2215 results about "Water supply network" patented technology

The invention discloses a water supply pipe network water hammer control method and system based on multifunctional module fusion, and relates to the technical field of data identification. A time sequence diagram neural network prediction and traceability module which realizes water hammer risk prediction and propagation path traceability based on a causal constraint graph neural network model; the reinforcement learning intervention decision module is used for generating an active intervention strategy through a reinforcement learning agent, forming closed-loop control, abstracting a water supply pipe network into a graph structure, and modeling in combination with a time sequence, so that a propagation path of pressure waves can be comprehensively reflected, a blind area of traditional local modeling is overcome, and the comprehensiveness and accuracy of water hammer event detection are improved; and a causal analysis result is input as an adjacent matrix, so that the interference of irrelevant edges on prediction in topology is effectively eliminated, and the learning efficiency and causal traceability of the model are improved.

The invention provides a water resource scheduling optimization method and system based on artificial intelligence. The method comprises the following steps: acquiring water resource multi-source heterogeneous data of a target area, generating a dynamic balance strategy according to a precipitation gradient change trend of precipitation distribution characteristic data and a consumption rate association relationship in user side water demand fluctuation mode data, and determining the water demand fluctuation mode of the user side on the basis of node pressure distribution characteristics of dynamic load data of a water delivery pipe network. Outputting a multi-target water delivery path optimization scheme in combination with a priority weight in a dynamic balance strategy, and converting the multi-target water delivery path optimization scheme into a scheduling instruction set for controlling a pump station valve in the geographic space topology; according to the technical scheme provided by the invention, the defects of non-uniform resource allocation, poor real-time response capability and the like of a traditional static scheduling strategy are overcome, the utilization rate of water resources is also improved, and the flexibility and the response speed of a water supply system are enhanced.

The invention relates to the technical field of data processing, and discloses a comprehensive dispatching system for a digital intelligent water service pipe network. Comprising a processing module, a layering module, an identification module, a scheduling module, an analysis module and an evaluation module which are connected in sequence. The processing module preprocesses the pipe network data and constructs a dynamic boundary area; the layering module generates operation parameters according to function classification; the identification module analyzes equipment fault features to obtain state data; the scheduling module formulates a preliminary scheme according to a time-sharing rule; the analysis module performs multi-target processing based on the dynamic weight to generate a scheduling instruction; and the evaluation module evaluates pressure, flow and water quality indexes to form a target strategy. The operation efficiency of the water supply network is improved.

The invention discloses a water supply network sound leakage monitoring method based on multi-modal data fusion and multi-task learning, and the method comprises the steps: inputting an original sound signal, and carrying out the labeling of a label; converting the original sound signal to obtain a corresponding logarithmic Mel spectrogram; performing feature extraction on the original sound signal to construct a time-frequency feature set; forming a data set by the original sound signal, the logarithmic Mel spectrogram, the label and the time-frequency feature set; constructing a multistage task prediction network based on a residual convolutional neural network framework; performing multi-task training on the multi-level task prediction network by using the data set to obtain a water supply network sound leakage monitoring model; and inputting the original sound signals of the collection points into the water supply network sound leakage monitoring model to obtain a prediction result. The invention further provides a water supply network sound leakage monitoring device. According to the method provided by the invention, the leakage detection aspect is promoted to more comprehensive sound leakage monitoring application through a data-driven acoustic detection 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 provides a water supply pipe network optimization scheduling method and system based on multi-model coupling, and the method comprises the steps: collecting the basic data of a regional pipe network, and building a water supply pipe network hydraulic model; obtaining a water supply network operation scheduling target, and establishing a water supply network scheduling scene model; historical monitoring data of a water supply network are acquired, a node water demand prediction model and a node water demand change curve prediction model are constructed by using a neural network algorithm, and node predicted water demand and a node water demand change curve during prediction are respectively output; constructing a water supply pipe network optimization scheduling model based on multi-model coupling, performing optimization by using an improved particle swarm algorithm, outputting a water supply pipe network optimization scheduling scheme, and introducing an optimization scheduling evaluation index to perform scheduling scheme evaluation; and constructing a water supply pipe network optimization scheduling scheme screening model, and obtaining an optimal water supply pipe network optimization scheduling scheme. According to the method, a multi-model coupling mode is adopted, the prediction error is effectively reduced, the optimization efficiency is improved, and intelligent and precise scheduling of the water supply pipe network is realized.

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 embodiment of the invention discloses a water supply network abnormity identification method and device, electronic equipment and a storage medium, and relates to the technical field of water supply network abnormity identification, and the method comprises the steps: collecting pressure data from each monitoring point, carrying out the preprocessing, employing a Pearson's correlation coefficient to construct a dynamic pressure correlation matrix according to the pressure data, carrying out abnormity judgment and restoration on the monitoring equipment; the pressure difference standard deviation of each monitoring point in adjacent time periods is calculated to recognize the water hammer effect; constructing a residual error distribution reference through a machine learning algorithm based on historical monitoring data, calculating a real-time pressure residual error of each monitoring point, calculating a comprehensive anomaly index according to the residual error distribution reference, and carrying out anomaly judgment and early warning; and a Kriging interpolation method and a semi-variation function are adopted to generate pressure residual error space distribution according to the real-time pressure residual error and display the pressure residual error space distribution. The problems that in the prior art, data verification is not systematic, space-time relevance is lacked, the abnormal classification dimension is single, misjudgment is prone to occurring, and the response efficiency is low are solved.

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 provides an optical fiber vibration auscultation method and system for water supply network leakage, and relates to the technical field of water supply network monitoring, and the method comprises the steps: carrying out the synchronous sampling of a dual-channel optical fiber vibration signal through an FPGA main clock, so as to obtain a pipeline vibration signal and an environment noise signal which are aligned in time; performing multi-dimensional feature fusion on the pipeline vibration signal and the environmental noise signal to obtain a pipeline leakage feature vector; and inputting the pipeline leakage feature vector into the CNN-LSTM hybrid model to obtain pipeline leakage information output after the CNN-LSTM hybrid model performs feature analysis and prediction processing on the pipeline leakage feature vector. The technical problem that the recognition accuracy of water supply pipeline leakage in the complex environment is low in the prior art is solved, and the technical effect of improving the recognition accuracy of water supply pipeline leakage in the complex environment is achieved.

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 embodiment of the invention provides a water supply network water quality area monitoring point optimization method which comprises the following steps: constructing and correcting a hydraulic water quality model based on monitoring data of existing monitoring points of a water supply network and a topological structure of the water supply network; a structure depth clustering algorithm is adopted, a node time sequence water quality change curve simulated by the model is combined with a topological structure of the water supply pipe network, and all nodes in the water supply pipe network are subjected to quality area division; scoring the nodes in each quality area by comprehensively considering the node coverage rate and the node water age, and sorting the nodes in each quality area according to the score from high to low; selecting a node combination of which the score is in the front and the node spacing meets a set requirement from each quality area, and generating a candidate monitoring point combination; and a monitoring point arrangement scheme meeting information gain and coverage range requirements is determined by integrating the multi-index information entropy and coverage rate optimization target and combining node spacing constraints. In this way, the layout of the monitoring points can be effectively optimized.

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 discloses a detection and positioning method, device and system for coping with multi-pipeline leakage of a water supply pipe network. The method comprises the following steps: constructing a pipe network model, deploying sensors, creating a complex hydraulic pipe network model, screening an optimal monitoring point, and generating a pressure and flow sensor arrangement scheme; outputting a pipe network topological graph and a sensor deployment result; performing scene approximation, constructing an optimization model to enable the hydraulic data under the original normal working condition to fit the hydraulic data under the leakage scene, finally introducing hydraulic data correction, and outputting possible leakage nodes and corresponding hydraulic parameters; reducing a leakage pipe network range, and continuously updating and screening a pipeline range possibly having leakage based on preliminary inference of leakage nodes; after iterative optimization, outputting a local pipe network area where leakage is most likely to occur; and establishing an observer, positioning a specific leakage point, dynamically estimating leakage parameters in a suspected leakage pipe section, comprehensively analyzing a filtering result, and outputting a specific leakage pipe section and a leakage position coordinate.

The invention discloses an intelligent pipe network management system based on the Internet of Things technology, and relates to the technical field of data management. Pressure bearing capacity and water pressure data of a pipeline are collected in real time through stress and pressure sensors, and whether water flow exceeds a pipeline safety range or not is accurately assessed by combining pipeline pressure bearing capacity monitoring, frequency analysis and risk assessment; based on the data, the risk division module divides risks into high, medium and low levels, and takes targeted management measures; and for medium-risk conditions, the maintenance module reduces the pipeline fracture risk by predicting and dynamically adjusting the starting frequency of the water pump and the opening degree of the valve, and effectively prevents secondary pollution and water quality decline, thereby ensuring safe and stable operation of the water supply system.

The invention discloses a pipe burst analysis method and system for a multi-water-source complex water supply pipe network, and the method comprises the steps: building a pipe network diagram based on a pipe network physical structure, and building an undirected adjacency list, a forward adjacency list and a reverse adjacency list for each pipe point; valve distribution at the two ends of the pipe explosion point is determined, and if the two ends are both valves, an optimal valve closing scheme and an affected pipe point and pipeline set are directly obtained; if not all of the two ends of the pipe explosion point are valves, a breadth-first search algorithm is used for starting from the non-valve end of the pipe explosion point, and an initial valve closing result and an affected area are determined; calculating the water inlet quantity of an unaccessed pipe point through a reverse adjacency list, starting from a downstream valve in the initial valve closing result, and based on the forward adjacency list, applying a breadth-first search algorithm, and updating the valve closing result and an affected area; and combining the initial valve closing result with the updated valve closing result to obtain a final valve closing result and an affected pipe point set. According to the invention, no matter how many water sources exist in the pipe network, all pipe network elements can be considered.

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 discloses a water supply network pressure and flow monitoring and early warning system based on an AI model, and relates to the technical field of water supply network monitoring, the system comprises the following components: a data acquisition module, a data transmission module, a quantum heuristic AI optimization algorithm processing module, a monitoring and early warning module and a user interaction module; pressure, flow and water quality data in a water supply network are collected in real time through the data collection module, the accuracy and reliability of the data are ensured by adopting a self-adaptive dynamic point distribution strategy and a self-calibration function, the data can be efficiently analyzed and processed through the quantum heuristic AI optimization algorithm processing module, and the accuracy and reliability of the data are ensured. And the monitoring and early-warning module monitors the condition of the pipe network in real time according to a prediction result, immediately sends out an early-warning signal once an abnormality is found, and ensures the timeliness and pertinence of early warning by adopting a multi-dimensional graded early-warning strategy.

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 embodiment of the invention provides a water supply network pollution tracing and positioning method and device based on a graph theory. The method comprises the following steps: S1, abstracting a topological structure of the water supply network based on a hydraulic model of the water supply network, constructing an adjacent matrix of the water supply network based on the topological structure, and determining an initial dynamic detection point candidate range by combining the adjacent matrix of the water supply network and utilizing a breadth-first search algorithm in a graph theory; s2, according to the dynamic detection point candidate range, dynamic detection point candidate schemes are generated, the information gain of each dynamic detection point candidate scheme is evaluated, and a dynamic detection point most beneficial to pollution source positioning is selected based on the information gain; s3, repeatedly executing the step S2 to obtain water quality pollution information, and continuously narrowing the candidate range of the dynamic detection points; and S4, when the calculated node pollution source result meets the positioning success condition or the dynamic detection point candidate range is empty, determining the position of the pollution source. In this way, the position of the pollution source can be quickly positioned.

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 bimodal cooperative control-based intelligent chemical adding optimization method for a water supply project. The method comprises the following steps of: S1, acquiring effluent quality and quantity data of a water supply system and a monitoring index of a running state of the water supply system in a plurality of continuous control periods adjacent to a current control period; s2, judging whether the running state of the water supply system is in a stable state or not according to the monitoring indexes and the effluent quality and quantity data, if so, entering step S4, and otherwise, entering step S3; s3, calculating the dosing pump control quantity in the current control period by adopting a fuzzy self-tuning PID algorithm; s4, determining a dynamic prediction time domain adjustment rule, inputting effluent quality and quantity data in a time domain into the machine learning model, and adopting a model prediction control MPC algorithm to obtain a dosing pump control quantity; and S5, according to the control quantity of the dosing pump in the step S3 or the step S4, controlling the dosing pump to add a medicament into the clean water tank, and returning to the step S1 when the water supply system is in a running state and the current control period is completed.

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 water quality monitoring, and provides a water supply network trihalomethane prediction method based on a fusion model. Comprising the steps of original water quality data set acquisition, data preprocessing, CNN-BiLSTM-MultiHead Attention fusion prediction model construction, hyper-parameter optimization space and model evaluation function definition, model prediction and output evaluation, model parameter iteration and verification evaluation, model verification, sample importance display, to-be-detected water quality data set collection and trihalomethane prediction. According to the method, the convolutional neural network, the bidirectional long-short-term memory network and the multi-head attention mechanism are fused, so that the change characteristics of a plurality of conventional water quality indexes in the water supply network are efficiently learned and extracted, and better prediction precision can still be kept under the condition of less training data; and the prediction performance and generalization ability of the model in the aspect of predicting a plurality of target variables by using a plurality of input variables are remarkably improved.

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.