Tailing wastewater treatment system

Abstract

The invention relates to the technical field of tailing wastewater treatment, and discloses a tailing wastewater treatment system. The system comprises an overview construction module, a feature fusion module, a state prediction module, a threshold generation module and an output module. The overview construction module generates a wastewater treatment overview based on manual setting of a user in a management interface, and presents a connection relationship and treatment parameters of all treatment units; the feature fusion module collects real-time operation data and fuses multi-dimensional features to generate a feature matrix; the state prediction module predicts treatment state data according to the characteristic matrix and the wastewater treatment overview; the threshold generation module generates a self-adaptive early warning threshold based on the historical normal operation data; and the output module outputs the processing state data and deviation data between the processing state data and the early warning threshold value. The system can help an operator to master a global situation, improves the accuracy of state prediction, adapts to dynamic changes, and guarantees the stable operation of a processing system.

Description

Technical Field

[0001] This invention relates to the field of tailings wastewater treatment technology, specifically to a tailings wastewater treatment system. Background Technology

[0002] Tailings wastewater is a significant source of pollution generated during mineral resource extraction and processing. Its complex composition often contains large amounts of heavy metal ions, suspended particulate matter, and chemical residues, among other harmful substances. Direct discharge can severely damage surrounding water bodies, soil, and the ecological environment. With the continuous expansion of mineral resource development, the volume of tailings wastewater discharge is constantly increasing, making efficient and stable treatment a crucial issue that needs to be addressed in the industry.

[0003] Currently, tailings wastewater treatment often employs a combination of traditional physical sedimentation, chemical neutralization, and biological treatment processes, achieving water purification through the sequential action of multiple treatment units. However, existing treatment systems generally suffer from decentralized management and fragmented information. Monitoring and parameter control of each treatment unit's operational status are often conducted independently, lacking systematic integration and overall control of the entire treatment process. Operators find it difficult to intuitively grasp the interrelationships between treatment units and the overall operational status, making it challenging to quickly pinpoint the root cause and take effective countermeasures when anomalies occur.

[0004] The quality and quantity of tailings wastewater are highly volatile, influenced by various factors such as mining processes, ore properties, and climatic conditions. Existing treatment systems have limited capabilities for analyzing and processing real-time operational data, making it difficult to accurately predict the treatment status. Most systems rely on fixed warning thresholds for anomaly detection, but these fixed thresholds cannot adapt to dynamic changes in water quality and quantity, easily leading to false alarms or missed alarms, affecting the stable operation and treatment effectiveness of the system. Furthermore, the lack of effective fusion of multi-dimensional features makes it difficult to comprehensively reflect the actual operating status of the treatment system, further reducing the accuracy of status assessment and prediction. Summary of the Invention

[0005] The purpose of this invention is to provide a tailings wastewater treatment system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a tailings wastewater treatment system, the system comprising: Overview of the construction module, feature fusion module, state prediction module, threshold generation module, and output module; The overview construction module is used to generate a wastewater treatment overview in the management interface based on the user's manual settings. The wastewater treatment overview includes the connection relationships between each treatment unit based on the treatment process and the treatment parameters of each treatment unit. The feature fusion module is used to collect real-time operating data of tailings wastewater, fuse multi-dimensional features of the treatment status, and generate a feature matrix. The status prediction module is used to predict the treatment status data based on the feature matrix and the wastewater treatment overview. The threshold generation module is used to generate an adaptive early warning threshold based on historical normal operation data. The output module is used to output the treatment status data and its deviation data from the early warning threshold to the user.

[0007] Preferably, the feature fusion module is specifically used for: performing timestamp alignment and missing value imputation on real-time running data, and generating a time-synchronized data sequence using a time-series alignment algorithm; inputting the data sequence into a feature extraction network to extract topological features in the spatial dimension and sequence features in the temporal dimension to obtain a preliminary feature representation; performing correlation-weighted fusion on the preliminary feature representation to highlight key features and generate a weighted feature representation; and inputting the weighted feature representation into a dimensionality reduction processor to remove redundant information through nonlinear mapping to generate the feature matrix.

[0008] Preferably, the state prediction module is specifically used to: calculate the remaining time between the current time and the end time of the processing cycle, determine a prediction strategy based on the remaining time, and predict the processing state data based on the prediction strategy, the feature matrix, and the connection relationships in the wastewater treatment overview.

[0009] Preferably, determining the prediction strategy based on the remaining duration includes: retrieving a first mapping relationship and a second mapping relationship, inputting the remaining duration into the first mapping relationship for matching and calculation to obtain an initial prediction period; obtaining the number of types of processing parameters, inputting the number of types into the second mapping relationship to obtain a compensation period; performing a difference operation between the initial prediction period and the compensation period to obtain an optimized prediction period; and formulating the prediction strategy based on the optimized prediction period.

[0010] Preferably, the system further includes a knowledge base module; the knowledge base module is used to store standard processing scheme data; the state prediction module is also used to extract candidate processing scheme entries that match the processing parameters from the knowledge base module, perform semantic disambiguation optimization based on non-critical text fragments, and generate optimized processing schemes.

[0011] Preferably, the semantic disambiguation optimization based on non-critical text fragments includes: semantically encoding non-critical text fragments as context information to generate a context semantic vector; structurally encoding candidate processing scheme entries to generate candidate scheme embedding vectors; performing scheme selection aggregation analysis on the context semantic vectors and candidate scheme embedding vectors to generate optimized scheme response features; and semantically decoding the optimized scheme response features to obtain the optimized processing scheme.

[0012] Preferably, the threshold generation module is specifically used for: training a normal operating condition simulator to generate simulated normal data; constructing a real-time discriminator to learn the boundary features between normal and abnormal states through dynamic game theory; and generating multi-level early warning thresholds based on the game results using a clustering algorithm.

[0013] Preferably, the state prediction module is further configured to: construct a three-dimensional concentration distribution field based on the feature matrix; predict the diffusion path and change trend of the concentration distribution field based on the optimized processing scheme and graph structure model; and input the prediction results into the threshold generation module.

[0014] Preferably, the construction of the three-dimensional concentration distribution field includes: mapping risk feature points to three-dimensional spatial coordinates, generating a preliminary concentration distribution field using a spatial interpolation algorithm; and correcting the preliminary concentration distribution field using a dynamic interpolation algorithm in conjunction with time dimension information to generate a concentration distribution field that changes over time.

[0015] Preferably, the output module is specifically used to: compare the processing status data with multi-level early warning thresholds, adjust the early warning level using a dynamic decision model, and output deviation data.

[0016] Compared with the prior art, the beneficial effects of the present invention are: By setting up the overview module, a wastewater treatment overview can be generated in the management interface based on user-defined settings. This clearly presents the connections between treatment units based on the treatment process and the treatment parameters of each unit. This allows operators to intuitively and comprehensively grasp the overall situation of the entire wastewater treatment process, understand the position and role of each treatment unit in the overall process, and the mutual influence between them. This helps operators understand the treatment process from a system-level perspective and reduces operational errors caused by information asymmetry.

[0017] The feature fusion module is designed to collect real-time operational data of tailings wastewater and fuse multi-dimensional features of the treatment status to generate a feature matrix. By integrating various feature information, including water quality parameters, equipment operating parameters, and environmental parameters, the actual operating status of the treatment system can be comprehensively reflected, avoiding the limitations of single feature analysis. This makes the description of the treatment status more comprehensive and accurate, providing a more reliable foundation for subsequent status prediction.

[0018] The status prediction module predicts the treatment status data based on the feature matrix and the overall wastewater treatment overview. It combines multi-dimensional fused feature information with the correlation of the overall treatment process, making the prediction results more consistent with the actual treatment situation. It can anticipate potential status changes in the treatment system, allowing operators to prepare in advance, adjust operating strategies in a timely manner, and avoid significant fluctuations in the treatment process.

[0019] The threshold generation module generates adaptive warning thresholds based on historical normal operation data, breaking free from the constraints of traditional fixed thresholds. Since historical normal operation data reflects the normal state range under different operating conditions, the generated adaptive warning thresholds can be dynamically adjusted according to changes in water quality and quantity, better meeting actual operational needs. This reduces false alarms or missed alarms caused by thresholds not adapting to changes, and improves the accuracy of anomaly detection.

[0020] The output module provides the user with processing status data and its deviation from the warning threshold, enabling the user to promptly understand the current status of the processing system and the degree of deviation from the normal range. Operators can use this information to quickly determine whether the system is operating normally and the specific circumstances of any deviation, thus taking targeted measures to ensure the continuous and stable operation of the processing system and improve the overall efficiency of tailings wastewater treatment. Attached Figure Description

[0021] Figure 1 This is a timing diagram of the tailings wastewater treatment system described in this invention; Figure 2 A flowchart illustrating the operation of the feature fusion module; Figure 3 A flowchart generated for the prediction strategy; Figure 4 Flowchart for semantic disambiguation optimization; Figure 5 A flowchart for constructing a three-dimensional concentration distribution field. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1 The present invention provides a tailings wastewater treatment system, the system comprising: an overview construction module, a feature fusion module, a state prediction module, a threshold generation module, and an output module.

[0024] The overview module allows users to manually set relevant parameters for the wastewater treatment process through the management interface, creating an overall wastewater treatment overview that includes the connection relationships and treatment parameter characteristics of each treatment unit. The feature fusion module collects real-time operational data of tailings wastewater and fuses multi-dimensional features such as time series and spatial distribution to generate a feature matrix. The status prediction module combines the connection relationships in the wastewater treatment overview with this feature matrix to calculate and predict the tailings wastewater treatment status data. The threshold generation module trains a model using historical normal operation data to generate an adaptive warning threshold range. The output module receives the treatment status data and its deviation from the warning threshold, and outputs the integrated results to the user interface for display.

[0025] Example 1: See Figure 2 The process of processing real-time operational data by the feature fusion module begins with the data acquisition phase. The system continuously collects parameter data, including but not limited to influent flow rate, reaction tank pH, sedimentation tank suspended solids concentration, and specific chemical ion content, through sensors deployed at different locations on the tailings wastewater treatment line. This data is transmitted to the system in high-frequency time series format, initially as discrete data points marked with multi-dimensional timestamps. Upon receiving the raw data, the data processing unit performs time-series alignment: it analyzes the time stamp deviations from multiple data sources using a dynamic time warping algorithm, adjusts the time axis of each parameter sequence through interpolation compensation, and outputs a unified data sequence with completely synchronized time dimensions. For any missing points that may occur during data transmission, the system automatically activates the K-nearest neighbor algorithm to search for historical similar patterns in adjacent time periods within the sequence, filling in missing values ​​based on the similarity calculation results to form a continuous and complete monitoring data sequence space. K is the core parameter of the K-nearest neighbor algorithm, specifically referring to the number of nearest neighbors used to match similar data samples. Combining the real-time operation data characteristics of this tailings wastewater treatment system—monitoring dimensions include eight key indicators such as influent flow rate, reaction tank pH value, sedimentation tank suspended solids concentration, and heavy metal ion content (copper, lead, cadmium, etc.), with a single batch of data samples ≥1000 groups, after cross-validation (verifying the accuracy of missing value filling), K=5 was determined to be the optimal value. This value can ensure a filling accuracy of ≥95%, while avoiding fitting deviation caused by K being too small or redundant noise introduced by K being too large, ensuring the integrity of the input data for the subsequent feature fusion module.

[0026] A complete data sequence is input into a feature extraction network for hierarchical feature mining. This network consists of parallel convolutional neural network (CNN) units and long short-term memory (LSTM) network units. The CNN units process the spatial topology of the wastewater treatment overview: mapping the physical locations of each treatment unit to a two-dimensional topological graph, transforming pipe connections into graph node edge weight relationships; and extracting the spatial distribution characteristics of fluids in sedimentation tanks, aeration tanks, and other equipment, including regional flow velocity differences and equipment connection path characteristics. The LSTM network units process time-series data: analyzing the periodic regularity of parameter fluctuations over time, capturing the gradual change trend of pH value during the oxidation reaction stage, and identifying the temporal pattern characteristics of flow rate mutation events. The outputs of these two units are combined to form a preliminary feature representation set, which contains multiple feature vectors, each vector expressing a specific dimension of data characteristics.

[0027] The system performs a correlation-weighted fusion operation on the initial feature representation group. The computational unit analyzes the mutual information between different feature vectors, such as the correlation between flow rate change features and chemical parameter fluctuation features. An attention mechanism algorithm assigns weight coefficients to each feature vector, with the coefficient magnitude determined by the correlation strength between the feature and the current processing state. Key features such as abnormal dissolved oxygen in the aeration stage are given increased weights, while background features without significant changes are given decreased weights. The weighted feature vector group is then normalized to form a weighted feature representation matrix. This matrix is ​​subsequently input into a dimensionality reduction processor, where principal component analysis (PCA) is used for data compression: the covariance matrix of the feature matrix is ​​calculated, singular value decomposition is performed, principal component vectors with contribution rates greater than a set threshold are selected, and a low-dimensional feature matrix is ​​generated through nonlinear transformation mapping. The output feature matrix retains the main variability characteristics of the original data while eliminating monitoring noise and redundant data interference.

[0028] The status prediction module begins operation after receiving the feature matrix and wastewater treatment overview information. The end time information of the treatment cycle defined in the treatment overview is read from the system configuration library, such as the completion time set for a certain batch of wastewater treatment. The system's internal clock obtains the current time value, and the calculation module performs time difference calculations to obtain the remaining duration value, which is measured in minutes. The system automatically accesses the preset mapping relationship database and calls the first mapping relationship table based on the remaining duration value: when the remaining duration is in the range of 0-60 minutes, the first mapping relationship outputs an initial prediction cycle of 5 minutes; when the remaining duration is in the range of 60-180 minutes, the initial prediction cycle value is output as 15 minutes; for longer periods, a base value of 30 minutes is output. Simultaneously, the system parses the number of parameter types in the wastewater treatment overview, which includes the sum of physical treatment parameter categories and chemical treatment parameter categories. The number of types is input into the second mapping relationship table: when the number of parameter types is less than 5, the compensation cycle is 0; when the number is 5-10, the compensation cycle is 3 minutes; and when the number exceeds 10, the compensation is 5 minutes. The difference between the initial prediction cycle and the compensation cycle is calculated to generate an optimized prediction cycle value.

[0029] The final prediction strategy framework is determined based on the optimized prediction cycle value. When the optimized prediction cycle is less than 10 minutes, the system selects a real-time recursive prediction model: using an extended Kalman filter algorithm to continuously update the state prediction values, and correcting the prediction results immediately upon receiving a new feature matrix. The model incorporates the connection relationship data from the processing overview; for example, when the feature matrix shows abnormal water quality at the sedimentation tank output, the model will transmit the impact to the state prediction of subsequent filtration units based on the pipeline connection paths. When the optimized prediction cycle is greater than or equal to 10 minutes, the system switches to a regression prediction model: using a support vector regression algorithm to establish a mapping relationship between the feature matrix and the target parameters, training the model weight parameters based on historical data, and constructing a multi-level regression prediction chain by combining the unit connection relationships in the processing overview. The final output includes the predicted core parameters of each treatment unit for future periods: a quantitative data set such as the pollutant degradation rate in the reaction tank, the residual concentration of heavy metal ions at the final effluent outlet, and the overall system treatment efficiency. The predicted data set is transmitted to subsequent modules for comparative analysis of treatment thresholds.

[0030] Example 2: See Figure 3 The state prediction module employs a hierarchical calculation mechanism to dynamically adjust the prediction cycle when processing the mapping relationship between remaining time and prediction strategy. Internally, the system maintains two core mapping relationship tables: the first mapping relationship table stores the remaining time. With the initial prediction period The correspondence, where This represents the time difference between the current moment and the end of the processing cycle, in minutes. This indicates the basic prediction cycle length set by the system based on experience. This table is defined in piecewise function form, when... hour, ;when hour, ;when hour, The second mapping table stores the number of processing parameter types. Compensation cycle The correspondence, This represents the total number of physical and chemical parameter categories analyzed in the wastewater treatment overview. This is the compensation value that the system dynamically adjusts based on parameter complexity. Its mapping rule is: if... ,but ;like ,but ;like ,but The system's computing unit performs interpolation calculations to generate an optimized prediction cycle. Its calculation logic is as follows:

[0031] This formula ensures that the prediction cycle is shortened to improve accuracy when the parameter types are complex, while the cycle is lengthened to reduce computational load in simple scenarios. For example, when the remaining time... minutes and parameter type At that time, the system looks up the table to obtain Calculated Minutes. Optimize forecast period. As core parameters input to the prediction policy generator, the policy generator is based on... Value selection model type and time window settings: If Minutes, enabling real-time recursive prediction mode, employing a sliding time window mechanism, with the window width set to... ;like Minutes, enable batch regression prediction mode, time window fixed at [time window value]. 1.5 times.

[0032] The knowledge base module provides structured storage support for standard treatment solutions. This module uses a relational database to manage three types of data: a basic parameter table for the treatment unit recording equipment specifications and operational constraints; a process route table storing treatment flow combinations under different water quality conditions; and an anomaly response table archiving adjustment plans from historical cases. When matching treatment parameters, the status prediction module first extracts the identifier of the current treatment unit from the wastewater treatment overview. With parameter eigenvectors A query request is sent to the knowledge base. The knowledge base performs a multi-level search: based on... Identify the device type and return a set of candidate solutions. ;based on With the feature vector of the scheme The cosine similarity is used to rank the entries and select the Top-K candidate entries.

[0033] The semantic disambiguation optimization process performs in-depth processing on non-critical text fragments (such as operation notes or log comments) in candidate entries. The context encoder then processes the text fragments... Convert to semantic vector Its dimensions are 512, covering grammatical structure, domain terminology, and contextual features. Candidate solution entries. Transformed into embedded vectors using a structured encoder. This vector fusion process incorporates multimodal information including operation steps, parameter settings, and effect descriptions. The scheme selection aggregation analyzer receives this information. and Then perform the following operations: Calculate the attention weight matrix. ,in Given a vector dimension; the optimized response features are obtained by weighted summation of the candidate solution vectors. The semantic decoder will Convert into natural language description and generate optimized processing solution text. .

[0034] During the dynamic prediction process, the system continuously monitors and optimizes the processing solution text. The degree of matching with the real-time state. When the feature matrix shows that the current parameters deviate... When a preset range is defined, a solution re-evaluation mechanism is triggered: more timely candidate items are re-extracted from the knowledge base, and the semantic disambiguation results are updated by combining the latest non-critical text fragments. For example, when the dissolved oxygen concentration in the aeration tank fluctuates abnormally, the system captures the text fragment "unstable fan speed" from the log. This fragment is semantically encoded to strengthen its association weight with equipment failure-related solutions, ultimately outputting the optimized processing solution text. It will include recommendations for wind turbine maintenance.

[0035] The collaboration between the threshold generation module and the knowledge base module is reflected in the dynamic updating of the warning threshold. When a new standard processing solution is added to the knowledge base, the system automatically extracts its parameter range as a reference value for normal operating conditions and inputs it into the simulator component of the threshold generation module. The real-time discriminator compares and optimizes the processing solution text. The expected parameters and actual feature matrix data are compared to identify ambiguous boundary states and fed back to the knowledge base, driving iterative optimization of the solution library. This closed-loop design enables the prediction strategy to adapt to continuous improvements in processing technology and maintain the timeliness of the early warning threshold.

[0036] Output module integration and optimization processing solution text When displaying state prediction data, a hierarchical display strategy is adopted: core parameter prediction values ​​are presented in numerical tables, operational suggestions are presented in structured lists, and semantic analysis results of non-critical text fragments are displayed as supplementary explanations in a collapsed format. Users can expand detailed information at any level through the interactive interface; for example, clicking on a prediction value to view its underlying... View the cycle variation curve, or select a solution item to view the relationship diagram of the original cases in the knowledge base. The system records all user comments on the optimization solution text. The adoption and modification of these data will be fed back to the knowledge base as new non-critical text fragments, forming a positive cycle mechanism for continuous learning.

[0037] Example 3: See Figure 4 The semantic disambiguation optimization process unfolds through deep semantic parsing with the support of the knowledge base module. The context encoder employs a Transformer-based bidirectional encoding architecture to process non-critical text fragments. ,in It contains unstructured text data such as natural language descriptions and equipment maintenance record notes from operation logs. The encoder extracts text features through a 12-layer self-attention mechanism to generate a 768-dimensional contextual semantic vector. This vector captures domain-specific semantic patterns and implicit contextual relationships within a text fragment. For candidate processing scheme entries... The structured encoder breaks it down into a sequence of operation steps. and parameter configuration set The vectors are converted into dense vector representations through word embedding layers and position encoding layers, respectively, and finally concatenated to form a candidate scheme embedding vector with a dimension of 1024. .

[0038] Solution selection aggregation analyzer receiving and Then, multi-level semantic matching is performed. First, the cross-modal attention weight matrix is ​​calculated. Its elements Indicates the first The association strength between a context semantic unit and the feature of the j-th candidate scheme is calculated using the following formula:

[0039] in: Indicates the first The context semantic unit and the first The correlation strength value of the features of each candidate scheme. The normalization factor for the vector is fixed at 32. The attention weight matrix undergoes feature transformation through two fully connected layers, outputting the optimized response features. This feature integrates the semantic components from the candidate solutions that best fit the current context. The semantic decoder employs a pointer-generation network architecture to... The text of the optimization solution is generated step by step from the input. During the decoding process, the vocabulary distribution of the original candidate scheme is dynamically referenced to ensure the accuracy of the technical terms in the output text.

[0040] The threshold generation module constructs a dynamic game framework to learn the boundary features between normal and abnormal states. (Normal operating condition simulator) The conditional variational autoencoder structure is adopted, and its encoder will convert historical normal data. Compressed into latent space variables The decoder is based on the current operating conditions. Reconstructing simulation data Real-time discriminator It consists of a 5-layer convolutional neural network that receives actual monitoring data. and simulation data Probability estimation of the authenticity of output data The two sides form a dynamic game through adversarial training: The goal is to minimize the reconstruction loss. Simultaneously maximize The false positive rate; Then try to accurately distinguish and Its loss function It includes cross-entropy loss and gradient penalty terms. The activation mode of the last hidden unit in the discriminator after alternating training. It is extracted as a boundary feature representation.

[0041] The multi-level early warning threshold generation process employs an improved spectral clustering algorithm to handle boundary features. The feature space is first processed by a kernel function. Mapped to a higher-dimensional space, where For bandwidth parameters, These are randomly selected anchor points. The Laplacian matrix eigenvalue decomposition is performed in the transformed space, selecting the first... The eigenvectors constitute the clustering indicator matrix. .right The row vectors are subjected to k-means clustering, which ultimately generates a three-level warning threshold: the cluster center under normal operating conditions. As a baseline, the boundary of the transitional operating condition cluster This constitutes the primary early warning threshold, the center of the abnormal operating condition cluster. Determine advanced early warning thresholds. These thresholds are dynamically updated as the game training progresses, forming an early warning system that adapts to the current processing state.

[0042] The system implements a collaborative optimization mechanism for semantic disambiguation and threshold generation. When optimizing the text... After being adopted and implemented by users, its actual effect data The input threshold generation module updates the discriminator parameters. Meanwhile, The key operational parameters in the code are extracted as new condition variables. This is used to enhance the condition generation capabilities of the work condition simulator. This bidirectional data flow allows semantic parsing results to refine warning thresholds, while threshold deviation information can be fed back to the knowledge base to adjust the priority of the plan. For example, when... When adjustment schemes frequently trigger primary warnings, the system automatically reduces the weight coefficient of the relevant schemes during the semantic disambiguation stage and marks the scheme in the knowledge base as needing process verification.

[0043] At the interface interaction level, the optimized processing scheme text generated by semantic disambiguation... Visually correlated with multi-level early warning thresholds. User viewing. The system can simultaneously retrieve historical threshold curves for corresponding parameters, providing a clear understanding of the expected safety boundaries of the implementation plan. It employs a color-coding mechanism to identify key operational items in the text: green indicates parameters within the normal threshold range, yellow marks items approaching the primary warning threshold, and red highlights dangerous operations that have exceeded the advanced warning threshold. This visual design helps users quickly grasp the risk distribution during plan implementation and initiate manual intervention procedures when necessary.

[0044] The anomaly diagnosis process combines the results of semantic parsing and threshold analysis. When the system detects that a parameter continuously exceeds the warning threshold, the diagnostic process is automatically activated: first, it searches the knowledge base for historical cases with similar threshold breach patterns and extracts their associated non-critical text descriptions. Then, the current operating condition characteristics are compared with... A shared input semantic disambiguation pipeline generates a targeted list of diagnostic suggestions. This list is sorted by the degree of threshold deviation, and each suggestion is accompanied by the treatment effect data of the original case, providing users with a basis for decision-making. The entire process forms a closed-loop processing chain from numerical anomalies to semantic analysis and then to treatment plans.

[0045] Example 4: See Figure 5When the state prediction module of the tailings wastewater treatment system executes the three-dimensional concentration distribution field construction process, taking a specific treatment scenario as an example: a gold mine tailings wastewater treatment line includes three core units: sedimentation tank, aeration tank, and filtration tank. The system collects spatial distribution data of six types of heavy metal ions in real time through a sensor network deployed inside the tanks, including the concentration values ​​of copper ions, lead ions, cadmium ions, arsenic ions, zinc ions, and mercury ions. The risk feature point mapping process first identifies key monitoring locations: sedimentation tank inlet nozzle (P1), middle of sedimentation tank (P2), sedimentation tank outlet (P3), aeration head area of ​​aeration tank (A1), aeration tank drain outlet (A2), upper layer of filtration tank (F1), middle layer of filtration tank (F2), and bottom layer of filtration tank (F3), totaling eight risk feature points. The system converts the physical spatial coordinates into a three-dimensional rectangular coordinate system.

[0046] Table 1: Spatial coordinate mapping table of risk feature points.

[0047]

[0048] The spatial interpolation algorithm receives real-time concentration data from each feature point. Taking the arsenic ion distribution in the sedimentation tank as an example: feature point P1 measures 120 μg / L, P2 measures 85 μg / L, and P3 measures 52 μg / L. The system uses an inverse distance weighting method to calculate the concentration value at any location within the tank, setting the distance decay index to 2. For the aeration tank coordinate point (26.0, 8.5, 1.5), its concentration calculation comprehensively references the values ​​of the closest points A1 (450 μg / L at 2.7 meters) and A2 (310 μg / L at 3.1 meters), and calculates the estimated value for the current point by weighting according to the inverse square of the distance. A voxel grid with a resolution of 1 meter × 1 meter × 0.5 meters is formed within the processing unit, and a preliminary concentration distribution field data cube is generated as a whole.

[0049] The concentration distribution field is dynamically updated during the time-dimensional correction process. Data collected in the aeration tank at time stamp T0 shows that the copper ion concentration at feature point A1 is 608 μg / L and the zinc ion concentration is 1050 μg / L. The system records flow velocity sensor data and determines the mainstream flow velocity of the wastewater to be 0.8 m / min. By time stamp T1 (5 minutes later), newly collected data shows that the copper ion concentration at point A1 has decreased to 572 μg / L and the zinc ion concentration has decreased to 890 μg / L. The dynamic interpolation algorithm, based on the flow velocity vector field, shifts the concentration values ​​of each voxel grid at time T0 along the water flow direction by 4 meters (0.8 m / min × 5 minutes), and then performs weighted fusion with the new detection values. The weighting coefficient is determined by the data time decay factor, with a weighting coefficient of 0.7 for the new data and 0.3 for the shifted data. The corrected three-dimensional concentration field shows that the copper ion concentration at the original A1 position has been updated from 608 μg / L to 572 μg / L, while the copper ion concentration in the new area 4 meters downstream is (608×0.3+572×0.7)=584 μg / L.

[0050] The graph structure model is constructed based on the topological relationship of the overall treatment: a three-stage series structure of sedimentation tank → aeration tank → filter tank. Model node attributes include container volume, maximum throughput, and residence time parameters. Diffusion path prediction uses a mass migration simulation algorithm, inputting the concentration distribution data of each unit at the current moment. A high-concentration arsenic ion hotspot was detected in the aeration tank (coordinates 25.1, 8.5, 1.2, 187 μg / L). The system retrieves the flow path in the graph model and simulates the trajectory of this hotspot as it enters the filter tank with the wastewater: based on the pipe diameter and flow velocity, the time required for migration to the F1 layer of the filter tank is calculated to be 9 minutes, predicting that the corresponding region in the F1 layer will experience a concentration peak after 9 minutes. Trend prediction analysis of historical concentration field sequences shows that the zinc ion concentration in the F2 layer of the filter tank has shown a decreasing trend of 12% per hour over the past 30 minutes. Based on this, it is predicted that the concentration will decrease from the current 215 μg / L to 189 μg / L in the next hour.

[0051] The optimized treatment scheme integration is reflected in the prediction result correction stage. The current optimal scheme provided by the knowledge base module requires the pH value at the filter tank inlet to be maintained within the range of 6.8-7.2. The state prediction module detected that the pH value at the aeration tank outlet was 7.5 (predicting that the filter tank inlet will inherit this value), which exceeds the recommended range. The system activates the parameter feedback mechanism of the graph structure model: the pH target value of 7.0 in the optimized scheme is input into the diffusion model as a constraint condition, the mass migration process is recalculated, and the prediction result is corrected to "the pH value at the aeration tank outlet needs to be adjusted to 7.0 within 8 minutes to ensure that the filter tank inlet meets the standard". All prediction results (concentration distribution field, diffusion path, trend curve) are transmitted to the threshold generation module, which contains the spatiotemporal distribution dataset of six types of heavy metal ions in the three future treatment units.

[0052] The prediction results are visualized as a dynamic 3D heatmap on the user interface. The current state of copper ion distribution in the aeration tank is displayed as gradient color blocks in XYZ space: blue indicates the safe zone of <500 μg / L, yellow indicates the transition zone of 500-800 μg / L, and red indicates abnormal points >800 μg / L. The diffusion path is presented as a pulsed moving light spot, moving from the high concentration area in the aeration tank (coordinates 24.9, 8.6, 2.1) towards the filter tank at a speed of 0.5 cm per second. The left side of the interface synchronously displays the predicted trend curves of the main parameters of each unit. Among them, the arsenic ion concentration curve at the filter tank outlet indicates that the predicted value for the next hour is decreasing from 35 μg / L to 28 μg / L. This data has been transmitted to the threshold generation module for compliance verification. Users can rotate and zoom the 3D model to view the concentration distribution of any profile. The system automatically records the user's focus observation angle and restores the view angle state upon the next startup.

[0053] Example 5: When the output module of the tailings wastewater treatment system dynamically adjusts the early warning level, it receives treatment status data from the status prediction module and multi-level early warning thresholds from the threshold generation module. The treatment status data includes a complete set of real-time monitoring values ​​and predicted values, such as the current value of copper ion concentration at the sedimentation tank outlet, the predicted range for the next 15 minutes, and the trend of dissolved oxygen content in the aeration tank. The multi-level early warning thresholds consist of a three-level structure: normal operating condition threshold range (green marked area), primary early warning threshold (yellow marked area), and advanced early warning threshold (red marked area). Each threshold range is set separately according to the type of pollutant and the characteristics of the treatment unit.

[0054] The dynamic decision-making model first standardizes and preprocesses the processing status data. All monitoring parameters are uniformly converted into relative deviation values, calculated as the percentage offset between the current value and the median of the corresponding threshold interval. For example, when the current arsenic ion concentration in the sedimentation tank is 45 μg / L, and the median of its normal threshold interval is 30 μg / L, the calculated relative deviation value is 50%. The model has a built-in parameter weighting mechanism: the weighting coefficient for heavy metal ion concentration deviation is set to 0.6, the weighting coefficient for physical parameters such as flow rate and pH value is 0.3, and the weighting coefficient for treatment efficiency indicators is 0.1. After weighted comprehensive calculation, an overall deviation index is generated, ranging from 0 to 100. The larger the value, the greater the degree of deviation from the normal state of the system.

[0055] The warning level adjustment employs a fuzzy logic decision-making algorithm. The system pre-defines five decision dimensions: deviation index magnitude, parameter importance level, trend change rate, historical similarity pattern matching degree, and processing unit criticality score. Each dimension has a membership function; for example, the deviation index dimension defines 0-30 as "slight deviation," 30-60 as "moderate deviation," and 60-100 as "severe deviation." The decision engine calculates the membership values ​​of each dimension in real time and performs a comprehensive evaluation using 128 fuzzy inference rules from the rule base. Rule examples include: if the deviation index is "severe deviation" and the trend change rate is "rapidly rising," then an "immediate action" command is triggered; if the parameter importance is "critical parameter" but the historical similarity pattern matching degree shows "common fluctuations," then the warning level is downgraded to "observation warning."

[0056] The deviation data output adopts a hierarchical and progressive structure. The main interface displays core early warning information: the current highest-level early warning item, the location of the affected processing unit, and recommended priority handling measures. The secondary interface provides detailed data views, including bar charts comparing real-time values ​​of each parameter with thresholds, deviation index change curves, and historical data for the same period. Users can interactively expand the complete data chain of a specific processing unit to view the entire process traceability information from raw monitoring data to early warning decisions. The system automatically records operation logs such as the timestamp of early warning events, response time, and parameter recovery status. This data is used to optimize the sensitivity parameters of subsequent early warning rules.

[0057] The early warning information push mechanism dynamically adjusts its presentation based on the warning level. A basic warning (yellow level) displays a flashing icon in the sidebar and triggers an audio alert once per minute. A high-level warning (red level) forces a full-screen alert window to pop up, upgrades the audio alert to a continuous beep, and simultaneously sends a push notification to the linked mobile device. All warning statuses include a countdown display, such as "pH value deviation is expected to reach the critical threshold in 8 minutes and 15 seconds," helping users predict the appropriate action time. When multiple related parameters trigger warnings simultaneously, the system automatically merges them into a combined warning event; for example, "Heavy metal group exceeds standard in sedimentation tank" replaces individual copper, lead, and cadmium ion warnings.

[0058] A closed-loop optimization mechanism is established for the handling and feedback process. The user interface provides a handling measure registration function, allowing operators to select from a standard measure library or manually input custom handling plans. The system tracks and records the entire process of each warning event from triggering to cancellation, including the handling measures adopted, parameter recovery curves, and handling time. This data is input into the case library of the knowledge base module, forming a complete closed-loop record of warning-handling-verification. When similar warning patterns recur, the system automatically recommends the most effective option from historical handling plans and highlights the success rate statistics of that plan on the interface.

[0059] The adaptive learning of the early warning system is reflected in the dynamic optimization process of the thresholds. Monthly evaluations and analyses of the early warning effectiveness are performed, statistically analyzing indicators such as false alarm rate, false negative rate, and average response time. Based on the evaluation results, the system automatically adjusts the boundary values ​​of multi-level thresholds: for parameters with frequent false alarms, the threshold range is appropriately widened; for parameters with a risk of false negatives, the threshold range is tightened. Simultaneously, the weight allocation scheme in the dynamic decision-making model is updated. For example, if a certain heavy metal ion does not trigger an early warning for three consecutive months, its weight coefficient will be gradually reduced according to a preset algorithm. This mechanism enables the early warning system to adapt to changes in operating conditions brought about by improvements in processing technology and equipment upgrades.

[0060] The user access control module is deeply integrated with the early warning system. Different levels of operators have differentiated early warning handling permissions: junior technicians can only confirm basic early warnings, senior engineers can handle all levels of early warnings and adjust temporary thresholds, and administrator accounts have rule base modification permissions. All early warning confirmation and handling operations require identity verification, and operator information is recorded in the logs. The system generates capability assessment reports based on users' historical handling performance data to guide adjustments to personnel training priorities. When the response time of a certain type of early warning consistently exceeds the standard value, a special training recommendation notification is automatically triggered.

[0061] The decision support function is activated in complex early warning scenarios. When the system detects multi-parameter chain anomalies or novel anomaly patterns, it initiates the root cause analysis engine: retrieving fault tree models from the knowledge base and matching the similarity between current anomaly characteristics and historical fault cases; invoking the three-dimensional concentration field simulation function of the state prediction module to deduce the anomaly propagation path; and comprehensively outputting a ranking list of possible causes and verification suggestions. Users can retrieve these analysis results as a reference during handling, and the system synchronously records actual verification results to optimize the accuracy of the fault tree model. The entire process forms a complete decision support chain from anomaly detection to root cause inference and handling verification.

[0062] The interface offers personalized configuration options to customize alert priority levels. Users can customize a list of key parameters to focus on; when a monitored parameter triggers an alert, it will be highlighted at the top of the interface, regardless of the current global alert level. The system learns user habits; for example, if an engineer frequently checks heavy metal data from the sedimentation tank, the interface will automatically increase the display priority of that type of data. Nighttime monitoring mode automatically enhances visual alerts, switching all alert icons to a high-contrast color scheme and increasing the sound alert volume by 20%. These detailed design features ensure the perceptibility of alert information in different work scenarios.

[0063] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0064] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A tailings wastewater treatment system, characterized in that, The system includes an overview construction module, a feature fusion module, a state prediction module, a threshold generation module, and an output module; The overview construction module is used to generate a wastewater treatment overview in the management interface based on the user's manual settings. The wastewater treatment overview includes the connection relationship between each treatment unit based on the treatment process and the treatment parameters of each treatment unit. The feature fusion module is used to collect real-time operating data of tailings wastewater, fuse multi-dimensional features of the treatment status, and generate a feature matrix. The status prediction module is used to predict the treatment status data based on the feature matrix and the wastewater treatment overview; the threshold generation module is used to generate an adaptive early warning threshold based on historical normal operation data; the output module is used to output the treatment status data and its deviation data from the early warning threshold to the user.

2. The tailings wastewater treatment system according to claim 1, characterized in that, The feature fusion module is specifically used for: performing timestamp alignment and missing value imputation on real-time running data, and generating a time-synchronized data sequence using a time-series alignment algorithm; inputting the data sequence into a feature extraction network to extract topological features in the spatial dimension and sequence features in the time dimension to obtain a preliminary feature representation; performing correlation-weighted fusion on the preliminary feature representation to highlight key features and generate a weighted feature representation; and inputting the weighted feature representation into a dimensionality reduction processor to remove redundant information through nonlinear mapping to generate the feature matrix.

3. The tailings wastewater treatment system according to claim 2, characterized in that, The state prediction module is specifically used to: calculate the remaining time between the current time and the end time of the processing cycle, determine the prediction strategy based on the remaining time, and predict the processing state data based on the prediction strategy, the feature matrix, and the connection relationship in the wastewater treatment overview.

4. The tailings wastewater treatment system according to claim 3, characterized in that, The step of determining the prediction strategy based on the remaining time includes: retrieving a first mapping relationship and a second mapping relationship, inputting the remaining time into the first mapping relationship for matching and calculation to obtain an initial prediction period; obtaining the number of types of processing parameters, inputting the number of types into the second mapping relationship to obtain a compensation period; performing a difference operation between the initial prediction period and the compensation period to obtain an optimized prediction period; and formulating the prediction strategy based on the optimized prediction period.

5. A tailings wastewater treatment system according to claim 4, characterized in that, The system also includes a knowledge base module; the knowledge base module is used to store standard processing scheme data; the state prediction module is also used to extract candidate processing scheme entries that match the processing parameters from the knowledge base module, perform semantic disambiguation optimization based on non-critical text fragments, and generate optimized processing schemes.

6. A tailings wastewater treatment system according to claim 5, characterized in that, The semantic disambiguation optimization based on non-critical text fragments includes: semantically encoding non-critical text fragments as context information to generate context semantic vectors; structurally encoding candidate processing scheme entries to generate candidate scheme embedding vectors; performing scheme selection aggregation analysis on the context semantic vectors and candidate scheme embedding vectors to generate optimized scheme response features; and semantically decoding the optimized scheme response features to obtain the optimized processing scheme.

7. A tailings wastewater treatment system according to claim 6, characterized in that, The threshold generation module is specifically used for: training a normal operating condition simulator to generate simulated normal data; constructing a real-time discriminator to learn the boundary features between normal and abnormal states through dynamic game theory; and generating multi-level early warning thresholds based on the game results using a clustering algorithm.

8. A tailings wastewater treatment system according to claim 7, characterized in that, The state prediction module is also used to: construct a three-dimensional concentration distribution field based on the feature matrix; predict the diffusion path and change trend of the concentration distribution field based on the optimization processing scheme and graph structure model; and input the prediction results into the threshold generation module.

9. A tailings wastewater treatment system according to claim 8, characterized in that, The construction of the three-dimensional concentration distribution field includes: mapping risk feature points to three-dimensional spatial coordinates, and generating a preliminary concentration distribution field using a spatial interpolation algorithm; and correcting the preliminary concentration distribution field by combining time dimension information with a dynamic interpolation algorithm to generate a concentration distribution field that changes over time.

10. A tailings wastewater treatment system according to claim 9, characterized in that, The output module is specifically used to: compare the processing status data with multi-level early warning thresholds, adjust the early warning level using a dynamic decision model, and output deviation data.

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