1019 results about "Water supply network" patented technology

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 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 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 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 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 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 water supply network monitoring, and discloses a water supply network leakage monitoring and positioning system and method based on big data analysis, and the method comprises the steps: obtaining continuous audio stream data and network topological graph data collected by a distributed sensor, and calculating background baseline parameters; performing multi-scale wavelet transform decomposition on the current audio frame, calculating an energy entropy feature and generating a single-frame abnormal score; applying a cumulative sum control chart algorithm to output a cumulative abnormal index; generating an enhanced anomaly score through a graph attention network; and performing leakage judgment and positioning based on the abnormal scores of the multiple sensors. The technical problem that weak leakage signal detection sensitivity is low is solved.

The invention discloses a water supply pipe network water supply amount prediction method based on deep learning. The method comprises the following steps: analyzing a convolutional neural network CNN and a long short-term memory network LSTM; constructing a water supply prediction model based on LSTM, analyzing the water supply prediction model, and setting training parameters; further fusing the CNN and the LSTM, and constructing a CNN-LSTM combination model to obtain an optimized water supply prediction model; and carrying out verification and comparative analysis on the optimized water supply prediction model. According to the method, the CNN-LSTM model is constructed, the model adopts an Adam gradient descent algorithm to optimize a traditional algorithm of the model, and the CNN-LSTM model strengthens the time sequence modeling capability of the LSTM through CNN feature extraction, so that the model not only can identify the microscopic fluctuation of water supply amount data, but also can grasp the macroscopic evolution rule of the water supply amount data; the problem that a traditional model is poor in stability and low in reliability is solved.

The invention relates to the technical field of water supply network operation and maintenance detection, in particular to a water supply network multi-pipeline leakage detection and positioning method, which comprises the following steps of: acquiring pressure time sequence data of a monitoring point of a water supply network, processing to generate a standardized pressure fluctuation sequence, and analyzing the data by utilizing a pre-trained leakage reasoning calculation model; leakage suspected degree scores and leakage type classifications are obtained, high-suspected-degree leakage events are screened, and corresponding positions and timestamps are obtained. A transient flow state inversion equation set is built in combination with a pipe network topological structure and a hydraulic model, a standardized pressure sequence is used as a boundary condition, pipe section hydraulic state changes are reversely deduced through a state inversion algorithm, pressure gradient abrupt change points are recognized, corresponding pipe sections are positioned, and leakage types and positioning information are fused to generate a complete detection positioning report. According to the method, leakage types can be accurately distinguished, multi-pipeline concurrent leakage pipe sections can be accurately positioned, non-leakage signal interference is reduced, and the method is adaptive to a complex pipe network leakage detection scene.

The invention relates to the technical field of leakage detection, in particular to a water supply network leakage detection method and system based on multi-source data analysis, and the method comprises the following steps: obtaining DMA subarea flow data, eliminating abnormities, complementing missing, down-sampling, denoising, calculating a fluctuation parameter set, collecting sound wave and pressure data, carrying out the alignment, calculating delay and synchronous proportion, and generating multi-source features; carrying out normalized difference comparison to screen a leakage feature combination set, carrying out matching statistics on a threshold exceeding calculation shortest distance to generate calibration information, and carrying out GIS mark superposition display to generate leakage detection visual information. According to the method, abnormal values are eliminated, missing data are complemented, data integrity and stability are guaranteed, flow characteristics are more accurate and reliable by combining calculation of fluctuation amplitude, duration and change rate after downsampling and denoising, and parameter combinations matched with leakage characteristic modes are screened by using normalized difference comparison. And counting the matching proportion and judging the leakage probability, and calculating the shortest distance point location based on the space coordinates to generate position calibration data.

The invention discloses a cross-modal causal coupling water supply network cascade failure deduction method and system, and relates to the field of safety prevention and control of urban water supply networks. The method comprises the following steps: firstly, constructing a water supply network heterogeneous graph, abstracting physical pipe sections as core nodes, associating the pipe sections as heterogeneous edges, and obtaining static and dynamic attributes of the heterogeneous edges; multi-source heterogeneous data space-time alignment is completed through an exclusive scale adaptive coding layer, and a standardized feature matrix is generated; taking the cross-modal causal directed acyclic graph as a hard constraint, and generating a dynamic coupling adjacency matrix through a cross-modal graph attention layer; dynamic weighted coupling of multi-modal characteristics is realized through a space-time dynamic gating unit; finally, cascade failure evolution is simulated based on the coupling characteristics and the adjacent matrix, critical inflection points are recognized, and graded early warning signals are output. The method solves the problems of modeling distortion, false correlation and inflection-point-free pre-judgment in the traditional technology, greatly improves the deduction precision and the engineering credibility, and is suitable for safety prevention and control and emergency early warning of the urban water supply pipe network.

The invention relates to the technical field of intelligent scheduling of water supply resources, in particular to an intelligent scheduling system and method for water supply resources in extreme weather, in which a time sequence coupling analysis unit collects pipe network pressure, turbidity and meteorological early warning data in real time, identifies the peak time period of the turbidity abrupt change rate through dual-channel verification, and sends the peak time period of the turbidity abrupt change rate to a cloud server; the dynamic strategy generation unit divides scenes according to the coefficients, starts staged pressure relief in a high-contact-ratio scene, controls total duration and single-stage pressure drop, integrates real-time compensation of flow velocity, generates a pre-pressure-relief instruction in a low-contact-ratio scene, and generates a pre-pressure-relief instruction according to the pre-pressure-relief instruction, and the dynamic strategy generation unit generates a pre-pressure-relief instruction according to the pre-pressure-relief instruction and the single-stage pressure drop in the high-contact-ratio scene. And the pipe network topology compensation correction triggering time is fused, and the execution intensity of two types of instructions is dynamically allocated in a mixed scene, so that the pipe network pressure and water quality collaborative guarantee in extreme weather is realized, and the safety and stability of a water supply system are maintained.

The invention discloses a water quality degradation early warning method and system for a water supply pipe network in a low-pressure area. The method comprises the following steps: S1, collecting topological structure data and pressure, flow, residual chlorine concentration and turbidity data of an urban water supply pipe network, and preprocessing the topological structure data and the pressure, flow, residual chlorine concentration and turbidity data; s2, constructing an initial node feature matrix, a pipe network node graph model, an adjacent matrix and a low-pressure area identification matrix; sequentially extracting a first pipe network spatial feature, a second pipe network spatial feature and a water flow stagnation time sequence feature, and fusing to obtain a space-time coupling feature; s3, extracting space-time coupling characteristics of the low-voltage area, and calculating to obtain a water age distribution matrix of each node of the low-voltage area; s4, residual chlorine attenuation characteristics are constructed, and then a low-pressure area residual chlorine concentration prediction sequence and a low-pressure area water quality degradation risk matrix are calculated; and S5, performing graded early warning. According to the method, the problems of inaccurate water age distribution inversion and inaccurate water quality risk prediction caused by residual chlorine attenuation prediction of the low-pressure area of the water supply network in a traditional method can be solved.

The invention provides a partition metering leakage monitoring management method and system based on the Internet of Things, and belongs to the technical field of water supply pipe network leakage monitoring, and the method comprises the steps: carrying out the metering partition division of a water supply pipe network, and deploying the Internet of Things monitoring equipment in the metering partition; acquiring position information of the Internet of Things monitoring equipment in the metering subarea to which the Internet of Things monitoring equipment belongs; acquiring geographic information data of a water supply pipe network in the metering partition, identifying the shortest path between adjacent Internet of Things monitoring equipment through a pipe network topological relation based on the geographic information data, and calculating the shortest pipeline length; constructing a pipe network hydraulic model, and simulating simulation parameters of the Internet of Things monitoring equipment under a normal water supply working condition and a pipe explosion working condition; acquiring actual parameters monitored by the Internet of Things monitoring equipment in each metering partition; and calculating the deviation between the normal simulation parameters and the actual parameters, and positioning and correcting the leakage position in combination with a leakage positioning algorithm and the abnormal simulation parameters. The leakage monitoring efficiency of the water supply network is improved, and the leakage point is accurately positioned.

The invention belongs to the technical field of water affair management, and relates to an urban water plant water supply balance automatic scheduling method and system. According to the method, the water pressure value of each monitoring point in the water supply network and the water consumption of the water metering unit are collected in real time, the water time sequence characteristics are extracted to identify the potential peak period, and the validity of the peak period is verified by combining the correlation between the water pressure and the water consumption; performing region division and water consumption aggregation based on peak period distribution and a pipe network topological structure, calculating water consumption deviation of adjacent regions, and when the deviation exceeds a historical synchronous fluctuation range, dynamically generating a target water supply pressure value and issuing a scheduling instruction; and evaluating the balance state of the water pressure and the water consumption between the areas after scheduling by executing the feedback data. According to the method, the problems of response lag and regional supply and demand imbalance in traditional scheduling are solved, accurate regulation and control of the water supply pressure and regional water consumption balance in the peak period are achieved, and the stability and the energy efficiency level of a water supply system are improved.

The invention discloses a water supply pipe network dynamic pressure control method and system based on multi-parameter fusion, and relates to the technical field of water supply pipe network control, and the method comprises the steps: carrying out the standardization processing and topological optimization of pipe network geographic information system data, and constructing an urban pipe network topological graph; identifying a pressure sensitive node and a distance sensitive node based on a hydraulic calculation result of the mechanism calculation model; performing association coding on historical monitoring data of the key nodes and a topological connection relationship, and training a machine learning model fusing pipe network topological constraints; collecting real-time monitoring data, inputting the real-time monitoring data into the mechanism calculation model and the machine learning model, and respectively obtaining offline adjustment parameters and online adjustment parameters; and determining a final adjusting instruction through consistency verification to control water supply pump set equipment. According to the method, the calculation precision of the mechanism model and the response speed of the machine learning model are fused, organic unification of accuracy and real-time performance is achieved, and the control stability and reliability under the abnormal working condition are improved.

The invention relates to the technical field of data processing, in particular to an urban water supply system water quality intelligent monitoring method based on the Internet of Things, and the method comprises the steps: obtaining a multi-dimensional water quality monitoring data sequence of any water quality monitoring node in an urban water supply system within a preset time period; dividing the multi-dimensional water quality monitoring data sequence into at least two water quality monitoring data sequences according to dimensions, and obtaining the data fluctuation degree of each time in each water quality monitoring data sequence according to the correlation between every two water quality monitoring data sequences and the data fluctuation characteristics of each water quality monitoring data sequence; and in combination with data fluctuation characteristics of each water quality monitoring data sequence, obtaining adaptive filtering parameters of each water quality monitoring data sequence at each moment in the current period, performing filtering processing on water quality monitoring data at each moment in the current monitoring period, and performing water quality monitoring on any water quality monitoring node in the current period. The water quality monitoring accuracy is improved.

The invention relates to the field of water quality prediction, and particularly discloses a water supply pipe network water quality prediction method and system based on big data analysis. The input mixed water quality signal is decoupled into a plurality of independent component branches in one-to-one correspondence with the water sources on the characteristic level. And then, through a time-space diagram convolutional network sharing parameters, parallel and independent evolution derivation is carried out on each component flow, so that pervasive transportation and attenuation laws followed by different source water in a pipe network can be respectively captured, and the fundamental defect that a traditional single model cannot be distinguished and processed is overcome. Finally, all independently predicted future component states are subjected to self-adaptive nonlinear fusion with definite physical significance according to the accurate volume ratio of the future component states at the target point, so that a final mixed water quality prediction result with physical interpretability and high precision is generated.

An ice making module (100), and an ice maker and refrigerator having same. In the ice making module (100), a lifting device drives an upper die (26) to ascend or descend. After descending, the upper die (26) matches with a lower die (13) to form a plurality of ice making cavities. The top of the upper die (26) is provided with a plurality of water inlet holes, which are respectively in communication with the cavities. A quantitative water supply system (7) is in communication with the water inlet holes (261) to achieve quantitative water supply. The lower die (13) is connected to a turning device. The bottom of the lower die (13) is attached to a heat transfer block (12). Further comprised is a separation apparatus and / or a vent hole (122). The separation apparatus is used for separating the heat transfer block (12) and the lower die (13). The vent hole (122) communicates a gap between the heat transfer block (12) and the lower die (13). By means of the present ice making module (100), ice can be rapidly made, the dies can be rapidly separated from the heat source, the dies are easy to turn over, and the ice maker can collect overflow water to prevent the overflow water from soaking the device. The refrigerator is connected in series with an evaporator (33) of the ice making module (100) by means of an evaporator (102) of the refrigerator, or directly uses air from an air port of an air duct of a freezing chamber of the refrigerator to cool the dies, thereby saving energy.

The invention discloses a time-sharing energy-saving scheduling method and device for a marine water supply system based on intelligent prediction, and relates to the technical field of data processing, the method comprises the following steps: constructing a hydraulic network based on a marine water supply pipeline and a time-sharing water supply scene water law gene pool, the scene being defined by time heterogeneity, space gradient and burst disturbance; through valve opening and water pump adjustment, a double-end decision maker is deployed to be embedded into a water supply system; and the auxiliary decision-making device performs primary and secondary pipeline classification and key valve and water pump motor parameter adjustment decision making, executes double-end balance adjustment to determine an adjustment strategy, decouples the strategy and issues the strategy to the equipment to complete water supply scheduling control. Therefore, the technical effects of accurate and stable water supply scheduling, and improvement of system operation stability and energy-saving efficiency are achieved.

The utility model discloses a cleaning machine, which relates to the technical field of electric appliances and comprises a shell component, a water storage container, a water mixing valve and a water storage component. The shell assembly is used for being installed on a wall face and provided with an upper end, a lower end and a containing cavity limited between the upper end and the lower end. The water storage container is installed in the containing cavity and provided with a first water cavity and a second water cavity, the first water cavity is used for being communicated with an external hot water source, and the second water cavity is used for being communicated with an external normal-temperature water source; the water mixing valve is respectively communicated with the first water cavity and the second water cavity; the water outlet assembly is provided with a water inlet end and a water outlet end, the water inlet end of the water outlet assembly is communicated with the water mixing valve, and the water outlet end of the water outlet assembly is arranged at the upper end of the shell assembly and used for being communicated with the water using end. The cleaning machine can be directly matched with a water supply system to supply normal-temperature water and hot water, and the applicability of the cleaning machine is improved.

The invention relates to the technical field of water supply network pollution monitoring, in particular to a water supply network water pollution rapid detection method. The method comprises the following steps: acquiring water supply pipe network operation data, extracting pipe network structure information, detecting a water flow direction, and constructing a water supply pipe network topological graph; on the basis of the topological graph and the operation demand data, the negative pressure overload condition of the pipe network is judged, the pipeline damage condition is estimated, then the conditions of external pollutant invasion and hydraulic unbalance are analyzed, and the pollutant backflow trend and the water flow retention area are determined; the conditions of microorganism breeding and internal water quality deterioration are evaluated by combining a water flow retention area and a pollutant backflow trend, and tracing and positioning of a pollution source are carried out; pipe network structure adjustment, water flow regulation and control and pollution prevention and control measures are implemented according to the pollution source tracing result, and optimization treatment of the water supply pipe network is achieved; optimization treatment of the water supply pipe network is achieved through pollution monitoring of the water supply pipe network.

The invention belongs to the technical field of water supply system toughness evaluation and optimization decision making, particularly relates to an urban water supply system toughness evaluation and optimization decision making method and system, and solves the problems that a traditional evaluation method is difficult to deal with uncertainty and large in subjective interference, and an improved method is subjective in weight and lacks dynamic performance and visualization. According to the method, qualitative toughness grade fuzziness and evaluation factor randomness are quantified through a cloud model, index cloudization, a weight cloud model and a multi-level cloud integration mechanism are constructed, toughness grade judgment and cloud parameter optimization are combined, and qualitative-quantitative natural conversion is achieved. According to the method, evaluation scientificity and reliability are improved, decision makers are helped to position short boards and adapt to system time-varying characteristics by means of the visual cloud picture and toughness-impact response corresponding relation, a basis is provided for operation and maintenance improvement and resource allocation, and a stable and adaptive water supply system toughness guarantee system is helped to be constructed.

The invention relates to the technical field of water supply system monitoring and control, and discloses an urban and rural water supply intelligent scheduling and monitoring system based on the Internet of Things. The system multiplexes transient pressure waves generated in the system into detection signals, and performs propagation damping and reflection echo analysis on the same pressure signal sequence from a pressure sensor in parallel, so as to obtain a pressure signal sequence; according to the method, the system can be controlled to obtain physical state information of the pipe network on line, and the information is utilized to update a hydraulic model used for guiding dispatching; through a non-disturbance detection process, information about a pipe network distributed health state and a discrete abnormal point position is obtained at the same time, a basis is provided for a scheduling decision, and thus the operation state of a system is influenced.

The invention provides a water supply network water leakage abnormity identification method based on NMF dimension reduction, and relates to the technical field of water supply network abnormity identification, and the method comprises the steps: embedding hydrophone sample data containing expert labeling information into a sample weight matrix as a row vector, and constructing a sample dimension loss item; hydrophone sample data containing industry feature priori are mapped to the feature basis matrix to serve as rows, and feature dimension loss items are constructed; combining the two loss items to construct a total loss function and solving the total loss function to obtain an optimal sample weight matrix; and performing clustering analysis based on the optimal sample weight matrix to obtain a clustering affiliation result of the hydrophone sample data so as to judge whether the water supply network to be identified has water leakage abnormity or not. According to the method, the double-constraint NMF framework fusing the expert knowledge of the sample dimension and the industry priori knowledge of the feature dimension is constructed, the accuracy and interpretability of hydrophone feature dimension reduction are considered, and the problems that an existing method is poor in interpretability, less in priori knowledge utilization and the like are solved.

The invention belongs to the technical field of water supply network leakage monitoring, and particularly relates to a water supply network leakage node and time window combined positioning method based on a hybrid neural network, which realizes spatial positioning of a water supply network leakage node and time positioning of a leakage time window. Comprising the following steps of 1, collecting node pressure and flow time sequence data of the water supply network in different scenes, and recording leakage nodes and leakage starting and ending time labels; 2, carrying out normalization preprocessing on the data, and constructing a standardized time sequence data set containing a multi-leakage scene mark; step 3, a dual-branch hybrid neural network model TCN-Transform-KAN is built, and a dual-branch hybrid neural network model TCN-Transform-KAN is built; 4, constructing a joint loss function of node classification loss and time regression loss; and step 5, training through a joint loss function constraint model, and outputting a leakage node and a center position and a continuous width of a leakage time window. According to the method, joint precise identification of the leakage nodes and the time range is realized, and technical support of time-space two-dimensional information is provided for intelligent operation and maintenance of a water supply pipe network.

The invention relates to the technical field of intelligent water affairs, and provides a water supply pipe network leakage monitoring method comprising the following steps: collecting pipe network multi-source data and pre-processing; constructing a pipe network directed graph topological structure, inputting data into a space-time graph convolutional neural network to extract space-time correlation characteristics, and predicting a dynamic reference pressure value and a flow value of each node in combination with environment temperature and historical water consumption; calculating a residual error between an actual monitoring value and a reference value, dynamically determining an adaptive threshold value based on historical residual error statistical characteristics, and judging that leakage occurs when the residual error continuously exceeds a limit; finally, the pipe network hydraulic micro model is called for reverse evolution, the virtual leakage amount of each pipe section is calculated through a leakage coefficient optimization algorithm, and the pipe section with the maximum value is determined as the leakage point. According to the method, deep learning and a hydraulic mechanism are fused, the anti-interference capacity is high, the false alarm rate can be effectively reduced, and early warning and accurate positioning of tiny leakage are achieved.