197 results about "Water quality management" patented technology

The invention relates to the technical field of water quality monitoring, and discloses a water quality multi-parameter real-time monitoring system. The system comprises a monitoring point position configuration module, a water quality parameter acquisition module, a pollution index calculation module, an anomaly detection module, a central control unit and the like. The monitoring point position configuration module sets monitoring point positions and generates acquisition rules; the water quality parameter acquisition module acquires a water quality parameter set according to rules; the pollution index calculation module calculates a pollution index; the anomaly detection module obtains an anomaly detection index; and the central control unit comprehensively analyzes and generates water quality evaluation parameters. In addition, the system is further provided with a pollution source tracking module, a storage unit and an alarm module, pollution sources can be tracked, data can be stored, and an alarm can be given according to evaluation parameters. The system realizes multi-parameter real-time monitoring, accurate analysis and evaluation and pollution source tracking, provides comprehensive support for water quality management, effectively improves the water environment monitoring level, and guarantees the safety of water resources.

The invention discloses a dynamic water quality monitoring system based on an unmanned ship and a data acquisition and distribution algorithm. Comprising an unmanned ship platform module, an initial navigation path setting module, a water quality data acquisition module, a dynamic adjustment and optimization module, a control interaction module and a dynamic adjustment and optimization module, wherein the dynamic adjustment and optimization module dynamically adjusts the navigation strategy and sampling frequency of the unmanned ship in a target to-be-detected water area through a second branch of a first neural network model; the unmanned ship can sail autonomously, continuous and global water quality monitoring is achieved, through an efficient data processing algorithm, the accuracy and reliability of data are improved, a scientific basis is provided for water quality management, a distributed algorithm model is achieved to conduct predictive analysis on each specific target sampling point, the accurate predictability of unmanned ship monitoring is improved, and the water quality monitoring efficiency is improved. The monitoring strategy is dynamically adjusted according to actual requirements, optimal configuration of monitoring resources is achieved, the monitoring cost is reduced, the water quality change trend is found in advance through prediction analysis of the neural network model, and powerful support is provided for early warning and emergency response.

The invention discloses a marine ranching early warning method and system based on a knowledge graph, and relates to the technical field of marine ranching management, and the method comprises the knowledge graph of large-class keywords of water quality management, equipment management and biological resource management and progressive-level subclass keywords of the large-class keywords, terminal subclass association preset acquisition parameter groups and state description; generating a marine ranching plane graph; equipment states and detection parameters are collected to form a normal state parameter group, and a normal planar graph representation model is constructed; generating an exception model based on a preset exception adjustment normal parameter group, and dynamically associating the exception model with small-class keywords in the knowledge graph; and collecting data in real time to construct a real-time model, comparing the real-time model with an abnormal model to trigger an alarm, and marking an abnormal root in the knowledge graph. According to the method, the limitation of traditional single-dimensional monitoring is broken through, multi-source data are integrated through knowledge graph semantization, dynamic threshold and spatial correlation analysis are combined, abnormal accurate positioning and cross-dimensional risk early warning are achieved, and the marine ranch management efficiency and safety are remarkably improved.

The invention discloses an AI-driven unattended water environment quality monitoring system, and relates to the technical field of water environment monitoring, and the system comprises a data collection module, a cloud processing module, an edge calculation module, an intelligent analysis and early warning module, and a system management module. Through comprehensive and real-time intelligent monitoring of a water body, monitoring of water quality physical indexes is concerned, aquatic organism behavior monitoring is introduced, so that the health condition of the water body can be reflected more comprehensively, and meanwhile, through the data preprocessing and local model fine tuning functions of the edge calculation module, the water quality monitoring accuracy is improved. The intelligent analysis and early warning module can perform correlation analysis, set an early warning threshold value and generate early warning information based on the AI model after cloud side end collaborative optimization, and provides powerful decision support for water quality management.

The invention relates to an intelligent regulation and control system for multi-phase conversion of water quality of a gate-controlled river reach, in particular to the field of water treatment, and realizes accurate dynamic perception of the multi-phase conversion process of the water quality of the gate-controlled river reach by constructing a high-fidelity digital twinborn body and fusing a real-time data assimilation technology; by means of a time-space diagram neural network agent model based on an attention mechanism, the system can predict a complex time-space pattern of pollutant concentration field and phase evolution under different regulation and control strategies in an ultra-fast manner; on the basis, a multi-agent reinforcement learning algorithm is adopted to automatically generate a globally optimal gate group coordinated regulation scheme, and multiple targets of water quality improvement, ecological protection, engineering operation and the like are effectively coordinated; and finally, a theoretical strategy is reliably converted into a physical action through rolling optimization and a closed-loop execution mechanism, and adaptive adjustment can be performed according to environmental feedback, so that the emergency response speed of sudden water pollution events, the scientificity of regulation and control decision and the intelligent level of water quality management of the whole river network are comprehensively improved.

The invention relates to the technical field of remote sensing spectrums, in particular to an urban black and odorous water body recognition method and system based on remote sensing spectrums. The method comprises the following steps: acquiring infrared spectrum and visible spectrum data based on a remote sensing satellite or an unmanned aerial vehicle, processing the data, eliminating noise and abnormal values in the data, and extracting spectral features to obtain a spectral feature data set; according to the invention, through infrared and visible spectrum data collected by a remote sensing satellite or an unmanned aerial vehicle and through combination with urban water body data of a geographic information system, the dimension and quality of the data are improved, so that monitoring of the urban black and odorous water body is not limited to surface coverage, and sensitivity of data analysis can be accurately regulated and controlled by dynamically adjusting a spectrum band weight; the method has the advantages that the method adapts to variable environmental conditions, recognition precision of various polluted water bodies is improved, continuity and accuracy of long-term monitoring are guaranteed by continuously updating and optimizing the processing flow, and urban water quality management and decision-making processes are effectively supported.

The invention relates to the technical field of water resource management, in particular to a cascade reservoir ecological scheduling system based on artificial intelligence, which comprises a multi-source data integrated processing module, an ecological influence prediction module, a scheduling strategy optimization module, a self-adaptive water quality management module and an intelligent user interaction module. Wherein the multi-source data integrated processing module is used for collecting various data of a meteorological station, a hydrometric station and an ecological monitoring point; the ecological influence prediction module is used for constructing an ecological influence model; the scheduling strategy optimization module is used for formulating a reservoir scheduling strategy; the self-adaptive water quality management module is used for adjusting the reservoir outlet flow and the adjusting strategy of the reservoir; and the intelligent user interaction module provides a human-computer interface. According to the invention, by integrating multi-source real-time data, applying reinforcement learning to optimize the scheduling strategy and providing an efficient user interaction interface, the real-time responsiveness and decision-making efficiency of reservoir management are significantly improved, and optimal utilization of water resources and protection of the ecological environment are ensured at the same time.

The invention relates to the technical field of data processing, and provides a water quality detection method, device and equipment and a storage medium, and the method comprises the following steps: obtaining the temperature and turbidity of a specific water sample, carrying out standardization treatment on the temperature to obtain a standardized temperature, analyzing the standardized temperature and the turbidity, obtaining long-term water quality change prediction, and carrying out data driving simulation. The method comprises the following steps: obtaining a simulated water quality change path and expected environmental influence, adjusting temperature by utilizing the simulated water quality change path to obtain a temperature adjusting scheme, optimizing turbidity by utilizing the expected environmental influence to obtain a turbidity optimization scheme, and combining the temperature adjusting scheme and the turbidity optimization scheme to obtain the turbidity optimization scheme. And obtaining a comprehensive water quality management scheme and generating a water quality maintenance report. Dynamic monitoring and accurate prediction are realized by using monitoring data of temperature and turbidity, and the problems of insufficient detection accuracy and prediction capability caused by incapability of effectively predicting and coping with a long-term water quality change trend when the water quality is detected are solved.

To provide a method that enables quantitative evaluation of water quality influencing factors and derivation of operating indicators for facilities to optimize water quality, which are necessary for optimal water quality management.SOLUTION: A water quality control indicator output device 10 includes a data storage part 13 that stores water quality-related data, a machine learning model estimation part 17 that outputs a trained model from the water quality-related data, a trained model storage part 18 that stores the trained model, a water quality effect quantity inference value output part 20 that infers and outputs the water quality effect quantity inference value from the water quality-related data and the trained model, a water quality effect quantity inference value storage part 21 that stores the water quality effect quantity inference value, a water quality optimization operation indicator output part 24 that infers and outputs a water quality optimization operation indicator from the water quality-related data and the trained model, and a water quality optimization operation indicator storage part 26 that stores the water quality optimization operation indicator.SELECTED DRAWING: Figure 2

The invention provides a water quality on-line monitoring method and system for a large-volume water tank, and the method comprises the steps: obtaining original data of a plurality of water quality sensors, estimating the water quality state of the whole water tank through a weighted interpolation method, and forming a water quality space distribution baseline; for multi-sensor data, the pollution deviation degree of the sensor data and the water quality space distribution base line is calculated, then according to the pollution deviation degree and a dynamic self-adaptive threshold value, an abnormal pollution event is recognized, and abnormal detection data is obtained; according to the abnormal detection data, utilizing a hydrodynamic model to predict the diffusion trend of pollutants, and evaluating future water quality change; determining an optimal water quality management strategy according to the pollution prediction data in combination with water tank physical parameters and water treatment resource information to obtain a water quality optimization decision scheme; and dynamically adjusting the hydrodynamic model according to the execution result of the water quality optimization decision scheme and the latest monitoring data. According to the method, the defects of an existing method in the aspects of monitoring comprehensiveness, real-time performance and accuracy are overcome.

The invention provides a marine ranch water quality management method and system, electronic equipment and a storage medium, and the method comprises the steps that a water quality monitoring device, an edge computing terminal and a water quality adjusting device are arranged on a navigation mark in a marine ranch in advance, and the edge computing terminal obtains water quality parameter data of the marine ranch at a plurality of continuous moments; the water quality parameter data is collected through a water quality monitoring device, and if the water quality parameter predicted value exceeds a preset water quality normal range, a target adjusting amount is determined based on the water quality parameter predicted value; the water quality adjusting device adjusts the water quality according to the target adjusting quantity; near-end water quality prediction and water quality deterioration accident treatment can be completed at the navigation mark, the defect that water quality data cannot be monitored in real time and water quality deterioration accidents cannot be treated in time due to the fact that the time interval of Beidou satellite data transmission is long is overcome, and ecological risks and economic losses are effectively reduced.

The invention discloses a water pollution composite treatment method and system based on water quality pollution real-time monitoring, and relates to the technical field of water quality monitoring. Sensor equipment is arranged to dynamically monitor the water quality of a specified water body in real time, and collected data is uploaded to a water quality management cloud platform; according to monitoring data, the concentration change trend of various pollutants in a water body is evaluated, the pollutant concentration is predicted and analyzed in combination with a long-short-term memory neural network and a short-term difference method, then an appropriate composite treatment scheme is determined based on the change trend of the pollutant concentration, and water pollution treatment measures are evaluated and optimized by utilizing the effects of different treatment means. Finally, whether the treatment scheme needs to be further optimized or not is determined by dynamically evaluating the treatment effect, the pollution condition of the water body can be monitored in real time and accurately predicted, the machine learning and data analysis technologies are combined, the treatment measures can be dynamically adjusted and optimized, and the accuracy of the treatment effect is improved.

The invention provides a water quality prediction and intelligent regulation and control system based on machine learning, and the system comprises a real-time collection module which is used for collecting the current water quality data of a culture pond in real time; the water quality prediction module is used for determining a water quality prediction result according to the current water quality data based on a pre-trained water quality prediction model; and the intelligent regulation and control module is used for carrying out adaptive regulation and control on a future water quality regulation plan and a bait feeding plan of the culture pond based on the water quality prediction result. Based on a machine learning algorithm, a water quality prediction model is trained, based on the water quality prediction model, a water quality prediction result is determined according to current water quality data, and based on the water quality prediction result, a future water quality regulation plan and a bait feeding plan of a culture pond are adaptively regulated and controlled. According to the invention, accurate water quality management and bait feeding management are automatically carried out on the culture pond, the labor cost is reduced, real-time accurate regulation and control are realized, and the aquaculture efficiency and the aquaculture quality are improved.

The invention provides a water quality environment detection method and system based on 5G communication, and the method comprises the steps: determining a target detection node through obtaining the spatial distribution data of a mobile detection node in a to-be-detected water area and the water area topographic feature data, and obtaining a detection data set based on the target detection node; heavy metal ion migration tracks in the detection data set are processed, a pollutant diffusion boundary prediction map is generated to simulate and generate a multi-scene diffusion envelope range, and an emergency management and control instruction set is generated based on the multi-scene diffusion envelope range; determining a pollution diffusion trend in combination with the emergency management and control instruction set and the environmental influence factor, and generating a water quality environment detection result based on the pollution diffusion trend; according to the technical scheme, the 5G communication technology and a traditional water quality detection method are effectively combined, automation, refinement and intelligentization of water quality environment detection are achieved, and the water quality management and protection capacity is improved.

The invention provides a water quality detection method based on combination of machine learning and spectrophotometry, and the method comprises the steps: measuring the absorbance of a water sample under a plurality of preset wavelengths through a spectrophotometer to form a multi-dimensional absorbance data set, mapping the multi-dimensional absorbance data set to a high-dimensional space through a kernel function technology to obtain a data set with enhanced non-linear features, and carrying out the detection of the non-linear features of the multi-dimensional absorbance data set. Extracting a known water sample and the pollutant concentration corresponding to the known water sample from historical water quality data, generating a training sample set, performing pollutant concentration prediction on the data set with the enhanced nonlinear characteristics by applying a machine learning model, and dynamically adjusting the training sample set by adopting an active learning strategy to obtain a pollutant concentration prediction value, and performing safety grade evaluation on the water sample and outputting a water quality report by using the predicted value of the pollutant concentration in combination with a water quality safety standard, the technical scheme provided by the invention improves the efficiency and accuracy of water quality detection, provides powerful technical support for environmental protection and public health guarantee, and has a wide application prospect. The whole process realizes integrated operation from data acquisition to result interpretation, and ensures scientificity and reliability of water quality management decision.

The invention relates to the field of water resource analysis models, discloses a drainage basin scale phosphorus two-stage output coefficient model construction method, and aims to accurately calculate drainage basin phosphorus output load. The method comprises the following steps: firstly, obtaining watershed related data including a digital elevation model (DEM), a river network system, phosphorus load, soil, meteorology, hydrology and total phosphorus concentration data of river water; secondly, calculating a sub-basin phosphorus output coefficient SPOC and a river channel phosphorus output coefficient RPOC, and evaluating phosphorus load transmission and attenuation conditions; then, constructing a regression model, and improving the accuracy of the model through regression analysis of factors such as rainfall, terrain gradient and river network density; then, calculating a basin total phosphorus output load based on a regression model, and analyzing a spatial-temporal change trend of the basin total phosphorus output load; finally, the model is verified and optimized through measured data, model parameters are adjusted through regression analysis and cross validation, and the prediction reliability is improved. The method can effectively evaluate the phosphorus output load change of the drainage basin, and provides support for water quality management.

The invention relates to the technical field of water quality analysis, in particular to a water quality evaluation method based on data analysis, which comprises the following steps: based on a satellite remote sensing technology, analyzing water spectral characteristics by adopting a deep neural network algorithm, and generating water characteristic data. According to the method, the satellite remote sensing technology and the deep neural network are combined, the accuracy of water body spectral feature analysis is improved, particulate matter in water is classified through image recognition, the analysis speed is increased, the result accuracy is improved, environmental DNA sampling is used for evaluating the biological diversity, and basic data are generated for microbial activity analysis; a micro-fluidic chip and a fluorescence labeling method are used for improving the activity analysis sensitivity of microorganisms and algae, a support vector machine algorithm is used for improving the water quality detection accuracy, an autoregression integral moving average model and a random forest algorithm are combined for deep water quality change analysis, and a comprehensive risk assessment model is used for comprehensively assessing the water quality risk. And a basis is provided for accurate water quality management.

The invention discloses an intelligent high-density pool water quality control method and system, and relates to the technical field of intelligent water quality treatment.The intelligent high-density pool water quality control method comprises the steps that newest data and local data in a high-density pool are collected, the newest data are preprocessed, the preprocessed data are input into a three-dimensional digital twinborn model to update the state of the three-dimensional digital twinborn model, and the state of the three-dimensional digital twinborn model is obtained; and meanwhile, exchanging the local data between adjacent agents to obtain local interaction data of the agents. Extreme working conditions are simulated in the digital twinborn model, a water quality change prediction result is obtained, a bionic swarm intelligence algorithm is started for distributed collaborative optimization, an optimized control instruction is obtained and distributed to each agent, a water quality control task is executed, and a high-density pool water quality state report is generated. According to the method, the ant colony algorithm is used for optimization, the requirement that the water quality reaches the standard is considered, the requirement of saving energy consumption is also considered, the economic adaptability is greatly improved, and it is ensured that efficient water quality management can be achieved through control instructions under various environment conditions.

The invention discloses an online monitoring system and method for water quality for environmental monitoring, and relates to the technical field of online monitoring of water quality. A data acquisition module is responsible for real-time acquisition of online monitoring data related to dissolved oxygen, chemical oxygen demand and total phosphorus of water quality, a data transmission module is responsible for real-time transmission of the collected online monitoring data to a remote data center, a data processing module is responsible for receiving and storing the online monitoring data from the data acquisition module, and an early warning and response module is responsible for triggering early warning information according to the water quality change trend displayed in a line graph and the set early warning conditions. The invention monitors three water quality parameters in real time, and integrates the three water quality parameters to achieve the purpose of comprehensive evaluation and monitoring, and has powerful data processing capabilities and stable data transmission, which not only improves the accuracy and efficiency of water quality monitoring, but also provides a scientific basis and intelligent early warning function for water quality management and protection.

The invention relates to the technical field of water quality monitoring, in particular to an automatic river water quality monitoring and analyzing method and system.The method comprises the following steps that on the basis of real-time and historical water quality monitoring data, multi-source monitoring information is integrated through the data fusion technology, and flow characteristics of a river water body are simulated through a finite element analysis method; and carrying out weighted average on data collected at multiple time points by implementing a Kalman filtering algorithm to generate an integrated hydrodynamics and spatial distribution prediction result. According to the analysis method, the analysis depth and accuracy of complex water body system behaviors such as rivers are improved through the analysis method integrating the water quality monitoring data and the fluid dynamic model, and the relation between the water quality change and the water flow dynamic process is comprehensively analyzed and predicted. The scientificity and effectiveness of a water quality management strategy are enhanced, and the capability of coping with complicated water environment challenges is improved.

The invention discloses a transportation environment intelligent adjusting method and system for long-distance lateolabrax japonicus transportation, and relates to the technical field of long-distance lateolabrax japonicus safe transportation. The method comprises the steps that lateolabrax japonicus transportation characteristics are constructed according to the estimated transportation duration, the average size of lateolabrax japonicus, the number of lateolabrax japonicus and the water temperature; constructing an anesthetic dosage recommendation model, importing the transport characteristics of lateolabrax japonicus into the trained anesthetic dosage recommendation model, obtaining the recommended anesthetic dosage for lateolabrax japonicus transport, anesthetizing to-be-transported lateolabrax japonicus, collecting the temperature, water quality and oxygen concentration of a transport water body in the transport process, and evaluating the activeness of lateolabrax japonicus bodies. And generating an adjustment gradient based on the activeness to dynamically adjust the transportation water body environment, and additionally setting an emergency and an emergency plan library to process the transportation emergency. By optimizing water quality management, improving an oxygen supply system and introducing an intelligent monitoring and regulating means, dynamic regulation and fine management in the whole transportation process are achieved, and health and survival of lateolabrax japonicus in the transportation process are ensured.

The invention discloses a hydrogen production water quality management system, which comprises an electrolytic bath device, a water storage device, a hydrogen separation device, an oxygen separation device and a water quality filtering device, and the hydrogen separation device and the oxygen separation device are respectively communicated with a hydrogen electrolysis outlet and an oxygen electrolysis outlet of the electrolytic bath device. A first water return main path of the oxygen separation device guides separated water to a water inlet of the electrolytic bath device, the first water return main path is provided with a first conductivity sensor and is in bypass connection with a water return bypass, and the water return bypass or the first water return main path located between the water return bypass and the electrolytic bath device is provided with a first valve. The first valve is in communication connection with the first conductivity sensor, and the water quality filtering device is connected with the water return bypass and a water supplementing pipeline of the water storage device in a converging mode and can guide filtered water to the first water return main path, so that water entering the system from the water storage device and circulating water from the first water return main path can be filtered at the same time. The electric conductivity fluctuation is avoided by reducing the electric conductivity in the water path of the system.

The present invention may provide a water quality management device including a spectral camera, a memory, and a processor, and an operating method thereof. The processor is configured to collect the plurality of spectral images for at least a portion of the stored water in response to a predefined event occurrence, classify the plurality of spectral images into a suspended material data set and a non-suspended material data set based on a presence or absence of a suspended material in the spectral images, perform learning to generate a water quality detection model used to detect water quality by using at least a portion of the suspended material data set and the non-suspended material data set, and store the water quality detection model generated through the learning.

The invention relates to the technical field of safety early warning, in particular to a safety protection early warning method based on a neural network, and the method comprises the following steps: collecting the pH value, dissolved oxygen concentration, turbidity, temperature and flow of effluent quality, carrying out data cleaning and abnormal value elimination, carrying out time sequence sorting, and generating water quality time sequence data. According to the method, the neural network model is applied to predict the water quality parameters such as the pH value, the dissolved oxygen concentration, the turbidity, the temperature and the flow, the prediction error is combined with the short-term trend value, and the water quality risk is dynamically evaluated. And meanwhile, the parameter weight is dynamically adjusted to match the real-time importance of each parameter, so that the prediction accuracy is improved. The analysis of the long-period trend and the fluctuation range enables the early warning system to perform effective early warning before the risk reaches a threshold value, and allows timely countermeasures to be taken, thereby enhancing the perspectiveness and initiative of water quality management.

The invention relates to the technical field of water body recognition, in particular to a black and odorous water body recognition method and system based on artificial intelligence, and the method comprises the following steps: collecting a water body image, carrying out the pixel-level analysis of the water body image, calculating the spectral response of each pixel, analyzing the color distribution, and comparing the spectral characteristics with the color standard. And screening the image areas matched with the black and odorous characteristics to generate spectral characteristic data. Through deep analysis of spectral response and color distribution of each pixel, pollution characteristics in a water body can be identified on a microscopic level, neural network weight adjustment and image processing can be optimized, images of various scales and angles can be effectively processed, generalization ability and environmental adaptability of the model are enhanced, and the method is suitable for being applied to the field of water quality detection. Through detailed forward and backward propagation optimization, the performance of the network in distinguishing water body images with different pollution degrees is remarkably improved, an accurate pollution grade evaluation result can be output, and data support is provided for formulating more targeted water quality management and pollution abatement measures.

The invention discloses an intelligent monitoring system and method for outlet water of a central air-conditioning cooling tower, and relates to the technical field of cooling tower outlet water quality monitoring. The intelligent outlet water monitoring system for the central air-conditioning cooling tower comprises an operation fluctuation analysis module, an outlet water quality monitoring module and a water quality discharge and supplement optimization module. According to the invention, fluctuation analysis is carried out on the operation efficiency of the central air-conditioning cooling tower, the outlet water flow of the central air-conditioning cooling tower is adjusted and the outlet water quality data is collected based on the fluctuation analysis result, and then the water quality of the outlet water of the central air-conditioning cooling tower is monitored. And finally, combining a water quality warning analysis result to carry out effluent water quality discharge compensation optimization on the effluent water quality of the central air-conditioning cooling tower so as to realize more efficient effluent water quality management in the intelligent effluent water monitoring process of the central air-conditioning cooling tower. The problem that in the prior art, interaction between water quality and operation is not considered when the water quality of the outlet water is managed in the intelligent monitoring process of the outlet water of the central air conditioner cooling tower is solved.

The invention provides an intelligent water quality management and control system in a fishing and light integrated mode, and relates to the technical field of intelligent water quality management and control systems.The intelligent water quality management and control system comprises a connecting box, a storage box is installed in the connecting box, a water inlet pipe is bonded to the bottom of the storage box, and one end of the storage box is fixedly connected with a water pump; a water inlet pipe is installed at the bottom of the storage box, a water outlet pipe is installed at the bottom of the storage box, an electromagnetic valve is installed on the surface of the water outlet pipe, the water inlet pipe and the water outlet pipe are inserted into the bottom of the connecting box in a penetrating mode, a signal emitter is assembled at the top of the connecting box, and a management and control system body is installed at the top of the storage box. And then the sliding pipe is manually controlled to slide on the inner wall of the sliding cylinder, so that the length of the sliding pipe can be increased to a certain extent, and the transverse length and the vertical length of the sliding pipe can be well changed.

Provided are a water quality control method capable of performing control for maintaining water quality management items at appropriate values at low cost, a method for creating a control table used therefor, and a control table creation program. 【Solution means】This method for creating a control table and the control table creation program create a control table using actually measured data in an operating management system. Further, for the insufficient part, it is calculated by an operation based on the actually measured data. Therefore, the control table can be created without using expensive equipment. Further, the acquisition of data necessary for creating the control table can be handled by existing equipment, and the introduction cost can be kept low. Further, in this water quality control method, a large calculation load does not occur in the management value setting device. Therefore, it is possible to use existing equipment for the management value setting device, and the introduction cost of the present invention can be kept low.

The invention discloses a water quality monitoring method and system based on multi-source data. The method comprises the following steps: acquiring multiple types of monitoring sensing data of a target water area; determining a plurality of pollution origin positions of the target water area based on the monitoring sensing data belonging to the first monitoring level; based on the pollution origin position, screening associated sensing data belonging to a second monitoring level from all the monitoring sensing data; and analyzing the water quality pollution condition of the target area according to the associated sensing data and the pollution origin position. Therefore, accurate water quality pollution assessment based on multi-stage monitoring and multi-source data can be realized, the accuracy and response efficiency of water quality management of the target water area are improved, and the risk of pollution diffusion is reduced.

The invention discloses a migratable surface water quality prediction method based on representation learning, and the method comprises the steps: 1, obtaining historical monitoring data, so as to construct a data set in a multi-site sequence format, carrying out the mask processing of the historical monitoring data in the data set, so as to obtain mask data, forming a training test set by the historical monitoring data and the mask data; 2, selecting a Transform model framework with the same total number of source domains to construct a prediction network; step 3, training the prediction network to obtain a prediction model; and step 4, inputting the water quality data collected in the station and the meteorological data of the current day into the prediction model to obtain the water quality data of the source domain where the station is located. The invention further provides a device for predicting the water quality of the transferable surface water. According to the method provided by the invention, the key water quality index of the source domain river monitoring section is predicted through the change of the water quality information of each station in the source domain, and more accurate and comprehensive reference is provided for subsequent water quality management.