14717 results about "Sewage" patented technology

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 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 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 relates to the technical field of water quality prediction, and discloses an industrial sewage water quality real-time prediction and early warning method and system. According to the method, the depth features of the internal treatment process state of each water quality treatment unit are extracted, so that the problem that the prediction precision of a prediction model is limited due to the fact that the internal deep features cannot be excavated in a traditional method is solved; a migration rule and a response relation of pollutants between every two adjacent water quality treatment units are analyzed through real-time water quality parameters, so that a water quality flow association graph with the water quality treatment units as nodes, pollutant migration paths as edges and cross-unit association strength as edge weights is constructed; the driving effect of the water quality change of the upstream water quality treatment unit on the treatment effect of the downstream water quality treatment unit is quantified, the accurate quantification of the cross-unit dynamic linkage effect is realized, and the problem that the linkage effect is caused by neglecting the transfer and conversion of pollutants among the water quality treatment units in the prior art is solved. Therefore, the water quality prediction accuracy is improved.

The invention relates to the technical field of sewage treatment, in particular to a coupling control system and method for deep denitrification of sewage, and the system comprises a real-time water quality monitoring module, a microorganism twinborn modeling module, a real-time prediction module, an intelligent decision module, an execution mechanism module and a prediction regulation and control module. Compared with the prior art that a passive feedback control strategy based on an effluent quality index is generally adopted, the hysteresis quality is high, and violent fluctuation of an inflow load cannot be coped with; according to the method, a digital twinborn body capable of reflecting the functional state of a microbial community in real time is constructed, and a control target is improved from a traditional process parameter set point to direct optimization of microbial ecological functions; according to the invention, the method achieves the fundamental crossing from the control of technological parameters to the regulation and control of microbial ecology, can carry out intervention from the root of the reaction process, enables the system to have the active health management capability, and remarkably improves the stability of the treatment efficiency and the intelligent level of coping with complex working conditions.

The application discloses a floor cleaning machine, which comprises a handle, a cleaning head and an integrated water tank. The cleaning head comprises a shell rotatably connected with the handle. The integrated water tank comprises a clean water cavity and a sewage cavity and is installed in a horizontal manner on the rear half of the shell in a single-side inlet-outlet mode, so as to provide clean water for cleaning and collect sewage after cleaning for the cleaning head. The cleaning head further comprises a motor installed on one side of the shell, a roller assembly sleeved on the motor and a brush assembly bridged on both sides of the shell. The motor drives the roller assembly to rotate, so as to drive the brush assembly to rotate synchronously. The floor cleaning machine provided by the application has simple structure, reduces the weight on the handle, and makes the user feel more light when pushing or pulling the floor cleaning machine. The floor cleaning machine can increase the standby time of the floor cleaning machine without increasing the volume of the floor cleaning machine, and can also reduce the noise of the floor cleaning machine in the working state.

The invention discloses a sewage treatment plant greenhouse gas optimization control method and device and a storage medium, and relates to the technical field of environmental protection, and the method comprises the steps: discretizing a sewage treatment process of an aeration tank into a plurality of complete mixing reactors connected in series, and constructing a sewage treatment process mechanism model based on an activated sludge model; optimizing parameters of the sewage treatment process mechanism model according to the target greenhouse gas concentration spatial distribution data, the target greenhouse gas emission flux and the water quality spatial distribution data to obtain a digital twinborn model; based on a digital twinborn model, a dissolved oxygen set value of each complete mixing reactor partition in an aeration tank is used as a decision variable, a multi-objective evolutionary algorithm is adopted for solving, a Pareto optimal control strategy is generated, and the technical problem that in the prior art, an optimal control strategy of greenhouse gas is not accurate is solved. And precise quantification and collaborative optimization control of greenhouse gas emission are realized.

The invention discloses a precise sewage aeration method, and relates to the field of water, wastewater, sewage or sludge treatment, the method comprises a feed-forward model and a feedback model, the feed-forward model collects historical data of aeration tank operation parameters, environmental parameters and oxygen demand, and a decision tree is iteratively generated to correct errors and determine a target dissolved oxygen value; the feedback model corrects the target dissolved oxygen value according to the aeration tank dissolved oxygen concentration and the mixed liquid suspended solid concentration; and controlling the power of the aeration equipment according to the target dissolved oxygen value. Through common coordination of the feedforward model and the feedback model, accurate aeration of sewage treatment is realized.

The invention discloses a gradient optimization control method for an aeration system of a sewage plant. The gradient optimization control method comprises the following steps: S1, acquiring key monitoring multi-source data of the aeration system of the sewage plant in real time through an online sensor; s2, setting a security threshold value of each monitoring data to form a security constraint condition; s3, taking unit nitrogen removal energy consumption minimization as an optimization target, and constructing a comprehensive loss function fusing an energy consumption item and a water quality deviation item; s4, iteratively updating the air volume of the aeration system by adopting a self-adaptive gradient algorithm; s5, the optimized and updated air volume is issued to the aeration equipment to be executed, and water quality data and energy consumption data after execution are collected in real time, a control effect is calculated, an award signal is generated, and the award signal serves as feedback to be input into an optimization module; and S6, dynamically adjusting key parameters including the learning rate and the gradient threshold according to a feedback result. According to the invention, the energy consumption can be effectively reduced and the control precision can be improved while ensuring that the effluent stably reaches the standard, and the stable operation of the aeration system of the sewage plant under a complex working condition is ensured.

The invention relates to the technical field of solid waste treatment, in particular to an intelligent waste sorting method based on multi-sensor fusion. Carrying out multi-level fusion decision making: carrying out sewage reflection suppression on the visible light image; carrying out matching identification and rejecting a liquid pollution area based on a near infrared spectrum; fusing the visible light texture, the spectral feature and the electromagnetic signal to generate a space grid feature vector; identifying the material category through a cascade classifier, and dynamically adjusting the weight of each sensor in the decision according to the environmental parameters; self-adaptive sorting execution: triggering a corresponding sorting execution mechanism according to an identification result; based on the transmission belt speed, the execution mechanism response delay and the material physical attribute, the action advance compensation amount is calculated; the action time of an execution mechanism is adaptively prolonged for the stacked materials according to the stacking hierarchy; and online feedback optimization. Through dynamic compensation and time sequence adjustment, the time sequence consistency of multi-sensor data is ensured, and the influence of band speed fluctuation is overcome.

The invention relates to the technical field of rural sewage treatment, and discloses a rural sewage intelligent monitoring and aeration regulation and control system based on Internet of Things. The system comprises a pollution parameter sensing layer, a pollution index analysis layer, an aeration path generation layer, a process parameter matching layer, a dynamic monitoring focusing layer, a cross-modal mapping layer, an equipment scheduling decision-making layer and an aeration parameter generation layer. Wherein the pollution parameter sensing layer collects operation parameters in real time by means of a multi-source sensing terminal; the pollution index analysis layer divides pollution parameter intervals and calculates a distribution coefficient; the aeration path generation layer generates an aeration intensity grading path in sequence; the process parameter matching layer outputs a process deviation parameter set; dynamically monitoring a focusing layer to adjust the density of monitoring points; the cross-modal mapping layer establishes space-time correlation mapping of water quality parameters and aeration intensity; the equipment scheduling decision-making layer sorts the calling sequence of the aeration equipment; and the aeration parameter generation layer converts the parameters into executable regulation and control parameters. The system realizes intelligent monitoring of rural sewage and accurate aeration regulation and control.

The invention discloses a sewage data management system and method based on kitchen garbage treatment, and relates to the technical field of data management. The system comprises a data acquisition and historical database construction module, a correlation analysis and dynamic response rule construction module, a sewage data trend pre-judgment module and an operation regulation and data updating module. The data acquisition and historical database construction module is used for acquiring garbage treatment load and sewage data and constructing a historical database covering time period fluctuation and seasonal periods; the correlation analysis and dynamic response rule construction module constructs a dynamic response rule and determines the time delay duration; the sewage data trend pre-judging module is used for acquiring load data in real time and pre-judging a future sewage change trend; and the operation regulation and data updating module adjusts parameters of the sewage treatment system, collects effluent data to construct a new sample, supplements a database and updates a dynamic response rule. According to the invention, accurate sewage data management is realized, and the operation efficiency and stability of the sewage treatment system are improved.

The invention discloses a sewage treatment equipment operation monitoring method based on big data, and relates to the technical field of sewage treatment.The sewage treatment equipment operation monitoring method comprises the steps that original sewage data flow is collected and preprocessed, and multi-source fusion data is generated; performing exception detection processing on the multi-source fusion data to generate exception mark data; performing time sequence prediction on the abnormal mark data by using a time sequence prediction model to generate a sewage prediction sequence, and combining the sewage prediction sequence with the abnormal mark data to form comprehensive risk features; performing early warning analysis on the comprehensive risk characteristics according to a risk level rule and a sewage prediction sequence, and generating pre-risk early warning; and carrying out graded warning and operation decision making on the preposed risk early warning, and generating an operation regulation and control suggestion. According to the method, the prediction result has advantages in two aspects of trend grasp and anomaly adaptation, and the effect of remarkably improving the prediction accuracy and robustness is achieved.

The invention relates to the technical field of sewage detection, provides an intelligent detection method for suspended particles in coking sewage based on image deep learning, and aims to solve the problems of detection lag and low precision in the prior art. According to the technical scheme, the method comprises the following steps: synchronously acquiring sound wave and image data through a high-frequency ultrasonic transducer and an industrial camera, and extracting acoustic parameters and optical characteristic parameters; using a dual-channel convolutional neural network to fuse features, and combining a cross-modal attention mechanism to perform dynamic weighting; and outputting a particle concentration classification result and a particle size distribution prediction value through a multi-task learning model, and performing online learning to compensate environmental interference factors. The device is mainly used for realizing real-time and high-precision suspended particle monitoring and automatic process adjustment of coking sewage treatment.

The invention relates to the technical field of sewage treatment supervision, and discloses a sewage plant supervision system based on digital twinning. The system comprises an operation mirror image generation module which collects data in real time and generates a digital twin mirror image of the sewage treatment facility; the virtual equipment state library construction module is used for extracting key equipment parameters based on the digital twin mirror image and dynamically updating a virtual equipment state library; the strategy optimization module is used for analyzing a sewage flow state and a pollutant migration path and generating a virtual operation strategy optimization parameter set; the abnormal response engine module is used for monitoring abnormal fluctuation of key nodes and generating an abnormal response intervention data set; and the efficiency improvement module is used for optimizing virtual processing path regulation and control parameters and generating a pollutant removal efficiency improvement scheme. According to the system, dynamic monitoring, intelligent optimization and quick response of the whole sewage treatment process are achieved through the digital twinning technology, supervision accuracy and timeliness are improved, and the system adapts to complex and changeable operation conditions.

The invention discloses a sewage treatment plant effluent prediction method based on multi-task learning. The method comprises the following steps: acquiring sewage treatment data; based on the sewage treatment data, establishing an effluent prediction model; the input of the effluent prediction model is inflow water quality data, process data, environmental data and sewage treatment unit data, and the output of the effluent prediction model is predicted effluent index data; predicting the water outlet index data in future time based on the water outlet prediction model and the input of the water outlet prediction model; according to the method, the water outlet prediction model is constructed in combination with multi-task learning, and the model can comprehensively consider the time sequence dependence and mutual influence relationship among the input data to perform prediction, so that the calculation redundancy is reduced, and the prediction demand that an actual process needs to cooperatively consider multiple targets is met; the effluent quality prediction precision and the process regulation and control efficiency are remarkably improved, and a solid foundation is laid for promoting intelligence of operation management of a sewage treatment plant.

The invention belongs to the technical field of oil-gas-water separation equipment, and discloses an ultrasonic detonation rotational flow microbubble flotation device which comprises a vertical container tank with inner and outer stepped double-barrel design and a separation inner barrel, an oil gathering umbrella is arranged on the upper portion of the interior, an oil discharge connector is arranged on the outer wall of one side, and surrounding physical demulsification coalescence filler is arranged on the upper portion of the area between the tank and the inner barrel. And the lower part of the separation inner cylinder is provided with a reverse symmetrical double-tangential rotational flow inlet, the bottom of the separation inner cylinder is externally provided with a purified backflow water inlet and is communicated with the purified water diversion outlet in a process manner, and the bottom of the separation inner cylinder is internally provided with a gas-water mixer and an ultrasonic knocker. By combining ultrasonic cavitation, rotational flow centrifugation, micro-nano bubbles and physical coalescence demulsification technologies, the diameter of the bubbles is reduced by 15 microns or below through the ultrasonic detonation cavitation effect, fine oil drops and suspended particles which are difficult to treat through traditional flotation and are 10 microns or below can be captured, the method is suitable for multi-phase separation of discrete high-concentration fine pollutants, and the flotation efficiency is improved. Complex separation scenes such as oily sewage, micro-plastics and mineral flotation can be treated.

The invention relates to the technical field of sewage treatment optimization, in particular to an intelligent sewage plant full-process management and control method based on digital twinning, which comprises the following steps: acquiring concentration data to calculate rate clustering fluctuation, setting a threshold value mark stable section to analyze sedimentation association, constructing dynamic threshold value output control information, and adjusting stirring to generate a sludge discharge instruction set. According to the method, through the time sequence clustering of the concentration change rate, the stable section in the sludge sedimentation process is identified, the concentration fluctuation identification precision is improved, the sedimentation hysteresis is judged based on the time difference between the sedimentation track and the standard curve, the particle layering identification capability is improved, and the dynamic threshold matrix is constructed by using the correlation degree and the particle size data; the concentration range, the settling velocity deviation and the particle size distribution dispersion are fused, the multidimensional property of a regulation and control basis is enhanced, the interface position movement trend is combined, the concentration range is matched, a stirring intervention instruction is output, real-time regulation and accurate control of the layered settling process are achieved, and the treatment stability and the reaction time efficiency are improved.

The invention discloses a low-temperature sewage deep denitrification treatment method and system based on an AOA process, and relates to the technical field of sewage treatment.The method comprises the steps that multiple types of sensing units are arranged in an anoxic zone and an aerobic zone to obtain multi-zone characteristics, and a low-temperature denitrification collaborative state tensor is constructed through space-time decoupling and correlation analysis; and inputting a denitrification task target, a state tensor and an equipment state into a collaborative decision model, outputting a combined control instruction of gas-liquid distribution, a reflux ratio, aeration intensity and the like through attention fusion, and issuing the combined control instruction to a related unit so as to realize deep denitrification control under a low-temperature condition. The method solves the technical problems of low denitrification efficiency and poor stability caused by the fact that the existing AOA process cannot effectively identify and regulate the coupling denitrification dynamic relationship of multiple reaction intervals under the low-temperature condition, realizes accurate regulation and control of multi-zone denitrification power under the low-temperature condition through multi-zone cooperative control and refined parameter optimization, and improves the denitrification efficiency. The denitrification efficiency and the stability are improved.

The present invention discloses an intelligent control method for sewage treatment equipment at dry bulk cargo terminals, relating to the technical field of sewage treatment equipment control, and is used to address the problem of poor control of sewage treatment equipment at terminals. The method comprises following steps: installing multiple types of sensors at key locations on dry bulk cargo terminals, utilizing edge computing nodes for real-time data acquisition and preprocessing, combining historical and temporal features with machine learning models to classify sewage types, achieving efficient dynamic adjustment of sewage treatment equipment operation parameters, then using weighted voting and confidence assessment to integrate multiple classification results to ensure optimal treatment results, and analyzing the actual sewage treatment situation to optimize equipment control continuously, thereby preventing failures, extending equipment life, and improving sewage treatment effectiveness.

The invention relates to the technical field of electronics, and discloses a drainage pipe network pollutant tracing method, device and equipment, a medium and a program product. According to the method, the map similarity between a first three-dimensional fluorescence spectrum map corresponding to a first water sample of a target node and a second three-dimensional fluorescence spectrum map corresponding to each upstream pipe network node is calculated; and determining at least one target upstream pipe network node in the plurality of upstream pipe network nodes based on the map similarity, and determining pollution source information of abnormal water in a target node according to first water quality data of the target upstream pipe network node and second water quality data on a sewage flowing path between the target upstream pipe network node and the target node, the advantages of high sensitivity, rapid detection and the like of the three-dimensional fluorescence spectrum technology are utilized in the pollutant traceability process, and efficient and accurate traceability of pipe network pollutants is achieved.

The invention discloses latex skeleton aerobic granular sludge and a preparation method thereof, and belongs to the technical field of sewage treatment. The aerobic granular sludge takes a porous natural latex carrier as a core skeleton, anaerobic sludge is fixed on the carrier, and after being mixed and wrapped with a sodium alginate solution, the mixture is dropwise added into a calcium chloride solution to be cured to form gel particles. The preparation method comprises the following steps: crushing the natural latex, screening to obtain particles with the particle size of 0.4-1.2 mm, and soaking the particles in alkali liquor with the pH value of 10-12; the method comprises the following steps: centrifugally concentrating anaerobic sludge to 10000 to 20000 mg / L; mixing the treated latex carrier, anaerobic sludge and a 1%-5% sodium alginate solution according to a volume ratio of 5: (2-5): (2-5); dropwise adding the mixture into a calcium chloride solution with the concentration of 3-7%, and curing to form gel particles; and putting the gel particles into a sewage treatment aerobic tank, injecting domestic sewage, and carrying out aeration domestication for 3-6 days until the particles become earthy yellow. The granular sludge prepared by the invention has the advantages of stable structure, good settling performance and high pollutant removal efficiency.

The invention belongs to the technical field of sewage treatment, and particularly relates to an anaerobic ammonia oxidation-photoelectric denitrification deep nitrogen removal method. The method comprises the following steps: firstly, adopting a double-chamber reactor; introducing part of sewage into the cathode chamber, carrying out Anammox reaction under a dark state condition to remove ammonia nitrogen and part of nitrite nitrogen, and synchronously generating nitrate nitrogen; turning on a light source to irradiate the anode chamber, exciting photo-induced electrons by a photosensitizer, transmitting the photo-induced electrons to the cathode chamber through an external circuit, and driving the sulfur autotrophic denitrification sludge to reduce residual nitrite nitrogen and nitrate nitrogen generated by Anammox; the total nitrogen removal rate is close to 100%. According to the invention, photo-induced electrons are used as the only denitrification electron donor, so that the addition of a carbon source / sulfide is thoroughly avoided, and the risk of secondary pollution is eliminated. The method is suitable for green low-carbon deep treatment of sludge digestion liquid and pharmaceutical wastewater high-ammonia-nitrogen low-carbon-nitrogen-ratio sewage.

The invention discloses a sewage plant multi-process-section collaborative intelligent decision fusion system, which comprises a sensing and rule fusion layer used for collecting inlet and outlet water quality parameters, process control parameters and operation state parameters of multiple process sections in real time, preprocessing data and fusing an expert rule base; the mechanism and data driving joint modeling layer is used for establishing a mechanism model and a data driving model under the constraint condition of the expert rule base and predicting control quantities respectively; and the collaborative optimization and fusion decision-making layer is used for executing cross-process-section multi-target collaborative optimization and fusion decision-making based on an output result of the mechanism and data driving joint modeling layer under the constraint condition of an expert rule base, calculating a dynamic fusion weight, generating a final control quantity, and issuing the final control quantity to execution equipment. And in combination with real-time feedback self-adaptive adjustment, closed-loop optimization is realized. According to the system, the sewage treatment stability, decision precision and resource utilization efficiency are improved, and the environmental risk is reduced.

The invention discloses a sewage high-risk pollutant screening and identification method based on fragmentation tree pre-training, and the method comprises the steps: constructing a fragmentation tree pre-training data set according to the second-level mass spectrum data of known high-risk pollutants, so as to carry out the self-supervision pre-training of a graph neural network encoder, and obtaining a fragmentation tree editor; the method comprises the following steps: acquiring secondary mass spectrum data of suspected high-risk pollutant related compounds in a to-be-detected sewage sample, constructing a fragmentation tree set, constructing a high-risk pollutant screening model based on a fragmentation tree encoder, performing high-risk pollutant signal screening on the fragmentation tree set to obtain a high-risk candidate fragmentation tree set, and performing high-risk pollutant signal screening on the high-risk candidate fragmentation tree set. And generating a candidate molecule set of each high-risk candidate fragmentation tree, and identifying a target molecular structure and a matching score of the high-risk candidate fragmentation tree from the candidate molecule set. According to the invention, rapid screening and priority ranking of high-risk pollutants in sewage are realized, and candidate output of structure identification is further provided on the basis of screening.

The invention provides a sewage treatment aeration control method and system based on a deviation compensation hybrid model, and relates to the technical field of intelligent optimization control of a sewage treatment process, a mechanism model driving layer in the hybrid model outputs a benchmark predicted value of effluent quality, and a process deviation compensation layer performs uncertain quantitative estimation on the benchmark predicted value, so that an aeration control result is obtained. Performing process deviation compensation on the reference predicted value; establishing an aeration optimization objective function by taking the minimum aeration rate as an optimization objective, solving to obtain the optimal aeration rate, outputting a control signal to an air blower according to the optimal aeration rate so as to adjust the rotating speed of the air blower or the opening degree of a valve, change the air supply rate to a reaction tank, and simultaneously, collecting inlet water quality data and operation parameters of an aeration system in real time; and updating the optimal aeration rate, and updating the control signal to form a feedback closed loop. Highly intelligent, stable, reliable, energy-saving and efficient aeration control can be realized, and the sewage treatment plant is ensured to stably realize that the effluent quality reaches the standard and the energy consumption is reduced to the greatest extent under complex working conditions.

The application discloses an aeration tank for sewage treatment and relates to the technical field of sewage treatment.The aeration tank comprises a tank body, a horizontal reciprocating moving mechanism and an aeration mechanism.The tank body is connected with a guide rail through a depth adjusting mechanism at the top.The guide rail is connected with a sliding seat through sliding connection.The bottom outer wall of the sliding seat is fixedly connected with a box.The aeration mechanism comprises a rotary driving assembly, a pipeline switching assembly, a water and air dual-purpose pump, a rotary pipe, an aeration head, an agitating impeller and a cleaning and anti-blocking assembly.The cleaning and anti-blocking assembly can automatically clean the inner wall of the aeration head in a continuous rotating state, thereby effectively avoiding the serious blocking of the air holes of the aeration head.The horizontal reciprocating moving mechanism can expand the aeration range of the aeration head and can uniformly distribute the aeration air into the sewage, thereby effectively improving the oxygenation effect of the sewage.

The invention discloses a fault detection method suitable for a permanent magnet motor sewage pump, and particularly relates to the technical field of motor equipment operation fault detection.The fault detection method comprises the following steps that stage division is conducted on the operation process of the permanent magnet motor sewage pump, and staged dynamic baselines corresponding to all operation stages are established; extracting transient abrupt change of the operating parameters under the dynamic baseline constraint and forming a transient abnormal event sequence; the method comprises the following steps: judging the damage degree grade of a transient abnormal event, continuously accumulating risk integrals, outputting early warning information when a risk reaches a trigger condition, and generating an operation disposal instruction; according to the invention, by constructing a staged dynamic baseline and extracting a transient abnormal event, accurate identification of abnormal behaviors in different operation stages is realized; and in combination with a continuous accumulation mechanism of the damage degree grade and the risk integral, when the risk reaches a triggering condition, an early warning is output, and an operation disposal instruction is generated, so that the accuracy, timeliness and operation safety of fault detection of the permanent magnet motor sewage pump are improved.

The invention discloses an intelligent decision-making method and system for a water impeller, and relates to the technical field of sewage treatment, and the method comprises the steps: obtaining first sewage state data; inputting the first sewage state data into the trained first model to obtain a predicted flow impeller action; inputting the action of the water impeller into the safety boundary model to obtain safety boundary information of the action of the water impeller; and the controller judges whether to execute the action of the water impeller or not according to the safety boundary information. According to the scheme, the water impeller has the intelligent decision-making capability through the first model, and the made water impeller action achieves the dual effects of guaranteeing safety and saving energy consumption through training and judgment of the safety boundary.