1268 results about "Graph Node" patented technology

The invention discloses a network attack AI detection analysis method and system based on the smart Internet, and belongs to the technical field of network security protection, and the method comprises the steps: building an attack feature library through distributed edge nodes in a cooperative manner, generating feature parameters of each node based on a local attack event, and transmitting the feature parameters to a central server for dynamic fusion through encryption; constructing a multi-modal interaction graph, identifying a potential attack link based on association strength among graph nodes, and deducing an attack intention to generate a defense strategy; deploying a virtualized network environment, dynamically injecting induction characteristics, and adjusting an induction strategy in real time according to the interaction behavior of an attacker; and monitoring an abnormal mode of the user behavior sequence, triggering an AI interaction verification process and storing a defense strategy. According to the method, rapid collection and fusion of network attack features are realized, the detection delay of network attacks is reduced, the accuracy of attack prediction is improved, the flexibility and effectiveness of network attack confrontation are enhanced, and the defense intelligence and adaptive ability of the whole network are improved.

The invention discloses a sensing data fusion-based radiator abnormal state detection method, relates to the technical field of radiator state detection, and is used for solving the problem of poor radiator state detection efficiency. According to the method, multiple types of sensors are arranged in combination with a radiator structure, acquisition time alignment is completed, segmentation and de-noising processing is performed on multi-modal data based on a sliding time window, and a frequency domain texture feature is constructed by extracting a frequency spectrum information entropy and a high-frequency energy ratio; constructing the feature values into graph nodes, establishing a complete connection graph, compressing the complete connection graph into a skeleton graph through conditional independence test, generating a directed acyclic graph in combination with an intersection structure and topological sorting, and calculating a causal weight to realize structure updating; and extracting a current window node state, comparing the current window node state with a prediction state, identifying an abnormal node, executing path backtracking, calculating path cost, tracing to a root cause node, extracting path evaluation information, and generating a response signal, thereby improving the abnormality diagnosis precision and scheduling linkage responsivity of the radiator.

The invention discloses a fault root cause positioning method and system driven by a dynamic knowledge graph, and relates to the technical field of fault root cause localization, and the method comprises the steps: collecting and obtaining a multi-source fault associated data set, carrying out the entity association extraction of the multi-source fault associated data set, and obtaining a fault entity set and an entity relationship set; performing graph node cascading and incremental learning updating, and constructing a fault updating knowledge graph; monitoring and acquiring target fault data, performing mode matching reasoning, and generating a fault mode candidate root cause set; and performing similarity matching on the fault mode candidate root cause set in combination with a historical fault case library, and determining a target fault root cause positioning result. The technical problem of low fault diagnosis efficiency caused by inaccurate fault root cause positioning and knowledge graph updating lagging in the prior art is solved, and the technical effects of realizing accurate positioning of the fault root cause and dynamic improvement of the knowledge graph and improving the fault diagnosis efficiency and accuracy are achieved.

The invention provides a multi-modal knowledge extraction method and system based on multi-agent collaborative optimization, and relates to the technical field of knowledge extraction, and the method comprises the steps: carrying out the multi-modal deconstruction of an original document to be extracted; constructing a multi-modal agent, respectively executing feature extraction and preliminary knowledge extraction, and outputting a single-modal multi-component system; based on a cross-modal knowledge graph, mapping information of different modals to a unified semantic node, and establishing cross-modal association and analyzing a logic chain through a graph neural network and a causal reasoning module; dynamically allocating resources according to the importance of map nodes, and screening structured knowledge; and through confidence analysis and node traceability evaluation, an intelligent agent cooperation mechanism is optimized, and increment correction is carried out on a result. According to the method and the device, the technical problem of low knowledge extraction accuracy and efficiency caused by insufficient multi-modal knowledge collaborative mining capability due to knowledge extraction of literatures by adopting a single agent in the prior art can be solved, and the knowledge extraction quality and efficiency are improved.

The invention relates to the technical field of automatic driving, and discloses an automatic driving lane changing trajectory planning method based on deep learning, and the method comprises the steps: carrying out the data collection and preprocessing of a multi-modal sensor; performing spatial feature extraction and time sequence modeling on the preprocessed multi-modal data by adopting a CNN-LSTM hybrid architecture, performing feature fusion through an attention mechanism, and outputting a first feature extraction vector; taking the detected vehicles as graph nodes to construct a traffic graph, learning an interaction relationship between the vehicles through a graph attention network and a message passing mechanism, and calculating a scene urgency score and a safety score; generating a lane changing decision based on the deep Q network and the strategy gradient; and generating a trajectory based on the generative adversarial network. The technical problems that an existing lane changing track planning method cannot adapt to the dynamic traffic environment, lacks the ability of understanding complex multi-vehicle interaction and is difficult to balance safety and urgent conflict requirements are solved, and intelligent, safe and efficient automatic driving lane changing track planning is achieved.

The invention discloses a marine ecological disaster early warning method based on multi-source heterogeneous big data fusion. The marine ecological disaster early warning method comprises the steps that remote sensing image data and actual measurement environment data of a target sea area are acquired respectively; sequentially performing boundary segmentation and feature fusion on the remote sensing image data based on the image segmentation network to obtain multispectral feature data; performing space-time alignment on the multispectral feature data and the environmental data to obtain a space-time alignment data set; mapping each sample pair data in the time-space alignment data set into graph node data, and analyzing a connection relationship between any two adjacent graph node data to construct an adjacency relationship matrix; performing neighborhood aggregation processing on the adjacency relation matrix to generate a fusion feature vector; and inputting the fusion feature vector into a pre-constructed marine disaster prediction model to obtain a multi-dimensional prediction result, and carrying out ecological disaster real-time early warning based on the multi-dimensional prediction result. According to the method, the real-time performance of early warning of the marine ecological disasters is improved.

The invention relates to the technical field of industrial Internet of Things, in particular to a machine equipment online state monitoring and fault diagnosis system, which comprises the following steps of: acquiring multi-source heterogeneous sensing data through an edge computing node deployed on an equipment body, performing adaptive noise filtering and feature dimension reduction processing on original data, and acquiring multi-source heterogeneous sensing data; outputting a standardized equipment state vector set; inputting the equipment state vector set into a dynamic knowledge graph engine, constructing a fault evolution network comprising space-time correlation characteristics based on an equipment operation entropy change quantification model, and generating a graph node connection relationship with a weight coefficient; and inputting the fault evolution network into a migration reinforcement learning module, and outputting a diagnosis decision set comprising a fault type, a severity degree and an evolution path through knowledge migration of a cross-device fault mode. According to the method, the problems of edge redundancy and single feature expression in traditional rule-based atlas construction are effectively avoided, and the structuring ability and physical traceability of fault recognition are improved.

The invention discloses an enterprise scientific and technological achievement adaptation method based on big data accurate retrieval and query, and relates to the technical field of big data processing and knowledge maps, and the method comprises the steps: constructing an enterprise technical field knowledge map based on a standardized multi-modal feature matrix, receiving incremental technical data in real time by adopting an Apache Flink streaming processing framework, and carrying out the real-time retrieval and query of the enterprise technical field knowledge map. Dynamically updating a graph node relationship, removing expired nodes through a pruning algorithm, and outputting a dynamic knowledge graph with a timestamp; and receiving a query request of a user, extracting a query vector by utilizing the cross-modal embedding model, retrieving a Top-N candidate node list through a dynamic knowledge graph, calculating a comprehensive similarity score in combination with a graph relation weight, and outputting a sorted technical achievement list. Multi-source heterogeneous data is mapped to a unified semantic space through a cross-modal embedding model, and the intelligent level and the actual application effect of scientific and technological achievement adaptation are comprehensively improved in combination with construction and updating of a dynamic knowledge graph.

The invention relates to the technical field of process optimization, in particular to a knowledge graph-driven manufacturing process optimization system, which comprises a graph node generation module, a dependency relationship construction module, a semantic entity matching module, a process path screening module and a trend-driven early warning module. According to the method, semantic implicit relation recognition is achieved through sequential alignment and numerical difference comparison, quantitative screening between the machining size and the tolerance grade is introduced in path construction, and it is ensured that a path connection structure is optimized under the condition that the size precision constraint is met; in combination with the time sequence trend splitting and continuous fluctuation consistency discrimination mechanism of the equipment operation state, the dynamic marking and early warning annotation set generation of the process deviation trend can be completed at the node path level, and the closed-loop processing from knowledge modeling, path screening to trend pre-judgment is realized. And the process path adaptability, the parameter configuration precision and the abnormality identification advancement are effectively improved.

The invention discloses a personalized learning resource recommendation method and system based on multi-agent collaboration and a dynamic knowledge graph, and the method comprises the steps: constructing the dynamic knowledge graph, enabling nodes to be associated with teaching resources (videos, test questions, teaching plans, PPT and the like), and enabling edges to represent the logic relation between the resources; collaborative decision is made through four layers of agents: a target determination agent generates a learning target based on student historical learning data and a graph node state; the path planning agent plans a learning path in combination with the target and the learner model; the resource screening agent matches personalized resources from the path nodes; the user portrait intelligent agent updates the learner model in real time; and finally, generating a dynamic recommendation result and feeding back the optimized knowledge graph. Through multi-agent hierarchical collaboration and dynamic interaction with the knowledge graph, the problems of cold start, incomplete resource coverage and path stiffness of a traditional recommendation system are solved, and precise and adaptive learning resource recommendation is realized.

Apparatuses, systems, and techniques to perform a neural network. In at least one embodiment, an application programming interface schedules two or more graph nodes to be performed by two or more parallel processing pipelines based, at least in part, on an order of layers in the neural network.

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

The invention relates to the technical field of fracture network fracture water channel identification, and discloses an identification method for analyzing a dominant flow channel in a three-dimensional fracture network based on a topological network. According to the method, probability distribution parameters such as geometric occurrence, gap width and density of a rock mass multi-scale fracture system in a target area are obtained through field surveying and mapping and three-dimensional scanning, and a spatial topological structure of the three-dimensional fracture system is reconstructed by adopting Monte Carlo method simulation and discrete fracture network modeling technology; secondly, abstracting the fracture network into a three-dimensional topological graph model based on a graph theory principle, defining fracture center points and cross points as graph nodes, and converting fracture sections into weighted edges; the method breaks through the continuous medium hypothesis limitation of traditional seepage analysis, and has important engineering application value in the fields of deep geological energy storage reservoir seepage risk assessment, shale gas fracture network optimization design, rock slope stability analysis and the like; compared with a traditional numerical simulation method, the method has the technical advantages of being high in calculation efficiency, good in prediction precision, high in multi-scale applicability and the like.

Apparatuses, systems, and techniques to perform an application programming interface (API) to cause dependency type information of one or more user-indicated graph nodes of a software graph to be indicated. In at least one embodiment, one or more dependency types from a graph are indicated.

The invention discloses a PHP taint type vulnerability detection method based on a heterogeneous graph neural network, and belongs to the field of software security. The method comprises the following steps: performing annotation removal, variable naming standardization and character string standardization processing on a PHP source code through a code preprocessing module to generate a standardized code; based on a vulnerability sub-attribute graph extraction module, reversely tracking vulnerability sinks to a taint source, extracting a simplified vulnerability sub-attribute graph, and removing redundant nodes and edges; fusing BERT semantic features and node type features through a graph node embedding module to generate an initial embedding vector, and constructing a heterogeneous graph comprising an abstract syntax tree edge, a program flow graph edge and a control dependence graph edge; a heterogeneous graph neural network vulnerability detection module is adopted to perform independent feature aggregation on multiple types of edges, dynamic weighted fusion is performed in combination with an attention mechanism, and key nodes are screened through Top-k graph pooling; and finally, inputting the graph-level features into a classifier to realize vulnerability detection.

The invention relates to the technical field of short message auditing and intercepting, in particular to a short message auditing and intercepting system based on artificial intelligence, which performs multi-level semantic feature extraction on short message content by constructing a deep learning architecture mixed by a convolutional neural network and a recurrent neural network. And traditional keywords are effectively identified, and camouflage keywords, homophonic replacement and interference symbols which are easy to bypass are filtered. Furthermore, a short message semantic map is constructed through a multi-head self-attention mechanism, and weighted analysis and abnormal node recognition are carried out by taking sensitive words and key semantic units as map nodes and taking semantic association relationships among the nodes as edges, so that the abnormal short message recognition precision is improved. Besides, a multi-dimensional short message risk assessment mechanism is constructed by combining the historical behavior mode of the sending number, the number reputation score and the incidence relation between the numbers, and a hierarchical auditing strategy is implemented, so that the system processing efficiency and the interception accuracy are effectively improved.

The invention discloses a sparse dynamic graph convolution-based electroencephalogram signal decoding method and system. The method comprises the following steps of: acquiring a multi-channel electroencephalogram signal and preprocessing the multi-channel electroencephalogram signal; performing multi-band filtering on each channel signal, extracting statistical characteristics on each band signal, calculating a covariance matrix of a task electroencephalogram signal, constructing image electroencephalogram data by taking an electroencephalogram channel as an image node, the multi-band spliced statistical characteristics on the channel as a node feature vector, and the covariance matrix between the channels as an adjacent matrix; finally, a dynamic graph convolutional neural network model is constructed, the model constructs a graph convolutional neural network based on an autoregression moving average filter, graph electroencephalogram data is used as input, the category of electroencephalogram signals is used as output, an adjacency matrix is dynamically generated in combination with bilinear mapping, fuzzy label learning and sparse constraint are added to improve the decoding capacity of the model, and the dynamic graph convolutional neural network model is obtained. And the frequency domain response capability and robustness of the model to the graph structure are enhanced.

The present application discloses a database query method and apparatus, an electronic device, and a non-volatile storage medium. The method comprises: determining a knowledge graph corresponding to a database to be queried, wherein the knowledge graph is used for representing a logical structure and an association relationship of data in said database; determining similarity scores between user question text and graph nodes in the knowledge graph, and determining a target node from among the graph nodes of the knowledge graph on the basis of the similarity scores, wherein the similarity scores are used for representing the degree of association between the graph nodes and the user question text; and on the basis of the target node, generating database schema information corresponding to said database, and using a large language model to generate, on the basis of the database schema information, a structured query language statement corresponding to the user question text.

The embodiment of the invention provides a bill collaborative management method and system based on multi-source heterogeneous data fusion, and belongs to the technical field of data processing. The method comprises the following steps: performing hash calculation according to standardized bill data to obtain a unique identifier; and obtaining a map node corresponding to the standardized bill data to obtain a target map node. The path from the map root node to the target map node is a target structure path. And obtaining a complete behavior sequence of the standardized bill data according to the unique identifier and the behavior log. And constructing a behavior structure joint sequence according to the target structure path and the complete behavior sequence. And inputting the behavior structure joint sequence into the structure perception embedding model to obtain a sequence embedding vector. And obtaining a structure behavior inconsistency score and an abnormal type label according to the structure behavior consistency evaluation function, the behavior structure joint sequence and the sequence embedding vector. And generating a suggestion text according to the structure behavior inconsistency score and the abnormal type label, and displaying the suggestion text. Accurate identification of abnormal bills is realized.

Apparatuses, systems, and techniques to perform signal processing operations in a fifth generation (5G) new radio (NR) signal. In at least one embodiment, one or more processors process a 5G NR signals according to one or more graph nodes.

The invention provides a Beidou intelligent early warning analysis method and system applied to a drainage basin-level reservoir dam group, and the method comprises the steps: obtaining a dam body deformation time sequence record and a corresponding hydrological environment time sequence record which are collected by a monitoring system of the drainage basin-level reservoir dam group through a Beidou satellite positioning terminal; dam body deformation response mode self-encoding processing is carried out on the associated time sequence data set, a deformation response feature element set representing the dam body structure state is generated, the deformation response feature element set is input into a pre-constructed basin reservoir dam group topological relation graph, abnormal state propagation calculation is carried out through the state dependency relation between graph nodes, and the abnormal state propagation calculation is carried out. And obtaining a chain risk transmission path set, performing risk evolution trend deduction on dam nodes in each path based on the chain risk transmission path set, generating a risk evolution index matrix, and generating a threatened risk identification sequence for key dam nodes. According to the method, the structuralization and response operability of the early warning information can be improved, and the scientificity and practicability of early warning analysis of the basin-level reservoir dam group are comprehensively improved.

The invention relates to the technical field of intelligent abstract generation, in particular to an intelligent archive abstract generation system and method based on a natural language processing technology. The system comprises a multi-layer semantic generation core unit which performs semantic hierarchical segmentation analysis on an original file text to construct a multi-layer semantic graph, and constructs an abstract generation model to generate abstract content; the knowledge graph fusion engine unit associates and extracts terms in an original file text, constructs an entity mapping relation, and sets semantic graph node expression weights in an abstract generation model according to semantic association confidence scores; a paging index cache retrieval unit performs fragment division processing on each node in the multi-layer semantic graph, and constructs an abstract content fragment index structure containing a node set; and the version tracing transaction management unit carries out version recording on generation and modification operations of the abstract contents. The invention discloses an intelligent archive abstract generation system which is constructed by fusing a multi-layer semantic graph and is associated with a knowledge graph.

The invention particularly relates to a ship multi-modal image fusion recognition method based on graph neural structure alignment, and the method comprises the following steps: 1, obtaining a ship multi-modal image, and constructing a backbone neural network to extract the features of the multi-modal image; 2, constructing graph nodes of the graph neural network, and generating an adjacent edge relationship; 3, for graph structures constructed in different modes, adopting a two-level graph attention mechanism to complete structure alignment; step 4, utilizing an optimal transmission mechanism to realize structure alignment between the infrared and visible light modal diagrams; step 5, feature re-injection is carried out to fuse space coordinates and global information, and the positioning and expression ability of node features is improved; and step 6, training the constructed ship multi-modal image fusion recognition network by adopting local feature alignment loss, graph-level semantic consistency loss and classification supervision loss. According to the method, the problem of alignment errors caused by inconsistency of infrared and visible light modal images is solved, the structure and semantic information of the infrared and optical images are fully fused, the accuracy and robustness of cross-modal target recognition are effectively improved, and the method is suitable for complex scenes such as multi-modal ship recognition.

The invention discloses a bearing life prediction method and system based on dynamic knowledge embedding, and the method comprises the following steps: S1, encoding bearing field data, expert experience and monitoring data into a structured triple, building a dynamic knowledge graph frame, and designing a sliding window confidence mechanism to achieve the online updating of a graph node relation; s2, extracting knowledge embedding vectors by using a relational graph convolutional network, and extracting vibration signal features by using a hierarchical convolutional network; s3, mapping the knowledge embedding vector and the vibration characteristics to a unified semantic space through a linear projection layer; s4, constructing a Transform encoder based on multi-head self-attention, and establishing a dynamic correlation model between vibration characteristics and knowledge embedding; s5, designing a full-connection network output life prediction result and feeding back the optimized knowledge graph; and S6, adaptively adjusting the size of the sliding window based on the change rate of the working condition, dynamically balancing the contribution weight of new and old knowledge in combination with a gating mechanism, and ensuring the adaptability of the model to the complex working condition.

The invention discloses a logistics transportation personalized recommendation path planning method and system based on a cloud platform, and belongs to the technical field of logistics transportation, and the method comprises the steps: constructing a heterogeneous traffic map model fusing road sections, geographic interest points and traffic event information; collecting historical transportation task records of a plurality of users, generating user semantic intention tags based on delivery behaviors, and mapping the user semantic intention tags to traffic map nodes and edge attributes to form a semantic constraint graph structure; performing joint modeling on the user intention and the path reachability by using a heterogeneous graph neural network to generate a path scoring sequence; after the transportation task is completed, abnormal events are collected, and the semantic intention label mapping relation is dynamically updated based on reverse alignment errors; the model and the mapping module are deployed on a cloud platform, the traffic state is obtained in real time in combination with edge equipment, and path recommendation online generation and rapid pushing are achieved; according to the method, the personalized matching degree and the real-time response capability of path recommendation can be effectively improved, and the method is suitable for intelligent logistics path planning in a complex scene.

The invention belongs to the technical field of wireless communication and antennas, and particularly relates to a phased array antenna pattern synthesis method and system based on a graph neural network, array element parameters and environmental parameters are converted into a graph node and edge feature matrix, and array element excitation parameters are optimized by using a multilayer graph attention network; according to the method, energy conservation and array element spacing constraint are ensured through a physical constraint module, and the performance of a directional diagram is verified and iteratively optimized through a fast moment method; under the 1024 array element scene, the calculation efficiency is greatly improved, second-level optimization is achieved, and meanwhile, the system has the dynamic self-adaptive capacity, array element faults and carrier deformation can be responded in real time, and the optimized directional diagram performance can be kept; according to the technical progress, the calculation efficiency is improved, the stability and adaptability of the system are enhanced, and an innovative solution is brought to the technical field of wireless communication and antennas.

The invention relates to the technical field of intelligent agriculture, and particularly discloses an orchard intelligent water and fertilizer integration-based edge end fruit tree fertilizer demand prediction method, which comprises the following steps of: constructing each fruit tree as a graph node, and fusing canopy spatial characteristics extracted by unmanned aerial vehicle multispectral remote sensing and nutrient time sequence characteristics monitored by a soil sensor; space influence weights among nodes are calculated according to the terrain, the soil texture and the space distance, and a fruit tree individual relation graph is established; then, through a space-time diagram convolutional network, aggregating space neighborhood features and capturing time dynamic changes, and outputting target state features subjected to multi-scale enhancement; when the canopy conflicts with the soil characteristic indication, combined diagnosis is carried out based on a neighborhood consistency index and an adjacent node state, systematic nutrient stress and local non-nutrient factors are distinguished, and final fertilizer demand prediction is generated after the conflict is eliminated; a digital fertilization prescription map is generated through spatial interpolation, soil characteristics and slope correction, and the water and fertilizer integrated equipment is driven to execute precise operation.

The invention provides a landslide deformation zone monitoring method and system based on combination of earth surface and deep layer data, and relates to the technical field of deformation measurement. The method comprises the following steps: acquiring earth surface deformation data and deep monitoring data of a landslide deformation zone in a plurality of observation periods, and constructing a time sequence monitoring sequence corresponding to each monitoring point in the landslide deformation zone based on the earth surface deformation data and the deep monitoring data; taking the monitoring point positions as graph nodes, and constructing a graph structure according to the spatial distribution relation of the monitoring point positions; adding the time sequence monitoring sequence as a node attribute to a corresponding node in the graph structure to obtain a multi-dimensional graph model; predicting the multi-dimensional graph model by using a graph neural network, and obtaining predicted deformation data of each monitoring point in a future observation period; and determining an expansion area of the landslide deformation zone by combining the predicted deformation data and the current landslide deformation zone image. According to the technical scheme, accurate prediction of the deformation trend and the expansion area of the landslide deformation zone can be realized.

The invention discloses an agricultural product supply chain abnormal event tracing method and system based on a knowledge graph, and relates to the technical field of agricultural product supply chain management, and the method comprises the steps: constructing a supply chain knowledge graph, and enabling graph nodes to cover agricultural product batches, production subjects, processing equipment, transport vehicles, storage warehouses and other entities; when an anomaly is detected, performing reverse traversal from an abnormal node based on a graph neural network to determine a candidate propagation path; calculating an abnormal source probability score by integrating the historical abnormal record, the time window goodness of fit and the association strength, and outputting a responsibility subject sequence; the method supports hypothetical reasoning to predict the downstream influence range, and solves the problems that a traditional traceability scheme is difficult to efficiently associate and analyze cross-link data and lacks abnormal path intelligent reasoning ability.

The invention provides an intelligent identification and early warning method for scientific and technological consultation project risk factors, and the method comprises the steps: obtaining a calculation cluster use record, training time period reservation data and department task distribution data in a scientific research consultation project, and carrying out the map node mapping of cluster conflict and training reservation data, constructing a resource competition distribution characteristic spectrum containing department cooperation contradictions; when the interference degree exceeds a threshold value, task priorities and resource requirements of all departments are obtained, a task overlapping dependency subgraph is constructed, and task allocation imbalance indexes are quantitatively analyzed; extracting distributed data interface configuration information, detecting inconsistency of a protocol and task distribution data in combination with a task distribution imbalance index, and identifying an influence path of data synchronization delay; and analyzing a dependency path between the data synchronization delay influence path and resource competition risks in the distribution characteristics of computing resource competition, and constructing a risk map containing department cooperation contradictions and linkage risk diffusion to obtain risk factor distribution.