A machine vision-based waste engine oil treatment monitoring method and system

Abstract

The present application is suitable for waste engine oil processing technical field, provide a kind of based on machine vision's waste engine oil processing monitoring method and system, the method includes: when receiving waste engine oil in processing container, determine the target viscosity characteristic of waste engine oil, and obtain the initial heat treatment parameters set for it, while, obtain the historical processing record of processing container, and determine the predetermined cleaning cycle of processing container.The present application fully considers the influence of the gradually intensified contamination degree of processing container inner wall in cleaning cycle progress on the adhesion of waste engine oil, and the influence of the contamination degree change of waste engine oil on heat treatment process.The system dynamically adjusts processing parameters by real-time monitoring and analyzing the contamination condition of processing container inner wall and the contamination degree of waste engine oil, to ensure that waste engine oil processing process can adapt to these changes.

Description

Technical Field

[0001] This invention belongs to the field of waste oil treatment technology, and particularly relates to a waste oil treatment monitoring method and system based on machine vision. Background Technology

[0002] In existing waste oil treatment technologies, the treatment of waste oil typically relies on static treatment parameter settings. These parameters, such as temperature, pressure, and time, are usually fixed and cannot be adjusted in real time according to the actual characteristics of the waste oil or the degree of contamination of the treatment container. Traditional methods often use uniform treatment parameters, and the treatment process is usually carried out under preset conditions regardless of the degree of contamination of the waste oil. While this "uniform treatment" approach can ensure the treatment of waste oil to a certain extent, it often ignores changes in the degree of contamination of the waste oil and the contamination status of the inner wall of the treatment container, resulting in insufficient precision and efficiency in the treatment effect.

[0003] Especially during waste engine oil treatment, the contamination of the inner wall of the treatment container gradually worsens with each cleaning cycle, and the viscosity of the waste engine oil also increases. However, existing technologies have failed to effectively address this change; the heat treatment parameters remain constant throughout the process, unable to adjust accordingly to the changes in the viscosity of the waste engine oil. Increased inner wall contamination not only affects the treatment efficiency of waste engine oil but may also lead to incomplete treatment and even damage to equipment performance. Furthermore, changes in the degree of contamination of the waste engine oil significantly impact the treatment effect; existing methods cannot dynamically adjust treatment parameters based on the specific contamination status of the waste engine oil, resulting in energy waste and unstable treatment quality.

[0004] Therefore, traditional waste oil treatment technologies suffer from low processing efficiency, high energy consumption, severe equipment wear, and unstable treatment results. They fail to adequately consider the varying viscosity and contamination levels of waste oil, resulting in an inflexible treatment process that cannot be optimized for different conditions. These problems have accumulated into technical deficiencies in waste oil treatment, necessitating a novel method that can monitor and dynamically adjust treatment parameters in real time to adapt to changes in the contamination levels of waste oil and the inner walls of the treatment container. This would improve the efficiency and quality of waste oil treatment and extend the service life of the equipment. Summary of the Invention

[0005] The purpose of this invention is to provide a machine vision-based method and system for monitoring and treating waste oil, aiming to solve the problems mentioned in the background art.

[0006] This invention is implemented as follows: a machine vision-based method for monitoring waste oil treatment, the method comprising:

[0007] When the waste oil is received in the processing container, the target viscosity characteristics of the waste oil are determined, and the initial heat treatment parameters set for it are obtained. At the same time, the historical processing records of the processing container are obtained, and the predetermined cleaning cycle of the processing container is determined.

[0008] The system intelligently analyzes historical processing records, filters out sub-processing records that match the target viscosity characteristics of waste engine oil, and determines the target cleaning cycle that contains the most of these sub-processing records.

[0009] Based on machine vision technology, the degree of adhesion of waste oil on the inner wall of the processing container in each sub-processing record within the target cleaning cycle is analyzed. Based on these degrees of adhesion, an adhesion curve that changes with time is generated, and the average slope of the adhesion curve is calculated and used as the first optimization coefficient.

[0010] The current contamination level of waste engine oil is evaluated based on machine vision technology, and the standard contamination level threshold corresponding to the waste engine oil with the target viscosity characteristics is determined. The deviation between the standard contamination level threshold and the current contamination level is used as the second optimization coefficient.

[0011] The initial heat treatment parameters are dynamically adjusted based on the first and second optimization coefficients.

[0012] As a further limitation of the technical solution of this embodiment of the invention, the target cleaning cycle should only include relevant processing records of waste oil that meets the target viscosity characteristics.

[0013] As a further limitation of the technical solution of this invention embodiment, the steps of analyzing the degree of adhesion of waste oil on the inner wall of the processing container in each sub-processing record within the target cleaning cycle based on machine vision technology, generating an adhesion degree curve that changes with the progress of time based on these waste oil adhesion degree values, calculating the average slope of the adhesion degree curve, and using it as the first optimization coefficient include:

[0014] Based on historical processing records, obtain images of the inner wall of the processing container corresponding to each sub-processing record within the target cleaning cycle, and use machine vision technology to intelligently analyze each inner wall image to determine the degree of waste oil adhesion in the corresponding sub-processing record.

[0015] Based on the timestamp of each inner wall image and the corresponding waste oil adhesion value, an adhesion curve that changes over time is generated.

[0016] Calculate the average slope of the adhesion curve and use it as the first optimization coefficient.

[0017] As a further limitation of the technical solution of this invention, the step of evaluating the current contamination level of waste engine oil based on machine vision technology, determining the standard contamination level threshold corresponding to the waste engine oil with the target viscosity characteristics, and using the deviation between the standard contamination level threshold and the current contamination level value as the second optimization coefficient includes:

[0018] Using machine vision technology to assess the current level of contamination in waste engine oil;

[0019] Retrieve the preset reference model, extract the standard contamination threshold corresponding to the target viscosity characteristics, calculate the deviation between the current contamination value and the standard contamination threshold, and use the deviation as the second optimization coefficient;

[0020] The reference model refers to a pre-set standard contamination assessment model established based on historical data and processing experience of different waste oil viscosity characteristics and contamination levels. This reference model includes contamination level thresholds that allow waste oil with different viscosity characteristics to be effectively accepted by the treatment container.

[0021] As a further limitation of the technical solution of this invention, the step of dynamically adjusting the initial heat treatment parameters based on the first optimization coefficient and the second optimization coefficient includes:

[0022] The preset parameter optimization formula is retrieved, and the initial heat treatment parameters are dynamically adjusted in combination with the first optimization coefficient and the second optimization coefficient to obtain the optimized heat treatment parameters.

[0023] The optimized heat treatment parameters are applied to the process of treating the current waste oil in the treatment vessel.

[0024] As a further limitation of the technical solution of this embodiment of the invention, the parameter optimization formula is as follows: T optimized This refers to the initial heat treatment parameters, T. initial This refers to the optimized heat treatment parameters. S refers to the average slope of the adhesion curve, i.e., the first optimization coefficient. W1 refers to the corresponding adjustment weight of the first optimization coefficient. W2 refers to the deviation between the current pollution level and the standard pollution level threshold, i.e., the second optimization coefficient. W2 refers to the corresponding adjustment weight of the second optimization coefficient.

[0025] A machine vision-based waste oil treatment monitoring system, comprising: a data acquisition module, a target cleaning cycle determination module, a first optimization coefficient generation module, a second optimization coefficient generation module, and a heat treatment parameter optimization module, wherein:

[0026] The data acquisition module is used to determine the target viscosity characteristics of the waste oil when the processing container receives the waste oil, and to acquire the initial heat treatment parameters set for it. At the same time, it acquires the historical processing records of the processing container and determines the predetermined cleaning cycle of the processing container.

[0027] The target cleaning cycle determination module is used to intelligently analyze historical processing records, filter out sub-processing records that are consistent with the target viscosity characteristics of waste engine oil, and determine the target cleaning cycle that contains the most of these sub-processing records. The target cleaning cycle should only include relevant processing records that process waste engine oil that meets the target viscosity characteristics.

[0028] The first optimization coefficient generation module is used to analyze the degree of adhesion of waste oil on the inner wall of the processing container in each sub-processing record within the target cleaning cycle based on machine vision technology, and generate an adhesion degree curve that changes with the progress of time based on these waste oil adhesion degree values, calculate the average slope of the adhesion degree curve, and use it as the first optimization coefficient.

[0029] The second optimization coefficient generation module is used to evaluate the current contamination level of waste oil based on machine vision technology, determine the standard contamination level threshold corresponding to the waste oil with the target viscosity characteristics, and use the deviation between the standard contamination level and the current contamination level as the second optimization coefficient.

[0030] The heat treatment parameter optimization module is used to dynamically adjust the initial heat treatment parameters based on the first optimization coefficient and the second optimization coefficient.

[0031] As a further limitation of the technical solution of this embodiment of the invention, the steps of the first optimization coefficient generation module specifically include:

[0032] The adhesion degree value determination unit is used to obtain the inner wall image of the processing container corresponding to each sub-processing record within the target cleaning cycle based on historical processing records, and to use machine vision technology to intelligently analyze each inner wall image to determine the adhesion degree value of waste oil in the corresponding sub-processing record.

[0033] The adhesion degree curve generation unit is used to generate an adhesion degree curve that changes over time based on the timestamp of each inner wall image and the corresponding waste oil adhesion degree value.

[0034] The average slope calculation unit is used to calculate the average slope of the adhesion curve and use it as the first optimization coefficient.

[0035] As a further limitation of the technical solution of this embodiment of the invention, the steps of the second optimization coefficient generation module specifically include:

[0036] The current pollution level assessment unit is used to assess the current pollution level of waste engine oil using machine vision technology;

[0037] The deviation value calculation unit is used to retrieve a preset reference model, extract the standard contamination level threshold corresponding to the target viscosity characteristics, calculate the deviation value between the current contamination level value and the standard contamination level threshold, and use the deviation value as the second optimization coefficient.

[0038] The reference model refers to a pre-set standard contamination assessment model established based on historical data and processing experience of different waste oil viscosity characteristics and contamination levels. This reference model includes contamination level thresholds that allow waste oil with different viscosity characteristics to be effectively accepted by the treatment container.

[0039] As a further limitation of the technical solution of this embodiment of the invention, the heat treatment parameter optimization module specifically includes:

[0040] The heat treatment parameter optimization unit is used to retrieve the preset parameter optimization formula and dynamically adjust the initial heat treatment parameters in combination with the first optimization coefficient and the second optimization coefficient to obtain the optimized heat treatment parameters.

[0041] The parameter optimization application unit is used to apply the optimized heat treatment parameters to the process of treating the current waste oil in the treatment container.

[0042] The parameter optimization formula is as follows: T optimized This refers to the initial heat treatment parameters, T. initial This refers to the optimized heat treatment parameters. S refers to the average slope of the adhesion curve, i.e., the first optimization coefficient. W1 refers to the corresponding adjustment weight of the first optimization coefficient. W2 refers to the deviation between the current pollution level and the standard pollution level threshold, i.e., the second optimization coefficient. W2 refers to the corresponding adjustment weight of the second optimization coefficient.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] This invention fully considers the impact of the gradually increasing contamination level of the inner wall of the treatment container on the adhesion of waste engine oil during the cleaning cycle, as well as the impact of changes in the degree of waste engine oil contamination on the heat treatment process. The system dynamically adjusts treatment parameters by real-time monitoring and analysis of the contamination status of the inner wall of the treatment container and the degree of contamination of the waste engine oil, ensuring that the waste engine oil treatment process can adapt to these changes. Specifically, as the contamination of the inner wall gradually worsens and the adhesion of the waste engine oil increases, the system automatically optimizes the heat treatment parameters to prevent excessive inner wall contamination from affecting the waste engine oil treatment efficiency. Simultaneously, the system also adjusts the treatment parameters according to the degree of contamination of the waste engine oil to ensure that the treatment process is always in optimal condition.

[0045] This dynamic adjustment mechanism addresses the shortcomings of traditional methods that fail to adequately consider changes in internal wall contamination and waste oil pollution, avoiding a one-size-fits-all approach and improving processing efficiency and quality. Through this mechanism, the waste oil treatment process can be continuously optimized, reducing energy consumption and extending equipment lifespan. Furthermore, the synergistic effect between the treatment container and the waste oil is fully realized, resulting in more refined and efficient treatment, providing crucial technical support for the efficient and environmentally friendly treatment of waste oil. Attached Figure Description

[0046] Figure 1 A flowchart of the method provided in the embodiments of the present invention;

[0047] Figure 2 This is a flowchart illustrating the generation of the first optimization coefficient in the method provided in this embodiment of the invention;

[0048] Figure 3 This is a flowchart illustrating the generation of the second optimization coefficient in the method provided in this embodiment of the invention;

[0049] Figure 4 This is a flowchart illustrating the optimization and adjustment of initial heat treatment parameters in the method provided in this embodiment of the invention;

[0050] Figure 5 Application architecture diagram of the system provided in the embodiments of the present invention;

[0051] Figure 6 This is a structural block diagram of the first optimization coefficient generation module in the system provided in the embodiments of the present invention;

[0052] Figure 7 This is a structural block diagram of the second optimization coefficient generation module in the system provided in the embodiment of the present invention;

[0053] Figure 8 This is a structural block diagram of the heat treatment parameter optimization module in the system provided in the embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0055] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0056] Specifically, a machine vision-based method for monitoring waste oil treatment includes the following steps:

[0057] Step S100: When the processing container receives waste oil, determine the target viscosity characteristics of the waste oil and obtain the initial heat treatment parameters set for it. At the same time, obtain the historical processing records of the processing container and determine the predetermined cleaning cycle of the processing container.

[0058] In this embodiment of the invention, the processing container refers to equipment specifically designed for waste engine oil treatment, typically including various industrial devices capable of heating, filtering, and decomposing waste engine oil. These containers are designed to efficiently and safely treat waste engine oil, ensuring it meets standards before being recycled or further processed. The structure of the processing container typically includes an inner wall, a heating system, and a discharge system, ensuring uniform heating and treatment of the waste engine oil during the heat treatment process.

[0059] The target viscosity characteristics of waste engine oil can be determined using various methods, with common techniques including viscosity measurement and rheological analysis. By measuring the flowability of waste engine oil, its viscosity characteristics can be obtained, allowing for adjustments to the treatment process based on the specific viscosity requirements. For example, commonly used equipment such as rotational viscometers and capillary viscometers can help obtain viscosity data for waste engine oil. Furthermore, the viscosity characteristics of waste engine oil can be comprehensively assessed by combining them with component analysis, considering factors such as oil type and impurity content.

[0060] Initial heat treatment parameters refer to a set of starting conditions set during waste engine oil treatment. These conditions are crucial for subsequent treatment processes. Initial heat treatment parameters include heating temperature, heating time, and heating rate, which affect the thermal decomposition effect and contaminant removal efficiency of the waste engine oil. Common existing technologies may involve setting these parameters based on preliminary analysis results of the waste engine oil, possibly through empirical data or computational models to achieve automated setting.

[0061] The historical processing records of the treatment containers include multiple aspects, primarily detailed records of past waste oil treatment processes. These records may include the initial characteristics of the waste oil (such as viscosity and degree of contamination), specific operations during the heat treatment process (such as temperature changes, treatment time, and heating rate), the treatment effect of the waste oil after treatment (such as viscosity changes and contaminant removal), and the history of cleaning and maintenance (such as container cleaning cycles, cleaning methods, and cleaning effects). This data helps to optimize the waste oil treatment process based on historical experience and data analysis.

[0062] A scheduled cleaning cycle refers to the regular cleaning operation cycle that a processing container needs to undergo after a certain period of time or number of processing cycles. This is usually set by the equipment manufacturer or user based on the equipment's operating conditions and the cleaning effect. In the prior art, scheduled cleaning cycles are used in many industrial equipment, especially those that require regular maintenance. The cleaning cycle is usually set based on the frequency of equipment use, the amount of contaminant accumulation, and the equipment's operating efficiency.

[0063] Furthermore, the machine vision-based waste oil treatment monitoring method also includes the following steps:

[0064] Step S200: Intelligently analyze historical processing records, filter out sub-processing records that match the target viscosity characteristics of waste engine oil, and determine the target cleaning cycle containing the most such sub-processing records. The target cleaning cycle should only include processing records related to waste engine oil that meets the target viscosity characteristics.

[0065] In this embodiment of the invention, the purpose of selecting the cleaning cycle containing the most records of that type of sub-processing as the target cleaning cycle is to find the most stable and effective cycle when processing similar waste engine oil (i.e., having the same or similar viscosity characteristics) using historical data. The selection of the cleaning cycle is based on data analysis of historical records to find the time period with the best processing effect in the past. This can improve the consistency and efficiency of the processing and ensure that each processing within the cleaning cycle achieves the best results.

[0066] The significance of "only processing records related to waste engine oil meeting the target viscosity characteristics should be included within the target cleaning cycle" lies in the fact that the viscosity characteristics of waste engine oil have a significant impact on its processing effectiveness. Waste engine oil with different viscosity characteristics may require different processing parameters or techniques to achieve the desired cleaning effect. Therefore, limiting the processing to waste engine oil meeting the target viscosity characteristics within the target cleaning cycle can avoid data errors caused by mixing processing records of waste engine oil with other different viscosities, thereby ensuring that parameter adjustments are consistent with the type of waste engine oil and improving the accuracy and efficiency of the final processing.

[0067] Furthermore, the machine vision-based waste oil treatment monitoring method also includes the following steps:

[0068] Step S300: Based on machine vision technology, analyze the degree of adhesion of waste oil on the inner wall of the processing container in each sub-processing record within the target cleaning cycle, and generate an adhesion degree curve that changes with the time progress based on these waste oil adhesion degree values. Calculate the average slope of the adhesion degree curve and use it as the first optimization coefficient.

[0069] Specifically, Figure 2 The flowchart for generating the first optimization coefficients is shown.

[0070] Specifically, based on machine vision technology, the degree of adhesion of waste oil on the inner wall of the processing container in each sub-processing record within the target cleaning cycle is analyzed. Based on these adhesion values, an adhesion degree curve is generated that changes over time. The average slope of the adhesion degree curve is calculated and used as the first optimization coefficient. The process includes the following steps:

[0071] Step S301: Based on historical processing records, obtain images of the inner wall of the processing container corresponding to each sub-processing record within the target cleaning cycle, and use machine vision technology to intelligently analyze each inner wall image to determine the degree of waste oil adhesion in the corresponding sub-processing record.

[0072] Step S302: Based on the timestamp of each inner wall image and the corresponding waste oil adhesion value, generate an adhesion curve that changes over time.

[0073] Step S303: Calculate the average slope of the adhesion degree curve and use it as the first optimization coefficient.

[0074] In this embodiment of the invention, machine vision technology is used to intelligently analyze each inner wall image to determine the degree of waste oil adhesion in the corresponding sub-processing record. Specifically, this is achieved by analyzing the image of the inner wall of the processing container using image recognition and image processing techniques. In existing technologies, image segmentation and edge detection algorithms are typically used to identify the residual waste oil in images. For example, deep learning models such as convolutional neural networks (CNNs) can be used to train and classify images, accurately identifying the adhesion layer of waste oil. By extracting and analyzing the pixel values ​​of the waste oil area in the inner wall image, and combining this with image clarity, lighting conditions, and the material of the inner wall of the processing container, machine vision technology can assess the degree of waste oil adhesion to the container's inner wall in real time. The key to this method is the intelligent processing of image data to identify the degree of adhesion, thereby providing accurate input data for subsequent optimization of heat treatment parameters.

[0075] The generation of the adhesion curve is of great significance, as it reflects the changing adhesion of waste engine oil to the inner wall of the treatment container. As the treatment process progresses, the adhesion degree of the waste engine oil changes. By combining continuous images on the time axis with adhesion degree data, a curve reflecting this trend can be generated. The adhesion curve helps assess the interaction between the waste engine oil and the container's inner wall, as well as the removal efficiency of the waste engine oil during heat treatment. Analyzing this curve provides intuitive data for optimizing heat treatment parameters, ensuring a more efficient and precise heat treatment process.

[0076] The significance of using the average slope of the adhesion curve as the first optimization coefficient lies in its ability to objectively reflect the rate of change in the adhesion degree of waste engine oil. A steeper slope indicates a more rapid change in the adhesion of waste engine oil to the container's inner wall, potentially requiring more efficient treatment methods. Calculating the average slope of the adhesion curve quantifies the changing trend of waste engine oil removal effectiveness, providing data for the dynamic adjustment of heat treatment parameters. As an optimization coefficient, the first optimization coefficient dynamically adjusts parameters during the heat treatment process through changes in its value, thereby achieving more precise waste engine oil treatment and improving equipment efficiency and treatment quality.

[0077] Furthermore, the machine vision-based waste oil treatment monitoring method also includes the following steps:

[0078] Step S400: The current contamination level of the waste oil is evaluated based on machine vision technology, and the standard contamination level threshold corresponding to the waste oil with the target viscosity characteristics is determined. The deviation between the standard contamination level threshold and the current contamination level threshold is used as the second optimization coefficient.

[0079] Specifically, Figure 3 A flowchart for generating the second optimization coefficients is shown.

[0080] The process of assessing the current contamination level of waste engine oil based on machine vision technology, determining the standard contamination level threshold corresponding to the target viscosity characteristics of the waste engine oil, and using the deviation between the standard threshold and the current contamination level as the second optimization coefficient specifically includes the following steps:

[0081] Step S401: Use machine vision technology to assess the current level of contamination of the waste engine oil;

[0082] Step S402: Retrieve the preset reference model, extract the standard contamination level threshold corresponding to the target viscosity characteristics, calculate the deviation between the current contamination level value and the standard contamination level threshold, and use the deviation value as the second optimization coefficient.

[0083] The reference model refers to a pre-set standard contamination assessment model established based on historical data and processing experience of different waste oil viscosity characteristics and contamination levels. This reference model includes contamination level thresholds that allow waste oil with different viscosity characteristics to be effectively accepted by the treatment container.

[0084] In this embodiment of the invention, the step of "assessing the current level of contamination of waste engine oil using machine vision technology" is typically achieved through a combination of image analysis and sensor data. Specifically, machine vision technology can acquire image data of waste engine oil in real time using camera equipment, and use image processing algorithms (such as edge detection, color segmentation, texture analysis, etc.) to assess the state of the waste engine oil. These images can display the distribution of impurities, deposits, and contaminants in the waste engine oil, and the degree of contamination can be determined through intelligent algorithms.

[0085] In existing technologies, image features are typically compared with known pollution standards. For example, the presence of pollutants is detected by color differences, or the degree of pollution is determined by the size and distribution pattern of particles in the image. Furthermore, machine vision technology can be combined with techniques such as spectral analysis and laser scanning to assess the concentration of suspended matter in waste engine oil, further quantifying the degree of pollution. This combination of technologies enables machine vision to effectively assess the pollution status of waste engine oil and determine its current level of contamination.

[0086] The degree of contamination was chosen as the secondary objective because it directly impacts the efficiency and quality of waste engine oil treatment. This is especially true when the waste engine oil contains a significant amount of impurities, which can lead to increased adhesion to the inner walls during treatment, thus affecting the working condition of the treatment container. The degree of adhesion to the inner walls is closely related to the degree of contamination; heavily contaminated waste engine oil tends to accumulate more easily on the container's inner walls, increasing cleaning difficulty. Therefore, assessing the degree of contamination in the waste engine oil is a crucial step in evaluating the overall treatment process's effectiveness. It provides more comprehensive feedback, helping to dynamically adjust treatment parameters and optimize the entire process.

[0087] In this invention, the reference model refers to a standard contamination level assessment model. This model is established based on extensive historical data and processing experience, and is used to quantify the contamination level thresholds for different viscosity characteristics of waste engine oil. By analyzing the treatment effects of waste engine oil with different viscosity characteristics, the reference model determines the contamination level thresholds at which various types of waste engine oil can be effectively accepted in treatment containers. The core of this model is that it provides a standardized contamination level boundary, helping to determine whether waste engine oil is within the treatable range or requires further adjustment.

[0088] Using the deviation value as a second optimization coefficient is of great significance. The deviation value represents the difference between the current level of contamination of the waste engine oil and the standard contamination threshold. By calculating this deviation value, the extent to which the contamination level of the waste engine oil exceeds or falls short of the standard threshold can be quantified, providing a reference for subsequent adjustments to heat treatment parameters. A larger deviation value indicates a higher level of contamination in the waste engine oil, which may require stronger heat treatment parameters; a smaller deviation value indicates a lower level of contamination, which can correspondingly reduce the heat treatment intensity, thereby saving energy and avoiding overtreatment. Therefore, the second optimization coefficient (i.e., the deviation value) can help to more accurately adjust heat treatment parameters to adapt to waste engine oils with different levels of contamination, improving treatment effectiveness and efficiency.

[0089] Furthermore, the machine vision-based waste oil treatment monitoring method also includes the following steps:

[0090] Step S500: Dynamically adjust the initial heat treatment parameters based on the first optimization coefficient and the second optimization coefficient.

[0091] Specifically, Figure 4 A flowchart is shown to optimize and adjust the initial heat treatment parameters.

[0092] The dynamic adjustment of initial heat treatment parameters based on the first and second optimization coefficients specifically includes the following steps:

[0093] Step S501: Retrieve the preset parameter optimization formula and dynamically adjust the initial heat treatment parameters in combination with the first optimization coefficient and the second optimization coefficient to obtain the optimized heat treatment parameters.

[0094] Step S502: Apply the optimized heat treatment parameters to the process of treating the current waste oil in the treatment container.

[0095] The parameter optimization formula is as follows: T optimized This refers to the initial heat treatment parameters, T. initial This refers to the optimized heat treatment parameters. S refers to the average slope of the adhesion curve, i.e., the first optimization coefficient. W1 refers to the corresponding adjustment weight of the first optimization coefficient. W2 refers to the deviation between the current pollution level and the standard pollution level threshold, i.e., the second optimization coefficient. W2 refers to the corresponding adjustment weight of the second optimization coefficient.

[0096] In this embodiment of the invention, the significance of dynamically adjusting the initial heat treatment parameters by combining the first optimization coefficient and the second optimization coefficient lies in the fact that they can comprehensively evaluate multiple key factors in the waste oil treatment process based on the different characteristics of waste oil, thereby achieving precise adjustment of heat treatment parameters. In the prior art, the waste oil treatment process usually relies on fixed treatment parameters, which is difficult to cope with the complexity of different types of waste oil, resulting in unstable treatment effects. Especially when the contamination level, viscosity, and other characteristics of waste oil vary greatly, their impact on the treatment parameters is often not fully considered.

[0097] In this invention, the first optimization coefficient reflects how the waste engine oil interacts with the inner wall of the treatment container during the treatment process by analyzing the degree of adhesion of the waste engine oil to the inner wall. Changes in adhesion directly affect treatment efficiency; therefore, the first optimization coefficient, by calculating the average slope of the adhesion curve, helps optimize heat treatment parameters, especially in terms of temperature and treatment time adjustments. If the adhesion is high, it may be necessary to increase the temperature or extend the treatment time to reduce oil adhesion to the inner wall.

[0098] The second optimization coefficient is adjusted based on the deviation between the degree of contamination of the waste engine oil and the standard contamination level. This deviation reflects the actual difference in the degree of contamination of the waste engine oil. When the degree of contamination of the waste engine oil is low, the deviation from the standard contamination threshold is negative, indicating that the oil is "cleaner" and may have less adhesion to the inner wall of the container. Therefore, the initial heat treatment parameters can be appropriately reduced (such as lowering the temperature or shortening the treatment time). Conversely, when the degree of contamination is high, the deviation is positive, meaning that the oil is more contaminated and the heat treatment intensity may need to be increased to improve the treatment effect.

[0099] By combining the first and second optimization coefficients, the system can comprehensively evaluate the treatment needs of waste engine oil from two dimensions: the degree of adhesion between the waste engine oil and the inner wall of the treatment container, and the degree of contamination. This correlation allows for more precise adjustment of heat treatment parameters, enabling automatic adaptation during the treatment of different types of waste engine oil, thereby achieving more efficient and energy-saving treatment results.

[0100] In summary, this invention considers the impact of the gradually increasing degree of contamination on the inner wall of the treatment container on the adhesion of waste engine oil as the cleaning cycle progresses, and also takes into account the influence of changes in the degree of waste engine oil contamination on the heat treatment process. By dynamically adjusting the treatment parameters, the system can optimize the treatment process according to changes in the degree of inner wall contamination and waste engine oil contamination. This dynamic adjustment mechanism ensures that the waste engine oil treatment process is always maintained in an optimal state, thereby avoiding the problem that traditional treatment methods fail to effectively address changes in inner wall contamination and waste engine oil contamination, improving waste engine oil treatment efficiency and quality, and ensuring that the synergistic effect of the treatment container and waste engine oil is fully utilized.

[0101] Furthermore, Figure 5 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0102] In another preferred embodiment of the present invention, a machine vision-based waste oil treatment monitoring system includes:

[0103] The data acquisition module 100 is used to determine the target viscosity characteristics of the waste oil when the processing container receives the waste oil, and to acquire the initial heat treatment parameters set for it. At the same time, it acquires the historical processing records of the processing container and determines the predetermined cleaning cycle of the processing container.

[0104] In this embodiment of the invention, the processing container refers to equipment specifically designed for waste engine oil treatment, typically including various industrial devices capable of heating, filtering, and decomposing waste engine oil. These containers are designed to efficiently and safely treat waste engine oil, ensuring it meets standards before being recycled or further processed. The structure of the processing container typically includes an inner wall, a heating system, and a discharge system, ensuring uniform heating and treatment of the waste engine oil during the heat treatment process.

[0105] The target viscosity characteristics of waste engine oil can be determined using various methods, with common techniques including viscosity measurement and rheological analysis. By measuring the flowability of waste engine oil, its viscosity characteristics can be obtained, allowing for adjustments to the treatment process based on the specific viscosity requirements. For example, commonly used equipment such as rotational viscometers and capillary viscometers can help obtain viscosity data for waste engine oil. Furthermore, the viscosity characteristics of waste engine oil can be comprehensively assessed by combining them with component analysis, considering factors such as oil type and impurity content.

[0106] Initial heat treatment parameters refer to a set of starting conditions set during waste engine oil treatment. These conditions are crucial for subsequent treatment processes. Initial heat treatment parameters include heating temperature, heating time, and heating rate, which affect the thermal decomposition effect and contaminant removal efficiency of the waste engine oil. Common existing technologies may involve setting these parameters based on preliminary analysis results of the waste engine oil, possibly through empirical data or computational models to achieve automated setting.

[0107] The historical processing records of the treatment containers include multiple aspects, primarily detailed records of past waste oil treatment processes. These records may include the initial characteristics of the waste oil (such as viscosity and degree of contamination), specific operations during the heat treatment process (such as temperature changes, treatment time, and heating rate), the treatment effect of the waste oil after treatment (such as viscosity changes and contaminant removal), and the history of cleaning and maintenance (such as container cleaning cycles, cleaning methods, and cleaning effects). This data helps to optimize the waste oil treatment process based on historical experience and data analysis.

[0108] A scheduled cleaning cycle refers to the regular cleaning operation cycle that a processing container needs to undergo after a certain period of time or number of processing cycles. This is usually set by the equipment manufacturer or user based on the equipment's operating conditions and the cleaning effect. In the prior art, scheduled cleaning cycles are used in many industrial equipment, especially those that require regular maintenance. The cleaning cycle is usually set based on the frequency of equipment use, the amount of contaminant accumulation, and the equipment's operating efficiency.

[0109] Furthermore, the machine vision-based waste oil treatment monitoring system also includes:

[0110] The target cleaning cycle determination module 200 is used to intelligently analyze historical processing records, filter out sub-processing records that are consistent with the target viscosity characteristics of waste engine oil, and determine the target cleaning cycle that contains the most of these sub-processing records. The target cleaning cycle should only include relevant processing records that process waste engine oil that meets the target viscosity characteristics.

[0111] In this embodiment of the invention, the purpose of selecting the cleaning cycle containing the most records of that type of sub-processing as the target cleaning cycle is to find the most stable and effective cycle when processing similar waste engine oil (i.e., having the same or similar viscosity characteristics) using historical data. The selection of the cleaning cycle is based on data analysis of historical records to find the time period with the best processing effect in the past. This can improve the consistency and efficiency of the processing and ensure that each processing within the cleaning cycle achieves the best results.

[0112] The significance of "only processing records related to waste engine oil meeting the target viscosity characteristics should be included within the target cleaning cycle" lies in the fact that the viscosity characteristics of waste engine oil have a significant impact on its processing effectiveness. Waste engine oil with different viscosity characteristics may require different processing parameters or techniques to achieve the desired cleaning effect. Therefore, limiting the processing to waste engine oil meeting the target viscosity characteristics within the target cleaning cycle can avoid data errors caused by mixing processing records of waste engine oil with other different viscosities, thereby ensuring that parameter adjustments are consistent with the type of waste engine oil and improving the accuracy and efficiency of the final processing.

[0113] Furthermore, the machine vision-based waste oil treatment monitoring system also includes:

[0114] The first optimization coefficient generation module 300 is used to analyze the degree of adhesion of waste oil on the inner wall of the processing container in each sub-processing record within the target cleaning cycle based on machine vision technology, and generate an adhesion degree curve that changes with the progress of time based on these waste oil adhesion degree values, calculate the average slope of the adhesion degree curve, and use it as the first optimization coefficient.

[0115] Specifically, Figure 6The diagram shows the structure of the first optimization coefficient generation module 300 in the system provided by an embodiment of the present invention.

[0116] In a preferred embodiment provided by the present invention, the first optimization coefficient generation module 300 specifically includes:

[0117] The adhesion degree value determination unit 301 is used to obtain the inner wall image of the processing container corresponding to each sub-processing record within the target cleaning cycle based on historical processing records, and to use machine vision technology to intelligently analyze each inner wall image to determine the adhesion degree value of waste oil in the corresponding sub-processing record.

[0118] The adhesion degree curve generation unit 302 is used to generate an adhesion degree curve that changes over time based on the timestamp of each inner wall image and the corresponding waste oil adhesion degree value.

[0119] The average slope calculation unit 303 is used to calculate the average slope of the adhesion degree curve and use it as the first optimization coefficient.

[0120] In this embodiment of the invention, machine vision technology is used to intelligently analyze each inner wall image to determine the degree of waste oil adhesion in the corresponding sub-processing record. Specifically, this is achieved by analyzing the image of the inner wall of the processing container using image recognition and image processing techniques. In existing technologies, image segmentation and edge detection algorithms are typically used to identify the residual waste oil in images. For example, deep learning models such as convolutional neural networks (CNNs) can be used to train and classify images, accurately identifying the adhesion layer of waste oil. By extracting and analyzing the pixel values ​​of the waste oil area in the inner wall image, and combining this with image clarity, lighting conditions, and the material of the inner wall of the processing container, machine vision technology can assess the degree of waste oil adhesion to the container's inner wall in real time. The key to this method is the intelligent processing of image data to identify the degree of adhesion, thereby providing accurate input data for subsequent optimization of heat treatment parameters.

[0121] The generation of the adhesion curve is of great significance, as it reflects the changing adhesion of waste engine oil to the inner wall of the treatment container. As the treatment process progresses, the adhesion degree of the waste engine oil changes. By combining continuous images on the time axis with adhesion degree data, a curve reflecting this trend can be generated. The adhesion curve helps assess the interaction between the waste engine oil and the container's inner wall, as well as the removal efficiency of the waste engine oil during heat treatment. Analyzing this curve provides intuitive data for optimizing heat treatment parameters, ensuring a more efficient and precise heat treatment process.

[0122] The significance of using the average slope of the adhesion curve as the first optimization coefficient lies in its ability to objectively reflect the rate of change in the adhesion degree of waste engine oil. A steeper slope indicates a more rapid change in the adhesion of waste engine oil to the container's inner wall, potentially requiring more efficient treatment methods. Calculating the average slope of the adhesion curve quantifies the changing trend of waste engine oil removal effectiveness, providing data for the dynamic adjustment of heat treatment parameters. As an optimization coefficient, the first optimization coefficient dynamically adjusts parameters during the heat treatment process through changes in its value, thereby achieving more precise waste engine oil treatment and improving equipment efficiency and treatment quality.

[0123] Furthermore, the machine vision-based waste oil treatment monitoring system also includes:

[0124] The second optimization coefficient generation module 400 is used to evaluate the current contamination level of waste oil based on machine vision technology, determine the standard contamination level threshold corresponding to the waste oil with the target viscosity characteristics, and use the deviation between the standard contamination level threshold and the current contamination level value as the second optimization coefficient.

[0125] Specifically, Figure 7 The diagram shows the structure of the second optimization coefficient generation module 400 in the system provided in the embodiment of the present invention.

[0126] In a preferred embodiment of the present invention, the second optimization coefficient generation module 400 specifically includes:

[0127] The current pollution level assessment unit 401 is used to assess the current pollution level of waste engine oil using machine vision technology;

[0128] The deviation value calculation unit 402 is used to retrieve a preset reference model, extract the standard contamination level threshold corresponding to the target viscosity characteristics, calculate the deviation value between the current contamination level value and the standard contamination level threshold, and use the deviation value as the second optimization coefficient.

[0129] The reference model refers to a pre-set standard contamination assessment model established based on historical data and processing experience of different waste oil viscosity characteristics and contamination levels. This reference model includes contamination level thresholds that allow waste oil with different viscosity characteristics to be effectively accepted by the treatment container.

[0130] In this embodiment of the invention, the step of "assessing the current level of contamination of waste engine oil using machine vision technology" is typically achieved through a combination of image analysis and sensor data. Specifically, machine vision technology can acquire image data of waste engine oil in real time using camera equipment, and use image processing algorithms (such as edge detection, color segmentation, texture analysis, etc.) to assess the state of the waste engine oil. These images can display the distribution of impurities, deposits, and contaminants in the waste engine oil, and the degree of contamination can be determined through intelligent algorithms.

[0131] In existing technologies, image features are typically compared with known pollution standards. For example, the presence of pollutants is detected by color differences, or the degree of pollution is determined by the size and distribution pattern of particles in the image. Furthermore, machine vision technology can be combined with techniques such as spectral analysis and laser scanning to assess the concentration of suspended matter in waste engine oil, further quantifying the degree of pollution. This combination of technologies enables machine vision to effectively assess the pollution status of waste engine oil and determine its current level of contamination.

[0132] The degree of contamination was chosen as the secondary objective because it directly impacts the efficiency and quality of waste engine oil treatment. This is especially true when the waste engine oil contains a significant amount of impurities, which can lead to increased adhesion to the inner walls during treatment, thus affecting the working condition of the treatment container. The degree of adhesion to the inner walls is closely related to the degree of contamination; heavily contaminated waste engine oil tends to accumulate more easily on the container's inner walls, increasing cleaning difficulty. Therefore, assessing the degree of contamination in the waste engine oil is a crucial step in evaluating the overall treatment process's effectiveness. It provides more comprehensive feedback, helping to dynamically adjust treatment parameters and optimize the entire process.

[0133] In this invention, the reference model refers to a standard contamination level assessment model. This model is established based on extensive historical data and processing experience, and is used to quantify the contamination level thresholds for different viscosity characteristics of waste engine oil. By analyzing the treatment effects of waste engine oil with different viscosity characteristics, the reference model determines the contamination level thresholds at which various types of waste engine oil can be effectively accepted in treatment containers. The core of this model is that it provides a standardized contamination level boundary, helping to determine whether waste engine oil is within the treatable range or requires further adjustment.

[0134] Using the deviation value as a second optimization coefficient is of great significance. The deviation value represents the difference between the current level of contamination of the waste engine oil and the standard contamination threshold. By calculating this deviation value, the extent to which the contamination level of the waste engine oil exceeds or falls short of the standard threshold can be quantified, providing a reference for subsequent adjustments to heat treatment parameters. A larger deviation value indicates a higher level of contamination in the waste engine oil, which may require stronger heat treatment parameters; a smaller deviation value indicates a lower level of contamination, which can correspondingly reduce the heat treatment intensity, thereby saving energy and avoiding overtreatment. Therefore, the second optimization coefficient (i.e., the deviation value) can help to more accurately adjust heat treatment parameters to adapt to waste engine oils with different levels of contamination, improving treatment effectiveness and efficiency.

[0135] Furthermore, the machine vision-based waste oil treatment monitoring system also includes:

[0136] The heat treatment parameter optimization module 500 is used to dynamically adjust the initial heat treatment parameters based on the first optimization coefficient and the second optimization coefficient.

[0137] Specifically, Figure 8 A structural block diagram of the heat treatment parameter optimization module 500 in the system provided in an embodiment of the present invention is shown.

[0138] In a preferred embodiment of the present invention, the heat treatment parameter optimization module 500 specifically includes:

[0139] The heat treatment parameter optimization unit 501 is used to retrieve a preset parameter optimization formula and dynamically adjust the initial heat treatment parameters in combination with the first optimization coefficient and the second optimization coefficient to obtain the optimized heat treatment parameters.

[0140] The optimization parameter application unit 502 is used to apply the optimized heat treatment parameters to the process of the treatment container treating the current waste oil.

[0141] The parameter optimization formula is as follows: T optimized This refers to the initial heat treatment parameters, T. initial This refers to the optimized heat treatment parameters. S refers to the average slope of the adhesion curve, i.e., the first optimization coefficient. W1 refers to the corresponding adjustment weight of the first optimization coefficient. W2 refers to the deviation between the current pollution level and the standard pollution level threshold, i.e., the second optimization coefficient. W2 refers to the corresponding adjustment weight of the second optimization coefficient.

[0142] In this embodiment of the invention, the significance of dynamically adjusting the initial heat treatment parameters by combining the first optimization coefficient and the second optimization coefficient lies in the fact that they can comprehensively evaluate multiple key factors in the waste oil treatment process based on the different characteristics of waste oil, thereby achieving precise adjustment of heat treatment parameters. In the prior art, the waste oil treatment process usually relies on fixed treatment parameters, which is difficult to cope with the complexity of different types of waste oil, resulting in unstable treatment effects. Especially when the contamination level, viscosity, and other characteristics of waste oil vary greatly, their impact on the treatment parameters is often not fully considered.

[0143] In this invention, the first optimization coefficient reflects how the waste engine oil interacts with the inner wall of the treatment container during the treatment process by analyzing the degree of adhesion of the waste engine oil to the inner wall. Changes in adhesion directly affect treatment efficiency; therefore, the first optimization coefficient, by calculating the average slope of the adhesion curve, helps optimize heat treatment parameters, especially in terms of temperature and treatment time adjustments. If the adhesion is high, it may be necessary to increase the temperature or extend the treatment time to reduce oil adhesion to the inner wall.

[0144] The second optimization coefficient is adjusted based on the deviation between the degree of contamination of the waste engine oil and the standard contamination level. This deviation reflects the actual difference in the degree of contamination of the waste engine oil. When the degree of contamination of the waste engine oil is low, the deviation from the standard contamination threshold is negative, indicating that the oil is "cleaner" and may have less adhesion to the inner wall of the container. Therefore, the initial heat treatment parameters can be appropriately reduced (such as lowering the temperature or shortening the treatment time). Conversely, when the degree of contamination is high, the deviation is positive, meaning that the oil is more contaminated and the heat treatment intensity may need to be increased to improve the treatment effect.

[0145] By combining the first and second optimization coefficients, the system can comprehensively evaluate the treatment needs of waste engine oil from two dimensions: the degree of adhesion between the waste engine oil and the inner wall of the treatment container, and the degree of contamination. This correlation allows for more precise adjustment of heat treatment parameters, enabling automatic adaptation during the treatment of different types of waste engine oil, thereby achieving more efficient and energy-saving treatment results.

[0146] In summary, this invention considers the impact of the gradually increasing degree of contamination on the inner wall of the treatment container on the adhesion of waste engine oil as the cleaning cycle progresses, and also takes into account the influence of changes in the degree of waste engine oil contamination on the heat treatment process. By dynamically adjusting the treatment parameters, the system can optimize the treatment process according to changes in the degree of inner wall contamination and waste engine oil contamination. This dynamic adjustment mechanism ensures that the waste engine oil treatment process is always maintained in an optimal state, thereby avoiding the problem that traditional treatment methods fail to effectively address changes in inner wall contamination and waste engine oil contamination, improving waste engine oil treatment efficiency and quality, and ensuring that the synergistic effect of the treatment container and waste engine oil is fully utilized.

[0147] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0148] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0149] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0150] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0151] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A machine vision-based method for monitoring waste oil treatment, characterized in that, The method includes: When the waste oil is received in the processing container, the target viscosity characteristics of the waste oil are determined, and the initial heat treatment parameters set for it are obtained. At the same time, the historical processing records of the processing container are obtained, and the predetermined cleaning cycle of the processing container is determined. The system intelligently analyzes historical processing records, filters out sub-processing records that match the target viscosity characteristics of waste engine oil, and determines the target cleaning cycle that contains the most of these sub-processing records. Based on machine vision technology, the degree of adhesion of waste oil on the inner wall of the processing container in each sub-processing record within the target cleaning cycle is analyzed. Based on these degrees of adhesion, an adhesion curve that changes with time is generated, and the average slope of the adhesion curve is calculated and used as the first optimization coefficient. The current contamination level of waste engine oil is evaluated based on machine vision technology, and the standard contamination level threshold corresponding to the waste engine oil with the target viscosity characteristics is determined. The deviation between the standard contamination level threshold and the current contamination level is used as the second optimization coefficient. The initial heat treatment parameters are dynamically adjusted based on the first and second optimization coefficients.

2. The waste oil treatment monitoring method based on machine vision according to claim 1, characterized in that, The target cleaning cycle should only include records of the treatment of waste engine oil that meets the target viscosity characteristics.

3. The waste oil treatment monitoring method based on machine vision according to claim 1, characterized in that, Based on machine vision technology, the steps of analyzing the degree of adhesion of waste oil on the inner wall of the processing container in each sub-processing record within the target cleaning cycle, generating an adhesion degree curve that changes over time based on these adhesion degree values, and calculating the average slope of the adhesion degree curve as the first optimization coefficient include: Based on historical processing records, obtain images of the inner wall of the processing container corresponding to each sub-processing record within the target cleaning cycle, and use machine vision technology to intelligently analyze each inner wall image to determine the degree of waste oil adhesion in the corresponding sub-processing record. Based on the timestamp of each inner wall image and the corresponding waste oil adhesion value, an adhesion curve that changes over time is generated. Calculate the average slope of the adhesion curve and use it as the first optimization coefficient.

4. The waste oil treatment monitoring method based on machine vision according to claim 1, characterized in that, The steps of evaluating the current contamination level of waste engine oil using machine vision technology, determining the standard contamination level threshold corresponding to the target viscosity characteristics of the waste engine oil, and using the deviation between the standard threshold and the current contamination level as the second optimization coefficient include: Using machine vision technology to assess the current level of contamination in waste engine oil; Retrieve the preset reference model, extract the standard contamination threshold corresponding to the target viscosity characteristics, calculate the deviation between the current contamination value and the standard contamination threshold, and use the deviation as the second optimization coefficient; The reference model refers to a pre-set standard contamination assessment model established based on historical data and processing experience of different waste oil viscosity characteristics and contamination levels. This reference model includes contamination level thresholds that allow waste oil with different viscosity characteristics to be effectively accepted by the treatment container.

5. The waste oil treatment monitoring method based on machine vision according to claim 1, characterized in that, The steps for dynamically adjusting the initial heat treatment parameters based on the first and second optimization coefficients include: The preset parameter optimization formula is retrieved, and the initial heat treatment parameters are dynamically adjusted in combination with the first optimization coefficient and the second optimization coefficient to obtain the optimized heat treatment parameters. The optimized heat treatment parameters are applied to the process of treating the current waste oil in the treatment vessel.

6. The waste oil treatment monitoring method based on machine vision according to claim 5, characterized in that, The parameter optimization formula is as follows: T optimized This refers to the initial heat treatment parameters, T. initial This refers to the optimized heat treatment parameters. S refers to the average slope of the adhesion curve, i.e., the first optimization coefficient. W1 refers to the corresponding adjustment weight of the first optimization coefficient. W2 refers to the deviation between the current pollution level and the standard pollution level threshold, i.e., the second optimization coefficient. W2 refers to the corresponding adjustment weight of the second optimization coefficient.

7. A machine vision-based waste oil treatment monitoring system, characterized in that, The system includes: a data acquisition module, a target cleaning cycle determination module, a first optimization coefficient generation module, a second optimization coefficient generation module, and a heat treatment parameter optimization module, wherein: The data acquisition module is used to determine the target viscosity characteristics of the waste oil when the processing container receives the waste oil, and to acquire the initial heat treatment parameters set for it. At the same time, it acquires the historical processing records of the processing container and determines the predetermined cleaning cycle of the processing container. The target cleaning cycle determination module is used to intelligently analyze historical processing records, filter out sub-processing records that are consistent with the target viscosity characteristics of waste engine oil, and determine the target cleaning cycle that contains the most of these sub-processing records. The target cleaning cycle should only include relevant processing records that process waste engine oil that meets the target viscosity characteristics. The first optimization coefficient generation module is used to analyze the degree of adhesion of waste oil on the inner wall of the processing container in each sub-processing record within the target cleaning cycle based on machine vision technology, and generate an adhesion degree curve that changes with the progress of time based on these waste oil adhesion degree values, calculate the average slope of the adhesion degree curve, and use it as the first optimization coefficient. The second optimization coefficient generation module is used to evaluate the current contamination level of waste oil based on machine vision technology, determine the standard contamination level threshold corresponding to the waste oil with the target viscosity characteristics, and use the deviation between the standard contamination level and the current contamination level as the second optimization coefficient. The heat treatment parameter optimization module is used to dynamically adjust the initial heat treatment parameters based on the first optimization coefficient and the second optimization coefficient.

8. The waste oil treatment monitoring system based on machine vision according to claim 7, characterized in that, The steps of the first optimization coefficient generation module specifically include: The adhesion degree value determination unit is used to obtain the inner wall image of the processing container corresponding to each sub-processing record within the target cleaning cycle based on historical processing records, and to use machine vision technology to intelligently analyze each inner wall image to determine the adhesion degree value of waste oil in the corresponding sub-processing record. The adhesion degree curve generation unit is used to generate an adhesion degree curve that changes over time based on the timestamp of each inner wall image and the corresponding waste oil adhesion degree value. The average slope calculation unit is used to calculate the average slope of the adhesion curve and use it as the first optimization coefficient.

9. The waste oil treatment monitoring system based on machine vision according to claim 8, characterized in that, The steps of the second optimization coefficient generation module specifically include: The current pollution level assessment unit is used to assess the current pollution level of waste engine oil using machine vision technology; The deviation value calculation unit is used to retrieve a preset reference model, extract the standard contamination level threshold corresponding to the target viscosity characteristics, calculate the deviation value between the current contamination level value and the standard contamination level threshold, and use the deviation value as the second optimization coefficient. The reference model refers to a pre-set standard contamination assessment model established based on historical data and processing experience of different waste oil viscosity characteristics and contamination levels. This reference model includes contamination level thresholds that allow waste oil with different viscosity characteristics to be effectively accepted by the treatment container.

10. The waste oil treatment monitoring system based on machine vision according to claim 9, characterized in that, The heat treatment parameter optimization module specifically includes: The heat treatment parameter optimization unit is used to retrieve the preset parameter optimization formula and dynamically adjust the initial heat treatment parameters in combination with the first optimization coefficient and the second optimization coefficient to obtain the optimized heat treatment parameters. The parameter optimization application unit is used to apply the optimized heat treatment parameters to the process of treating the current waste oil in the treatment container. The parameter optimization formula is as follows: T optimized This refers to the initial heat treatment parameters, T. initial This refers to the optimized heat treatment parameters. S refers to the average slope of the adhesion curve, i.e., the first optimization coefficient. W1 refers to the corresponding adjustment weight of the first optimization coefficient. W2 refers to the deviation between the current pollution level and the standard pollution level threshold, i.e., the second optimization coefficient. W2 refers to the corresponding adjustment weight of the second optimization coefficient.

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