1076 results about "Heterogeneous network" patented technology

The disclosed embodiments provide systems and methods for continuous exposure and attack surface management using a data fabric. Data from multiple heterogeneous cybersecurity sources, including vulnerability scanners, threat intelligence, cloud security tools, and endpoint monitoring systems, is ingested and integrated into a semantically harmonized representation, such as a security knowledge graph. This unified data model normalizes, correlates, and contextualizes diverse cybersecurity information, enabling comprehensive and real-time assessment of an organization's cybersecurity risk posture. Automated workflows trigger proactive remediation actions based on dynamically calculated exposure metrics. Additional embodiments leverage the same data fabric architecture to support specialized cybersecurity use cases, including unified vulnerability management (UVM), cyber asset attack surface management (CAASM), continuous threat exposure management (CTEM), and asset exposure management (AEM).

The invention discloses an Internet of Things data transmission optimization system based on intelligent routing, and relates to the technical field of data transmission optimization. The heterogeneous network access module supports adaptive switching of multiple protocols such as 5G, NB-IoT and LoRa, and monitors link quality; intelligent routing decision selects an optimal path by means of deep reinforcement learning, and end-to-end delay is reduced; data distribution scheduling split data streams are transmitted in a multi-path parallel mode, and forward error correction is combined to guarantee reliability. The network state prediction uses an LSTM to predict bandwidth, and a route is adjusted in advance; the secure transmission integrates iris authentication and dynamic encryption. According to the invention, efficient, reliable and secure transmission is cooperatively realized, energy collection and dormancy strategies are combined, the equipment endurance is greatly prolonged, the network energy consumption is reduced, and the method is suitable for industrial control, telemedicine and other scenes with high requirements on real-time performance and reliability.

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 wireless communication, discloses an air-sea cross-domain network reinforcement learning routing algorithm based on an SDN (Software Defined Network), and mainly aims to solve the problems of poor dynamic environment adaptability, insufficient multi-task QoS (Quality of Service) demand guarantee capability and the like caused by a static rule or local optimization of an existing air-sea cross-domain routing algorithm. Global network state perception and resource centralized scheduling are realized through an SDN controller, and a dynamic routing decision model driven by deep reinforcement learning (DRL) is constructed. A network state is sensed in real time through an SDN controller, a multi-target reward function is designed, and a cross-domain path selection strategy is dynamically optimized. According to the method, the limitation of a traditional routing protocol in a cross-domain heterogeneous network is broken through, the end-to-end transmission efficiency, the resource utilization rate and the multi-task differentiated service quality guarantee capability are remarkably improved, and the method can be widely applied to the scenes of marine environment monitoring, emergency rescue, cross-domain cooperative detection and the like.

The invention relates to a heterogeneous network engineering-oriented adaptive protocol conversion gateway system and a control method, and belongs to the technical field of Internet of Things, and the system comprises a protocol identification module which is used for monitoring a data stream in real time and identifying the type of a protocol used in current communication; the adaptive protocol selection module is used for selecting the most suitable protocol according to the network state and the data flow type; the protocol conversion module is used for data format conversion between different protocols and mapping of header information; the network resource optimization module is used for dynamically adjusting the transmission mode of the protocol according to the current network state; and the data caching and traffic scheduling module is used for scheduling the cache and the traffic when different protocols are switched. According to the method, the problem of compatibility among different network protocols is solved, the data transmission efficiency and the utilization of network resources are optimized, and the method can adapt to different application scenes and is widely applied to environments needing cross-protocol communication, such as the Internet of Things, a smart power grid and cloud computing.

The Internet can be configured to provide communications to a large number of Internet-of-Things (IoT) devices. Devices can be designed to address the need for network layers, from central servers, through gateways, down to edge devices, to grow unhindered, to discover and make accessible connected resources, and to support the ability to hide and compartmentalize connected resources. Network protocols can be part of the fabric supporting human accessible services that operate regardless of location, time, or space. Innovations can include service delivery and associated infrastructure, such as hardware and software. Services may be provided in accordance with specified Quality of Service (QoS) terms. The use of IoT devices and networks can be included in a heterogeneous network of connectivity including wired and wireless technologies.

The invention provides an ETC fraud detection method. The method comprises the following steps: collecting multi-source data and constructing a heterogeneous network; dynamically screening a high-risk node association topology from the heterogeneous network through a two-way graph propagation analysis method; generating a behavior feature vector through a neural network model, and optimizing the behavior feature vector based on attribute similarity weighted neighborhood aggregation and spatio-temporal trajectory fusion to obtain an optimized feature vector; fusing the static risk value, the dynamic risk value and the topological risk value to generate a composite risk index, and triggering a hierarchical response mechanism according to the composite risk index; and outputting a fraud judgment result containing the multi-dimensional scoring matrix and the associated topology evidence chain. According to the ETC fraud detection method, the technical bottlenecks of multi-entity correlation analysis, dynamic mode adaptation and evidence chain construction in ETC fraud detection are overcome, the fraud detection coverage rate is high, meanwhile, the normal passing efficiency is guaranteed, and the ETC fraud detection method has remarkable industrial application value.

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.

Disclosed embodiments generally relate to edge-based multi-Radio Access Technology (RAT) traffic management (TM) solutions to support delay-sensitive traffic over heterogeneous networks. Embodiments include delay-aware TM implementations that split and / or steer network traffic across different RATs for the edge network control plane. Embodiments also include utilization threshold-based implementations to achieve delay-aware multi-path TM at the network's edge. The multi-path TM includes multi-RAT, multi-access, or multi-connectivity traffic routes. Embodiments include strategies to sort users (or devices) for making multi-RAT traffic distribution decisions and to determinate the utilization thresholds. Embodiments also include message exchange mechanisms or learning utilization thresholds and other useful system properties. Other embodiments may be described and / or claimed.

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 discloses a generative AI endogenous communication network architecture and a hierarchical collaborative scheduling method, and relates to the technical field of communication networks, the communication network architecture comprises an intention extraction layer, a mapping adaptation layer and a network execution layer, the intention extraction layer processes multi-modal data by using a generative AI model to obtain a semantic demand representation of a user, and the mapping adaptation layer carries out mapping on the semantic demand representation of the user; the mapping adaptation layer maps the semantic demand representation into a service quality index by using a generative AI model, maps a real-time network state of a heterogeneous network in the network execution layer into a network performance index, and generates a transmission scheduling strategy based on the service quality index and the network performance index, and the network execution layer works based on the transmission scheduling strategy, so that the network performance of the heterogeneous network is improved. End-to-end deterministic transmission scheduling of data packets of a service is implemented. According to the invention, efficient deterministic transmission scheduling of intelligent computing services in a 6G network can be realized.

The invention provides a heterogeneous network resource virtualization modeling and intelligent arrangement method and system based on a knowledge graph, and relates to unified modeling, dynamic retrieval and intelligent resource arrangement of heterogeneous network equipment. The method specifically comprises: 1, a unified modeling method based on a knowledge graph: integrating protocol attributes, dynamic states and topological relationships of heterogeneous devices such as a 5G base station, an SDN switch, a router, an Internet of Things gateway and the like into a structured knowledge graph, breaking the barrier of a manufacturer private data model, and realizing semantic-level collaborative scheduling of cross-domain resources; 2, designing a semantic retrieval engine: querying dynamic conditional reasoning through a natural language, replacing traditional manual rule definition, and improving retrieval response speed and accuracy; and 3, developing a graph-driven intelligent arrangement framework: combining a graph neural network (GNN) and reinforcement learning (RL), automatically generating a resource allocation strategy according to a real-time network state, reducing manual intervention and improving the resource utilization rate.

The invention relates to the technical field of welding process optimization, and discloses a welding parameter intelligent acquisition and process optimization management system. The system comprises a multi-source data acquisition module for synchronously acquiring various welding data to generate a multi-dimensional data set; the signal preprocessing module is used for carrying out noise reduction processing on the data; the dynamic parameter regulation and control module dynamically adjusts welding parameters based on a reinforcement learning model; the heterogeneous network communication module is used for designing a dual-channel protocol to guarantee data transmission; and the process optimization decision module is used for generating an optimal welding parameter scheme by using a deep neural network. And a welding quality simulation module and an abnormal process mode library are also arranged. Intelligent collection, real-time regulation and control and process optimization of welding parameters are achieved, the welding quality and the production efficiency are improved, the cost is reduced, and it is guaranteed that welding production is conducted stably.

The invention relates to the technical field of directional delivery, in particular to a media promotion directional precise delivery system and method based on big data, and the system comprises a user interest mining module, a content correlation analysis module, a heterogeneous network optimization module, a user behavior insight module, an advertisement matching strategy module and a precise delivery effect monitoring module. According to the method, the advertisement content matching accuracy is further improved by analyzing user browsing contents and records through a big data technology, effectively extracting keywords, identifying interest points and predicting the behavior trend of the user, it is ensured that advertisements can be accurately put to directional target users every time, and the advertisement effect is monitored in real time, so that the advertisement quality is improved. The advertising strategy is adjusted according to the user feedback, so that the advertising efficiency and the user satisfaction degree are remarkably improved, accurate matching and advertising of the advertising content are achieved through deep analysis of user behaviors and interests, the pertinence and effectiveness of advertisements are greatly enhanced, meanwhile, the user experience is optimized, and interference of irrelevant advertisements is avoided.

The invention discloses an optical transmission cross-domain service real-time configuration method, an optical transmission cross-domain service real-time configuration device and a computer program product, and aims to realize unified planning and dynamic output of cross-domain service end-to-end service quality targets by constructing a multi-domain quality strategy fusion model and integrating service quality strategies and resource states of different network domains. By collecting performance data of each domain in real time and combining with a time sequence analysis algorithm, the network performance degradation trend can be accurately identified; based on quality propagation path analysis of a cross-domain service topological relation graph, a quality degradation root can be quickly positioned, the influence range of the quality degradation root can be quantified, and the service quality stability of a key service in a resource limited scene is ensured; by establishing a cross-domain service quality mapping system, heterogeneous network performance parameters are mapped to end-to-end service quality indexes, and the cross-domain collaborative operation and maintenance efficiency is greatly improved. A continuous iterative optimization mechanism driven by reinforcement learning is adopted, adaptive adjustment of network resource allocation and a routing strategy is realized, and a cross-domain service quality dynamic optimization closed loop is formed.

The invention discloses a multi-modal network state detection method based on large model alignment, which is used for processing multi-modal data of a communication network. The method comprises the steps that multi-modal data such as a time sequence and a text are converted into a unified semantic space through blocking and embedding, multi-modal features are aligned through cross-modal reprogramming by utilizing a text prototype library and an attention mechanism, and an injection task instruction and domain knowledge are enhanced by setting a dynamic prompt prefix so as to activate the LLM reasoning ability; then, LLM trunk model parameters are frozen, only an input and output adaptation layer is trained, and a lightweight optimal adaptation model is constructed; and generating a network state prediction result based on the adaptation layer. According to the method, dynamic alignment of multi-modal network data is achieved, the generated joint semantic representation can be further used for LLM reasoning, the purpose of more accurately sensing the network state is achieved, network state reasoning under few-sample and zero-sample scenes is supported, and the method is suitable for a heterogeneous network environment.

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 network security, and discloses a big data-based network security risk assessment method, which comprises the following steps of: 1, acquiring multi-source heterogeneous network security data; step 2, performing real-time cleaning and normalization processing on the data to generate a standardized feature vector; step 3, constructing a dynamic risk assessment model, and calculating a network node threat value by fusing a hybrid model of a correlation analysis algorithm, a random forest classifier and a long and short-term memory network; step 4, based on dynamic weight distribution of the threat value, optimizing a risk assessment result, and outputting a risk level and a protection suggestion; according to the method, the hybrid model fusing the correlation analysis algorithm, the random forest classifier and the long-short-term memory network is constructed and used for calculating the threat value of the network node, the advantages of different algorithms can be fully utilized through the hybrid model, the accuracy and robustness of risk assessment are improved, and meanwhile, the application of the dynamic weight distribution algorithm can improve the risk assessment accuracy and robustness. And the risk assessment result is more in line with the actual network condition.

The invention discloses an edge node heterogeneous network heterogeneous platform access management method and system, and relates to the technical field of Internet of Things. The method comprises the following steps: a monitoring platform performs reverse decoding on each piece of received access request sequence information mapped by a three-dimensional chaotic dynamic symbol so as to obtain decoded access request sequence information; generating a threat index corresponding to the edge node according to the access request sequence information; generating a sandbox adjustment strategy of the edge node according to the threat index; carrying out different treatments on the edge nodes according to a sandbox adjustment strategy; inputting the feature information of the edge nodes into the identification neural network to obtain identification information; and the monitoring platform judges whether the edge node in the preset sandbox is allowed to access according to the identification information, and if yes, the edge node is accessed. According to the method, the access request is encrypted and transmitted through three-dimensional chaotic dynamic symbol mapping, the data security is remarkably improved, and fine access control is realized in cooperation with a dynamic sandbox strategy.

The invention relates to a cross-network time synchronization method oriented to AUTBUS and TSN fusion, and belongs to the field of network time synchronization. The invention aims to solve the problems that the traditional Ethernet cannot meet the time-sensitive application requirement and the scale of the TSN is limited and the like. According to the method, a time compensation value prediction algorithm is placed in an AUTBUS terminal, time delay values of all parts in the heterogeneous network are collected and predicted, and finally a prediction result is compensated to a local clock of the AUTBUS terminal, so that end-to-end time synchronization of the AUTBUS and TSN fusion network is realized. The method comprises the four steps of TSN intra-network time synchronization, gateway intra-network time synchronization, AUTBUS intra-network time synchronization and AUTBUS terminal time compensation, time delay data are predicted through a linear regression prediction algorithm, and synchronization precision and efficiency are improved. The method is suitable for the fields of intelligent manufacturing, industrial automation and the like, and an effective solution is provided for time synchronization of the heterogeneous network.

The invention relates to the technical field of adaptive network shunting, and discloses an adaptive network shunting method and system based on deep reinforcement learning, and the method comprises the steps: obtaining the flow data of a heterogeneous network, building a protocol fingerprint database, and converting the data of different protocols into a unified network state feature vector; constructing a multi-agent reinforcement learning framework, including defining a state space, defining an action space and defining a reward function, and optimizing a strategy of each agent based on a differential game; adjusting the weight of each agent reward function by using a meta-learning model, training a multi-agent reinforcement learning framework by using deep reinforcement learning, and learning an optimal shunting strategy under different heterogeneous network conditions; and deploying the trained strategy to a heterogeneous network environment, and dynamically selecting an optimal network path according to the current network state. And the heterogeneous data processing capability is obviously 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 provides a network node classification method based on a multilayer graph neural network model, and the method comprises the steps: constructing a graph according to network nodes, carrying out the preprocessing of initial node features in the graph, and obtaining an initial embedded vector; according to the initial embedding vectors of the node and the neighbor node thereof, updating the node representation by adopting a layered message passing mechanism; according to the initial embedded vectors of the nodes and the neighbor nodes thereof, the graph attention network uses an attention mechanism to calculate weights for the neighbor nodes of each node and then dynamically updates feature vectors of the nodes; a multi-head attention mechanism is adopted to splice and merge the feature vectors of the nodes output by the graph attention network and the updated node representation, and the comprehensive feature vector of each node is obtained; the comprehensive feature vectors are further optimized through graph contrast learning and a node embedding module, and optimized features are obtained; and obtaining predicted node classification according to the optimization features. According to the method, complex features of heterogeneous graph data can be effectively captured, the method has good robustness for an over-smoothing problem, more accurate heterogeneous network representation learning is realized, and the method has advanced performance and robustness in a node classification task.

The invention relates to the technical field of Internet of Things routing management, in particular to a big data-based routing optimization method, which comprises the following steps of: acquiring spectrum data of an Internet of Things routing management area, performing correlation analysis on a spectrum state corresponding to a historical success rate and retransmission times of communication between nodes, and quantifying a probability of successful communication at a future target time. According to the method, the multi-band historical communication success rate and retransmission frequency data are dynamically acquired, the interference risk of the target time period is analyzed and predicted in combination with the time sequence, an interference avoidance strategy based on static channel assessment is upgraded to a probability-driven pre-judgment mechanism, and the anti-interference capability of a transmission path in a complex electromagnetic environment is improved. According to multi-protocol link characteristics of Bluetooth, WiFi, 2.4 G and the like, parameters such as delay, throughput, packet loss rate, flow cost and the like of a single path are synchronously quantified, a unified performance cost file of a heterogeneous network link is established, and end-to-end performance modeling of cross-protocol path combination is realized.

The invention discloses an emergency broadcast message scheduling system, a processing method and a broadcast terminal, and belongs to the technical field of message scheduling. Emergency event data are collected, public events and regional events are divided according to an initial influence range, and SIP labels are constructed to generate broadcast messages; constructing a dynamic list based on a terminal identity and a network state, starting multi-network integration to execute a differential transmission strategy, and ensuring message delivery; public event preemption, regional event dynamic polling and priority jump triggering during event diffusion are realized through a multi-stage scheduling queue; volume coverage mapping is established by using multi-dimensional information of the terminal, the volume is dynamically calibrated in combination with regional characteristics, formats are adapted, and non-sensitive regulation and control are realized. The objective of the invention is to solve the problems of rigid multi-event scheduling, unstable heterogeneous network transmission, insufficient terminal adaptation capability and the like of a traditional system, and to improve emergency scene message scheduling efficiency, transmission reliability and coverage accuracy.

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.