1380results about "Treatment control/steering" patented technology

The invention relates to the field of wastewater treatment, and provides an industrial wastewater membrane process intelligent optimization method and system based on multi-dimensional data modeling. The method comprises the following steps: acquiring wastewater quality parameters of a target scene and a production line equipment state in real time through a multi-dimensional sensor network, and constructing a digital twinborn model of a wastewater treatment system in the target scene; the membrane surface pollution type of each node in the wastewater treatment system is identified through a digital twin model, and the inner membrane pollution evolution condition in the future preset duration is predicted; a multi-strategy evolutionary reinforcement learning algorithm is adopted, the wastewater quality parameters, the production line equipment state and the membrane pollution prediction result are combined, multi-target dynamic optimization is conducted on a wastewater treatment system through a multi-agent collaborative decision-making algorithm, an optimization strategy is generated based on the multi-target dynamic optimization result, and membrane process operation parameters and production control parameters are dynamically adjusted. According to the method, real-time intelligent adjustment of membrane process operation can be realized, the stability of membrane process operation is guaranteed, and the treatment efficiency of membrane process operation is improved.

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 present disclosure provides a method for controlling a cleaning device and a cleaning device. The cleaning device includes a first distance sensor disposed at a bottom. The control method includes: in a process in which the cleaning device moves on a bottom of a pool, when the cleaning device approaches or collides with an obstacle, controlling the cleaning device to climb the obstacle; and in a process of climbing the obstacle, if the obstacle is determined as a step, controlling the cleaning device to move from a first surface of the step to a second surface of the step, where the step at least includes the approximately vertical first surface and the approximately horizontal second surface, where the obstacle is determined as the step at least based on a detection value of the first distance sensor.

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 belongs to the technical field of intelligent early warning, and particularly relates to an intelligent early warning method for a DMF waste liquid purification and recovery control platform, and the method comprises the steps: carrying out the principal component analysis of long-period historical data, and constructing a principal component transformation matrix of a static reference model; for a moment to be diagnosed, calculating a reconstruction value by using the static reference model to obtain a residual vector, carrying out eigenvalue decomposition on a covariance matrix of a residual matrix of a sliding time window, calculating a drift coherence index according to the distribution of drift eigenvalues, modulating a drift principal component vector of the sliding time window by combining the residual vector, and carrying out diagnosis on the moment to be diagnosed; and obtaining a drift compensation vector, superposing the drift compensation vector with a reconstruction value of a real-time data vector at a to-be-diagnosed moment to obtain an adaptive reconstruction value at the to-be-diagnosed moment, calculating a reconstruction error, comparing the reconstruction error with a fault alarm threshold, judging whether a fault exists at the to-be-diagnosed moment, and performing early warning. According to the invention, the early warning accuracy and robustness are improved.

The present invention relates to a modular and scalable system for intelligent vertical farming infrastructure, using the latest in water recycling sciences, and artificial-intelligence based automation. This helps to combat the issue of sustainable Agriculture+its minimizing usage of water, energy, and space. It combines a vertical farming structure with multiple levels of cultivation with an irrigation and drainage system to achieve recirculation of water. The water recycling unit consists of rainwater harvesting, wastewater collection and multi-stage treatment subsystems, which can guarantee a continuous supply of high-quality recycled water for irrigation. The invention also combines a network of Internet of Things (IoT)-enabled sensors and AI-based control circuitry to track and manage environmental conditions (e.g. light intensity, humidity, temperature, and nutrient levels).

The invention relates to the technical field of water treatment equipment, in particular to a coaxial nested ultrafiltration and nanofiltration integrated water purification device and system.The coaxial nested ultrafiltration and nanofiltration integrated water purification device comprises a main shell, and an annular ultrafiltration treatment area and an inner nanofiltration treatment area are coaxially arranged in the main shell. An energy storage adjusting assembly is arranged in the nanofiltration treatment area and comprises an elastic breathing sleeve coaxially arranged outside the nanofiltration membrane assembly in a sleeving mode. The sleeve elastically expands during filtering, so that the effects of buffering pressure and storing energy are achieved; and turbulent flow convex ribs can be arranged on the inner wall of the sleeve, so that the nanofiltration membrane is physically cleaned when the sleeve retracts. Besides, a backwashing pipeline and a valve are arranged, linkage control is carried out when the control unit detects that expansion of the sleeve exceeds the limit, and ultrafiltration produced water stored on the nanofiltration side is reversely pushed by utilizing pressure energy released by retraction of the sleeve so as to backwash an ultrafiltration membrane. According to the invention, the cooperation of pressure buffering, online cleaning of the nanofiltration membrane and energy storage backwashing of the ultrafiltration membrane is realized, the operation is stable, the efficiency is high, and the energy is saved.

The invention relates to the technical field of data transmission, in particular to an intelligent operation and maintenance data transmission method for an industrial sewage treatment system, which comprises the following steps: acquiring various water quality data of a current date and a target historical date at each sampling moment, obtaining a historical reference value and a trend reference value of each item of water quality data at the current sampling moment, and further obtaining a predicted baseline value of each item of water quality data at the current sampling moment; obtaining the abnormal degree of each item of water quality data at the current sampling moment according to the predicted baseline value and the fluctuation amplitude of each item of water quality data at the current sampling moment; based on the abnormal degree, the final abnormal value of each item of water quality data at the current sampling moment is obtained, then whether the water quality data is high-value abnormal data or low-value data is judged, different transmission modes are selected to transmit the data, the water quality data abnormity is accurately evaluated, and invalid early warning and unnecessary transmission cost are reduced.

The invention relates to the technical field of production line control, and discloses a drinking water production line water quality real-time monitoring and intelligent adjusting system based on the Internet of Things. Registration equipment is unified with units, and NTP time synchronization is performed to eliminate deviation; the shift / CIP calibrates the zero range, and the EWMA monitors the drift weight drop; constructing state vectors containing IDs / batches, and performing auditing and warehousing; median / bilateral filtering denoising is carried out, and grid interpolation is unified; sliding window multi-scale aggregation is carried out, and self-adaptive sampling is carried out according to curvature and events; normalizing and interpolating a near-time training window; estimating indexes difficult to measure by a soft sensor, and depicting residual chlorine by a mechanism model; and carrying out uncertainty weighted fusion, and outputting estimation and intervals and auditing. According to the method, the whole process of real-time monitoring, quick response and traceable compliance is realized, the risk caused by chemical shortage or ratio error and environmental protection constraint is remarkably reduced, the capacity loss caused by cleaning and plan conflict is reduced, and the factory water quality stability and the treatment transparency are improved.

The invention discloses an intelligent household water processing terminal interaction control system based on 2.4 G hotspot networking, and particularly relates to the technical field of household water processing terminal interaction control. The overall system time sequence risk is accurately assessed by generating a time sequence consistency loss index, dynamic sampling adjustment, code rate optimization and control action rectification strategies are triggered based on the index, key data are uploaded preferentially, key control actions are executed preferentially, the problems of data transmission conflicts and time sequence lagging are effectively relieved, and meanwhile, the data transmission efficiency is improved. A node execution result and a control action receipt record are written into a state mirror image, the state mirror image and a time sequence consistency loss index are jointly evaluated to generate a safety degradation condition, and an executable safety strategy is synchronously issued, so that self-adaptive degradation and quick recovery of a system under abnormal conditions are realized, and continuity and stability of a water treatment process are ensured.

The present invention relates to a method for automated data processing to assess the state of multiple filtration membranes used in liquid filtration. The method involves receiving data from state sensors positioned within or near a set of membranes, which process incoming water into permeate and concentrate flows. This data, collected as time series at predefined frequencies, pertains to external physical parameters. An operating indicator is determined from this data, forming a second time series. Both the first and second time series are recorded as point clouds over a specified acquisition period. An intermediate operating indicator is generated, representing the state of new, clean, or cleaned membranes, using a learned normalization model. Finally, a normalized operating indicator is produced, characterizing membrane fouling and aging, independent of environmental variations, and forming a third time series. This method enhances the accuracy of membrane state assessment in filtration systems.

The invention discloses a real-time sewage treatment method based on sewage data monitoring, particularly relates to the technical field of sewage treatment, and is used for solving the problem of how to realize real-time sewage treatment under the condition that quality problems such as sewage blockage, drifting and missing exist in online monitoring data under complex and fluctuating working conditions. The problem that real-time, stable and energy-saving self-adaptive regulation and control can still be conducted on the sewage treatment process based on sewage monitoring data is solved, and the problems that due to dirty data and sudden change of working conditions, outlet water exceeds the standard, and energy consumption / chemical consumption is abnormal are avoided. Key water quality soft measurement and multi-source difference checking are implemented under the constraint of working condition numbers by constructing a sewage working condition map and a data quality evaluation rule base and introducing sliding time window feature extraction, monitoring credibility grading and sewage state fingerprints. The control strategy can still generate reliable aeration control instructions, backflow control instructions and dosing control instructions when monitoring data distortion and working condition fluctuation, so that the effluent is ensured to stably reach the standard, and the risk of control deviation is reduced.

The invention relates to the field of direct drinking water of a fusion calculation model, and discloses a double-membrane mixing direct drinking water system and an operation method.The method comprises the steps that online data of a water supply end and a water quality monitoring end are collected for cleaning and time alignment, and a basic working condition data set is formed; the method comprises the following steps: establishing an operation characteristic model comprising water demand prediction, raw water quality prediction and membrane flux characteristic sub-models, calculating double-membrane target water yield and water quality indexes based on the model to generate a control parameter set, evaluating membrane pollution to generate an online cleaning task queue, performing joint simulation to generate a pressure stability control instruction set, and dynamically correcting the model. The conversion from passive response to active pre-judgment of the double-membrane mixing direct drinking water system is realized, and the operation stability, the water quality guarantee capability and the water-saving and energy-saving effects are improved.

The invention discloses a membrane filtration sewage treatment system based on fine control, relates to the technical field of sewage treatment, and solves the technical problems of delayed response to water quality and water quantity fluctuation and extensive biochemical reaction control caused by water inlet load impact in an existing sewage treatment system. The system comprises an inflow water pretreatment module used for predicting inflow water quality in real time by using a water quality prediction model based on a neural network, and carrying out water quantity balance and water quality adjustment on inflow water according to a prediction result to obtain a first water body; the biochemical reaction module is used for cooperatively controlling external carbon source addition, nitrification liquid backflow and aeration intensity in the biological reaction process of the first water body by utilizing a multivariable model predictive control method to obtain a second water body after biochemical reaction; and the deep purification module is used for carrying out solid-liquid separation and deep purification on the second water body to obtain an effluent water body, and triggering cleaning and maintenance operation of the membrane based on a prediction result of the membrane pollution characteristics.

The present disclosure provides a cleaning device and a cleaning device system. The cleaning device includes a cleaning device body. The cleaning device body includes a filtering mechanism, a drive mechanism, an adjustment assembly, a moving assembly, and a propulsion assembly. The filtering mechanism is at least partially disposed inside the cleaning device body. The drive mechanism is configured to generate a suction force to drive liquid to flow through the at least one filtering mechanism. The adjustment assembly is configured to adjust an action force applied to the cleaning device in a first direction or a second direction, so that the cleaning device is switched between a first motion state and a third motion state. The moving assembly is disposed at the bottom of the cleaning device body and configured to drive the cleaning device to move on a to-be-cleaned surface. The propulsion assembly is configured to drive the cleaning device to move away from the to-be-cleaned surface. The cleaning device can be switched between the first motion state and the third motion state to perform underwater cleaning and water surface cleaning to meet different use requirements.

The invention discloses an intelligent adding control platform for a water treatment process, and relates to the technical field of water treatment, and the intelligent adding control platform comprises a data processing module which is used for obtaining historical water treatment data of water treatment plants in different regions to obtain historical medicament use data; the feedforward control module is used for acquiring sewage data in the plant area to obtain basic dosing data; the prediction and supplement module is used for predicting a water quality change trend according to the basic dosing data to obtain a dosing effect, and performing medication adjustment to obtain supplementary dosing data; the dynamic adjustment module is used for obtaining a target quantification set of the plant area and carrying out weight distribution to obtain a dosing strategy; and the trust adjustment module is used for collecting the sensor state and sensor output data of each sensor to obtain the confidence coefficient of sewage treatment, and modifying the dosing strategy according to the confidence coefficient to obtain a target dosing strategy. The method has the effect of improving the accuracy and intelligence of automatic dosing on medicament control.

The invention relates to the technical field of wastewater treatment, and discloses a heavy metal wastewater treatment method and system.The method comprises the steps that the concentration, pH value, temperature and conductivity data of heavy metal ions are collected in real time through multiple types of sensors arranged in a wastewater pipe network and a treatment station, and standardized treatment is conducted; inputting the standardized data into a prediction algorithm unit, executing multi-parameter fusion analysis, and generating a concentration prediction result and a processing agent dosage; the coagulation reactor and the sedimentation tank are controlled to operate according to the prediction result, and chemical sedimentation and concentration of the heavy metal ions are achieved; the treated wastewater is deeply purified through adsorption, ion exchange and filtering processes, the final discharged water quality is obtained, the treatment efficiency is fed back, and closed-loop optimization control is formed. The system comprises a data acquisition and pretreatment module, a prediction and addition control module and a reaction and purification execution module, can realize automatic and intelligent optimization control of the wastewater treatment process, and remarkably improves the effluent standard-reaching rate and the system stability.

The invention relates to an organic combined polluted underground water in-situ remediation method based on a micro-nano iron-based material. The method comprises the following steps: acquiring underground water pollution information; establishing a composite reaction zone at a preset position, and constructing a reduction-oxidation chain reaction system; and operating the composite reaction zone, collecting monitoring data, and optimizing the composite reaction zone until the composite pollution is degraded and removed to a preset value. Through series reaction of powerful reduction of the micro-nano iron-based material and advanced oxidation of the iron-based activated persulfate, the performance of the micro-nano iron-based material serving as a strong reduction material and an active agent is fully played, pollutants such as halogenated hydrocarbon in underground water are sequentially subjected to reduction degradation, pollutants such as benzene series and volatile phenol are sequentially subjected to oxidative degradation, and the pollution to the underground water is reduced. Effective management and control of underground water combined pollution of medical and chemical parks and enterprises are realized; a monitoring well is configured based on a composite reaction zone, and remediation materials are discontinuously added in combination with management and control conditions, or other management and control materials are flexibly selected, so that long-term management and control of groundwater pollution risks are realized.

The invention discloses a distributed sewage treatment and automatic control system, and relates to the technical field of sewage automatic treatment, and the system comprises the steps: collecting the check-in reservation data and geographic coordinate information of a tourism business operation site in a target area in real time, weather rainfall forecast information, and a sewage pipe network topological structure; constructing a sewage load prediction model, and identifying check-in reservation data and meteorological rainfall forecast information of the tourism business operation site to obtain load prediction data; based on the geographic coordinate information, mapping the load prediction data of the tourism business operation site to a sewage pipe network topological structure to obtain sewage topological mapping data; determining a source position and a propagation path of load impact; constructing a treatment strategy optimization model, identifying the sewage topological mapping data and the meteorological rainfall forecast information, and dynamically generating a sewage treatment strategy; a sewage treatment strategy is transmitted to a sewage treatment actuator through a low-power-consumption wide-area internet of things communication protocol, and automatic control over sewage treatment is achieved.

The invention particularly relates to an intelligent monitoring method and system for the service life of an ion exchange resin mixed bed, and belongs to the technical field of ion exchange resin water treatment. The method comprises the following steps: collecting real-time flow and real-time conductivity of mixed bed inlet water; executing a mixed bed service life intelligent evaluation algorithm, and dynamically estimating a mixed bed exchange capacity consumption rate and a mixed bed residual operation duration; displaying the exchange capacity consumption rate of the mixed bed and the residual running time of the mixed bed in real time in the form of numbers, progress bars or trend curves; and when the exchange capacity consumption rate of the mixed bed and the residual operation duration of the mixed bed are lower than a preset threshold value, triggering an early warning prompt. The system implements the steps of the method. The invention provides the universal, convenient and economic intelligent monitoring method and system for the service life of the ion exchange resin mixed bed, and the real-time dynamic monitoring of the residual exchange capacity of the ion exchange resin mixed bed and the accurate prediction of the residual operation service life are realized.

The invention provides an ice making method and system, an intelligent electric appliance, a storage medium and a program product. The ice making method is used for the ice making device and comprises the steps that water quality parameters of ice making water are obtained, and the water quality parameters at least comprise a total dissolved solid value; determining target control parameters in the ice making process according to the water quality parameters; and controlling the ice making device to operate based on the target control parameter so as to form ice blocks. The method has the advantages that the water quality parameters are introduced to serve as the decision basis of ice making control, so that the ice making process can be adaptively adjusted according to different water quality conditions. According to the method, the fine control capability of the ice making process is improved, so that ice block products better meeting the expected quality can be obtained, and the consistency and stability of the ice making effect are improved.

The invention provides an intelligent dosing control method, device and equipment of a magnetic coagulation system and a storage medium, real-time process data of a plurality of sections of a magnetic coagulation process is collected, standardized time sequence data is generated by performing data cleaning and standardization processing on the real-time process data, feature screening and auxiliary feature construction are performed, and the real-time process data are subjected to data cleaning and standardization processing to generate standardized time sequence data; the expression ability of the key time sequence features is enhanced; a model input feature set is input into a dosing prediction model based on a multi-scale attention mechanism and a bidirectional sequential network, the attention mechanism is utilized to dynamically adjust weight contributions of different features at different moments to a dosing decision, and the bidirectional sequential network is utilized to capture a historical trend and a future dependency relationship at the same time. Quick response and accurate prediction of water quality mutation are realized; the working condition grade is judged based on the effluent quality index, the control instruction is generated in combination with the predicted value, closed-loop control of graded response is formed, it is ensured that the dosing strategy can be adjusted in time under different working conditions, and therefore the technical problem is effectively solved.

A UV disinfectant system may include a chamber having a wall that is transparent to a disinfecting radiation. Liquid may be flowed through the chamber for treatment by exposure to the radiation. The chamber may include a static mixer having vanes to impede laminar flow of the liquid during treatment. The vanes extend into the flow path of the liquid through the chamber. A gap is defined between the vanes and the transparent wall. A cabinet may house the chamber and radiation emitting bulbs. Blowers may be operably coupled to a temperature sensor and flow meter and positioned at a lower end and upper end of the cabinet to urge air out of the cabinet. The temperature sensor may include a thermocouple. The blowers may be variable speed blowers. The system may include a controller to control system operations. The controller may be remotely accessible to monitor or control operations.

A real-time wastewater treatment and water quality monitoring system includes a plurality of wastewater treatment facilities configured to purify wastewater generated from semiconductor manufacturing lines, a plurality of contaminant analysis apparatuses configured to obtain and analyze a sample from effluent water discharged through discharge pipes of the wastewater treatment facilities respectively, discharge rate sensors installed in the discharge pipes respectively, and an integrated monitoring apparatus configured to receive measurement result values from the contaminant analysis apparatuses and the discharge rate sensors and monitor in real time concentration of a contaminant in an entirety of the effluent water that is purified and discharged from the wastewater generated in the semiconductor manufacturing lines.

The invention relates to the technical field of water purification treatment, and discloses an intelligent water purification system and method based on membrane feature recognition and pulse cleaning, and the method comprises the following steps: collecting discrete hydraulics monitoring data to construct a space-time synchronization hydraulic state tensor, and extracting pressure component mapping to generate a membrane resistance evolution topology curved surface and a resistance gradient vector; after a dominant pollution mechanism is matched and identified by using a pollution morphological feature library and a fluid pulse modulation parameter set is generated, an advanced energy storage potential energy target is calculated based on pipe network load prediction, and then an energy storage saturation state is established; driving the target membrane module to execute a multi-stage composite physical regeneration process including first forward flushing, reverse deep cleaning and second forward flushing, and implementing pressure dynamic compensation based on an advanced energy storage target; according to the invention, the pressure fluctuation caused by regeneration switching is eliminated by utilizing active potential energy release while deep regeneration of the membrane module is realized through an original three-section cleaning process, so that the water supply continuity and the membrane flux recovery rate are remarkably improved.

A system includes an evaporator having sensors and selectable operational parameters and a controller configured to receive data and determine operational configuration for the evaporator. Selectable parameters relate to system heating, liquid flow rate, air flow rate, and environmental data.

The invention discloses an intelligent sewage treatment monitoring system and method, and relates to the technical field of sewage treatment.The intelligent sewage treatment monitoring system and method achieve transformation from parameter standard exceeding alarm to efficiency health degree evaluation by establishing a standard efficiency curve of each key unit and calculating a real-time health index; the degradation trend of the unit performance can be captured earlier and more accurately; the overall process efficiency coefficient based on the environmental parameter dynamic adjustment weight is introduced, so that the evaluation is more suitable for the actual operation condition, and the influence of bottleneck units under different conditions is highlighted; a cross-unit root cause tracing analysis mechanism can effectively distinguish unit faults from upstream impact conduction, so that the accuracy and efficiency of abnormality diagnosis are greatly improved, and wrong regulation and control are avoided; a coordinated regulation instruction set is generated based on root cause analysis, systematicness and collaboration of regulation actions are ensured, current abnormity is solved, and it is more emphasized to maintain stability and efficiency of the whole process; the intelligent operation level of a sewage treatment plant is obviously improved.

The invention relates to the technical field of automatic control, and discloses a wastewater treatment automatic control method and system.The method comprises the steps that water quality change related data are collected in real time through a detector, and the water quality change trend is obtained; determining a correction state estimation value according to the water quality change related data; if the correction state estimation value shows that the pollutant concentration exceeds a preset safety threshold value, calculating a standard-exceeding deviation vector, and obtaining a preliminary adjustment instruction through analysis; correcting the preliminary adjustment instruction to obtain a precise adjustment instruction; reconstructing a to-be-executed instruction queue according to the precise adjustment instruction and executing the to-be-executed instruction queue; re-collecting water quality data for prediction operation to obtain an updated water quality change trend; and obtaining an updated state estimation value according to the updated water quality change trend for continuous monitoring and adjustment of wastewater treatment. According to the method, the problem of poor wastewater treatment control accuracy can be solved.

The invention provides a chemical adding control method and a chemical adding control system. The dosing control method comprises the following steps: collecting target measuring point data, and generating a working condition family label based on an hour period, a water quality grade and a load grade; respectively training each candidate model according to a candidate model library and historical data so as to obtain a prediction file of each candidate model, obtaining a champion model of each hour period according to the prediction file and the working condition family label, and generating a time period model mapping table; and determining the dosage based on the time period model mapping table, and issuing the dosage to medicament adding equipment. According to the method, the hour segments serve as working condition family labels, the errors of the multiple candidate models are evaluated hour by hour in the back-testing window, the champion model is selected, the optimal model can be adopted in a self-adaptive mode according to the differences of SCD1 / SCD2 distribution, water diversion / generated water instantaneous flow change and the like of different hour segments, the problem that errors of a single model are large in part of the hour segments is avoided, and the accuracy of the model is improved. Therefore, the all-weather prediction stability is improved.

The invention provides a dual-wavelength multi-parameter self-compensation intelligent control system and method based on pollution grading. Comprising a sensing module used for acquiring a dual-wavelength optical measurement signal and a multi-parameter sensing measurement signal of a to-be-treated water body; the self-compensation module is used for generating a compensated pollution characterization characteristic quantity; the uncertainty generation module is used for performing consistency residual calculation on the compensated pollution characterization characteristic quantity and at least one water quality parameter signal and generating an uncertainty parameter; the pollution grading module is used for executing boundary correction on the initial pollution grade under the constraint of the uncertainty parameter and outputting the pollution grade and grading confidence; the decision control module is used for selecting a target control strategy cluster and generating a control set value of the water treatment process based on rolling optimization; and the execution control module is used for issuing the control set value to an execution mechanism of the water treatment process. Pollution grading accuracy can be improved, long-term stability of measurement is enhanced, and control fluctuation and energy consumption are reduced.