A marine component spray wastewater treatment system

Abstract

The present application relates to the field of sewage treatment, especially to a marine component spraying wastewater treatment system, the present application detects polarization resistance value and filler cluster image through the internet of things module, locks the community succession window through the observation and analysis module, carries out pulse aeration intervention observation in the community succession window, and further determines the scattered cluster characteristics, carries out edge morphological evolution analysis through the morphological analysis module, determines the evolution characteristics of each dimension edge morphological parameter, determines the abnormal precursors according to the pre-constructed evolution characteristic constraint atlas in a plurality of evolution processes through the biofilm intervention module, and corresponding optimizes the process parameters of the wastewater contact oxidation tank, the present application analyzes whether there is an abnormal precursor by sensing the scattered cluster characteristics and evolution characteristics in a plurality of evolution processes in the biofilm formation process using the evolution characteristic constraint atlas, and further timely outputs the optimization strategy, ensures the biofilm formation efficiency, and improves the stability of the wastewater treatment system.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment, and more particularly to a wastewater treatment system for marine component painting. Background Technology

[0002] Wastewater from marine component painting contains large amounts of uncured resin, organic solvents, and heavy metal ions leached from antifouling paint, characterized by high COD concentration, poor biodegradability, and strong biological toxicity. Traditional activated sludge processes are insufficient to handle the shock loads and toxicity inhibition of this type of wastewater; therefore, biological contact oxidation is often used as the core process for biological treatment. Biological contact oxidation involves filling the tank with packing material to allow microorganisms to attach and grow, forming a biofilm. As the wastewater flows through, the biofilm degrades pollutants.

[0003] For example, Chinese Patent Publication No. CN102225803A discloses a biofilm reactor, comprising: an outer cylinder with an inlet and an outlet at its bottom; an inner cylinder disposed within the outer cylinder, both ends of which are connected to the outer cylinder, and the top of the inner cylinder being lower than the top of the outer cylinder; packing material for a biological contact oxidation tank filled inside the inner cylinder; filter media for a biological filter tank filled between the inner and outer cylinders; and an aeration device disposed at the bottom of the outer cylinder, which is connected to the inner cylinder. This invention also provides a wastewater treatment system and a wastewater treatment method. The biofilm reactor provided by this invention combines the functions of a biological contact oxidation tank and a biological filter. Under the driving force of the aeration device, wastewater forms a circulation between the inner and outer cylinders and is sequentially purified by the packing material for the biological contact oxidation tank and the filter media for the biological filter tank, thereby improving the wastewater treatment effect.

[0004] The core process of biological contact oxidation is the biofilm formation stage, and the stability and morphology of the biofilm generated during this stage directly affect the subsequent degradation capacity. However, the following problems still exist in the existing technology. Existing biological contact oxidation treatment systems rely heavily on conventional water quality indicators such as dissolved oxygen, pH, and influent and effluent COD for monitoring process operation. However, these indicators show a lag in reflecting the microbial community after it has suffered substantial damage, making it impossible to promptly detect the biofilm community succession status during the biofilm formation stage, diagnose the health of the floc structure online, or proactively intervene when abnormal signs appear. This results in low biofilm formation efficiency and poor system stability. Summary of the Invention

[0005] To address this, the present invention provides a wastewater treatment system for marine component painting, which overcomes the problems in the prior art where detection indicators are lagging, making it impossible to promptly detect the biofilm community succession status during the biofilm formation stage, diagnose the health of bacterial floc structure online, and actively intervene when abnormal signs appear, resulting in low biofilm formation efficiency and poor system stability.

[0006] To achieve the above objectives, the present invention provides a wastewater treatment system for marine component painting, comprising: The Internet of Things (IoT) module includes an image acquisition unit deployed in the wastewater contact oxidation tank for acquiring images of packing clusters and an electrode assembly for acquiring polarization resistance values. The observation and analysis module is used to lock the community succession window based on the time-domain change trend and fluctuation of the polarization resistance value. Within the community succession window, pulse aeration intervention observation is carried out, including staged control of the aeration unit to perform pulse aeration, determining the observation field of the packing cluster in the direction of water flow during the pulse stage, and extracting the characteristics of scattered clusters within the observation field. The morphological analysis module is used to perform edge morphological evolution analysis based on the collected images of filler clusters within the community succession window. This includes summarizing the edge morphological parameters of filler clusters in multiple dimensions during the non-pulse stage, in order to analyze the evolution characteristics of edge morphological parameters in each dimension. The biofilm intervention module is used to analyze the scattered cluster characteristics and evolutionary characteristics based on a pre-constructed evolutionary characteristic constraint map in several evolutionary processes, so as to identify abnormal precursors and determine the optimization strategy for the wastewater contact oxidation tank in the evolutionary process. The evolutionary process refers to a sub-time period within the membrane attachment stage, and the evolutionary feature constraint map includes evolutionary feature constraints within different evolutionary processes and scattered cluster feature constraints.

[0007] It should be specifically explained that the observation and analysis module, used to lock the community succession window based on the time-domain variation trend and fluctuation of the polarization resistance value, includes the following: Real-time acquisition of the polarization resistance value over a certain period of time is used to determine the time period in which the downward trend exists. The fluctuation parameters of polarization resistance values ​​within each time period are determined in order to screen time periods that meet the screening conditions for community succession, which are then used as community succession windows. The community succession screening conditions include a decreasing polarization resistance value and a fluctuation parameter greater than a preset upper limit value within a time period.

[0008] It should be specifically explained that the observation and analysis module's phased control of the aeration unit to perform pulse aeration includes, The aeration unit is controlled to alternate between pulse and non-pulse phases to perform pulse aeration. The pulse phase is the period during which the aeration power is increased, and the non-pulse phase is the period during which the aeration power is maintained.

[0009] It should be specifically explained that the observation and analysis module determines the observation field of view of the packing clusters in the direction of water flow during the pulse phase, and extracts the features of scattered clusters within the observation field of view, including... Identify the outline of packing clusters based on images of packing clusters; Construct a directional reference line passing through the center of the packing cluster outline in the packing cluster image based on the water flow direction; A reference point is determined on the direction reference line at a predetermined distance from the center of the filler cluster outline, and a rectangular area is constructed based on the reference point as the field of view; Cluster the pixels within the observation field of view, identify several clusters, and filter the clusters based on the color parameters within the filler cluster outlines to select several scattered clusters. Based on the time-series arrangement of filler cluster images, the scattered clusters in the observation field are used to determine the characteristics of the scattered clusters, including the scattering frequency and the average area of ​​the scattered clusters.

[0010] It should be specifically noted that the morphology analysis module summarizes the edge morphology parameters of the packing clusters in the non-pulse stage across multiple dimensions, including... The outline of the packing cluster is identified based on the image of the packing cluster, and a strip-shaped observation area of ​​a predetermined width is determined along the outline of the packing cluster; Within the strip-shaped observation area, several sampling points are determined at intervals, and several central normals passing through the sampling points and the center of the filler cluster outline are constructed to obtain normal line segments divided by the strip-shaped observation area. Select several pixels on each of the normal segments and calculate the gradient of the corresponding gray value of the pixel. The mean gradient is determined as the edge blur dimension morphological parameter. The fractal dimension of the filler cluster profile is determined as the edge discrete dimension morphological parameter.

[0011] It needs to be specifically explained the evolution characteristics of the edge morphology parameters in each dimension of the morphology analysis module. The mean distribution and rate of change of the edge fuzzy dimension morphological parameters and the edge discrete dimension morphological parameters in different evolutionary processes are respectively determined as the corresponding evolutionary features.

[0012] It should be specifically explained that the membrane-attachment intervention module analyzes the characteristics and evolutionary features of the scattered clusters to determine the abnormal precursors, including... Based on the current evolutionary process, select the corresponding evolutionary feature constraints and scattered cluster feature constraints from the evolutionary feature constraint map; The scattered cluster features corresponding to the evolution process are compared with the corresponding scattered cluster feature constraints; The evolutionary features corresponding to the evolutionary process are compared with the corresponding evolutionary feature constraints; If the scattered cluster features break free from the scattered cluster feature constraints or / and the evolutionary features break free from the evolutionary feature constraints, then an abnormal precursor is determined to exist.

[0013] It should be specifically explained that the biofilm intervention module determines the optimized strategy for the wastewater contact oxidation tank during the evolution process. Based on the characteristics and evolution of scattered clusters when abnormal precursors are identified, corresponding optimization strategies are determined; Among them, there are pre-set optimization strategies for when scattered cluster features are freed from the constraints of the corresponding scattered cluster features, and pre-set optimization strategies for when evolutionary features are freed from the constraints of evolutionary features.

[0014] It should be specifically noted that the packing cluster image is an image frame containing packing clusters; The proportion of filler clusters in the image frame must meet the predetermined ratio requirement.

[0015] It should be specifically noted that it also includes a storage module, which is used to record the filler cluster images and polarization resistance values ​​collected by the IoT module.

[0016] Compared with existing technologies, this invention uses an IoT module to detect polarization resistance values ​​and packing cluster images. An observation and analysis module locks the community succession window and performs pulse aeration intervention within that window to determine the characteristics of scattered clusters. A morphology analysis module analyzes edge morphology evolution to determine the evolutionary characteristics of edge morphology parameters in various dimensions. A biofilm intervention module analyzes the scattered cluster characteristics and evolutionary features within several evolutionary processes based on a pre-constructed evolutionary feature constraint map to identify anomalies and optimize the process parameters of the wastewater contact oxidation tank accordingly. This invention senses the scattered cluster characteristics and evolutionary features within multiple evolutionary processes during biofilm formation, analyzes the presence of anomalies using an evolutionary feature constraint map, and promptly outputs optimization strategies to ensure biofilm formation efficiency and improve the stability of the wastewater treatment system.

[0017] The advantage lies in the fact that this invention locks the community succession window based on the temporal variation trend and fluctuation of polarization resistance. In the actual biofilm formation process, as metabolically active bacterial flocs gradually become dominant on the packing material, electron transfer becomes more active, and the polarization resistance value shows a trend reversal from a high point to a continuous decline. Furthermore, due to the emergence of metazoans throughout the process, the electron transfer activity of the biofilm is unstable during the growth and decline of ciliates, and the fluctuation rate of the polarization resistance value is significantly greater than in the early stage of biofilm formation. Therefore, the community succession window is a critical stage for biofilm formation. The community succession window is a period of rapid growth of biofilm, during which the state of the biofilm changes drastically, and the relevant characteristic signals are obvious and easy to capture, facilitating monitoring and intervention.

[0018] The advantage lies in the fact that the observation and analysis module of this invention performs pulse aeration intervention observation within the community succession window, observing the characteristics of scattered clusters. In the actual biofilm formation process, the adhesion of the biofilm is crucial. Therefore, pulse aeration can be used. Pulse aeration applies moderate hydraulic shear to the biofilm, which can stimulate the bacterial flocs to secrete extracellular polymers, enhance their anchoring ability with the packing material, and selectively peel off loose flocs that are not firmly attached. At the same time, the characteristics of scattered clusters can more intuitively reflect the adhesion effect, which is convenient to use as one of the indicators for identifying abnormal precursors, thereby outputting optimization strategies in a timely manner, ensuring biofilm formation efficiency, and improving the stability of the wastewater treatment system.

[0019] The advantage lies in the fact that this invention utilizes a morphological analysis module to analyze edge morphology evolution. Traditional biofilm analysis using dissolved oxygen, pH, and COD indicators reflects macroscopic changes in the aquatic environment after the microbial community has been damaged. Usually, at this point, the biofilm has already detached or been damaged over a large area. However, by directly characterizing the structural state of the bacterial flocs themselves during biofilm formation using edge morphology parameters of various dimensions, the extracellular polymers at the edge of the flocs will dissolve first, the bacterial cells will begin to loosen, and the edges will be relatively blurred. When filamentous bacteria just begin to proliferate abnormally, the cluster outline will become complex, and the fractal dimension will increase, reflecting the edge morphology changes throughout the entire biofilm formation process. Edge morphology parameters can capture abnormal precursors such as structural loosening and edge disintegration earlier and more sensitively, providing real-time and objective quantitative evidence for the refined control of the biofilm formation process. This allows for timely output of optimization strategies, ensuring biofilm formation efficiency and improving the stability of the wastewater treatment system.

[0020] The advantage of this invention lies in its consideration that the scattered cluster characteristics and edge morphology parameters should fluctuate within corresponding ranges under normal circumstances during several evolutionary stages of the biofilm formation process. Based on this, an evolutionary feature constraint map is constructed, and then anomaly precursors are identified by setting scattered cluster characteristic constraints and edge morphology characteristic constraints for each evolutionary stage. During system operation, the real-time extracted scattered cluster characteristics and edge morphology evolutionary characteristics are compared with the constraints corresponding to the current evolutionary stage to determine anomaly precursors. This makes anomaly detection more consistent with the actual developmental patterns of biofilms, and is more sensitive, allowing for early detection and intervention within the critical community succession window. This enables timely output of optimization strategies, ensuring biofilm formation efficiency and improving the stability of the wastewater treatment system. Attached Figure Description

[0021] Figure 1 Structural diagram of a marine component spraying wastewater treatment system according to an embodiment of the invention; Figure 2 A photograph of the packing sheet according to an embodiment of the invention; Figure 3 This is a schematic diagram illustrating the construction of normal line segments according to an embodiment of the invention; Figure 4 A logic diagram for determining abnormal precursors in an embodiment of the invention; In the diagram, 1 represents the normal line segment, and 2 represents the strip-shaped observation area. Detailed Implementation

[0022] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0024] Please see Figure 1 The diagram shown is a structural diagram of a marine component painting wastewater treatment system according to an embodiment of the present invention. The marine component painting wastewater treatment system according to an embodiment of the present invention includes: The Internet of Things (IoT) module includes an image acquisition unit deployed in the wastewater contact oxidation tank for acquiring images of packing clusters and an electrode assembly for acquiring polarization resistance values. The observation and analysis module is used to lock the community succession window based on the time-domain change trend and fluctuation of the polarization resistance value. Within the community succession window, pulse aeration intervention observation is carried out, including staged control of the aeration unit to perform pulse aeration, determining the observation field of the packing cluster in the direction of water flow during the pulse stage, and extracting the characteristics of scattered clusters within the observation field. The morphological analysis module is used to perform edge morphological evolution analysis based on the collected images of filler clusters within the community succession window. This includes summarizing the edge morphological parameters of filler clusters in multiple dimensions during the non-pulse stage, in order to analyze the evolution characteristics of edge morphological parameters in each dimension. The biofilm intervention module is used to analyze the scattered cluster characteristics and evolutionary characteristics based on a pre-constructed evolutionary characteristic constraint map in several evolutionary processes, so as to identify abnormal precursors and determine the optimization strategy for the wastewater contact oxidation tank in the evolutionary process. The evolutionary process refers to a sub-time period within the membrane attachment stage, and the evolutionary feature constraint map includes evolutionary feature constraints within different evolutionary processes and scattered cluster feature constraints.

[0025] The specific structure of the wastewater contact oxidation tank is not limited. In modern biological contact oxidation, the wastewater contact oxidation tank is usually used as a packing material and is also used for aeration to allow microorganisms to attach and grow, forming a biofilm. Then, when the wastewater flows through, the biofilm degrades pollutants.

[0026] Please see Figure 2As shown, it is a physical diagram of the packing sheet according to an embodiment of the invention. Regarding the packing method, a honeycomb packing sheet can be used. The honeycomb packing sheet has a regular geometric channel structure and a large specific surface area. During the biofilm formation process, microorganisms will be attached to the packing sheet, thereby forming a packing cluster. The overall structure is circular, which facilitates the acquisition of images of the packing cluster using an image acquisition unit. Of course, it should be noted that the above packing form is only an example. Those skilled in the art can choose other forms of packing according to their needs, which will not be elaborated here.

[0027] The form of cultured microbial strains is not limited. Common microbial strains used for biological contact oxidation treatment of spray painting wastewater include, but are not limited to: heterotrophic bacteria such as *Pseudomonas*, *Bacillus*, and *Flavobacterium*, which can degrade paint solvents such as benzene series compounds and alcohol ethers; and floc-forming bacteria such as *Aeromonas* and *Alcaligenes*, which have good flocculation and adhesion capabilities and are important contributors to biofilm structure. In artificial inoculation methods, commercially available compound microbial agents or mature activated sludge with these genera as the dominant components are often introduced. It should be noted that the above examples are merely illustrative descriptions based on conventional knowledge in the art and do not constitute any limitation on the scope of microbial strains applicable to the technical solutions of this invention.

[0028] In practice, there are no restrictions on the form of the image acquisition unit. For example, an industrial CCD camera can be used. Those skilled in the art can select the best shooting angle on site, such as shooting from above, as long as the image of the filler cluster can be captured. This will not be elaborated further.

[0029] In practice, there are no limitations on the arrangement of the electrode assembly. In one embodiment, the electrode assembly may include a working electrode, a reference electrode, and an auxiliary electrode, employing a three-electrode system for electrochemical impedance spectroscopy measurement. The electrode material can be corrosion-resistant and conductive materials such as titanium-based platinum-plated materials, graphite, or stainless steel to adapt to the long-term immersion conditions in the wastewater contact oxidation tank. In terms of placement, the electrode assembly can be fixed to the packing support, with its sensing end close to but not in contact with the packing clusters, to accurately sense changes in polarization resistance at the biofilm interface. The electrode assembly is a mature existing technology and will not be described in detail here.

[0030] An AC excitation signal is applied to the biomembrane interface using an electrode array, and the current response and phase shift at various frequencies are simultaneously acquired to obtain an electrochemical impedance spectroscopy (EIS). Based on the EIS, the polarization resistance value can be determined using an equivalent circuit fitting method. For example, the impedance spectrum can be fitted using a Randle equivalent circuit to deduce the polarization resistance value. Of course, other methods can also be used. The methods for deduce polarization resistance based on EIS are well known to those skilled in the art and will not be elaborated further here.

[0031] It should be noted that the multiple functional modules involved in this invention are merely a logical division based on the functions implemented according to this invention, and are not a strict limitation on the physical structure; in practical applications, the above functional modules can be implemented by one or more integrated circuits, a processor executing program code in memory, or a combination of the above devices. To elaborate, the observation and analysis module is used to lock the community succession window based on the time-domain variation trend and fluctuation of the polarization resistance value, including: Real-time acquisition of the polarization resistance value over a certain period of time is used to determine the time period in which the downward trend exists. The fluctuation parameters of polarization resistance values ​​within each time period are determined in order to screen time periods that meet the screening conditions for community succession, which are then used as community succession windows. The community succession screening conditions include a decreasing polarization resistance value and a fluctuation parameter greater than a preset upper limit value within a time period.

[0032] In practice, the time period was set to 24 hours to ensure that the polarization resistance value accumulated enough data points within the time period, so that the determination of the downward trend and the calculation of the fluctuation parameters have statistical reliability.

[0033] In practice, the fluctuation parameter is the variance of the polarization resistance value over a time period; In practice, the upper limit of fluctuation is determined based on the polarization resistance value during the initial observation period of the predetermined biofilm formation. Specifically, the average polarization resistance value during the initial observation period after biofilm formation is initiated is determined, and the average polarization resistance value is amplified by a predetermined factor and set as the upper limit of fluctuation.

[0034] During implementation, the initial observation period for biofilm formation was set to 2 days. At this time, only a small number of primary colonies were attached to the surface of the packing material, and the microorganisms were mainly free bacteria and loose flocs. The electron transfer activity was low and relatively stable at this time.

[0035] The community succession window typically occurs within 5 to 10 days after biofilm formation begins. During this window, the fluctuation range of polarization resistance increases dramatically compared to the initial observation period after biofilm formation, usually exceeding 1.5 times. Therefore, the magnification factor is set to 1.5 times. Of course, those skilled in the art can adjust this factor according to the type of spraying wastewater and the operating conditions of the contact oxidation tank, which will not be elaborated here.

[0036] This invention locks the community succession window based on the temporal variation trend and fluctuation of polarization resistance. In the actual biofilm formation process, as metabolically active bacterial flocs gradually become dominant on the packing material, electron transfer becomes more active, and the polarization resistance value shows a trend reversal from a high point to a continuous decline. Furthermore, due to the emergence of metazoans throughout the process, the electron transfer activity of the biofilm is unstable during the growth and decline of ciliates, and the fluctuation rate of the polarization resistance value is significantly greater than in the early stage of biofilm formation. Therefore, the community succession window is a critical stage for biofilm formation. The community succession window is a period of rapid growth of biofilm formation, during which the biofilm state changes dramatically, and the relevant characteristic signals are obvious, easy to capture, and convenient for monitoring and intervention.

[0037] To elaborate, the observation and analysis module's phased control of the aeration unit to perform pulse aeration includes: The aeration unit is controlled to alternate between pulse and non-pulse phases to perform pulse aeration. The pulse phase is the period during which the aeration power is increased, and the non-pulse phase is the period during which the aeration power is maintained.

[0038] During implementation, the increase in aeration power during the pulse phase should not be excessive. Within the community succession window, the biofilm is in a critical stage of transitioning from loose flocs to dense bacterial flocs, and the anchorage between the bacterial flocs and the packing material is not yet firmly established. If the aeration power during the pulse phase is too high, the resulting hydraulic shear force may exceed the adhesion strength of the initial bacterial flocs, causing a large number of unstable bacterial flocs to be forcibly detached, thus inhibiting the biofilm formation process. Simultaneously, excessively high aeration power may also cause excessive deformation of the packing material clusters in the direction of water flow, resulting in a large amount of debris in the scattered cluster characteristics within the observation field caused by normal hydraulic disturbance rather than structural fragility, interfering with the accuracy of anomaly detection. Therefore, the increase in aeration power during the pulse phase should be controlled within a range that moderately stimulates the biofilm to produce a stress response, avoiding structural damage as much as possible. Thus, a slight increase based on the initial aeration power is sufficient. In one embodiment, the aeration power during the pulse phase can be set to 1.1 to 1.3 times the aeration power during the non-pulse phase. It should be noted that the above multiple range is only an example and can be adjusted appropriately according to the type of packing material, the development stage of the biofilm, and the characteristics of the wastewater in actual applications.

[0039] To elaborate, the observation and analysis module determines the observation field of view of the packing clusters in the direction of water flow during the pulse phase, and extracts the features of scattered clusters within the observation field, including: Identify the outline of packing clusters based on images of packing clusters; Construct a directional reference line passing through the center of the packing cluster outline in the packing cluster image based on the water flow direction; A reference point is determined on the direction reference line at a predetermined distance from the center of the filler cluster outline, and a rectangular area is constructed based on the reference point as the field of view; Cluster the pixels within the observation field of view, identify several clusters, and filter the clusters based on the color parameters within the filler cluster outlines to select several scattered clusters. Based on the time-series arrangement of filler cluster images, the scattered clusters in the observation field are used to determine the characteristics of the scattered clusters, including the scattering frequency and the average area of ​​the scattered clusters.

[0040] During implementation, the scattering frequency is the number of clusters scattered within the observation period. Since the scattering frequency is suitable for short-term observation, the observation period is 1 minute.

[0041] In practice, there are no restrictions on the method for recognizing the outline of the filler cluster. For example, image segmentation algorithms can be used to recognize the edges of the filler cluster outline. Of course, other methods can also be used, which will not be elaborated here.

[0042] In practice, only cases of excessive or large-scale shedding of filler clusters are considered. Ideally, no shedding or only shedding of extremely small debris should occur. Therefore, the influence of extremely small debris needs to be filtered out during analysis. Only when the minimum diameter of the scattered clusters is greater than 1 mm can it be used to determine the characteristics of the scattered clusters.

[0043] Understandably, during wastewater treatment, wastewater needs to flow through the wastewater contact oxidation tank. Therefore, the water flow usually flows from the inlet to the outlet of the wastewater contact oxidation tank. The image acquisition unit takes pictures of the packing material from a top-down perspective. Therefore, in the acquired images of the packing material clusters, the direction of water flow can be pre-calibrated based on the actual location of the inlet and outlet of the tank.

[0044] In practice, setting the predetermined distance is significant because it positions the observation field of view in the downstream region immediately adjacent to the main body of the packing material cluster, effectively capturing tiny debris that has just detached from the cluster edge and has not yet been dispersed by the water flow. If the observation field of view is too close to the main body of the cluster, the inherent fluctuations in the cluster's outline may be misinterpreted as detached debris; if it is too far, the detached tiny debris may have already broken or settled out of the field of view during migration. The predetermined distance can be set according to the actual spatial distance. In some possible implementations, a distance of 5cm in actual space can be converted into the packing material cluster image according to the shooting ratio of the image acquisition unit and used as the predetermined distance.

[0045] The reference point can be used as the shortest distance between the rectangular area and the center of the filler cluster outline, and the rectangular area can be constructed based on this reference.

[0046] The width of the rectangular region is the same as the maximum width of the packing cluster, and the length of the rectangular region is the same as the maximum length of the packing cluster.

[0047] In practice, there are no restrictions on the method of clustering pixels. For example, K-means clustering can be used to identify clusters. Clusters reflect the results of separating scattered features from water features. Therefore, it is also necessary to screen clusters based on the color parameters within the outline of the filler clusters to select clusters that are close to the color parameters of the filler cluster outline.

[0048] In practice, clusters are screened based on color parameters within the filler cluster outline. This may include: extracting the color template of pixels within the filler cluster outline in HSV space, wherein the color template contains the mean vector of hue H and saturation S; calculating the mean vector of pixels in HSV space for each cluster identified within the observation field; calculating the Euclidean distance between the cluster mean vector and the color template mean vector; and determining that the cluster matches the filler cluster outline and is screened as a clustered cluster when the Euclidean distance is less than a preset threshold.

[0049] The preset threshold can be determined in advance. For example, several regions are randomly selected within the outline of the filler cluster, the mean vector of the HSV space corresponding to each region is calculated, and the Euclidean distance is calculated. The maximum value of the Euclidean distance is then used as the preset threshold.

[0050] The observation and analysis module of this invention performs pulse aeration intervention observation within the community succession window to observe the characteristics of scattered clusters. In the actual biofilm formation process, the adhesion of the biofilm is crucial. Therefore, pulse aeration can be used. Pulsed aeration applies moderate hydraulic shear to the biofilm, which can stimulate the bacterial flocs to secrete extracellular polymers, enhance their anchoring ability with the packing material, and selectively peel off loose flocs that are not firmly attached. At the same time, the characteristics of scattered clusters can more intuitively reflect the adhesion effect, which is convenient to use as one of the indicators for identifying abnormal precursors, thereby outputting optimization strategies in a timely manner, ensuring biofilm formation efficiency, and improving the stability of the wastewater treatment system.

[0051] Please see Figure 3 As shown, this is a schematic diagram of the construction of normal line segments according to an embodiment of the invention. More specifically, the morphology analysis module summarizes the edge morphology parameters of the filler clusters in multiple dimensions during the non-pulse stage, including... The outline of the packing cluster is identified based on the image of the packing cluster, and a strip-shaped observation area of ​​a predetermined width is determined along the outline of the packing cluster; Within the strip observation area 2, several sampling points are determined at intervals, and several central normals passing through the sampling points and the center of the filler cluster outline are constructed to obtain normal line segments 1 divided by the strip observation area 2. Select several pixels on each of the normal segments 1, calculate the gradient of the gray value corresponding to the pixel, and determine the mean gradient as the edge blur dimension morphological parameter. The fractal dimension of the filler cluster profile is determined as the edge discrete dimension morphological parameter.

[0052] The gradient is the average of the differences in gray values ​​between adjacent pixels; It is understandable that during the biofilm formation stage, the biofilm community is in a dynamic succession process. The density of the floc edges will change significantly with the changes in microbial activity and extracellular polymer secretion. For example, in the early stage of biofilm formation, the floc structure is loose, the edge cells are not firmly attached, and the extracellular polymer layer is thin and uneven. Microscopically, this is manifested as the bacteria gradually diffuse from the main body of the cluster into the water. Macroscopically, it is shown as the transition zone between the cluster edge and the water background widens and the grayscale change becomes gentler, that is, the edge blurring phenomenon occurs. The significance of setting up the strip observation area 2 is to observe the edge area of ​​the packing material cluster outline. The width of the strip area can be converted into the packing material cluster image according to the shooting ratio of the image acquisition unit based on the actual space distance of 1cm.

[0053] In practice, sampling points can be selected at intervals on the outline of the packing cluster, so that the normal segment 1 constructed later can be distributed along the outline of the packing cluster.

[0054] There are no restrictions on the method for determining the fractal dimension. For example, the most classic box counting method can be used to calculate the fractal dimension of the packing cluster profile. For instance, a series of square grids with different side lengths are used to cover the profile curve of the packing cluster in sequence, and the number of grids containing the profile is counted for each side length. Then, in a double logarithmic coordinate system, the logarithm of the reciprocal of the grid side length is used as the x-axis and the logarithm of the number of grids is used as the y-axis. The obtained data points are linearly fitted, and the slope of the fitted line is the fractal dimension of the cluster profile. This will not be elaborated further.

[0055] This invention utilizes a morphological analysis module to analyze edge morphology evolution. Traditional biofilm analysis using dissolved oxygen, pH, and COD indicators reflects macroscopic changes in the aquatic environment after the microbial community has been damaged. Typically, by this time, the biofilm has already detached or been damaged over a large area. However, by directly characterizing the structural state of the bacterial flocs themselves during biofilm formation using edge morphology parameters of various dimensions, the extracellular polymers at the edge of the flocs will dissolve first, the bacterial cells will begin to loosen, and the edges will become relatively blurred. When filamentous bacteria just begin to proliferate abnormally, the cluster outline will become complex, and the fractal dimension will increase, reflecting the edge morphology changes throughout the entire biofilm formation process. Edge morphology parameters can capture abnormal precursors such as structural loosening and edge disintegration earlier and more sensitively, providing real-time and objective quantitative evidence for the refined control of the biofilm formation process. This allows for timely output of optimization strategies, ensuring biofilm formation efficiency and improving the stability of the wastewater treatment system.

[0056] To elaborate, the evolution characteristics of the edge morphology parameters in each dimension of the morphology analysis module, The mean distribution and rate of change of the edge fuzzy dimension morphological parameters and the edge discrete dimension morphological parameters in different evolutionary processes are respectively determined as the corresponding evolutionary features.

[0057] In practice, the rate of change is the amount of change per unit time. Considering the relatively slow nature of microbial succession, the unit time here is set to 1 hour.

[0058] Please see Figure 4 As shown, this is a logic diagram for determining abnormal precursors according to an embodiment of the invention. Specifically, the membrane-attachment intervention module analyzes the characteristics and evolutionary features of the scattered clusters to determine the abnormal precursors, including... Based on the current evolutionary process, select the corresponding evolutionary feature constraints and scattered cluster feature constraints from the evolutionary feature constraint map; The scattered cluster features corresponding to the evolution process are compared with the corresponding scattered cluster feature constraints; The evolutionary features corresponding to the evolutionary process are compared with the corresponding evolutionary feature constraints; If the scattered cluster features break free from the scattered cluster feature constraints or / and the evolutionary features break free from the evolutionary feature constraints, then an abnormal precursor is determined to exist.

[0059] In practice, the duration of the evolution process was set to 24 hours, which matches the metabolic succession cycle of the microbial community on a daily basis. This allows for consideration of the distribution mean and rate of change of biofilm edge morphological parameters under each complete daily working cycle. In addition, to make the evolutionary feature constraint map summarized later more regular, the first evolution process corresponds to the first community succession window, and the last evolution process corresponds to the last community succession window.

[0060] In practice, the evolutionary feature constraint map is pre-constructed to reflect the scattered cluster features and the corresponding normal constraint boundaries of evolutionary features under typical no-abnormal biofilm attachment conditions at different evolutionary stages. Record the characteristics and evolution of scattered clusters during several biofilm formation processes. Those skilled in the art can identify and select several biofilm formation cases that are normal throughout the process. For different evolutionary processes, statistically analyze the normal distribution of scattered cluster characteristics within the corresponding evolutionary process and determine the 95% confidence interval. The characteristics of scattered clusters include scattering frequency and average area of ​​scattered clusters. Therefore, it is necessary to determine the scattering frequency constraint and the average area constraint of scattered clusters separately. Determine the normal distribution of the scattering frequency and the average area of ​​the scattered clusters, and determine the 95% confidence interval; Considering that in practice only excessive or large-scale shedding is taken into account, the ideal situation is no shedding or only the shedding of extremely small fragments. Therefore, The upper limit of the scatter frequency constraint is the upper limit of the 95% confidence interval corresponding to the scatter frequency, and the lower limit of the scatter frequency constraint is 0.

[0061] The upper limit of the cluster average area constraint is the upper limit of the 95% confidence interval corresponding to the cluster average area, and the lower limit of the cluster average area constraint is 0.

[0062] Since evolutionary characteristics include the distribution mean and the rate of change, evolutionary characteristic constraints also include distribution mean constraints and rate of change constraints. Considering that edge morphology parameters reflect both blurred and relatively complex edge conditions, ideally, these parameters should be kept below a certain limit. The normal distribution corresponding to the edge morphological parameters in the corresponding evolution process is statistically analyzed. The upper limit of the 95% confidence interval is used as the upper limit of the distribution mean constraint, and the lower limit of the distribution mean constraint is set to 0. To address the constraint on the rate of change, we statistically determine the mean distribution constraint within the corresponding evolutionary process, and statistically determine the normal distribution of the rate of change of the marginal morphological parameters within the corresponding evolutionary process. We then use the 95% confidence interval as the constraint on the rate of change within the corresponding evolutionary process.

[0063] To elaborate, the biofilm intervention module determines the optimal strategy for the wastewater contact oxidation tank during the evolutionary process. Based on the characteristics and evolution of scattered clusters when anomalies are identified, corresponding optimization strategies are determined. Among them, there are pre-set optimization strategies for when scattered cluster features are freed from the constraints of the corresponding scattered cluster features, and pre-set optimization strategies for when evolutionary features are freed from the constraints of evolutionary features.

[0064] In some possible implementations, When the scattered cluster characteristics exceed the upper limit of the scattered cluster characteristics constraint, that is, the scattering frequency exceeds the scattering frequency constraint or / and the average area of ​​the scattered cluster exceeds the average area constraint of the scattered cluster, it reflects that the biofilm has insufficient structural strength and reduced adhesion. Optimization strategies include suspending water intake, reducing aeration power, and extending the duration of non-pulse phase. When the mean of the evolutionary trait distribution exceeds the constraint of the mean distribution, it reflects a problem with the microbial morphology. There are two possible scenarios, including: If the mean distribution of the morphological parameter corresponding to the edge ambiguity dimension in the evolutionary characteristics exceeds the constraint of the corresponding mean distribution, it reflects that the edge ambiguity of the packing cluster is severe. This usually indicates that the bacterial flocs are suffering from acute toxicity shock, the extracellular polymers at the edge dissolve rapidly, and the structure quickly loosens. Optimization strategies include suspending water intake, reducing aeration power, and adding powdered activated carbon. Activated carbon can quickly adsorb residual bactericides, heavy metal ions, and other inhibitory substances in the water, providing a temporary detoxification microenvironment for microorganisms.

[0065] If the mean distribution of the morphological parameters of the discrete dimension at the edge in the evolutionary features exceeds the constraint of the corresponding mean distribution, it reflects that the structure of the filler cluster is relatively loose. Optimization strategies include supplementing nutrient substrates, such as sodium acetate solution, to improve the substrate competition ability of the floc bacteria. By taking advantage of the fact that the floc bacteria have a better growth rate than filamentous bacteria under nutrient-rich conditions, they can regain their dominant ecological niche.

[0066] When the rate of change of evolutionary characteristics exceeds the constraint of the distribution mean, it reflects a problem with microbial growth and attachment, i.e., the original biofilm formation process is not progressing well. If the rate of change is lower than the lower limit of the rate of change constraint, it reflects insufficient microbial proliferation and attachment motivation. The optimization strategy can reduce the aeration intensity and supplement the nutrient substrate.

[0067] If the rate of change exceeds the upper limit of the rate of change constraint, it is often because the pretreatment has failed to effectively remove suspended paint mist particles and high molecular weight organic matter in the water. These particles are physically adsorbed or adhered to the packing material in a non-biological manner. Optimization strategies include checking whether the aeration and stirring of the equalization tank are sufficient to avoid high-concentration wastewater from entering the biological treatment tank in a short period of time, causing excessive local instantaneous load, which prevents microorganisms from effectively degrading the wastewater and can only perform physical adsorption.

[0068] This invention considers that the scattered cluster characteristics and edge morphology parameters should fluctuate within corresponding ranges under normal circumstances during several evolutionary stages of the biofilm formation process. Based on this, an evolutionary feature constraint map is constructed, and then anomaly precursors are identified by setting scattered cluster characteristic constraints and edge morphology characteristic constraints for each evolutionary stage. During system operation, the real-time extracted scattered cluster characteristics and edge morphology evolutionary characteristics are compared with the constraints corresponding to the current evolutionary stage to determine anomaly precursors. This makes anomaly detection more consistent with the actual developmental patterns of biofilms, and is more sensitive, allowing for early detection and intervention within the critical community succession window. This enables timely output of optimization strategies to ensure biofilm formation efficiency and improve the stability of the wastewater treatment system.

[0069] To elaborate, the packing cluster image is an image frame containing packing clusters; The proportion of filler clusters in the image frame must meet the predetermined ratio requirement.

[0070] In practice, to avoid the filler clusters accounting for too small a proportion in the image frame, the area of ​​the filler clusters should not be less than 1 / 10 of the total image frame area.

[0071] In more detail, it also includes a storage module, which is used to record the filler cluster images and polarization resistance values ​​collected by the IoT module.

[0072] Example 1 This embodiment records the polarization resistance value and corresponding fluctuation parameters during 15 time periods in a single film attachment process. Each time period is 24 hours, and fluctuation parameters exceeding 400 are considered high.

[0073] Table 1. Record of polarization resistance values ​​and fluctuation parameters during a specific time period of a single biofilm formation. Please refer to Table 1. In the 6th, 7th, 8th, 9th, 10th and 11th time periods, the polarization resistance value showed a significant decrease and the fluctuation remained high. This indicates that the metabolically active bacterial flocs began to gradually become dominant on the packing material, and electron transfer became more active. The polarization resistance value showed a trend reversal from a high point to a continuous decrease. Furthermore, due to the emergence of metazoans throughout the process, the electron transfer activity of the biofilm was unstable during the process of ciliate growth and decline, and the fluctuation rate of the polarization resistance value increased significantly. This means that the 6th, 7th, 8th, 9th, 10th and 11th time periods entered the community succession window.

[0074] Example 2 This embodiment records the feature data of scattered clusters within the community succession window.

[0075] In practice, the wastewater contact oxidation tank has dimensions of 2.0m long, 1.0m wide, and 1.5m high, with an effective water depth of 1.2m and an aeration power of 0.8 m³ / s during the non-pulse stage. 3 / (m 2 •h), pulse aeration power 1.0 m 3 / (m 2 The pulse segment lasts for 60 seconds, the non-pulse segment lasts for 1 hour, and the hydrodynamic retention time is set to be relatively low during the biofilm formation stage, which is 24 hours.

[0076] Scattered cluster features were collected every 1 minute within the pulse segment duration and recorded as Table 2.

[0077] Table 2. Record of characteristics of scattered clusters within a single evolutionary process. Please refer to Table 2. The scattered cluster features are generally within a stable range. In practice, the scattered cluster feature constraints are 20-40 for the scattered frequency and 2.20-4.00 for the average area of ​​the scattered clusters. It can be seen that during the evolution process, the scattered cluster features have not deviated from the corresponding scattered cluster feature constraints.

[0078] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A wastewater treatment system for marine component painting, characterized in that, include: The Internet of Things (IoT) module includes an image acquisition unit deployed in the wastewater contact oxidation tank for acquiring images of packing clusters and an electrode assembly for acquiring polarization resistance values. The observation and analysis module is used to lock the community succession window based on the time-domain change trend and fluctuation of the polarization resistance value. Within the community succession window, pulse aeration intervention observation is carried out, including staged control of the aeration unit to perform pulse aeration, determining the observation field of the packing cluster in the direction of water flow during the pulse stage, and extracting the characteristics of scattered clusters within the observation field. The morphological analysis module is used to perform edge morphological evolution analysis based on the collected images of filler clusters within the community succession window. This includes summarizing the edge morphological parameters of filler clusters in multiple dimensions during the non-pulse stage, in order to analyze the evolution characteristics of edge morphological parameters in each dimension. The biofilm intervention module is used to analyze the scattered cluster characteristics and evolutionary characteristics based on a pre-constructed evolutionary characteristic constraint map in several evolutionary processes, so as to identify abnormal precursors and determine the optimization strategy for the wastewater contact oxidation tank in the evolutionary process. The evolutionary process is a sub-time period in the membrane attachment stage, and the evolutionary feature constraint map includes evolutionary feature constraints in different evolutionary processes and scattered cluster feature constraints. The observation and analysis module determines the observation field of view of the packing clusters in the direction of water flow during the pulse phase, and extracts the features of scattered clusters within the observation field, including... Identify the outline of packing clusters based on images of packing clusters; Construct a directional reference line passing through the center of the packing cluster outline in the packing cluster image based on the water flow direction; A reference point is determined on the direction reference line at a predetermined distance from the center of the filler cluster outline, and a rectangular area is constructed based on the reference point as the field of view; Cluster the pixels within the observation field of view, identify several clusters, and filter the clusters based on the color parameters within the filler cluster outlines to select several scattered clusters. Based on the time-series arranged images of filler clusters, the scattered cluster characteristics are determined within the corresponding observation field, including the scattering frequency and the average area of ​​the scattered clusters; The morphology analysis module summarizes the edge morphology parameters of the packing clusters in the non-pulse phase across multiple dimensions, including... The outline of the packing cluster is identified based on the image of the packing cluster, and a strip-shaped observation area of ​​a predetermined width is determined along the outline of the packing cluster; Within the strip-shaped observation area, several sampling points are determined at intervals, and several central normals passing through the sampling points and the center of the filler cluster outline are constructed to obtain normal line segments divided by the strip-shaped observation area. Select several pixels on each of the normal segments and calculate the gradient of the corresponding gray value of the pixel. The mean gradient is determined as the edge blur dimension morphological parameter. The fractal dimension of the filler cluster profile is determined as the edge discrete dimension morphological parameter; The membrane-attachment intervention module analyzes the characteristics and evolutionary features of the scattered clusters to determine abnormal precursors, including... Based on the current evolutionary process, select the corresponding evolutionary feature constraints and scattered cluster feature constraints from the evolutionary feature constraint map; The scattered cluster features corresponding to the evolution process are compared with the corresponding scattered cluster feature constraints; The evolutionary features corresponding to the evolutionary process are compared with the corresponding evolutionary feature constraints; If the scattered cluster features break free from the scattered cluster feature constraints or / and the evolutionary features break free from the evolutionary feature constraints, then an abnormal precursor is determined to exist; The biofilm intervention module determines the optimal strategy for the wastewater contact oxidation tank during the evolution process. Based on the characteristics and evolution of scattered clusters when abnormal precursors are identified, corresponding optimization strategies are determined; Among them, there are pre-set optimization strategies for when scattered cluster features are freed from the constraints of the corresponding scattered cluster features, and pre-set optimization strategies for when evolutionary features are freed from the constraints of evolutionary features.

2. The marine component spraying wastewater treatment system according to claim 1, characterized in that, The observation and analysis module is used to lock the community succession window based on the time-domain variation trend and fluctuation of the polarization resistance value. Real-time acquisition of the polarization resistance value over a certain period of time is used to determine the time period in which the downward trend exists. The fluctuation parameters of polarization resistance values ​​within each time period are determined in order to screen time periods that meet the screening conditions for community succession, which are then used as community succession windows. The community succession screening conditions include a decreasing polarization resistance value and a fluctuation parameter greater than a preset upper limit value within a time period.

3. The marine component spraying wastewater treatment system according to claim 1, characterized in that, The observation and analysis module controls the aeration unit to perform pulse aeration in stages, including... The aeration unit is controlled to alternate between pulse and non-pulse phases to perform pulse aeration. The pulse phase is the period during which the aeration power is increased, and the non-pulse phase is the period during which the aeration power is maintained.

4. The marine component spraying wastewater treatment system according to claim 3, characterized in that, The evolution characteristics of edge morphology parameters in each dimension of the morphology analysis module. The mean distribution and rate of change of the edge fuzzy dimension morphological parameters and the edge discrete dimension morphological parameters in different evolutionary processes are respectively determined as the corresponding evolutionary features.

5. The marine component spraying wastewater treatment system according to claim 1, characterized in that, The filler cluster image is an image frame containing filler clusters; The proportion of filler clusters in the image frame must meet the predetermined ratio requirement.

6. The marine component spraying wastewater treatment system according to claim 1, characterized in that, It also includes a storage module for recording the filler cluster images and polarization resistance values ​​collected by the IoT module.

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