Electroplating wastewater pollution early warning and regulation system based on Internet of Things

Abstract

The invention relates to the technical field of electroplating wastewater treatment, and discloses an electroplating wastewater pollution early warning and regulation system based on Internet of Things, comprising: a sensing layer for collecting characteristic parameters of an electroplating wastewater treatment link through distributed sensing nodes, the characteristic parameters including a water quality parameter, an equipment operation parameter and an environmental parameter, a wireless communication module is arranged in the sensing node; the network layer is used for constructing a wireless communication network and transmitting the parameters acquired by the sensing layer to the application layer through a multi-hop relay; and the application layer receives and processes the parameters transmitted by the network layer to realize pollution early warning, pollution risk prediction and regulation and control instruction generation, and the regulation and control instruction is fed back to the execution layer through the network layer. By adopting a channel frequency hopping mechanism, when it is monitored that the interference intensity of a current communication channel exceeds a preset interference threshold value, the standby channel can be automatically switched, instruction transmission of an application layer and an execution layer is not interrupted, and it is guaranteed that data such as water quality parameters and equipment operation parameters collected by a sensing layer are continuously and stably transmitted to the application layer.

Description

Technical Field

[0001] This invention relates to the field of electroplating wastewater treatment technology, specifically to an electroplating wastewater pollution early warning and control system based on the Internet of Things. Background Technology

[0002] Electroplating wastewater treatment technology is a key area of ​​industrial wastewater treatment and is of great significance for controlling heavy metal pollution and maintaining ecological balance.

[0003] The publication number (CN117164134B) proposes a method for analyzing and treating pollution sources in electroplating wastewater. By collecting water samples in stages, identifying characteristic pollutants, accurately classifying wastewater, and matching corresponding treatment processes, a highly targeted treatment system is formed. Its advantage lies in its ability to adapt wastewater of different properties to appropriate treatment through refined factor analysis and classification logic, thereby ensuring treatment effectiveness. Moreover, the overall operation process is relatively simple and easy to implement in practice.

[0004] However, this method has a drawback: although its feature-based classification model can improve targeting, the entire system is built on a static correspondence between standard categories and treatment processes. It fails to introduce dynamic data monitoring and prediction mechanisms, neither utilizing historical treatment data to build pollution trend prediction models nor responding to real-time parameter changes during wastewater treatment. This means that in practical applications, when key parameters such as pollutant content and pH value in wastewater experience sudden fluctuations or continuous changes, the system cannot predict and adjust its treatment strategy in advance, and can only passively execute fixed processes based on predetermined standard categories. In industrial scenarios with frequent or complex water quality fluctuations, this static characteristic significantly limits the flexibility and adaptability of the treatment process, potentially reducing treatment efficiency and posing a risk of substandard effluent due to failure to respond promptly to water quality changes, making it difficult to achieve dynamic optimization and proactive risk control throughout the entire treatment process. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an IoT-based early warning and control system for electroplating wastewater pollution, which solves the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an IoT-based early warning and control system for electroplating wastewater pollution, the system comprising: The perception layer collects characteristic parameters of the electroplating wastewater treatment process through distributed sensing nodes. These characteristic parameters include water quality parameters, equipment operating parameters, and environmental parameters. The sensing nodes have built-in wireless communication modules. The network layer constructs a wireless communication network and transmits the parameters collected by the perception layer to the application layer through multi-hop relays. The application layer receives and processes parameters transmitted from the network layer to achieve pollution early warning, pollution risk prediction, and generation of control instructions. The control instructions are fed back to the execution layer through the network layer. The execution layer receives control instructions from the application layer and executes corresponding operations to adjust the wastewater treatment process; The perception layer, network layer, application layer, and execution layer form a closed-loop control link through wireless communication.

[0007] Preferably, the network layer includes routing nodes, a coordinator, and a cloud gateway; Routing nodes relay data and dynamically select the transmission path with the best signal strength; The coordinator is responsible for managing the network access and communication synchronization of sensing nodes in the perception layer, and maintaining the network topology. The cloud gateway converts the wireless communication protocol into the TCP / IP protocol, enabling cloud data interaction with the application layer.

[0008] Preferably, the network layer adopts a channel frequency hopping mechanism, which does not interrupt the instruction transmission between the application layer and the execution layer during the frequency hopping process. When the interference intensity of the current communication channel is detected to exceed the preset interference threshold, it automatically switches to the backup channel. The frequency hopping interval is dynamically adjusted according to the network load, and the higher the load, the smaller the frequency hopping interval. The preset interference threshold is the signal-to-interference ratio threshold of the communication channel, with a value range of 8-12dB. When the real-time signal-to-interference ratio is lower than this signal-to-interference ratio threshold, it is determined that the current communication channel interference intensity exceeds the preset interference threshold. The backup channels are a preset set of channels, including channel 1, channel 3, channel 5, channel 7, channel 9, channel 11, channel 13, channel 15, channel 17, channel 19, channel 21, channel 23, channel 25, channel 27, channel 29, and channel 31.

[0009] Preferably, the application layer includes a data storage unit, an early warning analysis unit, a pollution risk prediction module, and a control strategy unit; The data storage unit receives and stores historical data, historical coupled datasets, and preset thresholds uploaded by the perception layer through the network layer, and the storage process uses an encrypted transmission protocol. The early warning analysis unit compares real-time parameters with preset thresholds, and generates early warning information when the real-time data exceeds the preset thresholds. The pollution risk prediction module is used to predict pollution risks based on historical coupled datasets and real-time data; The control strategy unit generates control instructions based on early warning information, pollution risk prediction results and historical data, and sends them down to the execution layer through the network layer.

[0010] Preferably, the pollution risk prediction module includes: The model training unit uses an LSTM neural network to train a historical coupled dataset. The LSTM neural network adds a feature attention mechanism layer between the input layer and the hidden layer. The attention weights are dynamically allocated according to the influence of the parameters on the pollution risk. The input is a multi-parameter time series with 20-30 dimensions, and the output is a pollution risk index of 0-100. The risk grading unit divides the predicted pollution risk values ​​into four levels: no risk, low risk, medium risk, and high risk, according to [0,20), [20,40), [40,70), and [70,100]. The prediction triggering unit generates an early warning signal when the risk value reaches medium risk or above for three consecutive prediction cycles. The early warning signal is synchronized to the execution layer and remote terminal through the network layer communication link.

[0011] Preferably, the construction process of the LSTM neural network includes: Step 1: Normalize the historical coupling data and map the parameter values ​​to the [0,1] interval; Step 2: Divide the dataset into training, validation, and test sets in a ratio of 7:2:1; Step 3: Initialize two bidirectional LSTM hidden layers, with 32 neurons in the first layer and 16 neurons in the second layer; Step 4: Connect a dropout layer to the output of the hidden layer, and set the dropout rate to 0.2; Step 5: Minimize the loss function using the Adam optimizer and iterate until the validation set loss does not decrease for 5 consecutive rounds.

[0012] Preferably, the historical coupling dataset, used as a training data source for the LSTM neural network, includes: Time-series data of the electroplating wastewater treatment process, including the corresponding relationship data of water quality parameters, equipment operating parameters and environmental parameters collected at regular intervals; The associated data for each parameter's abnormal fluctuation period, including the time of the abnormality, duration, magnitude of change of the associated parameter, and corresponding handling measures, provides a reference for the risk classification unit to determine abnormalities. A complete data set of parameters for the entire wastewater treatment process under different seasons and production loads, covering multiple sampling points daily, is used to optimize the adaptability of control strategy units. The preset threshold is the benchmark for threshold comparison performed by the early warning analysis unit, including: Water quality parameter thresholds: heavy metal ion concentration threshold, pH value threshold, chemical oxygen demand threshold, and suspended solids concentration threshold, used to determine whether wastewater quality exceeds standards; Equipment operating parameter thresholds: These include thresholds for power fluctuation range, flow deviation, and pressure stability range of the processing equipment, used to monitor whether the equipment is operating abnormally. Environmental parameter thresholds: hazardous gas concentration thresholds, temperature thresholds, and humidity thresholds in the treatment area are used to assess whether there are any risks in the treatment area environment.

[0013] Preferably, the control strategy unit has a built-in parameter correlation model, which is constructed based on historical data and interacts with the LSTM neural network of the pollution risk prediction module. The input of the parameter correlation model includes the pollution risk index output by the LSTM neural network and the warning level generated by the warning analysis unit. When the early warning analysis unit generates early warning information or the pollution risk prediction module generates an advanced early warning signal, the parameter association model calls the historical coupled dataset in the data storage unit, combines the real-time parameters to calculate the optimal adjustment parameters corresponding to the pollution risk, and generates a control instruction containing equipment control codes. The optimal adjustment parameters include the reagent addition correction coefficient of the dosing device, the pump flow rate adjustment ratio, and the stirring equipment speed adjustment value.

[0014] Preferably, the parameter correlation model includes multi-level control logic, with different control intensities corresponding to different pollution risk levels; When the pollution risk level is medium, the parameter association model generates control instructions according to the basic adjustment range, which is determined based on the average adjustment value of the same risk in the historical coupled dataset. When the pollution risk level is high, the parameter correlation model activates the emergency control mode. The adjustment range of the control command is 1.5-2 times the basic adjustment range, and priority is given to calling the execution terminals with redundant processing capabilities in the execution layer.

[0015] Preferably, the execution layer includes multiple intelligent execution terminals, each of which has a built-in communication module and a microcontroller. After receiving the control instructions from the application layer, the intelligent execution terminal parses the control code and drives the actuator to move. The actuator includes a dosing device, a pump, valves and a stirring device. The dosing device adjusts the dosage and frequency of the chemicals, the pump and valves adjust the flow rate and direction of the wastewater, and the stirring equipment adjusts the stirring speed.

[0016] This invention provides an IoT-based early warning and control system for electroplating wastewater pollution. It offers the following advantages: 1. By adopting a channel frequency hopping mechanism, when the interference intensity of the current communication channel is detected to exceed the preset interference threshold, it can automatically switch to the backup channel without interrupting the instruction transmission between the application layer and the execution layer. This ensures that the water quality parameters, equipment operating parameters and other data collected by the perception layer are continuously and stably transmitted to the application layer, providing a complete multi-parameter time series input for the LSTM neural network of the pollution risk prediction module. This enables the risk prediction model to accurately capture the changing trend of pollution risk and avoid early warning delays caused by data transmission problems.

[0017] 2. By adding a feature attention mechanism between the input layer and hidden layer of the LSTM neural network, attention weights can be dynamically allocated according to the influence of each parameter on pollution risk. This allows the model to focus more on the changes in key parameters, improving the accuracy of pollution risk prediction. At the same time, it is interconnected with the parameter association model. The parameter association model takes the real-time pollution risk index and warning level output by the neural network as input, combines it with historical coupled datasets to calculate the optimal adjustment parameters, and can dynamically match the control intensity according to different pollution risk levels. During medium risk, it can accurately control according to the historical average adjustment value, and during high risk, it can activate the emergency mode to increase the control amplitude, ensuring that the control measures match the pollution risk level in real time and reduce the risk of pollution spread.

[0018] 3. The perception layer, network layer, application layer, and execution layer form a closed-loop control link through wireless communication. The intelligent execution terminal of the execution layer transmits operation feedback data back to the application layer to correct the parameter correlation model, making the control commands more in line with the actual operating state. The routing nodes of the network layer dynamically select the optimal transmission path to further ensure data interaction efficiency and improve the operational stability of the entire system in complex industrial environments. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a schematic diagram of the application layer structure of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will now be clearly and completely described 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.

[0021] For examples, please refer to the appendix. Figure 1 This invention provides an IoT-based early warning and control system for electroplating wastewater pollution, the system comprising: The perception layer collects characteristic parameters of the electroplating wastewater treatment process through distributed sensing nodes. These characteristic parameters include water quality parameters, equipment operating parameters, and environmental parameters. The sensing nodes have built-in wireless communication modules. The network layer constructs a wireless communication network and transmits the parameters collected by the perception layer to the application layer through multi-hop relays. The application layer receives and processes parameters transmitted from the network layer to achieve pollution early warning, pollution risk prediction, and generation of control instructions. The control instructions are fed back to the execution layer through the network layer. The execution layer receives control instructions from the application layer and executes corresponding operations to adjust the wastewater treatment process; The perception layer, network layer, application layer, and execution layer form a closed-loop control link through wireless communication.

[0022] Specifically, a four-layer architecture enables comprehensive management of electroplating wastewater pollution. The sensing layer, as the data acquisition front-end, utilizes distributed sensor nodes to cover all key stages of wastewater treatment, such as equalization tanks, reaction tanks, and sedimentation tanks, ensuring comprehensive parameter acquisition. The network layer's wireless communication network solves the problems of difficult wiring and high maintenance costs in complex industrial environments, while multi-hop relays expand signal coverage, adapting to the needs of large wastewater treatment plants. The application layer, as the core processing unit, combines data processing with decision generation, realizing the transformation from data to action. The execution layer concretizes control commands into equipment operations, directly impacting the wastewater treatment process. The closed-loop control link between the four layers ensures bidirectional data flow, enabling the system to dynamically adjust based on real-time status. This avoids the control lag caused by the disconnect between acquisition, decision-making, and execution in traditional open-loop control, improving the overall response speed of pollution control.

[0023] The network layer includes routing nodes, coordinators, and cloud gateways; Routing nodes relay data and dynamically select the transmission path with the best signal strength; The coordinator is responsible for managing the network access and communication synchronization of sensing nodes in the perception layer, and maintaining the network topology. The cloud gateway converts the wireless communication protocol into the TCP / IP protocol, enabling cloud data interaction with the application layer.

[0024] Specifically, the routing nodes employ a dynamic path selection mechanism, monitoring the signal strength and packet loss rate of each transmission path in real time. When a path experiences signal weakening due to interference, it automatically switches to a better path, preventing data transmission interruptions caused by reliance on a single path. The coordinator, as the core network controller, manages the authentication of sensor node access requests, preventing data pollution caused by unauthorized nodes. Simultaneously, a time synchronization mechanism ensures that all nodes collect and transmit data in a unified sequence, avoiding parameter correlation errors due to time discrepancies. The cloud gateway's protocol conversion function establishes a data channel between the wireless sensor network and the cloud application layer, enabling locally collected real-time data to be quickly uploaded to the cloud for in-depth processing. Simultaneously, cloud-generated control commands can be efficiently distributed to the execution layer, achieving a collaborative model combining edge acquisition and cloud decision-making, balancing the real-time nature and complexity of data processing.

[0025] The network layer adopts a channel frequency hopping mechanism. During the frequency hopping process, the instruction transmission between the application layer and the execution layer is not interrupted. When the interference intensity of the current communication channel is detected to exceed the preset interference threshold, it automatically switches to the backup channel. The frequency hopping interval is dynamically adjusted according to the network load. The higher the load, the smaller the frequency hopping interval. The preset interference threshold is the signal-to-interference ratio threshold of the communication channel, with a value range of 8-12dB. When the real-time signal-to-interference ratio is lower than this signal-to-interference ratio threshold, it is determined that the current communication channel interference intensity exceeds the preset interference threshold. The backup channels are a preset set of channels, including channel 1, channel 3, channel 5, channel 7, channel 9, channel 11, channel 13, channel 15, channel 17, channel 19, channel 21, channel 23, channel 25, channel 27, channel 29, and channel 31.

[0026] Specifically, the channel frequency hopping mechanism is implemented based on real-time interference monitoring and dynamic channel management. The system continuously monitors the signal-to-interference ratio (SIR) of the current channel through a dedicated interference detection module. When the SIR falls below a preset threshold of 8-12 dB, channel quality is deemed degraded, and the frequency hopping control unit immediately initiates the handover process. The backup channel set consists of 16 odd-numbered channels (1-31) spaced at intervals of 2. This utilizes the frequency isolation characteristics between channels to reduce adjacent channel interference, and these channels are pre-screened for interference intensity to ensure that the initial interference level is below 5 dB, providing quality assurance for data transmission after handover. During frequency hopping, the system achieves seamless handover through a dual-channel parallel mechanism: while transmitting data on the current channel, a connection is established with the target backup channel in advance, and the data transmission status is synchronized. The original channel connection is terminated only after the backup channel is confirmed to be stable, avoiding data loss or command interruption during handover. The dynamic adjustment logic of the frequency hopping interval is based on network load calculation: when the number of data frames transmitted per unit time exceeds the preset limit (such as 1000 frames / second), the frequency hopping interval is shortened from the default 10 seconds to 5 seconds. More frequent channel switching is used to deal with the accumulation of interference under high load. Under low load, the interval is extended to 20 seconds to reduce the energy consumption and latency caused by channel switching. This adaptive mechanism optimizes network resource consumption while ensuring transmission stability.

[0027] Please see the appendix Figure 2 The application layer includes a data storage unit, an early warning analysis unit, a pollution risk prediction module, and a control strategy unit. The data storage unit receives and stores historical data, historical coupled datasets, and preset thresholds uploaded by the perception layer through the network layer, and the storage process uses an encrypted transmission protocol. The early warning analysis unit compares real-time parameters with preset thresholds, and generates early warning information when the real-time data exceeds the preset thresholds. The pollution risk prediction module is used to predict pollution risks based on historical coupled datasets and real-time data; The control strategy unit generates control instructions based on early warning information, pollution risk prediction results and historical data, and sends them down to the execution layer through the network layer.

[0028] Specifically, the data storage unit adopts a distributed storage architecture, classifying and storing high-frequency collected real-time data (such as water quality parameters every 5 minutes) and historical coupled datasets. Encrypted transmission protocols (such as AES-128) ensure that the data is not tampered with or leaked during the upload process, providing a reliable data foundation for subsequent analysis. The early warning analysis unit, as the first line of defense for real-time monitoring, quickly identifies real-time parameters exceeding standards (such as a sudden increase in heavy metal concentration) through threshold comparison. The generated early warning information includes the parameter exceeding the standard, the current value, the threshold, and the associated location, providing staff with a clear indication of the anomaly. The pollution risk prediction module breaks through the limitations of traditional post-event early warning, predicting future pollution risks based on historical data and real-time trends, making up for the lag of relying solely on real-time thresholds. The control strategy unit, as the core of decision-making, integrates early warning information and prediction results with historical processing experience to generate targeted control instructions, enabling decisions to not only address current anomalies but also prevent potential risks, achieving dual control combining real-time response and proactive intervention.

[0029] The pollution risk prediction module includes: The model training unit uses an LSTM neural network to train on a historical coupled dataset. The LSTM neural network adds a feature attention mechanism layer between the input layer and the hidden layer. The attention weights are dynamically allocated according to the influence of the parameters on the pollution risk. The input is a multi-parameter time series with 20-30 dimensions, and the output is a pollution risk index of 0-100. The risk grading unit divides the predicted pollution risk values ​​into four levels: no risk, low risk, medium risk, and high risk, according to [0,20), [20,40), [40,70), and [70,100]. The prediction triggering unit generates an early warning signal when the risk value reaches medium risk or above for three consecutive prediction cycles. The early warning signal is synchronized to the execution layer and remote terminal through the network layer communication link.

[0030] Specifically, the pollution risk prediction module achieves accurate predictions through deep learning and dynamic grading. The LSTM neural network in the model training unit excels at processing time-series data, capturing the dynamic changes of parameters over time (such as the lag correlation between pH decrease and heavy metal leaching). The added feature attention mechanism layer strengthens the influence of key parameters through weight allocation: for example, when fluctuations in heavy metal ion concentration are detected, its weight increases from 0.1 to 0.3, while the weight of environmental humidity, which has a smaller impact, decreases from 0.05 to 0.02. This allows the model to focus more on parameters that play a decisive role in pollution risk, improving prediction accuracy. The 20-30 dimensional multi-parameter input covers information from multiple dimensions such as water quality, equipment, and environment, avoiding the one-sidedness of single-parameter prediction. The output 0-100 risk index intuitively reflects the degree of pollution through quantitative methods. The four-level risk grading unit corresponds to different intervention needs: no risk (0-20) requires no intervention, low risk (20-40) requires enhanced monitoring, medium risk (40-70) requires mild regulation, and high risk (70-100) requires emergency handling, providing a clear basis for the intensity of subsequent regulation. The "three consecutive cycles" condition setting of the prediction trigger unit avoids false triggers caused by single prediction fluctuations, ensuring the reliability of the early warning signal. The design of synchronizing to the execution layer and remote terminals achieves dual protection of automatic on-site response and remote personnel awareness.

[0031] The construction process of an LSTM neural network includes: Step 1: Normalize the historical coupling data and map the parameter values ​​to the [0,1] interval; Step 2: Divide the dataset into training, validation, and test sets in a ratio of 7:2:1; Step 3: Initialize two bidirectional LSTM hidden layers, with 32 neurons in the first layer and 16 neurons in the second layer; Step 4: Connect a dropout layer to the output of the hidden layer, and set the dropout rate to 0.2; Step 5: Minimize the loss function using the Adam optimizer and iterate until the validation set loss does not decrease for 5 consecutive rounds.

[0032] Specifically, step 1, normalization, eliminates dimensional differences between parameters (such as pH value (0-14) and heavy metal concentration (mg / L)), ensuring that each parameter has equal weight in model training and avoiding training bias caused by differences in numerical ranges; step 2, dataset partitioning, allocates 70% of historical data for model learning patterns, 20% for validating model generalization ability, and 10% for final testing, ensuring that the model is not limited to a specific data distribution and can adapt to different working conditions; step 3, a two-layer bidirectional LSTM structure design: the first layer with 32 neurons is responsible for capturing short-term parameter fluctuations (such as small... The first layer handles time-level water quality changes, while the second layer, consisting of 16 neurons, processes long-term trends (such as day-level pollution accumulation). Bidirectional propagation utilizes both past and future parameter information to enhance the ability to capture temporal correlations. The dropout layer in step 4 prevents the model from over-relying on specific parameter combinations and reduces the risk of overfitting by randomly discarding 20% ​​of the neuron connections. The Adam optimizer in step 5 combines momentum and adaptive learning rate to accelerate the convergence of the loss function, while the stopping condition of "loss not decreasing for 5 consecutive validation sets" avoids overtraining, controlling computational resource consumption while ensuring model accuracy.

[0033] Historical coupled datasets, used as training data sources for LSTM neural networks, include: Time-series data of the electroplating wastewater treatment process, including the corresponding relationship data of water quality parameters, equipment operating parameters and environmental parameters collected at regular intervals; The associated data for each parameter's abnormal fluctuation period, including the time of the abnormality, duration, magnitude of change of the associated parameter, and corresponding handling measures, provides a reference for the risk classification unit to determine abnormalities. The wastewater treatment process parameter dataset under different seasons and production loads, covering a complete data chain of multiple sampling points per day, is used to optimize the adaptability of the parameter correlation model of the control strategy unit.

[0034] The preset threshold is the benchmark for threshold comparison performed by the early warning analysis unit, including: Water quality parameter thresholds: heavy metal ion concentration threshold, pH value threshold, chemical oxygen demand threshold, and suspended solids concentration threshold, used to determine whether wastewater quality exceeds standards; Equipment operating parameter thresholds: These include thresholds for power fluctuation range, flow deviation, and pressure stability range of the processing equipment, used to monitor whether the equipment is operating abnormally. Environmental parameter thresholds: hazardous gas concentration thresholds, temperature thresholds, and humidity thresholds in the treatment area are used to assess whether there are any risks in the treatment area environment.

[0035] Specifically, time-series data is collected periodically (e.g., every 5 minutes) to establish the temporal correspondence between water quality, equipment, and environmental parameters. For example, the flow rate of the dosing pump at 9 AM is correlated with the pH value of the sedimentation tank at 10 AM, allowing the model to learn the lagging impact of operational parameters on treatment effectiveness. Abnormal fluctuation data focuses on scenarios of sudden parameter changes (e.g., a sudden 20% increase in heavy metal concentration). Recorded treatment measures (e.g., increasing flocculant dosage) provide the model with "problem-solution" learning samples, enabling the risk grading unit to determine the current risk level based on historical anomaly amplitudes. Data sets from different seasons and production loads cover boundary conditions such as temperature changes (e.g., the impact of high summer temperatures on reaction rates) and production fluctuations (e.g., increased orders leading to a doubling of wastewater discharge), ensuring stable predictions under various operating conditions and avoiding accuracy degradation due to environmental or production changes. The pre-defined threshold classifications specifically cover different dimensions of monitoring needs: water quality thresholds are directly related to discharge standards, equipment thresholds ensure the normal operation of the treatment system, and environmental thresholds focus on operational safety. These three together constitute a multi-dimensional risk monitoring network, ensuring no abnormal situations are missed.

[0036] The control strategy unit has a built-in parameter correlation model, which is built based on historical data and interacts with the LSTM neural network of the pollution risk prediction module. The input of the parameter correlation model includes the pollution risk index output by the LSTM neural network and the warning level generated by the early warning analysis unit. When the early warning analysis unit generates early warning information or the pollution risk prediction module generates an advanced early warning signal, the parameter association model calls the historical coupled dataset in the data storage unit, combines the real-time parameters to calculate the optimal adjustment parameters corresponding to the pollution risk, and generates control instructions containing equipment control codes. The optimal adjustment parameters include the chemical dosing correction coefficient of the dosing device, the pump flow rate adjustment ratio, and the stirring equipment speed adjustment value.

[0037] Specifically, the real-time data interaction between the parameter association model and the LSTM neural network enables regulation not only based on current risk but also on predicted trends. For example, if the neural network predicts that the risk will rise to a moderate level in one hour, the model can initiate mild regulation in advance to prevent the risk from accumulating to a severe level. The historical coupled dataset includes treatment cases of similar risks (such as the optimal dosage for the past three instances of heavy metal concentration exceeding the standard). The model compares the similarity between current parameters and historical cases (such as pH value and water temperature differences) to correct historical adjustment values ​​and generate optimal parameters suitable for the current operating conditions. The output equipment control code adopts a standardized format, including equipment ID, adjustment value, execution time, and other information to ensure accurate parsing at the execution layer. Among the specific adjustment parameters, the reagent dosage correction coefficient (such as 1.2 times the base amount) directly affects the dosing device, the flow rate adjustment ratio (such as 80% of the design flow rate) controls the residence time of wastewater in the reaction tank, and the stirring speed adjustment value (such as 300 rpm) optimizes the reagent mixing efficiency. These three factors work synergistically in the wastewater treatment process to achieve targeted risk control.

[0038] The parameter correlation model contains multi-level control logic, with different control intensities corresponding to different pollution risk levels; When the pollution risk level is medium, the parameter association model generates control instructions according to the basic adjustment range, which is determined based on the average adjustment value of the same risk in the historical coupled dataset. When the pollution risk level is high, the parameter correlation model activates the emergency control mode. The adjustment range of the control command is 1.5-2 times the basic adjustment range, and priority is given to calling the execution terminals with redundant processing capabilities in the execution layer.

[0039] Specifically, the multi-level control logic achieves precise matching of control intensity through risk level adaptation. The basic adjustment range for medium-risk levels is calculated based on statistical analysis of historical data: the model extracts adjustment values ​​from nearly 100 medium-risk cases from the historical coupled dataset, removes extreme values, and takes the average (e.g., an average increase of 30% in pesticide dosage). This range effectively controls risk while avoiding pesticide waste or excessive equipment load caused by over-control. The emergency mode for high-risk situations strengthens the control effect through a dual mechanism: increasing the adjustment range to 1.5-2 times (e.g., an increase of 45%-60% in pesticide dosage) can quickly suppress the upward trend of risk, while prioritizing the use of redundant execution terminals (e.g., backup dosing pumps, backup mixing equipment) improves control reliability through multi-device collaborative operation, avoiding control failure caused by the failure of a single device.

[0040] The execution layer includes multiple intelligent execution terminals, each with a built-in communication module and microcontroller; After receiving the control instructions from the application layer, the intelligent execution terminal parses the control code and drives the actuator to move. The actuator includes a dosing device, a pump, valves and a stirring device. The dosing device adjusts the dosage and frequency of the chemicals, the pump and valves adjust the flow rate and direction of the wastewater, and the stirring equipment adjusts the stirring speed.

[0041] Specifically, the execution layer achieves the physical conversion of control commands through the parsing and driving of intelligent terminals. The distributed deployment of intelligent execution terminals enables them to control equipment in each treatment stage locally (such as the dosing terminal next to the equalization tank and the stirring terminal in the sedimentation tank), reducing command transmission distance and delay. The built-in microcontroller, as the parsing core, can quickly decode the equipment control code in the control commands (such as identifying "dosing device ID: 001, correction coefficient 1.2") and convert the digital commands into analog control signals (such as 4-20mA current signals) to drive the actuators. The actions of each actuator directly affect the wastewater treatment process: the dosing device changes the dosage of the reagent by adjusting the stroke frequency of the metering pump, and achieves precise dosing by coordinating the dosing frequency (such as once every 30 seconds); the coordinated action of the pump and valve can adjust the distribution ratio of wastewater in each treatment unit (such as diverting 60% of the wastewater to the standby reaction tank) to cope with flow fluctuations; the stirring equipment optimizes the mixing uniformity of the reagent and wastewater by changing the motor speed (such as increasing it from 200 rpm to 300 rpm), thereby improving reaction efficiency. This direct conversion of "command-action" ensures that decisions made at the application layer can be quickly implemented into actual measures, forming the final link in closed-loop control.

[0042] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An IoT-based early warning and control system for electroplating wastewater pollution, characterized in that, The system includes: The perception layer collects characteristic parameters of the electroplating wastewater treatment process through distributed sensing nodes. These characteristic parameters include water quality parameters, equipment operating parameters, and environmental parameters. The sensing nodes have built-in wireless communication modules. The network layer constructs a wireless communication network and transmits the parameters collected by the perception layer to the application layer through multi-hop relays. The application layer receives and processes parameters transmitted from the network layer to achieve pollution early warning, pollution risk prediction, and generation of control instructions. The control instructions are fed back to the execution layer through the network layer. The execution layer receives control instructions from the application layer and executes corresponding operations to adjust the wastewater treatment process; The perception layer, network layer, application layer, and execution layer form a closed-loop control link through wireless communication.

2. The IoT-based electroplating wastewater pollution early warning and control system according to claim 1, characterized in that, The network layer includes routing nodes, coordinators, and cloud gateways; Routing nodes relay data and dynamically select the transmission path with the best signal strength; The coordinator is responsible for managing the network access and communication synchronization of sensing nodes in the perception layer, and maintaining the network topology. The cloud gateway converts the wireless communication protocol into the TCP / IP protocol, enabling cloud data interaction with the application layer.

3. The IoT-based electroplating wastewater pollution early warning and control system according to claim 1, characterized in that, The network layer adopts a channel frequency hopping mechanism. During the frequency hopping process, the instruction transmission between the application layer and the execution layer is not interrupted. When the interference intensity of the current communication channel is detected to exceed the preset interference threshold, it automatically switches to the backup channel. The frequency hopping interval is dynamically adjusted according to the network load. The higher the load, the smaller the frequency hopping interval. The preset interference threshold is the signal-to-interference ratio threshold of the communication channel, with a value range of 8-12dB. When the real-time signal-to-interference ratio is lower than this signal-to-interference ratio threshold, it is determined that the current communication channel interference intensity exceeds the preset interference threshold. The backup channels are a preset set of channels, including channel 1, channel 3, channel 5, channel 7, channel 9, channel 11, channel 13, channel 15, channel 17, channel 19, channel 21, channel 23, channel 25, channel 27, channel 29, and channel 31.

4. The electroplating wastewater pollution early warning and control system based on the Internet of Things according to claim 1, characterized in that, The application layer includes a data storage unit, an early warning analysis unit, a pollution risk prediction module, and a control strategy unit. The data storage unit receives and stores historical data, historical coupled datasets, and preset thresholds uploaded by the perception layer through the network layer, and the storage process uses an encrypted transmission protocol. The early warning analysis unit compares real-time parameters with preset thresholds, and generates early warning information when the real-time data exceeds the preset thresholds. The pollution risk prediction module is used to predict pollution risks based on historical coupled datasets and real-time data; The control strategy unit generates control instructions based on early warning information, pollution risk prediction results and historical data, and sends them down to the execution layer through the network layer.

5. The IoT-based electroplating wastewater pollution early warning and control system according to claim 4, characterized in that, The pollution risk prediction module includes: The model training unit uses an LSTM neural network to train a historical coupled dataset. The LSTM neural network adds a feature attention mechanism layer between the input layer and the hidden layer. The attention weights are dynamically allocated according to the influence of the parameters on the pollution risk. The input is a multi-parameter time series with 20-30 dimensions, and the output is a pollution risk index of 0-100. The risk grading unit divides the predicted pollution risk values ​​into four levels: no risk, low risk, medium risk, and high risk, according to [0,20), [20,40), [40,70), and [70,100]. The prediction triggering unit generates an early warning signal when the risk value reaches medium risk or above for three consecutive prediction cycles. The early warning signal is synchronized to the execution layer and remote terminal through the network layer communication link.

6. The IoT-based electroplating wastewater pollution early warning and control system according to claim 5, characterized in that, The construction process of the LSTM neural network includes: Step 1: Normalize the historical coupling data and map the parameter values ​​to the [0,1] interval; Step 2: Divide the dataset into training, validation, and test sets in a ratio of 7:2:1; Step 3: Initialize two bidirectional LSTM hidden layers, with 32 neurons in the first layer and 16 neurons in the second layer; Step 4: Connect a dropout layer to the output of the hidden layer, and set the dropout rate to 0.2; Step 5: Minimize the loss function using the Adam optimizer and iterate until the validation set loss does not decrease for 5 consecutive rounds.

7. The electroplating wastewater pollution early warning and control system based on the Internet of Things according to claim 5, characterized in that, The historical coupled dataset, used as the training data source for the LSTM neural network, includes: Time-series data of the electroplating wastewater treatment process, including the corresponding relationship data of water quality parameters, equipment operating parameters and environmental parameters collected at regular intervals; The associated data for each parameter's abnormal fluctuation period, including the time of the abnormality, duration, magnitude of change of the associated parameter, and corresponding handling measures, provides a reference for the risk classification unit to determine abnormalities. A complete data set of parameters for the entire wastewater treatment process under different seasons and production loads, covering multiple sampling points daily, is used to optimize the adaptability of control strategy units. The preset threshold is the benchmark for threshold comparison performed by the early warning analysis unit, including: Water quality parameter thresholds: heavy metal ion concentration threshold, pH value threshold, chemical oxygen demand threshold, and suspended solids concentration threshold, used to determine whether wastewater quality exceeds standards; Equipment operating parameter thresholds: These include thresholds for power fluctuation range, flow deviation, and pressure stability range of the processing equipment, used to monitor whether the equipment is operating abnormally. Environmental parameter thresholds: hazardous gas concentration thresholds, temperature thresholds, and humidity thresholds in the treatment area are used to assess whether there are any risks in the treatment area environment.

8. The IoT-based electroplating wastewater pollution early warning and control system according to claim 5, characterized in that, The control strategy unit has a built-in parameter correlation model, which is built based on historical data and interacts with the LSTM neural network of the pollution risk prediction module. The input of the parameter correlation model includes the pollution risk index output by the LSTM neural network and the warning level generated by the warning analysis unit. When the early warning analysis unit generates early warning information or the pollution risk prediction module generates an advanced early warning signal, the parameter association model calls the historical coupled dataset in the data storage unit, combines the real-time parameters to calculate the optimal adjustment parameters corresponding to the pollution risk, and generates a control instruction containing equipment control codes. The optimal adjustment parameters include the reagent addition correction coefficient of the dosing device, the pump flow rate adjustment ratio, and the stirring equipment speed adjustment value.

9. The IoT-based electroplating wastewater pollution early warning and control system according to claim 8, characterized in that, The parameter correlation model contains multi-level control logic, with different control intensities corresponding to different pollution risk levels. When the pollution risk level is medium, the parameter association model generates control instructions according to the basic adjustment range, which is determined based on the average adjustment value of the same risk in the historical coupled dataset. When the pollution risk level is high, the parameter correlation model activates the emergency control mode. The adjustment range of the control command is 1.5-2 times the basic adjustment range, and priority is given to calling the execution terminals with redundant processing capabilities in the execution layer.

10. The electroplating wastewater pollution early warning and control system based on the Internet of Things according to claim 1, characterized in that, The execution layer includes multiple intelligent execution terminals, each of which has a built-in communication module and a microcontroller. After receiving the control instructions from the application layer, the intelligent execution terminal parses the control code and drives the actuator to move. The actuator includes a dosing device, a pump, valves and a stirring device. The dosing device adjusts the dosage and frequency of the chemicals, the pump and valves adjust the flow rate and direction of the wastewater, and the stirring equipment adjusts the stirring speed.

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