780 results about "Graph computation" patented technology

The invention discloses an auditing decision support system and method based on a dynamic knowledge graph, relates to the technical field of computers, and aims to solve the problems that auditing data are heterogeneous and complex, risk identification is not timely and causal interpretation is lacked. According to the system, multi-modal audit data is collected in real time through a streaming event processing framework, and a dynamic audit knowledge graph with timeliness weight is constructed. Based on a graph calculation engine and cross-domain rule mining, identifying a high-frequency risk mode, and generating a risk conduction path graph; further fusing a multi-modal graph attention network, identifying and positioning abnormal entities, and outputting abnormal nodes and risk links thereof; and finally, the abnormal node embedding representation is dynamically updated through the time sequence diagram attention network, an interpretable audit causal map is generated in combination with a structural causal model, and closed-loop support from data acquisition and risk identification to interpretive audit decision is realized. The intellectualization and transparency of audit decision making are improved, and an efficient and traceable decision making basis is provided for a complex audit scene.

The invention provides a knowledge graph construction and product recommendation method and system based on user data, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an initial knowledge graph through user historical behavior data, generating an optimization graph through a multi-layer neural network comprising a multi-granularity layer, a graph attention layer and a graph convolution layer, and performing bidirectional random walk sampling based on real-time behavior data of a target user to obtain a related sub-graph, calculating a product node importance score, and performing sorting pushing by adopting a multi-target optimization algorithm. According to the method, multi-dimensional accurate description of user interests can be realized, the recommendation accuracy and diversity are improved, and meanwhile, the commercial value is considered.

The invention discloses a digital twin three-dimensional scene modeling method based on a webGPU, and relates to the technical field of three-dimensional scene modeling and graphic computing, multi-source sensing data are asynchronously sampled from sparse point cloud, a video texture sequence, a scene semantic tag graph and a structure boundary tuple, and a six-dimensional structure unit group is generated through a normalization operator; constructing a structural unit atlas with nodes representing component entities and edges representing constraint relations, and introducing a tension balance mechanism and multi-scale constraints to generate a modeling path prior model; constructing a graph calculation and graph rendering dual-channel assembly line in the WebGPU, and executing parallel texture mapping and boundary fitting operation; a dynamic sensing module is used for capturing scene disturbance and driving atlas response, and incremental reconstruction of the model is achieved; and finally, mapping the model to a Web terminal, and supporting microscopic semantic query and multi-layer data linkage. According to the method, the response speed, semantic consistency and structural adaptability of three-dimensional modeling in a complex environment are improved.

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

The invention provides an intelligent analysis and execution method and system for database cascading operation, and relates to the technical field of database management.The intelligent analysis and execution method comprises the steps that a target table and an association table are extracted through a recursive query algorithm, field mapping analysis is carried out to generate a dependency weight matrix, cyclic dependency is recognized based on the matrix and decomposed into directed acyclic subgraphs, and the directed acyclic subgraphs are analyzed; node depth values and breadth values are calculated for topological sorting to generate an execution sequence; performing node classification on the execution sequence, organizing the execution sequence into a batch processing task group, calculating an optimal execution path through an adaptive path optimization algorithm, generating a distributed transaction control instruction, and allocating the task group to an execution thread pool for execution by using a consistent Hash algorithm; monitoring an execution process, dynamically adjusting thread pool resources according to a performance prediction value, executing cascade rollback when an exception occurs, and reconstructing a transaction execution path; according to the method, the execution efficiency and stability of the database cascade operation can be effectively improved, and the system resource consumption is reduced.

The invention relates to the field of financial science and technology, and discloses a financial risk dynamic assessment system and method based on artificial intelligence. The system comprises a multi-source heterogeneous data acquisition module which acquires transaction data, public opinion texts and association maps in real time; the adaptive feature engineering module dynamically screens key risk factors; the dynamic risk map construction module calculates a risk conduction coefficient through a map neural network; the multi-modal AI analysis engine cooperatively runs a time sequence prediction model, a text mining model and a graph calculation model; a risk conduction simulator quantifies a systematic risk path. The problems of data splitting processing, model static solidification and correlation risk quantification deficiency in the prior art are solved, the false alarm rate is reduced to 12%, the response speed reaches 90 seconds, the prediction deviation is reduced to 22%, and an interpretable supervision report is generated.

The invention discloses an electric power capital construction site operation environment data monitoring and safety management method, which belongs to the field of intelligent decision technology and electric power safety management, and comprises the following steps: constructing a semantic network framework according to a construction plan; collecting and calibrating multi-source environment data to generate a trusted data set; generating a real-time risk network graph based on the semantic framework and the trusted data set; calculating a robust risk index and performing sensitivity deconstruction; generating a closed-loop intervention instruction when the risk indicator exceeds a safety threshold; and finally, collecting, feeding back, iteratively optimizing the whole system, and generating a cross-project multiplexing intelligent template library. According to the method, a comprehensive technical path of semantic modeling, causal inference and closed-loop adaptive optimization is adopted, the operation situation can be deeply analyzed, potential risks can be quantified and attributed prospectively, the optimal intervention strategy is intelligently generated, and the intelligence, precision and prospective level of safety management of the electric power capital construction site is remarkably improved.

The invention provides a hospital infection prevention and control related data online management method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the dynamic graph calculation of an infection transmission path through a multi-modal deep learning model, and recognizing a high-risk department, equipment and personnel interaction mode, so as to obtain a real-time calculation result; generating a dynamic risk threshold based on the real-time calculation result, and generating a risk event vector; mapping the risk event vector into an executable supervision task list, and calculating a final task allocation path through a reinforcement learning algorithm; based on the final task distribution path, constructing a department-level risk portrait and a hospital-level risk topological graph; expected loss values of different prevention and control strategies are calculated through Monte Carlo simulation, and a department specific prevention and control scheme is generated; and when a multi-drug-resistant bacterium propagation risk is detected, automatically associating historical data of related departments, and calculating a final isolation region division scheme. The false alarm rate can be reduced.

The invention relates to the field of power system dispatching control, in particular to a virtual power plant collaborative optimization dispatching method, system and device based on multiple spatial-temporal scales and a storage medium. The method comprises the following steps: acquiring real-time supply and demand data of a multi-energy data source, constructing a dynamic operation data set by adopting distributed data acquisition, and performing time sequence analysis on the data set to extract a multi-energy fluctuation feature set; the fluctuation feature set constructs a network topology model in a spatial dimension, and a resource allocation weight of each energy node is determined through graph calculation to generate a resource allocation optimization scheme; when the real-time demand fluctuation exceeds a threshold value, a reinforcement learning algorithm is adopted to carry out optimization adjustment to obtain a real-time scheduling instruction set; in combination with real-time data of the electricity market, an optimized economic signal set is obtained through multi-objective optimization, and an equipment control instruction set is generated by adopting distributed control; and performing real-time monitoring by utilizing edge calculation according to the equipment control instruction set, and dynamically updating the scheduling instruction set through adaptive adjustment based on the system operation deviation to obtain a final resource optimization configuration scheme.

The invention relates to the technical field of online education, in particular to a componentized page rendering method and device for online education, equipment and a medium, and the method comprises the steps: obtaining a page rendering request, and determining component configuration information; constructing a component dependency graph; calculating component loading priority scores, and generating a loading queue; loading the execution component and managing the life cycle; establishing an inter-component communication bus; and executing a rendering process and performing performance optimization. According to the method, the user learning behavior and the component dependency relationship are intelligently analyzed, the component loading and rendering strategy is dynamically adjusted, and a dual-priority event management mechanism is introduced, so that efficient intelligent rendering of the education scene component is realized. Therefore, the problems of low online education component rendering efficiency, poor scene adaptability, insufficient learning behavior analysis, lack of performance optimization automation and the like in the prior art are solved, and the performance of an online education platform and the user experience are remarkably improved.

The invention relates to the technical field of knowledge graph construction, in particular to a node attenuation and edge similarity weight fused knowledge graph construction method and system, and the method comprises the steps: collecting multi-source heterogeneous data, and carrying out the preprocessing of the multi-source heterogeneous data; identifying entities in the preprocessed multi-source heterogeneous data, and extracting a relationship between the entities; establishing an initial graph structure based on the entities and the relationship between the entities, and determining the representation mode of nodes and edges in the graph; fusing time decay and space correlation factors to correct representation of nodes and edges in the constructed graph structure, and fusing semantic similarity on the basis of node and edge weight correction to further adjust an edge connection relation; after the graph structure is corrected and optimized, final knowledge graph data representation is organized and generated, and unified storage and graph calculation structured packaging are completed. According to the method, the defect that an existing map construction scheme only depends on time attenuation and neglects space factors can be effectively overcome.

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

The invention discloses an electronic material life cycle quality tracing method based on digital twinning, and relates to the technical field of industrial Internet of Things, the digital twinning of an electronic material is constructed, a material constitutive equation, a process parameter threshold library and historical quality data are integrated, and a multi-dimensional virtual model is formed; a production line real-time data stream including an equipment state, environmental parameters and material attributes is collected. According to the method, the virtual model containing the material constitutive equation and the process parameter threshold library is constructed, the real-time data flow dynamic evolution is combined, and the graph calculation and the causal reasoning algorithm are applied, so that the interaction effect of the equipment state, the environmental parameters and the material attributes can be associated, the core influence factor chain of the quality abnormality can be positioned, the single-point alarm limitation is broken through, and the quality abnormality can be accurately detected. The quality problem is deeply analyzed from the angle of multi-factor coupling, a comprehensive and systematic analysis framework is provided for accurate attribution, the source of the quality problem can be quickly and accurately found, and the efficiency and accuracy of quality tracing are improved.

The invention discloses an animation script automatic generation method and system, a storage medium and a program product, and the method comprises the steps: extracting a scene element set, a plot element set and a role element set in a static text; generating an element association graph containing weights; calculating the content matching degree of each element in the element association graph and each plot unit; selecting a core plot unit; calculating a semantic vector included angle cosine value between the core plot units as a semantic association degree, and constructing a plot unit association network by taking the semantic association degree as a connection weight; determining a plot development main line between the core plot units; determining an extended core plot unit; according to weight distribution of role elements in the element association graph, distributing corresponding roles for the extended core plot unit and generating role dialogue content to obtain a final core plot unit; and integrating the final core plot unit according to a preset animation script format to generate an animation script text. According to the method, the correlation between the generated script and the result required by the user is improved.

The invention provides a brain aneurysm intelligent detection and positioning method and system based on multi-feature fusion, and relates to the technical field of image processing, and the method comprises the steps: receiving and preprocessing a head angiography image; utilizing a multi-scale feature extraction network to obtain feature maps and fusing the feature maps; constructing anatomical candidate regions by using density clustering; extracting a tumor contour through a graph cut energy function; constructing a vascular network topological graph based on probability feature mapping, and calculating a position feature descriptor; and finally carrying out classification discrimination and marking and displaying a detection result. According to the method, various characteristics are fused, the accuracy and sensitivity of cerebral aneurysm detection are improved, and the misdiagnosis rate is reduced.

The invention discloses a network security malicious traffic tracing method based on a generative adversarial network, which comprises the following steps: collecting network traffic data, selecting a target traffic sample, extracting a protocol behavior, a communication structure and a time evolution characteristic of the target traffic sample, and carrying out semantic coding; mapping the multiple types of features to a unified embedding space through a multi-view fusion mode; constructing a manifold set based on known malicious samples, and generating an intermediate state sample sequence; constructing an attack context and path disturbance information in combination with the target sample, and generating a guide vector; inputting the guide vector and the random variable into a generator, and outputting a potential malicious sample representation; identifying the authenticity of the sample and evaluating the path similarity by using a discrimination network; and finally, constructing an attack path graph according to the similarity and the spatial relationship, and calculating a shortest path sequence to a known malicious sample to realize traceability identification of potential malicious behaviors. According to the invention, tracking analysis of malicious traffic sources is realized by using the generative adversarial network.

The invention relates to the technical field of deformation measurement, in particular to a building deformation monitoring system based on fusion and intersection of Beidou and machine vision technologies, and the system comprises a pose calculation module which is used for setting anchoring nodes, matching Beidou three-dimensional coordinates and inclination angle data of the anchoring nodes obtained in real time with two-dimensional pixel coordinates of the anchoring nodes in a visual image, and calculating the pose of the anchoring nodes; resolving a real-time pose, and converting the real-time pose into a deformation measurement result; the three-dimensional deformation field generation module is used for processing the topological graph of the building structure by adopting a graph calculation model, fusing multi-source heterogeneous information by aggregating and updating node features, and decoding to generate a three-dimensional deformation field of the whole building; and the measurement early warning module is used for constructing a normal behavior baseline model to predict a normal deformation state, carrying out deformation state evaluation and generating deformation early warning information by comparing deformation deviation degrees and combining context analysis and a deformation trend, so that the precision and comprehensiveness of building deformation monitoring are improved.

The invention discloses an electricity larceny identification method based on graph calculation, and particularly relates to the technical field of electricity utilization anomaly detection of an electric power system. Historical power consumption data and a power supply topological relation of power consumers are collected, and a multi-dimensional behavior graph model fusing behavior characteristics and structural information is constructed; performing structure disturbance analysis on each node in the graph, calculating information entropy change before and after node removal, performing attention fusion on a time sequence behavior feature of the node and a structure disturbance vector, constructing a joint feature vector, and enhancing feature expression through spectral clustering and linear reconstruction; a behavior propagation field and a disturbance adjustment mechanism are introduced into the graph to form a disturbance response graph, and an abnormal gathering area is identified through path energy analysis and focusing area fitting; calculating confidence scores of the nodes and outputting a suspicious user list; the method can realize efficient identification of electricity stealing behaviors with strong concealment and complex transmissibility, and has the advantages of high precision, strong interpretability and wide application scene adaptability.

The invention discloses a code fingerprint-based open source component identification and vulnerability detection method, which comprises the following steps of: receiving a local or remote code, extracting a difference file, generating an AST and constructing a code attribute graph; sHA-256 Hash fingerprints and GNN semantic fingerprints are calculated for the function level sub-graphs to form mixed fingerprints, accurate matching is conducted through a Bloom filter, semantic matching is completed through nearest neighbor, and a component version is determined through a distribution difference algorithm. The method comprises the steps that firstly, a component identifier is mapped into a PURL or an SWID, an OSV / NVD library is inquired to obtain a CVE, comprehensive risks are calculated in combination with CVSS, EPSS and dependency depth, and an SBOM and a vulnerability report conforming to CycloneDX or SPDX are output. The method is high in speed and high in accuracy, and the open source risk can be automatically treated in continuous integration.

The invention relates to the technical field of computer vision, and particularly discloses a corn ear harvester harvesting loss prediction method and system based on deep learning, and the method comprises the steps: collecting an ear image in an ear collection box of a corn harvester in real time, and cutting the ear image into a plurality of sub-images with preset sizes; pixel-level classification is carried out on the sub-images based on a pre-trained pixel-level classification model, and a three-channel label graph of each sub-image is generated; splicing the plurality of three-channel label graphs to form an integral label graph consistent with the original image in size; calculating the pixel proportion of the damaged corn ear based on the overall label graph; when the ear collection box is full, outputting a predicted value of corn ear harvesting loss quality based on a pre-trained harvesting loss prediction model; the method based on deep learning can more accurately detect the corn damage proportion during harvesting of the corn harvester under different illumination conditions, and solves the problem of low identification of damaged pixels by a model due to few damaged ear pixel samples at the same time.

Disclosed is a fraud-related gang incident recognition system and method based on intelligence sharing and graph calculation, and a related device. The solution mainly comprises an intelligence sharing platform and an incident model based on graph calculation. The intelligence sharing platform integrates a plurality of trained recognition models, establishes a model intelligence sharing system, and summarizes multivariate information of each model in real time. The incident model based on graph calculation is configured to perform data exchange with the intelligence sharing platform. The incident model based on graph calculation is configured to classify nodes based on a graph neural network, and perform analysis on same on the basis of economic characteristics and social characteristics, to construct a social network graph of the incident, further identify overlapping gang structures on the basis of the social network graph, and transmit the overlapping gang structures to the intelligence sharing platform for recognition processing. In the solution of the present invention, gang organizations in social networks can be effectively discovered and identified by combining a graph calculation method of node attributes and a network topology structure and by using an intelligence sharing platform.

The invention discloses a construction emergency early warning method and system, and the method comprises the following steps: collecting the three-dimensional coordinates of a constructor, combining a sliding time window with a wavelet packet energy entropy and other indexes, and generating a multi-scale movement disorder index through principal component analysis and fusion; continuously unstable persons are recognized according to the disorder index, after the trajectory of the persons is segmented, a weighted graph is constructed in combination with hidden Markov and building information model environment parameters, and the cognitive mismatch degree is calculated; mapping the cognitive mismatch degree to a space grid, calculating a local Moran index, fusing a density gradient and a mechanical operation sequence resonance result, and constructing a propagation weight matrix; constructing a heterogeneous graph based on weight matrix guidance, calculating risk influence propagation potential energy, combining historical disorder sequence analysis and relative entropy, and coupling to obtain a group-level instability pre-judgment value; and outputting a comprehensive critical level for the pre-judgment value over-limit individuals through motion trend prediction, spatial intersection and shortest path algorithms. The safety during building construction operation is improved.

The invention provides a full-text retrieval method and system fusing various types of documents, and relates to the technical field of information retrieval, and the method comprises the following steps: obtaining document representation through document content extraction and structure recognition, generating a cross-modal semantic vector by using word embedding and nonlinear transformation, constructing a hierarchical index and a cross-document association graph, and obtaining a full-text retrieval result; the basic correlation score is calculated after the query request is received, and the comprehensive score of the candidate content segments is calculated based on the association graph to determine the optimal retrieval result, so that unified representation and retrieval of heterogeneous documents are realized, the cross-document retrieval precision and relevance are improved, and the processing capability of a retrieval system on complex queries is enhanced.

The invention relates to the technical field of language information processing, and provides a large language model-based structured information retrieval method, which comprises the following steps of: deploying an adapter, accessing operation and maintenance data, carrying out timestamp synchronization on an original entry and identifying a source, mapping equipment, parts and personnel based on an asset directory and recording mapping confidence, extracting structural elements from the records; injecting three types of metadata into candidate evidences, establishing corresponding nodes and edges in a graph database, analyzing query into a structured retrieval intention, expanding a candidate evidence set along three chains to form candidate sub-graphs, and calculating comprehensive scores for sorting; the candidate evidences should be verified and constrained, meanwhile, LLM is called for inference, feasibility inference confidence is output, actual measurement results, the inference confidence and source confidence are fused according to preset weights, feasibility scores of the candidate evidences are obtained, and for the candidate evidences passing feasibility verification, accurate anchor points are calibrated for each evidence along a source chain; and generating a structured answer.

The invention relates to the technical field of artificial intelligence and industrial visual inspection, and discloses a Dinov3 and SAM-based few-sample industrial defect target detection method and system, and the method comprises the steps: building a reference feature library: extracting the features of a few defect reference images through a Dinov3 model, and generating a category prototype through weighted aggregation; generating a similarity response diagram: calculating the pixel-by-pixel similarity of the image to be detected and the reference prototype, and performing context enhancement filtering; generating a segmentation prompt: screening a salient region in combination with a space attention mechanism, and generating a geometric prompt required by the SAM model; fine segmentation is executed; an accurate mask of the defect instance is generated by using an SAM model; and confidence evaluation: multi-dimensional scoring is carried out, and a dynamic threshold value is adopted to screen results. Rapid deployment can be realized without fine adjustment of the model, the problem of data shortage in the initial stage is effectively solved, data is continuously accumulated through automatic detection, a foundation is laid for training a better special model, and the method is suitable for scenes such as new product import or new defect discovery.

The invention belongs to the technical field of vehicle fault prediction, and discloses a vehicle fault evolution law modeling method and system based on digital twinning. According to the method, vehicle running state data are obtained and mapped to a digital twin virtual state space, a fault feature evolution sequence is extracted in combination with historical fault records, a state transition topological graph is constructed, the spatial distance between a real-time state and a feature vector group is calculated to match an evolution path, and the state transition topological graph is obtained. And a fault evolution prediction result is determined based on the corresponding timestamp information, and a preventive maintenance strategy is generated. According to the invention, accurate modeling of the vehicle fault evolution rule is realized, and the fault prediction accuracy and the maintenance efficiency are improved.

The invention provides a data governance method and device based on data consanguinity and electronic equipment, and relates to the field of data governance. The method comprises the following steps: acquiring metadata of target data corresponding to a target governance demand; constructing a data blood relationship map of the target data according to the metadata; calculating a data quality propagation score, a data blood relationship path complexity, a data global influence score, a data leakage risk score and a data use efficiency score of the target data through the data blood relationship map; generating a data governance comprehensive score based on the data quality propagation score, the data consanguinity path complexity, the data global influence score, the data leakage risk score and the data use efficiency score; and performing data governance on the target data according to the data governance comprehensive score. By implementing the technical scheme provided by the invention, accurate treatment on the data is facilitated.

The invention provides a SegTCLIP glioma segmentation method and system based on data amplification and a semantic map and application, and relates to the technical field of medical image processing and image segmentation, the method comprises the following steps: pre-processing a glioma image collected in advance, and carrying out missing mode filling processing on the pre-processed glioma image; performing feature extraction on the glioma image after filling processing and the corresponding text data, mapping the features into embedded vectors, and constructing an image-text mixed feature map; and calculating a pixel-text score graph based on the multi-modal pixel feature graph, and inputting the image-text mixed feature graph and the pixel-text score graph into a multi-modal decoding network for feature decoding to obtain a segmentation result. According to the method, the problems of glioma image modal deficiency and insufficient glioma image and text information fusion are solved, and meanwhile, the accuracy of complex glioma region segmentation is improved.

The invention relates to the technical field of boxing path planning, and discloses a deep learning-based boxing path planning method and system, and the method comprises the steps: collecting the data of an object to be boxed, and constructing a three-dimensional coordinate parameter; performing spatial topology modeling by using the graph convolutional network to generate a geometric topological graph, and calculating a path density coefficient; carrying out graph Laplacian matrix spectrum decomposition on the path density coefficient to extract a spatial principal component vector, and generating a path planning weight through a genetic algorithm; and dynamically updating the reference path node based on the weight to generate an optimized path, and controlling the motion trail of the mechanical arm. The system comprises a three-dimensional data acquisition module, a topology modeling module, a density coefficient calculation module, a spectral decomposition module, a weight optimization module, a path generation module and a motion control module. According to the method, through combination of deep learning and an intelligent algorithm, complex geometric feature modeling and path optimization are realized, the boxing efficiency, accuracy and system adaptability are improved, and the method is suitable for boxing scenes such as logistics and warehousing.

A system for an automated real-time management of a directed acyclic graph (DAG) based on predictive analytics of DAG input-related data including a processor of a graph compute manager (GCM) node configured to host a machine learning (ML) module and connected to at least one DAG source entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire the DAG input-related data from the at least one DAG source entity node; parse the DAG input-related data to derive a plurality of key features; query a local DAGs' database to retrieve local historical DAGs'-related data associated with previous DAG parameters based on the plurality of key features; generate at least one feature vector based on the plurality of key features and the local historical DAGs'-related data; and provide the at least one feature vector to the ML module for generating a predictive model configured to produce at least one DAG update parameter for updating the DAG at the at least one DAG source entity.