An image recognition system applied to condensate water fine treatment of power plant

Abstract

The application discloses an image recognition system applied to condensate water fine treatment of a power plant, and particularly relates to the field of image recognition, and comprises an initialization module, a noise reduction module, an analysis module, a standardization module, a classification module, an evaluation module, a suggestion module and a feedback module. The initialization module is used for equipment deployment and basic data acquisition, and a three-dimensional space-time coordinate system is established. The noise reduction module is used for obtaining a water flow disturbance coefficient and a dynamic wavelet threshold value based on working condition parameters, and performing noise reduction processing on an original image. The analysis module is used for extracting static features and dynamic features of pollutant particles, and analyzing each feature parameter. The self-adaptive wavelet noise reduction algorithm is designed by fusing condensate water working condition parameters, the noise reduction threshold value is dynamically adjusted, the error of the static feature extraction of the pollutant particles is reduced, the accuracy of subsequent classification and recognition is significantly improved, and the misjudgment rate of fine components is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of image recognition technology, and more specifically, to an image recognition system applied to the polishing of condensate in power plants. Background Technology

[0002] In power plant thermal systems, condensate polishing is a core component for ensuring boiler feedwater quality meets standards, preventing equipment corrosion and scaling, and extending unit lifespan. Currently, the industry widely employs image recognition technology to assist in monitoring the condensate polishing process. By deploying cameras in the observation section of the polishing unit, image information of pollutants in the condensate is collected. Morphological analysis is then used to achieve preliminary identification of pollutants and assessment of water quality status, providing decision support for regeneration system switching and backwashing operations.

[0003] The application of existing image recognition technology in condensate polishing has advantages over traditional chemical detection methods, such as fast response speed, high visualization, and no need for frequent sampling. It can reduce manual intervention to a certain extent and improve monitoring efficiency. However, several technical shortcomings remain in actual operation: First, image recognition relies solely on static features such as grayscale and area of ​​a single frame, ignoring dynamic information such as the trajectory and aggregation characteristics of pollutant particles. This leads to a high misjudgment rate for fine particles, making it difficult to accurately distinguish between different types of pollutants with similar morphologies. Second, it does not incorporate actual operating parameters of condensate (flow rate, temperature, pressure). Image acquisition is easily affected by water flow disturbances and light reflection. Existing noise reduction algorithms use fixed thresholds and do not consider the impact of operating condition fluctuations on image quality, resulting in the loss of feature details or residual noise after noise reduction. Third, the pollutant classification system is simplistic, based solely on particle size, failing to accurately identify specific pollutant types such as resin damage, iron oxides, and organic flocs. This results in a lack of targeted treatment measures, easily leading to waste of regenerant or incomplete treatment. Fourth, water quality status assessment lacks quantitative indicators, providing only qualitative judgments and failing to provide data support for the fine-tuning of the treatment system, leading to problems of delayed or over-adjustment.

[0004] To address the shortcomings of existing technologies, such as high misjudgment rate, insufficient targeting, limited accuracy, and lack of quantitative evaluation, an image recognition system for condensate polishing in power plants is needed. This system should employ adaptive noise reduction, dynamic feature fusion, precise classification modeling, and quantitative evaluation feedback to achieve accurate identification of pollutant types, quantitative determination of water quality status, and dynamic optimization of treatment strategies, thereby comprehensively improving the intelligence level and treatment effect of condensate polishing. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an image recognition system for condensate polishing in power plants, which solves the problems mentioned in the background art through the following solutions.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an image recognition system applied to condensate polishing in power plants, comprising: Initialization module: Equipment deployment and basic data acquisition, and establishment of a three-dimensional spatiotemporal coordinate system; Noise reduction module: Based on the operating parameters, the water flow disturbance coefficient and dynamic wavelet threshold are obtained to perform noise reduction processing on the original image; Analysis module: Extracts the static and dynamic characteristics of pollutant particles and analyzes each characteristic parameter; Standardization module: Constructs the original feature vector and performs standardization processing on the feature vector; Classification module: Optimize the RBF kernel function parameters and use an improved SVM classifier to classify pollutant types; Assessment module: Analyzes the contribution factors of various pollutants and assesses the total pollution index based on the proportion of pollutant quantities; Recommendation module: Classifies water quality levels based on total pollution index and generates targeted operation adjustment recommendations based on operating condition adjustment coefficients; Feedback module: Regularly updates standardized parameters, combines chemical test results to correct deviations, and optimizes model accuracy.

[0007] Preferably, the equipment deployment includes: installing a high-definition industrial camera in the transparent observation section of the condensate polishing device, with the lens axis perpendicular to the condensate flow direction to ensure full coverage of the observation section cross-section; installing an electromagnetic flow meter, temperature sensor, and pressure sensor 3m from the inlet side of the observation section; the basic data includes operating condition acquisition and image parameters, the operating condition parameters including condensate flow rate Q, temperature T, and pressure P, and simultaneously setting standard reference parameters including standard temperature. Standard pressure Standard flow rate The image parameters include setting the camera acquisition frame rate and image resolution R; the three-dimensional spatiotemporal coordinate system is established with the center of the observation section cross-section as the origin O, the condensate flow direction as the X-axis, the pipe radial direction as the Y-axis, and the acquisition time as the T-axis, forming a three-dimensional spatiotemporal coordinate system O-XYT.

[0008] Preferably, the flow disturbance coefficient is obtained based on the coupling relationship between flow rate and pressure, as shown in the formula: ,in A larger value indicates a more severe water flow disturbance; the dynamic wavelet threshold is designed based on the image pixel size and the disturbance coefficient, and the formula is as follows: Where N×M is the total number of pixels in the image; the noise reduction process involves performing a 3-level wavelet decomposition on the acquired original image to obtain low-frequency approximation coefficients and high-frequency detail coefficients. A hard threshold is applied to the high-frequency detail coefficients, where the threshold value is greater than a certain threshold. The coefficient is retained, less than The coefficients are set to zero, and the image is reconstructed by inverse wavelet transform to obtain a clear image after noise reduction.

[0009] Preferably, the static features include equivalent particle size d, average gray value G, and particle roundness C. The formula for calculating the equivalent particle size d is as follows: Where S is the pixel area of ​​the particle, R is the image resolution, and the formula for calculating the grayscale mean G is: ,in Let C be the grayscale value of the i-th pixel. The formula for calculating the grain roundness C is: Where L is the perimeter of the particle outline pixels; the dynamic features include motion speed v and aggregation coefficient K, and the formula for calculating motion speed v is: ,in The formula for calculating the aggregation coefficient K is: [Formula omitted for brevity] where K represents the coordinates of the same particle in F consecutive frames of images. Where n is the number of particles within the trajectory cluster. D is the average equivalent particle size within the cluster, and D is the actual diameter of the circumcircle of the trajectory cluster. The method for analyzing each feature parameter is as follows: based on the denoised image and the three-dimensional spatiotemporal coordinate system, the basic data required for calculation is obtained through image segmentation, contour extraction, and trajectory tracking, and the parameters are calculated according to the corresponding formulas.

[0010] Preferably, the original feature vector The standardization process involves normalizing the feature vectors using the Z-score standardization method, with the following formula: ,in Let i be the standardized value of the i-th feature. This is the historical sample mean of this feature. The standard deviation of the historical samples for this feature is denoted as , and after standardization, the values ​​of all features are concentrated in the range of [-3, 3].

[0011] Preferably, the RBF kernel function parameters The formula is derived from the 2-norm of the standardized eigenvectors. ,in The 2-norm of the standardized feature vector is used; the improved SVM classifier is constructed based on the optimized RBF kernel function, using 500 sets of labeled pollutant images and working condition data as training samples; the pollutant type classification is performed by standardizing the feature vector... Input the trained SVM classifier and output the pollutant type label. The classification target is four core pollutants: resin broken particles Label=1, iron oxide particles Label=2, organic flocs Label=3, and inorganic impurities Label=4.

[0012] Preferably, the classification contribution factor is designed for different types of pollutants, with a contribution factor of resin broken particle Label=1. ,in The contribution factor for iron oxide particles with Label=2, representing the standard resin particle size. ,in The contribution factor of organic flocs Label=3 for standard particle velocity. Contribution factor of inorganic impurity Label=4 The total pollution index is calculated based on the proportion of each type of pollutant, using the following formula: ,in Let i be the number of particles of pollutant class i. The total number of all pollutants, PI, ranges from [0, +∞); the pollutant quantity percentage is calculated by statistically analyzing the particle count of various pollutants using image recognition. Calculate the quantity of each category and the total quantity. The ratio of .

[0013] Preferably, the method for classifying water quality grades is as follows: A PI threshold is set: PI < 0.3 is excellent (Grade A); 0.3 ≤ PI < 0.7 is good (Grade B); 0.7 ≤ PI < 1.2 is slightly polluted (Grade C); and PI ≥ 1.2 is heavily polluted (Grade D). The operating condition adjustment coefficient is calculated based on flow rate and temperature, using the following formula: The proposed adjustments are as follows: Level A should maintain its current operating status, and the backwashing frequency should remain unchanged. Level B: Adjust backwashing frequency Level C activates enhanced filtering mode and adjusts the filtering speed. ,in For standard filtration speed; for grade D, immediately stop the fine treatment system and start the resin regeneration program, using regenerant at the following dosage: Calculation, where The standard regenerant dosage is determined, and the regeneration process is optimized based on the distribution of pollutant types.

[0014] Preferably, the method for updating the standardized parameters is: every interval Collect 100 new sets of data, recalculate the characteristic mean and standard deviation, and update the standardized parameters; the bias correction method is as follows: perform chemical tests once a week, where the test indicators include conductivity, Content, iron content, converting chemical test results into a chemical pollution index. Calculate model bias ,in Calculate values ​​for the model using the formula. Correct the next PI calculation result, where The benchmark time interval is used as the reference time interval. The method for optimizing the model accuracy is as follows: adapting to fluctuations in operating conditions and changes in water quality through dynamic parameter updates, and combining deviation correction to avoid excessive impact of single deviations on the results, thereby ensuring the long-term stable accuracy of the model.

[0015] The technical effects and advantages of this invention are as follows: 1. This invention designs an adaptive wavelet denoising algorithm by integrating condensate working parameters and dynamically adjusts the denoising threshold, which solves the problem of image noise and detail loss caused by water flow disturbance in the prior art, reduces the error of static feature extraction of pollutant particles, significantly improves the accuracy of subsequent classification and recognition, and effectively reduces the misclassification rate of small particles. 2. This invention constructs a multi-dimensional feature system of "static + dynamic" and combines working condition-image fusion modeling with an improved SVM classifier to achieve accurate differentiation of four types of core pollutants, improve classification accuracy, and solve the defects of traditional technology that only classifies by particle size and lacks specificity. It provides a precise basis for optimizing treatment measures and reduces the risk of waste of regenerant and incomplete treatment. 3. This invention, by designing a multi-parameter-based pollution index quantification model and combining it with dynamic adjustment coefficients to generate targeted operational suggestions, realizes the transformation of water quality status from "qualitative judgment" to "quantitative control," solves the problems of lagging and over-adjustment in existing technologies, shortens the response time of the fine treatment system to changes in water quality, and effectively avoids equipment corrosion and scaling. 4. Through dynamic parameter updates and chemical detection feedback correction mechanisms, the model can adapt to fluctuations in operating conditions and changes in water quality, maintaining stable accuracy over long-term operation. At the same time, it reduces the frequency and intensity of manual sampling and testing, lowers labor costs and chemical reagent consumption, and realizes intelligent, efficient, and green operation of condensate polishing, providing a reliable guarantee for the safety and stability of power plant thermal systems. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] As attached Figure 1The image recognition system shown is applied to the polishing of condensate in power plants, including an initialization module, a noise reduction module, an analysis module, a standardization module, a classification module, an evaluation module, a suggestion module, and a feedback module.

[0019] The initialization module includes equipment deployment and basic data acquisition, and the establishment of a three-dimensional spatiotemporal coordinate system. In this embodiment, the specific details are as follows: The equipment deployment involves installing a high-definition industrial camera in the transparent observation section of the condensate polishing device, with the lens axis perpendicular to the condensate flow direction to ensure full coverage of the observation section cross-section; an electromagnetic flow meter, temperature sensor, and pressure sensor are installed 3m from the inlet side of the observation section; the basic data includes operating condition acquisition and image parameters, including condensate flow rate Q, temperature T, and pressure P, while standard reference parameters are set, including a standard temperature. Standard pressure Standard flow rate The image parameters include setting the camera's frame rate and image resolution R. The three-dimensional spatiotemporal coordinate system is established with the center of the observation section cross-section as the origin O, the condensate flow direction as the X-axis, the pipe radial direction as the Y-axis, and the acquisition time as the T-axis, forming a three-dimensional spatiotemporal coordinate system O-XYT. During equipment deployment, the axis of the high-definition industrial camera lens is perpendicular to the condensate flow direction to ensure complete coverage of the observation section cross-section and avoid blind spots. The operating condition sensor is installed three meters from the inlet side of the observation section to accurately capture the actual operating conditions of the condensate before it enters the observation section, avoiding interference from water flow disturbances within the observation section on parameter detection. Among the basic data, flow rate, temperature, and pressure are core operating condition parameters that directly affect the physical state of condensate and the behavior of pollutants, and must be prioritized for acquisition. The image parameter settings need to match the particle size range of condensate pollutants, especially fine particles. By reasonably setting the resolution and pixel size, it is possible to ensure clear capture of minute pollutants, meeting subsequent identification requirements. The establishment of a three-dimensional spatiotemporal coordinate system, with the center of the observation section as the origin and combined with the water flow direction, pipe radial direction, and acquisition time, enables precise positioning of pollutant particles in time and space, providing logical support for dynamic feature extraction and conforming to the comprehensive analysis logic of "space + time". High-definition industrial cameras, electromagnetic flowmeters, temperature sensors, and pressure sensors are all existing mature industrial detection equipment with low technical barriers and can be directly purchased and applied. The setting of image parameters is based on the actual inner diameter of the observation section and the required accuracy of pollutant detection, and can be determined through simple size conversion without technical obstacles. The construction of the three-dimensional spatiotemporal coordinate system is based on classical spatial coordinate theory, extended with the time dimension, with a solid theoretical foundation, allowing for direct implementation without any insurmountable technical difficulties. This module is the fundamental prerequisite for the entire image recognition method. The rationality of equipment deployment determines the completeness and accuracy of data acquisition. Comprehensive acquisition of basic data provides raw materials for subsequent noise reduction, feature extraction, and classification recognition. The three-dimensional spatiotemporal coordinate system provides a necessary spatiotemporal reference framework for the analysis of pollutant dynamic characteristics (such as movement speed). Without the support of this module, all subsequent technical steps would be impossible due to missing data or insufficient reference systems.

[0020] The noise reduction module: obtains the water flow disturbance coefficient and dynamic wavelet threshold based on the operating parameters, and performs noise reduction processing on the original image; In this embodiment, it should be specifically noted that the water flow disturbance coefficient is obtained based on the coupling relationship between flow rate and pressure, and the formula is as follows: ,in A larger value indicates a more severe water flow disturbance; the dynamic wavelet threshold is designed based on the image pixel size and the disturbance coefficient, and the formula is as follows: Where N×M is the total number of pixels in the image; the noise reduction process involves performing a 3-level wavelet decomposition on the acquired original image to obtain low-frequency approximation coefficients and high-frequency detail coefficients. A hard threshold is applied to the high-frequency detail coefficients, where the threshold value is greater than a certain threshold. The coefficient is retained, less than The coefficients are set to zero, and the image is reconstructed through inverse wavelet transform to obtain a clear image after noise reduction. Fluctuations in the flow rate, temperature, and pressure of condensate directly cause water flow disturbance, which is the main cause of image blurring and noise. Therefore, calculating the water flow disturbance coefficient through the coupling relationship of flow rate, temperature, and pressure can accurately quantify the degree of disturbance. Traditional fixed-threshold noise reduction does not consider changes in operating conditions and is prone to problems such as "incomplete noise reduction" or "loss of feature details." Dynamically adjusting the wavelet threshold based on the disturbance coefficient and the number of image pixels allows the noise reduction intensity to match the degree of water flow disturbance, preserving the subtle features of pollutants while eliminating noise, conforming to the correlation logic of "operating conditions-image quality." Wavelet decomposition, inverse transform, and hard thresholding are existing mature image processing technologies that have been widely used in industrial image noise reduction scenarios. The calculation of the water flow disturbance coefficient and the dynamic wavelet threshold are both based on the basic data (flow rate, temperature, pressure, and number of image pixels) collected by the initialization module, without adding any additional unknown parameters. The calculation logic conforms to mathematical principles and can be implemented through existing data processing algorithms, without any technological gaps. This module solves the problem of image noise caused by operating condition fluctuations. Through adaptive noise reduction processing, it removes interfering factors and provides clear and reliable image data for subsequent feature extraction. It avoids noise interference with feature recognition and directly improves the analysis accuracy of subsequent steps. It is a key bridge connecting basic data acquisition and feature extraction.

[0021] The analysis module extracts the static and dynamic characteristics of pollutant particles and analyzes each characteristic parameter. In this embodiment, it should be specifically noted that the static features include the equivalent particle size d, the average gray value G, and the particle roundness C. The formula for calculating the equivalent particle size d is as follows: Where S is the pixel area of ​​the particle, R is the image resolution, and the formula for calculating the grayscale mean G is: ,in Let C be the grayscale value of the i-th pixel. The formula for calculating the grain roundness C is: Where L is the perimeter of the particle outline pixels; the dynamic features include motion speed v and aggregation coefficient K, and the formula for calculating motion speed v is: ,in The formula for calculating the aggregation coefficient K is: [Formula omitted for brevity] where K represents the coordinates of the same particle in F consecutive frames of images. Where n is the number of particles within the trajectory cluster. Let D be the average equivalent particle size within the cluster, and D be the actual diameter of the circumcircle of the trajectory cluster. The method for analyzing each feature parameter is as follows: based on the denoised image and the three-dimensional spatiotemporal coordinate system, the basic data required for calculation is obtained through image segmentation, contour extraction, and trajectory tracking, and the parameters are calculated according to the corresponding formulas. A single feature cannot fully characterize the properties of pollutants. For example, resin particles and inorganic impurities of similar sizes cannot be distinguished by particle size alone, but their mean grayscale and roundness may differ significantly. Furthermore, the movement speed and aggregation behavior of the same type of pollutant in water flow will exhibit unique patterns (e.g., organic flocs are prone to aggregation and move slowly). Therefore, static features (equivalent particle size, mean grayscale, particle roundness) can reflect the physical properties of the pollutant itself, while dynamic features (movement speed, aggregation coefficient) can reflect the behavioral characteristics of the pollutant in water flow. Combining multi-dimensional features can achieve a comprehensive characterization of pollutants, overcoming the limitations of single-feature identification. Image segmentation, contour extraction, and trajectory tracking are all standard techniques in industrial image recognition and can be directly applied to pollutant feature extraction. The calculation of each feature parameter is based on the denoised image data and the three-dimensional spatiotemporal coordinate system established by the initialization module. The calculation logic conforms to geometric principles (e.g., equivalent particle size derived from area) and statistical principles (e.g., grayscale mean based on pixel grayscale statistics). All the basic data required for the calculation comes from the aforementioned modules, with no fictitious parameters, making it highly operable. This module breaks through the limitations of traditional single-feature recognition, constructing a "feature profile" of pollutants through multi-dimensional features. This provides comprehensive and effective data support for subsequent accurate classification, reducing misjudgments of pollutants due to insufficient features, and is a core prerequisite for achieving accurate pollutant identification.

[0022] The standardization module: constructs the original feature vector and performs standardization processing on the feature vector; In this embodiment, it should be specifically noted that: the original feature vector The standardization process involves normalizing the feature vectors using the Z-score standardization method, with the following formula: ,in Let i be the standardized value of the i-th feature. This is the historical sample mean of this feature. The standard deviation of the historical samples for this feature is denoted as . After standardization, all feature values ​​fall within the range of [-3, 3]. However, the dimensions of operating parameters (flow rate, temperature, pressure) and image features (equivalent particle size, grayscale mean, etc.) differ significantly. For example, flow rate is measured in cubic meters per hour, and the grayscale mean ranges from 0 to 255. Direct fusion analysis would allow features with larger dimensions to dominate the identification results, masking the role of other key features. Standardization unifies all features to the same numerical range, eliminating the weight imbalance caused by dimensional differences and ensuring that each feature plays an equally important role in classification and identification, aligning with the scientific logic of data fusion analysis. Z-score standardization is a classic method in data preprocessing, with mature technology and simple calculation. The historical sample mean and standard deviation can be statistically derived from a large amount of historical operating data accumulated by the power plant, requiring no additional specialized experiments. The standardization process is based solely on feature parameters extracted by the analysis module, with no new data requirements, and can be directly implemented. This module optimizes the effectiveness of feature data, avoids interference from dimensional differences on subsequent classification models, and enables classification models to learn the correlation between each feature and pollutant type more objectively. This provides high-quality input data for subsequent accurate classification and improves the reliability of the classification model.

[0023] The classification module optimizes the RBF kernel function parameters and uses an improved SVM classifier to classify pollutant types. In this embodiment, it is specifically necessary to explain that the RBF kernel function parameters The formula is derived from the 2-norm of the standardized eigenvectors. ,in The 2-norm of the standardized feature vector is used; the improved SVM classifier is constructed based on the optimized RBF kernel function, using 500 sets of labeled pollutant images and working condition data as training samples; the pollutant type classification is performed by standardizing the feature vector... The trained SVM classifier is input and outputs a pollutant type label. The classification targets are four core pollutants: resin breakage particles (Label=1), iron oxide particles (Label=2), organic flocs (Label=3), and inorganic impurities (Label=4). Condensate pollutant classification falls under the category of "small sample, high-dimensional data" classification scenarios. In such scenarios, the SVM classifier has advantages in high classification accuracy and strong generalization ability, making it suitable for distinguishing between the four core pollutants, including resin breakage particles and iron oxides. The parameters of the RBF kernel function directly affect the classifier's fitting ability. Traditional fixed parameters cannot adapt to changes in feature distribution under different operating conditions. Dynamically optimizing the parameters based on the L2 norm of standardized feature vectors allows the kernel function to better fit real-time feature data, improving the classifier's adaptability to changes in operating conditions and avoiding the decrease in classification accuracy caused by fixed parameters. Both the SVM classifier and the RBF kernel function are mature machine learning algorithms with well-established technical implementations. Training samples can be accumulated through annotation of historical monitoring data from power plants (500 sets of labeled data meet the needs of small-sample training in industrial settings). Kernel function parameter optimization is based on standardized feature vectors from a standardized module, with clear computational logic and no technical barriers, and can be implemented through existing algorithm programming. This module achieves accurate differentiation of pollutant types, overcoming the shortcomings of traditional technologies that only classify by particle size and lack specificity. It provides accurate pollutant type criteria for subsequent development of differentiated treatment strategies, avoiding inappropriate treatment measures due to classification errors (such as misjudging resin damage as inorganic impurities, resulting in waste of regenerant).

[0024] The assessment module analyzes the contribution factors of various pollutants and assesses the total pollution index based on the proportion of pollutant quantities. In this embodiment, it should be specifically noted that: the classification contribution factor is designed for different types of pollutants, with a contribution factor of resin broken particle Label=1. ,in The contribution factor for iron oxide particles with Label=2, representing the standard resin particle size. ,in The contribution factor of organic flocs Label=3 for standard particle velocity. Contribution factor of inorganic impurity Label=4 The total pollution index is calculated based on the proportion of each type of pollutant, using the following formula: ,in Let i be the number of particles of pollutant class i. The total number of all pollutants, PI, ranges from [0, +∞); the pollutant quantity percentage is calculated by statistically analyzing the particle count of various pollutants using image recognition. Calculate the quantity of each category and the total quantity. The ratio of pollutants to water quality is used to calculate the overall pollution index. Different pollutants pose varying degrees of harm to the power plant's thermal system. For example, resin damage directly affects condensate polishing, iron oxides easily cause boiler pipe corrosion, and organic flocs easily clog filters. Therefore, it is necessary to design specific contribution factors based on the hazard characteristics of each pollutant and quantify their impact weight on water quality. The calculation of the overall pollution index combines the hazard weight (contribution factor) of pollutants with their quantity proportion, comprehensively reflecting the overall pollution level of the water and avoiding assessment bias caused by considering only quantity or hazard, thus conforming to the scientific logic of water quality assessment.

[0025] The design of contributing factors is based on the physicochemical properties and actual hazards of various pollutants (e.g., the lower the roundness of the broken resin and the closer the particle size is to the standard resin, the greater the hazard), without any subjective fabrication factors. The calculation of the total pollution index integrates the characteristic parameters of the analysis module and the classification results of the classification module. All data comes from the preceding steps, the calculation logic is coherent, and it can be achieved through existing data processing technologies, without missing parameters or logical gaps. This module transforms water quality status from "qualitative judgment" to "quantitative assessment," solving the problem that traditional technologies cannot quantify the degree of water pollution. It provides accurate quantitative basis for subsequent water quality classification and treatment strategy formulation, making the water quality assessment results more scientific and convincing, and providing data support for refined operation.

[0026] The suggestion module: classifies water quality levels based on the total pollution index and generates targeted operation adjustment suggestions based on the operating condition adjustment coefficient; In this embodiment, it should be specifically noted that: the method for classifying water quality grades is as follows: A PI threshold is set, PI < 0.3 is excellent (Grade A), 0.3 ≤ PI < 0.7 is good (Grade B), 0.7 ≤ PI < 1.2 is slightly polluted (Grade C), and PI ≥ 1.2 is heavily polluted (Grade D); the operating condition adjustment coefficient is calculated based on flow rate and temperature, using the following formula: The proposed adjustments are as follows: Level A should maintain its current operating status, and the backwashing frequency should remain unchanged. Level B: Adjust backwashing frequency Level C activates enhanced filtering mode and adjusts the filtering speed. ,in For standard filtration speed; for grade D, immediately stop the fine treatment system and start the resin regeneration program, using regenerant at the following dosage: Calculation, where The standard regenerant dosage is set, and the regeneration process is optimized based on the distribution of pollutant types. The water quality grade thresholds are set based on industry standards and practical operating experience for power plant condensate polishing. For example, high-quality water corresponds to a PI value of less than 0.3, meeting the water quality requirements for boiler feedwater. The threshold range has been verified by historical data and accurately reflects the impact of water quality on unit operation. The operating condition adjustment coefficients combine flow rate and temperature, as these two parameters directly affect the treatment efficiency of the polishing system (e.g., increased flow rate accelerates pollutant passage, requiring increased backwashing frequency). Different adjustment recommendations correspond to different water quality grades, based on logical deduction of pollutant hazard and operating condition adaptability, ensuring the targetedness and effectiveness of the adjustment measures. The water quality grade thresholds can be calibrated using historical power plant operating data and industry standards, providing practical data support. The parameters in the adjustment recommendations, such as backwashing frequency, filtration speed, and regenerant dosage, are all conventional adjustable indicators of the polishing system. The calculation of the adjustment coefficients and the generation of recommendations are based on the quantitative data from the preceding modules, with no unenforceable operational requirements, conforming to the actual operating specifications of the power plant. This module transforms quantified water quality data into directly executable operational strategies, solving the problems of lagging, over-adjustment, or under-adjustment in traditional technologies. It enables refined and dynamic operation of the fine treatment system, allowing treatment measures to be precisely matched with water quality status and operating conditions, effectively reducing the risk of equipment corrosion and scaling, and reducing regenerant waste and energy consumption.

[0027] The feedback module periodically updates standardized parameters, combines chemical detection results to correct deviations, and optimizes model accuracy.

[0028] In this embodiment, it should be specifically noted that the method for updating the standardized parameters is as follows: every interval Collect 100 new sets of data, recalculate the characteristic mean and standard deviation, and update the standardized parameters; the bias correction method is as follows: perform chemical tests once a week, where the test indicators include conductivity, Content, iron content, converting chemical test results into a chemical pollution index. Calculate model bias ,in Calculate values ​​for the model using the formula. Correct the next PI calculation result, where The benchmark time interval is used. The method for optimizing model accuracy is as follows: dynamic parameter updates adapt to fluctuations in operating conditions and changes in water quality, combined with deviation correction to avoid excessive impact of single deviations on the results, ensuring stable long-term model accuracy. Specifically, the operating conditions of power plant condensate fluctuate dynamically due to factors such as random load and fuel type. Fixed standardized parameters (mean, standard deviation) will gradually become incompatible with new operating conditions. Regularly updating parameters allows the model to continuously adapt to changes in operating conditions, avoiding distortion in feature standardization. Chemical testing is the authoritative method for water quality assessment, and its results have the highest reliability. Combining chemical testing results with deviation correction can compensate for systematic errors in image recognition (such as grayscale feature deviations caused by changes in lighting), ensuring long-term model accuracy and conforming to the model iteration logic of "dynamic optimization + authoritative calibration". The parameter update interval (ten minutes) and data volume (one hundred sets) are set based on actual engineering conditions, avoiding system fluctuations caused by frequent updates while ensuring data representativeness. The chemical testing indicators (conductivity, silica content, iron content) are routine testing items in power plants, requiring no additional testing equipment. The deviation correction logic is based on reasonable correction of system errors, with a simple calculation process that can be automated iteratively implemented through the existing control system. This module ensures the long-term stability and accuracy of the model, avoiding identification deviations caused by changes in operating conditions or system errors. It reduces the frequency and intensity of manual sampling and testing, lowers labor costs and chemical reagent consumption, improves the intelligence and reliability of the fine processing system, and provides long-term assurance for the safe and stable operation of the power plant's thermal system.

[0029] In conclusion, 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, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An image recognition system for use in condensate polishing in power plants, characterized in that, include: Initialization module: Equipment deployment and basic data acquisition, and establishment of a three-dimensional spatiotemporal coordinate system; Noise reduction module: Based on the operating parameters, the water flow disturbance coefficient and dynamic wavelet threshold are obtained to perform noise reduction processing on the original image; Analysis module: Extracts the static and dynamic characteristics of pollutant particles and analyzes each characteristic parameter; Standardization module: Constructs the original feature vector and performs standardization processing on the feature vector; Classification module: Optimize the RBF kernel function parameters and use an improved SVM classifier to classify pollutant types; Assessment module: Analyzes the contribution factors of various pollutants and assesses the total pollution index based on the proportion of pollutant quantities; Recommendation module: Classifies water quality levels based on total pollution index and generates targeted operation adjustment recommendations based on operating condition adjustment coefficients; Feedback module: Regularly updates standardized parameters, combines chemical test results to correct deviations, and optimizes model accuracy.

2. The image recognition system for condensate polishing in power plants according to claim 1, characterized in that: The equipment deployment includes: installing a high-definition industrial camera in the transparent observation section of the condensate polishing unit, with the lens axis perpendicular to the condensate flow direction to ensure full coverage of the observation section cross-section; installing an electromagnetic flow meter, temperature sensor, and pressure sensor 3m from the inlet side of the observation section; and providing basic data including operating condition acquisition and image parameters, such as condensate flow rate Q, temperature T, and pressure P, while also setting standard reference parameters including a standard temperature. Standard pressure Standard flow rate The image parameters include setting the camera acquisition frame rate and image resolution R; the three-dimensional spatiotemporal coordinate system is established with the center of the observation section cross-section as the origin O, the condensate flow direction as the X-axis, the pipe radial direction as the Y-axis, and the acquisition time as the T-axis, forming a three-dimensional spatiotemporal coordinate system O-XYT.

3. The image recognition system for condensate polishing in power plants according to claim 2, characterized in that: The water flow disturbance coefficient is obtained based on the coupling relationship between flow rate and pressure; the dynamic wavelet threshold is designed according to the image pixel size and disturbance coefficient; the noise reduction process is as follows: the original image is subjected to three-level wavelet decomposition to obtain low-frequency approximation coefficients and high-frequency detail coefficients. The high-frequency detail coefficients are processed by hard thresholding, where coefficients greater than the dynamic wavelet threshold are retained and coefficients less than the dynamic wavelet threshold are set to zero. Then, the image is reconstructed by inverse wavelet transform to obtain a clear image after noise reduction.

4. The image recognition system for condensate polishing in power plants according to claim 2, characterized in that: The static characteristics include equivalent particle size d, average gray value G, and particle roundness C; the dynamic characteristics include motion velocity v and agglomeration coefficient K, the formula for calculating motion velocity v is: ,in The coordinates of the same particle in F consecutive frames of images are given. The method for analyzing each feature parameter is as follows: based on the denoised image and the three-dimensional spatiotemporal coordinate system, the basic data required for calculation is obtained through image segmentation, contour extraction, and trajectory tracking, and the parameters are calculated according to the corresponding formulas.

5. The image recognition system for condensate polishing in power plants according to claim 4, characterized in that: The original feature vector The standardization process involves using the Z-score standardization method to normalize the feature vectors. After standardization, all feature values ​​are concentrated in the range of [-3, 3].

6. The image recognition system for condensate polishing in power plants according to claim 5, characterized in that: The RBF kernel function parameters The improved SVM classifier is derived from the 2-norm of the standardized feature vector; it is constructed based on the optimized RBF kernel function using 500 sets of labeled pollutant images and operating condition data as training samples; the pollutant type classification involves classifying the standardized feature vector... Input the trained SVM classifier and output the pollutant type label. The classification target is four core pollutants: resin broken particles Label=1, iron oxide particles Label=2, organic flocs Label=3, and inorganic impurities Label=4.

7. The image recognition system for condensate polishing in power plants according to claim 5, characterized in that: The classification contribution factors are designed for different pollutant types, including contribution factors for resin broken particles (Label=1), iron oxide particles (Label=2), organic flocs (Label=3), and inorganic impurities (Label=4); the total pollution index... The calculation is based on the proportion of each type of pollutant; the proportion of pollutant quantity is calculated by statistically analyzing the number of particles of each type of pollutant through image recognition, and then calculating the ratio of the quantity of each type to the total quantity.

8. The image recognition system for condensate polishing in power plants according to claim 7, characterized in that: The method for classifying water quality grades is as follows: A PI threshold is set: PI < 0.3 is excellent (Grade A); 0.3 ≤ PI < 0.7 is good (Grade B); 0.7 ≤ PI < 1.2 is slightly polluted (Grade C); and PI ≥ 1.2 is heavily polluted (Grade D). The operating condition adjustment coefficient is calculated based on flow rate and temperature. The adjustment recommendation is: Grade A should maintain its current operating state, and the backwash frequency should remain at [value missing]. ; Level B adjusts the backwash frequency to ; Level C activates enhanced filtering mode, adjusting the filtering speed to... Level D: Immediately stop the operation of the fine treatment system, start the resin regeneration program, and optimize the regeneration process according to the distribution of pollutant types.

9. An image recognition system for condensate polishing in power plants according to claim 1, characterized in that: The method for updating the standardized parameters is as follows: every interval Collect 100 new sets of data, recalculate the characteristic mean and standard deviation, and update the standardized parameters; the bias correction method is as follows: perform chemical tests once a week, where the test indicators include conductivity, Content, iron content, converting chemical test results into a chemical pollution index. Calculate model bias ,in Calculate values ​​for the model using the formula. Correct the next PI calculation result, where The benchmark time interval is used as the reference time interval. The method for optimizing the model accuracy is as follows: adapting to fluctuations in operating conditions and changes in water quality through dynamic parameter updates, and combining deviation correction to avoid excessive impact of single deviations on the results, thereby ensuring the long-term stable accuracy of the model.

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