Method, system and equipment for monitoring operation state of degradable material granulator

Abstract

The invention discloses a degradable material granulator operation state monitoring method, system and equipment. The method comprises the following steps: acquiring a plurality of monitoring images and a plurality of CT scanning images of a target material strip in a unit monitoring period; respectively acquiring a surface defect trend value and a porosity trend value of the target material strip; obtaining an abnormal operation trend value of the granulator; inputting the surface defect trend value, the porosity trend value and the operation abnormity trend value into a preset risk assessment model to obtain a risk assessment value; judging whether the risk assessment value is greater than a preset risk threshold value or not, if so, sending first alarm information, and if not, obtaining the current weight of a particle material discharged from a pelletizing mechanism of the pelletizer; and judging whether the current weight is within a preset weight threshold interval, and if not, sending second alarm information, and the method and the device have the advantage of improving the accuracy of monitoring the abnormity in the granulation process.

Description

Technical Field

[0001] The present application relates to the technical field of equipment operation monitoring, and in particular to a method, system and equipment for monitoring the operation status of a degradation material granulator. Background Art

[0002] Degradable materials are materials that can decompose into harmless substances under specific conditions. They have attracted widespread attention due to their environmentally friendly properties. In the production process of degradable materials, granulation is an important process link, which can process loose granular degradable materials into granules for easy transportation and application.

[0003] At present, when a granulator is used to granulate biodegradable materials to produce granular materials, the biodegradable materials are first stirred and mixed and then extruded into material strips. The material strips are then air-dried, cooled and other process operations under the action of a conveyor, and finally pelletized to form granular materials. This process requires constant monitoring of the granulation production status. When abnormalities are detected in the material granulation process (including product abnormalities and equipment operation abnormalities), the granulator needs to be shut down for inspection and maintenance immediately. Currently, this is mainly done through manual visual monitoring. Due to the large number of monitoring items, problems such as missed detection and wrong detection are prone to occur, resulting in inaccurate monitoring. Summary of the Invention

[0004] The main purpose of this application is to provide a method, system and equipment for monitoring the operating status of a degradable material granulator, aiming to solve the technical problem that the existing granulator monitoring method has low accuracy in monitoring abnormalities in the granulation process.

[0005] To achieve the above objectives, the present application provides a method for monitoring the operating status of a degradable material granulator, comprising the following steps:

[0006] Acquire multiple monitoring images and multiple CT scan images of a target material strip within a unit monitoring cycle; wherein the target material strip is a material strip exiting an extruder of a granulator, and the target material strips in the multiple monitoring images are spliced ​​together to form a material strip having a preset length;

[0007] Obtain the surface defect trend value of the target material strip based on multiple monitoring images;

[0008] Obtain the porosity trend value of the target material strip based on multiple CT scan images;

[0009] Obtain the abnormal operation trend value of the granulator;

[0010] Inputting the surface defect trend value, the porosity trend value and the operation abnormality trend value into a preset risk assessment model to obtain a risk assessment value;

[0011] Determine whether the risk assessment value is greater than a preset risk threshold, and if so, send a first alarm message; if not, obtain the current weight of the granular material coming out of the granulating mechanism of the granulator;

[0012] Determine whether the current weight is within a preset weight threshold range. If not, send a second alarm message. If so, return to obtaining multiple monitoring images and multiple CT scan images of the target material strip within the unit monitoring cycle.

[0013] Optionally, based on multiple monitoring images, a surface defect trend value of the target material strip is obtained, including:

[0014] Identify the fracture characteristics of the target material strip in each monitoring image to obtain the fracture defect trend value η1 of the target material strip;

[0015] Identify the abnormal diameter characteristics of the target material strip in each monitoring image to obtain the abnormal diameter trend value η2 of the target material strip;

[0016] Identify the color dispersion of the target material strip in each monitoring image to obtain the color dispersion trend value η3;

[0017] Obtain the surface defect trend value η, the expression of η is:

[0018] η=K1·η1+K2·η2+K3·η3;

[0019] Wherein, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

[0020] Optionally, identifying the fracture characteristics of the target material strip in each monitoring image to obtain the fracture defect trend value η1 of the target material strip includes:

[0021] Eliminate monitoring images that do not have fault features, and sort the remaining monitoring images according to time series;

[0022] Obtain the fracture characteristic value ΔL of the target material strip in each monitoring image after sorting; where ΔL = N1·L, N1 is the number of fractures in a single monitoring image, and L is the longest fracture length in a single monitoring image;

[0023] Obtaining a first ratio of fracture characteristic values ​​ΔL corresponding to adjacent monitoring images;

[0024] Obtain a number M1 of first ratios greater than or equal to 1;

[0025] If the number M1 is greater than a preset first number threshold, the maximum value among the multiple first ratios is output as the fracture defect tendency value η1;

[0026] If the number M1 is less than or equal to the first number threshold, the output fracture defect tendency value η1 is 0.

[0027] Optionally, identifying the abnormal diameter feature of the target material strip in each monitoring image to obtain the abnormal diameter trend value η2 of the target material strip includes:

[0028] Eliminate monitoring images that do not have abnormal diameter features, and sort the remaining monitoring images according to time series;

[0029] Obtain the diameter anomaly value ΔD of the target material strip in each monitoring image after sorting; where ΔD = N2·Δd, N2 is the number of diameter anomalies in a single monitoring image, Δd is the maximum absolute value of the difference between the abnormal diameter and the standard diameter in a single monitoring image, and the abnormal diameter is the diameter of the part of the target material strip that is smaller or larger than the standard diameter;

[0030] Obtaining a second ratio of diameter anomaly values ​​ΔD corresponding to adjacent monitoring images;

[0031] Obtain a number M2 of second ratios greater than or equal to 1;

[0032] If the number M2 is greater than a preset second number threshold, the maximum value among the plurality of second ratios is output as the diameter abnormality trend value η2;

[0033] If the number M2 is less than or equal to the second number threshold, the output diameter abnormality trend value η2 is 0.

[0034] Optionally, identifying the color dispersion of the target material strip in each monitoring image to obtain a color dispersion trend value η3 includes:

[0035] Eliminate monitoring images with color dispersion within the standard range, and sort the remaining monitoring images according to time series;

[0036] Obtaining a third ratio of color dispersions corresponding to adjacent monitored images after sorting;

[0037] Obtaining a number M3 of third ratios greater than or equal to 1;

[0038] If the number M3 is greater than a preset third number threshold, the maximum value among the plurality of third ratios is output as the color dispersion trend value η3;

[0039] If the number M3 is less than or equal to the third number threshold, the output color dispersion trend value η3 is 0.

[0040] Optionally, obtaining a porosity trend value of the target material strip based on multiple CT scan images includes:

[0041] Obtain the average porosity of the target material strip in each CT scan image;

[0042] obtaining a fourth ratio of average porosities corresponding to adjacent monitoring images;

[0043] Obtaining a number M4 of fourth ratio values ​​greater than or equal to 1;

[0044] If the quantity M4 is greater than a preset fourth quantity threshold, the maximum value among the plurality of fourth ratios is output as the porosity trend value;

[0045] If the quantity M4 is less than or equal to the fourth quantity threshold, the output porosity trend value is 0.

[0046] Optionally, obtaining an average porosity of the target material strip in each CT scan image includes:

[0047] The target material strip in the CT scan image is divided into multiple sub-regions at equal distances;

[0048] Obtain the measured porosity corresponding to each sub-region;

[0049] Screen out the sub-regions where the measured porosity is greater than the theoretical porosity;

[0050] The average value of the measured porosity of the screened sub-areas is output as the average porosity of the target material strip.

[0051] Optionally, the risk assessment model is expressed as:

[0052] F=(W1·η+W2·Q)·P;

[0053] Where F is the risk assessment value, Q is the porosity trend value, P is the operation abnormality trend value, W1 is the first weight coefficient, and W2 is the second weight coefficient.

[0054] To achieve the above objectives, the present application also provides a degradation material granulator operation status monitoring system, comprising:

[0055] An image acquisition module is configured to acquire multiple monitoring images and multiple CT scan images of a target material strip within a unit monitoring cycle; wherein the target material strip is a material strip exiting an extruder of a granulator, and the target material strips in the multiple monitoring images are spliced ​​together to form a material strip of a preset length;

[0056] A first trend prediction module is used to obtain a surface defect trend value of a target material strip based on multiple monitoring images;

[0057] A second trend prediction module is used to obtain a porosity trend value of the target material strip based on multiple CT scan images;

[0058] The third trend prediction module is used to obtain the abnormal operation trend value of the granulator;

[0059] A risk assessment module, for inputting the surface defect trend value, the porosity trend value and the operation abnormality trend value into a preset risk assessment model to obtain a risk assessment value;

[0060] a first data processing module, configured to determine whether the risk assessment value is greater than a preset risk threshold, and if so, to send a first alarm message; if not, to obtain a current weight of the granular material coming out of the granulating mechanism of the granulator;

[0061] The second data processing module is used to determine whether the current weight is within a preset weight threshold range. If not, a second alarm message is sent. If so, it returns to obtaining multiple monitoring images and multiple CT scan images of the target material strip within a unit monitoring period.

[0062] To achieve the above objectives, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above method.

[0063] To achieve the above objectives, the present application also provides a computer-readable storage medium, on which a computer program is stored. A processor executes the computer program to implement the above method.

[0064] The beneficial effects that can be achieved by this application are as follows:

[0065] Since the molding quality of the target material strip is crucial to the quality of the granular material after subsequent pelletizing, this application first obtains multiple monitoring images and multiple CT scan images of the target material strip within a unit monitoring cycle to comprehensively evaluate the molding quality of the material strip with a preset length. Here, the surface defect trend change of the target material strip can be predicted based on the multiple monitoring images, and the trend change can be quantified and assessed by the surface defect trend value. The internal structural defect of the target material strip is mainly porosity, so the internal porosity trend change of the target material strip can be predicted by multiple CT scan images, and the trend change can be quantified and assessed by the porosity trend value. At the same time, it is also associated with the operation abnormality trend value of the granulator, and finally the surface defect trend value, porosity trend value and operation abnormality trend value are input into the preset risk assessment model to obtain a risk assessment value. The risk assessment value is obtained by analyzing the surface and internal structural defect trend of the target material strip. The trend of the equipment operation abnormality change of the granulator is obtained by simultaneous correlation calculation. By predicting the abnormal trend change, abnormal risk assessment is performed, which avoids the situation of product quality defects or equipment operation abnormality caused by accidental abnormalities, reduces the probability of false alarms, and is more reasonable and reliable than directly assessing the risk by abnormal characteristics. Then, it is judged whether the risk assessment value is greater than the preset risk threshold. If so, the first alarm message is sent. If not, taking into account the monitoring error, the current weight of the granular material coming out of the granulating mechanism of the granulator is further calculated to determine whether the current weight is within the preset weight threshold range. If not, it may be that the granular material has a low density and causes the weight to be low, or the granular material is not dried sufficiently and has a high moisture content, so a reasonable range of weight threshold range is set here as a judgment basis. At this time, the second alarm message is sent. If so, the next round of monitoring cycle continues to monitor. In summary, the present application is based on the front-end material strip forming defect trend change and the granulator equipment abnormal trend change, and combined with the weight monitoring after the back-end granulation, the various influencing factors are effectively correlated, and the overall abnormal situation of the granulation process can be comprehensively evaluated, which is more reasonable and efficient, and improves the monitoring accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] To more clearly illustrate the specific embodiments of this application or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0067] Figure 1 This is a structural diagram of a method, system, and device for monitoring the operating status of a degradation material granulator in an embodiment of the present application;

[0068] Figure 2A schematic diagram of eliminating monitoring images without fracture features in an embodiment of the present application;

[0069] Figure 3 This is a schematic diagram of eliminating monitoring images without abnormal diameter features in an embodiment of the present application.

[0070] Reference numerals:

[0071] 110-target material strip, 120-fracture characteristics, 130-diameter abnormality characteristics.

[0072] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0074] It should be noted that if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0075] Example 1

[0076] Reference Figure 1-Figure 3 This embodiment provides a method for monitoring the operating status of a degradation material granulator, comprising the following steps:

[0077] Acquire multiple monitoring images and multiple CT scan images of a target material strip 110 within a unit monitoring cycle; wherein the target material strip 110 is a material strip exiting an extruder of a pelletizer, and the target material strip 110 in the multiple monitoring images is spliced ​​together to form a material strip having a preset length;

[0078] Obtaining a surface defect trend value of the target material strip 110 based on the plurality of monitoring images;

[0079] Obtaining a porosity trend value of the target material strip 110 based on the multiple CT scan images;

[0080] Obtain the abnormal operation trend value of the granulator;

[0081] Inputting the surface defect trend value, the porosity trend value and the operation abnormality trend value into a preset risk assessment model to obtain a risk assessment value;

[0082] Determine whether the risk assessment value is greater than a preset risk threshold, and if so, send a first alarm message; if not, obtain the current weight of the granular material coming out of the granulating mechanism of the granulator;

[0083] Determine whether the current weight is within a preset weight threshold range. If not, send a second alarm message. If so, return to obtaining multiple monitoring images and multiple CT scan images of the target material strip 110 within the unit monitoring cycle.

[0084] In this embodiment, since the molding quality of the target material strip 110 is crucial to the quality of the granular material after subsequent pelletizing, this embodiment first obtains multiple monitoring images and multiple CT scan images of the target material strip 110 within a unit monitoring period, thereby comprehensively evaluating the molding quality of the material strip with a preset length. Here, the surface defect trend change of the target material strip 110 can be predicted based on the multiple monitoring images, and its trend change can be quantified and assessed by the surface defect trend value. The internal structural defect of the target material strip 110 is mainly porosity. Therefore, the internal porosity trend change of the target material strip 110 can be predicted by multiple CT scan images, and its trend change can be quantified and assessed by the porosity trend value. At the same time, it is also associated with the abnormal operation trend value of the pelletizer. Finally, the surface defect trend value, porosity trend value and abnormal operation trend value are input into a preset risk assessment model to obtain a risk assessment value. The risk assessment value is obtained by simultaneously associating and calculating the change trend of the surface and internal structural defects of the target material strip 110 and the abnormal operation trend of the equipment operation of the pelletizer. Abnormal risk assessment is performed based on abnormal trend changes, avoiding product quality defects or equipment operation abnormalities caused by accidental abnormalities (such as misoperation), and reducing the probability of false alarms. If the product defects or equipment operation abnormalities are caused by accidental abnormalities, a trend of gradually returning to normal is formed (i.e., an abnormal slowdown trend). Therefore, this embodiment is more reasonable and reliable than directly assessing risks based on abnormal characteristics. Then, it is determined whether the risk assessment value is greater than the preset risk threshold. If so, it indicates that there is an abnormal upward trend, and a first alarm message is sent to notify the staff to conduct timely inspection and maintenance. If not, taking into account the monitoring error, the current weight of the granular material coming out of the granulator's cutting mechanism is further calculated to determine whether the current weight is within the preset weight threshold range. If not, the granular material may have a low molding density, resulting in a low weight, or the granular material may have insufficient drying and high moisture content, resulting in a high weight. Therefore, a reasonable range of weight threshold range is set here as a judgment basis, and a second alarm message is sent at this time. If so, the next monitoring cycle is entered to continue monitoring. In summary, this embodiment is based on the trend changes of front-end material strip forming defects and the abnormal trend changes of granulator equipment, and combined with the weight monitoring after back-end pelletizing, to effectively correlate the various influencing factors, and can comprehensively evaluate the abnormal conditions in the granulation process as a whole, which is more reasonable and efficient, and improves the monitoring accuracy.

[0085] It should be noted that an industrial camera and a CT scanner can be configured at the extruder discharge position to continuously capture monitoring images and CT scan images. The CT scan image can use X-rays to penetrate the target material bar 110 and reconstruct its three-dimensional internal structure to quantify the porosity. Because the pelletizer's molding quality is affected by numerous factors, such as the uniformity of raw material mixing within the pelletizer, temperature, humidity, and extrusion pressure, and abnormal quality trends in the target material bar 110 are generally due to abnormal operating parameters in the pelletizer, when calculating the pelletizer's abnormal operating trend value, the type of abnormal operating parameter, such as temperature, is first detected using a corresponding sensor. The temperature change value is then output as the abnormal operating trend value. If multiple abnormal operating parameters are present, the change values ​​of the multiple abnormal operating parameters are weighted and summed to output the abnormal operating trend value. The first alarm message and the second alarm message can each include the corresponding parameter value to assist personnel in quickly screening for abnormalities.

[0086] As an optional embodiment, obtaining a surface defect trend value of the target material strip 110 based on multiple monitoring images includes:

[0087] Identify the fracture feature 120 of the target material strip 110 in each monitoring image to obtain a fracture defect trend value η1 of the target material strip 110;

[0088] Identify the abnormal diameter feature 130 of the target material strip 110 in each monitoring image to obtain the abnormal diameter trend value η2 of the target material strip 110;

[0089] Identify the color dispersion of the target material strip 110 in each monitoring image to obtain a color dispersion trend value η3;

[0090] Obtain the surface defect trend value η, the expression of η is:

[0091] η=K1·η1+K2·η2+K3·η3;

[0092] Wherein, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

[0093] In this embodiment, since the surface defects of the target material strip 110 are mainly poor continuity (i.e., having a fracture feature 120), poor diameter uniformity (i.e., having a diameter abnormality feature 130) and uneven surface color distribution (which can be represented by color dispersion), the defect feature identification can be performed separately after image processing based on the monitoring image to respectively identify the corresponding fracture feature 120, diameter abnormality feature 130 and color dispersion in each monitoring image, so as to quantitatively calculate the fracture defect trend value η1, the diameter abnormality trend value η2 and the color dispersion trend value η3. Based on these three key parameters, the surface defect change trend of the target material strip 110 can be characterized as a whole. The surface defect trend value η can be quantitatively calculated by inputting the above formula. Since the unit properties of the three parameters are different, the adjustment coefficients are used for conversion and adjustment respectively so that each parameter can be quantitatively superimposed to calculate an accurate and reliable surface defect trend value η.

[0094] It should be noted that, since the extrusion structure generally has multiple discharge holes, the collected monitoring image may contain multiple target material strips 110. When calculating the above-mentioned trend values, it is necessary to identify the defect features (i.e., fracture features 120, diameter abnormality features 130 and color dispersion) of all target material strips 110 in each monitoring image and superimpose the calculations to perform overall defect trend prediction.

[0095] As an optional embodiment, identifying the fracture feature 120 of the target material strip 110 in each monitoring image to obtain the fracture defect trend value η1 of the target material strip 110 includes:

[0096] Eliminate monitoring images that do not have the fault feature 120, and sort the remaining monitoring images according to the time series;

[0097] Obtaining a fracture characteristic value ΔL of the target material strip 110 in each monitoring image after sorting; wherein ΔL=N1·L, N1 is the number of fractures in a single monitoring image, and L is the longest fracture length in a single monitoring image;

[0098] Obtaining a first ratio of fracture characteristic values ​​ΔL corresponding to adjacent monitoring images;

[0099] Obtain a number M1 of first ratios greater than or equal to 1;

[0100] If the number M1 is greater than a preset first number threshold, the maximum value among the multiple first ratios is output as the fracture defect tendency value η1;

[0101] If the number M1 is less than or equal to the first number threshold, the output fracture defect tendency value η1 is 0.

[0102] In this embodiment, when calculating the fracture defect trend value η1, the monitoring images without fracture features 120 are first eliminated, and the remaining monitoring images are sorted according to the time series (i.e., the time sequence of acquiring the monitoring images), so as to facilitate the overall prediction of the continuity trend of the target material strip 110 to ensure the accuracy and reliability of the prediction results. Then, the fracture characteristic value ΔL of the target material strip 110 in each monitoring image after sorting is calculated, where ΔL=N1·L, i.e., the fracture characteristic value ΔL is characterized by two key factors, the number of fractures and the longest fracture length. Whether the number of fractures is too large or the fracture length is too long, the continuity can be characterized (when there are multiple fracture sites, the longest fracture length is taken here, which is more representative). Therefore, when the fracture characteristic value ΔL is characterized by the product of the two, no matter which parameter is too large or the two parameters are in the middle, the fracture characteristic value ΔL will be larger, which can accurately reflect the impact of the number of fractures and the fracture length on the continuity of the target material strip 110. The influence of , and then calculate the first ratio of the fracture characteristic values ​​ΔL corresponding to the adjacent monitoring images. The first ratio can reflect the change trend of the fracture defect. If the first ratio is greater than 1, it means an upward trend. The first ratio is equal to 1, which means the trend is flat, but it indicates that there is a certain degree of continuous fracture defect. The first ratio is less than 1, which means a downward trend. Therefore, the number M1 of first ratios greater than or equal to 1 is taken as the judgment benchmark, which can represent the upward trend or the flat trend as a whole. For example, taking 10 monitoring images with fracture characteristics as an example, 9 groups of first ratios can be calculated. The first number threshold is set to 3. Therefore, as long as there are at least 4 groups of first ratios greater than or equal to 1, it can be characterized that the fracture defect is in an overall upward trend. The maximum value of the multiple first ratios is output as the fracture defect trend value η1, which is more targeted and representative. On the contrary, if it indicates an overall downward trend, the fracture defect trend value η1 is output as 0. Therefore, this quantitative assessment method can accurately and reliably judge the fracture defect trend.

[0103] It should be noted that, considering the occurrence of major fracture defects, a fracture characteristic threshold can be set here. After calculating multiple sets of fracture characteristic values ​​ΔL, it is determined whether there is a fracture characteristic value ΔL greater than the fracture characteristic threshold. If so, the fracture defect trend value η1 is directly output as the default value (which can be set according to the empirical model, for example, set to 10). There is no need to calculate the first ratio to judge the defect trend, thereby amplifying the final risk assessment value, that is, increasing the alarm probability to characterize the emergency treatment when there are obvious major defects. The first quantity threshold here should be a floating value, which is related to the remaining monitoring images, that is, the smaller the number of remaining monitoring images (that is, the number of monitoring images with fracture characteristics), the smaller the first quantity threshold. Here, it can be determined based on the association formula, that is, E'=ε1E, where E' is the first quantity threshold, E is the number of remaining monitoring images, and ε1 is the first proportional coefficient, which is more reasonable. To further improve data reliability, a basic number threshold Z' can be set here, Z' = ε2Z, where Z is the number of monitoring images acquired, and ε2 is the second proportional coefficient. For example, if the number of monitoring images initially acquired is 10, ε2 = 0.3, then the basic number threshold Z' = 3. When the number of remaining monitoring images E is less than the basic number threshold Z', for example, the number of remaining monitoring images E is only 2, it means that only 2 of the 10 monitoring images initially acquired have fracture characteristics, which can be determined as accidental fractures. At this time, the fracture defect trend value η1 can be directly output as 0, which optimizes the judgment principle and is more reasonable and reliable.

[0104] As an optional embodiment, identifying the abnormal diameter feature 130 of the target material bar 110 in each monitoring image to obtain the abnormal diameter trend value η2 of the target material bar 110 includes:

[0105] Eliminate monitoring images that do not have the abnormal diameter feature 130, and sort the remaining monitoring images according to the time series;

[0106] Obtain the diameter abnormality value ΔD of the target material strip 110 in each monitored image after sorting; wherein ΔD = N2·Δd, N2 is the number of diameter abnormalities in a single monitored image, Δd is the maximum absolute value of the difference between the abnormal diameter and the standard diameter in a single monitored image, and the abnormal diameter is the diameter of the part of the target material strip 110 that is smaller or larger than the standard diameter;

[0107] Obtaining a second ratio of diameter anomaly values ​​ΔD corresponding to adjacent monitoring images;

[0108] Obtain a number M2 of second ratios greater than or equal to 1;

[0109] If the number M2 is greater than a preset second number threshold, the maximum value among the plurality of second ratios is output as the diameter abnormality trend value η2;

[0110] If the number M2 is less than or equal to the second number threshold, the output diameter abnormality trend value η2 is 0.

[0111] In this embodiment, similarly, when calculating the diameter anomaly trend value η2, it is necessary to first eliminate monitoring images that do not have the diameter anomaly feature 130, and sort the remaining monitoring images according to the time series, so as to predict the diameter uniformity trend of the target material bar 110 as a whole. Then, the diameter anomaly value ΔD of the target material bar 110 in each monitoring image is calculated, where ΔD=N2·Δd, that is, it is jointly characterized by the number of diameter anomalies and the anomaly difference. When the diameter is uneven, the diameter of some parts of the target material bar 110 is greater than or less than the standard diameter. Therefore, the maximum absolute value of the difference between the abnormal diameter and the standard diameter is taken as one of the key factors, which is more targeted and prominent, so as to amplify the diameter anomaly value ΔD, and then calculate the second ratio of the diameter anomaly values ​​ΔD corresponding to adjacent monitoring images. Similarly, the number M2 of second ratios greater than or equal to 1 is used as the judgment basis. When the number M2 is greater than the second number threshold, the maximum value of the multiple second ratios can be output as the diameter anomaly trend value η2. Otherwise, the output diameter anomaly trend value η2 is 0, and the diameter uniformity trend can be accurately and reliably judged.

[0112] As an optional implementation, identifying the color dispersion of the target material strip 110 in each monitoring image to obtain the color dispersion trend value η3 includes:

[0113] Eliminate monitoring images with color dispersion within the standard range, and sort the remaining monitoring images according to time series;

[0114] Obtaining a third ratio of color dispersions corresponding to adjacent monitored images after sorting;

[0115] Obtaining a number M3 of third ratios greater than or equal to 1;

[0116] If the number M3 is greater than a preset third number threshold, the maximum value among the plurality of third ratios is output as the color dispersion trend value η3;

[0117] If the number M3 is less than or equal to the third number threshold, the output color dispersion trend value η3 is 0.

[0118] In this embodiment, color dispersion is defined as the degree to which the numerical distribution of a specific color channel (such as the H channel in the HSV space) deviates from its mean, which is used to quantify the consistency of the surface color distribution of the target material strip 110. For example, when the surface color of the target material strip 110 is yellow, the color dispersion is relatively high. Similarly, when calculating the color dispersion trend value η3, it is necessary to first eliminate the monitoring images whose color dispersion is within the standard range, and sort the remaining monitoring images according to the time series, and then calculate the third ratio of the color dispersion corresponding to adjacent monitoring images, so as to predict the color dispersion trend from the overall perspective through multiple groups of third ratios. Here, the number M3 of third ratios greater than or equal to 1 is used as the judgment basis. If it is greater than the third number threshold, the maximum value of the multiple third ratios can be output as the color dispersion trend value η3. Otherwise, the output color dispersion trend value η3 is 0, thereby achieving an effective prediction of the color dispersion trend of the target material strip 110 from the overall perspective.

[0119] It should be noted that, assuming the color dispersion is Dc, the expression of Dc is:

[0120]

[0121] Where H(x,y) represents the hue value of the pixel (x,y). represents the regional hue mean, and MN represents the image resolution.

[0122] As an optional embodiment, obtaining the porosity trend value of the target material strip 110 based on multiple CT scan images includes:

[0123] Obtaining an average porosity of the target material strip 110 in each CT scan image;

[0124] obtaining a fourth ratio of average porosities corresponding to adjacent monitoring images;

[0125] Obtaining a number M4 of fourth ratio values ​​greater than or equal to 1;

[0126] If the quantity M4 is greater than a preset fourth quantity threshold, the maximum value among the plurality of fourth ratios is output as the porosity trend value;

[0127] If the quantity M4 is less than or equal to the fourth quantity threshold, the output porosity trend value is 0.

[0128] In this embodiment, the porosity of the target material strip 110 can be quantitatively calculated based on the CT scan image. Since the target material strip 110 has a certain length, the average porosity is used as the key parameter, and then the fourth ratio of the average porosity corresponding to adjacent monitoring images is calculated to predict the porosity change trend. Similarly, the number M4 of fourth ratios greater than or equal to 1 is used as the judgment basis. When the number M4 is greater than the fourth number threshold, the maximum value of the multiple fourth ratios is output as the porosity trend value. Otherwise, the output porosity trend value is 0, thereby predicting the porosity change trend as a whole.

[0129] As an optional embodiment, obtaining the average porosity of the target material strip 110 in each CT scan image includes:

[0130] Divide the target material strip 110 in the CT scan image into multiple sub-regions at equal intervals;

[0131] Obtain the measured porosity corresponding to each sub-region;

[0132] Screen out the sub-regions where the measured porosity is greater than the theoretical porosity;

[0133] The average value of the measured porosities of the screened sub-regions is output as the average porosity of the target material strip 110 .

[0134] In this embodiment, since the target material strip 110 has a certain length, in order to improve the calculation accuracy, the target material strip 110 in the CT scan image is first equidistantly divided into multiple sub-regions, and then the measured porosity corresponding to each sub-region is calculated, so as to screen out the sub-regions with measured porosity greater than the theoretical porosity as defective areas for consideration, which is more targeted. Then, the average value of the measured porosity of the screened sub-regions is calculated and output as the average porosity of the target material strip 110, which can better characterize the non-standard porosity changes of the target material strip 110.

[0135] As an optional implementation, the risk assessment model is expressed as:

[0136] F=(W1·η+W2·Q)·P;

[0137] Where F is the risk assessment value, Q is the porosity trend value, P is the operation abnormality trend value, W1 is the first weight coefficient, and W2 is the second weight coefficient.

[0138] In this embodiment, (W1·η+W2·Q) represents the weighted sum of the surface defect trend value η and the porosity trend value Q, thereby characterizing the trend change of the external and internal defects of the target material bar 110, and is associated with the abnormal operation trend value P of the granulator equipment, and the product of the two is output as the risk assessment value F. Regardless of whether any parameter (W1·η+W2·Q) or P is too large or the values ​​of the two parameters are in the middle, the overall risk assessment value F will be too large, so that hidden risks can be predicted from both the target material bar 110 product and the granulator equipment, thereby improving data reliability and accuracy, and having strong reference and guidance.

[0139] Example 2

[0140] Based on the same inventive concept as the above embodiment, this embodiment further provides a degradation material granulator operation status monitoring system, comprising:

[0141] An image acquisition module is configured to acquire multiple monitoring images and multiple CT scan images of a target material strip 110 within a unit monitoring cycle; wherein the target material strip 110 is a material strip produced by an extruder of a granulator, and the target material strip 110 in the multiple monitoring images is spliced ​​together to form a material strip of a preset length;

[0142] A first trend prediction module is used to obtain a surface defect trend value of the target material strip 110 based on multiple monitoring images;

[0143] A second trend prediction module is used to obtain a porosity trend value of the target material strip 110 based on multiple CT scan images;

[0144] The third trend prediction module is used to obtain the abnormal operation trend value of the granulator;

[0145] A risk assessment module, for inputting the surface defect trend value, the porosity trend value and the operation abnormality trend value into a preset risk assessment model to obtain a risk assessment value;

[0146] a first data processing module, configured to determine whether the risk assessment value is greater than a preset risk threshold, and if so, to send a first alarm message; if not, to obtain a current weight of the granular material coming out of the granulating mechanism of the granulator;

[0147] The second data processing module is used to determine whether the current weight is within a preset weight threshold range. If not, a second alarm message is sent. If so, it returns to obtaining multiple monitoring images and multiple CT scan images of the target material strip 110 within a unit monitoring period.

[0148] The relevant explanations and examples of each module in the system of this embodiment can refer to the methods of the aforementioned embodiments and will not be repeated here.

[0149] Example 3

[0150] Based on the same inventive concept as the above embodiment, this embodiment provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above method.

[0151] Example 4

[0152] Based on the same inventive concept as the above embodiment, this embodiment provides a computer-readable storage medium, on which a computer program is stored. A processor executes the computer program to implement the above method.

[0153] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for monitoring the operating status of a degradable material granulator, characterized in that: The following steps are involved: Acquire multiple monitoring images and multiple CT scan images of a target material strip within a unit monitoring cycle; wherein the target material strip is a material strip exiting an extruder of a granulator, and the target material strips in the multiple monitoring images are spliced ​​together to form a material strip having a preset length; Obtain the surface defect trend value of the target material strip based on multiple monitoring images; Obtain the porosity trend value of the target material strip based on multiple CT scan images; Obtain the abnormal operation trend value of the granulator; Inputting the surface defect trend value, the porosity trend value and the operation abnormality trend value into a preset risk assessment model to obtain a risk assessment value; Determine whether the risk assessment value is greater than a preset risk threshold, and if so, send a first alarm message; if not, obtain the current weight of the granular material coming out of the granulating mechanism of the granulator; Determine whether the current weight is within a preset weight threshold range. If not, send a second alarm message. If so, return to obtaining multiple monitoring images and multiple CT scan images of the target material strip within the unit monitoring cycle.

2. The method for monitoring the operating status of a degradable material granulator according to claim 1, wherein: Based on multiple monitoring images, the surface defect trend value of the target material strip is obtained, including: Identify the fracture characteristics of the target material strip in each monitoring image to obtain the fracture defect trend value η1 of the target material strip; Identify the abnormal diameter characteristics of the target material strip in each monitoring image to obtain the abnormal diameter trend value η2 of the target material strip; Identify the color dispersion of the target material strip in each monitoring image to obtain the color dispersion trend value η3; Obtain the surface defect trend value η, the expression of η is: η=K1·η1+K2·η2+K3·η3; Wherein, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

3. The method for monitoring the operating status of a degradable material granulator according to claim 2, wherein: Identify the fracture characteristics of the target material strip in each monitoring image to obtain the fracture defect trend value η1 of the target material strip, including: Eliminate monitoring images that do not have fault features, and sort the remaining monitoring images according to time series; Obtain the fracture characteristic value ΔL of the target material strip in each monitoring image after sorting; where ΔL = N1·L, N1 is the number of fractures in a single monitoring image, and L is the longest fracture length in a single monitoring image; Obtaining a first ratio of fracture characteristic values ​​ΔL corresponding to adjacent monitoring images; Obtain a number M1 of first ratios greater than or equal to 1; If the number M1 is greater than a preset first number threshold, the maximum value among the multiple first ratios is output as the fracture defect tendency value η1; If the number M1 is less than or equal to the first number threshold, the output fracture defect tendency value η1 is 0.

4. The method for monitoring the operating status of a degradable material granulator according to claim 2, wherein: Identify the abnormal diameter characteristics of the target material strip in each monitoring image to obtain the abnormal diameter trend value η2 of the target material strip, including: Eliminate monitoring images that do not have abnormal diameter features, and sort the remaining monitoring images according to time series; Obtain the diameter anomaly value ΔD of the target material strip in each monitoring image after sorting; where ΔD = N2·Δd, N2 is the number of diameter anomalies in a single monitoring image, Δd is the maximum absolute value of the difference between the abnormal diameter and the standard diameter in a single monitoring image, and the abnormal diameter is the diameter of the part of the target material strip that is smaller or larger than the standard diameter; Obtaining a second ratio of diameter anomaly values ​​ΔD corresponding to adjacent monitoring images; Obtain a number M2 of second ratios greater than or equal to 1; If the number M2 is greater than a preset second number threshold, the maximum value among the plurality of second ratios is output as the diameter abnormality trend value η2; If the number M2 is less than or equal to the second number threshold, the output diameter abnormality trend value η2 is 0.

5. The method for monitoring the operating status of a degradable material granulator according to claim 2, wherein: Identify the color dispersion of the target material strip in each monitoring image to obtain the color dispersion trend value η3, including: Eliminate monitoring images with color dispersion within the standard range, and sort the remaining monitoring images according to time series; Obtaining a third ratio of color dispersions corresponding to adjacent monitored images after sorting; Obtaining a number M3 of third ratios greater than or equal to 1; If the number M3 is greater than a preset third number threshold, the maximum value among the plurality of third ratios is output as the color dispersion trend value η3; If the number M3 is less than or equal to the third number threshold, the output color dispersion trend value η3 is 0.

6. The method for monitoring the operating status of a degradable material granulator according to claim 1, wherein: Based on multiple CT scan images, the porosity trend value of the target material strip is obtained, including: Obtain the average porosity of the target material strip in each CT scan image; obtaining a fourth ratio of average porosities corresponding to adjacent monitoring images; Obtaining a number M4 of fourth ratios greater than or equal to 1; If the quantity M4 is greater than a preset fourth quantity threshold, the maximum value among the plurality of fourth ratios is output as the porosity trend value; If the quantity M4 is less than or equal to the fourth quantity threshold, the output porosity trend value is 0.

7. The method for monitoring the operating status of a degradable material granulator according to claim 6, wherein: Obtain the average porosity of the target material strip in each CT scan image, including: The target material strip in the CT scan image is divided into multiple sub-regions at equal distances; Obtain the measured porosity corresponding to each sub-region; Screen out the sub-regions where the measured porosity is greater than the theoretical porosity; The average value of the measured porosity of the screened sub-areas is output as the average porosity of the target material strip.

8. A method for monitoring the operating status of a degradable material granulator according to any one of claims 2 to 7, characterized in that: The risk assessment model is expressed as: F=(W1·η+W2·Q)·P; Where F is the risk assessment value, Q is the porosity trend value, P is the operation abnormality trend value, W1 is the first weight coefficient, and W2 is the second weight coefficient.

9. A degradation material granulator operation status monitoring system, characterized in that: include: An image acquisition module is configured to acquire multiple monitoring images and multiple CT scan images of a target material strip within a unit monitoring cycle; wherein the target material strip is a material strip exiting an extruder of a granulator, and the target material strips in the multiple monitoring images are spliced ​​together to form a material strip of a preset length; A first trend prediction module is used to obtain a surface defect trend value of a target material strip based on multiple monitoring images; A second trend prediction module is used to obtain a porosity trend value of the target material strip based on multiple CT scan images; The third trend prediction module is used to obtain the abnormal operation trend value of the granulator; A risk assessment module, for inputting the surface defect trend value, the porosity trend value and the operation abnormality trend value into a preset risk assessment model to obtain a risk assessment value; a first data processing module, configured to determine whether the risk assessment value is greater than a preset risk threshold, and if so, to send a first alarm message; if not, to obtain a current weight of the granular material coming out of the granulating mechanism of the granulator; The second data processing module is used to determine whether the current weight is within a preset weight threshold range. If not, a second alarm message is sent. If so, it returns to obtaining multiple monitoring images and multiple CT scan images of the target material strip within a unit monitoring period.

10. A computer device, characterized in that: The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 8.

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