Sewage quality prediction method and system based on deep learning

Abstract

The invention discloses a sewage quality prediction method and system based on deep learning, and belongs to the technical field of sewage quality prediction. The method aims at solving the problems that a traditional sewage quality prediction method is low in precision and poor in adaptability. According to the method, through the steps of data generation and simulation, feature engineering, model fusion and migration, anomaly detection and restoration, intelligent decision support, multi-modal data fusion, real-time prediction and adjustment, complex event simulation and optimization and the like, a deep learning technology is utilized to capture a hidden mode and a dynamic change rule in water quality data, and the prediction precision and efficiency are improved; according to the invention, water quality data can be received and processed in real time, a prediction result is dynamically adjusted, real-time monitoring and early warning are realized, water quality data of various sources and types are fused, and the accuracy and comprehensiveness of prediction are improved; the method is suitable for sewage treatment under different environmental conditions, and has wide application prospects and remarkable social and economic benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological data correction, and in particular to a sewage quality prediction method and system based on deep learning. Background Art

[0002] The prediction and monitoring of sewage quality is an important part of water environment management and governance, and is of great significance for ensuring water quality safety and protecting the ecological environment. Existing sewage quality prediction methods mainly rely on statistical models, empirical formulas or expert systems. These methods can reflect the changing trend of water quality to a certain extent, but they have limitations in accuracy, real-time and adaptability.

[0003] At present, the detection methods of sewage water quality mainly include physical indicator detection, chemical indicator detection and biological indicator detection. Physical indicator detection involves the measurement of parameters such as color, turbidity, temperature, conductivity, suspended solids content of water samples; chemical indicator detection covers the determination of multiple parameters such as pH value, dissolved oxygen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), total nitrogen, total phosphorus, heavy metal ions, etc.; biological indicator detection evaluates the degree of biological pollution of water bodies by observing and analyzing the structure, quantity and activity of microbial populations in water bodies. With the development of science and technology, many advanced instrumental analysis technologies, such as high performance liquid chromatography (HPLC), gas chromatography (GC), atomic absorption spectroscopy (AAS), etc., have also been widely used in sewage water quality detection. These technologies can accurately determine trace or even ultra-trace harmful substances in water bodies.

[0004] However, in terms of sewage quality prediction, traditional methods such as statistical models and empirical formulas are often limited by data size, feature extraction capabilities and model complexity, making it difficult to accurately capture hidden patterns and dynamic changes in water quality data. In addition, these methods show weak adaptability and robustness when dealing with complex and changing pollution sources and environmental conditions.

[0005] Problems with existing technologies: Insufficient prediction accuracy: Traditional prediction methods have difficulty in effectively extracting key features when processing large-scale, high-dimensional water quality data, resulting in low prediction accuracy and difficulty in meeting increasingly stringent water quality management requirements.

[0006] Poor real-time performance: Due to the limitations of computational complexity and data processing speed, traditional prediction methods are difficult to achieve real-time water quality prediction and early warning, and it is difficult to respond to emergencies in a timely manner.

[0007] Poor adaptability: When faced with sewage treatment needs in different cities and regions, traditional prediction methods have weak model migration capabilities and are difficult to adapt to the diverse characteristics of water quality changes.

[0008] Data quality has a great impact: There are often outliers and noise in water quality monitoring data. Traditional prediction methods lack effective anomaly detection and repair mechanisms, and the prediction results are easily affected by data quality issues.

[0009] Insufficient decision support: Traditional prediction methods often only provide prediction results, lack of optimization suggestions and decision support for sewage treatment processes, and are difficult to meet the comprehensive needs in practical applications.

[0010] In view of the above problems, deep learning technology has shown great potential in the field of sewage quality prediction due to its powerful feature learning ability, nonlinear modeling ability and real-time processing ability. Through the construction of multi-layer neural networks and automatic feature learning, deep learning models can more accurately capture the hidden patterns and dynamic changes in water quality data, and improve the accuracy and efficiency of prediction. At the same time, deep learning models also have strong adaptability and robustness, and can cope with complex and changing pollution sources and environmental conditions, and achieve real-time prediction and early warning.

[0011] In summary, the sewage quality prediction method based on deep learning is expected to solve many problems in existing technologies and provide more accurate, real-time and effective technical support for water environment management and governance. Summary of the invention

[0012] The technical problem to be solved by the present invention is to provide a sewage water quality prediction method and system based on deep learning, so as to solve the long-standing problems in the field of sewage water quality prediction technology such as low prediction accuracy, low efficiency and difficulty in adapting to complex and changeable environmental conditions. Specifically, traditional sewage water quality prediction methods mostly rely on statistical models, empirical formulas or expert systems. These methods often show insufficient prediction capabilities when processing large amounts of complex water quality data, and are difficult to adjust dynamically in real time to adapt to different pollution sources and changes in environmental conditions.

[0013] In order to solve the above technical problems, the technical solution adopted by the present invention is a sewage quality prediction method based on deep learning, comprising the following steps: Step 1: Data generation and simulation: Generate synthetic sewage data through generative adversarial networks, and perform data simulation to simulate the impact of different pollution sources on water quality and create multiple hypothetical scenarios; Step 2: Feature engineering, extract key time series features from time series data, identify periodic changes, use spectral analysis, and perform dimensionality reduction on high-dimensional data; Step 3: Model fusion and migration: multiple prediction models are integrated, and the model is adapted from one field to another using ensemble learning methods, cascade model technology, and domain adaptation technology. Step 4: Anomaly detection and repair, applying anomaly detection technology to identify abnormal patterns in the data and adjusting the data through automatic repair methods; Step 5: Intelligent decision support, automatically optimizing the sewage treatment process based on the model prediction results, using deep reinforcement learning methods to make decisions, and generating easy-to-understand explanation reports; Step 6: Multimodal data fusion, combining visual sensor data with other sensor data for comprehensive analysis, and using multimodal convolutional neural network for data fusion; Step 7: Real-time prediction and adjustment: receive and process water quality data in real time, and dynamically adjust and optimize the prediction results based on the latest data; Step 8: Complex event simulation and optimization, simulate the impact of specific events such as industrial accidents or natural disasters on water quality, and perform complex event simulation and optimization.

[0014] In a preferred solution, the feature engineering in Step 2 also includes using principal component analysis to reduce the dimensionality of high-dimensional data to improve model efficiency.

[0015] In a preferred solution, the model fusion and migration in Step 3 further includes: Step 3.1: Domain adaptation unit, used to adapt the model from one domain to another, including sewage treatment in different cities, using MMD (Maximum Mean Discrepancy) domain adaptation technology for migration; Step 3.2: Cascade model unit is used to cascade multiple prediction models, take advantage of the advantages of different models, and use stacking cascade model technology and integrated learning methods.

[0016] In a preferred solution, the anomaly detection and repair in Step 4 further includes: Step 4.1: Anomaly Generative Adversarial Network unit, used to detect abnormal patterns in the data and automatically repair them, using Anomaly Generative Adversarial Networks (AnoGAN) for anomaly detection and repair; Step 4.2: Adaptive data repair unit, used to dynamically adjust the data repair strategy according to the anomaly detection results, using the adaptive interpolation method.

[0017] In a preferred solution, the intelligent decision support in Step 5 further includes: Step 5.1: Adaptive optimization unit, used to automatically adjust the sewage treatment process according to the prediction results, and use the DeepQ-Learning method to make optimization decisions; Step 5.2: Prediction explanation generation unit is used to generate explainable prediction results and decision support information, and use natural language generation (NLG) technology to generate explanation reports.

[0018] In a preferred solution, the multimodal data fusion in Step 6 further includes: Step 6.1: Fusion of visual sensor data unit, used to combine video surveillance data sensor data for comprehensive analysis, using multimodal convolutional neural network for data fusion; Step 6.2: Sensor fault detection unit is used to detect and correct faults in sensor data, and uses Kalman filtering method for fault detection.

[0019] In a preferred solution, the real-time prediction and adjustment in Step 7 further includes: Step 7.1: Adaptive prediction window unit, used to dynamically adjust the prediction window length according to real-time data, and use dynamic time window adjustment algorithm to make prediction adjustments; Step 7.2: The prediction result real-time adjustment unit is used to adjust the prediction result in real time according to the latest data during the system operation, and use the online learning algorithm for real-time adjustment.

[0020] In a preferred solution, the complex event simulation and optimization in Step 8 further includes: Step8.1: Event-driven simulation unit, used to simulate the impact of specific events on water quality; Step 8.2: Complex optimization strategy unit is used to formulate complex optimization strategies based on simulation results to deal with the impact of different events on water quality.

[0021] The implementation of any of the deep learning-based sewage quality prediction methods described above adopts a deep learning-based sewage quality prediction system, which includes a data generation simulation module, an advanced feature engineering module, a model fusion migration module, an anomaly detection and repair module, an intelligent decision support module, a multimodal data fusion module, a real-time prediction and response module, and a complex event simulation and optimization module. The modules cooperate with each other to achieve comprehensive prediction and optimization of sewage quality.

[0022] The sewage quality prediction method and system based on deep learning provided by the present invention have the following beneficial effects: 1. The present invention solves the long-standing problems in the field of sewage quality prediction technology, such as low prediction accuracy, low efficiency, and difficulty in adapting to complex and changing environmental conditions; 2. The present invention generates synthetic data by generating adversarial networks, which makes up for the problem of insufficient actual data; 3. The present invention improves the performance of the model in different environments through multi-model fusion and transfer learning technology, making up for the problem of poor generalization ability of the model; 4. The present invention uses anomaly detection and automatic repair technology to ensure data quality and solve the problem of difficulty in handling data anomalies; 5. The present invention provides intelligent decision support, including optimizing process parameters and generating explanation reports, which makes up for the problem of insufficient decision support of existing prediction methods; 6. The application of deep learning technology in the present invention can more accurately capture the hidden patterns and dynamic change laws in water quality data through the construction of multi-layer neural networks and automatic feature learning, thereby improving the accuracy and efficiency of prediction; 7. The multi-model fusion and transfer learning technology of the present invention uses integrated learning methods and cascade model technology to improve the performance of the model under different environmental conditions; 8. The anomaly detection and automatic repair technology of the present invention combines generative adversarial networks and interpolation methods to automatically identify and repair abnormal patterns in data to ensure data quality; 9. The present invention automatically optimizes the sewage treatment process according to the prediction results and generates an easy-to-understand explanation report to improve the intelligence and transparency of decision-making; 10. The present invention integrates water quality data from multiple sources and types, fully utilizes multimodal information, and improves the accuracy and comprehensiveness of prediction; 11. The deep learning technology of the present invention can more accurately capture the hidden patterns and dynamic changes in water quality data, and improve the accuracy and efficiency of prediction; 12. The multi-model fusion and transfer learning technology of the present invention enables the model to adapt to the changing characteristics of water quality under different environmental conditions; 13. The anomaly detection and automatic repair technology of the present invention ensures data quality and improves the robustness of the model; 14. The present invention automatically generates optimization plans and explanation reports, improving the intelligent level of decision-making and user satisfaction; 15. The multimodal data fusion and real-time prediction and adjustment functions of the present invention make the system more comprehensive and real-time, and can respond to water quality changes in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention will be further described below in conjunction with the accompanying drawings: Figure 1 A topological diagram of the sewage quality prediction system of the present invention; Figure 2 This is a flow chart of the sewage quality prediction method based on deep learning of the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments: Example 1 like Figure 2 As shown, the sewage quality prediction method based on deep learning includes the following steps: Step 1: Data generation and simulation: Generate synthetic sewage data through generative adversarial networks, and perform data simulation to simulate the impact of different pollution sources on water quality and create multiple hypothetical scenarios; Step 2: Feature engineering, extract key time series features from time series data, identify periodic changes, use spectral analysis, and perform dimensionality reduction on high-dimensional data; Step 3: Model fusion and migration: multiple prediction models are integrated, and the model is adapted from one field to another using ensemble learning methods, cascade model technology, and domain adaptation technology. Step 4: Anomaly detection and repair, applying anomaly detection technology to identify abnormal patterns in the data and adjusting the data through automatic repair methods; Step 5: Intelligent decision support, automatically optimizing the sewage treatment process based on the model prediction results, using deep reinforcement learning methods to make decisions, and generating easy-to-understand explanation reports; Step 6: Multimodal data fusion, combining visual sensor data with other sensor data for comprehensive analysis, and using multimodal convolutional neural network for data fusion; Step 7: Real-time prediction and adjustment: receive and process water quality data in real time, and dynamically adjust and optimize the prediction results based on the latest data; Step 8: Complex event simulation and optimization, simulate the impact of specific events such as industrial accidents or natural disasters on water quality, and perform complex event simulation and optimization.

[0025] In this embodiment, the feature engineering in Step 2 also includes using principal component analysis to reduce the dimensionality of high-dimensional data to improve model efficiency.

[0026] Furthermore, the model fusion and migration in Step 3 further includes: Step 3.1: Domain adaptation unit, used to adapt the model from one domain to another, including sewage treatment in different cities, using MMD domain adaptation technology for migration; Step 3.2: Cascade model unit is used to cascade multiple prediction models, take advantage of the advantages of different models, and use stacking cascade model technology and integrated learning methods.

[0027] Furthermore, the anomaly detection and repair in Step 4 also includes: Step 4.1: Anomaly Generative Adversarial Network Unit, used to detect abnormal patterns in the data and automatically repair them, using the Anomaly Generative Adversarial Network (AnoGAN) for anomaly detection and repair; Step 4.2: Adaptive data repair unit, used to dynamically adjust the data repair strategy according to the anomaly detection results, using the adaptive interpolation method.

[0028] Furthermore, the intelligent decision support in Step 5 also includes: Step 5.1: Adaptive optimization unit, used to automatically adjust the sewage treatment process according to the prediction results, and use the DeepQ-Learning method to make optimization decisions; Step 5.2: Prediction explanation generation unit is used to generate explainable prediction results and decision support information, and generate explanation reports using natural language generation (NLG) technology.

[0029] Furthermore, the multimodal data fusion in Step 6 further includes: Step 6.1: Fusion of visual sensor data unit, used to combine video surveillance data sensor data for comprehensive analysis, using multimodal convolutional neural network for data fusion; Step 6.2: Sensor fault detection unit is used to detect and correct faults in sensor data, and uses Kalman filtering method for fault detection.

[0030] Furthermore, the real-time prediction and adjustment in Step 7 also includes: Step 7.1: Adaptive prediction window unit, used to dynamically adjust the prediction window length according to real-time data, and use dynamic time window adjustment algorithm to make prediction adjustments; Step 7.2: The prediction result real-time adjustment unit is used to adjust the prediction result in real time according to the latest data during the system operation, and use the online learning algorithm for real-time adjustment.

[0031] Furthermore, the complex event simulation and optimization in Step 8 also includes: Step8.1: Event-driven simulation unit, used to simulate the impact of specific events on water quality; Step 8.2: Complex optimization strategy unit is used to formulate complex optimization strategies based on simulation results to deal with the impact of different events on water quality.

[0032] Example 2 In another preferred embodiment, based on the above embodiment 1, Figure 1 As shown, the deep learning-based sewage quality prediction method described in any one of the implementation examples 1 adopts a deep learning-based sewage quality prediction system, which includes a data generation simulation module, an advanced feature engineering module, a model fusion migration module, an anomaly detection and repair module, an intelligent decision support module, a multimodal data fusion module, a real-time prediction and response module, and a complex event simulation and optimization module. The modules cooperate with each other to achieve comprehensive prediction and optimization of sewage quality.

[0033] Example 3 In another preferred embodiment, based on the above embodiments 1 and 2, as Figure 1 As shown in the figure, the sewage water quality prediction system based on deep learning realizes the sewage water quality prediction function based on deep learning by integrating multiple modules; the system mainly includes the following modules: data generation simulation module, advanced feature engineering module, model fusion migration module, anomaly detection and repair module, intelligent decision support module, multimodal data fusion module, real-time prediction and response module and complex event simulation and optimization module.

[0034] 1. Data generation simulation module The data generation simulation module includes a synthetic data generation unit and a data simulation unit; the synthetic data generation unit uses generative adversarial networks (GANs), especially deep convolution generative adversarial networks (DCGAN), to generate synthetic sewage data to supplement the lack of actual data; the data simulation unit uses a physical simulation engine, such as Monte Carlo simulation based on physical models or statistical models, to simulate the impact of different pollution sources (such as industrial emissions, domestic sewage) on water quality, and create a variety of hypothetical scenarios to enhance the generalization ability of the model.

[0035] 2. Advanced Feature Engineering Module The advanced feature engineering module includes a time series feature extraction unit and a high-dimensional data dimension reduction unit; the time series feature extraction unit uses the autoregressive integrated moving average (ARIMA) model, spectral analysis (such as Fourier transform FFT, Fast Fourier Transform) and other tools to extract key time series features from time series data, such as trends, seasonality, and cyclical changes; the high-dimensional data dimension reduction unit uses principal component analysis (PPCA, Principal Component Analysis) and t-SNE (t-Distributed Stochastic Neighbor Embedding) and other methods to reduce the dimensionality of high-dimensional data to reduce the number of features and improve model training efficiency.

[0036] 3. Model fusion migration module The model fusion migration module includes a domain adaptation unit and a cascade model unit; the domain adaptation unit adopts the maximum mean difference (MMD) domain adaptation technology to adapt the model from the historical data environment to the new pollution source environment to cope with the differences in sewage treatment scenarios in different cities; the cascade model unit uses Stacking cascade model technology and integrated learning methods to connect multiple prediction models in series hierarchically, with the output of the previous model used as the input of the next model, and gradually refine the prediction results.

[0037] 4. Anomaly detection and repair module The anomaly detection and repair module includes an anomaly generative adversarial network unit and an adaptive data repair unit; the anomaly generative adversarial network unit uses the anomaly generative adversarial network (AnoGAN) to detect abnormal patterns in the data, such as extreme values ​​and abnormal trends; the adaptive data repair unit uses adaptive interpolation methods (such as linear interpolation and spline interpolation) to dynamically adjust the data repair strategy based on the anomaly detection results to ensure data quality.

[0038] 5. Intelligent decision support module The intelligent decision support module includes an adaptive optimization unit and a prediction and explanation generation unit; the adaptive optimization unit is based on deep reinforcement learning (DRL), especially the deep Q-Leaning Network (DQN), and automatically optimizes sewage treatment process parameters such as treatment volume and treatment time according to the model prediction results; the prediction and explanation generation unit uses natural language generation (NLG) technology to generate easy-to-understand explanation reports, including prediction results, optimization measures and reasons, to help users make decisions.

[0039] 6. Multimodal data fusion module The multimodal data fusion module includes a fusion visual sensor data unit and a sensor fault detection unit; the fusion visual sensor data unit combines video surveillance data and multi-source data such as chemical sensors and flow sensors, and uses a multimodal convolutional neural network for data fusion to improve the comprehensiveness and accuracy of the prediction; the sensor fault detection unit uses the Kalman filtering method to detect and correct faults in the sensor data to ensure data reliability.

[0040] 7. Real-time prediction response module The real-time prediction response module includes an adaptive prediction window unit and a prediction result real-time adjustment unit; the adaptive prediction window unit dynamically adjusts the prediction window length according to real-time data and uses a dynamic time window adjustment algorithm to make prediction adjustments; the prediction result real-time adjustment unit adjusts the prediction results in real time according to the latest data during system operation and uses an online learning algorithm to ensure the timeliness and accuracy of the prediction results.

[0041] 8. Complex event simulation optimization module The complex event simulation and optimization module includes an event-driven simulation unit and a complex optimization strategy unit; the event-driven simulation unit simulates the impact of specific events (such as industrial accidents and natural disasters) on water quality and generates corresponding water quality change data; the complex optimization strategy unit formulates complex optimization strategies based on the simulation results to cope with water quality changes under different event scenarios.

[0042] This embodiment uses multimodal data fusion to improve prediction accuracy, while ensuring data quality through anomaly detection and repair, enhancing generalization capabilities through model fusion migration, providing managers with instant strategic recommendations through intelligent decision support, and further optimizing the sewage treatment process through complex event simulation optimization, thus achieving full-chain intelligent management from data preprocessing to decision support. This solution not only improves sewage treatment efficiency, but also significantly reduces operating costs, setting an example for intelligent transformation in the environmental protection industry.

[0043] Example 4 In another preferred embodiment, based on the above embodiments 1, 2, and 3, as Figure 2 As shown, the sewage water quality prediction method based on deep learning based on the prediction system of Example 3, in actual application, specifically includes the following steps: S1. Data Generation and Simulation S1.1 Synthetic data generation: Generate synthetic sewage data using Generative Adversarial Networks (GAN); GAN consists of a generator (G) and a discriminator (D). G generates fake data and D distinguishes between real and fake data. Through iterative training, the data generated by G is close to the distribution of real data. Generative Adversarial Networks (GAN), where G and D are both neural networks, are trained by minimizing the difference between generated data and real data; S1.2 Data simulation: Using simulation software or platforms, the generated synthetic data is combined with actual data to simulate the impact of different pollution sources (including industrial emissions and domestic sewage) on water quality. Monte Carlo simulation based on physical models or statistical models is used to generate water quality changes under various hypothetical scenarios; S1.3 Scene creation: Based on the simulation results, a variety of hypothetical scenarios with different pollution sources, pollution levels and durations are created to train the model and enhance its generalization ability. The scenario generation algorithm is used to construct a diverse training data set based on parameter adjustment (including pollution source intensity, location, and time).

[0044] S2. Feature Engineering S2.1 Time series feature extraction: Extract key temporal features from time series data, including trends, seasonality, and cyclical changes, using the Autoregressive Integrated Moving Average (ARIMA) model to capture the temporal dependency in the data; S2.2 Spectrum analysis and processing: Use Fourier transform to identify periodic components in data and help understand the law of data change. Fourier transform (FFT) converts time series data from time domain to frequency domain and analyzes frequency components. S2.3 Dimensionality reduction of high-dimensional data: Dimensionality reduction is performed on high-dimensional data to reduce the number of features and improve model training efficiency. Principal component analysis (PCA) maps high-dimensional data to low-dimensional space through linear transformation and retains the main variation information of the data.

[0045] S3, model fusion and migration S3.1 Multi-model fusion: Fusion of prediction results from multiple prediction models (including neural networks, support vector machines, decision trees) to improve prediction accuracy, integrated learning methods, including bagging (random forest) or boosting (gradient boosting tree), to form a strong learner by combining multiple weak learners; S3.2 Cascade Model Technology: Connect multiple models in series hierarchically, using the output of the previous model as the input of the next model, gradually refining the prediction results, cascading regression or classification models, including stacking, and integrating predictions by training a series of base models and a meta-model; S3.3 Field Adaptation: Domain adaptation techniques are used to adapt the model from the historical data environment to the new pollution source environment, and transfer learning algorithms, including TrAdaBoost (Transfer Ada Boost, multivariate time series prediction), are used to reduce the differences between the source and target domains by adjusting the model weights.

[0046] S4. Anomaly detection and repair S4.1 Anomaly Detection: Apply anomaly detection techniques to identify abnormal patterns in data, including extreme values ​​and abnormal trends. GAN-based anomaly detection uses the generator to learn the normal distribution of data, and the discriminator to identify data points that deviate from the normal distribution. S4.2 Automatic repair method: Automatically repair detected abnormal data to ensure data quality. Interpolation methods, including linear interpolation and spline interpolation, are used to fill or smooth abnormal data points; S4.3 Data adjustment: Adjust the data set according to the repair results to ensure that high-quality data is used for model training. Data preprocessing algorithms, including normalization and standardization, ensure that the data is within the appropriate range to facilitate model training.

[0047] S5. Intelligent decision support S5.1 Prediction result optimization: Automatically optimize wastewater treatment process parameters, including treatment volume and treatment time, based on model prediction results. Deep reinforcement learning (DRL), including deep Q networks (DQN), learns optimal strategies through interaction with the environment. S5.2 Decision Making: Based on the optimization results, formulate specific sewage treatment decision plans, and use decision trees or random forests, combined with expert knowledge base, to generate feasible decision options; S5.3 Explanation report generation: Generate easy-to-understand explanation reports, including prediction results, optimization measures and reasons, to help users make decisions. Visualization technology, including scatter plots, line charts, heat maps, and natural language generation (NLG) technology, converts complex information into an intuitive and easy-to-understand format.

[0048] S6. Multimodal data fusion S6.1 Data collection: Combine cameras to monitor water quality changes with chemical sensors and flow sensors to collect multimodal data, and data collection algorithms, including time synchronization mechanisms, to ensure that data from different sources are aligned in time; S6.2 Data preprocessing: Preprocess the collected multimodal data, including denoising and normalization. Gaussian filtering is used to remove noise, and the normalization algorithm ensures that the data is on the same scale. S6.3 Multimodal Fusion: A multi-feature map-convolutional neural network (MM-CNN) is used for data fusion, to extract cross-modal features and improve prediction accuracy. MM-CNN fuses data features of different modalities through convolutional layers, pooling layers and fully connected layer structures for joint learning.

[0049] S7. Real-time prediction and adjustment S7.1 Dynamic Window Adjustment: According to the real-time data changes, the length of the prediction window is dynamically adjusted to ensure that the prediction results reflect the latest data trends. The sliding window algorithm adapts to data changes by adjusting the length of the time series; S7.2 Real-time prediction: Use the latest data to update forecast results in real time to ensure the timeliness and accuracy of the forecast; Online learning algorithms, including online gradient descent, continuously update model parameters to adapt to new data; S7.3 Model Updates: During system operation, the model is continuously updated based on new data to maintain predictive performance. Incremental learning algorithms, including the Incremental Support Vector Machine (ISVM), are updated by adding new samples to the model without retraining the entire model.

[0050] S8. Complex event simulation and optimization S8.1 Event simulation: Simulate the impact of specific events (including industrial accidents and natural disasters) on water quality and evaluate their impact on sewage treatment systems. Event-driven simulation technology, including agent-based simulation, evaluates the impact by simulating system behavior under different event scenarios; S8.2 Strategy Evaluation: Evaluate different optimization strategies and compare their effects in different event scenarios. Multi-objective optimization algorithms, including genetic algorithms (GA), find the optimal solution that meets multiple objectives (including cost, efficiency, and environmental protection) through iterative search; S8.3 Optimization plan determination: Complex combinatorial optimization technology is used to determine the best sewage treatment solution to deal with possible complex events. The combinatorial optimization algorithm is a combination of GA and simulated annealing, which finds the global optimal solution through exploration and utilization strategies.

[0051] The deep learning-based sewage quality prediction method of the present invention achieves accurate prediction of sewage quality and intelligent decision support by comprehensively using technical means such as data generation and simulation, feature engineering, model fusion and migration, anomaly detection and repair, intelligent decision support, multimodal data fusion, real-time prediction and adjustment, and complex event simulation and optimization. This method not only improves the prediction accuracy and efficiency, but also enhances the system's adaptability to complex and changeable environmental conditions. It has significant technical advantages and broad application prospects.

[0052] In summary, the sewage water quality prediction method and system based on deep learning provided by the present invention solve the long-standing problems of low prediction accuracy, low efficiency and difficulty in adapting to complex and changeable environmental conditions in the field of sewage water quality prediction technology; the prediction method of the present invention integrates deep learning technology and applies it to the field of sewage water quality prediction, which is significantly novel in data processing complexity and prediction accuracy compared with traditional statistical models, empirical formulas or expert systems; it uses a multimodal convolutional neural network to fuse visual sensor data with other sensor data. This data fusion method is rare in water quality prediction and provides more comprehensive and accurate data input, enhancing the model's understanding ability and prediction accuracy; it uses principal component analysis (PCA) Methods such as spectral analysis are used to reduce the dimensionality of high-dimensional data, and tools such as spectral analysis are used to process time series data and extract key features. These methods show new ideas in improving model efficiency and prediction accuracy; through methods such as deep reinforcement learning (DRL), not only water quality is predicted, but also the sewage treatment process is automatically optimized according to the prediction results to provide intelligent decision-making support. This link realizes the close integration of prediction and control and reflects a high level of intelligence; the present invention can simulate the impact of specific events (such as industrial accidents and natural disasters) on water quality and formulate optimization strategies accordingly. This function has important value in practical applications and provides new tools for emergency management and risk control; the present invention integrates learning methods and cascade model technology, Combined with domain adaptation technology, multiple prediction models are integrated and adapted to different environments. This method shows high creativity in improving the generalization ability of the model and the prediction accuracy. The application of the generative adversarial network (GAN) in data generation and simulation uses GAN to generate synthetic sewage data, and performs data simulation to simulate the impact of different pollution sources on water quality. This technical innovation effectively solves the problem of insufficient actual data and enhances the generalization ability of the model. The automatic anomaly detection and data repair of the present invention automatically identifies and repairs abnormal patterns in the data through generative adversarial networks and interpolation methods, ensures data quality, and improves the reliability of prediction results. This innovation has important practical value in water quality prediction data processing. The method can not only make high-precision predictions, but also generate easy-to-understand explanation reports to help users understand the prediction results and optimization measures. This feature shows a high degree of creativity in enhancing user trust and improving decision-making transparency. The present invention has a comprehensive system architecture design, which divides the sewage water quality prediction system into multiple modules (such as data generation simulation module, advanced feature engineering module, etc.), and describes in detail the functions and interactions of each module. This system architecture design not only improves the overall performance of the system, but also facilitates subsequent maintenance and upgrades, reflecting a high degree of system design and creativity. In addition, the present invention also incorporates the attention mechanism in deep learning, so that the model can automatically focus on key water quality parameters to further improve the prediction accuracy.Its user-friendly visual interface allows users to easily operate and monitor water quality changes in real time, bringing revolutionary changes to environmental monitoring and management.

Claims

1. A sewage quality prediction method based on deep learning, characterized in that: The method comprises the following steps: Step 1: Data generation and simulation: Generate synthetic sewage data through generative adversarial networks, and perform data simulation to simulate the impact of different pollution sources on water quality and create multiple hypothetical scenarios; Step 2: Feature engineering, extract key time series features from time series data, identify periodic changes, use spectral analysis, and perform dimensionality reduction on high-dimensional data; Step 3: Model fusion and migration: multiple prediction models are integrated, and the model is adapted from one field to another using ensemble learning methods, cascade model technology, and domain adaptation technology. Step 4: Anomaly detection and repair, applying anomaly detection technology to identify abnormal patterns in the data and adjusting the data through automatic repair methods; Step 5: Intelligent decision support, automatically optimizing the sewage treatment process based on the model prediction results, using deep reinforcement learning methods to make decisions, and generating easy-to-understand explanation reports; Step 6: Multimodal data fusion, combining visual sensor data with other sensor data for comprehensive analysis, and using multimodal convolutional neural network for data fusion; Step 7: Real-time prediction and adjustment: receive and process water quality data in real time, and dynamically adjust and optimize the prediction results based on the latest data; Step 8: Complex event simulation and optimization, simulate the impact of specific events such as industrial accidents or natural disasters on water quality, and perform complex event simulation and optimization.

2. The sewage quality prediction method based on deep learning according to claim 1 is characterized in that: The feature engineering in Step 2 also includes using principal component analysis to reduce the dimensionality of high-dimensional data to improve model efficiency.

3. The sewage quality prediction method based on deep learning according to claim 1 is characterized in that: The model fusion and migration in Step 3 further includes: Step 3.1: Domain adaptation unit, used to adapt the model from one domain to another, including sewage treatment in different cities, using MMD domain adaptation technology for migration; Step 3.2: Cascade model unit is used to cascade multiple prediction models, take advantage of the advantages of different models, and use stacking cascade model technology and integrated learning methods.

4. The sewage quality prediction method based on deep learning according to claim 1 is characterized in that: The anomaly detection and repair in Step 4 also includes: Step 4.1: Anomaly Generative Adversarial Network Unit, used to detect abnormal patterns in the data and automatically repair them, using the Anomaly Generative Adversarial Network AnoGAN for anomaly detection and repair; Step 4.2: Adaptive data repair unit, used to dynamically adjust the data repair strategy according to the anomaly detection results, using the adaptive interpolation method.

5. The sewage quality prediction method based on deep learning according to claim 1 is characterized in that: The intelligent decision support in Step 5 also includes: Step 5.1: Adaptive optimization unit, used to automatically adjust the sewage treatment process according to the prediction results, and use the DeepQ-Learning method to make optimization decisions; Step 5.2: Prediction explanation generation unit is used to generate explainable prediction results and decision support information, and use natural language generation (NLG) technology to generate explanation reports.

6. The sewage quality prediction method based on deep learning according to claim 1 is characterized in that: The multimodal data fusion in Step 6 also includes: Step 6.1: Fusion of visual sensor data unit, used to combine video surveillance data sensor data for comprehensive analysis, using multimodal convolutional neural network for data fusion; Step 6.2: Sensor fault detection unit is used to detect and correct faults in sensor data, and uses Kalman filtering method for fault detection.

7. The sewage quality prediction method based on deep learning according to claim 1 is characterized in that: The real-time prediction and adjustment in Step 7 also includes: Step 7.1: Adaptive prediction window unit, used to dynamically adjust the prediction window length according to real-time data, and use dynamic time window adjustment algorithm to make prediction adjustments; Step 7.2: The prediction result real-time adjustment unit is used to adjust the prediction result in real time according to the latest data during the system operation, and use the online learning algorithm for real-time adjustment.

8. The sewage quality prediction method based on deep learning according to claim 1 is characterized in that: The complex event simulation and optimization in Step 8 also includes: Step8.1: Event-driven simulation unit, used to simulate the impact of specific events on water quality; Step 8.2: Complex optimization strategy unit is used to formulate complex optimization strategies based on simulation results to deal with the impact of different events on water quality.

9. The method for predicting sewage quality based on deep learning according to any one of claims 1 to 8 is implemented by using a sewage quality prediction system based on deep learning, characterized in that: The system includes a data generation simulation module, an advanced feature engineering module, a model fusion migration module, an anomaly detection and repair module, an intelligent decision support module, a multimodal data fusion module, a real-time prediction and response module, and a complex event simulation and optimization module. The modules cooperate with each other to achieve comprehensive prediction and optimization of sewage quality.

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