Computer signal processing-based automatic detection method and system for surface defects of printed fabric

Abstract

This invention provides an automatic detection method and system for surface defects of printed fabrics based on computer signal processing, belonging to the field of data processing technology. The method includes: setting an ultrasonic transducer array above the fabric's travel path, and fixing a photoelectric position sensor at the beginning and end of the path respectively; determining a virtual reference direction based on the fabric edge position information detected in real time by the two photoelectric position sensors; resolving the virtual reference direction into multiple continuous analysis intervals; evaluating the tension and smoothness properties of the fabric in each analysis interval to obtain dynamic adjustment values; and adjusting the emission parameters of the ultrasonic transducer array in real time based on the dynamic adjustment values, emitting controlled ultrasonic waves of a specific frequency onto the fabric surface, and receiving the reflected echoes to obtain the original acoustic signal data of the fabric. This invention achieves highly adaptable automatic detection of surface and near-surface defects in printed fabrics.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an automatic detection method and system for surface defects of printed fabrics based on computer signal processing. Background Technology

[0002] Fabric surface defect detection is a core part of quality control, but traditional detection methods have shortcomings. The missed detection rate of manual visual inspection is as high as 8% to 25%, and the inspection volume per shift is less than 800 linear meters, which is difficult to match the needs of high-speed production lines. Although mainstream AI vision inspection systems have achieved automation, they are limited by optical imaging principles and have prominent problems in the inspection of complex patterns and flexible fabrics.

[0003] A company specializing in digitally printed home textile fabrics once failed to detect a 0.6mm registration deviation when inspecting cotton and linen printed fabrics with gradient patterns using a 5-megapixel visual inspection system. This resulted in a bedding set being returned by the customer due to substandard quality. This case exposed a typical technical flaw: visual inspection relies on surface light reflection features to extract information, which cannot effectively decouple the optical signal differences between the normal texture fluctuations of gradient patterns and defects such as registration deviations. Furthermore, micro-wrinkles caused by tension changes during fabric movement can cause imaging distortion, reducing the detection accuracy of small, hidden defects and making it difficult to meet the full-width, high-precision inspection requirements of high-end printed fabrics. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an automatic detection method and system for surface defects of printed fabrics based on computer signal processing, so as to achieve highly adaptable automatic detection of surface and near-surface defects of printed fabrics.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A first aspect is an automatic detection method for surface defects of printed fabrics based on computer signal processing, the method comprising:

[0007] An ultrasonic transducer array is set above the fabric travel path, and a photoelectric position sensor is fixedly installed at the beginning and end of the path, respectively. Based on the fabric edge position information detected in real time by the two photoelectric position sensors, a virtual reference direction is determined. The virtual reference direction is resolved into multiple continuous analysis intervals. The tension and smoothness properties of the fabric in each analysis interval are evaluated to obtain dynamic adjustment values. Based on the dynamic adjustment values, the emission parameters of the ultrasonic transducer array are adjusted in real time to emit controlled ultrasonic waves of a specific frequency onto the fabric surface and receive the reflected echoes to obtain the original acoustic signal data of the fabric.

[0008] The raw acoustic signal data is preprocessed to obtain a standardized material characteristic signal; the standardized material characteristic signal is then compared with a preset reference material signal corresponding to a flawless printed fabric to obtain the signal difference results.

[0009] Multi-dimensional feature analysis was performed on the signal difference results to distinguish signal fluctuations caused by normal changes in the color and material of the printed pattern, as well as abnormal signal features caused by defects. Potential defect areas were located by using a polygon area calculation algorithm.

[0010] Based on the abnormal signal characteristics corresponding to potential defect areas, the defect types are classified and the severity is determined, resulting in a judgment result that includes defect category and location information;

[0011] The judgment result is converted into a sorting control instruction, and the defective fabric segments are separated from the qualified products according to the sorting control instruction.

[0012] Furthermore, an ultrasonic transducer array is positioned above the fabric travel path, and a photoelectric position sensor is fixedly installed at the beginning and end of the path, respectively. Based on the real-time detection of the fabric edge position information by the two photoelectric position sensors, a virtual reference direction is determined. The virtual reference direction is resolved into multiple continuous analysis intervals. The tension and smoothness properties of the fabric in each analysis interval are evaluated to obtain dynamic adjustment values. Based on the dynamic adjustment values, the emission parameters of the ultrasonic transducer array are adjusted in real time to emit controlled ultrasonic waves of a specific frequency onto the fabric surface and receive the reflected echoes to obtain the original acoustic signal data of the fabric, including:

[0013] By setting two photoelectric position sensors at the beginning and end of the fabric travel path, the position information of the fabric edge is obtained in real time;

[0014] A dynamically changing virtual reference direction is determined based on location information;

[0015] The virtual reference direction is resolved into multiple consecutive analysis intervals; the tension and smoothness properties of the fabric are evaluated in each consecutive analysis interval to obtain the corresponding dynamic adjustment values;

[0016] The emission parameters of the ultrasonic transducer array are adjusted in real time according to the dynamic adjustment value; the adjusted ultrasonic transducer array is used to emit ultrasonic waves to the fabric surface and receive the reflected echoes to obtain the original acoustic signal data.

[0017] Furthermore, the raw acoustic signal data is preprocessed to obtain standardized material characteristic signals; these standardized material characteristic signals are then compared with preset reference material signals corresponding to flawless printed fabrics to obtain signal difference results, including:

[0018] The original acoustic signal data is denoised and filtered to obtain a purified acoustic signal;

[0019] The amplitude of the purified acoustic signal is normalized to obtain a standardized material property signal;

[0020] The standardized material characteristic signals are compared point by point with the preset reference material signals of flawless printed fabrics to obtain the comparison results.

[0021] The degree of difference between signals is calculated based on the comparison results, and the signal difference results characterizing the abnormal state of the fabric are obtained.

[0022] Furthermore, multi-dimensional feature analysis was performed on the signal difference results to distinguish signal fluctuations caused by normal variations in the color and material of the printed pattern, as well as abnormal signal features caused by defects. Potential defect areas were located using a polygon area calculation algorithm, including:

[0023] Multi-dimensional feature extraction in the time and frequency domains is performed on the signal difference results to generate a feature vector set;

[0024] Based on the feature vector set, the signal fluctuation features caused by normal changes in the color and material of the printed pattern and the abnormal signal features caused by defects are distinguished by feature classification.

[0025] The identified abnormal signal features are spatially clustered to form abnormal feature distribution data;

[0026] Based on the distribution data of abnormal features, the boundary of the clustered region of abnormal features is determined by the polygon area calculation algorithm; and the potential defective region is located based on the boundary of the clustered region.

[0027] Furthermore, based on the distribution data of abnormal features, the boundaries of clustered regions of abnormal features are determined using a polygon area calculation algorithm; potential defective regions are located based on the boundaries of these clustered regions, including:

[0028] Extract the coordinate point set of abnormal signal features from the abnormal feature distribution data;

[0029] Perform convex hull calculation on the coordinate point set to generate the smallest convex polygon containing all anomalous feature points;

[0030] Calculate the area of ​​the smallest convex polygon and compare it with a preset area threshold.

[0031] The boundary of the smallest convex polygon whose area exceeds a preset area threshold is defined as the boundary of the cluster region of abnormal features.

[0032] Potential defect areas are located by mapping the boundaries of the clustered areas onto the fabric surface.

[0033] Furthermore, based on the abnormal signal characteristics corresponding to the potential defect area, the defect type is classified and its severity is determined, resulting in a judgment result that includes defect category and location information, including:

[0034] Extract spectral features and temporal amplitude features for classification from the abnormal signal features corresponding to potential defective regions;

[0035] The extracted spectral features and temporal amplitude features are matched with a pre-defined defect feature rule base to obtain preliminary defect classification results.

[0036] Based on the intensity and spatial distribution characteristics of abnormal signals, the severity level of the defect is determined according to the preset severity assessment rules.

[0037] The preliminary classification results are combined with the severity level to obtain a judgment result that includes the specific category and severity of the defect.

[0038] Furthermore, the judgment result is converted into a sorting control instruction, and the defective fabric segments are separated from the qualified products according to the sorting control instruction, including:

[0039] Receive the judgment results and parse the location, category, and severity of the defects from the judgment results;

[0040] Based on the parsed defect location information and combined with the real-time detected fabric travel speed, the estimated time for the defect to reach the sorting station is calculated.

[0041] Based on the analyzed defect categories and severity, corresponding sorting control instructions are generated according to preset sorting rules;

[0042] When the calculated estimated time arrives, the generated sorting control instructions are executed, triggering the sorting mechanism to separate the defective fabric segments from the qualified products.

[0043] Secondly, an automatic detection system for surface defects of printed fabrics based on computer signal processing includes:

[0044] The acquisition module is used to set up an ultrasonic transducer array above the fabric travel path, and fix a photoelectric position sensor at the beginning and end of the path respectively; based on the fabric edge position information detected in real time by the two photoelectric position sensors, a virtual reference direction is determined; the virtual reference direction is resolved into multiple continuous analysis intervals; the tension and smoothness properties of the fabric in each analysis interval are evaluated to obtain dynamic adjustment values; based on the dynamic adjustment values, the emission parameters of the ultrasonic transducer array are adjusted in real time to emit controlled ultrasonic waves of a specific frequency to the fabric surface and receive the reflected echoes to obtain the original acoustic signal data of the fabric;

[0045] The comparison module is used to preprocess the raw acoustic signal data to obtain standardized material characteristic signals; the standardized material characteristic signals are then compared with preset reference material signals of corresponding flawless printed fabrics to obtain signal difference results.

[0046] The calculation module is used to perform multi-dimensional feature analysis on the signal difference results, distinguishing signal fluctuations caused by normal changes in the color and material of the printed pattern, as well as abnormal signal features caused by defects. Through the polygon area calculation algorithm, potential defect areas are located.

[0047] The judgment module is used to classify the defect type and determine the severity based on the abnormal signal characteristics corresponding to the potential defect area, and obtain the judgment result containing defect category and location information;

[0048] The processing module is used to convert the judgment result into sorting control instructions, and to separate the defective fabric segments from the qualified products according to the sorting control instructions.

[0049] Thirdly, a computing device, comprising:

[0050] One or more processors;

[0051] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0052] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0053] The above-described solution of the present invention has at least the following beneficial effects:

[0054] Because it employs a combination of ultrasonic transducer arrays and photoelectric position sensors to dynamically generate virtual reference directions and adjust emission parameters in real time to adapt to changes in fabric tension and flatness, preprocesses acoustic signals and compares them with reference signals, distinguishes normal signal fluctuations from abnormal defect features through multi-dimensional feature analysis, uses polygon area calculation algorithms to locate potential defect areas, and completes defect classification and sorting control based on feature matching, it overcomes the problems of low efficiency and high missed detection rate of manual inspection, as well as the limitations of visual inspection due to optical characteristics, such as interference from complex printed patterns and textures, and imaging distortion caused by fabric wrinkles, making it difficult to identify hidden or minor defects. Thus, it achieves high-precision and highly adaptable automatic detection of surface and near-surface defects of printed fabrics, realizing accurate positioning, classification, and automatic sorting of defects, and meeting the quality control requirements of high-speed production lines. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating the automatic detection method for surface defects of printed fabrics based on computer signal processing, provided in an embodiment of the present invention.

[0056] Figure 2 This is a schematic diagram of an automatic detection system for surface defects of printed fabrics based on computer signal processing, provided in an embodiment of the present invention. Detailed Implementation

[0057] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0058] like Figure 1 As shown, embodiments of the present invention propose an automatic detection method for surface defects of printed fabrics based on computer signal processing. The method includes the following steps:

[0059] Step 1: An ultrasonic transducer array is set above the fabric travel path, and a photoelectric position sensor is fixedly installed at the beginning and end of the path respectively; based on the fabric edge position information detected in real time by the two photoelectric position sensors, a virtual reference direction is determined; the virtual reference direction is resolved into multiple continuous analysis intervals; the tension and smoothness properties of the fabric in each analysis interval are evaluated to obtain dynamic adjustment values; based on the dynamic adjustment values, the emission parameters of the ultrasonic transducer array are adjusted in real time to emit controlled ultrasonic waves of a specific frequency onto the fabric surface and receive the reflected echoes to obtain the original acoustic signal data of the fabric;

[0060] Step 2: Preprocess the original acoustic signal data to obtain standardized material characteristic signals; compare the standardized material characteristic signals with the preset reference material signals of the corresponding flawless printed fabric to obtain the signal difference results.

[0061] Step 3: Perform multi-dimensional feature analysis on the signal difference results to distinguish signal fluctuations caused by normal changes in the color and material of the printed pattern, as well as abnormal signal features caused by defects. Using a polygon area calculation algorithm, locate potential defect areas.

[0062] Step 4: Based on the abnormal signal features corresponding to the potential defect area, classify the defect type and determine the severity to obtain a judgment result containing defect category and location information;

[0063] Step 5: Convert the judgment result into a sorting control instruction, and separate the defective fabric segments from the qualified products according to the sorting control instruction.

[0064] In this embodiment of the invention, by deploying an ultrasonic transducer array and photoelectric position sensors at both ends along the fabric travel path, determining the virtual reference direction and analyzing the interval by real-time detection of edge positions, dynamically adjusting ultrasonic emission parameters to obtain the original acoustic signal based on the fabric tension and flatness evaluation results, preprocessing the signal and comparing it with a flawless baseline signal, distinguishing normal signal fluctuations from abnormal defect features through multi-dimensional feature analysis, locating potential defect areas using a polygon area calculation algorithm, and finally classifying and judging defects based on abnormal features and converting them into sorting control instructions, this technical approach overcomes the problems of low efficiency and high missed detection rate of traditional manual inspection, as well as the technical defects of visual inspection being affected by the color and material of the printed pattern, the influence of fabric wrinkles on imaging, and the difficulty in identifying hidden or minor defects. This achieves a high-precision and highly adaptable automatic detection effect for surface and near-surface defects of printed fabrics, enabling precise positioning, classification and severity determination of defects, and automatic sorting of defective fabric segments, thus meeting the needs of efficient quality control in printed fabric production lines.

[0065] In a preferred embodiment of the present invention, step 1 above may include:

[0066] Step 1.1 involves using two photoelectric position sensors positioned at the beginning and end of the fabric travel path to acquire real-time position information of the fabric edge. Specifically, this includes fixing two photoelectric position sensors at corresponding positions on the production line frame at the beginning and end of the fabric travel path, ensuring that the detection lines of both sensors are perpendicularly aligned with the fabric edge area. The two photoelectric position sensors continuously emit infrared detection beams. When the fabric edge passes the detection beam during travel, the sensors capture the coordinate information of the fabric edge in real-time based on the positional change of the reflected beam, recording edge position data every 50 milliseconds to ensure real-time tracking of the dynamic offset of the fabric edge during travel.

[0067] Step 1.2, determining a dynamically changing virtual reference direction based on position information, specifically includes: using the edge coordinate points captured by two sensors as a basis, calculating the line connecting the two points and using it as the initial reference direction. Since the fabric may shift at the edge due to tension fluctuations during movement, the system control unit updates the edge position data of the two sensors every 100 milliseconds, recalculates the line connecting the two points, and dynamically corrects the initial reference direction, ultimately forming a virtual reference direction that adjusts in real time according to the change of the fabric edge position.

[0068] Step 1.3: The virtual reference direction is resolved into multiple continuous analysis intervals. The tension and smoothness properties of the fabric within each continuous analysis interval are evaluated to obtain the corresponding dynamic adjustment values. Specifically, the determined dynamic virtual reference direction is uniformly resolved into multiple continuous analysis intervals along the fabric width direction. The width of each interval is set to 10 cm, ensuring that each interval covers a local area of ​​the fabric surface and that there is no overlap between intervals. For each continuous analysis interval, the fluctuation amplitude of the edge position is calculated by comparing the edge position data at different times within the interval. Based on this, the tension level is classified. If the fluctuation amplitude is less than or equal to 5 mm, it is judged as stable tension, corresponding to a tension coefficient of 1.0; if the fluctuation amplitude is between 5 and 10 mm, it is judged as slightly unstable tension, corresponding to a tension coefficient of 1.2; if the fluctuation amplitude is greater than 10 mm, it is judged as severely unstable tension, corresponding to a tension coefficient of 1.5. This forms the tension assessment result. Simultaneously, low-power ultrasonic waves are pre-emitted into each interval, and the amplitude of the reflected echo is consistent. The flatness is assessed and classified into flatness levels: if the difference in echo amplitude is less than or equal to 15%, it is judged as flat, with a flatness coefficient of 1.0; if the difference in amplitude is between 15% and 30%, it is judged as slightly uneven, with a flatness coefficient of 1.2; if the difference in amplitude is greater than 30%, it is judged as severely uneven, with a flatness coefficient of 1.5. This forms the flatness assessment result. Subsequently, the tension coefficient and the flatness coefficient are each weighted at 50%, and the dynamic adjustment value for each interval is calculated. The dynamic adjustment value is equal to the tension coefficient multiplied by 50% plus the flatness coefficient multiplied by 50%.

[0069] Step 1.4: Adjust the emission parameters of the ultrasonic transducer array in real time according to the dynamic adjustment values. Use the adjusted ultrasonic transducer array to emit ultrasonic waves onto the fabric surface and receive the reflected echoes to obtain raw acoustic signal data. Specifically, this includes: transmitting the obtained dynamic adjustment values ​​for each analysis interval to the drive module of the ultrasonic transducer array; the drive module adjusts the emission parameters of the corresponding interval in the transducer array in real time according to the dynamic adjustment values, wherein the emission frequency is adapted and adjusted between 200kHz and 500kHz according to the adjustment values; if the dynamic adjustment value of a certain interval shows high tension and poor flatness, the emission power of the transducer corresponding to that interval is increased by 10% to 20%, and the pulse width is adjusted to 10 microseconds to enhance signal penetration. After the adjustment is completed, the ultrasonic transducer array emits ultrasonic waves of a specific frequency onto the fabric surface interval by interval according to the adjustment parameters of each interval. The transducer array simultaneously receives the echo signals reflected from the fabric surface and converts the echo signals into electrical signals, ultimately forming raw acoustic signal data that reflects the surface and near-surface conditions of the fabric.

[0070] In this embodiment of the invention, by employing a technical means of setting photoelectric position sensors at the beginning and end of the fabric travel path to acquire edge position information in real time, determining a dynamic virtual reference direction based on this information and resolving it into continuous analysis intervals, evaluating the fabric tension and flatness within each interval to obtain dynamic adjustment values, and then adjusting the ultrasonic transducer array emission parameters in real time according to the dynamic adjustment values ​​to acquire the original acoustic signal data, the technical problems of easy edge deviation and uneven tension and flatness in different areas during fabric travel, which lead to unstable signal acquisition and affect the accuracy of the original acoustic signal when the ultrasonic emission parameters are fixed, are overcome. Thus, the ultrasonic transducer array emission parameters can be dynamically adapted to the real-time state of the fabric, ensuring that the original acoustic signal data accurately reflects the actual situation of the fabric.

[0071] In a preferred embodiment of the present invention, step 2 above may include:

[0072] Step 2.1 involves denoising and filtering the original acoustic signal data to obtain a purified acoustic signal. Specifically, this includes: first, denoising the original acoustic signal data. The system collects interference noise in the production line environment in real time, including mechanical noise generated by equipment operation and airflow noise in the workshop. Adaptive noise cancellation technology generates a cancellation signal with the same frequency and amplitude as the interference noise but opposite phase. This cancellation signal is then superimposed on the original acoustic signal data to cancel out the environmental interference components. Subsequently, filtering is performed. Based on the ultrasonic frequency range emitted by the ultrasonic transducer array (200kHz to 500kHz), a bandpass filter for the corresponding frequency band is selected. The effective signal within this frequency band in the original acoustic signal is retained, while high-frequency interference signals above 500kHz and low-frequency noise signals below 200kHz are filtered out. After denoising and filtering, a purified acoustic signal containing only information about the surface and near-surface state of the fabric is obtained.

[0073] Step 2.2 involves performing amplitude normalization processing on the purified acoustic signal to obtain a standardized material characteristic signal. Specifically, this includes: first, extracting the amplitude data of all signal points in the obtained purified acoustic signal and identifying the maximum and minimum amplitudes; then, for each signal point in the purified acoustic signal, calculating the difference between the amplitude and the minimum amplitude, and simultaneously calculating the difference between the maximum and minimum amplitudes; subsequently, dividing the amplitude difference of each signal point by the difference between the maximum and minimum amplitudes to obtain the normalized amplitude of each signal point, ensuring that the amplitudes of all signal points are within a uniform range of 0 to 1; after the above amplitude normalization processing, the signal amplitude differences caused by fine-tuning of ultrasonic emission parameters in different detection periods and different fabric areas are eliminated, forming a standardized material characteristic signal.

[0074] Step 2.3 involves comparing the standardized material characteristic signal with the preset reference material signal of the flawless printed fabric point by point to obtain the comparison result. Specifically, this includes: first, acquiring the preset reference material signal of the flawless printed fabric. The reference signal must be generated using a flawless fabric with the same material and printing pattern as the fabric to be tested, under the same production line speed and ultrasonic emission parameters as the current test, after noise reduction filtering and amplitude normalization processing; then, aligning the obtained standardized material characteristic signal with the reference material signal in chronological order, corresponding to the same position point in the fabric's travel direction, comparing the amplitude of each corresponding position point in the two signals, recording the difference between the amplitude of the standardized material characteristic signal and the amplitude of the reference material signal at each position point, and whether the difference is within the preset normal fluctuation range, thus forming a comparison result that includes the comparison situation of each position point.

[0075] Step 2.4: Calculate the difference degree between signals based on the comparison results to obtain the signal difference result characterizing the abnormal fabric condition. Specifically, this includes: based on the obtained comparison results, first count the number of abnormal location points whose amplitude difference exceeds the normal fluctuation range, and calculate the proportion of abnormal location points to the total number of location points; simultaneously calculate the average value of the absolute values ​​of the amplitude differences of all location points, combine the average value with the proportion of abnormal location points, and calculate the difference degree that comprehensively reflects the overall difference between the two signals through the system's built-in weight allocation rules; if the difference degree exceeds the preset difference degree threshold, which is set to 0.08 based on multiple detection data of flawless fabric, it is determined that the current fabric has an abnormal condition. Integrate the difference degree and the corresponding abnormal location point distribution information to obtain the signal difference result characterizing the abnormal fabric condition.

[0076] In this embodiment of the invention, the technical means of denoising and filtering the original acoustic signal data to remove environmental noise and equipment interference, normalizing the amplitude of the purified acoustic signal to unify the signal scale, comparing the standardized material characteristic signal with the preset flawless printed fabric reference material signal point by point, and calculating the difference between signals based on the comparison results overcome the technical problems of the original acoustic signal being easily distorted by interference, the signal amplitude being inconsistent under different detection scenarios or fabric batches affecting the comparison accuracy, and the inability to clearly distinguish between normal signal fluctuations and abnormal differences. Thus, it achieves the goal of obtaining pure and standardized material characteristic signals, ensuring the accuracy of signal comparison, and effectively extracting signal difference results that characterize abnormal fabric conditions.

[0077] In a preferred embodiment of the present invention, step 3 above may include:

[0078] Step 3.1 involves multi-dimensional feature extraction in the time and frequency domains of the signal difference results to generate a feature vector set. Specifically, this includes: first, performing time-domain feature extraction on the obtained signal difference results, statistically analyzing the peak value, duration, amplitude change rate, and time interval between adjacent abnormal locations for the signal difference results corresponding to each abnormal location point in the time dimension. These features reflect the fluctuation pattern of signal differences in the time dimension. Then, performing frequency-domain feature extraction, decomposing the signal difference results into components of different frequencies through signal conversion, and extracting the characteristic frequencies, spectral energy distribution, bandwidth, and energy proportion of each frequency component. These features reflect the distribution characteristics of signal differences in the frequency dimension. Finally, integrating the time-domain and frequency-domain features corresponding to each abnormal location point to form a feature vector containing parameters such as peak value, duration, characteristic frequency, and energy proportion. The feature vectors of all abnormal locations together constitute the feature vector set.

[0079] Step 3.2: Based on the feature vector set, distinguish between signal fluctuation features caused by normal changes in the color and material of printed patterns and abnormal signal features caused by defects through feature classification. Specifically, this includes: first, constructing a feature classification rule system. The rule system uses pre-collected signal fluctuation features corresponding to changes in the color and material of different printed patterns on flawless fabrics, as well as abnormal signal features corresponding to common defects such as holes, printing deviations, and staining, as training data. After multiple training and optimizations, stable classification rules are formed. The generated feature vector set is input into the classification rule system. The rule system compares the feature vector to be detected with the normal signal fluctuation feature template and the abnormal signal feature template in the classification rule system one by one, and calculates the matching degree. If the matching degree of a feature vector with the normal signal fluctuation feature template is higher than a preset threshold, it is determined to be a signal fluctuation feature caused by normal changes in the color and material of printed patterns; if the matching degree with the abnormal signal feature template is higher than a preset threshold, it is determined to be an abnormal signal feature caused by defects, thereby completing the distinction between the two types of features.

[0080] Step 3.3 involves spatial clustering of the identified abnormal signal features to form abnormal feature distribution data. This includes: first, determining the basis for spatial clustering, using the detection coordinates on the fabric surface as a reference, and using the spatial coordinates corresponding to each abnormal signal feature as a clustering reference. A clustering distance threshold is set, determined based on the fabric travel speed and ultrasonic detection accuracy, to ensure that adjacent abnormal signal features generated by the same defect can be classified into the same category. A clustering algorithm is used to group all identified abnormal signal features, and abnormal signal features whose spatial coordinates are less than the clustering distance threshold are divided into a cluster. For each cluster, the spatial coordinates of all abnormal signal features, the corresponding difference value, and the feature vector parameters are recorded. The information is then organized by cluster to form abnormal feature distribution data containing the spatial distribution and feature information of each cluster.

[0081] Step 3.4: Based on the abnormal feature distribution data, determine the boundary of the clustered region of abnormal features using a polygon area calculation algorithm; locate potential defect areas based on the clustered region boundary. Specifically, this includes: first, extracting the spatial coordinates of all abnormal signal features within each cluster from the abnormal feature distribution data; using a convex hull algorithm to calculate these coordinates to generate the smallest convex polygon containing all coordinates within the cluster; the boundary of this convex polygon is the preliminary boundary of the abnormal feature clustered region; calculating the area of ​​each smallest convex polygon; comparing the area of ​​each smallest convex polygon with a preset area threshold; if the area of ​​a certain smallest convex polygon exceeds the preset area threshold, then the boundary of the convex polygon is confirmed as a valid boundary of the abnormal feature clustered region; if it does not exceed the threshold, it is determined to be an invalid cluster caused by detection error and is discarded; finally, based on the relative positional relationship between the detection device and the fabric, mapping the coordinates of the valid clustered region boundary to the actual position on the fabric surface; the mapped position is the potential defect area.

[0082] In this embodiment of the invention, a multi-dimensional feature extraction method is adopted to extract the signal difference results in the time and frequency domains to generate a feature vector set. Based on the feature vector set, the signal fluctuation features caused by normal changes in the color and material of the printed pattern and the abnormal signal features caused by defects are distinguished by feature classification. The abnormal signal features are spatially clustered to form abnormal feature distribution data. Then, based on the distribution data, the boundary of the abnormal feature aggregation area is determined and the potential defect area is located by a polygon area calculation algorithm. Therefore, this method overcomes the technical problems of traditional detection methods, which are difficult to effectively decouple the normal changes in the printed pattern and the difference in defect signals, and cannot accurately lock the spatial aggregation range of defect features, thus leading to fuzzy positioning of potential defect areas. This achieves the technical effect of accurately distinguishing normal signal fluctuations and defect abnormal features, clearly defining the aggregation range of defect features, and accurately locating potential defect areas.

[0083] In a preferred embodiment of the present invention, step 3.4 above may include:

[0084] Step 3.41: Extract the coordinate point set of abnormal signal features from the abnormal feature distribution data. Specifically, this includes: first, sorting out the abnormal feature distribution data, which contains the spatial coordinate information corresponding to each abnormal signal feature after spatial clustering of the abnormal signal features, and the coordinates have been associated with the actual position of the fabric during its movement. For example, the horizontal coordinate corresponds to the width direction of the fabric, and the vertical coordinate corresponds to the length direction of the fabric movement. From the abnormal feature distribution data, extract the spatial coordinate values ​​corresponding to all abnormal signal features one by one, and arrange the coordinate values ​​according to the order of detection to form a complete set of abnormal signal feature coordinate points, ensuring that each coordinate point can accurately correspond to a specific position on the fabric surface.

[0085] Step 3.42: Perform convex hull calculation on the coordinate point set to generate the minimum convex polygon containing all abnormal feature points. Specifically, this includes: importing the obtained coordinate point set into the convex hull calculation process. The process first analyzes the relative positional relationship between each coordinate point and all other coordinate points, and selects the outermost coordinate points that can form a closed figure around all coordinate points. That is, the line connecting any two adjacent outermost coordinate points will not pass through other abnormal coordinate points. Then, the outermost coordinate points are connected in counterclockwise order to form a closed figure that completely contains all abnormal signal feature coordinate points. The figure is the minimum convex polygon containing all abnormal feature points.

[0086] Step 3.43: Calculate the area of ​​the smallest convex polygon and compare it with a preset area threshold. Specifically, this includes: first, using the coordinates of each vertex of the smallest convex polygon, calculating the actual area of ​​the convex polygon using geometric methods. The calculation uses units consistent with the fabric size to ensure that the area value directly reflects the size of the corresponding fabric area. Then, retrieve the preset area threshold. The threshold is determined by repeatedly testing a large number of flawless printed fabrics and statistically analyzing the areas of convex polygons corresponding to abnormal point clusters caused by minor equipment errors and environmental interference during the testing. The maximum value of these areas is taken and a 10% margin is added to exclude minor abnormal clusters caused by non-defect factors. The calculated area of ​​the smallest convex polygon is then compared with the preset area threshold, and the relationship between the two is recorded.

[0087] Step 3.44: Determine the boundary of the smallest convex polygon whose area exceeds the preset area threshold as the boundary of the anomalous feature aggregation region. Specifically, this includes: judging based on the comparison results, if the area of ​​a certain smallest convex polygon is greater than the preset area threshold, it indicates that the aggregation of anomalous signal features within the convex polygon is not caused by detection errors or minor interferences, but by actual defects on the fabric surface. Therefore, the contour formed by each edge of the smallest convex polygon is determined as the boundary of the anomalous feature aggregation region. If the area of ​​the smallest convex polygon is less than or equal to the preset area threshold, the anomalous points in the region are determined to be invalid aggregations.

[0088] Step 3.45: Based on the mapping position of the aggregation area boundary on the fabric surface, locate the potential defect area. Specifically, this includes: first, obtaining the relative position parameters between the detection equipment and the fabric, including the installation height of the ultrasonic detection component, the distance between the photoelectric position sensor and the fabric edge, and the starting position of the detection area on the fabric travel path. Based on the above parameters, convert the coordinate values ​​of the determined abnormal feature aggregation area boundary into the actual physical position on the fabric surface. For example, determine the number of centimeters from the left edge in the fabric width direction and the number of meters from the detection starting point in the travel direction, forming the specific mapping range of the aggregation area boundary on the fabric surface. Based on the mapping range, clearly indicate the corresponding area on the fabric surface. The corresponding area is the potential defect area.

[0089] In this embodiment of the invention, because the technical means of extracting the coordinate point set of abnormal signal features from the abnormal feature distribution data, performing convex hull calculation on the coordinate point set to generate the minimum convex polygon containing all abnormal feature points, calculating the area of ​​the minimum convex polygon and comparing it with a preset area threshold, determining the boundary of the minimum convex polygon with an area exceeding the threshold as the boundary of the abnormal feature aggregation region, and then locating the potential defect area based on the mapping position of the boundary on the fabric surface, the technical problems of the dispersion of abnormal feature points leading to the fuzzy definition of the aggregation region boundary, the small abnormal point aggregation caused by detection error being easily misjudged as a defect, and the abnormal feature region not being accurately matched with the actual position of the fabric are overcome, thereby achieving the technical effect of clearly defining the effective aggregation range of abnormal features, effectively eliminating the interference of detection error, and accurately locating the potential defect area that perfectly matches the actual surface position of the fabric.

[0090] In a preferred embodiment of the present invention, step 4 above may include:

[0091] Step 4.1 involves extracting spectral and temporal amplitude features for classification from the abnormal signal features corresponding to the potential defective region. Specifically, for the located potential defective region, spectral and temporal amplitude features of the abnormal signal within that region are extracted. Spectral features include the energy proportion of the abnormal signal in different frequency bands, the offset of the characteristic frequency, and the distribution range of high-frequency components. Temporal amplitude features include the maximum amplitude, average amplitude, steepness of amplitude change, and duration of the abnormal signal. During the extraction process, all abnormal signal points within the potential defective region must be focused on to ensure that the features fully reflect the signal anomaly characteristics of the region.

[0092] Step 4.2: Match the extracted spectral features and temporal amplitude features with the preset defect feature rule library to obtain preliminary defect classification results. Specifically, the preset defect feature rule library stores feature templates for various common defects, including: spectral energy of overprinting deviation is concentrated in a specific low-frequency band and the temporal amplitude fluctuation is smooth; high-frequency components account for a high proportion of holes and the temporal amplitude change is obvious; and the feature frequency offset of staining is small but the duration is long. The extracted spectral features and temporal amplitude features are compared item by item with the features of each defect template in the rule library to calculate the overall matching degree. If the matching degree of a certain defect template is the highest and exceeds the preset matching threshold, the potential defect area is initially determined to be a defect of the corresponding category, forming a preliminary classification result.

[0093] Step 4.3: Based on the intensity and spatial distribution characteristics of the abnormal signal features, determine the severity level of the defect according to the preset severity assessment rules. Specifically, this includes: based on the intensity distribution of the abnormal signal features, statistically analyzing the maximum and average amplitude of the abnormal signal within the potential defect area. The larger the amplitude, the stronger the interference of the defect on the signal. Simultaneously, analyze the spatial distribution characteristics, including the area size of the potential defect area and the distribution density of abnormal signal points within the area. The preset severity assessment rules combine intensity and spatial characteristics. For example, an area exceeding 5 square centimeters with an average amplitude exceeding 30% of the baseline value is judged as severe; an area of ​​2-5 square centimeters with an average amplitude of 15%-30% is judged as moderate; and an area less than 2 square centimeters or an average amplitude less than 15% is judged as minor. Based on this, the severity level of the defect is determined.

[0094] Step 4.4 involves fusing the preliminary classification results with the severity levels to obtain a judgment result that includes the specific category and severity of the defect. Specifically, this includes integrating the preliminary classification results with the determined severity levels. During integration, the reasonableness of the matching between the classification results and levels must be verified. For example, it should be confirmed whether the severity level of the overprinting deviation is consistent with the typical impact of this type of defect to avoid logical contradictions. The final judgment result includes the specific category of the defect, the severity level, and the location information of the corresponding potential defect area.

[0095] In this embodiment of the invention, the technical means of extracting spectral features and temporal amplitude features from the abnormal signal features corresponding to the potential defect area, matching the extracted features with a preset defect feature rule base to obtain a preliminary classification result, determining the severity level based on the intensity distribution and spatial distribution features of the abnormal signal features according to preset rules, and then fusing the preliminary classification result with the severity level, overcomes the technical problems in traditional detection such as difficulty in accurately extracting defect classification features leading to misclassification, lack of assessment of defect severity leading to inconsistent quality control standards, and the disconnect between classification results and severity, which prevents the formation of a complete judgment basis. Thus, it achieves the technical effect of identifying the specific category of defect, classifying the severity level of defect, and forming a complete judgment result that includes defect category and severity.

[0096] In a preferred embodiment of the present invention, step 5 above may include:

[0097] Step 5.1: Receive the judgment result and extract the location information, category, and severity of the defect from the judgment result. Specifically, this includes: receiving the output judgment result containing defect information, which clearly records the specific location of the potential defect area on the fabric surface, the corresponding defect category, and the severity; parsing the judgment result, whereby the location information needs to be broken down into the specific coordinates of the fabric width and length directions, such as the number of centimeters from the left edge of the fabric in the width direction and the number of meters from the detection starting point in the length direction; the defect category needs to be clearly distinguished as printing deviation, hole, or staining; and the severity needs to be determined as three levels: slight, moderate, or severe.

[0098] Step 5.2: Based on the parsed defect location information and the real-time detected fabric travel speed, calculate the estimated time for the defect to reach the sorting station. Specifically, this includes: first, obtaining the coordinates of the length direction in the parsed defect location information; determining the straight-line distance between the current defect and the sorting station on the fabric travel path; the distance needs to be calculated based on the actual layout parameters of the production line and the real-time position of the defect; simultaneously, using a speed sensor installed next to the active roller of the production line, the fabric travel speed is detected in real time. This speed data needs to be updated every 10 milliseconds to cope with minor fluctuations in the production line speed; dividing the calculated distance from the defect to the sorting station by the real-time detected fabric travel speed gives the time required for the defect to move from its current position to the sorting station, which is the estimated time for the defect to reach the sorting station.

[0099] Step 5.3: Based on the analyzed defect categories and severity, generate corresponding sorting control instructions according to preset sorting rules. Specifically, this includes: retrieving preset sorting rules, which are formulated based on textile industry quality control standards and enterprise production needs. For example, the sorting rule for severe defects such as holes or printing deviations is to directly remove the fabric segment to the non-conforming product channel; the rule for medium-level color stains is to mark and temporarily store it in the re-inspection area; the rule for minor defects such as small-area defects is to record the location information and continue to transport with qualified products. Combine the analyzed defect categories and severity with the preset sorting rules to determine the corresponding handling method, and then convert the handling method into control instructions that the sorting mechanism can recognize, such as instructions to control the movement of pneumatic push rods, instructions to switch the direction of the sorting conveyor belt, or instructions to start the marking device, ensuring that the instruction content and handling method are completely matched.

[0100] Step 5.4: When the calculated estimated time arrives, execute the generated sorting control command to trigger the sorting mechanism to separate the defective fabric segment from the qualified products. Specifically, this includes: simultaneously generating the sorting control command, starting a timer and comparing the current time with the calculated estimated time in real time. When the timer reaches the estimated time, immediately send the corresponding sorting control command to the sorting mechanism. After receiving the command, the sorting mechanism executes the actions according to the command requirements. For example, if the command is to reject defective products, the pneumatic pusher will extend within a preset time to push the defective fabric segment to the defective product recycling channel; if the command is to mark for re-inspection, the marking device will print a mark at the corresponding position of the fabric segment. After the action is executed, the sorting mechanism will send a completion signal, confirming that the defective fabric segment has been separated from the qualified product conveying path. Then, the timer is reset to prepare for the next defective sorting task, ensuring that the entire sorting process is continuous and does not affect the normal travel speed of the production line.

[0101] In this embodiment of the invention, the method of receiving the judgment result and parsing the location information, category, and severity of the defect, calculating the estimated time for the defect to reach the sorting station based on the real-time detected fabric travel speed, generating sorting control instructions according to the defect category and severity and preset sorting rules, and executing the instructions to trigger the sorting mechanism to separate the defective fabric segment when the estimated time arrives, overcomes the technical problems of large deviation in defect location judgment, delayed sorting timing, and inconsistent handling of similar defects due to inconsistent sorting rules during manual sorting, which cannot adapt to the real-time sorting needs of high-speed production lines. This achieves precise control of defect sorting timing, targeted separation of defective fabric segments of different categories and severity according to standardized rules, and efficient and accurate differentiation between defective fabric segments and qualified products, thus meeting the technical requirements of real-time quality control and automated sorting for high-speed production lines of printed fabrics.

[0102] like Figure 2As shown, embodiments of the present invention also provide an automatic detection system for surface defects of printed fabrics based on computer signal processing, comprising:

[0103] The acquisition module is used to set up an ultrasonic transducer array above the fabric travel path, and fix a photoelectric position sensor at the beginning and end of the path respectively; based on the fabric edge position information detected in real time by the two photoelectric position sensors, a virtual reference direction is determined; the virtual reference direction is resolved into multiple continuous analysis intervals; the tension and smoothness properties of the fabric in each analysis interval are evaluated to obtain dynamic adjustment values; based on the dynamic adjustment values, the emission parameters of the ultrasonic transducer array are adjusted in real time to emit controlled ultrasonic waves of a specific frequency to the fabric surface and receive the reflected echoes to obtain the original acoustic signal data of the fabric;

[0104] The comparison module is used to preprocess the raw acoustic signal data to obtain standardized material characteristic signals; the standardized material characteristic signals are then compared with preset reference material signals of corresponding flawless printed fabrics to obtain signal difference results.

[0105] The calculation module is used to perform multi-dimensional feature analysis on the signal difference results, distinguishing signal fluctuations caused by normal changes in the color and material of the printed pattern, as well as abnormal signal features caused by defects. Through the polygon area calculation algorithm, potential defect areas are located.

[0106] The judgment module is used to classify the defect type and determine the severity based on the abnormal signal characteristics corresponding to the potential defect area, and obtain the judgment result containing defect category and location information;

[0107] The processing module is used to convert the judgment result into sorting control instructions, and to separate the defective fabric segments from the qualified products according to the sorting control instructions.

[0108] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An automatic detection method for surface defects of printed fabrics based on computer signal processing, characterized in that, The method includes: An ultrasonic transducer array is set above the fabric travel path, and a photoelectric position sensor is fixedly installed at the beginning and end of the path. Based on the fabric edge position information detected in real time by the two photoelectric position sensors, a virtual reference direction is determined. The virtual reference direction is resolved into multiple continuous analysis intervals. The tension and smoothness properties of the fabric in each analysis interval are evaluated to obtain dynamic adjustment values. This includes: uniformly resolving the determined dynamic virtual reference direction into multiple continuous analysis intervals along the fabric width direction; for each continuous analysis interval, calculating the fluctuation amplitude of the edge position by comparing the edge position data at different times within the interval, classifying the tension level accordingly, and forming a tension evaluation result; pre-emitting low-power ultrasonic waves into each interval, judging the smoothness based on the consistency of the amplitude of the reflected echo, classifying the smoothness level, and forming a smoothness evaluation result; dynamically adjusting the ultrasonic emission parameters in combination with the fabric tension and smoothness evaluation results; and adjusting the emission parameters of the ultrasonic transducer array in real time based on the dynamic adjustment values, emitting controlled ultrasonic waves of a specific frequency to the fabric surface, and receiving the reflected echoes to obtain the original acoustic signal data of the fabric. The raw acoustic signal data is preprocessed to obtain a standardized material characteristic signal; the standardized material characteristic signal is then compared with a preset reference material signal corresponding to a flawless printed fabric to obtain the signal difference results. Multi-dimensional feature analysis was performed on the signal difference results to distinguish signal fluctuations caused by normal changes in the color and material of the printed pattern, as well as abnormal signal features caused by defects. Potential defect areas were located by using a polygon area calculation algorithm. Based on the abnormal signal characteristics corresponding to potential defect areas, the defect types are classified and the severity is determined, resulting in a judgment result that includes defect category and location information; The judgment result is converted into a sorting control instruction, and the defective fabric segments are separated from the qualified products according to the sorting control instruction.

2. The automatic detection method for surface defects of printed fabrics based on computer signal processing according to claim 1, characterized in that, The raw acoustic signal data is preprocessed to obtain standardized material characteristic signals. These standardized material characteristic signals are then compared with preset reference material signals for corresponding flawless printed fabrics to obtain signal difference results, including: The original acoustic signal data is denoised and filtered to obtain a purified acoustic signal; The amplitude of the purified acoustic signal is normalized to obtain a standardized material property signal; The standardized material characteristic signals are compared point by point with the preset reference material signals of flawless printed fabrics to obtain the comparison results. The degree of difference between signals is calculated based on the comparison results, and the signal difference results characterizing the abnormal state of the fabric are obtained.

3. The automatic detection method for surface defects of printed fabrics based on computer signal processing according to claim 2, characterized in that, Multi-dimensional feature analysis was performed on the signal difference results to distinguish signal fluctuations caused by normal variations in the color and material of the printed pattern, as well as abnormal signal features caused by defects. Potential defect areas were located using a polygon area calculation algorithm, including: Multi-dimensional feature extraction in the time and frequency domains is performed on the signal difference results to generate a feature vector set; Based on the feature vector set, the signal fluctuation features caused by normal changes in the color and material of the printed pattern and the abnormal signal features caused by defects are distinguished by feature classification. The identified abnormal signal features are spatially clustered to form abnormal feature distribution data; Based on the distribution data of abnormal features, the boundary of the clustered region of abnormal features is determined by the polygon area calculation algorithm; and the potential defective region is located based on the boundary of the clustered region.

4. The automatic detection method for surface defects of printed fabrics based on computer signal processing according to claim 3, characterized in that, Based on the distribution data of abnormal features, the boundary of the clustering region of abnormal features is determined by a polygon area calculation algorithm; Potential defective areas were located based on the boundaries of the clustered areas, including: Extract the coordinate point set of abnormal signal features from the abnormal feature distribution data; Perform convex hull calculation on the coordinate point set to generate the smallest convex polygon containing all anomalous feature points; Calculate the area of ​​the smallest convex polygon and compare it with a preset area threshold. The boundary of the smallest convex polygon whose area exceeds a preset area threshold is defined as the boundary of the cluster region of abnormal features. Potential defect areas are located by mapping the boundaries of the clustered areas onto the fabric surface.

5. The automatic detection method for surface defects of printed fabrics based on computer signal processing according to claim 4, characterized in that, Based on the abnormal signal characteristics corresponding to potential defect areas, defect types are classified and severity is determined, resulting in a judgment result that includes defect category and location information, including: Extract spectral features and temporal amplitude features for classification from the abnormal signal features corresponding to potential defective regions; The extracted spectral features and temporal amplitude features are matched with a pre-defined defect feature rule base to obtain preliminary defect classification results. Based on the intensity and spatial distribution characteristics of abnormal signals, the severity level of the defect is determined according to the preset severity assessment rules. The preliminary classification results are combined with the severity level to obtain a judgment result that includes the specific category and severity of the defect.

6. The automatic detection method for surface defects of printed fabrics based on computer signal processing according to claim 5, characterized in that, The judgment result is converted into a sorting control instruction, and the defective fabric segments are separated from the qualified products according to the sorting control instruction, including: Receive the judgment results and parse the location, category, and severity of the defects from the judgment results; Based on the parsed defect location information and combined with the real-time detected fabric travel speed, the estimated time for the defect to reach the sorting station is calculated. Based on the analyzed defect categories and severity, corresponding sorting control instructions are generated according to preset sorting rules; When the calculated estimated time arrives, the generated sorting control instructions are executed, triggering the sorting mechanism to separate the defective fabric segments from the qualified products.

7. An automatic detection system for surface defects of printed fabrics based on computer signal processing, wherein the system implements the method as described in any one of claims 1 to 6, characterized in that, include: The acquisition module is used to set up an ultrasonic transducer array above the fabric travel path, and fix a photoelectric position sensor at the beginning and end of the path respectively; based on the fabric edge position information detected in real time by the two photoelectric position sensors, a virtual reference direction is determined; the virtual reference direction is resolved into multiple continuous analysis intervals; The tension and smoothness properties of the fabric are evaluated in each analysis interval to obtain dynamic adjustment values. Based on the dynamic adjustment values, the emission parameters of the ultrasonic transducer array are adjusted in real time to emit controlled ultrasonic waves of a specific frequency onto the fabric surface and receive the reflected echoes to obtain the original acoustic signal data of the fabric. The comparison module is used to preprocess the raw acoustic signal data to obtain standardized material characteristic signals; the standardized material characteristic signals are then compared with preset reference material signals of corresponding flawless printed fabrics to obtain signal difference results. The calculation module is used to perform multi-dimensional feature analysis on the signal difference results, distinguishing signal fluctuations caused by normal changes in the color and material of the printed pattern, as well as abnormal signal features caused by defects. Through the polygon area calculation algorithm, potential defect areas are located. The judgment module is used to classify the defect type and determine the severity based on the abnormal signal characteristics corresponding to the potential defect area, and obtain the judgment result containing defect category and location information; The processing module is used to convert the judgment result into sorting control instructions, and to separate the defective fabric segments from the qualified products according to the sorting control instructions.

8. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

Images