27995 results about "Sewage" patented technology

The invention discloses a sewage plant total nitrogen concentration real-time prediction and process regulation and control method based on an optimization integration algorithm, and belongs to the technical field of environment monitoring and treatment. According to the method, sewage plant data are monitored and collected, a sliding window and a time sequence are combined to analyze and clean the data and reconstruct features, total nitrogen concentration strong correlation variables are screened, data quality is standardized and optimized, a plurality of machine learning algorithms are adopted to construct a prediction model, and an optimal model is optimized through cross validation and performance evaluation. The robustness is improved by global parameter adjustment in combination with optimization algorithms such as a particle swarm, process schemes such as aeration intensity and carbon source adding are generated through multi-objective optimization after containerization deployment, and a whole-process intelligent management and control system is constructed by integrating virtual verification, graded early warning and a self-adaptive feedback mechanism. According to the method, the problems of detection lag, insufficient model generalization ability, regulation response delay and the like of a traditional method are solved, and the operation energy consumption and the medicament cost are remarkably reduced while it is guaranteed that the effluent quality stably reaches the standard.

The invention relates to the technical field of sewage treatment, and particularly discloses a sewage and wastewater treatment control system and method based on an intelligent optimization algorithm. Water quality data are collected through a data acquisition module, and a water quality characteristic matrix is generated through preprocessing. And the prediction module analyzes the feature matrix by using the trained water quality dynamic prediction model to obtain a water quality prediction result. And processing the prediction result by using a multi-objective optimization algorithm to obtain an initial control parameter. And the parameter optimization module calculates a water load fluctuation ratio, a model confidence coefficient and an equipment state according to the sewage and wastewater treatment data, inputs the water load fluctuation ratio, the model confidence coefficient and the equipment state into the adaptive fuzzy network and generates a multi-target parameter optimization suggestion. And the dynamic optimization module adjusts the multi-objective optimization algorithm parameters according to the parameters, and processes the prediction result again to obtain optimization control parameters. And the control module regulates and controls sewage and wastewater treatment according to the optimized parameters. The system realizes closed-loop management from data acquisition, prediction and optimization to control, can dynamically adapt to water quality change, and operates stably and efficiently.

The invention relates to the technical field of sewage treatment, and discloses a coagulant addition control method and system in sewage treatment, and the method comprises the steps: obtaining a floc image in a flocculation basin in real time, and inputting the floc image into an alumen ustum segmentation recognition model pre-trained based on a U-Net model after generative adversarial network optimization to obtain an alumen ustum recognition result; extracting floc characteristic parameters based on the alumen ustum identification result; acquiring water quality parameters of the sewage treatment plant in real time; inputting the floc characteristic parameters and the water quality parameters into a coagulant prediction model trained based on ensemble learning to obtain predicted coagulant adding data; and controlling the operation of the coagulant dosing pump based on the predicted coagulant dosing data. According to the invention, a proper coagulant addition amount can be accurately given according to real-time water quality data in combination with floc characteristics, and the method can be combined with an operation mechanism of a coagulation process, has very strong adaptability, is helpful to realize reasonable and accurate addition of the coagulant, optimizes resource utilization, and improves the cost performance of sewage treatment.

The invention provides an intelligent sewage treatment process optimization method and system. The method comprises the following steps: continuously collecting the flux attenuation rate, transmembrane pressure difference and pollutant spectrum data of a ceramic membrane, carrying out time sequence analysis, constructing a pollution accumulation dynamic model, and predicting a critical threshold value; when a threshold value is reached, triggering an ultrasonic cavitation device, adjusting high-frequency vibration to generate a microbubble group, stripping pollutants by utilizing shock waves, and calculating membrane flux recovery data; fusing the spectral data and the recovery data to extract an actual pollution stripping efficiency value, and comparing a theoretical value to obtain a difference proportion; if the actual value is lower than the theoretical value, the cleaning period is shortened, the fluid pressure is increased to strengthen the stripping effect, and meanwhile, dynamic model parameters are reversely corrected based on the pressure adjustment amplitude to form closed-loop regulation and control. According to the invention, intelligent prediction, self-adaptive cleaning and closed-loop optimization of ceramic membrane pollution are realized, the sewage filtering efficiency is improved, and the energy consumption and cleaning cost are reduced.

The invention relates to the technical field of environment detection, and discloses a GIS map-based multi-class environment parameter automatic prediction method, which comprises the following steps: collecting noise intensity, vibration spectrum, sewage turbidity, illumination intensity and PM2.5 concentration in real time through a distributed sensor network, combining social behavior data, carrying out space-time alignment, and generating a multi-dimensional space-time matrix; calculating an energy overlapping degree by adopting a space-time diagram attention network, marking a high-risk collaborative pollution area, and predicting a pollution diffusion path; constructing a self-adaptive prediction model containing physical and behavior driving channels, and dynamically adjusting the weight to optimize the prediction precision; based on the optimization model, reversely deducing a pollution source of an overproof area, matching equipment characteristics and generating a control instruction; and opening an AR (Augmented Reality) interface to verify the treatment effect, and when the virtual-real data deviation exceeds a data deviation threshold, triggering federal learning to update the global model, and generating an environmental protection compliance report. According to the invention, the accurate decision-making efficiency of environmental governance can be improved.

The invention relates to the technical field of town sewage pipe network detection, and discloses a town sewage pipe network leakage detection system and method. The system comprises a data acquisition module which uses a multi-source sensor to acquire real-time operation data such as pipe network pressure, flow and the like; the feature extraction module extracts spatio-temporal features based on the multi-scale convolutional neural network, and generates a pipe network state feature matrix; the anomaly detection module inputs the feature matrix into a pre-training model and marks a potential leakage area; the optimization analysis module constructs a multi-constraint dynamic optimization model, and pipe network pressure parameters are optimized by using an adaptive particle swarm algorithm; the hierarchical execution module generates a global regulation and control sequence, dynamically matches local pressure parameters and adjusts the valve opening and the pump station power through a decision layer, a region coordination layer and an execution layer. The system and the method are accurate in detection and reasonable in regulation and control optimization, leakage risks can be effectively reduced, the operation management level of a pipe network is improved, and water resource waste and environmental pollution are reduced.

The invention relates to the technical field of remote sensing image recognition, in particular to a remote sensing sewage area recognition method and system based on a graph structure and multi-stage enhancement. The method comprises the steps of performing data preprocessing and representation enhancement on an acquired remote sensing image; performing sewage salient region preliminary screening on the enhanced remote sensing image, including abnormal enhancement mapping construction based on local statistical distribution; pollution candidate graph extraction based on spatial structure prior driving; enhancing the response of the stable region based on a structure consistency enhancing mechanism of the polluted region; high-precision segmentation and identification of the sewage area comprises the following steps: constructing a multi-resolution residual pyramid structure; carrying out fine-grained boundary structure modeling and uncertainty suppression; generating a sewage distribution probability graph and optimizing structural consistency; according to the method, the multi-resolution residual pyramid structure is constructed, image context information under different perception scales is fully mined, and the sensitivity and edge integrity of the model to a sewage area under a complex texture background are remarkably enhanced.

The invention discloses an intelligent underground water quality supervision method and system. The method comprises heterogeneous sensing acquisition, water quality benchmark modeling, sewage traceability enhancement, water quality prediction and early warning and underground water quality supervision. The invention relates to the technical field of water quality supervision, in particular to an intelligent underground water quality supervision method and system, and adopts an underground water quality comprehensive supervision method combining water quality benchmark modeling, pollution traceability enhancement and dynamic threshold water quality prediction and early warning. The perception capability, the identification capability and the response capability of the underground water pollution event are effectively improved, and the supervision conversion from passive response to active pre-judgment is realized; carrying out water quality benchmark modeling by adopting an improved space-time diagram convolutional network method combined with a space-time dynamic baseline algorithm; pollution traceability enhancement is carried out by adopting an improved adversarial generative network method of virtual and real traceability modeling.

The invention discloses an urban life body full-cycle monitoring system based on a digital twinborn technology, relates to the technical field of urban governance, integrates multi-source data such as GIS data, remote sensing information, surface permeability data and an underground drainage pipe network structure, quantifies a flood high-risk area, and provides an urban life body full-cycle monitoring system based on the digital twinborn technology. The AI flood prediction module deduces the ponding range and the drainage capacity under different rainfall situations by adopting extreme weather analogue simulation based on a historical rainfall trend, a current meteorological condition and a drainage network state, and dynamically adjusts a flood diffusion trend prediction model, so that the flood risk identification is more accurate; the intelligent emergency scheduling module automatically matches flood control resources, combines flood high-risk areas, ponding point locations and traffic flow analysis, optimizes a drainage pump station start-stop strategy, remotely regulates and controls an underground drainage gate, dynamically adjusts the overflow adjustment capability of a sewage treatment plant, and optimizes an emergency risk avoiding route in combination with an intelligent traffic management system. And the accuracy and the execution efficiency of the flood control scheduling scheme are improved.

The invention discloses a cleaning head shell assembly and a cleaning head, the cleaning head shell assembly comprises a cleaning head shell body, the cleaning head shell body comprises a shell body and two side walls connected with the shell body, one side wall is fixedly connected with a hinge seat, the hinge seat is hinged to one end of a driving motor, and the other end of the driving motor is fixedly connected with the shell body. The output end of the other end of the driving motor is clamped with the other side wall; the driving motor can be sleeved with a cleaning roller, and the output end is coupled to drive the cleaning roller to rotate to scrub a to-be-cleaned surface; the shell body is provided with a water guide tip part which is propped against the outer layer of the cleaning roller and is used for guiding sewage; the cleaning head shell body can be detachably connected with the water tank assembly, and the water tank assembly provides clean water for washing the surface of the cleaning roller. A rotatable extrusion roller is clamped between the two side walls, clear water can be sprayed out from a clear water outlet formed in one side wall to directly wash the surface of the cleaning roller, the cleaning head shell body extends upwards away from the cleaning roller to form an anti-overflow plate, and the water guide tip, the anti-overflow plate, the extrusion roller and the cleaning roller define a water tank space.

The invention discloses an intelligent regulation and control method and system for sewage treatment and a program product, and relates to the technical field of sewage treatment.The method comprises the steps that a multi-modal water quality sensing network is constructed, and sewage quality parameter information and overall treatment target numerical value information are obtained; according to the equipment operation state and the overall treatment target numerical value information, sewage treatment process parameters are generated, and treatment units in the sewage treatment system are distributed in a plurality of areas; triggering a process regulation and control instruction according to the sewage treatment process parameters and the overall treatment target numerical value information; obtaining partition processing effect detection information, and comparing the partition processing effect detection information with the corresponding index value to obtain a partition difference value; if the partition difference value exceeds a preset change threshold range, triggering a partition self-adaptive regulation and control updating instruction; obtaining partition adjustment parameter information according to the partition self-adaptive regulation and control updating instruction; and triggering an adjustment instruction. The invention provides an intelligent regulation and control method based on multi-dimensional perception and adaptive decision.

The invention discloses a water and dust circulation cleaning head and a cleaning tool. The water and dust circulation cleaning head comprises a cleaning head shell assembly, a cleaning roller and an extrusion roller. The cleaning head shell assembly comprises a cleaning head shell body provided with two side walls, one side wall is fixedly connected with a hinge base, the hinge base is hinged to one end of a driving motor, and the output end of the other end of the driving motor is connected with the other side wall in a clamped mode. The cleaning roller sleeves the driving motor, and the output end of the cleaning roller is coupled to drive the cleaning roller to rotate to scrub a to-be-cleaned surface; the cleaning head shell body is provided with a water guide tip which abuts against the cleaning roller in the saturated state in the axial direction of the whole cleaning roller and guides sewage. The extrusion roller is rotatably clamped between the two side walls, clear water can be sprayed out from a clear water outlet formed in one side wall to directly wash the surface of the cleaning roller, an anti-overflow plate upwards extends from the cleaning head shell body away from the extrusion roller relative to the water guide tip part, and a water tank space is defined by the water guide tip part, the anti-overflow plate, the extrusion roller and the cleaning roller.

The present invention relates to the technical field of the control of wastewater treatment devices. Disclosed is an intelligent control method for wastewater treatment devices at a dry bulk cargo terminal, which is used for solving the problem of poor control of wastewater treatment devices at a terminal. The method comprises the following steps: installing a plurality of types of sensors at key locations of a dry bulk cargo terminal, and using edge computing nodes to perform real-time data collection and preprocessing; on the basis of historical features and temporal features, using a machine learning model to perform wastewater type classification, thereby realizing efficient dynamic adjustment of operating parameters of wastewater treatment devices; then, by means of weighted voting and confidence evaluation, integrating a plurality of classification results to ensure an optimal treatment effect; and analyzing actual wastewater treatment conditions to continuously optimize device control, thereby preventing faults, extending the service life of devices, and improving the wastewater treatment effect.

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.

The invention provides a sewage treatment system and method based on a large language model and multi-agent collaboration, and the system comprises a sensor network which is used for collecting the water quality parameters of sewage in real time; the monitoring agent is used for performing data verification on the water quality parameters; the at least one edge computing node is used for performing data preprocessing on the water quality parameters; the cloud decision intelligent agent is used for generating a sewage treatment strategy according to the preprocessed water quality parameters based on a large language model; the execution agent is used for executing the sewage treatment strategy and treating the sewage; the evaluation agent is used for analyzing and evaluating the sewage treatment effect and feeding back the sewage treatment effect to the cloud decision agent so as to optimize the sewage treatment strategy; by constructing an intelligent and dynamic treatment system, real-time monitoring, intelligent analysis and self-adaptive treatment of the mariculture sewage are realized, so that the sewage treatment stability and environmental protection benefits are improved, and the sustainable development of the mariculture industry is promoted.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment aeration control method and system.The method comprises the steps that according to collected historical data, abnormal values are removed through intelligent calculation in combination with optimization and correction, an accurate sample data set is obtained and then classified through a clustering model, and all classifications are subjected to training and verification testing; a trained BP neural network model is obtained, a follow-up controller outputs accurate aeration quantity through the BP neural network model according to latest data to control each aeration device, and the increase / decrease of the air quantity of the biochemical pool is automatically adjusted; the blast volume of the biochemical pool is controlled and the air volume matching working condition is controlled according to the inlet water quality and the water volume, so that the control precision of the aeration rate in the sewage treatment process can be effectively improved, the energy consumption waste is reduced, the process control efficiency is improved, the air volume of the biochemical pool under different working conditions is ensured to be at the optimal value, the outlet water quality stability and the standard-reaching rate are ensured, and the power consumption cost is reduced; the method is suitable for sewage treatment plants of various types and scales, and has a wide market application prospect.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment process model parameter correction method and system. The method comprises the following steps: acquiring sensing data of a multi-point facility through a sewage treatment sensing network, extracting a topological structure of a sewage pipeline, projecting the data into the topological structure, marking position information, constructing an initial sewage treatment process model based on the information, extracting a sewage data set of a treatment process, and establishing a sewage treatment process model; the system simulates a sewage treatment environment, generates virtual flow field data, tracks particle motion, draws a sedimentation curve and generates particle sedimentation track offset data, obtains morphological parameters of a sedimentation tank, reconstructs the framework of the sedimentation tank, predicts a sedimentation imbalance state, backtracks and analyzes model parameters through an abnormal state, and corrects the model parameters. And generating an optimized sewage treatment process model. According to the invention, the operation efficiency and the treatment effect of the sewage treatment facility are integrally improved, and the intelligent and refined development of the sewage treatment technology is promoted.

The invention provides a sewage plant effluent prediction method, system and equipment based on an improved Bi-LSTM model, and relates to the technical field of sewage treatment. The method comprises the following steps: acquiring historical operation data of a sewage plant, introducing an attention mechanism, a bidirectional structure and residual connection based on a standard LSTM unit, constructing a Bi-LSTM prediction model, taking a key kinetic equation of a simplified activated sludge model ASM as a physical constraint condition, inputting the operation data subjected to data preprocessing into the Bi-LSTM prediction model, and calculating the operation data of the sewage plant according to the operation data. The prediction result is subjected to multi-objective optimization based on the genetic algorithm to obtain an optimal process parameter combination, and the optimal process parameter combination is converted into an actual process control instruction to realize dynamic parameter adjustment, so that the prediction precision is greatly improved, and the energy consumption is reduced, the stability is improved and the abnormal working condition adaptive capacity is improved through multi-objective optimization.

The invention discloses an intelligent monitoring system for sewage treatment in the coal chemical industry, and relates to the technical field of sewage treatment.The intelligent monitoring system is characterized in that a water quality monitoring unit is used for collecting parameters such as COD, turbidity, pH and heavy metal ion concentration in real time, the change trend of water quality in a short time is predicted through a long-short term memory (LSTM) network, and then adding parameters of a flocculating agent are dynamically calculated; the defects of a fixed adding curve or artificial experience adjustment are overcome, so that the adding amount, the type and the speed of the flocculating agent can be adaptively adjusted to be matched with the change of sewage components; the addition optimization unit is combined with a CNN-LSTM model to analyze the rheological characteristics of the sludge, so that the addition of the flocculating agent adapts to the rheological change of the sludge, the flocculation effect is optimized, and the sludge settling performance is improved; meanwhile, the stirring control unit adjusts the stirring strength and time in real time based on ultrasonic online monitoring data, so that the flocculating agent is fully dispersed, the particle distribution state is optimized, the flocculation reaction is more uniform, and the subsequent precipitation separation efficiency is improved.

The invention relates to the technical field of sewage plant-oriented carbon neutralization regulation and control scheme design, in particular to a sewage plant-oriented carbon neutralization regulation and control method and a sewage plant-oriented carbon neutralization regulation and control system. The method comprises the steps of collecting water quality, energy consumption and gas emission data in real time through a multi-source sensor, inputting the data into a space-time diagram convolutional network to predict a carbon emission trend after edge computing node cleaning, and dynamically optimizing an aeration rate and a sludge reflux ratio based on a fuzzy PID controller. A whole-process carbon footprint is tracked by adopting a life cycle evaluation method, a multi-target compensation strategy is generated in combination with an improved NSGA-III algorithm, and photovoltaic, biogas and green power resources are scheduled preferentially. A digital twin platform and a block chain evidence storage module are innovatively integrated, the former calibrates model parameters in real time, and the latter generates a non-tampering carbon neutralization voucher. According to the method, the problems of one-sided data, regulation and control lag and insufficient credibility of a traditional method are solved, and minute-level sensing, prediction and offset closed-loop control of carbon emission in the sewage treatment process is realized.

The invention provides an intelligent aeration optimization control method and system for a sewage plant, and relates to the technical field of sewage treatment.The method comprises the steps that water quality, water quantity and aeration quantity data are collected in real time; analyzing and processing the data of the water quality, the water quantity and the aeration quantity to form a sewage treatment process data set which comprises the change rate of a water quality index, the fluctuation condition of the water quantity and aeration efficiency information; and according to the sewage treatment process data set, establishing and training a dynamic mathematical model in the sewage treatment process to reflect the relationship among the water quality, the water quantity and the aeration quantity. Real-time monitoring, model prediction and intelligent adjustment technologies are integrated, the aeration control strategy is effectively optimized, the sewage treatment efficiency is improved, the stability of water quality is ensured, and energy conservation and environmental protection are promoted.

The invention provides a sewage treatment plant terminal effluent carbon source quality detection and evaluation method, which realizes full-flow dynamic evaluation and regulation of carbon source quality through on-line monitoring and intelligent algorithm fusion. According to the method, an online water quality full-spectrum detector is used for collecting original spectrum data flow, and after preprocessing such as variational mode decomposition denoising and mutual information feature selection, organic matter content quantification, variation trend analysis and anomaly detection are completed in combination with algorithms such as a support vector machine and an autoregressive moving average model. An entropy weight method is introduced to dynamically adjust the weight of the evaluation model, model parameters are optimized based on a gradient descent algorithm, a process adjustment instruction is generated through reinforcement learning and fuzzy logic, and an automatic system is linked to execute regulation and control. According to the method, the problems of hysteresis and singleness of traditional offline analysis are solved, multi-dimensional real-time evaluation, abnormal quick response and process dynamic optimization of the quality of the carbon source are realized, the sewage treatment efficiency and the effluent quality stability are improved, and a technical support is provided for continuous standard reaching of the quality of the carbon source.

The invention relates to the technical field of sewage treatment, in particular to a dynamic simulation method for a lake and reservoir sewage sludge deposition process. The method comprises the following steps: collecting lake and reservoir hydrological station data, carrying out abnormal value filtering and missing value completion, generating a complete hydrological sequence, carrying out water flow state subdivision to obtain flow state hierarchical data, reconstructing a multi-layer hydrodynamic field, forming a hydrological dynamic field set, carrying out suspended matter analysis, and obtaining a hydrological dynamic field model; the method comprises the following steps of: extracting sewage sludge data, deconstructing transportation characteristics of the sewage sludge data, generating transportation dynamic data, performing water body stress tensor decomposition to obtain stress distribution data, performing substance transportation simulation on a complete hydrological sequence by using the stress distribution data, establishing a multi-scale sedimentation dynamic model, acquiring water body sedimentation change data in real time, comparing the water body sedimentation change data with the model, and if inconsistency exists, judging whether the water body sedimentation change data is consistent or not. If yes, the model is finely adjusted in real time, and an optimized multi-scale deposition dynamic model is generated. The more accurate dynamic simulation method for the lake and reservoir sewage and sludge deposition process is achieved.

The invention discloses a sewage plant accurate dosing method based on multi-mode automatic machine learning, and belongs to the technical field of sewage treatment. The method comprises the following steps: acquiring historical sensing data and image data of a sewage plant; extracting key features in the image by using a YOLO visual model, fusing the key features with sensor data, and constructing a multi-modal sample data set; carrying out modeling training on the fused data by adopting an automatic machine learning framework, and constructing a'water inlet-dosing-water outlet 'prediction model; generating a reference sample data set according to an existing dosing rule; inputting the sample data into the trained prediction model to obtain predicted effluent quality data, and adjusting the dosage until the predicted effluent quality of all samples meets a water quality standard condition; and finally outputting an optimized dosing sample data set. According to the invention, precision and real-time dosing of the sewage plant can be realized, chemical waste can be effectively reduced, the treatment efficiency is improved, the cost is saved, and the water quality is ensured to stably reach the standard.

The invention provides a plant-network integrated intelligent linkage scheduling method and system based on real-time monitoring, and the method comprises the steps: collecting drainage data in a drainage pipe network, obtaining the working condition data of a sewage treatment plant, and obtaining meteorological early warning information; performing comprehensive analysis on the drainage data, the working condition data and the meteorological early warning information based on an intelligent algorithm model, and performing prediction to obtain an analysis result; the intelligent algorithm model is used for simulating the running state of the drainage pipe network and predicting the sewage amount, the water quality change and the mixed sewage overflow risk; and according to the analysis result, generating a dynamic linkage scheduling instruction for the storage tank, the intelligent catch basin and the sewage treatment plant. According to the invention, the running state of the drainage pipe network is accurately simulated through the intelligent algorithm model, the sewage amount, the water quality change and the mixed sewage overflow risk are predicted, then the dynamic linkage scheduling instruction for the regulation and storage tank, the intelligent catch basin and the sewage treatment plant is generated, and the situation that the environment is damaged by overflow is avoided.

The invention discloses a sewage monitoring and early warning method and system, and aims to solve the problems of lagging and high misjudgment rate of traditional monitoring. The multi-modal acquisition module acquires water quality parameters, equipment states and pipe network data in real time through a distributed sensor network, a timestamp is calibrated through linear interpolation of a dynamic analysis engine, spatial features are extracted through concentration gradient calculation, and data are cleaned in combination with Kalman filtering and an isolated forest algorithm. And the intelligent analysis and decision module mines time sequence features based on the LSTM model and outputs pollution level indexes. And the grading response execution module triggers early warning according to three-level threshold values: a first-level APP pushes data, a second-level superposition short message and a third-level telephone alarm and links equipment, a standby purification device is started within 10 seconds, and a WebGL visual diffusion report is synchronously generated. The system constructs an acquisition-analysis-decision-response closed loop which is suitable for industrial and urban pipe network scenes.

The invention relates to the field of heat exchangers, in particular to a heat exchange module and a plate-type anti-fouling heat exchanger, the heat exchange module comprises two groups of box-type heat exchange subunits, heat exchange plates are arranged in each heat exchange subunit in parallel at intervals, inner cavities of the heat exchange plates form heat exchange medium flow channels, sewage flow channels are formed between the adjacent heat exchange plates, and the sewage flow channels are communicated with the heat exchange subunits. The partition plates and the heat exchange plates are arranged in a crossed and staggered mode and inserted into the sewage flow channels and the heat exchange medium flow channels at the same time, the sewage flow channels and the heat exchange medium flow channels are divided into S-shaped flow channels, and the flow directions of the sewage flow channels and the heat exchange medium flow channels in all the heat exchange subunits are opposite. While the size of the heat exchanger is reduced, it can be guaranteed that the heat exchanger has high heat exchange efficiency, and the interior of the heat exchanger is not prone to being blocked.

The invention relates to the technical field of microorganisms, and provides a low-temperature-resistant heterotrophic nitrification-aerobic denitrification pseudomonas and application thereof, the strain is pseudomonas (Pseudomonas sp.) SW1, and the preservation number is CCTCC (China Center for Type Culture Collection) NO: M 20221797. The low-temperature-resistant heterotrophic nitrification-aerobic denitrification bacterium Pseudomonas sp. SW1 has the characteristic of low temperature resistance, can well grow at 4-10 DEG C, and has relatively strong heterotrophic nitrification-aerobic denitrification nitrogen removal capability, and the enrichment culture of the strain is carried out in a relatively low nitrogen source, and the condition is extremely consistent with the application scene of the strain in domestic sewage.

The invention provides a sewage AI intelligent management and control system based on a neural network algorithm, which comprises multi-source data acquisition units, an algorithm processing unit and an execution feedback unit, and is characterized in that the multi-source data acquisition units are deployed at a water inlet and a water outlet of a sewage treatment assembly line and in a biological reaction tank; comprising a fast parameter detection array composed of a pH sensor, a conductivity sensor, a dissolved oxygen sensor, a turbidity sensor, a temperature sensor and an ORP sensor. The process state monitoring group consists of an aeration equipment rotating speed sensor, a reflux pump flowmeter and a sludge concentration meter; the fast parameter detection array and the process state detection group are connected with the edge computing node through an industrial bus, perform sliding window mean filtering preprocessing on original data, generate regulation and control instructions of aeration rate, reflux ratio and sludge discharge frequency through model calculation, and send the regulation and control instructions to the execution unit; the complete refined AI intelligent control sewage treatment system based on the neural network algorithm is realized.

The invention relates to an intelligent decision-making method, system, equipment and medium for sewage medicament addition, and the method comprises the following steps: detecting and preprocessing sewage water quality parameters, extracting feature vectors, and carrying out clustering analysis to obtain water quality categories. Historical dosing data is retrieved and subjected to statistical analysis, and an initial dosing scheme is generated; a water quality change curve is obtained by simulating the scheme, and then the optimal dosing scheme is obtained through optimization. And finally, generating and issuing an agent adding control instruction, and executing treatment, so that the technical problems that the traditional agent adding mode mostly depends on artificial experience or simple automatic control, and is difficult to cope with the complex working condition of dynamic change of water quality and water quantity, so that the agent adding is inaccurate, and the agent waste is possibly caused are solved.