Integrated industrial wastewater treatment system based on coal mine roadway goaf

Abstract

The present application relates to the technical field of industrial wastewater treatment, and discloses an integrated industrial wastewater treatment system based on a coal mine roadway goaf. A wastewater input unit of the system introduces industrial wastewater into a hierarchical treatment unit in the coal mine roadway goaf, and the wastewater is treated by using the space of the goaf, thereby reducing the land occupation and construction cost of ground facilities, and the special environment of the goaf is conducive to wastewater treatment reactions. A data acquisition unit continuously collects wastewater treatment sequences and effluent quality indicators of each treatment batch, an intelligent analysis unit analyzes the fluctuation characteristics of the wastewater treatment sequences accordingly, calculates the influence weight of the effluent quality at the monitoring time, combines the similarity measurement between the treatment batches to evaluate the significance level, extracts the key monitoring time, constructs an adaptive prediction model, and real-time predicts the treatment effect of the key monitoring time in the new batch of wastewater, thereby providing a basis for treatment process adjustment and optimization, and realizing efficient and accurate treatment of industrial wastewater.

Description

Technical Field

[0001] This invention relates to the field of industrial wastewater treatment technology, specifically to an integrated industrial wastewater treatment system based on the goaf of coal mine roadways. Background Technology

[0002] Current technologies for monitoring wastewater treatment processes typically rely on water quality testing at preset time intervals, failing to adjust monitoring strategies based on the dynamic changes in the treatment process. Monitoring point selection often depends on experience, lacking quantitative assessment methods for the importance of monitoring moments. Data processing methods mostly employ static threshold alarms or simple trend analysis, struggling to capture the complex nonlinear characteristics of the treatment process. Predictive models are usually built using all monitoring data, without considering the varying contributions of different time points to the treatment effect. The system needs to address key issues such as optimized monitoring point selection, extraction of dynamic features of the treatment process, and accurate construction of predictive models.

[0003] Traditional wastewater treatment monitoring systems have significant shortcomings in data acquisition. Interval sampling may miss critical changes during the treatment process, leading to the loss of important information. Data analysis methods are limited and cannot effectively identify the characteristic changes at different treatment stages. Predictive models lack specificity and cannot adapt to the unique requirements of different water qualities and treatment processes. Existing technologies require a new type of treatment system capable of intelligently identifying key monitoring moments, accurately analyzing the dynamic characteristics of the treatment process, and establishing adaptive predictive models. The wastewater treatment environment in coal mine goaf areas is complex, necessitating more intelligent monitoring and predictive methods to ensure treatment effectiveness. Summary of the Invention

[0004] The purpose of this invention is to provide an integrated industrial wastewater treatment system based on the goaf of coal mine roadways, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides an integrated industrial wastewater treatment system based on the goaf area of ​​a coal mine roadway, the system comprising:

[0006] Wastewater input unit, multiple staged treatment units, data acquisition unit, and intelligent analysis unit;

[0007] The wastewater input unit introduces industrial wastewater into the graded treatment unit in the goaf of the coal mine roadway. The data acquisition unit continuously collects the wastewater treatment sequence and effluent quality indicators for each batch of treatment. The wastewater treatment sequence forms a curve with treatment time as the horizontal axis and pollutant concentration as the vertical axis, and the treatment time axis is divided into multiple monitoring times at equal intervals.

[0008] The intelligent analysis unit, based on the wastewater treatment sequence and the effluent quality indicators, analyzes the fluctuation characteristics of the wastewater treatment sequence for each treatment batch and each monitoring time, calculates the influence weight of each monitoring time on the effluent quality, and then, combined with the similarity measure between treatment batches, assesses the significance level of the monitoring time. Subsequently, it uses time series pattern recognition technology to extract key monitoring times and finally constructs an adaptive prediction model to predict the treatment effect of the key monitoring times in the new batch of wastewater in real time.

[0009] Preferably, when the intelligent analysis unit is configured to analyze the fluctuation characteristics of a wastewater treatment sequence, the following steps are adopted:

[0010] For each processing batch, the wastewater treatment sequence is divided into multiple overlapping time windows;

[0011] Calculate the mean and standard deviation of pollutant concentrations within each time window to form an eigenvector;

[0012] Cluster analysis was performed on the feature vectors of all processed batches to identify typical fluctuation patterns;

[0013] The influence weight of each monitoring moment is calculated based on the correlation between the fluctuation pattern of the time window to which each monitoring moment belongs and the effluent quality indicators.

[0014] Preferably, when the intelligent analysis unit is configured to calculate the impact weight of each monitoring time on the effluent quality, it employs a mutual information method, specifically including:

[0015] Calculate the mutual information value between the pollutant concentration value and the effluent quality index at each monitoring time, and use it as the initial influence weight;

[0016] To address the temporal correlation between batches, a time graph model is constructed, where nodes represent monitoring times and edges represent temporal adjacency. The initial influence weights are adjusted using a random walk algorithm to obtain the corrected influence weights.

[0017] Preferably, when the intelligent analysis unit is configured to assess the significance level of a monitoring time, the following steps are taken:

[0018] The wastewater treatment sequence of each batch is treated as a time series sample, and the pollutant concentration matrix at all monitoring times is calculated.

[0019] Principal component analysis was used to extract principal components, and the loading values ​​on the principal components at each monitoring time were calculated.

[0020] The significance score for each monitoring time point is calculated based on the magnitude of the load value and the effluent quality indicators.

[0021] Preferably, when the intelligent analysis unit is configured to extract key monitoring moments, it employs an importance propagation algorithm, specifically including:

[0022] The monitoring time is constructed as network nodes, and the edges between nodes are weighted based on time proximity and pollutant concentration similarity;

[0023] Calculate the centrality score of each node using the PageRank algorithm;

[0024] By combining the influence weights and the significance scores, the centrality scores are weighted and fused to obtain a comprehensive importance score for each monitoring time.

[0025] Select the preset number of monitoring times with the highest overall importance score as the key monitoring times.

[0026] Preferably, when the intelligent analysis unit is configured to construct an adaptive prediction model, it employs a recurrent neural network model, specifically including:

[0027] The pollutant concentration sequence at key monitoring moments is used as input features, and the effluent quality index is used as output labels. A long short-term memory network architecture is used for training, where the network parameters are updated online according to the new batch of data. During training, the backpropagation algorithm is used to optimize the weights, and regularization is added to prevent overfitting.

[0028] Preferably, the system further includes a prediction execution unit, which, when predicting the effect of the wastewater to be treated, adopts the following steps: real-time acquisition of the wastewater treatment sequence of the wastewater to be treated, and extraction of pollutant concentration values ​​at key monitoring moments; inputting the concentration values ​​into an adaptive prediction model to obtain predicted effluent quality indicators; and simultaneously, dynamically adjusting the model parameters based on the deviation between the prediction results and the actual monitoring values.

[0029] Preferably, when the intelligent analysis unit is configured to identify typical fluctuation patterns, it employs a dynamic time warping algorithm, specifically including:

[0030] Calculate the dynamic time-normalized distance between wastewater treatment sequences of different treatment batches to form a distance matrix;

[0031] Hierarchical clustering is performed based on the distance matrix, grouping the distance matrix into multiple clusters; the center sequence of each cluster represents a typical fluctuation pattern.

[0032] Preferably, the graded treatment unit includes a physical sedimentation module, a chemical reaction module, and a biodegradation module. The physical sedimentation module is configured in the entrance area of ​​the goaf in the coal mine roadway, the chemical reaction module is configured in the middle area, and the biodegradation module is configured in the outlet area. Wastewater flows through each module in sequence. The data acquisition unit is equipped with multi-parameter sensors at the outlet of each module to simultaneously collect pollutant concentration, temperature, and pressure data, thus forming a multi-dimensional wastewater treatment sequence.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] For each treatment batch and monitoring time point, the impact weight of each monitoring time point on effluent quality is calculated by analyzing the fluctuation characteristics of the wastewater treatment sequence. Fluctuation characteristics include parameters such as the amplitude, frequency, and trend of concentration changes, reflecting the dynamic characteristics of the treatment process. The impact weight quantifies the contribution of different monitoring times to the final effluent quality, identifying key control points. The significance level of monitoring times is assessed by combining batch similarity metrics; by comparing the characteristic similarity of different batches at the same monitoring time, the stability and importance of that time point are determined. Monitoring times with high significance levels represent those with a sustained and significant impact on treatment effectiveness, and these times require focused attention. This assessment method based on fluctuation characteristics and batch similarity can automatically identify truly critical time points from a large amount of monitoring data.

[0035] Time series pattern recognition technology is used to extract key monitoring moments and analyze recurring feature patterns during the treatment process. Pattern recognition algorithms can identify time periods with similar changing patterns in different batches, which often correspond to specific treatment stages or reaction processes. The extracted key monitoring moments represent the most representative time points in the treatment process, providing key observation windows for subsequent predictions. An adaptive prediction model is constructed specifically to predict the treatment effect at key monitoring moments. The model is trained using the relationship between the features of key moments in historical data and the final effluent quality to establish an accurate mapping relationship. The adaptive mechanism enables the model to continuously adjust parameters based on new data, adapting to changes in water quality and process adjustments. Real-time prediction of the treatment effect at key monitoring moments in new batches of wastewater enables proactive monitoring of the treatment process. This prediction method based on key monitoring moments improves the targeting of monitoring and the accuracy of prediction, providing a reliable basis for optimizing and adjusting the treatment process. Attached Figure Description

[0036] Figure 1 This is a schematic diagram illustrating the working principle of the integrated industrial wastewater treatment system based on the goaf of a coal mine roadway, as described in this invention. Figure 2 A flowchart for analyzing the fluctuation characteristics of wastewater treatment sequences using an intelligent analysis unit;

[0037] Figure 3A flowchart for evaluating the significance level of monitoring time for the intelligent analysis unit;

[0038] Figure 4 A graph showing the similarity between batches of industrial wastewater treatment and the changes in pollutant concentrations;

[0039] Figure 5 This is a graph showing the changes in pollutant concentrations over time for multiple batches.

[0040] Figure 6 The trend graphs of pH and temperature changes for each module;

[0041] Figure 7 This is a graph showing the correlation between temperature and the total removal rate during the biodegradation stage, along with the temperature distribution analysis. Detailed Implementation

[0042] 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.

[0043] Please see Figure 1 This invention provides an integrated industrial wastewater treatment system based on the goaf area of ​​a coal mine roadway. The system includes a wastewater input unit, multiple graded treatment units, a data acquisition unit, and an intelligent analysis unit. The wastewater input unit introduces industrial wastewater into the goaf area of ​​the coal mine roadway via pipelines and pumping equipment. The graded treatment units are distributed along the roadway, forming a continuous treatment process. The data acquisition units are deployed at the inlet and outlet positions of each treatment unit, equipped with multi-parameter water quality sensors, to continuously record the wastewater treatment sequence for each batch. This sequence forms a curve with treatment time as the horizontal axis and pollutant concentration as the vertical axis, and multiple monitoring times are divided at fixed intervals on the treatment time axis. Simultaneously, effluent quality indicators such as chemical oxygen demand (COD) and suspended solids concentration are collected. The intelligent analysis unit receives information uploaded by the data acquisition unit and performs a multi-step analysis process for each treatment batch and each monitoring time. First, it analyzes the fluctuation characteristics of the wastewater treatment sequence and calculates the influence weight of each monitoring time on the final effluent quality. Then, it combines the similarity measure between different treatment batches to evaluate the significance level of the monitoring time. Based on this, it applies time series pattern recognition technology to extract key monitoring times and finally constructs an adaptive prediction model to predict the treatment effect of new batches of wastewater at these key monitoring times in real time, thereby achieving precise control of the treatment process.

[0044] Example 1: See Figure 2In a specific implementation, the process of the intelligent analysis unit analyzing the fluctuation characteristics of the wastewater treatment sequence begins with the preprocessing of data from each independent batch of treatment. The original wastewater treatment sequence uploaded by the data acquisition unit is a list of pollutant concentration values ​​ordered by time. The intelligent analysis unit needs to divide this continuous sequence into multiple local segments for feature extraction. In the specific implementation, the segmentation operation is achieved using a fixed-length sliding time window. Each time window covers several consecutive monitoring times, for example, the window length is set to cover ten monitoring times. Five overlapping monitoring times are set between adjacent windows. This overlapping design ensures that each transition region in the sequence can be covered by multiple windows, avoiding the omission of important local fluctuation characteristics due to window segmentation. After the segmentation is completed, for each time window, the intelligent analysis unit calculates the arithmetic mean of all pollutant concentration values ​​within the window to characterize the average pollution level within that time period. At the same time, it calculates the standard deviation of these concentration values ​​to quantify the dispersion or fluctuation range of pollutant concentrations within that time period. The mean and standard deviation together constitute a two-dimensional feature vector, which serves as a numerical summary of the corresponding time window.

[0045] In practice, after the feature vectors for all time windows of all processing batches have been calculated, the intelligent analysis unit aggregates these feature vectors into a high-dimensional dataset. Next, a clustering analysis algorithm is applied to identify a limited number of typical fluctuation patterns from these numerous local features. The clustering analysis process uses the K-means algorithm, which requires a preset number of clusters K, determined by the elbow rule combined with specific business knowledge. After the algorithm runs, all feature vectors are assigned to K different clusters, ensuring that feature vectors within the same cluster have high similarity in mean and standard deviation, while feature vectors between different clusters differ significantly. The mean-standard deviation combination represented by the center point of each cluster is defined as a typical fluctuation pattern; for example, one cluster represents a stable pattern of "high mean, low volatility," while another cluster represents a drastic change pattern of "low mean, high volatility." Each monitoring moment is assigned to a specific fluctuation pattern category based on its time window.

[0046] In practical implementation, the core of calculating the impact weight of monitoring time points is to analyze the correlation strength between the fluctuation pattern and the final effluent quality indicators. The intelligent analysis unit first calculates the correlation coefficient, such as the Pearson correlation coefficient, between each identified typical fluctuation pattern and the effluent quality indicators. The larger the absolute value of the correlation coefficient, the more significant the impact of the fluctuation pattern on the effluent quality. Specifically, the intelligent analysis unit first identifies typical fluctuation patterns through cluster analysis, with each pattern representing a group of local time windows with similar pollutant concentration change characteristics. For each identified fluctuation pattern, the intelligent analysis unit extracts the feature vectors (including the mean and standard deviation of pollutant concentrations) corresponding to all time windows belonging to that pattern, and performs correlation analysis between these feature vectors and the final effluent quality indicators (such as chemical oxygen demand or suspended solids concentration) of the corresponding treatment batch. When calculating the correlation coefficient, the Pearson correlation coefficient method is used, which analyzes the degree of linear correlation between the feature values ​​(such as the mean or standard deviation) of the fluctuation pattern and the effluent quality indicators. Specifically, the system calculates the ratio of the covariance and standard deviation of the characteristic sequence to the effluent quality index sequence for each fluctuation pattern, thereby quantifying the predictive ability of that fluctuation pattern for effluent quality. This process relies on historical batch data, ensuring the objectivity of the assessment and eliminating the need for preset empirical thresholds. Through correlation analysis, the system can screen out fluctuation patterns highly correlated with effluent quality, providing a basis for subsequent calculation of the influence weights at monitoring times. For any given monitoring time, its initial influence weight is determined by the absolute value of the correlation coefficient corresponding to the fluctuation pattern within its time window. This initial weight only considers the local window pattern within which the monitoring time is located and does not yet consider the mutual influence relationships between different treatment batches over time.

[0047] In practical implementation, to correct the initial influence weights and more accurately reflect the true importance of monitoring moments in continuous processing, a time-dependent model between processing batches needs to be introduced. This step is accomplished by constructing a time graph model. The nodes of the time graph model represent each monitoring moment, and the edges connect adjacent monitoring moment nodes on the time axis. The edge weights can be set inversely proportional to the time interval, indicating that nodes closer in time have a stronger influence. A random walk algorithm is then executed on the constructed time graph model. The random walk algorithm simulates a virtual walker moving randomly along the edges of the graph, and the "influence" carried by the walker spreads between nodes. Using the initial influence weight of each monitoring moment as the initial attribute value of the node, through multiple iterations of the random walk process, the weight value of each node changes due to the influence of the weight values ​​of its neighboring nodes, eventually reaching a stable state. In practical implementation, the iterative process enhances the final influence weight of monitoring moments with high initial weights and high initial weights in their surrounding neighboring nodes; while the final influence weight of monitoring moments with high initial weights but in a low-weight neighbor environment is moderately weakened. The corrected influence weights, obtained after adjustment by the random walk algorithm, not only include information about the local fluctuation patterns at the monitoring time, but also incorporate their position information in the overall time series context, thus making them more comprehensive and robust.

[0048] It is understandable that fluctuation feature analysis is the foundation of the entire weight calculation, and the granularity of the sliding window directly affects the precision of subsequent feature extraction. An excessively long window will contain too many heterogeneous fluctuations, causing the calculated mean and standard deviation to lose representativeness; a window that is too short will fail to capture meaningful fluctuation trends. The size of the overlapping region relates to the smoothness of the analysis; a larger overlapping region makes the analysis results less sensitive to the starting position of the window, enhancing stability. The number of clusters, K, is a key parameter in cluster analysis. A K value that is too small will cause different fluctuation patterns to be forcibly merged, masking important details; a K value that is too large will generate too many fragmented patterns, increasing computational complexity and leading to overfitting. In practical implementation, these parameters need to be optimally determined through cross-validation using historical data. Specifically, the intelligent analysis unit uses the K-means algorithm for cluster analysis, and the selection of the K value is achieved by combining the elbow rule with specific business knowledge. The core of the elbow rule is to analyze the sum of squared clustering errors (i.e., the sum of squared distances between data points within a cluster and the cluster center) corresponding to different K values. As the K value increases, the sum of squared errors decreases, but the rate of decrease gradually slows. The elbow rule identifies the inflection point (the elbow point) in the curve of decreasing sum of squared errors as a potential K value. This point indicates that increasing the number of clusters significantly reduces the contribution to improving model accuracy. Simultaneously, the system incorporates business knowledge for validation. For example, based on the number of stages in wastewater treatment processes or common fluctuation patterns in historical data, the K value determined by the elbow point is fine-tuned to ensure that the clustering results both conform to the data distribution characteristics and have practical interpretability. This method avoids subjective settings and guarantees the objectivity and adaptability of cluster analysis.

[0049] It is understandable that the application of mutual information methods provides a way to measure the nonlinear relationship between monitoring time and effluent quality. Compared to linear correlation coefficients, mutual information can capture more complex dependencies. In practice, before calculating the mutual information value, the continuous concentration values ​​and effluent quality indicators need to be discretized, i.e., divided into several intervals, transforming continuous variables into discrete variables, and then the mutual information value is calculated based on the discretized data. The number of discretized intervals needs to be balanced between computational accuracy and efficiency. The construction of the time graph model reflects the temporal continuity of the processing, and the random walk algorithm is an effective graph algorithm that utilizes this continuity to smooth and propagate node weights. Hyperparameters such as transition probability and restart probability in the random walk also need to be optimized experimentally to achieve the best weight correction effect.

[0050] In practice, the entire fluctuation characteristic analysis and impact weight calculation process is automated. Upon receiving a new batch of processed data, the intelligent analysis unit automatically triggers the above calculation process without manual intervention. The calculated corrected impact weight for each monitoring moment is stored for subsequent significance level assessment and key monitoring moment extraction steps. The entire process design fully considers the temporality, fluctuations, and batch-to-batch similarity of the wastewater treatment process, aiming to extract the moment information that truly has a decisive impact on the final effluent quality from massive amounts of monitoring data.

[0051] Example 2: See Figure 3 In a specific implementation, the process by which the intelligent analysis unit assesses the significance level of monitoring times begins with constructing a pollutant concentration matrix encompassing all historical treatment batch data. Each wastewater treatment sequence provided by the data acquisition unit is treated as an independent sample. The intelligent analysis unit rigorously aligns all samples according to the monitoring times on the timeline, forming a two-dimensional data matrix with rows equal to the total number of treatment batches and columns equal to the total number of monitoring times. Each element in the matrix represents the pollutant concentration value collected for a specific treatment batch at a specific monitoring time. This pollutant concentration matrix serves as the foundational data carrier for subsequent multivariate statistical analysis. In practice, the constructed pollutant concentration matrix typically undergoes preprocessing steps, such as standardizing the concentration values ​​in each column. This involves subtracting the mean of all values ​​in that column and dividing by the standard deviation, ensuring that the mean of the concentration data at each monitoring time is 0 and the variance is 1. This eliminates the impact of dimensional differences between different monitoring times on subsequent analysis.

[0052] In practice, principal component analysis (PCA) is applied to the standardized pollutant concentration matrix. PCA is a dimensionality reduction technique designed to compress relevant information from multiple monitoring times into a few uncorrelated principal components. The calculation process first solves for the covariance matrix of the pollutant concentration matrix, then calculates the eigenvalues ​​and eigenvectors of the covariance matrix. The magnitude of the eigenvalues ​​reflects the variance contribution along the corresponding eigenvector direction. The first few eigenvectors are selected as principal component directions in descending order of eigenvalues, ensuring that the cumulative variance contribution rate of the selected principal components reaches a preset high threshold (e.g., above 85%). Each principal component is a linear combination of the variables from the original monitoring times, and the coefficients are the elements in the eigenvectors. Next, the loading values ​​of each original monitoring time variable on each principal component are calculated. The loading values ​​are the correlation coefficients between the monitoring time variables and the principal components, and their absolute values ​​directly reflect the degree of contribution of the monitoring time variable to the formation of that principal component. A high loading value on a principal component with a large variance contribution indicates that the monitoring time plays an important role in explaining the variation pattern of the entire wastewater treatment sequence.

[0053] In practice, after obtaining the loading values ​​of each principal component at each monitoring time, it is necessary to further combine them with effluent quality indicators to calculate the significance score of the monitoring time. The intelligent analysis unit calculates the correlation coefficient between the pollutant concentration sequence and the effluent quality indicator vector at each monitoring time, such as the Pearson correlation coefficient. This correlation coefficient quantifies the predictive ability of the concentration level at a single monitoring time on the final effluent quality. The significance score of the monitoring time is a comprehensive reflection of the loading information and the correlation information. Specifically, the calculation can be to take a weighted sum of the absolute values ​​of the loadings of the monitoring time on the first few principal components to obtain a score representing the overall variability contribution of the monitoring time. Then, this score is multiplied or weighted and fused with the absolute value of the correlation coefficient between the monitoring time and the effluent quality indicator. The final value obtained is the significance score of the monitoring time. The higher the significance score, the more prominent the importance of the monitoring time, indicating that it not only has important variability characteristics during the treatment process, but also that these characteristics are closely related to the final treatment effect.

[0054] In practical implementation, factor analysis is used to isolate external interference when assessing the probability values ​​of non-treatment factors. The intelligent analysis unit systematically collects non-treatment factor data for each treatment batch during operation. This data typically includes environmental parameters such as ambient temperature, influent pH, and influent flow rate fluctuations, which affect the treatment process but are not core process control factors. The collected non-treatment factor data is integrated with the pollutant concentration matrix to form a larger mixed data table. Factor analysis is then performed on this mixed data table. The goal of factor analysis is to extract a smaller number of non-observable common factors from numerous observed variables. These common factors represent the underlying root causes driving the coordinated changes in all observed variables. Methods for extracting common factors can include principal component analysis or maximum likelihood analysis. Factor rotation is often used to simplify the structure of the factor loading matrix, facilitating the interpretation of the actual meaning of each common factor.

[0055] In practice, after extracting common factors, it is necessary to quantify the degree to which pollutant concentrations at each monitoring time are affected by these common factors. The correlation coefficient, or factor loading, between the pollutant concentration variable at each monitoring time and each extracted common factor is calculated. For a given monitoring time, the factor loading with the largest absolute value among all common factors is identified, and this maximum value is defined as the "influence degree" of the non-treatment factors at that monitoring time. The larger this influence degree value, the more the concentration change at that monitoring time is driven by the extracted common factors, rather than by the inherent laws of the treatment process itself. Next, the influence degree values ​​calculated for all monitoring times are normalized. The purpose of normalization is to make the sum of the influence degree values ​​for all monitoring times equal to 1. The normalized result is the probability value of the non-treatment factors for each monitoring time. The higher the probability value of the non-treatment factors, the greater the possibility that the concentration reading at that monitoring time is affected by external random factors, and the lower its reliability in the subsequent selection of key monitoring times.

[0056] Principal component analysis (PCA) is understood to be applied based on the correlations between variables at different monitoring times within the wastewater treatment sequence. Extracting principal components effectively identifies common patterns of sequence variation, and high loadings on the main patterns at monitoring times are important indicators of significance. Combining this with correlation analysis of effluent quality indicators ensures that significance assessment is always guided by the final treatment effect, avoiding bias by focusing solely on internal variations and deviating from the actual objective. Factor analysis provides another important perspective, helping to distinguish between intrinsic signals and external noise in the treatment process. Calculating the probability values ​​of non-treatment factors allows the system to prioritize monitoring times controlled by the process itself and with strong anti-interference capabilities as key points, thereby improving the robustness and accuracy of the constructed prediction model. The quality of the pollutant concentration matrix directly determines the effectiveness of PCA and factor analysis; therefore, accurate data alignment and proper handling of missing data are crucial prerequisites. The selection of the number of principal components and common factors needs to be based on objective standards and domain knowledge. There are different variations in the formulas for calculating the significance score and the probability value of untreated factors at the monitoring time. For example, different weights can be assigned to loadings and relevance in the calculation of the significance score, and the calculation of the probability value of untreated factors can consider some aggregation of all factor loadings rather than just the maximum value. The choice of these specific function forms can be determined by cross-validation on historical data. The entire evaluation process is completely data-driven, relying on data accumulated from historical batches. As the number of processing batches increases, the reliability of the evaluation results will continuously improve.

[0057] See Figure 4 In the intelligent analysis of integrated industrial wastewater treatment systems based on coal mine goaf areas, Figure 4In the graph, 'a' represents a heatmap of similarity between treatment batches (based on the last five monitoring times). By constructing a pollutant concentration matrix, a similarity metric is used to present the degree of correlation between different treatment batches. The intensity of color in the heatmap corresponds to the magnitude of the similarity value; darker areas indicate high similarity between treatment batches, reflecting strong consistency in the fluctuation characteristics of these batches in the wastewater treatment sequence. This analysis provides fundamental data support for subsequent clustering and assessment of the significance of monitoring times, helping to identify batch groups with similar treatment patterns. Figure 4 Figure 'b' in the graph represents the pollutant concentration variation of typical treatment batches from different clusters. After cluster analysis of the treatment batches, typical batches from each cluster are selected to show the pollutant concentration trends over monitoring time (hours) in the three stages of physical sedimentation, chemical reaction, and biodegradation. The differences in the pollutant concentration variation curves of typical batches from different clusters reflect the pollutant degradation patterns in the wastewater treatment process under different fluctuation modes. This graph allows for a direct comparison of the pollutant concentration change rates and stage transition characteristics of different cluster batches at each treatment stage. Combined with effluent quality indicators, it enables further analysis of the impact of different treatment modes on the final treatment effect, providing a basis for extracting key monitoring moments and constructing adaptive prediction models.

[0058] Example 3: In a specific implementation, the intelligent analysis unit extracts key monitoring moments using an algorithm based on network structure and node importance propagation. The core idea is to model the points and their relationships on the time series as a graph and score their importance. The implementation process begins with constructing a monitoring moment network graph. The nodes in the network graph correspond to each monitoring moment on the wastewater treatment time axis. The connecting edges between nodes are established according to a dual criterion. The first criterion is temporal proximity, i.e., an undirected edge is automatically established between directly adjacent monitoring moment nodes on the time axis. The second criterion is the morphological similarity of pollutant concentration sequences. The dynamic time-normalized distance or cosine similarity between the concentration subsequences within the local time window of any two monitoring moment nodes is calculated. If the similarity exceeds a preset threshold, an undirected edge is established between the two nodes. In practice, the weight of each edge needs to quantify the closeness of the relationship between nodes. For edges established based on time proximity, their weight can be set to be inversely proportional to the time interval between two monitoring nodes. The shorter the time interval, the higher the weight. For edges established based on morphological similarity, their weight is directly proportional to the calculated sequence similarity value. Finally, each edge obtains a comprehensive weight value.

[0059] In practice, after the network graph at the monitoring time is constructed, an improved PageRank algorithm is run on it to calculate the centrality score of each node. The classic PageRank algorithm simulates the random walk behavior of a random surfer on a web page graph, iteratively calculating until convergence to obtain the importance score of each web page. When applied to the network graph at the monitoring time, nodes in the network graph correspond to web pages, and edges correspond to hyperlinks. The improvement lies in incorporating the edge weights into the calculation of the transition probability; the probability that a random surfer will move from the current node to any neighboring node is proportional to the weight of the connecting edge. The iterative calculation formula of the PageRank algorithm is as follows:

[0060]

[0061] Where: PR(u) represents the PageRank centrality score of node u, d is the damping factor, usually set to 0.85, N represents the total number of nodes in the network, B(u) represents the set of all neighboring nodes pointing to node u, and W... v→u Let W(v) represent the weight of the edge pointing from node v to node u, and let W(v) represent the sum of the weights of all outgoing edges originating from node v. The algorithm iteratively updates the PageRank centrality scores of all nodes until the score change is less than a minimum threshold, indicating that the computation has converged. After convergence, each node receives a PageRank centrality score at each monitoring time. This score reflects the node's global importance in the network, depending not only on the node's direct connections but also on its location within the network.

[0062] In practice, after obtaining the PageRank centrality score, it needs to be fused with the previously calculated influence weights and significance scores to obtain a comprehensive importance score for each monitoring time. The influence weights reflect the strength of the correlation between the local fluctuation pattern and effluent quality at each monitoring time and its temporal context, while the significance score reflects the contribution of each monitoring time to the overall sequence variation pattern and its direct correlation with effluent quality. The comprehensive importance score is calculated using a weighted linear fusion method: Comprehensive Importance Score = α × (Normalized PageRank Centrality Score) + β × (Normalized Influence Weight) + γ × (Normalized Significance Score). Here, α, β, and γ are weight coefficients, and α + β + γ = 1. The specific values ​​of the weight coefficients α, β, and γ need to be determined through grid search or optimization algorithms on historical data, with the goal of ensuring that the finally selected key monitoring times can most accurately predict effluent quality. In some embodiments, a higher weight γ can be assigned to the significance score because it is directly related to sequence variation and effluent quality. After calculating the comprehensive importance score for all monitoring times, they are sorted from highest to lowest score.

[0063] In some embodiments, the strategy for selecting key monitoring moments is to pre-set a fixed number, such as selecting the top 20 monitoring moments in terms of overall importance score, or selecting the top 10%. Another strategy is to pre-set an overall importance score threshold and select all monitoring moments with scores higher than that threshold. These selected key monitoring moments are considered to be the most informative and predictive time points in the entire wastewater treatment process, and they will serve as input features for subsequently building predictive models. It is understood that the extraction method based on network importance propagation can capture the complex spatiotemporal dependencies between monitoring moments, while multi-indicator fusion ensures that the selection process considers both the structural importance of nodes in the network and their direct correlation with the target variable, thereby filtering out more representative key points.

[0064] In this specific implementation, the adaptive prediction model employs a Long Short-Term Memory (LSTM) network architecture within a recurrent neural network. The model's input is a sequence of pollutant concentration values ​​for each treatment batch at key monitoring moments, arranged chronologically. The output is the corresponding effluent quality index value. The internal structure of an LTM network unit includes an input gate, a forget gate, an output gate, and a cell state, enabling selective memorization and forgetting of information to effectively capture long-term dependencies in the time series. The network structure typically includes an input layer, one or more LTM hidden layers, and a fully connected output layer. Training uses historical treatment batch data as the training set. The loss function is typically mean squared error, and the optimization algorithm employs a variant of backpropagation combined with gradient descent, such as the Adam optimizer. To prevent overfitting on the training data, regularization techniques are introduced during training. For example, a Dropout layer is added after the LTM layers to randomly discard some neuron outputs, or an L2 regularization term is added to the loss function to penalize the network weights. The goal of model training is to find a set of network weight parameters that minimizes the model's prediction error on the training set.

[0065] In some embodiments, a key feature of the adaptive prediction model lies in its online learning capability. After initial training, when new batches of treatment are completed and new input-output data pairs are generated, the prediction model does not remain static but incrementally updates its parameters with the new data at a small learning rate. This online update mechanism allows the model to continuously adapt to slow changes occurring during the treatment process, such as microbial community succession and changes in packing material performance, thereby maintaining the durability of its prediction performance. It is understandable that the Long Short-Term Memory (LSTM) network model is chosen because wastewater treatment sequences are typical time-series data, with strong correlations between treatment states at different times; LTM models are well-suited for handling this type of data. Successful model construction heavily relies on the selection of high-quality key monitoring moments. If the input features contain truly important time-point information, the model can more effectively learn the mapping relationship from the treatment process to effluent quality. Specifically, model construction begins with the extraction of key monitoring moments, which are selected as the most representative time points through a comprehensive evaluation of influence weights, significance scores, and network centrality scores. The intelligent analysis unit uses the pollutant concentration sequence at key monitoring moments, arranged chronologically, as input features and effluent quality indicators as output labels. It employs a long short-term memory (LSTM) network architecture within a recurrent neural network to construct an adaptive prediction model. During training, the model learns the nonlinear mapping relationship between the input sequence and output labels using historical batch data. It optimizes network weights through a backpropagation algorithm and adds regularization techniques to prevent overfitting. The model's adaptive characteristics are reflected in its ability to update parameters online based on new batch data. For example, when a new batch is processed, the system adjusts the network weights incrementally to ensure the model continuously adapts to changes in water quality. The quality of the selected key monitoring moments directly determines the information content of the input features. If the moment contains the core dynamic features of the treatment process, the model can more accurately capture the mapping pattern from the treatment sequence to effluent quality, thereby improving prediction accuracy.

[0066] Example 4: In a specific implementation, the predictive execution unit is responsible for predicting the online effects of newly input wastewater using a pre-built adaptive predictive model. The predictive execution unit establishes a data interface with the data acquisition unit and the intelligent analysis unit. When a new batch of industrial wastewater enters the graded treatment unit in the goaf of a coal mine roadway through the wastewater input unit to begin the treatment process, the predictive execution unit is activated. In the specific implementation, the predictive execution unit first instructs the data acquisition unit to collect the flowing wastewater treatment sequence in real time at a higher frequency. This sequence contains a continuous data stream showing the change in pollutant concentration over time at each monitoring point from the start of treatment. The predictive execution unit obtains a pre-determined list of key monitoring moments from the intelligent analysis unit. These key monitoring moments are specific time points with the highest predictive power for the final effluent quality, derived from historical data analysis. The predictive execution unit continuously monitors the real-time inflowing wastewater treatment sequence. When the treatment time reaches each key monitoring moment, it accurately extracts the pollutant concentration measurement value corresponding to that moment and arranges these concentration values ​​from different key monitoring moments in chronological order to form a state vector representing the characteristics of the current treatment batch.

[0067] In practice, after extracting the feature vectors, the prediction execution unit uses them as input features and feeds them into a pre-trained adaptive prediction model. The adaptive prediction model is typically a recurrent neural network model whose internal parameters have learned a complex nonlinear mapping relationship between concentration patterns at key monitoring moments and effluent quality through extensive historical data. Upon receiving the input vectors, the model performs forward propagation calculations and generates one or more predicted values ​​at the output layer. These predicted values ​​represent the predicted final effluent quality indicators for this batch of wastewater, such as the predicted residual chemical oxygen demand (COD) concentration or total suspended solids (TSS) content. The prediction results are displayed in real-time on the operator's interface, providing a basis for decision-making regarding possible process parameter interventions and adjustments. This key-point-based prediction method significantly reduces reliance on continuous monitoring data, improves the timeliness and computational efficiency of predictions, and enables the prediction of the final results early in the treatment process.

[0068] In practice, the predictive execution unit's function extends beyond single-time predictions; its core lies in establishing a closed-loop dynamic feedback adjustment mechanism. As the wastewater treatment process progresses, the actual effluent quality indicators are eventually measured. The predictive execution unit compares the previously obtained predicted effluent quality indicators with the actual measured ones, calculating the deviation between the two. The deviation is typically calculated using absolute or relative error. The system presets a deviation threshold; when the calculated deviation exceeds this threshold, it indicates a significant decline in the model's predictive performance, possibly due to changes in influent water quality or the treatment environment. At this point, the predictive execution unit triggers a dynamic adjustment procedure for the model parameters. This dynamic adjustment procedure uses new input-output data pairs (i.e., the concentration vector at key monitoring moments and the actual effluent quality indicators for this batch of treatment) as samples for incremental learning, fine-tuning the network weight parameters of the adaptive prediction model with a small learning rate. This online learning method enables the model to continuously adapt to the latest system state, maintaining predictive accuracy and achieving model adaptation and evolution.

[0069] In some embodiments, the process of identifying typical fluctuation patterns relies on a combination of dynamic time warping (RTW) and hierarchical clustering. RTD is used to calculate the similarity distance between wastewater treatment sequences from different batches. RTD effectively handles the stretching and distortion phenomena that exist on the time axis of time series by finding the optimal nonlinear alignment path between two sequences and calculating the cumulative distance along this path as the RTD distance. The intelligent analysis unit calculates the RTD distance between all pairs of historical treatment batches, thereby constructing a symmetric distance matrix that quantitatively describes the degree of morphological difference between any two batch sequences. Referring to Table 1, a simplified RTD distance matrix is ​​shown, where rows and columns represent different treatment batch numbers.

[0070] Table 1: Dynamic Time-Regulated Distance Matrix Between Processing Batches

[0071]

[0072] In practice, based on the calculated dynamic time-warped distance matrix, a hierarchical clustering algorithm is used to group all processing batches. The hierarchical clustering algorithm initially treats each processing batch as an independent cluster, then iteratively merges the two closest clusters to form a new, larger cluster. The distance between clusters can be defined as the average or minimum dynamic time-warped distance between all member sequences in different clusters. The merging process continues, forming a tree-like clustering structure. By setting a distance threshold or a preset number of clusters, a cut-off point can be determined on this tree structure, thus grouping all processing batches into multiple clusters. Ultimately, the wastewater treatment sequences within each cluster are considered to have highly similar fluctuation patterns, i.e., belonging to the same typical fluctuation mode. For each cluster, the average sequence of all its sequences can be calculated, or the sequence with the smallest average dynamic time-warped distance to other sequences within the cluster can be selected as the cluster's center sequence. This center sequence represents its corresponding typical fluctuation mode and is used for subsequent fluctuation characteristic analysis and monitoring time-influence weight calculation. Alternatively, the number of clusters can be determined by combining the shape of the dendrogram with specific business requirements.

[0073] See Figure 5 The figure presents the sequence of pollutant concentration changes over treatment time (hours) for different batches (batch 001 to batch 010), with the vertical axis representing pollutant concentration (mg / L). Different batches are distinguished by different colors and shapes, and key monitoring times (times 3, 7, 12, 16, and 20) are also marked. From a professional perspective, these curves reflect the dynamic decay process of pollutant concentration over time in the graded treatment unit of the coal mine goaf area for each batch of wastewater. This can be used by the intelligent analysis unit to identify typical fluctuation patterns, calculate the influence weight of monitoring times, and extract key monitoring times. For example, different batches show differences in pollutant concentration at the initial time (0 hours), and the concentration generally decreases as treatment time progresses. Furthermore, the concentrations of each batch exhibit different fluctuation characteristics at key monitoring times. These characteristics form the basis for the intelligent analysis unit to perform cluster analysis, mutual information calculation, and principal component analysis, providing data support for building an adaptive prediction model to predict the treatment effect of new batches of wastewater in real time.

[0074] Example 5: In a specific implementation, the graded treatment unit is spatially arranged along the natural direction of the abandoned coal mine roadway, making full use of the continuous underground space formed by the goaf. The graded treatment unit includes three core parts: a physical sedimentation module, a chemical reaction module, and a biodegradation module. The physical sedimentation module is located in the entrance area of ​​the goaf in the coal mine roadway. This area is usually connected to the surface water inlet pipe and has a relatively open space, which facilitates the construction of a large sedimentation tank structure. After industrial wastewater is injected into the physical sedimentation module through the wastewater input unit, the water flow velocity is significantly reduced, and the suspended solid particles in the wastewater naturally settle to the bottom under the action of gravity, initially achieving mud-water separation. The supernatant after treatment by the physical sedimentation module enters the next module through an overflow weir or a bottom connecting pipe. The chemical reaction module is located in the middle section of the goaf in the coal mine roadway. This section of the roadway is relatively straight and has stable geological conditions. The chemical reaction module contains reaction tanks connected in series or parallel. Coagulants, neutralizing agents, or advanced oxidants are quantitatively added to the tanks through a dosing system. Wastewater and chemicals come into full contact in the reaction tank through mechanical stirring or hydraulic mixing, resulting in neutralization, coagulation and sedimentation, or redox reactions. This effectively removes dissolved heavy metal ions, some recalcitrant organic matter, and adjusts the pH of the wastewater. The turbidity and color of the wastewater treated by the chemical reaction module are usually significantly reduced. The biodegradation module is located in the outlet area of ​​the goaf in the coal mine roadway. This area is close to the final outlet, has a relatively closed environment, and a stable temperature. The biodegradation module is filled with biological packing material or maintains a certain concentration of activated sludge. Utilizing the metabolic action of attached or suspended microbial populations, the residual organic pollutants in the wastewater are decomposed into carbon dioxide, water, and biomass, achieving deep purification. The wastewater treated by the biodegradation module is finally discharged through the drainage system or reused. Throughout the entire treatment process, wastewater flows sequentially through the physical sedimentation module, chemical reaction module, and biodegradation module by gravity flow or low-head pumping, forming a continuous multi-stage purification chain.

[0075] In practical implementation, to comprehensively monitor the treatment process, the data acquisition unit installs a multi-parameter sensor array at the outlet of each treatment module. For example, sensors are installed on the outlet pipe of the physical sedimentation module to monitor changes in water quality after sedimentation. The multi-parameter sensor array integrates various sensor probes, including optical sensors for measuring turbidity and specific pollutant concentrations, electrochemical sensors for measuring pH, dissolved oxygen, and redox potential, and pressure sensors for monitoring fluid pressure within the pipe. These sensors operate in a synchronously triggered manner, simultaneously acquiring data at preset fixed time intervals, such as every five minutes. Each acquired data packet contains pollutant concentration values, water temperature readings, and pressure values ​​at the same timestamp. Over time, these time-sequentially arranged multi-dimensional data points constitute a multi-dimensional wastewater treatment sequence. This sequence not only records the evolution of pollutant concentrations but also includes key physical parameters affecting treatment efficiency. Optionally, the sensor array is installed in a pipe section with stable water flow at the module outlet to avoid interference from turbulence on measurement accuracy.

[0076] In some embodiments, the specific structure of the physical sedimentation module can be designed according to the cross-sectional shape of the tunnel. For arched tunnels, the sedimentation tank can be designed with a rectangular or trapezoidal cross-section, and a sludge scraper can be installed to periodically remove the bottom sludge. The reaction tank of the chemical reaction module can be constructed with corrosion-resistant materials and equipped with an automatic dosing pump to adjust the dosage of chemicals in real time according to the influent flow rate or water quality parameters. The biological packing material of the biodegradation module can be selected as a combination packing material or elastic packing material, providing a large attachment area for microorganisms. If necessary, an aeration system can be installed to provide oxygen for aerobic microorganisms. It can be understood that this layout method relying on the tunnels in the goaf not only saves surface construction land, but also utilizes the natural advantages of constant temperature and humidity in the underground space, providing a stable growth environment for microorganisms, especially in the biodegradation module. The multidimensional wastewater treatment sequence obtained by the data acquisition unit provides a rich data foundation for the intelligent analysis unit. Temperature data helps to determine the reaction rate of the chemical reaction module and the microbial activity of the biodegradation module, while pressure data can indirectly reflect pipeline blockage or flow changes. These multidimensional information are input into the intelligent analysis unit, making the subsequent fluctuation characteristic analysis, influence weight calculation, and prediction model construction more accurate and reliable.

[0077] Optionally, each sensor in the data acquisition unit must undergo rigorous calibration before installation to ensure the accuracy of the measurement data, and calibration records are stored for future reference. The raw analog signals acquired by the sensors are converted into digital signals by a signal conditioning circuit, then acquired by the data acquisition card, and transmitted to the intelligent analysis unit server located on the ground via an industrial bus or wireless transmission module. Optionally, to cope with the potentially humid and dusty environment in coal mines, the sensor housings and junction boxes must meet the corresponding protection level requirements. In some embodiments, the data acquisition unit can be powered by an intrinsically safe power supply to ensure safe operation in potentially explosive environments. The multidimensional wastewater treatment sequence undergoes data compression during transmission to save bandwidth, and is then decompressed and reconstructed at the intelligent analysis unit.

[0078] See Figure 6 The figure contains two subplots, each depicting the dynamic changes in pH and temperature across different processing modules within the system. Figure 6 The graph 'a' shows the pH changes of each module. The pH of the physical sedimentation module (blue curve) generally remained in the range of 7.4-7.9, exhibiting a multi-peak fluctuation, reflecting the dynamic adjustment of water acidity and alkalinity during the treatment process. The pH of the chemical reaction module (orange curve) mainly fluctuated in the range of 7.2-7.6, showing a significant difference from the pH change trend of the physical sedimentation module. This reflects the impact of acid-base neutralization and other reactions during the chemical reaction process on the water pH. Figure 6 Figure 'b' shows the temperature changes of each module. The temperature of the physical sedimentation module (blue curve) fluctuates approximately within the range of 18.0-18.6℃, while the temperature of the biodegradation module (orange curve) fluctuates within the range of 18.4-18.9℃, generally higher than that of the physical sedimentation module. This temperature difference is related to the treatment process characteristics of each module. The biodegradation module relies on microbial activity, and a suitable temperature range helps maintain the metabolic efficiency of the microorganisms. In contrast, the temperature changes of the physical sedimentation module are more affected by influent and environmental factors. These dynamic pH and temperature change data provide important multidimensional information support for the intelligent analysis unit to evaluate the treatment effect of each module, extract key monitoring moments, and build adaptive prediction models. This helps to deepen the understanding of the influence mechanism of non-treatment factors (such as pH and temperature) on the industrial wastewater treatment process.

[0079] Example 6: An integrated industrial wastewater treatment system based on coal mine goaf areas is adaptable to the comprehensive remediation of polluted coal mines, treating contaminated groundwater within the goaf areas. The wastewater input unit introduces groundwater rich in pollutants such as sulfides, ammonia nitrogen, sulfates, and chlorides into a multi-stage treatment unit deployed within the goaf area via pipelines. Physical sedimentation, chemical reaction, and biodegradation modules are sequentially distributed along the goaf roadways, allowing groundwater to flow through each module for multi-stage purification. The data acquisition unit deploys multi-parameter sensors at the outlets of each module to simultaneously collect the wastewater treatment sequence and effluent quality indicators for each batch. The wastewater treatment sequence is plotted as a curve with treatment time on the horizontal axis and pollutant concentration on the vertical axis. Multiple monitoring points are evenly spaced along the treatment time axis. The sensors simultaneously collect data such as pollutant concentration, temperature, pH value, and conductivity, forming a multi-dimensional wastewater treatment sequence.

[0080] After receiving the data, the intelligent analysis unit divides the wastewater treatment sequence into multiple overlapping time windows for each treatment batch and each monitoring time. It calculates the mean and standard deviation of pollutant concentrations within each window to form a feature vector. Cluster analysis is performed on the feature vectors of all batches to identify typical fluctuation patterns. Based on the correlation between the fluctuation pattern of the monitoring time's window and the effluent quality indicators, the influence weight of that monitoring time is calculated. The mutual information value between pollutant concentration and effluent quality indicators at each monitoring time is calculated using the mutual information method as the initial weight. A time-plot model is constructed, and the initial weights are adjusted using a random walk algorithm to obtain the corrected influence weights. The wastewater treatment sequence of each batch is treated as a time-series sample. The pollutant concentration matrix for all monitoring times is calculated. Principal component analysis is used to extract the principal components, and the loading values ​​of the monitoring times on the principal components are calculated. Combining the loading values ​​and effluent quality indicators, the significance score for each monitoring time is calculated. Data on non-treatment factors such as changes in ambient temperature and pH are collected and factor analysis is performed with the pollutant concentrations at the monitoring times. Common factors are extracted, and correlation coefficients are calculated as influence values. After normalization, the probability values ​​of the non-treatment factors are obtained. The monitoring times are constructed as network nodes. The edges between nodes are weighted based on time proximity and similarity of pollutant concentration. The PageRank algorithm is used to calculate the centrality score of each node. The centrality scores are combined with the influence weight and significance score to obtain a comprehensive importance score. The monitoring times with the highest scores are selected as the key monitoring times.

[0081] The intelligent analysis unit uses pollutant concentration sequences at key monitoring moments as input features and effluent quality indicators as output labels. It employs a long short-term memory (LSTM) network architecture to construct an adaptive prediction model, with regularization added during training to prevent overfitting. Model parameters can be updated online based on new batches of data. The prediction execution unit collects the wastewater treatment sequence in real time, extracts pollutant concentration values ​​at key monitoring moments, inputs them into the adaptive prediction model, and obtains predicted effluent quality indicators. Model parameters are dynamically adjusted based on the deviation between the predicted results and actual monitoring values. During treatment, for high-salinity wastewater, nanofiltration, reverse osmosis concentration, and MVR evaporation modules can be added after the staged treatment unit to further separate salts from the wastewater. The treated water can be used for coal pile spraying or roadside greening, while solid salts are disposed of according to relevant standards. Meanwhile, combined with the hydrogeological conditions of the polluted coal mine, the data acquisition unit can simultaneously monitor the changes in groundwater level and water quality on both sides of the fault around the goaf. When extracting key monitoring moments, the intelligent analysis unit incorporates relevant monitoring data on the water-retaining performance of the fault, making the treatment process adaptable to the needs of pollution boundary risk management and helping to achieve efficient treatment and risk management of polluted groundwater in the coal mine goaf.

[0082] See Figure 7 The diagram contains two sub-diagrams, which are analyzed from a professional perspective as follows: Figure 7 The graph 'a' shows the correlation between temperature and the total removal rate during the biodegradation stage. Plotting temperature (°C) on the horizontal axis and total removal rate (%) on the vertical axis, a scatter plot reveals the relationship between temperature and the total removal rate during the biodegradation stage. It can be observed that within a certain temperature range, the total removal rate exhibits a non-linear fluctuation with temperature. The discrete distribution of the total removal rate at different temperatures reflects the differences in the biodegradation process's response to temperature, which is of significant reference value for exploring the optimal temperature range for the biodegradation module. Figure 7 The temperature distribution in b is presented as a histogram, with temperature (°C) on the horizontal axis and frequency on the vertical axis, showing the frequency distribution of the temperature data. The distribution shows a higher frequency in the 26-28°C range, indicating that this temperature range appears frequently in the dataset. This provides statistical evidence for subsequent analysis of the suitable temperature range for biodegradation processes and the representativeness of the data, and also helps in combining... Figure 7 Further analysis of section a reveals the performance characteristics of the total removal rate within this high-frequency temperature range. By combining correlation scatter plots and distribution histograms, data support is provided for the study of the temperature influence on the biodegradation module in an integrated industrial wastewater treatment system based on coal mine goaf areas, considering both correlation and statistical distribution. This helps to deepen the understanding of the mechanism by which temperature factors affect the total removal rate in the biodegradation stage, as well as the distribution characteristics of the temperature data itself, thus providing a basis for optimizing the operating parameters of the wastewater treatment system.

[0083] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An integrated industrial wastewater treatment system based on the goaf of coal mine roadways, characterized in that, The system includes a wastewater input unit, multiple graded treatment units, a data acquisition unit, and an intelligent analysis unit. The wastewater input unit introduces industrial wastewater into the graded treatment unit in the goaf of the coal mine roadway. The data acquisition unit continuously collects the wastewater treatment sequence and effluent quality indicators for each batch of treatment. The wastewater treatment sequence forms a curve with treatment time as the horizontal axis and pollutant concentration as the vertical axis, and the treatment time axis is divided into multiple monitoring times at equal intervals. The intelligent analysis unit, based on the wastewater treatment sequence and the effluent quality indicators, analyzes the fluctuation characteristics of the wastewater treatment sequence for each treatment batch and each monitoring time, calculates the influence weight of each monitoring time on the effluent quality, and then, combined with the similarity measure between treatment batches, assesses the significance level of the monitoring time. Subsequently, it uses time series pattern recognition technology to extract key monitoring times and finally constructs an adaptive prediction model to predict the treatment effect of the key monitoring times in the new batch of wastewater in real time. When the intelligent analysis unit is configured to analyze the fluctuation characteristics of a wastewater treatment sequence, the following steps are adopted: For each processing batch, the wastewater treatment sequence is divided into multiple overlapping time windows; Calculate the mean and standard deviation of pollutant concentrations within each time window to form an eigenvector; Cluster analysis was performed on the feature vectors of all processed batches to identify typical fluctuation patterns; The influence weight of each monitoring moment is calculated based on the correlation between the fluctuation pattern of the time window to which each monitoring moment belongs and the effluent quality indicators. The intelligent analysis unit is configured to use a mutual information method when calculating the impact weight of each monitoring time on the effluent quality, specifically including: Calculate the mutual information value between the pollutant concentration value and the effluent quality index at each monitoring time, and use it as the initial influence weight; To address the temporal correlation between batches, a time graph model is constructed, where nodes represent monitoring times and edges represent temporal adjacency. The initial influence weights are adjusted using a random walk algorithm to obtain the corrected influence weights. When the intelligent analysis unit is configured to assess the significance level of a monitoring time, the following steps are performed: The wastewater treatment sequence of each batch is treated as a time series sample, and the pollutant concentration matrix at all monitoring times is calculated. Principal component analysis was used to extract principal components, and the loading values ​​on the principal components at each monitoring time were calculated. Calculate the significance score for each monitoring time point based on the magnitude of the load value and the effluent quality indicators; When the intelligent analysis unit is configured to extract key monitoring moments, it employs an importance propagation algorithm, specifically including: The monitoring time is constructed as network nodes, and the edges between nodes are weighted based on time proximity and pollutant concentration similarity; Calculate the centrality score of each node using the PageRank algorithm; By combining the influence weights and the significance scores, the centrality scores are weighted and fused to obtain a comprehensive importance score for each monitoring time. Select the monitoring times with the highest overall importance score from the preset number of monitoring times as the key monitoring times; When the intelligent analysis unit is configured to construct an adaptive prediction model, it employs a recurrent neural network model, specifically including: The pollutant concentration sequence at key monitoring moments is used as input features, and the effluent quality index is used as output labels. A long short-term memory network architecture is used for training, where the network parameters are updated online according to the new batch of data. During training, the backpropagation algorithm is used to optimize the weights, and regularization is added to prevent overfitting. When the intelligent analysis unit is configured to identify typical fluctuation patterns, it employs a dynamic time warping algorithm, specifically including: Calculate the dynamic time-normalized distance between wastewater treatment sequences of different treatment batches to form a distance matrix; Hierarchical clustering is performed based on the distance matrix, grouping the distance matrix into multiple clusters; the center sequence of each cluster represents a typical fluctuation pattern.

2. The integrated industrial wastewater treatment system based on the goaf of a coal mine roadway according to claim 1, characterized in that, The system also includes a prediction execution unit, which, when predicting the effect of the wastewater to be treated, adopts the following steps: real-time acquisition of the wastewater treatment sequence of the wastewater to be treated, and extraction of pollutant concentration values ​​at key monitoring moments; inputting the concentration values ​​into an adaptive prediction model to obtain predicted effluent quality indicators; and dynamically adjusting the model parameters based on the deviation between the prediction results and the actual monitoring values.

3. The integrated industrial wastewater treatment system based on the goaf of a coal mine roadway according to claim 1, characterized in that, The graded treatment unit includes a physical sedimentation module, a chemical reaction module, and a biodegradation module. The physical sedimentation module is located in the entrance area of ​​the goaf in the coal mine roadway, the chemical reaction module is located in the middle area, and the biodegradation module is located in the outlet area. Wastewater flows through each module in sequence. The data acquisition unit is equipped with multi-parameter sensors at the outlet of each module to simultaneously collect pollutant concentration, temperature, and pressure data, forming a multi-dimensional wastewater treatment sequence.

Images