133 results about "Computational topology" patented technology

The invention relates to the technical field of brain-computer interfaces, in particular to an electroencephalogram signal amplification method, device and equipment based on topological gradient field coding. The method comprises the following steps: acquiring a first data set corresponding to a scalp electroencephalogram of a testee through electroencephalogram acquisition equipment, and performing Hilbert transform to extract phase information so as to calculate a topological gradient field; key phase features meeting the energy concentration ratio feature and the duration feature are extracted from the topological gradient field, so that the key phase features with the energy concentration ratio threshold value larger than a preset value and the duration exceeding a time threshold value are screened out; inputting the key phase features to a pre-constructed generative adversarial network, and generating an enhanced phase field by a generator of the generative adversarial network through adversarial training; and combining the enhanced phase field with the low-frequency amplitude component, subjected to wavelet decomposition, of the first data set to construct a complex field, simulating spatio-temporal evolution of the complex field based on a nonlinear dynamic model, and generating a finally enhanced and amplified electroencephalogram signal. And efficient characterization of electroencephalogram phase information is realized.

The invention discloses an intelligent electric equipment monitoring and optimizing method, and relates to the technical field of electric power, and the method comprises the steps: collecting high-dimensional voltage-current time sequence data and transient event marks, calculating topological invariant features, inputting the topological invariant features to a lightweight neural network model, and recognizing the features of all electric equipment; performing abnormal attribution and anti-fact energy efficiency prediction by using causal reasoning and dynamic regularization regression based on the identified characteristics of each electric device, and constructing a multi-objective optimization function through the abnormal attribution and anti-fact energy efficiency prediction; and based on the multi-objective optimization function, generating an equipment operation scheduling strategy through a deep reinforcement learning agent, based on the operation scheduling strategy, sending a control instruction to the electric equipment, and collecting an operation result feedback in real time for optimization and updating. According to the method, through fusion of topological features, causal reasoning and safety reinforcement learning, the precision, robustness and safety of monitoring and optimization of the intelligent electric equipment are improved.

The invention belongs to the technical field of pipe network monitoring control, and particularly relates to an urban pipe network leakage detection method and system.The urban pipe network leakage detection method includes the steps that firstly, data are collected at key nodes and pipe sections of a pipe network through a data collection module, and a four-dimensional original data set with spatial positioning attributes is formed; the preprocessing module adopts an improved Kalman filtering algorithm containing a pipe network material attenuation coefficient to reduce noise, and outputs high-quality data; the space-time fusion module is combined with GIS pipe network topological data, the topological weight is calculated through a node degree centrality algorithm, and a time attenuation mechanism and adjacent node features are fused to generate a comprehensive feature matrix; the dynamic judgment module judges suspected leakage based on a multi-parameter weighting model and a sliding window dynamic threshold value; the positioning engine module positions a leakage point through a two-factor algorithm of signal time difference and improved hydraulic model residual error; and finally, the linkage control module starts secondary acoustic verification, valve control, early warning and maintenance scheduling instructions are automatically generated after confirmation, and closed-loop management from detection to disposal is achieved.

The invention relates to the technical field of water conservancy project structure health monitoring and intelligent sensing, in particular to a water conservancy project online monitoring and abnormity diagnosis system based on edge calculation, which comprises a data acquisition module used for acquiring current water level data, current temperature data and actually measured structure response data; the reference reconstruction module is used for generating an ideal state vector; the fault simulation module is used for generating a theoretical damaged simulation state vector; the topology judgment module is used for calculating a first difference value between the actual measurement structure response data and the ideal state vector, calculating a second difference value between the theoretical damaged simulation state vector and the ideal state vector, and calculating the topology similarity based on the first difference value and the second difference value; the risk assessment module is used for generating a structure safety risk index in combination with the fault strength scalar and the topological similarity; according to the method, the problems of data transmission lag and low online calculation efficiency in a traditional centralized processing mode are effectively solved, and the real-time requirement of an engineering site is met.

The invention discloses a three-dimensional CAD model feature recognition method. The method comprises the following steps: analyzing boundary representation data of a CAD model and constructing a topological graph structure; the feature complexity is quantified by calculating a topological entropy value; in combination with a predefined process semantic rule base and a graph neural network model, realizing association identification of geometric features and processing semantics; and outputting the parameterized feature tree and supporting CAM system integration. The technical problems that a traditional feature recognition method is poor in adaptability to a complex topological structure and lacks process semantic association are solved, and the recognition accuracy and the automation degree are remarkably improved.

The invention discloses an intelligent evaluation system and intervention method for mental health of teenagers, and the method comprises the following steps: collecting multi-mode psychological signal data of the teenagers, and carrying out the preprocessing; establishing a cognitive energy field, and generating a cognitive energy time sequence curve; performing spectrum resonance analysis, and combining the spectrum resonance analysis with a cognitive energy time sequence curve; constructing a psychological topological graph, and calculating a topological complexity index; when the topology complexity index is continuously abnormal or the cognitive energy disturbance amplitude exceeds a set threshold value, a psychological risk early warning result is generated; after the psychological unstable state is detected, a cognitive energy feedback parameter set is generated, and cognitive energy balance intervention is executed; and after the intervention is finished, calculating an energy balance improvement rate, and when the energy balance improvement rate exceeds an energy balance threshold, judging that the intervention is finished. According to the method, multi-mode psychological signals and cognitive energy field modeling are fused, intelligent evaluation and dynamic intervention of the psychological state of the teenagers are achieved, and the method has the advantages of being high in accuracy, high in interpretability and closed in intervention loop.

The invention discloses a semi-supervised medical image segmentation method based on causal uncertainty decomposition, and belongs to the field of medical image processing and artificial intelligence. According to the method, a segmentation model of teacher and student architectures is constructed, and a causal uncertainty decomposition module, a self-adaptive consistency learning module and a topology perception consistency loss module are integrated. The total uncertainty is decomposed into cognitive uncertainty and random uncertainty, so that targeted processing is realized; a dual-path weight fusion and differential modulation strategy is adopted to realize pixel-level adaptive learning; and a Betti number is introduced to calculate a topological distance, so that the integrity of an anatomical structure is kept. According to the method, under the condition that only 5%-20% of annotation data is used, the Dice coefficient on multiple medical image data sets is increased by 3.2%-4.8%, the segmentation precision and the boundary positioning accuracy are remarkably improved, the segmentation problem under the condition that medical image annotation is scarce is effectively solved, and the method has important clinical application value.

The invention provides an energy storage device damage diagnosis and interaction system based on penetration vision and a large model, and relates to the technical field of energy storage device damage diagnos.The energy storage device damage diagnosis and interaction system comprises a penetration type 3D sensing module, a periodic topology analysis module, a feature mapping and retrieval module and a large model reasoning and interaction module, and the energy storage device is scanned and reconstructed; the method comprises the following steps: constructing a time-space decoupling periodic Transform network, introducing a periodic mask matrix to force the network to pay attention to a repeatability rule of an internal structure of a battery, and identifying internal tiny deformation and structural damage by calculating topological consistency under the condition that a large amount of negative sample training is not needed; visual defect features are mapped into text embedding by utilizing a feature projection technology, a diagnostic report containing physical cause analysis and maintenance suggestions is generated by combining a retrieval enhancement generation technology and a large language model, and a user is supported to perform interactive questions and answers in a natural language. The method can solve the problems that in the prior art, three-dimensional deformation is difficult to quantify, small samples are difficult to train, and intelligent decision-making ability is lacked.

The invention discloses a hydroelectric equipment anomaly detection method based on physical mechanism guidance and sequential topological entropy fluctuation characteristics, and belongs to the technical field of hydroelectric equipment monitoring and fault diagnosis. The method comprises the steps that multi-source sensor data of hydroelectric equipment is collected and preprocessed; constructing a physical weighted distance function in combination with an equipment physical mechanism, and embedding time sequence data into a high-dimensional point cloud space; extracting persistent homology features through a sliding window, generating a persistent graph sequence and calculating topological feature indexes; a persistence graph entropy fluctuation index is provided, and anomaly detection is realized by quantifying time sequence fluctuation of topological entropy; and finally, visual output and an alarm mechanism are combined to assist diagnosis. According to the method, equipment physical characteristics and topological data analysis are fused, the problems that a traditional method is insufficient in nonlinear system modeling, insensitive to dynamic evolution and the like are solved, the early warning capacity and detection precision of early faults are improved, and the method is suitable for anomaly detection application of core equipment such as a water turbine and a generator.

The invention provides a historical load analysis-based power supply path dynamic generation and evaluation method, which comprises the following steps of: acquiring historical load data of a commercial district and topological structure data of a power distribution network, extracting load space distribution characteristics, and determining a dynamic change trend of load space distribution by adopting a clustering algorithm; performing time sequence analysis on the offset distance of the load center and the change rate of the power utilization density, and identifying the change period of the load in the commercial district and the sudden characteristics of sudden increase or sudden decrease of the load; key section distribution is identified according to the section identification standard, the load matching degree of the topological structure of the power distribution network is evaluated according to the key section distribution, and the load distribution balance and the line bearing capacity between topological nodes are calculated; and identifying weak nodes and areas with unbalanced load distribution according to the load distribution balance among the topological nodes and the line bearing capacity, and adjusting the line layout according to the identified weak nodes.

The invention relates to the technical field of power system automation and artificial intelligence, and particularly discloses a power network risk alarm event analysis method, which comprises the following steps of: firstly, carrying out standardization processing and space-time slicing on a multi-source heterogeneous original alarm flow, and constructing a dynamic fault propagation graph in combination with power grid physical topology; then, multi-dimensional indexes such as topology centrality, time sequence attenuation and semantic rare degree are comprehensively calculated, and the causal significance of each alarm node is quantified; on this basis, adaptive sparse sampling based on maximum interpretation gain is implemented, and high-score core events are precisely preserved and public precursor nodes with global collective power are forcibly recalled by evaluating common-mode interpretation utility and space-time coupling strength of nodes to downstream alarms on the premise of strictly complying with the limitation of context tokens of a large model. And finally, inputting the simplified high-value evidence chain into a large language model to carry out thinking chain reasoning, thereby realizing accurate positioning of complex fault root causes and automatic generation of a disposal strategy.

The invention discloses a meshless EFGM and style migration-based multi-material structure topological optimization method, which comprises the following steps of: (1) inputting material attributes such as Young modulus, shear modulus and Poisson's ratio of a structure, and discretizing a design domain by using meshless nodes; (2) transforming the designed meshless density field to obtain a physical density field, and performing length scale filtering; (3) an alternating active phase algorithm is executed, and the displacement field, the system flexibility and the related sensitivity of the structure are solved based on the meshless EFGM; (4) executing a neural style migration program to calculate the difference between the current topological structure and the reference style image, and calculating the derivative of the physical density field of the topological structure to the network parameter; and (5) updating a design variable by using a moving asymptote algorithm, and judging whether an optimization loop is converged or not. According to the method, based on meshless EFGM and style migration multi-material structure topological optimization, topological structures with different artistic styles can be designed, and the method has engineering application value.

The invention discloses a semantic auxiliary vision SLAM loopback detection method based on topological graph matching, and belongs to the field of computer vision. The implementation method comprises the following steps of: screening out candidate key frames without a common-view relationship by judging whether the common-view relationship exists between a current frame and each frame in a historical key frame sequence or not; and constructing a semantic topological graph, namely constructing nodes and undirected weighted edges in the topological graph and calculating node descriptors, and realizing topological expression of the key frame image by constructing the semantic topological graph and calculating a feature descriptor for each node in the topological graph. And calculating an affinity matrix, constructing the affinity matrix between the topological graphs by combining the similarity between the weights of the node descriptors and the edges in the topological graph of the current key frame and the topological graph of the candidate key frame, and calculating an incidence matrix between the nodes of the topological graphs at the same time. And calculating the topological graph similarity through the affinity matrix and the incidence matrix. And judging whether loopback detection succeeds or not by judging whether the topological graph similarity reaches a loopback detection threshold value or not.

The invention discloses a temperature control system and method of a micro-fluidic chip, and relates to the technical field of micro-fluidic temperature control, and the method comprises the following steps: in the manufacturing process of the micro-fluidic chip, embedding topological insulator nanowires into a substrate in a directional arrangement mode to form a pre-locking array, and meanwhile, generating an anisotropic heat conduction network under the continuous action of a sound-magnetic field, a topological vortex invariant is calculated, and a heat conduction weight coefficient is generated; arranging a fluorescent layer on the surface of a micro-reaction cavity of the anisotropic heat conduction network, collecting a fluorescence lifetime attenuation curve in real time, and calculating a transient temperature value and a temperature change rate according to a heat conduction constraint variation model and a heat conduction weight system; nonlinear feed-forward compensation power is calculated according to the transient temperature value and the temperature change rate, feedback compensation power is calculated in combination with a deviation field of the target temperature and the real-time temperature, and a compensation power instruction is generated; according to the method, the thermal pilot frequency domain filtering function is constructed through the compensation power instruction, so that the uniformity and response speed of thermal response are improved.

A computer-implemented method includes loading a simplistic network-on-chip (NoC) topology that is fully routed, and performing reinforcement learning on the NoC topology to identify a sequence of topology transformations that will produce a more optimal NoC topology. Performing the reinforcement learning includes running a plurality of training sessions. Running each training session includes using a machine learning model to apply a set of transformations to the NoC topology according to a policy, computing a cost of the NoC topology after the set of transformations has been applied, and updating the policy in response to the cost.

The invention relates to the technical field of drainage pipe channel monitoring, and discloses an underground drainage pipe channel abnormity identification and positioning technology based on topology high-speed exploration. The method comprises the following steps: S1, carrying out topological structure construction on GIS data of an underground drainage pipe channel, and obtaining a topological connection relation of the drainage pipe channel; s2, based on a technology of combining a cache and a search engine, carrying out topological high-speed tracing on the drainage pipe channel, and outputting an upstream and downstream topological relation of a pipeline; and S3, automatically identifying abnormal conditions, including mixed connection, clogging, rainfall inflow, end breakage, reverse slope and pipe diameter matching problems, in the drainage pipe channel by using a topology high-speed exploration technology. By adopting the technical scheme based on the combination of topological modeling and high-speed exploration, the technical effect of quickly analyzing and dynamically updating the topological structure of the underground drainage pipe channel is achieved; the defects that a topology model is slow in updating and difficult to adapt to real-time changes of a pipe network are overcome.

The invention discloses a defect hidden danger data diagnosis method based on a distribution network SVG single line diagram. The method comprises the steps that basic analysis is conducted on the input SVG single line diagram, basic geometric figure elements are extracted and normalized, a topological connection relation is preprocessed, and a multi-level index structure is constructed; a dynamic topology path reasoning mechanism is constructed, and reasoning and self-adaptive optimization of the topological relation of the electrical equipment are realized through path consistency calculation, topological entropy minimization and a dynamic topology updating strategy; a self-adaptive implicit relational knowledge graph is constructed, and semantic association between the electrical equipment is established through explicit topological relation fusion, implicit function dependence reasoning and dynamic semantic embedding optimization; and constructing a multi-modal time sequence feature vector, calculating the hidden danger risk of the equipment by adopting a space-time defect probability estimation model, and giving an alarm through a dynamic risk early warning strategy to realize prediction and early warning of the hidden danger of the defects of the electrical equipment. According to the method, the problems of semantic understanding, topological reasoning, state inference and defect prediction of the SVG single line diagram are effectively solved.

The invention relates to the technical field of short message transmission optimization, and discloses an intelligent short message resending system and method based on receipt state recognition. A communication data acquisition module of the system obtains historical short message transmission records in a preset time period of a target communication area and generates a communication state data set; a feature extraction storage module performs receipt feature extraction on the data set to obtain a historical receipt feature set and establish a receipt state change matrix; the topology modeling module constructs a terminal state atlas according to the matrix, and calculates the topology aggregation degree of the historical short message failure events to determine an abnormal characteristic index set; a real-time analysis module extracts real-time receipt features of the current short message transmission data to generate real-time state vectors, and calculates a topology correlation degree with the abnormal feature index set in a terminal state graph to generate real-time risk factors; and the decision generation module generates a short message resending strategy instruction according to the real-time risk factor, the historical receipt feature set and the receipt state change matrix.

The invention relates to the technical field of virtual scene construction, and discloses a virtual scene construction method driven by live-action three-dimensional data. The method comprises the steps of obtaining multi-source three-dimensional data of a target area, extracting geometric feature parameters of a scene data unit, calculating topological feature indexes, generating a construction input set, loading a pre-configured three-dimensional modeling algorithm to determine dynamic evolution parameters, labeling the parameters to generate a three-dimensional scene map, and constructing a virtual scene after space consistency detection. And if the parameter conflict is detected, fusing the parameters according to the multi-source data priority. And after construction is completed, monitoring a spatial difference value between a scene data unit and an adjacent unit in real time, and correcting parameters to update the scene when the spatial difference value exceeds a threshold value. According to the method, multi-source data is fully utilized, the accuracy and efficiency of virtual scene construction are improved, the problems existing in a traditional method are effectively solved, and the method has wide application prospects in the fields of urban planning, cultural heritage protection and the like.

The invention relates to the technical field of air transportation network analysis and optimization, in particular to a method and a device for identifying important nodes of an airport network, which can solve the problems of limitation of importance evaluation, attack simulation, practical application and the like in the prior art to a certain extent. The method comprises the following steps: data acquisition and processing: acquiring real-time aviation network data from different data sources, and cleaning, formatting and storing the acquired data to provide preparation for subsequent analysis; network modeling: establishing an airport network model according to the collected data; topology potential calculation: calculating a topology potential value of each node by adopting an improved topology potential formula; key node identification: identifying key nodes in the network based on topology potential centrality; dynamic analysis and optimization are carried out, network data are updated in real time, the topology potential and centrality are periodically recalculated to adapt to the dynamic change of the aviation network, and the network structure and the operation strategy are optimized according to the dynamic change.

The invention discloses a dynamic calculation topology optimization method based on an Flink SQL, relates to the field of big data and real-time calculation, and aims to re-realize self-defined calculation topology arrangement and complete dynamic compression of different SQL calculation topologies by matching a native Flink SQL execution plan. The invention discloses an Flink SQL (Structured Query Language) engine-based dynamic combination, adjustment and removal method, and aims at constructing an innovative optimization method based on an Flink SQL engine, and realizing dynamic combination, adjustment and removal of multiple types of SQL calculation operators including Project, Filter, Aggregate, Window and the like in a unified calculation topology aiming at typical calculation modes including data extraction, window aggregation and the like which are covered by a streaming SQL calculation scene based on the same data source, so as to realize dynamic combination, adjustment and removal of multiple types of SQL calculation operators including Project, Filter, Aggregate, Window and the like. Sharing and multiplexing of computing resources are achieved through an intelligent resource scheduling mechanism, dynamic evolution of real-time SQL logic is effectively supported, and the computing efficiency and the resource utilization rate are remarkably improved.

The invention discloses a data analysis management method and system based on ocean network security, and relates to the technical field of ocean network security, and the technical key points comprise the following steps: collecting the flow and log data of multi-source ocean network equipment in real time, constructing a dynamic heterogeneous data stream, and extracting a space-time correlation feature tensor; behavior topology modeling is carried out on the space-time correlation feature tensor, a network behavior dynamic topological graph is generated, and topology stability measure is calculated; abnormal behavior detection is carried out based on topological stability measurement, an abnormal behavior cluster is identified through a multi-scale spectral clustering algorithm, and a high-risk threat area coordinate set is generated; the technical effects are that the dynamic topology modeling adapts to the characteristics of ocean network equipment movement, communication intermittence and the like, and the environment adaptability is improved; and manifold mapping and adaptive clustering are combined, so that the anomaly recognition accuracy is improved, the missing report rate is reduced, and accurate threat positioning is realized.

The invention discloses a power distribution network fault data preprocessing method and system based on multi-source information fusion, and relates to the technical field of power systems, and the method comprises the steps: constructing a dynamic topology manifold based on the physical connection relation of power distribution network equipment and multi-source data; manifold deformation caused by line switching or sudden load change is responded, and abnormal data are recognized in combination with the manifold curvature and the equipment connection path distance; encoding the abnormal data, the meteorological data and the equipment state data into quantum states, and generating entanglement feature vectors through quantum gate operation; performing chaos sequence modulation on the basic features to generate modulation features; performing quantum fusion transformation on the modulation feature and the entanglement feature vector to generate an enhanced feature; calculating topological space correlation, chaos behavior similarity and quantum correlation based on the enhanced features, and constructing a multi-dimensional fusion weight; and performing weighted fusion on the enhanced features according to the multi-dimensional fusion weight, and outputting a preprocessed fault feature vector.

The invention discloses a water resource fine management method and system integrated with water consumption behavior recognition, and relates to the technical field of water resource management, and the method comprises the steps: calling a water consumption event record, mining time-space water consumption characteristics, and constructing a dynamic water consumption portrait; reconstructing a fog computing topology according to the resource request condition, and establishing a resource field to determine a resource scene; deploying a user-defined scheduler according to the dynamic water consumption portrait, and executing a game to determine a water consumption strategy; and performing standardized reconstruction on a water consumption strategy, and executing scheduling and control management according to a water resource management system. The technical problems that a traditional water resource management mode does not make full use of a big data processing technology and is difficult to deal with complex water use scenes are solved, and the purposes that the big data processing technology is applied, various water use behaviors are accurately recognized, real-time dynamic resource scheduling is achieved, multi-region water use data are efficiently integrated and analyzed, the fine water resource management level is improved, and the water resource management efficiency is improved are achieved. And the technical effect of effectively dealing with complex water use scenes is achieved.

A global path planning method for a pure electric automatic driving commercial vehicle in a logistics park scene is characterized by comprising the following steps: step 1, acquiring a high-definition map of a target logistics park, and preprocessing the high-definition map into a topological map; 2, calculating the arc length, curvature and gradient between any two adjacent points on the topological map; 3, calculating energy consumption / recovery of the vehicle between two adjacent points on the topological map according to the vehicle longitudinal dynamic model; 4, calculating the running time of the vehicle between two adjacent points on the topological map; 5, constructing a comprehensive weight algorithm of energy consumption and efficiency between two adjacent points on the topological map; 6, calculating the weight of each path on the topological map according to a comprehensive weight algorithm; 7, constructing a cost function by adopting an A * algorithm; 8, constructing a heuristic function according to the characteristics of the topological map; and 9, searching an optimal global path of the vehicle by adopting an improved A * algorithm according to the position information of the starting point and the ending point of the vehicle on the topological map.

The invention relates to the field of computer vision, in particular to a bovine embryo quality evaluation method and system based on a time sequence microscopic image, and the method comprises the steps: collecting a microscopic image and a timestamp, carrying out the embryo segmentation and inter-frame registration of the image, and obtaining the frame quality weight, morphological characteristics, image block embedding and topological data; respectively calculating a topological distance and an optimal transmission distance, determining a change point generation event sequence in combination with a frame quality weight, and aligning and outputting a penalty term based on a time Petri network; inferring a stage sequence and a quality posteriori based on the event sequence and a penalty term, and constructing a multi-instance set based on an event time window to obtain a multi-instance learning score; and fusing quality posteriori and multi-instance learning score to obtain an evaluation result, calculating a conflict coefficient, and relocating an event time window when a threshold value is exceeded to update the evaluation result. According to the method, rating consistency, noise immunity and traceability are improved.

The invention discloses a karst reservoir geometric heterogeneity evaluation method based on a continuous coherence theory, and the method comprises the steps: obtaining a rock sample pore image through a tomography technology, converting the spatial distribution of a pore body into sphere cloud data, and constructing a Rips complex through the continuous coherence theory, the birth time and the death time of the topological features of the data set are captured, then the duration of the topological features is calculated and output to obtain a PD graph, and probability density function weighted average is applied to determination of the PD mass center; and then the complexity and heterogeneity of the reservoir pore structure are quantified and represented by calculating the mean value and variance of the duration, the higher the mean value and variance are, the higher the heterogeneity of the rock pore structure is, and the proposed heterogeneity quantitative index H verifies that the analysis index has reasonability. According to the method, the quantitative evaluation capability of the heterogeneity of the pore structure of the geological reservoir is improved, and assistance is provided for research and production in the field of karst reservoirs.

The invention discloses a host application sensitive service multi-scale monitoring and protection method, relates to the technical field of data analysis, and realizes real-time monitoring of user multi-host behaviors by collecting user behavior types on each host and constructing and continuously updating a dynamic user behavior graph. A topological disturbance value, a boundary fuzzy value and a jump entropy index are calculated by analyzing the structural disturbance degree, the edge diffusion degree and the node jump confusion degree of the dynamic behavior graph, and whether the user behavior is abnormal or not is comprehensively judged. And if an exception is found, the system immediately stops the user login permission and performs upgrade protection on sensitive information of the logged-in host. The method breaks through the information island problem of traditional single-scale monitoring, and can track user behaviors across hosts, perceive potential risk association in time and quickly capture early abnormal features of threats, thereby effectively coping with attacks with strong concealment and great destructiveness, and enhancing the security protection capability of sensitive services.

The embodiment of the invention discloses a coupling scheduling method and device for large model computing resources and a medium, relates to the technical field of computers, and is used for solving the problem that a computing power center is not fully utilized due to an existing scheduling mode. The method comprises the steps that all computing tasks are split based on an algorithm and needed data, and computing subtasks are obtained; establishing a global data flow topological graph based on the calculation topological relation among the calculation sub-tasks; according to the calculation speed corresponding to each calculation task, determining the data processing concurrency of each edge in the global data flow topological graph, so as to determine the total concurrency of each algorithm based on the data processing concurrency of each edge; and determining an optimal computing power center based on the total concurrency of each algorithm and a preset hierarchical constraint condition, and creating an algorithm instance of each algorithm based on the optimal computing power center so as to realize resource allocation of the algorithm instance.

The invention relates to a GAT-based industrial user voltage sag sensitivity degree evaluation method and equipment, and the method comprises the steps: obtaining the characteristics, types and equipment connection relations of sensitive equipment influenced by voltage sag, constructing a topological graph, inputting the topological graph into a trained graph attention neural network, outputting a voltage sag sensitivity evaluation result, and carrying out the evaluation of the sensitivity degree of the industrial user voltage sag. Wherein the graph attention neural network is constructed by embedding a GAL layer in a message passing graph neural network, the GAL layer calculates an attention coefficient between nodes in a topological graph by using an attention mechanism, weighted aggregation is performed by using an update function, iterative update of node features is completed, and a voltage sag sensitivity evaluation result is finally mapped and output. Compared with the prior art, the method has the advantages of improving the evaluation accuracy and the like.