Optimized operation method and system for sewage treatment

Abstract

The invention relates to the technical field of sewage treatment, in particular to an optimized operation method and system for sewage treatment. The method comprises the following steps: acquiring a multi-source sewage original data set; performing data preprocessing on the multi-source sewage original data set to generate a multi-source sewage preprocessing data set; performing feature set refining processing on the multi-source sewage preprocessing data set to generate a multi-source sewage feature set; generating an environment adaptability dynamic strategy according to the multi-source sewage feature set, and generating a sewage environment adaptability dynamic strategy; therefore, through integration of multi-source data, generation of an environmental adaptability strategy and fuzzy logic optimization, the problems that a traditional sewage treatment system is poor in environmental change adaptability and insufficient in decision basis are solved, and the sewage treatment efficiency and the system stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to a sewage treatment optimization operation method and system. Background Art

[0002] The existing sewage treatment system has many deficiencies. First, it has poor responsiveness to environmental changes. Under the influence of factors such as temperature and rainfall, it relies on static and single treatment solutions, cannot use real-time environmental data to dynamically optimize the treatment process, and is slow to respond to operating parameters such as sewage flow and dosage of reagents, resulting in large fluctuations in treatment effects, increased energy consumption, and low efficiency. Secondly, the optimization means are backward. At present, it mainly relies on experience and simplified control models. Under the interaction of complex multi-source data, it lacks data-driven methods, has weak ability to integrate multi-source data sets, and is difficult to deal with the heterogeneity and inconsistency of data. It is impossible to accurately extract valuable features for real-time processing, and is prone to data noise and outliers, which affects the accuracy of optimization decisions. Furthermore, the evaluation system is imperfect. The existing evaluation relies on fixed standards and periodic inspections, lacks real-time and continuous effect tracking and feedback, and is difficult to promptly discover and solve potential operational problems. Due to the dynamic changes in influencing factors in sewage treatment, a single evaluation system cannot meet complex actual needs. Summary of the invention

[0003] Based on this, it is necessary to provide a sewage treatment optimization operation method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for optimizing operation of sewage treatment is provided, the method comprising the following steps: Step S1: obtaining a multi-source sewage original data set; performing data preprocessing on the multi-source sewage original data set to generate a multi-source sewage preprocessing data set; Step S2: performing feature set refinement processing on the multi-source sewage pretreatment data set to generate a multi-source sewage feature set; generating an environmental adaptability dynamic strategy based on the multi-source sewage feature set to generate a sewage environmental adaptability dynamic strategy; Step S3: Perform fuzzy logic processing on the dynamic strategy of sewage environmental adaptability and assign fuzzy set weight values ​​to generate a sewage treatment adaptability optimization strategy; Step S4: construct an optimization report for the sewage treatment adaptability optimization strategy and generate a sewage treatment optimization analysis report, thereby completing the sewage treatment optimization operation.

[0005] The beneficial effect of the present invention is that, through a series of steps such as systematic multi-source data collection and preprocessing, feature set refinement, environmental adaptability dynamic strategy generation, fuzzy logic optimization processing and optimization report generation, the automation and intelligence level of the sewage treatment process is significantly improved, and various problems faced by the sewage treatment system are effectively solved from the data level. In step S1, multi-source data collection technology is adopted to obtain real-time data from different monitoring points (such as temperature, dissolved oxygen, pH value, flow rate, etc.), and through data preprocessing, noise is removed, missing values ​​are filled, and outliers are eliminated, etc., to ensure the accuracy and integrity of the data, which provides a reliable basis for subsequent analysis and decision-making. In step S2, by refining the feature set of the preprocessed data set, key features closely related to sewage treatment efficiency are further screened out, thereby laying a solid foundation for the subsequent generation of more accurate environmental adaptability dynamic strategies. This process effectively considers the impact of external environmental factors (such as temperature, precipitation, etc.) on sewage treatment, improves the resilience and flexibility of the system, and enables the sewage treatment process to be dynamically adjusted according to real-time changes. In step S3, a fuzzy logic processing strategy is adopted to convert the environmental adaptability dynamic strategy into an operational optimization scheme. By allocating weight values ​​to various data and parameters through fuzzy set theory, the problem of data uncertainty is solved, the decision-making process is refined and optimally controlled, so that various operations of sewage treatment can adapt to complex changing conditions and achieve the best results. Finally, in step S4, the generated optimization report summarizes the key decision indicators, operation effects and optimization suggestions for sewage treatment, provides quantitative analysis results and operational optimization schemes for the sewage treatment process, and further supports scientific decision-making and management. Therefore, the present invention solves the problems of poor adaptability of traditional sewage treatment systems to environmental changes and insufficient decision-making basis through the integration of multi-source data, the generation of environmental adaptability strategies and fuzzy logic optimization, and improves the sewage treatment efficiency and system stability.

[0006] Preferably, step S1 comprises the following steps: Step S11: Acquire a multi-source sewage original data set; Step S12: performing preliminary noise elimination on the original data set of multi-source sewage to generate multi-source sewage noise elimination data; performing preliminary outlier detection and elimination on the multi-source sewage noise elimination data to generate multi-source noise screening data; Step S13: performing data standardization processing on the multi-source noise screening data to generate a multi-source sewage pretreatment data set, wherein the multi-source sewage pretreatment data set includes sewage flow processing data and water quality physical quantity processing data.

[0007] The present invention eliminates the noise introduced by factors such as equipment errors and environmental interference in the data through preliminary noise elimination processing, and generates multi-source sewage noise elimination data. This process effectively improves the stability and credibility of the data by applying filtering algorithms, data smoothing and other methods, thereby laying a more robust foundation for subsequent analysis. Then, in step S12, the data after noise elimination is also preliminarily detected and eliminated for abnormal values, thereby eliminating abnormal data introduced by sensor failure or other abnormal factors, further improving the quality of the data and ensuring the accuracy of the data analysis results. In step S13, a data standardization processing method is used to uniformly convert physical quantities of different dimensions (such as water flow, pH value, dissolved oxygen, etc.) to the same standard scale, eliminating the deviation between different data dimensions, so that various data can be compared and fused under the same processing framework, thereby generating a multi-source sewage pretreatment data set. The pretreatment data set not only includes sewage flow treatment data, but also covers water quality physical quantity treatment data, which provides multi-dimensional and cleaned data support for subsequent data analysis, feature extraction and decision optimization. Through this series of data processing, it is ensured that subsequent analysis can be carried out based on high-quality data, thereby improving the intelligence level of the sewage treatment system, reducing decision-making errors caused by inaccurate data, and providing scientific and effective decision-making support for the system.

[0008] Preferably, step S2 comprises the following steps: Step S21: Acquire real-time environmental data of sewage treatment; Step S22: performing real-time sewage treatment index analysis on the real-time sewage treatment environment data to generate real-time sewage treatment index analysis data; Step S23: performing feature set refinement processing on the multi-source sewage pre-processing data set to generate a multi-source sewage feature set; Step S24: Generate a dynamic strategy for environmental adaptability based on the multi-source sewage feature set and sewage treatment real-time indicator analysis data to generate a dynamic strategy for sewage environmental adaptability.

[0009] The present invention constructs a real-time dynamic data source by acquiring real-time environmental data of sewage treatment, including external environmental parameters (such as temperature, precipitation, humidity, etc.) and the working status of sewage treatment equipment, and provides a highly timely input for subsequent processing and decision-making. The real-time indicator analysis of the acquired real-time environmental data of sewage treatment is carried out. This process reveals potential problems in the sewage treatment process, such as reduced equipment efficiency and water quality fluctuations, through statistical analysis, trend prediction and anomaly detection of the data, and generates real-time indicator analysis data for sewage treatment. This step provides strong support for the dynamic adjustment of the system at the data level, so that the system can respond promptly to changes in the external environment and the operating status of the equipment, thereby optimizing the treatment efficiency and avoiding potential treatment failures. Combined with the multi-source sewage pretreatment data set, through feature set refinement processing, characteristic variables closely related to sewage treatment efficiency (such as changes in pollutant concentration in sewage, flow fluctuations, etc.) are further extracted, and a high-quality multi-source sewage feature set is generated, which provides accurate input for subsequent decision-making models. According to the multi-source sewage feature set and the real-time indicator analysis data of sewage treatment, combined with environmental changes and sewage treatment needs, a dynamic strategy for sewage environmental adaptability is generated. This strategy can adjust the operation mode and operating parameters of sewage treatment in real time according to the changes in sewage quality and environmental factors, realize dynamic optimization control, and improve the adaptability and stability of the treatment system under different environmental conditions. Through these steps, the present invention effectively solves the problem that traditional sewage treatment systems cannot respond to environmental changes and fluctuations in treatment efficiency in real time, improves the intelligence level of the sewage treatment process, ensures that water quality meets standards and reduces energy consumption.

[0010] Preferably, performing real-time sewage treatment index analysis on real-time sewage treatment environmental data comprises the following steps: Extract sewage treatment temperature based on real-time sewage treatment environmental data to generate real-time sewage treatment temperature data; Extract sewage treatment rainfall based on real-time sewage treatment environmental data to generate sewage treatment rainfall data; The real-time temperature data of sewage treatment is timestamped and mapped to a two-dimensional coordinate axis to generate temperature change data of sewage treatment; Perform time series integration according to sewage treatment rainfall data to generate sewage treatment rainfall time series integration data; The data sets are packaged according to the sewage treatment temperature change data and the sewage treatment rainfall time series integral data to generate real-time indicator analysis data for sewage treatment.

[0011] The present invention systematically obtains the key environmental parameters in the sewage treatment system by extracting the temperature and rainfall of the real-time environmental data of sewage treatment. This process first constructs the important physical quantities related to the external environment in the water quality treatment process by extracting the sewage treatment temperature and rainfall data according to the real-time environmental data, and provides stable input data for subsequent analysis. Subsequently, by timestamping the real-time temperature data of sewage treatment and mapping it to the two-dimensional coordinate axis, the sewage treatment temperature change data is generated. This step reveals the influence of temperature change on the sewage treatment process through the time series analysis method, ensures that the temperature change can be reflected in the data analysis process in real time, and provides strong support for the dynamic control of the sewage treatment system. Then, the system performs time series integration processing according to the sewage treatment rainfall data to generate sewage treatment rainfall time series integration data. This step reveals the long-term accumulation effect of precipitation change on the sewage treatment process by integrating the time series change of rainfall, and provides important long-term environmental change trends for the optimization strategy. Finally, by packaging the sewage treatment temperature change data and the sewage treatment rainfall time series integration data into a data set, the sewage treatment real-time indicator analysis data is generated. This data set integrates the short-term and long-term impacts of environmental changes (such as temperature, precipitation, etc.) on the sewage treatment process, providing multi-dimensional, high-quality input for subsequent decision-making and optimization strategy generation. Through this series of steps, the system effectively captures the dynamic impact of the external environment on sewage treatment at the data level, improves the response speed and decision-making accuracy of the treatment process, thereby achieving environmental adaptability optimization in the sewage treatment process, improving treatment efficiency and reducing energy consumption.

[0012] Preferably, step S23 includes the following steps: Step S231: extracting the dissolved oxygen concentration of sewage treatment from the multi-source sewage pretreatment data set to generate real-time dissolved oxygen concentration data of sewage treatment; performing biodegradation efficiency analysis based on the real-time dissolved oxygen concentration data of sewage treatment to generate the dissolved oxygen reaction interval of sewage treatment; dividing the dissolved oxygen reaction interval of sewage treatment into optimal standard values ​​to generate the optimal value of dissolved oxygen for sewage treatment; Step S232: performing an optimal reaction interval analysis of a sedimentation tank and a reaction tank on a multi-source sewage pretreatment data set to generate optimal interval data of a sewage treatment sedimentation tank-reaction tank; Step S233: Merge the data sets of the optimal dissolved oxygen value of sewage treatment and the optimal interval data of the sewage treatment sedimentation tank-reaction tank to generate a multi-source sewage feature set.

[0013] The present invention generates real-time dissolved oxygen concentration data by extracting the dissolved oxygen concentration in the multi-source sewage pretreatment data set. This data is a key parameter for measuring the biodegradation potential of water bodies and has an important impact on the microbial activity and degradation efficiency in the sewage treatment process. Based on the real-time dissolved oxygen concentration data, the biodegradation efficiency analysis is further performed to reveal the influence of dissolved oxygen on the microbial degradation efficiency in sewage treatment, thereby generating the dissolved oxygen reaction interval of sewage treatment, which provides a theoretical basis for subsequent treatment optimization. Then, by dividing the dissolved oxygen reaction interval into the optimal standard value, the optimal value of dissolved oxygen for sewage treatment is generated. The determination of this standard value is based on the optimal concentration interval of dissolved oxygen for pollutant degradation, so as to ensure that the sewage treatment system can achieve the best treatment effect in the biodegradation stage. By analyzing the optimal reaction interval of the sedimentation tank and the reaction tank in the multi-source sewage pretreatment data set, the optimal interval data of the sedimentation tank-reaction tank for sewage treatment is generated. This analysis can effectively reveal the synergistic effect of the sedimentation tank and the reaction tank in sewage treatment, find the most suitable reaction conditions, and ensure that the various operations of sewage treatment are in the best reaction state. By merging the optimal value of dissolved oxygen with the optimal interval data of the sedimentation tank-reaction tank, a multi-source sewage feature set is generated. This feature set integrates the combined effects of dissolved oxygen concentration and reaction tank conditions, providing accurate input for subsequent optimization decisions and dynamic adjustments. Through this series of data processing, the system can monitor key parameters in real time during the sewage treatment process and dynamically adjust the operation strategy according to real-time changes to ensure that the sewage treatment efficiency is maximized and energy consumption is effectively reduced.

[0014] Preferably, step S24 includes the following steps: Step S241: generating a dynamic strategy for environmental adaptability according to the multi-source sewage feature set and the sewage treatment real-time indicator analysis data, generating a dynamic strategy for sewage environmental adaptability, wherein the dynamic strategy for sewage environmental adaptability includes an abnormal temperature processing strategy and an abnormal water volume processing strategy; Step S242: dividing the sewage treatment temperature change data into rising temperature intervals to generate sewage treatment temperature rising interval data; performing oxygen concentration over-value processing on the sewage treatment dissolved oxygen optimum value based on the sewage treatment temperature rising interval data, thereby completing the abnormal temperature processing strategy; Step S243: Mark the abnormal rainy season with the sewage treatment rainfall time series integral data to generate the sewage treatment rainy season abundant data; generate a point cloud distribution map with the sewage treatment rainy season abundant data and the sewage treatment sedimentation tank-reaction tank optimal interval data, and formulate the optimal value of rainfall reaction time to generate the sewage treatment sedimentation tank-reaction tank optimal reaction time data, thereby completing the abnormal water volume processing strategy.

[0015] The present invention combines multi-source sewage feature sets and real-time indicator analysis data of sewage treatment, and the system dynamically generates a dynamic strategy for sewage environmental adaptability. The core of this strategy is to dynamically adjust the treatment strategy according to the real-time data changes to cope with the impact of environmental factors on the sewage treatment process, including abnormal temperature treatment strategy and abnormal water volume treatment strategy. By dividing the sewage treatment temperature change data into rising temperature intervals, the sewage treatment temperature rising interval data is generated, and then on this basis, the optimal value of sewage treatment dissolved oxygen is treated with oxygen concentration overvalue to ensure that the oxygen supply can be adjusted in time when the temperature rises, and the microbial activity and biodegradation efficiency are guaranteed, thereby completing the formulation of the abnormal temperature treatment strategy. This effectively avoids the adverse effects of temperature rise on the sewage treatment process, optimizes oxygen concentration control, and reduces the reduction in treatment efficiency caused by temperature fluctuations. Then, in step S243, by marking the sewage treatment rainfall time series integral data for abnormal rainy seasons, the sewage treatment rainy season abundant data is generated. This mark can accurately identify the period of abnormally increased precipitation, so as to carry out precise water volume regulation. Then, by generating a point cloud distribution map of the abundant rainy season data and the optimal interval data of the sewage treatment sedimentation tank-reaction tank, the system can intuitively identify the relationship between the reaction interval of sewage treatment and precipitation, provide data support for the formulation of the optimal value of the reaction time of rainfall, and generate the optimal reaction time data of the sewage treatment sedimentation tank-reaction tank, thereby completing the abnormal water volume treatment strategy. This strategy ensures that when precipitation increases, the sedimentation tank and reaction tank can respond in time and perform optimized operations, reducing the decline in treatment efficiency caused by the surge in water volume in the rainy season and ensuring the stability of the sewage treatment system. Through the above steps, the system can dynamically adjust the treatment strategy according to changes in environmental factors to ensure the continuous optimization of the sewage treatment process.

[0016] Preferably, step S3 comprises the following steps: Step S31: Perform fuzzy logic processing on the real-time environmental data of sewage treatment for unknown physical quantities of water quality to generate fuzzy factors for sewage treatment; Step S32: allocating weight values ​​of the fuzzy set of the dynamic strategy for sewage environmental adaptability based on the sewage treatment fuzzy factor to generate the sewage treatment fuzzy weight value; Step S33: using the sewage treatment fuzzy weight value to iterate the sewage environment adaptability dynamic strategy, and generate a sewage treatment adaptability optimization strategy.

[0017] The present invention performs fuzzy logic processing on real-time environmental data of sewage treatment for unknown physical quantities of water quality, and the system generates fuzzy factors for sewage treatment according to the fuzziness and uncertainty in the environmental data. This processing can convert traditional data indicators into fuzzy factors that are more suitable for actual operations, so that the system can handle the uncertainty of information when facing environmental changes, and further enhance the robustness of the processing model. After the fuzzy factors are generated, the system assigns weight values ​​to the fuzzy set of dynamic strategies for sewage environmental adaptability based on the fuzzy factors for sewage treatment. This process quantitatively weights the relative importance of different fuzzy factors, ensuring that the treatment system can make dynamic decisions based on the weight of each factor under different environmental conditions, thereby improving the accuracy and timeliness of the decision. In particular, when dealing with complex environmental influencing factors (such as temperature fluctuations, rainfall, etc.), the system can reasonably assign weights to each factor to ensure that the dynamic strategies in the sewage treatment process can adapt to environmental changes in real time. By using the fuzzy weight values ​​of sewage treatment to update and iterate the dynamic strategies for sewage environmental adaptability, an adaptive optimization strategy for sewage treatment is generated. This update and iteration process ensures that the sewage treatment process can be adjusted in a timely manner according to new environmental data, so that the treatment efficiency and water quality stability are further improved. This step effectively avoids the lag of traditional systems in the face of rapidly changing environmental conditions by updating the processing strategy in real time, and improves the flexibility and adaptability of the processing process.

[0018] Preferably, step S31 includes the following steps: Step S311: Perform fuzzy logic processing on the real-time environmental data of sewage treatment for unknown physical quantities of water quality to generate fuzzy impact data of sewage treatment; Step S312: Perform binary linear regression of chemical agent dosage and water quality physical quantity on the fuzzy impact data of sewage treatment to generate fuzzy linear regression data of sewage treatment; Step S313: Generate sewage treatment fuzzy factors based on the Bayesian reasoning of water quality physical quantities of sewage treatment fuzzy linear regression data.

[0019] The present invention generates fuzzy impact data of sewage treatment by performing fuzzy logic processing on real-time environmental data of sewage treatment. Fuzzy logic processing can cope with uncertain factors in the sewage treatment process, such as temperature fluctuations, precipitation changes, etc., and convert these uncertain environmental variables into fuzzy factors that are easy to operate and understand, thereby enhancing the adaptability of the system in complex environments. These fuzzy impact data reflect the potential impact of environmental factors on the sewage treatment process and provide accurate input for subsequent processing. Binary linear regression analysis of chemical agent dosage and water quality physical quantity is performed on the fuzzy impact data of sewage treatment. Through the linear regression model, the system can reveal the quantitative relationship between chemical agent dosage and water quality physical quantity, and further optimize the agent addition strategy. This analysis process provides a scientific basis by quantifying the correlation between chemical agents and water quality indicators, ensuring the accurate placement of agents to improve the efficiency and water quality of sewage treatment. Based on the fuzzy linear regression data of sewage treatment, Bayesian reasoning is applied to analyze the water quality physical quantity to generate fuzzy factors for sewage treatment. Bayesian reasoning is used here to further optimize and update the estimated values ​​of various variables in the sewage treatment process, especially when the data is incomplete or uncertain. It can infer through the existing fuzzy linear regression data to generate more accurate fuzzy factors, thereby providing a more refined basis for adjustment of the system. This reasoning process significantly improves the dynamic adaptability and optimization decision-making ability of sewage treatment, ensuring that the system can achieve the best treatment effect under complex and changing environmental conditions.

[0020] Preferably, step S4 comprises the following steps: Step S41: generating sewage treatment results for the sewage treatment adaptability optimization strategy to obtain sewage treatment optimization result data; Step S42: Evaluate the sewage treatment optimization result data to generate sewage treatment optimization result evaluation data; Step S43: construct an optimization report for the sewage optimization treatment result evaluation data, generate a sewage treatment optimization analysis report, and thus complete the sewage treatment optimization operation.

[0021] The present invention generates the processing results of the sewage treatment adaptability optimization strategy to obtain the sewage treatment optimization result data. This process generates the final optimization result of sewage treatment by combining the optimization strategy with the real-time processing data, laying the foundation for subsequent analysis and evaluation. The optimization result data can accurately reflect the improvement of sewage treatment effect under specific environmental conditions, especially in terms of treatment efficiency and sewage water quality, through the integration of multi-dimensional parameters by the system. The sewage treatment optimization result data is evaluated to generate sewage optimization treatment result evaluation data. The evaluation process analyzes the sewage treatment results in multiple dimensions, including comprehensive evaluation of water quality improvement, energy efficiency improvement and resource utilization. Through this process, the system can comprehensively examine the effect after the implementation of the optimization strategy, and further adjust and improve the treatment strategy based on the evaluation results to ensure the sustainability and efficiency of the sewage treatment process. The evaluation data provides quantitative and operational feedback information for subsequent optimization. The sewage optimization treatment result evaluation data is integrated, and a sewage treatment optimization analysis report is constructed. This report not only elaborates on the sewage treatment effect, but also provides optimization paths, improvement measures and future work directions, making the treatment process more scientific and systematic, and providing comprehensive decision support for managers and decision makers in the sewage treatment process. The generation of the optimization report helps relevant personnel clearly understand the deficiencies and room for improvement in the sewage treatment process, so as to make effective adjustments and optimizations in future operations to ensure the continued efficient operation of the sewage treatment system.

[0022] In this specification, a sewage treatment optimization operation system is provided, which is used to execute the above-mentioned sewage treatment optimization operation method, and the sewage treatment optimization operation system includes: The data acquisition and preprocessing module is used to obtain the original data set of multi-source sewage; perform data preprocessing on the original data set of multi-source sewage to generate a preprocessed data set of multi-source sewage; The feature extraction and strategy generation module is used to refine the feature set of the multi-source sewage pretreatment data set to generate a multi-source sewage feature set; generate an environmental adaptability dynamic strategy based on the multi-source sewage feature set to generate a sewage environmental adaptability dynamic strategy; The fuzzy logic and optimization strategy module is used to perform fuzzy logic processing on the dynamic strategy of sewage environmental adaptability and to assign fuzzy set weight values, thereby generating an adaptive optimization strategy for sewage treatment; The optimization report generation and analysis module is used to construct an optimization report for the sewage treatment adaptability optimization strategy and generate a sewage treatment optimization analysis report, thereby completing the sewage treatment optimization operation.

[0023] The beneficial effect of the present invention is that, through a series of steps such as systematic multi-source data collection and preprocessing, feature set refinement, environmental adaptability dynamic strategy generation, fuzzy logic optimization processing and optimization report generation, the automation and intelligence level of the sewage treatment process is significantly improved, and various problems faced by the sewage treatment system are effectively solved from the data level. In step S1, multi-source data collection technology is adopted to obtain real-time data from different monitoring points (such as temperature, dissolved oxygen, pH value, flow rate, etc.), and through data preprocessing, noise is removed, missing values ​​are filled, and outliers are eliminated, etc., to ensure the accuracy and integrity of the data, which provides a reliable basis for subsequent analysis and decision-making. In step S2, by refining the feature set of the preprocessed data set, key features closely related to sewage treatment efficiency are further screened out, thereby laying a solid foundation for the subsequent generation of more accurate environmental adaptability dynamic strategies. This process effectively considers the impact of external environmental factors (such as temperature, precipitation, etc.) on sewage treatment, improves the resilience and flexibility of the system, and enables the sewage treatment process to be dynamically adjusted according to real-time changes. In step S3, a fuzzy logic processing strategy is adopted to convert the environmental adaptability dynamic strategy into an operational optimization scheme. By allocating weight values ​​to various data and parameters through fuzzy set theory, the problem of data uncertainty is solved, the decision-making process is refined and optimally controlled, so that various operations of sewage treatment can adapt to complex changing conditions and achieve the best results. Finally, in step S4, the generated optimization report summarizes the key decision indicators, operation effects and optimization suggestions for sewage treatment, provides quantitative analysis results and operational optimization schemes for the sewage treatment process, and further supports scientific decision-making and management. Therefore, the present invention solves the problems of poor adaptability of traditional sewage treatment systems to environmental changes and insufficient decision-making basis through the integration of multi-source data, the generation of environmental adaptability strategies and fuzzy logic optimization, and improves the sewage treatment efficiency and system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a method for optimizing the operation of sewage treatment is provided; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 4 , a sewage treatment optimization operation method, the method comprising the following steps: Step S1: obtaining a multi-source sewage original data set; performing data preprocessing on the multi-source sewage original data set to generate a multi-source sewage preprocessing data set; Step S2: performing feature set refinement processing on the multi-source sewage pretreatment data set to generate a multi-source sewage feature set; generating an environmental adaptability dynamic strategy based on the multi-source sewage feature set to generate a sewage environmental adaptability dynamic strategy; Step S3: Perform fuzzy logic processing on the dynamic strategy of sewage environmental adaptability and assign fuzzy set weight values ​​to generate a sewage treatment adaptability optimization strategy; Step S4: construct an optimization report for the sewage treatment adaptability optimization strategy and generate a sewage treatment optimization analysis report, thereby completing the sewage treatment optimization operation.

[0029] The beneficial effect of the present invention is that, through a series of steps such as systematic multi-source data collection and preprocessing, feature set refinement, environmental adaptability dynamic strategy generation, fuzzy logic optimization processing and optimization report generation, the automation and intelligence level of the sewage treatment process is significantly improved, and various problems faced by the sewage treatment system are effectively solved from the data level. In step S1, multi-source data collection technology is adopted to obtain real-time data from different monitoring points (such as temperature, dissolved oxygen, pH value, flow rate, etc.), and through data preprocessing, noise is removed, missing values ​​are filled, and outliers are eliminated, etc., to ensure the accuracy and integrity of the data, which provides a reliable basis for subsequent analysis and decision-making. In step S2, by refining the feature set of the preprocessed data set, key features closely related to sewage treatment efficiency are further screened out, thereby laying a solid foundation for the subsequent generation of more accurate environmental adaptability dynamic strategies. This process effectively considers the impact of external environmental factors (such as temperature, precipitation, etc.) on sewage treatment, improves the resilience and flexibility of the system, and enables the sewage treatment process to be dynamically adjusted according to real-time changes. In step S3, a fuzzy logic processing strategy is adopted to convert the environmental adaptability dynamic strategy into an operational optimization scheme. By allocating weight values ​​to various data and parameters through fuzzy set theory, the problem of data uncertainty is solved, the decision-making process is refined and optimally controlled, so that various operations of sewage treatment can adapt to complex changing conditions and achieve the best results. Finally, in step S4, the generated optimization report summarizes the key decision indicators, operation effects and optimization suggestions for sewage treatment, provides quantitative analysis results and operational optimization schemes for the sewage treatment process, and further supports scientific decision-making and management. Therefore, the present invention solves the problems of poor adaptability of traditional sewage treatment systems to environmental changes and insufficient decision-making basis through the integration of multi-source data, the generation of environmental adaptability strategies and fuzzy logic optimization, and improves the sewage treatment efficiency and system stability.

[0030] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a process flow of a sewage treatment optimization operation method of the present invention. In this example, the sewage treatment optimization operation method includes the following steps: Step S1: obtaining a multi-source sewage original data set; performing data preprocessing on the multi-source sewage original data set to generate a multi-source sewage preprocessing data set; In the embodiment of the present invention, it is first necessary to collect the original data set of sewage treatment from multiple different sources. The data set comes from real-time data from sensors, monitoring sites, internal equipment of sewage treatment plants or external environments, such as water quality parameters, flow data, temperature data, chemical content, etc. The diversity of data and the complexity of sources make this step a key starting point in the sewage treatment optimization process. The key technical means in the data preprocessing stage include data cleaning, denoising, standardization and missing value processing. Specifically, by using statistical methods, such as mean filling or interpolation, the missing values ​​in the original data are processed to ensure that subsequent analysis will not be affected by incomplete data. At the same time, the removal of noise data is also crucial. This process is usually completed by using signal processing techniques such as high-pass filtering, low-pass filtering, Kalman filtering, etc., so as to exclude invalid data caused by equipment failure or external interference. In order to ensure the consistency and reliability of the data, it is also necessary to detect and eliminate outliers to avoid the influence of extreme values ​​caused by emergencies or erroneous sampling on subsequent processing results. Data standardization processing technology, such as Z-score standardization or Min-Max normalization, is used to unify the dimensional differences between different data sources, so that various types of data can be effectively compared and analyzed at the same scale. Through this series of data preprocessing methods, the original data will be converted into a structured, multi-source fusion data set, namely the "multi-source sewage pretreatment data set". This data set not only contains the sewage treatment related information after cleaning, denoising and standardization, but also has higher accuracy and applicability, providing a reliable data foundation for subsequent feature extraction, optimization strategy generation and other steps. As a basic operation, data preprocessing lays a solid digital support for the entire sewage treatment optimization system.

[0031] Step S2: performing feature set refinement processing on the multi-source sewage pretreatment data set to generate a multi-source sewage feature set; generating an environmental adaptability dynamic strategy based on the multi-source sewage feature set to generate a sewage environmental adaptability dynamic strategy; In the embodiment of the present invention, through in-depth analysis of the data, key features that can reflect the sewage treatment process and its effects are extracted. This process usually includes technical means such as feature selection, feature construction and feature conversion. In the feature selection stage, statistical methods (such as correlation analysis, variance analysis) or machine learning algorithms (such as LASSO regression, random forest) are used to screen out the most distinguishing and predictive feature variables. These feature variables include chemical oxygen demand (COD), ammonia nitrogen concentration, dissolved oxygen level, flow rate, etc. in sewage. These feature variables can largely reflect the changes in sewage water quality and the effects of sewage treatment. In the feature construction process, combined with expert experience and domain knowledge, the expression ability of the feature set can be further improved by combining the original data, differential analysis, time series modeling, etc., so as to better reflect the complex sewage treatment process. At the same time, feature conversion technologies such as principal component analysis (PCA) and linear discriminant analysis (LDA) can reduce the redundancy of data by dimensionality reduction, extract the most representative information, and provide more concise and effective data input for subsequent analysis. After completing the feature set refinement processing, the generated "multi-source sewage feature set" will be used as the input of downstream tasks to provide a decision-making basis for the dynamic optimization of sewage treatment. Based on this refined feature set, the generation of environmentally adaptive dynamic strategies is carried out. The key to this step is to generate dynamic strategies for sewage treatment by combining multi-source sewage feature sets with real-time environmental data (such as temperature, rainfall, etc.) using environmental perception algorithms or intelligent control algorithms (such as fuzzy control, reinforcement learning, expert systems, etc.). These strategies will dynamically adjust various parameters of sewage treatment according to current environmental conditions to ensure that the system can optimize treatment efficiency and effects according to different sewage water quality and external environmental changes. For example, under high temperature or rainy season conditions, it is necessary to increase the dissolved oxygen concentration or adjust the flow rate of the reaction tank to improve the biodegradation efficiency. In this way, the sewage treatment system can achieve adaptive adjustment to ensure efficient and stable operation under various environmental conditions. The generation of environmentally adaptive dynamic strategies not only improves the responsiveness of the system, but also reduces energy consumption, reduces resource waste, and further optimizes the overall performance of sewage treatment.

[0032] Step S3: Perform fuzzy logic processing on the dynamic strategy of sewage environmental adaptability and assign fuzzy set weight values ​​to generate a sewage treatment adaptability optimization strategy; In the embodiment of the present invention, the introduction of fuzzy logic processing enables the originally clearly determined sewage treatment strategy to adapt to the uncertainty and complexity in reality. In the actual sewage treatment process, environmental conditions such as temperature, rainfall, pollutant concentration, etc. usually have large fluctuations, and the relationship between these variables is usually fuzzy and nonlinear. Through fuzzy logic processing, the system can still make reasonable decisions in this uncertainty. The process of assigning fuzzy set weight values ​​reflects the degree of influence of different factors on the final decision by assigning different weights to each input parameter. The determination of weight values ​​is usually based on the analysis of historical data, expert experience and feedback on the effects of different strategies in actual operation. These factors are mapped in the fuzzy set and given appropriate influence, so that the strategy can comprehensively consider various factors, thereby obtaining a more accurate and flexible treatment plan. Through this process, the sewage treatment system can adjust the treatment strategy according to different input data under variable environmental conditions to optimize the sewage treatment effect, improve the treatment efficiency, and reduce energy consumption and equipment load. In addition, fuzzy logic processing also has strong fault tolerance. When some input data is incomplete or noisy, the system can still maintain high processing stability and adaptability. Ultimately, the generated sewage treatment adaptive optimization strategy not only enhances the flexibility of the system, but also ensures that it can adaptively adjust when responding to complex dynamic changes, thereby achieving the goal of optimizing sewage treatment effects, improving system operation stability and reducing energy consumption.

[0033] Step S4: construct an optimization report for the sewage treatment adaptability optimization strategy and generate a sewage treatment optimization analysis report, thereby completing the sewage treatment optimization operation.

[0034] In the embodiment of the present invention, the dynamic strategy of sewage environmental adaptability is further optimized and adjusted by fuzzy logic processing. The core idea of ​​fuzzy logic processing is to use fuzzy set theory to convert uncertain, fuzzy or difficult to quantify information into operational values, and make decisions based on these fuzzy data. First, by fuzzifying the "dynamic strategy of sewage environmental adaptability", various environmental parameters (such as sewage treatment temperature, flow, water quality indicators, etc.) are converted into fuzzy sets, which are expressed as fuzzy levels such as "high", "medium", and "low". The input values ​​of these fuzzy sets are not precise values, but are processed by fuzzy rules based on linguistic variables, taking into account the uncertainty and variability in the data during the sewage treatment process. For example, the dissolved oxygen concentration of sewage changes in different time periods and environmental conditions, and this change is difficult to be fully predicted by traditional precise calculation methods. Fuzzy logic processing can effectively handle this uncertainty. Next, the weight value of the fuzzy set is assigned. The process of weight value assignment is based on fuzzy logic rules, and the relative importance of each fuzzy set is determined by quantifying the degree of influence of different factors (such as temperature, precipitation, sewage concentration, etc.) on the sewage treatment process. For example, some environmental factors have a greater impact on the efficiency of sewage treatment, and their weights can be increased to make them have a greater impact on the final decision results. The core technical means of this process usually include fuzzy reasoning algorithms (such as Mamdani-type reasoning or Sugeno-type reasoning), in which the reasoning process based on fuzzy rules can generate corresponding fuzzy outputs according to the input fuzzy data, and match and reason through the "rule base" to obtain a reasonable fuzzy weight distribution. This weight value distribution provides a decision-making basis for the formulation of sewage treatment optimization strategies. Finally, after the fuzzy set weight value distribution, the generated "sewage treatment adaptability optimization strategy" will take into account the interaction of various environmental factors and the influence of different factors, thereby realizing intelligent adaptive adjustment in the sewage treatment process. This technical means can enable the sewage treatment system to make more accurate decisions in a complex and changing environment, and improve the system's resilience and treatment efficiency.

[0035] Preferably, step S1 comprises the following steps: Step S11: Acquire a multi-source sewage original data set; Step S12: performing preliminary noise elimination on the original data set of multi-source sewage to generate multi-source sewage noise elimination data; performing preliminary outlier detection and elimination on the multi-source sewage noise elimination data to generate multi-source noise screening data; Step S13: performing data standardization processing on the multi-source noise screening data to generate a multi-source sewage pretreatment data set, wherein the multi-source sewage pretreatment data set includes sewage flow processing data and water quality physical quantity processing data.

[0036] In the embodiment of the present invention, the original data set of multi-source sewage is obtained to lay the foundation for data processing. The data set usually contains original data from multiple sensors or monitoring devices, which include various environmental factors such as water quality indicators, flow, dissolved oxygen, temperature, etc. However, these data often contain noise and outliers and need to be processed later. First, the original data set of multi-source sewage is preliminarily denoised. Noise elimination technology generally uses filtering algorithms, such as mean filtering, Kalman filtering, etc., to remove meaningless fluctuations caused by factors such as sensor errors and environmental interference. This process can effectively improve the signal-to-noise ratio of the data and provide a clearer signal for subsequent analysis. Next, the data after noise elimination is processed using outlier detection technology. Outlier detection often uses statistical methods (such as standard deviation method, box plot method) or model-based methods (such as machine learning-based anomaly detection) to identify and eliminate values ​​that are significantly deviated from the normal data range to ensure the validity and consistency of the data set. After this series of processing, the generated "multi-source noise screening data" is more in line with the actual situation and reduces the interference data that causes analysis errors. For the "multi-source noise screening data", data standardization processing is performed. The main goal of this step is to eliminate the dimensional differences between different data dimensions so that each variable is within the same scale range for subsequent processing and model training. Commonly used data standardization methods include minimum-maximum standardization, Z-Score standardization, etc. These methods convert data with different characteristics into unified standardized values ​​so that the data can be compared at the same scale. After standardization, the data processing is more standardized, avoiding weight bias caused by inconsistent dimensions between features. In the end, the processed "multi-source sewage pretreatment data set" not only includes sewage flow treatment data, but also covers water quality physical quantity processing data. These data provide accurate and non-interference basic data for subsequent steps such as feature set generation and environmental adaptability strategy formulation, ensuring the effectiveness and accuracy of the sewage treatment process.

[0037] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: Acquire real-time environmental data of sewage treatment; Step S22: performing real-time sewage treatment index analysis on the real-time sewage treatment environment data to generate real-time sewage treatment index analysis data; Step S23: performing feature set refinement processing on the multi-source sewage pre-processing data set to generate a multi-source sewage feature set; Step S24: Generate a dynamic strategy for environmental adaptability based on the multi-source sewage feature set and sewage treatment real-time indicator analysis data to generate a dynamic strategy for sewage environmental adaptability.

[0038] In an embodiment of the present invention, real-time environmental data of sewage treatment is obtained. This data set usually includes real-time information collected from on-site monitoring equipment and sensors, such as water temperature, flow, dissolved oxygen, pH value, pollutant concentration and other key environmental parameters. In order to ensure the accuracy of the data, these data are usually calibrated and synchronized to ensure the consistency and timeliness of different sensor data. Real-time indicators of sewage treatment are analyzed using real-time environmental data of sewage treatment. At this time, various data analysis methods, such as statistical analysis, machine learning or calculation based on physical models, are usually applied to extract key indicators and calculate the real-time status and operation effect of sewage treatment. These indicators may include pollutant removal rate, energy consumption, system stability, etc., which are helpful to provide real-time basis for subsequent decision-making. Through this analysis, the generated "real-time indicator analysis data of sewage treatment" contains a comprehensive assessment of the current state of the sewage treatment system. The feature set of the multi-source sewage pretreatment data set is refined to generate a "multi-source sewage feature set". This step mainly involves dimensionality reduction, feature selection or cluster analysis of the data to extract features closely related to the sewage treatment effect to reduce data redundancy and improve analysis efficiency. Common methods include principal component analysis (PCA), canonical correlation analysis (CCA), clustering algorithms, etc., which aim to ensure that the selected features can fully reflect the operating status of the sewage treatment system and predict the adaptability of the system. Based on the generated "multi-source sewage feature set" and "sewage treatment real-time indicator analysis data", dynamic strategies for environmental adaptability are generated. This process usually combines data mining, optimization algorithms and dynamic modeling techniques to generate sewage treatment strategies that adapt to current environmental conditions through training of historical data and feedback adjustment of real-time data. For example, a rule-based reasoning system or optimization algorithm (such as genetic algorithm, simulated annealing algorithm, etc.) is used to dynamically adjust the treatment process and parameters according to the changes in real-time indicators, thereby optimizing the sewage treatment process.

[0039] Preferably, performing real-time sewage treatment index analysis on real-time sewage treatment environmental data comprises the following steps: Extract sewage treatment temperature based on real-time sewage treatment environmental data to generate real-time sewage treatment temperature data; Extract sewage treatment rainfall based on real-time sewage treatment environmental data to generate sewage treatment rainfall data; The real-time temperature data of sewage treatment is timestamped and mapped to a two-dimensional coordinate axis to generate temperature change data of sewage treatment; Perform time series integration according to sewage treatment rainfall data to generate sewage treatment rainfall time series integration data; The data sets are packaged according to the sewage treatment temperature change data and the sewage treatment rainfall time series integral data to generate real-time indicator analysis data for sewage treatment.

[0040] In the embodiment of the present invention, environmental parameters such as water body temperature and rainfall are extracted from the raw sewage treatment data by parsing the original data set provided by the sensor. Common technical means include data extraction methods based on time series, and signal processing techniques such as smoothing filtering and noise removal to improve the accuracy and reliability of the data. Next, the real-time temperature data of sewage treatment is timestamped and mapped to a two-dimensional coordinate axis to generate "sewage treatment temperature change data". This process usually involves time series indexing operations and data visualization techniques. Common methods include timestamp synchronization, interpolation technology, and coordinate axis mapping to ensure that the temperature data can correctly represent its trend over time and provide a stable time reference for subsequent analysis. For sewage treatment rainfall data, the time series integration method is used to generate "sewage treatment rainfall time series integral data". Time series integration is a technology commonly used to process cumulative or historically dependent data. It usually extracts precipitation changes in different time windows by accumulating or weighted integrating rainfall data. This operation can effectively consider the accumulation effect of precipitation and provide a dynamic reference for subsequent processing strategies. Finally, the temperature change data of sewage treatment and the time series integral data of sewage treatment rainfall are packaged into a data set to generate "real-time indicator analysis data of sewage treatment". This step is the core of data fusion, which aims to merge information from different data sources (such as temperature and rainfall) into a comprehensive data set for subsequent analysis and decision-making. This usually relies on data fusion techniques, such as data merging, data matching, and weighted averaging, to ensure that multi-dimensional data can express the real-time operating status of the sewage treatment system in a coordinated and unified manner. Ultimately, the generated "real-time indicator analysis data of sewage treatment" will provide an important basis for real-time decision-making.

[0041] Preferably, step S23 includes the following steps: Step S231: extracting the dissolved oxygen concentration of sewage treatment from the multi-source sewage pretreatment data set to generate real-time dissolved oxygen concentration data of sewage treatment; performing biodegradation efficiency analysis based on the real-time dissolved oxygen concentration data of sewage treatment to generate the dissolved oxygen reaction interval of sewage treatment; dividing the dissolved oxygen reaction interval of sewage treatment into optimal standard values ​​to generate the optimal value of dissolved oxygen for sewage treatment; Step S232: performing an optimal reaction interval analysis of a sedimentation tank and a reaction tank on a multi-source sewage pretreatment data set to generate optimal interval data of a sewage treatment sedimentation tank-reaction tank; Step S233: Merge the data sets of the optimal dissolved oxygen value of sewage treatment and the optimal interval data of the sewage treatment sedimentation tank-reaction tank to generate a multi-source sewage feature set.

[0042] In the embodiment of the present invention, "extracting the dissolved oxygen concentration of sewage treatment from the multi-source sewage pretreatment data set to generate real-time dissolved oxygen concentration data of sewage treatment" is a typical data feature extraction step, which aims to extract the dissolved oxygen concentration, a key water quality indicator, from the original multi-source data set. This is usually achieved by numerical extraction technology based on physical models or sensor output data. Common methods include signal filtering, interpolation, data smoothing and error correction to ensure the accuracy and stability of dissolved oxygen concentration data. Next, "analyzing the biodegradation efficiency based on the real-time dissolved oxygen concentration data of sewage treatment to generate the dissolved oxygen reaction interval of sewage treatment" to the analysis process of biodegradation efficiency. This analysis is usually based on the relationship between dissolved oxygen concentration and the degradation rate of organic matter in sewage with the help of biological reaction models or statistical analysis methods to derive the reaction interval. Common methods include regression analysis, nonlinear fitting and machine learning techniques, which are used to accurately describe the correlation between dissolved oxygen concentration and biodegradation efficiency. Next, "dividing the optimal standard value of the dissolved oxygen reaction interval of sewage treatment to generate the optimal value of dissolved oxygen for sewage treatment" is a further optimization step based on the reaction interval, the purpose of which is to standardize the dissolved oxygen value according to the actual reaction efficiency and system requirements. This step usually uses methods based on standardization theory, such as maximum and minimum normalization, z-score standardization, etc., to ensure that the distribution of dissolved oxygen values ​​is within a reasonable range, thereby providing a scientific basis for subsequent control and regulation. Secondly, "analyzing the optimal reaction interval of sedimentation tanks and reaction tanks for multi-source sewage pretreatment data sets to generate the optimal interval data of sewage treatment sedimentation tanks-reaction tanks" is to optimize the parameters of sewage treatment equipment at the system level. This step uses fluid mechanics models, chemical reaction kinetic models or fitting analysis of experimental data to determine the optimal reaction conditions of sedimentation tanks and reaction tanks. This process relies on complex numerical simulations and optimization algorithms to maximize treatment efficiency. Finally, "merging the optimal value of sewage treatment dissolved oxygen and the optimal interval data of sewage treatment sedimentation tanks-reaction tanks to generate a multi-source sewage feature set" is a key step in data fusion. This process merges data from different sources and dimensions, and fuses different features through weighted average, principal component analysis (PCA) and other methods to generate an integrated multi-source sewage feature set.

[0043] Preferably, step S24 includes the following steps: Step S241: generating a dynamic strategy for environmental adaptability according to the multi-source sewage feature set and the sewage treatment real-time indicator analysis data, generating a dynamic strategy for sewage environmental adaptability, wherein the dynamic strategy for sewage environmental adaptability includes an abnormal temperature processing strategy and an abnormal water volume processing strategy; Step S242: dividing the sewage treatment temperature change data into rising temperature intervals to generate sewage treatment temperature rising interval data; performing oxygen concentration over-value processing on the sewage treatment dissolved oxygen optimum value based on the sewage treatment temperature rising interval data, thereby completing the abnormal temperature processing strategy; Step S243: Mark the abnormal rainy season with the sewage treatment rainfall time series integral data to generate the sewage treatment rainy season abundant data; generate a point cloud distribution map with the sewage treatment rainy season abundant data and the sewage treatment sedimentation tank-reaction tank optimal interval data, and formulate the optimal value of rainfall reaction time to generate the sewage treatment sedimentation tank-reaction tank optimal reaction time data, thereby completing the abnormal water volume processing strategy.

[0044] In the embodiment of the present invention, "generating a dynamic strategy for environmental adaptability based on a multi-source sewage feature set and real-time sewage treatment index analysis data" is a multidimensional data analysis process that extracts key features from multiple data sources and uses them to generate environmental adaptability strategies. This process usually uses statistical analysis methods, machine learning algorithms or rule-based decision systems, combined with the actual needs of the sewage treatment system, to generate dynamic strategies that can cope with different environmental changes. This strategy includes two main parts: "abnormal temperature processing strategy" and "abnormal water volume processing strategy". The generation of abnormal temperature processing strategies depends on the relationship model between temperature changes and sewage treatment efficiency. By monitoring and analyzing temperature data, abnormal temperature intervals are identified, so as to make appropriate adjustments and optimizations. Specifically, "dividing sewage treatment temperature change data into rising temperature intervals to generate sewage treatment temperature rising interval data" is a step that discretizes the temperature change data through interval division technology, usually using methods such as cluster analysis or segmented regression to identify the key intervals of temperature rise, and then calibrates the intervals accordingly. Subsequently, "processing the optimal value of sewage treatment dissolved oxygen based on the sewage treatment temperature rising interval data for oxygen concentration over-value" to the over-limit processing of dissolved oxygen concentration. By setting the threshold over-limit, a dynamic adjustment algorithm (such as PID control or adaptive control method) is used for real-time adjustment to ensure that the oxygen concentration is in the optimal range to cope with the degradation efficiency changes caused by high temperature. Next, "marking the abnormal rainy season for the sewage treatment rainfall time series integral data" is an anomaly detection step, relying on time series analysis methods such as sliding average, anomaly detection algorithms (such as Z-Score or machine learning-based detection methods) to identify abnormal rainy seasons and mark the corresponding data. This marking process provides a basis for subsequent strategy adjustments. Then, "generating abundant data for sewage treatment rainy seasons" is based on marking, using data interpolation, time series reconstruction and other methods to generate abundant data for rainy seasons to ensure the consistency and integrity of rainfall data throughout the process. Next, "generating a point cloud distribution map of the abundant data for sewage treatment rainy seasons and the optimal interval data of sewage treatment sedimentation tank-reaction tank" to the spatial analysis of multidimensional data, usually using point cloud analysis or spatial interpolation technology, combined with data visualization methods, to generate a data point cloud map to intuitively display the relationship between rainfall and the optimal interval of the reaction tank / sedimentation tank. Finally, "the optimal rainfall response time is formulated to generate the optimal response time data for the sewage treatment sedimentation tank-reaction tank" is based on time series analysis and optimization algorithms (such as linear programming, genetic algorithms, etc.), which calculates the optimal response time of the sedimentation tank and reaction tank under a specific rainfall, thereby providing a theoretical basis and data support for time scheduling in the sewage treatment process, ensuring maximum treatment efficiency.

[0045] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: Perform fuzzy logic processing on the real-time environmental data of sewage treatment for unknown physical quantities of water quality to generate fuzzy factors for sewage treatment; Step S32: allocating weight values ​​of the fuzzy set of the dynamic strategy for sewage environmental adaptability based on the sewage treatment fuzzy factor to generate the sewage treatment fuzzy weight value; Step S33: using the sewage treatment fuzzy weight value to iterate the sewage environment adaptability dynamic strategy, and generate a sewage treatment adaptability optimization strategy.

[0046] In the embodiment of the present invention, the real-time environmental data of sewage treatment is subjected to fuzzy logic processing, and the step of generating the fuzzy factor of sewage treatment utilizes the method of fuzzy logic control (FLC), which is intended to perform fuzzy conversion on real-time environmental data. Specifically, this process converts precise environmental data (such as temperature, precipitation, etc.) into fuzzy set elements through fuzzification rules and membership functions, and constructs corresponding fuzzy factors. For example, by converting "temperature" into three fuzzy categories of "low", "medium" and "high", the corresponding membership values ​​are generated, so that the data can adapt to complex and uncertain environmental changes. This fuzzification process can eliminate noise and uncertainty in the data and improve the robustness of subsequent analysis. Secondly, based on the fuzzy factor of sewage treatment, the weight value of the fuzzy set of the dynamic strategy of sewage environmental adaptability is allocated, and the step of generating the fuzzy weight value of sewage treatment is weighted. The process determines the influence and importance of each factor by weighted averaging or other fuzzy operations (such as weighted center method, fuzzy mean method, etc.) on multiple fuzzy factors. Through this weighted method, different fuzzy factors will be given different importance according to their weights in a specific environment, thereby affecting the decision-making results of the sewage treatment strategy. For example, under certain environmental conditions, the impact of temperature changes is greater, so a higher weight will be given to the temperature-related fuzzy factors, which in turn affects the adjustment of the strategy. Finally, the fuzzy weight value of sewage treatment is used to update and iterate the dynamic strategy of sewage environmental adaptability, and the steps of generating the sewage treatment adaptability optimization strategy are to continuously optimize and update the existing sewage treatment strategy. This update process relies on the adaptive ability of fuzzy logic control. In the actual treatment process, the system will dynamically adjust the weight value according to the real-time monitored data, and modify the original strategy according to the new data feedback. This method can adapt to environmental changes more accurately and adjust the treatment measures in time, thereby ensuring that the sewage treatment system can achieve optimal performance under various environmental conditions.

[0047] Preferably, step S31 includes the following steps: Step S311: Perform fuzzy logic processing on the real-time environmental data of sewage treatment for unknown physical quantities of water quality to generate fuzzy impact data of sewage treatment; Step S312: Perform binary linear regression of chemical agent dosage and water quality physical quantity on the fuzzy impact data of sewage treatment to generate fuzzy linear regression data of sewage treatment; Step S313: Generate sewage treatment fuzzy factors based on the Bayesian reasoning of water quality physical quantities of sewage treatment fuzzy linear regression data.

[0048] In the embodiment of the present invention, the real-time environmental data of sewage treatment is first fuzzified using fuzzy logic processing technology to better deal with the uncertainty and ambiguity in the data. At this stage, the fuzzy logic system converts environmental data (such as temperature, chemical concentration, etc.) into fuzzy variables by defining a set of membership functions and fuzzy rules. These fuzzy variables can reflect the influencing factors of sewage treatment under different environmental conditions. Next, the fuzzy impact data of sewage treatment is further processed, and the relationship between the amount of chemical agent added and the physical quantity of water quality is analyzed by binary linear regression, thereby generating fuzzy linear regression data for sewage treatment. The linear regression method is to establish a mathematical relationship between input (such as the amount of agent added) and output (such as water quality change) and solve its optimal parameters, so that a quantitative impact model between the amount of addition and the physical quantity of water quality can be obtained. This model can help the system accurately predict the impact of the amount of chemical agent added under different conditions and provide corresponding adjustment strategies. Finally, based on the fuzzy linear regression data, Bayesian reasoning is used to further analyze the potential influencing factors of the physical quantity of water quality, and the fuzzy factor of sewage treatment is generated by updating the probability distribution. Bayesian reasoning combines prior knowledge with newly acquired data and uses Bayesian theorem to optimize the inference process of water quality physical quantities, enabling the system to adaptively update and adjust decisions in a changing environment. Overall, the process combines fuzzy logic, linear regression, and Bayesian reasoning techniques to generate more accurate and dynamic wastewater treatment optimization strategies through gradually refined data processing.

[0049] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes: Step S41: generating sewage treatment results for the sewage treatment adaptability optimization strategy to obtain sewage treatment optimization result data; Step S42: Evaluate the sewage treatment optimization result data to generate sewage treatment optimization result evaluation data; Step S43: construct an optimization report for the sewage optimization treatment result evaluation data, generate a sewage treatment optimization analysis report, and thus complete the sewage treatment optimization operation.

[0050] In the embodiment of the present invention, the system executes a series of control and decision rules, combined with previously processed environmental data, optimization strategies and their execution, to generate actual sewage treatment result data. These result data usually include multiple key performance indicators, such as water quality improvement, treatment efficiency and energy consumption, which provide the necessary basis for subsequent evaluation and optimization. In step S42, the sewage treatment optimization result data is evaluated for the treatment results, and sewage optimization treatment result evaluation data is generated. The specific method includes a comprehensive evaluation of the generated sewage treatment result data based on a preset evaluation standard. This evaluation usually includes multiple dimensions, such as water quality improvement level, chemical agent consumption, system operation cost and other factors. Through data analysis techniques such as multivariate regression analysis or weighted scoring model, the relative importance of different indicators is weighed, thereby forming sewage optimization treatment result evaluation data, which provides a basis for further optimization. In addition, in step S43, based on the sewage optimization treatment result evaluation data, a sewage treatment optimization analysis report is constructed through data visualization and analysis technology. This process converts the evaluation results into actionable optimization suggestions. The report not only presents the performance of each key indicator, but also combines historical data for trend analysis to predict future treatment effects. Through analysis reports, the system can provide operators with detailed optimization directions for sewage treatment, such as adjusting the dosage of chemical agents, optimizing reaction tank parameters, or making system operation adjustments.

[0051] In this specification, a sewage treatment optimization operation system is provided, which is used to execute the above-mentioned sewage treatment optimization operation method, and the sewage treatment optimization operation system includes: The data acquisition and preprocessing module is used to obtain the original data set of multi-source sewage; perform data preprocessing on the original data set of multi-source sewage to generate a preprocessed data set of multi-source sewage; The feature extraction and strategy generation module is used to refine the feature set of the multi-source sewage pretreatment data set to generate a multi-source sewage feature set; generate an environmental adaptability dynamic strategy based on the multi-source sewage feature set to generate a sewage environmental adaptability dynamic strategy; The fuzzy logic and optimization strategy module is used to perform fuzzy logic processing on the dynamic strategy of sewage environmental adaptability and to assign fuzzy set weight values, thereby generating an adaptive optimization strategy for sewage treatment; The optimization report generation and analysis module is used to construct an optimization report for the sewage treatment adaptability optimization strategy and generate a sewage treatment optimization analysis report, thereby completing the sewage treatment optimization operation.

[0052] The beneficial effect of the present invention is that, through a series of steps such as systematic multi-source data collection and preprocessing, feature set refinement, environmental adaptability dynamic strategy generation, fuzzy logic optimization processing and optimization report generation, the automation and intelligence level of the sewage treatment process is significantly improved, and various problems faced by the sewage treatment system are effectively solved from the data level. In step S1, multi-source data collection technology is adopted to obtain real-time data from different monitoring points (such as temperature, dissolved oxygen, pH value, flow rate, etc.), and through data preprocessing, noise is removed, missing values ​​are filled, and outliers are eliminated, etc., to ensure the accuracy and integrity of the data, which provides a reliable basis for subsequent analysis and decision-making. In step S2, by refining the feature set of the preprocessed data set, key features closely related to sewage treatment efficiency are further screened out, thereby laying a solid foundation for the subsequent generation of more accurate environmental adaptability dynamic strategies. This process effectively considers the impact of external environmental factors (such as temperature, precipitation, etc.) on sewage treatment, improves the resilience and flexibility of the system, and enables the sewage treatment process to be dynamically adjusted according to real-time changes. In step S3, a fuzzy logic processing strategy is adopted to convert the environmental adaptability dynamic strategy into an operational optimization scheme. By assigning weight values ​​to various data and parameters through fuzzy set theory, the problem of data uncertainty is solved, and the decision-making process is refined and optimally controlled, so that various operations of sewage treatment can adapt to complex changing conditions and achieve the best results. Finally, in step S4, the generated optimization report summarizes the key decision indicators, operating effects and optimization suggestions for sewage treatment, and provides quantitative analysis results and operational optimization solutions for the sewage treatment process, further supporting scientific decision-making and management. Therefore, the present invention solves the problems of poor adaptability of traditional sewage treatment systems to environmental changes and insufficient decision-making basis through the integration of multi-source data, the generation of environmental adaptability strategies and fuzzy logic optimization, thereby improving sewage treatment efficiency and system stability. Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0053] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for optimizing operation of sewage treatment, characterized in that: The following steps are involved: Step S1: Obtaining a multi-source sewage original data set; Preprocessing the original data set of multi-source sewage to generate a preprocessed data set of multi-source sewage; Step S2: performing feature set refinement processing on the multi-source sewage pretreatment data set to generate a multi-source sewage feature set; Generate a dynamic strategy for environmental adaptability based on a multi-source sewage feature set to generate a dynamic strategy for sewage environmental adaptability; Step S3: Perform fuzzy logic processing on the dynamic strategy of sewage environmental adaptability and assign fuzzy set weight values ​​to generate a sewage treatment adaptability optimization strategy; Step S4: construct an optimization report for the sewage treatment adaptability optimization strategy and generate a sewage treatment optimization analysis report, thereby completing the sewage treatment optimization operation.

2. The sewage treatment optimization operation method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire a multi-source sewage original data set; Step S12: performing preliminary noise elimination on the original data set of multi-source sewage to generate multi-source sewage noise elimination data; performing preliminary outlier detection and elimination on the multi-source sewage noise elimination data to generate multi-source noise screening data; Step S13: performing data standardization processing on the multi-source noise screening data to generate a multi-source sewage pretreatment data set, wherein the multi-source sewage pretreatment data set includes sewage flow processing data and water quality physical quantity processing data.

3. The sewage treatment optimization operation method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Acquire real-time environmental data of sewage treatment; Step S22: performing real-time sewage treatment index analysis on the real-time sewage treatment environment data to generate real-time sewage treatment index analysis data; Step S23: performing feature set refinement processing on the multi-source sewage pre-processing data set to generate a multi-source sewage feature set; Step S24: Generate a dynamic strategy for environmental adaptability based on the multi-source sewage feature set and sewage treatment real-time indicator analysis data to generate a dynamic strategy for sewage environmental adaptability.

4. The sewage treatment optimization operation method according to claim 3 is characterized in that: The real-time indicator analysis of sewage treatment based on real-time environmental data of sewage treatment includes the following steps: Extract sewage treatment temperature based on real-time sewage treatment environmental data to generate real-time sewage treatment temperature data; Extract sewage treatment rainfall based on real-time sewage treatment environmental data to generate sewage treatment rainfall data; The real-time temperature data of sewage treatment is timestamped and mapped to a two-dimensional coordinate axis to generate temperature change data of sewage treatment; Perform time series integration according to sewage treatment rainfall data to generate sewage treatment rainfall time series integration data; The data sets are packaged according to the sewage treatment temperature change data and the sewage treatment rainfall time series integral data to generate real-time indicator analysis data for sewage treatment.

5. The sewage treatment optimization operation method according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: extracting the dissolved oxygen concentration of sewage treatment from the multi-source sewage pretreatment data set to generate real-time dissolved oxygen concentration data of sewage treatment; performing biodegradation efficiency analysis based on the real-time dissolved oxygen concentration data of sewage treatment to generate the dissolved oxygen reaction interval of sewage treatment; dividing the dissolved oxygen reaction interval of sewage treatment into optimal standard values ​​to generate the optimal value of dissolved oxygen for sewage treatment; Step S232: performing an optimal reaction interval analysis of a sedimentation tank and a reaction tank on a multi-source sewage pretreatment data set to generate optimal interval data of a sewage treatment sedimentation tank-reaction tank; Step S233: Merge the data sets of the optimal dissolved oxygen value of sewage treatment and the optimal interval data of the sewage treatment sedimentation tank-reaction tank to generate a multi-source sewage feature set.

6. The sewage treatment optimization operation method according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: generating a dynamic strategy for environmental adaptability according to the multi-source sewage feature set and the sewage treatment real-time indicator analysis data, generating a dynamic strategy for sewage environmental adaptability, wherein the dynamic strategy for sewage environmental adaptability includes an abnormal temperature processing strategy and an abnormal water volume processing strategy; Step S242: dividing the sewage treatment temperature change data into rising temperature intervals to generate sewage treatment temperature rising interval data; performing oxygen concentration over-value processing on the sewage treatment dissolved oxygen optimum value based on the sewage treatment temperature rising interval data, thereby completing the abnormal temperature processing strategy; Step S243: Mark the abnormal rainy season with the sewage treatment rainfall time series integral data to generate the sewage treatment rainy season abundant data; generate a point cloud distribution map with the sewage treatment rainy season abundant data and the sewage treatment sedimentation tank-reaction tank optimal interval data, and formulate the optimal value of rainfall reaction time to generate the sewage treatment sedimentation tank-reaction tank optimal reaction time data, thereby completing the abnormal water volume processing strategy.

7. The sewage treatment optimization operation method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Perform fuzzy logic processing on the real-time environmental data of sewage treatment for unknown physical quantities of water quality to generate fuzzy factors for sewage treatment; Step S32: allocating weight values ​​of the fuzzy set of dynamic strategies for sewage environmental adaptability based on the fuzzy factors of sewage treatment to generate fuzzy weight values ​​for sewage treatment; Step S33: using the sewage treatment fuzzy weight value to iterate the sewage environment adaptability dynamic strategy, and generate a sewage treatment adaptability optimization strategy.

8. The method for optimizing operation of sewage treatment according to claim 7, characterized in that: Step S31 includes the following steps: Step S311: Perform fuzzy logic processing on the real-time environmental data of sewage treatment for unknown physical quantities of water quality to generate fuzzy impact data of sewage treatment; Step S312: Perform binary linear regression of chemical agent dosage and water quality physical quantity on the fuzzy impact data of sewage treatment to generate fuzzy linear regression data of sewage treatment; Step S313: Generate sewage treatment fuzzy factors based on the Bayesian reasoning of water quality physical quantities of sewage treatment fuzzy linear regression data.

9. The method for optimizing operation of sewage treatment according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: generating sewage treatment results for the sewage treatment adaptability optimization strategy to obtain sewage treatment optimization result data; Step S42: Evaluate the sewage treatment optimization result data to generate sewage treatment optimization result evaluation data; Step S43: construct an optimization report for the sewage optimization treatment result evaluation data, generate a sewage treatment optimization analysis report, and thus complete the sewage treatment optimization operation.

10. A sewage treatment optimization operation system, characterized in that: Used to execute the sewage treatment optimization operation method according to claim 1, the sewage treatment optimization operation system comprises: The data acquisition and preprocessing module is used to obtain the original data set of multi-source sewage; perform data preprocessing on the original data set of multi-source sewage to generate a preprocessed data set of multi-source sewage; The feature extraction and strategy generation module is used to refine the feature set of the multi-source sewage pretreatment data set to generate a multi-source sewage feature set; generate an environmental adaptability dynamic strategy based on the multi-source sewage feature set to generate a sewage environmental adaptability dynamic strategy; The fuzzy logic and optimization strategy module is used to perform fuzzy logic processing on the dynamic strategy of sewage environmental adaptability and to assign fuzzy set weight values, thereby generating an adaptive optimization strategy for sewage treatment; The optimization report generation and analysis module is used to construct an optimization report for the sewage treatment adaptability optimization strategy and generate a sewage treatment optimization analysis report, thereby completing the sewage treatment optimization operation.

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