Industrial equipment fault monitoring and diagnosing system based on infrared thermal imaging

Abstract

The invention discloses an industrial equipment fault monitoring and diagnosis system based on infrared thermal imaging, and the system comprises a pipeline blocking region recognition module which recognizes a pipeline blocking region; the thermal imaging characteristic index acquisition module is used for acquiring a thermal imaging characteristic index of a pipeline blockage area; the blockage degree evaluation module is used for constructing a pipeline blockage degree evaluation model and evaluating the blockage degree; the pipeline blockage prediction module is used for predicting a time point when the blockage degree is higher than a preset degree threshold value; the pipeline blockage area migration prediction module is used for predicting the blockage degree when the position of the pipeline is changed; the pipeline blockage area aggregation prediction module is used for predicting the aggregation position and aggregation time of the pipeline blockage area; and the prevention measure optimization module continuously optimizes the blockage risk prevention measures and the blockage migration risk prevention measures. The invention provides a comprehensive and efficient industrial transmission pipeline blockage monitoring and management scheme, the operation safety and maintenance efficiency of the industrial transmission pipeline are improved, and the blockage risk and pipeline faults are reduced.

Description

Technical Field

[0001] The invention relates to the field of industrial equipment monitoring, and in particular to an industrial equipment fault monitoring and diagnosis system based on infrared thermal imaging. Background Art

[0002] Industrial equipment fault monitoring is one of the important links to ensure the normal operation of industrial production systems. Especially in the oil, natural gas, chemical and other industries, equipment failure will not only lead to production stagnation, but also may bring serious safety hazards and economic losses. Industrial equipment failures manifest themselves in various forms, including wear, overheating, aging of mechanical parts, blockages in transmission pipelines, abnormal fluid flow and other problems. Among them, the blockage failure of industrial transmission pipelines is the most common, most hidden and most far-reaching type of problem in industrial production. At present, although traditional pipeline monitoring technologies such as pressure sensors and flow meters can detect the blockage inside the pipeline indirectly, they can usually only have obvious monitoring signals when the blockage problem is more serious and affects the overall operation of the pipeline. This lagging detection method often makes it impossible to detect and deal with the problem in time at an early stage, resulting in further deterioration of the blockage. In addition, traditional monitoring methods also have limitations in the spatial positioning of pipeline blockage detection. The complex structure of the pipeline, such as elbows, valves, branch points and height change points, will affect the location and distribution of the blockage, and traditional flow and pressure monitoring equipment often finds it difficult to determine the specific location of the blockage. Especially in complex pipeline systems, accurate positioning of the blocked area is critical for effective treatment and prevention of blockages, but traditional technologies have difficulty in achieving such fine spatial identification. In addition, pipeline blockage is dynamic, and blockages may migrate in the pipeline with the flow of fluid, causing changes in the blocked area. Traditional fixed-point monitoring methods make it difficult to track the migration of blockages in real time, resulting in some small-scale blockage areas gradually gathering after migration, forming larger blockage areas, and eventually causing serious pipeline blockage or system failure. Therefore, the lack of real-time monitoring of the dynamic migration of blockages is another major defect of traditional detection technology. In addition, with the continuous expansion of the scale of industrial pipeline systems, a single detection method is difficult to meet the needs of comprehensive, real-time and efficient monitoring. When multiple small-scale blockage areas gradually migrate and gather, the lack of comprehensive monitoring methods leads to the lag of risk prevention, which increases the maintenance cost and failure rate of the pipeline system. Therefore, there is an urgent need for a method that can accurately identify the blockage area and evaluate the degree of blockage, predict the migration trend of the blockage, and take corresponding preventive measures in a timely manner. Summary of the invention

[0003] The present invention solves the problems existing in the above-mentioned prior art and provides an industrial equipment fault monitoring and diagnosis system based on infrared thermal imaging, which mainly includes:

[0004] The pipeline blockage area recognition module is used to obtain thermal imaging images of industrial transmission pipelines, identify the temperature abnormality area where the pipeline is blocked, and mark it as the pipeline blockage area;

[0005] The thermal imaging characteristic index acquisition module is used to extract the maximum temperature, average temperature gradient, maximum temperature difference and gradient direction standard deviation of the thermal imaging image of the pipeline blockage area, and form the thermal imaging characteristic index of the pipeline blockage area;

[0006] The blockage degree assessment module is used to construct a pipeline blockage degree assessment model based on the thermal imaging characteristic indicators of the historical blockage areas of the industrial transmission pipeline, assess the blockage degree of the pipeline blockage area of ​​the industrial transmission pipeline, and formulate blockage risk prevention measures for the pipeline blockage area with a blockage degree higher than a preset degree threshold;

[0007] The pipeline blockage prediction module is used to determine the heat diffusion speed of the pipeline blockage area at different times according to the temperature value of the pipeline blockage area at different times, and to build a thermal imaging characteristic index prediction model to predict the time point when the blockage degree of the pipeline blockage area with a heat diffusion speed greater than a preset speed threshold is higher than a preset degree threshold, so as to implement blockage risk prevention measures in advance;

[0008] The pipeline blockage area migration prediction module is used to determine whether the pipeline blockage area has migrated according to the blockage position of the pipeline blockage area, and based on the migration data of the pipeline blockage area, predict the blockage degree when the position of the pipeline where the pipeline blockage area is located changes, and implement blockage migration risk prevention measures for pipeline blockage areas whose blockage degree is higher than a preset degree threshold;

[0009] The pipeline blockage area aggregation prediction module is used to predict the aggregation location and aggregation time of the pipeline blockage area according to the migration speed and migration direction of each pipeline blockage area, and implement blockage migration risk prevention measures for the aggregation area of ​​the pipeline blockage area in advance;

[0010] The preventive measures optimization module is used to continuously monitor thermal imaging images of industrial transmission pipelines, evaluate the implementation effect of blockage risk prevention measures and blockage migration risk prevention measures, and continuously optimize preventive measures.

[0011] Furthermore, the pipeline blockage area identification module is used to obtain the thermal imaging image of the industrial transmission pipeline, identify the temperature abnormality area where the pipeline blockage occurs, and mark it as the pipeline blockage area, including:

[0012] According to the predetermined scanning path and speed, the industrial transmission pipeline is comprehensively scanned to obtain and save the thermal imaging image of the industrial transmission pipeline; the thermal imaging image of the industrial transmission pipeline is converted into a grayscale image, and the temperature threshold is set as the segmentation standard, the grayscale image is binarized, and the image area with a temperature higher than the set temperature threshold is marked as a temperature abnormality area; through the historical monitoring database of the industrial transmission pipeline, the thermal imaging images of the historical temperature abnormality areas of the industrial transmission pipeline are obtained, and it is marked whether the pipeline is blocked, and the convolutional neural network is used for model training to build a pipeline blockage recognition model; according to the newly acquired thermal imaging images of the temperature abnormality area, the pipeline blockage recognition model is used to identify the temperature abnormality area where the pipeline is blocked and mark it as the pipeline blockage area.

[0013] Furthermore, the thermal imaging characteristic index acquisition module is used to extract the maximum temperature, average temperature gradient, maximum temperature difference and gradient direction standard deviation of the thermal imaging image of the pipeline blockage area, and form the thermal imaging characteristic index of the pipeline blockage area, including:

[0014] According to the thermal imaging image of the pipeline blockage area, the temperature value of each pixel in the thermal imaging image is extracted to obtain the temperature distribution data of each pixel; according to the temperature value in the thermal imaging image, the central difference method is used to calculate the temperature change rate between adjacent pixels to determine the average temperature gradient value of the pipeline blockage area; according to the temperature value in the thermal imaging image, the difference between the highest temperature and the lowest temperature is calculated to obtain the maximum temperature difference in the pipeline blockage area; the Sobel operator is used to calculate the gradient direction of each pixel to determine the standard deviation of the gradient direction; the highest temperature, average temperature gradient, maximum temperature difference and gradient direction standard deviation are standardized and combined into a feature vector to constitute the thermal imaging feature index of the pipeline blockage area.

[0015] Furthermore, the blockage degree assessment module is used for the blockage degree assessment module to construct a pipeline blockage degree assessment model based on the thermal imaging characteristic indicators of the historical blockage area of ​​the industrial transmission pipeline, assess the blockage degree of the pipeline blockage area of ​​the industrial transmission pipeline, and formulate blockage risk prevention measures for the pipeline blockage area with a blockage degree higher than a preset degree threshold, including:

[0016] Through the historical monitoring database of industrial transmission pipelines, the thermal imaging characteristic indicators, blockage degree, pipe diameter, material and location of the historical blockage areas of the industrial transmission pipelines are obtained, and the recurrent neural network is used for model training to build a pipeline blockage degree assessment model. The materials of the pipeline include steel, stainless steel and PVC, and the locations of the pipeline include main transmission pipelines, pipeline elbows, pipeline valves, pipeline branch points, pipeline connection points and height change points; based on the thermal imaging characteristic indicators of the pipeline blockage area obtained in real time, combined with the pipe diameter, material and location of the pipeline, the pipeline blockage degree assessment model is used to assess the blockage degree of the pipeline blockage area of ​​the current industrial transmission pipeline; for pipeline blockage areas with a blockage degree higher than the preset threshold, blockage risk prevention measures are formulated and implemented, including cleaning up debris inside the pipeline, increasing the monitoring frequency, regularly conducting thermal imaging inspections, and optimizing the transmission pressure or flow rate of the pipeline.

[0017] Furthermore, the pipeline blockage prediction module is used to determine the heat diffusion speed of the pipeline blockage area at different time periods according to the temperature values ​​of the pipeline blockage area at different times, and to construct a thermal imaging characteristic index prediction model to predict the time point when the blockage degree of the pipeline blockage area where the heat diffusion speed is greater than the preset speed threshold is higher than the preset degree threshold, and implement blockage risk prevention measures in advance, including:

[0018] By adopting the method of regular sampling, the thermal imaging images of the pipeline blockage area with a blockage degree lower than the preset threshold are obtained at preset time intervals, the temperature distribution data at each moment is extracted, and the temperature value of the pipeline blockage area at each moment is determined; according to the temperature values ​​of the pipeline blockage area at different moments, the heat diffusion rate calculation formula is used to calculate the heat diffusion rate. Calculate the heat diffusion rate at different time periods in the pipeline blockage area, where T i and T j t i Time and t j The temperature value at the moment, α is the thermal diffusion coefficient, which is determined based on the material properties of the pipeline; if the thermal diffusion rate of the pipeline blocked area in a certain period of time is greater than the preset speed threshold, the historical thermal imaging characteristic indicators of the pipeline blocked area are obtained through the historical monitoring database of the industrial transmission pipeline, and are sorted into time series data; according to the time series data of the historical thermal imaging characteristic indicators, the ARIMA algorithm is used for model training, and a thermal imaging characteristic indicator prediction model is constructed to predict the thermal imaging characteristic indicators in the future preset time period, and combined with the pipeline blockage degree assessment model, the time point when the blockage degree of the pipeline blocked area is higher than the preset degree threshold is determined, and blockage risk prevention measures are implemented in advance for the pipeline blocked area.

[0019] Furthermore, the pipeline blocked area migration prediction module is used to determine whether the pipeline blocked area has migrated according to the blocked position of the pipeline blocked area, and predict the blockage degree when the position of the pipeline where the pipeline blocked area is located changes based on the migration data of the pipeline blocked area, and implement blockage migration risk prevention measures for the pipeline blocked area whose blockage degree is higher than the preset degree threshold, including:

[0020] The blockage position of the pipeline blockage area is determined according to the position with the highest temperature value in the thermal imaging image of the pipeline blockage area; the historical blockage position data of the pipeline blockage area is obtained through the historical monitoring database of the industrial transmission pipeline, and combined with the current blockage position of the pipeline blockage area, it is determined whether the pipeline blockage area has migrated; if the pipeline blockage area has migrated, the migration data of the pipeline blockage area is obtained through the historical monitoring database of the industrial transmission pipeline, including the migration distance, migration speed and migration direction, and the position of the pipeline where the pipeline blockage area is located in the future time period is predicted using the ARIMA algorithm; if it is predicted that the position of the pipeline where the pipeline blockage area is located changes in the future time period, the thermal imaging feature index prediction model is used to predict the thermal imaging feature index when the position of the pipeline where the pipeline blockage area is located changes; according to the blockage migration prediction formula Predict the degree of blockage when the position of the pipeline where the pipeline is blocked changes, where S(t) is the degree of blockage at time t, S 1 is the degree of congestion before migration, D 1 and D 2 are the diameters of the pipeline before and after migration, respectively, and k is the coefficient of the effect of the change in pipe diameter on the degree of blockage, obtained by fitting historical data, M 1 and M 2 are the material parameters of the pipeline before and after migration, which are defined according to the friction coefficient and corrosion resistance of the material. l is the influence coefficient of material change on the degree of blockage, which is obtained by fitting historical data. F z It is an influencing factor related to the pipeline position z and is obtained by fitting historical data. m represents the number of pipeline positions passed during the migration process. For pipeline blockage areas with a blockage degree higher than the preset threshold, blockage migration risk prevention measures are implemented, including optimizing the interval time of fluid delivery, using pipeline regulating devices to guide the blockage to migrate in a specified direction, and optimizing the flow rate by adjusting the power of the pump or the opening and closing degree of the valve.

[0021] Furthermore, the pipeline blockage area aggregation prediction module is used to predict the aggregation position and aggregation time of the pipeline blockage area according to the migration speed and migration direction of each pipeline blockage area, and implement blockage migration risk prevention measures for the aggregation area of ​​the pipeline blockage area in advance, including:

[0022] If there are several pipeline blockage areas in the industrial transmission pipeline with a blockage degree lower than a preset threshold and they migrate, the blockage degree when the position of the pipeline where the pipeline blockage area is located changes is predicted according to the blockage migration prediction formula; if the predicted blockage degree when the position of the pipeline where the pipeline blockage area is located changes is lower than the preset threshold, the blockage position data of the pipeline blockage area is obtained through the historical monitoring database of the industrial transmission pipeline; based on the blockage position data of the pipeline blockage area, the ARIMA algorithm is used to predict the clustering position and clustering time of the pipeline blockage area; based on the predicted clustering position and clustering time of the pipeline blockage area, blockage migration risk prevention measures are implemented in advance for the clustering areas of the pipeline blockage area.

[0023] Furthermore, the preventive measures optimization module is used to continuously monitor the thermal imaging images of the industrial transmission pipeline, evaluate the implementation effect of the blockage risk prevention measures and the blockage migration risk prevention measures, and continuously optimize the preventive measures, including:

[0024] Continuously monitor industrial transmission pipelines through infrared thermal imaging technology, and regularly obtain thermal imaging images and corresponding temperature data of industrial transmission pipelines; evaluate the effectiveness of preventive measures by comparing pipeline thermal imaging images before and after the implementation of blockage risk prevention measures and blockage migration risk prevention measures; if the blockage degree is alleviated and the temperature distribution in the blocked area returns to normal, the blockage risk prevention measures and blockage migration risk prevention measures are judged to be effective; if the blockage degree is not alleviated, optimize the blockage risk prevention measures and blockage migration risk prevention measures, including increasing the frequency of pipeline cleaning, adjusting the transmission pressure or changing the flow rate, or changing the cleaning method of the blocked area; implement the optimized blockage risk prevention measures and blockage migration risk prevention measures, evaluate the effectiveness of the new preventive measures, and continuously optimize the blockage risk prevention measures and blockage migration risk prevention measures based on the effectiveness evaluation results of the preventive measures.

[0025] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0026] The present invention provides an industrial equipment fault monitoring and diagnosis system based on infrared thermal imaging. By applying infrared thermal imaging technology, the present invention can monitor the temperature distribution of industrial transmission pipelines in real time, accurately identify and mark abnormal temperature areas as pipeline blockage areas. According to the extracted temperature characteristic indicators, the blockage degree of the pipeline is further evaluated to ensure that the blockage problem can be discovered and handled in time at an early stage. The present invention can dynamically monitor the migration of blockages, predict the position change of the blocked area in the pipeline and the trend of the change in the blockage degree, and ensure that preventive measures are taken for areas with a high degree of blockage after migration to prevent the blockage from worsening. For the situation where multiple small-scale blockages gradually gather, the present invention can predict their gathering position and time, ensure intervention before the blockages form a cluster, and avoid greater risks caused by the accumulation of blockages. The present invention provides a comprehensive and efficient pipeline blockage monitoring and management solution, which improves the safety and maintenance efficiency of pipeline operation and reduces the risk of blockage and the occurrence of pipeline failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of an industrial equipment fault monitoring and diagnosis system based on infrared thermal imaging of the present invention;

[0028] Figure 2 A schematic diagram of an industrial equipment fault monitoring and diagnosis system based on infrared thermal imaging according to the present invention;

[0029] Figure 3 This is another schematic diagram of an industrial equipment fault monitoring and diagnosis system based on infrared thermal imaging according to the present invention. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical strategies and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] like Figure 1-3 In this embodiment, an industrial equipment fault monitoring and diagnosis system based on infrared thermal imaging may specifically include:

[0032] Step S101, a pipeline blockage area identification module is used to obtain a thermal imaging image of an industrial transmission pipeline, identify an abnormal temperature area where the pipeline is blocked, and mark it as a pipeline blockage area.

[0033] According to the predetermined scanning path and speed, the industrial transmission pipeline is fully scanned to obtain and save the thermal imaging images of the industrial transmission pipeline. The thermal imaging images of the industrial transmission pipeline are converted into grayscale images, and the temperature threshold is set as the segmentation standard. The grayscale image is binarized, and the image area with a temperature higher than the set temperature threshold is marked as an abnormal temperature area. Through the historical monitoring database of industrial transmission pipelines, the thermal imaging images of the historical temperature abnormality areas of the industrial transmission pipeline are obtained, and whether the pipeline is blocked is marked. The convolutional neural network is used for model training to build a pipeline blockage recognition model. According to the newly acquired thermal imaging images of the temperature abnormality area, the pipeline blockage recognition model is used to identify the temperature abnormality area where the pipeline is blocked and mark it as the pipeline blockage area.

[0034] Exemplarily, during the monitoring of industrial transmission pipelines, the pipeline is fully scanned at a speed of 1 meter per second according to a predetermined scanning path, and a thermal imaging image of a pipeline with a total length of 500 meters is obtained and saved. After converting the acquired thermal imaging image into a grayscale image, the temperature threshold is set to 70 degrees Celsius, and the areas with temperatures above 70 degrees Celsius are binarized and marked as temperature abnormality areas. Subsequently, the thermal imaging images of the corresponding pipelines in the past three years are obtained through the historical monitoring database of industrial transmission pipelines, and which temperature abnormality areas have caused pipeline blockages are marked. These annotated historical data are used to train convolutional neural networks to construct a pipeline blockage recognition model. When an area with a temperature above 70 degrees Celsius is detected in the newly acquired pipeline thermal imaging image, the model is used for analysis, and one of the temperature abnormality areas is successfully identified, located 150 meters from the starting point of the pipeline, and marked as a pipeline blockage area.

[0035] Step S102, a thermal imaging characteristic index acquisition module is used to extract the maximum temperature, average temperature gradient, maximum temperature difference and gradient direction standard deviation of the thermal imaging image of the pipeline blockage area, and form the thermal imaging characteristic index of the pipeline blockage area.

[0036] According to the thermal imaging image of the pipeline blockage area, the temperature value of each pixel in the thermal imaging image is extracted to obtain the temperature distribution data of each pixel. According to the temperature value in the thermal imaging image, the central difference method is used to calculate the temperature change rate between adjacent pixels to determine the average temperature gradient value of the pipeline blockage area. According to the temperature value in the thermal imaging image, the difference between the highest temperature and the lowest temperature is calculated to obtain the maximum temperature difference in the pipeline blockage area. The Sobel operator is used to calculate the gradient direction of each pixel and determine the standard deviation of the gradient direction. The highest temperature, average temperature gradient, maximum temperature difference and standard deviation of the gradient direction are standardized and combined into a feature vector to constitute the thermal imaging feature index of the pipeline blockage area.

[0037] For example, in the process of monitoring industrial transmission pipelines, a thermal imaging image of the pipeline blockage area is extracted, and the temperature value of each pixel is extracted from it, and a thermal imaging image with a resolution of 200x200 pixels is generated. By analyzing the temperature distribution of each pixel in the image, it is obtained that the temperature range of the entire area is between 60 degrees Celsius and 120 degrees Celsius, and then the temperature change rate between adjacent pixels is calculated by the central difference method, and the average temperature gradient of the pipeline blockage area is determined to be 10 degrees Celsius per pixel. The highest temperature in the area is 120 degrees Celsius and the lowest temperature is 60 degrees Celsius, so the maximum temperature difference in the area is 60 degrees Celsius. The Sobel operator is used to calculate the gradient direction of each pixel in the thermal imaging image, and the standard deviation of the gradient direction is determined to be 15 degrees, indicating that there is a certain volatility in the change direction of different temperature gradients. The highest temperature of 120 degrees Celsius, the average temperature gradient of 10 degrees Celsius, the maximum temperature difference of 60 degrees Celsius and the standard deviation of the gradient direction of 15 degrees are standardized and combined into a feature vector to describe the thermal imaging feature index of the pipeline blockage area.

[0038] Step S103, a blockage degree assessment module is used to construct a pipeline blockage degree assessment model based on the thermal imaging characteristic indicators of the historical blockage areas of the industrial transmission pipeline, assess the blockage degree of the pipeline blockage areas of the industrial transmission pipeline, and formulate blockage risk prevention measures for the pipeline blockage areas with a blockage degree higher than a preset degree threshold.

[0039] Through the historical monitoring database of industrial transmission pipelines, the thermal imaging characteristic indicators, blockage degree, pipe diameter, material and location of the historical blockage area of ​​the industrial transmission pipeline are obtained, and the recurrent neural network is used for model training to build a pipeline blockage degree assessment model. The materials of the pipeline include steel, stainless steel and PVC, and the locations of the pipeline include the main transmission pipeline, pipeline elbow, pipeline valve, pipeline branch point, pipeline connection point and height change point. According to the thermal imaging characteristic indicators of the pipeline blockage area obtained in real time, combined with the pipe diameter, material and location of the pipeline, the pipeline blockage degree assessment model is used to assess the blockage degree of the pipeline blockage area of ​​the current industrial transmission pipeline. For pipeline blockage areas with a blockage degree higher than the preset degree threshold, blockage risk prevention measures are formulated and implemented, including cleaning up debris inside the pipeline, increasing the monitoring frequency, regularly conducting thermal imaging inspections, and optimizing the transmission pressure or flow rate of the pipeline.

[0040] For example, in the process of monitoring industrial transmission pipelines, the thermal imaging characteristic indicators, blockage degree, pipe diameter, material and location data of 20 different pipeline blockage areas in the past five years were obtained through the historical monitoring database, where the pipe diameter ranged from 15 to 50 cm, the materials included steel, stainless steel and PVC, etc., and the locations covered the main transmission pipeline, pipeline elbows, pipeline valves, pipeline branch points, pipeline connection points and height change points, etc. By inputting these data into the recurrent neural network, the training of the pipeline blockage degree assessment model was completed, and the pipeline blockage degree assessment model was constructed. During real-time monitoring, thermal imaging characteristic indicators of a main transmission pipeline were obtained, with a maximum temperature of 85 degrees Celsius, an average temperature gradient of 12 degrees Celsius, a maximum temperature difference of 45 degrees Celsius, and a standard deviation of 18 degrees in the gradient direction. Combined with the fact that the pipeline is made of stainless steel, has a diameter of 30 centimeters, and is located at a special location at a pipe elbow, these data were input into a trained pipeline blockage assessment model, and the assessment showed that the blockage level in the blocked area of ​​the pipeline was 35%, which was higher than the preset threshold of 30%. It was decided to implement blockage risk prevention measures, including cleaning up debris inside the pipeline, increasing the monitoring frequency to once a week, conducting regular thermal imaging inspections, and appropriately adjusting the pipeline's transmission pressure to alleviate the blockage.

[0041] Step S104, a pipeline blockage prediction module is used for the pipeline blockage prediction module to determine the heat diffusion speed of the pipeline blockage area at different time periods according to the temperature value of the pipeline blockage area at different times, and to construct a thermal imaging characteristic index prediction model to predict the time point when the blockage degree of the pipeline blockage area where the heat diffusion speed is greater than the preset speed threshold is higher than the preset degree threshold, and to implement blockage risk prevention measures in advance.

[0042] By adopting the method of regular sampling, the thermal imaging images of the pipeline blockage area with a blockage degree lower than the preset threshold are obtained at preset time intervals, the temperature distribution data at each moment is extracted, and the temperature value of the pipeline blockage area at each moment is determined. According to the temperature values ​​of the pipeline blockage area at different times, the heat diffusion rate calculation formula is used Calculate the heat diffusion velocity v in the pipeline blockage area at different time periods d , where T i and T j t i Time and t jThe temperature value at the moment, α is the thermal diffusion coefficient, which is determined based on the material properties of the pipeline. If the thermal diffusion rate of the pipeline blockage area in a certain period of time is greater than the preset speed threshold, the historical thermal imaging characteristic indicators of the pipeline blockage area are obtained through the historical monitoring database of the industrial transmission pipeline and organized into time series data. According to the time series data of the historical thermal imaging characteristic indicators, the ARIMA algorithm is used for model training to build a thermal imaging characteristic indicator prediction model to predict the thermal imaging characteristic indicators in the future preset time period, and combined with the pipeline blockage degree assessment model, it is determined that the time point when the blockage degree of the pipeline blockage area is higher than the preset degree threshold, and blockage risk prevention measures are implemented in advance for the pipeline blockage area.

[0043] For example, in the process of monitoring an industrial transmission pipeline, a thermal imaging image of a pipeline blockage area with a blockage degree of 10% is obtained at a preset time interval every half an hour. At each sampling, the temperature distribution data of the pipeline blockage area is extracted, including: i = 10:00, the temperature T in the blocked area of ​​the pipe i is 80℃, and at time t j =11:00, temperature T j Rising to 95°C. Combined with the thermal diffusion coefficient of stainless steel of the pipe material α = 0.02, the thermal diffusion rate calculation formula is used Calculate the heat diffusion rate v d The thermal diffusion rate in this period is 0.3℃ / minute. By comparing the preset speed threshold of 0.2℃ / minute, it is determined that the thermal diffusion rate in this period is greater than the preset threshold. Therefore, the thermal imaging characteristic indicators of the pipeline blockage area in the past six months are extracted from the historical monitoring database of industrial transmission pipelines and organized into time series data. By inputting these historical data into the ARIMA model for training, the thermal imaging characteristic indicators of the pipeline area in the next two hours are predicted. Combined with the pipeline blockage degree assessment model, it is judged that the pipeline blockage degree may reach a severity of 30% in the next two hours. Therefore, blockage risk prevention measures such as cleaning the pipeline, adjusting the transmission pressure and increasing the monitoring frequency are taken in advance.

[0044] Step S105, a pipeline blockage area migration prediction module is used to determine whether the pipeline blockage area has migrated according to the blockage position of the pipeline blockage area, and based on the migration data of the pipeline blockage area, predict the blockage degree when the position of the pipeline where the pipeline blockage area is located changes, and implement blockage migration risk prevention measures for pipeline blockage areas whose blockage degree is higher than a preset degree threshold.

[0045] The blockage position of the pipeline blockage area is determined based on the position with the highest temperature value in the thermal imaging image of the pipeline blockage area. The historical blockage position data of the pipeline blockage area is obtained through the historical monitoring database of industrial transmission pipelines, and combined with the current blockage position of the pipeline blockage area, it is determined whether the pipeline blockage area has migrated. If the pipeline blockage area has migrated, the migration data of the pipeline blockage area is obtained through the historical monitoring database of the industrial transmission pipeline, including the migration distance, migration speed and migration direction, and the ARIMA algorithm is used to predict the position of the pipeline where the pipeline blockage area is located in the future time period. If it is predicted that the position of the pipeline where the pipeline blockage area is located will change in the future time period, then the thermal imaging feature index prediction model is used to predict the thermal imaging feature index when the position of the pipeline where the pipeline blockage area is located changes. According to the blockage migration prediction formula Predict the degree of blockage when the position of the pipeline where the pipeline is blocked changes, where S(t) is the degree of blockage at time t, S 1 is the degree of congestion before migration, D 1 and D 2 are the diameters of the pipeline before and after migration, respectively, and k is the coefficient of the effect of the change in pipe diameter on the degree of blockage, obtained by fitting historical data, M 1 and M 2 are the material parameters of the pipeline before and after migration, which are defined according to the friction coefficient and corrosion resistance of the material. l is the influence coefficient of material change on the degree of blockage, which is obtained by fitting historical data. F z is an influencing factor related to the pipeline position z, obtained by fitting historical data, and m represents the number of pipeline positions passed during the migration process. For pipeline blockage areas with blockage levels higher than the preset threshold, preventive measures for blockage migration risks are implemented, including optimizing the interval time of fluid delivery, using pipeline adjustment devices to guide blockages to migrate in a specified direction, and optimizing flow rate by adjusting the power of the pump or the opening and closing degree of the valve.

[0046] For example, during the monitoring of industrial transmission pipelines, the location of the blockage of a certain section of the pipeline is determined to be 120 meters away from the starting point of the main transmission pipeline through the position of the highest temperature value in the thermal imaging image. According to the historical monitoring database of industrial transmission pipelines, it is found that the blockage area has migrated from the straight section of the main transmission pipeline 100 meters away from the starting point to the pipeline elbow position 120 meters away from the starting point. It is calculated that the migration distance of the blockage is 20 meters, the migration speed is 5 meters per hour, and the migration direction has changed along the pipeline transportation direction. Using the ARIMA algorithm to predict, the blockage may continue to migrate to 140 meters in the next 2 hours and enter a pipeline branch point. Since the position of the pipeline has migrated from the straight section of the main transmission pipeline to the pipeline elbow, and may then migrate to the pipeline branch point, the risk of blockage needs to be re-evaluated. After the migration, the diameter of the pipeline is reduced from 30 cm to 20 cm at the pipeline elbow. At the same time, the pipeline material is changed from stainless steel with a material friction coefficient of 0.05 to PVC with a material friction coefficient of 0.1. If the degree of blockage before migration S 1 The influence coefficient of pipe diameter change on the degree of blockage is k = 0.8, and the influence coefficient of material change on the degree of blockage is l = 0.5. When the blockage migrates from the main pipeline to the pipeline elbow, it passes through two key positions, namely the pipeline valve, and the influence factor F 1 =1.1, and the pipeline branch point, the influence factor F 2 =1.05, using the blocking migration prediction formula Predict the degree of blockage when the position of the pipeline where the pipeline is blocked changes, where S(t) is the degree of blockage at time t, S 1 is the degree of congestion before migration, D 1 and D 2 are the diameters of the pipeline before and after migration, respectively, and k is the coefficient of the effect of the change in pipe diameter on the degree of blockage, obtained by fitting historical data, M 1 and M 2 are the material parameters of the pipeline before and after migration, which are defined according to the friction coefficient and corrosion resistance of the material. l is the influence coefficient of material change on the degree of blockage, which is obtained by fitting historical data. F z It is the influencing factor related to the pipeline position z, such as the main transmission pipeline, pipeline elbow, pipeline valve, pipeline branch point, pipeline connection point and height change point, etc. It is obtained by fitting historical data. The influencing factor F of the pipeline position ZIt will be adjusted as the position of the pipeline changes, and m represents the number of pipeline positions passed during the migration process. The predicted blockage degree when the position of the pipeline where the pipeline blockage area is located changes is 70.14%. This shows that the blockage after migration is very serious, so it is necessary to immediately implement blockage risk prevention measures, including adjusting the power of the pump, optimizing the interval time of fluid delivery, and using pipeline adjustment devices to guide the blockage to migrate to a safe position. At the same time, increase the monitoring frequency of pipeline elbows and branch points to prevent further blockage.

[0047] Step S106, a pipeline blockage area aggregation prediction module is used to predict the aggregation position and aggregation time of the pipeline blockage area according to the migration speed and migration direction of each pipeline blockage area, and implement blockage migration risk prevention measures for the aggregation area of ​​the pipeline blockage area in advance.

[0048] If there are several pipeline blockage areas in the industrial transmission pipeline with a blockage degree lower than the preset degree threshold and they migrate, the blockage degree when the position of the pipeline where the pipeline blockage area is located changes is predicted according to the blockage migration prediction formula. If the predicted blockage degree when the position of the pipeline where the pipeline blockage area is located changes is lower than the preset degree threshold, the blockage location data of the pipeline blockage area is obtained through the historical monitoring database of the industrial transmission pipeline. Based on the blockage location data of the pipeline blockage area, the ARIMA algorithm is used to predict the clustering location and clustering time of the pipeline blockage area. Based on the predicted clustering location and clustering time of the pipeline blockage area, blockage migration risk prevention measures are implemented in advance for the clustering areas of the pipeline blockage area.

[0049] For example, during the monitoring of industrial transmission pipelines, it was found that the blockage levels of multiple blocked areas in the pipeline were all lower than the preset 30% threshold, and these blocked areas were located at 150 meters, 200 meters and 250 meters from the starting point of the pipeline. Through the blockage migration prediction formula analysis, it is predicted that these low-blockage areas will migrate to the pipeline branch point 250 meters away from the starting point within the next 3 hours. Since the blockage level of these blocked areas is still lower than the preset threshold of 30% after migration, the historical blockage location data of these blocked areas are obtained through the historical monitoring database of industrial transmission pipelines. Combined with the data of the past few months, it is observed that the blockage gradually migrates in the pipeline at a speed of 5 meters per hour. The ARIMA algorithm is used to analyze and model the historical location data of the blocked areas, and it is predicted that in the next 5 hours, two of these low-blockage areas will gather at the pipeline branch point 300 meters away from the starting point of the pipeline, and the gathering time will be about 5 hours later. Based on this prediction result, preventive measures for blockage migration risk were immediately implemented at the branch points of the pipeline, including slowing down the migration speed of blockages by adjusting the fluid flow rate and installing temporary adjustment devices at the branch points of the pipeline to guide the blockages to migrate to safe areas outside the main pipeline. At the same time, the frequency of real-time monitoring of the area was increased to prevent serious blockages caused by the accumulation of blockages.

[0050] Step S107, a preventive measures optimization module is used to continuously monitor the thermal imaging images of the industrial transmission pipeline, evaluate the implementation effects of the blockage risk prevention measures and the blockage migration risk prevention measures, and continuously optimize the preventive measures.

[0051] Industrial transmission pipelines are continuously monitored through infrared thermal imaging technology, and thermal imaging images and corresponding temperature data of industrial transmission pipelines are regularly obtained. The effectiveness of preventive measures is evaluated by comparing the thermal imaging images of pipelines before and after the implementation of blockage risk prevention measures and blockage migration risk prevention measures. If the degree of blockage is alleviated and the temperature distribution in the blocked area returns to normal, the blockage risk prevention measures and blockage migration risk prevention measures are judged to be effective. If the degree of blockage is not alleviated, the blockage risk prevention measures and blockage migration risk prevention measures are optimized, including increasing the frequency of pipeline cleaning, adjusting the transmission pressure or changing the flow rate, or changing the cleaning method of the blocked area. Implement the optimized blockage risk prevention measures and blockage migration risk prevention measures, evaluate the effectiveness of the new preventive measures, and continuously optimize the blockage risk prevention measures and blockage migration risk prevention measures based on the effectiveness evaluation results of the preventive measures.

[0052] For example, when continuously monitoring an industrial transmission pipeline, infrared thermal imaging technology is used to obtain thermal imaging images and temperature data every hour. After analysis, it was found that the degree of blockage in a blocked area in the pipeline was 50%, and the temperature in the area was higher than the normal temperature, reaching 85°C. Based on this situation, blockage risk prevention measures were implemented, including adjusting the flow rate and clearing some blockages, and comparing the pipeline conditions before and after the implementation of the preventive measures through thermal imaging images. Within 2 hours after the implementation of the preventive measures, the temperature in the blocked area dropped to the normal level of 55°C, and the degree of blockage was reduced to 20%. By comparing the thermal imaging images and the changes in the degree of blockage, it is judged that the blockage risk prevention measures are effective, the temperature distribution in the blocked area has returned to normal, and the risk of blockage has been significantly reduced. In another blocked area, after the implementation of preventive measures, the situation in the blocked area did not improve, the degree of blockage remained at 50%, and the temperature in the area was still high. Based on this, it was decided to optimize the current blockage risk prevention measures and blockage migration risk prevention measures, including increasing the pipeline cleaning frequency from once a week to once every 3 days, adjusting the pipeline transmission pressure from 10MPa to 8MPa, and increasing the flow rate from 1.2 m / s to 1.5 m / s to facilitate further evacuation of blockages. In addition, the method of cleaning the blocked area was changed, and a combination of mechanical cleaning and chemical cleaning was adopted. After implementing the optimized preventive measures, continuous monitoring for 2 hours found that the temperature in the blocked area dropped to the normal value of 70°C, and the degree of blockage dropped to 10%. Through this result, it was confirmed that the optimized blockage risk prevention measures were effective, and it was decided to continue to adjust the cleaning frequency and flow rate according to actual conditions in the next monitoring cycle, further optimize the preventive measures, and ensure the long-term safe operation of the pipeline.

[0053] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the concept of the present application. For example, the above features are replaced with the technical features with similar functions disclosed in the present application (but not limited to) to form a technical solution.

Claims

1. An industrial equipment fault monitoring and diagnosis system based on infrared thermal imaging, characterized in that: The system comprises: The pipeline blockage area recognition module is used to obtain thermal imaging images of industrial transmission pipelines, identify the temperature abnormality area where the pipeline is blocked, and mark it as the pipeline blockage area; The thermal imaging characteristic index acquisition module is used to extract the maximum temperature, average temperature gradient, maximum temperature difference and gradient direction standard deviation of the thermal imaging image of the pipeline blockage area, and form the thermal imaging characteristic index of the pipeline blockage area; The blockage degree assessment module is used to construct a pipeline blockage degree assessment model based on the thermal imaging characteristic indicators of the historical blockage areas of the industrial transmission pipeline, assess the blockage degree of the pipeline blockage area of ​​the industrial transmission pipeline, and formulate blockage risk prevention measures for the pipeline blockage area with a blockage degree higher than a preset degree threshold; The pipeline blockage prediction module is used to determine the heat diffusion speed of the pipeline blockage area at different times according to the temperature value of the pipeline blockage area at different times, and to build a thermal imaging characteristic index prediction model to predict the time point when the blockage degree of the pipeline blockage area with a heat diffusion speed greater than a preset speed threshold is higher than a preset degree threshold, so as to implement blockage risk prevention measures in advance; The pipeline blockage area migration prediction module is used to determine whether the pipeline blockage area has migrated according to the blockage position of the pipeline blockage area, and based on the migration data of the pipeline blockage area, predict the blockage degree when the position of the pipeline where the pipeline blockage area is located changes, and implement blockage migration risk prevention measures for pipeline blockage areas whose blockage degree is higher than a preset degree threshold; The pipeline blockage area aggregation prediction module is used to predict the aggregation location and aggregation time of the pipeline blockage area according to the migration speed and migration direction of each pipeline blockage area, and implement blockage migration risk prevention measures for the aggregation area of ​​the pipeline blockage area in advance; The preventive measures optimization module is used to continuously monitor thermal imaging images of industrial transmission pipelines, evaluate the implementation effect of blockage risk prevention measures and blockage migration risk prevention measures, and continuously optimize preventive measures.

2. The system according to claim 1, wherein: The pipeline blockage area identification module is used to obtain the thermal imaging image of the industrial transmission pipeline, identify the temperature abnormality area where the pipeline blockage occurs, and mark it as the pipeline blockage area, including: According to the predetermined scanning path and speed, the industrial transmission pipeline is comprehensively scanned to obtain and save the thermal imaging image of the industrial transmission pipeline; the thermal imaging image of the industrial transmission pipeline is converted into a grayscale image, and the temperature threshold is set as the segmentation standard, the grayscale image is binarized, and the image area with a temperature higher than the set temperature threshold is marked as a temperature abnormality area; through the historical monitoring database of the industrial transmission pipeline, the thermal imaging images of the historical temperature abnormality areas of the industrial transmission pipeline are obtained, and it is marked whether the pipeline is blocked, and the convolutional neural network is used for model training to build a pipeline blockage recognition model; according to the newly acquired thermal imaging images of the temperature abnormality area, the pipeline blockage recognition model is used to identify the temperature abnormality area where the pipeline is blocked and mark it as the pipeline blockage area.

3. The system according to claim 1, wherein: The thermal imaging characteristic index acquisition module is used to extract the maximum temperature, average temperature gradient, maximum temperature difference and gradient direction standard deviation of the thermal imaging image of the pipeline blockage area, and form the thermal imaging characteristic index of the pipeline blockage area, including: According to the thermal imaging image of the pipeline blockage area, the temperature value of each pixel in the thermal imaging image is extracted to obtain the temperature distribution data of each pixel; according to the temperature value in the thermal imaging image, the central difference method is used to calculate the temperature change rate between adjacent pixels to determine the average temperature gradient value of the pipeline blockage area; according to the temperature value in the thermal imaging image, the difference between the highest temperature and the lowest temperature is calculated to obtain the maximum temperature difference in the pipeline blockage area; the Sobel operator is used to calculate the gradient direction of each pixel to determine the standard deviation of the gradient direction; the highest temperature, average temperature gradient, maximum temperature difference and gradient direction standard deviation are standardized and combined into a feature vector to constitute the thermal imaging feature index of the pipeline blockage area.

4. The system according to claim 1, wherein: The blockage degree assessment module is used to construct a pipeline blockage degree assessment model based on the thermal imaging characteristic indicators of the historical blockage area of ​​the industrial transmission pipeline, assess the blockage degree of the pipeline blockage area of ​​the industrial transmission pipeline, and formulate blockage risk prevention measures for the pipeline blockage area with a blockage degree higher than a preset degree threshold, including: Through the historical monitoring database of industrial transmission pipelines, the thermal imaging characteristic indicators, blockage degree, pipe diameter, material and location of the historical blockage areas of the industrial transmission pipelines are obtained, and the recurrent neural network is used for model training to build a pipeline blockage degree assessment model. The materials of the pipeline include steel, stainless steel and PVC, and the locations of the pipeline include main transmission pipelines, pipeline elbows, pipeline valves, pipeline branch points, pipeline connection points and height change points; based on the thermal imaging characteristic indicators of the pipeline blockage area obtained in real time, combined with the pipe diameter, material and location of the pipeline, the pipeline blockage degree assessment model is used to assess the blockage degree of the pipeline blockage area of ​​the current industrial transmission pipeline; for pipeline blockage areas with a blockage degree higher than the preset threshold, blockage risk prevention measures are formulated and implemented, including cleaning up debris inside the pipeline, increasing the monitoring frequency, regularly conducting thermal imaging inspections, and optimizing the transmission pressure or flow rate of the pipeline.

5. The system according to claim 1, wherein: The pipeline blockage prediction module is used to determine the heat diffusion speed of the pipeline blockage area at different time periods according to the temperature value of the pipeline blockage area at different times, and to build a thermal imaging characteristic index prediction model to predict the time point when the blockage degree of the pipeline blockage area with a heat diffusion speed greater than a preset speed threshold is higher than a preset degree threshold, and to implement blockage risk prevention measures in advance, including: By adopting the method of regular sampling, the thermal imaging images of the pipeline blockage area with a blockage degree lower than the preset threshold are obtained at preset time intervals, the temperature distribution data at each moment is extracted, and the temperature value of the pipeline blockage area at each moment is determined; according to the temperature values ​​of the pipeline blockage area at different moments, the heat diffusion rate calculation formula is used to calculate the heat diffusion rate. Calculate the heat diffusion rate at different time periods in the pipeline blockage area, where T i and T j t i Time and t j The temperature value at the moment, α is the thermal diffusion coefficient, which is determined based on the material properties of the pipeline; if the thermal diffusion rate of the pipeline blocked area in a certain period of time is greater than the preset speed threshold, the historical thermal imaging characteristic indicators of the pipeline blocked area are obtained through the historical monitoring database of the industrial transmission pipeline, and are sorted into time series data; according to the time series data of the historical thermal imaging characteristic indicators, the ARIMA algorithm is used for model training, and a thermal imaging characteristic indicator prediction model is constructed to predict the thermal imaging characteristic indicators in the future preset time period, and combined with the pipeline blockage degree assessment model, the time point when the blockage degree of the pipeline blocked area is higher than the preset degree threshold is determined, and blockage risk prevention measures are implemented in advance for the pipeline blocked area.

6. The system according to claim 1, wherein: The pipeline blockage area migration prediction module is used to determine whether the pipeline blockage area has migrated according to the blockage position of the pipeline blockage area, and based on the migration data of the pipeline blockage area, predict the blockage degree when the position of the pipeline where the pipeline blockage area is located changes, and implement blockage migration risk prevention measures for the pipeline blockage area whose blockage degree is higher than the preset degree threshold, including: The blockage position of the pipeline blockage area is determined according to the position with the highest temperature value in the thermal imaging image of the pipeline blockage area; the historical blockage position data of the pipeline blockage area is obtained through the historical monitoring database of the industrial transmission pipeline, and combined with the current blockage position of the pipeline blockage area, it is determined whether the pipeline blockage area has migrated; if the pipeline blockage area has migrated, the migration data of the pipeline blockage area is obtained through the historical monitoring database of the industrial transmission pipeline, including the migration distance, migration speed and migration direction, and the position of the pipeline where the pipeline blockage area is located in the future time period is predicted using the ARIMA algorithm; if it is predicted that the position of the pipeline where the pipeline blockage area is located changes in the future time period, the thermal imaging feature index prediction model is used to predict the thermal imaging feature index when the position of the pipeline where the pipeline blockage area is located changes; according to the blockage migration prediction formula Predict the degree of blockage when the position of the pipeline where the pipeline blockage area is located changes, where S(t) is the blockage degree at time t, S1 is the blockage degree before migration, D1 and D2 are the diameters of the pipeline before and after migration, respectively, k is the coefficient of the influence of the pipe diameter change on the blockage degree, obtained by fitting historical data, M1 and M2 are the material parameters of the pipeline before and after migration, defined according to the friction coefficient and corrosion resistance characteristics of the material, l is the influence coefficient of the material change on the blockage degree, obtained by fitting historical data, and F z It is an influencing factor related to the pipeline position z and is obtained by fitting historical data. m represents the number of pipeline positions passed during the migration process. For pipeline blockage areas with a blockage degree higher than the preset threshold, blockage migration risk prevention measures are implemented, including optimizing the interval time of fluid delivery, using pipeline regulating devices to guide the blockage to migrate in a specified direction, and optimizing the flow rate by adjusting the power of the pump or the opening and closing degree of the valve.

7. The system according to claim 1, wherein: The pipeline blockage area aggregation prediction module is used to predict the aggregation position and aggregation time of the pipeline blockage area according to the migration speed and migration direction of each pipeline blockage area, and implement blockage migration risk prevention measures for the aggregation area of ​​the pipeline blockage area in advance, including: If there are several pipeline blockage areas in the industrial transmission pipeline with a blockage degree lower than a preset threshold and they migrate, the blockage degree when the position of the pipeline where the pipeline blockage area is located changes is predicted according to the blockage migration prediction formula; if the predicted blockage degree when the position of the pipeline where the pipeline blockage area is located changes is lower than the preset threshold, the blockage position data of the pipeline blockage area is obtained through the historical monitoring database of the industrial transmission pipeline; based on the blockage position data of the pipeline blockage area, the ARIMA algorithm is used to predict the clustering position and clustering time of the pipeline blockage area; based on the predicted clustering position and clustering time of the pipeline blockage area, blockage migration risk prevention measures are implemented in advance for the clustering areas of the pipeline blockage area.

8. The system according to claim 1, wherein: The preventive measures optimization module is used to continuously monitor the thermal imaging images of the industrial transmission pipeline, evaluate the implementation effect of the blockage risk prevention measures and the blockage migration risk prevention measures, and continuously optimize the preventive measures, including: Continuously monitor industrial transmission pipelines through infrared thermal imaging technology, and regularly obtain thermal imaging images and corresponding temperature data of industrial transmission pipelines; evaluate the effectiveness of preventive measures by comparing pipeline thermal imaging images before and after the implementation of blockage risk prevention measures and blockage migration risk prevention measures; if the blockage degree is alleviated and the temperature distribution in the blocked area returns to normal, the blockage risk prevention measures and blockage migration risk prevention measures are judged to be effective; if the blockage degree is not alleviated, optimize the blockage risk prevention measures and blockage migration risk prevention measures, including increasing the frequency of pipeline cleaning, adjusting the transmission pressure or changing the flow rate, or changing the cleaning method of the blocked area; implement the optimized blockage risk prevention measures and blockage migration risk prevention measures, evaluate the effectiveness of the new preventive measures, and continuously optimize the blockage risk prevention measures and blockage migration risk prevention measures based on the effectiveness evaluation results of the preventive measures.

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