Metal surface treatment wastewater intelligent monitoring and regulation system based on Internet of Things

Abstract

The invention relates to the field of wastewater treatment, and discloses an intelligent monitoring and regulation system for metal surface treatment wastewater based on Internet of Things, comprising: a wastewater data acquisition and purification unit for performing real-time acquisition of wastewater parameters through a sensor array to obtain multi-dimensional wastewater original data; performing abnormal data identification and noise filtration on the multi-dimensional wastewater original data to obtain a purified wastewater basic parameter set; according to the method, the real-time data of the wastewater are acquired through the sensor array of the Internet of Things, and the accuracy and reliability of the data are ensured by adopting an advanced anomaly detection and noise filtering algorithm; the system can adjust the wastewater treatment process in real time according to environmental factors and production line operation loads, multi-objective optimization is achieved through a deep reinforcement learning algorithm, and it is ensured that the wastewater treatment process can be efficiently operated under various complex environmental conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wastewater treatment, in particular to a metal surface treatment wastewater intelligent monitoring and regulation system based on the Internet of Things. BACKGROUND

[0002] The wastewater generated in the process of metal surface treatment contains a large amount of harmful substances, and if not effectively treated, it may cause serious pollution to the environment. The traditional wastewater treatment system usually relies on manual intervention and fixed treatment process, lacks flexibility, and has certain limitations in treatment effect and efficiency. With the increasing strictness of environmental regulations and the diversification of industrial production needs, how to realize the intelligent and fine management of the wastewater treatment process has become a problem to be solved.

[0003] Although the existing wastewater treatment technology has realized automatic control to a certain extent, it lacks real-time adaptability adjustment to the composition of wastewater and external environmental changes, and the data processing and analysis process is complex and easily affected by noise. At the same time, how to monitor the pollution components in wastewater in real time, predict the pollution diffusion path and optimize the treatment process is still an important technical challenge in the field of wastewater treatment. SUMMARY

[0004] The purpose of the present application is to provide a metal surface treatment wastewater intelligent monitoring and regulation system based on the Internet of Things to solve the above-mentioned problems of lack of real-time adaptability adjustment to the composition of wastewater and external environmental changes, and complex data processing and analysis process, easy to be affected by noise.

[0005] To achieve the above-mentioned purpose, the metal surface treatment wastewater intelligent monitoring and regulation system based on the Internet of Things comprises: A wastewater data acquisition and purification unit is used to collect wastewater parameters in real time through a sensor array to obtain multi-dimensional wastewater original data, identify abnormal data and filter noise from the multi-dimensional wastewater original data to obtain a set of purified wastewater basic parameters, and analyze the correlation of pollution components based on the set of wastewater basic parameters to obtain wastewater pollution component correlation feature data. A pollution feature modeling and evaluation unit is used to model the spatial and temporal distribution of pollution sources based on the wastewater pollution component correlation feature data to obtain wastewater pollution anomaly spatial and temporal distribution data, simulate the pollution diffusion path based on the wastewater pollution anomaly spatial and temporal distribution data to obtain pollution diffusion dynamic trajectory data, and evaluate the adaptability of the treatment process based on the pollution diffusion dynamic trajectory data to obtain initial treatment process adaptation parameters. The parameter dynamic correction analysis unit is configured to acquire environmental temperature and humidity data and production line operation load data; perform environmental factor dynamic weight distribution on initial processing process adaptive parameters according to the environmental temperature and humidity data to obtain environmental correction processing parameters; and perform production capacity correlation influence analysis on the environmental correction processing parameters based on the production line operation load data to obtain load correlation processing parameters. The wastewater intelligent regulation and control execution unit is configured to perform multi-objective optimization modeling on the load correlation processing parameters through a deep reinforcement learning algorithm to obtain a wastewater treatment dynamic regulation and control model; perform protocol docking of the wastewater treatment dynamic regulation and control model and an Internet of Things execution mechanism to obtain a real-time regulation and control instruction set; and start an automatic dosing system and a flow rate adjusting device based on the real-time regulation and control instruction set to complete intelligent monitoring and regulation and control of the metal surface treatment wastewater.

[0006] Further, the wastewater parameter is collected in real time through a sensor array to obtain a purified wastewater basic parameter set, including: The multi-modal sensor array is dynamically calibrated, and the sensor sensitivity threshold is automatically adjusted according to the change of the environmental temperature and humidity to obtain a calibrated original data set; The calibrated original data set is labeled with time and space features based on an edge computing node, and a collection time stamp and a spatial position code are added to each data point to obtain an original data stream with time and space labels; an improved isolated forest algorithm is used to detect abnormal points of the original data stream with time and space labels, and data segments deviating from the normal distribution range are identified and marked to obtain an abnormal data marking result; An adaptive noise filtering model is constructed according to the abnormal data marking result, and a wavelet transform algorithm is used to perform multi-scale decomposition on the abnormal data segments to identify and filter out high-frequency noise components to obtain an intermediate data set after denoising; the intermediate data set is dynamically smoothed through a Kalman filtering algorithm to obtain a preliminary purified wastewater parameter set; Multi-sensor data fusion is performed based on the preliminary purified wastewater parameter set, the D-S evidence theory is used to weight the measurement values of different sensors at the same time to obtain a fused basic parameter set; the fused basic parameter set is encrypted and compressed through an Internet of Things gateway and then transmitted to a cloud platform to complete real-time collection and purification of the wastewater parameters.

[0007] Further, pollution component correlation analysis is performed according to the wastewater basic parameter set to obtain wastewater pollution component correlation feature data, including: A multi-dimensional pollution component feature matrix is constructed based on the wastewater basic parameter set, the pH value, the conductivity and the heavy metal ion concentration parameters are taken as matrix row vectors, and the sampling time sequence is taken as a column vector to form a time and space feature fusion matrix; the Z-score standardization algorithm is used to perform data normalization processing on the time and space feature fusion matrix to obtain a standardized pollution feature matrix; A mutual information calculation model is constructed based on a standardized pollution feature matrix, a maximum relevance minimum redundancy algorithm is used to screen pollution component pairs with nonlinear correlation, and a mutual information correlation weight matrix is generated; a topological structure model of the mutual information correlation weight matrix is established through a graph neural network, a pollution component correlation graph is constructed, and dynamic correlation graph structure data is obtained; An improved Louvain community detection algorithm is applied to the dynamic correlation graph structure data, and a pollution component community with strong cohesion is identified through modularity optimization; community characteristic parameters are extracted to form a multi-dimensional community feature vector set; A dynamic correlation verification model is constructed based on the multi-dimensional community feature vector set, a time series analysis algorithm is combined to predict the time series evolution of the community feature vector, and a stable correlation community feature is obtained; the stable community feature is associated and mapped with the wastewater treatment process parameters to obtain wastewater pollution component correlation characteristic data.

[0008] Further, according to the wastewater pollution component correlation characteristic data, a pollution source spatiotemporal distribution model is established, and wastewater pollution anomaly spatiotemporal distribution data is obtained, including: A spatiotemporal feature fusion cube is constructed based on the wastewater pollution component correlation characteristic data, the correlation feature vector of each sampling point is three-dimensionally bound with geographic coordinate information and a timestamp to form a multi-modal data cube; a generative adversarial network is used to perform data enhancement on a sparse sampling area to obtain an expanded spatiotemporal feature fusion cube; A spatiotemporal convolution graph attention network is constructed based on the expanded spatiotemporal feature fusion cube, a graph attention mechanism is used in the spatial dimension to capture the pollution correlation of adjacent sampling points, a one-dimensional convolutional neural network is used in the time dimension to extract the evolution trend of the pollution component, and a pollution source spatiotemporal distribution baseline model is obtained through spatiotemporal joint training; a dynamic weight adjustment strategy is introduced, and model parameters are automatically updated according to real-time sensor data streams to obtain an adaptive spatiotemporal distribution model.

[0009] Further, according to the wastewater pollution component correlation characteristic data, a pollution source spatiotemporal distribution model is established, and wastewater pollution anomaly spatiotemporal distribution data is obtained, which also includes: Based on the adaptive spatiotemporal distribution model, pollution anomaly detection is performed, a variational autoencoder is used to encode normal pollution distribution patterns in a hidden space, and the reconstruction error of real-time data in the hidden space is calculated; when the error exceeds a preset threshold, an anomaly alarm is triggered; a density peak clustering algorithm is combined to perform spatial clustering on the abnormal area to identify pollution anomaly blocks with contiguous features, and pollution anomaly spatiotemporal distribution data is obtained; The abnormal pollution spatial and temporal distribution data is associated and verified with known pollution source geographic information, the abnormal block boundary is corrected through a spatial interpolation algorithm, and a high-precision pollution source spatial and temporal distribution heat map is generated; based on the heat map data, a pollution diffusion prediction model is constructed, the evolution path of the pollutants in space and time is simulated combined with meteorological data, and abnormal spatial and temporal distribution data of wastewater pollution is obtained.

[0010] Further, based on the abnormal spatial and temporal distribution data of wastewater pollution, the pollution diffusion path simulation is performed, and dynamic trajectory data of pollution diffusion is obtained, including: Based on the abnormal spatial and temporal distribution data of wastewater pollution, a dynamic diffusion graph model is constructed, and each pollution abnormal block is regarded as a graph node; a fluid dynamics simulation algorithm is used to calculate the potential diffusion path between the graph nodes, a directed edge weight is constructed combined with the actual water flow direction data, and initial pollution diffusion graph structure data is generated; A space-time graph convolution network is deployed on the initial pollution diffusion graph structure data, the spatial dimension adopts a graph attention mechanism to capture the diffusion correlation between nodes, and the time dimension adopts a gated recurrent unit to simulate the time sequence evolution of pollution concentration; a meta-learning framework is introduced to quickly adapt the ST-GCN to the environment, and preliminary diffusion dynamic trajectory data is obtained.

[0011] Further, based on the abnormal spatial and temporal distribution data of wastewater pollution, the pollution diffusion path simulation is performed, and dynamic trajectory data of pollution diffusion is obtained, including: Based on the preliminary diffusion dynamic trajectory data, a digital twin simulation environment is constructed, and meteorological data and hydrological data are integrated as external disturbance inputs; a multi-agent reinforcement learning algorithm is used to perform diffusion path stress testing in the simulation environment, to identify abnormal diffusion patterns under extreme weather or hydrological mutations, and to obtain diffusion trajectory data with enhanced robustness; The diffusion trajectory data with enhanced robustness is dynamically compared with real-time sensor monitoring data, and when the deviation between the actual diffusion path and the simulation trajectory exceeds a threshold, an online model correction mechanism is triggered; the ST-GCN and MADDPG model parameters are updated using an incremental learning strategy, and the generated diffusion dynamic trajectory data of pollution is obtained.

[0012] Further, according to the pollution diffusion dynamic trajectory data, a process adaptability evaluation is performed, and initial process adaptation parameters are obtained, including: Based on the pollution diffusion dynamic trajectory data, a process adaptation feature space is constructed, the spatial and temporal attributes of the diffusion trajectory are multi-dimensionally mapped with the process parameters, a process adaptation feature matrix is formed, and a generative adversarial network is used to perform data enhancement on the process adaptation feature matrix to generate a virtual process adaptation sample set; A learning framework is deployed on the virtual process adaptation sample set, a contrastive learning mechanism is used to calculate the inhibition effect of different process combinations on pollution diffusion, and an initial process adaptation parameter set is output; A dynamic weight adjustment strategy is introduced to modify the initial adaptation parameter set based on real-time monitored wastewater parameters using environmental factors; a Bayesian optimization model is constructed to search the parameter space to obtain candidate process parameter combinations. Candidate process parameters are input into a digital twin simulation platform and the entire process is simulated and verified by combining fluid dynamics and chemical reaction kinetics models. A multi-agent reinforcement learning algorithm is used to stress test the simulation process, identify process failure modes under extreme pollution diffusion scenarios, and obtain the initial treatment process adaptation parameters.

[0013] Furthermore, real-time environmental temperature and humidity data, as well as production line operating load data, are acquired to obtain environmental correction parameters, including: Real-time environmental data is collected by a sensor array deployed in the wastewater treatment workshop. The temperature and humidity sensors are manufactured using MEMS technology, and the load sensor integrates a current transformer and a vibration analysis module. A federated learning framework is used to preprocess the multi-sensor data at the edge, and each node independently completes anomaly detection and noise filtering to generate purified environmental data segments. A dynamic weight allocation model is constructed, and a spatiotemporal attention mechanism is used to extract features from environmental data fragments. The spatial attention module focuses on the vertical distribution differences of sensors, and the temporal attention module captures the temporal evolution of environmental parameters. A transfer learning strategy is introduced to transfer the prior knowledge obtained from training on historical environmental data to the current model and generate an initial environmental factor weight matrix. The initial environmental factor weight matrix and the initial treatment process adaptation parameters are fused in multiple dimensions. The DS evidence theory is used to solve the confidence conflict between the weight parameters and the process parameters. A Bayesian network model is constructed to quantify the probability of the influence of environmental factors on the treatment process. The environmental correction treatment parameter set is generated through probabilistic inference. An environmental correction processing parameter set is deployed in a digital twin simulation platform, and the processing effect under different environmental conditions is simulated by combining a fluid dynamics model. The simulation process is trained by reinforcement learning using a near-end policy optimization algorithm. When the processing efficiency is detected to be lower than the threshold, a weight rollback mechanism is automatically triggered to obtain the environmental correction processing parameters.

[0014] Furthermore, based on production line operating load data, a capacity correlation impact analysis was conducted on the environmental correction parameters to obtain load correlation parameters, including: Real-time work load data is collected by deploying smart meters and vibration sensor arrays at key nodes of the production line; wavelet transform algorithm is used to decompose the original load data into multi-scale data to generate purified work load time series data. A production capacity and environmental parameter correlation graph model is constructed, and the time series data of work load is mapped with environmental correction parameters in multiple dimensions. A graph attention network is used to extract features from the correlation graph, and a multi-head attention mechanism is used to capture the nonlinear correlation between parameters to generate an initial production capacity correlation feature matrix. A dynamic weight adjustment strategy is introduced to correct the load factor of the initial capacity correlation feature matrix based on real-time work load data; a multi-objective optimization model is constructed and the NSGA-III algorithm is used to perform efficient search in the parameter space to generate a set of candidate load correlation processing parameters. The candidate load association processing parameter set is input into the digital twin simulation platform, and the entire process is simulated and verified by combining the 3D model of the production line and the fluid dynamics model. The simulation process is trained by reinforcement learning using a deep deterministic strategy gradient algorithm. When the deviation between the actual production capacity and the simulation result exceeds the threshold, the parameter rollback mechanism is triggered to obtain the load association processing parameters.

[0015] Compared with the prior art, the beneficial effects of the present invention are: (1) Real-time wastewater data is collected through an IoT sensor array and advanced anomaly detection and noise filtering algorithms are used to ensure the accuracy and reliability of the data and avoid human error in traditional wastewater monitoring methods. (2) Based on the basic parameters of wastewater, the correlation analysis of pollutant components can accurately identify pollution sources and their spatiotemporal distribution characteristics. Advanced algorithms such as graph neural networks can be used to optimize the identification and prediction of pollution sources, thereby improving the precision of pollutant control. (3) The system can adjust the wastewater treatment process in real time according to environmental factors and production line load, and achieve multi-objective optimization through deep reinforcement learning algorithm to ensure that the wastewater treatment process can operate efficiently under various complex environmental conditions; (4) Through intelligent process adaptability assessment and dynamic correction, over-processing and resource waste are avoided, while processing efficiency and system stability are improved. Attached Figure Description

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

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

[0018] Please see Figure 1This invention provides a technical solution: an intelligent monitoring and control system for metal surface treatment wastewater based on the Internet of Things, comprising: The wastewater data acquisition and purification unit is used to acquire wastewater parameters in real time through a sensor array to obtain multi-dimensional raw wastewater data; to identify abnormal data and filter noise from the multi-dimensional raw wastewater data to obtain a set of basic parameters for purified wastewater; and to perform correlation analysis of pollutant components based on the set of basic parameters to obtain correlation characteristic data of wastewater pollutant components. Among them, the sensor array is a set of sensors used to detect and measure different parameters in wastewater; multi-dimensional raw wastewater data refers to various wastewater parameter data collected by the sensor array; abnormal data identification and noise filtering is a data cleaning process that uses algorithms to filter out invalid or abnormal data to ensure the accuracy and reliability of the data; pollutant component correlation analysis is to establish a data model based on the relationship between various pollutants in wastewater to analyze their interactions and influences. The pollution characteristic modeling and evaluation unit is used to model the spatiotemporal distribution of pollution sources based on the correlation characteristic data of wastewater pollution components, and obtain abnormal spatiotemporal distribution data of wastewater pollution; to simulate the pollution diffusion path based on the abnormal spatiotemporal distribution data of wastewater pollution, and obtain dynamic trajectory data of pollution diffusion; and to evaluate the adaptability of treatment processes based on the dynamic trajectory data of pollution diffusion, and obtain the initial treatment process adaptability parameters. Among them, the spatiotemporal distribution modeling of pollution sources is to establish a model of the changes of pollutants in time and space based on the distribution of pollutants, and analyze the distribution characteristics of pollution sources; the pollution diffusion path simulation is to simulate the propagation path of pollutants in wastewater flow, understand how pollutants diffuse, and predict their future behavior; the treatment process adaptability assessment is to evaluate whether the existing treatment process can effectively cope with the current pollution situation based on the characteristics of wastewater pollution diffusion. The parameter dynamic correction analysis unit is used to acquire environmental temperature and humidity data and production line operating load data; based on the environmental temperature and humidity data, it performs dynamic weight allocation of environmental factors on the initial treatment process adaptation parameters to obtain environmental correction treatment parameters; based on the production line operating load data, it performs capacity correlation impact analysis on the environmental correction treatment parameters to obtain load correlation treatment parameters. Among them, environmental temperature and humidity data refers to the monitored environmental temperature and humidity information, which can affect the efficiency of the wastewater treatment process; production line load data is an indicator reflecting the production line load, which affects the wastewater generation rate and the concentration of pollutants in the wastewater; dynamic weight allocation of environmental factors is to adjust the parameters in the treatment process according to environmental data to make it more adaptable to the current environmental conditions; capacity correlation impact analysis is to analyze the impact of production line load on wastewater treatment capacity to ensure the adaptability and stability of the wastewater treatment system under different load conditions. The wastewater intelligent control and execution unit is used to perform multi-objective optimization modeling of load-related processing parameters through deep reinforcement learning algorithms to obtain a dynamic control model for wastewater treatment; the dynamic control model for wastewater treatment is then connected to the Internet of Things (IoT) actuator to obtain a real-time control instruction set; based on the real-time control instruction set, the automatic dosing system and flow rate adjustment device are activated to complete the intelligent monitoring and control of metal surface treatment wastewater; Among them, deep reinforcement learning algorithm is an artificial intelligence-based algorithm that can learn the optimal behavioral strategy through interaction with the environment; multi-objective optimization modeling refers to the optimization problem not having just one objective, but multiple objectives that need to be balanced; IoT actuator is a device used for actual operation; real-time control instruction set is an instruction set generated based on the best operation scheme output by intelligent algorithm, used to control field equipment to perform specific operations.

[0019] It should be noted that during operation, wastewater data is collected in real time through a sensor array, and intelligent algorithms are used for data processing and analysis to ensure accurate monitoring and timely adjustment of the wastewater treatment process. Through pollutant component correlation analysis and pollution diffusion path simulation, the system can accurately identify pollution sources and predict the diffusion trend of pollutants, thereby optimizing treatment strategies. The system can dynamically adjust the treatment process based on environmental factors and production load data to ensure that wastewater treatment remains highly efficient under different conditions. Through deep reinforcement learning algorithms, multi-objective optimization is performed to achieve dynamic control of the wastewater treatment process. Furthermore, through an automatic dosing system and flow rate adjustment device, real-time control commands are automatically executed to improve treatment efficiency and reduce the need for manual intervention.

[0020] In one embodiment, wastewater parameters are collected in real time using a sensor array to obtain a set of basic parameters for the purified wastewater, including: Dynamic calibration of the multimodal sensor array is performed, and the sensor sensitivity threshold is automatically adjusted according to changes in ambient temperature and humidity to obtain the calibrated raw dataset. Spatiotemporal feature annotation is performed on the calibrated raw dataset based on edge computing nodes. A collection timestamp and spatial location code are added to each data point to obtain a raw data stream with spatiotemporal labels. An improved isolated forest algorithm is used to detect outliers in the raw data stream with spatiotemporal labels, identify and mark data segments that deviate from the normal distribution range, and obtain the outlier data marking results. An adaptive noise filtering model is constructed based on the abnormal data labeling results. The abnormal data segments are decomposed into multiple scales using the wavelet transform algorithm to identify and filter out high-frequency noise components, resulting in a denoised intermediate dataset. The intermediate dataset is then dynamically smoothed using the Kalman filter algorithm to obtain a preliminary set of wastewater parameters. Wavelet transform multi-scale noise filtering formula: , in, Wavelet coefficients Scale factor, unit: dimensionless, value range: 21 to 25. Translation factor, unit: seconds. Morlet wavelet basis functions:

[0021] in =6, center frequency; Based on the parameter set of the pre-purified wastewater, multi-sensor data fusion is performed. The DS evidence theory is used to weight the confidence of the measurements from different sensors at the same time to obtain the fused basic parameter set. The fused basic parameter set is then encrypted and compressed and transmitted to the cloud platform through an IoT gateway to complete the real-time acquisition and purification of wastewater parameters. This design, through dynamic calibration of the multimodal sensor array, combined with edge computing to label spatiotemporal features, utilizes an improved isolated forest algorithm to detect anomalies, wavelet transform and Kalman filtering for noise reduction and smoothing, DS evidence theory to fuse data, and finally encrypted transmission to the cloud platform. Dynamic calibration improves sensor adaptability, edge computing reduces latency, accurately identifies anomalies and noise, data fusion improves accuracy, and encrypted transmission ensures data security, providing high-quality basic data for subsequent processing.

[0022] In one embodiment, a correlation analysis of pollutant components is performed based on a set of basic wastewater parameters to obtain correlation characteristic data of wastewater pollutant components, including: A multi-dimensional pollutant component feature matrix is ​​constructed based on the wastewater basic parameter set. The parameters of pH value, conductivity and heavy metal ion concentration are used as the matrix row vectors, and the sampling time series is used as the column vectors to form a spatiotemporal feature fusion matrix. The spatiotemporal feature fusion matrix is ​​normalized by the Z-score normalization algorithm to obtain a standardized pollutant feature matrix. A mutual information calculation model is constructed based on a standardized pollution feature matrix. The maximum correlation minimum redundancy algorithm is used to screen pollution component pairs with nonlinear correlations to generate a mutual information association weight matrix. The topological structure of the mutual information association weight matrix is ​​modeled by a graph neural network to construct a pollution component association graph and obtain dynamic association graph structure data. Mutual information calculation model formula: , in, Mutual information between variables X and Y, unit: bits. The joint probability density function is calculated using kernel density estimation. , Marginal probability density function Non-linear association filtering: retain Variable pairs with a value greater than 0.3; An improved Louvain community detection algorithm is applied to dynamic relational graph structure data. By optimizing the modularity, it identifies pollution component communities with strong cohesion. Community feature parameters are extracted to form a multi-dimensional community feature vector set. Improved Louvain community detection formula: , Modularity gain is used to determine whether node movement optimizes the community structure. Community internal edge weight sum, : The sum of all edge weights in the community The sum of edge weights at node i. Total edge weights of the entire graph; A dynamic association verification model is constructed based on a multi-dimensional community feature vector set. The time series analysis algorithm is combined to predict the temporal evolution of the community feature vectors to obtain stable community features. The stable community features are then mapped with wastewater treatment process parameters to obtain wastewater pollutant component association feature data.

[0023] This design constructs and normalizes a multi-dimensional feature matrix, uses a mutual information calculation model to screen related components, builds a correlation graph using graph neural network modeling, identifies communities using an improved Louvain algorithm, extracts features to build a verification model and correlates with process parameters, accurately screens related components, intuitively displays correlation relationships, accurately identifies strongly correlated communities, and obtains reliable correlation feature data through time series prediction and correlation mapping, thus helping to accurately analyze pollutant components.

[0024] In one embodiment, the spatiotemporal distribution of pollution sources is modeled based on the correlation feature data of wastewater pollutants to obtain abnormal spatiotemporal distribution data of wastewater pollution, including: A spatiotemporal feature fusion cube is constructed based on the correlation feature data of wastewater pollutants. The correlation feature vector of each sampling point is three-dimensionally bound with geographic coordinate information and timestamp to form a multimodal data cube. A generative adversarial network is used to augment the data in the sparse sampling area to obtain the expanded spatiotemporal feature fusion cube. A spatiotemporal convolutional graph attention network is constructed based on the expanded spatiotemporal feature fusion cube. In the spatial dimension, a graph attention mechanism is used to capture the pollution correlation between adjacent sampling points, and in the temporal dimension, a one-dimensional convolutional neural network is used to extract the evolution trend of pollution components. A baseline model of the spatiotemporal distribution of pollution sources is obtained through spatiotemporal joint training. A dynamic weight adjustment strategy is introduced to automatically update the model parameters according to the real-time sensor data stream, resulting in an adaptive spatiotemporal distribution model.

[0025] This design constructs a spatiotemporal feature fusion cube and data augmentation, builds a spatiotemporal convolutional graph attention network, introduces a dynamic weight adjustment strategy to solve the sparse sampling problem, and uses graph attention and convolutional neural networks to capture spatiotemporal correlations and evolution trends. Dynamic weight adjustment enables the model to automatically update according to real-time data, improves the model's adaptability, and more accurately simulates the spatiotemporal distribution of pollution sources.

[0026] In one embodiment, the modeling of the spatiotemporal distribution of pollution sources is performed based on the correlation feature data of wastewater pollutants to obtain abnormal spatiotemporal distribution data of wastewater pollution, and the method further includes: Pollution anomaly detection is based on an adaptive spatiotemporal distribution model. A variational autoencoder is used to encode the latent space of the normal pollution distribution pattern. The error between real-time data and latent space reconstruction is calculated. When the error exceeds a preset threshold, an anomaly alarm is triggered. The density peak clustering algorithm is combined to perform spatial clustering of the abnormal area, identify pollution anomaly blocks with contiguous features, and obtain the spatiotemporal distribution data of pollution anomalies. The spatiotemporal distribution data of abnormal pollution are correlated and verified with the geographic information of known pollution sources. The boundaries of abnormal blocks are corrected by spatial interpolation algorithm to generate a high-precision spatiotemporal distribution heat map of pollution sources. Based on the heat map data, a pollution diffusion prediction model is constructed, and the evolution path of pollutants in time and space is simulated by combining meteorological data to obtain the spatiotemporal distribution data of abnormal wastewater pollution.

[0027] This design, based on an adaptive model, uses a variational autoencoder to detect anomalies, density peak clustering to identify anomalous blocks, correlation verification to generate a heat map, and a diffusion prediction model to build a diffusion prediction model. This accurately identifies anomalous areas, improves data accuracy through correlation verification, provides a visual representation of the distribution through the heat map, and allows the diffusion prediction model to simulate evolution paths in conjunction with meteorological data, thus providing a scientific basis for pollution prevention and control.

[0028] In one embodiment, pollution diffusion path simulation is performed based on wastewater pollution anomaly spatiotemporal distribution data to obtain pollution diffusion dynamic trajectory data, including: A dynamic diffusion graph model is constructed based on the spatiotemporal distribution data of wastewater pollution anomalies, with each pollution anomaly block regarded as a graph node. A fluid dynamics simulation algorithm is used to calculate the potential diffusion paths between graph nodes, and directed edge weights are constructed by combining actual water flow direction data to generate the initial pollution diffusion graph structure data. A spatiotemporal graph convolutional network is deployed on the initial pollution diffusion map structure data. In the spatial dimension, a graph attention mechanism is used to capture the diffusion correlation between nodes, and in the temporal dimension, a gated recurrent unit is used to simulate the temporal evolution of pollution concentration. A meta-learning framework is introduced to train the ST-GCN for rapid environmental adaptation, and preliminary diffusion dynamic trajectory data is obtained.

[0029] This design constructs a dynamic diffusion graph model, uses a fluid dynamics simulation algorithm to generate the initial graph structure, deploys a spatiotemporal graph convolutional network, and introduces a meta-learning framework. It treats polluted areas as graph nodes and constructs directed edges based on the water flow direction, which can accurately calculate the diffusion path. The spatiotemporal graph convolutional network captures node relationships and temporal evolution, and the meta-learning framework enables the model to quickly adapt to new environments and obtain more accurate preliminary dynamic diffusion trajectory data.

[0030] In one embodiment, pollution diffusion path simulation is performed based on abnormal spatiotemporal distribution data of wastewater pollution to obtain dynamic trajectory data of pollution diffusion, and the method further includes: A digital twin simulation environment is constructed based on preliminary diffusion dynamic trajectory data, integrating meteorological and hydrological data as external disturbance inputs; a multi-agent reinforcement learning algorithm is used to conduct diffusion path stress tests in the simulation environment, identify abnormal diffusion patterns under extreme weather or hydrological changes, and obtain robust diffusion trajectory data. The robust diffusion trajectory data is dynamically compared with real-time sensor monitoring data. When the deviation between the actual diffusion path and the simulated trajectory exceeds a threshold, the online correction mechanism of the model is triggered. The ST-GCN and MADDPG model parameters are updated using an incremental learning strategy to generate dynamic pollution diffusion trajectory data.

[0031] This design constructs a digital twin simulation environment, uses a multi-agent reinforcement learning algorithm for stress testing, dynamically compares data to trigger model correction, employs an incremental learning strategy to update parameters, simulates diffusion under complex environments, identifies abnormal patterns, enhances the robustness of trajectory data, and through dynamic comparison and incremental learning, enables the model to correct deviations in a timely manner, generating more realistic dynamic trajectory data of pollution diffusion.

[0032] In one embodiment, the adaptability of the treatment process is evaluated based on the dynamic trajectory data of pollution diffusion to obtain initial treatment process adaptability parameters, including: A process adaptation feature space is constructed based on the dynamic trajectory data of pollution diffusion. The spatiotemporal attributes of the diffusion trajectory are mapped to the treatment process parameters in multiple dimensions to form a process adaptation feature matrix. A generative adversarial network is used to augment the process adaptation feature matrix to generate a virtual process adaptation sample set. A learning framework is deployed on a virtual process adaptation sample set, and a contrastive learning mechanism is used to calculate the inhibition effect of different process combinations on pollution diffusion, and the initial treatment process adaptation parameter set is output. A dynamic weight adjustment strategy is introduced to modify the initial adaptation parameter set based on real-time monitored wastewater parameters using environmental factors; a Bayesian optimization model is constructed to search the parameter space to obtain candidate process parameter combinations. Candidate process parameters are input into a digital twin simulation platform and the entire process is simulated and verified by combining fluid dynamics and chemical reaction kinetics models. A multi-agent reinforcement learning algorithm is used to stress test the simulation process, identify process failure modes under extreme pollution diffusion scenarios, and obtain the initial treatment process adaptation parameters.

[0033] This design constructs a process-adaptive feature space and augments the data, deploys a learning framework to output initial parameters, introduces dynamic weight adjustment and Bayesian optimization, verifies through digital twin simulation, conducts multi-agent reinforcement learning stress testing, comprehensively maps the relationship between diffusion trajectories and process parameters, enriches the samples through data augmentation, enables the learning framework to quickly output initial parameters, dynamically adjusts and optimizes to improve parameter accuracy, and ensures the effectiveness of the process under various scenarios through simulation verification and stress testing, thus obtaining reliable initial process-adaptive parameters.

[0034] In one embodiment, real-time ambient temperature and humidity data and production line operating load data are acquired to obtain environmental correction parameters, including: Real-time environmental data is collected by a sensor array deployed in the wastewater treatment workshop. The temperature and humidity sensors are manufactured using MEMS technology, and the load sensor integrates a current transformer and a vibration analysis module. A federated learning framework is used to preprocess the multi-sensor data at the edge, and each node independently completes anomaly detection and noise filtering to generate purified environmental data segments. Federated Learning Framework Formula: , in, Global model parameters : No. The local loss function of each node. Learning rate, initial value 0.01, dynamically decaying. L2 regularization coefficient

[0035] Add Gaussian noise during gradient uploading: ,in =0.1; A dynamic weight allocation model is constructed, and a spatiotemporal attention mechanism is used to extract features from environmental data fragments. The spatial attention module focuses on the vertical distribution differences of sensors, and the temporal attention module captures the temporal evolution of environmental parameters. A transfer learning strategy is introduced to transfer the prior knowledge obtained from training on historical environmental data to the current model and generate an initial environmental factor weight matrix. The initial environmental factor weight matrix and the initial treatment process adaptation parameters are fused in multiple dimensions. The DS evidence theory is used to solve the confidence conflict between the weight parameters and the process parameters. A Bayesian network model is constructed to quantify the probability of the influence of environmental factors on the treatment process. The environmental correction treatment parameter set is generated through probabilistic inference. An environmental correction processing parameter set is deployed in a digital twin simulation platform, and the processing effect under different environmental conditions is simulated by combining a fluid dynamics model. The simulation process is trained by reinforcement learning using a near-end policy optimization algorithm. When the processing efficiency is detected to be lower than the threshold, a weight rollback mechanism is automatically triggered to obtain the environmental correction processing parameters.

[0036] This design uses a sensor array to collect data and perform edge preprocessing, constructs a dynamic weight allocation model to extract features, fuses parameters to resolve confidence conflicts, uses a Bayesian network to quantify the impact, and obtains parameters through digital twin simulation and reinforcement learning training. It collects environmental data in real time and accurately, improves efficiency through edge preprocessing, focuses on key features through dynamic weight allocation, resolves conflicts through parameter fusion, quantifies the impact through a Bayesian network, and uses simulation training to adapt parameters to different environments, thus obtaining accurate environmental correction parameters.

[0037] In one embodiment, based on production line operating load data, an impact analysis of the environmental correction parameters on capacity correlation is performed to obtain load correlation parameters, including: Real-time work load data is collected by deploying smart meters and vibration sensor arrays at key nodes of the production line; wavelet transform algorithm is used to decompose the original load data into multi-scale data to generate purified work load time series data. A production capacity and environmental parameter correlation graph model is constructed, and the time series data of work load is mapped with environmental correction parameters in multiple dimensions. A graph attention network is used to extract features from the correlation graph, and a multi-head attention mechanism is used to capture the nonlinear correlation between parameters to generate an initial production capacity correlation feature matrix. A dynamic weight adjustment strategy is introduced to correct the load factor of the initial capacity correlation feature matrix based on real-time work load data; a multi-objective optimization model is constructed and the NSGA-III algorithm is used to perform efficient search in the parameter space to generate a set of candidate load correlation processing parameters. The candidate load association processing parameter set is input into the digital twin simulation platform, and the entire process is simulated and verified by combining the 3D model of the production line and the fluid dynamics model. The simulation process is trained by reinforcement learning using a deep deterministic strategy gradient algorithm. When the deviation between the actual production capacity and the simulation result exceeds the threshold, the parameter rollback mechanism is triggered to obtain the load association processing parameters.

[0038] This design involves collecting and purifying operational load data, constructing a production capacity-environment parameter correlation graph model to extract features, dynamically adjusting the weight correction matrix, searching for parameters through multi-objective optimization, obtaining parameters through digital twin simulation and reinforcement learning training, collecting load data in real time and accurately, capturing nonlinear correlations through the correlation graph model, dynamically adjusting to adapt to changes, finding the optimal parameters through multi-objective optimization, verifying the effectiveness of the parameters through simulation training, and obtaining environmental correction parameters that meet production capacity requirements.

[0039] In one embodiment, a dynamic control model for wastewater treatment is obtained by performing multi-objective optimization modeling on load-related treatment parameters using a deep reinforcement learning algorithm, including: A multimodal state space is constructed based on load-related processing parameters. Process parameters, environmental factors, and capacity indicators are fused into vectors to generate a high-dimensional state representation. A variational autoencoder is used to compress and encode the state space, extract key features to generate a low-dimensional latent state vector, and obtain environmental state representation data. A course learning strategy is introduced to gradually transition from simple single-objective scenarios to complex multi-objective scenarios, generating initial dynamic adjustment strategy data. A dynamic weight adjustment mechanism is introduced to correct the weights of multiple objectives online based on real-time environmental temperature and humidity data and production line operating load data; a meta-learning framework is constructed to quickly adapt to new scenarios using historical control data to obtain control strategy data; A protocol conversion gateway is constructed based on a meta-reinforcement learning model. Edge computing nodes are used to decode abstract policy parameters in real time and convert them into raw instruction formats that conform to the communication protocols of IoT devices. Blockchain smart contracts are introduced to realize secure transmission and tamper-proof storage of instructions. A federated learning framework is used to aggregate feedback data from the execution agency and construct a decentralized instruction optimization closed loop to obtain a real-time control instruction set.

[0040] This design constructs a multimodal state space and compresses and encodes it, introduces course learning to generate initial policies, dynamically adjusts and corrects target weights, uses a meta-learning framework to adapt to new scenarios, and builds a protocol conversion gateway to achieve secure transmission and instruction optimization. It fully integrates multi-source information, compresses and encodes to reduce dimensionality, gradually improves policy capabilities through course learning, dynamically adjusts and uses meta-learning to adapt to complex environments, and the protocol conversion gateway ensures the safe and accurate execution of instructions, resulting in an efficient and reliable dynamic control model for wastewater treatment.

[0041] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. An intelligent monitoring and control system for metal surface treatment wastewater based on the Internet of Things, characterized in that, include: The wastewater data acquisition and purification unit is used to collect wastewater parameters in real time through a sensor array to obtain multi-dimensional raw wastewater data. Anomaly identification and noise filtering were performed on the raw wastewater data from multiple dimensions to obtain a set of basic parameters for the purified wastewater. Based on the set of basic parameters, correlation analysis of pollutant components was conducted to obtain correlation characteristic data of wastewater pollutant components. The pollution characteristic modeling and evaluation unit is used to model the spatiotemporal distribution of pollution sources based on the correlation characteristic data of wastewater pollution components, and obtain abnormal spatiotemporal distribution data of wastewater pollution; to simulate the pollution diffusion path based on the abnormal spatiotemporal distribution data of wastewater pollution, and obtain dynamic trajectory data of pollution diffusion; and to evaluate the adaptability of treatment processes based on the dynamic trajectory data of pollution diffusion, and obtain the initial treatment process adaptability parameters. The parameter dynamic correction analysis unit is used to acquire environmental temperature and humidity data and production line operating load data; Based on the ambient temperature and humidity data, the environmental factors are dynamically weighted to adjust the initial treatment process parameters to obtain the environmental correction treatment parameters. Based on production line operating load data, an analysis of the capacity correlation impact of environmental correction parameters was conducted to obtain load correlation parameters. The wastewater intelligent control and execution unit is used to perform multi-objective optimization modeling of load-related processing parameters through deep reinforcement learning algorithms to obtain a dynamic control model for wastewater treatment; the dynamic control model for wastewater treatment is then connected to the Internet of Things (IoT) actuator through protocol to obtain a real-time control instruction set; The automatic dosing system and flow rate regulating device are activated based on the real-time control command set to complete the intelligent monitoring and control of metal surface treatment wastewater.

2. The intelligent monitoring and control system for metal surface treatment wastewater based on the Internet of Things as described in claim 1, characterized in that, Wastewater parameters are collected in real time using a sensor array to obtain a basic parameter set of the purified wastewater, including: Dynamic calibration of the multimodal sensor array is performed, and the sensor sensitivity threshold is automatically adjusted according to changes in ambient temperature and humidity to obtain the calibrated raw dataset. Spatiotemporal feature annotation is performed on the calibrated raw dataset based on edge computing nodes. A collection timestamp and spatial location code are added to each data point to obtain a raw data stream with spatiotemporal labels. An improved isolated forest algorithm is used to detect outliers in the raw data stream with spatiotemporal labels, identify and mark data segments that deviate from the normal distribution range, and obtain the outlier data marking results. An adaptive noise filtering model is constructed based on the abnormal data labeling results. The abnormal data segments are decomposed into multiple scales using the wavelet transform algorithm to identify and filter out high-frequency noise components, resulting in a denoised intermediate dataset. The intermediate dataset is then dynamically smoothed using the Kalman filter algorithm to obtain a preliminary set of wastewater parameters. Based on the preliminary purified wastewater parameter set, multi-sensor data fusion is performed. The DS evidence theory is used to weight the confidence of the measurements from different sensors at the same time to obtain the fused basic parameter set. The fused basic parameter set is then encrypted and compressed and transmitted to the cloud platform through an IoT gateway to complete the real-time acquisition and purification of wastewater parameters.

3. The intelligent monitoring and control system for metal surface treatment wastewater based on the Internet of Things according to claim 2, characterized in that, Correlation analysis of pollutant components was conducted based on the wastewater baseline parameter set to obtain correlation characteristic data of wastewater pollutant components, including: A multi-dimensional pollutant component feature matrix is ​​constructed based on the wastewater basic parameter set. The parameters of pH value, conductivity and heavy metal ion concentration are used as the matrix row vectors, and the sampling time series is used as the column vectors to form a spatiotemporal feature fusion matrix. The spatiotemporal feature fusion matrix is ​​normalized by the Z-score normalization algorithm to obtain a standardized pollutant feature matrix. A mutual information calculation model is constructed based on a standardized pollution feature matrix. The maximum correlation minimum redundancy algorithm is used to screen pollution component pairs with nonlinear correlations to generate a mutual information association weight matrix. The topological structure of the mutual information association weight matrix is ​​modeled by a graph neural network to construct a pollution component association graph and obtain dynamic association graph structure data. An improved Louvain community detection algorithm is applied to dynamic relational graph structure data. By optimizing the modularity, it identifies pollution component communities with strong cohesion. Community feature parameters are extracted to form a multi-dimensional community feature vector set. A dynamic association verification model is constructed based on a multi-dimensional community feature vector set. The time series analysis algorithm is combined to predict the temporal evolution of the community feature vectors to obtain stable community features. The stable community features are then mapped with wastewater treatment process parameters to obtain wastewater pollutant component association feature data.

4. The intelligent monitoring and control system for metal surface treatment wastewater based on the Internet of Things according to claim 3, characterized in that, Based on the correlation characteristic data of wastewater pollutants, a spatiotemporal distribution model of pollution sources is performed to obtain anomalous spatiotemporal distribution data of wastewater pollution, including: A spatiotemporal feature fusion cube is constructed based on the correlation feature data of wastewater pollutants. The correlation feature vector of each sampling point is three-dimensionally bound with geographic coordinate information and timestamp to form a multimodal data cube. A generative adversarial network is used to augment the data in the sparse sampling area to obtain the expanded spatiotemporal feature fusion cube. A spatiotemporal convolutional graph attention network is constructed based on the expanded spatiotemporal feature fusion cube. In the spatial dimension, a graph attention mechanism is used to capture the pollution correlation between adjacent sampling points, and in the temporal dimension, a one-dimensional convolutional neural network is used to extract the evolution trend of pollution components. A baseline model of the spatiotemporal distribution of pollution sources is obtained through spatiotemporal joint training. A dynamic weight adjustment strategy is introduced to automatically update the model parameters according to the real-time sensor data stream, resulting in an adaptive spatiotemporal distribution model.

5. The intelligent monitoring and control system for metal surface treatment wastewater based on the Internet of Things according to claim 4, characterized in that, Based on the correlation characteristic data of wastewater pollutants, the spatiotemporal distribution of pollution sources is modeled to obtain the spatiotemporal distribution data of abnormal wastewater pollution, which also includes; Pollution anomaly detection is based on an adaptive spatiotemporal distribution model. A variational autoencoder is used to encode the latent space of the normal pollution distribution pattern. The error between real-time data and latent space reconstruction is calculated. When the error exceeds a preset threshold, an anomaly alarm is triggered. The density peak clustering algorithm is combined to perform spatial clustering of the abnormal area, identify pollution anomaly blocks with contiguous features, and obtain the spatiotemporal distribution data of pollution anomalies. The spatiotemporal distribution data of abnormal pollution are correlated and verified with the geographic information of known pollution sources. The boundaries of abnormal blocks are corrected by spatial interpolation algorithm to generate a high-precision spatiotemporal distribution heat map of pollution sources. Based on the heat map data, a pollution diffusion prediction model is constructed, and the evolution path of pollutants in time and space is simulated by combining meteorological data to obtain the spatiotemporal distribution data of abnormal wastewater pollution.

6. The intelligent monitoring and control system for metal surface treatment wastewater based on the Internet of Things according to claim 5, characterized in that, Based on the spatiotemporal distribution data of abnormal wastewater pollution, pollution diffusion path simulation was performed to obtain dynamic trajectory data of pollution diffusion, including: A dynamic diffusion graph model is constructed based on the spatiotemporal distribution data of wastewater pollution anomalies, with each pollution anomaly block regarded as a graph node. A fluid dynamics simulation algorithm is used to calculate the potential diffusion paths between graph nodes, and directed edge weights are constructed by combining actual water flow direction data to generate the initial pollution diffusion graph structure data. A spatiotemporal graph convolutional network is deployed on the initial pollution diffusion map structure data. In the spatial dimension, a graph attention mechanism is used to capture the diffusion correlation between nodes, and in the temporal dimension, a gated recurrent unit is used to simulate the temporal evolution of pollution concentration. A meta-learning framework is introduced to train the ST-GCN for rapid environmental adaptation, and preliminary diffusion dynamic trajectory data is obtained.

7. The intelligent monitoring and control system for metal surface treatment wastewater based on the Internet of Things according to claim 6, characterized in that, Based on the spatiotemporal distribution data of abnormal wastewater pollution, pollution diffusion path simulation is performed to obtain dynamic trajectory data of pollution diffusion, which also includes; A digital twin simulation environment is constructed based on preliminary diffusion dynamic trajectory data, integrating meteorological and hydrological data as external disturbance inputs; A multi-agent reinforcement learning algorithm was used to conduct diffusion path stress tests in a simulation environment to identify abnormal diffusion patterns under extreme weather or hydrological changes, and to obtain robust diffusion trajectory data. The robust diffusion trajectory data is dynamically compared with real-time sensor monitoring data. When the deviation between the actual diffusion path and the simulated trajectory exceeds a threshold, the online correction mechanism of the model is triggered. The ST-GCN and MADDPG model parameters are updated using an incremental learning strategy to generate dynamic pollution diffusion trajectory data.

8. The intelligent monitoring and control system for metal surface treatment wastewater based on the Internet of Things according to claim 7, characterized in that, Based on the dynamic trajectory data of pollution diffusion, the suitability of the treatment process is assessed to obtain initial treatment process suitability parameters, including: A process adaptation feature space is constructed based on the dynamic trajectory data of pollution diffusion. The spatiotemporal attributes of the diffusion trajectory are mapped to the treatment process parameters in multiple dimensions to form a process adaptation feature matrix. A generative adversarial network is used to augment the process adaptation feature matrix to generate a virtual process adaptation sample set. A learning framework is deployed on a virtual process adaptation sample set, and a contrastive learning mechanism is used to calculate the inhibition effect of different process combinations on pollution diffusion, and the initial treatment process adaptation parameter set is output. A dynamic weight adjustment strategy is introduced to modify the initial adaptation parameter set based on real-time monitored wastewater parameters using environmental factors; a Bayesian optimization model is constructed to search the parameter space to obtain candidate process parameter combinations. Candidate process parameters are input into a digital twin simulation platform and the entire process is simulated and verified by combining fluid dynamics and chemical reaction kinetics models. A multi-agent reinforcement learning algorithm is used to stress test the simulation process, identify process failure modes under extreme pollution diffusion scenarios, and obtain the initial treatment process adaptation parameters.

9. The intelligent monitoring and control system for metal surface treatment wastewater based on the Internet of Things according to claim 8, characterized in that, Acquire real-time ambient temperature and humidity data and production line operating load data to obtain environmental correction parameters, including: Real-time environmental data is collected by a sensor array deployed in the wastewater treatment workshop. The temperature and humidity sensors are manufactured using MEMS technology, and the load sensor integrates a current transformer and a vibration analysis module. A federated learning framework is used to preprocess the multi-sensor data at the edge, and each node independently completes anomaly detection and noise filtering to generate purified environmental data segments. A dynamic weight allocation model is constructed, and a spatiotemporal attention mechanism is used to extract features from environmental data fragments. The spatial attention module focuses on the vertical distribution differences of sensors, and the temporal attention module captures the temporal evolution of environmental parameters. A transfer learning strategy is introduced to transfer the prior knowledge obtained from training on historical environmental data to the current model and generate an initial environmental factor weight matrix. The initial environmental factor weight matrix and the initial treatment process adaptation parameters are fused in multiple dimensions. The DS evidence theory is used to solve the confidence conflict between the weight parameters and the process parameters. A Bayesian network model is constructed to quantify the probability of the influence of environmental factors on the treatment process. The environmental correction treatment parameter set is generated through probabilistic inference. An environmental correction processing parameter set is deployed in a digital twin simulation platform, and the processing effect under different environmental conditions is simulated by combining a fluid dynamics model. The simulation process is trained by reinforcement learning using a near-end policy optimization algorithm. When the processing efficiency is detected to be lower than the threshold, a weight rollback mechanism is automatically triggered to obtain the environmental correction processing parameters.

10. The intelligent monitoring and control system for metal surface treatment wastewater based on the Internet of Things according to claim 9, characterized in that, Based on production line load data, an analysis of the capacity correlation impact of environmental correction parameters was conducted to obtain load correlation parameters, including: Real-time work load data is collected by deploying smart meters and vibration sensor arrays at key nodes of the production line; wavelet transform algorithm is used to decompose the original load data into multi-scale data to generate purified work load time series data. A production capacity and environmental parameter correlation graph model is constructed, and the time series data of work load is mapped with environmental correction parameters in multiple dimensions. A graph attention network is used to extract features from the correlation graph, and a multi-head attention mechanism is used to capture the nonlinear correlation between parameters to generate an initial production capacity correlation feature matrix. A dynamic weight adjustment strategy is introduced to correct the load factor of the initial capacity correlation feature matrix based on real-time work load data; a multi-objective optimization model is constructed and the NSGA-III algorithm is used to perform efficient search in the parameter space to generate a set of candidate load correlation processing parameters. The candidate load association processing parameter set is input into the digital twin simulation platform, and the entire process is simulated and verified by combining the 3D model of the production line and the fluid dynamics model. The simulation process is trained by reinforcement learning using a deep deterministic strategy gradient algorithm. When the deviation between the actual production capacity and the simulation result exceeds the threshold, the parameter rollback mechanism is triggered to obtain the load association processing parameters.

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