729 results about "Turbidity" patented technology

The invention discloses a modularized full-process high-quality direct drinking water treatment system and a control method, and belongs to the technical field of direct drinking water treatment.The modularized full-process high-quality direct drinking water treatment system is characterized in that an online sensor is deployed at a water inlet of a flocculation basin, raw water turbidity, temperature and flow parameters are collected in real time, and qualified samples are screened to construct a standardized water quality characteristic data set in combination with historical water quality and agent adding records; based on the data set, performing multi-dimensional clustering on historical water quality parameters, generating a characteristic working condition cluster, calculating a three-dimensional efficiency index, generating a dynamic dosage reference interval by using a sliding window confidence interval and a time decay factor, establishing a mapping relation between water quality characteristics and medicament dosage, and generating a graded dosage strategy; the transmembrane pressure difference and the membrane flux change rate are continuously monitored at the water inlet end in the membrane treatment stage, the membrane pollution trend is predicted through time sequence analysis, the prediction result is corrected according to the agent adding deviation, the precipitation effluent turbidity and the membrane inflow COD data, a cleaning early warning signal is generated, and graded response is performed according to the pollution degree.

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

The invention belongs to the technical field of water quality monitoring, and particularly relates to a construction method and device of a turbidity testing system based on a photoelectric detector. Optical signal data and multi-wavelength optical signal data are obtained; performing intelligent switching and spectrum matching according to the signal data, determining a target wavelength, and obtaining characteristic parameters related to turbidity by combining a scattering and transmission combined measurement principle; measuring different standard solutions according to the characteristic parameters to obtain corresponding characteristic parameters, constructing a turbidity characteristic parameter data set, and establishing a characteristic ratio turbidity calibration model according to the data set; measuring and calculating the turbidity according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm, and meanwhile, performing temperature compensation; and transmitting the parameter information to a cloud database for storage, performing hierarchical processing, performing regular intelligent self-inspection at the same time, and feeding back abnormal data when an abnormality occurs. Therefore, the problems of insufficient range coverage, weak automatic calibration capability, susceptibility to temperature drift and the like in the prior art are solved.

The invention relates to the technical field of three-dimensional imaging, and discloses a high-precision underwater structured light three-dimensional point cloud imaging method and system, and the method comprises the following steps: deploying a plurality of sensor nodes, monitoring the temperature, salinity and turbidity key parameters of a water body in real time, selecting proper light source wavelength and power according to the collected data, and carrying out the three-dimensional point cloud imaging of the underwater structured light. It is ensured that the optimal signal-to-noise ratio is obtained under the current condition, and meanwhile the exposure time and gain setting of the camera are initialized. According to the method, the optimal signal-to-noise ratio is obtained by monitoring water body conditions in real time and dynamically adjusting light source characteristics, the optimal structured light pattern is predicted by using a machine learning model, scattering noise is removed, advanced registration and fusion algorithms and an accurate phase unwrapping method are applied, and a high-quality comprehensive structured light pattern is generated. Point cloud gaps are filled through a geometric correction algorithm and an interpolation / filtering algorithm, so that a reconstruction model is closer to the surface of a real object.

The invention provides a multi-source data fusion modeling method and system in an aeration process, and is applied to the field of intelligent aeration control in sewage treatment. The method comprises the steps that multi-source time sequence data such as dissolved oxygen, turbidity, flow, temperature, power and pool bottom pressure pulsation signals are collected, dissolved oxygen response lag is calculated through cross-correlation analysis with power change as the reference, time sequence alignment is carried out, and a dissolved oxygen reference interval is predicted by utilizing calibration data in combination with a physical constraint LSTM model; performing spectral analysis on the pressure pulsation signal to extract a gas-liquid coupling characteristic value, and generating a cooperative regulation instruction of the frequency of the blower and the rotating speed of the stirrer based on the information; by means of the scheme, control oscillation caused by lag of the dissolved oxygen sensor can be effectively overcome, online monitoring of bubble form distribution is achieved, the gas-liquid mass transfer efficiency is improved, invalid aeration is avoided, and system energy consumption is remarkably reduced on the premise that stable effluent quality is guaranteed.

The invention relates to the technical field of industrial water system safety monitoring, in particular to an intelligent source tracing method for a process medium leaked in circulating water, which comprises the following steps of: acquiring multi-dimensional operating parameters such as conductivity, pH value, turbidity, dissolved oxygen, temperature, pressure and characteristic ion concentration; a standardized water quality parameter matrix is generated after space-time alignment and wavelet noise reduction; the method comprises the following steps: extracting an abnormal fluctuation signal by using a leakage feature recognition model based on transfer learning, simulating a diffusion process through a three-dimensional leakage diffusion model, realizing leakage source positioning by combining reverse particle tracking and kernel density estimation, associating a high-probability leakage region with upstream process equipment, extracting backtracking path features, and matching a process medium feature library, thereby realizing leakage source positioning. The leakage medium type is judged; and finally generating a structured traceability report. According to the method, high-precision identification, positioning and medium analysis of process leakage in a complex circulating water system can be realized, and the method has relatively high practicability and popularization value.

The invention relates to the technical field of water quality monitoring, in particular to a water quality monitoring analysis method and system of a water conservancy system. The method comprises the following steps: acquiring area scale and water area type distribution, and carrying out sensing planning layout and water quality index real-time monitoring to obtain a water conservancy system water quality index data set; performing regional water quality index distribution coupling and regional time step division on the water conservancy system to obtain a corresponding water conservancy pH value, water conservancy dissolved oxygen concentration, water conservancy turbidity, water conservancy heavy metal concentration and water conservancy microorganism concentration; performing microorganism growth efficiency evaluation and microorganism pollution attenuation analysis on the water conservancy microorganism concentration to obtain a water conservancy area microorganism pollution degradation attenuation rate; and performing water quality pollution anomaly quantification and anomaly category decision management on the water quality index data set of the water conservancy system to generate a water quality anomaly category management strategy so as to execute corresponding regional water quality category management work of the water conservancy system. The accuracy and efficiency of water quality monitoring can be improved.

The invention discloses a self-adaptive intelligent device system for water ecological restoration and water quality improvement, and particularly relates to the field of water ecological restoration, the self-adaptive intelligent device system comprises a monitoring module, a control module, a restoration module, an energy supply module and a remote communication module, and the modules are connected through signals; the monitoring module is used for monitoring the temperature, the pH value, the dissolved oxygen, the turbidity and the nutritive salt content of the water body in real time as well as the water level change, the water flow speed and the meteorological conditions; the control module analyzes and processes the monitoring data, automatically generates a control instruction according to a preset target and rule, and adjusts the operation parameters and the working state of the repair module; the restoration module is used for restoring the water body; the energy supply module is used for providing stable and reliable energy supply for the whole device system. The change trend of the water quality parameters can be predicted in advance, so that the system can take measures in advance, manual intervention is reduced, and the automation degree and the operation efficiency of the system are improved.

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention relates to the technical field of ballast water treatment, in particular to a ballast water treatment-ultraviolet cooperative control method and system based on multi-parameter feedback, and the method comprises the steps: collecting turbidity, pressure gradient, ultraviolet light transmittance and biological activity data in real time through multiple types of sensors arranged at an inlet of a filter unit, the surface of a filter screen and an ultraviolet reactor; a composite control model fusing hydromechanics and optical characteristics is constructed, and closed-loop cooperation of aperture dynamic adjustment, ultraviolet dose compensation and directional backwashing is realized. The method is characterized in that an electrostriction filter screen is adopted to realize local aperture gradient control so as to balance flux and precision; an ultraviolet dose dynamic compensation algorithm is designed, and the irradiation efficiency is enhanced and improved in combination with a vortex flow field; and establishing a linkage optimization mechanism of the backwashing period and the service life of the equipment. A real ship verifies that the method can effectively reduce comprehensive energy consumption, can adapt to water quality fluctuation of different sea areas in the world, and meets the long-acting compliance requirement of the IMOD-2 standard.

The invention discloses an inland water body turbidity satellite remote sensing method based on optical classification and spectral simulation, which comprises the following steps: S1, acquiring water body spectral data, water body turbidity, satellite remote sensing images and meteorological data of a to-be-detected area, and calculating water body remote sensing reflectivity; s2, preprocessing the satellite remote sensing image to obtain a water body area; performing equivalent calculation on the remote sensing reflectivity data of the water body actual measurement spectrum to obtain the remote sensing reflectivity of each channel of the satellite sensor; performing water body optical classification by combining the actually measured water body spectral shape and turbidity distribution characteristics of the research area; s3, performing correlation analysis on different optical types of the actually measured spectrum according to the actually measured turbidity and the remote sensing reflectivity of multiple channels of the satellite, selecting channel remote sensing reflectivity data with high turbidity correlation as a sample, and constructing a turbidity remote sensing model; the turbidity remote sensing model is trained and tested, and the optimal turbidity remote sensing model is screened out. The method can improve the precision of remote sensing inversion of the turbidity of the inland water body.

The invention belongs to the technical field of river and lake water quality monitoring, and discloses a river and lake water quality intelligent traceability monitoring system and method.Dissolved organic matter fluorescent fingerprints are analyzed in real time through a three-dimensional fluorescent monitoring unit, pollution markers are detected, and parameters such as COD, ammonia nitrogen and total phosphorus are synchronously detected through a full-spectrum monitoring unit; the video monitoring unit is used for collecting section images / videos of rivers and lakes, other sensors are used for collecting real-time values of chlorophyll, pH, conductivity, turbidity and dissolved oxygen in a water body, then the data processing module is used for carrying out normalization processing on multi-source data, and the analysis and early warning module is used for analyzing water quality early warning scores, spectrum early warning scores and video early warning scores. All scores are synthesized to obtain a comprehensive early warning score, traceability is started when comprehensive early warning is larger than or equal to the grade II, pollution sources are recognized based on three-dimensional fluorescence spectrum comparison and upstream patrol, and a river and lake water body monitoring-early warning-traceability-verification closed loop is achieved.

The invention discloses a river organic carbon flux inversion method based on multi-source data and related equipment. The method comprises the following steps: acquiring multi-source data of a river basin range in a target research area; obtaining a normalized difference index based on remote sensing image conversion; river parameters of the target research area are obtained based on the normalized difference water body indexes and multi-source data processing, and river runoff is obtained through conversion of a hydraulic geometrical relationship; on the basis of the remote sensing image, the normalized difference chlorophyll index and the normalized difference turbidity index, the particle organic carbon concentration is obtained through inversion by means of a first inversion model; based on the population number and the rainfall capacity, a second inversion model is used for inversion to obtain the concentration of dissolved organic carbon; and converting the river runoff, the particle organic carbon concentration and the dissolved organic carbon concentration to obtain the river organic carbon flux. The method can significantly improve the accuracy and reliability of monitoring on a high-dynamic river, accurately realizes the inversion of the organic carbon flux of the river, and can be widely applied to the technical field of data processing.

The invention discloses a flocculation treatment device and method for removing high turbidity of a water sample. The method comprises the following steps: monitoring an inflow turbidity parameter in real time, continuously collecting original optical characteristic data of water through an optical sensor array, and calculating a turbidity fluctuation rate parameter after noise reduction treatment; carrying out graded rotational flow mixing treatment, forming a pressure gradient through three stages of tapered rotational flow pipes in sequence, activating an ultrasonic frequency field, and synchronously adding main and auxiliary flocculants; the method comprises the following steps: introducing a water sample into a multi-stage gradient flocculation reaction zone, and sequentially executing turbulent flow enhanced flocculation, slow stirring flocculation and pulsed electric field assisted flocculation to form a floc structure with different particle size distribution; running parameters of a sedimentation module are adjusted according to real-time effluent turbidity feedback, and the inclination angle of an inclined plate assembly and the opening degree of a mud discharging valve are dynamically adjusted; a parameter set is updated through a prediction model based on periodically collected system operation data, a closed-loop optimization control strategy is formed, the adaptability to extreme turbidity is enhanced, the solid-liquid separation efficiency is improved, the overall energy consumption is reduced, and the efficient and stable requirements of emergency treatment are met.

The invention provides an underwater target identification method and system based on 4K visible light polarization imaging, and relates to the technical field of underwater target identification. High-resolution spectral intensity information and polarization physical characteristics of a target are synchronously captured by using a pixel-level registration RGB and polarization four-dimensional light field tensor coding technology; the limitation of information loss of single-mode data in a low-illumination or high-turbidity scene is overcome; the polar coordinate symmetry design of the annular direction sensitive convolution kernel is combined with the polarization degree matrix decomposition technology of the Fresnel reflection law to accurately quantify the optical reflection characteristic difference of the material; based on a cross-modal attention mechanism, dynamically learning a correlation weight of a spatial feature and a polarization feature, and strengthening a joint response of a target edge contour and an internal texture; a lightweight recognition network architecture is adopted, a polarization sensitive convolution kernel and a dynamic channel re-parameterization module are integrated, parallel efficient reasoning of target position, category and material information is achieved, and accurate recognition of an underwater target is achieved.

The invention provides a method for efficiently detecting a fish target in a complex underwater environment based on a priori knowledge guide network, which comprises the following steps of: 1, acquiring an underwater image and preprocessing the underwater image; 2, establishing a recovery subnet module, a relation reasoning attention module and a self-adaptive feature fusion module, and performing complex underwater environment fish target detection; the recovery subnet module is used for guiding network learning to remove underwater turbid features through an underwater scattering model, generating a clear image through a water body recovery decoder WR, and reconstructing an underwater turbid image; the relation reasoning attention module is used for constructing a co-occurrence relation graph, performing relation reasoning by using a graph convolution network, and dynamically adjusting attention weight; and the adaptive feature fusion module is used for optimizing feature expression in combination with a Sigmoid function and a channel-by-channel weighting mechanism. The method can effectively cope with the conditions of sudden change of turbidity of a water body or complex background and the like, and can keep higher detection performance under different underwater conditions.

The invention relates to the technical field of concentration calculation, in particular to a water suspended matter concentration calculation method based on multi-wavelength detection, which comprises the following steps of: adopting a multi-spectral measurement method, utilizing light beams with different wavelengths to penetrate through a water body, and identifying particle size distribution and morphological characteristics of suspended matter particles; separating and correcting the turbidity contribution of the particles with different particle sizes through a least square fitting algorithm; decomposing the measured optical signal into a basic scattering component, a particle size dependent component and a background signal component by adopting a multi-level signal decoupling algorithm; and in combination with data after particle characteristic correction and optical signal decoupling, constructing a suspended matter concentration calculation model based on particle dynamics, calculating the water suspended matter concentration by using the dynamic model, and outputting suspended matter concentration data. According to the method, dynamic modeling is carried out on the concentration of the suspended matter, a dynamic equilibrium equation is solved through numerical values, and the concentration change of the suspended matter in time and depth is accurately described.

The invention discloses an underwater LED fish gathering lamp control method based on a light field scattering model, and the method comprises the following steps: obtaining water optical characteristic parameters and environmental parameters of a target water area in real time, and building an underwater light field scattering model through employing a radiation transfer equation in combination with a Monte Carlo simulation method; with improvement of target fish school gathering efficiency and reduction of energy consumption as optimization objectives, environment parameters are taken as constraint conditions, illumination effects under different LED parameter combinations are simulated through a light field scattering model, and LED control parameters with optimal comprehensive performance are determined; a control instruction is generated according to the LED control parameters, and the working state of each LED unit is adjusted; meanwhile, fish school gathering data and current change values of water temperature, salinity and turbidity are monitored in real time and fed back to the light field scattering model for parameter correction, and closed-loop control is formed. The underwater LED fish gathering lamp control method solves the technical problems of low light field distribution prediction precision, insufficient fish school visual matching performance, poor environmental adaptability and unbalanced energy consumption optimization in the existing underwater LED fish gathering lamp control method.

The invention focuses on the field of online turbidity analysis of industrial circulating water, and provides an innovative analysis method. According to the method, a two-stage defoaming and dynamic calibration collaborative strategy is adopted to solve the problem that a traditional online turbidity meter is easily interfered by bubbles and temperature fluctuation in a low-turbidity environment, so that the measurement error is large. Primary defoaming is completed by arranging a porous bubble adsorption layer in a flow cell, and secondary defoaming is realized by virtue of centrifugal force of rotating blades of a centrifugal degassing unit; double light paths (infrared light and visible light) are used for cross validation of turbidity signals, and the light path sensitivity is dynamically adjusted according to the water turbidity; and constructing a temperature-turbidity correlation model to correct the temperature drift influence in real time, and constructing a historical data trend library to predict the turbidity change. Through application of a chemical plant circulating water system, the error in a low-turbidity scene is remarkably reduced, and the stability is greatly improved.

The invention discloses a water quality parameter prediction method and system based on multi-sensor data fusion, and the method comprises the steps: collecting and preprocessing original dissolved oxygen data and turbidity data, and obtaining dissolved oxygen data and turbidity data in a plurality of time windows; according to the dissolved oxygen data and the turbidity data in each time window, calculating a water quality feature vector, a dynamic convolution kernel and an abnormal discrimination probability of each sensor in the time window, and performing traversal elimination based on the abnormal discrimination probability to obtain a sensor sequence; according to geographic coordinates and water flow directions of sensors corresponding to data points in the sensor sequence, calculating spatial relationship weights among the sensors, according to the water quality feature vectors, calculating frequency dynamic features, and combining the spatial relationship weights and the frequency dynamic features to generate global feature vectors; and calculating a water quality parameter tensor according to the global feature vector and the long-short term memory network, and generating a water quality parameter thermodynamic diagram. According to the method, the problem of insufficient prediction precision caused by poor spatial collaboration of single-point abnormal data and multi-source data is solved.

The invention belongs to the technical field of hydropower station monitoring and early warning, and particularly relates to a hydropower station intelligent inspection and fault early warning linkage method, which comprises the following steps: arranging a redundant sensor network at key equipment parts, synchronously acquiring pressure, flow, vibration and water turbidity data, establishing a flow-pressure loss reference relationship, and calculating real-time pressure-flow ratio deviation; performing spectral analysis on the vibration signals, extracting characteristic frequency band energy, performing collaborative study and judgment in combination with a turbidity change trend, and ensuring data credibility through data cross validation of main and auxiliary sensors; performing time sequence difference operation on the pressure-flow ratio deviation to obtain a change rate, fusing vibration-turbidity abnormal performance, and comprehensively diagnosing water diversion blockage or foreign matter invasion; early warning levels are divided according to the deviation and the change rate, corresponding response measures are triggered, response actions are executed according to a preset risk priority sequence, and linkage control of the whole process from state sensing, anomaly recognition, fault diagnosis to intelligent response is achieved.

The invention discloses a surveying and mapping method based on remote sensing big data analysis, and relates to the technical field of remote sensing big data. During operation of the system, images and spectral data of a target water body area are collected through satellite remote sensing, unmanned aerial vehicle remote sensing or a ground sensor, the collected remote sensing data are preprocessed, and the target water body area is obtained through waveband combination or spectral line analysis; the method comprises the following steps: extracting characteristic parameters of a water body by utilizing spectral information in remote sensing data, carrying out multi-dimensional analysis on field data, calculating to obtain a water body turbidity coefficient Ct, an algal bloom coefficient Ca and an oil pollution coefficient Co, and analyzing the influence of potential pollution sources, agricultural runoff and industrial wastewater discharge on the water body by combining the remote sensing data and geographic information. And performing spatial-temporal change analysis on the water body pollution condition, identifying the trend of pollutant expansion or degradation, and based on the results of pollution assessment and spatial-temporal analysis, comparing the comprehensive water quality index WQI with a preset threshold to generate water quality level early warning information.

The invention discloses a water environment treatment method and platform based on a water environment mathematical model and artificial intelligence, and belongs to the technical field of water environment treatment, and the method comprises the following steps: S1, obtaining water quality data including water depth, water level, flow velocity, flow direction, temperature, salinity, dissolved oxygen, pH value, chemical oxygen demand, ammonia nitrogen, total phosphorus and turbidity; s2, constructing a water environment mathematical model and an artificial intelligence model; the method specifically comprises the following steps: constructing a water environment mathematical model by using water quality data; the water environment mathematical model is used for simulating a hydrodynamic process and spatial and temporal distribution of water quality parameters; the method comprises the following steps: firstly, selecting a basic artificial intelligence model, then carrying out training learning on the artificial intelligence model by using water quality data, and finally obtaining a trained artificial intelligence model; s3, analyzing a water environment by using the artificial intelligence model to obtain spatial and temporal distribution characteristics and dominant factors of water quality parameters; and S4, making a water environment treatment scheme according to the spatial and temporal distribution characteristics of the water quality parameters and the dominant factors.

The invention discloses a water environment monitoring method based on image vision, and relates to the technical field of water environments, and the method comprises the steps: continuously shooting water body images of different depths at preset intervals in the vertical direction through an underwater camera; calculating the color difference of two adjacent pictures, and determining a water quality mutation layer; when it is detected that the turbidity in the water quality abrupt change layer interval exceeds a preset threshold value, infrared spectral imaging and ultrasonic ranging cooperative work is switched, the penetrability characteristic of an infrared image and ultrasonic depth calibration data are combined, the layering positioning error is corrected, and the water quality abrupt change layer interval is obtained. Through vertical interval shooting, color difference analysis, infrared and ultrasonic collaborative correction and a dynamic sampling strategy, the precision and efficiency of water quality layered monitoring are improved.

The invention discloses an urban road rainwater runoff stage treatment system and method, and relates to the technical field of urban rainwater pollution treatment. In order to solve the problems that an existing system is difficult to monitor multi-index water quality in real time, the pollution degree is accurately recognized, and distribution control lacks dynamics, the system is provided with a multi-source spectrum water quality monitor at the tail end of a pipe network, turbidity, COD, TOC and heavy metal concentration are monitored in real time, rainfall intensity data are accessed into a control center, the pollutant load instantaneous rate L is calculated through a weighted summation formula, and the real-time monitoring of the pollution degree is achieved. A controllable flow distribution module in the flow distribution well distributes the rainwater to an enhanced treatment unit, a biological aerated filter system or a horizontal flow sedimentation tank, wherein the enhanced treatment unit comprises a rotational flow sand setting unit, an electrochemical heavy metal degradation unit and a chemical flocculation unit. The system is mainly used for realizing quality-divided efficient treatment and resource recycling of rainwater and guaranteeing the safety of a receiving water body.

The invention discloses an AI-based raw water pretreatment dosing intelligent control system and method, and relates to the technical field of artificial intelligence, and the system comprises a sensor layer, an edge calculation layer, a cloud platform layer and an execution layer. The sensor layer consists of a distributed sensor network, comprises a turbidity meter, a pH electrode, an electromagnetic flowmeter, a COD (Chemical Oxygen Demand) online monitor and a distributed optical fiber temperature measuring device, covers a raw water inlet, a mixing tank and a sedimentation tank node, and transmits a signal to the edge calculation layer through an RS485 bus. According to the raw water pretreatment dosing intelligent control system and method provided by the invention, through multi-modal data fusion and a dynamic prediction model, the water quality of raw water is accurately sensed and predicted, the dosing amount is adjusted in advance, the hysteresis of a coagulation reaction is effectively compensated, the standard reaching rate of the water quality is improved, and by utilizing an NSGA-II multi-target optimization algorithm, the quality of the raw water is effectively improved. The medicament cost and the treatment effect are balanced, excessive dosing of medicaments in a traditional mode is avoided, and the operation cost is reduced.

The invention relates to a water depth estimation method of a KAN network based on self-adaption and physical constraint, and belongs to the technical field of remote sensing data processing. Grades are divided according to different water body turbidity, different KAN expert networks are adopted for different grades to learn the complex nonlinear relation between B3 and B4 wave band reflectivity and water depth, and KAN initial model parameters corresponding to different grades of water bodies are obtained through independent self-adaptive training; taking the B3 and B4 wave band reflectivity after normalization processing as input, selecting a corresponding KAN network model according to the water turbidity grade, and outputting predicted water depth; and training the KAN network by using the physical constraint loss function L, and finally extracting an interpretable mathematical inversion formula of the wave band reflectivity and the water depth after the training is completed. Through deep fusion of B3 / B4 waveband spectral characteristics of the KAN, a remote sensing technical solution with high precision, low cost and strong adaptation is provided for dynamic monitoring of the water depth of the shallow water area.

The invention provides an underwater small target self-adaptive detection method based on multi-scale dynamic perception and cross-domain prototype migration, and belongs to the technical field of self-adaptive detection.The method is based on a collaborative architecture of environmental perception, dynamic decision-making, feature enhancement and closed-loop optimization, environmental data are obtained through an optical camera and a depth sensor, and the detection accuracy is improved. An improved Beer-Lambert model is utilized to accurately estimate turbidity, a dynamic routing network is driven to adaptively select FPN feature levels (P2-P5), and feature distribution migration is realized through KL divergence and MMD loss in combination with a cross-domain alignment mechanism of a double-prototype library (source domain / target domain). A high-frequency component is adopted to extract and enhance small target details, prototype matching and edge scoring composite confidence indexes are fused, a target frame and a classification result are output, a target domain prototype library is dynamically updated through confidence feedback, a feature selection strategy is optimized, a sensing, detection and optimization closed-loop link is formed, and the target domain prototype library is subjected to feature selection. And the detection precision and robustness in turbid, low-illumination and shielding scenes are obviously improved.

A deep neural network-based energy-saving control method for a sedimentation tank sludge scraper, comprising: acquiring turbidity information, operating parameters and temperature information in a sedimentation tank, and inputting same into a deep learning neural network model to acquire prediction results of a sludge level and starting time of a sludge scraper, thereby controlling the starting and stopping of the sludge scraper. The deep learning neural network model comprises a convolutional neural network sub-model and a long short-term memory network sub-model, the convolutional neural network sub-model is used for adaptively extracting local features of water quality on the basis of received turbidity information, operating parameters and temperature information, and the long short-term memory network sub-model is used for performing secondary feature extraction on the extracted local features of the water quality to acquire time-series information of the features, and ultimately outputting the prediction results. The method allows for identification of a sludge level and prediction of starting time of a sludge scraping plate, has a certain anti-interference capability, and ultimately achieves automatic control of the starting and stopping of the sludge scraping plate, saving energy.

The invention discloses a regulation and control method for stably improving the water quality of water produced by a reverse osmosis system of a power plant, and relates to the field of water quality regulation and control, which comprises the following steps: configuring a water inlet flow regulation device, performing initial setting, configuring the position of an online sensor, performing data acquisition, setting judgment rules of turbidity, differential pressure and transmembrane pressure difference, and determining the water quality. And configuring a flocculant adding device and adding control logic, configuring a backwashing and adding linkage control state machine, and carrying out data smoothing, trend analysis and self-adaptive threshold adjustment. When the water quality of the raw water fluctuates for a short time or the pollution load is increased, dynamically adjusting the water inlet flow, the backwashing intensity and the flocculant adding concentration based on parameters such as turbidity, pollution index, conductivity and pressure difference which are acquired by an online sensor in real time; and meanwhile, the concentrated water reflux proportion and the high-pressure pump frequency are automatically corrected by continuously calculating the desalting rates of the first-stage and second-stage reverse osmosis devices, so that the running state of the membrane component is kept in an optimal interval.