2417 results about "Tubular network" patented technology

The invention discloses an underground pipe network leakage detection method fusing big data analysis and machine learning. The underground pipe network leakage detection method comprises the step of deploying an acoustic sensor, a pressure sensor and a flow sensor at preset positions of an underground pipe network. Performing noise reduction and standardization processing on the acquired operation data, extracting frequency domain, energy and time sequence characteristics of acoustic signals to generate acoustic characteristic vectors, and calculating initial space coordinates of a leakage position based on time difference of arrival of the acoustic signals and sensor arrangement; and generating a pressure feature vector, and analyzing the spatial-temporal correlation of the flow data to generate a flow feature vector. And fusing acoustic, pressure and flow feature vectors to form a multi-dimensional feature vector, inputting the multi-dimensional feature vector and the initial space coordinates into a pre-trained cascade deep learning model, and outputting the leakage probability and the leakage position of underground pipe network leakage. And when the leakage probability exceeds a preset threshold value, triggering an alarm mechanism. According to the invention, efficient detection and accurate positioning of underground pipe network leakage are realized, and high real-time performance and positioning accuracy are achieved.

The invention provides a digital twinborn-based park pipe network monitoring and early warning method and system, and relates to the technical field of pipe network monitoring and early warning, and the method comprises the steps: constructing a park pipe network domain knowledge graph, collecting real-time monitoring data and historical operation and maintenance data, and forming a knowledge enhanced pipe network data set; extracting pipe network performance attenuation characteristics by using a bidirectional gating recurrent neural network, and deeply fusing the pipe network performance attenuation characteristics with expert empirical rules in the domain knowledge graph to construct a performance attenuation analysis model; a performance degradation curve and remaining useful life are calculated based on the model, potential fault risks are predicted, and maintenance suggestions are generated. According to the invention, precise evaluation of pipe network performance degradation, early warning of fault risks and intelligent generation of maintenance suggestions are realized, and the intelligent level and reliability of operation and maintenance of the park pipe network are improved.

The invention discloses a gas pipe network fault positioning method based on a space-time diagram neural network, and the method employs an edge-cloud collaborative architecture, and achieves the precise detection of leakage points through simulation data generation, multi-modal perception, dynamic space-time modeling and hierarchical positioning. Firstly, simulation modeling is conducted on a pipe network, a multi-working-condition leakage data set is generated, and monitoring point layout is optimized through fuzzy clustering; a multi-modal sensing unit and a lightweight anomaly detection module are deployed at an edge end to realize coarse-grained anomaly detection and data hierarchical transmission; the cloud constructs a dynamic space-time diagram neural network, integrates a pipe network topological structure, multi-source time sequence data and physical constraints, and realizes high-precision positioning of leakage points through a hierarchical positioning strategy; and finally, realizing online evolution of the model through elastic weight solidification and hierarchical parameter updating. According to the method, the space-time diagram neural network and the physical characteristics of the pipe network are deeply fused, and the problems that space-time coupling features are difficult to extract and physical constraints are missing in complex pipe network fault positioning are solved.

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 discloses an intelligent monitoring system for a municipal drainage pipe network, and particularly relates to the technical field of drainage pipe network monitoring. The node operation mode identification module carries out real-time classification and confidence evaluation on the operation state of the pipe network, constructs a multi-attribute pipe network weighted graph based on pipe diameter difference, gradient and confluence density, and extracts multi-scale features through graph Fourier transform. A hybrid anomaly detection link is constructed in combination with an LSTM self-encoder, an isolated forest model and chemical oxygen demand and turbidity water quality verification, and the problems that traditional single-index monitoring is prone to false alarm and missing alarm and inaccurate in positioning are solved; sensor data compensation is realized through cooperation with digital twinning, a rapid detection mode is started during rainstorm early warning, key nodes are processed preferentially, and drainage scheduling is controlled in a closed-loop mode; and target nodes which are easy to accumulate grease are screened based on pipe network topology connectivity, accumulation risks are predicted by fusing multi-sensor data, and a preventive clearing instruction is triggered.

The invention relates to the field of pipe network operation state prediction, in particular to an intelligent urban pipe network operation prediction method based on a physical information graph neural network, and the method comprises the steps: obtaining monitoring data and topological structure information of a pipe network, and generating time sequence graph data; constructing a prediction model, wherein the prediction model is a mixed model combining a graph neural network and a physical information neural network; performing feature extraction on the time sequence diagram data based on a graph neural network to model dynamic characteristics of a pipe network; embedding the physical constraint into a loss function based on a physical information neural network; generating an operation state prediction result of the pipe network according to the prediction model; and a prediction result is verified, and the model is optimized and adjusted according to verification. The method has the advantages that the pipe network state is accurately predicted, the key parameters are inversed in real time, and reliable support is provided for optimization scheduling and fault diagnosis of the pipe network.

This invention disclosed a full-space intelligent detection method and system for underground drainage networks, as well as storage media, including the following steps: image acquisition, intelligent image denoising, internal pipe defect segmentation, concealed defect detection around the pipe, 3D reconstruction with volume quantification, and pipeline life prediction. This invention introduced a bionic four-wheel-drive, all-terrain detection robot that can effectively navigate through mud and flowing water-challenges that hinder traditional detection devices. By leveraging deep learning algorithms as well as various techniques of computing vision, 3D reconstruction, and point cloud processing, the system thoroughly analyzed collected data to determine defect types and precise locations. Utilizing this analysis, precise location of different defect type and quantitative measurement of their dimensions can be realized. Based on the data analysis results, a deep-learning driven model was developed for predicting pipeline longevity to support maintenance staff with timely information on pipe defects and operational lifespan.

The invention discloses an intelligent heat supply regulation and control system based on digital twinning and deep reinforcement learning. The system comprises a physical layer, a control layer and a control layer, wherein the physical layer is a physical heat supply system composed of heat source equipment, a transmission and distribution pipe network and a user terminal; according to the digital twinborn layer, a virtual heat supply system mapped with the physical layer in real time is constructed, the virtual heat supply system comprises a multi-physics field coupling model based on the thermodynamics and fluid mechanics principle, operation data of the physical layer are collected through a distributed sensor network, and the state vector of the virtual system is dynamically updated; and the intelligent decision-making layer is integrated with a DRL intelligent agent, the state space of the DRL intelligent agent is defined as a virtual system state vector output by the digital twin layer, the action space of the DRL intelligent agent is a regulation and control instruction combination of heat source power and pump valve opening, and a reward function fuses an energy consumption penalty term, a room temperature comfort reward term and a pipe network stability constraint term. According to the method, global optimization, high-precision continuous regulation and control and collaborative balance are realized through deep collaboration of digital twinning and deep reinforcement learning.

The invention relates to the technical field of town sewage pipe network detection, and discloses a town sewage pipe network leakage detection system and method. The system comprises a data acquisition module which uses a multi-source sensor to acquire real-time operation data such as pipe network pressure, flow and the like; the feature extraction module extracts spatio-temporal features based on the multi-scale convolutional neural network, and generates a pipe network state feature matrix; the anomaly detection module inputs the feature matrix into a pre-training model and marks a potential leakage area; the optimization analysis module constructs a multi-constraint dynamic optimization model, and pipe network pressure parameters are optimized by using an adaptive particle swarm algorithm; the hierarchical execution module generates a global regulation and control sequence, dynamically matches local pressure parameters and adjusts the valve opening and the pump station power through a decision layer, a region coordination layer and an execution layer. The system and the method are accurate in detection and reasonable in regulation and control optimization, leakage risks can be effectively reduced, the operation management level of a pipe network is improved, and water resource waste and environmental pollution are reduced.

The invention discloses a multi-source heterogeneous data fusion pipe network intelligent scheduling decision-making system, which comprises a multi-modal data acquisition cabin module, a space-time alignment fusion center module, a digital twin deduction cabin module, a self-adaptive decision-making matrix module, an elastic execution feedback chain module and a credibility tracing platform module, the multi-modal data acquisition cabin module comprises a heterogeneous protocol analysis unit, an unstructured processing engine and an edge preprocessing mechanism, and the space-time alignment fusion center module comprises a space-time reference mapping engine, a federal learning cleaning tower and a dynamic semantic association library. The problem that data of a traditional system cannot be effectively integrated is solved, fusion of multi-source heterogeneous data is achieved, data islands are broken, the response speed is increased, the dynamic response capacity is enhanced, in addition, decision making efficiency and accuracy can be improved, decision making intellectualization can be enhanced, optimal configuration of pipe network energy efficiency can be achieved, and the system is suitable for popularization and application. And the energy efficiency of the pipe network is greatly optimized.

The invention discloses a modeling analysis method for optimizing an urban water supply pipe network, and belongs to the technical field of intelligent water affairs, and the method comprises the steps: building a comprehensive pipe network database, collecting hydraulic parameters through Internet of Things equipment, and fusing the hydraulic parameters with GIS topological data; constructing a dynamic hydraulic simulation model, coupling real-time data of a pressure sensor with an EPANET model, and correcting a friction resistance coefficient of a pipe section; a hybrid optimization algorithm architecture is designed, a double-target genetic algorithm is adopted for old pipe network transformation, and a constrained particle swarm algorithm is adopted for new pipe network planning; executing multi-stage optimization calculation, generating a pipe diameter feasible solution space in combination with geographical constraints, and iteratively outputting a scheme meeting a pressure threshold value; and generating a pipe network optimal configuration scheme, and outputting an engineering implementation list through spatial overlay analysis and valve regulation logic. According to the method, the problem of modeling distortion caused by a multi-source data island is solved, the defect that a static model is difficult to adapt to dynamic water demands is overcome, and the economical efficiency and the engineering implementability of a pipe network optimization scheme are remarkably improved.

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 provides a civil air defense engineering concealed conduit leakage positioning method based on acoustic characteristics, and belongs to the technical field of civil air defense engineering. An acoustic signal acquisition network is constructed by deploying a high-sensitivity hydrophone array, and signals are preprocessed by using a time division multiple access technology and a pulse compression technology; and carrying out time-frequency analysis by applying wavelet transform to extract acoustic features. A time reversal mirror technology is introduced to identify direct propagation and multipath reflection signals, a fluid acoustic coupling propagation equation is constructed to analyze a leakage sound source mechanism, and pipe network topological information and a sound wave speed correction function are combined to compensate a measurement error. Wherein the deep learning pipe network acoustic propagation multi-modal model is fused with a pipeline structure encoder, an acoustic feature extractor and a position prediction decoder, and high-precision leakage positioning and degree evaluation in a complex environment are realized through a three-stage pre-training strategy; the technical problem that it is difficult to accurately locate the position of a leakage point in the complex pipe network environment of civil air defense engineering is solved.

The invention discloses an indoor temperature real-time regulation and control method of a heat distribution pipeline and a control system of the indoor temperature real-time regulation and control method, relates to the technical field of dynamic control of a heat distribution pipe network, and solves the problems of hydraulic oscillation and temperature control hysteresis caused by the fact that local valve regulation neglects whole-network coupling and a first-order linear model is difficult to describe multi-order thermal inertia and large heat capacity in the prior art. According to the scheme, on the basis of pipe network distributed PDE / lumped parameter hybrid modeling and in combination with extended Kalman filtering and unscented Kalman filtering on-line identification, a feedforward decoupling compensation item is generated through spectral decomposition, a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed, a control instruction is issued according to a pump-first and valve-second serialization strategy, and a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed. Meanwhile, the model weight and the prediction time domain are dynamically adjusted; according to the method, the global balance capability and the temperature tracking precision of heat distribution pipeline regulation and control are remarkably improved.

The invention relates to the technical field of data processing, and provides a multi-heat-source networking heat supply optimization operation method and system.The method comprises the steps that environmental parameters, heat source data, market dynamic information, user behavior characteristics and a pipe network topological graph are collected by deploying IoT equipment; acquiring a thermal load time sequence predicted value in a future preset time period; the heat source data and the pipe network topological graph are processed, and a pipe network operation state matrix is obtained; performing feature dimension alignment processing on the pipe network operation state matrix to obtain a pipe network spatial topology feature mapping value; performing weighted fusion on the thermal load time sequence prediction value and the pipe network spatial topological feature mapping value to obtain a final thermal load prediction value; and optimal operation of heat supply is realized according to the final heat load predicted value. According to the method, a real-time response mechanism for environmental parameters, market dynamics and user behavior characteristics can be realized, collaborative optimization of multiple heat sources can be realized, the heat source collaborative efficiency is improved, and energy waste and operation cost are reduced.

The invention relates to the technical field of water pump control, and discloses a water pump energy-saving optimization control method and system. The method comprises the steps of obtaining water pump outlet pressure, pipe network tail end flow and water pump motor power monitoring signals; calculating a flow-pressure propagation time compensation coefficient and a pressure fluctuation compensation value to eliminate the influence of signal propagation lag and amplitude fluctuation; extracting an operation condition feature vector by combining the motor power frequency domain energy distribution feature and the pressure fluctuation compensation value, and matching the operation condition feature vector with an energy-saving parameter mapping rule base to generate an energy-saving parameter combination; correcting a water pump efficiency reference curve in real time based on the combination, outputting an efficiency attenuation factor, and generating a control deviation value in combination with a target water supply demand; according to the control deviation value, the water pump rotating speed adjusting quantity and a start-stop priority sequence are calculated, and the priority sequence is optimized in combination with historical operation data to generate a machine number control instruction; and finally synthesizing a control signal to drive an execution mechanism.

The invention discloses a pipe network on-line monitoring system, which relates to the field of urban capital construction and comprises a sensing deployment module, a signal acquisition module, a signal processing module, a pipe network modeling module, a leakage positioning module, an edge calculation module, an energy scheduling module, an anomaly prediction module, a deposition evaluation module, an early warning visual module and a center management module. According to the method, the recognition capability of the system on a hidden and complex pipeline structure is improved, accurate and efficient three-dimensional modeling can be carried out, the path deviation resistance capability is improved, and the more comprehensive and robust leakage sensing capability is realized; the method is advantaged in that communication burden and response time delay are substantially reduced, sensitivity and foresight of abnormal evolution trend identification are improved, overall identification accuracy of the system is improved, a global optimal risk solution can be efficiently output, a high-risk area is visually displayed, and rapid pre-judgment and decision making of operation and maintenance personnel are facilitated.

The invention belongs to the technical field of intelligent monitoring, and particularly relates to an urban underground pipe network real-time monitoring algorithm and system based on multi-source data fusion, and the method comprises the steps: obtaining multi-source heterogeneous monitoring data; performing multi-source data preprocessing; carrying out multi-source heterogeneous feature coding and fusion; carrying out real-time monitoring and anomaly detection on a pipe network state; fault diagnosis and prediction are carried out; and generating decision support information and early warning. The system comprises a data acquisition module, a data preprocessing module, a multi-source heterogeneous feature coding and fusion module, a pipe network state real-time monitoring and anomaly detection module, a fault diagnosis and prediction module and a decision support and early warning module. According to the scheme, multi-source heterogeneous data are integrated, spatial-temporal feature coding and fusion are carried out through deep learning, accurate sensing, early warning and intelligent fault diagnosis of the operation state of the pipe network are achieved, and the safe operation level and maintenance management efficiency of the urban underground pipe network are improved.

The invention relates to the technical field of fault detection, in particular to a heating and ventilation system fault positioning system and method based on big data, and the system comprises a multi-source sensing module, a disturbance feature module, a path modeling module, a frequency spectrum matching module and a fault positioning module. In the method, a real-time disturbance sequence is constructed through time window segmentation and parameter offset calculation, separation of an active response chain and an abnormal propagation path is realized through a directional joint state vector and a topological relation table, and time asynchronism of multi-device signal transmission is eliminated by adopting a dynamic time warping algorithm. In combination with a real-time parameter bidirectional verification mechanism of a frequency domain main frequency band energy mark, a valve opening degree and a pump rotating speed, the problem of path confusion in a multi-node parameter coupling scene of a traditional method is solved, the tracing efficiency of concurrent faults in a complex pipe network system is improved, the adaptability limitation of a single-dimensional threshold mechanism to equipment performance degradation is overcome, and the method is suitable for a complex pipe network system. And the error positioning probability caused by signal delay superposition is reduced.

The invention discloses a GIS-based urban underground pipe network monitoring and early warning platform, and relates to the technical field of underground pipe network early warning. A data acquisition module is used for acquiring a pressure fluctuation signal in a pipeline, fluid flow data, a pipe wall vibration spectrum and surrounding soil moisture content change data in real time to form a multi-source time sequence data set; the feature extraction module is used for performing wavelet packet decomposition on the pressure signals, extracting high-frequency-band micro pressure pulsation features, processing vibration data by adopting empirical mode decomposition, and separating a normal operation mode and an abnormal disturbance component of a pipeline, so that the early detection capability of micro leakage is remarkably improved, the false alarm rate is reduced, and meanwhile, high-precision positioning is realized; reliable technical guarantee is provided for safe operation of the underground pipe network, and resource waste and safety accidents caused by tiny leakage are effectively avoided.

The invention provides a pipe jacking construction digital twinning optimization system and method based on high-precision physical simulation, and relates to the technical field of underground pipe network pipe jacking construction. Construction data of multiple physical entities during pipe jacking construction are collected in real time through a physical simulation engine module and then transmitted to an established digital twin model, virtual mapping synchronously updated with the construction data of the physical entities is established, and an optimization and decision module optimizes construction parameters by adopting a joint simulation algorithm fusing finite element analysis (FEA) and computational fluid dynamics (CFD), so that the construction parameters are optimized. The construction feedback module realizes automatic parameter adjustment based on PLC control according to optimized construction parameters, the analysis report module analyzes time sequence data and generates a construction efficiency evaluation report, a stress concentration area is accurately simulated, the simulation precision is improved, high synchronization of a virtual model and physics is realized, and the precision and real-time performance of system execution are improved.

The invention discloses an intelligent heat supply pipe network optimization control method and system based on the Internet of Things, and relates to the technical field of heat supply pipe network optimization control, and the system comprises environmental parameter collection modules which are distributed in different areas covered by a heat supply pipe network and are used for collecting environmental data of each area in real time; and the Internet of Things control platform is used for receiving the environmental data from the environmental parameter acquisition module. According to the intelligent heat supply pipe network optimization control method and system based on the Internet of Things, flow and temperature data are monitored in real time, the valve opening degree is accurately controlled according to a PID control algorithm, a closed-loop control system is formed, heat supply demand changes are quickly responded, energy waste is reduced, heat supply quality is guaranteed, meanwhile, energy consumption is reduced, and the energy utilization efficiency is improved; environmental parameters and user heat using habit changes are monitored in real time, a heat supply strategy is adjusted in time, heat supply demand fluctuation is quickly responded, and it is ensured that heat supply always meets the actual demand of a user.

The invention discloses a distributed natural gas pipeline network safety monitoring system and method based on edge computing, and belongs to the field of natural gas pipeline network safety monitoring, and the method comprises the steps: deploying monitoring units at a plurality of target nodes of a natural gas pipeline network, collecting the operation data of a local node by each monitoring unit, and carrying out the multi-dimensional anomaly detection through a first deep learning model; a local security risk event is identified. And the monitoring unit uploads the node operation data and the security risk event to the cloud monitoring center. And the cloud end summarizes and analyzes the data of the plurality of monitoring units to obtain global operation data of the pipe network, and identifies global safety risks through a second deep learning model. And based on the global security risk identification result, the cloud generates a corresponding control instruction and issues the control instruction to the target monitoring unit. And an execution unit connected with the target monitoring unit executes a safety action according to the control instruction. According to the method, accurate, real-time and distributed safety monitoring of the natural gas pipeline network can be realized, and the operation safety and the risk prevention and control capability of the pipeline network are improved.

The invention relates to the technical field of fire-fighting monitoring, in particular to a fire-fighting water system fault detection and early warning method and system based on the fire-fighting Internet of Things, and the method comprises the steps: 1, periodically collecting pressure data through pressure sensors disposed at a fire-fighting water pump outlet, a pipe network main pipe branch point and the most unfavorable tail end; 2, according to the current valve opening degree, the pump state and historical normal working condition data, theoretical pressure expected values and dynamic allowable deviation zones of all nodes are generated; 3, when actually measured pressure deviates from a theoretical pressure expected value and exceeds a dynamic allowable deviation band, marking abnormal nodes, reversely constructing a fault propagation tree along the topological model, and allocating weight factors for associated nodes according to fault types; and 4, triggering graded early warning based on the number of the abnormal nodes, the fault propagation path and the weight factor accumulated value. And the system can automatically take measures when a fault occurs through an equipment linkage function, so that the efficient operation of the fire fighting water system is guaranteed, and the efficiency and safety of fire emergency response are improved.

The invention relates to an intelligent water service pipe network monitoring method based on Internet of Things fusion, and aims to solve the problems in heterogeneous sensor data accurate acquisition, consistent processing, efficient anomaly recognition and trend prediction. According to the core technical scheme, the method comprises the steps that deployment of multiple types of sensors is optimized, standardized calibration is implemented, efficient collection and local preprocessing of original data are achieved through a wireless communication protocol, and data uniformity and reliability are guaranteed through data normalization, noise suppression and abnormal value elimination; performing historical operation trend and short-term fluctuation feature extraction and conventional trend prediction by adopting space-time mixed feature perception and a deep neural network, and integrating an adaptive anomaly detection and correction mechanism to realize emergency response and cause explanation; and finally, an analysis result is fed back to an early warning and resource scheduling system, and the model is periodically optimized. According to the scheme, the sensing precision, intelligent analysis and abnormal response capability of the operation data of the water service pipe network are remarkably improved.

The invention discloses a heat supply system pipe network anomaly detection method based on a multi-mode AI large model, and the method comprises the steps: deploying a plurality of types of detection devices at key nodes of a heat supply system pipe network, and collecting the detection data of different modes and different types in the operation process of the heat supply system pipe network, forming a heat supply system pipe network multi-mode data set by combining virtual measurement of key data in different operation states of the pipe network and various types of historical abnormal event information of the pipe network; integrating various modal data by using a multi-modal large model, extracting key information from the various modal data, and constructing a heat supply system pipe network anomaly multi-modal knowledge graph; forming a pre-training large model with a heat supply system pipe network abnormal event detection knowledge basis; finely adjusting the pre-trained large model according to the heat supply system pipe network anomaly multi-mode knowledge graph, optimizing the large model by using a reinforcement learning algorithm, and constructing a heat supply system pipe network anomaly event detection large model; and outputting an abnormal event detection result by using the large model.

The invention discloses a three-dimensional GIS (geographic information system) system for safety assessment of an urban gas pipeline internet of things. A gas pipe network monitoring and assessment system combining the internet of things, a mobile internet and BIM / GIS information is established. Firstly, an overall framework based on sensing, transmission, knowing and use of the internet of things is established; secondly, a front-end sensing system deployment scheme is established, a database system is built, and index data are collected and stored; thirdly, a gas leakage hazard model is constructed, and possible hazard consequences are evaluated; 4, a pipe network monitoring system application layer module is constructed, and underground space information management, safety analysis, prediction and early warning and an auxiliary decision making module are achieved; and establishing a gas pipe network risk identification module, a basic information checking module, a monitoring and explosion early warning module, a three-dimensional visualization module and an auxiliary decision-making module. The system monitors the operation condition of the gas pipe network in real time, analyzes the operation rule of the gas pipe network, evaluates the risk of the gas pipe network, predicts, early warns and decides in time, changes passive first-aid repair into active protection, and is high in monitoring stability, precision and efficiency.

The invention relates to the technical field of heat load prediction, and discloses a heat supply system load prediction method and system, and the method comprises the steps: collecting multi-source sensing data in real time, and collecting outdoor meteorological parameters and building structure information; based on building distribution, a pipe network structure and user load characteristics of a heat supply area, a multi-stage heat supply load prediction model is constructed. And constructing a thermal topological graph model of the heat supply area based on the graph neural network. And periodically collecting parameters of the building-level edge prediction model, the heat exchange station-level aggregation prediction model and the thermal topological graph model, performing global aggregation optimization, and updating and optimizing each edge node model. And obtaining an edge prediction result according to the optimized model, and jointly controlling the heat source output power, the main pump rotating speed and the area valve opening according to the edge prediction result and the heat source level scheduling prediction model. According to the method, the depiction capability of the system on the dynamic load change and the space heat conduction path is improved, and the generalization capability of model updating and the real-time responsiveness of edge deployment are ensured.

The invention discloses a mass concrete intelligent temperature monitoring system and method, and the system comprises a high-precision temperature sensor, a data collection node which is connected with the high-precision temperature sensor and is responsible for carrying out the primary processing and integration of temperature data collected by the sensor, and a data collection terminal which serves as a data convergence center of the whole monitoring system and is used for collecting the temperature data. The intelligent temperature monitoring system comprises a plurality of data acquisition nodes which are connected in a wired or wireless communication mode, a data monitoring platform which operates at a server end and is a core control and management unit of the whole intelligent temperature monitoring system, and a circulating water cooling control system which is composed of a cooling water pump, a cooling tower, a cooling water pipe network, a temperature control main station and the like. The cooling water pipe network is embedded in the concrete in advance to form a closed water circulation loop; the temperature control master station receives a cooling control instruction sent by the data monitoring platform, and adjusts the rotating speed of a cooling water pump and the working state of a cooling tower according to the instruction, so that the flow and temperature of cooling water in a cooling water pipe network are accurately controlled; through flowing of circulating water in the concrete, redundant heat in the concrete is taken away, effective regulation and control over the temperature in the concrete are achieved, key indexes such as the highest temperature and the temperature gradient of the concrete are controlled within a reasonable range all the time, and concrete cracks caused by temperature stress are avoided.

The invention discloses a pipe network operation and maintenance management system based on data analysis, which relates to the technical field of municipal infrastructure operation and maintenance management and comprises an equipment feature modeling module, a group anomaly identification module, a common-mode risk tracing module, a dynamic risk modeling module, a regulation and control strategy optimization module and a closed-loop self-adaptive updating module. Based on historical operation data and real-time acquisition parameters, feature coding is performed on the model, batch, position and time sequence of the sensor, and an equipment attribute mapping matrix is constructed. According to the method, the equipment attribute mapping matrix and the group anomaly identification mechanism are constructed, and external induction factor positioning and the dynamic risk weight map are combined, so that intelligent adjustment of regulation and control parameters and anomaly interference avoidance are realized, a closed-loop optimization process is constructed, and the identification accuracy, the control safety and the operation toughness of a pipe network system in a multi-disturbance scene are improved.