Wastewater multistage treatment method and system based on big data analysis

Abstract

The invention relates to the technical field of wastewater treatment, and discloses a wastewater multi-stage treatment method and system based on big data analysis, and the method comprises the steps: collecting multi-dimensional parameters such as pollutant concentration, pH value, temperature and flow in a wastewater treatment process in real time through a sensor array, and generating standardized data after filtering cleaning and space-time alignment; identifying an instant association mode among parameters by adopting principal component analysis and a clustering algorithm; a neural network virtual environment model is constructed to predict key working conditions such as sudden flow increase, and virtual parameters such as sedimentation tank retention time are adjusted based on the dynamic mapping relation; a regulation and control strategy of a multi-stage processing link is generated by using a decision tree and is converted into a real-time instruction set executable by equipment through feedback mapping, and a physical system is driven to perform precise regulation and control; and finally, based on the water outlet state data, continuously optimizing the treatment strategy through a cyclic feedback mechanism. The method can solve the problem of insufficient regulation and control precision in the wastewater treatment process in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of wastewater treatment, and particularly to a multi-stage wastewater treatment method and system based on big data analysis. Background Art

[0002] At present, wastewater treatment, as a key link in environmental engineering, the stability of its treatment effect directly relates to the up-to-standard discharge of pollutants and the ecological safety of the water environment. In the scenario of industrial wastewater treatment, due to significant fluctuations in the influent water quality and quantity, it is necessary to sense the changes of multiple parameters in real time and establish their internal correlations to achieve precise regulation and forward-looking optimization of the treatment process.

[0003] In an existing technology, key parameters such as pollutant concentration, pH value, temperature, and flow rate are mainly monitored independently by deploying a sensor network, and alarms or simple linkage controls are performed for abnormal states based on threshold judgments. For example, when the detected pollutant concentration exceeds the standard, the system only triggers the start and stop operations of the dosing pump, without comprehensively considering the influence of pH value fluctuations and flow rate changes on the treatment effect; in terms of generating treatment strategies, it mostly relies on manual experience or static models, and adjusts parameters such as the residence time in the sedimentation tank and the aeration volume through preset rules with fixed strategies, lacking the quantitative analysis and prediction ability for the dynamic coupling relationship between multiple parameters.

[0004] Due to the failure of the existing technology to achieve in-depth fusion and correlation analysis of multi-dimensional parameters, the system is difficult to capture the instant correlation patterns between parameters and cannot predict complex working conditions such as sudden increase in flow rate and synchronous increase in pollutant concentration; at the same time, there is no effective dynamic mapping mechanism between the physical treatment process and the virtual analysis environment, making the control strategy lag behind the changes in the actual state of the wastewater, resulting in slow regulation response and unstable effluent water quality. Therefore, there is a problem of insufficient regulation accuracy in the existing technology during the wastewater treatment process. Summary of the Invention

[0005] The present invention provides a multi-stage wastewater treatment method and system based on big data analysis to solve the problem of insufficient regulation accuracy in the existing technology during the wastewater treatment process.

[0006] In a first aspect, to solve the above technical problems, the present invention provides a multi-stage wastewater treatment method based on big data analysis, including:

[0007] Obtain multi-dimensional raw data of the wastewater flow, and perform filtering, cleaning, and spatio-temporal alignment processing on the multi-dimensional raw data to obtain real-time parameter data;

[0008] According to the real-time parameter data, perform data fusion and correlation pattern analysis to determine the instant correlation pattern between parameters;

[0009] Obtain parameter data corresponding to the real-time association mode, and obtain the dynamic mapping relationship between the physical processing process and the virtual environment through simulation and prediction;

[0010] Based on the dynamic mapping relationship, the parameters of the processing unit in the virtual environment are adjusted, and a digital representation is generated;

[0011] Based on the digital representation, strategies are allocated to multi-level processing stages, and real-time adjustment instructions are generated.

[0012] According to the real-time adjustment command, the configuration of the wastewater treatment equipment is updated, the outlet data is collected, and the effluent status data after real-time adjustment is obtained.

[0013] Based on the effluent status data, an analysis report log is generated, and the entire strategy generation process is iteratively optimized to obtain a continuously optimized adaptive processing strategy.

[0014] Preferably, multidimensional raw data of wastewater flow is acquired, and the multidimensional raw data is filtered, cleaned, and spatiotemporally aligned to obtain real-time parameter data, including:

[0015] Obtain multidimensional raw data of wastewater flow;

[0016] Based on the multidimensional raw data, mean filtering and outlier removal operations are performed to obtain preliminary cleaned data.

[0017] Based on the preliminary cleaning data, timestamp alignment and data standardization are performed to obtain standardized parameters.

[0018] Based on the standardized parameters, the Pearson correlation coefficient under a sliding window is calculated to obtain the correlation analysis results;

[0019] By integrating the correlation analysis results with the standardized parameters, real-time parameter data is obtained.

[0020] Preferably, based on the real-time parameter data, data fusion and correlation pattern analysis are performed to determine the instantaneous correlation patterns between parameters, including:

[0021] Based on the real-time parameter data, principal component analysis is performed to reduce dimensionality and obtain the first dimensionality-reduced feature.

[0022] Based on the first dimensionality reduction feature, K-means clustering is performed to obtain the grouping results of pollutant concentration and pH value;

[0023] Based on the grouping results, the Spearman correlation coefficients between pollutant concentration and pH, temperature, and flow rate are calculated to obtain the parameter correlation strength.

[0024] If the correlation strength of the parameters exceeds a preset strength threshold, it is determined to be a strong correlation, and the instantaneous correlation mode between the parameters is obtained.

[0025] Preferably, acquiring parameter data corresponding to the instant association mode, and obtaining the dynamic mapping relationship between the physical processing process and the virtual environment through simulation prediction, includes:

[0026] Data on pollutant concentration, pH fluctuation, flow surge, and temperature change corresponding to the real-time correlation mode are obtained from the real-time parameter data.

[0027] The data on pollutant concentration, pH fluctuation, flow rate surge, and temperature change are subjected to dimensionality reduction processing to extract the main feature vector and obtain the second dimensionality reduction feature.

[0028] The second dimensionality reduction feature is input into a preset virtual environment model, and the prediction result of the traffic surge is output.

[0029] If the magnitude of the sudden increase in the predicted flow rate is greater than a preset magnitude threshold, the correlation strength between the pollutant concentration and the pH fluctuation is calculated using the Spearman correlation coefficient.

[0030] Based on the correlation strength, a dynamic relationship mapping is performed to obtain the dynamic mapping relationship between the physical processing process and the virtual environment.

[0031] Preferably, adjusting the parameters of the processing unit in the virtual environment according to the dynamic mapping relationship and generating a digital representation includes:

[0032] Based on the dynamic mapping relationship, it is determined whether the sudden increase in flow rate and the pollutant concentration increase synchronously, and the correlation judgment result is obtained;

[0033] If the correlation judgment result is yes, then modify the residence time parameter of the virtual sedimentation tank to obtain the optimized virtual environment parameters;

[0034] Based on the optimized virtual environment parameters, data representation and visualization transformation are performed to obtain a digital representation.

[0035] Preferably, based on the digital representation, strategy allocation is performed on multi-level processing stages to generate real-time adjustment instructions, including:

[0036] Based on the digital characterization, pollutant concentration distribution and flow load characteristics are extracted to obtain the input feature set for multi-stage treatment processes;

[0037] Based on the input feature set, a decision tree strategy is assigned to obtain preliminary strategy schemes for each processing stage;

[0038] Based on the preliminary strategy, feedback mapping and physical process matching are performed to obtain the real-time adjustment parameters for each stage;

[0039] Based on the real-time adjustment parameters, adjustment instructions are generated to obtain real-time adjustment instructions.

[0040] Preferably, according to the real-time adjustment command, the configuration of the wastewater treatment equipment is updated, and outlet data is collected to obtain the real-time adjusted effluent status data, including:

[0041] The real-time adjustment command is executed to dynamically adjust the operating parameters of the wastewater treatment equipment, and the outlet data of the equipment after adjustment is collected to obtain time series data of the adjusted pollutant concentration and pH value.

[0042] The time series data is denoised, and feature vectors of pollutant concentration and pH fluctuations are extracted to obtain adjusted feature data.

[0043] Based on the adjusted feature data, the correlation strength between pollutant concentration and pH value is analyzed, and it is verified whether the effluent indicators meet the preset discharge standards, thus obtaining the real-time adjusted effluent status data.

[0044] Preferably, based on the effluent status data, an analysis report log is generated, and the entire strategy generation process is iteratively optimized to obtain a continuously optimized adaptive processing strategy, including:

[0045] Based on the water discharge status data, the deviation between the actual water discharge index and the preset target value is calculated to obtain the effect evaluation result;

[0046] Based on the effect evaluation results, the parameters in the multi-dimensional data fusion, virtual environment modeling and strategy allocation stages are corrected to obtain optimized parameters;

[0047] Based on the optimization parameters and historical process data, an analysis report log is generated.

[0048] Based on the analysis report log, the strategy generation process is iteratively optimized to obtain a continuously optimized adaptive processing strategy.

[0049] Secondly, the present invention provides a multi-stage wastewater treatment system based on big data analysis, comprising:

[0050] The data acquisition module is used to acquire multidimensional raw data of wastewater flow, and to perform filtering, cleaning and spatiotemporal alignment processing on the multidimensional raw data to obtain real-time parameter data.

[0051] The data analysis module is used to perform data fusion and correlation pattern analysis based on the real-time parameter data to determine the real-time correlation patterns between parameters.

[0052] The dynamic mapping module is used to acquire parameter data corresponding to the real-time association mode and obtain the dynamic mapping relationship between the physical processing process and the virtual environment through simulation prediction.

[0053] The parameter optimization module is used to adjust the parameters of the processing unit in the virtual environment according to the dynamic mapping relationship and generate a digital representation.

[0054] The strategy decision-making module is used to allocate strategies to multi-level processing links based on the digital representation and generate real-time adjustment instructions.

[0055] The execution control module is used to update the configuration of the wastewater treatment equipment according to the real-time adjustment command, collect effluent data, and obtain the effluent status data after real-time adjustment.

[0056] The feedback optimization module is used to generate an analysis report log based on the effluent status data and iteratively optimize the entire strategy generation process to obtain a continuously optimized adaptive processing strategy.

[0057] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the wastewater multi-stage treatment method based on big data analysis described in any one of the above.

[0058] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the wastewater multi-stage treatment method based on big data analysis described above.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] (1) This invention uses principal component analysis to reduce the dimensionality of real-time parameter data, and combines K-means clustering and Spearman correlation coefficient analysis to effectively extract and quantify the real-time correlation patterns between pollutant concentration and key parameters such as pH value from high-dimensional data, providing a quantitative basis for understanding the dynamic characteristics of wastewater treatment process.

[0061] (2) The present invention constructs a virtual environment model based on the real-time correlation mode, and establishes a dynamic mapping relationship between the physical treatment process and the virtual environment by simulating and predicting key scenarios such as sudden increase in flow and calculating the correlation strength between pollutant concentration and pH value fluctuation, thereby realizing the ability to predict the future state of the system.

[0062] (3) The present invention optimizes the parameters of the virtual sedimentation tank and other treatment units according to the dynamic mapping relationship and generates digital representations, transforming the data analysis results into operable process parameters, providing direct input for the precise control of the physical system.

[0063] (4) This invention uses decision trees to allocate strategies for multi-level processing links and generates real-time adjustment instructions through feedback mapping, realizing the automated and intelligent conversion from data analysis to control instructions, and improving the system's response speed and decision accuracy in the control process.

[0064] (5) This invention executes real-time adjustment commands and collects effluent data for effect verification, forming a closed-loop control, and drives a cyclic feedback mechanism based on the verification results to continuously optimize the entire data processing and analysis chain, ultimately forming an adaptive processing strategy framework that can learn and evolve on its own, which significantly improves the stability and processing efficiency of the wastewater treatment system under complex working conditions. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of a multi-stage wastewater treatment method based on big data analysis provided in the first embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of a multi-stage wastewater treatment system based on big data analysis provided in the second embodiment of the present invention. Detailed Implementation

[0067] 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.

[0068] Reference Figure 1 The first embodiment of the present invention provides a multi-stage wastewater treatment method based on big data analysis, including the following steps:

[0069] S11, acquire multidimensional raw data of wastewater flow, and perform filtering, cleaning and spatiotemporal alignment processing on the multidimensional raw data to obtain real-time parameter data;

[0070] S12, Based on the real-time parameter data, perform data fusion and correlation pattern analysis to determine the real-time correlation pattern between parameters;

[0071] S13, Obtain parameter data corresponding to the real-time association mode, and obtain the dynamic mapping relationship between the physical processing process and the virtual environment through simulation prediction;

[0072] S14, Based on the dynamic mapping relationship, adjust the parameters of the processing unit in the virtual environment and generate a digital representation;

[0073] S15, Based on the digital representation, strategy allocation is performed on the multi-level processing links to generate real-time adjustment instructions;

[0074] S16, according to the real-time adjustment instruction, update the configuration of the wastewater treatment equipment, collect the outlet data, and obtain the real-time adjusted outlet status data;

[0075] S17. Based on the water discharge status data, generate an analysis report log and iteratively optimize the entire strategy generation process to obtain a continuously optimized adaptive processing strategy.

[0076] In step S11, multidimensional raw data of the wastewater flow is acquired, and the multidimensional raw data is filtered, cleaned, and spatiotemporally aligned to obtain real-time parameter data, including:

[0077] Obtain multidimensional raw data of wastewater flow;

[0078] Based on the multidimensional raw data, mean filtering and outlier removal operations are performed to obtain preliminary cleaned data.

[0079] Based on the preliminary cleaning data, timestamp alignment and data standardization are performed to obtain standardized parameters.

[0080] Based on the standardized parameters, the Pearson correlation coefficient under a sliding window is calculated to obtain the correlation analysis results;

[0081] By integrating the correlation analysis results with the standardized parameters, real-time parameter data is obtained.

[0082] First, multidimensional raw data of the wastewater flow is acquired. Sensor arrays are deployed in key process units of the wastewater treatment system, such as the inlet, equalization tank, biological treatment tank, and outlet. These include electrochemical sensors to measure pollutant concentration, pH sensors to monitor acidity and alkalinity, thermistors to detect temperature, and turbine flow meters to record instantaneous flow. The sensor sampling frequency is set to once per minute. This frequency can capture most process fluctuations and avoid data redundancy. Too low a frequency will lose peak information, while too high a frequency will increase the storage burden. All sensors are connected to the data acquisition unit via RS485 bus and use the Modbus protocol for data transmission to ensure data integrity and real-time performance.

[0083] Secondly, mean filtering is performed on the multidimensional raw data. A moving average is calculated using a sliding window with a window size of 5, that is, the arithmetic mean of the current point and the two data points before and after it is taken as the filter output, which can effectively suppress high-frequency noise caused by electromagnetic interference or mechanical vibration. Subsequently, reasonable threshold ranges for each parameter are set based on the range of each parameter under normal operating conditions in historical operating data: pollutant concentration 0-1000mg / L, pH value 4-10, temperature 5-50°C, and flow rate 0-200m³ / h. Data points exceeding the thresholds are identified and removed to obtain preliminary cleaning data. For example, in the practice of a wastewater treatment plant in a chemical industrial park, the normal biochemical treatment unit requires the pH to be maintained at 6.5-8. At a certain moment, the pH sensor returned an extreme value of 2.5. According to the threshold, this value far exceeds the possible acidity and alkalinity range of the wastewater, which is very likely a sensor malfunction or signal transmission error. Therefore, this abnormal point is directly removed to prevent it from misleading the analysis of pH fluctuation trends.

[0084] Subsequently, a timestamp alignment operation is performed on the preliminary cleaned data, and the network time protocol is used to ensure that the time synchronization error of each sensor data does not exceed 1 second; then, data standardization processing is performed, and the min-max normalization processing is used to normalize each parameter value to the [0,1] interval. The normalization standard is determined by the historical minimum and historical maximum values ​​of each parameter.

[0085] Then, based on the standardized parameters, a sliding window with a window size of 60 minutes and a step size of 10 minutes is used to calculate the Pearson correlation coefficient between pollutant concentration and parameters such as temperature, pH value, and flow rate, and obtain the correlation analysis results. It should be noted that this window size corresponds to a typical hydraulic residence time cycle, which can capture the complete process dynamics.

[0086] Finally, the standardized parameters and correlation analysis results are integrated by timestamp to generate real-time parameter data and stored in a time-series database. For example, within a 60-minute window, the system calculates the correlation coefficient between pollutant concentration and temperature. =0.82, indicating a strong positive correlation between the two in the current period, suggesting that increased water temperature may be a significant factor contributing to the increase in pollutant concentration; the correlation coefficient between pollutant concentration and pH value is... = -0.65, indicating a moderate negative correlation between the two, suggesting that increased acidity (decreased pH) is associated with increased pollutant concentration; the correlation coefficient between pollutant concentration and flow rate is... =0.15, indicating only a very weak positive correlation between the two, suggesting that flow rate changes are not the primary cause of pollutant concentration fluctuations during this window period. It's worth noting that real-time parameter data can be saved in JSON format, for example: {"timestamp":"2024-05-20T14:00:00Z","parameters":{"pollutant_concentration":0.65,"temperature":0.72,"ph":0.58,"flow_rate":0.81},"correlation_analysis":{"pollutant_temperature":0.82,"pollutant_ph":-0.65,"pollutant_flow":0.15,"temperature_ph":-0.43,"temperature_flow":0.28,"ph_flow":-0.12},"window_info":{"window_size":60,"window_step":10,"data_points":360}}

[0087] In step S12, based on the real-time parameter data, data fusion and correlation pattern analysis are performed to determine the instantaneous correlation patterns between parameters, including:

[0088] Based on the real-time parameter data, principal component analysis is performed to reduce dimensionality and obtain the first dimensionality-reduced feature.

[0089] Based on the first dimensionality reduction feature, K-means clustering is performed to obtain the grouping results of pollutant concentration and pH value;

[0090] Based on the grouping results, the Spearman correlation coefficients between pollutant concentration and pH, temperature, and flow rate are calculated to obtain the parameter correlation strength.

[0091] If the correlation strength of the parameters exceeds a preset strength threshold, it is determined to be a strong correlation, and the instantaneous correlation mode between the parameters is obtained.

[0092] First, parameters of four dimensions—pollutant concentration, pH value, temperature, and flow rate—are extracted from real-time parameter data, and principal component analysis is performed for dimensionality reduction. The covariance matrix of the parameters and its eigenvalues ​​and eigenvectors are calculated. The eigenvalues ​​are sorted from largest to smallest, and the first two principal components with a cumulative variance contribution rate ≥ 85% are selected. This threshold aims to retain most of the original information while significantly reducing data dimensionality and computational complexity, constructing a new feature space, and mapping the original four-dimensional data to two-dimensional feature data to obtain the first dimensionality-reduced feature. For example, principal component analysis was performed on real-time data containing 500 sample points. The eigenvalues ​​of the four principal components were calculated as follows: λ1=2.8, λ2=0.9, λ3=0.2, λ4=0.1. The cumulative variance contribution rate of the first two principal components was (2.8+0.9) / (2.8+0.9+0.2+0.1)=92.5%, which far exceeded the threshold of 85%. This indicates that using only these two new features, PC1 and PC2, is sufficient to capture more than 92% of the information in the original four parameters, thus achieving effective dimensionality reduction.

[0093] K-means clustering was performed on the first dimensionality-reduced feature, with the number of clusters K=3. Based on Euclidean distance, the data points were divided into three typical fluctuation patterns: high concentration-low pH, medium concentration-medium pH, and low concentration-high pH. The cluster centers were initialized using the K-means++ algorithm, iterating until the change in the sum of squares within the cluster was less than 1×10. -4 Alternatively, clustering can be stopped when the maximum number of iterations, such as 100, is reached, and the grouping results are obtained. For example, the algorithm converged after 15 iterations, ultimately identifying three cluster centers: Cluster 1, a high-concentration-low-pH pattern, with its center located at (2.1, -1.5) in the two-dimensional feature space composed of principal components. Mapping back to the original parameter space, this cluster is characterized by pollutant concentration > 80 mg / L and pH < 6.2, which typically corresponds to high organic load influent or accidental acidic wastewater discharge. Cluster 2, a medium-concentration-medium-pH pattern, with its center located at (0.3, 0.2), is characterized by pollutant concentration between 30-80 mg / L and pH between 6.5-7.5, representing the normal stable operating state and the most common and ideal operating condition of the system. Cluster 3, a low-concentration-high-pH pattern, with its center located at (-1.8, 1.8), is characterized by pollutant concentration < 30 mg / L and pH > 7.8, which typically occurs after a large amount of rainwater dilution or in cases of excessive subsequent alkaline agent addition.

[0094] Based on the grouping results, Spearman correlation coefficients were calculated between pollutant concentration and pH, temperature, and flow rate within each cluster. A strength threshold of 0.7 was set; when the absolute value of the Spearman correlation coefficient exceeded this threshold, it was considered a strong correlation pattern. The final output included real-time correlation patterns containing strongly correlated nodes and their correlation strengths, used to guide subsequent virtual environment modeling. For example, by calculating the Spearman correlation coefficients for the three clusters, the following typical results were obtained: In cluster 1, the correlation coefficient between pollutant concentration and pH was -0.85. This strong negative correlation indicates that under this high-load mode, the sharp increase in pollutant concentration is closely coupled with the rapid decrease in pH, which is usually due to the impact of acidic chemical wastewater. This strong correlation pattern suggests that the virtual environment model should focus on the dynamic response of the acid-base neutralization unit under this mode. In cluster 2, the correlation coefficient between pollutant concentration and temperature was 0.72. This strong positive correlation indicates that under normal operating conditions, water temperature is a key factor affecting the pollutant degradation rate, and increased temperature accelerates the reaction. This correlation pattern will guide the virtual environment model, and temperature should be used as the core input variable for predicting the effectiveness of biochemical treatment when simulating this mode. In cluster 3, the correlation coefficient between pollutant concentration and flow rate is -0.68. The absolute value of this coefficient is less than the threshold of 0.7, so it is judged as a moderate correlation rather than a strong correlation. This indicates that under dilution conditions, although increasing the flow rate helps to reduce the concentration, it is not the only dominant factor, and other dilution or precipitation mechanisms may be at play. Through the above analysis, the real-time correlation pattern finally output by the system can be clearly expressed as follows: in the high concentration-low pH mode, concentration-pH is the key strong correlation pair (-0.85); in the medium concentration-medium pH mode, concentration-temperature is the key strong correlation pair (0.72).

[0095] In step S13, parameter data corresponding to the instant association mode is acquired, and the dynamic mapping relationship between the physical processing process and the virtual environment is obtained through simulation prediction, including:

[0096] Data on pollutant concentration, pH fluctuation, flow surge, and temperature change corresponding to the real-time correlation mode are obtained from the real-time parameter data.

[0097] The data on pollutant concentration, pH fluctuation, flow rate surge, and temperature change are subjected to dimensionality reduction processing to extract the main feature vector and obtain the second dimensionality reduction feature.

[0098] The second dimensionality reduction feature is input into a preset virtual environment model, and the prediction result of the traffic surge is output.

[0099] If the magnitude of the sudden increase in the predicted flow rate is greater than a preset magnitude threshold, the correlation strength between the pollutant concentration and the pH fluctuation is calculated using the Spearman correlation coefficient.

[0100] Based on the correlation strength, a dynamic relationship mapping is performed to obtain the dynamic mapping relationship between the physical processing process and the virtual environment.

[0101] First, pollutant concentration, pH fluctuation, flow surge, and temperature change data corresponding to the immediate correlation pattern are extracted from real-time parameter data as model input features. Principal component analysis is performed on the model input features for secondary dimensionality reduction, and the first three principal components are extracted as the main feature vectors (cumulative variance contribution rate ≥90%), forming the second dimensionality reduction features.

[0102] It should be noted that the virtual environment model is a pre-built prediction model. After receiving the input three-dimensional feature vector, the model will predict the magnitude of the traffic surge and output the prediction result of the traffic surge.

[0103] For example, this invention employs a three-layer feedforward neural network to construct a virtual environment model. The input layer of this network receives a dimensionality-reduced three-dimensional feature vector, the hidden layer contains 15 nodes and uses the ReLU activation function for nonlinear transformation, and the output layer is a single-node linear output used to predict the magnitude of traffic surges. The model is trained using supervised learning, with the dimensionality-reduced feature vector from historical data as input. The training data covers at least six months of historical operating data, including normal and abnormal operating conditions, and the corresponding actual traffic observations are used as the target output. Mean squared error is used as the loss function, and the Adam optimizer is used for iterative training on a large amount of data. Five-fold cross-validation is used to prevent overfitting until the model converges. The trained model can accurately predict future traffic surge trends based on real-time collected dimensionality-reduced features.

[0104] Real-time data is input into the model, which outputs a predicted flow surge for the next 15 minutes. A threshold for the flow surge is set at 150% of the normal flow rate. This threshold is determined based on the maximum acceptable load ensuring system stability from historical operating data. When the predicted result exceeds this threshold, a correlation analysis process is triggered. Based on the most recent 200 sample points, the Spearman correlation coefficient between pollutant concentration and pH fluctuations is calculated to obtain the correlation strength. A quantitative relationship between key parameters is established based on this correlation strength, ultimately generating a dynamic mapping relationship between the physical treatment process and the virtual environment.

[0105] For example, if the baseline processing flow rate is 100 m³ / h, then the flow surge threshold is set at 150 m³ / h. When the model predicts that the flow rate will reach 165 m³ / h in the next 15 minutes, this predicted value exceeds the threshold. The correlation analysis process is immediately initiated, and the Spearman correlation coefficient between pollutant concentration and pH is calculated to be -0.82, indicating a strong negative correlation between the two. This means that under the current flow surge condition, the increase in pollutant concentration is accompanied by a significant decrease in pH. Based on this, a dynamic mapping relationship is established: when the flow rate exceeds 150 m³ / h and continues to rise, the pollutant concentration and pH show a strong negative correlation (r ≤ -0.8).

[0106] In step S14, the parameters of the processing unit in the virtual environment are adjusted according to the dynamic mapping relationship, and a digital representation is generated, including:

[0107] Based on the dynamic mapping relationship, it is determined whether the sudden increase in flow rate and the pollutant concentration increase synchronously, and the correlation judgment result is obtained;

[0108] If the correlation judgment result is yes, then modify the residence time parameter of the virtual sedimentation tank to obtain the optimized virtual environment parameters;

[0109] Based on the optimized virtual environment parameters, data representation and visualization transformation are performed to obtain a digital representation.

[0110] First, the dynamic mapping relationship is analyzed to determine whether the sudden increase in flow rate and the pollutant concentration show a synchronous upward trend. The synchronization criterion is set as follows: the magnitude of the sudden increase in flow rate > a threshold (150% of the normal flow rate) and the pollutant concentration-flow rate correlation coefficient > 0.6. When this criterion is met, a positive correlation judgment result is generated. For example, when the current instantaneous flow rate is monitored to have risen to 160 m³ / h, exceeding 150% of the baseline flow rate (100 m³ / h), and simultaneously, the Spearman correlation coefficient between the pollutant concentration and flow rate calculated based on the most recent data window is 0.75, which is greater than the criterion requirement of 0.6, then it is determined that the sudden increase in flow rate and the pollutant concentration show a significant synchronous upward trend, generating a positive correlation judgment result. This result clearly indicates that the increased flow rate is not clean dilution water, but wastewater carrying a high concentration of pollutants. The corresponding parameter adjustment strategy must be immediately initiated in the virtual environment to simulate and cope with the impending pollution load impact.

[0111] Based on the correlation judgment results, the residence time parameter of the virtual sedimentation tank is modified through a dynamic adjustment algorithm. Specifically, when a sudden increase in flow rate and a simultaneous increase in pollutant concentration are detected, the residence time is linearly adjusted according to the correlation strength, using the following formula:

[0112]

[0113] in The baseline retention time is 2 hours, which is determined based on the standard hydraulic retention time of the sedimentation tank in this wastewater treatment plant under design operating conditions. The adjustment coefficient was set at 0.5. This coefficient was derived by analyzing the residence time in historical operational data to adjust the sensitivity to settlement effects, and was optimized after multiple rounds of simulation verification. To determine the correlation strength, optimized virtual environment parameters are obtained. For example, the correlation strength between the system currently detects a sudden increase in flow and the pollutant concentration. =0.75, substitute this value into the adjustment formula for calculation: =2.0×(1+0.5×0.75)=2.75 hours. The calculation results show that the residence time in the virtual sedimentation tank will be extended from the baseline of 2 hours to 2.75 hours. This adjustment quantitatively reflects the degree of increase in pollution load and aims to cope with the treatment load impact caused by the simultaneous increase in flow rate and pollutant concentration by extending the settling time. In this way, the treatment effect under this condition can be simulated and optimized in advance in the virtual environment.

[0114] The optimized virtual environment parameters are subjected to data characterization operations to generate standardized records containing key parameters such as timestamps, pollutant concentrations, flow rates, and residence times. The parameter change trends are presented in the form of curves and heat maps through visualization components, forming an intuitively interpretable digital representation to support subsequent decision-making.

[0115] In step S15, based on the digital representation, strategies are allocated to the multi-level processing stages, and real-time adjustment instructions are generated, including:

[0116] Based on the digital characterization, pollutant concentration distribution and flow load characteristics are extracted to obtain the input feature set for multi-stage treatment processes;

[0117] Based on the input feature set, a decision tree strategy is assigned to obtain preliminary strategy schemes for each processing stage;

[0118] Based on the preliminary strategy, feedback mapping and physical process matching are performed to obtain the real-time adjustment parameters for each stage;

[0119] Based on the real-time adjustment parameters, adjustment instructions are generated to obtain real-time adjustment instructions.

[0120] First, pollutant concentration distribution characteristics and instantaneous flow load characteristics are extracted from digital representations to construct an input feature set containing 12 feature dimensions for multi-level processing, including current concentration, concentration change rate, flow load rate, etc.

[0121] The input feature set is fed into a pre-trained decision tree model, and a strategy assignment operation is performed. This decision tree model is constructed using the CART framework, recursively dividing the feature space into different decision regions. During the training phase, the model uses the information gain ratio as the node splitting criterion. This criterion, based on information gain, considers the inherent information of the features, effectively suppressing the bias of multi-valued features and improving the model's generalization ability. A pre-pruning strategy limits the maximum tree depth to 10, ensuring model complexity while preventing overfitting. During inference, the input feature vectors, such as current pollutant concentration, concentration change rate, and flow load rate, start from the root node and traverse downwards along the corresponding branches based on the comparison between their values ​​and the node splitting threshold, until a leaf node is reached. Each leaf node corresponds to a pre-learned multi-output strategy combination, which encapsulates coordinated control suggestions for multiple treatment stages such as primary sedimentation, secondary biochemical treatment, and deep filtration. Finally, the model generates preliminary strategy schemes for primary sedimentation, secondary biochemical treatment, and deep filtration, including adjustments to chemical dosage, aeration control, and reflux ratio settings.

[0122] The preliminary strategy is matched with the physical processing through a feedback mapping mechanism. Taking into account factors such as the current state of the equipment and processing capacity limitations, real-time adjustment parameters such as valve opening, pump station frequency, and chemical dosage are calculated for each stage to form real-time adjustment parameters. For example, the initial strategy suggested by the decision tree model is to increase the coagulant dosage by 20% in the primary sedimentation stage. Upon receiving this strategy, the feedback mapping mechanism does not directly convert it into an execution command. Instead, it first checks and verifies the current state of the physical system, verifying the equipment status. The system detects that pump No. 1 is currently operating normally and has sufficient backup capacity, meeting the hardware requirements for increasing the dosage. It then assesses the treatment capacity, determining, based on the real-time sludge interface height in the sedimentation tank and the load of the sludge discharge pump, that the current solids load capacity of the sedimentation tank allows for an increase in flocculant production. Finally, it calculates specific parameters. After passing the above verification, the mechanism transforms the qualitative strategy of "increasing by 20%" into precise, equipment-executable real-time adjustment parameters, such as increasing the frequency of pump No. 1 from the current 35Hz to 42Hz. This frequency corresponds to a dosage increase of approximately 20% compared to the current baseline dosage (e.g., 50L / h). Similarly, for strategies to increase the aeration rate in the secondary biological treatment tank, the mechanism will combine real-time dissolved oxygen monitoring values. The system calculates the target aeration rate based on blower current and air pressure, and maps this to adjusting the valve opening of a specific blower from 55% to 68%. For mismatches, the invention employs a multi-level processing mechanism to ensure robustness. When critical equipment is detected as unavailable (e.g., dosing pump 1 is under maintenance), the system automatically switches to backup equipment and recalculates the parameter mapping. When the processing capacity approaches its limit (e.g., sludge interface height reaches a warning value), a degradation scheme is activated, proportionally reducing the adjustment range (e.g., adjusting a 20% increase to 10%), and simultaneously activating the sludge discharge procedure. When multiple adjustment requests conflict in resources, the system coordinates based on priority, ensuring that core process parameters are prioritized. All abnormal situations and handling measures are recorded in the system log, providing data support for subsequent strategy optimization. Ultimately, all these physically constrained and precisely quantified instructions, such as dosing pump 1: 42Hz, blower valve 1: 68% opening, are integrated into a unified set of real-time adjustment parameters.

[0123] Based on the real-time adjustment parameters, the system generates real-time adjustment commands that the equipment can directly execute. The command format is {device ID, parameter type, target value, execution time}, and is sent to the field control system via industrial communication protocol. For example, based on the real-time adjustment parameters, the system generates the following three commands with a unified format, forming a command set: {Pump_CF-101,Frequency,42.0,2024-05-20 14:05:00}, which indicates that at the specified time, the frequency of the dosing pump with ID Pump_CF-101 will be set to 42.0Hz; {Valve_VX-203,Opening,68,2024-05-20 14:05:00}, which indicates that at the same time, the opening degree of the aeration pipeline regulating valve with ID Valve_VX-203 will be set to 68%; {Feeder_AL-305,Dosage,125,2024-05-20 At 14:05:00, this instruction indicates that at the same time, the dosage of the alkaline neutralizing agent dosing device with ID Feeder_AL-305 will be set to 125 L / h. This instruction set, via the Modbus TCP / IP industrial communication protocol, is synchronously sent to the field execution equipment at the specified execution time, driving the corresponding frequency converters, electric actuators, and metering pumps to execute precisely, thereby achieving coordinated control of the wastewater treatment equipment.

[0124] In step S16, according to the real-time adjustment instruction, the configuration of the wastewater treatment equipment is updated, and outlet data is collected to obtain the real-time adjusted effluent status data, including:

[0125] The real-time adjustment command is executed to dynamically adjust the operating parameters of the wastewater treatment equipment, and the outlet data of the equipment after adjustment is collected to obtain time series data of the adjusted pollutant concentration and pH value.

[0126] The time series data is denoised, and feature vectors of pollutant concentration and pH fluctuations are extracted to obtain adjusted feature data.

[0127] Based on the adjusted feature data, the correlation strength between pollutant concentration and pH value is analyzed, and it is verified whether the effluent indicators meet the preset discharge standards, thus obtaining the real-time adjusted effluent status data.

[0128] First, execute the real-time adjustment command to dynamically adjust the operating parameters of key equipment such as the frequency of the dosing pump, the air volume of the aeration device, and the opening of the sludge return valve, and control the adjustment response time within 30 seconds.

[0129] Immediately after the control operation was executed, pollutant concentration and pH value data at the outlet were collected, with the sampling frequency increased to once every 30 seconds, and continuous monitoring for 10 minutes to obtain high-resolution time series data. Wavelet transform denoising processing was performed on the time series data, and a Daubechies wavelet basis such as db4 was used for three-level decomposition to extract low-frequency trend components as effective signals, generating adjusted feature data.

[0130] For example, after the control command was issued at 14:05:00, starting at 14:05:30, the system began intensive sampling of the outlet at 30-second intervals. During the 10-minute monitoring period, a total of 20 sets of pollutant concentration and pH value data were collected, forming two high-resolution time series. The pollutant concentration series (mg / L) was [15.2, 14.8, 14.5, 13.9, 12.5, 11.0, 10.2, 9.8, 9.5, 9.3, 9.1, 8.9, 8.8, 8.7, 8.6, 8.5, 8.5, 8.4, 8.4, 8.3].

[0131] The pH value sequence is: [6.52, 6.55, 6.58, 6.62, 6.68, 6.75, 6.82, 6.88, 6.92, 6.95, 6.97, 6.98, 6.99, 7.00, 7.00, 7.01, 7.01, 7.01, 7.02, 7.02]

[0132] After wavelet decomposition of the above pollutant concentration sequence, sub-bands of different frequencies were obtained. Among them, the detail components D1, D2, and D3 containing high-frequency noise were filtered out. The finally reconstructed low-frequency approximation component A3 formed a smooth, denoised sequence that reflects the true trend of change. The denoised pollutant concentration sequence (mg / L) is [15.1,14.9,14.6,14.0,13.0,11.5,10.5,9.9,9.6,9.4,9.2,9.0,8.9,8.8,8.7,8.6,8.5,8.5,8.4,8.4]. This denoised data effectively eliminated random fluctuation interference and clearly showed the process of pollutant concentration continuously decreasing and stabilizing after adjustment.

[0133] Based on the adjusted feature data, the Pearson correlation coefficient between pollutant concentration and pH value is calculated, and the correlation strength is analyzed. At the same time, the effluent indicators are compared and verified with the Class A standard in the "Discharge Standard of Pollutants for Municipal Wastewater Treatment Plants" (GB 18918-2022). Finally, the real-time adjusted effluent status data containing the evaluation of the control effect is output.

[0134] In step S17, based on the effluent status data, an analysis report log is generated, and the entire strategy generation process is iteratively optimized to obtain a continuously optimized adaptive processing strategy, including:

[0135] Based on the water discharge status data, the deviation between the actual water discharge index and the preset target value is calculated to obtain the effect evaluation result;

[0136] Based on the effect evaluation results, the parameters in the multi-dimensional data fusion, virtual environment modeling and strategy allocation stages are corrected to obtain optimized parameters;

[0137] Based on the optimization parameters and historical process data, an analysis report log is generated.

[0138] Based on the analysis report log, the strategy generation process is iteratively optimized to obtain a continuously optimized adaptive processing strategy.

[0139] First, based on the real-time adjusted effluent status data, the absolute and relative deviations between the actual values ​​and target values ​​of key indicators such as chemical oxygen demand and ammonia nitrogen are calculated to obtain quantitative effect evaluation results.

[0140] For example, the set effluent target values ​​are: chemical oxygen demand (COD) ≤ 50 mg / L and ammonia nitrogen (NH3-N) ≤ 5 mg / L. An assessment of chemical oxygen demand (COD) revealed that the adjusted characteristic data showed an actual COD value of 42 mg / L, with an absolute deviation of 42 mg / L - 50 mg / L = -8 mg / L and a relative deviation of (42 - 50) / 50 × 100% = -16%. The assessment concluded that the COD met the standard and had a margin of safety. An assessment of ammonia nitrogen showed that the adjusted characteristic data showed an actual ammonia nitrogen value of 6.8 mg / L, with an absolute deviation of 6.8 mg / L - 5 mg / L = +1.8 mg / L and a relative deviation of (6.8 - 5) / 5 × 100% = +36%. The assessment concluded that the ammonia nitrogen exceeded the standard, and the control effect did not meet expectations. The generated quantitative effect assessment results were: {COD: [absolute deviation: -8 mg / L, relative deviation: -16%], NH3-N: [absolute deviation: +1.8 mg / L, relative deviation: +36%]}.

[0141] Based on the performance evaluation results, the number of principal components, clustering algorithm parameters, network weights of the virtual environment model, and splitting criteria of the decision tree in the multidimensional data fusion are collaboratively corrected. The correction process aims to improve the final effluent compliance rate and employs a gradient descent-based collaborative optimization strategy. For example, when the evaluation results show that the ammonia nitrogen removal effect remains substandard, the system initiates the following targeted correction process: Virtual environment model correction; the mean square error between the model's predicted ammonia nitrogen concentration and the actual ammonia nitrogen concentration is calculated as the loss function; through backpropagation, the neural network weights are adjusted using gradient descent with a learning rate of 0.001. For example, the weight matrix of a hidden layer neuron is adjusted from [[0.12,-0.45],[0.67,0.23]] to [[0.15,-0.41],[0.72,0.28]] after iterative optimization, making the model more sensitive to the prediction of the flow-ammonia nitrogen correlation. The following adjustments were made: First, the clustering algorithm parameters were corrected. Analysis revealed that the high ammonia nitrogen-medium pH pattern in historical data was incorrectly classified into the medium concentration-medium pH cluster. Therefore, the number of K-means clusters, K, was increased from 3 to 4, and a new cluster center specifically for identifying high ammonia nitrogen was added, enabling more precise separation of special operating conditions with high ammonia nitrogen loads. Second, the decision tree splitting criteria were corrected. For the ammonia nitrogen exceedance problem, the information gain weight of the ammonia nitrogen concentration change rate feature was increased during decision tree training. This encourages the decision tree to prioritize this feature when splitting nodes. For example, the optimized decision tree might add a new rule: IF ammonia nitrogen concentration change rate > 0.2 mg / L / min THEN Activate enhanced nitrification strategy, thus responding more promptly to ammonia nitrogen shocks. Third, the multidimensional data fusion was corrected. The number of principal components in principal component analysis was increased from 2 to 3, increasing the cumulative variance contribution rate from 92.5% to 96.8%, ensuring that more weak signals related to ammonia nitrogen fluctuations are retained during dimensionality reduction. Through the above-mentioned synergistic correction, optimized parameters are obtained, thereby systematically improving the prediction accuracy and the effectiveness of the treatment strategy for ammonia nitrogen exceeding the standard.

[0142] The optimized parameters, along with historical process data, control command records, and effluent effect data, are integrated into a standardized format to generate an analysis report log, which is then stored in a knowledge base. Based on the analysis report log, the parameter information during the treatment strategy generation process is iteratively updated to form an adaptive treatment strategy that can continuously optimize and adjust treatment parameters according to influent characteristics.

[0143] In summary, this invention discloses a multi-stage wastewater treatment method based on big data analysis. It uses a sensor array to collect multi-dimensional parameters such as pollutant concentration, pH value, temperature, and flow rate in real time during the wastewater treatment process. After filtering, cleaning, and spatiotemporal alignment, standardized data is generated. Principal component analysis and clustering algorithms are used to identify real-time correlation patterns between parameters. A neural network virtual environment model is constructed to predict key operating conditions such as sudden flow surges, and virtual parameters such as sedimentation tank retention time are adjusted based on dynamic mapping relationships. A decision tree is used to generate control strategies for the multi-stage treatment process, which are then converted into a set of real-time executable instructions through feedback mapping, driving precise control of the physical system. Finally, based on effluent status data, the treatment strategy is continuously optimized through a cyclical feedback mechanism to solve the problem of insufficient control precision in the wastewater treatment process in existing technologies.

[0144] Reference Figure 2 The second embodiment of the present invention provides a multi-stage wastewater treatment system based on big data analysis, comprising:

[0145] The data acquisition module is used to acquire multidimensional raw data of wastewater flow, and to perform filtering, cleaning and spatiotemporal alignment processing on the multidimensional raw data to obtain real-time parameter data.

[0146] The data analysis module is used to perform data fusion and correlation pattern analysis based on the real-time parameter data to determine the real-time correlation patterns between parameters.

[0147] The dynamic mapping module is used to acquire parameter data corresponding to the real-time association mode and obtain the dynamic mapping relationship between the physical processing process and the virtual environment through simulation prediction.

[0148] The parameter optimization module is used to adjust the parameters of the processing unit in the virtual environment according to the dynamic mapping relationship and generate a digital representation.

[0149] The strategy decision-making module is used to allocate strategies to multi-level processing links based on the digital representation and generate real-time adjustment instructions.

[0150] The execution control module is used to update the configuration of the wastewater treatment equipment according to the real-time adjustment command, collect effluent data, and obtain the effluent status data after real-time adjustment.

[0151] The feedback optimization module is used to generate an analysis report log based on the effluent status data and iteratively optimize the entire strategy generation process to obtain a continuously optimized adaptive processing strategy.

[0152] It should be noted that the wastewater multi-stage treatment device based on big data analysis provided in this embodiment of the invention is used to execute all process steps of the wastewater multi-stage treatment method based on big data analysis in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0153] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a multi-stage wastewater treatment program based on big data analysis. When the processor executes the computer program, it implements the steps in the various embodiments of the multi-stage wastewater treatment method based on big data analysis described above, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module in the above-described device embodiments, such as the data acquisition module.

[0154] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0155] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0156] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0157] The memory can be used to store the computer program or module. The processor implements various functions of the electronic device by running or executing the computer program or module stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0158] If the modules integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0159] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0160] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A multi-stage wastewater treatment method based on big data analysis, characterized in that, include: Multidimensional raw data of wastewater flow is acquired, and the multidimensional raw data is filtered, cleaned, and spatiotemporally aligned to obtain real-time parameter data. Based on the real-time parameter data, data fusion and correlation pattern analysis are performed to determine the real-time correlation patterns between parameters; Obtain parameter data corresponding to the real-time association mode, and obtain the dynamic mapping relationship between the physical processing process and the virtual environment through simulation and prediction; Based on the dynamic mapping relationship, the parameters of the processing unit in the virtual environment are adjusted, and a digital representation is generated; Based on the digital representation, strategies are allocated to multi-level processing stages, and real-time adjustment instructions are generated. According to the real-time adjustment command, the configuration of the wastewater treatment equipment is updated, the outlet data is collected, and the effluent status data after real-time adjustment is obtained. Based on the effluent status data, an analysis report log is generated, and the entire strategy generation process is iteratively optimized to obtain a continuously optimized adaptive processing strategy.

2. The multi-stage wastewater treatment method based on big data analysis according to claim 1, characterized in that, The process of acquiring multidimensional raw data of wastewater flow and performing filtering, cleaning, and spatiotemporal alignment on the multidimensional raw data to obtain real-time parameter data includes: Obtain multidimensional raw data of wastewater flow; Based on the multidimensional raw data, mean filtering and outlier removal operations are performed to obtain preliminary cleaned data. Based on the preliminary cleaning data, timestamp alignment and data standardization are performed to obtain standardized parameters. Based on the standardized parameters, the Pearson correlation coefficient under a sliding window is calculated to obtain the correlation analysis results; By integrating the correlation analysis results with the standardized parameters, real-time parameter data is obtained.

3. The multi-stage wastewater treatment method based on big data analysis according to claim 1, characterized in that, The step of performing data fusion and correlation pattern analysis based on the real-time parameter data to determine the instantaneous correlation pattern between parameters includes: Based on the real-time parameter data, principal component analysis is performed to reduce dimensionality and obtain the first dimensionality-reduced feature. Based on the first dimensionality reduction feature, K-means clustering is performed to obtain the grouping results of pollutant concentration and pH value; Based on the grouping results, the Spearman correlation coefficients between pollutant concentration and pH, temperature, and flow rate are calculated to obtain the parameter correlation strength. If the correlation strength of the parameters exceeds a preset strength threshold, it is determined to be a strong correlation, and the instantaneous correlation mode between the parameters is obtained.

4. The multi-stage wastewater treatment method based on big data analysis according to claim 1, characterized in that, The step of acquiring parameter data corresponding to the real-time association mode and obtaining the dynamic mapping relationship between the physical processing process and the virtual environment through simulation prediction includes: Data on pollutant concentration, pH fluctuation, flow surge, and temperature change corresponding to the real-time correlation mode are obtained from the real-time parameter data. The data on pollutant concentration, pH fluctuation, flow rate surge, and temperature change are subjected to dimensionality reduction processing to extract the main feature vector and obtain the second dimensionality reduction feature. The second dimensionality reduction feature is input into a preset virtual environment model, and the prediction result of the traffic surge is output. If the magnitude of the sudden increase in the predicted flow rate is greater than a preset magnitude threshold, the correlation strength between the pollutant concentration and the pH fluctuation is calculated using the Spearman correlation coefficient. Based on the correlation strength, a dynamic relationship mapping is performed to obtain the dynamic mapping relationship between the physical processing process and the virtual environment.

5. The multi-stage wastewater treatment method based on big data analysis according to claim 1, characterized in that, The step of adjusting the parameters of the processing unit in the virtual environment and generating a digital representation based on the dynamic mapping relationship includes: Based on the dynamic mapping relationship, it is determined whether the sudden increase in flow rate and the pollutant concentration increase synchronously, and the correlation judgment result is obtained; If the correlation judgment result is yes, then modify the residence time parameter of the virtual sedimentation tank to obtain the optimized virtual environment parameters; Based on the optimized virtual environment parameters, data representation and visualization transformation are performed to obtain a digital representation.

6. The multi-stage wastewater treatment method based on big data analysis according to claim 1, characterized in that, The step of allocating strategies to multi-level processing stages based on the digital representation and generating real-time adjustment instructions includes: Based on the digital characterization, pollutant concentration distribution and flow load characteristics are extracted to obtain the input feature set for multi-stage treatment processes; Based on the input feature set, a decision tree strategy is assigned to obtain preliminary strategy schemes for each processing stage; Based on the preliminary strategy, feedback mapping and physical process matching are performed to obtain the real-time adjustment parameters for each stage; Based on the real-time adjustment parameters, adjustment instructions are generated to obtain real-time adjustment instructions.

7. The multi-stage wastewater treatment method based on big data analysis according to claim 1, characterized in that, The process of updating the configuration of the wastewater treatment equipment according to the real-time adjustment command, collecting effluent outlet data, and obtaining the real-time adjusted effluent status data includes: The real-time adjustment command is executed to dynamically adjust the operating parameters of the wastewater treatment equipment, and the outlet data of the equipment after adjustment is collected to obtain time series data of the adjusted pollutant concentration and pH value. The time series data is denoised, and feature vectors of pollutant concentration and pH fluctuations are extracted to obtain adjusted feature data. Based on the adjusted feature data, the correlation strength between pollutant concentration and pH value is analyzed, and it is verified whether the effluent indicators meet the preset discharge standards, thus obtaining the real-time adjusted effluent status data.

8. The multi-stage wastewater treatment method based on big data analysis according to claim 1, characterized in that, The step of generating an analysis report log based on the effluent status data and iteratively optimizing the entire strategy generation process to obtain a continuously optimized adaptive processing strategy includes: Based on the water discharge status data, the deviation between the actual water discharge index and the preset target value is calculated to obtain the effect evaluation result; Based on the effect evaluation results, the parameters in the multi-dimensional data fusion, virtual environment modeling and strategy allocation stages are corrected to obtain optimized parameters; Based on the optimization parameters and historical process data, an analysis report log is generated. Based on the analysis report log, the strategy generation process is iteratively optimized to obtain a continuously optimized adaptive processing strategy.

9. A multi-stage wastewater treatment system based on big data analysis, characterized in that, include: The data acquisition module is used to acquire multidimensional raw data of wastewater flow, and to perform filtering, cleaning and spatiotemporal alignment processing on the multidimensional raw data to obtain real-time parameter data. The data analysis module is used to perform data fusion and correlation pattern analysis based on the real-time parameter data to determine the real-time correlation patterns between parameters. The dynamic mapping module is used to acquire parameter data corresponding to the real-time association mode and obtain the dynamic mapping relationship between the physical processing process and the virtual environment through simulation prediction. The parameter optimization module is used to adjust the parameters of the processing unit in the virtual environment according to the dynamic mapping relationship and generate a digital representation. The strategy decision-making module is used to allocate strategies to multi-level processing links based on the digital representation and generate real-time adjustment instructions. The execution control module is used to update the configuration of the wastewater treatment equipment according to the real-time adjustment command, collect effluent data, and obtain the effluent status data after real-time adjustment. The feedback optimization module is used to generate an analysis report log based on the effluent status data and iteratively optimize the entire strategy generation process to obtain a continuously optimized adaptive processing strategy.

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