99 results about "Degree distribution" patented technology

The invention discloses a bridge crack feature fine quantification method and system based on deep learning, and relates to the technical field of bridge detection, and the method comprises the steps: carrying out the preprocessing of bridge surface image data; inputting the preprocessed image data into a U-Net network fused with a multi-scale channel space attention module for semantic segmentation and outputting a crack segmentation mask graph; performing morphological refinement operation to obtain fracture skeleton line data, and calculating a trend angle and curvature distribution; measuring the crack width along the normal direction of the skeleton point to generate a width distribution thermodynamic diagram; accumulating skeleton point intervals to calculate the total length and identifying branch features; according to the method, the segmentation IoU reaches 85% or above, the width precision is superior to 0.05 mm, and the method has the crack development trend prediction capability.

The invention discloses a multi-dimensional distribution offset adaptive threshold detection method and system, and the method comprises the steps: obtaining real-time data, extracting a multi-dimensional statistical feature, and obtaining a feature vector; based on historical normal data, using an expectation maximization algorithm to train a Gaussian mixture model, and determining parameters to obtain a normal distribution model; inputting the feature vector into the model, and calculating a probability value of the feature vector belonging to normal distribution as a first offset judgment index; based on the real-time data distribution of a plurality of detection objects in the same group, the distribution difference of any two objects is calculated by using a Wasserstein distance, and the similarity between the objects is obtained; and constructing a similarity network and calculating connectivity as a second offset judgment index. Setting a fixed-length sliding window, dynamically updating two indexes in the window, and obtaining a first self-adaptive threshold value and a second self-adaptive threshold value; and when any index is lower than a corresponding threshold value, determining distribution offset and giving an alarm, and updating model parameters in real time by using an incremental expectation maximization algorithm. According to the invention, accurate detection and intelligent analysis of data distribution offset are realized.

The invention relates to the technical field of constructional engineering quality detection, and discloses a method for testing the apparent quality of mirror-surface bare concrete. The method comprises the following steps: acquiring a multiband multispectral image of the surface of a component; through image fusion and feature decoupling, an independent specular glossiness distribution diagram, a microscopic fluctuation topological diagram and a chromaticity coordinate mapping diagram are obtained; registering and associating the three images to construct a three-dimensional total element apparent characteristic field; performing gridding scanning on the characteristic field to extract glossiness uniformity, texture roughness and chromaticity stability indexes of each grid; inputting a pre-trained apparent quality analysis network to output a local quality evaluation value and a defect type label of each grid; and integrating and generating an overall quality index and generating a repair guidance map according to the defects. According to the method, multi-dimensional, full-field and intelligent fine detection of the apparent quality is realized, the gloss, the texture and the color can be independently and quantitatively analyzed, and the defect can be accurately positioned.

The invention relates to a supply chain demand prediction and risk early warning method and system based on multi-source data. The method comprises the steps of collecting multi-source heterogeneous data and constructing a feature vector set; constructing a supply chain network topology structure; inputting the feature vector set and the supply chain network topology structure into a risk propagation model to obtain a risk space-time distribution diagram; inputting the feature vector set and the risk space-time distribution diagram into a demand prediction model to obtain collaborative prediction result information; based on a supply chain network topology structure, generating a node criticality distribution map, comparing, identifying high-risk nodes, obtaining risk level information, and generating graded risk early warning information; according to the method, the feature vector set and the supply chain network topology structure are constructed by integrating the multi-source heterogeneous data, combined optimization of dynamic risk simulation and demand prediction is realized by combining cooperative calculation of the risk propagation model and the demand prediction model, and the method has the advantages of improving prediction accuracy, dynamically identifying high-risk nodes and generating hierarchical early warning information.

This invention discloses a method for constructing a natural-artificial water network model and analyzing its connectivity, including: collecting basic data; generalizing nodes and edges of the natural and artificial water networks and adding weights to the edges; constructing a natural-artificial water network model based on complex networks; constructing the degree distribution of structure and flow based on node degrees; and calculating the structural connectivity and flow connectivity of the natural-artificial water network based on the degree distribution. This invention combines complex network theory to model natural-artificial water networks and analyze their connectivity, solving the technical problem of lacking comprehensive modeling and connectivity assessment in existing water network analysis methods, and providing new analytical ideas and technical support for water resource management and water network structure optimization.

The present disclosure belongs to the technical field of network security, and particularly relates to a network anomaly monitoring method, device and program product based on traffic fingerprint learning, which comprises the following steps: extracting metadata irrelevant to encryption of a target network; the metadata comprises five-tuple, timestamp, length and protocol flag information of a data packet; the metadata is aggregated with a source IP address as a primary key to define a network entity; session feature extraction is performed on a session of the target network entity and other network entities; the extracted session features comprise timing features, operation sequence features and scale distribution features; the operation sequence features comprise Shannon entropy of a continuous request and response type pair; an action fingerprint of the target network entity is constructed according to the session features; and whether the target network and the target network entity are abnormal is judged according to a deviation degree of a current action fingerprint of the target network entity and a fingerprint baseline. The present disclosure can realize accurate identification of network anomalies.

The invention relates to the technical field of modern communication, and discloses a wireless network card power adaptive control method and system. The method comprises the following steps: acquiring real-time interference data, performing spectrum change according to the real-time interference data to obtain an interference intensity distribution diagram, performing interference correlation analysis and power optimization calculation according to the interference intensity distribution diagram to obtain a power configuration scheme, performing parameter updating according to the power configuration scheme to obtain a preliminary power correction result, and performing power optimization calculation according to the preliminary power correction result. The method comprises the steps of obtaining a preliminary power correction result, carrying out interference comparison evaluation according to the preliminary power correction result to obtain deviation information, carrying out power optimization according to the deviation information to obtain an optimized power configuration scheme, and carrying out synchronous configuration and index feedback analysis according to the optimized power configuration scheme to obtain a final regulation command set. According to the method, the problem that interference chain reaction is easily caused by equipment power adjustment can be solved, so that stable communication and interference suppression in a complex network environment are realized.

The present application relates to the field of engineering detection technology, in particular to a deep compaction degree nondestructive testing method and system for large-thickness water-stable base course. A multi-source heterogeneous dataset of a to-be-detected area is obtained using an integrated detection device; a construction process compliance index and a process uniformity index of the to-be-detected area are obtained; each data source in the multi-modal nondestructive testing data is deeply corrected to obtain a deep correction compaction degree estimation set of each data source; the deep correction compaction degree estimation set of each data source is input into an adaptive weight fusion model to obtain a preliminary compaction degree distribution map; a fusion compaction degree distribution map of the to-be-detected area and a dynamic confidence score of each spatial point are calculated; the compaction quality of the to-be-detected area is determined, and a detection strategy optimization instruction is generated for the area with a confidence score lower than a confidence threshold. The present application can realize high-precision, high-confidence and engineering-actual nondestructive testing of deep compaction degree of large-thickness water-stable base course.

The invention relates to the technical field of physical field prediction, in particular to a physical field prediction method and device based on gradient recognition parameter tuning and a medium, and the method comprises the steps: determining a space-time computational domain based on a physical problem, and determining a control equation, an initial condition constraint and a boundary condition constraint of the physical problem; constructing a training sample data set; initializing parameters of the neural network, and pre-training the neural network; obtaining pre-trained neural network parameters, and identifying high-contribution neural blocks; on the basis of the high-contribution-degree nerve blocks, parameter fine tuning iteration is carried out on the weights of the combination points of the neural network, and a physical field prediction model is obtained; and inputting the space-time coordinates of a to-be-solved point into the physical field prediction model to obtain a physical field prediction value of the point. The adaptive algorithm framework and the staged training strategy provided by the method enable the model to adapt to different partial differential equations and gradient distribution characteristics, thereby reducing the use threshold of the physical information neural network.

The application belongs to the technical field of target identification, and provides a dynamic target identification method and system based on a low-illumination environment. An illumination component and a reflection component are separated frame by frame through a Retinex decomposition network, and an illumination distribution map is generated based on the illumination component. The convolution kernel weight of an edge branch is modulated based on the illumination distribution map, and an edge feature map and a semantic feature map are extracted. A candidate region and a corresponding uncertainty distribution are generated based on the illumination component and the reflection component to determine a fusion weight and complete weighted fusion, thereby improving the integrity and recognition degree of the fusion feature map. Target detection and confidence determination of the candidate region are performed based on the fusion feature map, local secondary enhancement is performed on a low-confidence region, and a target region is output after inter-frame residual fusion based on the fusion feature map of adjacent frames, thereby improving the stability and environmental adaptation capability of cross-frame dynamic target identification.

This invention provides a refined design method for explosive charge quantity based on rock mineral particle structure, relating to the field of geotechnical engineering blasting technology. This invention maps the mineral composition and particle size data of rock samples to mineral hardness distribution characteristics and particle structure characteristics, respectively, and generates a high-dimensional feature vector through nonlinear encoding fusion. This vector is input into a graph neural network, and a rock structure feature relationship map is formed through node representation and attention mechanisms. Using this map as a condition, under the constraint of baseline explosive consumption, a deep generative network learns and outputs a differentiated charge distribution matrix corresponding to spatial location. After alignment and transformation with blasting hole mesh parameters, a charge allocation design map is generated. Finally, after blasting based on this design map, actual effect indicators are collected to construct a multi-objective optimization function. An optimization algorithm is used to adjust the parameters of the graph attention mechanism and the generative network in reverse according to the effect differences, achieving iterative optimization of the model.

The application discloses a natural gas load interval estimation method based on large model coupling working condition clustering, and belongs to the technical field of natural gas pipeline network operation optimization and artificial intelligence load prediction. The method extracts working condition semantic constraints by using a large model, and obtains fuzzy working condition clusters by combining historical operation data clustering; a natural gas load point prediction model is trained for each cluster, and residual probability density distribution is estimated; semantic and numerical weights are fused in real time, and the final point prediction value and the natural gas load prediction interval at the prediction time are obtained by dynamic weighting, which are taken as the natural gas load prediction result and output. The application introduces a large model into the natural gas working condition expression and clustering constraint construction process, and no longer uses the large model as a simple downstream feature generation tool, but solves the problem of interval estimation failure of the natural gas load under fuzzy working conditions such as holiday switching, peak-valley transition and extreme weather through deep coupling of the large model and working condition clustering, so that high-reliability dynamic prediction interval output under complex working conditions is realized.

This application relates to the field of smart property management and discloses a resource scheduling method for smart property management based on deep learning. The method includes: constructing a spatial topology influence map of the target property management area; spatially aligning the multi-source heterogeneous data streams according to the nodes of the spatial topology influence map and generating a sequence of node feature vectors corresponding to each node; inputting the node feature vector sequence into a spatiotemporal influence propagation prediction model; performing spatial feature aggregation on the node feature vector sequence based on the graph attention layer and the static topology constraint parameters in the spatiotemporal influence propagation prediction model to generate a spatiotemporal influence representation characterizing the degree of impact of sudden events on each functional area; predicting the resource demand heat value of each functional area within a preset time period based on the spatiotemporal influence representation to form a resource demand heat distribution; and generating a resource scheduling instruction set based on the resource demand heat distribution. This technical solution improves the user's property service experience.

The application relates to the technical field of intelligent auditing, and discloses a digital import and export intelligent auditing method based on a CV-NLP fusion algorithm, which comprises the following steps: performing image coordinate standardization processing on an image sequence of an import and export document package, generating a set of mark candidate blocks containing continuous and reliable weights by using semantic probability and prior attributes of a layout, calculating a layout level complexity parameter representing the fragmentation degree of the hierarchical structure in the document package according to the normalized area scale distribution of the block set, generating a multi-level resolution sampling weight distribution based on the parameter, and performing multi-scale identification and probability fusion on the candidate blocks by using the weight determined by the distribution, calculating a character structure fragmentation measurement value representing the structural fragmentation bias and a region positioning volatility measurement value representing the dispersion degree of the cross-page layout landing point in combination with the layout geometric attributes, and finally constructing a comprehensive risk assessment model to output an intelligent auditing result quantitatively representing the document level identity observability risk.

The application belongs to the technical field of communication system, and particularly relates to a method and system for deriving optimal degree distribution of regular LDPC code based on AWGN channel. The method comprises the following steps: S1, taking the maximization of code rate of the regular LDPC code as a criterion and taking the decoding success as a prerequisite, a target function with a constraint condition is constructed; S2, fixing the degree d v of the variable node, based on the fixed point analysis theory, a theoretical analytic expression of the degree d c of the check node in the LDPC code is derived, and an inverse function of the target function in the non-convex optimization problem is solved; S3, based on the mathematical set theory, the uncertainty in the inverse function solving problem in the step S2 is solved, and the optimal degree distribution of the LDPC code under the AWGN channel is obtained. The application has the characteristics of improving the channel coding performance in the communication system.

The invention provides a business scene recommendation method which can be applied to the technical field of artificial intelligence. The business scene recommendation method comprises the steps of obtaining a demand description text of a target business; matching degrees between the demand description text and m preset hot word banks are obtained, first matching degree distribution is obtained, the m preset hot word banks are set based on keywords of m service scenes, the m service scenes comprise n preset service scenes, m is an integer larger than 1, and n is an integer larger than or equal to 1; in response to the target service as the preset service scene, identifying the demand description text by using the pre-training language model to obtain second matching degree distribution, the second matching degree distribution being matching degree distribution of the target service and n preset service scenes; and obtaining a recommendation result based on the first matching degree distribution and the second matching degree distribution. The invention further provides a service scene recommendation device and equipment, a storage medium and a program product.

The invention discloses an SOME / IP service flow-oriented spatio-temporal behavior perception and dynamic shaping method, which comprises the steps of collecting SOME / IP message mirror images on a bypass of a vehicle-mounted network controller main control platform, analyzing and extracting spatio-temporal multi-dimensional features, constructing a service granularity flow sample set based on a sliding window, completing kernel density estimation through self-adaptive gridding discretization and frequency domain accelerated convolution, and obtaining a service granularity flow sample set. Service flow probability density distribution is deeply represented, and time-space communication behaviors of the service flow are quantified through comprehensive information entropy; on-line comparison is decided based on a self-adaptive double-entropy threshold value, and a hierarchical shaping strategy is accurately triggered; and driving the release end ECU to execute in real time through a standard interface, constructing closed-loop recursive feedback by fusing a bottom layer physical state to carry out dynamic correction, and setting a state deviation degree-based severe boundary constraint and deterministic failure rollback mechanism. According to the method, SOME / IP service-level precise management and control, low-delay shaping and vehicle-gauge-level safety guarantee are realized, a bottom-layer protocol stack does not need to be modified, and a data-driven dynamic traffic management normal form is constructed for a vehicle-mounted network.

The application relates to the technical field of mold design, and discloses a mold conformal runner intelligent optimization design method based on deep learning, which comprises the following steps: acquiring a mold three-dimensional model, extracting a glue position plane, a hollowed-out plane and thickness distribution data, obtaining processed point cloud data, inputting the processed point cloud data into a three-dimensional point cloud convolutional neural network to output a feature semantic graph, inputting the feature semantic graph and cooling parameters into a GNN model, performing mechanical constraint through Kangaroo2 in the GNN model to output a runner guide curve coordinate sequence, obtaining a runner layout based on the runner guide curve coordinate sequence, and analyzing temperature distribution and stress concentration areas in a simulation cooling process of the generated runner layout; and if the simulation result is substandard, dynamically adjusting runner parameters based on a PPO algorithm to output an optimized runner layout. The application significantly reduces the cost of manual intervention, comprehensively improves cooling performance, improves product qualification rate, and has self-adaptive capacity in complex scenes.

The invention provides a joint degree distribution design and optimization method for fountain codes. The method comprises the steps of obtaining to-be-transmitted test data in a current scene; using to-be-transmitted test data to form an optimized joint degree distribution model by taking the minimum average decoding overhead as an optimization target under the joint degree distribution model; the joint degree distribution model is obtained by joint construction of probability distribution of modified Poisson distribution, probability distribution of ideal soliton distribution and probability distribution of sliding robust soliton distribution; based on the optimized joint degree distribution model, performing global optimal solution search processing on a to-be-optimized weight coefficient in the optimized joint degree distribution model by adopting a Monte Carlo simulation method and a double-layer nested search method to obtain a global optimal weight; and substituting the global optimal weight into the joint degree distribution model to obtain final fountain code joint degree distribution in the current scene. Therefore, the rationality of the degree distribution structure, the consistency of the optimization target and the system performance and the improvement of the global optimality of the design method are realized.

The invention discloses a self-adaptive multi-modal entity disambiguation method and system for enterprise big data, and relates to the technical field of enterprise big data analysis. The method comprises the following steps: constructing an influence graph by extracting stock right relation data and guarantee relation data, and screening core nodes by adopting a PageRank algorithm; adaptively adjusting the influence propagation weight based on the relation strength to obtain node score distribution; recognizing a fracture relationship in combination with historical name change, and repairing the atlas through multi-modal feature similarity; conflict intensity distribution is generated by comparing node score changes before and after restoration, and time sequence optimization is carried out on attenuation parameters in combination with enterprise business transformation events; and finally, enterprise entity identity determination is completed based on the optimized conflict intensity distribution. The entity disambiguation accuracy and stability in a complex enterprise network can be improved.

The application provides a hypergraph adaptive sampling method for structure feature reservation, and belongs to the technical field of computer models. The hypergraph adaptive sampling method for structure feature reservation comprises initializing a hypergraph and sampling parameters, the hypergraph comprising a node set and a hyperedge set, setting a sampling ratio, a batch size and a weight adjustment parameter, and calculating a global attribute of the hypergraph; calculating an initial sampling weight of each hyperedge in the hyperedge set based on a hyperedge size, a node degree distribution and a combination ratio; randomly selecting a batch of hyperedges from the hyperedge set according to the sampling weight, adding the batch of hyperedges to a sampling hypergraph, and adding nodes corresponding to the batch of hyperedges to the node set of the sampling hypergraph; calculating a difference value of the sampling hypergraph and the hypergraph in the global attribute, and adjusting the sampling weight according to the difference value. The application can solve the problem that the existing technology cannot realize efficient sampling while maintaining the structure features of the hypergraph.

The invention discloses a graph convolutional network confrontation defense method based on adaptive frequency spectrum filtering. Firstly, spectral analysis is performed on a graph structure, and spectral distribution of graph data is obtained and evaluated; then, an adaptive spectrum filter is constructed based on the distribution, and the filter can automatically adjust parameters according to the degree distribution and the maximum characteristic value of the graph, so that the most robust spectrum interval under the attack resistance is adaptively determined; on the basis, invalid or harmful frequency spectrum components introduced by disturbance are weakened by a filter and are embedded into the propagation process of the graph convolutional network, and finally, the resistance of the model to confrontation disturbance is improved at the frequency domain level, and a stable prediction result is output. According to the method, negative effects caused by disturbance can be inhibited without adding additional filtering plug-ins, so that the node classification precision and the stability of the model in an adversarial environment are improved.

The invention discloses an equipment operation and maintenance intelligent question answering method and system based on knowledge graph context fusion, and the method comprises the steps: firstly constructing a knowledge text data set in the field of industrial equipment operation and maintenance, obtaining an industrial equipment operation and maintenance knowledge graph, carrying out the community division of the knowledge graph, and obtaining a knowledge community; secondly, respectively performing vectorization representation on questions and knowledge communities input by the user; and then, calculating a diversity association relationship between the problem vector and the community vector, generating similarity reliability distribution reflecting different association degrees, and carrying out adaptive adjustment through a community reliability discount strategy. And finally, fusing the similarity reliability distribution subjected to adaptive adjustment, constructing a knowledge graph context, inputting the knowledge graph context into an industrial equipment operation and maintenance big language model, and generating an answer to an industrial equipment operation and maintenance problem. According to the invention, the reliability of the context of the constructed knowledge graph is improved, and an efficient and credible decision-making intelligent question-answering method for equipment operation and maintenance is provided for industrial equipment operation and maintenance.

The invention discloses a drainage basin water engineering intelligent joint scheduling method and system based on a knowledge graph, particularly relates to the technical field of intelligent decision support, and is used for solving the problem of cross-scale decision mismatching caused by single representation of the knowledge graph in an existing drainage basin scheduling method. The method comprises the following steps: constructing a multi-scale watershed knowledge graph fusing a macroscopic entity and a microscopic entity, calculating topological potential field gradient distribution of a macroscopic scheduling instruction to identify a mismatch risk, performing analogy analysis in a historical case library based on a key energy efficiency loss node to obtain an optimization strategy, dynamically adjusting a scheduling rule in the knowledge graph, and obtaining an optimal scheduling result. And finally, a multi-scale collaborative joint scheduling scheme is generated to realize accurate collaboration and dynamic optimization of cross-scale scheduling.

The application relates to the technical field of artificial intelligence, and provides a prospecting prediction method and system based on dynamic deduction and a computer, the prospecting prediction method based on dynamic deduction comprises the following steps: constructing an atomic fact layer, a logic rule layer and a dynamic context layer to form a geological dynamic hierarchical knowledge graph; obtaining a confidence degree distribution of a prospecting prediction set, obtaining an updated query text based on the confidence degree distribution; introducing D-S evidence theory to the geological dynamic hierarchical knowledge graph to perform energy diffusion, and evolving into a global activated knowledge graph; obtaining a vector database, constructing a dependency triple, searching in the vector database according to the dependency triple to obtain a plurality of reasoning knowledge segments; obtaining a comprehensive complexity score based on the plurality of reasoning knowledge segments and the updated query text, selecting a reasoning mode according to the comprehensive complexity score to generate a reasoning prediction result. Through the above method, professional knowledge illusion can be avoided, and the model robustness and the prediction result accuracy are improved.

The invention discloses a top coal crushing degree prediction method based on image recognition, and belongs to the field of image recognition, and the method comprises the steps: collecting a top coal image in real time, analyzing the definition and edge sharpness, automatically adjusting the focus of a camera to obtain a high-quality image, extracting the shape and size distribution characteristics of a coal briquette through an edge detection algorithm, and determining a crushing degree parameter. And if the parameters are matched with the preset threshold value, predicting crushing degree distribution through a neural network in combination with the geological strength and the mining depth, analyzing a change trend and judging whether mining process parameters need to be adjusted or not. And the optimized parameters process historical data through a feedback learning mechanism, a self-learning adjustment value is determined, and the neural network model is updated, so that more accurate crushing degree prediction and mining process closed-loop control are realized. According to the method, through fusion of image processing, feature extraction and neural network prediction, the top coal crushing degree identification precision and the mining efficiency are remarkably improved, and the dynamic adjustment capability of process parameters is optimized.

The application belongs to the field of house data processing, and is a mass house risk hidden danger visualization evaluation method and system, which comprises the following steps: performing building comprehensive risk evaluation on a single house to calculate a risk hidden danger coefficient of the single house; performing spatial aggregation processing of different degrees on mass houses from multiple administrative region levels to obtain a risk hidden danger distribution situation of the aggregated point houses after aggregation; converting discrete vector points into continuous grid network data for display through kernel density analysis; and performing result analysis and visual rendering display according to the heat space distribution characteristic grid network data. The application calculates heat distribution based on the density weight of the risk hidden danger index, enhances the display effect of the geographical space distribution characteristics of the house risk hidden danger index, and solves the technical problems of lag, full memory occupation and the like during the rendering of the billion-level building surface and attribute data.

Input data is received for storage by a system. The input data is encoded using a low-density parity-check (LDPC) matrix to generate encoded data, wherein the LDPC matrix is selected from a plurality of LDPC matrices, each of the plurality of LDPC matrices having a common size and a unique degree distribution. The encoded data is then stored on a memory device of the system.

The invention discloses a bridge scouring form generation method based on a generative adversarial network, and relates to the technical field of image processing, and the method comprises the following steps: S01, obtaining multiple groups of bridge scouring pit sample data; s02, constructing a generative adversarial network model, wherein the model at least comprises a generator and a discriminator; s03, performing adversarial training on the generative adversarial network model by using the training data set to obtain an optimized adversarial network model; s04, inputting a to-be-predicted bridge pier mask image and corresponding environmental parameters into the optimized adversarial network model to obtain a predicted two-dimensional depth distribution thermodynamic diagram of the scouring pit around the bridge pier; and S05, performing image processing on the two-dimensional depth distribution thermodynamic diagram output in the step S04, and extracting and generating an isobath diagram of the scour pit. According to the method, bridge and water flow information is converted into a two-dimensional scour pit form graph containing depth distribution and range and an isobath through a generative adversarial network;

The application discloses a low-latency slice resource intelligent allocation method for a 5G network, which comprises the following steps: collecting the connection state and performance index of a network node in real time, dynamically adjusting the collection frequency according to network load fluctuation, and generating structured topology data; calculating the node degree distribution entropy value based on the topology data, generating a virtual resource topology graph, and predicting the resource demand of the network node; constructing a dynamic confidence interval by using the virtual resource topology graph and the entropy value change rate, designing a resource pre-allocation strategy; detecting network burst traffic, quickly adjusting resource allocation through incremental update of a graph neural network, and ensuring that the reconfiguration latency is lower than 1 millisecond; through topology perception driven by the graph neural network, prediction guided by the entropy value and the adaptive allocation strategy, the low-latency resource allocation of the uRLLC slice is realized, resource waste is avoided, and the strict performance requirements of the 5G network are met.