990 results about "Complex network" patented technology

The invention relates to a risk management and control method and system based on real-time behavior analysis, and the method comprises the steps: carrying out the structural processing of multi-source behavior data through lightweight protocol decoding and behavior label embedding, and constructing an original behavior data set of a user and an entity; extracting multi-dimensional behavior characteristics by using a sliding window analysis and sparse representation mechanism, and constructing a user behavior graph by combining graph embedding learning; constructing a time-sensitive behavior trend model through streaming modeling and an incremental learning strategy, identifying an abnormal evolution trajectory in real time, and introducing a dynamic risk threshold regulation and control mechanism; adopting a high-throughput flow data processing and fast similarity matching algorithm to construct a fusion discrimination model, giving risk levels to abnormal behaviors and classifying the abnormal behaviors; and finally, performing closed-loop optimization in combination with a historical treatment effect. The system has the advantages of high real-time performance, high calculation efficiency, adaptability to complex network environments and the like, and the network security protection capability can be effectively improved.

The invention relates to the technical field of computer network security, and discloses a computer network security access control management method based on big data. The method comprises the following steps: constructing a network security situation knowledge graph, collecting a real-time access behavior sequence through a probe, and synchronizing the real-time access behavior sequence to the knowledge graph; simulating a network entity interaction state in the knowledge graph, and predicting a threat propagation path and a potential intrusion behavior; setting a dynamic access control strategy, constructing a multi-dimensional feature matrix in combination with a real-time access behavior sequence association influence degree and a strategy execution priority constraint condition, calculating a strategy conflict risk score by using a deep learning model, comparing with a preset threshold to judge whether a conflict exists or not, and if yes, reconstructing the strategy; and automatically executing access blocking, session termination and data encryption operations according to the reconstructed strategy, recording an execution log and security feedback data, and updating the knowledge graph in real time. According to the method, the dynamic property and the security of access control are improved, and security threats in a complex network environment can be effectively handled.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention discloses a non-direct connection-oriented data routing method, which comprises the following steps of: S1, acquiring topological structures, link bandwidths, time delays, loads and energy consumption states of nodes in a network in real time, and constructing a time sequence dynamic matrix of the nodes and links; s2, on the basis of the dynamic matrix, adopting a neural network adaptive enhanced ant colony optimization method to generate alternative paths; s3, generating a grey wolf optimization algorithm initial population by using the alternative paths, and constructing a multi-dimensional composite fitness function; s4, according to the fitness function, driving the grey wolf optimization algorithm to perform multi-scale iteration to update the path; s5, topology and node state prediction is carried out based on the dominant path, and the prediction path is optimized in advance; and S6, issuing the optimal path and the alternative path at the same time, carrying out data parallel forwarding, and driving a neural enhanced ant colony optimization algorithm to update online. The method improves the network path selection efficiency and the resource utilization rate, and is suitable for data routing in a complex network environment.

The invention discloses an AI-based network security event tracing method, system and device and a medium, and the method specifically comprises the steps: constructing a network entity association graph based on a multi-modal data set, mining the implicit association between entities through a graph convolutional network, recognizing an APT attack chain, and obtaining graph feature data; based on the multi-modal data set, an LSTM-Transform hybrid model is adopted to analyze time sequence characteristics of network traffic, slow penetration and low-frequency detection behaviors are detected, and time sequence characteristic data are obtained; based on the graph feature data and the time sequence feature data, high-value features are screened through a genetic algorithm, and cross-modal combination features are generated by using a depth auto-encoder; based on cross-modal combination features, a network environment digital twin is constructed, an attack diffusion path is simulated, and a service influence range is quantified. According to the method, accurate tracing of the network security event is realized, and the detection and tracking capabilities of complex network attacks and the intelligent level of a response strategy are comprehensively improved.

The invention discloses a multidirectional frame audio stream transmission method, device, equipment and medium, and the method is realized through cooperation of a transmitting end and a receiving end: the transmitting end cuts an original audio stream into independent audio frames, gives priority identifiers to the independent audio frames, and determines redundant coding parameters and transmission paths for different priority frames in combination with a predefined static strategy; generating a data packet containing an original data block and a redundant data block, and sending the data packet through at least one network path; and a receiving end caches the multi-path data packet, recovers lost data by using redundant data blocks to recombine a complete audio frame, and executes error concealment processing on the frame which cannot be recombined to generate a replacement frame. According to the method, based on a multi-path parallel transmission, forward error correction (FEC) redundancy mechanism and a cost-aware static scheduling strategy, lossless forwarding and instantaneous recovery of audio streams are realized on the premise of not waiting for network feedback and avoiding inter-frame dependence, and high-quality real-time audio transmission service can still be provided in a complex network environment.

The invention discloses a method based on a large language model and ATTamp; the invention discloses a CK framework penetration test automation method and device, and the method comprises the steps: firstly carrying out the structural analysis of multi-source input information and tool output, and guaranteeing that key fields are not discarded; then combining a retrieval enhancement generation technology and a network security knowledge base to provide domain knowledge support for the large language model, so as to generate a model with ATTamp; a penetration test task tree marked by CK tactics, technologies and sub-technologies; on the basis, an optimal tool is automatically selected through a tool resource library and a multi-dimensional screening mechanism, an execution instruction is generated, and finally an execution result is returned to the input analysis module to form a self-adaptive optimization test closed loop. According to the method, semantic fidelity compression and standardization processing of long information can be realized aiming at the problems of large output format difference, more information redundancy and the like of different penetration testing tools, and efficient, explainable and auditory technical support can be provided for automatic penetration testing in a complex network environment.

The invention belongs to the technical field of cloud computing and edge computing collaboration, and particularly discloses an intelligent scheduling and optimizing method for a cloud-edge collaboration network based on reinforcement learning. By constructing the state sensing matrix and generating the action decision vector, the problem that a traditional scheduling method is insufficient in correlation analysis of multi-dimensional operation state data in a complex network environment is solved, and the comprehensive sensing capability of the operation state of the network node is improved; a dynamic mapping mechanism among the running state, the resource limitation and the task allocation strategy is established, the task allocation and resource scheduling strategy is automatically and differentially adjusted according to the real-time state of the node, and the optimal matching between the task demand and the resource supply and the dynamic balance between the performance and the efficiency are realized; through performance index monitoring and closed-loop feedback optimization, the scheduling effect is mastered in real time, continuous iterative optimization is performed on the reinforcement learning model according to objective data, and resource waste and scheduling delay are reduced.

The invention relates to the technical field of network security, and particularly discloses an edge device network threat detection method and system based on an electric power large model, and the method comprises the steps: capturing a network message sequence in real time, extracting a time sequence randomness feature and a semantic deviation feature from a time dimension and a protocol dimension, and carrying out the fusion to form a comprehensive threat feature vector; performing multi-dimensional feature analysis and time sequence modeling by adopting a lightweight electric power large model to realize millisecond-level threat assessment; establishing a multi-level response mechanism, dynamically triggering a differential protection strategy according to the threat level, and ensuring the reliability and consistency of response actions through digital signature and collaborative verification; according to the method, the complex network attack in the power edge equipment can be effectively identified, the threat detection accuracy and the system defense capability are improved, and the strict requirements of a power system on real-time performance and reliability are met.

The invention provides a knowledge graph-based IT operation and maintenance fault tracing method and device, and the method solves the problem of low fault positioning efficiency in a high-complexity heterogeneous network through fusing a topological path and a knowledge graph of a fault propagation mode, and specifically comprises the steps: firstly, carrying out the fusion of the knowledge graph, and carrying out the positioning of a fault through the fusion of the knowledge graph; the incidence relation between the physical connection and the fault logic is represented, so that the reduction accuracy of a cross-equipment fault propagation path is improved; secondly, manual troubleshooting participation degree is reduced through semantic similarity analysis, and priority ranking of fault roots is achieved in a complex network environment; and finally, the reliability of fault positioning is guaranteed through consistency verification, and misjudgment caused by simple rule matching is avoided.

The invention discloses a power distribution network protection resource dynamic allocation method, system and device and a storage medium, and relates to the field of power system network security protection, and the method comprises the steps: collecting the operation data of a power distribution network in real time, constructing a multi-dimensional fusion data set, and predicting a potential attack path through an attack path prediction model in combination with a historical attack mode library; performing risk assessment on the predicted potential attack path, and calculating the protection resource demand quantity of each region of the power distribution network in combination with the topological structure of the power distribution network and the importance of key nodes; according to a risk assessment result, dynamic allocation and real-time scheduling of protection resources are realized in combination with a resource constraint condition; the method can grasp the operation of the power distribution network in real time, accurately predict the potential attack path, accurately evaluate the risk and calculate the protection resource demand. Dynamic allocation and real-time scheduling of protection resources are realized, the resources are reasonably utilized, the cost is reduced, the protection effect is improved, and safe and stable operation of the power distribution network under complex network attacks is ensured.

The invention discloses a complex scene self-adaption-based multi-ship-lock collaborative ship navigation scheduling method, which comprises the following steps of: acquiring static and dynamic data of a multi-ship-lock system, constructing a basic complex network model, and performing calculation by utilizing the basic complex network model to obtain a basic data calculation result; obtaining new real-time data based on the basic data calculation result, and preprocessing the new real-time data to obtain processed dynamic feature data; optimizing the basic complex network model by using the processed dynamic feature data, and constructing a cascade effect prediction model; and performing congestion propagation prediction based on the cascade effect prediction model to obtain a prediction result, and executing cooperative scheduling to realize intelligent monitoring and cascade effect prevention and control of the multi-ship-lock system. According to the method, the association relationship between the ship lock operation synchronism and congestion diffusion is quantified, accurate prediction of the risk propagation path is realized, the congestion cross-ship lock propagation probability is effectively reduced, and the time required for average fault recovery is shortened.

The invention discloses a traffic scheduling method and an electronic device, and relates to the technical field of traffic scheduling, and the method comprises the steps: determining the priority weight of a micro-service, and predicting a target traffic according to the historical traffic information of a network device; constructing a graph model according to the topological information of the network equipment and the dependency relationship of the micro-service, and performing embedded learning on nodes in the graph model to generate a state vector representing a network state; the priority weight, the state vector and the target traffic of the micro-service serve as input of a reinforcement learning model, and a traffic scheduling strategy of the network equipment is obtained; performing iterative search according to iterative particles formed by encoding the strategy network parameters of the reinforcement learning model and the feature learning network parameters of the graph model to determine reinforcement learning model parameters; and issuing the traffic scheduling strategy to the network equipment and executing the traffic scheduling strategy so as to solve the technical problem that a traffic scheduling method in related technologies is difficult to adapt to a dynamic and complex network environment and service requirements under a micro-service architecture, and the reliability of traffic scheduling is improved.

The invention discloses a flow control method based on a neural network, and relates to the technical field of network flow control, and the method comprises the steps: collecting network flow metadata, constructing a standardized feature data set, carrying out the flow data classification and prediction based on a hybrid neural network, generating a differential flow control strategy, and carrying out the strategy optimization and dynamic adjustment; statistical features are extracted by using flow metadata acquired in multiple environments, spatial-temporal feature fusion and attention weighting are carried out in combination with a convolutional neural network and a long-short-term memory network, and network state recognition and flow trend analysis are realized; based on an analysis result, generating a control strategy adaptive to different network environments, and performing strategy optimization and real-time adjustment through reinforcement learning; the problems of accurate flow control and dynamic strategy adjustment in a complex network environment are effectively solved, and the accuracy, the adaptivity and the system stability of flow control are improved.

The invention relates to the field of multimedia communication and network control, in particular to an intelligent network control method for low-delay video return and related equipment. The intelligent network control method comprises the following steps: acquiring network key indexes including bandwidth, delay, jitter and packet loss rate of a network link in real time, and providing real-time network environment data support for transmission strategy adjustment. According to the method, an intelligent control mechanism combining network state perception and video content feature recognition is constructed, so that the technical problem of low-delay video return in a complex network environment is effectively solved. Specifically, key indexes of a network link are collected in real time, and a lightweight CNN model is introduced to analyze the video content activeness, so that dual perception capabilities for a network environment and content features are formed, data support is provided for dynamic adjustment of coding parameters, and accurate balance between video quality and network adaptability is realized.

The invention discloses a data encryption method for multilevel key stream control in an industrial gateway, and particularly relates to the technical field of data encryption and network security. The method comprises the steps of obtaining a session identification code and communication context information; generating a first-level basic key based on the session identification code, and generating a multi-level key stream control matrix in combination with communication context information and a multi-source dynamic random factor; according to the hierarchical relationship of the matrix, key stream sequences of corresponding levels are distributed to data packets of different priorities, and the key stream sequences are switched or combined in real time according to data packet types, transmission paths and security policies in the encryption process, and time fragmentation processing and disturbance coding are executed; the encrypted data packet is sent through a multi-protocol forwarding module of the industrial gateway, and the receiving end carries out decryption by using the key stream control matrix; according to the invention, dynamic generation, hierarchical calling and safe switching of the key stream can be realized, and the data confidentiality, integrity and anti-attack capability of the industrial gateway in a complex network environment are remarkably improved.

The invention provides a multi-receptive-field network abnormal flow detection method and system based on endogenous security attributes, and relates to the technical field of network security and abnormal flow intelligent detection. The method comprises the following steps: firstly, performing multi-scale flow representation, preprocessing and data enhancement on network flow data to obtain enhanced input flow data; local features are extracted through basic convolution, and local and global fusion features are obtained based on a double-branch network comprising a multi-receptive field convolution branch and a Mama-self-attention branch; deep fusion representation is formed through multi-round feature extraction and tensor fusion, and finally binary classification and fine-grained classification results are output through global pooling and a linear classification layer. According to the method, high-precision, high-robustness and high-real-time detection of the abnormal traffic of the complex network is realized under low calculation overhead.

The invention belongs to the technical field of network security, and particularly relates to a network security analysis system based on an AI algorithm. Comprising a data acquisition and preprocessing module, an abnormal behavior characteristic index construction module, a safety mode evolution law index construction module and a threat risk comprehensive evaluation module. According to the system, multi-source heterogeneous data such as network flow, equipment logs, threat intelligence and user behaviors are analyzed through deep learning, a graph neural network and an attention mechanism, potential attack time periods are recognized in real time, the threat evolution trend is predicted, and dynamic early warning is achieved by fusing multi-dimensional risk factors. Compared with a traditional detection mode based on rules, the method has higher unknown threat detection capacity, interpretability and real-time performance, the network security protection level can be greatly improved, and the method is suitable for various complex network environments.

The invention relates to the technical field of wireless streaming media, in particular to a real-time video transmission method and system based on 5G, and the method comprises the following steps: based on mobile terminal equipment, analyzing the signal strength and service bearing change of a 5G base station, screening an optimal access sequence, comparing channel capability with video partition characteristics, and adjusting coding parameter distribution. And analyzing link capability and packet loss performance, and optimizing uplink and downlink compression parameters to obtain a compression change trend. According to the invention, by dynamically identifying the signal association and network load change between the mobile terminal and the 5G base station, the multi-dimensional access priority sequence is generated in combination with the real-time bandwidth demand, the coding and allocation parameters are flexibly adjusted according to the video partition content characteristics and the wireless channel adaptation condition, and the access priority sequence is optimized in combination with the uplink and downlink performance and the frame type change. Dynamic regulation and control and synchronous updating of compression parameters are automatically completed, continuous transmission of video streams in a complex network environment is achieved, and the end-to-end consistency of video content and the integrity of picture data are improved.

The invention relates to a ship network security digital twinning full life cycle maintenance and monitoring method, which comprises the following steps: step 1, constructing a digital twinning 3D virtual model of a ship network, the 3D virtual model is based on an actual network topology structure of a ship, and comprises all network nodes including a switch, a router, a sensor and a control system; and real-time rendering is carried out on local equipment or a cloud platform. According to the method, the real-time mapping of the high-fidelity 3D virtual model and the physical network is constructed, the three-dimensional and visual monitoring of the ship network state is realized, the cognitive efficiency of operation and maintenance personnel on the complex network topology is greatly improved, the cloud-side collaborative intelligent analysis architecture is adopted, and the real-time monitoring of the ship network state is realized. According to the method, the local real-time threat detection capability in an unstable network environment is ensured, the defense level of the system is continuously improved through cloud deep learning and model optimization, multi-source heterogeneous data acquisition and intelligent analysis are combined, and hidden attacks and abnormal behaviors which are difficult to find by a traditional method can be accurately recognized.

The invention discloses a network traffic anomaly detection and identification method based on artificial intelligence, and the method comprises the following steps: obtaining original network traffic data, carrying out the preprocessing, and carrying out the fusion construction of a multi-layer traffic language field tensor; behavior semantic modeling and path structure coding are carried out; constructing a behavior event sequence tensor, inputting the behavior event sequence tensor into the improved RetNet model, and outputting a behavior drift response vector; performing deviation analysis on the behavior drift response vector and the target behavior intention trajectory tensor, and constructing an abnormal evolution graph of abnormal growth and propagation; performing type classification and causal analysis according to the abnormal evolution graph, and outputting an abnormal type label and a causal path set; constructing a minimum disturbance sample, and performing comparative reasoning to generate an abnormal credibility change interval and an anti-robust score; and updating the abnormal knowledge graph and executing incremental updating. According to the invention, high-precision, interpretable and robust anomaly detection and classification analysis in a complex network environment can be realized.

A self-optimizing and self-programming computing system (SOSPCS) design framework that achieves both programmability and flexibility and exploits computing heterogeneity [e.g., CPUs, GPUs, and hardware accelerators (HWAs)] is provided. First, at compile time, a task pool consisting of hybrid tasks with different processing element (PE) affinities according to target applications is formed. Tasks preferred to be executed on GPUs or accelerators are detected from target applications by neural networks. Tasks suitable to run on CPUs are formed by community detection to minimize data movement overhead. Next, a distributed reinforcement learning-based approach is used at runtime to allow agents to map the tasks onto the network-on-chip-based heterogeneous PEs by learning an optimal policy based on Q values in the environment.

The invention relates to the technical field of network security detection, and discloses a malicious network traffic detection and analysis method based on artificial intelligence. The method comprises the following steps: acquiring network flow data through preset equipment, and constructing a network flow characteristic representation containing a time sequence statistical characteristic and a protocol attribute characteristic; determining a multi-level associated entity of each traffic fragment in combination with the network security knowledge graph, and aggregating the features to generate aggregated network traffic features; performing malicious probability evaluation on the aggregation features, and determining target malicious traffic by means of the maximum response value of the thermodynamic map; determining a similar malicious traffic mode based on the aggregation feature similarity; generating a detection prompt text in combination with the target malicious traffic and the similar mode, and inputting a preset model to output a detection result; and adjusting model noise covariance matrix parameter optimization detection according to the flow dynamic index. The method can comprehensively capture traffic characteristics, mine associated information, improve the accuracy and adaptability of malicious traffic detection, and effectively cope with malicious attacks in a complex network environment.

The invention discloses a complex network disintegration method based on evolution deep reinforcement learning. According to the method, an encoder-decoder model fusing a graph convolutional neural network and a deep Q network is constructed, and is used for efficiently extracting importance features of nodes in a complex network and realizing dynamic decision-making of a node disassembling sequence according to the importance features. In order to optimize model parameters and improve search capability, an evolutionary algorithm is introduced to perform global exploration on the model parameters, and the problem that a directional optimization strategy is easy to fall into local optimum is avoided. Meanwhile, deep mining is carried out on an evolution result in combination with a reinforcement learning strategy, the overall optimization process is accelerated, and advantage complementation of parameter evolution and strategy learning is achieved. Experimental results show that the method significantly improves the efficiency and precision of network disassembly while maintaining the robustness of the model, and has good practical value and wide application prospects.

The invention discloses a crack width measurement method and system based on dynamic topological structure analysis. A system hardware basis comprises an image acquisition module, an image processing unit, a data storage module and a result display module. According to the method, the working process of an image processing unit is divided into two stages according to a time sequence: the first stage is used for realizing data processing and crack segmentation, and the second stage is used for completing dynamic topology analysis and width measurement of cracks. In the second stage, the steps of form closed operation, skeleton extraction and enhancement, end point bridging, topological statistics and segment width measurement are executed in sequence, and finally a visual result and quantized data are output. The method comprises the following steps: processing an initial skeleton image by adopting an independently researched and developed topology enhancement algorithm; the method is especially good at processing complex net-shaped cracks, and the defect that measurement is inaccurate at crossed and bent cracks in a traditional method is effectively overcome through a dynamic topology analysis method.

The invention discloses an asset vulnerability detection method and device, electronic equipment and a storage medium, and relates to the technical field of network security, and the method comprises the steps: actively sending a multi-protocol detection packet to scan a target network segment, and obtaining a first asset set; passively monitoring network traffic to extract asset feature information, and obtaining a second asset set to generate an asset list; port scanning tasks of all assets are dispatched to a plurality of scanning nodes in a distributed and parallel mode, dynamic port scanning is carried out according to a descending order of a plurality of key elements in combination with a port scanning optimization model based on risk prediction, and a full-amount port risk map is constructed; the static layer is matched with known vulnerabilities; the dynamic layer identifies suspicious behaviors deviating from a normal behavior baseline through an anomaly detection algorithm, and obtains an asset vulnerability detection result in combination with a cross validation method; according to the invention, the detection requirements of asset full coverage and early threat discovery in a complex network environment are met.

The invention provides a data center interconnection link fault self-healing and path switching method, which comprises the following steps of: acquiring multi-dimensional link performance monitoring data, establishing a standardized time sequence database, realizing link health degree multi-index trend prediction by combining an LSTM-Attention model, and determining the link health degree according to the LSTM-Attention model. Risk-aware dynamic path selection and switching for multiple business scenes are realized by combining business service sensitivity modeling, path inherent stability quantification, nonlinear adaptive switching criteria and a debounce switching process, the response accuracy and the business adaptation degree of path switching are improved, false triggering and service interruption risks can be remarkably reduced, and the path switching efficiency is improved. And the high availability and robustness of routing in a complex network environment are enhanced.

The invention discloses an Internet enterprise multi-mode identity verification method and system, and belongs to the technical field of Internet enterprise security, and the method comprises the steps: obtaining user historical behavior data, current transaction request data, equipment environment parameters and initial biological signal data, carrying out the risk assessment, and generating a verification path; obtaining a personalized verification sequence instruction, collecting a user face dynamic video stream, a real-time voice stream and response action time sequence data, carrying out cross-modal association comparison with a user reference biological feature template, outputting a biological feature confidence matrix, carrying out association analysis in combination with the obtained structured identity feature vector and risk assessment, and obtaining a personalized verification result; and generating a verification decision feature vector to judge a verification result state, and obtaining a pass instruction, a rejection instruction or a manual auditing request instruction. According to the method, dynamic risk-driven multi-modal verification path generation, cross-modal biological feature association decision and incremental learning mechanisms are adopted, so that the optimal balance between security and user experience can be realized in a complex network environment.

The invention discloses a high-resolution and low-delay audio and video transmission method and system, which realize high-quality and low-delay remote audio and video transmission by dynamically negotiating transmission resolution and self-adaptive compression coding. The method comprises the following steps: constructing a resolution negotiation matrix through extended display identification data based on receiving end equipment, and determining an optimal transmission resolution parameter; the compression ratio is dynamically adjusted in combination with network bandwidth fluctuation, a compressed data stream is generated by adopting inter-frame prediction coding, and the anti-interference capability is enhanced through forward error correction and time division multiplexing packaging; and finally, the differential signal pair is transmitted to a receiving end to be decoded and restored into a standard audio and video signal. According to the method, the transmission delay is remarkably reduced while the high-resolution image quality is ensured, the method effectively adapts to a complex network environment, the transmission distance and stability are considered, and the method is suitable for application scenes such as remote conferences and real-time monitoring which have high real-time requirements.

The invention relates to a meteorological-based dynamic graph network photovoltaic power station group ultra-short-term prediction method, which is characterized in that a dynamic space-time graph network is constructed, photovoltaic power stations in a region are regarded as a complex network system connected by meteorological fluctuations, each power station is taken as a node, and the propagation relationship of the meteorological fluctuations among the power stations is represented by edges of a graph. The weight and time delay of the edge are dynamically adjusted according to real-time meteorological data, and dynamic interaction between power stations, the overall trend under the stable meteorological condition and rapid fluctuation caused by sudden weather events are accurately captured in combination with a mixed model of a graph neural network and a recurrent neural network. Compared with a traditional method, the method has the advantages that the prediction precision, the real-time response capability and the physical interpretability are remarkably improved, scattered photovoltaic power stations are integrated into a mutually associated dynamic system, an efficient and reliable solution is provided for power prediction of a power station group, and the operation stability and the power grid dispatching efficiency of a large-scale photovoltaic system are enhanced.