21835 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 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 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 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 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 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 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 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.

The invention discloses a sewage waste heat recovery heating control method and system based on a digital twinning heat pump. The method comprises the steps that a digital twinning platform of a sewage waste heat recovery heating system is constructed; based on the operation parameters of the entity equipment of the sewage waste heat recovery heating system, establishing a digital twinborn model of the sewage waste heat recovery heating system; deploying the established digital twinborn model of the sewage waste heat recovery heating system in the established digital twinborn platform of the sewage waste heat recovery heating system; based on the physical equipment operation parameters of the sewage waste heat recovery heating system and the virtual data of the digital twinborn model, an optimization target model of the sewage waste heat recovery heating system is constructed, and an optimal control strategy is obtained; dynamic optimization control is carried out on the heating system entity equipment according to the optimal control strategy; the prediction precision is improved through real-time data calibration, model prediction control is fused, second-level response and multivariable cooperative regulation and control are achieved, and the problems that a traditional sewage waste heat recovery heating system is low in energy efficiency, slow in response and high in maintenance cost are effectively solved.

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 liquid level detection, in particular to a scrubber sewage tank water level detection system and method based on a liquid level sensor, and the system comprises a sensing data acquisition module, a liquid level signal conversion module, a multi-source numerical value comparison module, a liquid level fluctuation recognition module and an alarm action linkage module. According to the invention, through acquisition of a sensor capacitance change sequence, elimination of abnormal fluctuation, construction of a stable reference curve, enhancement of data immunity, mapping of a liquid level section based on an average capacitance value, accurate identification of liquid level elevation is realized, synchronous comparison of liquid level state information in various communication protocols is realized, and data consistency and reliability are improved. A liquid level fluctuation boundary is accumulatively recognized through trend direction change, a change section is dynamically marked, alarm triggering is based on boundary area and state mark double verification, warning response accuracy and effectiveness are enhanced, and a high-precision and high-stability liquid level detection and early warning system is constructed.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment control process based on dynamic regulation and control of aeration and carbon source addition, which comprises the following steps: acquiring microbial metabolism heat change data in real time; biochemical treatment: monitoring the dielectric constant of a water body and the Reynolds number of sewage flow in real time, and constructing a multi-parameter coupling regulation and control model and dynamically regulating and controlling the aeration rate in combination with microbial metabolism heat change data obtained in the pretreatment step; accurately adding the carbon source according to a calculation result of the carbon source demand prediction model; sewage subjected to biochemical treatment is subjected to ultrafiltration through a nanofiber membrane and then subjected to combined disinfection treatment through ultraviolet rays and ozone, treatment parameters are regulated and controlled in real time according to the membrane flux attenuation trend in the treatment process, and dynamic and accurate control over sewage treatment is achieved. According to the method, the problem of energy consumption waste caused by excessive aeration or low degradation efficiency caused by insufficient aeration in a traditional process is solved, and accurate matching of dissolved oxygen supply and microbial requirements is realized.

The invention relates to the technical field of sewage treatment, and discloses a supervision system and method for a sewage treatment process, and the system comprises a multi-parameter real-time monitoring module, a data fusion and analysis module, an automatic dosing control module and a feedback optimization module. The method is applied to the system. According to the supervision system and method for the sewage treatment process, dynamic and accurate supervision of the sewage treatment process is realized through the synergistic effect of the multi-parameter real-time monitoring module, the data fusion and analysis module, the automatic dosing control module and the closed-loop feedback optimization module; the problems of parameter simplification, control lag, high energy consumption and the like in the prior art are solved, the standard reaching rate of effluent is increased, and meanwhile, the agent cost and the equipment maintenance cost are reduced.

The invention discloses a sewage denitrification dosing method and system based on machine learning and a storage medium, and belongs to the technical field of sewage treatment.The method includes the steps that data are collected and preprocessed, and variable data influencing biochemical pool carbon source dosing behaviors are obtained; and lagging influence of carbon source input on the denitrification amount index is analyzed, and the duration time range of the drug effect is determined. And adopting the trained prediction model, and based on the denitrification amount index and the prediction variable of the future t + X period, obtaining the dosage of the (t + 1) th period. Through a correlation analysis method, the correlation rule of nitrogen conversion in the future X period after the carbon source is added is analyzed, the duration time of the drug effect is determined, the lag effect is accurately quantified, and the problem of mismatching of regulation and control opportunities is avoided. The hysteresis effect is captured and subjected to multi-factor coupling analysis based on the prediction model, the carbon source adding amount and time are optimized, system load fluctuation caused by excessive carbon sources or incomplete nitrogen removal caused by insufficient carbon sources are avoided, and the stability of an original sewage ecological system is gradually improved.

The invention relates to the technical field of information, and discloses an agricultural sewage treatment control method and system based on the Internet of Things, and the method comprises the steps: obtaining sewage water quality data in different time periods, and carrying out the preprocessing of the data, and obtaining standard water quality data; extracting time sequence data of the water quality indexes from the water quality indexes, and obtaining smoothed time sequence data by using a time sequence analysis method; according to the smoothed time sequence data, through a trained long-short-term memory neural network model, obtaining prediction data of a future time sequence of the water quality index; according to the prediction data, constructing an objective function of an optimization problem so as to establish a multi-objective optimization model; solving the multi-objective optimization model by adopting a particle swarm optimization algorithm to obtain an optimal combination of process parameters; and converting the optimal combination into a control instruction, issuing the control instruction to execution equipment of the sewage treatment system, and controlling the operation state of the execution equipment. According to the method, the process parameters can be adjusted in time, and the efficiency and the effect of agricultural pollution treatment are improved.

The invention discloses a quality detection method and system for a main structure of a buried sewage treatment plant, relates to the technical field of structure quality detection, and aims to solve the problem that early recognition of local micro cracks of the structure and dynamic tracking of the crack development trend cannot be realized. Water level depth change and displacement deformation quantity are collected, the displacement change trend is analyzed, and an abnormal area is judged in combination with water level data. Transmitting ultrasonic waves to the abnormal area, obtaining reflection echoes, marking the position and the trend of the crack, analyzing the stress characteristics of the structure based on the trend, collecting material characteristic data, analyzing the crack type, and generating a crack damage index by integrating the stress characteristics and the crack type. And setting detection time, collecting state information, calculating a closing amplitude and a dislocation rate, judging a state trend, and analyzing an early warning level in combination with a damage index, so that intelligent monitoring and early warning of the structural crack are realized, and the monitoring 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 provides a chemical sewage advanced treatment system and method based on multistage membrane separation, and relates to the technical field of chemical sewage treatment.The method comprises the steps that pollution load data under preset membrane flux is collected, and the cascade reflux amount in the current membrane separation process is determined through the pollution load data and a real-time pressure index during membrane separation filtration; according to the separation interference deviation, the steady-state filtering period of the membrane separation filtering grade in the constant flux change is predicted, the steady-state filtering period is synchronously screened, and the full-flow separation limit of the multistage membrane separation unit in different pollution loads is obtained; and generating a separation regulation and control index when the equivalent flux generates peak offset according to the cascade reflux quantity and the full-flow separation limit, and performing starting point triggering on the treatment flow of the multistage membrane separation sewage and dirt. Under the dynamic working condition of operation of the multistage membrane separation system, balance adjustment can be carried out on the advanced treatment process trigger of the chemical sewage, so that the standard-reaching rate of membrane separation chemical sewage advanced treatment is increased.