A method for monitoring the status of a tin stripping wastewater treatment device

Abstract

The present invention relates to the technical field of device status monitoring, and in particular to a method for status monitoring of a tin stripping wastewater treatment device. The method comprises the following steps: collecting process flow data of the tin stripping wastewater treatment device to obtain wastewater treatment process data; identifying high-risk areas of the tin stripping wastewater treatment device according to the wastewater treatment process data to obtain device high-risk area data; real-time monitoring of the electrochemical impedance spectrum of the tin stripping wastewater treatment device according to the device high-risk area data to obtain a regional electrochemical reaction dynamic model; real-time monitoring of the microstructure deformation of the tin stripping wastewater treatment device to obtain a regional microstructure deformation data set; collecting a mechanical structure diagram of the tin stripping wastewater treatment device to obtain a device mechanical structure diagram. The present invention realizes a comprehensive status diagnosis of the tin stripping wastewater treatment device and provides accurate device status data.

Description

Technical Field

[0001] The present invention relates to the technical field of device status monitoring, and in particular to a method for monitoring the status of a tin stripping wastewater treatment device. Background Art

[0002] In current industrial production, the treatment of tin stripping wastewater is an important link, because these wastewaters contain heavy metal ions and harmful chemicals. If they are not properly treated, they will cause serious pollution to the environment. Traditional tin stripping wastewater treatment devices often lack effective status monitoring methods, which makes it impossible to monitor the operating status of the device in real time, making it difficult to detect and deal with problems in a timely manner. For example, some parts of the device will gradually be damaged due to long-term contact with corrosive chemicals, but this damage is difficult to detect without monitoring until a failure occurs, causing the entire treatment process to be interrupted.

[0003] High-risk areas of tin stripping wastewater treatment devices often require special attention, because these areas will become high-incidence areas of failure due to problems such as chemical corrosion, mechanical wear or structural deformation. However, existing technical means are often unable to accurately identify these high-risk areas, and it is even more difficult to monitor them in real time. This not only increases maintenance costs, but also reduces treatment efficiency and safety. Existing tin stripping wastewater treatment devices also face challenges in terms of material-medium compatibility. Since the chemicals in the wastewater react with the device materials and cause material corrosion, this not only reduces the service life of the device, but also affects the quality of wastewater treatment due to the dissolution of corrosion products. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a state monitoring method for a tin stripping wastewater treatment device to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above object, a state monitoring method for a tin stripping wastewater treatment device comprises the following steps:

[0006] Step S1: collecting process data of the tin stripping wastewater treatment device to obtain wastewater treatment process data; identifying high-risk areas of the tin stripping wastewater treatment device according to the wastewater treatment process data to obtain high-risk area data of the device;

[0007] Step S2: performing real-time electrochemical impedance spectroscopy monitoring on the tin stripping wastewater treatment device according to the high-risk area data of the device to obtain a regional electrochemical reaction dynamic model; performing real-time monitoring on the microstructure deformation of the tin stripping wastewater treatment device to obtain a regional microstructure deformation data set;

[0008] Step S3: collecting a mechanical structure diagram of the tin stripping wastewater treatment device to obtain a mechanical structure diagram of the device; analyzing the equipment wear state of the tin stripping wastewater treatment device based on the mechanical structure diagram of the device to obtain equipment component wear assessment data;

[0009] Step S4: performing material-medium compatibility analysis on the tin stripping wastewater treatment device to obtain a device material corrosion area distribution map; performing a surface corrosion state assessment on the tin stripping wastewater treatment device according to the device material corrosion area distribution map to obtain component metal surface corrosion assessment data;

[0010] Step S5: Perform electrochemical fatigue analysis on the tin stripping wastewater treatment device based on the regional electrochemical reaction dynamic model to obtain the electrochemical fatigue data of the equipment surface; determine the microstructural stress-strain behavior of the regional microstructural deformation data set to obtain the equipment structural deformation stress distribution data; perform a comprehensive status diagnosis on the tin stripping wastewater treatment device based on the equipment surface electrochemical fatigue data, equipment structural deformation stress distribution data, equipment component wear assessment data and component metal surface corrosion assessment data to obtain the treatment device status data.

[0011] The present invention can timely discover and deal with potential problems by real-time collection of process data and identification of high-risk areas, effectively avoiding failures and environmental pollution caused by lack of monitoring. At the same time, through real-time monitoring of electrochemical impedance spectroscopy and microstructure deformation, high-risk areas are accurately identified and continuously monitored, reducing the occurrence of unexpected failures, reducing maintenance costs, and improving the safety of the treatment process. Through the analysis of the mechanical structure diagram and the evaluation of the wear state of the equipment, the wear of components can be predicted, and maintenance or replacement can be carried out in time, extending the service life of the device. Through the analysis of material-medium compatibility, the material selection and treatment effect are further optimized, and the corrosion resistance and treatment efficiency of the device are improved. Through comprehensive electrochemical fatigue analysis, microstructure stress-strain behavior analysis, equipment wear evaluation and metal surface corrosion evaluation, the present invention realizes a comprehensive status diagnosis of the device and provides accurate device status data. The present invention not only improves environmental and economic benefits, reduces the risk of environmental pollution and production interruption, but also enhances the reliability and stability of the system, ensures the continuity and stability of the wastewater treatment process, and is of great significance to maintaining the normal operation of industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Other features, objects and advantages of the present invention will become more apparent from the detailed description made with reference to the following drawings:

[0013] Figure 1 A schematic flow chart of the steps of a method for monitoring the state of a tin stripping wastewater treatment device according to an embodiment is shown.

[0014] Figure 2A detailed flow chart of step S3 of an embodiment is shown.

[0015] Figure 3 A detailed flow chart of step S4 of an embodiment is shown. DETAILED DESCRIPTION

[0016] 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.

[0017] 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.

[0018] 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.

[0019] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for monitoring the state of a tin stripping wastewater treatment device, comprising the following steps:

[0020] Step S1: collecting process data of the tin stripping wastewater treatment device to obtain wastewater treatment process data; identifying high-risk areas of the tin stripping wastewater treatment device according to the wastewater treatment process data to obtain high-risk area data of the device;

[0021] Step S2: performing real-time electrochemical impedance spectroscopy monitoring on the tin stripping wastewater treatment device according to the high-risk area data of the device to obtain a regional electrochemical reaction dynamic model; performing real-time monitoring on the microstructure deformation of the tin stripping wastewater treatment device to obtain a regional microstructure deformation data set;

[0022] Step S3: collecting a mechanical structure diagram of the tin stripping wastewater treatment device to obtain a mechanical structure diagram of the device; analyzing the equipment wear state of the tin stripping wastewater treatment device based on the mechanical structure diagram of the device to obtain equipment component wear assessment data;

[0023] Step S4: performing material-medium compatibility analysis on the tin stripping wastewater treatment device to obtain a device material corrosion area distribution map; performing a surface corrosion state assessment on the tin stripping wastewater treatment device according to the device material corrosion area distribution map to obtain component metal surface corrosion assessment data;

[0024] Step S5: Perform electrochemical fatigue analysis on the tin stripping wastewater treatment device based on the regional electrochemical reaction dynamic model to obtain the electrochemical fatigue data of the equipment surface; determine the microstructural stress-strain behavior of the regional microstructural deformation data set to obtain the equipment structural deformation stress distribution data; perform a comprehensive status diagnosis on the tin stripping wastewater treatment device based on the equipment surface electrochemical fatigue data, equipment structural deformation stress distribution data, equipment component wear assessment data and component metal surface corrosion assessment data to obtain the treatment device status data.

[0025] In this embodiment, an automated data acquisition system, such as Siemens SIMATIC S7 series PLC, is used to monitor and record the key operating parameters of the wastewater treatment device in real time, including flow, temperature, pH value, chemical component concentration, etc., to obtain wastewater treatment process data. The collected wastewater treatment process data is combined with GIS software (such as ArcGIS) for spatial analysis to identify areas with high chemical concentrations and large temperature changes in wastewater, and obtain high-risk area data for the device. Electrochemical sensors are deployed in high-risk areas, and electrochemical impedance spectrometers (such as old versions of FRA32) are used for real-time monitoring to record impedance spectrum data at different time points to obtain a regional electrochemical reaction dynamic model. Using high-precision displacement sensors and video monitoring systems, the microstructural changes of the device are monitored in real time to obtain a regional microstructural deformation data set. The mechanical structure diagram of the device is drawn by CAD software, including information such as component layout, size, and material properties. Based on the mechanical structure diagram, finite element analysis (FEA) software (such as ANSYS) is used to simulate the stress distribution and wear of the device during operation to obtain equipment component wear assessment data. The compatibility of the device materials with the chemicals in the wastewater is evaluated using a materials science database (such as NACE MR0175) to obtain a regional distribution map of the device material corrosion. Combined with the regional distribution map of the device material corrosion, an electrochemical test method (such as polarization curve test) is used to evaluate the corrosion state of the device surface and obtain the corrosion evaluation data of the metal surface of the component. Using an electrochemical workstation (such as Autolab PGSTAT128N) and electrochemical impedance spectroscopy (EIS) technology, based on the regional electrochemical reaction dynamic model, the key components of the tin stripping wastewater treatment device are subjected to electrochemical fatigue analysis. By applying a small-amplitude AC signal and measuring its impedance change, the dynamic behavior of the electrochemical reaction is analyzed. The data are analyzed using Nyquist and Bode diagrams to extract electrochemical parameters such as charge transfer resistance and double-layer capacitance to obtain the surface electrochemical fatigue data of the device. The regional microstructure deformation data set is imported into data analysis software such as MATLAB or Python. These data sets contain the spatial coordinates, strain amplitude, and strain rate of the component deformation. Selecting a suitable stress-strain relationship model, such as an elastic model, a plastic model, or a viscoelastic model, depends on the material properties and loading conditions. Use finite element analysis (FEA) software (such as ANSYS or ABAQUS) to simulate and calculate the stress distribution of the structure based on the spatial coordinates and strain amplitude of the component deformation. In the model, apply the actual loads and boundary conditions and run the simulation to obtain the stress field. Perform time series analysis on the component strain rate data to determine the trend and characteristics of the strain rate. This helps to evaluate the fatigue and damage state of the material. Analyze the stress distribution data of the equipment structure deformation to identify stress concentration areas and potential structural weaknesses. Organize the analysis results into a report, including stress distribution diagrams, the location of stress concentration areas, and related component deformation data.Integrate the equipment surface electrochemical fatigue data, equipment structural deformation stress distribution data, equipment component wear assessment data, and component metal surface corrosion assessment data into a database. Use SQL or Python's Pandas library to manage and integrate this data. Extract key features from each data set, such as fatigue cycle number from electrochemical fatigue data, maximum stress value from structural deformation stress distribution data, wear rate from wear assessment data, and corrosion depth from corrosion assessment data. Select a machine learning model suitable for multi-source data fusion, such as random forest or gradient boosting decision tree (GBDT). These models can handle nonlinear relationships and give feature importance scores. Split the integrated data set into training and test sets in a ratio of 70:30 or 80:20. Use the training set to train the machine learning model. In Python, the scikit-learn library can be used to implement the training of random forest or GBDT models. Apply the model to the entire data set to obtain a comprehensive status score for each component. Interpret the results of the model output to determine the potential failure points and performance degradation areas of the equipment. Generates a detailed condition diagnostic report (i.e., processing unit condition data) including a condition score for each component.

[0026] Preferably, step S1 comprises the following steps:

[0027] Step S11: collecting process data of the tin stripping wastewater treatment device to obtain wastewater treatment process data;

[0028] Specifically, the data acquisition system can be used to monitor the key operating units of the tin stripping wastewater treatment device, including but not limited to the wastewater input port, treatment tank, pump, filter and output port. The data acquisition system includes a sensor array, a data acquisition card and software. The sensor array is responsible for real-time monitoring and recording of parameters such as pH value, temperature, flow rate, pressure and chemical concentration. The data acquisition card is responsible for collecting data from the sensor and transmitting it to the central database via wired or wireless means to obtain wastewater treatment process data.

[0029] Step S12: identifying the chemical risk area of ​​the tin stripping wastewater treatment device according to the wastewater treatment process data to obtain chemical risk area data;

[0030] Specifically, wastewater treatment process data include wastewater flow, pH value, temperature, chemical composition (such as heavy metal ion concentration, organic matter content), etc. Use chemical analysis software (such as ChemCAD or Aspen Plus) to analyze the collected chemical composition data. These software can simulate chemical reaction processes and predict chemical reaction products under different conditions. Use existing risk assessment models, such as the Nernst model, to assess the corrosiveness and reactivity of chemicals in wastewater. The Nernst model predicts chemical reactions by calculating the equilibrium potential of chemical reactions. Use hot spot analysis tools in GIS software (such as ArcGIS or QGIS) to identify chemical risk areas. Hot spot analysis tools can help identify statistically significant high-risk areas. Use standards such as ASTM E1681 or DuPont Risk Zones to predict toxic gases, sediments, or corrosive substances that will be produced by chemical reactions. Integrate the above analysis results to determine chemical risk areas. In GIS software, these areas are marked with different colors or layers. Record the identified chemical risk area data in the database, including the location of the area, the types of chemicals involved, and the potential chemical reaction risks.

[0031] Step S13: Perform three-dimensional modeling on the tin stripping wastewater treatment device to obtain a three-dimensional model of the wastewater treatment device;

[0032] Specifically, a 3D scanner can be used to scan the physical structure of the tin stripping wastewater treatment device to obtain the precise size and shape data of the tin stripping wastewater treatment device. Then, these data are input into 3D modeling software, such as AutoCAD, SolidWorks or CATIA, to construct a 3D model of the wastewater treatment device.

[0033] Step S14: performing fluid path simulation on the tin stripping wastewater treatment device based on the three-dimensional model of the wastewater treatment device and the wastewater treatment process data to obtain wastewater flow simulation path data;

[0034] Specifically, professional fluid dynamics simulation software, such as ANSYS Fluent or COMSOL Multiphysics, can be used to simulate the fluid path of the three-dimensional model of the wastewater treatment device. Before simulation, the wastewater treatment process data collected in steps S11 and S12 need to be input into the simulation software. These data include the flow rate, temperature, chemical composition concentration, etc. of the wastewater. Next, set the simulation parameters, such as inlet and outlet boundary conditions, operating pressure, and physical properties of the fluid (such as density and viscosity). In the software, create a calculation domain, import the three-dimensional model of the wastewater treatment device into the domain, and define the inlet and outlet of the fluid flow. Use the built-in meshing tool in the software to mesh the model and generate a grid system for calculation. Subsequently, select a suitable fluid dynamics model, such as a laminar flow model or a turbulent flow model, and select a suitable turbulence model (such as a k-ε model or a k-ω model) according to the characteristics of the wastewater. Start the simulation calculation, and the software will calculate the flow path and flow velocity distribution of the wastewater in the device based on the input fluid characteristics and boundary conditions. During the calculation process, the software will update the flow state of the fluid in real time and generate simulated path data of wastewater flow after the calculation is completed. These data include velocity vector diagrams, pressure distribution diagrams, and temperature field distribution diagrams.

[0035] Step S15: Identify high-risk areas of the tin stripping wastewater treatment device based on the chemical risk area data and the wastewater flow simulation path data to obtain high-risk area data of the device.

[0036] Specifically, please refer to the sub-steps of step S15 for the detailed implementation process of this embodiment.

[0037] The present invention can identify chemical risk areas in wastewater treatment process data, and can monitor those high-risk areas for chemical leakage or reaction in a targeted manner, greatly improving the efficiency and effectiveness of monitoring. At the same time, the established three-dimensional model provides an intuitive analysis method for the structure and fluid path of the wastewater treatment device, optimizing the planning of monitoring points and the prediction of potential problem areas. The simulation of the fluid path further predicts the flow characteristics of the wastewater, which plays an important role in understanding and improving the efficiency of wastewater treatment. By combining the chemical risk area with the fluid path simulation data to locate the high-risk area, the optimal allocation of resources and the early control of risks are achieved. The present invention improves the ability of preventive maintenance, reduces unexpected downtime, and enhances the operational reliability of the device. Accurate identification of risk areas helps to reduce environmental pollution and chemical leakage and ensure the safety of operators.

[0038] Preferably, step S15 comprises the following steps:

[0039] Step S151: performing turbulence and shear force area identification on the wastewater flow simulation path data to obtain fluid mechanics risk area data, wherein the fluid mechanics risk area data includes high turbulence areas and high shear force areas;

[0040] Specifically, fluid dynamics analysis software (such as ANSYS Fluent or CFX) can be used to post-process the wastewater flow simulation path data to identify turbulence and shear force areas. First, import the simulated fluid velocity field and pressure field data. In the software, a turbulence model (such as the k-ε or k-ω SST model) is applied to analyze the turbulence intensity of the fluid. By calculating the Reynolds number of the fluid, determine whether the fluid flow is laminar or turbulent. The turbulent area is manifested as rapid changes in fluid velocity and pressure, which can be identified by the distribution diagram of turbulent kinetic energy and turbulent dissipation rate, and finally the fluid mechanics risk area data is obtained, where the fluid mechanics risk area data includes high turbulence areas and high shear force areas.

[0041] Step S152: determining the operating conditions of the wastewater treatment process to obtain a set of operating conditions for the treatment process;

[0042] Specifically, the detailed operating procedures and technical requirements of the wastewater treatment process can be reviewed to determine the operating conditions. Use process simulation software (such as Aspen Plus or SIMUL8) to simulate the various stages of wastewater treatment, including chemical treatment, sedimentation, filtration and biological treatment. In the software, enter the initial conditions such as the composition, temperature, pH value, flow rate of the wastewater, as well as the design parameters and operating limits of each treatment unit. These operating conditions are organized into an operating condition set, including but not limited to chemical addition, pressure, flow rate, temperature and equipment operation mode.

[0043] Step S153: mapping the operation area of ​​the three-dimensional model of the wastewater treatment device according to the wastewater treatment process data to obtain a device operation area mapping model;

[0044] Specifically, 3D modeling software (such as AutoCAD 3D or SolidWorks) and process simulation software can be used to combine wastewater treatment process data with the 3D model of the wastewater treatment plant to create an operation area mapping model. First, the 3D model of the wastewater treatment plant is imported into the modeling software, and the model is divided into different operation areas, such as pretreatment area, main treatment area, sedimentation area, and sludge treatment area, according to the requirements of the process flow. Then, specific operating conditions and monitoring requirements are assigned to each operation area based on the wastewater treatment process data, such as flow, chemical addition amount, and equipment operating parameters. In the software, labels and attributes are added to each area, such as operating temperature, pressure limit, chemical composition requirements, etc. Finally, the device operation area mapping model is generated.

[0045] Step S154: performing structural stress and thermal distribution identification on the device operation area mapping model according to the processing flow operation condition set to obtain structural high stress area data and structural thermal distribution data;

[0046] Specifically, finite element analysis (FEA) software, such as ANSYS or ABAQUS, can be used to identify the structural stress and thermal distribution of the device operation area mapping model. First, the device operation area mapping model is imported into the FEA software, and the material type is assigned to the model according to the material properties (such as elastic modulus, Poisson's ratio, thermal expansion coefficient, etc.). Next, the boundary conditions of the structure are defined, such as the position of the fixed support, the applied external force or torque, etc. Then, according to the set of processing flow operating conditions, the simulated working conditions are set, including fluid pressure, temperature change, chemical corrosion conditions, etc. In the software, thermal analysis and structural analysis are run to calculate the structural stress and thermal distribution of the device under different working conditions. Through these analyses, high stress areas in the structure can be identified, which are fatigued or cracked due to long-term excessive mechanical stress. At the same time, the thermal distribution data of the structure can also be obtained to identify overheating areas, which are caused by material performance degradation or thermal stress concentration due to excessive temperature. After the analysis is completed, the software will generate structural stress cloud map and thermal distribution cloud map to clearly show the structural stress and temperature distribution of the entire device. Based on these data, the high stress area data and the structural thermal distribution data of the structure can be determined.

[0047] Step S155: collecting fault maintenance records of the tin stripping wastewater treatment device to obtain device fault maintenance record data;

[0048] Specifically, a fault maintenance record database can be established, and database management software such as Microsoft Access or SQL Server can be used to store and manage the historical fault and maintenance data of the tin stripping wastewater treatment device. By accessing the maintenance log, maintenance report and operator's record of the device, information such as the device's fault history, maintenance activities, replaced parts, fault causes and solutions can be collected. Using data collection tools such as tables or special data collection software, the collected fault maintenance records are entered into the database. In the database, an index is established for each record, including the equipment name, fault date, fault type, maintenance measures and maintenance results.

[0049] Step S156: identifying frequently-failed components based on the device failure maintenance record data to obtain frequently-failed component data;

[0050] Specifically, the fault maintenance record data can be imported into data analysis software, such as using Python's Pandas library for data sorting and preprocessing. Then, by writing scripts or using the query function of the software, filter out the components that appear frequently in the fault records. Next, perform statistical analysis on these data to calculate the failure rate of each component, that is, the ratio of the number of component failures to the total operating time. Visualization tools such as histograms or pie charts can be used to display the failure frequency of different components, so as to intuitively identify components with frequent failures. Finally, organize the analysis results into data on frequently failing components, including component name, fault type, failure frequency, maintenance history and other information.

[0051] Step S157: Based on the chemical risk area data, fluid mechanics risk area data, structural high stress area data, structural thermal distribution data and failure-prone component data, the device operation area mapping model is used to locate and map the high-risk area to obtain the device high-risk area data.

[0052] Specifically, the data of chemical risk areas, structural high stress areas, structural thermal distribution data, and failure-prone component data can be integrated into a building information model (BIM) software, such as Revit. Then, using the analysis tools of the BIM software, these data are superimposed on the operating area mapping model of the device. By setting different colors or transparency, the distribution of different risk areas can be intuitively displayed in the three-dimensional model. For example, high turbulence areas can be marked in red, high shear areas in blue, high structural stress areas in yellow, and overheating areas in orange. Next, spatial analysis tools such as buffer analysis or overlay analysis are used to determine the relationship between different risk areas and potential cross-risk areas. Through these analyses, the highest risk areas in the device can be identified. Finally, the analysis results are organized into high-risk area data of the device, including the location, type, and risk level of the high-risk areas.

[0053] By deeply analyzing the wastewater flow simulation path data, the present invention can accurately identify high turbulence and high shear force areas, enhance the identification of fluid mechanics risks, make monitoring more targeted, and improve the effectiveness of preventive measures. At the same time, by setting operating conditions based on wastewater treatment process data, clear guidance is provided for the stable and safe operation of the device. The identification of structural stress and thermal distribution further improves the structural safety assessment of the device, predicts and evaluates potential structural problems, and helps to take reinforcement or cooling measures in time to avoid the occurrence of structural failures. By collecting fault maintenance records and identifying fault-prone components, the present invention optimizes the maintenance strategy, improves the efficiency of maintenance work and the reliability of the device. By comprehensively considering chemical risks, fluid mechanics risks, structural stresses, thermal distribution and fault-prone data, the present invention achieves accurate positioning of high-risk areas and provides a comprehensive perspective for risk management. The present invention improves risk prevention capabilities, reduces accidents, and enhances the safety of the device and protection of the environment. At the same time, accurate risk area positioning promotes the rational allocation of resources, ensures that key areas receive appropriate attention and maintenance, and avoids waste of resources.

[0054] Preferably, step S2 comprises the following steps:

[0055] Step S21: marking the chemical risk area of ​​the tin stripping wastewater treatment device according to the high-risk area data of the device to obtain electrochemical monitoring area marking data, and determining the electrochemical monitoring components of the tin stripping wastewater treatment device according to the electrochemical monitoring area marking data to obtain the electrochemical monitoring component data of the device;

[0056] Specifically, software tools can be used to visually mark high-risk areas on the three-dimensional model of the tin stripping wastewater treatment device based on the high-risk area data of the device. In the software, these areas will be marked with different colors or symbols to determine the electrochemical monitoring area marking data. Based on the electrochemical monitoring area marking data, determine which components require continuous electrochemical monitoring. For example, if the potential reading of a component indicates that corrosion has occurred, then this component needs to be installed with a potential monitoring sensor. Create a detailed component monitoring plan, record each selected component, and finally obtain the electrochemical monitoring component data of the device.

[0057] Step S22: obtaining wastewater flow simulation path data and device operation area mapping model;

[0058] Specifically, wastewater flow simulation path data can be exported from fluid dynamics simulation software (such as ANSYS Fluent or COMSOL Multiphysics). These data include information such as the flow velocity, flow direction, and pressure distribution of the fluid inside the device. Then, use 3D modeling software (such as SolidWorks or AutoCAD) to open the operating area mapping model of the device. This model already contains the physical structure of the device, the division of the operating area, and the location information of key components.

[0059] Step S23: Based on the device operation area mapping model and the wastewater flow simulation path data, the chemical corrosion degree of the device electrochemical monitoring component data is identified to obtain a component corrosion degree data set;

[0060] Specifically, the operating area mapping model of the device can be opened using 3D modeling software (such as AutoCAD or SolidWorks), and the wastewater flow simulation path data can be imported into the model. These data contain information such as the flow rate, flow direction, and chemical composition distribution of the fluid inside the device. Next, the electrochemical monitoring component data is analyzed using chemical corrosion analysis software (such as Corrosion Analyzer or Electrochemical Impedance Spectroscopy (EIS) software). By combining the flow path data with the electrochemical response of the monitored components, the degree of corrosion of each component under different flow conditions is identified. This process involves comparing the actual measured current density, corrosion potential, and electrochemical impedance parameters to generate a component corrosion degree data set. Ultimately, the output corrosion degree data set will contain the corrosion rate and corrosion type of each component.

[0061] Step S24: determining the sensor material type of the device electrochemical monitoring component data according to the component corrosion degree data set to obtain sensor material type data;

[0062] Specifically, data analysis software (such as Excel or Python's Pandas library) can be used to analyze the corrosion rate, chemical composition, and fluid conditions based on the component corrosion degree dataset to determine the type of sensor material required for each component. For example, for metal components in a highly corrosive environment, corrosion-resistant materials such as titanium alloys or polymer materials need to be selected. Next, the sensor material type of each component is recorded to form sensor material type data.

[0063] Step S25: selecting a monitoring sensor for the electrochemical monitoring component data of the device according to the sensor material type data to obtain sensor selection data;

[0064] Specifically, sensor selection software or databases (such as Sensor Selection Tool or Manufacturer Catalogs) can be used to find sensor options that meet material requirements and environmental conditions. This includes electrochemical sensors, pH sensors, temperature sensors, etc. Then, compare the sensor's technical parameters (such as measurement range, sensitivity, response time, and corrosion resistance) to select the most suitable sensor type and model. Finally, record the model, specifications, and installation location of the selected sensor to form sensor selection data.

[0065] Step S26: performing sensor deployment operations on the tin stripping wastewater treatment device according to the device electrochemical monitoring component data and sensor selection data, and performing real-time electrochemical impedance spectroscopy monitoring to obtain a regional electrochemical reaction dynamic model;

[0066] Specifically, please refer to the sub-steps of step S26 for the detailed implementation process of this embodiment.

[0067] Step S27: performing real-time monitoring of microstructural deformation of the tin stripping wastewater treatment device according to the high-risk area data of the device to obtain a regional microstructural deformation data set.

[0068] Specifically, please refer to the sub-steps of step S27 for the detailed implementation process of this embodiment.

[0069] The present invention ensures the accuracy and reliability of electrochemical monitoring by accurately marking chemical risk areas, providing a scientific basis for the correct selection of monitoring components. By real-time monitoring of the electrochemical impedance spectrum, chemical corrosion risks can be dynamically captured and timely evaluated, effectively preventing and controlling corrosion problems. By optimizing sensor deployment and accurately matching the degree of chemical corrosion and sensor material type, the accuracy of monitoring data and the long-term stability of the sensor are ensured. The enhanced microstructural deformation monitoring capability enables timely detection and evaluation of microscopic damage to the device, providing important support for maintaining structural integrity. By integrating electrochemical and microstructural monitoring data, the present invention comprehensively evaluates the operating status of the device, reduces unexpected failures, and thus significantly improves the safety and stability of the device.

[0070] Preferably, step S26 comprises the following steps:

[0071] Step S261: performing sensor deployment operations on the tin stripping wastewater treatment device according to the electrochemical monitoring component data of the device and the sensor selection data, and using the deployed sensors to perform multi-band impedance scanning on the components corresponding to the electrochemical monitoring component data of the device to obtain a component impedance response spectrum segment set, wherein the component impedance response spectrum segment set includes a plurality of component impedance response spectrum segments, and the component impedance response spectrum segments are electrochemical response spectrum segments under different current density conditions;

[0072] Specifically, sensors can be installed on designated components of the tin stripping wastewater treatment device using automated deployment machinery or manual methods based on the device electrochemical monitoring component data and sensor selection data. These sensors include electrochemical impedance spectroscopy (EIS) sensors or other types of electrochemical sensors that can measure electrochemical responses under different current density conditions. Use an electrochemical workstation (such as CH Instruments or Autolab electrochemical workstation) to perform a multi-band impedance scan on the deployed sensors. This involves measuring the impedance of the component at different frequencies and current densities to obtain its electrochemical response. Under different current density conditions, the impedance values ​​measured by the sensor are recorded to form a component impedance response spectrum segment set. These spectrum segment sets contain electrochemical response information under different conditions. The collected impedance response data is organized into component impedance response spectrum segment sets to create archives for the electrochemical response of each component at different current densities.

[0073] Step S262: extracting key characteristic variables from the component impedance response spectrum segment set to obtain a multidimensional electrochemical characteristic matrix, wherein the multidimensional electrochemical characteristic matrix includes a plurality of electrochemical reaction path vectors, and the key characteristic variables include current density, impedance spectrum modulus, electrode potential change, double layer capacitance, and charge transfer resistance;

[0074] Specifically, the component impedance response spectrum segment set can be imported into data analysis software such as MATLAB or Python (using NumPy and SciPy libraries). The impedance response spectrum of each component is analyzed using the software's signal processing tools to extract key characteristic variables. These variables include: Current density: current density value under different test conditions. Impedance spectrum modulus: impedance modulus at different frequencies. Electrode potential change: change in electrode potential during electrochemical reaction. Double layer capacitance: double layer capacitance value extracted from impedance spectrum. Charge transfer resistance: charge transfer resistance value extracted from impedance spectrum. The extracted characteristic variables are organized into a multidimensional electrochemical characteristic matrix. The electrochemical response data of each component is taken as a vector containing all the above key characteristic variables.

[0075] Step S263: performing chemical reaction path identification on the multi-dimensional electrochemical characteristic matrix to obtain a component chemical reaction path characteristic set;

[0076] Specifically, chemical reaction path identification can be performed using chemical informatics software or specialized data analysis tools, such as Python's scikit-learn library or related packages in the R language. First, the multidimensional electrochemical feature matrix is ​​imported into the data analysis software. These data include current density, impedance spectrum modulus, electrode potential change, double layer capacitance, and charge transfer resistance. Feature selection algorithms, such as principal component analysis (PCA) or recursive feature elimination (RFE), are used to identify the feature variables that have the greatest impact on chemical reaction path identification. Machine learning algorithms, such as random forests, support vector machines (SVMs), or artificial neural networks (ANNs), are used to train models for the selected features to identify different chemical reaction paths. The trained model is used to analyze the electrochemical response data of each component, identify its corresponding chemical reaction path, and organize this information into a component chemical reaction path feature set.

[0077] Step S264: performing regional current density and reaction intensity partitioning on the components corresponding to the device electrochemical monitoring component data based on the component chemical reaction path feature set to obtain electrode surface partition area data;

[0078] Specifically, the electrochemical monitoring component data can be imported into electrochemical analysis software such as Zahner Zennium or EC-Lab. These data include electrochemical response data obtained from electrochemical impedance spectroscopy (EIS) or current-voltage (IV) tests. Electrochemical features, including current density, charge transfer resistance, double layer capacitance, etc., are extracted using the software's built-in tools. Based on the electrochemical features, the electrode surface is divided into different regions using the k-means clustering algorithm. Each region has similar electrochemical properties. Based on the electrochemical features, the electrode surface is divided into different regions using the k-means clustering algorithm. Each region has similar electrochemical properties. For each region, the Bard-FAulkner model is used to analyze the current density distribution. This model is a general electrochemical kinetic model that can describe the relationship between current density and potential on the electrode surface. The reaction intensity of each region is evaluated from the IV curve using the Tafel equation. The Tafel equation describes the relationship between reaction rate and electrode kinetics at different potentials. The current density distribution and reaction intensity evaluation data of each region are recorded in the database to form the electrode surface partition area data.

[0079] Step S265: Perform regional dynamic mapping on the electrode surface partition area data to obtain a regional electrochemical dynamic distribution map, and perform spatiotemporal chemical reaction correlation on the components corresponding to the device electrochemical monitoring component data based on the regional electrochemical dynamic distribution map to obtain a regional electrochemical reaction dynamic model.

[0080] Specifically, data visualization software, such as Tableau or Python's matplotlib library, can be used to visualize the data of the electrode surface partition area and generate regional electrochemical dynamic distribution diagrams. These charts can show the real-time changes in current density and reaction intensity in different regions. Using time series analysis or dynamic system modeling methods, such as system dynamics (SD) models or state space models, the electrochemical responses of different regions are analyzed in time and space. Based on the results of the time and space correlation analysis, a regional electrochemical reaction dynamic model is constructed. This model can describe the dynamic changes and interactions of electrochemical reactions in different regions of the electrode surface.

[0081] The present invention enhances the sensitivity and comprehensiveness of monitoring by implementing multi-band impedance scanning, ensuring the comprehensive capture of electrochemical responses under different current density conditions. The complexity and dynamic changes of electrochemical processes are revealed by extracting key characteristic variables and constructing a multidimensional electrochemical characteristic matrix. By accurately identifying chemical reaction paths, a scientific basis can be provided for the understanding of electrochemical processes and the optimization of wastewater treatment. By regionalizing the management of current density and reaction intensity, differentiated management can be implemented in different regions to further optimize the electrochemical treatment effect. By generating regional electrochemical dynamic distribution maps, dynamic visualization of electrochemical processes is achieved, which is convenient for real-time monitoring and evaluation of device status. By analyzing the spatiotemporal correlation of chemical reactions, the understanding of the electrochemical response relationship at different time and space scales is enhanced, providing rich information for the optimized operation and fault diagnosis of the device. By combining these electrochemical characteristics and dynamic distribution maps, the present invention significantly improves the accuracy and timeliness of fault prediction and diagnosis.

[0082] Preferably, step S27 includes the following steps:

[0083] Step S271: deploying a fiber Bragg grating sensing network for the tin stripping wastewater treatment device according to the high-risk area data of the device to obtain a component strain sensing network;

[0084] Specifically, the specific locations where sensors need to be deployed can be determined based on the data of high-risk areas of the device. These locations include stress concentration areas, corrosion-prone areas, or critical operating components. Select suitable FBG sensors that can measure strain, temperature, or other relevant parameters. Use a dedicated fiber optic fusion machine to install the FBG sensor to the predetermined location of the device. Ensure that the optical fiber fits tightly to the surface of the component to obtain accurate strain readings. The deployed sensor network is called the component strain sensing network, and the location and characteristics of each sensor are recorded.

[0085] Step S272: performing phase-locked filtering processing on the component strain sensing network to obtain phase-synchronized strain analysis data;

[0086] Specifically, a fiber Bragg grating interrogator (such as the SM130 fiber Bragg grating interrogator) can be used to collect the reflection spectrum data of the FBG sensor in real time. These data include the wavelength shift of the grating, which is related to strain and temperature changes. The phase-locked loop (PLL) algorithm is used to demodulate the reflection spectrum of the FBG sensor. PLL is a feedback control system that can lock to the frequency and phase of the input signal. In this case, it is used to track the wavelength change of the FBG sensor. The output of the PLL is the wavelength change information of the FBG sensor, which is related to strain and temperature changes. The PLL algorithm can provide an accurate measurement of the wavelength in the form of wavelength shift. Convert the wavelength shift to a strain value. This requires the use of a calibration curve for the FBG sensor, which describes the relationship between wavelength shift and strain. Phase synchronization technology is used to process the wavelength data output by the PLL. Phase synchronization ensures that the measurement is accurate and consistent within each cycle of the wavelength change. The strain value is parsed from the data obtained by PLL and phase synchronization processing. This includes calculating the mean and standard deviation of the wavelength change. The parsed strain data is recorded in a database, and phase-synchronized strain parsing data is finally obtained.

[0087] Step S273: performing temperature compensation grating integration on the component strain sensing network according to the phase-synchronous strain analysis data to obtain a strain sensing temperature self-correction network;

[0088] Specifically, a temperature compensation algorithm based on a physical model can be selected, such as a temperature compensation algorithm based on the relationship between the refractive index and temperature of the FBG sensor. The algorithm takes into account the characteristics of the thermal expansion coefficient and refractive index of the optical fiber material changing with temperature. Use a fiber grating demodulator (such as the SM130 fiber grating demodulator) to collect the reflection spectrum data of the FBG sensor in real time. Synchronously collect the temperature sensor data at a position adjacent to the FBG sensor to obtain accurate ambient temperature information. Based on the collected reflection spectrum data and the synchronously collected temperature data, a temperature compensation algorithm is applied to calculate the temperature compensation factor. The algorithm includes the following formulas: in, is the change in the Bragg wavelength, is the wavelength change caused by strain, is the thermal expansion coefficient of the FBG sensor material, is the thermal expansion coefficient of the optical fiber material, is the temperature change, is the initial value of the Bragg wavelength; the calculated temperature compensation factor is applied to the original strain reading to obtain the corrected strain data. The temperature compensation algorithm is integrated into the data processing flow of the FBG sensor network to construct a strain-aware temperature self-correction network. The network can automatically perform real-time temperature compensation on the data of each sensor, thereby obtaining a strain-aware temperature self-correction network.

[0089] Select a suitable temperature compensation algorithm that can correct the strain readings according to temperature changes. Apply the temperature compensation algorithm to the grating reflection spectrum acquisition network to perform real-time temperature compensation on the data of each FBG sensor. By integrating the temperature compensation algorithm, a strain-aware temperature self-correction network is constructed. This network can automatically adjust the sensor readings to eliminate the effects of temperature changes on strain measurements.

[0090] Step S274: performing acoustic-optical interaction enhanced monitoring on the strain-sensing temperature self-correction network to obtain a strain-sensing temperature self-correction enhanced network;

[0091] Specifically, an acousto-optic interaction device can be designed and set up, which can generate ultrasonic waves and guide them to the location of the fiber Bragg grating (FBG) sensor. When the ultrasonic wave passes through the optical fiber, the light wave in the optical fiber is modulated due to the acousto-optic interaction, thereby enhancing the FBG sensor's ability to sense strain. The acousto-optic interaction device is integrated with the strain-sensing temperature self-correction network to ensure that the emission of the acoustic wave and the monitoring of the grating are synchronized. A system test is performed to verify whether the acousto-optic interaction effectively enhances the monitoring signal of the sensor, and the parameters are adjusted to optimize the signal enhancement effect. The monitoring data after the acousto-optic interaction enhancement is recorded, and the monitoring network that integrates the acousto-optic interaction is called the strain-sensing temperature self-correction enhancement network.

[0092] Step S275: collecting strain fields of the tin stripping wastewater treatment device through a strain-sensing temperature self-correction enhancement network to obtain component strain data streams;

[0093] Specifically, a data acquisition system can be configured, such as using NI DAQ hardware and software, to collect data from FBG sensors in the strain-sensing temperature self-correction enhanced network. Start the data acquisition system and begin collecting strain data from the FBG sensors. These data reflect the real-time strain state of the components during the operation of the device. Organize the collected data into component strain data streams, which can reflect the strain changes of key components of the device in real time. Use real-time monitoring software, such as LabVIEW or a customized monitoring system, to display and record the component strain data streams in real time.

[0094] Step S276: reconstructing the dynamic strain field of the component strain data stream to obtain a dynamic strain field model of the device;

[0095] Specifically, the component strain data stream can be imported into data processing software, such as MATLAB or Python environment, which has powerful data processing and analysis capabilities. Use finite element analysis (FEA) software or a customized computational model to reconstruct the dynamic strain field of the device based on the processed data. This involves building a mathematical model to map the measured strain data to the physical model of the device to simulate the strain distribution of the entire device. Finally, a dynamic strain field model of the device is generated, which can reflect the strain state of the device under different working conditions in real time.

[0096] Step S277: extracting deformation data of strained parts of the dynamic strain field model of the device to obtain a regional microstructure deformation data set, wherein the regional microstructure deformation data set includes a plurality of regional microstructure deformation data, and the regional microstructure deformation data includes component deformation space coordinates, component strain amplitude, and component strain rate.

[0097] Specifically, FEA software or specialized data analysis tools can be used to extract deformation data of key parts from the dynamic strain field model. This includes parameters such as the spatial coordinates, strain amplitude, and strain rate of component deformation. The extracted deformation data is organized into regional microstructure deformation data sets. Each data set contains multiple deformation data points from different regions, and each data point records the deformation characteristics of a specific location.

[0098] The present invention realizes accurate monitoring of strain changes of key components by deploying a fiber Bragg grating sensing network, ensuring high sensitivity and high resolution of the monitoring means. By applying phase-locked filtering processing and temperature compensation grating integration technology, the signal-to-noise ratio of the signal is effectively improved, and the reliability and stability of the monitoring data are enhanced. By introducing a strain-sensing temperature self-correction network, the influence of temperature changes on strain measurement is automatically corrected, further improving the accuracy of the data. By applying the acoustic-optical interaction enhanced monitoring technology, the detection capability of small strain changes is enhanced. Through the real-time acquisition of strain field data, continuous data support is provided for dynamic monitoring and analysis. Through the construction of a dynamic strain field model, a powerful tool is provided for in-depth analysis of the operating status of the device and prediction of structural problems. By extracting the deformation data of the dynamic strain field model of the device, detailed regional microstructure deformation data including deformation space coordinates, strain amplitude and strain rate can be obtained. By integrating these data, the present invention can comprehensively evaluate the structural integrity of the device, timely discover and prevent potential structural damage, thereby ensuring the long-term stable operation of the device. The present invention not only improves the accuracy and efficiency of monitoring.

[0099] Preferably, step S3 comprises the following steps:

[0100] Step S31: collecting a mechanical structure diagram of the tin stripping wastewater treatment device to obtain a mechanical structure diagram of the device;

[0101] Specifically, you can obtain the design drawings or blueprints of the device from the engineering archives. These drawings contain detailed mechanical structure information of the device. Use CAD software (such as AutoCAD or SolidWorks) to draw the mechanical structure diagram of the device based on the site survey and drawing information. Make sure the drawings accurately reflect the size, component location and connection method of the device.

[0102] Step S32: performing component identification on the mechanical structure diagram of the device to obtain a spatial distribution diagram of key components, wherein the component identification includes rotating component identification, pump identification and valve identification;

[0103] Specifically, in CAD software, you can annotate each component on the mechanical structure drawing, including rotating parts (such as motors and turbines), pumps (such as centrifugal pumps and diaphragm pumps), and valves (such as ball valves and butterfly valves). Define properties for each annotated component, such as component type, material, size, and operating parameters. Use the layers and visualization tools of CAD software to generate a spatial distribution map of key components, which clearly shows the location and relationship of key components in the device.

[0104] Step S33: obtaining a three-dimensional model of a wastewater treatment device;

[0105] Specifically, the three-dimensional model of the wastewater treatment device obtained in step S13 may be obtained in the database.

[0106] Step S34: annotating components of the three-dimensional model of the wastewater treatment device according to the spatial distribution diagram of key components to obtain an annotated three-dimensional component model of the device;

[0107] Specifically, you can use 3D modeling software, such as SolidWorks, to open the existing 3D model of the wastewater treatment plant. Identify and clarify the function and location of each key component through the key component spatial distribution diagram. Then, use the annotation tool in the software to add labels and annotations to each key component, including the component name, model, and key parameters. After completing the annotation, save the model to ensure that all key components are clearly marked and recorded.

[0108] Step S35: extracting the topological structure of the device annotated three-dimensional component model to obtain a device topological structure map;

[0109] Specifically, you can use professional structural analysis software, such as ANSYS or ABAQUS. First, import the device's annotated 3D component model into the analysis software and define the material properties and connection relationships. The software will automatically analyze and generate a topological structure map showing how the materials and components are best arranged. This map will help understand the structural strength and potential weaknesses of the device under different working conditions.

[0110] Step S36: performing finite element mesh modeling on the annotated three-dimensional component model of the device to obtain a finite element mesh model of the device;

[0111] Specifically, finite element analysis software, such as ANSYS or SolidWorks Simulation, can be used to import the device annotated three-dimensional component model and set the analysis parameters, including defining material properties, applied loads, and boundary conditions. Then, meshing is performed, and the appropriate element type and size are selected to capture the key details of the model. After the meshing is completed, the mesh quality is checked to ensure that there are no errors or defects. Finally, the finite element mesh model is saved to obtain the finite element mesh model of the device.

[0112] Step S37: Analyze the equipment wear status of the tin stripping wastewater treatment device based on the finite element mesh model of the device and according to the device topology map to obtain equipment component wear assessment data.

[0113] Specifically, please refer to the sub-steps of step S37 for the detailed implementation process of this embodiment.

[0114] The present invention provides an in-depth understanding of the layout of the device and the spatial distribution of key components by accurately collecting and analyzing mechanical structure drawings. Clear identification of key components such as rotating parts, pumps and valves makes maintenance work more targeted and improves the speed and accuracy of fault diagnosis. Through the effective application of three-dimensional models and accurate labeling of components, intuitive tools are provided for visual representation and structural analysis, optimizing design and improvement measures. In-depth analysis of the topological structure reveals the internal connections and structural relationships of the device, which helps to identify structural weaknesses. Through the efficient implementation of finite element meshing modeling, stress analysis and vibration analysis are more accurate, and fatigue and wear of components are effectively predicted. Through comprehensive equipment wear status assessment, timely maintenance or replacement of necessary components is ensured, maintenance strategies are optimized, and maintenance costs are reduced. The present invention extends the service life of the device, reduces downtime, improves operating efficiency, and significantly improves the safety performance of the device, ensuring the safety of operators and environmental protection.

[0115] Preferably, step S37 includes the following steps:

[0116] Step S371: performing equipment vibration modal analysis on the tin stripping wastewater treatment device based on the finite element mesh model of the device to obtain device vibration characteristic parameters, wherein the device vibration characteristic parameters include the natural vibration frequency of the tin stripping wastewater treatment device and the vibration mode shape of the tin stripping wastewater treatment device;

[0117] Specifically, you can open the ANSYS software and prepare for modal analysis. In ANSYS, import a pre-built finite element mesh model of the device. This model should already contain all the key components and material properties of the device. In the material property manager of the model, specify the correct material properties for the different parts of the device, including density, Young's modulus, Poisson's ratio, etc. These properties should match the materials actually used. In the analysis settings of ANSYS, select the modal analysis type. Modal analysis is a method used to determine the natural vibration frequencies and mode shapes of a structure under undamped free vibration conditions. According to the actual operation and support conditions of the device, apply appropriate boundary conditions to the model. For example, if some parts of the device are fixed on the base, fixed constraints should be applied to these parts. Run the modal analysis. ANSYS will calculate the first few natural vibration frequencies and corresponding mode shapes of the model. These data will show the vibration mode of each mode, including the location of the nodes and anti-nodes. After the analysis is completed, extract the natural vibration frequency and mode shape data from the results interface of ANSYS. These data will be used to identify the vibration characteristics of the device.

[0118] Step S372: Drawing a vibration modal spectrum of the tin stripping wastewater treatment device according to the vibration characteristic parameters of the device to obtain vibration modal spectrum data;

[0119] Specifically, the vibration characteristic parameters can be used to draw the vibration modal spectrum of the tin stripping wastewater treatment device. First, the natural frequency and vibration mode data of each vibration mode are exported from the finite element analysis software. Then, a vibration modal spectrum is created using data visualization software, such as the drawing library in MATLAB or Python. In these spectra, different lines and colors are used to clearly represent the vibration shapes of different modes, and the corresponding natural vibration frequencies are marked next to or below the spectra, and finally the vibration modal spectrum data is obtained. This representation method helps to intuitively understand the behavior of the device under different vibration modes.

[0120] Step S373: performing vibration node identification on the vibration modal spectrum data to obtain a device vibration node set;

[0121] Specifically, the vibration mode spectrum data can be imported into the MATLAB or Python environment. In Python, the Pandas library can be used to read data files, such as CSV or Excel formats. Pandas is used for data cleaning, including removing missing values, removing outliers (IQR method can be used), and data standardization. The NumPy library of Python is used to process the vibration mode data and extract the node position of each vibration mode. The node position can be determined by finding the minimum point in the vibration mode data, because the node does not displace during vibration. A threshold is set to identify nodes with large vibration amplitudes. This threshold can be set based on the statistical characteristics of the vibration data, for example, points with vibration amplitudes two standard deviations below the mean are selected as nodes. Use signal processing tools in the SciPy library, such as the find_peaks function, to identify local maximum points in the vibration mode data, which correspond to vibration nodes. The identified node information is stored in a DataFrame to form a set of device vibration nodes. The information of each node includes its position coordinates in the structure, the corresponding vibration mode frequency, and the vibration amplitude.

[0122] Step S374: simulating the acoustic wave propagation path of the tin stripping wastewater treatment device based on the device finite element mesh model and the device vibration node set to obtain an acoustic wave propagation path diagram of the treatment device;

[0123] Specifically, you can select sound wave propagation simulation software, such as COMSOL Multiphysics or ANSYS, and import the finite element mesh model of the device. Then, according to the device vibration node set, set the location and propagation direction of the sound wave source. Next, define the propagation characteristics of the sound wave, including parameters such as sound speed, frequency, and wavelength. Run the sound wave propagation simulation, and the software will calculate the propagation path of the sound wave inside the device and generate a sound wave propagation path diagram. The diagram will show the propagation path, reflection, and interference phenomena of the sound wave in the device.

[0124] Step S375: identifying high stress areas of the tin stripping wastewater treatment device according to the device topology map to obtain a high stress area distribution map of the device;

[0125] Specifically, you can use topology analysis software, such as ANSYS or ABAQUS, to import the topology diagram of the device and combine it with the finite element analysis results. Then, set the stress analysis parameters, select appropriate loads and boundary conditions, and run the high stress area identification analysis. The software will calculate the stress distribution of each component under different working conditions and identify the stress concentration areas. Through visualization tools, a distribution map of high stress areas of the device is generated, clearly marking the areas with higher stress. These areas are potential failure points and require special attention.

[0126] Step S376: determining the acoustic emission monitoring points of the tin stripping wastewater treatment device according to the acoustic wave propagation path diagram of the treatment device and the distribution diagram of the high stress area of ​​the device, obtaining the layout diagram of the acoustic emission monitoring points of the device, and installing the acoustic emission sensor array of the tin stripping wastewater treatment device according to the layout diagram of the acoustic emission monitoring points of the device, and obtaining the acoustic emission monitoring network of the device;

[0127] Specifically, the acoustic emission monitoring points of the tin stripping wastewater treatment device can be determined based on the acoustic wave propagation path diagram of the treatment device and the distribution map of the high stress area of ​​the device. First, use visualization software (such as MATLAB or AutoCAD) to superimpose the acoustic wave propagation path diagram of the treatment device and the distribution map of the high stress area of ​​the device, so as to intuitively identify the overlapping parts of the key areas of acoustic wave propagation and the high stress areas. Next, analyze these overlapping areas and select suitable locations for installing acoustic emission sensors, which should be able to effectively capture potential acoustic emission signals. After determining the monitoring points, draw a layout diagram of the acoustic emission monitoring points, marking the location and number of each monitoring point. Finally, according to the layout diagram, install an acoustic emission sensor array on the device to ensure that each sensor can cover the key monitoring area, thereby forming a complete device acoustic emission monitoring network.

[0128] Step S377: collecting multi-channel synchronous acoustic emission signals of the tin stripping wastewater treatment device through the device acoustic emission monitoring network to obtain a set of acoustic emission signals of the treatment device;

[0129] Specifically, the AE monitoring network can be used to collect multi-channel synchronous AE signals for the tin stripping wastewater treatment device. First, use an AE data acquisition system (such as PAC or Kistler's AE monitoring system) to connect the installed AE sensors to the data acquisition device to ensure that all sensors can work synchronously. Next, configure the parameters of the data acquisition system, including sampling frequency, signal gain, and trigger threshold, to ensure that enough AE signals are captured. Start the data acquisition system, start multi-channel synchronous signal acquisition, and record AE signal data. During the data acquisition process, the signal strength and frequency will be monitored in real time, and the data will be stored in the computer. Finally, after the signal acquisition is completed, the AE signal set of the processing device is sorted out.

[0130] Step S378: Analyze the equipment wear status of the tin stripping wastewater treatment device based on the acoustic emission signal set of the processing device to obtain equipment component wear assessment data.

[0131] Specifically, please refer to the sub-steps of step S378 for the detailed implementation process of this embodiment.

[0132] The present invention provides a scientific basis for in-depth understanding of the dynamic behavior of the device and identification of abnormal vibrations through the implementation of equipment vibration modal analysis, while the visualization of the vibration modal spectrum makes the vibration characteristic parameters more intuitive, which is convenient for technicians to analyze and diagnose. The accurate identification of vibration nodes helps to determine the key vibration sources and optimize the structural design and vibration reduction measures. Through the simulation of the sound wave propagation path and the effective identification of high-stress areas, the monitoring work can be targeted and focus on areas where fatigue and cracks will occur. Through the optimized layout of the acoustic emission monitoring network and the multi-channel synchronous signal acquisition, the real-time and accuracy of the monitoring are enhanced, providing strong data support for the comprehensive evaluation of the wear status of the device. The present invention realizes the early diagnosis and prevention of faults, reduces unexpected downtime and maintenance costs, improves the reliability and safety of the device operation, and ensures the continuity of the wastewater treatment process and the safety of the environment.

[0133] Preferably, step S378 includes the following steps:

[0134] Step S3781: extracting signal features from the acoustic emission signal set of the processing device to obtain an acoustic emission feature set of the device;

[0135] Specifically, the processed device acoustic emission signal set can be imported into signal processing software, such as MATLAB or LabVIEW. The time domain signal is converted into a frequency domain signal using Fourier transform (FFT). Next, signal processing techniques, such as wavelet transform or spectral correlation analysis, are applied to extract the features of the signal, including amplitude, frequency, energy, duration, and time-frequency characteristics of the signal. These features will be integrated into the device acoustic emission feature set.

[0136] Step S3782: clustering the acoustic emission events of the device acoustic emission feature set to obtain acoustic emission event classification data;

[0137] Specifically, data mining tools, such as Python's scikit-learn library or R language's clustering analysis package, can be used to perform cluster analysis based on the characteristics of the acoustic emission signal. Select a suitable clustering algorithm, such as K-means or hierarchical clustering, to classify events into different categories based on the similarity of signal characteristics. The optimal number of clusters is determined by setting a suitable number of clusters or using the elbow method. After clustering is completed, the acoustic emission event classification data is obtained, and each category represents a specific acoustic emission mode or fault type.

[0138] Step S3783: performing component wear type identification on the acoustic emission event classification data to obtain a component wear type identification data set;

[0139] Specifically, the acoustic emission event classification data can be imported into data processing software, such as MATLAB or Python environment. The necessary preprocessing steps are performed, including filtering and denoising, signal amplification and normalization. Features that are helpful for wear type identification are extracted from the preprocessed acoustic emission data. These features include the peak frequency, energy distribution, duration of the signal, waveform characteristics of the signal (such as kurtosis and skewness), etc. There is no label of wear type in the initial data, and unsupervised learning methods, such as K-means clustering or hierarchical clustering algorithms, are used to identify patterns in the acoustic emission data. These algorithms will divide the data into different groups based on the similarity of acoustic emission features. The clustering results are analyzed to identify acoustic emission patterns associated with known wear types. This requires cooperation with domain experts or through correlation analysis with historical maintenance records and equipment logs. Based on the results of the pattern analysis, a wear type label is assigned to each group. For example, the acoustic emission characteristics of a group are related to bearing failure, so it can be labeled as "bearing wear". The accuracy of clustering and label assignment is verified by comparing with actual equipment inspection and maintenance data. The identified wear type labels are associated with the corresponding acoustic emission events to create a component wear type identification dataset. This dataset will contain component identification, acoustic emission features, clustering results, and wear type labels.

[0140] Step S3784: evaluating the degree of component wear of the tin stripping wastewater treatment device according to the component wear type identification data set, and obtaining a component wear degree evaluation data set;

[0141] Specifically, the component wear types in the component wear type identification dataset can be labeled and encoded, and the text type wear types can be converted into numerical types. Features that are helpful for wear assessment are extracted from the device acoustic emission feature set, such as the frequency, energy, duration of the acoustic emission signal, and the waveform characteristics of the signal. Select a suitable machine learning algorithm to establish a wear assessment model. For example, a support vector machine (SVM) or a decision tree algorithm can be selected. Use the training dataset to train the wear assessment model. During the training process, adjust the model parameters, such as the kernel function of the SVM and the depth of the decision tree. Use the final model to score the wear data of all components and generate a dataset containing component identification, wear type, location, and wear score. Analyze the scores output by the model to determine the wear level of each component. Based on the analysis results, generate a component wear assessment dataset to record the wear level of each component in detail.

[0142] Step S3785: Perform an overall assessment of the equipment wear status of the tin stripping wastewater treatment device based on the component wear degree assessment data set to obtain equipment component wear assessment data.

[0143] Specifically, equipment management software or database systems, such as Maximo or SAP PM, can be used to integrate the wear assessment data sets of all components. Then, statistical analysis methods, such as weighted scoring systems or risk matrices, are applied to comprehensively assess the wear status of the entire device. This includes considering the degree of wear of different components, their importance to the operation of the device, and the potential risk of failure. In this way, the components and areas that have the greatest impact on the overall performance of the device can be identified. Finally, equipment component wear assessment data is generated.

[0144] The present invention enhances the signal analysis capability through feature extraction, captures key acoustic emission features, and provides rich information for event identification and analysis. Accurate signal classification is achieved through acoustic emission event clustering, and different types of events are identified, so as to more accurately understand the operating status of the device. Through the analysis of classified data, the wear types of different components are effectively identified, providing a targeted basis for maintenance and repair. Through accurate assessment of the degree of component wear and comprehensive assessment of the overall wear status, comprehensive and accurate data support is provided for equipment maintenance decisions. These measures help to optimize maintenance strategies, reduce maintenance costs, ensure that key components are maintained in a timely manner, extend the service life of equipment, and improve economy and reliability. At the same time, accurate wear assessment and timely maintenance prevent failures and accidents caused by component wear, significantly improve the safety of equipment operation, and ensure the safety of operators and environmental protection.

[0145] Preferably, step S4 comprises the following steps:

[0146] Step S41: collecting chemical components of the wastewater to be treated to obtain chemical component data of the wastewater;

[0147] Specifically, an automatic sampler can be used to collect wastewater samples from the inlet of the wastewater treatment device. Then, the collected wastewater samples are sent to the chemical analysis laboratory, and the metal ion concentration, organic pollutant content, pH value and other key chemical parameters in the wastewater are measured using analytical instruments such as atomic absorption spectrometer (AAS), inductively coupled plasma mass spectrometry (ICP-MS) or high performance liquid chromatography (HPLC). Through these analyses, the chemical composition data of the wastewater are obtained.

[0148] Step S42: collecting the element composition of the device material of the tin stripping wastewater treatment device to obtain the element composition data of the device material;

[0149] Specifically, a portable X-ray fluorescence spectrometer (XRF) or energy dispersive X-ray spectrometer (EDX) can be used to scan different parts of the device on site to quickly obtain the elemental composition data of the device material in a non-destructive manner. For parts that are difficult to analyze on site, samples can be taken and sent to the laboratory for analysis using more precise instruments such as electron probe microanalysis (EPMA) or scanning electron microscopy (SEM-EDS). These analyses will provide detailed elemental composition information of the device material, including the content of major elements and trace elements.

[0150] Step S43: performing material-medium compatibility analysis on the tin stripping wastewater treatment device according to the wastewater chemical component data and the device material element composition data to obtain material corrosion reaction characteristic data;

[0151] Specifically, chemical engineering software (such as Aspen Plus or ChemCAD) or material science databases (such as NACE MR0175 or SSC-JIS G 3141) can be used to combine wastewater chemical composition data with device material element composition data to evaluate the corrosion resistance of materials in wastewater media. Through calculation and simulation, the corrosion reactions that will occur in materials in wastewater environments can be predicted, including corrosion rates, corrosion products, and potential local corrosion phenomena. Ultimately, material corrosion reaction characteristic data can be obtained.

[0152] Step S44: performing stress corrosion crack area identification on the tin stripping wastewater treatment device according to the material corrosion reaction characteristic data to obtain a corrosion area distribution map of the device material;

[0153] Specifically, corrosion monitoring software, such as Corrosion Analyst, can be used to input material corrosion reaction characteristic data into the software. These data include the corrosion rate, corrosion type, and sensitivity of the material in a specific chemical environment. Next, combined with the stress distribution data of the device, finite element analysis (FEA) software is used to simulate and analyze stress corrosion cracking. Through simulation, areas prone to stress corrosion cracking under the combined action of chemical and mechanical factors are identified. Finally, these areas are marked on the three-dimensional model of the device to generate a distribution map of the corrosion areas of the device material.

[0154] Step S45: performing electrochemical impedance spectroscopy measurement on the tin stripping wastewater treatment device according to the device material corrosion area distribution map to obtain device electrochemical impedance data;

[0155] Specifically, the key areas where EIS measurements need to be performed can be determined based on the distribution map of the corrosion areas of the device materials. Then, electrochemical impedance spectroscopy measurements are performed on these areas using an electrochemical workstation, such as CH Instruments or Autolab, in conjunction with corresponding electrochemical sensors, such as a rotating disk electrode (RDE) or an electrochemical impedance spectrometer. During the measurement, a small-amplitude AC signal is applied to measure the impedance response of the device material at different frequencies. By analyzing the impedance spectrum, the electrochemical impedance data of the device are obtained, including parameters such as charge transfer resistance, double layer capacitance, and diffusion resistance. These data reflect the electrochemical activity and corrosion state of the device material in the wastewater environment.

[0156] Step S46: performing impedance spectrum equivalent circuit modeling on the electrochemical impedance data of the device to obtain a surface electrochemical behavior model of the device;

[0157] Specifically, the device electrochemical impedance data can be imported into electrochemical analysis software, such as ZView or EC-Lab. These software can process and analyze EIS data and support equivalent circuit modeling. Then, according to the electrochemical principles and the characteristics of the device materials, select a suitable equivalent circuit model, such as the Randles equivalent circuit or the Warburg impedance model. In the software, the experimental data is fitted by adjusting the component parameters (such as resistance, capacitance, and constant phase elements) in the equivalent circuit until the impedance spectrum predicted by the model is in good agreement with the experimental data. Through this process, a model of the electrochemical behavior of the device surface can be obtained, including charge transfer process, diffusion process, and double layer behavior.

[0158] Step S47: Perform surface corrosion depth simulation evaluation on the device surface electrochemical behavior model to obtain component metal surface corrosion evaluation data.

[0159] Specifically, the surface electrochemical behavior model of the device can be used, combined with corrosion dynamics and material science principles, to simulate the surface corrosion depth using simulation software (such as COMSOL Multiphysics or ANSYS). During the simulation process, the corrosion rate, environmental conditions (such as temperature, pH value) and time of the material are set to simulate the impact of the corrosion process on the material surface. Through numerical calculation, a distribution diagram of the corrosion depth of the material surface over time is obtained. Then, the simulation results are analyzed to evaluate the degree of metal surface corrosion of different components, including corrosion rate, corrosion depth and corrosion products. Finally, these evaluation data are organized into a component metal surface corrosion evaluation data set.

[0160] The present invention provides a detailed data basis for material-medium compatibility through comprehensive analysis of the chemical composition of wastewater and the elemental composition of device materials, making the prediction and evaluation of corrosion behavior more accurate. High-risk areas are located in a timely manner through effective identification of stress corrosion cracking areas. The electrochemical behavior of materials in wastewater is deeply revealed through electrochemical impedance spectroscopy measurement and equivalent circuit modeling. Accurate simulation and evaluation of surface corrosion depth provide quantitative support for the formulation of maintenance and replacement plans, optimize maintenance strategies, reduce maintenance costs, and ensure timely maintenance of key components. These measures help to extend the service life of the device, improve the economy and reliability of the equipment, and prevent equipment failures and accidents through accurate corrosion evaluation, thereby enhancing the safety of device operation. By optimizing material selection and process flow, this solution enhances the adaptability and stability of the device to different chemical environments and improves the durability of the device under various operating conditions.

[0161] 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.

[0162] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for monitoring the status of a tin stripping wastewater treatment device, characterized in that: The following steps are involved: Step S1: collecting process data of the tin stripping wastewater treatment device to obtain wastewater treatment process data; According to the wastewater treatment process data, the high-risk areas of the tin stripping wastewater treatment device are identified to obtain the high-risk area data of the device; Step S2: performing real-time electrochemical impedance spectroscopy monitoring on the tin stripping wastewater treatment device according to the high-risk area data of the device to obtain a regional electrochemical reaction dynamic model; The microstructure deformation of the tin stripping wastewater treatment device is monitored in real time to obtain a regional microstructure deformation data set; wherein step S2 includes the following steps: Step S21: marking the chemical risk area of ​​the tin stripping wastewater treatment device according to the high-risk area data of the device to obtain electrochemical monitoring area marking data, and determining the electrochemical monitoring components of the tin stripping wastewater treatment device according to the electrochemical monitoring area marking data to obtain the electrochemical monitoring component data of the device; Step S22: obtaining wastewater flow simulation path data and device operation area mapping model; Step S23: Based on the device operation area mapping model and the wastewater flow simulation path data, the chemical corrosion degree of the device electrochemical monitoring component data is identified to obtain a component corrosion degree data set; Step S24: determining the sensor material type of the device electrochemical monitoring component data according to the component corrosion degree data set to obtain sensor material type data; Step S25: selecting a monitoring sensor for the electrochemical monitoring component data of the device according to the sensor material type data to obtain sensor selection data; Step S26: Perform sensor deployment operations on the tin stripping wastewater treatment device according to the device electrochemical monitoring component data and sensor selection data, and perform real-time monitoring of the electrochemical impedance spectrum to obtain a regional electrochemical reaction dynamic model; wherein step S26 includes the following steps: Step S261: performing sensor deployment operations on the tin stripping wastewater treatment device according to the electrochemical monitoring component data of the device and the sensor selection data, and using the deployed sensors to perform multi-band impedance scanning on the components corresponding to the electrochemical monitoring component data of the device to obtain a component impedance response spectrum segment set, wherein the component impedance response spectrum segment set includes a plurality of component impedance response spectrum segments, and the component impedance response spectrum segments are electrochemical response spectrum segments under different current density conditions; Step S262: extracting key characteristic variables from the component impedance response spectrum segment set to obtain a multidimensional electrochemical characteristic matrix, wherein the multidimensional electrochemical characteristic matrix includes a plurality of electrochemical reaction path vectors, and the key characteristic variables include current density, impedance spectrum modulus, electrode potential change, double layer capacitance, and charge transfer resistance; Step S263: performing chemical reaction path identification on the multi-dimensional electrochemical characteristic matrix to obtain a component chemical reaction path characteristic set; Step S264: performing regional current density and reaction intensity partitioning on the components corresponding to the device electrochemical monitoring component data based on the component chemical reaction path feature set to obtain electrode surface partition area data; Step S265: performing regional dynamic mapping on the electrode surface partition area data to obtain a regional electrochemical dynamic distribution map, and performing spatiotemporal chemical reaction correlation on the components corresponding to the device electrochemical monitoring component data based on the regional electrochemical dynamic distribution map to obtain a regional electrochemical reaction dynamic model; Step S27: performing real-time monitoring of microstructure deformation of the tin stripping wastewater treatment device according to the high-risk area data of the device to obtain a regional microstructure deformation data set; Step S3: collecting a mechanical structure diagram of the tin stripping wastewater treatment device to obtain a mechanical structure diagram of the device; analyzing the equipment wear state of the tin stripping wastewater treatment device based on the mechanical structure diagram of the device to obtain equipment component wear assessment data; Step S4: performing material-medium compatibility analysis on the tin stripping wastewater treatment device to obtain a device material corrosion area distribution map; performing a surface corrosion state assessment on the tin stripping wastewater treatment device according to the device material corrosion area distribution map to obtain component metal surface corrosion assessment data; Step S5: Perform electrochemical fatigue analysis on the tin stripping wastewater treatment device based on the regional electrochemical reaction dynamic model to obtain the electrochemical fatigue data of the equipment surface; determine the microstructural stress-strain behavior of the regional microstructural deformation data set to obtain the equipment structural deformation stress distribution data; perform a comprehensive status diagnosis on the tin stripping wastewater treatment device based on the equipment surface electrochemical fatigue data, equipment structural deformation stress distribution data, equipment component wear assessment data and component metal surface corrosion assessment data to obtain the treatment device status data.

2. The state monitoring method of the tin stripping wastewater treatment device according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting process data of the tin stripping wastewater treatment device to obtain wastewater treatment process data; Step S12: identifying the chemical risk area of ​​the tin stripping wastewater treatment device according to the wastewater treatment process data to obtain chemical risk area data; Step S13: Perform three-dimensional modeling on the tin stripping wastewater treatment device to obtain a three-dimensional model of the wastewater treatment device; Step S14: performing fluid path simulation on the tin stripping wastewater treatment device based on the three-dimensional model of the wastewater treatment device and the wastewater treatment process data to obtain wastewater flow simulation path data; Step S15: Identify high-risk areas of the tin stripping wastewater treatment device based on the chemical risk area data and the wastewater flow simulation path data to obtain high-risk area data of the device.

3. The state monitoring method of the tin stripping wastewater treatment device according to claim 2 is characterized in that: Step S15 includes the following steps: Step S151: performing turbulence and shear force area identification on the wastewater flow simulation path data to obtain fluid mechanics risk area data, wherein the fluid mechanics risk area data includes high turbulence areas and high shear force areas; Step S152: determining the operating conditions of the wastewater treatment process to obtain a set of operating conditions for the treatment process; Step S153: mapping the operation area of ​​the three-dimensional model of the wastewater treatment device according to the wastewater treatment process data to obtain a device operation area mapping model; Step S154: performing structural stress and thermal distribution identification on the device operation area mapping model according to the processing flow operation condition set to obtain structural high stress area data and structural thermal distribution data; Step S155: collecting fault maintenance records of the tin stripping wastewater treatment device to obtain device fault maintenance record data; Step S156: identifying frequently-failed components based on the device failure maintenance record data to obtain frequently-failed component data; Step S157: Based on the chemical risk area data, fluid mechanics risk area data, structural high stress area data, structural thermal distribution data and failure-prone component data, the device operation area mapping model is used to locate and map the high-risk area to obtain the device high-risk area data.

4. The state monitoring method of the tin stripping wastewater treatment device according to claim 1 is characterized in that: Step S27 includes the following steps: Step S271: deploying a fiber Bragg grating sensing network for the tin stripping wastewater treatment device according to the high-risk area data of the device to obtain a component strain sensing network; Step S272: performing phase-locked filtering processing on the component strain sensing network to obtain phase-synchronized strain analysis data; Step S273: performing temperature compensation grating integration on the component strain sensing network according to the phase-synchronous strain analysis data to obtain a strain sensing temperature self-correction network; Step S274: performing acoustic-optical interaction enhanced monitoring on the strain-sensing temperature self-correction network to obtain a strain-sensing temperature self-correction enhanced network; Step S275: collecting the strain field of the tin stripping wastewater treatment device through the strain-sensing temperature self-correction enhancement network to obtain the component strain data stream; Step S276: reconstructing the dynamic strain field of the component strain data stream to obtain a dynamic strain field model of the device; Step S277: extracting deformation data of strained parts of the dynamic strain field model of the device to obtain a regional microstructure deformation data set, wherein the regional microstructure deformation data set includes a plurality of regional microstructure deformation data, and the regional microstructure deformation data includes component deformation space coordinates, component strain amplitude, and component strain rate.

5. The state monitoring method of the tin stripping wastewater treatment device according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: collecting a mechanical structure diagram of the tin stripping wastewater treatment device to obtain a mechanical structure diagram of the device; Step S32: performing component identification on the mechanical structure diagram of the device to obtain a spatial distribution diagram of key components, wherein the component identification includes rotating component identification, pump identification and valve identification; Step S33: obtaining a three-dimensional model of a wastewater treatment device; Step S34: annotating components of the three-dimensional model of the wastewater treatment device according to the spatial distribution diagram of key components to obtain an annotated three-dimensional component model of the device; Step S35: extracting the topological structure of the device annotated three-dimensional component model to obtain a device topological structure map; Step S36: performing finite element mesh modeling on the annotated three-dimensional component model of the device to obtain a finite element mesh model of the device; Step S37: Analyze the equipment wear status of the tin stripping wastewater treatment device based on the finite element mesh model of the device and according to the device topology map to obtain equipment component wear assessment data.

6. The state monitoring method of the tin stripping wastewater treatment device according to claim 5 is characterized in that: Step S37 includes the following steps: Step S371: performing equipment vibration modal analysis on the tin stripping wastewater treatment device based on the finite element mesh model of the device to obtain device vibration characteristic parameters, wherein the device vibration characteristic parameters include the natural vibration frequency of the tin stripping wastewater treatment device and the vibration mode shape of the tin stripping wastewater treatment device; Step S372: Drawing a vibration modal spectrum of the tin stripping wastewater treatment device according to the vibration characteristic parameters of the device to obtain vibration modal spectrum data; Step S373: performing vibration node identification on the vibration modal spectrum data to obtain a device vibration node set; Step S374: simulating the acoustic wave propagation path of the tin stripping wastewater treatment device based on the device finite element mesh model and the device vibration node set to obtain an acoustic wave propagation path diagram of the treatment device; Step S375: identifying high stress areas of the tin stripping wastewater treatment device according to the device topology map to obtain a high stress area distribution map of the device; Step S376: determining the acoustic emission monitoring points of the tin stripping wastewater treatment device according to the acoustic wave propagation path diagram of the treatment device and the distribution diagram of the high stress area of ​​the device, obtaining the layout diagram of the acoustic emission monitoring points of the device, and installing the acoustic emission sensor array of the tin stripping wastewater treatment device according to the layout diagram of the acoustic emission monitoring points of the device, and obtaining the acoustic emission monitoring network of the device; Step S377: collecting multi-channel synchronous acoustic emission signals of the tin stripping wastewater treatment device through the device acoustic emission monitoring network to obtain a set of acoustic emission signals of the treatment device; Step S378: Analyze the equipment wear status of the tin stripping wastewater treatment device based on the acoustic emission signal set of the processing device to obtain equipment component wear assessment data.

7. The state monitoring method of the tin stripping wastewater treatment device according to claim 6 is characterized in that: Step S378 includes the following steps: Step S3781: extracting signal features from the acoustic emission signal set of the processing device to obtain an acoustic emission feature set of the device; Step S3782: clustering the acoustic emission events of the device acoustic emission feature set to obtain acoustic emission event classification data; Step S3783: performing component wear type identification on the acoustic emission event classification data to obtain a component wear type identification data set; Step S3784: evaluating the degree of component wear of the tin stripping wastewater treatment device according to the component wear type identification data set, and obtaining a component wear degree evaluation data set; Step S3785: Perform an overall assessment of the equipment wear status of the tin stripping wastewater treatment device based on the component wear degree assessment data set to obtain equipment component wear assessment data.

8. The state monitoring method of the tin stripping wastewater treatment device according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: collecting chemical components of the wastewater to be treated to obtain chemical component data of the wastewater; Step S42: collecting the element composition of the device material of the tin stripping wastewater treatment device to obtain the element composition data of the device material; Step S43: performing material-medium compatibility analysis on the tin stripping wastewater treatment device according to the wastewater chemical component data and the device material element composition data to obtain material corrosion reaction characteristic data; Step S44: performing stress corrosion crack area identification on the tin stripping wastewater treatment device according to the material corrosion reaction characteristic data to obtain a corrosion area distribution map of the device material; Step S45: performing electrochemical impedance spectroscopy measurement on the tin stripping wastewater treatment device according to the device material corrosion area distribution map to obtain device electrochemical impedance data; Step S46: performing impedance spectrum equivalent circuit modeling on the electrochemical impedance data of the device to obtain a surface electrochemical behavior model of the device; Step S47: Perform surface corrosion depth simulation evaluation on the device surface electrochemical behavior model to obtain component metal surface corrosion evaluation data.

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