1676 results about "Water supply network" patented technology

The invention discloses a water flow intelligent monitoring system and method, and relates to the technical field of water flow intelligent monitoring, in combination with an actuator health degree evaluation and multi-target prediction control method, the characteristics of a valve and a water pump are finely described by arranging sensors and constructing a digital twinborn model; model predictive control or multi-agent reinforcement learning is adopted to dynamically generate a reliable and efficient control instruction; furthermore, through real-time closed-loop deviation detection and self-adaptive correction, delay, faults or communication abnormity of the actuator can be accurately dealt with; 4, quickly completing self-healing and fault-tolerant control by using a fault prediction and standby scheduling strategy to prevent failure diffusion; and 5, scenes such as rainfall flood, fire fighting and reclaimed water are incorporated into the same framework, and multi-scene coupled comprehensive water resource management is realized, so that water hammer impact and water leakage risks can be effectively reduced, and the safety of the water supply system under complex working conditions or equipment aging is remarkably improved.

The invention provides a water supply data real-time collection and analysis method, device and equipment and a medium, and the method comprises the steps: obtaining multi-source monitoring data of a water supply network, and carrying out the space-time alignment and spatial interpolation of the multi-source monitoring data, and obtaining a water supply data set; according to the topological structure of the water supply network, the water supply data set is mapped into a dynamic topological graph, and the dynamic topological graph comprises node attributes, edge attributes, real-time pressure and flow data; according to the dynamic topological graph, calculating stability parameters of each node based on a fluid mechanics principle; the dynamic topological graph and the stability parameters are input into a pre-trained machine learning model, a system balance degree quantitative index is generated, and the system balance degree quantitative index is used for representing the operation stability of the water supply pipe network. By adopting the method, the overall balance state of the water supply system can be accurately quantified to realize early risk warning.

The invention discloses a method and system for predicting leakage of a water supply pipe network, and the method comprises the steps: modeling nodes and pipe sections of the pipe network into a graph topological structure, and endowing the nodes and the pipe sections with static attributes; collecting operation data of the water supply network, and constructing time-varying graph data corresponding to the graph topology; combining the time-varying graph data with the static attributes to form space-time input features; constructing a graph time sequence prediction model based on a deep learning framework, performing graph structure feature extraction on node graph features and pipe section graph features of each time step to obtain node space features and pipe section space features, and outputting a node and pipe section space-time representation set; evaluating and analyzing the leakage level of each DMA or pressure partition; generating a pipe section leakage risk space distribution set; constructing a joint loss function, and training and updating the graph time sequence prediction model; and inputting operation data acquired in real time into the trained graph time sequence prediction model, and generating a leakage rate prediction value of each partition and a leakage risk index of each pipe section on line for leakage prediction and operation and maintenance decision.

The invention belongs to the technical field of intelligent control, and particularly relates to a water pump multi-mode intelligent control method based on a PLC. Operation parameters are acquired in real time through a data acquisition module, a target mode is selected from constant-pressure water supply, constant-flow water supply, energy-saving optimization and fault emergency modes through a mode judgment module according to preset triggering conditions, and an operation strategy including a start-stop sequence, a target rotating speed, a frequency converter output frequency and a pressure set value is generated through a strategy generation module. The control execution module converts the strategy into a control instruction and outputs the control instruction to the water pump driving module, and linkage operation and mode switching of the multiple water pumps are achieved in combination with the mode linkage module. And the recording and optimizing module is used for storing and analyzing the operation data and optimizing mode switching conditions and operation strategies. Self-adaptive control and multi-pump cooperative scheduling in multiple modes are achieved, the stability and energy saving performance of a water supply system are improved, and the service life of equipment is prolonged.

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 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 intelligent water affair and urban water supply system informatization, in particular to a water supply pipe network hydraulic model calibration and leakage locating method and system, and the method comprises the steps: obtaining the pressure and flow data of a plurality of monitoring points in a pipe network in real time; taking the model as a boundary condition to drive a hydraulic model to carry out real-time simulation, and calculating a theoretical value; calculating a residual error between a theoretical value and an actual value of the monitoring point; judging a triggering model calibration event or a leakage suspicion event based on a residual error abnormal mode; if calibration is triggered, inverting and updating global parameters of the model through an optimization algorithm to realize self-calibration; if leakage is triggered, a suspected area is determined by combining pressure space distribution analysis, and a leakage pipe section is accurately positioned through analog simulation and correlation analysis. Through closed-loop feedback of real-time data and the model, the model can adapt to changes of a pipe network system, dynamic self-calibration of the water supply pipe network hydraulic model is achieved, and the problem of precision attenuation of a traditional static model is solved.

The invention relates to the technical field of intelligent operation and maintenance management of urban water supply pipe networks, in particular to a double-model urban water supply pipe explosion risk prediction method based on multi-dimensional dynamic indexes. The method comprises the following steps: collecting multi-dimensional data of a water supply pipe network, and combining with gas-containing water hammer simulation analysis to form a standardized index feature set; establishing a dual-model prediction system comprising a subjective and objective weight fusion model and a machine learning model, and dynamically adjusting and setting a risk assessment weight proportion of the model according to the change of model prediction precision in the dual-model prediction system; and calculating the risk probability score of the pipe section according to the dynamically adjusted weight ratio, marking the pipe section when the prediction result difference of the double-model prediction system exceeds a preset threshold value, and further dividing the risk level of the pipe network. According to the method, closed-loop management from prediction to decision making is realized based on multi-dimensional data, dual-model prediction and operation efficiency evaluation, and the scientificity and efficiency of operation and maintenance of the water supply network are improved.

The invention relates to the technical field of water supply pipe network monitoring and computers, and discloses a precise water leakage positioning method and system based on water supply pipe network leakage monitoring, and the method comprises the steps: obtaining pipe network time sequence data and a topological structure, and constructing a structured model; performing multi-source data cross validation according to the model, and determining a deviation source node; historical data of the deviation source node are extracted for simulation, and a confirmation result of the leakage event is obtained; analyzing the space-time gradient change of the upstream pipeline flow, and accurately positioning the coordinates of the water leakage point; the water leakage point coordinate enhancement model is fed back, consistency verification is carried out, and accurate coordinates are obtained; and integrating physical details of the pipe network to generate a final positioning result. By establishing a set of intelligent analysis closed loop from data fusion positioning to feedback verification, the problem of insufficient water leakage point positioning accuracy in the prior art is solved.

The invention discloses a time-phased intelligent pressure regulation management method and system for secondary water supply, and belongs to the technical field of water supply management, and the method specifically comprises the steps: dividing a day into a water consumption peak period and a water consumption low period based on historical water consumption flow data, and determining the starting and ending time nodes of each time period; through hydraulic calculation and actual water supply pressure monitoring, a pressure control point at the tail end of the water supply system is locked, and water supply pressure data of the point are collected in real time; in the water consumption peak period, a peak pressurization control strategy is executed; in the water consumption low-peak period, analysis is conducted according to historical water supply pressure data of a tail end pressure control point, and a low-peak period pressure safety threshold value is set; a pressure instruction is sent to a control system according to the threshold value, and water supply equipment is controlled to output pressure according to the threshold value; the water supply pressure of a tail end pressure control point is monitored in real time, and when the pressure is lower than a low-peak-period pressure safety threshold value, pre-alarm is triggered, and pressure changes are continuously recorded; and if the pre-alarm state is not finished continuously, switching to a peak-period peak pressurization control strategy.

The invention discloses a water supply network GIS data physical attribute filling method based on a graph neural network, and the method comprises the following steps: constructing a pipe section connection relation topological graph through a spatial index and geometric alignment operation, extracting pipe section physical attributes, and generating a missing mark; a pipe section attribute filling model is constructed based on a graph attention neural network, a graph attention feature aggregation module is adopted, a Gaussian kernel function is combined to express a multi-hop spatial dependency relationship, a composite attention weight is formed, neighbor effective attribute features are aggregated, and prediction missing attributes are decoded; and designing a weighted filling accuracy rate, and comprehensively evaluating the filling precision of the model through a weighting mode. According to the method, the topological connection relation between the pipe sections is fused into the graph neural network model, and the limitation that a traditional attribute filling method ignores pipe network spatial relevance is broken through; according to the constructed graph neural network attribute filling model, the pipe section space and the attribute association relationship are jointly incorporated into the model through a graph attention feature aggregation module, so that the inference accuracy is improved.

The invention discloses a fire hydrant pressure adaptive regulation and control system based on Internet of Things data, and relates to the technical field of urban water supply pipe network fire hydrant pressure control, and the system comprises a multi-source data collection module which is used for collecting fire hydrant pressure time sequence data in real time from a plurality of Internet of Things nodes deployed in an urban water supply pipe network, and historical pressure data, pipeline topological structure data and user water consumption mode data of the water supply network are obtained from a historical database. Real-time and historical data are integrated through the multi-source data acquisition module, a comprehensive basis is provided for pressure regulation and control in combination with a pipe network topology and a user water consumption mode, and the integrity of data support is improved; the pipe network state prediction module is used for extracting multi-dimensional features and predicting pressure changes by means of an LSTM model, the pressure abnormal risk can be recognized in advance, and hysteresis of traditional regulation and control is avoided.

The invention discloses a water supply network global water quality prediction method based on a double flow-graph convolutional network, and belongs to the field of urban water supply. According to the method, a pipe network model of a directed graph is constructed, information flows in the upstream direction and the downstream direction are extracted respectively, feature learning is carried out through a parallel graph convolution module, a random mask mechanism is introduced during training to simulate sensor missing, and accurate prediction of the water quality concentration under the sparse monitoring condition is achieved. According to the double flow-graph convolution water quality prediction model provided by the invention, the practicability and coverage capability of the water quality prediction model under the condition of sparse monitoring data are remarkably improved; a double flow-graph convolution structure and a dynamic mask training mechanism are adopted, so that the robustness of the complexity of a pipe network is effectively enhanced; the constructed water quality prediction model has the characteristic of high response speed, can realize real-time prediction of water quality in combination with historical monitoring data and network structure information, is suitable for various scenes such as water quality monitoring, abnormal early warning and intelligent regulation and control, and is beneficial to improving the operation efficiency and management level of a water supply system.

The invention belongs to the technical field of water supply network adjustment, and particularly relates to a water supply network pressure and flow adjusting method. The method comprises the following steps: firstly, collecting pressure, flow and water temperature data of a monitoring node at present and in a preset time period, wherein the preset time period is set according to a water consumption period; preprocessing flow data through a sliding window method and fitting a curve, and marking an instantaneous disturbance time period; the dynamic response coefficient of each disturbance time period is calculated, and water temperature correction is needed; combining the flow standard score, the dynamic response coefficient and the real-time deviation coefficient to obtain a flow dynamic regulation index; a pressure stability index is obtained through pressure and water temperature trend feature vector matching; the two are subjected to weighted fusion to form a pipe network comprehensive regulation factor, and the valve opening and the water pump power are regulated accordingly. The method can accurately identify instantaneous disturbance, improve the adjustment precision and response speed, and guarantee stable water supply.

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 a water supply system scheduling optimization method and device, electronic equipment and a storage medium, and relates to the field of intelligent scheduling of water supply systems. The method comprises the steps of collecting and preprocessing multi-dimensional data to obtain a data set; the pressure data is analyzed from the space-time dimension, and the unfavorable points and the pressure requirements thereof are accurately identified; the method comprises the following steps: constructing a water volume prediction model by adopting a time sequence model, and constructing a total water head difference prediction model by adopting a Light GBM gradient boosting tree in combination with a MultiOutputRegressor multi-output regression framework; constructing a minimum total water production cost objective function based on a prediction result, and outputting an optimal scheduling scheme by combining water volume and pressure constraint iterative optimization; and establishing a model updating mechanism to ensure dynamic adaptation of the strategy. According to the method, the problems of insufficient pressure guarantee, extensive cost control and weak model practicability and generalization ability are solved, the inherent contradiction that a traditional mechanism model is high in complexity and a pure data driving pressure prediction model is poor in generalization and lacks physical significance is overcome, and safe, stable and efficient intelligent technical support is provided for a water supply system.

The invention discloses a water supply pipe network leakage locating method based on a lightweight gradient elevator algorithm, and belongs to the technical field of intelligent water affair and pipe network monitoring. The method comprises the following steps: integrating hydraulic modeling basic information, and constructing an initial hydraulic model by utilizing hydraulic software; actual measurement values of key node pressure and main pipe flow under different working conditions are collected and compared with simulation values to be calibrated to reach the standard, and a high-precision hydraulic model is obtained; simulating each leakage working condition in different time periods on a selected potential leakage node / pipe section by using the calibration model, collecting pressure change data of a pressure monitoring node, and performing preprocessing and data division; the data set is used for training a LightGBM model, the LightGBM model learns the relevance of node pressure in spatial topology and the dynamic change characteristics of a time sequence at the same time, a spatial position differentiation weight mechanism is introduced, and accurate positioning of leakage points and quantitative estimation of leakage coefficients are achieved. According to the method, the pipe network leakage positioning precision and efficiency are remarkably improved, and support is provided for rapid and accurate leakage detection and leakage amount evaluation.

The invention relates to the technical field of water pump intelligent control, and discloses a water pump controller parameter self-adaptive adjustment control method and system.The water pump controller parameter self-adaptive adjustment control method comprises the steps that meteorological early warning data, pressure pulsation signals of all pump stations and pipe network topology data are obtained; generating a time-space synchronous disaster monitoring data set through a data fusion algorithm; inputting the disaster monitoring data set into the deep space-time diagram neural network, and outputting a pipe network damage probability distribution diagram; generating a pulsation propagation prediction model containing propagation time delay and an attenuation coefficient based on a fluid dynamics equation and the pipe network damage probability distribution diagram; calculating a PID controller parameter coordinated regulation scheme of each pump station by minimizing an objective function including pressure deviation, flow fluctuation and parameter regulation quantity; and system response data is acquired in real time, and adaptive optimization of a control strategy is realized. According to the invention, stable operation of the water supply system under disaster conditions is realized.

The invention discloses a machine learning enhanced real-time hydraulic modeling method for a water supply network. The method comprises the following steps: acquiring position numbers of sensors in the water supply network and correspondingly acquired historical hydraulic data; based on position numbers of sensors in the water supply network and time nodes in an acquisition period, constructing a sensor sampling matrix corresponding to each monitoring target, and filling historical hydraulic data into the corresponding sensor acquisition matrixes to form a training set; constructing an initial model; constructing a physical constraint loss function and training the initial model by using the training set to obtain a hydraulic prediction model for predicting a global hydraulic state; and inputting the sensor acquisition matrix of the monitoring target in the water supply pipe network into the hydraulic prediction model to output global pipe network hydraulic data. The invention further provides a real-time hydraulic monitoring system for the water supply network. The model constructed by the method provided by the invention can realize real-time estimation of the hydraulic state of the water supply network, and provides accurate data support for daily maintenance of the water supply network.

The invention discloses an urban intelligent water affair risk dynamic identification and early warning method based on deep learning, and the method comprises the following steps: S1, collecting the water pressure, flow, residual chlorine concentration, elevation, rainfall, valve state, pump station state and accident label of each node in a water supply network, and constructing a time alignment data sequence; s2, constructing a dynamic adjacency matrix according to the pipe network connection relation and the event state information; s3, inputting the data sequence and the dynamic adjacency matrix into an improved space-time diagram wavelet neural network to generate space-time feature representation; s4, multi-scale features are extracted and fused through the high-frequency branches and the low-frequency branches; s5, constructing a hyperedge set, executing graph structure propagation, and generating a risk representation tensor; s6, inputting the risk representation tensor into the risk prediction network, and outputting a node risk probability and a confidence interval; and S7, determining a risk level according to the risk probability and the confidence interval, and generating a corresponding early warning signal. According to the invention, fine modeling and dynamic early warning of urban water supply risks are realized.

The invention provides a robust control automatic setting method for a constant-pressure water supply system, and the method comprises the steps: obtaining a system working condition in real time, and extracting multi-dimensional state parameters including real-time flow, pressure deviation, current deviation and power deviation; the current load working condition is judged according to the pressure deviation, disturbance characteristics are analyzed by integrating all parameters, and the disturbance grade and type are determined. On the basis, robust control parameters are automatically set in combination with real-time working conditions, load conditions and disturbance characteristics of the system. And finally, performing multi-stage cooperative control on the system by using the set parameters, so that the constant-pressure water supply system is automatically set. According to the robust control automatic setting method for the constant-pressure water supply system, load working conditions and disturbance characteristics are recognized based on multi-dimensional state parameters, robust control parameters are self-set in a targeted mode, and automatic setting of the constant-pressure water supply system is achieved through multi-stage cooperative control. Therefore, the intelligent level of the constant-pressure water supply system and the adaptability in different operation environments are effectively improved.

The invention discloses a regional water supply system optimization scheduling method considering the influence of a salt tide. The method comprises the following steps: S1, constructing a multi-water-source-multi-water-plant-multi-water-demand-area layered water supply topological structure covering a target area influenced by the salt tide; s2, judging a salt tide period according to real-time water quality monitoring data of a reservoir in an area influenced by the salt tide, dynamically adjusting a target function and constraint conditions of the multi-target multi-water-source optimal scheduling model, and preferentially scheduling water sources with safe water quality; and S3, on the basis of the topological structure model constructed in the step S1, by taking the salt tide state in the step S2 as a dynamic parameter, constructing a three-dimensional objective function of'water deficit, salt tide influence and cost difference 'of the multi-objective multi-water-source optimal scheduling model, and solving a Pareto optimal solution set through an improved NSGA-III algorithm in combination with constraint conditions. The method is suitable for collaborative scheduling optimization of the multi-water-source system influenced by the salt tide, and has wide application value in the aspects of improving the water resource allocation efficiency, guaranteeing the water supply safety and the like.

The invention provides a smart city water supply pipe network emergency supervision Internet of Things large model system and method, and relates to the field of city water pipe monitoring, and the system comprises a government supervision management platform, a government supervision sensing network platform, a government supervision object platform, a water affair company sensing network platform, and a smart water affair equipment object platform. The government supervision and management platform is configured to determine time sequence flow corresponding to the multiple water pipe node groups based on water flow characteristics of the multiple water pipe nodes in the water supply network; based on the time sequence flow, determining water and soil loss coefficients corresponding to the one or more target areas; based on the water and soil loss coefficient, temporary control parameters are generated through a parameter generation model; and on the basis of the temporary control parameters, the valve opening degrees of the corresponding valves in the one or more target areas are controlled. The system can determine the control parameters of the adjusting device according to the water and soil loss coefficient of the target area, and realizes informatization and intelligence of urban water pipe emergency monitoring.

The invention relates to a water supply network leakage identification method and device based on data enhancement and hybrid neural network architecture, equipment and a medium, and the method comprises the steps: employing a generative adversarial network to generate a simulation pipeline vibration audio signal, and combining the simulation pipeline vibration audio signal with a real pipeline vibration audio to construct a pipeline vibration audio signal data set; performing feature fusion on the Mel-frequency cepstral coefficient feature matrix and a first-order difference matrix of Mel-frequency cepstral coefficients to determine a feature fusion matrix of each pipeline vibration audio signal frame; by taking the feature fusion matrix as a training sample and the leakage state of the pipeline vibration audio signal as a sample label, training a CNN-Bi LSTM hybrid neural network model to determine a water supply network leakage detection model; and inputting the to-be-identified pipeline vibration audio signal into the water supply network leakage detection model to determine whether the to-be-identified pipeline vibration audio signal is in a leakage state or a non-leakage state. According to the invention, the precision, robustness and generalization ability of leakage detection are significantly improved.

The invention discloses an intelligent water operation management and control system based on the Internet of Things, and relates to the technical field of industrial Internet of Things crossing, and the system comprises the steps: collecting multi-dimensional operation data in a water supply network, carrying out the preliminary time mark alignment and preprocessing, and generating a multi-source synchronous perception data set; importing the comprehensive risk list into a digital twinborn body, dynamically injecting corresponding fault and abnormal scene parameters in a digital twinborn environment, and performing large-scale parallel deduction through a Monte Carlo simulation algorithm to generate a rehearsal consequence data set; and carrying out multi-objective decision analysis on the rehearsal consequence data set, carrying out prediction comprehensive plan utility scoring based on a preset safety weight and an economic weight, and calculating an optimized management and control plan through iterative optimization. According to the invention, the multi-source heterogeneous data is deeply fused through the graph neural network of the risk identification module, the dynamic impedance characteristics of the pipe network are accurately extracted, the early abnormality is identified, and advanced diagnosis of the multi-modal coupling risk is realized.

The invention provides an operation condition evaluation method and system for a water supply system. Belongs to the technical field of water supply system intelligent management. The method comprises the following steps: acquiring real-time data of a water supply system through various types of sensors and monitoring equipment arranged in each link of the water supply system; meanwhile, collecting non-real-time data of the water supply system to form a multi-dimensional heterogeneous water supply system data set; aiming at data of different types and sources, adopting a self-adaptive preprocessing strategy to carry out data preprocessing; and generating a standard multi-dimensional heterogeneous water supply system data set. The spatio-temporal data analysis technology is adopted, spatio-temporal characteristics in water supply system data are deeply excavated, and the operation condition and dynamic change of the water supply system can be reflected more accurately.

The invention relates to the technical field of water quality detection, and discloses a water supply network water quality abnormity tracing method and system based on data fusion, and the method comprises the steps: carrying out the time alignment of water quality parameter data of distributed monitoring points in a water supply network, and obtaining a standardized water quality parameter sequence; according to the measurement precision of the parameters in the standardized water quality parameter sequence, distributing the weight coefficient of the parameters, and according to the hydraulic retention time in the water supply network, correcting the weight coefficient to obtain a correction coefficient of the water quality parameter data; performing weighted fusion on the standardized water quality parameter sequence based on the correction coefficient to obtain a fusion feature vector; constructing a node connection relation matrix according to the topological structure data of the fusion feature vector; performing anomaly tracing on the fusion feature vector according to the node connection relation matrix to obtain an anomaly result; a pollution source in the water supply network is positioned according to the abnormal result, and a water quality early warning signal is generated; the accuracy of water quality abnormity tracing can be improved.

The invention belongs to the technical field of power grid systems, and particularly relates to a cooperative scheduling method for a water-electricity-heat combined supply negative carbon building light-storage direct-flexible energy microgrid group. Comprising the following steps: constructing a negative carbon building light storage direct-flexible energy microgrid group mathematical model; wherein the mathematical model comprises a generator constraint model, an external power grid and micro-grid group exchange power constraint model, a power balance constraint model, a peak-valley difference constraint model, a storage battery constraint model, a phase change heat storage system operation constraint model and a water supply system operation constraint model; constructing a target function of the scheduling model by taking the minimum total operation cost of the negative carbon building light storage direct-flexible energy microgrid group in a scheduling period as a target; wherein the total operation cost comprises electric power procurement cost, carbon emission cost, energy storage cost, fuel cost and peak-valley difference punishment cost. Through linkage of the electricity storage system, the heat storage system and the water storage system, the utilization rate of renewable energy sources and the flexibility of the system are maximized.

The invention relates to the technical field of pipe network water quality monitoring, in particular to a water supply pipe network water quality intelligent monitoring system. The system comprises: a spatial feature analysis module, which is used for collecting the water age of each monitoring point in the water supply network and various water quality parameters at each moment; acquiring a spatial consistency index of the target monitoring point at the moment by utilizing the numerical value variation amplitude of various water quality parameters of the target monitoring point and upstream and downstream adjacent monitoring points at the same moment; the flow velocity regulation factor acquisition module is used for acquiring a flow velocity regulation factor of the target monitoring point at one moment; the real abnormal index acquisition module is used for acquiring a real abnormal index of the target monitoring point at one moment; and the abnormal score correction module is used for correcting the initial abnormal score of the target monitoring point at the moment by using the flow velocity regulation factor and the real abnormal index of the target monitoring point at the moment to obtain a corrected abnormal score. The water quality of the water supply network can be accurately monitored.

The invention provides a water quality intelligent monitoring method and system, and the method comprises the steps: obtaining the historical water quality data of a water transmission and distribution pipe network, and obtaining parameters in a water quality dynamic system model through a least square method based on the historical data; a parameter estimation result is applied in a laboratory simulation environment, if the difference between model output and measured data does not exceed a preset threshold value, parameters are deployed in an actual water supply system, and the model is dynamically calibrated according to real-time operation data of a water transmission and distribution pipe network; and acquiring real-time water quality data through an Internet of Things sensor, inputting the real-time water quality data into the water quality dynamic system model to predict future water quality indexes, and adjusting water treatment process parameters in advance according to a prediction result. Through the method and the corresponding system, a water quality supervisor can be helped to make an adjustment decision in advance before the water quality is deteriorated, and more intelligent monitoring is realized.