727 results about "Heterogeneous network" patented technology

A system for adaptively caching network communication protocols enhances efficiency across heterogeneous device environments through a multi-level cache architecture with device-capability-based tiers. The system collects endpoint telemetry data including device capabilities and operational constraints to classify endpoints and generate context-aware protocol variants optimized for specific device types. Protocol optimization opportunities are determined through structural analysis of message patterns and state transitions. The system performs protocol deduplication by identifying functionally equivalent variants and maintaining canonical representations to reduce cache redundancy. Cache synchronization across distributed nodes uses enhanced Merkle tree structures with protocol normalization processing. The system predicts communication needs based on historical patterns, network context, and endpoint constraints, enabling proactive cache management tailored to device capabilities. Integration with event-driven data communication systems enables seamless protocol selection and translation while maintaining compatibility between diverse endpoint types, from high-performance servers to resource-constrained IoT devices.

The invention relates to the technical field of communication, in particular to an underground power distribution room communication system and method based on heterogeneous network and multi-modal fusion, and the system comprises a three-dimensional heterogeneous network cooperation module, a cross-modal feature fusion module, a dynamic routing decision engine module, a self-adaptive interference suppression system and a cross-layer cooperation optimization module. The three-dimensional heterogeneous network cooperation module comprises a 4G / 5G wireless communication sub-layer, a power line carrier communication sub-layer and an edge computing management sub-layer; the cross-modal feature fusion module comprises a multi-modal encoder group, a shared feature projection layer, a twin network structure and a cross-modal attention fusion module; and the dynamic routing decision engine module comprises a state space monitoring unit, a routing optimization unit and a millisecond-level path switching strategy. Through the arrangement, the reliability, the real-time performance and the energy efficiency of communication of the underground power distribution room are systematically improved, and a high-robustness communication infrastructure is provided for an intelligent power grid.

The invention discloses a vehicle-mounted cross-protocol data time sequence cooperative processing method, which comprises the following steps: establishing a unified time reference, and providing an accurate space-time coordinate system for heterogeneous network nodes by electing a global master clock and constructing a clock relation model and a periodic correction mechanism; systematic cognition and management of communication resources are realized, and a verified protocol capability table is generated by automatically identifying protocol types and variants; constructing an automatic mapping system from demand to execution, performing formalized packaging on a target scene by introducing a unified intermediate model, generating a cross-protocol mapping rule based on the model and a protocol capability table, and then forming a global scheduling table in combination with a unified time reference; the cooperative communication of all nodes is finally realized by using the global scheduling table, and the end-to-end delay is constrained within the preset range, so that the problem that the end-to-end time sequence in the vehicle-mounted heterogeneous network is uncontrollable is solved, and the controllability and reliability of the time sequence of the vehicle-mounted network are improved.

The invention discloses a network space security situation awareness detection analysis system and method, and the system comprises a heterogeneous network flow feature deep extraction unit which obtains a flow feature vector through a distributed probe array and an adaptive sampling strategy; the edge computing gateway multi-dimensional threat feature mapping unit realizes feature space mapping through a multi-dimensional feature mapping matrix; the dynamic weight multi-head attention feature fusion unit fuses features by using an optimized multi-head attention mechanism; the time-space sequence security situation evolution modeling unit constructs an evolution model in combination with the time sequence and the fusion features; the security threat risk quantitative evaluation unit carries out risk quantification; and the heterogeneous security policy collaborative response unit generates a response instruction. According to the method, flow collection, feature mapping, fusion, modeling, quantification and response operation are sequentially executed based on all units of the system. According to the method, efficient perception and accurate response of the network space security situation are realized through a multi-unit cooperation and innovation model.

The invention relates to the technical field of network communication, and discloses a vehicle-mounted edge computing data recording system and method based on protocol adaptive analysis, and the system comprises a multi-protocol network access controller which is used for capturing an original data frame and extracting a physical feature triple; the identification analysis engine is used for executing hash operation on the triple to generate a physical feature code and retrieving a physical logic address mapping table; the direct memory access controller is used for responding to a target memory address pointer hit by retrieval and directly writing a data load into an input buffer area of the functional operation module, and by constructing a direct addressing mechanism based on Hash mapping, thorough decoupling of vehicle-mounted heterogeneous network physical topology and edge computing logic is achieved.

The invention relates to the technical field of wireless communication, in particular to a wireless communication network system and method for intelligently fusing a multi-mode heterogeneous network. Comprising a multi-mode access unit; a protocol compatibility and conversion unit; the heterogeneous network intelligent fusion unit completes cross-network dynamic flow collaboration and resource optimization decision through cross-layer feature association mining, service-network two-dimensional weight calibration and a double-closed-loop feedback optimization mechanism, generates a resource scheduling instruction and transmits the resource scheduling instruction to the intelligent scheduling and resource control unit; an intelligent scheduling and resource control unit; and a system control and cooperation unit. According to the invention, the service-network two-dimensional dynamic fusion weight is constructed through the intelligent fusion unit of the heterogeneous network, and bandwidth, frequency spectrum and power resources are allocated according to the priority and margin balance principle in combination with the intelligent scheduling unit, so that the problem of insufficient two-dimensional coordination of resource scheduling is solved, the resource scheduling is matched with the service demand and the network load, and the resource scheduling efficiency is improved. And resource waste or incapability of meeting business requirements is avoided.

The invention discloses an unmanned aerial vehicle anti-interference communication link control method based on heterogeneous network convergence, and relates to the technical field of unmanned aerial vehicle anti-interference communication, and the method comprises the steps: S1, interference sensing and network state modeling, S2, intelligent link decision making, S3, spectrum avoidance and resource scheduling, S4, distributed feedback and cooperative anti-interference, and S5, heterogeneous network cooperative relay. S6, resource allocation and energy efficiency optimization; S7, performance evaluation and parameter optimization; and S8, multi-link redundancy backup. According to the invention, through S1, S2 and S7, a sensing, decision-making and optimization closed-loop system is constructed, LSTM interference prediction and three-dimensional network modeling in S1 provide accurate input, a dual deep Q network in S2 realizes low-delay link decision-making, and KPI evaluation and genetic algorithm dynamic parameter adjustment in S7, the signal-to-interference ratio is improved, the anti-interference adaptability is improved compared with a traditional passive response mode, and the method has the advantages that the method is simple and convenient to operate, and the cost is low. The creative breakthrough that interference does not reach strategy precedence is realized; through S3 and S4, an active anti-interference mechanism is formed, and compared with a single avoidance or suppression method, the interference elimination efficiency is improved.

The invention relates to the technical field of mobile communication transmission, and discloses a heterogeneous network multilink parallel mobile communication transmission method and system. A dynamic link aggregation module of the system collects link state parameters including signal strength, delay jitter and packet loss rate from a plurality of heterogeneous network interfaces in real time; a flow distribution decision module calculates available bandwidth weight of each link according to the data and generates a dynamic flow distribution strategy; the data fragment packaging module splits to-be-transmitted data into a plurality of data fragments according to the strategy, and adds a sequence identifier and a link identifier to each fragment; the multi-channel concurrent transmission module sends corresponding data fragments in parallel through a specified heterogeneous network link; and the receiving recombination verification module sorts the recombination data fragments according to the sequence identifiers, and detects the data integrity through a verification mechanism. According to the system, a set of complete multi-link parallel transmission solution is formed, and the problems existing in a traditional single link and an existing multi-link transmission technology can be effectively solved.

The invention discloses a multi-protocol channel data aggregation method based on 5G communication, and relates to the technical field of heterogeneous network data processing under the fusion of 5G communication and edge computing, and the method comprises the steps: constructing a protocol adaptive conversion engine, recognizing an input protocol type through learning, and automatically selecting a conversion strategy and a data packaging format. An edge-cloud collaborative intelligent scheduling architecture is introduced, a data aggregation task is dynamically determined, a dynamic channel aggregation algorithm of a 5G network state and protocol characteristics is designed, and a transmission path and an aggregation strategy are intelligently selected. By constructing a protocol adaptive conversion engine, an edge-cloud collaborative intelligent scheduling architecture and a dynamic channel aggregation algorithm fusing 5G network state and protocol characteristics, automatic identification, efficient aggregation and optimized transmission of multi-protocol data are realized, dynamic scheduling of tasks is realized in combination with an SDN / NFV technology, and the dynamic scheduling efficiency of the multi-protocol data is improved. The channel selection mechanism based on the utility function comprehensively considers the network state and protocol characteristics, and guarantees the transmission quality of high-priority and time delay sensitive data.

The invention discloses a multi-source heterogeneous network security alarm aggregation noise reduction method, system and device. The method comprises the following steps: step 1, constructing a network security alarm ontology model; step 2, standardization processing of multi-source alarm; 3, performing dynamic alarm aggregation based on an entity relationship; 4, multi-level alarm noise reduction and intelligent study and judgment are carried out; according to the method, deep standardization is carried out on multi-source heterogeneous alarms through the network security alarm ontology model, and a data semantic gap is eliminated; a dynamic aggregation strategy based on a core entity and a relationship is adopted, related alarms surrounding the same core entity in a specific time window are aggregated into aggregated alarms, the order of magnitude of the number of the alarms is reduced, and a complete attack scene description is formed; a three-layer progressive noise reduction mechanism is adopted, false alarms and low-value alarms are eliminated, and high credibility of output safety events is guaranteed; automatic conversion from massive original alarms to a small number of high-value security events is realized, and the security operation response speed and decision quality are improved.

The invention relates to a cross-domain authentication method based on a block chain, and the method comprises the steps: obtaining distributed identity data, carrying out the coding through employing a preset hash function, and obtaining a coded identity; recording an identity verification log based on the encoded identity label, and retrieving label consistency data from an account book according to the verification request to obtain a legality verification result; obtaining a dynamic permission allocation strategy through a legality verification result, generating a temporary access credential meeting the access requirement, and determining the content of the dynamic credential; extracting a permission range based on the dynamic voucher content, encrypting the permission range by adopting a zero-knowledge proof protocol, acquiring proof data, distributing the proof data to a cross-domain access target domain, and if the target domain feeds back a verification request, checking credibility according to the proof data, and obtaining an access permission validity result, extracting an access demand matching degree for comparison verification, and determining a cross-domain access passing state. According to the method, the trusted identity management requirement in a complex heterogeneous network environment can be met.

The invention relates to the technical field of adaptive routing optimization, and particularly discloses a topology self-recognition and routing optimization method and system for a concentrator and a collector, and the method comprises the steps: carrying out the topology modeling of a multi-layer heterogeneous network; executing high-speed link state monitoring based on the initial topological graph; global loop detection and link optimization are completed based on the link state matrix; a weighted robust multi-dimensional path selection method is adopted to configure a main path and a standby path for each node; and monitoring the path state in real time and dynamically switching. In the prior art, a static route, a single protocol path or a fixed main and standby path is mainly adopted, and especially in a cross-protocol, cross-level and dynamic multi-node environment in a smart city, rapid path switching and high-reliability route redundancy guarantee cannot be realized. Due to the fact that the multi-dimensional path selection method and the cross-level and cross-protocol self-adaptive switching strategy are adopted, the problems of routing interruption and performance reduction are avoided, and the reliability of the whole network is improved.

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 relates to the field of network communication, and discloses a multi-source heterogeneous network data cooperative transmission method based on dynamic multi-dimensional evaluation and intelligent disaster recovery, which comprises the following steps: collecting system network parameters of a 5G network and a long-distance wired network in real time through a software definition interface module; performing weighted evaluation on the acquired system network parameters based on a dynamic link selection engine, the evaluation dimensions including real-time bandwidth, transmission delay and current link traffic, and generating an optimal communication link combination scheme; carrying out fragmentation and protocol adaptation on the data by adopting a general packaging framework and an intelligent label technology; distributing multi-path parallel transmission according to a priority strategy; at a receiving end, data recombination and disaster recovery supplementary transmission are realized through network coding and multi-path cooperation; and the evaluation weight is dynamically optimized in combination with reinforcement learning. According to the invention, the problems of rigid link selection, low protocol conversion efficiency and insufficient disaster tolerance in the prior art are solved.

The invention discloses a smart power grid fault-tolerant communication system and method based on multi-operator channel switching, and the method comprises the steps: collecting heterogeneous network and equipment state data through an intelligent sensing layer, achieving the network quality prediction and multi-target optimization through an AI decision engine, integrating spectrum resources and network slices through a resource cooperative control layer, and achieving the fault-tolerant communication of a smart power grid. The edge execution layer executes hardware-level fast switching, the emergency disaster recovery layer provides multi-dimensional fault recovery, and the security trusted layer guarantees data security. The method breaks through the bottleneck of traditional network switching, realizes high-reliability and low-delay transmission of power business, is suitable for scenes of intelligent substations, distribution network automation and the like, and remarkably improves the fault-tolerant capability and resource utilization efficiency of intelligent power grid communication.

The invention is suitable for the technical field of unmanned aerial vehicle communication, and particularly relates to an unmanned aerial vehicle environment monitoring and data processing method supported by edge computing, and the method comprises the steps: obtaining a time sequence data stream collected by a multi-source environment sensing device, and carrying out the compression and processing of the time sequence data stream based on a linear projection compression model, and obtaining a compressed feature vector; calculating a communication cost based on the current heterogeneous network condition and the energy constraint through a preset communication cost function; and executing different data processing strategies based on different communication costs. According to the method, efficient edge compression is realized in an embedded resource limited scene; resource-aware data uploading control is realized through a communication cost function; a Bayesian state fusion method based on a compressed feature space reconstructs a Kalman filtering matrix structure, effectively supports local state estimation and uncertainty control, instantly triggers early warning when an environment index crosses a boundary, and effectively enhances the risk perception and response capability of a system.

The invention discloses a three-network fault propagation risk assessment method and system for complex network analysis, and belongs to the technical field of power system toughness assessment and disaster risk management, and the method comprises the steps: collecting node and edge data of a power network, an information network and a traffic network, and forming a heterogeneous network topology structure; constructing a node importance evaluation function; carrying out weighted correction, and outputting a three-network coupling node importance degree sequence; generating a fault event triggering list; updating the node state matrix until the node state does not change any more, and outputting a fault influence range and a fault propagation path; and calculating a three-network overall connectivity loss rate, function recovery time estimation, key node fault sensitivity and a coupling dependence vulnerability index, and outputting a three-network fault propagation risk assessment index. According to the method, the dependency relationship and the influence strength among the three networks can be truly reflected, the contribution degree of the nodes to the system toughness is quantified, and the multi-dimensional, quantifiable and explainable effective evaluation of the fault propagation risk under the complex network is realized.

The invention discloses a BIM building intelligent integrated management system based on Internet of Things control, and relates to the technical field of networking technology and building intelligent management, and the system comprises a sensing layer, a transmission layer, a platform layer and an application layer. The sensing layer comprises a multi-source heterogeneous sensor cluster and is divided into an environment monitoring unit, an equipment state acquisition unit, a personnel positioning unit and a structure health monitoring unit, and each unit is provided with a corresponding sensor; the transmission layer adopts a LoRaWAN and 5G fusion architecture and comprises an edge computing gateway, a TSN switch and a data security encryption unit, and the gateway can perform frequency domain analysis and the like; the platform layer is based on micro-service and is provided with a multi-source data fusion engine and a BIM model dynamic updating module; the application layer provides an equipment fault prediction module, an energy optimization module and a safety risk early warning module, and all the modules adopt corresponding algorithms. According to the invention, a hierarchical collaborative architecture is constructed, a multi-source sensor, a heterogeneous network and the like are integrated, deep integration of multi-dimensional management of a building is realized, and the problems of subsystem independence and data obstruction of a traditional system are solved.

The invention provides a vision-based cross-network interaction method and system, and relates to the technical field of intelligent interaction, and the method comprises the steps: obtaining a visual interaction sequence, constructing multi-modal feature representation, achieving cross-domain semantic alignment, deconstructing visual information into a hierarchical control instruction set, and transmitting the hierarchical control instruction set to a target network environment for execution after security classification. And a bidirectional mapping relation graph is constructed to realize incremental optimization. According to the method, semantic bridging between heterogeneous networks can be established, the cross-domain control precision is improved, and meanwhile safe interaction in a network isolation environment is guaranteed.

The invention relates to the technical field of network switching, in particular to a heterogeneous network dual-mode self-adaptive switching method and system. According to the method, the multi-dimensional environment sensing data and the initial signal intensity are acquired, the signal intensity is calibrated in a dynamic environment, the dynamic response of the network state to real-time change is realized, and the connection state characteristic and the side network consumption index of the dual-mode network are acquired by combining the calibrated signal intensity. The switching demand can be evaluated on the basis of the data, the switching demand can be judged more accurately through the multi-dimensional evaluation mechanism, the limitation that a traditional method only depends on signal strength is avoided, and the switching demand can be evaluated by generating an optimized directed time sequence graph and a network performance data set. According to the technical scheme, the equipment can make a scientific and reasonable switching decision according to the real-time network state snapshot data and the preset optimization time sequence graph, and the connection requirements of the equipment can be dynamically adapted under the variable network conditions, so that the optimal network experience is kept, and the network connection quality is improved.

The invention discloses a wide-area time traceability credible authentication method in a power heterogeneous network environment, and belongs to the technical field of power system informatization and automation. Comprising the following steps: acquiring various high-precision time source information in a power heterogeneous network environment, and acquiring an initial time reference of the power system time synchronization device; generating uniform reference time, and embedding a digital watermark containing time source information and credibility thereof for each timestamp by using the digital watermark embedding module; controlling the power system time synchronization device to sequentially generate time transmission fingerprints at all levels of network nodes; constructing a unique time fingerprint chain covering the whole link; packaging the time fingerprint of each time transmission node and the traceability information thereof, and performing uplink storage; precise prediction and optimal control of time transmission errors are realized; if the overall cumulative time error exceeds a range of + / -10 [mu] s, the predictive correction is maintained until the error is controlled within + / -10 [mu] s.

The embodiment of the invention provides a terminal threat event detection method and device, computer equipment and a storage medium. Comprising the steps of obtaining interaction data of a target terminal to construct a heterogeneous network behavior graph, and determining a target node needing to be detected and an associated path element graph; for each path element graph, according to the node similarity between the neighbor node and the target node, adjusting the neighbor node vector to obtain a first node embedding vector, and obtaining a graph embedding representation based on the plurality of first node embedding vectors; for each neighbor node, fusing the neighbor node vector and the graph embedding representation according to the calculated attention score to obtain a second node embedding vector; aggregating the plurality of second node embedding vectors and the target node to obtain a comprehensive embedding representation of the path element graph; and fusing the plurality of comprehensive embedding representations to obtain a target node, embedding and inputting the target node into the target classification model to obtain a threat event detection result. Therefore, the efficiency and accuracy of threat event detection can be improved.

The invention provides an internal medicine multi-parameter real-time remote monitoring system based on the Internet of Things, and belongs to the technical field of remote monitoring, the internal medicine multi-parameter real-time remote monitoring system comprises an integrated data acquisition terminal, a heterogeneous network communication module, an intelligent remote analysis server and a user interaction terminal, and the integrated data acquisition terminal adopts a medical grade flexible wearable design. According to the internal medicine multi-parameter real-time remote monitoring system based on the Internet of Things provided by the invention, the efficiency of remote monitoring of internal medicine diseases is remarkably improved through multi-dimensional technical innovation. On the data processing level, efficient fusion and stable transmission of multi-source data are achieved, and the real-time performance and integrity of monitoring data are guaranteed; in an analysis level, a specialized deep neural network model is combined with environmental factors, so that the accuracy of health state assessment and risk prediction is greatly improved; according to the application level, through a personalized rehabilitation scheme, intelligent early warning and remote equipment management, the medical service process is optimized, and patient compliance and medical cooperation efficiency are improved.

The invention relates to a computing power resource scheduling method, a computing power resource scheduling device and computer equipment, through a randomly walking dynamic heterogeneous network, a computing power scheduling strategy is learned under the condition of no label data, the problem of less label data is avoided, and the computing power resource scheduling method and device are suitable for a computing environment with relatively high task unknown. According to the method, tasks and resources are modeled into nodes of a heterogeneous network, edge connection represents the interaction relation, graph vector representation is constructed, the matching relation between task requirements and computing power resources can be more accurately captured, and more accurate task allocation is achieved. And finally, the scheduling decision-making efficiency and accuracy of the computing power resources are improved.

The invention discloses a rail transit multi-network integration method based on a digital trunking technology, relates to the technical field of communication, and aims to solve the problem that a traditional narrowband trunking system cannot meet the requirements of voice and data comprehensive bearing and vehicle-ground large-bandwidth service real-time transmission. The method comprises the following steps: constructing a time division duplex long-term evolution broadband digital trunking unified bearing platform; an intermediate media access control layer fusion architecture is deployed, and seamless mobility management of the heterogeneous network is realized through unified authentication and multi-link aggregation; an intelligent vertical switching algorithm based on prediction and multi-criterion decision is introduced, a bidirectional long-short-term memory network model is utilized to predict a network state trend, and an improved entropy weight method and an analytic hierarchy process are combined to dynamically select an optimal access network according to service requirements. According to the invention, the network operation efficiency and the user experience can be obviously improved, and high reliability and high service quality of the rail transit communication system are ensured.

The invention relates to the field of low-altitude communication network planning methods, in particular to a 5G three-dimensional coverage and capacity planning design method for low-altitude economy, which comprises the following steps: performing airspace range limitation and application scene division on a target airspace, establishing a mobile model of a low-altitude terminal, and providing dynamic input for simulation; the method comprises the following steps: establishing a dual-frequency heterogeneous network, and determining base station addresses covering targets at different heights and meeting different signal station spacing; performing three-dimensional coverage and quality simulation on the established dual-frequency heterogeneous network, performing network KPI evaluation on the simulation until the standard is reached, and outputting a network deployment planning scheme; deploying according to a network deployment planning scheme, predicting low-altitude service flow, and adjusting a beam direction and a PRB reservation ratio to realize on-demand allocation of resources; and carrying out actual measurement on the deployed network, collecting an air measurement index, comparing a comparison deviation between the air measurement index and a simulation result, and carrying out fine adjustment on the network according to the comparison deviation. According to the invention, diversified communication requirements are met, and the success rate of terminal access is improved.

The invention relates to the technical field of heterogeneous network optimization, and discloses a heterogeneous network dynamic routing collaborative optimization method based on reinforcement learning, which comprises the following steps: S1, deploying an agent at each node of a heterogeneous network, and initializing a state sampling period and an action space; s2, sensing the state of the heterogeneous network in real time and obtaining sensing data; s3, encoding the sensing data into a state vector; s4, designing a layered dynamic reward function, including short-term reward and long-term reward; dynamically setting a weight ratio according to the scene type and outputting the weight ratio as a comprehensive reward; s5, carrying out multi-agent collaborative decision-making, which comprises the following steps: constructing a local topological graph based on neighbor state information broadcasted by nodes; each agent selects an action based on the state vector through the DQN; and updating the network weight of the DQN by using the comprehensive reward. According to the invention, transmission real-time performance and sustainability can be considered, transmission delay and transmission cost are reduced, and the method is suitable for film and television production and industrial Internet of Things scenes with variable topology and complex interference.

The invention discloses a multi-layer heterogeneous sequential network feature alignment and clustering method and device based on tensor self-representation. The method comprises the steps that sequential feature matrix sequences {},..., {} of data views in a multi-view heterogeneous network in time windows are obtained; based on the matrix sequence of the first time window, constructing a Laplacian regular term by introducing a self-representation learning mechanism, modeling multiple views by adopting a tensor structure and introducing a Schatten p-norm regularization mode to construct an optimization objective function for optimization, and representing shared feature representation of each data view in the first time window; and solving the function and carrying out clustering processing on the obtained function, wherein the clustering result of each time window is used for carrying out characteristic analysis on nodes in the network. According to the method, on the premise that the original structure and semantics of the network are guaranteed, unified modeling can be carried out on the incomplete multi-view heterogeneous network, and feature expression is aligned.

Disclosed are systems, apparatuses, methods, and computer-readable media for heterogenous network services using platform agnostic extensions. A method includes: instantiating a first service having a first data plane control point; instantiating a second service configured to access the first control point in a data plane; receiving a first packet at the first service in a network path; providing at least one of the first packet and first metadata associated with the first packet to the second service to analyze or process the first packet and the first metadata in conjunction with the first service; processing at least one of the first packet or the first metadata in the second service based on external bytecode and generating at least second metadata based on the processing; receiving second metadata to the first service; and processing the first packet or a second packet.

The invention provides a drug-disease association prediction method and system, computer equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: constructing a drug-protein-disease heterogeneous network, and extracting a plurality of element path sub-graphs; inputting the meta-path sub-graph into a multi-scale diffusion graph convolution module, executing learnable multi-step graph diffusion on the basis of graph convolution, synchronously capturing local adjacency and high-order topological information, and generating node embedding; and performing dynamic weighted fusion by utilizing meta-path attention to obtain unified representation. In order to relieve imbalance of positive and negative samples, implementing difficult negative sampling in the embedding space, and constructing a balance training set with the positive samples; medicine-disease features are spliced, a regularization XGBoost classifier is trained, and unknown correlation accurate prediction is achieved. By adopting the method, the drug-disease association prediction precision and efficiency are improved, multi-scale topology and priori knowledge are fused, and a powerful calculation tool is provided for drug relocation.