Rapid Thin-Layer Drying Method for Sludge Based on Industrial Images

Abstract

This invention relates to the field of sludge treatment technology, and more particularly to a rapid thin-layer drying method for sludge based on industrial images. The method includes: data acquisition; data extraction; anomaly event identification; obtaining anomaly determination results; parameter adjustment; threshold correction; and optimization of the drying process. This invention acquires real-time images of the sludge moisture content and key locations, extracts key data from the images, first identifies anomalies; then determines whether there is localized over-drying; subsequently, it adjusts operating parameters based on the anomaly determination results and sludge thickness; it corrects a preset correlation threshold using the sludge moisture content and vapor density; and optimizes the drying process based on the adjusted parameters redefined after the correction, ensuring the dryer is always in optimal operating condition. This effectively solves the problem of low drying efficiency and unstable drying quality caused by over-reliance on predictive models, which prevents timely responses to changes in sludge characteristics and operating conditions.

Description

Technical Field

[0001] This invention relates to the field of sludge treatment technology, and in particular to a rapid thin-layer drying method for sludge based on industrial images. Background Technology

[0002] With the rapid pace of urbanization and heightened environmental awareness, sludge treatment and disposal have become a major concern. Sludge dryers, especially thin-layer dryers, utilize steam sludge drying, achieving continuous feeding and discharging. They offer advantages such as high efficiency, low energy consumption, and high safety, and can be custom-designed to meet specific sludge treatment needs. However, in actual operation, the efficiency of thin-layer dryers is easily affected by various factors such as temperature, humidity, and material characteristics, leading to reduced drying efficiency and impacting the performance of the entire sludge treatment system. Furthermore, since thin-layer sludge drying technology relies heavily on precise control of parameters such as feed rate and rotation speed to achieve the sludge drying target, improper control may result in unstable product quality after drying, such as clumping or discoloration.

[0003] Patent document CN118084292A discloses a high-efficiency operating device and control method for thin-layer sludge drying, including: a feeding system, a drying system, an energy recovery system, a steam system, a tail gas emission system, and a control system; wherein, the feeding system is used to introduce sludge to be dried; the drying system is used to dry the sludge transported by the feeding system; the steam system is used to provide the heat energy required by the drying system; the energy recovery system is used to recover the waste heat generated in the drying system; the tail gas emission system is used to treat the tail gas generated by the drying system; the control system includes a data acquisition module, a prediction module, and a parameter control module, wherein, the data acquisition module is configured to acquire real-time data during the sludge treatment process; the prediction module is configured to predict the moisture content of the sludge produced from the thin-layer drying, the outlet temperature of the waste gas heat exchanger, and the amount of condensate generated based on historical data and a prediction model established by an artificial neural network; the parameter control module is configured to automatically adjust the operating parameters of the drying system based on the output results of the prediction module and the real-time data.

[0004] Therefore, the sludge thin-layer drying high-efficiency operation device has the following problems: the accuracy of the prediction results depends not only on the accuracy of data acquisition, but also on the representativeness of historical data and the training degree of the model; it relies on the prediction model and real-time data to automatically adjust the operating parameters of the drying system, and when changes occur, it takes time to adapt to the new operating conditions, resulting in a lag in the response of the control system. Summary of the Invention

[0005] To address this, the present invention provides a rapid thin-layer drying method for sludge based on industrial images. This method overcomes the problems of low drying efficiency and unstable drying quality caused by over-reliance on prediction models in existing technologies, which prevent timely responses to changes in sludge characteristics and operating conditions. This is achieved through the acquisition of industrial images, multi-dimensional data analysis, and dynamic adjustment mechanisms.

[0006] To achieve the above objectives, the present invention provides a rapid thin-layer drying method for sludge based on industrial images, comprising:

[0007] Real-time acquisition of the sludge moisture content at the discharge port, steam image at the steam discharge port, cylinder wall image at each detection point inside the cylinder, and blade image during the operation of the thin-layer dryer based on the preset sludge feed speed and preset rotor speed.

[0008] Extract the vapor density and grayscale value of vapor color of each region in the vapor image, and extract the sludge thickness in the cylinder wall image and the dry sludge area on the blade surface in the blade image.

[0009] An abnormal event is determined based on the vapor density, the gray value, the dry mud area, and a preset correlation threshold, and an abnormality determination result is obtained.

[0010] The anomaly type is determined based on the anomaly determination result, the sludge thickness, and the dry sludge area.

[0011] The preset sludge feed rate is adjusted according to the abnormality type and the total sludge thickness to obtain the adjusted sludge feed rate, and the preset rotor speed is adjusted according to the adjusted sludge feed rate and the sludge thickness to obtain the adjusted rotor speed.

[0012] Based on the adjustment of sludge feed rate and the adjustment of rotor speed, the preset correlation threshold is corrected according to the sludge moisture content and the vapor density within the preset correction time to obtain the corrected correlation threshold;

[0013] The thin-layer dryer is controlled by adjusting the sludge feed rate and the rotor speed, which are redetermined based on the corrected correlation threshold.

[0014] Furthermore, the process of determining the occurrence of an abnormal event based on the vapor density, the gray value, the dry mud area, and a preset correlation threshold, and obtaining the anomaly determination result, includes:

[0015] Calculate the standard deviation of all the vapor densities to obtain the density dispersion;

[0016] When the density dispersion is greater than a preset dispersion threshold, the current timestamp is recorded; when the density dispersion is less than or equal to the preset dispersion threshold, recording is stopped, and the duration is obtained.

[0017] When the duration exceeds a preset duration threshold, an abnormal event is determined based on the gray value, the dry mud area, and a preset correlation threshold, and an abnormality determination result is obtained.

[0018] Furthermore, the process of determining the occurrence of an abnormal event and obtaining the anomaly determination result based on the grayscale value, the dry mud area, and the preset correlation threshold includes:

[0019] Calculate the standard deviation of all gray values ​​to obtain the color non-uniformity.

[0020] The color unevenness is statistically analyzed within a preset judgment time period to obtain an unevenness dataset;

[0021] The area of ​​all the dry mud within the preset determination time period is counted to obtain an area dataset.

[0022] Calculate the correlation coefficient between the non-uniformity dataset and the area dataset to obtain the correlation degree of change;

[0023] When the correlation of the change is greater than the preset correlation threshold, an abnormal event is determined to have occurred, and an abnormality determination result is obtained.

[0024] Furthermore, the process of determining the type of the abnormal event as local over-drying based on the anomaly determination result, the sludge thickness, and the dry sludge area, and obtaining the anomaly determination result, includes:

[0025] When an anomaly determination result is obtained, the standard deviation of the sludge thickness from the initial time to each time within the preset determination time is calculated to obtain several thickness fluctuation values.

[0026] Calculate the standard deviation of the dry mud area from the initial time to each time within the preset judgment period to obtain several area fluctuation values;

[0027] Based on all the thickness fluctuation values ​​and all the area fluctuation values, the type of the abnormal event is determined to be localized over-drying, thus obtaining the abnormality determination result.

[0028] Furthermore, the process of determining the type of the abnormal event as localized over-drying based on all the thickness fluctuation values ​​and all the area fluctuation values, and obtaining the abnormality determination result, includes:

[0029] Plot the variation curves of all the thickness fluctuation values ​​to obtain the thickness fluctuation variation curve;

[0030] Plot the change curves of all the area fluctuation values ​​to obtain the area fluctuation change curves;

[0031] Calculate the cosine similarity between the thickness fluctuation curve and the area fluctuation curve to obtain the consistency of change;

[0032] When the consistency of change is greater than the preset consistency threshold, the type of the abnormal event is determined to be local over-drying, and an abnormality determination result is obtained.

[0033] Furthermore, the process of adjusting the preset sludge feed rate based on the anomaly determination result and the total sludge thickness includes:

[0034] Calculate the average thickness of all sludge at each detection point on the cross-section of the inner cylinder wall to obtain the average thickness;

[0035] Plot the change curve of all the average thicknesses within the preset adjustment time to obtain the thickness change curve;

[0036] Calculate the slope change rate of the thickness change curve to obtain the thickness change rate;

[0037] The preset sludge feed rate is adjusted according to the thickness change rate to obtain the adjusted sludge feed rate.

[0038] Furthermore, adjusting the preset sludge feed rate based on the thickness change rate, the process of adjusting the sludge feed rate includes:

[0039] When the thickness change rate is greater than a preset change rate threshold, the preset sludge feed rate is reduced based on the relative deviation between the thickness change rate and the preset change rate threshold and the preset feed rate adjustment coefficient, thereby obtaining an adjusted sludge feed rate.

[0040] Furthermore, the process of adjusting the preset rotor speed based on adjusting the sludge feed rate and sludge thickness to obtain the adjusted rotor speed includes:

[0041] Calculate the standard deviation of the sludge thickness at each detection point on the vertical cross section of the inner cylinder wall to obtain the thickness dispersion.

[0042] When the thickness dispersion is greater than a preset dispersion threshold, the preset rotor speed is increased according to the relative deviation between the thickness dispersion and the preset dispersion threshold and the preset speed adjustment coefficient to obtain the adjusted rotor speed.

[0043] Further, the process of correcting the preset correlation threshold based on the sludge moisture content and the vapor density within a preset correction period to obtain the corrected correlation threshold includes:

[0044] When the sludge moisture content is greater than the preset sludge moisture content threshold, the standard deviation of the sludge moisture content within the preset correction time is calculated to obtain the moisture content fluctuation value.

[0045] Calculate the standard deviation of all the vapor densities to obtain the corrected dispersion;

[0046] Calculate the average value of all the corrected dispersions within the preset correction time period to obtain the density dispersion mean;

[0047] The preset correlation threshold is corrected based on the moisture content fluctuation value and the density dispersion mean to obtain the corrected correlation threshold.

[0048] Further, the process of correcting the preset correlation threshold based on the moisture content fluctuation value and the density dispersion mean to obtain the corrected correlation threshold includes:

[0049] The relative deviation between the moisture content fluctuation value and the preset moisture content fluctuation threshold is calculated to obtain the fluctuation deviation;

[0050] The relative deviation between the density dispersion mean and the preset dispersion threshold is calculated to obtain the dispersion deviation;

[0051] The preset correlation threshold is obtained by reducing the preset correlation threshold based on the fluctuation deviation, the dispersion deviation, the preset fluctuation deviation weight, the preset dispersion deviation weight, and the preset correction coefficient.

[0052] Compared with existing technologies, the beneficial effects of this invention are as follows: By acquiring key images of sludge moisture content and different locations in real time, and extracting key data from the images, abnormal events are determined based on vapor density, grayscale value, dry sludge area, and a preset correlation threshold; further, it is determined whether there is local over-drying, thus accurately locating the problem; based on the determined abnormal events and sludge thickness, the sludge feed rate and rotor speed are adjusted to optimize the sludge drying process, ensuring uniform sludge distribution and thorough drying; the preset correlation threshold is corrected using sludge moisture content and vapor density, making subsequent abnormal judgments more accurate; and the sludge feed rate and rotor speed are adjusted again based on the corrected threshold to control the operation of the thin-layer dryer, forming a closed-loop optimization control, ensuring that the dryer is always in the best operating state. This effectively solves the problem of low drying efficiency and unstable drying quality caused by over-reliance on predictive models, which prevents timely responses to changes in sludge characteristics and operating states.

[0053] Furthermore, the density dispersion is obtained by calculating the standard deviation of the vapor density. The standard deviation of the vapor density reflects the stability of the vapor emission. When the density dispersion exceeds the preset dispersion threshold, it indicates that the fluctuation of the vapor density has increased abnormally. This usually means that the drying process has become unstable, causing large fluctuations in the vapor emission density. Moreover, if the duration of this state is longer than the preset duration threshold, it indicates that it is a persistent problem rather than an occasional anomaly. Further determination is needed to determine whether an abnormal event has occurred in order to accurately identify the abnormal event and take corresponding measures for adjustment and optimization to ensure the stable operation of the dryer.

[0054] Furthermore, color non-uniformity is obtained by calculating the standard deviation of grayscale values, which reflects the degree of variation in vapor color across different regions. Data sets are collected to accumulate changes over a period of time; then, the correlation coefficient between the two datasets is calculated to obtain the correlation degree, revealing the relationship between color change and dry mud area change. When the correlation degree exceeds a preset correlation threshold, an abnormal event is identified, indicating a strong positive correlation between color non-uniformity and dry mud area. As color non-uniformity increases, the dry mud area also increases, suggesting that as the drying process progresses, sludge on the leaves may detach due to over-drying, leading to increased vapor impurities, i.e., increased color non-uniformity. Simultaneously, the dry mud area also increases due to the increased area of ​​completed drying, indicating an abnormality in the drying process. At this point, an abnormal event is identified.

[0055] Furthermore, by calculating the standard deviation of sludge thickness and dry sludge area within a previously preset judgment period, thickness fluctuation values ​​and area fluctuation values ​​were obtained, respectively. These fluctuation values ​​reflect the degree of change in sludge thickness and dry sludge area during that time period. The thickness fluctuation value reflects the uniformity of sludge accumulation, while the area fluctuation value reflects the stability of changes in the sludge adhesion range. By further analyzing the overall trend of these fluctuation values, it is possible to determine whether there is localized over-drying.

[0056] Furthermore, by plotting thickness and area fluctuation curves, the changing trends of sludge thickness and area at different locations can be visually observed. Calculating the cosine similarity of these two curves yields the consistency of change, quantifying the similarity of their trends. The calculation of cosine similarity is existing technology and will not be elaborated upon here. When the consistency of change exceeds a preset threshold, it indicates a high degree of consistency between thickness and area fluctuations in their changing trends. This consistency typically indicates localized over-drying, as some areas experience rapid sludge loss, leading to a rapid thinning of the local thickness and even the formation of voids, while other areas show little change, resulting in increased thickness fluctuation. Simultaneously, the amount of dry sludge adhering to the blades also increases with sludge drying, causing area fluctuation changes. This effectively identifies abnormal events of localized over-drying and helps to adjust the drying process in a timely manner.

[0057] Furthermore, by calculating the average thickness of the sludge in the cross-section and plotting its variation curve, the dynamic changes in sludge thickness can be visually presented; the slope change rate reflects the rate and trend of thickness change. Adjusting the sludge feed rate based on the thickness change rate ensures that the sludge feed volume matches the current drying capacity. On this basis, adjusting the rotor speed in conjunction with the sludge thickness distribution in the vertical cross-section of the inner cylinder wall can further optimize the sludge distribution in the vertical direction and the drying effect, linking the sludge feed rate and rotor speed to achieve precise control of the drying process.

[0058] Furthermore, by monitoring the sludge thickness change rate, the accumulation speed of sludge on the inner cylinder wall can be reflected in a timely manner. When the thickness change rate exceeds a preset threshold, the sludge feed rate is reduced by combining the relative deviation and adjustment coefficient, which can prevent excessive sludge accumulation and maintain the sludge thickness on the inner cylinder wall within a reasonable range. This ensures the drying efficiency and operational stability of the dryer, avoids uneven drying and equipment failure caused by sludge accumulation, and achieves precise dynamic control of the sludge feed rate.

[0059] Furthermore, by adjusting the sludge feed rate and combining this with the sludge thickness distribution along the vertical cross-section of the inner cylinder wall, the rotor speed can be adjusted to further optimize the vertical sludge distribution and drying effect. This links the sludge feed rate and rotor speed, enabling precise control of the drying process. The thickness dispersion is obtained by calculating the standard deviation of the sludge thickness along the vertical cross-section, which quantifies the uniformity of sludge distribution. When the thickness dispersion exceeds a preset threshold, it indicates uneven sludge thickness at the top and bottom. In this case, increasing the rotor speed based on the relative deviation and adjustment coefficient enhances the stirring force, redistributes the sludge, and prevents localized accumulation or uneven drying.

[0060] Furthermore, the correlation threshold is dynamically adjusted by monitoring fluctuations in the sludge moisture content and vapor density. When the sludge moisture content exceeds the standard, its fluctuation value and the dispersion mean of the vapor density are calculated. The sludge moisture content directly reflects the drying effect, while the vapor density reflects the stability of the drying process. The greater the fluctuation in both, the more unstable the equipment operation, requiring a reduction in the correlation threshold to improve the sensitivity of anomaly detection. By combining these two indicators to adjust the correlation threshold, stable and efficient operation of the equipment under different working conditions is ensured.

[0061] Furthermore, the fluctuation deviation is obtained by calculating the relative deviation between the moisture content fluctuation value and the preset threshold, and the dispersion deviation is obtained by calculating the relative deviation between the density dispersion mean and the preset threshold. These two deviations reflect the degree of deviation in drying effect and process stability. The preset correlation threshold is then reduced based on preset weights and correction coefficients to obtain the corrected threshold. The correction based on moisture content and vapor density directly reflects the sludge drying effect and process stability, and the correlation threshold can better adapt to the actual operating conditions of the equipment. Attached Figure Description

[0062] Figure 1 This is a flowchart of the rapid thin-layer drying method for sludge based on industrial images described in this embodiment;

[0063] Figure 2 This is a logic diagram for determining the occurrence of abnormal events in this embodiment;

[0064] Figure 3 This is a logic diagram for determining the anomaly determination result in this embodiment;

[0065] Figure 4The logic diagram for adjusting the preset sludge feed rate in this embodiment is shown. Detailed Implementation

[0066] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0067] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0068] Please see Figure 1 As shown, it is a flowchart of the rapid thin-layer drying method for sludge based on industrial images described in this embodiment;

[0069] This embodiment provides a rapid thin-layer drying method for sludge based on industrial images, including:

[0070] Real-time acquisition of the sludge moisture content at the discharge port, steam image at the steam discharge port, cylinder wall image at each detection point inside the cylinder, and blade image during the operation of the thin-layer dryer based on the preset sludge feed speed and preset rotor speed.

[0071] Extract the vapor density and grayscale value of vapor color of each region in the vapor image, and extract the sludge thickness in the cylinder wall image and the dry sludge area on the blade surface in the blade image.

[0072] An abnormal event is determined based on the vapor density, the gray value, the dry mud area, and a preset correlation threshold, and an abnormality determination result is obtained.

[0073] Based on the anomaly determination result, the sludge thickness, and the dry sludge area, the type of the abnormal event is determined to be local over-drying, and the anomaly determination result is obtained.

[0074] The preset sludge feed rate is adjusted based on the anomaly determination result and the total sludge thickness to obtain the adjusted sludge feed rate. The preset rotor speed is then adjusted based on the adjusted sludge feed rate and the sludge thickness to obtain the adjusted rotor speed.

[0075] Based on the adjustment of sludge feed rate and the adjustment of rotor speed, the preset correlation threshold is corrected according to the sludge moisture content and the vapor density within the preset correction time to obtain the corrected correlation threshold;

[0076] The thin-layer dryer is controlled by adjusting the sludge feed rate and the rotor speed, which are redetermined based on the corrected correlation threshold.

[0077] The preset sludge feed rate and preset rotor speed mentioned in this embodiment refer to the sludge feed rate and rotor rotation speed preset before the thin-layer dryer is put into operation, based on factors such as equipment performance, sludge characteristics, and treatment objectives. As the properties of the sludge change, the preset sludge feed rate and preset rotor speed need to be adaptively adjusted to ensure drying effect and efficiency.

[0078] The discharge port of the thin-layer dryer is where the sludge is discharged after drying; the steam exhaust port is the channel for the water vapor and other exhaust gases generated during the drying process to be discharged; the inner cylinder wall is the heat transfer part in contact with the sludge, providing the main heat exchange area and the carrier for forming the sludge thin layer; the discharge port and the steam exhaust port are located at the two ends of the dryer, respectively, the inner cylinder wall constitutes the internal space of the dryer, and the blades are located inside the inner cylinder wall, which realize the turning and renewal of the sludge by rotation.

[0079] The sludge moisture content at the discharge port refers to the ratio of the mass of water contained in the sludge discharged from the discharge port to the total mass of the sludge, which is collected by an infrared moisture analyzer.

[0080] An industrial camera is installed at the steam emission outlet to capture images of the steam emission outlet and obtain steam images, reflecting the real-time status of steam emission. "Divided areas" refers to dividing the steam image into multiple grid-like areas in order to more accurately analyze the characteristics of steam in each area.

[0081] Vapor density refers to the mass of vapor per unit volume. By analyzing the grayscale histogram of a vapor image and setting a grayscale threshold to distinguish between vapor and background, vapor density can be calculated. The grayscale value of vapor color refers to the numerical value obtained after converting the color information in the vapor image into grayscale levels. It is used to quantify the depth of vapor color and is usually related to the concentration of particulate matter or pollutants in the vapor. The lower the grayscale value, the darker the vapor color, which may mean that the vapor contains more pollutants or impurities. By converting the vapor image from the RGB color space to the grayscale color space, the grayscale value of each pixel can be directly read from the converted grayscale image.

[0082] Endoscopes are evenly arranged on the inner cylinder wall to capture images of the inside of the dryer, resulting in images of the cylinder wall and blades. "Each detection point" refers to the specific location of an endoscope evenly distributed on the inner cylinder wall to detect the sludge thickness and the area of ​​dry sludge adhering to the blade surface. In this embodiment, the endoscopes are equidistantly arranged along the inner cylinder wall in both vertical and horizontal directions, forming a grid-like array of detection points.

[0083] Vertical (height direction): Several rows of endoscopes are evenly distributed along the height of the cylinder wall, from the top to the bottom of the cylinder wall, to ensure that the changes in sludge thickness and dry sludge adhesion at different heights can be captured.

[0084] Lateral (circumferential / circumferential): Several rows of endoscopes are equidistantly arranged along the circumference of the cylinder wall, so that each row corresponds to the adjacent angular area, realizing full-circumference monitoring without blind spots.

[0085] The intersection of the vertical rows and horizontal columns marks the specific detection points. The endoscopes at each detection point are used to acquire images of the sludge thickness on the cylinder wall and the dry sludge area on the blade surface. Through vertical and horizontal gridding, multi-point image data in both height and circumference can be obtained simultaneously, providing comprehensive and balanced data support for calculating indicators such as dispersion and correlation.

[0086] Sludge thickness refers to the straight-line distance from the inner cylinder wall surface to the outer surface of the sludge layer. The cylinder wall image acquired by the endoscope is processed into grayscale, and then the outer boundary of the sludge layer and the boundary of the inner cylinder wall are determined by the edge detection algorithm. The vertical distance between these two boundaries is measured to obtain the sludge thickness.

[0087] The dry mud area refers to the area occupied by the dried mud formed after the sludge attached to the blade surface dries. The blade image is processed into grayscale, and then the sludge area is separated from the blade surface. The number of pixels in the segmented sludge area is counted, and then converted into the actual area according to the image resolution, thus obtaining the dry mud area.

[0088] The preset correlation threshold is a critical value used to determine the degree of correlation between color unevenness and changes in dried sludge area. It depends on the properties of the sludge, drying process requirements, historical data of equipment operation, and the type and severity of abnormal events, and is usually set between 0.6 and 0.9. In this embodiment, it is set to 0.8, which can more accurately filter out data combinations with significant correlation, thereby improving the accuracy of abnormal event judgment and reducing the possibility of misjudgment and omission.

[0089] The preset correction time is the length of time used to correct the preset correlation threshold. It depends on the operating characteristics of the thin-layer dryer, the sludge drying rate, and the required response speed, and is usually set between 5 and 30 minutes. In this embodiment, it is set to 10 minutes to ensure that the correction of the correlation threshold is timely and effective, enabling the equipment to respond quickly to changes in operating status.

[0090] By acquiring real-time images of sludge moisture content and key locations at different points, and extracting vapor density, grayscale values ​​of vapor color, sludge thickness, and the area of ​​dry sludge attached to the blades from the images, the machine determines whether an abnormal event has occurred based on the acquired vapor density, grayscale values, dry sludge area, and a preset correlation threshold. This results in an anomaly determination. Next, combining the anomaly determination, sludge thickness, and dry sludge area, it is determined whether there is localized over-drying. Based on the identified abnormal event, the preset sludge feed rate and preset rotor speed are adjusted according to the total sludge thickness, resulting in adjusted sludge feed rate and rotor speed. Then, within a preset correction time, the preset correlation threshold is corrected based on the sludge moisture content and vapor density, resulting in a corrected correlation threshold. Finally, based on the corrected correlation threshold, the sludge feed rate and rotor speed are re-determined and adjusted, and the thin-layer dryer is controlled according to these two adjusted parameters.

[0091] The underlying logical relationships between the parameters follow the basic principles of mass and energy conservation: the preset sludge feed rate and rotor speed determine the residence time and mechanical shear intensity of the sludge in the dryer, affecting heat transfer efficiency; the solids content (or residual moisture content) at the discharge port reflects the overall degree of moisture evaporation and is positively correlated with the steam density and gray value measured at the steam discharge port—the more moisture, the denser the steam, and the darker the gray value; at the same time, the sludge thickness and dry sludge adhesion area measured in the inner cylinder wall and blade images correspond to the local moisture removal rate and dry solids accumulation, respectively, and the changes in their standard deviation and correlation coefficient reveal the non-uniformity in the drying process; based on the dynamic feedback of these parameters, when the steam density or gray value fluctuation exceeds the threshold, or the consistency of the thickness and dry sludge area changes reaches the preset standard, it is determined that local over-drying has occurred. By adjusting the feed rate and rotor speed to change the residence time and shearing action, the heat-mass transfer is rebalanced, thereby achieving optimal coupling between energy input and moisture removal efficiency, ensuring stable and efficient system operation.

[0092] By acquiring real-time images of sludge moisture content and key locations, and extracting key data from these images, abnormal events are identified based on vapor density, grayscale value, dry sludge area, and a preset correlation threshold. Further analysis is conducted to determine if localized over-drying is the cause, precisely pinpointing the problem. Based on the identified abnormal events and sludge thickness, the sludge feed rate and rotor speed are adjusted to optimize the sludge drying process, ensuring uniform sludge distribution and thorough drying. The preset correlation threshold is corrected using sludge moisture content and vapor density, making subsequent anomaly detection more accurate. Based on the corrected threshold, the sludge feed rate and rotor speed are re-determined to control the operation of the thin-layer dryer, forming a closed-loop optimization control system. This ensures the dryer is always in optimal operating condition, effectively solving the problem of low drying efficiency and unstable drying quality caused by over-reliance on predictive models, which prevents timely responses to changes in sludge characteristics and operating conditions.

[0093] Specifically, the process of determining the occurrence of an abnormal event based on the vapor density, the gray value, the dry mud area, and a preset correlation threshold, and obtaining the anomaly determination result, includes:

[0094] Calculate the standard deviation of all the vapor densities to obtain the density dispersion;

[0095] When the density dispersion is greater than a preset dispersion threshold, the current timestamp is recorded; when the density dispersion is less than or equal to the preset dispersion threshold, recording is stopped, and the duration is obtained.

[0096] When the duration exceeds a preset duration threshold, an abnormal event is determined based on the gray value, the dry mud area, and a preset correlation threshold, and an abnormality determination result is obtained.

[0097] The preset dispersion threshold is a critical value used to measure the degree of dispersion of vapor density changes. It depends on the specific model and specifications of the thin-layer dryer, the type of sludge being treated, and the operating requirements, and is usually set between 0.5 and 2.0. In this embodiment, it is set to 1.2, which can effectively distinguish between normal operating conditions and possible abnormal situations. It is neither too sensitive, leading to frequent false alarms, nor too insensitive, causing important abnormal changes to be missed.

[0098] The preset duration threshold refers to the length of time during which the density dispersion exceeds the threshold. It depends on the operating characteristics of the dryer and the kinetics of the sludge drying process, and is typically set between 30 seconds and 5 minutes. In this embodiment, it is set to 1 minute to strike a balance between rapid response to anomalies and avoiding misjudgments, thus improving the stability and reliability of the system.

[0099] The density dispersion is obtained by calculating the standard deviation of the steam density in each divided area of ​​the steam emission outlet. If the density dispersion is greater than a preset dispersion threshold, the current timestamp is recorded; otherwise, recording stops and the duration is obtained. Finally, when the duration exceeds a preset duration threshold, the grayscale value of the steam color, the area of ​​sludge attached to the blade surface, and a preset correlation threshold are further combined to comprehensively analyze and determine whether an abnormal event has occurred, thus obtaining an anomaly determination result.

[0100] Density dispersion is obtained by calculating the standard deviation of vapor density. The standard deviation of vapor density reflects the stability of vapor emission. When the density dispersion exceeds the preset dispersion threshold, it indicates that the fluctuation of vapor density has increased abnormally. This usually means that the drying process has become unstable, causing large fluctuations in vapor emission density. Furthermore, if the duration of this state is longer than the preset duration threshold, it indicates that it is a persistent problem rather than an occasional anomaly. Further determination is needed to determine whether an abnormal event has occurred in order to accurately identify the abnormal event and take corresponding measures for adjustment and optimization to ensure the stable operation of the dryer.

[0101] Please continue reading. Figure 2 As shown, this is the logic diagram for determining the occurrence of an abnormal event in this embodiment;

[0102] The process of determining the occurrence of an abnormal event based on the gray value, the dry mud area, and a preset correlation threshold, and obtaining the abnormality determination result includes:

[0103] Calculate the standard deviation of all gray values ​​to obtain the color non-uniformity.

[0104] The color unevenness is statistically analyzed within a preset judgment time period to obtain an unevenness dataset;

[0105] The area of ​​all the dry mud within the preset determination time period is counted to obtain an area dataset.

[0106] Calculate the correlation coefficient between the non-uniformity dataset and the area dataset to obtain the correlation degree of change;

[0107] When the correlation of the change is greater than the preset correlation threshold, an abnormal event is determined to have occurred, and an abnormality determination result is obtained.

[0108] The preset judgment time is the length of time used to determine abnormal events based on data changes. It depends on the operating cycle of the thin-layer dryer, the sludge drying rate, and the representativeness of the required data, and is usually set between 1 and 5 minutes. In this embodiment, it is set to 3 minutes to ensure data sufficiency while promptly reflecting changes in the dryer's operating status, thus ensuring timely judgment of abnormal events.

[0109] Color unevenness is obtained by calculating the standard deviation of all gray values; then, all color unevenness within a preset judgment period is statistically analyzed to form an unevenness dataset, and all dry mud areas within the preset judgment period are statistically analyzed to form an area dataset; next, the correlation coefficient between the unevenness dataset and the area dataset is calculated to obtain the change correlation; finally, when the change correlation is greater than a preset correlation threshold, an abnormal event is determined to have occurred, and an abnormal judgment result is obtained.

[0110] Color uniformity is obtained by calculating the standard deviation of grayscale values, which reflects the degree of variation in vapor color across different regions. Data sets are collected to accumulate changes over a period of time. The correlation coefficient between the two datasets is then calculated to obtain the correlation between color changes and changes in the dried mud area. When the correlation coefficient exceeds a preset threshold, it indicates a strong positive correlation between color uniformity and dried mud area. As color uniformity increases, the dried mud area also increases, suggesting that as the drying process progresses, sludge on the leaves may detach due to over-drying, leading to increased vapor impurities and thus increased color uniformity. Simultaneously, the dried mud area increases due to the increased area of ​​dried material, indicating an abnormality in the drying process. At this point, an abnormal event is identified.

[0111] Specifically, the process of determining the type of the abnormal event as local over-drying based on the anomaly determination result, the sludge thickness, and the dry sludge area, and obtaining the anomaly determination result includes:

[0112] When an anomaly determination result is obtained, the standard deviation of the sludge thickness from the initial time to each time within the preset determination time is calculated to obtain several thickness fluctuation values.

[0113] Calculate the standard deviation of the dry mud area from the initial time to each time within the preset judgment period to obtain several area fluctuation values;

[0114] Based on all the thickness fluctuation values ​​and all the area fluctuation values, the type of the abnormal event is determined to be localized over-drying, thus obtaining the abnormality determination result.

[0115] By calculating the standard deviation of sludge thickness from the initial time to each time within the preset judgment period, multiple thickness fluctuation values ​​are obtained; then, the standard deviation of dry sludge area at each time within the same preset judgment period is calculated, multiple area fluctuation values ​​are obtained; finally, by combining the characteristics of all thickness fluctuation values ​​and area fluctuation values, it is determined whether there is local over-drying.

[0116] By calculating the standard deviations of sludge thickness and dry sludge area over a pre-defined time period, thickness fluctuation values ​​and area fluctuation values ​​were obtained. These fluctuation values ​​reflect the degree of change in sludge thickness and dry sludge area during that time period. Thickness fluctuation values ​​reflect the uniformity of sludge accumulation, while area fluctuation values ​​reflect the stability of changes in the sludge adhesion range. By further analyzing the overall trend of these fluctuation values, it can be determined whether there is localized over-drying.

[0117] Please continue reading. Figure 3 As shown, it is the logic diagram for determining the result of the anomaly determination in this embodiment;

[0118] The process of determining the type of the abnormal event as localized over-drying based on all the thickness fluctuation values ​​and all the area fluctuation values, and obtaining the abnormality determination result, includes:

[0119] Plot the variation curves of all the thickness fluctuation values ​​to obtain the thickness fluctuation variation curve;

[0120] Plot the change curves of all the area fluctuation values ​​to obtain the area fluctuation change curves;

[0121] Calculate the cosine similarity between the thickness fluctuation curve and the area fluctuation curve to obtain the consistency of change;

[0122] When the consistency of change is greater than the preset consistency threshold, the type of the abnormal event is determined to be local over-drying, and an abnormality determination result is obtained.

[0123] The preset consistency threshold is a critical value used to judge the similarity between the thickness fluctuation curve and the area fluctuation curve. It depends on the characteristics of the sludge processed by the thin-layer dryer, the fluctuation characteristics during normal operation of the equipment, and historical data of abnormal events, and is usually set between 0.7 and 0.95. In this embodiment, it is set to 0.85, which can accurately distinguish between normal fluctuations and abnormal situations, effectively avoid misjudgment and omission, and ensure stable operation of the equipment.

[0124] A thickness fluctuation curve is formed by plotting the change curves of all thickness fluctuation values, and an area fluctuation curve is formed by plotting the change curves of all area fluctuation values. Then, the cosine similarity between these two curves is calculated to obtain the change consistency. Finally, if the change consistency is greater than the preset consistency threshold, the abnormal event is determined to be local over-drying, and the abnormality determination result is obtained.

[0125] By plotting thickness and area fluctuation curves, the changing trends of sludge thickness and area at different locations can be visually observed. The cosine similarity of these two curves yields the consistency of change, quantifying the similarity of their trends. The calculation of cosine similarity is a current technique and will not be elaborated upon here. When the consistency of change exceeds a preset threshold, it indicates a high degree of consistency between thickness and area fluctuations in their trends. This consistency typically indicates localized over-drying, as some areas experience rapid sludge loss, leading to a rapid thinning of the sludge and even the formation of voids, while other areas show little change, resulting in increased thickness fluctuation. Simultaneously, the amount of dry sludge adhering to the blades increases with sludge drying, causing area fluctuations. This effectively identifies abnormal events of localized over-drying and helps in timely adjustments to the drying process.

[0126] Specifically, the process of adjusting the preset sludge feed rate based on the anomaly determination result and the total sludge thickness includes:

[0127] Calculate the average thickness of all sludge at each detection point on the cross-section of the inner cylinder wall to obtain the average thickness;

[0128] Plot the change curve of all the average thicknesses within the preset adjustment time to obtain the thickness change curve;

[0129] Calculate the slope change rate of the thickness change curve to obtain the thickness change rate;

[0130] The preset sludge feed rate is adjusted according to the thickness change rate to obtain the adjusted sludge feed rate.

[0131] The preset adjustment time is a period used to calculate and analyze changes in sludge thickness. It depends on the operating characteristics of the thin-layer dryer, the sludge drying rate, and the required adjustment response speed, and is typically set between 5 and 30 minutes. In this embodiment, it is set to 10 minutes to ensure that the adjustment of the sludge feed rate and rotor speed is timely and effective, enabling the equipment to respond quickly to changes in operating status and optimize the drying effect.

[0132] The cross-section of the inner cylinder wall refers to the section formed by cutting along the horizontal direction of the inner cylinder wall (i.e., parallel to the ground). It shows the sludge thickness distribution of the inner cylinder in the horizontal direction. It can be used to analyze whether the sludge thickness distribution in the circumferential direction (horizontal direction around the cylinder wall) of the inner cylinder wall is uniform, and whether there is local accumulation or missing parts.

[0133] The average thickness is obtained by calculating the average thickness of all sludge in the cross section; then, the thickness change curve is obtained by plotting the change curve of the average thickness within the preset adjustment time; then, the slope change rate of the thickness change curve is calculated to obtain the thickness change rate; the preset sludge feed speed is adjusted according to the thickness change rate to obtain the adjusted sludge feed speed; finally, based on the adjusted sludge feed speed, the preset rotor speed is adjusted according to the total sludge thickness of the vertical cross section of the inner cylinder wall to obtain the adjusted rotor speed.

[0134] By calculating the average thickness of the sludge across the cross-section and plotting its variation curve, the dynamic changes in sludge thickness can be visually presented; the slope change rate reflects the rate and trend of thickness change. Adjusting the sludge feed rate based on the thickness change rate ensures that the sludge feed volume matches the current drying capacity.

[0135] Please continue reading. Figure 4 As shown, it is the logic diagram for adjusting the preset sludge feed rate in this embodiment;

[0136] The process of adjusting the preset sludge feed rate based on the thickness change rate includes:

[0137] When the thickness change rate is greater than a preset change rate threshold, the preset sludge feed rate is reduced based on the relative deviation between the thickness change rate and the preset change rate threshold, and a preset feed rate adjustment coefficient, to obtain an adjusted sludge feed rate. Wherein, Q'=Q×[1-k×(S-S0) / S0], Q' is the adjusted sludge feed rate, Q is the preset sludge feed rate, k is the preset feed rate adjustment coefficient, S is the thickness change rate, and S0 is the preset change rate threshold.

[0138] The preset change rate threshold is a critical value used to determine whether the sludge thickness change rate is too fast. It depends on the processing capacity of the thin-layer dryer, the characteristics of the sludge, and the stability of the equipment operation, and is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.3, which can effectively detect situations where the sludge thickness changes too quickly, adjust the sludge feed rate in time, and avoid sludge accumulation or uneven distribution on the inner cylinder wall.

[0139] The preset feed rate adjustment coefficient is a factor used to control the adjustment range of the sludge feed rate. It depends on the response speed of the equipment, the fluidity of the sludge, and the sensitivity requirements of the adjustment, and is usually set between 0.5 and 0.9. In this embodiment, it is set to 0.7 to avoid excessive feed rate adjustment range leading to system instability and to ensure that the adjustment of the sludge feed rate is both timely and stable.

[0140] When the thickness change rate exceeds the preset change rate threshold, the relative deviation between the thickness change rate and the preset change rate threshold is calculated. Then, based on the relative deviation and the preset feed rate adjustment coefficient, the preset sludge feed rate is reduced to obtain the adjusted sludge feed rate.

[0141] By monitoring the sludge thickness change rate, the accumulation speed of sludge on the inner cylinder wall can be reflected in a timely manner. When the thickness change rate exceeds the preset threshold, the sludge feed rate is reduced by combining the relative deviation and adjustment coefficient, which can prevent excessive sludge accumulation and maintain the sludge thickness on the inner cylinder wall within a reasonable range. This ensures the drying efficiency and operational stability of the dryer, avoids uneven drying and equipment failure caused by sludge accumulation, and achieves precise dynamic control of the sludge feed rate.

[0142] Specifically, the process of adjusting the preset rotor speed based on adjusting the sludge feed rate and sludge thickness to obtain the adjusted rotor speed includes:

[0143] Calculate the standard deviation of the sludge thickness at each detection point on the vertical cross section of the inner cylinder wall to obtain the thickness dispersion.

[0144] When the thickness dispersion is greater than a preset dispersion threshold, the preset rotor speed is increased according to the relative deviation between the thickness dispersion and the preset dispersion threshold, as well as a preset speed adjustment coefficient, to obtain the adjusted rotor speed. Wherein, R'=R×[1+a×(M-M0) / M0], R' is the adjusted rotor speed, R is the preset rotor speed, a is the preset speed adjustment coefficient, M is the thickness dispersion, and M0 is the preset dispersion threshold.

[0145] The vertical section of the inner cylinder wall refers to the section formed by cutting along the vertical direction (i.e., perpendicular to the ground) of the inner cylinder wall. It shows the distribution of sludge thickness in the vertical direction of the inner cylinder and can be used to analyze the accumulation thickness of sludge at different heights in the vertical direction of the inner cylinder wall, and whether there is uneven thickness in the upper and lower parts.

[0146] The preset dispersion threshold is a standard value used to judge the uniformity of sludge thickness distribution in the vertical section. It depends on the sludge carrying capacity of the equipment design and historical operating data statistics, and is usually set between 0.1 and 0.3. In this embodiment, it is set to 0.2 to accurately identify uneven sludge thickness at the top and bottom, which is both sensitive and does not trigger adjustments too frequently.

[0147] The preset speed adjustment coefficient is a factor used to control the magnitude of rotor speed adjustment. Based on the rotor's maximum speed limit and the equipment's response speed requirements, it is typically set between 0.2 and 0.5. In this embodiment, it is set to 0.3 to achieve smooth speed adjustment and avoid impacting the equipment due to excessive adjustment.

[0148] The thickness dispersion is obtained by calculating the standard deviation of the total sludge thickness in the vertical section of the inner cylinder wall. If the thickness dispersion is greater than the preset dispersion threshold, the preset rotor speed is increased based on the relative deviation between the thickness dispersion and the preset dispersion threshold and the preset speed adjustment coefficient, and finally the adjusted rotor speed is obtained.

[0149] By adjusting the rotor speed in conjunction with the sludge thickness distribution along the vertical cross-section of the inner cylinder wall, the vertical distribution of sludge and the drying effect can be further optimized. This links the sludge feed rate and rotor speed, enabling precise control of the drying process. The standard deviation of the sludge thickness in the vertical cross-section is calculated to obtain the thickness dispersion, which quantifies the uniformity of sludge distribution. When the thickness dispersion exceeds a preset threshold, it indicates uneven sludge thickness at the top and bottom. In this case, increasing the rotor speed based on the relative deviation and adjustment coefficient enhances the stirring force, redistributes the sludge, and prevents localized accumulation or uneven drying.

[0150] Specifically, the process of correcting the preset correlation threshold based on the sludge moisture content and the vapor density within a preset correction period to obtain the corrected correlation threshold includes:

[0151] When the sludge moisture content is greater than the preset sludge moisture content threshold, the standard deviation of the sludge moisture content within the preset correction time is calculated to obtain the moisture content fluctuation value.

[0152] Calculate the standard deviation of all the vapor densities to obtain the corrected dispersion;

[0153] Calculate the average value of all the corrected dispersions within the preset correction time period to obtain the density dispersion mean;

[0154] The preset correlation threshold is corrected based on the moisture content fluctuation value and the density dispersion mean to obtain the corrected correlation threshold.

[0155] The preset sludge moisture content threshold is a critical value used to determine whether the sludge moisture content exceeds the allowable range. It depends on the sludge treatment process requirements, subsequent treatment or disposal needs, and environmental standards, and is typically set between 30% and 60%. In this embodiment, it is set to 33% to ensure that the sludge moisture content is within a reasonable range, meeting the requirements for subsequent treatment or disposal.

[0156] When the sludge moisture content exceeds a preset threshold, the standard deviation of the sludge moisture content within a preset correction period is calculated to obtain the moisture content fluctuation value; then the standard deviation of all vapor densities is calculated to obtain the corrected dispersion; then the average value of all corrected dispersions within the preset correction period is calculated to obtain the density dispersion mean; finally, the preset correlation threshold is corrected based on the moisture content fluctuation value and the density dispersion mean to obtain the corrected correlation threshold.

[0157] By monitoring fluctuations in the sludge moisture content and vapor density, the correlation threshold is dynamically adjusted. When the sludge moisture content exceeds the standard, its fluctuation value and the dispersion mean of the vapor density are calculated. The sludge moisture content directly reflects the drying effect, while the vapor density reflects the stability of the drying process. The greater the fluctuation in both, the more unstable the equipment operation, requiring a reduction in the correlation threshold to improve the sensitivity of anomaly detection. By combining these two indicators to adjust the correlation threshold, stable and efficient operation of the equipment under different working conditions is ensured.

[0158] Specifically, the process of correcting the preset correlation threshold based on the moisture content fluctuation value and the density dispersion mean to obtain the corrected correlation threshold includes:

[0159] The relative deviation between the moisture content fluctuation value and the preset moisture content fluctuation threshold is calculated to obtain the fluctuation deviation;

[0160] The relative deviation between the density dispersion mean and the preset dispersion threshold is calculated to obtain the dispersion deviation;

[0161] The preset correlation threshold is obtained by reducing the preset correlation threshold based on the fluctuation deviation, the dispersion deviation, the preset fluctuation deviation weight, the preset dispersion deviation weight, and the preset correction coefficient. Wherein, T'=T×[1+b×(W1×B1+W2×B2], T' is the corrected correlation threshold, T is the preset correlation threshold, b is the preset correction coefficient, W1 is the preset fluctuation deviation weight, B1 is the fluctuation deviation, W2 is the preset dispersion deviation weight, and B2 is the dispersion deviation.

[0162] The preset moisture content fluctuation threshold is a key indicator for evaluating the stability of sludge moisture content. It depends on the sludge characteristics, the performance of the drying equipment, and the process requirements, and is usually set between 2% and 5%. In this embodiment, it is set to 3% to promptly monitor abnormal fluctuations in the drying process, facilitating timely adjustments to process parameters and ensuring the stability and consistency of the sludge drying effect.

[0163] The preset fluctuation deviation weight and preset dispersion deviation weight are coefficients used to measure the influence of moisture content fluctuation and density dispersion mean on the correction correlation threshold. They depend on the importance and sensitivity assessment of the impact of sludge moisture content and vapor density dispersion on equipment operating status, and are typically set between 0 and 1, with the sum of the two being 1. In this embodiment, the preset fluctuation deviation weight is set to 0.6, and the preset dispersion deviation weight is set to 0.4, which balances the influence of moisture content fluctuation and density dispersion on the correlation threshold correction, making the correction result more accurately reflect the equipment operating status.

[0164] The preset correction coefficient is a factor used to control the magnitude of threshold correction. It depends on the sensitivity and stability of the correlation threshold correction and is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.2, which provides a moderate correction magnitude, avoiding excessively large or small corrections and ensuring the stability of equipment operation and the accuracy of anomaly detection.

[0165] The fluctuation deviation is obtained by calculating the relative deviation between the moisture content fluctuation value and the preset moisture content fluctuation threshold. Then, the dispersion deviation is obtained by calculating the relative deviation between the density dispersion mean and the preset dispersion threshold. Finally, the fluctuation deviation, dispersion deviation, preset fluctuation deviation weight, dispersion deviation weight and correction coefficient are combined to reduce the preset correlation threshold, thereby obtaining the corrected correlation threshold.

[0166] The fluctuation deviation is obtained by calculating the relative deviation between the moisture content fluctuation value and the preset threshold, and the dispersion deviation is obtained by calculating the relative deviation between the density dispersion mean and the preset threshold. These two deviations reflect the degree of deviation in drying effect and process stability. The preset correlation threshold is then reduced according to preset weights and correction coefficients to obtain the corrected threshold. Based on the corrections for moisture content and vapor density, the correlation threshold directly reflects the sludge drying effect and process stability, and can better adapt to the actual operating conditions of the equipment.

[0167] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for rapid thin-layer drying of sludge based on industrial images, characterized in that, include: Real-time acquisition of the sludge moisture content at the discharge port, steam images at the steam discharge port, cylinder wall images at various detection points inside the cylinder, and blade images during the operation of the thin-layer dryer based on preset sludge feed speed and preset rotor speed. Extract the vapor density and grayscale value of vapor color of each region in the vapor image, and extract the sludge thickness in the cylinder wall image and the dry sludge area on the blade surface in the blade image. An abnormal event is determined based on the vapor density, the gray value, the dry mud area, and a preset correlation threshold, and an abnormality determination result is obtained. Based on the anomaly determination result, the sludge thickness, and the dry sludge area, the type of the abnormal event is determined to be local over-drying, and the anomaly determination result is obtained. The preset sludge feed rate is adjusted based on the anomaly determination result and the total sludge thickness to obtain the adjusted sludge feed rate. The preset rotor speed is then adjusted based on the adjusted sludge feed rate and the sludge thickness to obtain the adjusted rotor speed. Based on the adjustment of sludge feed rate and the adjustment of rotor speed, the preset correlation threshold is corrected according to the sludge moisture content and the vapor density within the preset correction time to obtain the corrected correlation threshold; The thin-layer dryer is controlled by adjusting the sludge feed rate and the rotor speed, which are redetermined based on the corrected correlation threshold.

2. The rapid thin-layer drying method for sludge based on industrial images according to claim 1, characterized in that, The process of determining an anomaly result based on the vapor density, gray value, dry mud area, and a preset correlation threshold includes: Calculate the standard deviation of all the vapor densities to obtain the density dispersion; When the density dispersion is greater than a preset dispersion threshold, the current timestamp is recorded; when the density dispersion is less than or equal to the preset dispersion threshold, recording is stopped, and the duration is obtained. When the duration exceeds a preset duration threshold, an abnormal event is determined based on the gray value, the dry mud area, and a preset correlation threshold, and an abnormality determination result is obtained.

3. The rapid thin-layer drying method for sludge based on industrial images according to claim 2, characterized in that, The process of determining the occurrence of an abnormal event based on the gray value, the dry mud area, and a preset correlation threshold, and obtaining the abnormality determination result includes: Calculate the standard deviation of all gray values ​​to obtain the color non-uniformity. The color unevenness is statistically analyzed within a preset judgment time period to obtain an unevenness dataset; The area of ​​all the dry mud within the preset determination time period is counted to obtain an area dataset. Calculate the correlation coefficient between the non-uniformity dataset and the area dataset to obtain the correlation degree of change; When the correlation of the change is greater than the preset correlation threshold, an abnormal event is determined to have occurred, and an abnormality determination result is obtained.

4. The rapid thin-layer drying method for sludge based on industrial images according to claim 3, characterized in that, The process of determining the type of the abnormal event as localized over-drying based on the anomaly determination result, the sludge thickness, and the dry sludge area, and obtaining the anomaly determination result includes: When an anomaly determination result is obtained, the standard deviation of the sludge thickness from the initial time to each time within the preset determination time is calculated to obtain several thickness fluctuation values. Calculate the standard deviation of the dry mud area from the initial time to each time within the preset judgment period to obtain several area fluctuation values; Based on all the thickness fluctuation values ​​and all the area fluctuation values, the type of the abnormal event is determined to be localized over-drying, thus obtaining the abnormality determination result.

5. The rapid thin-layer drying method for sludge based on industrial images according to claim 4, characterized in that, The process of determining the type of the abnormal event as localized over-drying based on all the thickness fluctuation values ​​and all the area fluctuation values, and obtaining the abnormality determination result, includes: Plot the variation curves of all the thickness fluctuation values ​​to obtain the thickness fluctuation variation curve; Plot the change curves of all the area fluctuation values ​​to obtain the area fluctuation change curves; Calculate the cosine similarity between the thickness fluctuation curve and the area fluctuation curve to obtain the consistency of change; When the consistency of the change is greater than a preset consistency threshold, the type of the abnormal event is determined to be local over-drying, and an abnormality determination result is obtained.

6. The rapid thin-layer drying method for sludge based on industrial images according to claim 5, characterized in that, The process of adjusting the preset sludge feed rate based on the anomaly determination result and the total sludge thickness includes: Calculate the average thickness of all sludge at each detection point on the cross-section of the inner cylinder wall to obtain the average thickness; Plot the change curve of all the average thicknesses within the preset adjustment time to obtain the thickness change curve; Calculate the slope change rate of the thickness change curve to obtain the thickness change rate; The preset sludge feed rate is adjusted according to the thickness change rate to obtain the adjusted sludge feed rate.

7. The rapid thin-layer drying method for sludge based on industrial images according to claim 6, characterized in that, The process of adjusting the preset sludge feed rate based on the thickness change rate includes: When the thickness change rate is greater than a preset change rate threshold, the preset sludge feed rate is reduced based on the relative deviation between the thickness change rate and the preset change rate threshold and the preset feed rate adjustment coefficient, thereby obtaining an adjusted sludge feed rate.

8. The rapid thin-layer drying method for sludge based on industrial images according to claim 7, characterized in that, The process of adjusting the preset rotor speed by adjusting the sludge feed rate and sludge thickness includes: Calculate the standard deviation of the sludge thickness at each detection point on the vertical cross section of the inner cylinder wall to obtain the thickness dispersion. When the thickness dispersion is greater than a preset dispersion threshold, the preset rotor speed is increased according to the relative deviation between the thickness dispersion and the preset dispersion threshold and the preset speed adjustment coefficient to obtain the adjusted rotor speed.

9. The rapid thin-layer drying method for sludge based on industrial images according to claim 8, characterized in that, The process of correcting the preset correlation threshold based on the sludge moisture content and the vapor density within a preset correction time period, and obtaining the corrected correlation threshold, includes: When the sludge moisture content is greater than the preset sludge moisture content threshold, the standard deviation of the sludge moisture content within the preset correction time is calculated to obtain the moisture content fluctuation value. Calculate the standard deviation of all the vapor densities to obtain the corrected dispersion; Calculate the average value of all the corrected dispersions within the preset correction time period to obtain the density dispersion mean; The preset correlation threshold is corrected based on the moisture content fluctuation value and the density dispersion mean to obtain the corrected correlation threshold.

10. The rapid thin-layer drying method for sludge based on industrial images according to claim 9, characterized in that, The process of correcting the preset correlation threshold based on the moisture content fluctuation value and the density dispersion mean, to obtain the corrected correlation threshold, includes: The relative deviation between the moisture content fluctuation value and the preset moisture content fluctuation threshold is calculated to obtain the fluctuation deviation; The relative deviation between the density dispersion mean and the preset dispersion threshold is calculated to obtain the dispersion deviation; The preset correlation threshold is obtained by reducing the preset correlation threshold based on the fluctuation deviation, the dispersion deviation, the preset fluctuation deviation weight, the preset dispersion deviation weight, and the preset correction coefficient.

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