Sewage recycling intelligent regulation and control method and system based on deep learning

Abstract

The invention relates to the technical field of sewage recycling regulation and control, and discloses a sewage recycling intelligent regulation and control method and system based on deep learning, and the method comprises the steps: collecting and processing sewage multi-dimensional data, constructing a feature library, and extracting key features; a multi-level monitoring network is established, and a monitoring data set is obtained in real time. And fusing the feature library, real-time data and historical experience to construct an intelligent decision-making mechanism, and identifying a feature relationship generation rule. And coupling the DPSIR framework with a circulation principle to form a recycling strategy. And a resource regulation and control mechanism is constructed, a balance constraint is generated, and supply and demand intelligent management optimization is realized. And training a prediction model by using rules, constraints and historical data, and capturing a water quality trend. And when target deviation is predicted, a regulation response is automatically triggered, and intelligent optimization regulation is carried out by integrating decisions, strategies and constraints. The regulation and control result is fed back and learned after matching verification and effect evaluation, the strategy is continuously optimized, and finally the sewage recycling intelligent regulation and control closed-loop system which is high in adaptability and suitable for multiple scenes is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage resource utilization regulation and control, and more specifically, to a sewage resource utilization intelligent regulation method and system based on deep learning. Background Art

[0002] With the acceleration of urbanization and the continuous advancement of industrial development, wastewater treatment and resource utilization have become essential components of environmental protection and sustainable development. While traditional wastewater treatment methods have made progress in basic treatment technologies, monitoring equipment application, and operational management, they still lack systematic solutions to core issues such as intelligent processing of multidimensional data and the construction of feature libraries, the integration of real-time monitoring networks and decision-making mechanisms, and resource utilization strategies and balanced regulation. These challenges make it difficult to meet the treatment needs of diverse regions, wastewater scales, and wastewater types.

[0003] Therefore, how to integrate multi-source data, deeply explore the key characteristic relationships in the sewage treatment process, accurately capture the trend of water quality changes, and develop intelligent control methods with strong adaptability has become an urgent problem to be solved. Summary of the Invention

[0004] The present invention provides a deep learning-based intelligent regulation method and system for sewage resource utilization, which solves the technical problems in the existing technology of how to integrate multi-source data, deeply explore the key feature relationships in the sewage treatment process, accurately capture the trend of water quality changes, and have strong adaptability.

[0005] The present invention provides a deep learning-based intelligent control method and system for sewage resource utilization, including: First, a deep learning-based intelligent control method for wastewater resource utilization includes: Collect multi-dimensional data from the sewage treatment process and build a feature library for sewage resource utilization through data preprocessing and feature engineering; Based on the historical data patterns and feature associations in the feature library, a multi-level intelligent monitoring network is established to obtain monitoring data sets that match the feature library in real time. Deeply integrate and analyze the feature library with the monitoring data set, and build an intelligent regulation decision-making mechanism based on historical regulation experience; Construct a DPSIR conceptual framework, couple it with the intelligent control decision-making mechanism, and integrate the recycling principle to obtain a wastewater resource utilization strategy; Based on the wastewater resource recycling strategy, a resource balance control mechanism for demand forecasting and supply optimization is constructed. The resource balance control mechanism generates balance constraints by receiving the constraint results of the wastewater resource recycling strategy; Based on the decision rules of the intelligent regulation and control decision mechanism, the balance constraint conditions, and the guidance of the sewage resource utilization strategy, combined with the historical monitoring data set, a prediction model for predicting the water quality state is trained and constructed; Based on the real-time acquisition of water quality parameter prediction values by the regulation and control model, when the prediction values deviate from the target range, the regulation and control response mechanism is automatically triggered, combined with the intelligent regulation and control decision mechanism, the sewage resource utilization strategy and the balance constraint conditions, the intelligent optimization regulation and control processing of the sewage treatment is carried out, and the regulation and control results are generated; The regulation and control results are matched and verified with the feature information in the data feature library and consistency check, the regulation and control results are quantitatively fed back by effect evaluation; according to the feedback learning results, the regulation and control strategy is optimized, and the comprehensive intelligent regulation and control operation of sewage resource is formed.

[0006] By constructing a multi-dimensional data feature library, an intelligent monitoring network, a DPSIR conceptual framework and a resource balance regulation and control mechanism, intelligent regulation and control and resource utilization of the sewage treatment process are realized. The entire technical scheme includes five core links of data acquisition and processing, intelligent decision mechanism construction, prediction model training, regulation and control execution and feedback optimization, forming a complete comprehensive intelligent regulation and control operation system of sewage resource.

[0007] Further, the DPSIR conceptual framework comprises: Identify driving factors, analyze the root cause of sewage generation and social development needs; Determine the pressure index, quantify the direct pressure and load of sewage discharge on the environment; Evaluate state parameters, monitor and evaluate the current water environment quality state and the operation state of the sewage treatment system; Analyze the impact effect, analyze the potential impact of sewage discharge on the ecological environment and human health, and obtain the analysis result; Develop response measures based on the analysis results to develop corresponding management and resource utilization countermeasures.

[0008] Further, the data feature library and the monitoring data set are fused and associated to construct an intelligent regulation and control decision mechanism, and the decision rules are obtained, including: Standardize the data format and align the time stamp of the historical data and real-time monitoring data in the feature library, and generate a unified standard format of the sewage treatment data set; Based on the sewage treatment data set, through multi-dimensional data association analysis, the internal correlation and dependency relationship between the data are identified, the association relationship includes three dimensions of time sequence association, spatial position association and parameter change association, forming a multi-dimensional association feature set of the sewage treatment process; Deeply fuse the multi-dimensional association feature set with historical regulation and control experience to obtain the fusion result; Based on the fusion result, combined with expert knowledge system, a sewage resource treatment rule base is constructed; According to the sewage resource treatment rule base, an intelligent regulation and control reasoning engine matched with the multi-dimensional correlation feature set is constructed to obtain a reasoning result; Based on the mapping relationship between the reasoning result and the multi-dimensional correlation feature set, a sewage resource intelligent regulation and control decision mechanism is constructed; the intelligent regulation and control decision mechanism identifies the operation state of the sewage treatment data and adjusts the sewage treatment parameters; data sharing and cooperative decision are realized by cooperating with the multi-dimensional correlation feature set; According to the current sewage treatment data operation state, multi-dimensional correlation feature and historical regulation and control experience, an optimal resource regulation and control decision rule is generated, which includes parameter regulation and control threshold, operation timing rule and emergency response strategy.

[0009] Further, the DPSIR conceptual framework is coupled with the intelligent regulation and control decision mechanism, including: The driving force factor in the DPSIR framework is taken as an input parameter of the intelligent regulation and control decision mechanism, and a mapping relationship between the driving force and the decision is established; The pressure index in the DPSIR framework is associated and matched with the monitoring data in the intelligent regulation and control decision mechanism to form a pressure regulation and control response mechanism; The state parameter set in the DPSIR framework is integrated into the real-time parameter change state of the intelligent regulation and control decision mechanism to realize the linkage between the state and the decision; The impact effect evaluation result in the DPSIR framework is taken as a constraint condition of the intelligent regulation and control decision mechanism to optimize the regulation and control strategy; The response measures in the DPSIR framework are combined with the execution of the intelligent regulation and control decision mechanism to form an intelligent regulation and control system.

[0010] Further, the sewage resource utilization strategy is obtained by integrating the recycling principle, including: Based on the deep coupling result of the DPSIR framework and the intelligent regulation and control decision mechanism, the key nodes and optimization paths of the resource utilization are identified, and a three-level recycling system of reduction, reuse and recycling is established; The driving force analysis result of the DPSIR framework is combined with the recycling principle, and based on the key node analysis of the three-level recycling system, the priority and implementation order of the sewage resource are determined, and the mapping relationship between the driving force and the decision generated by the deep coupling result is used to construct a dynamic recycling utilization decision mechanism matched with the three-level recycling system; Based on the impact effect evaluation result and the output of the dynamic recycling utilization decision mechanism, combined with the optimization path of the three-level recycling system, a target function of maximizing recycling is established, and a sewage resource utilization strategy coordinated with the decision mechanism is developed; By deeply coupling the results with the corresponding collaborative operation of each link of recycling, integrating the execution effect of sewage resource utilization strategy and multi-dimensional sewage resource utilization evaluation index, a recycling performance evaluation system is established, and the evaluation results are obtained; The evaluation results are fed back to the three-level recycling system and the dynamic decision-making mechanism, and the feedback results are obtained; When the feedback results deviate from the recycling principles, return to the step of constructing a dynamic recycling decision-making mechanism for reoperation until the feedback results meet the recycling principles.

[0011] Further, a resource balance regulation mechanism is constructed to generate balance constraint conditions, including: The execution results of the three-level recycling system are taken as input, combined with resource allocation constraint conditions and recycling efficiency indexes, and under the constraint of recycling strategy, the constraint results are output; the constraint results are deeply analyzed to obtain the analysis results; According to the analysis results, the constraint conditions are analyzed for relevance to identify the key restricting factors of resource supply and demand balance, establish the mutual dependence relationship and influence weight between the constraint conditions, and form the analysis results; Based on the analysis results, combined with the intelligent regulation and control decision-making mechanism of sewage treatment, sewage treatment quality standards and environmental emission requirements, the balance constraint conditions are generated to construct a multi-dimensional balance constraint condition system covering water balance, water quality balance and energy balance; Based on the multi-dimensional balance constraint condition system, according to the real-time monitoring data and the feedback of the execution effect of recycling strategy, the feedback results are obtained, the balance constraint conditions are dynamically adjusted according to the feedback results, and the adjustment results are fed back to the constraint condition relevance analysis link; The constraint condition relevance analysis link analyzes and processes again according to the adjustment results combined with the analysis results to obtain the second analysis results; repeat the operation to iteratively adjust the constraint conditions to obtain the final balance constraint conditions.

[0012] Further, based on the decision rules, balance constraint conditions and guidance of sewage resource utilization strategy, and combined with the historical monitoring data set, a prediction model for predicting water quality state is trained and constructed, including: Based on the decision rules, balance constraint conditions and guidance of sewage resource utilization strategy, and combined with the historical monitoring data set, a prediction model for predicting water quality state is trained and constructed, including: The obtained historical monitoring data set is generated into structured data, the structured data is encoded into sequence data, combined with the decision rules, balance constraint conditions and sewage resource utilization strategy, and the water quality state prediction model is trained and constructed; The sequence data is input into the water quality state prediction model, and the water quality state prediction model includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer; The intermediate representation data obtained by processing the sequence data through multiple hidden layers is finally transmitted to an output layer, and the output layer outputs a prediction result representing the water quality state in the sewage treatment process; At least one data item in the newly acquired real-time monitoring data set is input into the water quality state prediction model, and the output layer outputs a prediction result representing the water quality state in the newly acquired real-time monitoring data set.

[0013] Further, when the water quality state prediction result output by the prediction model deviates from the target range value, an automatic control response mechanism is triggered, including: According to the water quality state prediction result, the deviation degree of the prediction value of each water quality index from the set target range is obtained, and the comprehensive deviation degree data of each water quality index is transmitted to the early warning response mechanism; Wherein, the deviation degree of a single water quality index is: wherein: represents the deviation degree of the i-th water quality index; represents the prediction value of the i-th water quality index; represents the target value of the i-th water quality index; represents the upper limit value of the target range of the i-th water quality index, represents the lower limit value of the target range of the i-th water quality index; Comprehensive deviation degree: wherein: represents the comprehensive deviation degree; represents the weight coefficient of the i-th water quality index; represents the total number of monitored water quality indexes; The early warning response mechanism pre-establishes a multi-level early warning response system, including a normal range, an abnormal range and an emergency range; When the early warning response mechanism receives the comprehensive deviation degree data, if the comprehensive deviation degree data is in the abnormal range or the emergency range, the corresponding level of early warning response is started, and the corresponding early warning information is output; According to the prediction value deviation degree of each water quality index, the deviation trend change rate is obtained by The deviation trend change rate is obtained, wherein: represents the change rate of the deviation trend of the i-th water quality index; represents the deviation degree of the i-th index at the current time t; represents the deviation degree of the i-th index at the previous time t;1; represents the time interval; According to the early warning information and the deviation trend change rate, if the deviation trend change rate shows a sharp drop or a sharp rise, an automatic control response mechanism is triggered, the control demand is identified and the control priority is determined, and the control demand and priority information are transmitted to the intelligent control decision mechanism for intelligent control of the parameters of the sewage treatment.

[0014] Further, the intelligent regulation and control decision mechanism, the sewage resource utilization strategy, and the balance constraint condition are combined to intelligently regulate and control the sewage resource, to generate a regulation and control result, including: When the intelligent regulation and control decision mechanism receives the regulation and control demand and the priority information, integrates the optimization strategy of the intelligent regulation and control decision mechanism, including the parameter regulation threshold, the operation timing rule, and the emergency response strategy, forms a targeted regulation and control scheme, and delivers the regulation and control scheme to the sewage resource utilization strategy for fusion; When the sewage resource utilization strategy receives the regulation and control scheme, combines the three-level circulation system requirements, makes the regulation and control process meet the resource utilization target, and outputs the fused regulation and control strategy to further optimize the regulation and control mechanism in combination with the balance constraint condition; When the fused regulation and control strategy combines the water balance, the water quality balance, and the energy consumption balance requirements in the balance constraint condition system, optimizes the regulation and control parameter configuration, generates the final intelligent regulation and control execution scheme, and performs the regulation and control execution operation; According to the intelligent regulation and control execution scheme, the intelligent optimization regulation and control processing is performed, including automatically adjusting the influent flow, the reagent dosage, the aeration intensity, and the sludge return ratio key operation parameters, and feeding the execution state information to the effect monitoring; According to the regulation and control execution state information, the regulation and control execution effect is monitored in real time, the sewage treatment strategy is dynamically regulated and controlled through the feedback control mechanism, the sewage quality parameter is gradually returned to the target range, the monitoring result and the adjustment information are generated as the regulation and control result; The regulation and control result includes the regulation and control action record, the parameter change trajectory, and the result report of the effect evaluation index, and feeds back the regulation and control result to the water quality state prediction model and the early warning threshold system to iteratively process the sewage quality parameter index.

[0015] In a second aspect, an intelligent sewage resource regulation and control system based on deep learning is used to execute an intelligent sewage resource regulation and control method based on deep learning, including: A data acquisition module is used to acquire multi-dimensional data of the sewage treatment process; A data processing module is used to preprocess and normalize the multi-dimensional data to construct a data feature library of sewage resource; An intelligent monitoring module is used to establish an intelligent monitoring network based on the data feature library, and to obtain a monitoring data set in real time; A decision mechanism module is used to fuse and correlate the data feature library and the monitoring data set, to construct an intelligent regulation and control decision mechanism, and to obtain a decision rule; A DPSIR framework module is used to couple the DPSIR conceptual framework and the intelligent regulation and control decision mechanism, to integrate the recycling principle, and to obtain a sewage resource utilization strategy; A balance regulation and control module is used to construct a resource balance regulation and control mechanism, and to generate a balance constraint condition; The model training module is used for training a prediction model for predicting water quality states based on decision rules, balance constraint conditions and guidance of sewage resource utilization strategies, and in combination with historical monitoring data sets; The intelligent regulation and control module is used for automatically triggering a regulation and control response mechanism when the water quality state prediction result output by the prediction model deviates from the target range value, intelligently regulating and controlling sewage resources in combination with an intelligent regulation and control decision mechanism, sewage resource utilization strategies and balance constraint conditions, and generating a regulation and control result; The feedback learning module is used for matching and verifying the regulation and control result with feature information in the data feature library and performing consistency checking, quantitatively feeding back the regulation and control result through effect evaluation, optimizing the regulation and control strategy according to the feedback learning result, and forming a comprehensive intelligent regulation and control operation of sewage resources The present application has the advantages that the present application collects multi-dimensional data of the sewage treatment process, constructs a feature library of sewage resources through data preprocessing and feature engineering, solves the technical difficulties of intelligent processing of multi-dimensional data and construction of the feature library, and realizes systematic integration and deep mining of key features of sewage treatment; Based on the historical data mode and feature correlation relationship in the feature library, a multi-level intelligent monitoring network is established, real-time monitoring data sets matched with the feature library are obtained, the technical difficulties of fusion of the real-time monitoring network and the decision mechanism are solved, and dynamic matching and real-time updating of the monitoring data and the feature library are realized; The feature library and the monitoring data set are deeply fused and analyzed, an intelligent regulation and control decision mechanism is constructed in combination with historical regulation and control experience, precise identification of key feature relationships in the sewage treatment process and automatic generation of intelligent decision rules are realized; The DPSIR concept framework is constructed, the DPSIR concept framework and the intelligent regulation and control decision mechanism are coupled with each other, the sewage resource utilization strategy is obtained by integrating the recycling principle, the technical difficulties of the resource utilization strategy and the balance regulation and control are solved, and systematic optimization of sewage resources and effective improvement of recycling efficiency are realized; Based on the sewage resource recycling strategy, a resource balance regulation and control mechanism for demand prediction and supply optimization is constructed, the resource balance regulation and control mechanism generates balance constraint conditions by receiving constraint results of the sewage resource recycling strategy, and realizes intelligent management of supply and demand balance and dynamic optimization of resource allocation; Based on the decision rules of the intelligent regulation and control decision mechanism, the balance constraint conditions and the guidance of the sewage resource utilization strategies, in combination with the historical monitoring data sets, a prediction model for predicting water quality states is trained and constructed, precise capture of water quality change trends and effective improvement of prediction accuracy are realized; Real-time water quality parameter prediction values are obtained based on the regulation model, and a regulation response mechanism is automatically triggered when the prediction values deviate from the target range, intelligent optimization regulation and control processing of sewage treatment are realized by combining the intelligent regulation and control decision mechanism, sewage resource utilization strategy and balance constraint conditions, and regulation results are generated, realizing automatic regulation and control and continuous optimization of the processing effect of the sewage treatment process. The regulation results are matched, verified and consistency checked with the feature information in the data feature library, the regulation results are quantitatively fed back by effect evaluation, the regulation strategy is optimized according to the feedback learning results, the comprehensive intelligent regulation and control operation of sewage resource is formed, the adaptive improvement of the regulation strategy and the continuous improvement of the overall performance of the system are realized, and good adaptability is achieved, which can meet the processing needs of different regions, different scales and different sewage types. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a deep learning-based sewage resource intelligent regulation and control method flowchart provided in an embodiment of the present application; Figure 2 is a deep learning-based sewage resource intelligent regulation and control system module schematic diagram provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is merely meant to provide a better understanding of the subject matter described herein and can be changed without departing from the scope of the present specification. Various processes or components can be omitted, substituted, or added according to desired implementations. Additionally, features described in some examples can be combined in other examples.

[0018] At least one embodiment of the present application discloses a deep learning-based sewage resource intelligent regulation and control method and system, comprising: As shown in Figure 1 A deep learning-based sewage resource intelligent regulation and control method comprises the following steps: Step 1: Collecting multi-dimensional data of the sewage treatment process; Step 2: Constructing a data feature library of sewage resource after pre-processing and normalization of the multi-dimensional data; Step 3: Establishing an intelligent monitoring network based on the data feature library to obtain real-time monitoring data sets; Step 4: Fusing and correlating the data feature library and the monitoring data sets to construct an intelligent regulation and control decision mechanism and obtain decision rules; Step 5: Coupling the DPSIR conceptual framework with the intelligent regulation and decision-making mechanism, integrating the recycling principle to obtain the wastewater resource utilization strategy; Step 6: Building a resource balance regulation mechanism to generate balance constraints; Step 7: Based on the decision rules, balance constraints, and wastewater resource utilization strategy guidance, combined with historical monitoring data sets, training and building a prediction model to predict water quality status; Step 8: When the water quality status prediction result output by the prediction model deviates from the target range value, automatically trigger the regulation and response mechanism, combine the intelligent regulation and decision-making mechanism, wastewater resource utilization strategy, and balance constraints to intelligently regulate and process the wastewater resources, and generate the regulation result.

[0019] Example 1: Multi-dimensional data acquisition and wastewater resource deep learning feature library construction.

[0020] Deploy sensor networks at key nodes in the wastewater treatment process, including the inlet, biochemical reaction tank, secondary sedimentation tank, and outlet, to collect real-time multi-source data and build a wastewater resource deep learning feature library.

[0021] The feature library includes: water quality feature data: deploy pH sensors, dissolved oxygen sensors, NH4 + ; N sensors, NO3 - ; N sensors, COD sensors, BOD sensors, SS sensors, TN sensors, TP sensors, collect key water quality parameters such as pH, dissolved oxygen, NH4 + ; N, NO3 - ; N, COD, BOD, SS, TN, TP, etc., and build historical data patterns and feature correlation relationships through time series analysis and statistical modeling.

[0022] Flow feature data: monitor inflow, outflow, and reflux ratio, etc. flow parameters, use sliding window algorithm to extract time series features and change rules, and establish a flow fluctuation prediction model.

[0023] Equipment operation feature data: collect aeration quantity, sludge quantity, and reagent dosage, etc. equipment operation parameters, identify optimization features and correlation patterns through machine learning algorithms, and build an equipment operation efficiency evaluation system.

[0024] Environmental feature data: monitor temperature, humidity, air pressure, etc. environmental parameters, analyze the influence of environmental factors on treatment effect features, and establish an environmental; treatment effect correlation model.

[0025] Historical regulation experience features: collect successful regulation cases, extract feature patterns and resource management learning data, and build an expert knowledge base and experience rule set.

[0026] Data preprocessing uses a sliding window smoothing algorithm to eliminate noise, and uses Z-score normalization method for data normalization. Feature engineering extracts key features through principal component analysis (PCA), and constructs a multi-dimensional feature vector containing time series features, statistical features and frequency domain features. The feature library is stored in a time series database, supporting high-frequency data writing and fast querying.

[0027] Example 2: Multi-level intelligent monitoring network establishment.

[0028] Based on the historical data patterns and feature correlation in the wastewater resource deep learning feature library, a multi-level intelligent monitoring network matching the feature library structure is established, and the monitoring data set is obtained in real time and the feature library is updated: Sensing layer: Deploy a sensor array corresponding to the water quality feature data in the feature library, including pH sensor, dissolved oxygen sensor, NH4 + ; N sensor, NO3 - ; N sensor, COD sensor, BOD sensor, SS sensor, TN sensor, TP sensor, to realize real-time monitoring of all parameters.

[0029] Transmission layer: Use wireless communication technologies such as LoRa, NB; IoT, 5G, etc. to realize data transmission, ensure that monitoring data is transmitted in time to the feature library for updating. Establish a multi-path redundant transmission mechanism to ensure data transmission reliability.

[0030] Processing layer: Preprocess and feature engineer the collected multi-source heterogeneous data to ensure matching with the feature library structure, and feed the processed data back to the feature library. Use edge computing nodes for data preprocessing and anomaly detection.

[0031] Intelligent monitoring layer of pipe network: Real-time monitoring of wastewater collection pipe network coverage, pipe network flow distribution, pipe network blockage status, monitoring data and flow feature data in the feature library form a closed-loop feedback, solving the problem of pipe network supporting lag.

[0032] The monitoring data set is collected and distributed through a real-time data stream processing framework (such as Apache Kafka), ensuring dynamic matching with the feature library.

[0033] Example 3: Intelligent control decision mechanism construction.

[0034] Deeply integrate and analyze the wastewater resource deep learning feature library and real-time monitoring data set, combine historical control experience and resource management learning, and construct an intelligent control decision mechanism based on data-driven: Deep integration module: Use deep learning algorithms to integrate and analyze feature library data and real-time monitoring data, and the integration results are directly input into the subsequent control model training link.

[0035] Correlation analysis includes: time series correlation: identify time series patterns through dynamic time warping (DTW) algorithm, analyze the time evolution of water quality parameters; spatial position correlation: based on graph neural network modeling spatial dependence, identify the mutual influence between different processing units; parameter change correlation: use mutual information theory to quantify the correlation between parameters, establish parameter linkage control mechanism.

[0036] Rule base construction: combined with expert knowledge to construct rule base, containing 1000+ control rules, covering three types of scenes: normal operation, abnormal treatment and emergency response. The intelligent reasoning engine uses a hybrid reasoning method combining fuzzy reasoning and Bayesian networks to generate control decision rules.

[0037] Decision rule generation: according to the current wastewater treatment data running state, multi-dimensional correlation characteristics and historical control experience, generate the optimal resource control decision rule, including parameter control threshold, operation timing rule and emergency response strategy.

[0038] Example 4: DPSIR framework and recycling strategy.

[0039] Build the DPSIR conceptual framework, couple the DPSIR conceptual framework with intelligent control decision mechanism, integrate the recycling principle to get the wastewater resource utilization strategy: Specific implementation of DPSIR framework: driving force (Driving forces): identify population growth, industrial development, policy and regulations, etc. Driving factors, analyze the root cause of wastewater generation and social development needs; pressure (Pressures): quantify pollutant emission load, water resource consumption, etc. Pressure indicators, determine the direct pressure and load on the environment caused by pressure indicators; state (State): monitor water environment quality, ecosystem health, assess current water environment quality and wastewater treatment system operation status; impact (Impact): assess the impact on human health, ecological environment, analyze the potential impact of wastewater discharge on ecological environment and human health; response (Response): develop governance measures and resource utilization strategies, based on the analysis results, develop corresponding governance and resource utilization countermeasures Recycling strategy is based on a three-level system of "reduction, reuse, and recycling": reduction: reduce wastewater generation through source control, optimize process flow to reduce energy consumption; reuse: treated water is used for landscaping, cooling and other non-drinking purposes, improving water resource utilization efficiency; recycling: resource utilization of sludge, extracting valuable substances, realizing waste resource.

[0040] Example 5: Resource balance control mechanism.

[0041] Based on the sewage resource recycling strategy, a resource balance regulation mechanism of demand prediction and supply optimization is constructed, and the balance constraint condition is generated by receiving the constraint result of the sewage resource recycling strategy: Multi-objective optimization model construction: the objective function includes: Water balance: ; Wherein, is the inflow, is the outflow, is the reuse water flow.

[0042] Water quality balance: ; Wherein: is the actual concentration of the i-th water quality parameter, is the target concentration.

[0043] Energy balance: ; Wherein: is the total energy consumption of the system, including aeration energy consumption, pumping energy consumption, equipment operation energy consumption.

[0044] Resource benefit: ; Wherein: is the resource recycling benefit, including the value of reclaimed water reuse and the value of sludge resource utilization.

[0045] Setting of equipment constraint condition: Capacity constraint: treatment capacity, equipment operation range limitation; Environmental protection standard constraint: the effluent quality must meet the discharge standard; Economic cost constraint: the operation cost is controlled within the budget range; resource utilization constraint: the quality of reclaimed water and sludge disposal requirements Genetic algorithm and particle swarm optimization algorithm are used to solve the multi-objective optimization problem, generate balance constraint condition, realize intelligent management of supply and demand balance and dynamic optimization of resource allocation.

[0046] Example 6: Training of prediction model.

[0047] Based on the decision rules of intelligent regulation and control decision mechanism, balance constraint condition and guidance of sewage resource utilization strategy, combined with the historical monitoring data set, the prediction model of predicting water quality state is trained and constructed: Deep neural network construction: Input layer: receiving 128-dimensional feature vector (including water quality, flow, equipment operation, environment, etc.); First hidden layer: 256 neurons, ReLU activation function, Dropout=0.2; Second hidden layer: 128 neurons, ReLU activation function, Dropout = 0.2; Third hidden layer: 64 neurons, ReLU activation function; Output layer: Predict water quality parameters (COD, BOD, NH4 + ; N, TN, TP) in the next 24 hours.

[0048] Model training strategy: Training data: Contains 3 years of historical data, a total of 1 million records; Optimizer: Adam optimizer, learning rate 0.001, β1 = 0.9, β2 = 0.999; Batch size: 32, training rounds: 1000; Validation method: 5-fold cross-validation, prediction accuracy reaches more than 95%; Loss function: Mean square error (MSE) combined with custom water quality compliance loss function.

[0049] Example 7: Regulatory response mechanism and intelligent optimization regulation.

[0050] Based on the predicted value of water quality parameters obtained in real time by the prediction model, when the predicted value deviates from the target range, the regulatory response mechanism is automatically triggered, combined with the intelligent regulation decision mechanism, sewage resource utilization strategy and balance constraint conditions to intelligently optimize and regulate the sewage treatment: Three-level early warning system is established: Normal range: Comprehensive deviation degree <0.1, system normal operation; Abnormal range: 0.1 ≤ comprehensive deviation degree <0.3, start early warning regulation; Emergency range: Comprehensive deviation degree ≥0.3, start emergency response.

[0051] Regulation execution strategy: Influent flow regulation: Adjusted by frequency conversion pump control, adjustment range 50; 150% rated flow; Optimize reagent addition: PAC addition 0; 50 mg / L, PAM addition 0; 5 mg / L, accurately added according to water quality prediction results; Aeration intensity control: Dissolved oxygen is controlled at 2; 4 mg / L, dynamically adjusted according to biochemical oxygen demand; Sludge return ratio adjustment: Controlled at 50; 200%, optimize biological treatment effect; Resource equipment regulation: Water reuse system, sludge dewatering system, etc. Collaborative regulation Example 8: Feedback learning and strategy optimization.

[0052] Match the regulation results with the feature information in the data feature library for matching verification and consistency check, and quantitatively regulate the regulation results through effect evaluation for feedback learning; According to the feedback learning results, optimize the regulation strategy to form a comprehensive intelligent regulation operation of sewage resourceization: Effect evaluation system: Water quality compliance rate: The proportion of effluent water quality meeting the standard; Resource utilization rate: Water reuse rate, sludge resourceization rate; Energy efficiency: Unit processing energy consumption change; Economic benefit: Operating cost savings, resourceization benefits; Feedback learning mechanism: online learning: real-time update of model parameters to adapt to water quality changes; reinforcement learning: reward mechanism based on regulation effect to optimize decision strategy; transfer learning: successful experience is promoted to similar working conditions; Strategy optimization method: parameter adaptive adjustment: dynamically adjust control parameters according to historical effects; rule base update: expand expert rules based on new successful cases; model retraining: periodically retrain the prediction model using the latest data.

[0053] As shown in Figure 2 A sewage resourceization intelligent regulation and control system based on deep learning includes: Data acquisition module: for collecting multi-dimensional data of sewage treatment process; Data processing module: for constructing sewage resourceization data feature library after pre-processing and normalization of multi-dimensional data; Intelligent monitoring module, for establishing an intelligent monitoring network based on the data feature library, and obtaining a monitoring data set in real time; Decision mechanism module, for fusing and correlating the data feature library and the monitoring data set, constructing an intelligent regulation and control decision mechanism, and obtaining a decision rule; DPSIR framework module, for coupling the DPSIR conceptual framework with the intelligent regulation and control decision mechanism, integrating the recycling principle to obtain a sewage resourceization utilization strategy; Balancing regulation module, for constructing a resource balancing regulation mechanism and generating balancing constraints; Model training module, for training a prediction model for predicting water quality status based on the decision rule, balancing constraints, and sewage resourceization utilization strategy, and combining historical monitoring data sets; Intelligent regulation and control module, for automatically triggering a regulation response mechanism when the prediction result of the prediction model deviates from the target range value, combining the intelligent regulation and control decision mechanism, the sewage resourceization utilization strategy, and the balancing constraints to intelligently regulate and control the sewage resource, and generating a regulation result; Feedback learning module, for matching and verifying the regulation result with the feature information in the data feature library and performing consistency check, quantifying the regulation result through effect evaluation for feedback learning; optimize the regulation strategy according to the feedback learning result to form a comprehensive intelligent regulation and control operation of sewage resourceization.

[0054] III. Application examples Example 1: A city sewage treatment plant with a daily treatment capacity of 100,000 tons adopts A 2 / O process.

[0055] The implementation process after deploying the technical solution of the present application: 1. Constructing sewage resourceization deep learning feature library: 60 sensor nodes are deployed at the inlet, anaerobic tank, anoxic tank, aerobic tank, secondary sedimentation tank, and outlet to collect key water quality parameters such as pH, dissolved oxygen, NH4 + -N, NO3 - -N, COD, BOD, SS, TN, TP, etc.; 2. Establishing a multi-level intelligent monitoring network: a feature library containing 150 features of water quality, process, and weather is constructed to realize real-time monitoring matching the feature library structure; 3. Constructing DPSIR framework: identifying urban development and population growth as the main driving force, quantifying pollution load pressure, monitoring water environment status, assessing ecological impact, and developing resourceization response strategies; 4. Establishing resource balance regulation mechanism: constructing a three-level recycling system, with a reclaimed water reuse rate of 30% and a sludge resource utilization rate of 85%; 5. Deploying intelligent regulation system: based on deep learning prediction model and intelligent decision mechanism, realizing automatic regulation.

[0056] Technical effects: effluent water quality is stable and reaches level A standard, COD removal rate is increased to 95%, NH4 + -N removal rate reaches 98%; energy consumption is reduced by 15%, reagent consumption is reduced by 20%, and operating cost is saved by 25%; reclaimed water reuse amount is increased by 8000 tons / day, sludge resourceization output value is increased by 500,000 yuan / year; abnormal working condition early warning accuracy rate reaches 98%, fault handling time is shortened by 60%; pipe network supporting lag problem is effectively solved, and pipe network utilization efficiency is increased by 30%.

[0057] Example 2: A certain chemical industrial park centralized sewage treatment facility treats various industrial wastewater, and the water quality fluctuates greatly, with a daily treatment capacity of 50,000 tons.

[0058] Implementation process after deploying the technical solution of the present application: 1. Building a classification feature library: according to the characteristics of wastewater from different enterprises, a professional feature library containing heavy metals, organic matter, salt content, etc. is constructed; 2. Establishing multi-source data fusion mechanism: integrating enterprise production plans and discharge data to realize predictive regulation; 3. DPSIR framework application: focusing on industrial production pressure and environmental impact assessment, establishing a risk early warning system; 4. Building a differentiated treatment strategy: implementing differentiated treatment and resource recovery according to wastewater characteristics; 5. Deploying emergency response system: establishing an intelligent early warning and rapid response mechanism for sudden water quality incidents.

[0059] Technical effect: processing efficiency is improved by 25%, effluent is stable and meets standards, heavy metal removal rate reaches 99%; heavy metal recovery rate reaches 90%, organic solvent recovery rate reaches 75%, realizing resource utilization; operating cost is reduced by 30%, automation degree is greatly improved, labor cost is saved by 40%; environmental risk early warning ability is enhanced, zero environmental accident occurs; ecological industry chain is constructed, wastewater resource utilization output value reaches 2 million yuan / year.

[0060] Example 3: In view of the problem of difficult operation and maintenance of decentralized wastewater treatment facilities in rural areas, 100 decentralized treatment points are deployed in a county area.

[0061] The implementation process after deploying the technical solution of the application: 1. Develop a simplified feature library: a simplified feature library and monitoring network suitable for small-scale facilities, focusing on monitoring key parameters such as COD, NH4 + ; N, TP, etc. 2. Deploy a wireless monitoring network: use a solar-powered wireless sensor network to achieve remote monitoring; 3. Build a rural DPSIR framework: focus on agricultural non-point source pollution and establish a rural water environment management system; 4. Establish an agricultural recycling model: wastewater recycling model, treated wastewater used for farmland irrigation; 5. Implement regional collaborative management: multiple treatment points operate in coordination to achieve regional water resource integrated management.

[0062] Technical effect: unmanned operation, remote monitoring and management, operation and maintenance efficiency improved by 80%; treated wastewater used for farmland irrigation, achieving zero discharge, irrigation area increased by 2000 mu; operation and maintenance cost reduced by 50%, normal operation rate of facilities reached 95%; improve rural water environment, promote the development of ecological agriculture, and improve the quality of agricultural products; build a comprehensive solution to wastewater resource utilization in areas with severe water shortage Example 4: Multi-scenario application expansion Intelligent transformation of urban wastewater treatment plants: intelligent upgrading of old wastewater treatment plants, deploying intelligent monitoring networks and control systems; pipeline network supporting optimization, solving the problems of pipeline network coverage deficiency and supporting lag; processing efficiency improved by 30%, energy consumption reduced by 20%.

[0063] Regional water resource integrated management: establish a regional water resource management platform to achieve optimal allocation of multiple water sources; integrate surface water, groundwater, and reclaimed water; improve the efficiency of regional water resource utilization by 25%.

[0064] Emergency response mechanism construction: establish an intelligent early warning system for sudden water quality incidents, with an accuracy rate of 95%; build a rapid generation and optimization algorithm for emergency treatment plans, with a response time reduced by 70%; achieve multi-department collaborative emergency response decision support; establish a dynamic allocation mechanism for resource allocation and processing capacity under emergency conditions.

[0065] Through application verification in different types of sewage treatment facilities, the technical solution of the present application has achieved remarkable results in solving the core problems raised in the background art: Multi-dimensional data intelligent processing and feature library construction: The constructed sewage resourceization deep learning feature library contains five categories of features, including water quality, flow, equipment operation, environment and historical control experience, covering multi-dimensional features such as time series, statistics and frequency domain, with an extraction accuracy rate of 98%, providing a reliable data foundation for intelligent decision-making.

[0066] Real-time monitoring network and decision-making mechanism integration: The multi-level intelligent monitoring network established realizes real-time matching of monitoring data and feature library, with a data processing delay of less than 1 second and a decision response time of less than 5 minutes, solving the problem of lagging pipe network support.

[0067] Resource utilization strategy and balanced regulation: Through the integration of the DPSIR framework and the principle of recycling, the resource utilization rate is increased by more than 30%, the reclaimed water reuse rate reaches 35%, and the sludge resource utilization rate reaches 90%, achieving double improvement of economic and environmental benefits.

[0068] Intelligent regulation and prediction accuracy: The prediction model based on deep learning has an accuracy rate of more than 95%, the regulation response time is shortened by 60%, and the abnormal working condition early warning accuracy rate reaches 98%, achieving accurate capture of water quality change trends.

[0069] Adaptability and generalizability: The technical solution has been successfully applied to urban sewage treatment plants, industrial parks, rural decentralized treatment and other scenarios, with good adaptability and ability to meet the treatment needs of different regions, different scales and different types of sewage.

[0070] The technical solution of the present application realizes comprehensive intelligent management of the sewage treatment process by constructing a complete sewage resourceization comprehensive intelligent regulation and control operation system, has good application prospect, and can provide effective technical support for the intelligent upgrading and resource utilization of the sewage treatment industry.

[0071] The above describes embodiments of the present application, but the embodiments are not limited to the specific implementation described above, which is only illustrative and not limiting, and those skilled in the art can make more forms of equivalent embodiments under the inspiration of the embodiments, which are all within the protection scope of the embodiments.

Claims

1. A deep learning-based intelligent control method for sewage resource utilization, characterized in that: include: Collect multi-dimensional data of sewage treatment process; After pre-processing and normalizing the multi-dimensional data, a data feature database for wastewater resource utilization is constructed; Establish an intelligent monitoring network based on the data feature library to obtain monitoring data sets in real time; The data feature library is integrated with the monitoring data set and correlation analysis is performed to build an intelligent control decision-making mechanism and obtain decision rules; The DPSIR conceptual framework is coupled with the intelligent control decision-making mechanism, and the recycling principle is integrated to obtain the wastewater resource utilization strategy; Build a resource balance control mechanism and generate balance constraints; Based on the guidance of decision rules, balance constraints and wastewater resource utilization strategies, and combined with historical monitoring data sets, a prediction model for water quality status is trained and constructed; When the water quality prediction results output by the prediction model deviate from the target range, the control response mechanism is automatically triggered. The intelligent control decision-making mechanism, sewage resource utilization strategy and balance constraints are combined to intelligently control sewage resources and generate control results. Match and verify the control results with the feature information in the data feature library and check the consistency. Then, quantify the control results through effect evaluation and conduct feedback learning. Optimize the control strategy based on the feedback learning results to form a comprehensive intelligent control operation for sewage resource utilization.

2. The method for intelligent regulation of sewage resource utilization based on deep learning according to claim 1 is characterized in that: The DPSIR conceptual framework includes: Identify driving factors, analyze the root causes of sewage generation and social development needs; Determine pressure indicators and quantify the direct pressure and load caused by sewage discharge on the environment; Evaluate status parameters, monitor and evaluate the current water environment quality status and sewage treatment system operation status; Analyze the impact effects, analyze the potential impact of sewage discharge on the ecological environment and human health, and obtain analysis results; Formulate response measures and formulate corresponding governance and resource utilization strategies based on the analysis results.

3. The method for intelligent regulation of sewage resource utilization based on deep learning according to claim 1 is characterized in that: The data feature library is integrated with the monitoring data set and correlation analysis is performed to build an intelligent control decision-making mechanism and obtain decision rules, including: Perform data format standardization and timestamp alignment on the historical data and real-time monitoring data in the feature library to generate a sewage treatment data set in a unified standard format; Based on the sewage treatment data set, multi-dimensional data association analysis is performed to identify the inherent correlation and dependency relationships between the data. The correlation relationships include three dimensions: time series correlation, spatial location correlation, and parameter change correlation, forming a multi-dimensional correlation feature set of the sewage treatment process; Deeply fusing the multidimensional correlation feature set with historical control experience to obtain a fusion result; Based on the fusion results and combined with the expert knowledge system, a wastewater resource treatment rule base is constructed; According to the sewage resource treatment rule base, an intelligent control reasoning engine matching the multi-dimensional correlation feature set is constructed to obtain the reasoning results; Based on the mapping relationship between the inference results and the multi-dimensional correlation feature set, an intelligent regulation and decision-making mechanism for wastewater resource utilization is constructed; the intelligent regulation and decision-making mechanism identifies the operating status of wastewater treatment data and adjusts wastewater treatment parameters; and coordinates the multi-dimensional correlation feature set to achieve data sharing and collaborative decision-making; Based on the operating status, multi-dimensional correlation characteristics and historical control experience of the current sewage treatment data, the optimal resource control decision rules are generated. The decision rules include parameter control thresholds, operation timing rules and emergency response strategies.

4. The method for intelligent regulation of sewage resource utilization based on deep learning according to claim 3 is characterized in that: Coupling the DPSIR conceptual framework with the intelligent regulation decision-making mechanism includes: The driving force factors in the DPSIR framework are used as input parameters of the intelligent control decision-making mechanism to establish a mapping relationship between driving force and decision-making; Correlate and match the pressure indicators in the DPSIR framework with the monitoring data in the intelligent control decision-making mechanism to form a pressure control response mechanism; Integrate the state parameters in the DPSIR framework into the real-time parameter change state of the intelligent control decision-making mechanism to achieve the linkage between state and decision-making; Use the impact effect assessment results in the DPSIR framework as constraints for the intelligent regulation decision-making mechanism to optimize the regulation strategy; The response measures in the DPSIR framework are combined with the execution of the intelligent control decision-making mechanism to form an intelligent control system.

5. The method for intelligent regulation of sewage resource utilization based on deep learning according to claim 4 is characterized in that: The integrated recycling principle leads to the wastewater resource utilization strategy, including: Based on the deep coupling results of the DPSIR framework and the intelligent control decision-making mechanism, key nodes and optimization paths for resource utilization are identified, and a three-level cycle system of reduction, reuse, and recycling is established; Combining the driving force analysis results of the DPSIR framework with the recycling principles, the key nodes of the three-level recycling system are analyzed to determine the priority and implementation sequence of wastewater resource utilization. The mapping relationship between driving forces and decisions generated by deep coupling results is used to construct a dynamic recycling decision-making mechanism that matches the three-level recycling system. Based on the impact assessment results and the output of the dynamic recycling decision-making mechanism, combined with the optimization path of the three-level recycling system, an objective function for maximizing recycling is established, and a wastewater resource utilization strategy that is coordinated with the decision-making mechanism is formulated; By deeply coupling the results with the corresponding coordinated operations of each recycling link, the implementation effect of the wastewater resource utilization strategy and the multi-dimensional wastewater resource utilization evaluation indicators are integrated to establish a recycling performance evaluation system and obtain the evaluation results; Feedback the evaluation results to the three-level circulation system and dynamic decision-making mechanism to obtain feedback results; When the feedback result deviates from the recycling principle, return to the step of building a dynamic recycling decision-making mechanism and repeat the operation until the feedback result meets the recycling principle.

6. The method for intelligent regulation of sewage resource utilization based on deep learning according to claim 5 is characterized in that: Build a resource balance control mechanism and generate balance constraints, including: The execution results of the three-level recycling system are used as input, combined with resource allocation constraints and recycling efficiency indicators, and under the constraints of the recycling strategy, the constraint results are output; the constraint results are deeply analyzed to obtain the analytical results; Conduct correlation analysis on the constraints based on the analytical results to identify the key constraints on resource supply and demand balance, establish the interdependence and impact weights between the constraints, and form the analysis results; Based on the analysis results, combined with the intelligent regulation and decision-making mechanism of sewage treatment, sewage treatment quality standards and environmental emission requirements, balance constraints are generated, and a multi-dimensional balance constraint system covering water quantity balance, water quality balance and energy consumption balance is constructed; Based on a multi-dimensional balance constraint system, feedback is obtained based on real-time monitoring data and feedback on the execution effect of recycling strategies. The balance constraints are dynamically adjusted based on the feedback results, and the adjustment results are fed back to the constraint correlation analysis link; The constraint correlation analysis step performs analysis again based on the adjustment results combined with the analytical results to obtain the second analysis results; the operation is repeated to iteratively adjust the constraints to obtain the final balanced constraints.

7. The method for intelligent regulation of sewage resource utilization based on deep learning according to claim 6 is characterized in that: Based on decision-making rules, balance constraints, and wastewater resource utilization strategies, and combined with historical monitoring data sets, a predictive model for predicting water quality status is trained and constructed, including: Based on decision-making rules, balance constraints, and wastewater resource utilization strategies, and combined with historical monitoring data sets, a predictive model for predicting water quality status is trained and constructed, including: Generating structured data from the acquired historical monitoring data set, encoding the structured data into sequence data, and training and constructing the water quality state prediction model by combining decision rules, balance constraints, and wastewater resource utilization strategies; Inputting the sequence data into the water quality state prediction model, the water quality state prediction model comprising an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer; The intermediate representation data obtained by processing the sequence data through multiple hidden layers is finally transmitted to the output layer, and the output layer outputs the prediction result of the water quality status during the sewage treatment process; At least one data item in the new monitoring data set acquired in real time is input into the water quality state prediction model, and the output layer outputs a water quality state prediction result in the new monitoring data set acquired in real time.

8. The method for intelligent regulation of sewage resource utilization based on deep learning according to claim 7 is characterized in that: When the water quality prediction results output by the prediction model deviate from the target range, the control response mechanism is automatically triggered, including: Based on the water quality status prediction results, obtain the deviation degree between the predicted value of each water quality indicator and the set target range, and transmit the comprehensive deviation degree data of each water quality indicator to the early warning response mechanism; Among them, the deviation degree of a single water quality indicator is: It represents the degree of deviation of the i-th water quality indicator; represents the predicted value of the i-th water quality indicator; represents the target value of the i-th water quality indicator; represents the upper limit of the target range of the i-th water quality indicator, represents the lower limit of the target range of the i-th water quality indicator; Comprehensive deviation degree: Among them: Indicates the comprehensive degree of deviation; represents the weight coefficient of the i-th water quality index; Indicates the total number of water quality indicators monitored; The early warning response mechanism pre-establishes a multi-level early warning response system, including normal range, abnormal range and emergency range; When the early warning response mechanism receives the comprehensive deviation degree data, when the comprehensive deviation degree data is in the abnormal range or emergency range, it initiates the corresponding level of early warning response and outputs the corresponding early warning information; According to the deviation degree of the predicted value of each water quality index, Get the deviation trend change rate, where: represents the rate of change of the deviation trend of the i-th water quality indicator; Indicates the degree of deviation of the i-th indicator at the current time t; Indicates the degree of deviation of the i-th indicator at the previous moment t;1; Indicates a time interval; If the early warning information and the rate of change of the deviation trend show a sudden drop or a sudden rise, the control response mechanism will be automatically triggered, and the control demand will be identified and the control priority will be determined. The control demand and priority information will be transmitted to the intelligent control decision-making mechanism for intelligent control of various sewage treatment parameters.

9. The method for intelligent regulation of sewage resource utilization based on deep learning according to claim 8, characterized in that: Combined with intelligent control decision-making mechanism, wastewater resource utilization strategy and balance constraints, intelligent control and treatment of wastewater resources are carried out to generate control results, including: When the intelligent control decision-making mechanism receives control demand and priority information, it integrates the optimization strategy of the intelligent control decision-making mechanism, including parameter control thresholds, operation timing rules and emergency response strategies, to form a targeted control plan, and then transmits the control plan to the sewage resource utilization strategy for integration; When the wastewater resource utilization strategy receives the control plan, combined with the requirements of the three-level circulation system, the control process is made to meet the resource utilization goal, and the integrated control strategy output is combined with the balance constraint conditions to further optimize the control mechanism; When the integrated control strategy is combined with the water quantity balance, water quality balance, and energy consumption balance requirements in the balance constraint system, the control parameter configuration is optimized, the final intelligent control execution plan is generated, and the control execution operation is carried out; According to the intelligent control execution plan, intelligent optimization control processing is carried out, including automatic adjustment of key operating parameters such as water flow, chemical dosage, aeration intensity, and sludge return ratio, and the execution status information is fed back to the effect monitoring system; Based on the control execution status information, the control execution effect is monitored in real time, and the sewage treatment strategy is dynamically adjusted through the feedback control mechanism to gradually return the sewage water quality parameters to the target range. The monitoring results and adjustment information are used to generate the control results; The control results include control action records, parameter change trajectories, and result reports of effect evaluation indicators, and the control results are fed back to the water quality status prediction model and early warning threshold system to iteratively process the sewage water quality parameter indicators.

10. A deep learning-based intelligent control system for sewage resource utilization, used to execute a deep learning-based intelligent control method for sewage resource utilization according to any one of claims 1 to 9, characterized in that: include: Data acquisition module: used to collect multi-dimensional data of sewage treatment process; Data processing module: used to pre-process and normalize multi-dimensional data to build a data feature library for sewage resource utilization; Intelligent monitoring module, used to establish an intelligent monitoring network based on the data feature library and obtain monitoring data sets in real time; The decision-making mechanism module is used to integrate and analyze the data feature library with the monitoring data set, build an intelligent control decision-making mechanism, and obtain decision rules; The DPSIR framework module is used to build a DPSIR conceptual framework that couples with the intelligent control and decision-making mechanism, integrating the recycling principle to obtain a wastewater resource utilization strategy; The balance control module is used to build a resource balance control mechanism and generate balance constraints; The model training module is used to train and construct a predictive model for water quality status based on decision rules, balance constraints, and guidance on wastewater resource utilization strategies, combined with historical monitoring data sets; The intelligent control module is used to automatically trigger the control response mechanism when the water quality status prediction result output by the prediction model deviates from the target range value. It combines the intelligent control decision-making mechanism, sewage resource utilization strategy and balance constraints to perform intelligent control processing on sewage resources and generate control results; The feedback learning module is used to match and verify the control results with the feature information in the data feature library and perform consistency checks, and to conduct feedback learning by quantifying the control results through effect evaluation; Optimize the control strategy based on the feedback learning results to form a comprehensive intelligent control operation for sewage resource utilization.

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