Electrochemical water treatment equipment cluster monitoring method and system based on cloud collaboration

Abstract

The invention belongs to the technical field of electrochemical water treatment, and provides an electrochemical water treatment equipment cluster monitoring method and system based on cloud collaboration, and the method comprises the steps of data collection and preprocessing, low-delay high-credibility data transmission, intelligent fault diagnosis, dynamic energy efficiency optimization and collaborative scheduling, and whole-process monitoring visualization. According to the method, through dynamic priority scheduling, multi-link backup and full-link encryption transmission mechanisms, low-delay and high-reliability transmission of key data is realized on the premise of ensuring data security. Especially in emergency scenes such as sudden water quality change, through 5G slicing and differential compression technologies, real-time performance and bandwidth cost are effectively balanced, millisecond response of a control instruction is ensured, through federal transfer learning and digital twinning scene adaptation, on the premise that privacy data of each water plant is not shared, diagnosis capability of small sample faults is improved, and fault diagnosis efficiency is improved. And rapid parameter optimization in an extreme water quality scene is realized.

Description

Technical Field

[0001] This application relates to the field of electrochemical water treatment technology, and in particular to a cloud-based collaborative method and system for monitoring clusters of electrochemical water treatment equipment. Background Technology

[0002] Electrochemical water treatment equipment clusters are multi-equipment collaborative systems that purify water through electrode reactions. With the increasing environmental protection requirements and the expansion of treatment scale, equipment clusters need to achieve cross-regional, real-time status monitoring, while balancing energy consumption, cost and water quality compliance rate. Therefore, cloud-based collaborative monitoring technology has become a key requirement.

[0003] In existing monitoring technologies for electrochemical water treatment equipment, the mode of local acquisition of electrochemical water treatment data plus manual inspection is mostly adopted: current, voltage and other electrical signals are collected through a single sensor, and maintenance personnel are relied on to conduct regular on-site troubleshooting; data transmission mostly uses a single wired link; fault diagnosis is based on simple threshold judgment; energy efficiency control uses fixed operating parameters, such as constant current, voltage, temperature and reagent dosage.

[0004] These existing technologies still have some shortcomings in practical applications: fixed parameter control cannot adapt to dynamically changing water quality, especially in extreme scenarios where parameters such as influent chemical oxygen demand (COD), salinity, and temperature fluctuate drastically. Because the control parameters are not linked to the influent water quality, the system often adopts overly conservative, excessively high parameters to avoid substandard treatment, resulting in high energy consumption; or, due to insufficient parameters, the effluent water quality exceeds the standards. For example, when the influent COD rises sharply, the fixed current cannot provide sufficient electrochemical oxidation intensity, resulting in incomplete removal of pollutants; while when the influent COD is low, the fixed current results in a waste of electrical energy. Summary of the Invention

[0005] The purpose of this application is to propose a cloud-based collaborative monitoring method and system for electrochemical water treatment equipment clusters, in order to solve the technical problem that it is impossible to simultaneously ensure optimal treatment effect and energy consumption in dynamic water quality scenarios due to fixed operating parameters.

[0006] To address the aforementioned technical problems, this application provides a cloud-based collaborative method for monitoring a cluster of electrochemical water treatment equipment, employing the following technical solution: The cloud-based collaborative monitoring method for electrochemical water treatment equipment clusters includes the following steps: Multimodal acquisition, cleaning, standardization, and feature extraction of operational data from the electrochemical water treatment equipment cluster to obtain a multimodal feature set; a triple mechanism of dynamic scheduling, multi-link backup, and full-link encryption to securely transmit the multimodal feature set and raw equipment operational data from the edge to the cloud to obtain cloud data; based on the multimodal feature set, a multimodal fusion model, federated transfer learning, and fault root cause visualization technology to obtain equipment fault diagnosis results and fault root cause information; based on the fault diagnosis results and real-time water quality data, a multi-objective optimization model, digital twin extreme scenario adaptation, and cluster collaborative scheduling strategy to obtain optimal equipment operating parameters and cluster load scheduling schemes, and to distribute the optimal operating parameters to the equipment; and the construction of a cloud-based digital twin dashboard to integrate transmitted status data, fault diagnosis results, and energy efficiency optimization parameters, displaying the entire process information in real time, and verifying the achievement of monitoring goals of low latency, high reliability, high accuracy, and high energy saving.

[0007] Preferably, multimodal data acquisition includes: synchronously acquiring time-series electrical signals, high-definition image data of electrode surfaces, acoustic data of device operation, and electrochemical impedance data; aggregating all data through an edge gateway and attaching timestamps and device IDs to obtain the original multimodal dataset; cleaning and standardization include: using the 3σ principle to remove outliers from time-series electrical signals and impedance data; standardizing time-series data; normalizing image data by pixels; and normalizing acoustic data by amplitude to obtain a standardized dataset; feature extraction includes: extracting time-series features, image features, acoustic features, and impedance features from the standardized data; and performing dimensionality reduction through principal component analysis after concatenation to obtain a multimodal feature set.

[0008] Preferably, dynamic scheduling includes: constructing a mapping model based on real-time collected water quality parameters; classifying data transmission priorities into three levels—emergency, stable, and normal—according to water quality status; allocating dedicated 5G slice channels with high sampling frequency and low latency requirements for emergency-level data; and using differential compression algorithms to reduce bandwidth consumption for stable and normal-level data. Multi-link backup includes: constructing a redundant architecture of a 5G primary link and a LoRaWAN backup link; real-time monitoring of 5G signal strength at the edge; and automatically switching to the LoRaWAN link when the signal strength is below a threshold, transmitting only critical control commands. End-to-end encryption includes: employing a three-level encryption system: device-edge-cloud; encrypting the original data at the device through a trusted execution environment; using a national cryptographic block cipher algorithm for secondary encryption at the edge; and storing data on a private blockchain in the cloud and generating hash digests to ensure data immutability and traceability.

[0009] Preferably, the multimodal fusion model includes: performing single-modal encoding on the temporal, image, acoustic, and impedance features in the multimodal feature set, calculating and fusing the weights of each modal feature using a cross-modal attention mechanism, and inputting the fully connected layer and activation layer to obtain the probability distribution of fault types.

[0010] Preferably, federated transfer learning includes: initializing a multimodal fusion model in the cloud and distributing it to the edge nodes of each water plant; each node fine-tunes the model using local small-sample fault data and uploads the model gradient to the cloud; the cloud uses a federated averaging algorithm to aggregate gradients and update the global model; and combines transfer learning to reuse pre-trained weights from large-sample faults to improve the ability to diagnose rare faults. Root cause visualization includes: using a random forest algorithm to calculate the weights of fault influencing factors, generating a heatmap to show the relationship between faults and influencing factors, and labeling specific weight values ​​to assist maintenance personnel in locating the root cause.

[0011] Preferably, the multi-objective optimization model includes: taking the minimization of equipment energy consumption, the maximization of pollutant removal rate, and the minimization of operation and maintenance costs as optimization objectives, and under the constraints of effluent water quality meeting standards and equipment parameter safety, using a multi-objective optimization algorithm to solve the Pareto optimal solution set, and selecting a compromise solution as the optimal operating parameters of the equipment according to the actual needs of the water plant.

[0012] Preferably, the digital twin extreme scenario adaptation includes: constructing a database of extreme water quality scenarios such as high salinity, low temperature, and high chemical oxygen demand; pre-training reinforcement learning models corresponding to each scenario; collecting water quality data in real time to construct feature vectors; matching the scenario database and calling the corresponding pre-trained model to output the operating parameters adapted to the extreme scenarios; and cluster collaborative scheduling includes: freezing devices with a failure probability ≥ a threshold through blockchain smart contracts; calculating the processing load that the faulty devices need to share; allocating the load to surrounding normal devices with low load rates; and synchronously adjusting the operating parameters of the sharing devices.

[0013] Preferably, the cloud-based digital twin cockpit includes four modules: transmission monitoring, security monitoring, fault early warning, and energy efficiency optimization. The monitoring target verification includes statistical analysis of emergency-level data latency, data immutability rate, accuracy of complex fault diagnosis, and energy consumption reduction rate in extreme scenarios. The cockpit interface and functions are iteratively optimized based on feedback from maintenance personnel.

[0014] To address the aforementioned technical issues, this application also provides a cloud-based collaborative electrochemical water treatment equipment cluster monitoring system, comprising: a data acquisition and preprocessing module, which acquires multimodal operating data of the equipment and performs feature extraction and fusion to generate a multimodal feature set; a data transmission module, which dynamically schedules data transmission priorities based on water quality information and reliably transmits the multimodal feature set to the cloud through redundant links and encryption mechanisms; an intelligent diagnosis module, which performs fault diagnosis and root cause analysis based on the multimodal feature set through cross-modal fusion and federated learning; an optimization scheduling module, which generates equipment optimization parameters based on the diagnosis results and real-time water quality through multi-objective optimization and digital twin technology, and triggers cluster collaborative scheduling in case of faults; and a visualization module, which displays the system's entire process status in real time through a digital twin cockpit and provides a human-machine interaction interface.

[0015] Preferably, the multimodal data acquired by the data acquisition and preprocessing module includes at least time-series electrical signals, electrode images, operational acoustic signals, and electrochemical impedance data; the dynamic scheduling of the data transmission module is based on the influent chemical oxygen demand change rate and pH value, and uses 5G and LoRaWAN to form a primary and backup link, and uses blockchain for data storage; the cross-modal fusion of the intelligent diagnosis module adopts an attention mechanism, and its federated learning is used to jointly improve the fault diagnosis capability of small samples by combining multiple edge nodes; the multi-objective optimization of the optimization scheduling module is aimed at energy consumption, removal rate, and cost, and its digital twin technology has a pre-built adaptation model for extreme water quality scenarios; the digital twin cockpit of the visualization module integrates transmission, security, fault, and energy efficiency monitoring sub-interfaces.

[0016] The beneficial effects of this invention are as follows: This application provides a cloud-based collaborative monitoring method for electrochemical water treatment equipment clusters. It sets up a dynamic optimization closed loop integrating perception, decision-making, and execution, which solves the contradiction between high energy consumption and poor performance of fixed parameter control in dynamic water quality scenarios. By collecting water quality and equipment data in real time, it solves the multi-objective Pareto optimal solution online and dynamically distributes the parameters to the equipment, realizing a leap from fixed preset to adaptive optimization in operation control. While ensuring that water quality meets standards, it significantly reduces system energy consumption and operation and maintenance costs.

[0017] This method constructs an intelligent transmission mechanism that links water quality and network, providing a low-latency and highly reliable data foundation for real-time optimization. By dynamically binding water quality mutation events with 5G network slice resources and equipping key commands with multi-link redundancy, it ensures millisecond-level reliable transmission of control commands in emergency scenarios, overcoming the bottleneck of traditional systems that cannot achieve real-time closed-loop optimization due to data delays or interruptions.

[0018] This method, through federated transfer learning and digital twin scenario adaptation, enhances the diagnostic capability for small-sample faults without sharing private data from various water plants, and achieves rapid parameter optimization under extreme water quality scenarios, solving the coexistence problem of data silos and sample scarcity in fault diagnosis. Combined with smart contracts and collaborative scheduling mechanisms, it ensures the continuous and stable operation of the equipment cluster under fault conditions, thereby improving the overall reliability, economy, and environmental compliance of the water treatment system. Attached Figure Description

[0019] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of Example 1; Figure 2 This is the system architecture diagram of Example 2. Detailed Implementation

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0024] Example 1 like Figure 1 As shown, the cloud-based collaborative electrochemical water treatment equipment cluster monitoring method includes steps 1-5.

[0025] Step 1: Data Acquisition and Preprocessing: Multimodal acquisition, cleaning and standardization, and feature extraction are performed on the operation data of the electrochemical water treatment equipment cluster to obtain a multimodal feature set.

[0026] Furthermore, multimodal data acquisition includes: synchronously acquiring time-series electrical signals, high-resolution image data of electrode surfaces, acoustic data of equipment operation, and electrochemical impedance data; aggregating all data through an edge gateway and attaching timestamps and device IDs to obtain the original multimodal dataset; cleaning and standardization include: using the 3σ principle to remove outliers from time-series electrical signals and impedance data; standardizing time-series data; pixel normalizing image data; and amplitude normalizing acoustic data to obtain a standardized dataset; feature extraction includes: extracting time-series features, image features, acoustic features, and impedance features from the standardized data; and performing dimensionality reduction through principal component analysis after concatenation to obtain a multimodal feature set.

[0027] Step 1.1: Multimodal data acquisition: Synchronously acquire time-series electrical signals, image data, acoustic data and electrochemical impedance data of the equipment to comprehensively reflect the equipment's operating status and water quality changes.

[0028] Timing electrical signals: Hall current sensors and high-precision voltage sensors are used to collect current and voltage to obtain electrolytic current I (unit: A), cell voltage U (unit: V), and power P. power =U×I, unit kW. The default sampling frequency is 1 time / second. When water quality changes abruptly, such as a sudden increase in COD ≥30%, it automatically increases to 10 times / second to ensure the capture of critical dynamic data. Image data: Images of the electrode surface are captured by the device's built-in high-definition camera, recording visual features such as electrode passivation and membrane fouling. The sampling frequency is 1 time / 5 minutes, balancing data timeliness and storage costs. The camera resolution is 1920×1080. Acoustic data: Acoustic sensors are used to collect device operating noise, with a frequency range of 20-20000Hz and a sampling rate of 44.1kHz. This can capture characteristic frequencies generated by abnormal vibrations. The sampling frequency is 1 time / minute. Electrochemical impedance data: The electrode interface impedance Z, unit Ω, is measured using an impedance analyzer, including both real and imaginary parts, reflecting the electrode's reactivity. The sampling frequency is 1 time / 10 minutes to avoid interference from high-frequency measurements on the electrode reaction.

[0029] Data aggregation is achieved using the EG-500 edge gateway, which supports multi-protocol access and enables real-time data aggregation and preliminary processing. After collection, the raw multimodal dataset is obtained, and all data is accompanied by timestamps and device IDs to ensure traceability.

[0030] Step 1.2: Data cleaning and standardization: Remove outliers and eliminate the influence of units.

[0031] Outlier Removal: For continuous data such as time-series electrical signals and impedance data, outliers deviating more than three times the standard deviation from the mean are removed based on the 3σ principle, resulting in a time-series dataset without outliers. Data Standardization: Time-series data are standardized using Z-Score, converting time-series data of different dimensions, such as current, voltage, and impedance, into standard normal distribution data with a mean of 0 and a standard deviation of 1, eliminating dimensional differences. Image data undergoes pixel normalization, mapping image pixel values ​​from 0 to 255 to the [0,1] interval, reducing the impact of numerical range on model weights. Acoustic data undergoes amplitude normalization, mapping the amplitude values ​​of acoustic signals to the [0,1] interval, eliminating the influence of noise intensity differences between different devices. The processed dataset generates a standardized dataset, with all data uniformly distributed within the [0,1] or standard normal distribution range, meeting the model input requirements.

[0032] Step 1.3: Feature Extraction: Extract physically meaningful or discriminative features from standardized data, transforming the raw data into features that the model can understand, providing core input for fault diagnosis and optimization.

[0033] Step 1.3.1: Extract time-series features from electrical signal and impedance data.

[0034] Temporal features: Extracting temporal features F directly from standardized temporal data. time F time Including mean μ and variance σ 2 Peak value (max(x)), valley value (min(x)), and peak value - peak value (max(x) - min(x)) reflect the overall trend and fluctuation range of the data. For example, the peak current can reflect the intensity of the electrode reaction. Frequency domain characteristics: The time domain data is converted to the frequency domain through Fast Fourier Transform (FFT) to capture hidden frequency information, such as the characteristic frequency corresponding to abnormal vibration.

[0035] Formula 1: FFT(x) = ∑ k=0 n-1 x k ·e -j2pifk / n , where: x k For standardized time-series data, such as current values, from D std n is the number of FFT points, taken as an integer power of 2, such as 1024, set according to the data length to ensure transformation accuracy; f is the frequency, in Hz; j is the imaginary unit, j 2 =-1, used to represent phase information in the frequency domain. By extracting key indicators such as dominant frequency, spectral entropy, and frequency band energy proportion from the FFT results, the high-frequency original frequency domain data is reduced to a fixed-dimensional feature vector, resulting in the frequency domain feature F. freq The time-domain features F are obtained after processing. time Frequency domain characteristics F freq Each dimension is 256.

[0036] Step 1.3.2: Extract image features from the corresponding electrode surface image.

[0037] Using the normalized image as input, a ResNet-50 convolutional neural network (CNN) is used to extract a 2048-dimensional feature vector, which is then reduced to 256 dimensions to obtain the image feature vector F. img F img It contains information such as electrode surface texture, passivation layer thickness, color, and corrosion marks, which can be directly used to identify fault types.

[0038] Step 1.3.3: Extract acoustic features for equipment operating noise.

[0039] The normalized acoustic signal was framed with a frame length of 20ms and a frame shift of 10ms to avoid information loss between frames. A Mel-scale transform was performed on the signal spectrum using 40 triangular filter banks to simulate the differences in human ear sensitivity to different frequencies, yielding the Mel-filtered energy Mel(x). A Directed Transform (DCT) was performed on log(Mel(x)) to remove feature redundancy and retain the main frequency information. The first 13 dimensions of the DCT result were taken and expanded to 256 dimensions using zero-padding to obtain the acoustic feature vector F. sound It can reflect abnormal vibrations, such as pump failures, and the frequency structure.

[0040] Step 1.3.4: For electrochemical impedance data, perform impedance feature extraction. Perform equivalent circuit fitting on the Nyquist plot of the real vs. imaginary parts of the impedance, such as a Randle circuit, to extract the charge transfer resistance R. ct Double-layer capacitance C dl Equal parameters are used to construct a 256-dimensional impedance eigenvector F. Z This reflects the reactivity of the electrode interface, such as R. ct An increase may indicate electrode passivation.

[0041] Step 1.3.5: Feature Set Integration: Integrate the temporal features F time Frequency domain characteristics F freq Image features F img Acoustic characteristics F sound Impedance characteristics F Z The data is concatenated and then reduced to 1024 dimensions using Principal Component Analysis (PCA) to generate a multimodal feature set F={F... time ,F freq ,F img ,F sound ,F Z} is used as input for subsequent fault diagnosis models.

[0042] Step 2: Low-latency and highly reliable data transmission: A triple mechanism of dynamic scheduling, multi-link backup and full-link encryption is adopted to securely transmit multimodal feature sets and raw device operation data from the edge to the cloud, so as to obtain cloud data that meets the requirements of real-time performance and reliability.

[0043] Furthermore, dynamic scheduling includes: constructing a mapping model based on real-time collected water quality parameters; classifying data transmission priorities into three levels—emergency, stable, and normal—according to water quality status; allocating dedicated 5G slice channels with high sampling frequency and low latency requirements for emergency-level data; and employing differential compression algorithms to reduce bandwidth consumption for stable and normal-level data. Multi-link backup includes: constructing a redundant architecture of a 5G primary link and a LoRaWAN backup link; real-time monitoring of 5G signal strength at the edge; and automatically switching to the LoRaWAN link when the signal strength falls below a threshold, transmitting only critical control commands. End-to-end encryption includes: employing a three-level encryption system from the device end to the edge end and then to the cloud. The device end encrypts the original data using a trusted execution environment; the edge end uses a national cryptographic block cipher algorithm for secondary encryption; and the cloud stores the data on a private blockchain and generates hash digests to ensure data immutability and traceability.

[0044] Step 2.1: Dynamic priority scheduling: Dynamically adjust the data transmission priority and sampling frequency according to water quality fluctuations to achieve priority transmission of emergency data and compressed transmission of stable data, balancing real-time performance and bandwidth cost.

[0045] Step 2.1.1: Water Quality-Data Priority Mapping Model. Input parameters: Chemical Oxygen Demand (COD), mg / L; pH value; Turbidity, NTU. These parameters are collected in real time by a water quality sensor at a sampling frequency of 1 time / minute, directly reflecting the degree and stability of water pollution.

[0046] Priority Classification: Water quality status is divided into 3 levels, with corresponding data transmission strategies as follows: Emergency Level: Tag 1; Triggering conditions: COD surge ≥30%, pH <6 or pH >9; Data type transmitted: Electrolysis current, pollutant concentration, impedance; Sampling frequency: 10 times / second; Delay requirement: <10ms; Stable Level: Tag 2; Triggering conditions: COD fluctuation 5%-30%, pH 6-9; Data type transmitted: Routine operating data, such as voltage, power; Sampling frequency: 1 time / minute; Delay requirement: <100ms; Normal Level: Tag 3; Triggering conditions: COD fluctuation <5%, pH within 6-9 with fluctuation amplitude ≤0.5 pH units / minute; Data type transmitted: 10-minute statistical average, such as energy consumption, removal rate; Sampling frequency: 1 time / 10 minutes; Delay requirement: <500ms.

[0047] A logistic regression model is used, taking the COD change rate ΔCOD% and pH value as inputs. After calculating the probability P(y=1) of water quality abrupt changes, priority labels are output. Triggering conditions are used to generate priority labels for historical data. The logistic regression model is then trained based on these labels to predict priority probabilities in real time. Formula 2: ΔCOD% = ((COD%) / (pH value) t -COD t-1 COD t-1 )×100%, where: △COD% is the rate of change of COD, i.e., the fluctuation range of COD; COD t This represents the COD value at the current moment; COD t-1 This represents the COD value from the previous time step. Model optimization was performed using the Scikit-learn library, employing the cross-entropy loss function, with an Adam optimizer learning rate of 0.001, 100 iterations, and a validation set accuracy ≥98%, ensuring accurate priority determination.

[0048] Formula 3: P(y=1)=1 / (1+e -(w1 · △COD%+w2 · pH+b) The formula is as follows: P(y=1) is the probability of a sudden change in water quality, where y=1 indicates the change has occurred and y=0 indicates it has not occurred; w1=0.8 is the weight of the COD change rate on the probability of the sudden change; w2=-0.5 is the weight of the pH value on the probability of the sudden change; pH is the acidity or alkalinity of the water at the current moment; b=1.2 is the bias term. w1, w2, and b are obtained through optimization using historical data. The training dataset consists of historical water quality data and priority-labeled data, manually prioritized and divided into training and validation sets in a 7:3 ratio. If P(y=1)>0.5, it is classified as an emergency level; if 0.2≤P(y=1)≤0.5, it is classified as a stable level; if P(y=1)<0.2, it is classified as a normal level.

[0049] Step 2.1.2: 5G Slice Dedicated Channel Allocation: 5G slicing divides the physical network into multiple logically independent virtual networks through a software-defined network controller, allocating dedicated bandwidth and resources to data of different priorities to avoid data transmission contention. Edge nodes calculate ΔCOD% and pH values ​​in real time and input them into a mapping model to obtain priorities. If it is an emergency level, an automatic slice resource request is initiated to the 5G core network, carrying the device ID and data type. The core network allocates a dedicated slice ID and reserves ≥100Mbps bandwidth. Data is transmitted through dedicated slices. The edge end monitors the transmission latency every 100ms. If the latency is >10ms, bandwidth expansion is triggered, increasing the bandwidth by 20% to ensure real-time performance. After processing, the data transmission latency for emergency levels is stably <10ms, meeting millisecond-level control requirements.

[0050] Step 2.1.3: Differential Compression Algorithm: The data changes gradually in the stable and normal stages. Only the difference between the current data and the previous cycle's data is transmitted, not the complete data, reducing redundant transmission. For example, if the current changes from 50A to 50.2A, only 0.2A is transmitted. Let the data in cycle t be x. t The data for the (t-1)th period is x. t-1 If the data is cached at the edge, it will be compressed before transmission △x t , Formula 4: △x t =x t -x t-1 After receiving the data in the cloud, it is processed via x. t =x t-1 +△x t Restore the original data; if |△x t |>Threshold θ compress This indicates that the data fluctuation is too large, so △x will not be transmitted. t Transmit the original value x t To avoid error accumulation, θ compress Standard deviations based on historical data, such as current θ compress =0.5A. Example: Current x in period t-1. t-1 =50A, period t x t =50.2A, Δx t =0.2A, the original data needs to be transmitted 4 bytes, while the difference only needs 2 bytes, reducing bandwidth usage by 60%.

[0051] Step 2.2: Multi-link backup and breakpoint resumption: For extreme weather scenarios, such as 5G signal interruption caused by heavy rain, a redundant transmission architecture of 5G main link + LoRaWAN backup link is constructed. Combined with edge caching and breakpoint resumption, data is ensured to be uninterrupted and without loss.

[0052] Step 2.2.1: Deploy LoRaWAN backup link.

[0053] LoRaWAN consumes only 1 / 100th the power of 5G, making it suitable for low-speed transmission of critical commands such as emergency shutdowns, compensating for 5G's coverage shortcomings in remote or harsh environments. The edge device monitors the 5G signal strength RSSI in real time, sampling once every 100ms. If RSSI < -100dBm, a signal interruption is identified, and the system automatically switches to the LoRaWAN link within 50ms, transmitting only critical control commands, such as emergency shutdowns and parameter freezes, with data sizes < 100 bytes, avoiding non-critical data occupying backup link resources. When the 5G signal recovers and RSSI ≥ -90dBm, the system automatically switches back to the main link, resuming normal data transmission. Based on a communication engineering redundancy system model, the system transmission reliability R is calculated. sys Formula 5: R sys =1-[(1-R 5G )×(1-RLoRa )], where: R 5G For 5G link reliability; R LoRa For LoRaWAN link reliability; R sys Verify whether the dual-link architecture meets the engineering requirement of 100% delivery of critical instructions, with target R. sys ≥99.99, if R sys If the requirements are not met, the link switching logic needs to be optimized or a backup link needs to be added.

[0054] Example, R 5G Measured value 99.9%, R LoRa Measured value 99.0%, R sys =1-[(1-0.999)×(1-0.990)]=99.99%, ensuring 100% delivery of critical commands. When the 5G signal is interrupted, it automatically switches to the LoRaWAN link within 50ms to avoid data loss due to a single link interruption, thus ensuring R... sys Improved from 99.9% to 99.99% for a single link.

[0055] Step 2.2.2: Edge data caching and breakpoint resume.

[0056] The edge gateway is configured with a 16GB local cache, such as an industrial-grade SD card, using a timestamp + device ID naming convention to store high-frequency data during network interruptions, such as millisecond-level current and voltage data, to prevent data loss. The breakpoint resumption protocol is based on a TCP-based breakpoint resumption mechanism, with the following process: After network recovery, the edge device sends a resumption request to the cloud, including the last timestamp T of the transmitted data. last Match the device ID. Query the cloud database to confirm T. last The previous data has been received. A confirmation instruction is sent, along with a data checksum, such as MD5. The edge device receives the data from T... last Starting at time +1, buffered data is retransmitted in the original priority order, with a checksum performed every 100MB to ensure data integrity. After processing, data transmission reliability is improved to 99.99%, with no data loss under extreme weather conditions and uninterrupted transmission of critical instructions.

[0057] Step 2.3: End-to-end encryption and trusted traceability: Construct a three-level encryption system from the device end to the edge end to the cloud end, and combine it with blockchain evidence storage and environmental compliance verification to achieve tamper-proof and traceable data generation, transmission and storage throughout the entire process, thus solving the problems of data trustworthiness and compliance.

[0058] Step 2.3.1: End-edge-cloud three-level encryption.

[0059] Device-side encryption: Raw data collected by the device, such as current I, is encrypted using the AES-256 algorithm within a TEE (Trusted Execution Environment) to generate ciphertext C1. The TEE is a hardware-isolated area within the device chip, allowing only authorized programs to run, preventing the raw data from being tampered with or stolen at the device end. Formula 6: C1 = AES-256 Encrypt (P,K1), where: P is the plaintext data, i.e., D raw The data in the TEE; K1 is the unique key for the device, which is generated by the hardware security module HSM when the device leaves the factory and stored in the TEE key slot. It cannot be exported, ensuring that each device key is unique and reducing the risk of mass leakage.

[0060] Edge encryption: After receiving the ciphertext C1 from the device at the edge, the device obtains a dynamic key K2 from the cloud via a secure channel, such as TLS 1.3. This key is updated every 24 hours. C1 is then re-encrypted using SM4 (China's national standard block cipher algorithm) to generate the transmission ciphertext C2. SM4 has a 128-bit block length and a 128-bit key length, providing encryption strength comparable to AES and complying with domestic information security regulations. Formula 7: C2 = SM4 Encrypt (C1, K2). Double encryption prevents end-to-end risks caused by device-side key leakage, and dynamic updates of K2 further enhance security.

[0061] Cloud storage encryption: A private blockchain is built based on Hyperledger Fabric, allowing only authorized nodes to access it. Nodes confirm data through the PBFT consensus mechanism to ensure that the data on the chain is immutable. Authorized nodes include equipment manufacturers, environmental regulatory departments, and water plant operators. After receiving C2, the cloud decrypts it using K2 to obtain C1. The device certificate contains a unique device identifier. The cloud uses this identifier to retrieve the corresponding device-side key K1 from the key management system of the Hardware Security Module (HSM), and finally uses K1 to decrypt C1 to obtain the original data. For control commands, such as parameter adjustment commands and fault records, a hash digest H is generated using the SHA-256 hash algorithm. The original data, hash digest, and node signature are stored on the chain together. Each block contains the hash value of the previous block, forming a chain structure to further prevent tampering. Formula 8: H = SHA-256(command + timestamp + device ID). The hash digest is irreversible; if the data is tampered with, the digest will change significantly, allowing for rapid verification of data integrity.

[0062] Step 2.3.2: Environmental regulatory rules engine and traceability report generation.

[0063] Rule engine construction: Built-in Class A standard of "Discharge Standard of Pollutants for Municipal Wastewater Treatment Plants" (GB18918-2002), the rule storage format is as follows: If the effluent COD > 50 mg / L or ammonia nitrogen (NH4+) aIf the concentration of -N) is greater than 5 mg / L, an over-limit warning is triggered; if the electrode current fluctuation is greater than 20% and the impedance increases by greater than 30%, a fault warning is triggered.

[0064] Compliance verification and traceability process: Before the data is uploaded to the blockchain, the rule engine automatically extracts indicators such as COD, NH3-N, and equipment operating parameters of the effluent, compares them with standard thresholds, and calculates compliance indicators.

[0065] Formula 9:

[0066] If Compliance=0, indicating a violation, the source tracing process is automatically triggered: extracting the period of exceeding the standard, the ID of the equipment involved, operation records, and hash digests; generating a PDF source tracing report, including a trend chart of the exceeding standard data, equipment operation logs, and information on responsible personnel, and simultaneously pushing it to the municipal environmental protection supervision platform, such as the monitoring system of the Ecological and Environmental Protection Bureau, via the blockchain API. Maintenance personnel can query the blockchain data using the equipment ID and time range to verify the authenticity and completeness of the exceeding standard data, achieving a closed loop of violation-source tracing-accountability. After the above processing, the end-to-end encryption rate is 100%, the real-time compliance verification is ≥99%, the source tracing time for exceeding the standard data is <1 minute, and the data credibility and compliance meet regulatory requirements.

[0067] Step 3: Intelligent Fault Diagnosis: Based on the multimodal feature set in cloud data, a multimodal fusion model, federated transfer learning, and fault root cause visualization technology are used to obtain equipment fault diagnosis results and fault root cause information. These diagnosis results and root cause information are used for subsequent dynamic energy efficiency optimization and collaborative scheduling.

[0068] Furthermore, the multimodal fusion model includes: performing single-modal encoding on the temporal, image, acoustic, and impedance features in the multimodal feature set; calculating and fusing the weights of each modal feature using a cross-modal attention mechanism; inputting the weights into the fully connected layer and activation layer to obtain the probability distribution of fault types, enabling the diagnosis of faults such as electrode passivation, membrane fouling, coating peeling, and pump failure; wherein, the dimensions of each modal feature are unified, and the fusion improves the accuracy of diagnosing complex faults. Federated transfer learning includes: initializing the multimodal fusion model in the cloud and distributing it to the edge nodes of each water plant; each node fine-tunes the model using local small-sample fault data; uploading the model gradient to the cloud; the cloud uses a federated averaging algorithm to aggregate gradients and update the global model; and combining transfer learning to reuse the pre-trained weights of large-sample faults to improve the ability to diagnose rare faults. Root cause visualization includes: using the random forest algorithm to calculate the weights of fault influencing factors, generating a heatmap to show the correlation between faults and influencing factors, and labeling specific weight values ​​to assist maintenance personnel in locating the root cause.

[0069] Step 3.1: Construction of a four-modal fusion model. Features from four types of data—time series, image, acoustic, and impedance—are fused. A cross-modal attention mechanism is used to capture intermodal correlations, such as the correspondence between current fluctuations and electrode passivation, thereby improving the prediction accuracy of complex faults, such as electrode passivation and membrane fouling.

[0070] Step 3.1.1: Input the multimodal feature set F={F time ,F freq ,F img ,F sound ,F Z}, transformed into a four-modal feature set {F time,seq ,F img ,F sound ,F Z}, where F time,seq For time series characteristics, by F time and F freq After merging, the dimensionality is reduced to 256. The feature dimensions of each modality are unified to 256. Through feature concatenation and dimensionality reduction, the consistency of the model input dimension is ensured.

[0071] Step 3.1.2: Modal fusion architecture.

[0072] Phase 1: Single-modal feature encoding: Four independent fully connected layers are used to encode features of each modality, reducing dimensionality and enhancing feature discriminativity. The original features from different modalities are mapped to a unified feature space, outputting 128-dimensional single-modal encoded features, laying the foundation for cross-modal attention fusion. Taking temporal features as an example: Equation 10: F 1,time =W1×F time +b1, where: F 1,time This is the result of temporal feature encoding, 128-dimensional; F time b1 represents the temporal features; W1 is the weight matrix of the fully connected layer, responsible for projecting the temporal features into a low-dimensional space; b1 is the bias vector, 128-dimensional, optimized through model training.

[0073] Phase 2: Cross-modal attention fusion: A multi-head attention mechanism is used to calculate the attention weights of each modal feature, highlighting the contribution of key modes to fault diagnosis. For example, in the case of electrode faults, the image modality weight should be higher than the acoustic modality weight.

[0074] Formula 11: Where: Attention(Q,K,V) is the feature after attention weight fusion; Q is the query matrix; K is the key matrix; V is the value matrix, Q=K=V, which is the matrix after concatenating single-modal encoded features, with a dimension of 4×128; d k=128 represents the feature dimension; Softmax is the activation function, which normalizes the attention weights to [0,1] to ensure that the sum of the weights is 1, which can intuitively reflect the importance of each modality. The attention output is a 512-dimensional vector. Multi-head splitting and merging: The attention mechanism is split into 8 heads, and the attention of different subspaces is calculated separately. Then the results are concatenated to obtain a 512-dimensional fused feature vector F. fuse This enhances the model's ability to capture multimodal correlations.

[0075] Phase 3: Fault Classification Output: F fuse Input two fully connected layers and a Softmax layer, and output the probability distribution of fault types.

[0076] Formula 12: P prob =Softmax(W3×(W2×F fuse +b2)+b3), where: P prob The probability distribution for fault types is represented by a 5-dimensional matrix, corresponding to 5 operating states. These states include 4 fault types and 1 normal type: electrode passivation, film contamination, coating peeling, pump failure, and normal. Each element represents the probability of belonging to that fault type, and the category with the highest probability is the predicted fault type. W2 is the weight matrix of the first-level fully connected layer, with a dimension of 512×256, used to weight F. fuse Further feature transformation; W3 is the weight matrix of the second-level fully connected layer, with a dimension of 256×5, which maps the features to the probability space of 5 running states; b2 is the bias vector of the first-level fully connected layer, with a dimension of 256; b3 is the bias vector of the second-level fully connected layer, with a dimension of 5: bias vector; Softmax is used to convert the output of the second-level fully connected layer into a probability distribution.

[0077] Step 3.1.2: Model Training and Validation. Training Dataset: Historical fault data from 100 water plants were collected, containing 2000 samples for each of 5 operating states. Each sample corresponds to a multimodal feature set and a fault label. The dataset was divided into training and validation sets in a 7:3 ratio. Loss Function: The cross-entropy loss function was used to measure the difference between the predicted probability and the true label.

[0078] Formula 13: Loss=-∑ k=1 5 y k ×log(P k ), where: Loss is the cross-entropy loss; y k For the true label, the value is 0 or 1. For example, if the true fault is electrode passivation, then y = [1, 0, 0, 0, 0], corresponding to y1 = 1 when k = 1, and 0 otherwise; P kis the probability of the k-th class predicted by the model; log is the natural logarithm. Loss is used to quantify the difference between the model's predicted distribution and the true label distribution; the smaller the value, the more accurate the prediction.

[0079] Training Process: Weights are initialized using a He normal distribution, with biases initialized to 0. The Adam optimizer is used with a learning rate of 0.001, a batch size of 32, and weight decay of 0.0001 to prevent overfitting. During training, Adam minimizes the loss through backpropagation, adjusting the model's weights and biases to gradually teach the model to accurately identify faults from multimodal features. During the training phase, the model calculates the loss on the training set and iteratively updates parameters through the optimizer to continuously reduce the loss on the training set. After each training round, the model performance is tested on the validation set, and the validation accuracy is calculated. If the loss does not decrease for five consecutive rounds, training is stopped to avoid overfitting. Validation Results: The model achieves a prediction accuracy of ≥95% for complex faults (electrode passivation, membrane contamination) on the validation set, a 35 percentage point improvement over the original LSTM-CNN model, meeting the requirements of engineering applications.

[0080] Step 3.2: Federated Transfer Learning: To address the problem of insufficient data, such as electrode coating peeling or sample size <50, a combination of federated learning and transfer learning is adopted. Without sharing the private data of each water plant, the model is jointly trained to improve the ability to diagnose small sample faults.

[0081] Federated Learning: Each water plant acts as an edge node, training its model using local data and uploading only the model's gradients to the cloud. The cloud aggregates these gradients to update the global model, ensuring the model operates on static data, thus protecting data privacy. The global model starts with a multimodal fusion model, and subsequent iterations optimize its parameters. Transfer Learning: Using the pre-trained weights of the four-modal fusion model as initial weights, and fine-tuning them with small sample data, the learned fault features are transferred to rare fault scenarios, reducing reliance on small sample data.

[0082] The federated migration framework is as follows: Global model initialization: The four-modal fusion model, i.e., the global model, is initialized in the cloud. Pre-trained weights from large-sample fault data are loaded, such as training weights for electrode passivation and membrane fouling. Model parameters, such as W1 and W2, are sent to the edge nodes of each water plant, such as Plant A and Plant B. Local model training: Each edge node uses local small-sample data, such as 30 electrode coating detachment samples from Plant A. Model fine-tuning: The first 80% of the model parameters are frozen, retaining the pre-trained general fault features. Only the last 20% of the parameters are trained to adapt to the local small-sample scenario. The local cross-entropy loss and gradient Δθ are calculated. i , △θ i =θ local -θ pre θ local The parameters of the model after local training, θpre Here, represents the pre-trained model parameters, and ... i Data is sent to a cloud server, not the raw data, to protect the water plant's privacy. Global model update: The cloud uses the FedAvg algorithm to aggregate gradients from each node and generate new global model parameters θ. new The data is then distributed back to the edge nodes.

[0083] Formula 14: θ new =θ old +η×(1 / M)∑ i=1 M △θ i , where: θ new The updated global model parameters; θ old This represents the old parameters of the global model, including all trainable parameters such as weight matrices W1, W2, W3 and bias vectors b1, b2, b3; η = 0.001 is the learning rate, controlling the magnitude of parameter updates; M is the number of edge nodes participating in training, such as 10 water plants, ensuring the model learns small sample features from multiple scenarios; △θ i Let be the model gradient of the i-th edge node. A pre-trained model regularization term is added to prevent the global model from deviating from the general features of the pre-trained model. Equation 14 is adjusted to obtain Equation 15.

[0084] Formula 15: θ new =θ old +η×((1 / M)∑ i=1 M △θ i -λ(θ old -θ pre ), where λ(θ) old -θ pre ) represents the regularization term of the pre-trained model, and λ=0.01 is the regularization coefficient, which controls the contribution of the pre-trained model.

[0085] Iterative optimization: Repeat the local training-gradient upload-global update process. After each iteration, each edge node downloads the updated global model and verifies the accuracy using local data until the global model achieves an accuracy of ≥90% on small sample data across all nodes, at which point the iteration stops.

[0086] Through repeated iterations, the global model continuously absorbs the knowledge of each edge node, ultimately achieving high-precision diagnostic processing results for small sample faults, such as electrode coating peeling, thus solving the pain point of traditional models having poor performance due to limited data.

[0087] Step 3.3: Fault Root Cause Heatmap Visualization: By quantifying the weight of fault influencing factors, an intuitive heatmap is generated to help maintenance personnel quickly locate the root cause of the fault, such as whether electrode passivation is caused by excessive chloride ions or excessive temperature, thus shortening the troubleshooting time.

[0088] Step 3.3.1: Based on electrochemical principles and historical fault data, determine the key influencing factors for various fault types: Electrode passivation: Chloride ion concentration C Cl- Temperature T, electrolysis time t; membrane fouling: turbidity, COD concentration, backwashing cycle; pump failure: vibration frequency, running time, voltage fluctuation.

[0089] Step 3.3.2: Calculate influence weights using the random forest algorithm: Construct a random forest of 100 decision trees. Each tree is trained by randomly selecting features and samples, and the fault type is finally determined by voting. Simultaneously, the contribution of each feature to fault prediction is reduced by calculating the Gini coefficient. The smaller the Gini coefficient, the higher the sample purity and the greater the feature contribution. Formula 16: G = 1 - ∑ k=1 K p k 2 Where: G is the Gini coefficient, which measures the concentration of the category distribution; K is the number of fault categories; p k p1 represents the proportion of the k-th class of samples in the dataset. For example, if the passivated samples account for 80% in a certain node, then p1 = 0.8.

[0090] Formula 17: Importance(X)=(1 / 100)∑ t=1 100 +△G t (X), where: Importance(X) is the importance of feature X; X is a feature variable, such as chloride ion concentration, temperature, etc., which affect the failure of electrochemical water treatment equipment); △G t (X) represents the reduction in the Gini coefficient after adding feature X to the t-th decision tree, reflecting the contribution of feature X to improving sample purity in the t-th tree.

[0091] Step 3.3.3: Heatmap generation and display: The heatmap function of the Matplotlib library is used to draw the heatmap, combined with Seaborn beautification styles to ensure visual intuitiveness.

[0092] The heatmap design is as follows: the horizontal axis represents the factors affecting the fault, such as C. Cl-The heatmap uses T and t as axes; the vertical axis represents the fault type, such as electrode passivation, membrane fouling, and pump failure; color depth represents weight, with darker colors indicating high weight (≥0.6), lighter colors indicating low weight (≤0.3), and medium colors indicating medium weight (0.3-0.6). Specific weight values ​​are labeled in each cell for accurate reading. The heatmap is integrated into a cloud monitoring platform. Maintenance personnel can click on a cell, such as electrode passivation - chloride ion concentration, to view the historical trend of that factor, aiding in root cause identification. After heatmap processing, the root cause location time for maintenance personnel has been reduced from 2 hours to 5 minutes, improving troubleshooting efficiency by 80% and avoiding prolonged equipment downtime caused by traditional trial-and-error methods.

[0093] Step 4: Dynamic Energy Efficiency Optimization and Collaborative Scheduling: Based on fault diagnosis results and real-time water quality data, a multi-objective optimization model, digital twin extreme scenario adaptation, and cluster collaborative scheduling strategy are adopted to obtain the optimal operating parameters of the equipment and the cluster load scheduling scheme. The optimal operating parameters are then sent to the equipment to achieve energy efficiency optimization, and the cluster scheduling scheme ensures the continuity of processing under fault conditions.

[0094] Furthermore, the multi-objective optimization model includes: minimizing equipment energy consumption, maximizing pollutant removal rate, and minimizing operation and maintenance costs as optimization objectives; under the constraints of effluent quality compliance and equipment parameter safety, a multi-objective optimization algorithm is used to solve for the Pareto optimal solution set; and a compromise solution is selected as the optimal operating parameters of the equipment based on the actual needs of the water plant. Digital twin extreme scenario adaptation includes: constructing a database of extreme water quality scenarios such as high salinity, low temperature, and high chemical oxygen demand; pre-training reinforcement learning models corresponding to each scenario; collecting water quality data in real time to construct feature vectors; matching the scenario database; calling the corresponding pre-trained model; and outputting operating parameters adapted to the extreme scenarios. Cluster collaborative scheduling includes: freezing equipment with a failure probability ≥ a threshold through blockchain smart contracts; calculating the processing load that the failed equipment needs to share; allocating the load to surrounding normal equipment with low load rates; and synchronously adjusting the operating parameters of the sharing equipment.

[0095] Step 4.1: Multi-objective optimization model construction: In response to the core requirements of electrochemical water treatment, a three-objective optimization model is constructed to minimize energy consumption, maximize pollutant removal rate, and minimize operation and maintenance costs. The Pareto optimal solution set is solved by the NSGA-III algorithm to provide a basis for adjusting equipment control parameters.

[0096] Step 4.1.1: Optimize the target definition.

[0097] Objective 1: Energy Minimization: Minimize the total energy consumption of equipment operation and reduce energy costs. Formula 18: E min =min(P×t), where: E is the total energy consumption of the equipment, in kWh. minThe minimum total energy consumption is represented by P, where P is the equipment power in kW, and t is the running time in hours, which is considered a fixed value in the optimization.

[0098] Objective 2: Maximize pollutant removal rate: Ensure effluent quality meets standards while improving treatment efficiency. Formula 19: R max =max((C in -C out ) / C in (×100%), where: R is the pollutant removal rate, in %, R max For the maximum removal rate; C in The concentration of pollutants in the influent, such as COD, is expressed in mg / L; C out This refers to the effluent concentration, in mg / L; it must meet the C standard. out ≤Standard threshold C std For example, COD ≤ 50 mg / L.

[0099] Objective 3: Minimize operation and maintenance costs: Take into account energy consumption, reagents, and electrode wear costs to reduce overall operation and maintenance expenditures.

[0100] Formula 20: Cost min =min(C e ×E+C chem ×V chem +C electrode ×m electrode ), where: Cost is the total operation and maintenance cost, in yuan. min To minimize total operating costs; C e Electricity price, unit: yuan / kWh; C chem V represents the unit price of the drug, expressed in yuan / L; chem This refers to the dosage of the drug, expressed in L / h, and is positively correlated with COD concentration; C electrode This is the unit price of the electrode, in yuan / kg; m electrode This represents electrode loss, expressed in kg / h, and is positively correlated with the square of the current.

[0101] Constraints: Ensure optimization parameters remain within the range required for equipment safety and water quality compliance; Effluent water quality: C out ≤C std COD≤50mg / L, NH3-N≤5mg / L; Equipment parameters: Current I∈[10,100]A, Voltage U∈[5,20]V, Temperature T∈[20,40]℃, Reagent dosage V chem ∈[0.1,0.5]L / h.

[0102] Step 4.1.2: Solve using the NSGA-III algorithm.

[0103] S1. Initialize the population: Randomly generate 100 candidate solutions, each solution corresponding to a set of control parameters (I, U, T, V). chem The parameter values ​​are randomly selected within the constraints, such as I=60A, U=12V, T=30℃, V chem= 0.3L / h.

[0104] S2. Non-dominated ranking: Calculate the three objective function values ​​for each candidate solution and categorize the solutions into different fronts based on their non-dominated relationships. Non-dominated solutions: If no other solution is superior to this solution in all objectives, it is a non-dominated solution and is assigned to Front1, i.e., the optimal front. Dominated solutions: If another solution is superior to this solution in at least one objective, and no objective is inferior to this solution, it is a dominated solution and is assigned to Front2, Front3, etc., with the ranking decreasing sequentially. For example: Solution A: Energy consumption 8 kWh, removal rate 90%, cost 12 yuan / h; Solution B: Energy consumption 9 kWh, removal rate 92%, cost 11 yuan / h. Solutions A and B are not mutually dominant and are both assigned to Front1.

[0105] S3. Reference point allocation: Set 20 uniformly distributed reference points in the three-objective space. For example, (1,0,0) represents optimizing only energy consumption, and (0,1,0) represents optimizing only removal rate. Assign the solutions in Front1 to the nearest reference points to ensure the diversity of solutions and avoid all solutions being concentrated in a single objective direction.

[0106] S4. Selection and Genetic Operations: Selection: Select solutions from Front1 according to the allocation results of reference points, selecting at least one solution for each reference point to ensure the retention of high-quality solutions. Crossover: Use a single-point crossover strategy with a crossover probability of 0.8 to exchange parameters of the selected solutions, such as exchanging the current of solution A with the current of solution B, generating offspring solutions. Mutation: Use a polynomial mutation strategy with a mutation probability of 0.1 to make small-scale random adjustments to the parameters of the offspring solutions, such as changing the current from 60A to 61A, increasing population diversity.

[0107] S5. Iteration Termination: Merge the parent solution with the child solution, repeat S2-S4, and after 50 iterations, the solution in Front1 is the Pareto optimal solution set.

[0108] Step 4.1.3: Optimal Parameter Selection and Application: Based on the actual needs of the water plant, such as prioritizing cost reduction during the dry season and prioritizing water quality protection during the flood season, a compromise solution is selected from the Pareto optimal solution set. For example: prioritizing cost reduction: the solution with energy consumption of 8.5 kWh, removal rate of 92%, and cost of 10 yuan / h is selected, corresponding to parameters I=60A, U=12V, T=30℃, V chem =0.5L / h. Prioritizing water quality: Select the solution with energy consumption of 9kWh, removal rate of 95%, and cost of 12 yuan / h, corresponding to parameters I=70A, U=14V, T=32℃, Vchem =0.4L / h. Parameter distribution: Selected parameters, including current, voltage, temperature, and reagent dosage, are distributed to the equipment controller via an encrypted channel to adjust operating parameters in real time and ensure the optimization effect is implemented.

[0109] After the above processing, the system uses a multi-objective optimization model to solve for minimizing energy consumption as a clear optimization objective, thus directly addressing the problem of excessive energy consumption caused by fixed parameters. Simultaneously, digital twin extreme scenario adaptation ensures timely optimization response in the event of parameter mutations. Therefore, under the premise of meeting water quality standards, compared to fixed parameter control, this solution achieves a significant optimization effect of reducing operation and maintenance costs by an additional 10% and energy consumption by 8% through dynamic parameter adjustment.

[0110] Step 4.2: Digital Twin Extreme Scenario Adaptation: For extreme water quality scenarios such as high salinity, low temperature, and high COD, traditional models have slow response times for parameter adjustment and are prone to failing to meet the standards. Pre-trained scenario models are used and matched and called in real time.

[0111] Step 4.2.1: Construction of an Extreme Water Quality Scenario Database: Three typical extreme scenarios were identified, and historical operational data was collected to construct a scenario database. Scenario definitions are as follows: High-salinity wastewater scenario: salinity > 5000 mg / L, chloride ion > 1000 mg / L; Low-temperature wastewater scenario: water temperature < 10℃, microbial activity reduced by 50%; High-COD wastewater scenario: COD... in >500mg / L, exceeding the normal treatment range; data for each scenario include: current, voltage, temperature, COD in COD out Energy consumption, sample size 500 groups. Scene feature extraction: For each group of scene data, extract key features to construct a scene feature vector S=[salt content, T, COD]. in pH], salinity refers to the salt concentration of the influent, T is the influent temperature, COD in The influent chemical oxygen demand (COD) is given by the influent pH value. For example, the feature vector for a high-salinity scenario is S_highsalinity = [6000, 25, 300, 7.5].

[0112] Step 4.2.2: PPO (Proximal Policy Optimization) Reinforcement Learning Model. PPO is a reinforcement learning algorithm that uses a policy network to output parameter selection probabilities and a value network to evaluate the long-term value of the parameters. It continuously optimizes the parameters through interaction with the environment, aiming to maximize long-term cumulative reward. The training process is as follows: Environmental modeling: A digital twin model of electrochemical water treatment is built based on MATLAB / Simulink. The input is the control parameters (I,U,T), and the output is the indicators such as energy consumption, removal rate, and cost, which simulate the real treatment process.

[0113] Reward Function Design: Taking into account multiple objectives, a reward function is designed. The higher the reward value, the better the parameters: Formula 21: R'=0.5×R-0.3×E-0.2×Cost, where: R' is the reward value of reinforcement learning; R is the pollutant removal rate; E is the total energy consumption of equipment operation; Cost is the total operation and maintenance cost; 0.5 is the removal rate weight, -0.3 is the energy consumption weight, and -0.2 is the cost weight, which are determined through expert experience and experimental optimization.

[0114] Model training: For each extreme scenario, the PPO model is trained with 500 sets of data corresponding to the scenario, iterating for 1000 rounds until the average reward value of the model converges, that is, the fluctuation is less than 5% for 50 consecutive rounds, resulting in three pre-trained models for the scenarios, including the high-salt PPO model, the low-temperature PPO model, and the high-COD PPO model.

[0115] Step 4.2.3: Scene matching and model invocation.

[0116] Real-time scene matching: The digital twin collects influent water quality data in real time, including salinity, T, and COD. in pH, constructing a real-time feature vector S real =[salt content, T, COD] in pH], S lib S is the set of all predefined scene feature vectors in the scene library, with one vector corresponding to each scene. lib =[salt content] lib ,T lib COD in,lib pH lib ], where lib represents "from the scene library", calculate S real With each S lib The cosine similarity is used, and the maximum value is taken as the matching criterion. Formula 22: Sim(S real ,S lib )=(S real ·S lib ) / (|S real |×|S lib |), where: Sim is the cosine similarity; · is the vector dot product, |S real |、|S lib | represents the vector magnitude, and Sim takes values ​​in the range [0,1]. If Sim≥0.8, the match is considered successful.

[0117] Fast model invocation: After a successful match, the digital twin invokes the corresponding pre-trained model within 2 seconds and outputs the optimal control parameters. For example: Real-time water quality S real=[5800,9,320,7.2], and Sim=0.85≥0.8 for S_high_salt=[6000,25,300,7.5], call the high-salt PPO model, output parameters I=80A,U=15V,T=35℃.

[0118] When Sim < 0.8, it indicates that the current water quality scenario does not belong to the predefined "high salinity, low temperature, high COD" extreme scenario. The system will then revert to the normal optimization process: calling the multi-objective optimization model, based on the normal parameters of the current water quality, simultaneously optimizing the three objectives of "minimum energy consumption, maximum removal rate, and lowest cost," and generating equipment control parameters to ensure water quality compliance and energy efficiency optimization. After the above processing, the parameter adjustment response time in extreme scenarios is shortened from 10 seconds to 2 seconds, energy consumption in low temperature scenarios is reduced by 12%, and the COD compliance rate of effluent in high salinity scenarios remains at 100%, avoiding energy waste and water quality exceeding standards caused by traditional delayed adjustments.

[0119] Step 4.3: Equipment Cluster Collaborative Scheduling: When a device fails, the faulty device is frozen through a smart contract, and surrounding devices are scheduled to share the load to ensure the stability of the total processing capacity of the cluster and avoid interruption of wastewater treatment.

[0120] Step 4.3.1: Freezing the Faulty Equipment. Smart Contract Design: An automatically executed contract deployed on a private blockchain. The core logic is solidified in code and cannot be tampered with. The triggering conditions and execution actions are as follows: Triggering Conditions: The fault probability output by the fault diagnosis model is ≥95% (ensuring a low false positive rate), and the device ID is in the cluster's authorized list. Execution Actions: Send a freeze command to the faulty equipment, prohibiting parameter adjustment and stopping water intake to prevent the fault from escalating. If the electrode breaks and the equipment continues to operate, it may cause a short circuit. The freeze record is stored on the blockchain as a basis for maintenance accountability. The freeze record includes the device ID, fault type, freeze time, and node signature. The maintenance personnel are simultaneously notified via SMS, platform notifications, etc., triggering the maintenance process.

[0121] Contract execution process: The cloud-based diagnostic model outputs the faulty device ID and fault probability, and sends the results to the blockchain node; the node verifies that the fault probability is ≥95% and the device ID is valid, triggering the smart contract execution; the contract sends a freeze command to the faulty device, and simultaneously generates an on-chain transaction, which is written to the blockchain after PBFT consensus. From the output of the fault probability to the freezing of the device, the entire process takes less than 1 second, preventing the spread of the fault.

[0122] Step 4.3.2: Load sharing of peripheral equipment.

[0123] Calculate the processing load of the faulty equipment. Formula 23: Q fault =Q total ×(N fault / N total ), where: Qfault Q represents the load that the faulty equipment needs to share, in m³ / h. total N represents the total processing capacity of the cluster, in m³ / h. fault N represents the number of faulty devices. total This represents the total number of devices in the cluster.

[0124] Equipment sharing selection: Select equipment with a load rate of <70% from normal equipment within a 3km radius of the faulty equipment to avoid equipment overload. For example, select equipment Dev-001 with a load rate of 60% and equipment Dev-003 with a load rate of 50%.

[0125] Load sharing strategy: Q is inversely proportional to the load factor. fault Equipment with lower load rates receives more load, ensuring load balance. Formula 24: Q i =Q fault ×(1-LR i ) / ∑ j=1 K (1-LR j ), where: Q i The additional load that equipment i needs to bear, in m³ / h; LR i Let LR be the load factor of device i, where LR = actual load / rated load, such as LR = 0.6 for Dev-001; K is the number of devices participating in the load sharing.

[0126] Parameter adjustment: based on Q i The system invokes a multi-objective optimization model to generate new control parameters for Dev-001 and Dev-003, such as increasing the current of Dev-001 from 60A to 70A, ensuring that energy consumption and water quality requirements are still met after the addition of load. After processing, the freeze response time of faulty equipment is less than 1 second, and after the load is shared by surrounding equipment, the fluctuation of the total processing capacity of the cluster is less than 5%. For example, if the capacity is reduced from 1000m³ / h to 980m³ / h, there is no risk of wastewater treatment interruption, ensuring the continuous operation of the water plant.

[0127] The input multimodal feature set for step 3 comes from the data acquisition and preprocessing in step 1, and the data transmission depends on the transmission from the edge to the cloud in step 2. The output fault diagnosis result of step 3 provides key input for step 4. If the diagnosis result is a pump failure, step 4.3 will trigger cluster collaborative scheduling, freeze the pump, and allocate its load to surrounding devices to avoid energy waste caused by optimizing the parameters of the faulty device.

[0128] Step 5: Full-process monitoring visualization: Build a cloud-based digital twin cockpit, integrate the transmission status data, fault diagnosis results and energy efficiency optimization parameters from Steps 2-4, display the full-process information in real time, and verify the achievement of monitoring goals of low latency, high reliability, high accuracy and high energy saving, so as to realize transparent, traceable and intervention-friendly operation and maintenance of equipment clusters.

[0129] Furthermore, the cloud-based digital twin cockpit includes four modules: transmission monitoring, security monitoring, fault early warning, and energy efficiency optimization. The monitoring target verification includes statistical analysis of emergency-level data latency, data immutability rate, accuracy of complex fault diagnosis, and energy consumption reduction rate in extreme scenarios. The cockpit interface and functions are iteratively optimized based on feedback from maintenance personnel.

[0130] Step 5.1: Building a Digital Twin Cockpit. Based on the principles of intuitiveness, ease of use, and real-time performance, four core modules are designed to integrate data from the entire process, providing maintenance personnel with a one-stop monitoring tool. The core modules are as follows: Transmission Monitoring Module: Real-time display of link status, latency, and bandwidth. Security Monitoring Module: Displays encryption status, compliance results, and a source tracing report entry point. Fault Early Warning Module: Displays a device topology diagram showing fault status, including normal, warning, and fault, with a floating root cause heatmap. Energy Efficiency Optimization Module: Displays real-time energy consumption, removal rate, and cost, comparing indicators before and after optimization.

[0131] Data refresh: High-frequency data, such as transmission latency, fault status, and energy consumption, are refreshed once per second using the WebSocket protocol to ensure real-time performance. Low-frequency data, such as compliance verification and historical trends, are refreshed once every 5 minutes to balance real-time performance and server load.

[0132] Interactive functions: Supports historical data retrieval, diagnostic report viewing, and remote parameter adjustment. Retrieving the device's historical data for the past 3 months allows maintenance personnel to modify control parameters via the parameter adjustment button, changing the current from 60A to 65A. After submission, a secondary verification using the maintenance password is required. Once verified, the parameters are sent to the device in real time.

[0133] Step 5.2: Verification of Monitoring Objectives. Through cockpit data statistics and offline experiments, verify the achievement of the four monitoring objectives of low latency, high reliability, high accuracy, and high energy efficiency, providing a basis for solution optimization.

[0134] Low latency: Metric: Emergency-level data latency; Target value: <10ms; Verification method: Record data sending and receiving timestamps in the cloud, calculate latency (Delay), where Delay = t receive -t send -Processing Time , where t receive For cloud-received timestamps, in milliseconds; t send Send timestamps at the edge, in milliseconds; Processing TimeThe edge processing time is in milliseconds. High reliability: Metric: Data immutability rate, target value: 100%; Verification method: Randomly select 100 on-chain data entries and verify the consistency between the hash digest and the original data. High accuracy: Metric: Accuracy of complex fault diagnosis, target value: ≥95%; Verification method: Statistically analyze fault diagnosis results from the past 30 days and compare with manual detection conclusions. High energy efficiency: Metric: Reduced energy consumption per unit processing volume in extreme scenarios, target value: ≥12%; Verification method: Compare energy consumption in extreme scenarios before and after optimization; Reduced operation and maintenance costs, target value: ≥10%; Verification method: Statistically analyze monthly operation and maintenance costs before and after optimization.

[0135] Feedback Collection: Quarterly questionnaires are distributed to water plant operation and maintenance personnel to collect feedback on the ease of operation, data readability, and functional completeness of the control panel. A 5-point scale is used for scoring, with 5 being the best. Iterative Optimization: Ease of Operation: If the score is less than 4 points, optimize the interface layout and add voice control. Data Readability: If the heatmap feedback is unclear, adjust the color gradient and label specific weight values. Functionality Expansion: Add mobile adaptation and data visualization reports as needed. After processing, the full-process monitoring visualization achieves the operation and maintenance goals of transparency, traceability, and intervention; through goal verification and user feedback, a closed loop of monitoring-verification-optimization is formed, supporting long-term solution iteration and meeting the stable operation requirements of the electrochemical water treatment equipment cluster.

[0136] Example 2 Based on the same inventive concept as the cloud-based collaborative electrochemical water treatment equipment cluster monitoring method provided in the embodiments of this application, the embodiments of this application also provide a cloud-based collaborative electrochemical water treatment equipment cluster monitoring system. If there is anything unclear about the content in the system embodiments, please refer to the corresponding content in the method embodiments.

[0137] like Figure 2 As shown, a cloud-based collaborative electrochemical water treatment equipment cluster monitoring system includes: The data acquisition and preprocessing module, deployed at the edge, synchronously acquires multimodal data of electrochemical water treatment equipment, including sequential electrical signals, images, acoustic and electrochemical impedance data, and performs cleaning, standardization and feature extraction to obtain a multimodal feature set. The data transmission module dynamically schedules data transmission priorities and allocates dedicated network slices based on real-time water quality data. It ensures transmission reliability through primary and backup link redundancy and breakpoint resume mechanism, and adopts end-edge-cloud three-level encryption and blockchain evidence storage technology to achieve data security and trusted traceability. The intelligent diagnostic module, deployed in the cloud, receives multimodal feature sets, identifies fault types through a cross-modal attention fusion model, improves the diagnostic accuracy of small-sample faults based on a federated transfer learning framework, and generates a fault root cause heatmap for visualization. The optimized scheduling module is deployed in the cloud. Based on the fault diagnosis results and real-time influent water quality, it uses a multi-objective optimization algorithm to solve the Pareto optimal solution set of equipment operating parameters. It also uses digital twin technology to match extreme water quality scenarios to quickly call the pre-trained optimization model. When equipment fails, it automatically triggers a smart contract to freeze the faulty equipment and coordinates the scheduling of surrounding equipment to share the processing load. The visualization module provides users with an interactive interface in the form of a digital twin cockpit, which is used to centrally display the key indicators and status of the entire process from data acquisition and transmission, fault diagnosis to energy efficiency optimization in real time, and supports historical data query, remote parameter adjustment and monitoring target achievement verification.

[0138] Furthermore, the data acquisition and preprocessing module includes a multimodal data acquisition unit, a data cleaning and standardization unit, and a feature extraction and fusion unit. The multimodal data acquisition unit includes a Hall current sensor, a voltage sensor, a high-definition camera, an acoustic sensor, and an electrochemical impedance analyzer, and aggregates data through an edge gateway. The feature extraction and fusion unit is used to extract time-domain features, frequency-domain features, image features, acoustic features, and impedance features from the standardized data, and performs dimensionality reduction and fusion through principal component analysis. The data transmission module includes a dynamic priority scheduling unit, a multi-link backup and breakpoint resumption unit, and a full-link encryption and trusted traceability unit. The dynamic priority scheduling unit dynamically allocates data transmission priorities based on the influent chemical oxygen demand change rate and pH value using a logistic regression model. The multi-link backup and breakpoint resumption unit uses a 5G network as the main link and a LoRaWAN network as the backup link, and sets up data caching at the edge. The full-link encryption and trusted traceability unit uses AES-256 and SM4 algorithms for hierarchical encryption and stores key data and operation records based on blockchain technology. The intelligent diagnostic module includes a four-modal fusion model unit, a federated transfer learning unit, and a fault root cause analysis unit. The four-modal fusion model unit uses a cross-modal attention mechanism to fuse features from different modalities. The federated transfer learning unit coordinates multiple edge nodes to jointly train and optimize the global diagnostic model without uploading local data. The fault root cause analysis unit uses a random forest algorithm to quantify the weights of each influencing factor and generate a heatmap. The optimized scheduling module includes a multi-objective optimization unit, a digital twin extreme scenario adaptation unit, and a device cluster collaborative scheduling unit. The multi-objective optimization unit aims to achieve the lowest energy consumption, the highest pollutant removal rate, and the lowest operation and maintenance cost, and uses the NSGA-III algorithm for solution. The digital twin extreme scenario adaptation unit is pre-loaded with reinforcement learning models for high-salt, low-temperature, and high-COD wastewater scenarios, and performs real-time scenario matching through cosine similarity. The device cluster collaborative scheduling unit automatically executes the freeze command for faulty equipment based on blockchain smart contracts, and schedules normal equipment to share the load according to the inverse load ratio allocation principle. The digital twin cockpit of the visualization module integrates transmission monitoring, safety monitoring, fault warning and energy efficiency optimization sub-panels, and supports real-time data refresh via the WebSocket protocol.

[0139] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A cloud-based collaborative method for monitoring clusters of electrochemical water treatment equipment, characterized in that: Includes the following steps: At the edge, multimodal acquisition, cleaning and standardization, and feature extraction are performed on the operation data and real-time water quality data of the electrochemical water treatment equipment cluster to generate a multimodal feature set; By employing a triple mechanism of dynamic scheduling, multi-link backup, and full-link encryption, multimodal feature sets and raw device operation data are securely transmitted from the edge to the cloud to obtain cloud data. Based on multimodal feature sets, a multimodal fusion model is used for fault diagnosis. Federated transfer learning is adopted. The multimodal fusion model is initialized in the cloud and distributed to each water plant edge node. The node adjusts the model gradient with local small sample fault data and uploads it. The cloud aggregates the gradient update through the federated averaging algorithm. Fault root cause information is generated by fault root cause visualization to obtain equipment fault diagnosis results and fault root cause information. Based on the fault diagnosis results and real-time water quality data, a multi-objective optimization model is used to solve the optimal operating parameters of the equipment. A digital twin extreme scenario adaptation optimization is used to optimize the operating parameters under extreme water quality scenarios. A cluster collaborative scheduling strategy is used to generate a cluster load scheduling scheme. The optimal operating parameters of each electrochemical water treatment device are sent to the corresponding electrochemical water treatment device, and the load scheduling scheme of the electrochemical water treatment device cluster is executed. Build a cloud-based digital twin cockpit to integrate, verify, and visualize the entire process data and results of preceding steps, providing an entry point for human-computer interaction and decision intervention.

2. The cloud-based collaborative electrochemical water treatment equipment cluster monitoring method according to claim 1, characterized in that, The multimodal acquisition includes: synchronously acquiring the timing electrical signals of the device operation, high-definition image data of the electrode surface, acoustic data of the device operation, and electrochemical impedance data; aggregating all data through an edge gateway and attaching timestamps and device IDs to obtain the original multimodal dataset. The cleaning and standardization process includes: using the 3σ principle to remove outliers from time-series electrical signals and impedance data; standardizing time-series data; normalizing image data by pixels; and normalizing acoustic data by amplitude, to obtain a standardized dataset. The feature extraction includes: extracting time-series features, image features, acoustic features, and impedance features from standardized data, concatenating them, and then performing dimensionality reduction through principal component analysis to obtain a multimodal feature set.

3. The cloud-based collaborative electrochemical water treatment equipment cluster monitoring method according to claim 1, characterized in that, The dynamic scheduling includes: constructing a mapping model based on real-time collected water quality parameters; dividing the data transmission priority into three levels—emergency, stable, and normal—according to the water quality status; allocating a dedicated 5G slice channel for emergency data; and using a differential compression algorithm for stable and normal data. The multi-link backup includes: constructing a redundant architecture of 5G primary link and LoRaWAN backup link, monitoring 5G signal strength in real time at the edge, and automatically switching to the LoRaWAN link when the signal strength is below the threshold, transmitting only critical control commands; The end-to-end encryption includes three levels of encryption: device-edge-cloud. The device encrypts the original data through a trusted execution environment, the edge uses a national cryptographic block cipher algorithm for secondary encryption, and the cloud stores the data based on a private blockchain and generates a hash digest.

4. The cloud-based collaborative electrochemical water treatment equipment cluster monitoring method according to claim 1, characterized in that, The multimodal fusion model includes: performing single-modal encoding on the temporal, image, acoustic, and impedance features in the multimodal feature set; using a cross-modal attention mechanism to calculate and fuse the weights of each modal feature; and inputting the fully connected layer and activation layer to obtain the probability distribution of fault types.

5. The cloud-based collaborative electrochemical water treatment equipment cluster monitoring method according to claim 4, characterized in that, The federated transfer learning includes: initializing a multimodal fusion model in the cloud and distributing it to each water plant edge node; each node fine-tuning the model using local small sample fault data; uploading the model gradient to the cloud; the cloud using a federated averaging algorithm to aggregate gradients and update the global model; and reusing the pre-trained weights of large sample faults in conjunction with transfer learning. The root cause visualization includes: using a random forest algorithm to calculate the weights of the factors influencing the fault, and generating a heatmap to show the relationship between the fault and the influencing factors.

6. The cloud-based collaborative monitoring method for electrochemical water treatment equipment clusters according to claim 1, characterized in that, The multi-objective optimization model includes: minimizing equipment energy consumption, maximizing pollutant removal rate, and minimizing operation and maintenance costs as optimization objectives; under the constraints of effluent water quality meeting standards and equipment parameter safety, using a multi-objective optimization algorithm to solve the Pareto optimal solution set; and selecting a compromise solution as the optimal operating parameters of the equipment based on the actual needs of the water plant.

7. The cloud-based collaborative electrochemical water treatment equipment cluster monitoring method according to claim 6, characterized in that, The digital twin extreme scenario adaptation includes: constructing a database of extreme water quality scenarios with high salinity, low temperature, and high chemical oxygen demand; pre-training a reinforcement learning model corresponding to each scenario; collecting water quality data in real time to construct feature vectors; matching the scenario database and calling the corresponding pre-trained model to output the operating parameters adapted to the extreme scenarios. The cluster collaborative scheduling includes: freezing devices with a failure probability ≥ a threshold through blockchain smart contracts, calculating the processing load that the faulty devices need to share, allocating it to normal devices with low load rates in the surrounding area, and synchronously adjusting the operating parameters of the sharing devices.

8. The cloud-based collaborative monitoring method for electrochemical water treatment equipment clusters according to claim 6, characterized in that, The cloud-based digital twin cockpit includes four modules: transmission monitoring, security monitoring, fault early warning, and energy efficiency optimization. The verification of the monitoring targets includes statistical analysis of emergency-level data latency, data immutability rate, accuracy of complex fault diagnosis, and energy consumption reduction rate in extreme scenarios.

9. A cloud-based collaborative electrochemical water treatment equipment cluster monitoring system, used in any one of claims 1 to 8, characterized in that, include: The data acquisition and preprocessing module collects multimodal operating data from the equipment and performs feature extraction and fusion to generate a multimodal feature set; The data transmission module dynamically schedules data transmission priorities based on water quality information and reliably transmits the multimodal feature set to the cloud through redundant links and encryption mechanisms. The intelligent diagnostic module, based on the multimodal feature set, performs fault diagnosis and root cause analysis through cross-modal fusion and federated learning; The optimization scheduling module generates equipment optimization parameters based on diagnostic results and real-time water quality through multi-objective optimization and digital twin technology, and triggers cluster collaborative scheduling in the event of a fault. The visualization module uses a digital twin cockpit to display the system's entire process status in real time and provides a human-machine interaction interface.

10. The cloud-based collaborative electrochemical water treatment equipment cluster monitoring system according to claim 9, characterized in that, The multimodal data acquired by the data acquisition and preprocessing module includes at least time-series electrical signals, electrode images, operating acoustic signals, and electrochemical impedance data. The dynamic scheduling of the data transmission module is based on the change rate of influent chemical oxygen demand and pH value, and uses 5G and LoRaWAN to form a primary and backup link, and uses blockchain for data storage. The intelligent diagnostic module employs an attention mechanism for cross-modal fusion, and its federated learning is used to jointly enhance the ability to diagnose small-sample faults by combining multiple edge nodes. The multi-objective optimization of the optimization scheduling module is aimed at energy consumption, removal rate and cost, and its digital twin technology has a pre-built adaptation model for extreme water quality scenarios. The digital twin cockpit of the visualization module integrates sub-interfaces for transmission, safety, fault, and energy efficiency monitoring.

Images