584 results about "Real time networks" patented technology

The present invention relates to the technical field of data transmission. Disclosed is an application-driven three-dimensional spatial data transmission method. The method comprises: determining system key performance indicators (KPIs) by means of qualitative and quantitative analysis; constructing an AI-driven adaptive three-dimensional data transmission mechanism, and dynamically adjusting a transmission strategy on the basis of a real-time network state, a device capability, an application scenario and the KPIs; developing an adaptive compression algorithm set oriented to three-dimensional data, so as to meet differentiated compression requirements of different application scenarios; performing loop execution of a test, and adjusting and optimizing the data transmission mechanism and the compression algorithm set on the basis of a test feedback result and the real-time network state; and deploying an optimized transmission method to a production environment, and collecting field data to optimize the system performance and verify the achievement of the KPIs. By constructing a qualitative and quantitative analysis framework based on machine learning, the present invention quantifies differentiated transmission requirements of different application scenarios and formulates transmission strategies meeting the scenario requirements.

The invention relates to the field of communication, in particular to a method for protocol optimization and dynamic routing for a network communication system. According to the invention, based on deep learning and big data analysis technologies, protocols in network communication are intelligently optimized. Through analysis of historical communication data, information such as a flow mode and a congestion condition in network communication is learned and predicted, so that communication protocol parameters are dynamically adjusted, and the efficiency and the stability of the network communication are improved. And meanwhile, a multi-objective optimization algorithm and a real-time network monitoring technology are adopted to realize dynamic routing selection. By monitoring network states such as bandwidth, delay, packet loss rate and the like in real time, an optimal routing path is calculated in combination with an objective function, and a data packet is transmitted along the path. By optimizing a data packet structure and a transmission mechanism of a protocol, redundant data is reduced, and the data transmission efficiency is improved. The method can effectively improve the transmission efficiency of network communication, reduce the time delay and packet loss rate, and improve the network stability and reliability.

The invention discloses a multi-source heterogeneous data synchronization system, and relates to the field of computer information management application. The system fuses key technologies such as flow dynamic regulation and control, self-adaptive load balancing and intelligent semantic mapping. Through a real-time network state monitoring and self-adaptive flow regulation and control algorithm, the data transmission rate is dynamically adjusted according to the server resource utilization rate, the data priority and the network bandwidth, data backlog and system overload are avoided, and the robustness and the real-time performance of a synchronization task are remarkably improved. The intelligent field mapping engine is based on semantic analysis, word vector representation and a machine learning model, automatically recognizes field semantic consistency of heterogeneous data sources, generates an optimal mapping rule and dynamically optimizes a mapping result, the manual configuration cost is reduced, and the mapping accuracy rate reaches 98% or above. The system has high expandability and self-learning ability, is suitable for a large-scale multi-source heterogeneous data integration scene, and provides an efficient, intelligent and self-adaptive full-stack solution for a data synchronization task.

The invention discloses a remote centralized control distributed data processing and analysis method based on a JMS, and the method comprises the steps: achieving the message routing and resource scheduling through the real-time monitoring of the state of a system, reinforcement of learning decision, deep learning optimization and multi-dimensional message distribution; dynamically adjusting message flow rate and queue length based on real-time network conditions, node load and flow fluctuation; through edge node intelligent task scheduling, distributed collaboration of calculation and storage is realized; adaptive compression algorithm selection is carried out by using deep learning, and the consumption of message transmission bandwidth is reduced; non-tampering storage of messages is realized through a block chain technology; based on a container arrangement technology, dynamic expansion and contraction of computing resources are realized; and dynamic resource scheduling and optimization decision are realized by utilizing reinforcement learning and multi-dimensional monitoring data. According to the invention, the bottleneck problem of the traditional message transmission system is overcome, and the system can efficiently and reliably process mass data in a high-concurrency and high-throughput scene.

The embodiment of the invention provides a dynamic allocation method and device for AI reasoning tasks, and the method comprises the steps: monitoring the workload, memory availability, network delay, bandwidth and energy consumption of each computing node in a cloud edge cooperation system in real time, the computing node comprising a local edge device, an edge server and a cloud platform; according to the delay requirement, the calculation requirement and the data privacy requirement of the task and the real-time state of each calculation node, dynamically distributing the task to different calculation nodes; adjusting the complexity of an AI model used for executing the task according to the resource use condition of the computing node; and according to the historical performance data and the real-time network condition in the task execution process, optimizing a task allocation strategy.

Methods, systems, and computer-readable storage media for receiving, from probes, probe data indicative of vulnerabilities of one or more devices to cybersecurity threats. The devices are connected over a network. Aggregated probe data is generated by mapping the probe data using data relationships obtained from a relational database, The data relationships define vulnerability types of cybersecurity threats. A cybersecurity compliance score of each of the one or more devices is determined using a correlation of the aggregated probe data to cybersecurity status scenarios defining consequences related to the cybersecurity threats. A cybersecurity assessment report including the compliance score of the one or more devices and an action plan preventing the consequences related to the cybersecurity threats are provided.

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 provides a QoS routing optimization method and system, a computer and a readable storage medium, and the method comprises the steps: S1, obtaining a real-time network performance data set which comprises link bandwidth, delay and packet loss rate; s2, modeling a network topology into a graph structure based on the real-time network performance data set, and generating a network state feature matrix; s3, constructing a state space and an action space according to the network state feature matrix, wherein the action space comprises a plurality of candidate paths from a source node to a target node; s4, selecting a candidate path from the action space through a main network, evaluating the performance value of the candidate path by adopting a target network, updating the main network according to the performance value, and generating an empirical data set; and S5, optimizing a path selection strategy according to the empirical data set, and updating a routing table. According to the method and the device, the optimal path meeting the QoS requirement can be found for the data flow in different service environments in the dynamic network environment.

The invention relates to the technical field of communication networks, in particular to a real-time network flow prediction and resource optimization distribution method and system based on a space-time multi-mode generation model. Multi-modal feature extraction and fusion are carried out; model prediction is generated; reinforcement learning scheduling optimization; self-supervision and feedback closed loop are realized; executing resource allocation; the method has the advantages that multiple data sources such as spatio-temporal data, user behaviors and application types are fused through the multi-modal large model, future network traffic distribution is generated through the generation model, and the accuracy and timeliness of traffic prediction are improved. The network resources of different areas and time periods are intelligently scheduled through the reinforcement learning model, real-time and automatic resource allocation is realized, and the network quality and user experience of high-demand areas are ensured.

The invention discloses a hydrogen fuel engine electronic thermostat heating assembly control method based on the Internet of Things, particularly relates to the technical field of hydrogen fuel engine control, and is used for solving the problems of cooling response lag and action conflict caused by disconnection of control logic and a real-time network state of an existing temperature control system in a dynamic network environment. Electronic thermostat temperature data and network state data are collected in real time to generate a dynamic synchronization coefficient, network topology toughness evaluation and causal influence analysis are combined to predict instruction conflict areas and divide priorities, and gradient correction is performed on heating power and valve opening based on a target deviation compensation value. And a correction instruction is issued through the distributed control unit and a communication reliability weight is synchronously updated, so that dynamic coordination of a network state and a temperature control parameter is realized, and efficient and stable operation of the hydrogen fuel engine under a complex working condition is ensured.

The invention relates to the technical field of satellite telemetering and remote control, and discloses a port task processing method for a satellite measurement, operation and control system, which comprises the following steps of: 1, detecting all available network interfaces through a port scanner, and establishing a multi-path communication channel comprising a public network, a private network and a satellite link; step 2, acquiring packet loss rate, delay, bandwidth and jitter parameters of each path in the multi-path communication channel in real time, and updating the parameters based on a preset period; and step 3, generating a comprehensive score of each path according to the packet loss rate, the delay, the bandwidth and the jitter parameters. According to the method and the device, the technical scheme of dynamic scoring and switching of the multi-path communication channel is adopted, and the technical effect of automatically selecting the optimal path according to the real-time network performance is achieved; the problems of high transmission delay, large packet loss rate and incapability of adaptive adjustment caused by network fluctuation are solved.

The invention provides a power distribution station house wireless communication method based on a cooperative coding algorithm, which comprises the following steps: data processing of dynamic environment adaptation: according to the real-time requirement of power distribution station house equipment data, segmenting original data into data blocks carrying priority labels, and dynamically generating a redundant coding strategy based on a real-time network state; network-aware cooperative transmission: transmitting data blocks and redundant information through multi-node cooperation, wherein a transmission path and a redundancy distribution proportion are dynamically adjusted according to network signal quality, node load and fault state; and fault-tolerant recovery of closed-loop feedback: the receiving end jointly decodes the data block and the redundant information, triggers a local retransmission instruction according to a decoding result, and feeds back the local retransmission instruction to the network architecture to optimize a subsequent transmission path.

The invention relates to the technical field of artificial intelligence, can be applied to the fields of finance, medical treatment and the like, and provides a network fault processing method based on artificial intelligence, and the method comprises the following steps: carrying out the trend prediction and anomaly detection through a machine learning model based on real-time network performance indexes and historical alarm data, and obtaining a network fault detection result; generating an alarm set containing fault type inference; performing association analysis on the alarm set and network topology and change records, constructing a fault propagation map, and outputting a root cause node list sorted according to probability; generating an equipment parameterized repair script according to the root cause node list; executing the repair script and monitoring an execution state in real time to obtain a repair result; and verifying the repair result. Compared with the prior art, the method has the advantages that the end-to-end fault processing period is remarkably shortened, the manual intervention requirement is reduced, and the service continuity is guaranteed in a complex network environment.

The invention provides a reliability-aware dependent task unloading method based on topology reconstruction in industrial internet edge computing, which comprises the following steps of: constructing a system model covering an edge cloud network platform, an industrial cloud platform and internet of things equipment, and establishing a transmission delay model and a reliability model; constructing a task unloading mathematical model with the maximum reliability level; the method comprises the following steps: modeling a micro-service dependency relationship into a weighted directed acyclic graph based on a network flow theory, and carrying out topology reconstruction on micro-services applied to the Internet of Things through a Ford-Fulkerson approximation algorithm to obtain a micro-service grouping structure formed by minimum cut division; the micro-service grouping structure serves as priori knowledge to be input into the deep Q network, the deep Q network is used for solving a task unloading mathematical model, a task unloading strategy is dynamically adjusted, resource allocation is optimized, and an optimal calculation unloading scheme in the industrial internet edge calculation environment is obtained. The micro-service deployment is optimized, the communication overhead is reduced, and the system reliability and the resource utilization rate are improved through real-time network state dynamic decision making.

The invention discloses an AI-based automatic connection and handshake protocol for a trusted data space connector. The AI-based automatic connection and handshake protocol comprises a data supply end connector, a data demand end connector and a platform operation end, the data supply end connector is used for collecting and preprocessing data; the demand end connector is used for receiving and processing data; the platform operation end is used for connection management, authority distribution and security auditing; the method has the beneficial effects that through automatic connection and an intelligent handshake protocol, manual intervention is reduced, and the data circulation cost is reduced; multi-factor authentication, dynamic encryption and privacy protection technologies are adopted to ensure the safety of the whole life cycle of the data; aI and machine learning technologies are introduced, so that the system can intelligently select an optimal strategy according to real-time network conditions and data features; interoperation and data sharing among different data spaces are realized, and circulation and value mining of data elements are promoted.

The invention discloses an edge-cloud collaborative civil aviation equipment intelligent detection platform, which relates to the technical field of intelligent detection and comprises an edge computing layer, a cloud computing layer, a collaborative scheduling layer, a data collaborative channel and an image processing layer. According to the method, a dynamic edge-cloud task scheduling mechanism is adopted, a traditional static task allocation mode is broken through, millisecond-level elastic task migration is achieved through real-time network state monitoring and computing power perception, incremental federated learning collaborative modeling and a gradient aggregation mechanism based on differential privacy are adopted, collaborative optimization of a multi-edge-node model is supported, and the task migration efficiency is improved. Multi-modal sensor fusion and lightweight anomaly detection are fused, the composite fault detection precision is improved, adaptive sampling and an edge knowledge graph are adopted, the sampling rate is dynamically adjusted based on the equipment risk level, offline rapid diagnosis can be achieved in combination with a graph database, and the problems that data redundancy is caused by fixed sampling, and the detection accuracy is high are solved. And the traditional system loses the diagnosis capability when the network is interrupted.

The invention provides an SIM (Subscriber Identity Module) card switching system and an SIM card switching method. Belongs to the technical field of mobile communication. The method comprises the following steps: analyzing behavior information of a user based on a machine learning algorithm, and establishing a user behavior model based on an analysis result; collecting network quality data of the current position in real time through the Internet of Things communication module, evaluating the network quality, and generating a network quality report based on an evaluation result; based on the user behavior model, the network quality report and a preset switching strategy, intelligent switching decision making is carried out through an intelligent decision making engine, and the optimal SIM card is selected for communication. Through application of a machine learning algorithm and a deep learning algorithm, intelligent decision making can be performed according to a behavior model of a user and real-time network quality, it is ensured that an optimal scheme is selected when the SIM card is switched, and the reliability and efficiency of communication are improved.

The invention relates to the technical field of network optimization, in particular to a cloud-based computer network resource management system, which comprises a network bandwidth demand analysis module, a cloud platform computer network task management module, a cloud platform computer network task management module, a cloud platform computer network task management module, a cloud platform computer network task management module, a cloud platform computer network task management module and a cloud platform computer network task management module, and according to the peak value change and the distribution condition of the task bandwidth demand, marking the bandwidth demand area exceeding a preset frequency threshold value, and generating network bandwidth distribution characteristic data. According to the invention, a resource management process taking data driving, characteristic analysis and real-time prediction as cores is formed through dynamic distribution and optimization management of network resources. In the bandwidth demand analysis, the over-limit region is marked through the peak change of the time sequence data, the accuracy of resource distribution is optimized, and the problem of bandwidth waste or resource overload is effectively avoided.

The invention discloses a network space map surveying and mapping method and system based on multi-source data fusion, and the method comprises the steps: obtaining a multi-source data set in a unified format based on network flow data, equipment information data and geographic position data; obtaining a network asset entity and an incidence relation graph thereof through entity identification and correlation analysis based on the multi-source data set with the uniform format; obtaining a network space three-dimensional map model through three-dimensional space mapping and visual rendering based on the network asset entity and the association relationship map thereof; based on the network space three-dimensional map model, performing dynamic updating according to the accessed real-time data flow to obtain real-time network space situation data; and obtaining a network security risk assessment result through anomaly detection and threat identification based on the real-time network space situation data. According to the invention, visual display and security situation awareness of the network space are realized, and a brand new decision support tool is provided for network security management.

The invention provides an automatic perception model method and system for illegal access in a network security isolation area, and belongs to the technical field of computer systems based on specific calculation models.The method comprises the steps that firstly, a network topological graph matrix of the security isolation area is constructed, an equipment asset list is established, and then distributed flow collection nodes are deployed to obtain real-time network data; a deep packet detection technology is used for extracting features to establish an equipment behavior baseline library, a multi-target risk assessment function is used for carrying out risk grade division on equipment, a multi-layer perceptron and a time sequence anomaly detection algorithm are used for identifying abnormal communication, and an equipment fingerprint identification mechanism based on physical layer characteristics is established to verify the legality of the identity of the equipment. A security isolation intelligent sensing network model is utilized to analyze network behaviors, a multi-dimensional abnormal scoring system is constructed to calculate risk scores, a response mechanism based on a rule engine is realized, a federal learning technology can be selectively adopted to optimize the model, and an all-dimensional and multi-level illegal access automatic sensing protection system is formed.

The invention relates to the field of network resource scheduling, in particular to a network resource scheduling method based on artificial intelligence. The method comprises the steps of collecting resource scheduling data, and dynamically adjusting task priorities; constructing a network load prediction model and a resource demand prediction model to obtain a network load prediction value and a resource demand prediction value; calculating the comprehensive benefit of the task based on the task priority, the network load predicted value and the resource demand predicted value; and based on the comprehensive benefit of the task, a task scheduling decision is optimized through a reinforcement learning algorithm. The problem that a traditional resource scheduling method lacks adaptability to real-time network states and task demand changes is solved. A network load and resource demand prediction method generally adopts simple historical data analysis, and periodic fluctuation and a load attenuation effect are not considered; the problems of low task scheduling efficiency and unreasonable resource allocation due to the fact that scheduling decisions are mostly based on fixed rules and scheduling strategies cannot be adjusted according to real-time comprehensive benefits of tasks in the prior art are solved.

The invention provides a real-time network security monitoring and protection method and system based on deep learning, and the method comprises the steps: extracting the local features of network traffic through a convolutional neural network, analyzing the time sequence features of abnormal network traffic through a long-short term memory network, and recognizing a network attack type; calculating the intensity of the network attack based on the traffic rate, the duration and the number of source IPs of the network attack, calculating a risk score based on the local features of the network traffic, and carrying out weighted calculation on the risk score and the intensity of the network attack to obtain a comprehensive score; and in response to different types of network attacks and in combination with the comprehensive scores of the different types of network attacks, executing different network attack protection measures, adjusting the protection level in real time according to the strength and risk scores of the network attacks, recording network attack information and protection measures, and generating a security log. The network flow can be analyzed in real time, the network attack type can be identified, corresponding protection measures can be taken, the accuracy of network attack detection is improved, and the risk is reduced.

The invention provides a real-time network topology and parameter identification method and system for a power distribution system, and the method comprises the steps: collecting the data of a node for installing an intelligent electric meter in a historical power distribution network, and constructing a data set; calculating an admittance matrix, deducing a network topology structure, and preliminarily estimating line parameters; modeling the power grid topology based on a GCN (Graph Convolutional Network), calculating the importance of each node, and iteratively optimizing the placement strategy of the SMD through a loss function; on this basis, SMD data is adopted as the input of a graph neural network GNN, and real-time network topology and parameter identification are carried out on the system; and if the change of the network topology is identified, performing fine adjustment on the pre-trained GNN parameters through a transfer learning method to adapt to a new topological structure, and re-identifying the parameters. According to the method, under the condition of limited measurement equipment, the placement strategy of the measurement equipment can be optimized, and the identification real-time performance and precision of the topology and parameters of the power distribution network are improved.

The invention provides a network traffic scheduling optimization method based on deep learning. The method is applied to the technical field of communication networks, and comprises the following steps: S1, collecting flow data, link state, delay and packet loss rate indexes deployed at network nodes in real time, and constructing a sample database; s2, according to the sample database, constructing a traffic prediction model fusing a retrieval enhancement diffusion model and a mixed linear expert model to perform multi-modal traffic prediction; s3, constructing a state space of a reinforcement learning agent according to a real-time network state and the future traffic prediction result; s4, performing strategy optimization based on a multi-objective optimization mechanism; and S5, deploying the trained and optimized model to a network controller or an edge computing node to realize real-time sensing and dynamic scheduling control of network resources. According to the method, the traffic prediction precision is remarkably improved, the scheduling strategy is updated and optimized in time according to the network environment change, and the comprehensive performance of the network is improved.

The invention discloses a distributed computing power dynamic scheduling method and device and a medium, and the method comprises the steps: packaging heterogeneous computing power resources of a home terminal and an edge cloud node through a lightweight containerization technology, and collecting the hardware resource state data of the home terminal and the load data of the edge cloud node in real time; generating a dynamic scheduling strategy according to the task calculation type and the real-time network state of the to-be-calculated task, and splitting the to-be-calculated task into a plurality of sub-tasks according to the dynamic scheduling strategy; distributing the sub-tasks to home terminals or edge cloud nodes according with a dynamic scheduling strategy, and aggregating calculation results of the sub-tasks; and performing end-to-end encryption and fragmentation verification on the cross-domain transmitted data stream, and dynamically adjusting the task allocation permission of the home terminal based on the equipment security score.

The invention discloses a shared cache system applied to a real-time network terminal chip. The shared cache system comprises queue management, enqueue scheduling control, dequeue scheduling control, write bus control, read bus control, a data cache, a register module, an RXFIFO module and a TXFIFO module. According to the cache allocation strategy, the variable-length data is stored by using the fixed-length unit. The user can customize and configure the private space, the residual space is the shared space, and the storage utilization rate is high. The shared cache is mainly used for storing data packets sent by an external application layer through a host interface. And storing the received data packet, extracting frame information according to a network flow protocol, and directly discarding frames which do not meet the protocol. The shared cache is provided with a dynamic cache structure, the state of each queue can be updated in real time, frame loss is prevented, and the data stream processing capacity is improved. And meanwhile, complex hybrid scheduling of a real-time network is met. The whole shared cache is subjected to anti-radiation reinforcement, the scene requirement of space navigation application is met, and high reliability is achieved.

The invention relates to the technical field of block chain security, and discloses a decentralized network access authority management system based on a block chain, and the system comprises a data collection module which is used for collecting and preprocessing multi-dimensional attribute data, and then outputting the data to a dynamic tensor processing module; the dynamic tensor processing module receives the data of the data acquisition module, constructs a three-dimensional attribute tensor and is used for executing dynamic rank decomposition and then outputting a nuclear tensor to the strategy generation module; and the strategy generation module is connected with the dynamic tensor processing module and the cross-chain synchronization module and is used for generating an access control strategy matrix based on the kernel tensor. A dynamic strategy matrix with environment perception capability is constructed by fusing real-time network situation data and a global reference strategy generated by federal learning, and balance optimization of a local strategy and a network consensus is realized by adopting a gradient correction fusion algorithm, so that the problem that a traditional static strategy is difficult to cope with complex network environment changes is solved.

The invention relates to the technical field of network optimization, and discloses an AI-driven real-time network optimization algorithm, which comprises the following steps: data optimization: preprocessing network state data by using a generative adversarial network, extracting optimization features and generating enhanced data; topology modeling: modeling network topology by adopting a graph neural network, and updating node features; resource optimization: dynamically adjusting calculation, storage and bandwidth resources by using deep reinforcement learning; global scheduling: optimizing resource allocation based on a multi-objective optimization method; secondary optimization: combining a Lagrangian relaxation method and a graph optimization technology to further optimize a scheduling strategy; and adaptive learning: improving the adaptive capacity of the system in a dynamic environment through self-supervised learning and meta-learning. Through a matrix operation technology based on a regular equation and in cooperation with an efficient data preprocessing process, the effect of improving the solving speed of the regression model is achieved, the problem that calculation is slow on a big data set in a traditional method is solved, and rapid and accurate regression analysis is achieved.

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 multi-network communication method and device applied to unmanned driving, and belongs to the technical field of unmanned driving, and the method comprises the steps: obtaining 5G network signal parameter data from a 5G multi-network link channel, and filtering out the 5G multi-network link channel reaching the standard; the NPU neural network is used for conducting quantization processing on the standard 5G multi-network link channel network signal parameters, a comprehensive score of the standard 5G multi-network link channel network signal parameters is obtained, and the sequence of the current optimal link channel and other backup link channels is further obtained; adjusting the optimal link channel in real time according to a comparison result of a preset condition and the real-time 5G network signal parameter data associated with the current optimal link channel; and keeping communication with the cloud server based on the optimal link channel adjusted in real time. The multi-network communication module is composed of at least three independent 5G + C-V2X full-network communication modules and an auxiliary USIM card slot, the network can be optimized in real time along with vehicle operation, and it is ensured that the vehicle always uses the optimal 5G network.