectral component analysis module: Perform spectral component analysis on the straw raw material to generate fiber component distribution information, and fit and process the fiber component distribution information based on the multiple linear regression algorithm to determine the areas at risk of local structure solidification.

[0165] Microscopic image recognition module: Collect microscopic images of the straw raw material to generate local microstructure maps, and identify and mark potential solidification feature areas based on feature extraction and classification.

[0166] Adjustment area generation module: Generate a pre-treatment risk adjustment area based on the areas at risk of local structure solidification and potential solidification feature areas.

[0167] Fiber offset evaluation module: Evaluate whether the offset amplitude of the fiber orientation arrangement is normal by analyzing the gradient change of the fiber arrangement direction in the pre-treatment risk adjustment area.

[0168] Interface interaction evaluation module: By analyzing the interface interaction between cellulose and hemicellulose in the preprocessing risk adjustment area, evaluate whether the asymmetric coupling strength of the fiber component interface interaction is reasonable.

[0169] Parameter optimization judgment module: Based on the offset amplitude of the fiber direction arrangement and the evaluation result of the asymmetric coupling strength of the fiber component interface interaction, judge whether the preprocessing risk adjustment area needs to be immediately processed for parameter optimization.

[0170] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0171] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0172] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0173] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0174] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0175] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] In addition, in each embodiment of the present application, the various functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0177] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0178] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

[0179] Finally, the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, 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. An intelligent management method for efficient utilization of straw feed resources, characterized in that: The steps include: Spectral component analysis of straw raw materials is performed to generate fiber component distribution information, and the fiber component distribution information is fitted and processed based on a multivariate linear regression algorithm to determine the local structure solidification risk area; Microscopic images of straw raw materials are collected to generate local microstructure maps, and potential solidification feature areas are marked based on feature extraction and classification identification; Generate pre-treatment risk adjustment areas based on local structural solidification risk areas and potential solidification feature areas; By analyzing the fiber alignment gradient changes in the pre-treatment risk adjustment area, it is assessed whether the deviation amplitude of the fiber alignment is normal; By analyzing the interface interaction between cellulose and hemicellulose in the pretreatment risk adjustment area, the asymmetric coupling strength of the interface interaction between fiber components is evaluated to see whether it is reasonable. Based on the evaluation results of the deviation amplitude of the fiber direction arrangement and the asymmetric coupling strength of the fiber component interface interaction, it is judged whether the pretreatment risk adjustment area needs immediate parameter optimization.

2. The intelligent management method for efficient utilization of straw feed resources according to claim 1, characterized in that: The fiber component distribution information is generated by spectral component analysis of straw raw materials. The fiber component distribution information is fitted and processed based on the multivariate linear regression algorithm to determine the local structure solidification risk area, including: The spectral characteristics of the straw raw material are collected by near infrared spectroscopy equipment to obtain spectral reflectance data including cellulose and hemicellulose; Extract the distribution characteristics of cellulose and hemicellulose based on the spectral reflectance data to generate fiber component distribution information; The fiber component distribution information was fitted using a multivariate linear regression algorithm. Based on the regression parameter weights obtained from the fitting, local areas where the fiber components had a risk of easy solidification were determined and marked as local structural risk areas of easy solidification.

3. The intelligent management method for efficient utilization of straw feed resources according to claim 2, characterized in that: Microscopic images of straw raw materials are collected to generate local microstructure maps, and potential solidification feature areas are marked based on feature extraction and classification identification, including: Microscopic images of straw raw materials are collected by using microscopic imaging equipment, and the microscopic images of straw raw materials are denoised and contrast enhanced by using image preprocessing methods. The edge features of fiber microstructure in the microscopic image of straw raw material are extracted by edge detection algorithm; The edge features of the fiber microstructure were classified and identified using the support vector machine classification algorithm, and the potential solidification feature areas in the microscopic images of the straw raw materials were marked according to the classification results.

4. The intelligent management method for efficient utilization of straw feed resources according to claim 3, characterized in that: Based on the local structure easy solidification risk area and potential solidification characteristic area, the pre-processing risk adjustment area is generated, including: Extract the spatial location data of the local structure prone to solidification risk areas and potential solidification feature areas; The spatial location data of the local structure prone to solidification risk area and the potential solidification feature area are combined to calculate the minimum bounding box covering the two areas and generate the preprocessing risk adjustment area.

5. The intelligent management method for efficient utilization of straw feed resources according to claim 4, characterized in that: By analyzing the fiber arrangement direction gradient changes in the pre-treatment risk adjustment area, it is evaluated whether the deviation amplitude of the fiber direction arrangement is normal, including: Extracting fiber arrangement direction data in the pre-processing risk adjustment area, and generating a two-dimensional distribution map of the fiber arrangement direction based on the fiber arrangement direction data; Perform gradient analysis on the two-dimensional distribution map of fiber arrangement direction to calculate the gradient change of fiber arrangement direction at different spatial positions; The deviation amplitude of the fiber direction arrangement in the pretreatment risk adjustment area is calculated based on the gradient change of the fiber arrangement direction at different spatial positions, and the deviation amplitude includes the average gradient change amplitude and the discreteness of the deviation amplitude; The deviation amplitude of the fiber direction arrangement is compared with the preset normal range of the deviation amplitude to evaluate whether the deviation amplitude of the fiber direction arrangement in the pretreatment risk adjustment area is normal.

6. The intelligent management method for efficient utilization of straw feed resources according to claim 5, characterized in that: The method for obtaining the discreteness of the average gradient change amplitude and the offset amplitude is specifically as follows: Average gradient change: ;in, is the average gradient change within the pretreatment risk-adjusted region, is the total number of grids, Represents the row number of the grid in the two-dimensional distribution map, Indicates the column number of the grid in the two-dimensional distribution map, Indicates that the Line The magnitude of the gradient change in the fiber arrangement direction within the grid of the column; Discreteness of offset amplitude: ;in, Represents the discreteness of the magnitude of the shift within the pretreatment risk-adjusted region.

7. The intelligent management method for efficient utilization of straw feed resources according to claim 6, characterized in that: By analyzing the interfacial interaction between cellulose and hemicellulose in the pretreatment risk adjustment area, the asymmetric coupling strength of the interfacial interaction between fiber components is evaluated to see whether it is reasonable, including: Extract the spectral data of cellulose and hemicellulose in the pretreatment risk adjustment area, and generate the interface characterization information of fiber components by combining molecular structure detection methods; Preprocess the interface characterization information and extract interface interaction variables by combining chemical feature analysis methods; A high-dimensional feature matrix of interface interaction variables is constructed, and the high-dimensional feature matrix is ​​decomposed by a coupling degree calculation method to obtain the coupling coefficient distribution of the interface interaction between cellulose and hemicellulose. The coupling coefficient distribution includes the correlation strength between the spatial position and the feature dimension, and the correlation strength between the feature dimension and the interface interaction variable. Based on the positive and negative differences in the coupling coefficient distribution, the asymmetric coupling strength of the fiber component interface interaction is calculated; The asymmetric coupling strength is compared with the reasonable range of coupling strength to determine whether the asymmetric coupling strength of the fiber component interface interaction in the pretreatment risk adjustment area is reasonable.

8. The intelligent management method for efficient utilization of straw feed resources according to claim 7, characterized in that: Based on the evaluation results of the deviation amplitude of the fiber direction arrangement and the asymmetric coupling strength of the fiber component interface interaction, it is judged whether the pretreatment risk adjustment area needs to be immediately optimized, including: When the offset amplitude of the fiber direction arrangement in the pretreatment risk adjustment area is normal and the asymmetric coupling strength of the fiber component interface interaction is reasonable, it is determined that the pretreatment risk adjustment area does not need to be immediately optimized; otherwise, it is determined that the pretreatment risk adjustment area needs to be immediately optimized.

9. An intelligent management system for efficient utilization of straw feed resources, used to implement the intelligent management method for efficient utilization of straw feed resources according to any one of claims 1 to 8, characterized in that: It includes a spectral component analysis module, a microscopic image recognition module, an adjustment area generation module, a fiber offset evaluation module, an interface interaction evaluation module, and a parameter optimization judgment module; Spectral component analysis module: Spectral component analysis of straw raw materials is performed to generate fiber component distribution information, and the fiber component distribution information is processed based on a multivariate linear regression algorithm to determine the local structure solidification risk area; Microscopic image recognition module: collect microscopic images of straw raw materials to generate local microstructure maps, and mark potential solidification feature areas based on feature extraction and classification identification; Adjustment area generation module: Generates pre-processing risk adjustment areas based on local structure easy-to-solidify risk areas and potential solidification feature areas; Fiber deviation assessment module: By analyzing the fiber arrangement direction gradient change in the pre-treatment risk adjustment area, it is assessed whether the deviation amplitude of the fiber direction arrangement is normal; Interface interaction assessment module: By analyzing the interface interaction between cellulose and hemicellulose in the pretreatment risk adjustment area, it is evaluated whether the asymmetric coupling strength of the fiber component interface interaction is reasonable; Parameter optimization judgment module: Based on the evaluation results of the offset amplitude of the fiber direction arrangement and the asymmetric coupling strength of the fiber component interface interaction, it is judged whether the pretreatment risk adjustment area needs to be immediately optimized.

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