53 results about "Network migration" patented technology

The invention discloses a machine room energy-saving operation and maintenance management method and system, and particularly relates to the technical field of machine room management. Computing resources are integrated in combination with a virtualization technology, virtual machine allocation is dynamically adjusted, migration flow is optimized by adopting an intelligent network bandwidth scheduling mechanism, and migration efficiency is improved; by combining a temperature and humidity control system and equipment operation temperature anomaly detection, the operation states of the air conditioner and the cooling equipment are dynamically optimized, energy consumption is reduced, early warning is performed based on the equipment operation temperature and the network load condition through a fault prediction model, equipment faults are prevented, and fuzzy logic control is adopted; based on the energy consumption efficiency and the fault prediction result, resource configuration is dynamically adjusted, the energy efficiency of the whole machine room is optimized, the utilization rate of computing resources is effectively improved, the influence of network migration on services is reduced, the energy consumption of the machine room is reduced, the equipment stability is improved, the management cost is optimized, and efficient, energy-saving and intelligent machine room operation and maintenance management is achieved.

The invention relates to the technical field of dredging engineering, in particular to a dredger autonomous path planning method based on reinforcement learning, which comprises the following steps: acquiring a multi-dimensional state space vector in real time; constructing a digital twinborn scene, training a strategy network by adopting a three-stage course learning strategy through increasing environment complexity, and dynamically adjusting a path planning priority through a layered reward function; migrating the trained strategy network to a ship entity; recognizing an obstacle based on a target multi-mode sensing system, evaluating a collision risk through a dynamic probability model, and performing local path reconstruction when the collision risk exists; and in a multi-ship collaborative operation scene, constructing a global optimization model, performing dynamic task redistribution on each ship by adopting a distributed iterative solution and incremental updating strategy until convergence, and outputting a collaborative optimization scheme of each ship. According to the method, the path planning efficiency in a multi-ship collaborative operation scene can be improved, and the training cost is reduced.

The invention discloses a hyperspectral remote sensing cross-scene classification method and system based on multi-view differential convolution and a side network migration strategy, and the method comprises the steps: obtaining a hyperspectral remote sensing image, and carrying out the preprocessing of the hyperspectral remote sensing image, and obtaining to-be-detected data; to-be-detected data is input into a pre-trained classification model for identification and classification, the classification model comprises a backbone network and a side network, the backbone network and the side network each comprise a feature extraction layer, a multi-view feature enhancement layer and a result output layer, the multi-view feature enhancement layer comprises a plurality of multi-view difference convolution modules, and the multi-view difference convolution modules comprise a plurality of multi-view difference convolution modules; the side network is obtained by training the backbone network through a side network migration strategy, and the backbone network and the side network are connected with each other through a gating connection layer. According to the method, the efficient and accurate classification of the complex hyperspectral remote sensing images is realized under the condition of not depending on a large amount of annotated data.

A computing system and method are disclosed for converting network configurations across different platforms using a language model. This system preprocesses a source network configuration from a first network platform, generates a prompt based on the preprocessed source network configuration and additional network data for input to the language model. In response to the prompt, the language model converts the source network configuration into a target network configuration for a second platform, ensuring that common configuration elements are preserved while unsupported protocols are adapted to supported alternatives on the second network platform. The system outputs the converted configuration, facilitating seamless network migration with minimal, or no, manual intervention. Additional features include the ability to receive and process active device states and network diagrams, suggesting alternative solutions for unsupported protocols, validating the target configuration against the second platform's specifications, and interacting with users to refine the conversion based on their input.

The present application relates to service coordination and perception data sharing for V2X. According to one aspect of the present application, provided is a system, comprising: one or more processors; and one or more memories having computer program instructions stored thereon, wherein the computer instructions, when executed by the one or more processors, cause an operation to be performed. The operation comprises: when cross-network migration of a service of a mobile terminal occurs between a V2X network and a 5G cellular network, on the basis of a service type of the service of the mobile terminal, implementing service coordination between the V2X network and the 5G cellular network.

The application relates to a network migration method and device, computer equipment and a storage medium, relates to the technical field of computers, and can be applied to the field of financial technology or other related fields.The method comprises the following steps: selecting a first user and a second user from each application user according to the user identifier of each application user; distributing the network request of the first user to a first private cloud server for response; distributing the network request of the second user to a non-private cloud server for response; updating a distribution rule and a distribution threshold value when it is detected that the first private cloud server is stable; updating the user identifier of each application user according to the updated distribution rule and the distribution threshold value; and returning to select the first user and the second user from each application user according to the updated user identifier of each application user until the number of the second user is zero.The above scheme can realize gradual migration of network requests under the condition that the private cloud server is stable.

The application provides a light field multispectral temperature inversion system and method based on a physical information neural network, first constructs a deep learning network infrastructure, adopts a double-layer U-Net architecture to extract radiation and spatial distribution characteristics of data; then designs a physical information embedding module, modularizes a Planck radiation law, and guides the network to establish a physical correlation between radiation information and temperature; subsequently, light field multispectral radiation data are collected through experiments, the network is trained after data division and preprocessing are completed, and a mapping relationship between radiation characteristics and temperature characteristics is established; finally, the trained network is migrated to actual test data, and high-precision and rapid temperature inversion is realized. The application has both the advantages of deep learning in processing complex data and the constraint of a physical model, not only improves the precision and calculation efficiency of light field multispectral temperature inversion, but also enhances the applicability of the light field multispectral temperature inversion in a high-temperature complex environment.

The invention discloses a dual-stack selection method for DNS (domain name system) area transmission and a related device, a first server respectively sends query requests of authorization start records SOA to a plurality of servers providing area data for the first server, P pieces of response information are obtained after waiting for a first time length, if IP addresses corresponding to the P pieces of response information are IPv4 addresses, the first server sends the query requests to the plurality of servers, and if the IP addresses corresponding to the P pieces of response information are IPv4 addresses, the first server sends the query requests to the plurality of servers; if yes, continuing to wait for a second time length to obtain Q pieces of response information, improving the probability of receiving the response information of the IPv6 address, then preferentially sorting the P pieces of response information and M IP addresses with the same serial number in the Q pieces of response information according to the IPv6 address to obtain a sorting result, and finally sending an area transmission XFR request according to the sorting result, therefore, the first server can preferentially select the server with the IPv6 address to carry out regional data synchronization, and the migration of traffic to an IPv6 network is accelerated.

The invention discloses a signal compensation method of an optical fiber communication system, which relates to the technical field of optical fiber communication and comprises the following steps: acquiring a baseband signal output after carrier phase recovery in a receiving end; a triple in the baseband signal is extracted, the triple is input into a target CLDNN network to obtain optical fiber nonlinear damage, the target CLDNN network is obtained by migrating a CLDNN network in a source domain to a target domain through migration learning, and the CLDNN network in the source domain is a network of a cascade CNN network, an LSTM network and a DNN network; and compensating the baseband signal through the nonlinear damage of the optical fiber. According to the technical scheme, the calculation complexity of the algorithm can be reduced, meanwhile, the high system transmission performance can be obtained under the low calculation complexity, and the compensation scheme can obtain high universality.

By providing and using a machine-to-machine (M2M) Embedded Universal Integrated Circuit Card (eUICC) in a consumer cellular telephone, a wireless carrier is able to download and manage profiles on the eUICC and is able to enable it for service on new networks, such as a wireless carrier's 5G or other network and migrate the cellular telephone to the new network without any interaction of the consumer with the cellular telephone and, in most cases, without any knowledge of the consumer that anything on their cellular telephone or SIM has changed. This is different from consumer style eUICCs where interaction of the consumer with the cellular telephone is needed.

The present invention relates to a system (108) and a method (600) for identifying customers eligible for migration from an existing network (106) to a new network (110) The disclosed system (108) and method (600) aim at enhancing the experience of the customer as well as service providers. More particularly, the present invention provides a unique approach of predicting and identifying eligible customers for migration from the existing cellular network technology to new cellular network technology based on the prediction performed by the AI / ML model (216) utilizing the historical data and trend analysis, focusing on pre-defined or dynamically set parameters. This ensures the customers can experience better performance after migrating from the existing cellular network technology to the new network cellular technology.

The invention provides an equipment state prediction method and evaluation method, and the method comprises the steps: collecting the historical state data of equipment, carrying out the normalization processing of the historical state data, and obtaining time series data; training the TimeGAN-LSTM model on the basis of the time sequence data and an anti-overfitting strategy, and performing joint optimization on the TimeGAN-LSTM model according to weights on the basis of an adversarial loss, a supervision loss and an embedding loss; migrating the supervision network into an LSTM prediction network to obtain a state prediction model; and inputting the state data at the current moment into the state prediction model to obtain state data at a plurality of continuous moments in the future. The invention provides an equipment state prediction method and an evaluation method, which are used for solving the problems that a traditional state prediction method is difficult to process non-linear, non-stationary and time dependence characteristics in time sequence data and TimeGAN is not fully applied in the field of state prediction.

The present invention relates to a system (108) and a method (600) for identifying customers eligible for migration from an existing network (106) to a new network (110) The disclosed system (108) and method (600) aim at enhancing the experience of the customer as well as service providers. More particularly, the present invention provides a unique approach of predicting and identifying eligible customers for migration from the existing cellular network technology to new cellular network technology based on the prediction performed by the AI / ML model (216) utilizing the historical data and trend analysis, focusing on pre-defined or dynamically set parameters. This ensures the customers can experience better performance after migrating from the existing cellular network technology to the new network cellular technology.

The present technology relates to an automated end-to-end network migration process from a locally deployed management platform to a cloud-based management platform (or vice versa). The disclosed technology describes an enhanced Plug and Play (PnP) software agent that is operatively coupled to a device and is capable of interacting with a PnP service hosted on a target management platform to initiate a migration process. Upon receiving a migration request from the device PnP agent, the PnP service on the target management platform maps the requesting device to a client profile present on the target management platform and identifies an associated default controller for the device based on the client profile. Subsequently, the device is directed to obtain and boot in accordance with a new software image that is compatible with the target management platform, and after converting the device's existing configuration to a version compatible with the new software image, the device is migrated to the associated default controller.

The invention provides a method and a device for cleaning residual data of a network element after user migration and a medium, and relates to the technical field of communication, and when the method is applied to the device for cleaning the residual data of the network element, the method comprises the following steps: establishing a trusted communication pipeline with a network element interface machine of a home subscriber server (HSS) of a first network; verifying that the user is a user whose communication is affected in the second network after being migrated from the first network to the second network; and sending an instruction to the HSS network element interface machine through the trusted communication pipeline, so that the HSS network element interface machine queries and clears the residual data of the user in the network element of the first network according to the instruction. The trusted communication pipeline is established through the network element residual data cleaning device and the HSS network element interface machine of the first network, and the residual data of the user in the network element of the first network is inquired and cleaned based on the trusted communication pipeline, so that the user is prevented from being influenced in communication in the second network after being migrated from the first network to the second network, and the user experience is improved. And the user experience is improved.

The invention provides a personal mobile network number anomaly detection method, device and equipment, a medium and a product, and relates to the technical field of data processing and communication security. Comprising the following steps: acquiring mobile network signaling ticket data and user tag data; according to a preset double encryption algorithm and in combination with a preset salt value, performing encryption processing on the main-end mobile phone number to obtain a desensitized mobile phone number; performing association processing on the desensitized mobile phone number and the user tag data to obtain a user identifier corresponding to the desensitized mobile phone number; according to the account period field, determining the accumulated number of opposite-end mobile phone numbers on the day corresponding to the main-end mobile phone number in the current account period; and obtaining a detection result of the desensitized mobile phone number according to the opposite-end mobile phone number, the user identifier, the accumulated number of the opposite-end mobile phone numbers on the day corresponding to the main-end mobile phone number in the current account period, a pre-stored acquaintance call record and a historical call statistical table. According to the method provided by the invention, on the premise of ensuring the security of user data, the real-time anomaly detection of the mobile network number call behavior is realized.

The present application relates to service coordination and perception data sharing for V2X. According to one aspect of the present application, provided is a system, comprising: one or more processors; and one or more memories having computer program instructions stored thereon, wherein the computer instructions, when executed by the one or more processors, cause an operation to be performed. The operation comprises: when cross-network migration of a service of a mobile terminal occurs between a V2X network and a 5G cellular network, on the basis of a service type of the service of the mobile terminal, implementing service coordination between the V2X network and the 5G cellular network.

This application discloses an unsupervised multi-source information domain adaptation method, device, medium, and product for rotating machinery migration diagnosis, relating to the field of mechanical fault diagnosis. The method comprises: acquiring multi-source information; fusing and compressing the multi-source information into a single fused sample; constructing a domain-adaptive self-attention network transfer learning model enhanced by information fusion; and inputting the single fused sample into the domain-adaptive self-attention network transfer learning model to obtain a migration diagnosis result for the rotating machinery. This application can address the problems of negative transfer, information loss, and high computational burden that exist in existing multi-source information domain adaptation methods.

The present disclosure provides a network slice migration method, device, electronic device and storage medium, which relate to the field of optical network technology. The method includes: when the target service is assigned to the target slice, the predicted network resources of the target slice are determined according to the strong prediction model, and the network resources include at least one of the predicted computing resources and the predicted spectrum resources; determining whether the predicted network resources of the target slice meet the first preset condition; if the predicted network resources do not meet the first preset condition, the nodes and paths of the target slice are migrated; if the migrated nodes and paths meet the second preset condition, the corresponding network resources are occupied. The present disclosure can realize the migration of the target slice in advance based on the prediction of future traffic, avoid network migration lag, effectively improve the flexibility and reliability of network slice migration, and improve service quality.

The invention provides a light field multispectral temperature inversion system and method based on a physical information neural network, and the method comprises the steps: firstly constructing a deep learning network infrastructure, and employing a double-layer U-Net architecture to extract the radiation and spatial distribution characteristics of data; then designing a physical information embedding module, modularizing the Planck's law of radiation, and guiding the network to establish physical association between radiation information and temperature; then, light field multispectral radiation data are collected through experiments, the network is trained after data division and preprocessing are completed, and the mapping relation between radiation characteristics and temperature characteristics is established; and finally, migrating the trained network to actual test data to realize high-precision and rapid temperature inversion. The method has the advantage of processing complex data through deep learning and the constraint of a physical model, the precision and the calculation efficiency of light field multispectral temperature inversion are improved, and the applicability of the method in a high-temperature complex environment is enhanced.

A computing system and method are disclosed for converting network configurations across different platforms using a language model. This system preprocesses a source network configuration from a first network platform, generates a prompt based on the preprocessed source network configuration and additional network data for input to the language model. In response to the prompt, the language model converts the source network configuration into a target network configuration for a second platform, ensuring that common configuration elements are preserved while unsupported protocols are adapted to supported alternatives on the second network platform. The system outputs the converted configuration, facilitating seamless network migration with minimal, or no, manual intervention. Additional features include the ability to receive and process active device states and network diagrams, suggesting alternative solutions for unsupported protocols, validating the target configuration against the second platform's specifications, and interacting with users to refine the conversion based on their input.

Disclosed are a virtualized network migration method, apparatus, device and medium, which are applied to the technical field of virtualized network migration, comprising: in a virtualized network migration process, detecting first version information of a first network resource fragment in a source node and second version information of a second network resource fragment in a target node, the first network resource fragment and the second network resource fragment are the same network resource fragment; when the first version information and the second version information meet a preset conflict condition, obtaining conflict resources corresponding to the first network resource fragment and the second network resource fragment; determining a corresponding target conflict processing strategy based on the influence of the conflict resource on a strategy dependency chain of the second network resource fragment; and executing the target conflict processing strategy to perform conflict repair. In this way, the conflict problem in virtual network migration can be detected in time and repaired in time, and the user experience is improved.

The invention discloses a method and a device for predicting potential mobile network loss users. The method comprises the following steps: acquiring target user feature data in a first time period; inputting the feature data of the target user into a potential mobile network loss user prediction model for prediction to obtain a loss probability prediction value of the target user; the potential network migration loss user prediction model is obtained by performing training and algorithm parameter tuning by adopting a cross validation stacking classifier based on historical user feature data in a second time period; the second time period is longer than the first time period; according to the loss probability prediction value of the target user, judging whether the target user is a potential network migration loss user; and when the loss probability predicted value of the target user is greater than a loss probability preset value, determining that the target user is a potential network migration loss user. The method can accurately predict the potential network migration churn users, provides accurate identification for retention measures, and can effectively retain the potential network migration churn users.

The invention relates to a network migration technology, and provides a transfer network and a borne intelligence method. Connecting the original network bearing intelligence with a carrier network; and operating the original network and the carrier network to promote the two networks to perform information interaction and network fusion so as to reduce the percentage of the intelligence distributed in the original network, improve the percentage of the intelligence distributed in the carrier network, transfer the intelligence distributed in the original network to the carrier network and realize the migration of the intelligence.

The present invention relates to the field of migration management technology and provides a multi-protocol cross-platform network migration management system, which includes a data acquisition module, a protocol identification module, a migration optimization module, an anomaly monitoring module, a protocol conversion module and a load scheduling module; the system realizes in-depth analysis and intelligent identification of different protocol fields through the protocol identification module, effectively solving the protocol compatibility problem in cross-platform network migration; by introducing a double-critical strategy optimization algorithm in the migration optimization module, the optimal migration strategy is automatically generated and optimized to ensure the reasonable allocation of resources and the high efficiency of the migration process; the system can automatically adapt to a dynamically changing network environment, provide intelligent protocol identification and migration optimization, significantly improve the reliability and efficiency of network migration, and is particularly suitable for complex, multi-platform network migration scenarios.

The present application relates to the technical field of dredging engineering, and particularly relates to a kind of autonomous path planning method of dredging ship based on reinforcement learning, comprising: real-time acquisition multidimensional state space vector;Construct digital twin scene, through the incremental environment complexity, adopt three-stage course learning strategy training strategy network, and pass through hierarchical reward function dynamic adjustment path planning priority;The trained strategy network is migrated to ship entity;Based on target multimodal perception system, obstacles are identified, collision risk is evaluated through dynamic probability model, and local path reconstruction is carried out when there is collision risk;In the multi-ship cooperative operation scene, a global optimization model is constructed, distributed iterative solution and incremental updating strategy are used to dynamically redistribute tasks for each ship until convergence, and the cooperative optimization scheme of each ship is output.The present application can improve the path planning efficiency in the multi-ship cooperative operation scene, and reduce the training cost.

The application discloses a cloud host network migration scheme based on DPU, and comprises the following steps: S1, basic network configuration; S2, out direction traffic identification and forwarding flow table configuration; and S3, in direction traffic identification and forwarding configuration. The scheme does not need additional planning calculation of private IP addresses, does not involve SNAT, and does not additionally design vpc and overlay, so that network migration channels are realized by utilizing PF and reusing original calculation internal network addresses, and the bandwidth of the migration network is ensured.

The application provides a network migration method and device, electronic equipment and storage medium. The method comprises the following steps: obtaining network configuration information of a traditional network from a gateway device, converting the traditional network configuration into a network architecture of an SDN network, then keeping the stability and performance of the network through each layer connection, gradually migrating virtual machines under a subnet to be migrated of the traditional network to corresponding virtual subnets in the SDN network, and finally switching the new and old gateways at one key, so that the network automatic migration is smoothly completed without affecting the business operation, the business traffic interruption time in the network migration process is minimized, the dependence on manual experience in the network migration process is reduced, and the network migration efficiency and accuracy are improved.

Per one example embodiment, a method is provided of shifting an OS (operating system) and associated native application platform from a private network to a cloud-based target environment, where the OS comprises an operating system not configured for web access. An image of the OS is provided that is suitable for installation on a target virtual machine in the cloud-based target platform. In lieu of moving user credentials of numerous current private network-based users of the OS—native application platform to the target environment, a passwordless credential-based OS command level communication brokering service is provided at the cloud-based target environment.

The present application relates to a kind of based on integrated weighting subdomain self-adapting network migration diagnosis method, belong to mechanical equipment fault diagnosis technical field.The method includes: S1: the original vibration signal on mechanical equipment is collected by sensor;Then, the original vibration signal collected is expanded sample using sliding window sampling technique;S2: EWSAN is constructed;S3: training sample is input into the EWSAN constructed, using the classification loss function L c Of source domain labeled sample, the EJDA domain adaptation loss function L EJDA Between source domain and unlabeled target domain, the edge distribution alignment loss function L MDA Between source domain and unlabeled target domain and the information entropy loss function L IE Of unlabeled target domain is used to EWSAN iterative update training;After multiple iterations training, error curve tends to be stable, EWSAN training is completed, and is used for the closed set and partial set migration diagnosis of bearing across.This application can improve the accuracy of migration diagnosis.