A sewage treatment simulation control method and system

Abstract

The present invention relates to the field of data mining technology, and in particular to a method and system for simulating and controlling sewage treatment. The method comprises the following steps: obtaining the structural data of sewage treatment facilities, analyzing and establishing the topological structure model of sewage treatment facilities, and dividing the main and secondary treatment process structures; constructing a sewage treatment sensor network based on sensor data, filtering abnormal data, and obtaining abnormal filtering batch data; processing abnormal data, establishing the main structural model of the abnormal filtering process, and evaluating the suspended matter sedimentation error; using the suspended matter sedimentation error data to simulate the hydrodynamics and evaluate the filter blockage; evaluating the abnormal dosage in the secondary topological structure model, and obtaining abnormal dosage data; optimizing the suspended matter sedimentation structure based on the abnormal blockage and dosage data, generating optimized structural data, and adjusting the processing strategy through the topological model, and uploading to the management platform to perform the optimization task. The present invention can improve the efficiency of sewage treatment.

Description

Technical Field

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

[0002] Wastewater treatment is a key link in modern urban environmental protection and water resource management. Wastewater treatment usually includes three stages: physical, chemical and biological treatment. Physical treatment removes large particle pollutants through sedimentation and filtration; chemical treatment removes soluble pollutants through reagent reactions; biological treatment uses the metabolism of microorganisms to degrade organic matter and nutrients such as nitrogen and phosphorus. With the development of technology, various advanced sewage treatment processes have emerged, such as activated sludge method, biofilm method, sewage treatment reactor, etc. These processes involve complex dynamic processes and the interaction of multiple factors. Traditional sewage treatment systems often rely on fixed operating parameters and empirical regulation. Although this method can guarantee the sewage treatment effect to a certain extent, it is difficult to achieve precise and flexible control when faced with complex situations such as fluctuations in treatment load and changes in influent water quality. Summary of the invention

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

[0004] To achieve the above purpose, a sewage treatment simulation control method comprises the following steps:

[0005] Step S1: Obtaining sewage treatment facility structure data, and performing sewage treatment facility topology structure analysis based on the sewage treatment facility structure data, thereby obtaining a sewage treatment facility topology structure model; dividing the sewage treatment process structure of the sewage treatment facility topology structure model, thereby obtaining a sewage treatment main topology structure model and a sewage treatment secondary topology structure model;

[0006] Step S2: acquiring sensor data of the sewage treatment facility, and constructing a sewage treatment sensor network for the sewage treatment facility topology model according to the sensor data of the sewage treatment facility, thereby obtaining a sewage treatment sensor network; performing sewage treatment abnormal filtration analysis based on the sewage treatment sensor network, thereby obtaining abnormal filtration batch sensor data;

[0007] Step S3: Based on the abnormal filtration batch sensor data, the main topological structure model of the sewage treatment is divided into an abnormal filtration process structure, so as to obtain the main structure model of the abnormal filtration process, and the suspended solids sedimentation error of the main structure model of the abnormal filtration process is evaluated, so as to obtain the suspended solids sedimentation error data;

[0008] Step S4: performing a hydrodynamic simulation on the main structural model of the abnormal filtration process according to the suspended matter sedimentation error data, thereby obtaining hydrodynamic simulation data, and performing a filter blockage evaluation based on the hydrodynamic simulation data, thereby obtaining abnormal filter blockage data;

[0009] Step S5: evaluating the abnormal dosage of the sewage treatment secondary topological structure model based on the suspended solids sedimentation error data, thereby obtaining abnormal dosage data;

[0010] Step S6: Optimize the suspended solids sedimentation structure based on the abnormal filter blockage data and the abnormal dosage data to obtain the suspended solids sedimentation optimization structure data; optimize and regulate the suspended solids sedimentation optimization structure data through the sewage treatment facility topology model to obtain the sewage treatment optimization regulation strategy, and upload it to the sewage treatment management platform to execute the treatment regulation task.

[0011] The present invention can accurately establish a model of a sewage treatment facility by acquiring structural data of the sewage treatment facility and performing topological structure analysis. This helps to understand the operating mechanism of the facility and the interaction between its parts. By dividing the process structure of the topological structure model, the main and secondary processing modules can be identified, the design and operation strategies can be optimized, and key modules can be monitored and controlled. Understanding the main and secondary topological structure models helps to optimize the configuration of the sewage treatment facility and ensure that the entire sewage treatment system can maintain good performance under various operating conditions. Acquiring sensor data and building a sensor network can achieve real-time monitoring of the sewage treatment process, and timely discover and respond to abnormal situations. Performing abnormal filtering analysis on the sensor network helps to remove noise data and ensure that subsequent analysis and decision-making are based on reliable information. Through abnormal filtering data, problems in the sewage treatment process can be more accurately analyzed and diagnosed, and the reliability and treatment effect of the system can be improved. Based on the abnormal filtering data, the main topological structure model is divided into abnormal filtering process structures, and abnormal situations in the treatment process can be identified and analyzed. Through the suspended matter sedimentation error evaluation, the sedimentation effect of suspended matter in the treatment process can be quantitatively understood, providing a basis for subsequent optimization. After identifying the abnormal process structure, targeted improvements can be made to improve the treatment efficiency and effect. Based on the hydrodynamic simulation data, the water flow distribution can be optimized to improve the sewage treatment effect. By evaluating the filter blockage, the blockage problem of the filter system can be discovered in time, the equipment maintenance cost can be reduced, and the normal operation of the system can be maintained. Optimizing the hydrodynamics and filter blockage evaluation can improve the efficiency and reliability of the entire sewage treatment process, reduce treatment time and resource waste. By evaluating the abnormal dosage of the secondary topological structure model, the dosage of the agent can be optimized to avoid excessive or insufficient dosage and ensure the treatment effect. Reasonable evaluation and adjustment of the dosage can reduce the cost of the agent and improve the effect of sewage treatment. By optimizing the suspended solids sedimentation structure, the overall efficiency of sewage treatment can be improved, the residual suspended solids can be reduced, and the water quality can be improved. Applying the optimized structural data to the sewage treatment optimization and control strategy will help the system to make intelligent adjustments in the face of load fluctuations and water quality changes, and improve the treatment stability and flexibility. Uploading to the sewage treatment management platform can achieve centralized management and remote monitoring, improve management efficiency and response speed. In summary, the comprehensive application of these steps enables the sewage treatment system to be accurately and flexibly regulated when faced with complex changes, thereby improving the overall performance and treatment effect of the entire sewage treatment system.

[0012] Optionally, step S1 specifically includes:

[0013] Step S11: acquiring sewage treatment facility structure data, and classifying the sewage treatment facility structure data into facility structure, thereby obtaining functional component structure data and connection component structure data;

[0014] Step S12: extracting connection structure features from the functional component structure data, thereby obtaining functional component connection structure data;

[0015] Step S13: Calculating the connection structure similarity of the connection component structure data and the function component connection structure data, thereby obtaining connection structure similarity data;

[0016] Step S14: integrating the component connection relationship of the functional component structure data and the connection component structure data according to the connection structure similarity data, thereby obtaining the sewage treatment facility component connection relationship data;

[0017] Step S15: performing a topological structure analysis of the sewage treatment facility components according to the connection relationship data of the sewage treatment facility components, thereby obtaining a topological structure model of the sewage treatment facility;

[0018] Step S16: dividing the sewage treatment facility topology structure model into sewage treatment process structures, thereby obtaining a sewage treatment primary topology structure model and a sewage treatment secondary topology structure model.

[0019] By acquiring facility structure data, the present invention can fully grasp the various components and layouts of sewage treatment facilities, laying the foundation for subsequent analysis. By classifying the structural data, the specific functions and relationships of each functional component and connection component can be clarified, thereby improving the organization and operability of the data. This helps to provide a more accurate data basis for subsequent analysis. Obtaining functional component and connection component structure data helps to identify the functional role of each component and understand how they are connected and coordinated with each other. Connection structure feature extraction can reveal the connection characteristics and relationships between functional components, making this information more specific and operational. This helps to understand the interaction between components and their impact on the overall facility. By extracting connection structure features, potential optimization opportunities between functional components can be found, such as improving the connection method to improve efficiency or reduce energy consumption. Calculating the connection structure similarity can help identify components and connection methods with similar structures, which is very useful for standardized design and maintenance work. By identifying structural similarities, the design process can be simplified, duplication of work can be reduced, and design efficiency can be improved. By understanding the similarity of components and connection structures, maintenance work of similar components can be predicted and arranged to improve the operational reliability of facilities. Integrating component connection relationship data can unify scattered information to form a comprehensive component connection relationship diagram. This is crucial for the overall understanding and optimization of the system. By integrating data, information redundancy can be reduced and data processing efficiency can be improved, thereby accelerating the decision-making process. Understanding the connection relationship between components can help optimize facility configuration and improve the overall efficiency and stability of the sewage treatment system. Topological structure analysis can provide an overall structural view of sewage treatment facilities to help understand how the various components work together. Through the topological structure model, potential bottlenecks and weaknesses can be identified, providing a basis for optimized design and improvement. The topological structure model helps to simulate the operating status of the system and predict the performance under different operating conditions, providing guidance for actual operation. By dividing the process structure of the topological structure model, the main and secondary treatment links can be more clearly identified, thereby optimizing the treatment process and improving the treatment efficiency. Dividing the system into primary and secondary topological structure models helps modular design, making maintenance and upgrades more flexible and efficient. The structural division of the sewage treatment process can help quickly locate and solve problems in specific treatment links and improve the operational stability and reliability of the system.

[0020] Optionally, step S16 is specifically:

[0021] Divide the sewage treatment facility structure data into functional structures to obtain chemical treatment structure data and environmental control structure data;

[0022] Extracting structural material features from chemical treatment structural data, thereby obtaining chemical treatment structural material data;

[0023] Performing structural material property analysis on chemically treated structural material data, thereby obtaining structural material property data;

[0024] According to the chemical treatment structure data, the topological structure model of the sewage treatment facility is divided into chemical treatment associated structures, thereby obtaining a chemical treatment topological structure model;

[0025] Mapping the structural material characteristics of the chemical treatment topological structure model according to the structural material characteristic data, thereby obtaining the main topological structure model of sewage treatment;

[0026] The topological structure model of the sewage treatment facility is divided into environmental control associated structures according to the environmental control structure data, thereby obtaining a secondary topological structure model of the sewage treatment.

[0027] The present invention can clarify the functional role of each part by decomposing the structural data of the sewage treatment facility into chemical treatment and environmental control parts, thereby improving the overall efficiency and manageability of the system. Extracting the material characteristics of the chemical treatment structure helps to select suitable materials, enhance the durability and processing capacity of the facility, and reduce maintenance costs. In-depth analysis of material properties can optimize the use of materials, improve their performance in actual operation, and ensure the long-term stability and reliability of the structure. Establishing a topological model based on chemical treatment structural data helps to understand and optimize the layout and connection of the chemical treatment process and improve the processing efficiency of the system. Mapping material properties to the topological model can accurately reflect the actual performance of the material, make the model more realistic, and improve the accuracy of the design. Associative division of the environmental control part helps to optimize the layout and function of the environmental control system and improve the environmental compatibility and convenience of operation of sewage treatment.

[0028] Optionally, step S2 specifically includes:

[0029] Step S21: acquiring sensor data of the sewage treatment facility, and performing sensor space feature extraction on the sensor data of the sewage treatment facility, thereby obtaining sensor space data;

[0030] Step S22: performing spatial structure association according to the sensor spatial data and the sewage treatment facility topological structure model, thereby obtaining a sensor spatial association topological structure model;

[0031] Step S23: performing spatial sensing data fusion on the sensor spatial correlation topological structure model according to the sensing data of the sewage treatment facility, thereby obtaining a sewage treatment sensor network;

[0032] Step S24: Perform abnormal filtration analysis on sewage treatment based on the sewage treatment sensor network to obtain abnormal filtration batch sensor data.

[0033] The sensor data obtained by the present invention is the basis for analysis. By collecting various sensor data (such as flow, concentration, temperature, etc.) in the sewage treatment facility, raw data support can be provided for subsequent data analysis. By extracting spatial features from the sensor data, the distribution law and change trend of the data at different locations can be revealed. This helps to understand the impact of the spatial distribution of sensors on the sewage treatment effect. Spatial feature extraction can identify which sensor data has significant patterns or anomalies in space, which is very helpful for accurately locating the problem area. By associating sensor data with the topological structure model of the sewage treatment facility, a model reflecting the spatial relationship between sensors can be constructed. This helps to understand how different sensors interact with each other in the spatial layout of the facility. Through spatial structure association, the advantages and disadvantages of the sensor layout can be identified, and then the position of the sensor can be optimized to improve the comprehensiveness and accuracy of the data. The established spatial association topological structure model can more accurately reflect the actual operation of the sewage treatment facility and enhance the practical application effect of the model. By fusing the sensor spatial data with the topological structure model, data from different sensors can be integrated to form a comprehensive sewage treatment sensor network. This helps to fully grasp all kinds of information in the sewage treatment process. The integrated sensor network provides a global perspective, making the operation, data flow and abnormal conditions of sewage treatment facilities clearer and more intuitive. The perfect sensor network provides decision makers with more comprehensive data support, which helps to make more accurate management and adjustment decisions. Through data analysis based on the sensor network, abnormal data can be effectively identified and filtered out. This is crucial for timely detection and treatment of problems in the sewage treatment process. Abnormal filtering analysis can reduce false alarms and false positives, thereby improving the stability and reliability of the system. The obtained abnormal filtering batch sensor data is filtered and more accurate data, which is helpful for subsequent analysis and processing, and improves the accuracy and availability of data.

[0034] Optionally, step S24 is specifically:

[0035] Step S241: extracting TSS sensing data and turbidity sensing data based on the sewage treatment sensor network, thereby obtaining TSS sensing data and turbidity sensing data;

[0036] Step S242: filtering the TSS sensor data to obtain suspended solid concentration statistics, thereby obtaining a standard suspended solid concentration limit, and performing abnormal suspended solid concentration batch clustering on the TSS sensor data according to the standard suspended solid concentration limit, thereby obtaining abnormal suspended solid concentration batch data;

[0037] Step S243: filtering turbidity statistics of the turbidity sensor data to obtain standard turbidity limits, and clustering abnormal turbidity batches of the turbidity sensor data according to the standard turbidity limits to obtain abnormal turbidity batch data;

[0038] Step S244: performing a filtering batch intersection operation on the abnormal suspended solids concentration batch data and the abnormal turbidity batch data, thereby obtaining abnormal filtering batch data;

[0039] Step S245: extracting abnormal filtering batch sensor data from the sewage treatment sensor network according to the abnormal filtering batch data, thereby obtaining the abnormal filtering batch sensor data.

[0040] The present invention can monitor the quality of sewage in real time and obtain data in time for subsequent analysis by extracting TSS and turbidity data. It provides the original data basis for subsequent data analysis and processing, ensuring the accuracy and integrity of the data. Through statistical and cluster analysis, it can be detected which data batches have abnormal suspended solids concentration, thereby identifying potential treatment problems or equipment failures. The standard suspended solids concentration limit helps to set a reasonable threshold, making the abnormal data more targeted and improving the processing accuracy. Through statistical and cluster analysis of turbidity data, it is possible to identify turbidity data that exceeds the standard and timely warn of possible sewage treatment anomalies. The identification of abnormal turbidity batches helps to locate the specific source of the problem and improve the maintenance efficiency of the overall sewage treatment system. Through intersection operations, the data of TSS and turbidity can be comprehensively considered to obtain more comprehensive abnormal data, which helps to diagnose problems more accurately. Intersection operations help to eliminate false alarms caused by a single factor and improve the reliability of anomaly detection. By extracting abnormal filtering batch data, more accurate abnormal data can be obtained, which is helpful for further analysis and processing, and provides clear abnormal data, so that the decision-making in the processing process is more based on evidence, helping to optimize the performance and stability of the sewage treatment system. Real-time extraction and processing of TSS and turbidity data can detect problems in time and prevent major failures in the sewage treatment system. Accurate identification and processing of abnormal data are ensured through various methods such as statistics, clustering and intersection operations. Detailed abnormal data is provided to help managers make more effective decisions and improve the overall performance and stability of the sewage treatment system.

[0041] Optionally, step S3 specifically includes:

[0042] Step S31: performing sensor fluctuation statistics of the sewage filtration process based on the sewage treatment sensor network, thereby obtaining standard sensor fluctuation data of the sewage filtration process;

[0043] Step S32: Calculating the filtration process sensor fluctuations for the abnormal filtration batch sensor data, thereby obtaining filtration process sensor fluctuation data;

[0044] Step S33: clustering the filtration process sensor fluctuation data into abnormal sensor fluctuation filtration process data according to the standard sensor fluctuation data of the sewage filtration process, thereby obtaining abnormal sensor fluctuation filtration process data, and calculating the suspended matter sedimentation degree of the abnormal sensor fluctuation filtration process data, thereby obtaining abnormal filtration process sedimentation degree data;

[0045] Step S34: performing filtering process structure association division on the main topological structure model of sewage treatment according to the abnormal sensing fluctuation filtering process data, thereby obtaining the main structure model of the abnormal filtering process;

[0046] Step S35: performing Monte Carlo sewage sensor simulation according to the sewage treatment sensor network to obtain sewage sensor simulation data, and performing sewage filtration simulation on the sewage sensor simulation data through the main structure model of the abnormal filtration process to obtain sewage filtration simulation data;

[0047] Step S36: Calculate the sedimentation rate of suspended matter in the filtration process for the sewage filtration simulation data, thereby obtaining sedimentation rate data for the simulated filtration process;

[0048] Step S37: Calculate the suspended matter sedimentation error of the abnormal filtration process sedimentation data and the simulated filtration process sedimentation data, so as to obtain the suspended matter sedimentation error data.

[0049] The present invention can establish a benchmark by statistically analyzing the standard sensor fluctuation data of the sewage filtration process, helping to identify the fluctuation range under normal operating conditions, thereby effectively monitoring and maintaining the system. By analyzing the data of abnormal batches, potential problems or faults can be identified, ensuring that the treatment process is within the normal fluctuation range, and reducing the risk of system failure. By clustering and calculating the sedimentation of abnormal fluctuation data, the specific cause of the problem can be determined, the filtration process can be optimized and the operation strategy can be improved to improve the processing efficiency. Applying abnormal data to the association division of the main topological structure model can help locate key structural problems in the system and provide targeted suggestions for maintenance and upgrading. Using Monte Carlo simulation for data generation and analysis, the system performance under different operating conditions can be predicted, the filtration process design can be optimized, and the sewage treatment effect can be improved. The sedimentation calculation of the simulated data can evaluate the treatment effect under different conditions, thereby verifying the accuracy of the model and improving the treatment plan. The gap between the model and the actual data can be quantified through error calculation, providing a basis for further system optimization and adjustment, and ensuring the stability and efficiency of the sewage treatment process.

[0050] Optionally, step S4 is specifically:

[0051] Step S41: Perform process error statistics on the suspended matter sedimentation error data, thereby obtaining high-value sedimentation error process data;

[0052] Step S42: dividing the main structure model of the abnormal filtering process into a high-value sedimentation error process structure according to the high-value sedimentation error process data, thereby obtaining a high-value sedimentation error process structure model;

[0053] Step S43: performing a hydrodynamic simulation on the sewage sensor simulation data through a high-sedimentation error process structure model, thereby obtaining hydrodynamic simulation data;

[0054] Step S44: extracting the water flow velocity characteristics of the filter mesh and the pressure characteristics of the filter mesh from the water flow dynamics simulation data, thereby obtaining the simulated water flow velocity data of the filter mesh and the simulated water flow pressure data of the filter mesh;

[0055] Step S45: performing flow velocity change statistics according to the simulated water flow velocity data of the filter net, thereby obtaining the flow velocity change data of the filter net; performing filter net pressure difference calculation according to the simulated water flow pressure data of the filter net, thereby obtaining the filter net pressure difference data;

[0056] Step S46: constructing a filter blockage evaluation system according to the filter flow rate change data and the filter pressure difference data, and performing filter blockage evaluation according to the filter blockage evaluation system to obtain abnormal filter blockage data.

[0057] The present invention can identify and correct the errors in the system by statistically analyzing the suspended matter sedimentation error data. This helps to ensure the accuracy of the data and improve the reliability of subsequent analysis. The main sources of sedimentation errors can be identified, thereby providing a basis for optimizing the design and operation of the filtration system. By analyzing high-value sedimentation error data, abnormal structures or processes in the filtration system can be identified, so as to carry out targeted optimization. Structural division can help determine which process steps or components cause errors, so as to optimize or replace these parts and improve the performance and stability of the overall system. Hydrodynamic simulation can provide detailed predictions of fluid behavior to help understand the flow characteristics of sewage in the filtration system. Potential problem areas and performance bottlenecks can be predicted before the actual system is operated, so as to make targeted adjustments. Extracting flow rate and pressure characteristics helps to better understand the actual working state of the filter. Through these characteristics, the operating efficiency and health state of the filter can be evaluated. The basic data required for subsequent analysis and evaluation are provided to ensure the accuracy and reliability of the analysis results. Flow rate change statistics can reveal the flow rate changes caused by filter clogging, and help identify the degree of clogging and its impact on system performance. By calculating the pressure difference, the resistance change of the filter can be determined, and the degree of blockage can be indirectly inferred. By building an evaluation system, the blockage degree of the filter can be quantified to help operators understand the status of the filter and perform maintenance or replacement. The blockage evaluation results can provide data support for system maintenance and optimization, thereby improving the overall efficiency and life of the filtration system.

[0058] Optionally, step S5 specifically includes:

[0059] Step S51: performing sedimentation error change statistics on the suspended matter sedimentation error data, thereby obtaining high-value sedimentation error change data;

[0060] Step S52: dividing the error change process structure of the sewage treatment secondary topological structure model according to the high-value sedimentation error change data, thereby obtaining a high-value sedimentation error change process structure model;

[0061] Step S53: obtaining the dosage data of the sewage treatment facility, and performing sewage treatment sedimentation simulation on the dosage data of the sewage treatment facility and the sewage sensor simulation data through the high-cost sedimentation error change process structure model, thereby obtaining sewage treatment sedimentation simulation data;

[0062] Step S54: performing suspended solids sedimentation calculation on the sewage treatment sedimentation simulation data to obtain sedimentation simulation sedimentation data, and performing drug-effect sedimentation effect benchmark statistics on the sedimentation simulation sedimentation data to obtain drug-effect sedimentation effect benchmark data;

[0063] Step S55: classifying the sewage treatment sedimentation simulation data according to the drug-effect sedimentation effect benchmark data to obtain abnormal drug-effect sedimentation simulation data;

[0064] Step S56: Correlate the dosage data of sewage treatment facilities with abnormal dosage data according to the abnormal dosage data, thereby obtaining abnormal dosage data.

[0065] The present invention can identify the system error or operation error that may occur in the sewage treatment process by statistically analyzing the sedimentation error change. This helps to understand which factors cause the abnormal change of sedimentation. Error statistics can reveal the deficiencies in the treatment process, so that the sewage treatment process and equipment settings can be improved in a targeted manner to improve the overall treatment effect. By dividing the error change data into process structures, the sewage treatment model can be adjusted more accurately to make it more in line with the actual situation, thereby improving the prediction accuracy of the model. The design defects in the model can be identified and improved, so that the sewage treatment system performs more stably and efficiently when facing different types of sewage. According to the simulation data, the dosage of the agent can be optimized to achieve a better sedimentation effect, reduce the waste of the agent, and improve the treatment efficiency. Through simulation, the influence of different dosages of the agent on the sedimentation effect can be predicted, so that scientific adjustments can be made before actual operation. By calculating the sedimentation data and performing benchmark statistics, the actual sedimentation effect of the agent can be evaluated and compared with the expected effect to ensure that the treatment effect meets the requirements. The standard of the sedimentation effect is established through benchmark statistics, providing a reliable reference basis for subsequent optimization and adjustment. Through classification, those data with abnormal sedimentation effects can be identified. This helps to identify problems in the treatment process, such as insufficient reagents or treatment facility failures. The identification of abnormal data can guide targeted improvements in treatment plans, solve abnormal sedimentation problems, and improve the stability and treatment capacity of the system. By correlating abnormal sedimentation data with the dosage, the dosage or strategy of the reagent can be adjusted to optimize the treatment effect. Treatment failures caused by improper reagent addition can be predicted and prevented, improving the reliability and stability of sewage treatment facilities.

[0066] Optionally, step S6 specifically includes:

[0067] Step S61: optimizing the blocked filter of the main topological structure model of sewage treatment according to the abnormal filter blockage data, thereby obtaining the optimized process blocked filter structure data;

[0068] Step S62: constructing an abnormal dosage detection model according to the abnormal dosage data and the sewage treatment sedimentation simulation data, and optimizing the abnormal dosage process of the sewage treatment facility dosage data through the abnormal dosage detection model, so as to obtain optimized process dosage data;

[0069] Step S63: merging the optimized process blockage filter structure data and the optimized process dosage data to obtain the suspended matter settling optimized structure data;

[0070] Step S64: Optimize and regulate the suspended solids settling optimization structure data for sewage treatment through the sewage treatment facility topology model, thereby obtaining a sewage treatment optimization regulation strategy, and uploading it to the sewage treatment management platform to execute the treatment regulation task.

[0071] The present invention optimizes the filter structure to reduce the frequency of blockage, thereby maintaining the normal operation of the treatment system and improving the efficiency of sewage treatment. By predicting and preventing blockage, the frequency of cleaning and replacing the filter can be reduced, thereby reducing maintenance costs. The optimized filter structure can reduce the pressure and loss on the system and extend the service life of related equipment. By detecting and optimizing the dosage, it can be ensured that the amount of medicine used matches the actual needs, thereby improving the effect and quality of sewage treatment. Optimizing the dosage can reduce the waste of medicine, save costs and reduce the impact on the environment. Abnormal dosage detection helps to find problems and make adjustments in time to maintain the stability of the treatment process. By merging and optimizing data, the optimization effects of the filter structure and the dosage of medicine can be comprehensively considered to obtain an overall optimization plan. The merged data can provide a more comprehensive understanding of the sewage treatment process, thereby making more accurate adjustments and optimizations. Merging data helps to improve data consistency and integrity, thereby reducing errors and problems caused by inconsistent data. By optimizing the control strategy, the efficiency of sewage treatment can be maximized and the treatment results can be guaranteed to achieve the expected goals. After uploading to the management platform, real-time monitoring and adjustment can be achieved to improve the system's responsiveness to emergencies. Systematic optimization and control strategies can help improve the level of sewage treatment management and achieve more scientific and effective management.

[0072] Optionally, the present invention further provides a sewage treatment simulation control system for executing the sewage treatment simulation control method as described above, the sewage treatment simulation control system comprising:

[0073] A topological structure analysis module is used to obtain the structural data of sewage treatment facilities, and perform topological structure analysis of sewage treatment facilities based on the structural data of sewage treatment facilities, so as to obtain a topological structure model of sewage treatment facilities; divide the topological structure model of sewage treatment facilities into sewage treatment process structures, so as to obtain a main topological structure model of sewage treatment and a secondary topological structure model of sewage treatment;

[0074] The abnormal filtering analysis module is used to obtain the sensor data of the sewage treatment facility, and construct the sewage treatment sensor network of the sewage treatment facility topology model according to the sensor data of the sewage treatment facility, so as to obtain the sewage treatment sensor network; perform sewage treatment abnormal filtering analysis based on the sewage treatment sensor network, so as to obtain abnormal filtering batch sensor data;

[0075] The sedimentation error evaluation module is used to divide the abnormal filtration process structure of the main topological structure model of sewage treatment based on the abnormal filtration batch sensor data, so as to obtain the main structure model of the abnormal filtration process, and to evaluate the suspended solid sedimentation error of the main structure model of the abnormal filtration process, so as to obtain the suspended solid sedimentation error data;

[0076] The filter blockage evaluation module is used to perform a hydrodynamic simulation on the main structural model of the abnormal filtration process according to the suspended matter sedimentation error data, thereby obtaining hydrodynamic simulation data, and to evaluate the filter blockage based on the hydrodynamic simulation data, thereby obtaining abnormal filter blockage data;

[0077] The abnormal dosage evaluation module is used to evaluate the abnormal dosage of the sewage treatment secondary topological structure model based on the suspended solids sedimentation error data, so as to obtain the abnormal dosage data;

[0078] The sewage treatment optimization and control module is used to optimize the suspended solids sedimentation structure based on abnormal filter blockage data and abnormal dosage data, so as to obtain suspended solids sedimentation optimization structure data; the sewage treatment optimization and control of the suspended solids sedimentation optimization structure data is carried out through the sewage treatment facility topology model, so as to obtain the sewage treatment optimization and control strategy, and upload it to the sewage treatment management platform to execute the treatment and control tasks.

[0079] The sewage treatment simulation control system of the present invention can implement any sewage treatment simulation control method of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the sewage treatment simulation control method. The internal modules of the system cooperate with each other to improve the stability of sewage treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0081] Figure 1 It is a schematic diagram of the steps of the sewage treatment simulation control method of the present invention;

[0082] Figure 2 Detailed step flow diagram of step S1 in the present invention;

[0083] Figure 3Detailed step flow diagram of step S2 in the present invention;

[0084] 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

[0085] The technical method of the present invention is described clearly and completely below 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.

[0086] 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.

[0087] 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.

[0088] To achieve this, please refer to Figures 1 to 3 The present invention provides a sewage treatment simulation control method, the method comprising the following steps:

[0089] Step S1: Obtaining sewage treatment facility structure data, and performing sewage treatment facility topology structure analysis based on the sewage treatment facility structure data, thereby obtaining a sewage treatment facility topology structure model; dividing the sewage treatment process structure of the sewage treatment facility topology structure model, thereby obtaining a sewage treatment main topology structure model and a sewage treatment secondary topology structure model;

[0090] In this embodiment, laser scanners and high-resolution camera equipment are used to obtain detailed structural data of sewage treatment facilities. These data include the geometry, size and location of each component of the facility, such as sedimentation tanks, reaction tanks, aeration tanks, etc. Then, computer-aided design (CAD) software is used to model these data and generate a three-dimensional topological structure model of the facility. The model is subjected to topological structure analysis, such as identifying the path through which sewage flows and the connection relationship between the components, and further dividing the main and secondary topological structure models. The main topological structure model includes the main treatment units, such as sedimentation tanks and aeration tanks, while the secondary topological structure model includes auxiliary equipment and piping systems.

[0091] Step S2: acquiring sensor data of the sewage treatment facility, and constructing a sewage treatment sensor network for the sewage treatment facility topology model according to the sensor data of the sewage treatment facility, thereby obtaining a sewage treatment sensor network; performing sewage treatment abnormal filtration analysis based on the sewage treatment sensor network, thereby obtaining abnormal filtration batch sensor data;

[0092] In this embodiment, sensor data is obtained from various locations of the sewage treatment facility (such as the water inlet and the reaction tank outlet). These sensors can monitor flow, chemical oxygen demand (COD), suspended solids concentration, etc. These data are used to build a sensor network in the topological structure model, and a detailed record of the location, type and data flow of each sensor is established. Through statistical analysis of the sensor data, abnormal data is identified, such as abnormal fluctuations or mutations in sensor readings. These abnormal data are filtered to generate abnormal filtered batch sensor data to remove data that does not conform to the normal data pattern.

[0093] Step S3: Based on the abnormal filtration batch sensor data, the main topological structure model of the sewage treatment is divided into an abnormal filtration process structure, so as to obtain the main structure model of the abnormal filtration process, and the suspended solids sedimentation error of the main structure model of the abnormal filtration process is evaluated, so as to obtain the suspended solids sedimentation error data;

[0094] In this embodiment, based on the abnormal filtration batch sensor data, the main topological structure model of sewage treatment is divided into process structures to identify abnormal links that may occur during the treatment process. For example, it is found that the sedimentation efficiency of a sedimentation tank is lower than expected, so further structural evaluation is required. The main structural model is evaluated for the error of suspended solids sedimentation, and the error data of suspended solids sedimentation is calculated by comparing the actual sedimentation effect with the theoretical design value. These data are used to determine deviations and potential problems in the treatment process.

[0095] Step S4: performing a hydrodynamic simulation on the main structural model of the abnormal filtration process according to the suspended matter sedimentation error data, thereby obtaining hydrodynamic simulation data, and performing a filter blockage evaluation based on the hydrodynamic simulation data, thereby obtaining abnormal filter blockage data;

[0096] In this embodiment, computational fluid dynamics (CFD) software is used to simulate the hydrodynamics of the sewage treatment process based on the suspended solids sedimentation error data. The flow rate, flow direction and mixing effect of the sewage are taken into account during the simulation. The blockage of the filter is analyzed through the simulation results. For example, the filter at the outlet of the reaction tank may cause flow obstruction due to excessive suspended solids. By calculating the blockage data, the performance of the filter is evaluated and improvement measures are proposed.

[0097] Step S5: evaluating the abnormal dosage of the sewage treatment secondary topological structure model based on the suspended solids sedimentation error data, thereby obtaining abnormal dosage data;

[0098] In this embodiment, the suspended solids sedimentation error data is used to analyze the drug delivery system in the secondary topology model. By comparing the actual dosage with the theoretical demand, the abnormal dosage data is calculated. For example, it is found that the dosage of a certain treatment link is higher than the set value, which may lead to poor treatment effect. This step helps determine whether the dosage of the drug needs to be adjusted to optimize the treatment effect.

[0099] Step S6: Optimize the suspended solids sedimentation structure based on the abnormal filter blockage data and the abnormal dosage data to obtain the suspended solids sedimentation optimization structure data; optimize and regulate the suspended solids sedimentation optimization structure data through the sewage treatment facility topology model to obtain the sewage treatment optimization regulation strategy, and upload it to the sewage treatment management platform to execute the treatment regulation task.

[0100] In this embodiment, the suspended solids settling structure of the sewage treatment facility is optimized by combining the suspended solids settling error data, the filter blockage data and the abnormal dosage data. The treatment efficiency is improved by adjusting the design parameters of the sedimentation tank or optimizing the agent placement strategy. The optimized structure data is applied to the sewage treatment facility topology model to generate the sewage treatment optimization control strategy. Finally, these strategies are uploaded to the sewage treatment management platform to automatically adjust the operating parameters of the facility to perform the optimized treatment tasks.

[0101] Optionally, step S1 specifically includes:

[0102] Step S11: acquiring sewage treatment facility structure data, and classifying the sewage treatment facility structure data into facility structure, thereby obtaining functional component structure data and connection component structure data;

[0103] In this embodiment, the structural data of the sewage treatment facility is obtained using sensors, design drawings and field survey data. The data includes the location, size, type and function of each component. The acquired structural data is classified according to the function of the component, for example, the data is divided into reaction tank, sedimentation tank, filter, etc. The components of each facility are classified according to functional characteristics and structural features for subsequent analysis. For example, the structural data of a sewage treatment plant is obtained, which includes a reaction tank (including an agitator), a sedimentation tank (including an outlet), a sand filter tank, etc. These components are classified by function, the reaction tank belongs to the "reaction component", the sedimentation tank belongs to the "sedimentation component", and the sand filter tank belongs to the "filtration component".

[0104] Step S12: extracting connection structure features from the functional component structure data, thereby obtaining functional component connection structure data;

[0105] In the present embodiment, the structural data of each functional component is extracted with connection features, such as the number and position of connection mode and connection points. The feature extraction tool in the graphic recognition technology or CAD software can be used. The extracted feature data is converted into a format that can be analyzed, such as an adjacency matrix or a connection map, to facilitate subsequent calculation and analysis. For example, for a reaction tank, its structural features include the connection points of the water inlet, water outlet and agitator. The data of these connection points are extracted, and their specific positions and connection modes in the facility are recorded to form the connection feature data of the functional component.

[0106] Step S13: Calculating the connection structure similarity of the connection component structure data and the function component connection structure data, thereby obtaining connection structure similarity data;

[0107] In this embodiment, an algorithm (such as cosine similarity, Jaccard coefficient) is used to calculate the similarity of the connection structure of the connected component and the functional component. The algorithm needs to consider the connection mode, position and number of connection points of the components. Generate a similarity matrix to represent the similarity between different components, and visualize these data to help identify component groups with high similarity. For example, calculate the similarity of the connection features of the reaction tank and the sedimentation tank. Assume that the reaction tank has two water inlets and one water outlet, and the sedimentation tank has one water inlet and two water outlets. By calculating the similarity of these features, it is found that the two have certain similarities in the connection structure, and a similarity matrix is ​​generated.

[0108] Step S14: integrating the component connection relationship of the functional component structure data and the connection component structure data according to the connection structure similarity data, thereby obtaining the sewage treatment facility component connection relationship data;

[0109] In this embodiment, similar components are combined based on similarity data to form an overall view of the connection relationship. The component connection relationship is integrated using a clustering algorithm or a network analysis tool in graph theory. The integrated data is used to build a component connection relationship model to represent the actual connection mode and relationship between the components. For example, a reaction tank and a sedimentation tank with high similarity are integrated into a component connection relationship model, and a network diagram is created to show how the reaction tank is connected to the sedimentation tank through a pipeline, and their specific connection relationship is recorded.

[0110] Step S15: performing a topological structure analysis of the sewage treatment facility components according to the connection relationship data of the sewage treatment facility components, thereby obtaining a topological structure model of the sewage treatment facility;

[0111] In this embodiment, graph theory and network analysis tools are used to perform topological structure analysis on component connection relationship data. The connection mode, path and flow between components are analyzed. Based on the analysis results, a topological structure model of the sewage treatment facility is constructed, including nodes (components) and edges (connection relationships). For example, a topological analysis is performed on the constructed component connection relationship model, and it is found that the connection relationship between the reaction tank, the sedimentation tank and the filter forms a complete treatment process network. Based on the analysis results, a complete topological structure model of the sewage treatment facility is established.

[0112] Step S16: dividing the sewage treatment facility topology structure model into sewage treatment process structures, thereby obtaining a sewage treatment primary topology structure model and a sewage treatment secondary topology structure model.

[0113] In this embodiment, according to the topological structure model, the sewage treatment process is divided into primary and secondary treatment stages. The primary stage includes the core treatment unit, and the secondary stage includes the auxiliary treatment unit. The primary and secondary topological structures are marked in the topological structure model to form a clear view of the treatment process. For example, according to the topological structure model of the sewage treatment facility, the reaction tank and the sedimentation tank are marked as the primary treatment units because they treat the core process of sewage; and the sand filter tank is marked as the secondary treatment unit because it is used for further treatment and filtration, forming a primary topological structure model and a secondary topological structure model.

[0114] Optionally, step S16 is specifically:

[0115] Divide the sewage treatment facility structure data into functional structures to obtain chemical treatment structure data and environmental control structure data;

[0116] In this embodiment, a functional analysis is performed on the overall structure of the sewage treatment facility and it is divided into different functional modules. For example, the facility can be divided into pretreatment, chemical treatment, biological treatment, sludge treatment and other parts. In this step, attention is paid to how to decompose the functional modules of the facility. For example, the sewage treatment facility is set to include modules such as primary sedimentation tank, chemical sedimentation tank, flotation device, bioreactor and sludge thickening tank. By analyzing the function of each module, the primary sedimentation tank and the flotation device are used to remove solid particles; the chemical sedimentation tank is used to add chemical agents to remove dissolved substances; the bioreactor is used for microbial decomposition of organic matter; and the sludge thickening tank is used to treat residual sludge.

[0117] Extracting structural material features from chemical treatment structural data, thereby obtaining chemical treatment structural material data;

[0118] In this embodiment, after determining the chemical treatment structure data, feature extraction is performed on the structural materials in these data. The main focus is on the physical and chemical properties of the material, such as corrosion resistance, strength, temperature resistance, etc. For example, for a chemical precipitation tank, feature data of the concrete and lining materials used in the tank body can be extracted. Concrete may require acid and alkali corrosion resistance, and the lining material may require good wear resistance.

[0119] Performing structural material property analysis on chemically treated structural material data, thereby obtaining structural material property data;

[0120] In this example, the extracted structural material properties are analyzed to assess their impact on the overall structural performance. This includes aspects such as material durability, maintenance requirements, and safety. For example, the corrosion resistance of the concrete of the chemical sedimentation tank is analyzed to ensure that it will not corrode during long-term use. In addition, the wear resistance of the lining material is analyzed to ensure that severe wear will not occur during the chemical reaction process.

[0121] According to the chemical treatment structure data, the topological structure model of the sewage treatment facility is divided into chemical treatment associated structures, thereby obtaining a chemical treatment topological structure model;

[0122] In this embodiment, a topological structure model of chemical treatment is constructed based on the chemical treatment structure data. This model shows the relationship and connection between the various chemical treatment units. For example, a connection diagram between a chemical precipitation tank and other chemical treatment units (such as a mixing reaction tank) is established to describe the fluid flow path and reaction process. The model should include the chemical agent addition point, the fluid flow direction and its interface with other modules. The structure associated with chemical treatment in the sewage treatment facility topological structure model is divided out to obtain a chemical treatment topological structure model.

[0123] Mapping the structural material characteristics of the chemical treatment topological structure model according to the structural material characteristic data, thereby obtaining the main topological structure model of sewage treatment;

[0124] In this embodiment, the structural material characteristic data is mapped to the chemical treatment topology model to form the main topology model of sewage treatment. For example, the application areas of different materials are marked in the chemical treatment topology model. For example, in the chemical sedimentation tank model, the application areas of concrete and lining materials are marked to show their influence on the overall structure.

[0125] The topological structure model of the sewage treatment facility is divided into environmental control associated structures according to the environmental control structure data, thereby obtaining a secondary topological structure model of the sewage treatment.

[0126] In this embodiment, the topological structure model of the sewage treatment facility is divided into environmental control associated structures according to the environmental control structure data to form a secondary topological structure model of sewage treatment. This step focuses on how the environmental control part of the facility affects the overall structure. For example, the ventilation system, exhaust device, noise control facilities, etc. of the sewage treatment facility are analyzed to construct a secondary topological structure model to show how these environmental control facilities are related to the main treatment unit. For example, the air flow path between the ventilation system and the chemical precipitation tank is marked in the model. The structure associated with environmental control in the topological structure model of the sewage treatment facility is divided out, thereby obtaining a secondary topological structure model of sewage treatment.

[0127] Optionally, step S2 specifically includes:

[0128] Step S21: acquiring sensor data of the sewage treatment facility, and performing sensor space feature extraction on the sensor data of the sewage treatment facility, thereby obtaining sensor space data;

[0129] In this embodiment, multiple sensors, such as flow sensors, temperature sensors, pH sensors, and dissolved oxygen sensors, are installed in the sewage treatment facility. First, real-time data from these sensors are collected, including the measurement value and timestamp of each sensor. Then, these data are processed using spatial feature extraction techniques, such as principal component analysis (PCA) or independent component analysis (ICA) to identify the main features and patterns in the data. Through these methods, the sensor data can be converted into feature vectors, which can reflect the spatial distribution of the sensors and the relationship between the data.

[0130] Step S22: performing spatial structure association according to the sensor spatial data and the sewage treatment facility topological structure model, thereby obtaining a sensor spatial association topological structure model;

[0131] In this embodiment, a topological structure model is established based on the actual layout of the sewage treatment facility. The model describes the physical location of the sensor in the sewage treatment facility and its relationship with the treatment unit (such as a sedimentation tank, aeration tank). Then, the obtained sensor spatial feature data is matched with the topological structure model. For example, if a flow sensor in a certain area shows an abnormal value, the topological structure model can help determine whether the abnormality is related to a failure of a nearby sedimentation tank or other facility. Using graph theory methods, a spatial structure association model is constructed. This model maps the features of the sensor data to the nodes of the topological structure, thereby obtaining a sensor spatial association topological structure model.

[0132] Step S23: performing spatial sensing data fusion on the sensor spatial correlation topological structure model according to the sensing data of the sewage treatment facility, thereby obtaining a sewage treatment sensor network;

[0133] In this embodiment, spatial sensor data fusion is implemented based on the obtained sensor spatial correlation topological structure model. Data fusion algorithms, such as weighted average method or Bayesian fusion, are used to integrate data from different sensors. These fusion methods consider the spatial relationship and measurement accuracy of sensors to optimize the integrity and accuracy of data. For example, assuming that there are significant differences in temperature data detected by multiple sensors in a certain area, the fusion algorithm will integrate these data and generate a unified temperature distribution map by considering the sensor location and historical data, thereby forming an accurate sewage treatment sensor network model.

[0134] Step S24: Perform abnormal filtration analysis on sewage treatment based on the sewage treatment sensor network to obtain abnormal filtration batch sensor data.

[0135] In this embodiment, the generated sewage treatment sensor network is used to apply anomaly detection algorithms (such as isolation forest, local outlier factor (LOF) or support vector machine (SVM)) for anomaly filtering analysis. These algorithms can identify and filter out abnormal data that is inconsistent with normal data patterns. For example, by analyzing the data trends and patterns in the sensor network, it is found that the dissolved oxygen level reported by a sensor is significantly lower than the normal value, then the data may indicate an abnormal situation in the system, such as an aeration system failure. The data after abnormal filtering will be further processed and analyzed so that necessary measures can be taken to ensure the normal operation of the sewage treatment facility.

[0136] Optionally, step S24 is specifically:

[0137] Step S241: extracting TSS sensing data and turbidity sensing data based on the sewage treatment sensor network, thereby obtaining TSS sensing data and turbidity sensing data;

[0138] In this embodiment, in the sewage treatment sensor network, high-precision TSS (total suspended solids) sensors and turbidity sensors are used to collect data respectively. The TSS sensor monitors the suspended solids content in the sewage in real time and records data points. The turbidity sensor measures the turbidity of the sewage. Through data extraction, the TSS and turbidity data of each time period are obtained for subsequent analysis.

[0139] Step S242: filtering the TSS sensor data to obtain suspended solid concentration statistics, thereby obtaining a standard suspended solid concentration limit, and performing abnormal suspended solid concentration batch clustering on the TSS sensor data according to the standard suspended solid concentration limit, thereby obtaining abnormal suspended solid concentration batch data;

[0140] In this embodiment, the data obtained from the TSS sensor is pre-processed, for example, using a moving average filter to remove short-term fluctuations and noise. Then, the suspended solids concentration statistics for each time period are calculated. A standard suspended solids concentration limit is set, for example, to 50 mg / L. If the concentration of some data points exceeds this limit, they are marked as abnormal data. Using the K-means clustering algorithm, the exceeded data are grouped into multiple batches, and batches with abnormal suspended solids concentrations are identified. For example, the K-means algorithm divides these data into different clusters for further analysis and processing.

[0141] Step S243: filtering turbidity statistics of the turbidity sensor data to obtain standard turbidity limits, and clustering abnormal turbidity batches of the turbidity sensor data according to the standard turbidity limits to obtain abnormal turbidity batch data;

[0142] In this embodiment, a filtering algorithm, such as Gaussian filtering, is applied to the data obtained from the turbidity sensor to remove noise and unstable data. Turbidity statistics are calculated for each time period and compared with a set standard limit (e.g., the standard limit is 10 NTU). DBSCAN (density clustering algorithm) is used to batch cluster the exceeded data to identify batches with abnormal turbidity. For example, the exceeded turbidity data points are divided into multiple density clusters to form an abnormal turbidity batch data set.

[0143] Step S244: performing a filtering batch intersection operation on the abnormal suspended solids concentration batch data and the abnormal turbidity batch data, thereby obtaining abnormal filtering batch data;

[0144] In this embodiment, the obtained abnormal suspended solids concentration batch data and abnormal turbidity batch data are subjected to intersection operation. Through the intersection operation, the data batch that satisfies both the suspended solids concentration and turbidity abnormalities is found. This process involves set operation, which screens out the common parts of the two abnormal data batches to obtain the final abnormal filtered batch data.

[0145] Step S245: extracting abnormal filtering batch sensor data from the sewage treatment sensor network according to the abnormal filtering batch data, thereby obtaining the abnormal filtering batch sensor data.

[0146] In this embodiment, based on the abnormal filtering batch data obtained, sensor data related to the abnormal filtering batch is extracted from the sewage treatment sensor network and analyzed in detail, such as by creating an abnormal report or alarm system to track and record these abnormal data in real time. These data help the sewage treatment plant to identify potential system failures or process anomalies and take corresponding measures to repair or adjust the treatment process.

[0147] Optionally, step S3 specifically includes:

[0148] Step S31: performing sensor fluctuation statistics of the sewage filtration process based on the sewage treatment sensor network, thereby obtaining standard sensor fluctuation data of the sewage filtration process;

[0149] In this embodiment, multiple sensors (such as pressure sensors, flow meters, and turbidity sensors) can be arranged in the sewage treatment plant, and these sensors are located in different processing units (such as primary sedimentation tanks, activated sludge tanks, and filtration tanks). By collecting and recording sensor data in real time, the fluctuation range of each sensor under normal operating conditions is calculated. Using data analysis tools (such as statistical software or NumPy and Pandas in Python libraries), the mean, standard deviation, maximum, and minimum values ​​of the statistics are calculated to establish a standard sensor fluctuation data model for the sewage filtration process. These data can be used to set the normal operating range to identify potential abnormalities.

[0150] Step S32: Calculating the filtration process sensor fluctuations for the abnormal filtration batch sensor data, thereby obtaining filtration process sensor fluctuation data;

[0151] In this embodiment, for the abnormal filtration batches found (e.g., abnormal increase in filter tank pressure), corresponding abnormal data are extracted from the sensor network. The same data analysis tool as step S31 is used to perform fluctuation calculations on these abnormal data. The specific method includes calculating the mean and standard deviation of the abnormal data and comparing them with the standard sensor fluctuation data. Through this comparison, the fluctuation characteristics of the abnormal data can be quantified to help identify the source of the abnormality.

[0152] Step S33: clustering the filtration process sensor fluctuation data into abnormal sensor fluctuation filtration process data according to the standard sensor fluctuation data of the sewage filtration process, thereby obtaining abnormal sensor fluctuation filtration process data, and calculating the suspended matter sedimentation degree of the abnormal sensor fluctuation filtration process data, thereby obtaining abnormal filtration process sedimentation degree data;

[0153] In this embodiment, a clustering algorithm in machine learning (such as K-means or DBSCAN) is used to cluster the sensor fluctuation data that is different from the standard sensor fluctuation data of the sewage filtration process to obtain abnormal sensor fluctuation filtration process data. Each cluster represents an abnormal fluctuation pattern. The suspended solid sedimentation degree is then calculated for the data of each cluster. For example, the sedimentation rate is calculated using a sedimentation tester or a data model, and the sedimentation degree is evaluated. In this way, the sedimentation degree data of the abnormal filtration process can be obtained, and the impact of different abnormal conditions on the sedimentation degree can be identified.

[0154] Step S34: performing filtering process structure association division on the main topological structure model of sewage treatment according to the abnormal sensing fluctuation filtering process data, thereby obtaining the main structure model of the abnormal filtering process;

[0155] In this embodiment, based on the abnormal sensor fluctuation data, the mutual relationship between each unit of the main topological structure of the sewage treatment system (such as filter tank, sedimentation tank, pump station, etc.) is analyzed. Using system modeling tools (such as MATLAB / Simulink or professional sewage treatment simulation software), the process data with abnormal fluctuations is mapped to the topological structure of the treatment system. The filtration process structure association is divided to identify which structural units are affected by the abnormality, forming the main structural model of the abnormal filtration process.

[0156] Step S35: performing Monte Carlo sewage sensor simulation according to the sewage treatment sensor network to obtain sewage sensor simulation data, and performing sewage filtration simulation on the sewage sensor simulation data through the main structure model of the abnormal filtration process to obtain sewage filtration simulation data;

[0157] In this embodiment, based on the Monte Carlo method, the sensor data simulation of the sewage treatment system is performed. The error range of the sensor, the sewage flow change and other parameters are set, and a large amount of simulation data is generated by random sampling. Then, the main structural model of the abnormal filtration process is obtained by using the fluid dynamics method to process these simulation data to obtain simulated sewage filtration data. In the specific implementation, Monte Carlo simulation software or a custom simulation script can be used for calculation.

[0158] Step S36: Calculate the sedimentation rate of suspended matter in the filtration process for the sewage filtration simulation data, thereby obtaining sedimentation rate data for the simulated filtration process;

[0159] In this embodiment, the obtained sewage filtration simulation data is subjected to suspended solids sedimentation calculation. The suspended solids sedimentation in the simulation data is measured using a sedimentation calculation formula such as the Stokes formula or a sedimentation test instrument (such as an optical sedimentation instrument or a sedimentation tank). The accuracy of the model can be verified by comparing the actual sedimentation data with the simulation data, and the results can be statistically analyzed to obtain sedimentation data of the simulated filtration process.

[0160] Step S37: Calculate the suspended matter sedimentation error of the abnormal filtration process sedimentation data and the simulated filtration process sedimentation data, so as to obtain the suspended matter sedimentation error data.

[0161] In this embodiment, the obtained sedimentation data of the abnormal filtration process is compared with the obtained sedimentation data of the simulated filtration process. The difference between the two is calculated using an error calculation formula (such as mean square error MSE or absolute error). Specifically, the sedimentation difference of each data point is taken, squared and averaged, or the average of the absolute differences is calculated to obtain the suspended matter sedimentation error data. These data can be used to evaluate the simulation accuracy of the sewage treatment process and its room for improvement.

[0162] Optionally, step S4 is specifically:

[0163] Step S41: Perform process error statistics on the suspended matter sedimentation error data, thereby obtaining high-value sedimentation error process data;

[0164] In this embodiment, the collected suspended sediment sedimentation error data is statistically analyzed in detail to analyze the error sources and their distribution. In specific implementation, statistical software (such as R or Python's Pandas library) is used to calculate the mean, standard deviation and error rate of sedimentation. By comparing the actual sedimentation with the sedimentation predicted by the theoretical model, the data points with errors exceeding the threshold are identified to generate high-value sedimentation error process data. For example, if the actual sedimentation of some samples is 30% higher than the predicted value, these samples will be marked as high-value errors.

[0165] Step S42: dividing the main structure model of the abnormal filtering process into a high-value sedimentation error process structure according to the high-value sedimentation error process data, thereby obtaining a high-value sedimentation error process structure model;

[0166] In this embodiment, the main structure of the abnormal filtering process is analyzed using the high sedimentation error data obtained. When building the structural model, a flowchart tool (such as Visio) is used to divide in detail the various links where high errors occur and their influencing factors. For example, if the error is concentrated at the inlet of the filter, the structure of the filter is divided out from the main structural model of the abnormal filtering process.

[0167] Step S43: performing a hydrodynamic simulation on the sewage sensor simulation data through a high-sedimentation error process structure model, thereby obtaining hydrodynamic simulation data;

[0168] In this embodiment, based on the high-precipitation sedimentation error process structure model, the sewage sensor simulation data is subjected to hydrodynamic simulation. Computational fluid dynamics software (such as ANSYS Fluent or COMSOL Multiphysics) is used to simulate water flow behavior. Simulation parameters such as flow velocity, flow rate and fluid properties are set, and numerical simulation is performed to finally obtain hydrodynamic simulation data to describe the dynamic changes of water flow in the filtration system.

[0169] Step S44: extracting the water flow velocity characteristics of the filter mesh and the pressure characteristics of the filter mesh from the water flow dynamics simulation data, thereby obtaining the simulated water flow velocity data of the filter mesh and the simulated water flow pressure data of the filter mesh;

[0170] In this embodiment, the water flow dynamics simulation data is processed to extract the water flow velocity characteristics and pressure characteristics of the filter. The simulation data is processed using a data analysis tool (such as MATLAB) to extract the flow velocity and pressure data. For example, the flow velocity distribution on the surface of the filter is analyzed, the flow velocity of each mesh is calculated, and the pressure value of each point of the filter is measured. This will generate the filter simulated water flow velocity data and the filter simulated water flow pressure data.

[0171] Step S45: performing flow velocity change statistics according to the simulated water flow velocity data of the filter net, thereby obtaining the flow velocity change data of the filter net; performing filter net pressure difference calculation according to the simulated water flow pressure data of the filter net, thereby obtaining the filter net pressure difference data;

[0172] In this embodiment, the simulated water flow velocity data of the filter is statistically analyzed to calculate the change of the flow velocity. In specific implementation, statistical analysis software (such as SPSS) is used to calculate the mean, variance and change rate of the flow velocity. At the same time, the pressure difference at each point of the filter is calculated based on the simulated water flow pressure data of the filter. For example, the inlet and outlet pressure difference is measured and the pressure difference data is calculated to evaluate the clogging of the filter.

[0173] Step S46: constructing a filter blockage evaluation system according to the filter flow rate change data and the filter pressure difference data, and performing filter blockage evaluation according to the filter blockage evaluation system to obtain abnormal filter blockage data.

[0174] In this embodiment, a filter blockage evaluation system is constructed, and the filter blockage is evaluated using a weighted scoring model (such as AHP or expert scoring method) in combination with the flow rate change data and pressure difference data of the step. In the specific implementation, the blockage level threshold is set according to the comprehensive results of the flow rate change and the pressure difference, and the blockage data of the abnormal filter is finally calculated. For example, if the flow rate of the filter drops by more than 50% and the pressure difference increases by 20%, it is marked as severe blockage.

[0175] Optionally, step S5 specifically includes:

[0176] Step S51: performing sedimentation error change statistics on the suspended matter sedimentation error data, thereby obtaining high-value sedimentation error change data;

[0177] In this embodiment, the sedimentation data of suspended solids during sewage treatment are collected, and the sedimentation error is calculated for each time period. Statistical analysis methods (such as standard deviation or coefficient of variation) are used to identify the trend of high-value sedimentation errors. For example, statistical software is used to perform regression analysis on the data, identify situations where the error exceeds the threshold, and record the error change data, which will be used for further analysis and modeling.

[0178] Step S52: dividing the error change process structure of the sewage treatment secondary topological structure model according to the high-value sedimentation error change data, thereby obtaining a high-value sedimentation error change process structure model;

[0179] In this embodiment, the error change process structure of the sewage treatment secondary topological structure model is constructed based on the obtained high-value sedimentation error change data. This involves using graph theory or process modeling software to associate the high-value sedimentation error change data with each node and process in the model to identify the source and impact of the error, and finally obtain a process structure model representing the error change.

[0180] Step S53: obtaining the dosage data of the sewage treatment facility, and performing sewage treatment sedimentation simulation on the dosage data of the sewage treatment facility and the sewage sensor simulation data through the high-cost sedimentation error change process structure model, thereby obtaining sewage treatment sedimentation simulation data;

[0181] In this embodiment, the actual dosage data of the sewage treatment facility and the simulation data of the sewage sensor are obtained through the sewage treatment facility, and these data are simulated using the high-cost sedimentation error change process structure model. For example, the sewage treatment process is modeled by a numerical simulation tool, the influence of different dosages on sedimentation is simulated, and the results are compared with the actual data to obtain sewage treatment sedimentation simulation data.

[0182] Step S54: performing suspended solids sedimentation calculation on the sewage treatment sedimentation simulation data to obtain sedimentation simulation sedimentation data, and performing drug-effect sedimentation effect benchmark statistics on the sedimentation simulation sedimentation data to obtain drug-effect sedimentation effect benchmark data;

[0183] In this embodiment, the suspended solids sedimentation degree is calculated for the sewage treatment sedimentation simulation data, and the simulation data is analyzed using a sedimentation degree calculation formula such as the Stokes formula or a sedimentation model. Then, the drug efficacy sedimentation effect benchmark statistics are performed, such as calculating the mean value and standard deviation of the sedimentation degree to determine the benchmark effect data of the drug efficacy.

[0184] Step S55: classifying the sewage treatment sedimentation simulation data according to the drug-effect sedimentation effect benchmark data to obtain abnormal drug-effect sedimentation simulation data;

[0185] In this embodiment, the sewage treatment sedimentation simulation data is classified into abnormal sedimentation according to the benchmark data of the medicinal effect sedimentation effect. The sedimentation data is analyzed using a classification algorithm (such as a decision tree or K-means clustering) to identify abnormal data that are significantly different from the benchmark data, thereby obtaining abnormal medicinal effect sedimentation simulation data.

[0186] Step S56: Correlate the dosage data of sewage treatment facilities with abnormal dosage data according to the abnormal dosage data, thereby obtaining abnormal dosage data.

[0187] In this embodiment, based on the abnormal drug effect sedimentation simulation data, the dosage data of the sewage treatment facility is analyzed to find the dosage related to the abnormal sedimentation. A statistical analysis method (such as correlation analysis or regression analysis) is used to identify the relationship between abnormal drug effect sedimentation and dosage, thereby obtaining abnormal dosage data. These data can help adjust the dosing strategy to optimize the treatment effect.

[0188] Optionally, step S6 specifically includes:

[0189] Step S61: optimizing the blocked filter of the main topological structure model of sewage treatment according to the abnormal filter blockage data, thereby obtaining the optimized process blocked filter structure data;

[0190] In this embodiment, the blockage of the filter can be monitored in real time by installing sensors in the sewage treatment facility. The data recorded by the sensor includes flow rate, pressure difference and sewage flow. The sensor data is processed using data analysis tools (such as machine learning algorithms) to identify blockage patterns under different time periods and treatment conditions. The data with abnormal blockage degree is screened out by an anomaly detection algorithm. According to the abnormal filter blockage data, the design parameters of the filter in the main topological structure model of sewage treatment, such as aperture, filter material and mesh layout, are adjusted to optimize the structure of the filter. The performance of the optimized filter under different working conditions is verified using simulation tools to ensure its effectiveness in practical applications. The optimized process blockage filter structure data includes the optimized filter design parameters and expected performance indicators. For example, in sewage treatment plant A, the blockage data of the filter is monitored in real time, and it is found that certain filter areas are often blocked through machine learning model analysis. After simulation optimization, a new filter is designed with an enlarged aperture and improved materials. It is verified by simulation that it performs better than the old design under various operating conditions.

[0191] Step S62: constructing an abnormal dosage detection model according to the abnormal dosage data and the sewage treatment sedimentation simulation data, and optimizing the abnormal dosage process of the sewage treatment facility dosage data through the abnormal dosage detection model, so as to obtain optimized process dosage data;

[0192] In this embodiment, the dosage data and sedimentation simulation data in the sewage treatment process can be collected, including the type, dosage, dosage time and sedimentation effect of the chemical agent. An abnormal dosage detection model is constructed using statistical analysis and machine learning algorithms (such as decision trees and neural networks). The model identifies normal and abnormal dosage modes by learning from historical data. The model is trained using historical data (abnormal dosage data and sewage treatment sedimentation simulation data) and tested on new dosage data to verify the accuracy and robustness of the model. The detection accuracy is improved by continuously adjusting the model parameters. The dosage is adjusted according to the detection results of the model to reduce the occurrence of abnormal situations. The optimized process data includes the adjusted dosage of the agent, the time arrangement and its effect on the sedimentation effect. For example, in sewage treatment plant B, monitoring found that the dosage was abnormal in certain time periods. Through the constructed model, it is identified that these abnormal dosages may be caused by uneven mixing of the agents or equipment failure. After optimization, the agent delivery system is adjusted to ensure uniform mixing of the agents and improve the sedimentation effect.

[0193] Step S63: merging the optimized process blockage filter structure data and the optimized process dosage data to obtain the suspended matter settling optimized structure data;

[0194] In this embodiment, the optimized filter structure data and the optimized dosage data are integrated into a comprehensive database. Data fusion technology (such as data warehouse and data mining) is used to standardize data from different sources. Through comprehensive data analysis, the comprehensive effect of the filter structure and dosage is evaluated. An optimization algorithm (such as genetic algorithm or particle swarm optimization) is applied to determine the best structural solution for suspended solids sedimentation. The optimized structure data of suspended solids sedimentation is output, including the filter design, the drug delivery strategy and the optimized processing flow.

[0195] Step S64: Optimize and regulate the suspended solids settling optimization structure data for sewage treatment through the sewage treatment facility topology model, thereby obtaining a sewage treatment optimization regulation strategy, and uploading it to the sewage treatment management platform to execute the treatment regulation task.

[0196] In this embodiment, the operating parameters and control strategies of the sewage treatment facility are adjusted based on the suspended solids settling optimization structure data. A detailed optimization and control plan is formulated, including adjusting the filter cleaning cycle, dosage and treatment process. The optimization and control strategies are uploaded to the sewage treatment management platform, and the platform executes these strategies through an automated control system. The platform provides real-time monitoring and feedback functions to ensure the implementation effect of the strategy. The actual effect of the optimization strategy is monitored through the management platform, and feedback data is collected for subsequent adjustments. Ensure that the sewage treatment facility achieves the best treatment effect and operating efficiency.

[0197] Optionally, the present invention further provides a sewage treatment simulation control system for executing the sewage treatment simulation control method as described above, the sewage treatment simulation control system comprising:

[0198] A topological structure analysis module is used to obtain the structural data of sewage treatment facilities, and perform topological structure analysis of sewage treatment facilities based on the structural data of sewage treatment facilities, so as to obtain a topological structure model of sewage treatment facilities; divide the topological structure model of sewage treatment facilities into sewage treatment process structures, so as to obtain a main topological structure model of sewage treatment and a secondary topological structure model of sewage treatment;

[0199] The abnormal filtering analysis module is used to obtain the sensor data of the sewage treatment facility, and construct the sewage treatment sensor network of the sewage treatment facility topology model according to the sensor data of the sewage treatment facility, so as to obtain the sewage treatment sensor network; perform sewage treatment abnormal filtering analysis based on the sewage treatment sensor network, so as to obtain abnormal filtering batch sensor data;

[0200] The sedimentation error evaluation module is used to divide the abnormal filtration process structure of the main topological structure model of sewage treatment based on the abnormal filtration batch sensor data, so as to obtain the main structure model of the abnormal filtration process, and to evaluate the suspended solid sedimentation error of the main structure model of the abnormal filtration process, so as to obtain the suspended solid sedimentation error data;

[0201] The filter blockage evaluation module is used to perform a hydrodynamic simulation on the main structural model of the abnormal filtration process according to the suspended matter sedimentation error data, thereby obtaining hydrodynamic simulation data, and to evaluate the filter blockage based on the hydrodynamic simulation data, thereby obtaining abnormal filter blockage data;

[0202] The abnormal dosage evaluation module is used to evaluate the abnormal dosage of the sewage treatment secondary topological structure model based on the suspended solids sedimentation error data, so as to obtain the abnormal dosage data;

[0203] The sewage treatment optimization and control module is used to optimize the suspended solids sedimentation structure based on abnormal filter blockage data and abnormal dosage data, so as to obtain suspended solids sedimentation optimization structure data; the sewage treatment optimization and control of the suspended solids sedimentation optimization structure data is carried out through the sewage treatment facility topology model, so as to obtain the sewage treatment optimization and control strategy, and upload it to the sewage treatment management platform to execute the treatment and control tasks.

[0204] The sewage treatment simulation control system of the present invention can implement any sewage treatment simulation control method of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the sewage treatment simulation control method. The internal modules of the system cooperate with each other to improve the stability of sewage treatment.

[0205] 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.

[0206] 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 sewage treatment simulation control method, characterized in that: The following steps are involved: Step S1: Obtaining the structural data of the sewage treatment facility, and performing a topological structure analysis of the sewage treatment facility according to the structural data of the sewage treatment facility, thereby obtaining a topological structure model of the sewage treatment facility; dividing the sewage treatment process structure of the topological structure model of the sewage treatment facility, thereby obtaining a main topological structure model of the sewage treatment and a secondary topological structure model of the sewage treatment. Step S1 is specifically as follows: Step S11: acquiring sewage treatment facility structure data, and classifying the sewage treatment facility structure data into facility structure, thereby obtaining functional component structure data and connection component structure data; Step S12: extracting connection structure features from the functional component structure data, thereby obtaining functional component connection structure data; Step S13: Calculating the connection structure similarity of the connection component structure data and the function component connection structure data, thereby obtaining connection structure similarity data; Step S14: integrating the component connection relationship of the functional component structure data and the connection component structure data according to the connection structure similarity data, thereby obtaining the sewage treatment facility component connection relationship data; Step S15: performing a topological structure analysis of the sewage treatment facility components according to the connection relationship data of the sewage treatment facility components, thereby obtaining a topological structure model of the sewage treatment facility; Step S16: dividing the sewage treatment facility topology structure model into sewage treatment process structures, thereby obtaining a sewage treatment main topology structure model and a sewage treatment secondary topology structure model; Step S2: acquiring sensor data of the sewage treatment facility, and constructing a sewage treatment sensor network for the sewage treatment facility topology model according to the sensor data of the sewage treatment facility, thereby obtaining a sewage treatment sensor network; Abnormal filtration analysis of sewage treatment is performed based on the sewage treatment sensor network to obtain abnormal filtration batch sensor data; Step S3: Based on the abnormal filtration batch sensor data, the main topological structure model of the sewage treatment is divided into an abnormal filtration process structure, so as to obtain the main structure model of the abnormal filtration process, and the suspended solids sedimentation error of the main structure model of the abnormal filtration process is evaluated, so as to obtain the suspended solids sedimentation error data; Step S4: performing a hydrodynamic simulation on the main structural model of the abnormal filtration process according to the suspended matter sedimentation error data, thereby obtaining hydrodynamic simulation data, and performing a filter blockage evaluation based on the hydrodynamic simulation data, thereby obtaining abnormal filter blockage data; Step S5: evaluating the abnormal dosage of the sewage treatment secondary topological structure model based on the suspended solids sedimentation error data, thereby obtaining abnormal dosage data; Step S6: Optimizing the suspended matter settling structure based on the abnormal filter blockage data and the abnormal dosage data, thereby obtaining suspended matter settling optimized structure data; The sewage treatment optimization control strategy is obtained by optimizing the suspended solids sedimentation optimization structure data through the sewage treatment facility topology model, and then uploaded to the sewage treatment management platform to execute the treatment control task.

2. The sewage treatment simulation control method according to claim 1, characterized in that: Step S16 is specifically as follows: Divide the sewage treatment facility structure data into functional structures, thereby obtaining chemical treatment structure data and environmental control structure data; Extracting structural material features from chemical treatment structural data, thereby obtaining chemical treatment structural material data; Performing structural material property analysis on chemically treated structural material data, thereby obtaining structural material property data; According to the chemical treatment structure data, the topological structure model of the sewage treatment facility is divided into chemical treatment associated structures, thereby obtaining a chemical treatment topological structure model; Mapping the structural material characteristics of the chemical treatment topological structure model according to the structural material characteristic data, thereby obtaining the main topological structure model of sewage treatment; The topological structure model of the sewage treatment facility is divided into environmental control associated structures according to the environmental control structure data, thereby obtaining a secondary topological structure model of the sewage treatment.

3. The sewage treatment simulation control method according to claim 1, characterized in that: Step S2 is specifically as follows: Step S21: acquiring sensor data of the sewage treatment facility, and performing sensor space feature extraction on the sensor data of the sewage treatment facility, thereby obtaining sensor space data; Step S22: performing spatial structure association according to the sensor spatial data and the sewage treatment facility topological structure model, thereby obtaining a sensor spatial association topological structure model; Step S23: performing spatial sensing data fusion on the sensor spatial correlation topological structure model according to the sensing data of the sewage treatment facility, thereby obtaining a sewage treatment sensor network; Step S24: Perform abnormal filtration analysis on sewage treatment based on the sewage treatment sensor network to obtain abnormal filtration batch sensor data.

4. The sewage treatment simulation control method according to claim 3 is characterized in that: Step S24 is specifically as follows: Step S241: extracting TSS sensing data and turbidity sensing data based on the sewage treatment sensor network, thereby obtaining TSS sensing data and turbidity sensing data; Step S242: filtering the TSS sensor data to obtain suspended solid concentration statistics, thereby obtaining a standard suspended solid concentration limit, and performing abnormal suspended solid concentration batch clustering on the TSS sensor data according to the standard suspended solid concentration limit, thereby obtaining abnormal suspended solid concentration batch data; Step S243: filtering turbidity statistics of the turbidity sensor data to obtain standard turbidity limits, and clustering abnormal turbidity batches of the turbidity sensor data according to the standard turbidity limits to obtain abnormal turbidity batch data; Step S244: performing a filtering batch intersection operation on the abnormal suspended solids concentration batch data and the abnormal turbidity batch data, thereby obtaining abnormal filtering batch data; Step S245: extracting abnormal filtering batch sensor data from the sewage treatment sensor network according to the abnormal filtering batch data, thereby obtaining the abnormal filtering batch sensor data.

5. The sewage treatment simulation control method according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: performing sensor fluctuation statistics of the sewage filtration process based on the sewage treatment sensor network, thereby obtaining standard sensor fluctuation data of the sewage filtration process; Step S32: Calculating the filtration process sensor fluctuations for the abnormal filtration batch sensor data, thereby obtaining filtration process sensor fluctuation data; Step S33: clustering the filtration process sensor fluctuation data into abnormal sensor fluctuation filtration process data according to the standard sensor fluctuation data of the sewage filtration process, thereby obtaining abnormal sensor fluctuation filtration process data, and calculating the suspended matter sedimentation degree of the abnormal sensor fluctuation filtration process data, thereby obtaining abnormal filtration process sedimentation degree data; Step S34: performing filtering process structure association division on the main topological structure model of sewage treatment according to the abnormal sensing fluctuation filtering process data, thereby obtaining the main structure model of the abnormal filtering process; Step S35: performing Monte Carlo sewage sensor simulation according to the sewage treatment sensor network to obtain sewage sensor simulation data, and performing sewage filtration simulation on the sewage sensor simulation data through the main structure model of the abnormal filtration process to obtain sewage filtration simulation data; Step S36: Calculating the sedimentation rate of suspended matter in the filtration process for the sewage filtration simulation data, thereby obtaining sedimentation rate data for the simulated filtration process; Step S37: Calculate the suspended matter sedimentation error of the abnormal filtration process sedimentation data and the simulated filtration process sedimentation data, so as to obtain the suspended matter sedimentation error data.

6. The sewage treatment simulation control method according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: Perform process error statistics on the suspended matter sedimentation error data, thereby obtaining high-value sedimentation error process data; Step S42: dividing the main structure model of the abnormal filtering process into a high-value sedimentation error process structure according to the high-value sedimentation error process data, thereby obtaining a high-value sedimentation error process structure model; Step S43: performing a hydrodynamic simulation on the sewage sensor simulation data through a high-sedimentation error process structure model, thereby obtaining hydrodynamic simulation data; Step S44: extracting the water flow velocity characteristics of the filter mesh and the pressure characteristics of the filter mesh from the water flow dynamics simulation data, thereby obtaining the simulated water flow velocity data of the filter mesh and the simulated water flow pressure data of the filter mesh; Step S45: performing flow velocity change statistics according to the simulated water flow velocity data of the filter net, thereby obtaining the flow velocity change data of the filter net; performing filter net pressure difference calculation according to the simulated water flow pressure data of the filter net, thereby obtaining the filter net pressure difference data; Step S46: constructing a filter blockage evaluation system according to the filter flow rate change data and the filter pressure difference data, and performing filter blockage evaluation according to the filter blockage evaluation system to obtain abnormal filter blockage data.

7. The sewage treatment simulation control method according to claim 1, characterized in that: Step S5 is specifically as follows: Step S51: performing sedimentation error change statistics on the suspended matter sedimentation error data, thereby obtaining high-value sedimentation error change data; Step S52: dividing the error change process structure of the sewage treatment secondary topological structure model according to the high-value sedimentation error change data, thereby obtaining a high-value sedimentation error change process structure model; Step S53: obtaining the dosage data of the sewage treatment facility, and performing sewage treatment sedimentation simulation on the dosage data of the sewage treatment facility and the sewage sensor simulation data through the high-cost sedimentation error change process structure model, thereby obtaining sewage treatment sedimentation simulation data; Step S54: performing suspended solids sedimentation calculation on the sewage treatment sedimentation simulation data to obtain sedimentation simulation sedimentation data, and performing drug-effect sedimentation effect benchmark statistics on the sedimentation simulation sedimentation data to obtain drug-effect sedimentation effect benchmark data; Step S55: classifying the sewage treatment sedimentation simulation data according to the drug-effect sedimentation effect benchmark data to obtain abnormal drug-effect sedimentation simulation data; Step S56: Correlate the dosage data of sewage treatment facilities with abnormal dosage data according to the abnormal dosage data, thereby obtaining abnormal dosage data.

8. The sewage treatment simulation control method according to claim 1, characterized in that: Step S6 is specifically as follows: Step S61: optimizing the blocked filter of the main topological structure model of sewage treatment according to the abnormal filter blockage data, thereby obtaining the optimized process blocked filter structure data; Step S62: constructing an abnormal dosage detection model according to the abnormal dosage data and the sewage treatment sedimentation simulation data, and optimizing the abnormal dosage process of the sewage treatment facility dosage data through the abnormal dosage detection model, so as to obtain optimized process dosage data; Step S63: merging the optimized process blockage filter structure data and the optimized process dosage data to obtain the suspended matter settling optimized structure data; Step S64: Optimize and regulate the suspended solids settling optimization structure data for sewage treatment through the sewage treatment facility topology model, thereby obtaining a sewage treatment optimization regulation strategy, and uploading it to the sewage treatment management platform to execute the treatment regulation task.

9. A sewage treatment simulation control system, characterized in that: Used to execute a sewage treatment simulation control method as claimed in claim 1, the sewage treatment simulation control system comprises: A topological structure analysis module is used to obtain the structural data of sewage treatment facilities, and perform topological structure analysis of sewage treatment facilities based on the structural data of sewage treatment facilities, so as to obtain a topological structure model of sewage treatment facilities; divide the topological structure model of sewage treatment facilities into sewage treatment process structures, so as to obtain a main topological structure model of sewage treatment and a secondary topological structure model of sewage treatment; The abnormal filtering analysis module is used to obtain the sensor data of the sewage treatment facility, and construct the sewage treatment sensor network of the sewage treatment facility topology model according to the sensor data of the sewage treatment facility, so as to obtain the sewage treatment sensor network; perform sewage treatment abnormal filtering analysis based on the sewage treatment sensor network, so as to obtain abnormal filtering batch sensor data; The sedimentation error evaluation module is used to divide the abnormal filtration process structure of the main topological structure model of sewage treatment based on the abnormal filtration batch sensor data, so as to obtain the main structure model of the abnormal filtration process, and to evaluate the suspended solid sedimentation error of the main structure model of the abnormal filtration process, so as to obtain the suspended solid sedimentation error data; The filter blockage evaluation module is used to perform a hydrodynamic simulation on the main structural model of the abnormal filtration process according to the suspended matter sedimentation error data, thereby obtaining hydrodynamic simulation data, and to evaluate the filter blockage based on the hydrodynamic simulation data, thereby obtaining abnormal filter blockage data; The abnormal dosage evaluation module is used to evaluate the abnormal dosage of the sewage treatment secondary topological structure model based on the suspended solids sedimentation error data, so as to obtain the abnormal dosage data; The sewage treatment optimization and control module is used to optimize the suspended solids sedimentation structure based on abnormal filter blockage data and abnormal dosage data, so as to obtain suspended solids sedimentation optimization structure data; the sewage treatment optimization and control of the suspended solids sedimentation optimization structure data is carried out through the sewage treatment facility topology model, so as to obtain the sewage treatment optimization and control strategy, and upload it to the sewage treatment management platform to execute the treatment and control tasks.

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