4195results about "Testing water" patented technology

The invention relates to the technical field of offshore area monitoring, in particular to an offshore area pollutant diffusion real-time tracking and traceability monitoring system. The method comprises the following steps: a data perception and fusion unit collects and preprocesses data, carries out space-time calibration and multi-source fusion, and outputs a standardized fusion space-time sequence; the pollutant diffusion simulation and prediction unit calculates a flow velocity field, simulates a diffusion process and predicts a trend; the pollutant traceability positioning unit tracks a pollution path and inverts pollution source parameters; the emergency decision support unit determines a risk level and generates an emergency plan. And through a space-time alignment algorithm and a multi-source fusion mechanism, the data consistency and reliability are improved, a high-quality data basis is provided for subsequent processing, and the monitoring accuracy is guaranteed.

The invention relates to a water quality real-time detection system which comprises the following modules: a multi-source sensing module which is based on a multi-parameter sensing array, adopts a self-adaptive sampling strategy, realizes sensor time sequence synchronization through a state estimation algorithm, completes water body multi-dimensional parameter acquisition in combination with a micro-fluidic chip, generates a multi-modal sensing data set, and transmits the multi-modal sensing data set to a data processing module; the multi-source sensing module comprises a multi-source sensing sub-module, a signal conditioning sub-module, a time sequence synchronization sub-module and an anomaly capture sub-module. The method has the advantages that through the synergistic effect of the adaptive sampling strategy and the state estimation algorithm, the multi-sensor time sequence synchronization precision is remarkably improved, the phase deviation problem caused by traditional fixed frequency sampling is effectively eliminated, the sliding window polynomial fitting is combined with the wavelet threshold de-noising technology, and the multi-sensor time sequence synchronization precision is improved. High-frequency noise interference is greatly suppressed on the premise that effective components of the signals are reserved, and meanwhile, the abnormal value detection accuracy is improved through a dynamic threshold mechanism.

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention discloses an urban water body pollution traceability system based on multi-source sensing data fusion, and the system comprises a data acquisition module which is used for deploying a multi-source water quality sensor to collect initial multi-source water body data, and carrying out the time-space unified alignment processing, and obtaining the time-space aligned multi-source time-space water body data; the pollution factor tracing module is used for constructing a pollution event deconstructor and a factor tracing reasoning engine based on a water network topological graph neural network on the basis of multi-source space-time water body data, and outputting pollution component vectors through pollution component decomposition driven by the pollution event deconstructor; inputting the pollution component vector into a tracing reason inference engine to carry out tracing reason space-time correlation to obtain a tracing reason pollution fusion map; and the traceability decision module is used for performing inversion through a reverse traceability algorithm based on the traceability pollution fusion map, calculating the probability that each upstream area is a pollution source, mapping the probability that each upstream area is the pollution source to a GIS platform, obtaining a pollution traceability confidence distribution map, and realizing accurate traceability of the urban water pollution source.

The invention discloses a water quality time sequence prediction method of an SSA-VMD-LSTM-XGBoost hybrid model, and belongs to the technical field of water quality monitoring and prediction. Comprising the following steps: (1) data preparation and preprocessing; (2) optimizing the water quality time sequence decomposition of the VMD based on SSA: optimizing a penalty factor and a modal number of the VMD by adopting a sparrow search algorithm (SSA), and decomposing the water quality time sequence into a plurality of sub-components with high stability and low complexity by utilizing the optimized VMD; (3) construction and training of an LSTM-XGBoost hybrid prediction model: constructing a hybrid prediction model fusing long-short term memory (LSTM) and extreme gradient boost (XGBoost), inputting a high-frequency component into the LSTM model, inputting a low-frequency component into the XGBoost model, and finally performing superposition and integration on prediction results of the models; and (4) multi-component prediction result integration and performance verification. According to the method, adaptive optimization of VMD parameters is realized through SSA, the feature extraction and time sequence modeling capability is improved by combining the advantages of LSTM and XGBoost, and the prediction precision and stability of the water quality time sequence are effectively improved.

The invention relates to the field of water quality monitoring analysis, in particular to a multi-modal water quality monitoring data intelligent processing method and system. The method comprises the following steps: obtaining a water quality monitoring parameter stream and a water quality monitoring image, and carrying out adaptive processing to obtain a filtering optimization parameter stream and an optimization monitoring image; performing water quality state evolution perception according to the adaptive filtering optimization parameter flow, and constructing a water quality state evolution graph; and performing biological index response analysis and water quality ecological influence evaluation based on the water quality state trend evolution graph to obtain a water quality evaluation report. And carrying out pollutant spatial distribution change analysis based on the optimized monitoring image, and executing final water quality monitoring evaluation according to a water quality evaluation report. The invention provides efficient and accurate water quality status analysis.

The invention relates to the technical field of hydrological detection, and discloses a water quality detection equipment calibration system based on artificial intelligence assistance, which comprises a data preprocessing module, a drift identification module, a model comparison module, a self-adaptive calibration module and a feedback updating module. According to the water quality detection equipment calibration system based on artificial intelligence assistance provided by the invention, the aspects of data processing precision, drifting identification capability, calibration response efficiency and model adaptability are remarkably improved. Through cooperative work of multiple modules, the system can realize full-process intelligent operation from original data acquisition to calibration execution to model self-updating, the human intervention frequency is reduced, and the stability and accuracy of long-term operation of equipment are improved.

The invention discloses an intelligent environment monitoring and regulation method for a marine ranch, and belongs to the field of environment monitoring, and the method comprises the steps: collecting water body parameters, employing a data fusion algorithm to generate a comprehensive environment state index, and solving the business problems of data dispersion, response lag and inaccurate adjustment in water body environment monitoring and regulation. A time sequence analysis model and an optimization algorithm are combined, potential anomalies are analyzed, a risk change trend is predicted, environment adjustment requirements are dynamically calculated, a precise regulation and control instruction set is generated and transmitted to equipment to be executed, and meanwhile, through deviation comparison between real-time feedback data and a preset threshold value, comprehensive indexes and regulation and control parameters are iteratively updated, so that environment stability is ensured. Through multi-level data processing and a self-adaptive adjustment mechanism, the timeliness and accuracy of water body environment regulation and control are remarkably improved, closed-loop management from monitoring to optimization is achieved, and efficient support is provided for intelligent decision making in a complex environment.

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

The invention relates to the technical field of soil monitoring, in particular to a soil-groundwater pollution risk threshold dynamic adjustment method and system based on an environment model, and the method comprises the steps: collecting soil and groundwater monitoring data, constructing an environment model containing the soil pollution concentration, the groundwater pollution concentration and the pollutant migration rate, obtaining comprehensive pollution characteristic data, extracting main characteristics, and determining the contribution degree of each characteristic to risk assessment; calculating the initial weight of each feature according to the contribution degree, and obtaining a weight distribution scheme; calculating pollution risk values of the soil and the underground water in combination with the pollution load data and the weight values, and preliminarily setting a risk threshold value; monitoring pollutant concentration fluctuation data in real time, predicting a pollutant migration trend through a time sequence analysis algorithm, and obtaining a risk change prediction result; and combining the risk change prediction result and the environmental capacity constraint optimization risk threshold to obtain a dynamically adjusted risk threshold. According to the invention, accurate extraction of pollution characteristics and dynamic adjustment of weights are realized.

The invention belongs to the technical field of pollution tracing, and discloses a distributed water body pollution tracing method and system, and the method comprises the steps: constructing a plurality of directed node pairs according to a topological structure of each partition node in a monitored water area; according to the pollutant concentration data in the monitored water area, determining a time delay interval of a pollutant concentration peak value between each directed node pair; determining an effective node pair of which the time delay interval meets the space-time constraint from the plurality of directed node pairs, and constructing a plurality of backtracking paths according to the effective node pair; performing particle tracking simulation on the pollutant concentration data to obtain a plurality of simulation paths, and determining a particle intersection area of pollutants according to the plurality of simulation paths; and determining a pollution source area according to the plurality of backtracking paths and the space intersection of the particle intersection area, and further determining traceability positioning. According to the method, the backtracking path is constructed through dynamic time-delay analysis, high-precision identification of the pollution source is realized by combining particle tracking and gridding intersection positioning, and the problems of insufficient space-time dynamics and large positioning deviation of a traditional method are solved.

The invention discloses an offshore red tide multi-parameter real-time monitoring method and monitoring system based on a buoy array. The method comprises the following steps: S1, performing array deployment and topology configuration according to a sea area sensitivity degree, tide characteristics and a historical red tide trajectory; according to the invention, through risk differentiation buoy array deployment and redundancy topology, traditional isolated monitoring limitation is solved, and sea area full coverage and red tide diffusion tracking are realized; physical-chemical-biological multi-source sensing and in-situ detection are combined with data fusion, so that the problem of single parameter is solved, and the monitoring accuracy is improved; by means of liftable stratified sampling and in-situ algae species and toxicity analysis, the vertical monitoring blank is filled up, and the complete life cycle process of red tide from occurrence to regression is completely captured; through linkage of edge-cloud multi-model early warning and field equipment, a'monitoring-early warning-disposal 'closed loop is formed, and the red tide monitoring real-time performance and the emergency response capability are greatly improved.

The invention relates to the technical field of water quality pollution detection, in particular to a water quality pollution rapid detection method driven by a quantum sensing technology. A quantum sensor array is deployed in a water area to be detected, and quantum signal response data of the water area is collected; constructing a pollutant quantum recognition model, and inputting quantum signal response data into the model to obtain an initial predicted value of pollutant concentration; generating an initial detection parameter of the quantum detection instrument based on the initial predicted value, executing primary pollution detection by the instrument according to the parameter, and collecting primary deviation data of an actual detection value and an expected detection value; determining a detection error according to the deviation data, further correcting the initial detection parameter, and generating an optimized detection parameter; and finally, the instrument performs secondary pollution detection based on the optimized detection parameters. According to the method, complex water sample pretreatment is not needed, water quality pollution detection can be rapidly and accurately completed, detection requirements of different water areas are met, and the detection efficiency and accuracy are improved.

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

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 river sediment multi-parameter sensing self-adaptive sampling method and system, and relates to the technical field of environment monitoring, and the method comprises the following steps: S1, synchronously collecting the pH value, electrochemical potential, heavy metal binding state proportion, water body flow velocity and hydrodynamic parameters of sediment through a sensing device, and establishing a multi-parameter response baseline; and S2, based on the multi-parameter response baseline, collecting monitoring data in real time and executing differential analysis, and identifying a high-risk time period when the pH value change rate exceeds a threshold value and the heavy metal binding state proportion fluctuates. By constructing a multi-parameter response baseline and a linkage recognition mechanism, early sensing and accurate sampling of heavy metal release are realized, a closed-loop response system is constructed by combining chemical form analysis, self-correction and data feedback optimization recognition model, and the accuracy and intelligent level of pollution monitoring are improved.

The invention discloses a water heavy metal concentration simulation and prediction method, which comprises the following steps: integrating original monitoring data, hydrodynamic data, total suspended solids, image remote sensing data and human activity data, and generating a multi-source cleaning sequence data packet; executing cross-modal adsorption capacity estimation by using image remote sensing data in the data packet, inferring particle chemical composition and adsorption isotherm parameters from image textures, and generating a capacity feature packet containing an adsorption capacity upper bound; time-varying travel time is calculated based on the hydrodynamic data and the human activity data, causal alignment is performed on the capacity feature packet and the upstream signal, and a travel time alignment feature packet is generated; and in combination with metal fingerprint parameters, applying an adsorption capacity upper bound as a physical constraint on a form distribution constraint head, explicitly decoupling and predicting the form, and generating a prediction result packet. According to the method, the hydrodynamic physical mechanism and the particle adsorption chemical mechanism are deeply coupled, and the prediction precision and the physical consistency of the model under the unsteady state condition are improved.

The invention provides an intraday high-frequency automatic monitoring method and system for lake cyanobacterial bloom based on a GOCI-II satellite, and aims to solve the problems that an existing method is susceptible to interference of thin cloud, low in recognition precision, high in false positive rate, low in efficiency and the like, and the processing flow depends on manpower. The system is based on an improved AFAI index, fine cloud detection, cloud expansion processing and classification correction strategies are combined, misrecognition caused by thin clouds and shadows is effectively restrained, and the extraction accuracy and robustness are improved. The system has the whole-process unattended processing capacity from GOCI-II data automatic downloading, preprocessing, algal bloom recognition, thematic map making to report output, can generate a monitoring report within one hour after satellite imaging, and supports intra-day multi-temporal cyanobacterial bloom dynamic monitoring. The method has been successfully applied to lakes such as Taihu Lake, lakes, Chaohu Lake and Hongze Lake, is particularly suitable for monitoring and early warning cyanobacterial bloom in large and medium lakes, and has wide application prospects in the fields of water environment supervision, water quality risk control, ecological assessment and the like.

The invention provides a water quality inversion method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring wide-area hyperspectral image data, regional multispectral image data and actually measured water quality parameters; performing correction and space-time registration processing on the wide-area hyperspectral image data and the regional multispectral image data, and performing feature fusion to generate a fusion feature vector; based on the fusion feature vector, performing inversion through a first inversion model to obtain a first type of water quality parameters; based on the first type of water quality parameters and the actually measured water quality parameters, performing inversion through a second inversion model to obtain second type of water quality parameters; a water quality parameter spatial distribution diagram is obtained based on inversion of the first type of water quality parameters and the second type of water quality parameters; and according to the water quality parameter spatial distribution map, when the actually measured water quality parameter is greater than a preset threshold value, generating an early warning signal. Based on the scheme of the invention, the precision, timeliness and automation level of water quality parameter inversion in a complex water area environment can be improved.

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

The invention relates to the technical field of water environment monitoring, and discloses a sewage detection and traceability system based on multi-source data fusion. The system comprises an unmanned aerial vehicle data acquisition module, an edge end preprocessing module, a cloud data fusion and modeling module, a traceability decision module and a closed-loop optimization module, and all the modules work cooperatively to realize sewage detection and traceability full-process automation. Hyperspectral, thermal infrared, LiDAR and meteorological data are collected in an integrated mode through a multi-source sensor, pollution area identification, pollutant concentration quantification, sewage draining exit positioning and pollution diffusion path tracing are accurately completed through edge end preprocessing and cloud deep fusion modeling, a detection strategy is dynamically adjusted through a closed-loop optimization mechanism, and the detection accuracy is improved. And high precision, high efficiency and real-time response of sewage detection under complex terrains are realized.

The invention relates to the field of underground water pollution monitoring, in particular to a dynamic intelligent monitoring method and system for underground water pollution. The method comprises the following steps: constructing and updating an underground water solute transport model; generating a comprehensive pollution feature vector by using the obtained multi-source data which is related to the groundwater pollution and comprises a plurality of pollution indexes; respectively constructing a pollution source positioning model and a pollution diffusion model; triggering a pollution alarm according to the comprehensive pollution feature vector; according to the multi-source data and the current hydrogeological data, utilizing the pollution source positioning model to obtain a pollution source position; respectively utilizing the pollution diffusion model and the groundwater solute transport model to carry out prediction simulation to obtain first pollution distribution changing along with time and second pollution distribution changing along with time; and carrying out weighted fusion on the first pollution distribution and the second pollution distribution to generate predicted pollution distribution changing along with time. The problem that underground water pollution monitoring is not timely and accurate enough in the prior art is solved.

The invention discloses a complex water body pollution detection method and system based on an adaptive cruise technology, and belongs to the technical field of environment monitoring. The method overcomes the inherent defect that an existing monitoring scheme based on a static preset path cannot adapt to a dynamic pollution environment by constructing a closed-loop feedback control architecture of real-time sensing, central decision making and dynamic execution. Generating initial global pollution distribution estimation and planning an initial path by the main aircraft; the subordinate vehicle sails along the path and estimates the local pollution concentration and the gradient direction in real time; and the main aircraft fuses all data to update global estimation, and dynamically generates an instruction for guiding the subordinate aircraft to move towards a pollution concentration gradient rising direction after identifying a pollution plume or a hot spot, thereby realizing active tracking and high-resolution surveying and mapping. According to the invention, the efficiency, precision and intelligent response capability of pollution monitoring are significantly improved, and accurate and adaptive tracking of a dynamic pollution field is realized.

The invention relates to the technical field of sewage treatment, in particular to a dynamic control method applied to a culture tail water circulation treatment system. The method comprises the following steps: continuously detecting aquaculture tail water on a water inlet pipeline, calculating the ratio of chemical oxygen demand to five-day biochemical oxygen demand as a first degradation index, and calculating the ratio of ultraviolet absorbance to total organic carbon concentration as a second degradation index; according to the first degradation index and the second degradation index, identifying aging complementary and range complementary organic load degradation characteristics, and determining a process treatment path based on the organic load degradation characteristics; performing primary pretreatment on the aquaculture tail water in different process treatment paths to obtain intermediate water quality data; detecting the ammonia nitrogen concentration of the intermediate water quality data, and extracting the time change rate of the ammonia nitrogen concentration as an ammonia nitrogen conversion parameter. According to the invention, the oxygen utilization efficiency and the removal rate of organic matters and nitrogen are improved through regional dissolved oxygen and function configuration optimization.

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 the technical field of water conservancy projects, and discloses a water conservancy project water quality detection method and system based on artificial intelligence, and the method comprises the steps: collecting first multi-source data for a water conservancy project, carrying out the alignment of water quality parameter data, remote sensing image data and meteorological and hydrological data in the first multi-source data based on a timestamp, and obtaining second multi-source data; performing data preprocessing and feature extraction processing on the second multi-source data to obtain target feature data; designing a multi-task space-time diagram convolutional network, performing parameter optimization by adopting a genetic algorithm, and constructing a water quality detection model; inputting the target characteristic data into a water quality detection model, and outputting a water quality detection result through the water quality detection model; when the water quality detection result determines that the water quality is abnormal, generating a pollution diffusion simulation map based on a space-time diagram structure and a water quality diffusion rule, and providing emergency decision suggestions according to a water conservancy project scheduling scheme; the accuracy and functional integrity of water quality detection are improved.

The invention discloses a sampling frequency energy-saving control method and system for a water quality monitoring sensor, and belongs to the field of water quality monitoring and self-adaptive control. The method comprises the steps of obtaining real-time water quality monitoring data and an energy consumption state, generating a water quality change trend prediction model, calculating a dynamic energy-saving sampling frequency based on the model and energy consumption, controlling a sensor to dynamically adjust the sampling frequency, and continuously monitoring, feeding back and updating the model. According to the method and system, a machine learning model is adopted to predict the water quality change trend, multi-objective optimization calculation of the dynamic energy-saving sampling frequency is achieved, the sampling frequency is smoothly adjusted through a progressive strategy, and the method and system have the pattern recognition and self-adaptive model updating capacity; on the premise of guaranteeing the monitoring precision, the sensor energy consumption and the data transmission cost can be remarkably reduced, and the intelligence, the stability and the battery life of the water quality monitoring system are effectively improved.

The invention relates to the technical field of water quality monitoring, in particular to an aquaculture water quality monitoring and self-adaptive regulation and control platform and method.The aquaculture water quality monitoring and self-adaptive regulation and control platform comprises a data processing module used for obtaining water quality parameter data of aquaculture at multiple monitoring points and multiple time points and conducting preprocessing and time sequence serialization fitting on the water quality parameter data to obtain a data processing module; obtaining a water quality parameter time sequence; the feature extraction module is used for carrying out space-time semantic recognition and feature extraction on the water quality parameter time sequence and constructing a feature sequence of each water quality parameter; and the risk early warning module is used for carrying out inter-parameter space-time correlation calculation and dynamic risk threshold value adaptation according to the feature sequence to obtain water quality risk early warning features. By combining the water quality parameters and the culture organism state data, the system can accurately evaluate the risk level of the water quality, timely warn potential risk factors, help a culturist to take effective precautionary measures, and avoid the death or health problem of the culture organism.

The invention discloses a system and a method for classified sewage treatment, and belongs to the technical field of environmental engineering and water treatment. The system comprises the following components: an intelligent water quality detection module, which is used for real-time and accurate monitoring of COD, BOD, ammonia nitrogen, total phosphorus, suspended matter concentration and heavy metal content; the sectional bioreactor realizes deep removal of nitrogen and phosphorus and efficient biological treatment by flexibly adjusting reaction conditions of each section; the intelligent flocculation control module is used for automatically and accurately adjusting the adding amount of a flocculating agent, and is combined with integrated operation of efficient inclined tube precipitation and sludge concentration, so that the water quality of discharged water is stable, and the medicament consumption is reduced; the ultrafiltration membrane bioreactor effectively removes residual suspended matters and colloids, and ensures that the effluent stably reaches the standard for a long time through the synergistic effect of catalytic ozonation; and the sludge treatment module is used for realizing collaborative optimization management of sludge reduction, recycling and energy recycling.