249 results about "Topology construction" patented technology

The invention relates to a charging pile intelligent operation management method and system based on the Internet of Things. The method comprises the following steps: performing Internet of Things topology construction on charging pile nodes to obtain a node communication distance matrix and a network connectivity matrix; performing deep learning dimension reduction and node screening based on the network connectivity matrix to obtain a fault feature vector; the fault feature vector and the charging pile operation state data are input into a hybrid neural network for risk calculation, a fault risk assessment value is obtained, and the hybrid neural network comprises a space perception sub-network and a time sequence analysis sub-network; performing normalization and dimension reduction mapping on the power response data of the charging pile to obtain a time domain mapping matrix; and performing particle swarm optimization calculation based on the time domain mapping matrix and the fault risk assessment value to obtain a charging power distribution scheme, and generating a control instruction set according to the charging power distribution scheme. According to the invention, the dynamic optimization of the charging power distribution scheme is realized, and the charging efficiency and the load balance are effectively balanced.

The invention relates to the technical field of artificial intelligence, in particular to an agent decision path optimization method fusing explicit topology and implicit semantics. The method comprises the following steps: acquiring multi-modal data; performing document analysis according to the multi-modal data to obtain document analysis data; performing knowledge injection according to the document analysis data to obtain knowledge injection data; carrying out implicit semantic detection according to the knowledge injection data to obtain implicit semantic data; constructing a user demand agent blueprint according to the implicit semantic data; constructing an agent workflow according to the agent blueprint required by the user; performing task execution scheduling according to the agent workflow to obtain task execution data; performing node explicit topology construction according to the task execution data to obtain node explicit topology data; and performing communication anomaly analysis based on the node explicit topology data to obtain node communication anomaly data. According to the invention, agent task scheduling and path optimization are realized based on an artificial intelligence technology, and the resource utilization rate of the system is improved.

The invention relates to the technical field of industrial internet, and discloses a water affair industry internet data acquisition and instruction control middleware, which comprises a data acquisition module for acquiring water affair data through a multi-source heterogeneous interface and transmitting the water affair data in a classified manner; the data processing module is used for performing protocol normalization processing and standardized data packet generation; the instruction control module is used for receiving and analyzing an instruction and generating an equipment executable instruction set; and the collaborative scheduling engine is used for distributing transmission paths based on priorities and load states and coordinating module interaction. The data processing module comprises a protocol adaptation unit and a topology construction unit, and the collaborative scheduling engine has a load balancing strategy. In addition, each module is further subdivided into units to improve functions, such as an equipment interface unit of the data acquisition module, a protocol analysis unit and the like. According to the invention, the problems of multi-source heterogeneous data acquisition and instruction control in the water affair industry are solved, the data processing efficiency, the instruction control accuracy and the system stability are improved, and intelligent management of a water affair system is realized.

The invention relates to the technical field of construction stage management, in particular to a BIM-based road construction digital management system, which comprises a component parameter acquisition module, a process topology construction module, a state trigger mapping module, a logic conflict judgment module and a scheduling path adjustment module. According to the method, through continuous collection and analysis of the component state, dynamic monitoring of the connection state, geometric intersection and contact time is achieved, the construction change sensing capacity is improved, the dependency relationship is constructed in combination with component space adjacency and time overlapping, the dispatching path and field matching degree is enhanced, and through linkage updating of the component state and model attributes, the construction efficiency is improved. According to the method, the execution state is visual and controllable, scheduling reconstruction is carried out after time dependence conflicts are recognized, synchronous updating of model views is completed, a closed-loop dynamic scheduling mechanism is formed, the task allocation accuracy, the flow connection efficiency and the field scheduling response capability are integrally improved, and the real-time performance and the intelligent level of construction management are enhanced.

The invention discloses an automatic protocol matching system for intelligent gateway sensor equipment of a power distribution station house. The automatic protocol matching system comprises a protocol feature deep analysis unit, a graph convolutional neural network topology construction unit, an equipment protocol suitability evaluation unit, a real-time data transmission monitoring unit, an abnormal protocol correction unit and a protocol matching result output unit. The system analyzes sensor protocol characteristics, combines power station house sensor deployment and transmission path construction graph models, evaluates protocol suitability, monitors transmission data, corrects abnormal protocols, and finally outputs a matching scheme. The graph convolutional neural network and the power station room data transmission detection model are utilized to realize automatic protocol matching, improve the fit degree of adaptation and an actual scene, form an exception handling closed loop, reduce interaction failures, improve the data transmission efficiency and stability of the power distribution station room, and meet the requirement of intelligent reconstruction.

The invention relates to the technical field of new energy storage regulation and control, and discloses an electricity-hydrogen coupling energy storage system and method for a new energy field station. The system comprises a feature analysis unit, a topology construction unit, a regulation and control domain positioning unit and a decision optimization unit. The feature analysis unit receives operation condition information of the new energy station, and generates a condition feature vector according to real-time load fluctuation, a historical output curve and an equipment operation database by using a deep feature extraction network; the topology construction unit constructs an energy topological graph for distributed energy storage equipment and hydrogen production equipment in the electricity-hydrogen coupling system, dynamically updates a graph structure and a node state based on a real-time monitoring mechanism, and deploys the graph structure and the node state in a heterogeneous computing framework; a regulation and control domain positioning unit positions a matched regulation and control domain in a dynamic representation learning mode according to the working condition feature vector; and the decision optimization unit utilizes a multi-source decision fusion algorithm to screen regulation and control instructions in a regulation and control domain and generate a scheduling sequence, so that the adaptability and the operation efficiency of the system to working conditions are improved.

The invention relates to the technical field of production scheduling, in particular to an optimal scheduling method for hardware mold production. The method comprises the following steps: collecting hardware mold parameters and carrying out topological structure analysis to generate a hardware topological structure and a skeleton, then simulating a skeleton assembly process and recording a movement track, carrying out time sequence arrangement on the track by using mold production historical data, identifying a conflict track and a coordination track, and finally, carrying out hardware mold assembly. Resource requirements are calculated according to mold parameters, parallel production scheduling is carried out, meanwhile, the dependency relation between the steps, conflict track rearrangement steps, coordination and conflict rearrangement production plans are deduced, scheduling optimization is carried out, and intelligent scheduling of hardware mold production is achieved. The optimal scheduling method for hardware mold production is more efficient.

The invention relates to the technical field of optical storage direct-flexible scheduling, and discloses a method for establishing an energy storage scheduling model of a multi-node optical storage direct-flexible system. The method comprises the following steps: acquiring real-time operation data of each node of a system, including a photovoltaic output fluctuation curve, an energy storage charge state, a load demand time sequence, a power grid interaction power limit value and an environmental irradiance change characteristic, and performing multi-dimensional alignment processing on the real-time operation data to form a multi-dimensional operation data set; performing operation state mapping topology construction processing on the data set, and separating out target operation data and state indication data; based on the target operation data, in combination with scheduling priority configuration information of each node, generating a multi-node collaborative energy storage scheduling initial model containing an energy storage charging and discharging time sequence strategy and a power distribution scheme; and identifying node operation abnormal characteristics based on the state indication data, dynamically reconstructing the initial model according to the node operation abnormal characteristics, and outputting the updated energy storage scheduling model to guarantee stable and efficient operation of the system.

The invention relates to an underground pipe network topological relation analysis method and device, and belongs to the technical field of underground pipeline management.The underground pipe network topological relation analysis method comprises the steps that a spatial index structure is constructed, an adjacent node set of each node is determined based on the spatial index structure, and a spatial ray corresponding to each node is constructed; determining a correlation score between each node and the nodes in the corresponding adjacent node set based on the space ray and pipe orifice multi-dimensional information corresponding to each node; determining a candidate connection node of each node based on a correlation score and a score threshold value between each node and the nodes in the corresponding adjacent node set, and constructing an initial pipe network topology based on the candidate connection node of each node; and optimizing the initial pipe network topology based on a preset topology construction logic to obtain the pipe network topology. According to the method, the topological relation analysis efficiency of the underground pipe network is improved, and meanwhile, the topological relation analysis accuracy is guaranteed.

The invention provides an industrial network topology construction method and system based on active and passive linkage, and the method comprises the following steps: S1, extracting equipment interface information and a neighbor link relation through an SNMP protocol, and screening stable and credible links in combination with multi-point routing link detection and time sequence analysis; s2, collecting flow data, carrying out direction modeling, analyzing and detecting abnormal links based on communication frequency, and filtering short-time interference links; s3, integrating active detection and passive acquisition equipment and link information, and realizing equipment and link merging based on the unique identifier of the equipment; and S4, performing link clustering, feeding back to an active detection module and a passive acquisition module, and optimizing an initiating node of the next round of active detection and an acquisition node of flow acquisition. The efficient and accurate network topology construction method is provided for solving the problems of multiple IP identifiers, complex topology links, noise link interference and the like of equipment in an industrial network, the noise link interference can be remarkably reduced, and the efficiency and accuracy of network management are improved.

The invention relates to the technical field of intelligent operation and maintenance, in particular to a fault insight and self-healing method and system based on chain control. According to the chain control-based fault insight and self-healing method, indexes, logs and tracking data of a micro-service environment are dynamically collected and fused; the method comprises the following steps: dynamically constructing and visualizing a service dependent topology, realizing dynamic topology construction and intelligent visual rendering, performing intelligent root cause analysis and predictive diagnosis based on multi-dimensional data, realizing self-adaptive early warning, generating an intelligent decision, performing closed-loop learning optimization based on an execution result, and realizing automatic fault repair and continuous optimization. According to the fault insight and self-healing method and system based on chain control, a predictive early warning mechanism is established, automatic fault self-healing is realized, and through intelligent and automatic means, manual intervention is reduced, the operation and maintenance cost is reduced, the average repair time is shortened, the service continuity is guaranteed, and the system availability is improved.

The invention discloses a method and equipment for determining a wiring state and a storage medium. The method comprises the following steps: receiving target image data containing image information at a wiring position, performing target detection and feature extraction processing on the target image data, automatically identifying a connecting element (a first target) and an identifier (a second target) thereof in an image, and simultaneously extracting an appearance visual feature (a first feature) of the connecting element and an identification code feature (a second feature) of the identifier; and performing topology construction processing based on the obtained target and characteristics to generate topological graph data capable of accurately representing the connection relationship of the electrical devices. And finally, comparing the topological graph data with a preset standard wiring template topological graph, and automatically generating judgment information for indicating whether the wiring connection state is correct or not according to a topological matching result. Therefore, misjudgment caused by artificial fatigue, negligence and experience difference is effectively avoided.

The invention discloses a route planning method and system, and relates to the technical field of unmanned aerial vehicles, and the method comprises the steps: calculating a cross-modal attention weight based on a feature vector, combining the attention weight with the feature vector to obtain a weighted feature vector, calculating an attention score based on the weighted feature vector, generating an integrated feature based on the attention score through linear transformation, and carrying out the calculation of the integrated feature. And integrating the features and combining the communication topology, and inputting the features into a Transform architecture to generate 3D environment representation. A cross-modal attention mechanism is utilized, attention weight dynamic modulation feature vector contribution of each modal is calculated, the environmental perception accuracy in a complex environment is improved, feature vectors of neighbor unmanned aerial vehicles are aggregated through a distributed communication topology construction method in combination with a multi-head attention mechanism, and the multi-modal attention mechanism is established. The group stability is still kept when communication is interrupted or obstacles change, and the environmental adaptability, the cooperation efficiency and the navigation precision of the unmanned aerial vehicle group in a complex environment are improved.

The invention discloses an unmanned aerial vehicle data link communication system for beyond-visual-range cluster measurement and control, and relates to the field of communication transmission. The system comprises a dynamic topology construction module which constructs a dynamic network topology according to unmanned aerial vehicle node parameters and selects an optimal communication path; the spectrum resource management module is used for optimizing spectrum allocation of multiple frequency bands and dynamically allocating spectrum resources; the relay node selection module is used for selecting an optimal relay node and optimizing the energy consumption and the signal quality of the relay node; the clock synchronization module is used for performing high-precision clock synchronization through clock skew modeling and correcting frequency drift; the channel distribution module is used for dynamically distributing communication channels; and the fault detection module monitors the fault state of the communication link of the unmanned aerial vehicle in real time through a fault monitoring model, and performs anomaly detection. Through dynamic topology construction, intelligent relay node selection and multi-dimensional utility function optimization, the flexibility, stability and resource allocation efficiency of a communication network are effectively improved, and efficient and reliable communication of an unmanned aerial vehicle cluster is ensured.

The invention relates to the technical field of supply chain monitoring, and particularly discloses an automatic sensor detection method and system based on a supply chain, and the method comprises the steps: generating a hierarchical pressure gradient field and an event node distribution diagram through obtaining the transportation vibration energy; if a creep coefficient resetting algorithm is triggered, mapping the event node graph into a weighted directed graph, extracting a key conduction path, and positioning an unpacking release type drift node; and introducing an unpacking stress correction term expansion cross-correlation entropy model, and determining a priority compensation strategy in combination with a time window information entropy to realize dynamic correction of sensor drift. The system comprises a hierarchy analysis module, a topology construction module, a lag positioning module and a drift compensation module. Through cross-scale modeling of vibration energy, material creep and drift compensation, the problem of sensor reading lag caused by stress accumulation in a supply chain can be reduced, and the packaging optimization decision efficiency is improved.

The invention relates to the technical field of photovoltaic power prediction, and provides a distributed photovoltaic power prediction method and system based on machine learning. The method comprises the following steps: interacting grid-connected position nodes and installation parameters of photovoltaic base stations in a photovoltaic jurisdiction area, and determining a distributed topology; mining photovoltaic power elements, taking modal decomposition-reconstruction-prediction as underlying logic, developing a power prediction module, and carrying out convergence learning and embedded connection; accessing a front-end acquisition card, and constraining and acquiring a sampling matrix of photovoltaic power elements through a timestamp; and importing the sampling matrix into a power prediction module, performing interval prediction decision through dynamic and static combination, and determining a power triple. According to the method, the technical problem of insufficient prediction precision caused by complex topological structure and data acquisition asynchronism of a distributed photovoltaic system is solved, and the technical effect of high-precision and robust power prediction is achieved through distributed topology construction, a modal decomposition-reconstruction prediction mechanism and dynamic and static combined interval prediction decision.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a multi-agent communication and decision-making method, device, equipment and medium. The method comprises the following steps: obtaining state information and communication demand information of each agent in a multi-agent system, determining a communication weight based on the information, a dynamic communication topology is constructed in the system, an intelligent agent is used as a graph node, information of neighbor nodes in communication connection with the node is aggregated to generate interactive feature information, a corresponding decision strategy is generated by combining state information of the intelligent agent and the interactive feature information, and joint modeling and optimization of communication and decision are achieved. According to the method, dynamic topology construction is driven through communication weight, interactive feature information is introduced in strategy generation, and a collaborative optimization mechanism is formed by communication and decision, so that redundant communication is reduced, the communication efficiency is improved, and the multi-agent collaborative precision and the task execution effect are enhanced.

The invention relates to the technical field of wireless communication, and discloses a method and a device for constructing a wireless ad hoc network without a signal area based on chain networking, and the method comprises the steps: carrying out the collaborative fusion of an identity label and a communication capability of a node in the chain networking, and obtaining the node capability information of the chain networking; performing relay capability evaluation on a target node in the wireless ad hoc network to obtain a topology construction basis; performing optimization forming on candidate nodes in the wireless ad hoc network to obtain a chain network topology of the wireless ad hoc network; establishing a bidirectional communication link between nodes in the chain network topology to form a preliminary communication network of the wireless ad hoc network; detecting the link communication quality of the preliminary communication network step by step to obtain a hierarchical link state of the wireless ad hoc network; dynamically negotiating a hierarchical link state, and establishing a stable communication path of the wireless ad hoc network; according to the invention, the construction accuracy of the chain networking-based wireless ad hoc network in the non-signal area can be improved.

The invention provides an intelligent agile networking method and device for a large emergency wireless communication network. The method comprises the following steps: designing an agent learning environment Env containing wireless communication network node initial position distribution, environment constraint and target constraint based on orthogonal reward multi-agent depth deterministic policy gradient reinforcement learning (OTR-MADDPG) of topology perception and road network optimization; designing an improved action space based on road network search for the MOVBS intelligent agent, and designing an updating mode of an improved strategy network pi based on state input of neighbor nodes in a connected branch; designing an access / rejection action space based on an LEO satellite for the LEO intelligent agent, and designing an updating mode of a strategy network # imgabs0 # based on an MOVBS coordinate-overhead satellite state pair; a reward function # imgabs1 # is designed; a learning environment Env, an action generation mode of an intelligent agent, a strategy network updating mode and a reward function R are integrated into an existing framework of MADDPG. According to the invention, the problems of long topology construction time and poor node collaboration of the emergency communication network are effectively solved.

The invention discloses an OHT track node automatic identification and path planning method and system, and relates to the technical field of OHT tracks, and the method comprises the following steps: identifying track node positions based on vehicle motion track feature analysis, point cloud geometric clustering processing and a multi-mode verification method combining visual and magnetic features; performing semantic recognition according to the node positions to obtain node types, and constructing a track topological graph according to the node types; a candidate path set is generated through the track topological graph, and local optimization is conducted on the task execution sequence of the candidate path set and the vehicle matching relation; based on a local optimization result, performing synchronous adjustment on path selection and task allocation, and outputting an integrated scheduling scheme; according to the method, high-precision node positioning, semantic classification, structured track topology construction and integrated optimization of multi-vehicle path and task scheduling can be realized, and the operation efficiency, the scheduling reliability and the resource utilization rate of an OHT system are remarkably improved.

The invention provides a hierarchical clock tree synthesis method and system based on a hybrid topological structure, and aims to collaboratively optimize clock skew and clock delay in a chip clock network. The method comprises the following steps: firstly, introducing a balanced greedy search clustering algorithm, clustering registers at the initial stage of clock network construction, and reducing local clock skew and delay; and generating a bottom layer clock tree in each cluster by adopting a bounded deviation construction method so as to ensure the clock synchronization quality in the cluster. In a top-layer clock tree construction stage, the invention provides a self-adaptive H-tree topology construction algorithm, and the H-tree structure is dynamically adjusted according to the position relationship among all layers of clusters, so that a global clock distribution path is optimized under multiple layers. Besides, in order to further improve the CTS quality, the invention further designs a clock network optimization strategy based on buffer post-processing, and local buffer insertion and position adjustment are carried out for the problems of fan-out unevenness and load overrun so as to eliminate potential illegal paths.

The invention provides a photographing dynamic tracking optimization system and method based on a topological structure, and belongs to the field of photographing dynamic tracking. The system comprises an action acquisition module, a dynamic rate calculation module, a topology construction module and a camera shooting switching module. The action acquisition module detects a sound source user and acquires a sound source user action set; the dynamic rate calculation module calculates the behavior dynamic rate of the sound source user action set by using a dynamic rate function; the topology construction module constructs a camera module topological structure comprising a first type of camera module topological layer and a second type of camera module topological layer, wherein the tracking sensitivity of each camera module in the first type of camera module topological layer is smaller than that of the second type of camera module topological layer; and the camera switching module performs optimization in the corresponding camera module topology layer according to a comparison result of the behavior dynamic rate and a preset dynamic rate threshold to determine switching of the camera module. The tracking accuracy is improved and the resource configuration is optimized by intelligently matching the tracking sensitivity of the camera according to the dynamic rate of the user behavior.

The invention relates to the technical field of data processing, in particular to an enterprise right and interest management system based on a cloud platform, which comprises a right and interest topology construction module, a candidate node mapping module, a local conflict graph generation module, an enterprise right and interest management module, an enterprise right and interest management module and an enterprise right and interest management module, the weight iteration screening module locks the maximum weight value nodes to generate a locked node set, and the compatible combination generation module generates a right and interest combination list according to the set. According to the method, the mutual exclusion topology network is established by analyzing the original record, the flattened data is converted into the graph structure, the conflict graph is constructed to present the conflict state, the graph is traversed, the node with the maximum weight value is preferentially locked, and the connected conflict nodes are removed, so that the full-table scanning loss of the traditional database is effectively avoided; right and interest combination value maximization calculation under the complex mutual exclusion logic is achieved, and the right and interest distribution accuracy and the system processing efficiency are greatly improved.

The invention discloses a drainage system state space-time prediction method and system based on a neural network, and belongs to the technical field of computer system modeling and artificial intelligence, and the method comprises the steps: obtaining pipe network geographic structure data and operation observation data, carrying out the time marking, carrying out the event-driven space-time alignment and missing completion, generating an event alignment sequence, and carrying out the event-driven time-time alignment and missing completion; fusing the pipeline connection relation and the flow direction rule to construct a reachable topological graph; performing combined feature extraction on the event alignment sequence to generate a topological space-time representation tensor, applying constraints based on mass conservation and capacity limitation, and calculating and generating a physical constraint deviation value; and fusing the topological space-time representation tensor and the physical constraint deviation value to generate a space-time prediction field, and carrying out secondary mapping based on uncertainty quantization and error feedback to generate an adaptive correction field. By integrating event-driven data alignment, physical topology construction and constraint fusion learning and combining error correction, space-time prediction of the future state of the drainage system can be realized.

The invention relates to a power line network topology construction method and device, computer equipment and a storage medium. The method comprises the following steps: selecting two terminal nodes from a node set to be reconstructed, and identifying a first intermediate node in a power line network; under the condition that the first distance between the first intermediate node and the root node is smaller than or equal to a distance threshold value, determining a father node of the two terminal nodes as the root node; when the first distance is greater than a distance threshold, identifying a second intermediate node near the first intermediate node, and determining the second intermediate node as the first intermediate node; and writing the first intermediate node into the reconstructed node set, deleting the two terminal nodes and the second intermediate node from the to-be-reconstructed node set, and returning to the step of selecting the two terminal nodes from the to-be-reconstructed node set until the number of the terminal nodes in the to-be-reconstructed node set is smaller than the number threshold value. By adopting the method, the accuracy of power line network topology construction can be improved.

The invention discloses a digital twin intelligent water-saving system based on a distributed optical fiber sensing network, which belongs to the technical field of water-saving management and specifically comprises the distributed optical fiber sensing network laid along a water delivery pipeline in a tree topology; the water use event recognition module collects optical fiber vibration signals, converts the optical fiber vibration signals into digital waveforms and recognizes trigger points through an amplitude threshold value; the water supply topology construction module constructs a water supply dependency topological graph containing nodes and directed edges according to the spatial position and time delay of the trigger point; a hydraulic model generation module superposes the topological graph and the digital map of the water delivery pipeline to generate a dynamic hydraulic balance model containing node water intensity weight coefficients; the intelligent water-saving control module sends a periodic intermittent closing instruction to the intelligent water valve of the low-weight node according to the node weight change, and the duration of the instruction is in direct proportion to the weight difference value of the adjacent node; according to the invention, refined intelligent water-saving management of a centralized water consumption scene is realized.

The invention relates to the technical field of display, and discloses an LED screen correction coefficient adjusting method and system, and the method comprises the steps: collecting the original brightness and chromaticity data of each LED unit; constructing a global topological connection relation graph; calculating an initial correction coefficient; the corrected residual error of the boundary pixels of the adjacent units is analyzed; establishing a consistency constraint equation set aiming at minimizing the difference between the boundary brightness and the chromaticity; and performing joint solution to obtain a global optimization correction matrix and writing the matrix into a driving module. The system comprises a data acquisition module, a topology construction module, an initial correction module, a residual analysis module, a constraint construction module, a global optimization module and a coefficient writing module, and integrates a temperature drift compensation and visual verification mechanism. Through cross-unit collaborative optimization, seamless fusion of splicing areas is realized, and brightness uniformity, chromaticity consistency and long-term operation stability are improved.

The invention relates to the technical field of intelligent traffic, and discloses a vehicle passing track auditing analysis system and method based on spatial-temporal feature clustering, and the system comprises a topology construction module which is configured to construct a highway networking charging attribute graph based on nodes and edge sets, and form a topology network of a node reachable relationship; the feature processing module is configured to perform standardization processing on the spatio-temporal features of the historical passing tracks and eliminate dimensional differences; the clustering analysis module is configured to perform adaptive clustering based on the standardized spatio-temporal characteristics and determine division of a time cluster and a space cluster; and the trajectory reasoning module is configured to generate candidate paths in combination with the space-time cluster characteristics, calculate a comprehensive score through space-time similarity calculation and weight distribution, and output an optimal reasoning path. The method corresponds to the system. According to the invention, the efficiency of the networking charging auditing work is greatly improved, and the national networking charging order is effectively maintained.

The invention discloses a micro-service fault diagnosis method and system based on a large language model and a knowledge graph. The method comprises the steps that a service dependency graph is automatically generated through static topology construction; converting the heterogeneous log into a standardized entity relationship triple by utilizing a semantic fusion engine; a diagnosis sub-graph associated with attributes is created through a dynamic sub-graph generation mechanism; and a workflow inference engine is adopted to realize multi-attribute collaborative root cause positioning, and a diagnosis report containing fault root causes and diagnosis results is generated. According to the method, operation and maintenance personnel can realize cross-service, high-fidelity and self-adaptive fault diagnosis in a fragmented observation data environment, and the diagnosis efficiency and accuracy are remarkably improved.

The invention discloses a server fault diagnosis system based on service topology correlation analysis, and relates to the technical field of computer data processing and automatic fault localization, and the system comprises a service topology construction module which is used for collecting multi-source heterogeneous data to construct infrastructure, runtime environment and business performance feature vectors, a service dependence topological graph is maintained; the fault storm diagnosis module is used for identifying a fault storm by calculating node state weights and utilizing spatial compactness and a time synchronization coefficient, and positioning a root cause node based on reverse breadth-first search and a fault propagation index; and the fusion alarm generation module is used for calculating the response time contribution degree of the root cause node to verify the causal relationship so as to distinguish dominant storm and hidden faults. The system executes cascade alarm convergence for dominant storm, triggers snapshot capture during deep operation for hidden faults, locates hidden root causes under complex topology, and realizes automatic fault closed-loop processing.