Impurity detection method for foreign fiber removing machine based on photoelectric detection technology

Abstract

The invention provides a photoelectric detection technology-based foreign fiber removing machine impurity detection method, and belongs to the field of data processing. The method comprises the following steps: obtaining a plurality of interception intervals; obtaining an abnormal factor of each interception interval according to the distribution difference of the signals to be detected in different interception intervals; clustering the to-be-detected signals in each interception interval to obtain an upper bound and a lower bound of a normal clustering cluster and a normal signal range in each interception interval; the final upper bound and the final lower bound of the normal signal range are obtained by combining the difference between the abnormal factors of each interception interval and the abnormal factors of other interception intervals; and obtaining the to-be-cleared degree of each interception interval according to the distribution of the to-be-detected signals in each interception interval. The invention aims to solve the problem that when foreign fibers are cleared through a K-means clustering algorithm, all data need to be clustered every time new data is added, so that abnormity confirmation is carried out on the new data, and the detection efficiency is too low.

Description

Technical Field

[0001] This invention belongs to the field of data processing, specifically relating to a method for detecting impurities in a foreign fiber removal machine based on photoelectric detection technology. Background Technology

[0002] With the development of related technologies and hardware in the industry, the use of photoelectric detection technology for detecting foreign fibers such as plastic filaments, hair, hemp fibers, and fabric fragments in cotton has become increasingly necessary. On the one hand, traditional manual foreign fiber detection is not only inefficient and costly, but also susceptible to human error, leading to missed detections or false positives. Photoelectric detection technology, however, can detect foreign fibers in real time through high-speed, non-contact methods, effectively improving detection accuracy and stability, meeting the requirements of modern textile industry for product quality and automation. On the other hand, the large amount of signal data generated during photoelectric detection often contains mixed information of normal and foreign fibers. Introducing clustering algorithms to filter abnormal signals in the received signals can utilize their adaptive characteristics to automatically distinguish normal and abnormal signals based on signal differences. This not only improves the accuracy and robustness of foreign fiber detection but also effectively reduces false alarm and false negative rates, enabling the detection system to maintain strong adaptability and intelligence even in complex production environments. Therefore, the K-means clustering algorithm is used as an adaptive clustering algorithm to detect abnormal signals in the collected signals, followed by foreign fiber removal.

[0003] Starting from the foreign fiber removal task, the sensor will acquire signal data every second according to the sampling frequency. In the foreign fiber removal task, each new data acquisition will expand the data sample size of the cluster. The addition of new data may affect the determination of the centroid position and the division of clusters in the algorithm. This means that after each new signal data is added, all the signal data need to be clustered together to confirm whether the new signal data is abnormal, which reduces the efficiency of abnormal data screening through clustering algorithm. Summary of the Invention

[0004] To address the issue that current methods using K-means clustering to continuously extract abnormal signal data require clustering all signal data after each new signal data entry for anomaly detection, resulting in low efficiency in filtering abnormal data, this invention proposes a foreign fiber removal machine impurity detection method based on photoelectric detection technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The cotton to be tested is placed on a foreign fiber removal machine to obtain a data sequence of the signal to be tested; the area that the spray valve in the foreign fiber removal machine can cover under a set air pressure, the width of the conveyor belt, and the running speed of the conveyor belt are obtained; the data sequence of the signal to be tested is segmented to obtain several interception intervals; each signal to be tested corresponds to a region of the cotton to be tested; based on the distribution difference of the signals to be tested in each interception interval and its surrounding interception intervals, an anomaly factor for each interception interval is obtained; the signals to be tested in each interception interval are clustered to obtain several clusters; the cluster containing the most signals to be tested in each interception interval is denoted as each interception interval. Select normal clusters within each interval; based on the distribution of the signals to be detected in the normal clusters within each interval, obtain the upper and lower bounds of the normal signal range for each interval; based on the difference in anomalous factors between each interval and other intervals, the anomalous factor of each interval, and the number of signals to be detected in the normal clusters within each interval, obtain the confidence factor of the normal signal range for each interval; combining the upper and lower bounds of the normal signal range for each interval, obtain the final upper and lower bounds of the normal signal range; based on the final upper and lower bounds of the normal signal range and the distribution of the signals to be detected within each interval, obtain the degree of removal required for each interval, and perform foreign fiber removal.

[0006] Further, the specific steps for obtaining several interception intervals are as follows: Set the air pressure of the spray valve inside the foreign fiber removal machine, and obtain the area that the spray valve can cover under the set air pressure; divide the area that the spray valve can cover under the set air pressure by the width of the conveyor belt inside the foreign fiber removal machine, and record the result as the coverage length; the specific calculation formula for obtaining the time length of the interception interval is as follows: In the formula, Indicates the length of the time interval to be extracted. Indicates the length that can be covered. This indicates the running speed of the conveyor belt inside the foreign fiber removal machine; based on the acquisition time corresponding to each signal to be detected in the data sequence of the signal to be detected, and the time length of the intercept interval, the data sequence of the signal to be detected is divided into several intercept intervals.

[0007] Furthermore, the specific calculation formula for obtaining the anomaly factor of each intercepted interval is as follows: In the formula, Indicates the first Outlier factors within a cutoff interval Indicates the first The variance of all signals to be detected within a cutoff interval. Indicates the first The variance of all signals to be detected within a cutoff interval. Indicates the first The mean of all signals to be detected within a cutoff interval. Indicates the first The mean of all signals to be detected within a cutoff interval. Indicates the first The mean of all signals to be detected within a cutoff interval. Represents the absolute value function. To prevent hyperparameters with a denominator of 0.

[0008] Furthermore, the specific steps for clustering the signal to be detected within each intercepted interval to obtain several clusters are as follows: Using the elbow method, obtain the clusters using the K-means clustering algorithm. The number of clusters formed when clustering all signals to be detected within a specified interval is denoted as the i-th cluster. The number of clusters in each cutoff interval; using Euclidean distance as the distance metric, based on the... The values ​​of all signals to be detected within the specified interval are taken, and the number of clusters is set to the th interval. The number of clusters in the th cutoff interval is used to perform K-means clustering on the th interval. All signals to be detected within a cutoff interval are clustered to obtain several clusters.

[0009] Furthermore, the specific steps for obtaining the upper and lower bounds of the normal signal range for each intercepted interval are as follows: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] The minimum value among all the signals to be detected in the normal clusters within the intercept interval is denoted as the i-th value. The lower bound of the normal signal range of the first intercept interval; the second... The maximum value among all the signals to be detected in the normal clusters within the intercept interval is denoted as the i-th value. The upper limit of the normal signal range of the intercepted interval.

[0010] Furthermore, the specific formula for calculating the confidence factor of the normal signal range for each intercepted interval is as follows: In the formula, Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first Outlier factors within a cutoff interval Indicates the first Outlier factors within a cutoff interval Indicates the number of intervals to be extracted. This represents a second hyperparameter to prevent the denominator from being zero. Indicates the first The number of signals to be detected in normal clusters within a cutoff interval. Indicates the first The number of signals to be detected within a single intercept interval. To prevent the third hyperparameter from having a denominator of 0, It is an absolute value function.

[0011] Furthermore, the specific calculation formulas for obtaining the final upper and lower bounds of the normal signal range are as follows: The specific calculation formula for obtaining the final upper bound of the normal signal range is as follows: In the formula, This represents the final upper bound of the normal signal range. Indicates the number of intervals to be extracted. Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first The upper bound of the normal signal range of each intercept interval; the specific calculation formula for obtaining the final lower bound of the normal signal range is as follows: In the formula, This represents the lower bound of the normal signal range. Indicates the number of intervals to be extracted. Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first The lower bound of the normal signal range of the intercept interval.

[0012] Furthermore, the specific steps for obtaining the degree of removal to be determined for each truncated interval are as follows: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] Signals to be detected that fall above the upper bound of the normal signal range or below the lower bound of the normal signal range within the first intercept interval are recorded as suspected abnormal signals; the first... Other signals to be detected within the first intercept interval, excluding suspected abnormal signals, are denoted as suspected normal signals; if the first... Within the selected interval, the first... If the first suspected abnormal signal is greater than the final upper bound of the normal signal range, then the first... Within the selected interval, the first... The absolute value of the difference between the final upper bound of the range of the suspected abnormal signal and the normal signal is denoted as the nth. Within the selected interval, the first... The degree of abnormality of the first suspected abnormal signal; if the first Within the selected interval, the first... If the first suspected abnormal signal is less than the final lower bound of the normal signal range, then the first... Within the selected interval, the first... The absolute value of the difference between the final lower bound of the range of the suspected abnormal signal and the normal signal is denoted as the i-th. Within the selected interval, the first... The degree of abnormality of the first suspected abnormal signal; the first Each suspected abnormal signal within a cutoff interval is connected to the suspected abnormal signals adjacent to its left and right sides to obtain the first... Several suspected abnormal signal segments were extracted within the intercept interval; according to the... The mean of the abnormality of all suspected abnormal signals within the first intercept interval, the first... The number of suspected normal signals within each intercept interval, the first The maximum value among the number of suspected abnormal signals in all suspected abnormal signal segments within the first intercept interval is obtained. The degree of need to be cleared in each intercepted interval.

[0013] Furthermore, the statement based on the first The mean of the abnormality of all suspected abnormal signals within the first intercept interval, the first... The number of suspected normal signals within each intercept interval, the first The maximum value among the number of suspected abnormal signals in all suspected abnormal signal segments within the first intercept interval is obtained. The specific formula for calculating the degree of removal required for each cutoff interval is as follows: In the formula, Indicates the first The degree of need to be cleared in each selected interval. Indicates the first The number of suspected normal signals within a single intercept interval. Indicates the first The number of signals to be detected within a single intercept interval. Indicates the first The mean of the abnormality level of all suspected abnormal signals within a cutoff interval. Indicates the first The maximum value among all suspected abnormal signal segments within a given intercept interval. This represents the sigmoid function. It is an exponential function with the natural constant as its base.

[0014] Furthermore, the specific steps for removing foreign fibers are as follows: According to the first... Each signal to be detected within a certain interval corresponds to a region of cotton to be detected, thus obtaining the first... The area of ​​cotton to be detected corresponding to each intercept interval; a preset threshold for removal. If the first The degree of uncleared areas in each selected interval Then for the first Foreign fibers are removed from the area of ​​the cotton to be tested corresponding to the intercepted interval.

[0015] The foreign fiber removal machine impurity detection method based on photoelectric detection technology provided by this invention has the following beneficial effects: Before removing foreign fibers from the cotton to be tested, this invention first divides the cotton to be tested into multiple regions according to the area that the spray valve can cover under the set spray pressure and the width of the conveyor belt, obtaining multiple interception intervals. This ensures that removing foreign fibers from each region will not affect other regions, and when removing foreign fibers from one region, all positions within that region can be removed, improving the accuracy of foreign fiber removal; subsequently, based on the fact that the signal to be detected contains a large number of normal signals, and the normal signals are relatively similar... Based on similar characteristics, the signals to be detected within each intercepted interval are clustered to obtain the upper and lower bounds of the normal signal range for each intercepted interval. Then, based on the characteristic that the values ​​and distributions of the signals to be detected in different intercepted intervals are relatively similar when all signals to be detected are normal, a confidence factor for the normal signal range of each intercepted interval is obtained according to the distribution differences of the signals to be detected in different intercepted intervals. Finally, based on the confidence factor and the upper and lower bounds of the normal signal range for each intercepted interval, the final upper and lower bounds of the normal range for each intercepted interval are obtained. This invention addresses the problem that when directly using the upper and lower bounds of the normal signal range within each intercepted interval to obtain the final upper and lower bounds of the normal signal range, the upper and lower bounds of the normal signal range in some intercepted intervals containing more anomalous signals have an excessive influence on the final upper and lower bounds of the normal signal range, thus lowering the reliability of the final upper and lower bounds of the normal signal range. Instead, this invention obtains the suspected anomalous signals within each intercepted interval based on the value of each signal to be detected. This solves the problem that when directly using the K-means clustering algorithm to obtain suspected anomalous signals, it is necessary to perform clustering on all data every time a new data point is added. This leads to low efficiency in anomaly detection. Based on the characteristic that foreign fibers are morphologically continuous (meaning multiple signals affected by foreign fibers should appear consecutively when a signal is present), the distribution of suspected anomaly data within each segmentation interval determines the degree of need for removal in that interval. This solves the problem that directly determining whether to remove foreign fibers from each segmentation interval based on the number of suspected anomaly signals fails to reduce the impact of noise on the signal to be detected. This invention improves the reliability of the degree of need for removal in each segmentation interval and increases the accuracy of foreign fiber removal. Attached Figure Description

[0016] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a foreign fiber removal machine impurity detection method based on photoelectric detection technology, according to an embodiment of the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0019] Example 1: This invention provides a method for detecting impurities in a foreign fiber removal machine based on photoelectric detection technology, specifically as follows: Figure 1 As shown, the process includes: Step S001: Place the cotton to be tested on the foreign fiber removal machine to obtain the data sequence of the signal to be tested; obtain the area that the spray valve in the foreign fiber removal machine can cover under the set air pressure, the width of the conveyor belt and the running speed of the conveyor belt, and divide the data sequence of the signal to be tested to obtain several intercept intervals.

[0020] It should be noted that the foreign fiber removal machine based on photoelectric detection technology removes foreign fibers by controlling the opening and closing of the airflow valve to blow them out. Since the airflow intensity from the spray valve is limited, the ratio of the area that the spray valve can cover under a preset spray intensity (i.e., spray pressure) to the width of the conveyor belt is used to determine the length that the spray valve can cover with each spray. Then, based on the length that the spray valve can cover with each spray and the conveyor belt speed, the time length of the cutting interval is determined. This ensures that every part corresponding to each cutting interval is blown out with each spray, and that the parts within each cutting interval are not affected by the airflow used to remove foreign fibers from other cutting intervals.

[0021] Specifically, the cotton to be tested, which needs to be inspected for foreign fibers and has been fully opened, is laid on the conveyor belt of the foreign fiber removal machine at a uniform thickness to complete the arrangement of the cotton to be tested.

[0022] Furthermore, during the inspection, a polarization module is placed above the conveyor belt perpendicular to it, and the light source on the polarization module emits light at a rate of [insert speed per second]. The light signal is emitted at a frequency of [frequency value missing]. At the source of the light, a polarizer converts the light signal into linearly polarized light. When the linearly polarized light shines on the cotton, it will be scattered to different degrees depending on the properties of the irradiated surface. The preset frequency value in this embodiment [details missing]. This example is used to illustrate the concept; other values ​​can be set in other implementations. And per second... The frequency of emitting light signals refers to the light source emitting light at uniform intervals per second. sub-optical signal.

[0023] Furthermore, an analyzer is placed in front of the receiving sensor. The receiving sensor receives linearly polarized light scattered to varying degrees, obtaining several signals to be detected, and thus a data sequence of these signals. The polarization direction of the analyzer in front of the receiving sensor is perpendicular to the polarizer direction. Each signal to be detected corresponds to a specific acquisition time. Each signal to be detected corresponds to a region of the cotton being tested.

[0024] At this point, the data sequence of the signal to be detected is obtained.

[0025] Furthermore, the area that the spray valve in the foreign fiber removal machine can cover under the set air pressure is divided by the width of the conveyor belt in the foreign fiber removal machine, and this value is recorded as the coverage length. In this embodiment, the preset air pressure of the spray valve in the foreign fiber removal machine is 0.6 MPa; other values ​​can be used in other embodiments. Obtaining the area that the spray valve can cover under the set air pressure is existing known technology and will not be elaborated upon in this embodiment.

[0026] Furthermore, the specific formula for calculating the time length of the intercepted interval is as follows: In the formula, Indicates the length of the time interval to be extracted. Indicates the length that can be covered. This indicates the operating speed of the conveyor belt inside the foreign fiber removal machine.

[0027] Furthermore, based on the acquisition time corresponding to each signal to be detected within the data sequence of the signal to be detected, and the time length of the truncated interval, the data sequence of the signal to be detected is divided into several truncated intervals. Each truncated interval does not contain any signals to be detected acquired at the same acquisition time. Dividing the data sequence into several segments based on the acquisition time is a known technique and will not be elaborated upon in this embodiment.

[0028] Thus, several cutoff intervals have been obtained.

[0029] Step S002: Based on the distribution difference of the signal to be detected in each intercepted interval and its surrounding intercepted intervals, obtain the anomaly factor of each intercepted interval.

[0030] It should be noted that since the intensity of the light signal emitted by the light source is consistent, and the properties of normal cotton are consistent, even if there are slight differences in the thickness during laying, causing fluctuations in the intensity signal received after reflection, the received intensity signals should be quite similar and the fluctuations should be small when the reflecting medium is cotton within the same interval. That is, under normal circumstances, the distribution of the detected signal within different intercept intervals where the reflecting medium is cotton is quite similar.

[0031] It should be further noted that due to the relatively large differences in properties between normal cotton and foreign fibers, the intensity signal of the light signal received after reflection by foreign fibers differs significantly from the intensity signal received after reflection by normal cotton. That is, the distribution of the detected signal acquired when the reflecting medium within the interval is entirely cotton differs from the distribution acquired when the reflecting medium within the interval contains both cotton and foreign fibers. Therefore, based on the difference in the distribution of the detected signal within each intercepted interval compared to the two intercepted intervals before and after it, the anomaly factor for each intercepted interval is obtained.

[0032] Specifically, the formula for calculating the outlier factor for each cutoff interval is as follows: In the formula, Indicates the first Outlier factors within a cutoff interval Indicates the first The variance of all signals to be detected within a cutoff interval. Indicates the first The variance of all signals to be detected within a cutoff interval. Indicates the first The mean of all signals to be detected within a cutoff interval. Indicates the first The mean of all signals to be detected within a cutoff interval. Indicates the first The mean of all signals to be detected within a cutoff interval. Represents the absolute value function. To prevent hyperparameters with a denominator of 0, this embodiment sets... In other embodiments, this can be set to other values. Wherein, if Then the third cutoff interval is taken as the cutoff interval before the first cutoff interval, that is, the third cutoff interval. The 3rd cutoff interval is the first cutoff interval; if the 3rd cutoff interval is the second cutoff interval... If the first interval is the last interval, then the third-to-last interval is taken as the interval following the last interval. That is, at this point, the first interval... The third-to-last cutoff interval is the selected interval.

[0033] It should be noted that, The smaller the value, the more... The more similar the mean of the signal to be detected within a certain intercept interval is to the mean of the signal to be detected within its surrounding intercept intervals, the better. The more similar the distribution of the signal to be detected within a certain intercept interval is to the distribution of the signal to be detected in its two surrounding intercept intervals, the more likely it is that the signal distribution is similar to that within the first intercept interval. The more the intercepted interval conforms to the characteristic that the distribution of the detected signal is relatively similar in different intercepted intervals when the reflecting medium is cotton under normal circumstances, then the first intercepted interval... The smaller the outlier of each intercepted interval; The smaller the value, the more... The first intercept interval and the first The more similar the mean values ​​of the signals to be detected within each intercepted interval, the more it conforms to the characteristic that the distribution of the signals to be detected within different intercepted intervals is relatively similar under normal circumstances, when the reflecting medium is cotton. The first intercept interval and the first The probability of an abnormal signal to be detected within a certain intercept interval is relatively small. It better reflects the first The probability that an abnormal signal is contained within a selected interval; and The smaller the value, the more... The smaller the difference between the different signals to be detected within each intercepted interval, the more it indicates that the... The smaller the difference between different signals to be detected within each intercept interval, that is, the smaller the difference between the first and second intercept intervals. With the The more a selected interval matches the characteristic that the signals to be detected are relatively similar within the selected interval where the reflecting medium is cotton, then... It better reflects the first The degree of anomaly in each intercepted interval.

[0034] Thus, the outlier factors for each intercepted interval are obtained.

[0035] Step S003: Cluster the signals to be detected within each intercepted interval to obtain several clusters; the cluster containing the most signals to be detected within each intercepted interval is denoted as the normal cluster within each intercepted interval; based on the distribution of signals to be detected in the normal clusters within each intercepted interval, the upper and lower bounds of the normal signal range for each intercepted interval are obtained; based on the difference in anomalous factors between each intercepted interval and other intercepted intervals, the anomalous factor of each intercepted interval, and the number of signals to be detected in the normal clusters within each intercepted interval, the confidence factor of the normal signal range for each intercepted interval is obtained; combined with the upper and lower bounds of the normal signal range for each intercepted interval, the final upper and lower bounds of the normal signal range are obtained.

[0036] It should be noted that since the signals to be detected obtained by reflection from normal cotton and the signals to be detected by reflection from foreign fibers differ in each intercepted interval, the K-means clustering algorithm is used to cluster the signals to be detected in each intercepted interval according to the value of each signal to be detected, and the signals to be detected in each intercepted interval are divided into several clusters.

[0037] It should be further noted that, since the amount of foreign fibers in the cotton being tested is relatively small, the number of signals to be detected obtained through reflection from these foreign fibers within each intercepted interval is also relatively small. Therefore, the cluster containing the most signals to be detected within each intercepted interval is designated as the normal cluster within that interval, thus obtaining the range of normal signals within each intercepted interval. Then, based on the ranges of normal signals across all intercepted intervals, the final range of normal signals is obtained.

[0038] It should be further noted that, since the number and value of the detected signals reflected by the foreign fiber may differ in different intercept intervals, when obtaining the final range of normal signals based on the range of normal signals in all intercept intervals, it is necessary to assign different weights to the range of normal signals in each intercept interval. When an intercept interval contains a large number of detected signals reflected by the foreign fiber, a smaller weight is assigned to that intercept interval; when an intercept interval contains a small number of detected signals reflected by the foreign fiber, a larger weight is assigned to that intercept interval.

[0039] It should be further noted that the more signals to be detected contained in the largest cluster of a cutoff interval, the more normal detection signals (i.e., signals to be detected obtained through cotton reflection) are contained in that cutoff interval. Therefore, the confidence factor of each cutoff interval, i.e., the weight of the cutoff interval, is calculated based on the number of signals to be detected contained in the largest cluster of each cutoff interval.

[0040] It should be further noted that a larger anomaly factor for a cutoff interval indicates a greater likelihood that the interval contains an abnormal detection signal, i.e., the signal to be detected obtained through fiber reflection. Therefore, the anomaly factor for each cutoff interval is used as a parameter in calculating the confidence factor for each cutoff interval.

[0041] It should be further noted that, under normal circumstances, the distribution of the signals to be detected in different cut-off intervals where the reflective medium is cotton is quite similar, which makes the anomaly factors of different cut-off intervals where the reflective medium is cotton also quite similar. Therefore, the difference in anomaly factors between each cut-off interval and other cut-off intervals is used as a parameter for calculating the confidence factor of each cut-off interval.

[0042] Specifically, using Euclidean distance as the distance metric, according to the... The values ​​of all signals to be detected within the intercepted interval are analyzed using the K-means clustering algorithm. All signals to be detected within a specified interval are clustered to obtain several clusters. The cluster containing the most signals to be detected is denoted as the i-th cluster. The first truncation interval contains normal clusters. The K-means clustering algorithm is used to select the th truncation interval. When clustering all signals to be detected within a certain intercept interval, the number of clusters is determined by the elbow method. Obtaining the number of clusters when clustering a set of data using the elbow method is a well-known existing technique, and will not be described in detail in this embodiment.

[0043] Furthermore, the first The minimum value among all the signals to be detected in the normal clusters within the intercept interval is denoted as the i-th value. The lower bound of the normal signal range of the intercept interval. The maximum value among all the signals to be detected in the normal clusters within the intercept interval is denoted as the i-th value. The upper limit of the normal signal range of the intercepted interval.

[0044] Furthermore, obtain the first The specific formula for calculating the confidence factor of the normal signal range of each intercept interval is as follows: In the formula, Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first Outlier factors within a cutoff interval Indicates the first Outlier factors within a cutoff interval Indicates the number of intervals to be extracted. This represents a second hyperparameter to prevent the denominator from being zero. Indicates the first The number of signals to be detected in normal clusters within a cutoff interval. Indicates the first The number of signals to be detected within a single intercept interval. To prevent the third hyperparameter from having a denominator of 0, It is an absolute value function. This embodiment sets... , In other embodiments, it can be set to other values.

[0045] It should be noted that since the number of signals to be detected obtained through reflection from the cotton in each truncated interval is relatively large, the ratio of the number of signals to be detected in the normal clusters within each truncated interval to the total number of signals to be detected in the entire truncated interval is... This is considered as the proportion of normal signals within the detection interval, therefore The larger the value, the more significant the [value]. The lower the probability of an abnormal signal within the intercepted interval, that is, the lower the probability of the first intercepted interval containing an abnormal signal. The more reliable the normal signal range of each intercepted interval, the better. The larger the value; The larger the value, the more significant the [value]. The greater the likelihood that an abnormal signal is contained within a specific intercept interval; The larger the value, the more significant the [value]. The greater the difference between the first intercept interval and other intercept intervals, the less it conforms to the characteristic that different intercept intervals with cotton as the reflecting medium also have relatively similar anomalous factors, i.e., the first... The greater the probability that the selected interval contains an abnormal signal, that is, the higher the probability that the selected interval contains an abnormal signal. The less reliable the normal signal range of a segmented interval is.

[0046] Thus, the confidence factor for the normal signal range of each intercepted interval is obtained.

[0047] Furthermore, the specific calculation formula for obtaining the final upper bound of the normal signal range is as follows: In the formula, This represents the final upper bound of the normal signal range. Indicates the number of intervals to be extracted. Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first The upper limit of the normal signal range of the intercepted interval.

[0048] Furthermore, the specific calculation formula for obtaining the final lower bound of the normal signal range is as follows: In the formula, This represents the lower bound of the normal signal range. Indicates the number of intervals to be extracted. Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first The lower bound of the normal signal range of the intercept interval.

[0049] It should be noted that, through Different weights are assigned to the upper and lower bounds of different intercept intervals, so that when an intercept interval contains more normal signals, the upper and lower bounds of the intercept interval are given a larger weight, and when an intercept interval contains fewer normal signals, the upper and lower bounds of the intercept interval are given a smaller weight.

[0050] Thus, the final upper and lower bounds of the normal signal range are obtained.

[0051] Step S004: Based on the final upper and lower limits of the normal signal range and the distribution of the signal to be detected in each intercepted interval, obtain the degree of removal required for each intercepted interval and perform foreign fiber removal.

[0052] It should be noted that when determining the degree of removal required for each intercepted interval based on the final upper and lower bounds of each normal signal range, the more detectable signals within an intercepted interval that are greater than the final upper bound or less than the final lower bound of the normal signal range, the greater the likelihood that the intercepted interval requires foreign fiber removal. Therefore, the degree of removal required for each intercepted interval is calculated based on the numerical distribution of the detectable signals within each intercepted interval.

[0053] It should be further explained that, since foreign fibers are morphologically continuous, although their position, angle, or thickness mixed with normal cotton may vary, their influence on the received normal signal should be continuous. That is, when a signal affected by foreign fibers appears, multiple signals affected by foreign fibers should appear consecutively. Therefore, the degree of removal required for each intercepted interval is calculated based on the number of consecutive signals affected by foreign fibers.

[0054] It should be further noted that when the signal to be detected is greater than the upper limit of the normal signal range, the greater the difference between the signal to be detected and the upper limit of the normal signal range, the greater the likelihood that the signal to be detected is an abnormal signal. Similarly, when the signal to be detected is less than the lower limit of the normal signal range, the greater the difference between the signal to be detected and the lower limit of the normal signal range, the greater the likelihood that the signal to be detected is an abnormal signal. Therefore, based on the value of the signal to be detected within each intercepted interval, the degree of removal required for each intercepted interval is calculated.

[0055] Specifically, the first Signals to be detected that fall above the upper bound of the normal signal range or below the lower bound of the normal signal range within the intercept interval are categorized as suspected abnormal signals. Other signals to be detected within a cutoff interval, excluding suspected abnormal signals, are denoted as suspected normal signals.

[0056] Furthermore, if the first Within the selected interval, the first... If the first suspected abnormal signal is greater than the final upper bound of the normal signal range, then the first... Within the selected interval, the first... The absolute value of the difference between the final upper bound of the range of the suspected abnormal signal and the normal signal is denoted as the nth. Within the selected interval, the first... The degree of abnormality of a suspected abnormal signal.

[0057] Furthermore, if the first Within the selected interval, the first... If the first suspected abnormal signal is less than the final lower bound of the normal signal range, then the first... Within the selected interval, the first... The absolute value of the difference between the final lower bound of the range of the suspected abnormal signal and the normal signal is denoted as the i-th. Within the selected interval, the first... The degree of abnormality of a suspected abnormal signal.

[0058] Furthermore, the first Each suspected abnormal signal within a cutoff interval is connected to the suspected abnormal signals adjacent to its left and right sides to obtain the first... The method involves extracting several suspected abnormal signal segments within a given interval. Specifically, connecting each signal within an interval to its adjacent signals on either side to form a data segment is a well-known technique and will not be described in detail in this embodiment.

[0059] Furthermore, obtain the first The specific formula for calculating the degree of removal required for each cutoff interval is as follows: In the formula, Indicates the first The degree of need to be cleared in each selected interval. Indicates the first The number of suspected normal signals within a single intercept interval. Indicates the first The number of signals to be detected within each intercept interval Indicates the first The mean of the abnormality level of all suspected abnormal signals within a cutoff interval. Indicates the first The maximum value among all suspected abnormal signal segments within a given intercept interval. This represents the sigmoid function, which is used for normalization in this embodiment. As an exponential function with the natural constant as its base, this embodiment uses it for inverse proportional processing.

[0060] It should be noted that, The larger the value, the more significant the [value]. The fewer signal data points within the intercepted interval that are greater than the final upper bound or less than the final lower bound, the better. The likelihood of containing foreign fibers within the selected interval is relatively small; The larger the value, the more significant the [value]. The greater the probability that a suspected abnormal signal within a given interval is indeed an abnormal signal, that is, the higher the probability that the first abnormal signal is actually an abnormal signal. The selected interval is likely to contain foreign fibers; The larger the value, the more significant the [value]. The more a segment of suspected abnormal data within a selected interval conforms to the characteristic that when a signal affected by a foreign fiber appears, multiple signals affected by a foreign fiber should appear consecutively, i.e., the first segment... The greater the chance that a cut-off interval contains foreign fibers.

[0061] At this point, the degree of clearing required for each selected interval is obtained.

[0062] Furthermore, according to the first Each signal to be detected within a certain interval corresponds to a region of cotton to be detected, thus obtaining the first... The area of ​​cotton to be detected corresponds to each intercept interval. A preset threshold for removal is set. ,like Then for the first The foreign fiber is removed from the area of ​​the cotton to be detected corresponding to each intercept interval. In this embodiment, a preset threshold for removal is used. This example is used for illustration; other values ​​can be set in other embodiments. Removing foreign fibers from a region is a well-known technique and will not be described in detail in this embodiment.

[0063] This concludes the embodiment.

Claims

1. A method for detecting impurities in a foreign fiber removal machine based on photoelectric detection technology, characterized in that, include: The cotton to be tested is placed on the foreign fiber removal machine to obtain the data sequence of the signal to be tested; The area that the spray valve can cover under a set jet pressure, the width of the conveyor belt, and the running speed of the conveyor belt are obtained within the foreign fiber removal machine. The data sequence of the signal to be detected is segmented to obtain several intercept intervals; each signal to be detected corresponds to a region of cotton to be detected; based on the distribution difference of the signal to be detected in each intercept interval and its surrounding intercept intervals, the anomaly factor of each intercept interval is obtained; the signal to be detected in each intercept interval is clustered to obtain several clusters; the cluster containing the most signal to be detected in each intercept interval is denoted as the normal cluster in each intercept interval; based on the normal clusters in each intercept interval... Based on the distribution of the signals to be detected, the upper and lower bounds of the normal signal range for each intercepted interval are obtained. According to the difference in anomalous factors between each intercepted interval and other intercepted intervals, the anomalous factor of each intercepted interval, and the number of signals to be detected in the normal clusters within each intercepted interval, the confidence factor of the normal signal range for each intercepted interval is obtained. Combining the upper and lower bounds of the normal signal range for each intercepted interval, the final upper and lower bounds of the normal signal range are obtained. Based on the final upper and lower bounds of the normal signal range, and the distribution of the signals to be detected within each intercepted interval, the degree of removal required for each intercepted interval is determined, and foreign fiber removal is performed.

2. The method for detecting impurities in a foreign fiber removal machine based on photoelectric detection technology according to claim 1, characterized in that, The specific steps for obtaining several cut-off intervals are as follows: Set the air pressure of the spray valve inside the foreign fiber removal machine, and obtain the area that the spray valve can cover under the set air pressure; divide the area that the spray valve can cover under the set air pressure by the width of the conveyor belt inside the foreign fiber removal machine, and record the result as the coverage length; the specific calculation formula for obtaining the time length of the cut-off interval is as follows: In the formula, Indicates the length of the time interval to be extracted. Indicates the length that can be covered. This indicates the running speed of the conveyor belt inside the foreign fiber removal machine; based on the acquisition time corresponding to each signal to be detected in the data sequence of the signal to be detected, and the time length of the intercept interval, the data sequence of the signal to be detected is divided into several intercept intervals.

3. The method for detecting impurities in a foreign fiber removal machine based on photoelectric detection technology according to claim 1, characterized in that, The specific calculation formula for the anomaly factor of each intercepted interval is as follows: In the formula, Indicates the first Outlier factors within a cutoff interval Indicates the first The variance of all signals to be detected within a cutoff interval. Indicates the first The variance of all signals to be detected within a cutoff interval. Indicates the first The mean of all signals to be detected within a cutoff interval. Indicates the first The mean of all signals to be detected within a cutoff interval. Indicates the first The mean of all signals to be detected within a cutoff interval. Represents the absolute value function. To prevent hyperparameters with a denominator of 0.

4. The method for detecting impurities in a foreign fiber removal machine based on photoelectric detection technology according to claim 1, characterized in that, The specific steps for clustering the signal to be detected within each intercepted interval to obtain several clusters are as follows: Using the elbow method, obtain the clusters for the first... The number of clusters formed when clustering all signals to be detected within a specified interval is denoted as the i-th cluster. The number of clusters in each cutoff interval; using Euclidean distance as the distance metric, according to the... The values ​​of all signals to be detected within the specified interval are taken, and the number of clusters is set to the th interval. The number of clusters in the selected interval is determined, and the K-means clustering algorithm is used to analyze the i-th interval. All signals to be detected within a cutoff interval are clustered to obtain several clusters.

5. The method for detecting impurities in a foreign fiber removal machine based on photoelectric detection technology according to claim 1, characterized in that, The specific steps for obtaining the upper and lower bounds of the normal signal range for each intercepted interval are as follows: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] The minimum value among all the signals to be detected in the normal clusters within the intercept interval is denoted as the i-th value. The lower bound of the normal signal range of the first intercept interval; the second... The maximum value among all the signals to be detected in the normal clusters within the intercept interval is denoted as the i-th value. The upper limit of the normal signal range of the intercepted interval.

6. The method for detecting impurities in a foreign fiber removal machine based on photoelectric detection technology according to claim 1, characterized in that, The specific formula for calculating the confidence factor of the normal signal range for each intercepted interval is as follows: In the formula, Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first Outlier factors within a cutoff interval Indicates the first Outlier factors within a cutoff interval Indicates the number of intervals to be extracted. This represents a second hyperparameter to prevent the denominator from being zero. Indicates the first The number of signals to be detected in normal clusters within a cutoff interval. Indicates the first The number of signals to be detected within a single intercept interval. To prevent the third hyperparameter from having a denominator of 0, It is an absolute value function.

7. The method for detecting impurities in a foreign fiber removal machine based on photoelectric detection technology according to claim 1, characterized in that, The specific calculation formulas for obtaining the final upper and lower bounds of the normal signal range are as follows: The specific calculation formula for obtaining the final upper bound of the normal signal range is as follows: In the formula, This represents the final upper bound of the normal signal range. Indicates the number of intervals to be extracted. Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first The upper bound of the normal signal range of each intercept interval; the specific calculation formula for obtaining the final lower bound of the normal signal range is as follows: In the formula, This represents the lower bound of the normal signal range. Indicates the number of intervals to be extracted. Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first Confidence factor for the normal signal range of a cut-off interval Indicates the first The lower bound of the normal signal range of the intercept interval.

8. The method for detecting impurities in a foreign fiber removal machine based on photoelectric detection technology according to claim 1, characterized in that, The specific steps for obtaining the degree of removal to be achieved for each truncated interval are as follows: [The text abruptly ends here, likely due to an incomplete sentence or a formatting error.] Signals to be detected that fall above the upper bound of the normal signal range or below the lower bound of the normal signal range within the first intercept interval are recorded as suspected abnormal signals; the first... Other signals to be detected within the first intercept interval, excluding suspected abnormal signals, are denoted as suspected normal signals; if the first... Within the selected interval, the first... If the first suspected abnormal signal is greater than the final upper bound of the normal signal range, then the first... Within the selected interval, the first... The absolute value of the difference between the final upper bound of the range of the suspected abnormal signal and the normal signal is denoted as the nth. Within the selected interval, the first... The degree of abnormality of a suspected abnormal signal; If the first Within the selected interval, the first... If the first suspected abnormal signal is less than the final lower bound of the normal signal range, then the first... Within the selected interval, the first... The absolute value of the difference between the final lower bound of the range of the suspected abnormal signal and the normal signal is denoted as the i-th. Within the selected interval, the first... The degree of abnormality of a suspected abnormal signal; The first Each suspected abnormal signal within a cutoff interval is connected to the suspected abnormal signals adjacent to its left and right sides to obtain the first... Several suspected abnormal signal segments were extracted within the intercept interval; according to the... The mean of the abnormality of all suspected abnormal signals within the first intercept interval, the first... The number of suspected normal signals within each intercept interval, the first The maximum value among the number of suspected abnormal signals in all suspected abnormal signal segments within the first intercept interval is obtained. The degree of need to be cleared in each intercepted interval.

9. The method for detecting impurities in a foreign fiber removal machine based on photoelectric detection technology according to claim 8, characterized in that, According to the first The mean of the abnormality of all suspected abnormal signals within the first intercept interval, the first... The number of suspected normal signals within each intercept interval, the first The maximum value among the number of suspected abnormal signals in all suspected abnormal signal segments within the first intercept interval is obtained. The specific formula for calculating the degree of removal required for each cutoff interval is as follows: In the formula, Indicates the first The degree of need to be cleared in each selected interval. Indicates the first The number of suspected normal signals within a single intercept interval. Indicates the first The number of signals to be detected within a single intercept interval. Indicates the first The mean of the abnormality level of all suspected abnormal signals within a cutoff interval. Indicates the first The maximum value among all suspected abnormal signal segments within a given intercept interval. This represents the sigmoid function. It is an exponential function with the natural constant as its base.

10. The method for detecting impurities in a foreign fiber removal machine based on photoelectric detection technology according to claim 1, characterized in that, The specific steps for removing foreign fibers are as follows: According to the... Each signal to be detected within a certain interval corresponds to a region of cotton to be detected, thus obtaining the first... The area of ​​cotton to be detected corresponding to each intercept interval; a preset threshold for removal. If the first The degree of uncleared areas in each selected interval Then for the first Foreign fibers are removed from the area of ​​the cotton to be tested corresponding to the intercepted interval.

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