7358results about "General water supply conservation" patented technology

The invention relates to the technical field of drainage basin water scheduling, and discloses a drainage basin water regulation and control optimization method based on ecological element changes. The method comprises the following steps: deploying a drainage basin monitoring system, and collecting ecological element real-time data such as a hydrological parameter sequence and a remote sensing image; after the data is cleaned and converted, hydrological trend features and spatial distribution features are extracted by adopting a feature learning model, and the hydrological trend features and the spatial distribution features are fused into unified ecological representation through a cross-modal alignment mechanism; inputting the unified ecological representation into a physically constrained neural network prediction model, and outputting a water regimen dynamic prediction value; and finally, based on the predicted value, a water resource regulation and control instruction is generated and executed by using a multi-objective decision algorithm so as to optimize the watershed water circulation process. According to the method, feature extraction comprehensiveness is improved through multi-source data fusion and cross-modal analysis, prediction reliability is enhanced in combination with physical constraints, reasonable allocation of water resources is achieved by means of multi-target decision, the ecological condition of a drainage basin can be improved, and the water utilization efficiency is improved.

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 discloses an intelligent water affair monitoring and management system based on digital twinning, and relates to the technical field of intelligent water affair. The intelligent water affair monitoring and management system comprises a water affair monitoring and management platform, and the water affair monitoring and management platform is in communication connection with the following modules: a multi-source data acquisition module; the water affair monitoring system is used for collecting and preprocessing water affair monitoring data from a plurality of monitoring points of the water affair system, monitoring changes of a pipe network topological structure and obtaining dynamic data of a pipe network connection relation and geometric parameters. Through the digital twinborn technology, data of multiple monitoring points can be integrated in real time, abnormal events such as water quality pollution, equipment faults, water shortage and hydraulic change can be rapidly recognized in combination with the abnormal trend analysis module, early warning signals are automatically generated, the response time is remarkably shortened through an instant early warning mechanism, and the early warning efficiency is improved. Therefore, the management personnel can take measures at the initial stage of the abnormal event, the problem expansion is effectively prevented, and the timeliness and accuracy of water management are improved.

The invention relates to a water quality prediction method and system based on gating residual enhancement and feature fusion, and belongs to the technical field of water environment intelligent analysis and deep learning. Taking each water quality index as a node of the graph, and constructing two complementary variable relation graph structures by utilizing a Pearson's correlation coefficient and mutual information; respectively inputting the two graph structures into a graph convolutional network, extracting deep dependency features among indexes, and splicing and fusing the deep dependency features. A multi-head attention mechanism is used as a trunk to extract global time dependence, a GRU network is introduced to extract local time sequence features, GRU output is used as an adjustable residual term to be injected into the attention trunk through a residual gating mechanism, self-adaptive enhancement of local dynamic features is achieved, and finally a self-adaptive fusion mechanism is introduced to generate comprehensive representation. According to the method, the complex dependency relationship between the water quality indexes and the time dynamic evolution process can be modeled in a collaborative manner, the response capability to key local change and sudden change events is remarkably enhanced, and the accuracy and robustness of water quality prediction are improved.

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 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 relates to the cross technical field of artificial intelligence and environment monitoring, and discloses an algal community structure change prediction algorithm and system based on multi-source data fusion, and the algorithm comprises the steps: obtaining water quality, weather and plankton multi-source time sequence data; performing time alignment and missing value interpolation; eliminating and screening key environment factors through recursive features; performing dynamic weighted fusion on the multi-modal features by using a space-time attention mechanism; inputting a three-layer stacked LSTM network to output future algae dominant species abundance prediction; and model parameters are corrected on line based on measured data. The system comprises a multi-source data acquisition module, a preprocessing module, a key factor extraction module, a space-time attention fusion module, a dynamic prediction module and an adaptive correction module. According to the method, the prediction accuracy and stability are remarkably improved, and algal bloom early warning and ecological regulation are effectively supported.

A method for large-scale prediction on water requirement of crop based on a spatiotemporal fusion model under a physical constraint, is applied to the field of prediction of water requirement of crop, wherein the method includes: acquiring multi-source data at the starting time and multi-source data for at least one sampling interval; encoding and fusing the multi-source data at the starting time and the multi-source data for at least one sampling interval by 1DCNN-MLP to obtain a comprehensive feature expression; and inputting the comprehensive feature expression into a trained spatiotemporal feature fusion model to obtain a prediction result of water requirement of crop at a target time output by the trained spatiotemporal feature fusion model, where the spatiotemporal feature fusion model includes a graph convolution network model and an Informer model.

The invention discloses a reservoir water regimen analysis method and system based on artificial intelligence, and the method comprises the steps: collecting original signals from multi-source monitoring indexes, such as water level, flow, rainfall and water quality, carrying out the standardized conversion through employing distributed calculation nodes, and forming a unified multi-source data set; based on this, using a feature extraction network to fuse upstream rainfall and reservoir flow, extracting space-time correlation features, and determining a short-term water level change trend; historical water quality abnormal data are integrated through a sequence prediction network, a time sequence is modeled, and potential pollution risks are judged; when the risk exceeds a threshold value, dynamically adjusting the weight of the prediction model, and generating an optimized water regimen simulation scene; finally, resource scheduling logic is fused, multi-scene risks are evaluated, and an optimization management strategy is output. Through deep fusion of spatial-temporal feature extraction and dynamic prediction, accurate water regimen prediction and flood control water supply decision support are realized, and the water resource management efficiency and the pollution prevention and control capability are improved.

The invention relates to the technical field of water conservancy management, and discloses a water conservancy dynamic decision-making method and system based on digital twinning and multi-source heterogeneous data, and the method comprises the steps: carrying out the real-time information capturing of a target water conservancy system, and generating a system operation information scheduling matrix based on a grid geographic model and a time dislocation mark; performing hierarchical slicing on the system operation information scheduling matrix according to a multi-scale time window to form a multi-channel flow framework of regional hydrological dynamic behaviors; analyzing the evolution trends of the water level, the flow and the water quality by using a multi-channel flowing framework and a dynamic fusion analysis operator; dividing a river reach, a reservoir and a regulation and control facility corresponding to the potential risk event as candidate scheduling areas; and based on the local risk coupling network, dynamically optimizing a scheduling strategy, a pump gate operation sequence and a data acquisition frequency, and generating a real-time scheduling and emergency response scheme of the candidate region. The method has the advantage of improving flood early warning accuracy and water supply scheduling efficiency.

The invention relates to a water plant intelligent chemical adding amount prediction method based on data preprocessing, and belongs to the technical field of deep learning and intelligent chemical adding. Calculating a theoretical dosage based on historical flow, pH, water temperature and turbidity; dividing a plurality of clusters and splicing to query historical dosage data; weighting and fusing the theoretical dosing amount and the inquired historical dosing amount as a pre-treatment dosing amount; learning the relationship among the flow, the pH, the water temperature, the turbidity and the pretreatment dosage to perform forward feedback optimization; building an alumen ustum image recognition model, classifying alumen ustum, and associating the alumen ustum with corresponding dosage to form a dosage feedback algorithm model; building a dosage feedback model based on the sedimentation tank outlet water quality monitoring data; and correcting the weight in the preprocessed dosage based on the adjusted data of alumen ustum identification and water quality feedback on the dosage. The method can effectively reduce the influence of the dosage data error on the effectiveness of the model, can reduce the complexity of the algorithm model, and improves the robustness of the model.

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 non-point source pollution emergency bypass decision control method, particularly relates to the technical field of automation and process control, and aims to solve the problems that existing scheduling cannot align multi-source measurement and evaluate credibility in minutes under a unified time base, and is lack of risk measurement integrated with a tide level phase and a moisture regain path and same-layer gating. The bypass rhythm is easy to misjudge, and the oscillation is difficult to audit. A minute-level risk density is constructed through multi-source alignment and credibility grading under a unified time reference, injected tide level phases and moisture regaining paths, bypass rhythms are constrained by same-layer gating and tide windows, and beam search, boundary arbitration and fixed window closed-loop correction are matched, so that the risk density is improved. Therefore, under the superposition of rainstorm and tide, decision making and execution are stably limited in a non-backflow red line, bearing and equipment limit, misjudgment and oscillation are remarkably reduced, the problems of unavailability and difficult auditing caused by multi-source asynchronization and moisture regain coupling are solved, intelligent decision support is provided for non-point source pollution treatment, and it is ensured that the treatment technology is accurate and efficient.

The invention discloses a drainage basin water environment intelligent management method and system, and belongs to the technical field of environment monitoring and water resource management. Multi-source heterogeneous water environment data is obtained, and a multi-modal pollution sensing map is constructed through fusion; extracting a time mutation vector and a spatial offset path of a pollution abnormal diffusion source point based on the map, and generating a pollution propagation sensitive weight map; performing cross-space-time matching on the sensitive weight map and historical climate disturbance data, generating a pollution causal chain path map by using a deep causal learning model, identifying a potential hidden pollution source distribution unit, predicting a response trend of the potential hidden pollution source distribution unit to future water quality change, and forming a multi-scene pollution evolution simulation result; and finally, constructing a pollution response priority index map, and generating a treatment resource optimal allocation sequence and an intervention strategy in combination with the drainage basin management database. According to the invention, intelligent identification and dynamic treatment optimization of complex pollution diffusion behaviors can be realized, and scientificity and timeliness of water environment management are improved.

The invention relates to the technical field of water quality data analysis, and discloses a river water quality pollution assessment method and system based on big data analysis, and the method comprises the steps: building a water quality parameter set; obtaining a preprocessed water quality data set; obtaining a global statistical analysis result through descriptive statistical analysis; carrying out hierarchical multi-time evaluation analysis to obtain a hierarchical evaluation result; obtaining a deviation value between the current water quality data and standard water quality data of a laboratory; reliable water quality characteristic data are obtained; a difference trend analysis result is obtained; performing relevance verification on the river water quality distribution map and the time change map of the target area according to the difference trend analysis result to form a comprehensive analysis result; and finally generating a comprehensive water quality pollution assessment report containing water quality traceability information and a comprehensive analysis result. According to the invention, a multi-parameter sensing technology and intelligent data processing are organically combined, so that the water quality of the river is efficiently, accurately and carefully analyzed, and the pollution degree is evaluated.

The invention relates to the technical field of environment intelligent supervision, and discloses a visual river and lake water area health ecological supervision and management system. A data acquisition and feature extraction module of the system is responsible for acquiring multi-source ecological monitoring data of water areas of rivers and lakes, extracting spatial change features of motion trails of aquatic organisms and a time sequence of water quality parameters from the data, and identifying abnormal features according to an index variation mode corresponding to a predefined ecological health level; and generating an ecological health abnormity response label set associated with the specific geographic position. And the health state analysis module obtains key information of the marked water area section based on the label set, analyzes a change track and a deviation interval of the health state in the supervision time frame, and determines a health state lagging time period. And the evaluation delay processing module is used for extracting related time information to calculate a time difference according to the water area subareas related to the lagging time period, screening out the water area subareas which are not evaluated completely and sorting the water area subareas, and generating an adjustable configuration scheme of the monitoring time period of the water area subareas.

The invention provides a water environment model coupling data processing method and system, and relates to the technical field of hydrology and water quality monitoring. The method comprises the following steps: determining a soil permeability characteristic descending trend and estimating an uncertainty range of a runoff coefficient; obtaining source model data according to the uncertainty range of the runoff coefficient by using a regional pollution production and convergence model; using the one-dimensional river network model to obtain error information according to the source model data, and outputting prediction data; and determining a prediction deviation mode by comparing the prediction data with the real-time monitoring data, and correcting parameters of all related models in the whole model chain. The method aims at solving the technical problem that the permissible discharge amount set based on a three-model coupling system does not conform to the actual absorption capacity of a water body due to continuous nonlinear deviation of river network model prediction and actually measured water quality data in the three-model coupling system, and accurate analysis of the urban pollution load and water environment quality response relation is achieved. And real-time and self-adaptive adjustment of the permissible emission amount is carried out.

The invention discloses a drainage pipeline multi-parameter sensing trenchless repair dynamic regulation and control method and system. Through a fusion technology path of multi-source comprehensive data acquisition, edge calculation real-time cross validation of abnormity, pipeline state simulation analysis of defect levels, intelligent algorithm dynamic adjustment of repair parameters and geographic information visualization platform full-process monitoring, closed-loop management from abnormity identification to repair regulation and control is realized. A comprehensive data set is formed through multi-source data acquisition, an edge computing technology is utilized to quickly identify abnormities, a simulation technology is combined to evaluate the severity of defects, then process parameters are optimized and repaired based on defect levels, and a regulation and control instruction is generated through linkage of a visual platform and a pump station dispatching system. And finally, a pipeline repair and operation regulation and control scheme is formed through integration, and the repair effect and the system stability are ensured. According to the method, the pipeline abnormity processing accuracy and the repairing efficiency are remarkably improved, and a technical guarantee is provided for safe and stable operation of an urban drainage system.

The present invention discloses a three-dimensional fluorescence portable water quality analyzer for pollution component determination and source tracing, and relates to the technical field of water quality analysis, the analyzer comprises the following components: a sample collection and processing module for collecting a water sample from a to-be-detected water body, filtering to remove impurities, and diluting the water sample according to a self-adaptive dilution multiple algorithm; fluorescence intensity information of a water sample under different excitation wavelengths and emission wavelengths is obtained through the three-dimensional fluorescence detection module, rich three-dimensional fluorescence spectrum data is formed, the real-time fluorescence lifetime imaging module is introduced, and by measuring the fluorescence lifetime of a fluorescent substance and utilizing the principle that different fluorescent substances have different fluorescence lifetime characteristics, the real-time fluorescence lifetime of the water sample is obtained. Various pollution components in the water sample are further distinguished and recognized, the pollution components can be accurately distinguished through fluorescence lifetime measurement and imaging, the accuracy of pollution component judgment is greatly improved, and a reliable basis is provided for accurate treatment of water quality pollution.

The invention discloses a sewage treatment supervision method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: extracting a medium-time feature vector in multi-scale feature vectors, inputting a Transform model, predicting a chemical parameter, extracting a long-time feature vector, predicting a water quality parameter for a linear predictable segment through employing LSTM, and predicting a non-linear segment through employing TVF. Obtaining a comprehensive predicted value and a confidence interval, carrying out anomaly classification, defining an optimization function target, and optimizing process parameters by using a genetic algorithm; the prediction precision of nonlinear water quality parameters is improved by introducing subinterval division of spectral intensity data and multi-scale feature vector construction in combination with LSSVM, Transform and LSTM models, real-time dynamic adjustment of process parameters is realized through a BP neural network optimized by chaotic mapping and a genetic algorithm, and the intelligent level of a sewage treatment system is improved.

The invention specifically relates to an intelligent water affair dynamic supervision method and platform based on digital twinning, and relates to the technical field of intelligent water affair supervision. Risk early warning and situation deduction; emergency decision making and dispatching command; and post-treatment evaluation and knowledge precipitation. According to the method, a comprehensive and scientific risk quantification system is constructed through a combined weighting mechanism of multi-source data fusion, an analytic hierarchy process and an entropy weight method, and dynamic and accurate description of risks of each section of pipeline and each node of a pipe network is achieved in combination with a real-time working condition correction coefficient and a second-level updating mechanism; and grading accurate early warning triggered by a dynamic threshold value is matched with risk situation deduction of a simulation model, so that the influence range and the development trend of risks such as pipe explosion and water quality pollution can be pre-judged in advance.

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 discloses a multi-source environment monitoring data fusion analysis and abnormity early warning method based on deep learning. According to the method, the spatial attention weight is calculated by fusing the feature similarity and the geographic distance, and the modeling precision of the complex spatial dependency relationship between the environmental monitoring nodes is remarkably improved. Specifically, the model firstly divides a monitoring area into fine grid units, each unit serves as an independent node to participate in calculation, the direction consistency of node feature vectors is quantified through cosine similarity, and meanwhile, a geographic distance attenuation factor is introduced to dynamically adjust weight distribution. The double consideration enables nodes with similar environmental characteristics in adjacent areas to obtain higher attention, and effectively avoids the problem of spatial correlation misjudgment easily occurring in a complex terrain in a traditional fixed adjacent matrix. When local pollution diffusion occurs in a monitoring area, the model can quickly position a propagation path of a core pollution source and surrounding affected nodes, and the space correlation identification efficiency is improved.

The invention discloses a source network, plant, river and lake integrated model simulation method and device, a medium and equipment in the technical field of regional water environment pollution treatment. The method comprises the following steps: inputting a boundary condition and an initial condition of a first model into a pre-constructed surface runoff production and convergence model of a target area, and outputting a flow rate of surface runoff and a water quality index time sequence; inputting the boundary condition and the initial condition of the second model into a pre-constructed pipe network model of a target area, and outputting a flow and water quality index time sequence of key nodes in a pipe network system; and inputting the boundary condition and the initial condition of the third model into a pre-constructed riverway and lake grid model of a target area, and simulating the river and lake hydrodynamics and water quality change process of the target area. According to the invention, coupling simulation of surface runoff, pipe networks, rivers and lakes is realized, and a propagation path and an influence mechanism of pollutants are comprehensively reflected; and high precision and reliability of the model are ensured through parameter calibration.

The invention relates to the field of water environment monitoring and treatment, in particular to a lake and reservoir pollution flux dynamic evaluation and optimization system and method.According to the system, hydrological, water flow, meteorological and remote sensing data are collected in real time through a data processing module, and a pollution flux space-time matrix is generated; the atlas generation module constructs a mechanism model based on the matrix and outputs a pollution flux thermodynamic atlas; the scheme simulation module determines a pollution source emission reduction scheme according to the map and simulates the influence of the pollution source emission reduction scheme on lake and reservoir water quality and ecology; and the feedback optimization module performs treatment efficiency scoring on the simulation result, compares the evaluation result with a preset threshold value, and adjusts the emission reduction scheme and updates the thermodynamic map when the evaluation result does not reach the standard. According to the method, through data driving, map visualization, scene simulation and dynamic feedback closed loop, accurate evaluation and optimal regulation and control of the lake and reservoir pollution flux are realized, and a scientific basis is provided for pollution abatement decision.

The invention discloses a water quality safety monitoring and early warning method based on big data, and relates to the technical field of monitoring and early warning. Setting sampling points according to areas, pasting scene labels, and arranging sensors to obtain water quality information; establishing a water quality evaluation model based on a support vector machine improved model, inputting the water quality information into the water quality evaluation model, outputting to obtain a water quality category, and dividing the sampling points into normal sampling points and key sampling points according to the water quality category; based on the normal point data, using LSTM to predict water quality safety and performing graded early warning; based on the key point data, positioning a pollution source by using space-time Kriging interpolation, and simulating a pollution diffusion path by using multivariable collaborative interpolation; and combining normal point early warning and key point diffusion simulation to obtain a water quality safety monitoring early warning result. According to the method, a global risk grading report is generated by fusing a normal sampling point early warning result and a key sampling point diffusion path, and emergency response and long-term treatment strategies are matched.

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 utility model discloses a length-adjustable concrete spreader, which relates to the technical field of concrete spreader, and comprises a support mechanism, a conveying pipeline, a cross arm and an adjusting mechanism, the adjusting mechanism comprises a connecting pipe, an adjusting assembly and a telescopic assembly, connecting flanges are arranged at the two ends of the connecting pipe, the adjusting assembly is arranged at one end of the connecting pipe and comprises an adjusting pipe and an adjusting bolt, the adjusting pipe is inserted into one end of the connecting pipe, an adjusting flange is arranged at one end of the adjusting pipe, and the adjusting bolt is inserted into the adjusting flange and the connecting flange. The telescopic assembly comprises a telescopic pneumatic rod and a moving pipe; the telescopic pneumatic rod is arranged on the side face of the connecting pipe, the movable pipe is inserted into the adjusting pipe, a movable flange is arranged at one end of the movable pipe, and the pneumatic driving end of the telescopic rod is fixedly connected with the movable flange. The length of the conveying pipeline is adjusted by designing an adjusting structure, the construction range is expanded, the installation fault tolerance is improved, and incomplete pouring caused by too large installation deviation is avoided.

The invention relates to the technical field of digital twinborn and drainage basin environment monitoring simulation, in particular to a drainage basin environment data simulation deduction system based on a digital twinborn technology, which comprises a digital twinborn management center used for calling multi-source heterogeneous monitoring data of a drainage basin environment to obtain an aligned environment data sequence; the causal coupling analysis unit is used for obtaining a steady-state logic signal or a dynamic decoupling signal; when a dynamic decoupling signal is generated, the topology adaptive reconstruction unit is used for performing structural mutation recognition feedback analysis on a calculation map of the digital twin model to obtain a reconstructed topology map; when the steady-state logic signal is generated, the simulation deduction execution unit is used for performing environment evolution deduction analysis on the aligned environment data sequence based on the current calculation map to obtain a simulation deduction result; according to the method, model calculation divergence or misleading output caused by sensor faults is effectively avoided, and the calculation problem caused by multi-source data heterogeneity is solved.