405 results about "Turbidity" patented technology

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 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 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 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.

Methods and systems for water quality assessment are disclosed. The method includes obtaining a first input data indicative of properties of a first liquid sample, the first input data including turbidity data and total suspended solids data for the first liquid sample, where the first liquid sample is acquired from a liquid source. The method further includes determining, using a computer processor and a machine learning model, a first predicted particle-size distribution of the first liquid sample based on the first input data, where particle-size distribution is controlled, at least in part, by a set of dosage parameters configurable by a water quality system. The method further includes determining, with an optimizer applied to the machine learning model, an optimal set of dosage parameters based on the first predicted particle-size distribution and adjusting the set of dosage parameters of the water quality system to the optimal set of dosage parameters.

The invention relates to the technical field of underwater facility measurement, and provides an underwater facility defect geometric feature measurement method and system, a medium and equipment, and the method comprises the steps: calculating the actual sound velocity based on the water temperature, water pressure and salinity, correcting a sonar point cloud, calculating the sound velocity change rate, and adjusting the size of a filtering window, performing smooth filtering on the corrected sonar point cloud; for the surface image, calculating an image dark channel, after smoothing the dark channel image through scattering filtering, calculating transmissivity, fusing a scattering coefficient, removing surface image scattered light, and correcting the scattering coefficient through turbidity; based on the salinity and the water temperature, the underwater refraction angle of the laser is calculated, and the three-dimensional scanning point cloud is corrected; registering and fusing the point clouds to generate a facility surface point cloud model; based on the surface image, texture information of the facility surface is extracted, after the texture information is mapped to the facility surface point cloud model, a defect area is segmented, and geometric feature parameter extraction is carried out. And the detection reliability in a complex water area environment is improved.

The invention relates to the technical field of intelligent scheduling of water supply resources, in particular to an intelligent scheduling system and method for water supply resources in extreme weather, in which a time sequence coupling analysis unit collects pipe network pressure, turbidity and meteorological early warning data in real time, identifies the peak time period of the turbidity abrupt change rate through dual-channel verification, and sends the peak time period of the turbidity abrupt change rate to a cloud server; the dynamic strategy generation unit divides scenes according to the coefficients, starts staged pressure relief in a high-contact-ratio scene, controls total duration and single-stage pressure drop, integrates real-time compensation of flow velocity, generates a pre-pressure-relief instruction in a low-contact-ratio scene, and generates a pre-pressure-relief instruction according to the pre-pressure-relief instruction, and the dynamic strategy generation unit generates a pre-pressure-relief instruction according to the pre-pressure-relief instruction and the single-stage pressure drop in the high-contact-ratio scene. And the pipe network topology compensation correction triggering time is fused, and the execution intensity of two types of instructions is dynamically allocated in a mixed scene, so that the pipe network pressure and water quality collaborative guarantee in extreme weather is realized, and the safety and stability of a water supply system are maintained.

The invention discloses a water quality degradation early warning method and system for a water supply pipe network in a low-pressure area. The method comprises the following steps: S1, collecting topological structure data and pressure, flow, residual chlorine concentration and turbidity data of an urban water supply pipe network, and preprocessing the topological structure data and the pressure, flow, residual chlorine concentration and turbidity data; s2, constructing an initial node feature matrix, a pipe network node graph model, an adjacent matrix and a low-pressure area identification matrix; sequentially extracting a first pipe network spatial feature, a second pipe network spatial feature and a water flow stagnation time sequence feature, and fusing to obtain a space-time coupling feature; s3, extracting space-time coupling characteristics of the low-voltage area, and calculating to obtain a water age distribution matrix of each node of the low-voltage area; s4, residual chlorine attenuation characteristics are constructed, and then a low-pressure area residual chlorine concentration prediction sequence and a low-pressure area water quality degradation risk matrix are calculated; and S5, performing graded early warning. According to the method, the problems of inaccurate water age distribution inversion and inaccurate water quality risk prediction caused by residual chlorine attenuation prediction of the low-pressure area of the water supply network in a traditional method can be solved.

The invention relates to the technical field of underwater vehicle detection, and discloses a self-adaptive underwater vehicle suitable for an estuary high-salinity and high-turbidity environment and a detection method, and the vehicle integrates an environment perception module, a density inversion module, an acoustic configuration module, a buoyancy control module, a silent observation module and the like. By fusing the temperature-salinity-depth and acoustic backscattering data, the additional density caused by the suspended sediment is inverted, and a total environment density parameter is constructed; according to the parameter, a variable buoyancy system is driven by feedforward control logic to adjust the drainage volume, and buoyancy fluctuation caused by density abrupt change is counteracted in advance. The system dynamically switches the acoustic detection frequency according to the local turbidity and corrects the time-varying gain curve; cutting off propelling power in a neutral hovering state to enter a silent drifting mode, and executing sediment flux observation without mechanical disturbance. According to the invention, the problems of buoyancy adjustment lag, acoustic signal attenuation and propulsion disturbance in the complex water body of the estuary are effectively solved, and the dynamic stability and the observation data quality are remarkably improved.

The invention provides a wetland ecological restoration effect comprehensive evaluation method and system, and belongs to the technical field of big data analysis. According to the method, real-time data of multi-dimensional ecological elements such as water turbidity, vegetation coverage density and soil acidity and alkalinity are collected, a multi-dimensional element correlation matrix based on time sequence correlation is constructed, and the dynamic change frequency of each element is captured. Core influence factors are screened by quantifying the contribution degree of each element to ecological restoration, weight distribution corresponding to clustered element clusters is determined, and then an accurate quantitative score is generated and mapped into a clear restoration condition level through multiplication and summation of inter-cluster correlation coefficients and weights item by item. Meanwhile, an evolution track is compared and analyzed by combining historical time sequence data, and the weight is dynamically adjusted to ensure that evaluation adapts to an actual repair process. The comprehensive, dynamic and accurate evaluation of the wetland ecological restoration effect is realized, scientific support is provided for the accurate adjustment of the restoration strategy, and the fine management of the wetland and the sustainable development of the natural environment are guaranteed.

The invention relates to the technical field of water quality detection, in particular to a riverway water quality detection analysis method and device for river environment protection, and the method comprises the following steps: pre-treating a collected riverway water sample, equally dividing the pre-treated riverway water sample into two parts, and measuring the turbidity of any one of the riverway water samples, performing water quality absorbance detection on the other part of the river channel water sample; forming a water sample absorbance sequence by the absorbance of all wavelengths of the river channel water sample, and correspondingly, obtaining a pure water absorbance sequence of an equal-volume pure water sample; obtaining a calibration water sample sequence by utilizing the difference between the water sample absorbance sequence and the pure water absorbance sequence; determining an absorbance correction value of each preset water quality influence element; and obtaining the content of each preset water quality influencing element in the river water sample by utilizing the absorbance correction value and combining with the Lambert-Beer law. Therefore, the accuracy of river water quality detection is improved.

The invention discloses a water body apparent spectrum synchronous acquisition and water quality parameter inversion system based on an unmanned ship, and relates to the technical field of intelligent sensing, and the system comprises the following steps: acquiring environmental parameters of the unmanned ship, constructing a deep reinforcement learning model, inputting the environmental parameters, and outputting a phase control instruction; constructing a water quality parameter inversion model, inputting noise-free spectrum data, and outputting water quality parameters; when the water quality turbidity in the water quality parameters exceeds a dynamic turbidity sudden change threshold value, triggering a laboratory to collect water quality verification data, comparing the water quality verification data with the water quality parameters to output a turbidity error, and when the turbidity error exceeds an inversion verification error threshold value, updating the weight of the water quality parameter inversion model; and performing spatial interpolation calculation based on the updated water quality parameter inversion model, outputting water quality parameter distribution, and displaying a water quality parameter thermodynamic diagram and a traceability pollution path through an electronic map. According to the method, the PLSR weight is updated online through the incremental learning algorithm, and the model adaptation speed is increased.

The invention discloses a method for cooperatively carrying out dredging and ecological protection in a near-shore sensitive area, belongs to the technical field of port and channel engineering desilting, and aims to solve the technical problem that construction efficiency and ecological environment protection are difficult to consider when dredging operation is carried out in the near-shore sensitive area. Comprising an operation system of at least one dredging sub-ship, a path is planned through underwater topographic survey, the sub-ship is fixed through a gauge pile system, a multi-joint hinged arm with a cover type cutter suction head is lowered to be attached to a mud bed, sludge is liquefied through a high-pressure water system, and slurry is conveyed through a sand pump set; the slurry concentration and the turbidity of a surrounding water area are respectively monitored through an inner turbidity sensor and an outer turbidity sensor, and closed-loop feedback control is formed to dynamically adjust the power of a sand pump, the pressure of high-pressure water and the speed of a hinged arm; and meanwhile, in the dredging process, an ecological base material containing a biological agent is sprayed to the bare seabed through an ecological base material spray head, and dredging and ecological restoration are synchronously carried out. The method is mainly used for dredging construction and ecological protection of near-shore sensitive water areas.

The invention discloses a water plant inlet water quality change identification method and system, and the method comprises the steps: collecting water plant inlet water turbidity, pH value, dissolved oxygen content, COD concentration, ammonia nitrogen concentration, total phosphorus concentration and other parameters, removing abnormal values, and constructing a water quality parameter time sequence data set; causal association among parameters is mined through a causal inference-based hybrid model, a weight matrix is generated, after the matrix and time sequence data are fused, bidirectional time features are extracted through a bidirectional long-short-term memory network in combination with an attention mechanism, attention weights are given, and then a gradient elevator integrated model is input; a plurality of base learners are used for parallel learning and dynamic weighted fusion of local results to obtain a preliminary recognition result, and finally the preliminary recognition result is mapped to a water quality change type space to determine a change type. The system comprises six units, a complete and efficient treatment flow is formed from water quality parameter collection to final recognition result output, and the actual requirements of a water plant for high-precision and high-reliability recognition of inflow water quality changes are practically met.

The invention provides a cooling tower cleaning period decision-making method based on filler layer resistance characteristic online identification, which comprises the following steps: acquiring multi-dimensional time sequence data such as differential pressure, water temperature, conductivity, turbidity and microbial concentration based on a multi-source sensor, and constructing a structured trend characteristic matrix through time synchronization, abnormal value elimination and statistical characteristic extraction; in combination with expert knowledge, constructing a multi-modal combined pollution knowledge graph, and learning pollution factor interaction weights by using a graph neural network to realize causal reasoning of combined pollution mechanisms such as scaling, biological slime and the like; according to the method, an abnormal causal chain path is efficiently matched in a knowledge graph by adopting improved A * search, causes and evolution stages are output, automatic attribution and intelligent cleaning suggestion optimization are realized, and the resistance abnormity diagnosis accuracy and the adaptability of a cleaning strategy are improved by continuously feeding back diagnosis results and dynamically expanding a knowledge graph structure.

The embodiment of the invention relates to the field of water quality detection, and provides a water turbidity detection method and device for a swimming pool circulating robot. The method comprises the following steps: scanning a swimming pool contour of a target swimming pool to construct a swimming pool three-dimensional model; monitoring multi-dimensional environment data of the target swimming pool, and predicting a pollutant distribution state of each feature region in the swimming pool three-dimensional model based on the multi-dimensional environment data; generating a cruise detection strategy based on the pollutant distribution state of each feature area, and dynamically setting sampling points in each feature area according to the cruise detection strategy; in the cruising process of the swimming pool circulating robot, detecting water turbidity data of each sampling point through a detection module; synchronously detecting the water flow fluctuation data and the water body monitoring image of each sampling point to obtain the turbidity offset of each sampling point; correcting the water turbidity data of each sampling point through the turbidity offset; the water quality turbidity distribution diagram corresponding to the swimming pool three-dimensional model is constructed and dynamically updated based on the water quality turbidity data, and the water quality detection accuracy and detection efficiency are improved.

The invention discloses a composite electric field catalytic oxidation synergistic oil and gas field produced water treatment method which comprises the following steps: acquiring an electric flocculation effect generated by a corrosion-resistant titanium-based coating anode and a stainless steel cathode under optimized pulsed electric field parameters through a turbidity sensor and a particle size analyzer, and superposing low-frequency pulses in a composite pulse mode, maintaining the temperature of the reactor to be 20-40 DEG C in combination with a temperature control system to obtain initial floc particle size distribution and concentration data; the concentration state of the floc in the concentration tank is monitored by adopting a conductivity sensor according to the amount of the floc in the concentration tank treatment pollution discharge scheduling scheme, the content of remaining oil drops and suspended solids is analyzed in combination with an emulsion monitoring module, and the water quality parameter change of the concentration tank is recorded through a data analysis system, so that a final water quality treatment result is determined.

The invention relates to the technical field of image data processing, in particular to an MBR water production turbidity detection method and system based on image processing, and the method comprises the steps: obtaining an original image of an MBR water production pipeline, and extracting a water body region by using a mask matrix; calculating the local contrast of the pixel points relative to the neighborhood, constructing a suppression weight based on the noise standard deviation of the image sensor, and weighting the local contrast to obtain scattering response intensity; obtaining the texture disorder degree based on the ratio of the geometric mean value to the arithmetic mean value of the feature values of the structure tensor; the particle confidence is obtained based on the scattering response intensity and the texture disorder degree, the weighted particle confidence is obtained through weighting of a Sigmoid function, and the average value of the particle confidence is calculated to serve as the comprehensive turbidity so as to evaluate the water production state. The scattering response intensity and the texture disorder degree are fused, a weight suppression and soft threshold mechanism is introduced, pipe wall scratches and thermal noise interference are reduced, and the detection accuracy is improved.

The invention relates to the technical field of simulation laboratories, and discloses a multifunctional laboratory capable of simulating different marine environments based on ship body carrying, which comprises a ship body capable of simulating different marine environments and manufacturing various controllable flow fields from laminar flow to turbulent flow in an experiment cabin through a controllable flow field generation unit; through the controllable wave generation unit, waves with specified wave heights and periods can be generated in the experiment module; the water body temperature and salinity regulation and control unit is used for quickly and accurately regulating the temperature and salinity of the water body in the experiment module; water bodies with different turbidity and eutrophication levels are simulated through the environmental biological simulation unit; through an outside-in test mode, a sealing door at the bottom of the experiment module is opened, the underwater robot and other equipment are allowed to freely enter and exit, the dynamic response and control algorithm of the underwater robot when entering a complex environment (external real ocean) from a calm environment (in the experiment module) can be tested, and in-situ storage of a sampled sample can be realized.

The invention relates to the technical field of fluid dynamics experimental measurement, and discloses a water tank experimental device and method for muddy current transportation of micro-plastics, the device comprises a water tank device main body, an optical measurement subsystem and an ultrasonic Doppler flow velocity measurement system, an ultrasonic emission path and an optical measurement window are overlapped in space, and the optical measurement subsystem and the ultrasonic Doppler flow velocity measurement system are arranged in the water tank device main body. The method comprises the following steps: performing first fluorescence labeling and second fluorescence labeling on the micro-plastic particles and the sediment particles; synchronously acquiring optical data and acoustic data in the cross window; respectively establishing a concentration correction function and a flow velocity correction function by taking acoustic backscattering intensity data and flow velocity data as references in the cross window; applying the correction function to the whole optical window to obtain a globally corrected concentration field and a globally corrected flow velocity field; and distinguishing and quantifying the micro-plastics and the sediments based on the corrected data. Through the cross correction principle, the problem of signal distortion of optical measurement in high-turbidity fluid is solved.

The application discloses a kind of intelligent switching water sample pretreatment system and method, system includes: combined sand filter sample barrel, for collecting water sample to be pretreated, initial water sample turbidity value acquisition is carried out, while water sample preliminary pre-filtering treatment is carried out;Positioning floating sampling device, for fixing to take supernatant after water sample settlement;Microfiltration module, for the microfiltration of ultrahigh turbidity water sample;Sample water transfer pump, for transmitting supernatant in combined sand filter sample barrel to microfiltration module;Sample collection barrel, for collecting and detecting water sample quantity after processing, for instrument or laboratory personnel to sample analysis;Valve assembly, by valve opening and closing, water sample is switched different pretreatment mode;Cleaning assembly, including rotary high-pressure spray head and self-heating ultrasonic cleaning device, for cleaning system after pretreatment internal residual adherend.

The invention discloses a water quality turbidity intelligent monitoring method and system based on vision, and relates to the technical field of water environment monitoring and intelligent perception, an industrial camera and an event camera perform coaxial acquisition, and an electric control polaroid and an adjustable incident angle realize angle-polarization dual scanning and construct a polarization prior tensor. Adaptive standardization is carried out on the dazzle removal reference frame, a forward operator is established by combining polarization geometry and particle size mapping, and a latent image is obtained through physical consistency inversion of split iteration. The method comprises the following steps: extracting multi-scale features from RGB / HSV / Lab three branches and a cavity pyramid by using time domain polarization transformation and reliability selection, carrying out cross-space fusion based on particle size spectrum estimation and spatial gating, outputting a turbidity value, and synchronously giving a particle size spectrum, depolarization confidence and flicker intensity. And the internet-of-things execution subsystem performs hierarchical linkage according to threshold values and performs online self-calibration.

The invention relates to the field of water quality detection, in particular to a water quality sampling detection method and device applied to a small and micro wetland unit. The method comprises the following steps: determining a turbidity coefficient according to scattered light intensity of a target water sample under different wavelengths, absorbance of a target interval in an ultraviolet absorption spectrum and fluorescence intensity of a target area in a fluorescence spectrum; determining a scattering equivalent absorption value of each wavelength according to the scattered light intensity of the target water sample under each wavelength, the absorbance corresponding to each wavelength in the ultraviolet absorption spectrum and the turbidity coefficient; determining a real ultraviolet absorption signal of each wavelength according to the absorbance corresponding to each wavelength in the ultraviolet absorption spectrum and the scattering equivalent absorption value of the wavelength; correcting the fluorescence spectrum according to the turbidity coefficient and the scattering equivalent absorption value of each wavelength to obtain a corrected fluorescence spectrum; and detecting the water quality according to the corrected fluorescence spectrum and the real ultraviolet absorption signal of each wavelength. According to the invention, accurate water quality sampling detection can be provided.

The invention discloses an XGBoost chlorophyll a concentration aerial remote sensing inversion method based on characteristic wave band selection, and relates to the technical field of environmental monitoring. By constructing an integrated input characteristic vector, an optimal characteristic wave band most relevant to the chlorophyll a concentration, an enhanced chlorophyll a index and specially designed correction characteristics are fused; and more targeted information is provided for the model. Moreover, correction features obtained through calculation of spectral difference values of adjacent shadow regions and non-shadow regions are introduced, and training data containing samples of the two regions are utilized to train an XGBoost model, so that the XGBoost model can autonomously learn and quantify a composite interference effect brought by illumination and suspended matter concentration change. Therefore, according to the technology, inversion noise caused by complex environmental factors can be effectively inhibited, high-precision and full-coverage inversion of the chlorophyll a concentration under different illumination and turbidity conditions is realized, and the robustness and the practical application value of the method are remarkably improved.

The invention provides a self-adaptive control system and method for a mud scraper of a secondary sedimentation tank based on computer vision and turbidity sensing. The self-adaptive control system and method are used for self-adaptive energy-saving control of the mud scraper of the secondary sedimentation tank in an urban sewage treatment plant. The method comprises the following steps: acquiring an image of a sludge accumulation area at the bottom of a tank in real time through an image acquisition device erected above a secondary sedimentation tank; identifying a sludge area by using a YOLO thickness detection model, estimating the visual thickness of sludge accumulation, comparing the visual thickness with a preset threshold value, a start-stop threshold value and a thickness interval threshold value, when the visual thickness reaches or exceeds the start-stop threshold value, starting the sludge scraper, and adjusting the running speed of the sludge scraper in a grading manner according to the interval to which the visual thickness belongs; and then the mud scraping device pauses the image recognition function, an online turbidity sensor installed at the effluent weir is started to monitor the turbidity of the water body, and the mud scraper is controlled to stop running according to the turbidity of the effluent water. The self-adaptive control of the mud scraper of the secondary sedimentation tank is realized, and the purposes of saving energy, reducing emission and prolonging the service life of the mud scraper are achieved.

This invention discloses a water treatment dosing control method and system based on quadratic programming, belonging to the field of water treatment process control and optimization technology. It collects historical water treatment operation data and constructs a mechanistic feature set, using the mechanistic feature set as the independent variable and turbidity reduction as the target variable. The turbidity reduction is used to represent the change in flocculation or sedimentation of suspended impurities in the water, and a full-variable regression model is constructed. Variables in the mechanistic feature set are screened, and the full-variable regression model is optimized using a stepwise regression method to obtain a simplified prediction model. Real-time influent water quality parameters are acquired and input into the simplified prediction model. The process of maximizing turbidity reduction in the simplified prediction model is transformed into minimizing a convex loss function, and the convex loss function is iteratively optimized using a hierarchical constrained projection gradient descent method to obtain the optimal dosing scheme. Through mechanism-driven modeling, convex function optimization, and hierarchical constrained projection, intelligent, efficient, and reliable control of the dosing process is achieved.

The invention belongs to the technical field of water quality sampling, and particularly relates to a circulating water quality turbidity monitoring, sampling and filtering device which comprises a sampling box and a sliding arc-shaped filtering frame located in the sampling box, a stirring impeller is rotationally installed on one side of an inner cavity of the sampling box, two sets of supporting frames are distributed on the inner wall of the sampling box, and a plurality of chain wheels are rotationally installed on the supporting frames; a plurality of supporting frames are in transmission through chains, a plurality of groups of n-shaped frames which are arranged along the axes of the supporting frames at equal intervals are arranged between the chains on the two sides, detachable sampling barrels are arranged on the n-shaped frames, and when the sampling barrels extrude the arc-shaped filter frames, the arc-shaped filter frames move downwards in water and can also press the water to a certain extent, so that the sampling efficiency is improved; according to the invention, the automatic locking assembly and the chain transmission are combined to form a complete'sample loading-locking-sampling-unlocking-sample unloading 'automatic cycle, and the automatic locking assembly and the chain transmission are combined, so that the device is simple in structure, convenient to operate and high in practicability.

The invention relates to the technical field of water quality detection, in particular to water quality turbidity detection equipment with a self-cleaning function, which comprises a base, a supporting plate is mounted on the base, a positioning disc is rotatably arranged on the supporting plate, a detection mechanism is mounted on the supporting plate, and the positioning disc is rotatably arranged on the supporting plate. And the detection mechanism comprises an electric sliding rail mounted on the supporting plate. When the detection head is reset, the air suction column is driven to move, the air suction column exhausts air in the fixed column into the protective sleeve through the air supply column, and through a second one-way valve in the air supply column and a first one-way valve in the fixed column, the air in the fixed column is discharged into the protective sleeve through the second one-way valve in the air supply column and the first one-way valve in the fixed column; and the reset detection head and the cleaned protection sleeve are air-dried, so that the residual time of water on the protection sleeve and the detection head is shortened, and the situation that the water is left on the protection sleeve and the detection head for a long time, and consequently the protection sleeve and the detection head are corroded is avoided.

The application discloses an ultrasonic intelligent cleaning equipment and a control method thereof. The ultrasonic intelligent cleaning equipment comprises a rack and a structure module, a water and gas supply and temperature control module, a circulation and filtration module, a cleaning and execution module, a drainage module and a control and interaction module. The application is characterized in that a multi-parameter detection unit comprising an electric conductivity probe, a turbidity probe and a granularity probe is integrated, and cooperates with a controller to realize intelligent closed-loop control of the cleaning process. Through a man-machine interface, a user can select multiple modes such as ultrasonic cleaning, mixed cleaning and self-cleaning. The control method automatically judges the cleaning end point based on comparison of real-time monitored water quality data and a preset threshold value, and can perform safety protection according to the number of cycles. The application effectively overcomes problems of traditional cleaning machines, such as dependence on manual experience, unstable cleaning effect, serious waste of water resources and energy and the like, and significantly improves the cleaning efficiency, the degree of automation and the cleaning quality.