1447 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 discloses an intelligent water quality monitoring system and method based on a turbidity sensor, and relates to the technical field of water quality monitoring. Data acquisition nodes are arranged in a water area to be monitored, data are acquired by using the turbidity sensor and an intelligent water quality monitoring equipment group, an environment data set S is constructed, and preprocessing is performed; acquiring a turbidity change coefficient # imgabs0 # according to a turbidity data set in the preprocessed environment data set S, performing primary evaluation, marking abnormal data acquisition nodes, acquiring a particulate matter dynamic index KL and a pollution index WR according to a particulate matter data set and an organic pollutant data set in the environment data set S, and performing secondary evaluation according to the turbidity change coefficient # imgabs1 # and the turbidity change coefficient # imgabs0 # in combination with the particulate matter dynamic index KL and the pollution index WR. Acquiring a comprehensive water quality score Q, performing secondary evaluation, and constructing a water quality prediction model to predict the water quality change and regulate and control the water quality for the data acquisition nodes which are normally evaluated for the second time; the intelligent level of water quality monitoring is improved, the accuracy of water quality monitoring is enhanced, and the safety of water quality is ensured.

The invention provides a reinforcement learning-based desilting strategy optimization method and system, and relates to the technical field of intelligent water area desilting, and the method comprises the steps: carrying out the multi-scale decomposition, adaptive histogram equalization, contrast optimization and edge sharpening processing of a collected image, and obtaining an optimized desilting environment image; then constructing a three-dimensional voxel grid model based on the optimized image, and establishing an environment interference source mathematical model and a dynamic evolution rule of sludge distribution; and finally, inputting the image features into a dredging strategy optimization network, through interaction with a simulation environment, updating network parameters based on a dual time sequence difference algorithm, generating a motion track planning scheme and an operation parameter configuration scheme of the dredging robot, and optimizing a value function on line according to an actual dredging effect. The desilting efficiency can be effectively improved, and the desilting cost is reduced.

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 relates to an intelligent water temperature control system and method based on machine learning. The method comprises the steps that water temperature, flow, pressure and environmental parameter data of a constant water temperature control system are collected, and a standardized time sequence data set is generated; constructing a time-varying dynamic model based on the standardized time sequence data set, and performing parameter identification by using a variable forgetting factor generalized subspace tracker to obtain a time-varying pseudo-modal temperature parameter; training a multi-branch long short-term memory network according to the time-varying pseudo-modal temperature parameters and the standardized time sequence data set to obtain an initial water temperature prediction model; performing system operation anomaly detection and prediction based on the initial water temperature prediction model to obtain an anomaly detection and prediction result; solving a control strategy based on the anomaly detection and prediction result to obtain an optimal water temperature control strategy; and executing water temperature control based on the optimal water temperature control strategy, and updating the initial water temperature prediction model to obtain a target water temperature prediction model. According to the intelligent water temperature control system, accurate control over intelligent water temperature is achieved.

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 relates to the technical field of intelligent water affair management, and discloses a method and system for dynamically adjusting the reporting moment of an NB water meter, and the system comprises NB water meter terminal equipment which is responsible for collecting water meter data in real time and uploading the data to a data management platform through an NB-IoT network; the data management platform is responsible for receiving, processing and storing data from the plurality of NB water meter terminal devices; the reporting moment adjusting module is used for dynamically adjusting the reporting moment of the NB water meter terminal equipment according to the strategy generated by the data management platform; the communication management module is responsible for communication between the NB water meter terminal equipment and the data management platform; the intelligent alarm and event processing module is responsible for monitoring the running state of the system in real time; and an equipment remote control module. By dynamically adjusting the reporting time, the system can reduce unnecessary frequent reporting, especially in a time period when the water consumption is relatively small or relatively stable, so that the power consumption of the equipment is remarkably reduced. Therefore, the battery life of the NB water meter terminal equipment can be prolonged, and the maintenance cost is reduced.

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 relates to the technical field of automatic control, in particular to an intelligent Internet of Things water meter control method and system.The intelligent Internet of Things water meter control method comprises the steps that the water consumption of a water meter at the current time node is obtained, the water consumption of the current time node is input into an improved machine learning model, the water consumption state of the current time node is output, and if the water consumption state is abnormal, the water meter is started; if yes, controlling the water meter to send out an early warning signal and stopping water supply; wherein the improved machine learning model comprises the steps of carrying out iterative training on the machine learning model by utilizing a loss function and calculating a loss function value of a current time node, and the historical loss function value is positively correlated with the abnormal degree of the water consumption of the historical time node and is inversely correlated with the surge coefficient of the water consumption of the historical time node. According to the method, the problem that the overall performance and reliability of the intelligent water meter are not high possibly due to potential insufficiency of an existing algorithm in the aspects of detection efficiency and accuracy is solved.

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 multi-mode sensing fusion intelligent water meter anomaly detection system, and relates to the technical field of industrial production water monitoring, and the system comprises an equipment composition obtaining module which interactively obtains a target production line composition. The production log acquisition module acquires an associated index set. The sensing equipment laying module is used for laying multi-mode sensing equipment for production equipment. The water consumption associated data set acquisition module acquires data to obtain a data set. The water consumption prediction edge node construction module constructs nodes of a local server. The cloud detection module pre-constructs a water meter anomaly detection cloud. The communication connection module connects the cloud and the node. And the dynamic detection module performs water meter abnormity dynamic detection. The technical problems that an existing water meter monitoring technology only depends on water meter reading, water meter abnormity cannot be accurately detected in real time, and water management efficiency and precision are affected are solved, and the technical effects that stable supply and accurate metering of water are guaranteed, and the production interruption risk and water resource waste are reduced are achieved.

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 discloses a water pump control system based on an intelligent water pump data collection function, and relates to the technical field of water pump operation data analysis, the system collects static lift, water absorption lift and drainage lift data in real time through a lift monitoring module, a comprehensive first data set is generated, and lift changes are dynamically presented. The pipeline resistance evaluation module is combined with liquid characteristic data, obtains fluid dynamic viscosity, flow and cavitation allowance data in real time, comprehensively calculates a pipeline friction loss index, a local resistance loss index and a cavitation allowance, is used for accurately evaluating the pipeline resistance, avoids energy consumption increase caused by resistance increase, and meanwhile, improves the reliability of the pipeline resistance. The liquid characteristic data acquisition module monitors the cavitation allowance, and the cavitation risk is recognized in advance in combination with the prediction analysis module.

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 data statistics, in particular to an intelligent water meter missing data complementation method and system.The intelligent water meter missing data complementation method includes the steps that initial segmentation points of a water consumption data curve are determined by analyzing the slope change condition of the water consumption data curve, and the variation index of the initial segmentation points is further analyzed to determine segmentation points of the water consumption data curve; performing clustering analysis on all segmentation points in the water consumption data curve of the user in all days, and constructing a standard water consumption data curve of the user based on a clustering result; and respectively using a Bezier curve fitting method and a regression analysis method, and combining the change trend of the standard water consumption data curve to carry out data completion on the missing data segment. Through the method and the system provided by the invention, complementation data which conform to the overall trend and are in smooth transition can be obtained, the integrity and the accuracy of the data are remarkably improved, and a more reliable data basis is provided for subsequent water resource management and decision support.

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 relates to an intelligent water meter warning method and system, a terminal and a storage medium, and relates to the technical field of water meters, and the method comprises the following steps: obtaining a water volume real-time detection value on a water meter and a water pressure detection value collected by a water pressure detection device preset on the water meter; calculating a difference value between the water pressure detection value and a preset water pressure reference value and taking the difference value as a water pressure deviation value; analyzing and determining a water pressure reference deviation interval according to the water volume real-time detection value; judging whether the water pressure deviation value falls into a water pressure reference deviation interval or not; if yes, continuing to obtain a water volume real-time detection value and a water pressure detection value; if not, water leakage early warning information is analyzed and determined according to the water pressure deviation value and the water pressure reference deviation interval, and the water leakage early warning information is output to the terminal held by the manager. The water leakage early warning device has the effect of conveniently and timely carrying out early warning on the water leakage condition.

According to the intelligent hydraulic power plant operation management system and method based on digital twinning, the digital twinning model of the hydraulic power plant is constructed through the digital twinning technology, the operation data of the hydraulic power plant are collected through the sensing technology, and real-time data synchronization between the digital twinning model and the hydraulic power plant is achieved through the Internet of Things technology; meanwhile, the operation data of the hydraulic power plant are intelligently analyzed, whether the working state of the turbine is abnormal or not is intelligently analyzed based on a time sequence interaction mode between the generating capacity and multiple operation parameters influencing the generating capacity, and the analysis result is synchronously displayed in a digital twinborn model of the hydraulic power plant. In this way, comprehensive sensing, real-time monitoring and intelligent analysis of the operation state of the hydraulic power plant can be effectively achieved, and therefore the overall operation efficiency and reliability of the hydraulic power plant are improved.