Electroplating rinsing sewage complex breaking electro-catalysis device based on titanium-based nano conductive ceramic

Abstract

The invention relates to the technical field of electrocatalytic oxidation devices, and discloses an electroplating rinse sewage complex breaking electrocatalytic device based on titanium-based nano conductive ceramics, which comprises an oxidation pond and an acquisition module, the acquisition module acquires catalytic liquid information in real time, and the catalytic liquid information comprises components and state data of a catalytic liquid. And the catalysis module is responsible for treating a catalytic liquid and destroying a complex by utilizing an electro-catalysis reaction. The central control module analyzes complex components of a catalyst through a convolutional neural network (CNN) and a recurrent neural network (RNN), and dynamically adjusts electro-catalysis parameters on the basis of a fuzzy control algorithm in combination with historical data and real-time information so as to optimize the catalytic effect. And the early warning module monitors the changes of the catalytic liquid and the electro-catalysis parameters, judges whether early warning needs to be given out or not by comparing the changes with a preset threshold value, and takes corresponding measures according to the risk level. Through real-time data analysis and intelligent control, the accuracy and efficiency of the catalysis process are improved, and the safety and stability of the treatment process are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of electrocatalytic oxidation devices, and in particular to a chelation-breaking electrocatalytic device for electroplating rinsing wastewater based on titanium-based nano-conductive ceramics. Background Art

[0002] As industrialization progresses, the electroplating industry generates large quantities of rinse water during production, which contains a large number of metal ions and chemicals. If this wastewater is not effectively treated and discharged into the environment, it will cause serious pollution to water bodies and ecosystems. Therefore, the treatment of electroplating rinse water has become an urgent environmental issue.

[0003] Currently, common treatment methods include chemical precipitation, electrolysis, and membrane separation. However, these traditional methods suffer from low treatment efficiency, high costs, and difficulty coping with complex wastewater compositions. There is an urgent need for an efficient and intelligent treatment solution. Furthermore, traditional electrocatalytic complex breaking technology faces the challenges of low catalytic efficiency and unstable catalytic processes. In particular, during the treatment process, catalyst optimization and adjustment typically rely on manual experience, lacking intelligent adjustment methods for different complex types and concentrations. This prevents the complex breaking effect of electroplating rinse wastewater from being fully improved.

[0004] Therefore, there is an urgent need to invent a catalytic device to solve the problems of low efficiency, high cost, unstable catalytic process, and lack of intelligent adjustment means in traditional electroplating rinsing wastewater treatment methods, which leads to the failure to fully improve the chelation breaking effect. Summary of the Invention

[0005] In view of this, the present invention proposes an electrocatalytic device for de-coordination of electroplating rinsing wastewater based on titanium-based nano-conductive ceramics, aiming to solve the problems in current technology of low efficiency, high cost, unstable catalytic process and lack of intelligent adjustment means, resulting in the failure to fully improve the de-coordination effect of traditional electroplating rinsing wastewater treatment methods.

[0006] The present invention proposes an electrocatalytic device for breaking up electroplating rinsing wastewater plexiforms based on titanium-based nano-conductive ceramics, comprising an oxidation tank and:

[0007] A collection module is disposed inside the oxidation tank, and is configured to collect information about the catalytic liquid inside the oxidation tank;

[0008] The catalytic module is disposed inside the oxidation tank and is configured to catalyze the catalytic liquid inside the oxidation tank;

[0009] a central control module, electrically connected to the acquisition module and the catalytic module, respectively, the central control module being configured to analyze the complex composition of the catalyzed substance based on a convolutional neural network and a recurrent neural network, and further configured to determine the electrocatalytic parameters of the catalytic module based on a fuzzy control algorithm, combined with historical data, the complex composition of the catalyzed substance, and real-time catalytic liquid information;

[0010] The early warning module is electrically connected to the catalytic module and the central control module respectively. The early warning module is configured to determine whether to issue an early warning based on the relationship between the catalytic liquid information and the electrocatalytic parameters.

[0011] Furthermore, the electrode material of the catalytic module is specifically titanium-based nano-conductive ceramic.

[0012] Furthermore, the acquisition module includes:

[0013] a spectral sensor configured to monitor the sewage composition of the catalytic liquid in real time;

[0014] an ultrasonic sensor configured to detect a flow rate of the catalytic liquid in real time;

[0015] The electrochemical sensor is configured to detect the current density and pH value of the catalytic solution in real time.

[0016] Furthermore, when the central control module analyzes the complex composition of the catalyzed substance based on the convolutional neural network and the recurrent neural network, it includes:

[0017] The central control module is also configured to perform feature extraction on the spectral signal and electrochemical data based on a convolutional neural network, and to extract the spectral absorption characteristics and electrochemical response characteristics of the complex based on the convolutional layer;

[0018] The central control module is also configured to reduce the data dimension based on the pooling layer and extract the complex composition information of the catalyzed substance;

[0019] The central control module is also configured to analyze the time series data of the complex concentration based on a recurrent neural network, and to determine the dynamic change trend of the complex components to be catalyzed based on a long short-term memory network.

[0020] Furthermore, the central control module extracts features from spectral signals and electrochemical data based on a convolutional neural network, and extracts spectral absorption features and electrochemical response features of the complex based on the convolutional layer, including:

[0021] The central control module is also configured to denoise the spectral signal based on a filtering algorithm and perform baseline correction on the electrochemical data;

[0022] The central control module is further configured to scan the spectral signal based on the convolution layer to extract the spectral absorption characteristics, including the absorption peak position and absorbance intensity. The central control module is further configured to perform convolution operations on the electrochemical data based on the convolution layer to extract the change patterns of current and potential;

[0023] The central control module is also configured to perform dimensionality reduction on the spectral and electrochemical features based on a pooling layer;

[0024] The central control module is also configured to fuse spectral features and electrochemical features based on a fully connected layer to form a complete feature vector of the complex.

[0025] Furthermore, the central control module analyzes the time series data of the complex concentration based on the recurrent neural network and determines the dynamic change trend of the complex components to be catalyzed based on the long short-term memory network, including:

[0026] The central control module is also configured to obtain real-time time series data of complex concentration and obtain concentration changes at different catalytic stages;

[0027] The central control module is also configured to perform denoising and normalization on the concentration data based on a data preprocessing algorithm;

[0028] The central control module is also configured to extract time series features of concentration data based on an LSTM network, identify patterns of concentration changes over time, and use an attention mechanism to increase the weight of concentration changes at key time points.

[0029] The central control module is also configured to predict the future change trend of the complex concentration based on regression analysis, and dynamically determine the catalytic parameters based on the prediction results.

[0030] Furthermore, the central control module is further configured to determine the electrocatalytic parameters of the catalytic module based on the fuzzy control algorithm, combined with historical data, the complex composition of the catalytic substance and the real-time catalytic liquid information, including:

[0031] The central control module is also configured to perform fuzzy processing on variables such as complex concentration, catalytic current, and catalytic time based on fuzzy logic, and map each variable to a fuzzy set based on a membership function;

[0032] The central control module is further configured to reason about the catalytic parameters based on preset rules in conjunction with the fuzzy rule base, and determine the catalytic parameters of the complex components based on the fuzzy reasoning;

[0033] The central control module is also configured to model historical catalytic data based on time series analysis and optimize real-time parameter calculations using Bayesian reasoning or Kalman filtering;

[0034] The central control module is further configured to determine the electrocatalytic parameters of the catalytic module based on real-time parameter calculations.

[0035] Furthermore, the central control module combines the fuzzy rule base to reason about the catalytic parameters based on preset rules, and determines the catalytic parameters of the complex components based on the fuzzy reasoning, including:

[0036] The central control module is also configured to construct a mapping relationship between complex components and catalytic parameters based on historical data to form fuzzy rules for catalytic current, catalytic time, and pH value;

[0037] The central control module is also configured to map the complex concentration variable, the current density variable, and the redox potential variable into fuzzy sets;

[0038] The central control module is also configured to perform rule reasoning based on the Mamdani reasoning method to determine the fuzzy output of catalytic current and catalytic time;

[0039] The central control module is also configured to convert the fuzzy inference results into specific numerical values ​​using the maximum membership method;

[0040] The central control module is also configured to optimize specific values ​​based on historical data and real-time catalyst liquid information and based on Bayesian reasoning, and use the optimized specific values ​​as execution parameters of the catalytic module.

[0041] Furthermore, the early warning module determines whether to issue an early warning based on the relationship between the catalyst liquid information and the electrocatalytic parameters, including:

[0042] The early warning module is further configured to determine whether to issue an early warning based on the relationship between the execution parameter and the preset execution parameter configured by the catalysis module:

[0043] When the execution parameter is lower than the preset execution parameter, the warning module determines not to issue a warning;

[0044] When the execution parameter is higher than or equal to the preset execution parameter, the warning module determines to issue a warning and determines the warning level based on the relationship between the execution parameter and the preset execution parameter.

[0045] Furthermore, the early warning module determines the early warning level based on the relationship between the execution parameter and the preset execution parameter, including:

[0046] The early warning module is further configured to obtain a parameter difference between the execution parameter and a preset execution parameter, and determine an early warning level according to a relationship between the parameter difference and a first preset parameter difference and a second preset parameter difference configured by the early warning module;

[0047] When the parameter difference is lower than or equal to the first preset parameter difference, the warning level is determined to be a low risk level;

[0048] When the parameter difference is higher than the first preset parameter difference and the parameter difference is lower than or equal to the second preset parameter difference, the warning level is determined to be a medium risk level;

[0049] When the parameter difference is higher than the second preset parameter difference, the warning level is determined to be a high risk level;

[0050] Among them, the first preset parameter difference is less than the second preset parameter difference, and the severity of the warning levels is low risk level, medium risk level and high risk level, respectively.

[0051] Compared with the prior art, the beneficial effect of the present invention is that by combining convolutional neural networks (CNN) and recurrent neural networks (RNN) to intelligently analyze the complex components of the catalyst, the accuracy and intelligence level of the electrocatalytic device for breaking the complex of electroplating rinse wastewater are significantly improved. The central control module accurately identifies the complex components based on the deep learning algorithm, and combines the fuzzy control algorithm, historical data and real-time catalyst liquid information to dynamically optimize the electrocatalytic parameters (such as current density, catalytic time, etc.), realizing adaptive adjustment of the catalytic process, thereby greatly improving the catalytic efficiency and system stability. Through this intelligent control mechanism, the device can respond to changes in sewage composition in real time, avoiding the catalytic instability or inefficiency caused by lack of experience in traditional methods. In addition, through the introduction of the early warning module, abnormal conditions in the catalytic process (such as changes in catalyst liquid composition, catalytic parameters deviating from the preset range, etc.) can be monitored and warned in time to avoid over-catalysis or catalytic failure, thereby ensuring the consistency and stability of the breaking effect. Through intelligent parameter optimization and real-time feedback control, the entire treatment process is not only more efficient and accurate, but also reduces the need for human intervention, reducing operational risks and costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0053] Figure 1 This is a functional block diagram of a chelation-breaking electrocatalytic device for electroplating rinsing wastewater based on titanium-based nano-conductive ceramics provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0055] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides an electrocatalytic device for breaking up electroplating rinsing wastewater plexiforms based on titanium-based nano-conductive ceramics, including: an oxidation tank, a collection module, a catalytic module, a central control module and an early warning module.

[0056] Specifically, the acquisition module is configured inside the oxidation tank, and the acquisition module is configured to collect catalytic liquid information inside the oxidation tank; the catalytic module is configured inside the oxidation tank, and the catalytic module is configured to catalyze the catalytic liquid inside the oxidation tank; the central control module is electrically connected to the acquisition module and the catalytic module respectively, and the central control module is configured to analyze the complex composition of the catalyzed substance based on the convolutional neural network and the recurrent neural network, and the central control module is also configured to determine the electrocatalytic parameters of the catalytic module based on the fuzzy control algorithm, combined with historical data, the complex composition of the catalyzed substance and real-time catalytic liquid information; the early warning module is electrically connected to the catalytic module and the central control module respectively, and the early warning module is configured to determine whether to issue an early warning based on the relationship between the catalytic liquid information and the electrocatalytic parameters.

[0057] As can be understood, the synergistic effect of the acquisition module and the catalytic module, through real-time collection of catalytic liquid information within the oxidation tank and real-time data from the catalytic process, provides comprehensive processing environment data support to the central control module. The central control module uses a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) to conduct in-depth analysis of the complex composition of the catalyst. Using deep learning techniques, it automatically identifies the characteristics of different complexes and uses these characteristics to achieve more precise control of the catalytic process. Secondly, the central control module incorporates a fuzzy control algorithm, combining historical data, real-time catalytic liquid information, and complex composition to dynamically adjust catalytic parameters such as current density and catalytic time. By processing fuzzy and uncertain information, the fuzzy control algorithm can better cope with complex catalytic processes, enabling adaptive optimization of catalytic parameters based on different wastewater compositions. This dynamic adjustment mechanism significantly improves catalytic efficiency and ensures the stability of the complex decomposition effect. Finally, the introduction of an early warning module further enhances the system's intelligence and safety. By monitoring the relationship between catalytic liquid information and electrocatalytic parameters, the early warning module can promptly identify abnormalities in the catalytic process and issue warning signals. This can prevent over-catalysis or catalytic failure during the catalytic process, ensure the stability and efficiency of the catalytic process, and ultimately achieve efficient catalytic decomposition and intelligent treatment of electroplating rinse wastewater.

[0058] It can be seen that the synergistic effect of the acquisition module and the catalytic module enables real-time monitoring of the catalytic liquid information within the oxidation tank, providing the necessary real-time data support for the central control module. The central control module utilizes convolutional neural networks (CNN) and recurrent neural networks (RNN) to analyze the complex composition of the catalyzed product, accurately identifying the types and characteristics of different complexes. This intelligent analysis process enables the system to dynamically optimize electrocatalytic parameters based on the different complex compositions, thereby achieving more efficient catalytic reactions and more precise complex destruction. Secondly, by combining the application of fuzzy control algorithms with historical data, real-time catalytic liquid information, and complex composition, the central control module makes adjustments to the catalytic process more intelligent and adaptive. The fuzzy control algorithm can handle the uncertainty and complexity of the catalytic process, enabling real-time optimization of catalytic parameters (such as current density and catalytic time). This adaptive control significantly improves catalytic efficiency, reduces manual intervention, and ensures stable and consistent complex destruction of electroplating rinse wastewater. Finally, the introduction of an early warning module enhances the safety and stability of the system. By monitoring the relationship between catalytic fluid information and electrocatalytic parameters, the early warning module can promptly identify potential anomalies during the catalytic process, such as changes in catalytic fluid composition or deviations from preset electrocatalytic parameters. When the system detects a potential anomaly, the early warning module issues an alarm, preventing over-catalysis or catalytic failure and ensuring the catalytic process remains optimal. This highly efficient and intelligent approach ensures efficient, stable, and safe wastewater treatment.

[0059] Specifically, the electrode material of the catalytic module is titanium-based nano-conductive ceramic.

[0060] As can be seen, the electrodes of the catalytic module are made of titanium-based nano-conductive ceramics. This material selection effectively improves the electrical conductivity and catalytic efficiency during the catalytic process. Titanium-based nano-conductive ceramics possess excellent electrical conductivity and chemical stability, enabling higher current densities in the electrocatalytic reaction while maintaining high stability at high currents. This characteristic makes the catalytic process more efficient, effectively breaking up the complexes in electroplating rinse wastewater in a shorter time, reducing treatment time and energy consumption. Furthermore, the corrosion and oxidation resistance of titanium-based nano-conductive ceramics extend the service life of the catalytic module in the complex environment of electroplating rinse wastewater. Compared with traditional electrode materials, titanium-based nano-conductive ceramics can better withstand high concentrations of corrosive substances and avoid rapid electrode wear during the catalytic process. This not only improves the long-term stability of the system but also reduces maintenance and replacement costs, further enhancing the economic efficiency of the wastewater treatment system. Finally, the nanostructured surface of titanium-based nano-conductive ceramics provides more reactive sites, which significantly impacts the efficiency of the catalytic reaction. The nanoscale surface structure accelerates the electrochemical reaction, increasing the reaction rate while enhancing the catalyst's affinity and catalytic activity. This enables the catalytic module to quickly and effectively decompose complex complexes when treating electroplating rinse wastewater, significantly improving the complex breaking effect and ultimately achieving efficient wastewater treatment.

[0061] Specifically, the acquisition module includes: a spectral sensor configured to monitor the sewage composition of the catalytic liquid in real time; an ultrasonic sensor configured to detect the flow rate of the catalytic liquid in real time; and an electrochemical sensor configured to detect the current density and pH value of the catalytic liquid in real time.

[0062] As can be understood, the acquisition module uses spectral sensors to monitor the wastewater composition of the catalytic liquid in real time, effectively detecting changes in the concentrations of various chemical components and pollutants in the wastewater. Spectral sensors can quickly identify harmful substances in the catalytic liquid by analyzing the absorption or reflection characteristics of light, providing important real-time data support for the subsequent catalytic process. Real-time monitoring of these components allows for timely adjustments to the catalytic strategy during the wastewater treatment process to ensure maximum chelation-breaking effectiveness. Secondly, the ultrasonic sensor is configured to detect the flow rate of the catalytic liquid in real time, effectively monitoring its flow state. Flow rate has a significant impact on the efficiency of the catalytic process. Too low a flow rate may prevent reactants in the catalytic liquid from fully contacting the electrodes, while too high a flow rate may result in inadequate catalyst reaction. By monitoring the flow rate in real time, the acquisition module ensures that the catalytic liquid flows through the catalytic module at the optimal rate, thereby optimizing the electrocatalytic reaction and improving treatment efficiency. Finally, electrochemical sensors are used to monitor the current density and pH of the catalytic liquid in real time. These two parameters are crucial to the progress of the electrocatalytic reaction. Current density determines the intensity and rate of the electrocatalytic reaction, while pH affects the activity of the catalyst and the stability of the catalytic process. By monitoring these two key parameters in real time, the acquisition module can promptly detect possible deviations in the catalytic process, such as current density that is too low or abnormal pH value, and then provide data support for the central control module, so that the catalytic process can be quickly adjusted, thereby maintaining the efficiency and stability of the catalytic reaction.

[0063] Specifically, when the central control module analyzes the complex composition of the substance to be catalyzed based on the convolutional neural network and the recurrent neural network, it includes: the central control module is also configured to extract features of spectral signals and electrochemical data based on the convolutional neural network, and extract the spectral absorption characteristics and electrochemical response characteristics of the complex based on the convolution layer; the central control module is also configured to reduce the data dimension based on the pooling layer and extract the complex composition information of the substance to be catalyzed; the central control module is also configured to analyze the time series data of the complex concentration based on the recurrent neural network, and determine the dynamic change trend of the complex composition of the substance to be catalyzed based on the long short-term memory network.

[0064] Specifically, when the central control module extracts features from spectral signals and electrochemical data based on a convolutional neural network, and extracts spectral absorption features and electrochemical response features of the complex based on a convolutional layer, it includes: the central control module is also configured to denoise the spectral signal based on a filtering algorithm, and perform baseline correction on the electrochemical data; the central control module is also configured to scan the spectral signal based on the convolutional layer, and extract spectral absorption features, including absorption peak position and absorbance intensity. The central control module is also configured to perform convolution operations on electrochemical data based on the convolutional layer, and extract the change patterns of current and potential; the central control module is also configured to perform dimensionality reduction processing on spectral features and electrochemical features based on a pooling layer; the central control module is also configured to fuse spectral features and electrochemical features based on a fully connected layer to form a complete feature vector of the complex.

[0065] Specifically, the central control module analyzes the time series data of the complex concentration based on the recurrent neural network, and determines the dynamic change trend of the complex components to be catalyzed based on the long short-term memory network, including: the central control module is also configured to obtain the time series data of the complex concentration in real time, and obtain the concentration changes in different catalytic stages; the central control module is also configured to denoise and normalize the concentration data based on the data preprocessing algorithm; the central control module is also configured to extract the time series features of the concentration data based on the LSTM network, identify the change pattern of the concentration over time, and combine the attention mechanism to enhance the weight of the concentration change at the key time point; the central control module is also configured to predict the future change trend of the complex concentration based on regression analysis, and dynamically determine the catalytic parameters based on the prediction results.

[0066] It can be understood that by using the convolutional layer to scan the input spectral signals and electrochemical data, spectral absorption features (such as absorption peak position and absorbance intensity) and electrochemical response features (such as current and potential change patterns) are extracted. This process, through the filtering effect of the convolutional layer, can effectively identify useful information in the data while removing noise, making subsequent analysis more accurate. Feature extraction of spectral signals can reveal the chemical composition of the catalyzed substance, while feature extraction of electrochemical data helps to reveal the dynamic process of the catalytic reaction. Secondly, the application of pooling layers in convolutional neural networks further reduces the dimensionality of the data and extracts the most representative features. By downsampling the feature maps output by the convolutional layer, the pooling layer can effectively reduce the computational complexity of the data while retaining key information. This dimensionality reduction process can reduce the interference of redundant features, making the model more efficient and better helping to capture the main characteristics of the complex. In this way, the spectral and electrochemical features are effectively integrated, providing high-quality input data for subsequent analysis by the central control module. Next, the central control module also integrates the spectral and electrochemical features through a fully connected layer to form a complete feature vector for the complex. The fully connected layer, through weighted connections between multiple neurons, comprehensively considers all input features and generates a feature vector that comprehensively represents the complex composition. This feature vector incorporates spectral absorption characteristics, electrochemical response characteristics, and their interrelationships, providing comprehensive information support for subsequent analysis. To analyze the time series data of complex concentrations, the central control module utilizes recurrent neural network (RNN) technology, specifically long short-term memory (LSTM) networks. LSTMs, with their unique memory units, effectively capture the time series characteristics of concentration data and identify patterns in concentration changes over time. LSTM networks overcome the vanishing gradient problem common in traditional neural networks when processing time series data, enabling the model to better understand and predict complex temporal patterns. Finally, the central control module combines an attention mechanism with regression analysis to predict the dynamic trends of complex concentrations. The attention mechanism assigns different weights to key time points in the time series, allowing the model to focus more on concentration changes at critical moments, thereby improving prediction accuracy. Based on the time series features extracted by the LSTM network and the predicted concentration trends, the central control module dynamically adjusts catalytic parameters to ensure the efficiency and stability of the catalytic process, thereby optimizing the catalytic reaction.

[0067] It can be seen that CNN is able to extract important features from spectral signals and electrochemical data, particularly spectral absorption features (such as absorption peak position and absorbance intensity) and electrochemical response characteristics (such as current and potential change patterns). This technical approach enables more accurate identification of complex components and provides a reliable data foundation for subsequent catalytic analysis. Secondly, by reducing data dimensionality through the use of pooling layers, CNN reduces information redundancy and computational burden while extracting the most representative features. This not only improves model efficiency but also enhances the stability of feature extraction. Pooling effectively compresses the input data size, reduces computing resource consumption, and preserves key information, further enhancing the analytical capabilities of spectral and electrochemical features. Furthermore, the combination of CNN and pooling layers significantly enhances the performance of the central control module when processing complex data, especially in high-dimensional data compression and information extraction. When the spectral signal and electrochemical data features are integrated into the fully connected layer, the central control module is able to form a comprehensive complex feature vector that fully describes the chemical properties of the catalyzed product. The use of fully connected layers allows the integration of different types of features, resulting in more accurate and comprehensive compositional analysis results. In addition, a recurrent neural network based on a long short-term memory (LSTM) network can analyze time series data on complex concentrations and identify patterns in concentration changes. Combined with the attention mechanism, the LSTM can more accurately focus on key change points in the time series, improving the model's responsiveness to dynamic changes. This technology provides significant performance improvements when processing complex time-varying data, especially in identifying and predicting trends in concentration changes. Finally, by predicting future trends in complex concentrations through regression analysis, and combining real-time concentration data and catalytic stage information, the central control module can dynamically adjust catalytic parameters based on the predicted results. This prediction-based adjustment strategy not only optimizes the catalytic process, but also ensures the stability and efficiency of the catalytic reaction, thereby maximizing the treatment effect and catalytic efficiency.

[0068] Specifically, the central control module is also configured to determine the electrocatalytic parameters of the catalytic module based on the fuzzy control algorithm, combined with historical data, the complex composition of the catalyzed substance and the real-time catalytic liquid information, including: the central control module is also configured to fuzzy process the variables such as complex concentration, catalytic current, catalytic time based on fuzzy logic, and map each variable to a fuzzy set based on the membership function; the central control module is also configured to infer the catalytic parameters based on preset rules in combination with the fuzzy rule base, and determine the catalytic parameters of the complex composition based on fuzzy reasoning; the central control module is also configured to model the historical catalytic data based on time series analysis, and optimize the real-time parameter calculation in combination with Bayesian reasoning or Kalman filtering; the central control module is also configured to determine the electrocatalytic parameters of the catalytic module based on the real-time parameter calculation.

[0069] Specifically, when the central control module combines the fuzzy rule base to infer the catalytic parameters based on preset rules, and determines the catalytic parameters of the complex components based on fuzzy reasoning, it includes: the central control module is also configured to construct a mapping relationship between the complex components and the catalytic parameters based on historical data to form fuzzy rules for catalytic current, catalytic time, and pH value; the central control module is also configured to map the complex concentration variable, current density variable, and redox potential variable into fuzzy sets; the central control module is also configured to perform rule reasoning based on the Mamdani reasoning method to determine the fuzzy output of the catalytic current and catalytic time; the central control module is also configured to use the maximum membership method to convert the fuzzy reasoning results into specific values; the central control module is also configured to optimize the specific values ​​based on historical data and real-time catalytic liquid information and based on Bayesian reasoning, and use the optimized specific values ​​as execution parameters of the catalytic module.

[0070] It's understandable that by converting these variables into fuzzy sets and mapping them to specific fuzzy sets using membership functions, traditional control methods can address their shortcomings in handling uncertainty and ambiguity. Fuzzification enables the system to better handle complex variable relationships and incomplete input data, ensuring more flexible and precise adjustment of catalytic parameters. The central control module then infers the catalytic parameters by combining a fuzzy rule base with pre-set rules. During this process, historical data is used to construct a mapping between complex composition and catalytic parameters, generating fuzzy rules related to complex composition, such as catalytic current, catalytic time, and pH. The fuzzy rule base combines actual catalytic conditions with theoretical reasoning to enable intelligent adjustment of catalytic parameters, effectively improving the stability and efficiency of the catalytic process. Furthermore, the fuzzy inference system employs the Mamdani inference method to infer fuzzy rules and derive outputs such as catalytic current and catalytic time. This inference method generates fuzzy output values ​​by matching input fuzzy variables with fuzzy rules in the rule base. These fuzzy output values ​​accurately reflect the variables that require adjustment during the catalytic process, but still require subsequent processing to be converted into specific execution parameters. In order to convert the results of fuzzy reasoning into specific numerical values, the central control module adopts the maximum membership principle and selects the most appropriate output value according to the maximum membership principle. This method can ensure that the parameters that best meet the requirements of the actual catalytic process are selected from the multiple possible outputs obtained from fuzzy reasoning, thereby improving the accuracy of the catalytic process. Finally, by optimizing the calculation of real-time data and historical catalytic data through Bayesian reasoning or Kalman filtering, the central control module can dynamically adjust the electrocatalytic parameters of the catalytic module. Through Bayesian reasoning and Kalman filtering technology, the central control module can optimize the catalytic parameters based on real-time input data to ensure that the electrocatalytic process always maintains the best operating state under different conditions. This optimization calculation method can adaptively adjust the catalytic parameters, thereby improving the catalytic efficiency and system stability.

[0071] As can be seen, fuzzy processing of key variables such as complex concentration, catalytic current, and catalytic time based on a fuzzy control algorithm enables the system to handle multiple uncertainties and ambiguities. In actual production, the properties of catalytic fluids are often affected by multiple factors. Using fuzzy logic to convert these variables into fuzzy sets helps avoid the problem of traditional control methods failing to accurately reflect system changes, thereby ensuring that the system can make accurate decisions in real time. Secondly, inferring catalytic parameters using a fuzzy rule base facilitates intelligent adjustment of key parameters in the catalytic process. By establishing a mapping relationship between historical data and catalytic parameters, fuzzy rules can reflect the optimal catalytic current, catalytic time, and pH value for different complex compositions, providing greater adaptability and flexibility to the catalytic process. This process enables precise adjustments to different situations, improving catalytic efficiency and reaction speed. Thirdly, using the Mamdani inference method to infer parameters such as catalytic current and catalytic time helps the system comprehensively consider multiple factors and arrive at more appropriate control decisions when faced with complex catalytic reactions. The Mamdani inference method effectively considers multiple factors when dealing with problems involving uncertainty and ambiguity, improving the accuracy of catalytic reactions and ensuring their stability and efficiency. Furthermore, by converting the fuzzy reasoning results into specific numerical values ​​through the maximum membership method, it is possible to ensure that the control variables in the catalytic process obtain accurate numerical outputs. This method effectively solves the uncertainty problem in fuzzy reasoning, realizes the automation and precision of the catalytic process, avoids human errors, and ensures the efficient operation of the catalytic module. Finally, optimizing the calculation of real-time data based on Bayesian reasoning or Kalman filtering technology helps the central control module to more accurately adjust the electrocatalytic parameters of the catalytic module. Real-time optimization can cope with fluctuations and complex reactions of the components in the catalytic liquid, further improve the adaptability of the catalytic process, and ensure that the execution effect of the catalytic module always remains in the best state. In addition, the combination of historical data and real-time information also provides more comprehensive decision-making support for the central control module, making the catalytic process more efficient, stable and accurate.

[0072] Specifically, when the early warning module determines whether to issue an early warning based on the relationship between the catalytic liquid information and the electrocatalytic parameters, it includes: the early warning module is also configured to determine whether to issue an early warning based on the relationship between the execution parameters and the preset execution parameters configured by the catalytic module: when the execution parameters are lower than the preset execution parameters, the early warning module determines not to issue an early warning; when the execution parameters are higher than or equal to the preset execution parameters, the early warning module determines to issue an early warning, and determines the early warning level based on the relationship between the execution parameters and the preset execution parameters.

[0073] Specifically, when the early warning module determines the early warning level based on the relationship between the execution parameter and the preset execution parameter, it includes: the early warning module is also configured to obtain the parameter difference between the execution parameter and the preset execution parameter, and determine the early warning level according to the relationship between the parameter difference and the first preset parameter difference and the second preset parameter difference configured by the early warning module; when the parameter difference is lower than or equal to the first preset parameter difference, the early warning level is determined to be a low risk level; when the parameter difference is higher than the first preset parameter difference, and the parameter difference is lower than or equal to the second preset parameter difference, the early warning level is determined to be a medium risk level; when the parameter difference is higher than the second preset parameter difference, the early warning level is determined to be a high risk level; wherein, the first preset parameter difference is less than the second preset parameter difference, and the severity of the early warning levels is low risk level, medium risk level and high risk level, respectively.

[0074] As can be understood, the early warning module can detect in real time whether there are any anomalies in the catalytic process by monitoring the difference between the execution parameters and the preset execution parameters. When the execution parameters are lower than the preset execution parameters, the early warning module determines that no early warning is issued, indicating that the catalytic process is within the normal range and no further intervention is required. However, when the execution parameters are higher than or equal to the preset execution parameters, the early warning module issues an early warning, indicating that potential risks may arise in the catalytic process. Secondly, by analyzing the difference between the execution parameters and the preset execution parameters, the early warning module can determine different levels of risk and thus determine different early warning levels. This technical principle, by measuring the parameter difference, classifies potential risks into low, medium, and high risk levels, thereby effectively managing potential risks in a tiered manner. In this way, the system can take appropriate countermeasures based on the different risk levels to ensure the safety of the catalytic process. Finally, by setting a first preset parameter difference and a second preset parameter difference, the early warning module can further refine the risk assessment mechanism. The relationship between the parameter difference and these preset differences directly influences the determination of the early warning level, making the risk assessment process more accurate. For example, when the parameter difference is lower than or equal to the first preset difference, the system determines it as a low risk level, and when the difference exceeds this range, it enters a medium risk or high risk state. This hierarchical risk assessment method enhances the flexibility and accuracy of the early warning system, and helps to identify and deal with potential safety issues in a timely manner during the catalytic process.

[0075] In the above embodiment, by combining convolutional neural networks (CNN) and recurrent neural networks (RNN) to perform intelligent analysis of the complex components of the catalyst, the accuracy and intelligence level of the electrocatalytic device for breaking the complex of electroplating rinse wastewater are significantly improved. The central control module accurately identifies the complex components based on the deep learning algorithm, and combines the fuzzy control algorithm, historical data and real-time catalytic liquid information to dynamically optimize the electrocatalytic parameters (such as current density, catalytic time, etc.), realizing adaptive adjustment of the catalytic process, thereby greatly improving the catalytic efficiency and system stability. Through this intelligent control mechanism, the device can respond to changes in sewage composition in real time, avoiding the catalytic instability or inefficiency caused by lack of experience in traditional methods. In addition, through the introduction of the early warning module, abnormal conditions in the catalytic process (such as changes in catalytic liquid composition, catalytic parameters deviating from the preset range, etc.) can be monitored and issued in time to issue an early warning, avoiding the phenomenon of over-catalysis or catalytic failure, thereby ensuring the consistency and stability of the breaking effect. Through intelligent parameter optimization and real-time feedback control, the entire treatment process is not only more efficient and accurate, but also can reduce the need for human intervention, reducing operational risks and costs.

[0076] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware embodiments. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0078] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A titanium-based nano-conductive ceramic electrocatalytic device for decomposing electroplating rinsing wastewater, comprising an oxidation tank, characterized in that: include: A collection module is disposed inside the oxidation tank, and is configured to collect information about the catalytic liquid inside the oxidation tank; The catalytic module is disposed inside the oxidation tank and is configured to catalyze the catalytic liquid inside the oxidation tank; a central control module, electrically connected to the acquisition module and the catalytic module, respectively, the central control module being configured to analyze the complex composition of the catalyzed substance based on a convolutional neural network and a recurrent neural network, and further configured to determine the electrocatalytic parameters of the catalytic module based on a fuzzy control algorithm, combined with historical data, the complex composition of the catalyzed substance, and real-time catalytic liquid information; The early warning module is electrically connected to the catalytic module and the central control module respectively. The early warning module is configured to determine whether to issue an early warning based on the relationship between the catalytic liquid information and the electrocatalytic parameters.

2. The electrocatalytic device for decomposing electroplating rinsing wastewater based on titanium-based nano-conductive ceramics according to claim 1, characterized in that: The electrode material of the catalytic module is specifically titanium-based nano-conductive ceramic.

3. The electrocatalytic device for decomposing electroplating rinsing wastewater based on titanium-based nano-conductive ceramics according to claim 1, characterized in that: The acquisition module includes: a spectral sensor configured to monitor the sewage composition of the catalytic liquid in real time; an ultrasonic sensor configured to detect a flow rate of the catalytic liquid in real time; The electrochemical sensor is configured to detect the current density and pH value of the catalytic solution in real time.

4. The electrocatalytic device for decomposing electroplating rinsing wastewater based on titanium-based nano-conductive ceramics according to claim 3, characterized in that: When the central control module analyzes the complex composition of the catalyzed substance based on convolutional neural networks and recurrent neural networks, it includes: The central control module is also configured to perform feature extraction on the spectral signal and electrochemical data based on a convolutional neural network, and to extract the spectral absorption characteristics and electrochemical response characteristics of the complex based on the convolutional layer; The central control module is also configured to reduce the data dimension based on the pooling layer and extract the complex composition information of the catalyzed substance; The central control module is also configured to analyze the time series data of the complex concentration based on a recurrent neural network, and to determine the dynamic change trend of the complex components to be catalyzed based on a long short-term memory network.

5. The electrocatalytic device for decomposing electroplating rinsing wastewater based on titanium-based nano-conductive ceramics according to claim 4, characterized in that: The central control module extracts features from spectral signals and electrochemical data based on a convolutional neural network, and extracts the spectral absorption characteristics and electrochemical response characteristics of the complex based on the convolutional layer, including: The central control module is also configured to denoise the spectral signal based on a filtering algorithm and perform baseline correction on the electrochemical data; The central control module is also configured to scan the spectral signal based on the convolution layer to extract the spectral absorption characteristics, including the absorption peak position and absorbance intensity. The central control module is also configured to perform convolution operations on the electrochemical data based on the convolution layer to extract the change pattern of current and potential. The central control module is also configured to perform dimensionality reduction on the spectral and electrochemical features based on a pooling layer; The central control module is also configured to fuse spectral features and electrochemical features based on a fully connected layer to form a complete feature vector of the complex.

6. The electrocatalytic device for decomposing electroplating rinsing wastewater based on titanium-based nano-conductive ceramics according to claim 5, characterized in that: The central control module analyzes the time series data of the complex concentration based on the recurrent neural network and determines the dynamic change trend of the complex components to be catalyzed based on the long short-term memory network, including: The central control module is also configured to obtain real-time time series data of complex concentration and obtain concentration changes at different catalytic stages; The central control module is also configured to perform denoising and normalization on the concentration data based on a data preprocessing algorithm; The central control module is also configured to extract time series features of concentration data based on an LSTM network, identify patterns of concentration changes over time, and use an attention mechanism to increase the weight of concentration changes at key time points. The central control module is also configured to predict the future change trend of the complex concentration based on regression analysis, and dynamically determine the catalytic parameters based on the prediction results.

7. The electrocatalytic device for decomposing electroplating rinsing wastewater based on titanium-based nano-conductive ceramics according to claim 6, characterized in that: The central control module is further configured to determine the electrocatalytic parameters of the catalytic module based on a fuzzy control algorithm, combined with historical data, the complex composition of the catalytic substance to be catalyzed, and real-time catalytic liquid information, including: The central control module is also configured to perform fuzzy processing on variables such as complex concentration, catalytic current, and catalytic time based on fuzzy logic, and map each variable to a fuzzy set based on a membership function; The central control module is further configured to reason about the catalytic parameters based on preset rules in conjunction with the fuzzy rule base, and determine the catalytic parameters of the complex components based on the fuzzy reasoning; The central control module is also configured to model historical catalytic data based on time series analysis and optimize real-time parameter calculations in conjunction with Bayesian reasoning or Kalman filtering; The central control module is further configured to determine the electrocatalytic parameters of the catalytic module based on real-time parameter calculations.

8. The electrocatalytic device for decomposing electroplating rinsing wastewater based on titanium-based nano-conductive ceramics according to claim 7, characterized in that: The central control module combines the fuzzy rule base to reason about the catalytic parameters based on preset rules, and determines the catalytic parameters of the complex components based on fuzzy reasoning, including: The central control module is also configured to construct a mapping relationship between complex components and catalytic parameters based on historical data to form fuzzy rules for catalytic current, catalytic time, and pH value; The central control module is also configured to map the complex concentration variable, the current density variable, and the redox potential variable into fuzzy sets; The central control module is also configured to perform rule reasoning based on the Mamdani reasoning method to determine the fuzzy output of catalytic current and catalytic time; The central control module is also configured to convert the fuzzy inference results into specific numerical values ​​using the maximum membership method; The central control module is also configured to optimize specific values ​​based on historical data and real-time catalyst liquid information and based on Bayesian reasoning, and use the optimized specific values ​​as execution parameters of the catalytic module.

9. The electrocatalytic device for decomposing electroplating rinsing wastewater based on titanium-based nano-conductive ceramics according to claim 8, characterized in that: The early warning module determines whether to issue an early warning based on the relationship between the catalyst liquid information and the electrocatalytic parameters, including: The early warning module is further configured to determine whether to issue an early warning based on the relationship between the execution parameter and the preset execution parameter configured by the catalysis module: When the execution parameter is lower than the preset execution parameter, the warning module determines not to issue a warning; When the execution parameter is higher than or equal to the preset execution parameter, the warning module determines to issue a warning and determines the warning level based on the relationship between the execution parameter and the preset execution parameter.

10. The electrocatalytic device for decomposing electroplating rinsing wastewater based on titanium-based nano-conductive ceramics according to claim 9, characterized in that: The warning module determines the warning level based on the relationship between the execution parameters and the preset execution parameters, including: The early warning module is further configured to obtain a parameter difference between the execution parameter and a preset execution parameter, and determine an early warning level according to a relationship between the parameter difference and a first preset parameter difference and a second preset parameter difference configured by the early warning module; When the parameter difference is lower than or equal to the first preset parameter difference, the warning level is determined to be a low risk level; When the parameter difference is higher than the first preset parameter difference and the parameter difference is lower than or equal to the second preset parameter difference, the warning level is determined to be a medium risk level; When the parameter difference is higher than the second preset parameter difference, the warning level is determined to be a high risk level; Among them, the first preset parameter difference is less than the second preset parameter difference, and the severity of the warning levels is low risk level, medium risk level and high risk level, respectively.

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