1070 results about "Smart water" patented technology

The invention relates to the technical field of intelligent water affairs, in particular to an intelligent water affairs dynamic monitoring system and method based on digital twinning, and aims to detect data drift in real time, calculate a standard deviation change rate to generate abnormal judgment, analyze model stability and drift rate synchronism to output deviation indexes, screen data abnormal items to form quality evaluation, and realize dynamic monitoring of the intelligent water affairs. And comparing node synchronization difference to trigger compensation correction, and generating a monitoring adjustment result through integrity verification. According to the invention, through comparison of the multi-cycle standard deviation change rate and the environmental parameters, sensor drift classification and confidence evaluation are realized, the reliability of the model is enhanced by constructing a composite attenuation coefficient, abnormal sampling is positioned by combining the data change rate and an outlier map, and data synchronization is guaranteed by adopting a difference matrix and state feedback. And version verification and repair records are introduced to realize closed-loop tracing.

The invention relates to the technical field of water and fertilizer integration, and provides an intelligent water and fertilizer integration optimization method and device based on the Internet of Things technology, and the method comprises the following three core stages and interaction mechanisms: 1, a data perception and edge processing stage; 2, an intelligent decision-making and dynamic optimization stage; and step 3, an accurate execution and closed-loop control stage. Soil parameters, meteorological parameters and crop physiological parameters are comprehensively collected through a multi-modal sensor network, the data transmission efficiency is improved by using an LoRa / NB-IoT hybrid transmission protocol, data cleaning and standardization processing are completed in combination with edge computing nodes, and the data fusion depth and quality are improved; a multi-objective optimization function is constructed based on a crop growth model, a water and fertilizer regulation and control strategy is dynamically generated through a machine learning algorithm, and collaborative optimization of water saving, yield increasing and environmental protection is achieved.

The invention discloses an urban inland inundation water recession process numerical simulation method and system based on physical-data fusion, and belongs to the technical field of urban flood control and intelligent water affairs. A hydraulic model and a data driving model are fused, a prediction result is dynamically weighted through gating attention, and real-time correction is performed by using Kalman filtering; the method comprises the steps of multi-source data fusion and enhancement, wherein data collection and processing are carried out, space-time alignment is achieved, and feature construction is carried out; mechanism and mathematical double-engine collaborative prediction: collaborative prediction is carried out based on a physical model engine and a data driving engine, and adaptive fusion is realized; online reasoning and dynamic correction, including Kalman filtering dynamic correction and extreme scene emergency processing; and performing model evaluation and iteration. According to the method, the problem of high-precision prediction of the recession time and the ponding range in a complex urban environment is solved, the generalization ability of the model in non-seen scenes such as pipeline blockage and rainstorm extrapolation is improved, and a reliable decision basis is provided for urban inland inundation emergency scheduling and drainage facility optimization.

The invention discloses an intelligent discharge port dynamic regulation and control system and method based on multi-source time sequence fusion, and relates to the technical field of intelligent water affairs, and the system comprises a data collection module which is used for obtaining the multi-source data of a target area in real time; the space-time fusion module is used for mapping the meteorological radar data to the topological space of the drainage pipe network through a space-time alignment encoder, dynamically fusing the meteorological radar data, the surface runoff sensor data and the historical drainage data by adopting a multi-head attention mechanism, and generating a space-time fusion tensor; the prediction model module is used for constructing a TCN-LSTM hybrid model, taking the space-time fusion tensor as input and taking future drainage flow as output; and the dynamic regulation and control module is used for optimizing the gate opening degree and the pump station power control sequence in a rolling manner based on a model predictive control algorithm, and performing dual-target optimization by combining real-time electricity price data and overflow risks. According to the system, the prediction precision of the drainage flow can be improved, and double-target balance of the overflow risk and the energy consumption cost can be achieved.

The invention discloses an intelligent water quality monitoring device based on Internet of Things, relates to the technical field of environmental monitoring, and aims to analyze a complex nonlinear relationship in water quality data, accurately predict a water quality change trend and identify potential water quality problems in advance through a deep learning model and a system. The dynamic weight calculation module can dynamically adjust the importance of water quality parameters according to real-time data and environmental changes, the monitoring precision and adaptability are improved, an intelligent feedback mechanism automatically adjusts the sampling frequency and the sensor position when an early warning signal is triggered, accurate monitoring of the water quality changes is ensured, and the monitoring accuracy is improved. The Internet of Things technology supports real-time transmission of water quality data and analysis results to the cloud, managers can check water quality states, historical trends and early warning records through a remote monitoring interface, the response capability and management efficiency of the system are improved, and the intelligent water quality monitoring device has the advantages of being high in precision, high in intelligence and high in adaptability.

The invention discloses an intelligent water affair monitoring management method and system based on the Internet of Things, particularly relates to the technical field of water affair management, and is used for solving the problems of control instruction mismatching, redundant execution and equipment overload caused by the fact that a static topology model cannot sense the dynamic change of a pipe network in real time in the prior art. Through real-time collection of pipe network operation data and analysis of time-space correlation characteristics of water flow propagation delay parameters and pressure mutation, a pipe network topology change event is dynamically perceived; the flow direction sudden change reasonability is verified in combination with fluid mechanics conservation constraint, and a corrected topological mapping table is generated through reverse calculation; candidate paths are screened based on water flow inertial parameters and pressure gradient threshold values, a safety control instruction set is generated through a multi-stage verification rule, dynamic matching of a pipe network regulation and control instruction and a real topological structure is achieved, the matching degree of the control instruction and the physical state of a pipe network is effectively improved, the leakage risk and energy waste are reduced, the manual maintenance requirement is reduced, and the safety of the pipe network is improved. The service life of equipment is prolonged.

The invention provides a digital twin water conservancy hyper-fusion method for coupling multi-source data and an all-in-one machine, and relates to the technical field of intelligent water conservancy. By constructing a unified water conservancy spatio-temporal data link protocol, efficient fusion of multi-source heterogeneous data of hydrology, meteorology, engineering operation and the like is realized, and the problem of water conservancy system data islands is solved; a coupling distributed hydrological model and a hydrodynamic model are established, and real-time accurate simulation of the watershed hydrological process and the river channel hydrodynamic process is achieved; a multi-target dynamic weight reinforcement learning algorithm is adopted to optimize a water conservancy scheduling scheme, model parameters are dynamically corrected in real time through a time sequence error regression analysis model, and closed-loop adaptive optimization is achieved; according to the method, closed-loop cooperation of water conservancy data fusion, real-time accurate simulation, intelligent scheduling optimization and dynamic parameter self-correction is realized, and the real-time performance and accuracy of water conservancy intelligent decision making are remarkably improved.

The invention relates to the technical field of water affair pipe network operation and maintenance, in particular to an intelligent analysis processing and decision support method for mass data of an intelligent water affair integrated platform based on a GIS (Geographic Information System) and the Internet of Things. According to the method, GIS topology and data of the internet of things are fused, the problems that the estimation deviation of the influence range of traditional pipe explosion is large, and the valve closing instruction lacks accuracy are solved, and the valve closing effectiveness is verified. The pipe explosion emergency response efficiency is improved, the water resource waste is reduced, the water supply stability is guaranteed, and the method is suitable for urban water refinement management.

The invention provides an intelligent water affair scheduling method based on LSTM and multi-objective optimization, and belongs to the technical field of intelligent water affair management and urban water supply system optimization. Comprising the following steps: based on water consumption data, performing analysis through a long short term memory neural network to obtain water consumption demand prediction; based on the water consumption demand prediction, combining pipeline information and pipe network layout data, performing pipe network pressure partition optimization, and obtaining a pipe network layout optimization result; and based on the pipe network layout optimization result, combining the historical operation data of the equipment and the water demand prediction, carrying out efficiency analysis on the pump station equipment to obtain an operation combination scheme of the equipment.

The invention discloses an intelligent water conservancy three-dimensional twinborn scene construction method and system, and relates to the technical field of intelligent water conservancy, and the method comprises the steps: collecting scene data in real time through a multi-source sensor disposed at a water conservancy facility site, including hydrological parameters, facility structure data, environment image data and ecological monitoring data; performing space-time alignment and deviation correction on the scene data by using a deep learning driven multi-modal feature fusion algorithm to generate a three-dimensional water conservancy grid model of a unified coordinate system; according to the real-time data updating frequency and the environment change trend, a local or global updating strategy of the three-dimensional water conservancy grid model is adaptively adjusted, the optimized three-dimensional water conservancy grid model and a virtual reality engine are integrated, and a twin scene supporting gesture interaction is constructed; an ecological simulation module is embedded in the twinborn scene to dynamically evaluate the influence of water conservancy activities on water quality and biological habitats.

The invention relates to the technical field of intelligent water affairs, in particular to an intelligent water affairs management and control system based on the Internet of Things, and aims to solve the problems that in the prior art, multi-source water quality data and user behavior characteristics can be fused to construct a risk assessment model based on fuzzy logic and a Bayesian network, dynamic assessment of a complex water quality state cannot be achieved, and the risk assessment accuracy is poor. Abnormity cannot be quickly recognized through a similarity matching mechanism, and the response speed and accuracy of early warning are reduced; multi-source water quality data and user behavior characteristics are fused through the water quality intelligent early warning module, a risk assessment model based on fuzzy logic and a Bayesian network is constructed, the method has high nonlinear modeling and uncertainty processing capacity, dynamic assessment of a complex water quality state is achieved, abnormity is rapidly recognized through a similarity matching mechanism, and the risk assessment efficiency is improved. The method improves the early warning response speed and accuracy, combines the countercurrent tracking and GIS technology, accurately locates the pollution source, and enhances the emergency disposal and decision support capability.

The invention discloses an urban resident water consumption behavior classification method based on an intelligent water meter, and relates to the technical field of intelligent water affairs. The method is used for improving water consumption behavior recognition and anomaly detection precision. The method comprises the steps that firstly, a water flow change direction sequence is generated in real time through an intelligent water meter, and flow gradient and timestamp density characteristics are captured and extracted based on a stable persistence trigger event; then, eliminating water hammer noise by adopting an adaptive filtering mode, extracting a key turning point in combination with curvature analysis, comparing the key turning point with a behavior pattern library topology, and generating a dual-channel behavior feature vector; further, metering grids are divided according to valve distribution in the building water supply system, distribution clustering is executed in combination with the pressure gradient, and a cross-grid abnormal feature conduction path is constructed; and finally, tracing an abnormal diffusion path by means of a multilayer graph convolutional network, positioning a water pressure abrupt change inflection point, mapping the water pressure abrupt change inflection point to a building component, and outputting a leakage point identifier and a behavior mode label, thereby realizing high-precision classification and abnormal identification of residential water consumption behaviors.

The invention relates to the technical field of monitoring management, in particular to an intelligent water affair monitoring management system based on the Internet of Things, the system comprises a processor and a memory, and the processor executes a computer program stored in the memory to realize the following steps: acquiring data correlation coefficients and differential correlation coefficients corresponding to different monitoring moments, obtaining a target abnormal coefficient of the first clustering cluster and a target abnormal coefficient of the second clustering cluster according to a data volume ratio and a variable coefficient in the first clustering cluster and the second clustering cluster obtained by clustering the data correlation coefficient and the difference correlation coefficient corresponding to the monitoring moment; and obtaining a target abnormal index value corresponding to the current monitoring moment according to the data correlation coefficient corresponding to the current monitoring moment and the target abnormal coefficient of the cluster to which the differential correlation coefficient belongs, and monitoring the water affair system according to the target abnormal index value. And the timeliness and the reliability of abnormal monitoring and early warning of the water affair system can be improved.

The invention discloses a riverway three-dimensional water flow numerical simulation method, and relates to the technical field of intelligent water conservancy simulation, and the method comprises the steps: generating a longitudinal long and narrow hexahedral mesh of a main riverway in a C gradient direction through employing a mucous network growth algorithm, and outputting a hexahedral mesh with a shear layer identifier; turbulence viscosity coefficient distribution is dynamically initialized through a DDPG reinforcement learning model, and turbulence parameters are output; inputting the hexahedral mesh with the shear layer identifier and turbulence parameters into a coupling solver, performing parallel numerical solution, and outputting transient flow field data; a lambda 2 criterion is adopted to identify a vortex core area and calculate a vorticity modulus, a transverse circulation is defined, an on-way distribution cloud picture is generated, an immersive VR scene is constructed through a Unity 3D engine, and a three-dimensional visual water flow simulation scene with a physical feature label is output. Through fusion of lambda2 criterion vortex identification and a Unity 3D visualization technology, a complete solution with both calculation precision and engineering practicability is provided for river flow simulation.

The invention relates to an intelligent fish blocking and ship passing system based on multi-mode AI fusion and self-adaptive control, and belongs to the field of water conservancy projects and intelligent control. Aiming at the problems that a traditional electric fish blocking device is low in ship communication efficiency, poor in environmental adaptability and high in maintenance cost, a multi-mode sensor (laser radar, sonar, infrared thermal imaging and the like) fusion technology is adopted, and a reinforcement learning optimization proportion-integral-differential (PID) control parameter and a graph neural network (GNN) path planning algorithm are combined. All-weather high-precision sensing and dynamic gate regulation and control are realized; and equipment faults are predicted through a long short-term memory (LSTM) model, so that the shutdown risk is reduced. According to the system, the ship dredging efficiency is improved by 25%, the accident rate is reduced by 70%, the maintenance cost is reduced by 40%, the system adapts to extreme environments, and intelligent water conservancy construction is supported.

The invention relates to the technical field of remote monitoring, in particular to a remote monitoring method and system for intelligent water purification equipment. The system comprises a sensing execution unit, a data routing unit, an intelligent central unit and an interactive service unit. The intelligent central unit is used for comparing underground water quality data collected in real time with corresponding month threshold values over the years, water quality fluctuation caused by natural changes such as seasonal replacement can be accurately distinguished, the water quality data interpretation accuracy is improved, when the water quality data deviates from a normal range and influences of natural laws are eliminated, the system introduces real-time weather data, and the water quality data interpretation accuracy is improved. And the analysis module considers the effect of weather on water quality by means of a dynamic structure equation model and resets a threshold value. The intelligent self-adaptive mechanism can effectively distinguish water quality abnormity caused by non-natural laws, for example, normal fluctuation of water quality after rainstorm is recognized, accuracy and intelligence of water quality monitoring are enhanced through multiple times of accurate judgment, and abnormity can be quickly recognized and early warned.

The invention discloses a water conservancy knowledge base construction method and system based on a large model, and relates to the technical field of intelligent water conservancy. Water conservancy related knowledge is integrated and managed, water conservancy knowledge dispersed in different regions and different departments is organically integrated through a large model, an information island is broken, and knowledge sharing and collaboration are achieved. Through data cleaning, standardization processing and other technologies, the large model can convert disordered data into standard and ordered knowledge, and more accurate and reliable information services are provided; the water conservancy knowledge base is constructed based on the knowledge graph, structured, unstructured and semi-structured water conservancy data can be flexibly and conveniently accessed, and multiple data types are compatible; massive water conservancy data and information are efficiently integrated and analyzed by using a large model, valuable information and knowledge are effectively extracted through deep mining of multi-source heterogeneous data, and water conservancy related technicians are effectively assisted in managing water conservancy projects.

The invention discloses an intelligent water affair management method and system based on artificial intelligence, and relates to the technical field of intelligent water affair management, and the method comprises the steps: initializing a pipe network digital twinborn model, loading pre-stored pipe network GIS data, collecting pipeline pressure data, water quality parameters and user water consumption in real time, inputting a risk assessment model, calculating a real-time risk probability, and carrying out the real-time risk assessment. When the real-time risk probability exceeds an early warning threshold value, generating a high-risk early warning signal; based on the high-risk early warning signal, a differential order parameter and an integral gain parameter are calculated through a pipe state fractional order controller, a voltage stabilization control instruction is generated and executed, and meanwhile a control error signal in the voltage stabilization process is extracted; inputting the control error signal and the water source type label into the element reinforcement learning model, and dynamically optimizing the differential order parameter and the integral gain parameter to obtain a water source risk coefficient; according to the method, personalized modeling and dynamic updating of risk assessment are realized by outputting the dynamic response characteristic data.

The invention provides a water network monitoring method and system based on intelligent water supply and drainage, and the method comprises the steps: obtaining multi-source heterogeneous monitoring data of a water network in real time, carrying out the unified format conversion and time-space synchronization processing of the multi-source heterogeneous monitoring data based on a protocol adaptive fusion mechanism, and obtaining a standardized multi-source time series data set; performing exception identification processing on the standardized multi-source time sequence data set based on the time-varying statistical characteristics and the dynamic control limit to obtain a candidate exception event set; performing multi-sensor cross validation processing on the candidate abnormal event set based on a pipe network topological space association knowledge graph to obtain a high-confidence abnormal event set; performing reverse scene matching analysis on the high-confidence abnormal event set based on a hydraulic numerical simulation model to obtain a preliminary positioning area; performing positioning optimization processing on the preliminary positioning area based on a graph theory pressure wave propagation algorithm to obtain an abnormal position; and performing multi-dimensional fusion display processing on the abnormal position to generate a monitoring report. According to the method, accurate positioning of the abnormal position is realized.

The invention relates to the technical field of error verification, and particularly discloses an error verification method and system for an intelligent water meter, which adopts a neural network model based on deep learning to carry out fine-grained time sequence feature extraction on time sequence data of temperature and pressure of a water body flowing through a multifunctional intelligent water meter in a target time period; according to the method, the time sequence change mode of the water body temperature and the pressure in each local time domain is captured, time sequence integral coding is carried out on the time sequence change characteristics of the water body temperature and the pressure in each local time domain to understand the time sequence accumulation effect of the water body temperature and the pressure on the water consumption in the global time domain, and intelligent prediction of the water consumption is achieved. And error verification is carried out on the intelligent water meter according to the difference between the prediction result and the measured value of the water meter. In this way, the measuring error of the intelligent water meter can be reflected more accurately, and then the error checking precision and the intelligent level of water affair management are improved.

The invention discloses a water outlet control method, system and device for an intelligent water valve, and relates to the technical field of water outlet control of water valves. The water outlet control method for the intelligent water valve comprises the following steps of water outlet prediction, intelligent water valve adjustment evaluation and water outlet control effect evaluation. The real-time water outlet data corresponding to each intelligent water valve control mode is collected, the water outlet prediction index is obtained in combination with the corresponding historical water outlet data so as to adjust the opening degree of the valve, then the intelligent water valve adjustment evaluation index is obtained according to the real-time water consumption data and the real-time water return data, and whether the intelligent water valve control mode is switched or not is judged. Finally, water outlet control adjustment measures are taken according to the water outlet control effect evaluation index, the effect of improving the water outlet control effectiveness of the intelligent water valve is achieved, and the problem that in the prior art, water outlet control of the intelligent water valve is not sufficiently associated with user requirements is solved.

The invention discloses an intelligent water conservancy integrated big data analysis method based on a clustering algorithm, and the method comprises the following steps: S1, collecting hydrological data in an intelligent water conservancy system, and carrying out the preprocessing of the hydrological data; s2, performing data enhancement on the preprocessed hydrological data; s3, constructing and training a SimCLR self-supervised contrast learning model, and outputting embedded feature representation; s4, taking the embedded feature representation as the input of an improved fuzzy clustering model, and outputting a membership matrix and a clustering center set; s5, according to the distribution of the membership matrix and the structure of the clustering center set, identifying a hydrological characteristic region, a pollution diffusion region and an abnormal hydrological mode; and S6, inputting an identification result into the intelligent water conservancy scheduling system. According to the method, self-supervised contrast learning and an improved fuzzy clustering model are fused, complex hydrological data recognition is achieved, and the method has the advantages of being excellent in feature expression, high in clustering precision and high in application adaptability.

The invention discloses an intelligent water meter operation monitoring method and system based on the Internet of Things, and relates to the technical field of intelligent water meters. According to the method, a disturbance consistency scoring index Ipd and a behavior map variation score Simv are constructed, and a multi-parameter coupling backtracking risk grade scoring function Rtrace is introduced; the disturbance sudden change intensity, the period stability, the flow direction consistency and the historical behavior deviation degree can be comprehensively considered, and the recognition precision of the unnatural disturbance water flow and the potential abnormal water consumption behavior is effectively improved. Particularly, under the condition that disturbance abnormity and behavior map abnormity are coupled, secondary comparison evaluation is carried out on a backtracking risk grade scoring function Rtrace and a risk interval threshold value, so that risk judgment and risk grade division with higher distinction can be realized, illegal water taking, backflow and backward flow and other abnormal water consumption behaviors can be accurately locked under the condition of a low misjudgment rate, and the accuracy and the reliability of the system are improved. And the intelligent sensing capability and the behavior modeling reliability of the urban water supply system are enhanced.

The invention discloses an intelligent water wave analysis method and system for a water conservancy project, and relates to the technical field of image processing, and the method comprises the steps: collecting a water surface image through a multispectral camera and a laser radar, extracting static environment features based on a target detection algorithm, and generating a water surface two-dimensional static environment map; capturing a dynamic image by using a high-speed camera, recognizing the movement direction of the water ripples at the moment when the object is in contact with the water surface according to the timestamp and the recognized dynamic water ripple shape, and generating a dynamic direction data set; performing multi-dimensional training on the neural network model according to a time sequence and a fluid mechanics constraint condition in combination with an object form parameter and a natural condition parameter, outputting a water wave event classification label, and obtaining event label classifications corresponding to different water wave features; water surface and underwater conditions are comprehensively identified, water waves are comprehensively analyzed, the water waves are efficiently analyzed according to physical constraint characteristics of fluid mechanics, a multi-dimensional prediction model is constructed, and the water waves are efficiently, comprehensively and comprehensively classified and predicted.

The invention discloses a municipal pipe network overflow detection and intelligent repair method, and relates to the technical field of municipal pipe network management, and the method comprises the following specific steps: data fusion and basic analysis: employing a plurality of sensors to collect various data in real time, and carrying out the fusion and preprocessing in a data processing center, comparing and analyzing historical and real-time data to set a threshold value, and finding out an external environment change factor influencing the operation of the pipe network to provide basic data; through deep integration of meteorological, hydrological and municipal pipe network related data and by means of an intelligent hydrodynamic simulation technology, high-precision simulation of water flow in a pipe network can be realized, the occurrence position, time and influence range of overflow can be accurately predicted in advance, and the risk of overflow can be accurately evaluated based on accurate overflow risk evaluation. The system can automatically formulate and adjust a pipe network operation parameter regulation and control strategy in real time, parameters of the drainage pumping station and the opening degree of a pipe network valve are intelligently adjusted according to different overflow risk grades, and therefore the pipe network operation parameters can be effectively optimized.

The invention relates to an intelligent water affair remote monitoring and control system based on the Internet of Things, in particular to the field of intelligent water affair remote monitoring and control, and the system comprises the steps: firstly, pre-training a long and short-term memory network model through a cloud, and generating a lightweight edge prediction model to reduce calculation and transmission loads; the data transmission module effectively reduces the occupation of transmission bandwidth based on a dynamic quantization threshold value of a residual error and a differential coding technology, meanwhile, the high precision of the data is kept, the verification feedback module monitors the accuracy of the transmission data in real time through confidence coefficient detection, and triggers model re-calibration when the confidence coefficient is reduced, so that the long-term stability of the system is ensured, and the reliability of the system is improved. The sampling optimization module dynamically adjusts the sampling frequency according to the confidence coefficient and the residual fluctuation characteristics, balance is achieved between bandwidth occupation and reconstruction precision by using the Lyapunov optimization theory, the real-time performance and accuracy of water affair monitoring are improved, and meanwhile bandwidth consumption and energy consumption are effectively reduced.

The present invention relates to the technical field of aquatic product traceability management, and in particular to an intelligent aquaculture traceability method and system based on the Internet of Things. The method comprises the following steps: obtaining aquaculture area location data; connecting the aquaculture area location data to a sensor network to generate an aquaculture Internet of Things network; performing real-time aquaculture data collection on the aquaculture Internet of Things network to obtain a real-time aquaculture sensor data stream; performing distributed time synchronization on the real-time aquaculture sensor data stream to generate standard aquaculture environment perception data; obtaining aquatic information data; and using the aquaculture area location data to perform real-life aquaculture environment scene mapping on the standard aquaculture environment perception data to generate real-life aquaculture environment scene mapping data. The present invention improves the intelligence and transparency of aquaculture traceability by combining the Internet of Things, blockchain and real-time data synchronization technology.

The invention relates to the technical field of intelligent monitoring, and particularly discloses an intelligent water meter metering data abnormity monitoring system and method.Metered data of an intelligent water meter is sent to a remote monitoring center through a built-in communication module of the intelligent water meter, and meanwhile a data processing algorithm is deployed in the remote monitoring center; the method comprises the following steps: segmenting intelligent water meter measurement data into continuous time sequence fragments according to a preset time step length, taking a measurement data fragment at the tail end as a verification reference, and carrying out secondary stacking time sequence coding on the intelligent water meter measurement data fragments in a historical period so as to capture deep time sequence characteristics of the intelligent water meter measurement data; and performing water meter measurement data reasoning under the current time step, and further realizing abnormality judgment on the measurement data based on comparison between a reasoning result and a verification reference. The method can effectively reduce dependence on preset rules, dynamically adapt to diversified water use scenes, and reduce misinformation and missed detection risks.

The invention discloses a drainage basin water engineering joint intelligent scheduling system and scheduling method for intelligent water conservancy. The system comprises a drainage basin water engineering joint scheduling knowledge graph distiller, a drainage basin water conservancy model set and a water engineering joint scheduling plan inference device. The drainage basin water engineering joint scheduling knowledge graph distiller starts from a drainage basin data base plate and an expert instruction, and bidirectionally distills to obtain a water engineering joint scheduling operation mode; the drainage basin water conservancy model set comprises engineering scheduling, drainage basin simulation, an artificial intelligence module, service drainage basin water engineering joint scheduling knowledge graph distillation and water engineering joint scheduling plan reasoning; and the water engineering joint dispatching plan inference device constructs a driving feedback mechanism two-way mapping relation between water engineering dispatching measures and disposal effects, and carries out water engineering optimization and dispatching measure matching inference according to the dispatching boundary condition of the current disposal event. According to the invention, drainage basin water engineering joint scheduling suggestions are provided based on big data, knowledge reasoning and a water conservancy professional model.

The invention discloses a meteorological water conservancy disaster intelligent grading early warning linkage call routing method and a meteorological water conservancy disaster intelligent grading early warning linkage call routing system. A meteorological water conservancy two-factor early warning matrix is constructed, a cross-department data fusion channel is established, an early warning coupling model is developed, and multi-dimensional early warning fusion is achieved. And formulating a water conservancy special response strategy library, wherein the water conservancy special response strategy library comprises river water level grading routing and mountain torrent risk area customization strategies. And developing an intelligent water conservancy enhancement module, such as a flood control project topology analysis engine and a water and rain work condition semantic analyzer. The method is characterized in that a three-element risk rating model is constructed by an initiative dynamic coupling algorithm, and a water conservancy emergency response topology network based on double dimensions and a water conservancy project BIM data and meteorological early warning space superposition analysis module are adopted. The accuracy and comprehensiveness of disaster early warning can be improved, the timeliness and effectiveness of emergency response are ensured, and the intelligent level and disaster response capacity of the system are improved.