829 results about "Graph model" patented technology

The invention discloses a modeling method based on shield tunneling data feature analysis and parameter relevance, and relates to the field of tunnel engineering data processing. The method comprises the steps that shield tunneling time sequence parameters are obtained, and a non-uniform time sequence is resampled into a space-aligned standardized footage domain sequence through state cleaning and coordinate domain transformation; by means of mixed variable rejection and lagging correlation analysis, environment common cause interference is stripped, physical response delay among parameters is recognized, and a time-delay directed correlation graph model is constructed; and inputting the footage domain sequence and the graph model into a graph neural network, performing feature learning by using a time delay compensation aggregation mechanism, and outputting a key parameter influence degree set with symbols based on a prediction gradient. According to the method, the problem of data space-time dislocation caused by propelling speed fluctuation and the problem of parameter relevance misjudgment caused by physical response lag are solved, and accurate identification and explanation of shield tunneling key parameters are achieved.

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

The invention relates to the technical field of data processing and analysis, and discloses a parallel sensor data analysis method and system for cable fault detection, and the method comprises the steps: synchronously collecting cable fault transient traveling wave signals through distributed monitoring terminals, and forming a multi-channel signal data set; and carrying out wavelet packet decomposition and reconstruction, and denoising to obtain a denoised data set. The method comprises the following steps: firstly, generating node time sequence data with a timestamp, constructing an undirected weighted graph model, calculating a plurality of candidate fault positions through a double-end positioning formula on the basis of corrected time data, and finally, allocating dynamic weights to effective candidate positions according to signal quality and confidence, and generating a fusion positioning result by adopting a weighted fusion algorithm. And physical correction is carried out based on cable laying constraints, and accurate fault position coordinates are output. According to the method, through multi-level data processing and fusion, the fault positioning precision and the system robustness under complex working conditions are improved.

The invention discloses a low earth orbit satellite network digital twin drive switching optimization method and system, and relates to the technical field of inter-satellite communication, and the method comprises the steps: building a digital twin system which is synchronously evolved with a physical LEO satellite network; predicting the link quality between each candidate satellite and the user terminal in a future time window based on a digital twin system; dividing a time window into a plurality of continuous time slices, constructing a directed weighted dynamic graph model which takes a satellite-time slice as a node and takes link comprehensive utility cost as an edge weight, and searching an optimal path in the directed weighted dynamic graph model by utilizing a shortest path algorithm so as to determine an optimal switching opportunity and a target satellite sequence; the optimal switching strategy is issued to a user terminal to be executed, and a link quality prediction result is corrected in real time through a feedback mechanism of the digital twin system to form closed-loop optimization; the method can effectively meet differentiated service quality requirements in a multi-service scene, and is suitable for a high-dynamic LEO satellite communication system.

The invention discloses a real-time multi-dimensional sensitivity evaluation and self-adaptive safety prevention and control method and system for natural resource geographic information. The method comprises the following steps: acquiring real-time natural resource geographic information data; obtaining a preset behavior graph model; updating a preset behavior graph model according to the real-time natural resource geographic information data so as to obtain an updated behavior graph model; acquiring an updated node risk vector according to the updated behavior graph model; obtaining a trained Bayesian risk prediction model; inputting the updated node risk vector into a trained Bayesian risk prediction model so as to obtain a real-time abnormal behavior identification result; and generating a personalized prevention and control strategy scheme according to the real-time abnormal behavior recognition result. According to the method, intelligent identification, dynamic evaluation and active protection of natural resource geographic information in a full life cycle are realized by constructing a multi-dimensional sensitivity quantitative model, a dynamic risk perception mechanism based on a graph structure and a safety prevention and control strategy capable of being updated in real time.

The invention discloses an oil product pipeline leakage monitoring method, device and equipment and a medium, and relates to the technical field of pipeline monitoring, the method comprises the steps that a graph model of a pipeline monitoring network is constructed, and each node pipeline in the graph model is provided with a measuring point; collecting pipeline data of each measuring point in real time, wherein the pipeline data comprises temperature, pressure and flow data; performing feature extraction on the pipeline data to obtain various feature data; determining a pipeline working condition for the various characteristic data corresponding to each measuring point, and adjusting a fusion weight for fusing the various characteristic data in real time by adopting a reinforcement learning algorithm based on the pipeline working condition; according to each fusion weight, performing feature fusion on the multiple feature data to obtain a multi-source fusion feature; inputting the multi-source fusion feature of each measuring point into a sensor fault recognition model, and outputting a sensor fault mark; and inputting the multi-source fusion feature of each node carrying the sensor fault mark into a leakage identification and positioning model, and outputting the leakage probability of each node. The prediction accuracy can be improved.

The invention discloses an airborne radar ground moving target stable tracking method, and belongs to the technical field of avionics, and the method comprises the following steps: S1, obtaining and fusing multi-modal measurement data; s2, clutter suppression and pretreatment; s3, target state estimation; s4, data association and track repair; s5, group target collaborative tracking; and S6, outputting a tracking result. According to the airborne radar ground moving target stable tracking method, multi-mode fusion and track adhesion are used for supplementing a single measurement short plate and repairing a broken track; by means of clutter map and optimization filtering strong clutter suppression, the state precision is improved; the multi-target tracking is optimized by using the graph model and the group target modeling, the complexity is reduced, the prior is fused, and the tracking stability of the complex scene is enhanced.

The invention discloses a data center power distribution cabinet energy-saving heat dissipation method based on heat flow optimization, and the method comprises the steps: arranging a multi-source sensing unit for collecting monitoring data, and constructing a heat flow map containing the relation between nodes and edges; establishing a self-evolution heat flow neural map model based on the heat flow map, and dynamically updating an edge weight to output a prediction result; determining a temperature difference and an air flow direction according to a prediction result, and setting a phase change memory air guide composite layer structure; the central controller receives the prediction and air flow state and generates a heat flow control instruction linkage control parameter; an energy bond scheduling mechanism is established, a credit account is set, and cross-cabinet cooling capacity borrowing and repayment operation is executed; and updating the model by using feedback and deviation data, and periodically executing retraining and parameter optimization. According to the invention, intelligent heat dissipation and energy-saving optimization of the data center power distribution cabinet are realized through self-evolution heat flow neural map modeling and a synergistic effect of the phase change memory air guide composite layer and an energy bond scheduling mechanism.

Disclosed are a data query method and system, and a related device, relating to the field of artificial intelligence. The method comprises the following steps: a data query system acquires input data; obtains, on the basis of the input data, metrics to be queried as requested by a user; uses, on the basis of said metrics, a multi-agent collaborative processing strategy to confirm a data query result, the multi-agent collaborative processing strategy being used for indicating a manner of implementing data retrieval on the basis of at least one of a graph model and a vector database; and presents the data query result, wherein the graph model expresses, in a graph-based form, correspondences between the metrics and between the metrics and database table data, and the vector database expresses, in a vector-based form, descriptive texts and computational formulas of the metrics. In this way, even if the expression of input data differs from the expression of fields in the database, or if metrics to be read are not present in the database, corresponding SQL statements can still be obtained by means of the graph model or the vector database, thereby reducing the error rate of natural language query, and meeting the query requirements of users.

The invention relates to the technical field of intelligent scheduling, and particularly discloses an intelligent scheduling decision control method and system for a distributed car washing robot, and the method comprises the steps: constructing a weighted graph model which comprises a vehicle position, obstacle distribution and path reachability, and dynamically adjusting the weight of an edge according to the path length, energy consumption and time; optimizing the graph structure by adopting an improved minimum spanning tree algorithm, and calculating a path efficiency index in combination with fuzzy logic and a neural network technology; on the basis of the optimized subgraph, applying a shortest path algorithm to obtain a local optimal path, and introducing a dynamic adaptation coefficient to evaluate the response capability of the path to environment change; fusing the path efficiency index and the dynamic adaptation coefficient into a comprehensive scheduling feature vector, inputting the comprehensive scheduling feature vector into a trained deep learning scheduling model, and predicting an optimal scheduling strategy in combination with a task priority and a robot resource state; and dynamically adjusting the moving path and the operation density of the robot according to a scheduling result, and realizing self-adaptive evolution and cooperative control of the system.

The invention provides an intelligent data consanguinity tracking and visualization method based on graph calculation, and the method comprises the steps: carrying out the data structure analysis and metadata extraction of original data assets, so as to generate a standardized data asset package; performing graph node attribute definition on the data entities in the standardized data asset package, and performing graph edge attribute definition on the association relationship between the data entities to generate a first data blood relationship model; performing blood relationship path mining on graph nodes and graph edges in the first data blood relationship graph model to obtain a basic blood relationship path set, and performing quantitative calculation and feature labeling on path association strength in the basic blood relationship path set to generate a second data blood relationship graph model; and performing visual rule mapping on graph node attributes and graph edge attributes in the second data blood relationship graph model to construct a standardized visual data set and generate a data blood relationship visual interaction interface according to the standardized visual data set.

The invention provides an intelligent fault diagnosis method and system for a photovoltaic system. The method comprises the following steps: collecting multi-source heterogeneous operation parameter data of the photovoltaic system based on an intelligent sensor network and carrying out standardized preprocessing; local feature extraction is carried out on edge nodes, a frequency moment is calculated, data summary aggregation is carried out through a frequency moment optimization algorithm, and a fusion key feature vector set is constructed by using a multi-modal data fusion model; performing anomaly detection based on the normal behavior mode baseline, and generating an abnormal state alarm signal and a preliminary anomaly type indication; constructing a weighted graph model based on a system topological structure, constructing a fault association sub-graph by using an approximate spanning tree algorithm, accurately positioning a fault unit and diagnosing a fault type; and generating and executing a self-healing operation scheme according to the fault diagnosis conclusion. Through the distributed multi-modal data fusion and approximate spanning tree fault positioning technology, rapid and accurate diagnosis and autonomous recovery of the photovoltaic system fault are realized, and the system reliability and the operation efficiency are improved.

The invention discloses an SVG valve hall cooling efficiency evaluation method and system based on a probabilistic graph model, and the method comprises the steps: obtaining original time sequence data which is obtained through the collection of a cooling system multi-parameter monitoring sensor group disposed in an SVG valve hall in continuous T sampling periods; preprocessing the original time series data to obtain a credible time series data set; constructing a Bayesian network topological structure comprising three-level nodes of an environment layer, a component layer and an efficiency layer and causal dependence edges, and optimizing parameters of the Bayesian network topological structure by adopting a maximum likelihood estimation method to form a dynamic Bayesian network model after parameter calibration; and the credible time sequence data set is used as an evidence variable to be input into the Bayesian network model after parameter calibration, calculation is carried out through a belief propagation reasoning algorithm, a final control instruction set is generated through probability weighted scoring processing, the final control instruction set is fed back to a valve group monitoring system, and early warning and automatic load reduction are achieved. The problems of large evaluation deviation and early warning lag in the prior art are solved.

The invention discloses a multi-network cascading failure propagation prediction method based on a heterogeneous graph neural network under a disaster. The method comprises the following steps: acquiring multi-source data of disaster, electric power, communication and traffic networks; constructing a four-layer heterogeneous graph model, and defining multi-type interlayer edges; establishing a mapping relation between disaster physical quantities and physical node health states, and predicting an initial fault state at a disaster impact moment; defining a multi-scale and multi-mechanism propagation rule based on interlayer edges, and simulating a cascading failure process; a space-time heterogeneous graph neural network model is constructed, a heterogeneous graph neural network module and a gating circulation unit module are stacked in the model, and a double-end prediction layer is adopted to predict the continuous operation state and the discrete function state of nodes in an autoregression mode; and post-processing a state time sequence output by the model, and reconstructing a cross-domain cascading failure propagation path through a causal attribution algorithm. According to the method, the whole evolution process from disaster occurrence to multi-network cascading failure can be accurately simulated, and multi-scale failure prediction and causal analysis are realized.

The invention relates to a global passenger flow direction real-time tracking method for rail transit station video monitoring, and belongs to the technical field of intelligent traffic, and the method comprises the following steps: S1, collecting video data and business data in a station, and carrying out the time-space synchronization and preprocessing of multi-source heterogeneous data; s2, extracting and fusing fine-grained clothing textures, carried objects and gait features of passengers in real time; s3, compressing a search space based on a station topology and a space-time reachability model, and performing global identity association by adopting a probability graph model; s4, maintaining and correcting the continuity of the track through forward-backward joint reasoning and external data verification, and finally outputting a continuous passenger track with consistent identity; s5, carrying out association and semantic annotation on the continuous passenger tracks and the business data; and S6, performing analysis based on the fused data and generating a regulation and control instruction.

The invention relates to the technical field of new energy power system instability risk assessment, in particular to a new energy power system frequency instability risk assessment method based on a heterogeneous graph attention network. The method comprises the following steps: constructing a wind power-photovoltaic heterogeneous graph model according to a power grid topology; performing combined sampling on different operation modes and anticipated disturbances, and determining corresponding input feature vectors; calculating a frequency stability index value label of the sample set; expanding the training set through an active learning iteration process, and carrying out model training; and inputting the collected operation data into the trained heterogeneous graph attention model, outputting a frequency stability index value, and evaluating the system frequency instability risk in combination with the risk matrix. By adopting the frequency instability risk assessment method for the new energy power system based on the heterogeneous graph attention network, the problem of low efficiency of risk assessment in a high-dimensional uncertain scene is solved, and the heterogeneous graph attention network can reflect the influence of different types of devices at different positions and disturbance types on the dynamic frequency of the system; and the accuracy of frequency instability risk assessment is improved.

The invention discloses a heterogeneous graph neural network-based microgrid cooperative scheduling prediction and security check method. The method comprises the steps of constructing a heterogeneous graph model; different micro-grid operation scene graphs are generated through analogue simulation; solving an optimal collaborative scheduling strategy vector for each scene graph to serve as an optimal scheduling label, generating a training data set of a'scene graph-optimal scheduling label 'data pair, and completing offline training of the graph neural network model; dynamically generating an instantiated heterogeneous graph; taking the instantiated heterogeneous graph as the input of the trained graph neural network model, carrying out first forward reasoning, and outputting a predicted collaborative scheduling strategy vector; and injecting a virtual fault, generating a virtual fault heterogeneous graph, inputting the virtual fault heterogeneous graph and the predicted cooperative scheduling strategy vector into the trained graph neural network model, carrying out second forward reasoning, predicting the state of the faulty micro-grid system, obtaining the predicted fault node voltage, and carrying out safety check on the predicted cooperative scheduling strategy vector.

The invention relates to the technical field of navigation control, in particular to an obstacle light-based unmanned aerial vehicle navigation auxiliary system, which comprises the following steps of: combining an image sequence with inertia time information, segmenting a light source area and extracting a frequency label, so that the stable identification capability on an optical signal is enhanced; the signal state and the time index can be accurately judged especially in a complex shielding environment, the robustness of obstacle light recognition in multiple angles and multiple scales is improved through direction vector extraction and a comparison mechanism of a graph neural network, an edge weight matrix is established in combination with light intensity calculation and shielding state elimination, and the robustness of obstacle light recognition in multiple angles and multiple scales is improved. The method effectively enhances the understanding of the spatial layout of the obstruction lights and the navigability of the graph model, extracts the vector group through the graph model, eliminates the shielding, and compares the path instruction selection logic of the action score, so that the path planning process has stronger dynamic adaptability and error tolerance, and enhances the navigation accuracy and flight safety in the complex airspace.

The invention relates to the technical field of electric vehicle charging pile planning, in particular to an intelligent charging pile layout optimization method based on a multi-scale space-time diagram neural network. Comprising the following steps: S1, constructing a heterogeneous dynamic graph which represents a potential charging pile position node set, epsilon t represents a time-varying edge set, At represents a time-varying adjacent matrix, and Xt represents a node feature matrix; s2, calculating a self-adaptive adjacency matrix with a specific relationship, and calculating a multi-type dynamic relationship between capture nodes of the self-adaptive adjacency matrix based on the constructed heterogeneous dynamic graph model; s3, updating a structure bias matrix, and capturing dynamic change characteristics of the network; s4, calculating distance measurement between nodes, and fusing geographic information and semantic information; s5, through graph neural network learning, based on the constructed heterogeneous dynamic graph and the calculated adaptive adjacency matrix, executing a graph neural network learning process, and extracting node space-time representation; and S6, predicting a future charging demand based on node representation obtained by graph neural network learning, and generating based on the future charging demand.

The invention discloses a power distribution network cluster local control method and device, equipment and a medium. The method adopts a cloud edge end three-level collaborative architecture, and comprises the following steps: acquiring electrical parameters through a terminal equipment layer; constructing a graph model based on an electrical distance at the edge calculation layer; performing initial cluster division and issuing on the cloud computing layer; dynamically optimizing cluster division in an edge calculation layer through a lightweight deep reinforcement learning model; local control strategies such as voltage reactive power cooperative control, frequency active power balance control and fault rapid isolation and self-healing are executed based on the final division. The system comprises a terminal device layer, an edge computing layer and a cloud computing layer. According to the method, decoupling of global optimization and local real-time control is realized through cloud-side cooperation, the problems of high data processing pressure, high communication delay, expanded local fault influence range and the like of centralized control are effectively solved, and the real-time performance, reliability and disaster resistance of the power distribution network are remarkably improved.

The invention belongs to the field of computer resource management, particularly relates to a computer resource dynamic allocation management method based on cloud computing, and aims to solve the problems of locality of a decision view angle and hysteresis of a time dimension in related technologies. The method comprises the following steps: acquiring the resource utilization rate of each virtual machine in a cloud environment, wherein the resource utilization rate comprises a historical resource utilization rate and a real-time resource utilization rate; according to the resource utilization rate, predicting a resource demand of each virtual machine in a future preset time period; determining a resource competition conflict according to the predicted resource demand, and constructing a resource competition graph model; mapping the resource competition graph model into a non-cooperative game model; and determining a scheduling scheme in the cloud environment based on the non-cooperative game model, and executing a resource allocation operation according to the scheduling scheme. According to the method, potential conflicts can be identified and scheduled before resource competition actually occurs, and performance reduction caused by insufficient resources is reduced.

The invention belongs to the technical field of medical data processing, and particularly provides a multi-mode heterogeneous medical data dynamic weighting intelligent disease analysis system. The system specifically comprises the following modules: a multi-modal heterogeneous data preprocessing module, a multi-modal data quality dynamic evaluation and quality control module, a cross-modal embedding space conversion network, a medical knowledge graph engine, a large model training architecture, a multi-modal decision fusion module, a disease analysis interpretability enhancement module and a Bayesian probability graph model integration module. And an edge cloud collaborative reasoning module. According to the invention, the accuracy and robustness of multi-modal disease analysis are improved, the adaptability quality control of different modal data is realized through the multi-dimensional dynamic quality evaluation module, and the reliability of basic data is improved in combination with intelligent quality control restoration.

Approaches in accordance with various embodiments provide unified training and inference frameworks useful for co-training, and performing inferencing using, models such as language and graphing models. The frameworks can be hierarchical, in that an inner layer can include a language model for generating semantic embeddings from input text content, and these semantic embeddings can be passed as input to an outer layer that can generate graph labels based, at least in part, upon a knowledge graph having these semantic embeddings substituted for textual content. Nodes of a knowledge graph that are determined to be related to the input content can then be used for various purposes, such as to generate tags for the content or determine how to route that content.

The invention relates to the technical field of meteorological early warning, and discloses a meteorological AI abnormity early warning method and system for extreme weather risk perception, and the method comprises the steps: collecting and preprocessing multi-source meteorological data, and obtaining standardized multi-source meteorological data; feature extraction and anomaly recognition are carried out on the standardized multi-source meteorological data in the continuous time period, and preliminary risk information and a first confidence value are obtained; and constructing a space-time diagram model and applying consistency constraint in the training and reasoning process of the space-time diagram model to obtain a risk identification result. And according to the risk identification result, determining a dynamic alarm threshold value to generate graded meteorological anomaly alarm information, and adjusting the sampling frequency. And sending the meteorological abnormity alarm information and the sampling adjustment instruction to a cloud platform and a terminal display device, and carrying out risk perception and early warning release of extreme weather. According to the invention, high-credibility, low-time-delay and full-period dynamic perception and hierarchical response of extreme weather risks are realized.

The invention relates to the technical field of medical big data analysis, and discloses a hospital medical record data anomaly analysis method based on big data. According to the method, multi-source medical record data including diagnosis and treatment event records, nursing operation sequences and patient sign monitoring streams in a hospital information system are collected, and structured reforming is carried out. And constructing a medical record element interaction graph based on the reformed data, and detecting an abnormal state conduction path between the data elements through the graph model. And performing hierarchical traceability calculation according to the detected conduction path, positioning an abnormal source and generating a positioning report, and finally outputting a calibration instruction set according to the report. According to the method, the abnormal conduction chain is systematically identified from the correlation perspective, and the root source of the abnormality can be automatically and accurately traced, so that the logicality and the positioning accuracy of the medical record data abnormality analysis are improved.

The invention provides a method and system for dispatching heterogeneous GPUs through cross-cluster management software, and relates to the technical field of GPU resource dispatching.The method comprises the steps that physical topology data, hardware specification data and historical task records of GPU nodes are obtained through the cross-cluster management software, an association degree matrix between GPU units is established, and a topology grouping set is generated; calculating a communication efficiency index based on topology detection analysis, constructing a topological graph model, and determining an optimal communication path of a multi-card task; generating a perception scheduling strategy in combination with a multi-objective optimization algorithm, and selecting an optimal GPU combination from the grouping set; in the task execution process, a scheduling strategy is dynamically adjusted according to the real-time cluster state and task efficiency data, continuous adaptation of a physical topological structure and task requirements is kept, and finally efficient collaborative scheduling of cross-cluster heterogeneous GPU resources is achieved. According to the method, the scheduling efficiency of heterogeneous GPU resources in a distributed computing scene and the overall performance of the system are improved.

The invention provides a dynamic updating method and system of a knowledge graph based on reasoning enhancement. The method belongs to the cross technical field of artificial intelligence, knowledge engineering and dynamic system modeling. The method comprises the following steps: performing multi-dimensional feature extraction on original knowledge data to generate a knowledge feature vector set; constructing an initial knowledge graph structure based on the knowledge feature vector set, and defining an initial association rule between knowledge nodes to form a basic knowledge graph model; building a multi-order causal inference engine according to the basic knowledge graph model, performing deep mining on potential causal relationships among knowledge nodes, and generating a knowledge causal relationship network; and performing confidence evaluation on each causal chain in the knowledge causal relationship network to obtain the knowledge causal relationship network subjected to quality verification. Based on a multi-order causal reasoning engine, the system can mine and verify potential causal relationships among knowledge nodes in real time, and continuous updating and self-optimization of the knowledge graph are ensured.

The invention belongs to the technical field of intelligent operation and maintenance of micro-service architecture information systems, and particularly relates to a micro-service anomaly positioning method and system based on dynamic topology and multi-modal data, and the method comprises the steps: obtaining a dynamic service topological graph of a micro-service system; constructing a directed weighted graph model of the service nodes in the micro-service system based on the obtained topological graph; according to the constructed directed weighted graph model, determining a calling link between system micro-services; acquiring a multi-modal observation index of each service node in the micro-service system on the calling link; calculating an observation entropy value of the micro-service system based on the obtained multi-modal observation index of each service node and the sliding window; and judging the abnormal state of the service node of the micro-service system according to the obtained observation entropy value, and completing the micro-service abnormity positioning based on the dynamic topology and the multi-modal data.

The invention discloses an electric power-traffic coupling network vulnerability identification method and system. The method comprises the following steps: S1, coupling an electric power network and a traffic network to form a graph model; the graph model comprises a node set and an edge set; constructing an adjacent matrix and a node feature matrix based on the node set and the edge set; s2, inputting the adjacent matrix and the node feature matrix into a graph convolutional neural network model, performing feature extraction model training through a semi-supervised learning mechanism, and outputting an updated node feature matrix; and S3, learning an optimal strategy by using a deep Q network, and identifying a fragile node sequence through interaction with the environment. According to the method, a traditional single network evaluation mode is broken through, accurate quantification of the cross-network coupling effect is achieved, complex topological information can be captured, intelligent dynamic recognition is achieved, dependence on artificial experience is reduced, decision visualization can be supported, the system practicability is high, and the method can be applied to power-traffic coupling network catastrophe scenes.

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.