29 results about "Network migration" patented technology

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.

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 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.

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 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 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.

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 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.

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.

The invention discloses a heterogeneous configuration robot neural network migration method and device, equipment and a medium, and belongs to the technical field of robot control. The method comprises the steps that configuration parameters of a target robot are extracted and coded, and configuration codes are obtained; collecting a small amount of target domain data to obtain a target domain data set; based on a general skill kernel network, the target domain data set and the configuration code, training a configuration adapter to obtain a trained configuration adapter; and obtaining a control instruction of the target robot based on the configuration code, the trained configuration adapter and the general motor skill kernel. By means of the method and device, the technical problems that in the prior art, data dependence is heavy, model reuse is difficult, and the development period is long are solved.

In some implementations, a network device may transmit user equipment (UE) route selection (URSP) rules during a registration of a UE with a non-standalone (NSA) network, wherein the URSP rules exclude network slicing related rules based on a lack of network slicing support in the NSA network. The network device may transmit, in response to a handover of the UE from the NSA network to a standalone (SA) network that supports network slicing, updated URSP rules during a registration of the UE with the SA network, wherein the updated URSP rules include a flag in each rule that controls the mode of migration of ongoing session(s) at the UE from the NSA network to the SA network during mobility procedures.

A network system can include a memory and processing circuitry, in communication with the memory, configured to: grant a client device access to a first network; determine that the client device has received authentication credentials for a second network; in response to determining that the client device has received the authentication credentials for the second network, prevent the client device from reconnecting to the first network; and cause the client device to disconnect from the first network.

The application provides a border network migration signal coverage problem processing method and device and a readable storage medium. The method comprises: classifying coverage problem data of a current network, establishing a database based on the data classification result, and the data classification result at least comprises a weak coverage class, an out-of-coverage class and a no-problem class; obtaining test data of a border area network migration signal; detecting and analyzing the test data based on the database to obtain a test analysis result; and determining a corresponding signal coverage problem processing strategy according to the test analysis result. The application provides a border network migration signal coverage problem processing method, which uses a test software carried by a drone to fly over a border port and a sea surface to perform signal testing, classifies signal coverage problems of a test result, and uses a matching algorithm to effectively solve various problems of border area signal coverage.

The application discloses a network migration method and system, electronic equipment, storage medium and program product, and relates to the technical field of Internet, which comprises the following steps: generating a migration plan according to a network migration request, network related information, first configuration information of a network to be migrated and second configuration information of a target network; establishing a transition tunnel to generate network traffic on a first network card of the target network; generating an initial security group policy according to the network traffic and deploying the initial security group policy to the first network card to obtain a performance index and a policy accuracy rate of the target network; if the performance index is within a preset baseline range and the policy accuracy rate reaches a gating threshold, adjusting the policy according to a preset weight to determine a traffic switching step, and switching network traffic corresponding to the network to be migrated to the target network. The application solves the problem that traffic switching during migration of a traditional network easily leads to service interruption, and the network after migration can only be verified after the migration is completed, and potential performance degradation defects in the traditional network cannot be found in time.

The invention discloses a computer data center resource scheduling method and system based on artificial intelligence, and relates to the technical field of data center resource scheduling and storage management, and the method comprises the steps: obtaining a time sequence sample, calculating a physical access time interval through the time sequence sample, carrying out the normalization of the physical access time interval, and obtaining a normalized access time interval; constructing a log-likelihood function based on maximum likelihood estimation, calculating an iterative update step length by adopting a Newton-Raphson incremental algorithm in combination with a fractional order log-bound function, and iteratively updating a shape distribution parameter and a scale distribution parameter; inputting the normalized access time interval, the optimal shape distribution parameter and the optimal scale distribution parameter into a risk assessment model, calculating an instantaneous risk rate, converting the instantaneous risk rate into a target access probability accessed in the next unit time, and constructing an inequality constraint trigger by combining the node retention penalty cost of a target data block and the network migration physical cost; and distributing the target data block to the matched target node.

The invention provides a 4G network migration bearing method and device, equipment and a storage medium. The 4G network migration bearing method comprises the following steps: newly adding an IP address and VLAN data on a 4G BBU, wherein the newly added IP address and VLAN data are used for cutting the 4G BBU from a PTN network to an SPN network; a physical port, an IP address and VLAN data are newly added on the SPN network; disconnecting the physical optical fiber connection between the PTN network and the 4G BBU; and performing physical optical fiber connection on a newly added physical port on the SPN network and a physical port of the 4G BBU, and realizing communication between 4G and 5G by using the SPN network based on the newly added IP address and VLAN data. Therefore, in the process of realizing communication between 4G and 5G, the service path can be shortened, the time delay can be reduced, and the interaction efficiency can be improved.

A system identifies devices to be migrated from a first network to a second network. Identifying the devices includes determining that the devices are eSIM-capable of being upgraded to operate using the second network. For each device, an eSIM profile is determined. Each device has a SIM card or a previous eSIM profile installed. The eSIM profile matches a level of service to be provided to the device by the second cellular network. The eSIM profile is pushed to the device. The eSIM profile is downloaded and installed on the device. The system determines that the eSIM profile has been installed on the device. The level of service to be provided to the device is enabled on the eSIM profile. The SIM card or previous eSIM profile on the device is disabled.

The invention discloses a large language model multi-domain training data dynamic allocation method based on meta reinforcement learning, and relates to the technical field of natural language processing. The method aims at solving the problems of low training efficiency, task forgetting and the like caused by an existing static data matching strategy. The method comprises the steps that firstly, on a small model which is consistent with a target large language model in structure but small in parameter quantity, a data matching strategy network is trained through meta reinforcement learning; then, the trained strategy network is migrated to a training environment of the target large language model; then, at the initial stage of training of the target large language model, a small number of real training tracks are utilized to perform fine adjustment on the strategy network; and finally, dynamically adjusting the sampling proportion of data in each field according to a real-time state in a training process by applying the fine-tuned strategy network. The large model training efficiency can be remarkably improved, disastrous forgetting is relieved, and the method has the advantages of being low in cost and high in mobility.

The invention relates to the technical field of service network migration, and provides a service network migration method and system. The method comprises the steps of determining a to-be-migrated user list based on a network equipment networking topology of a target user; the target user is a home user whose set top box is connected to the OTT service network; generating a to-be-migrated work order based on the to-be-migrated user list, and verifying the to-be-migrated work order to obtain a target migrated work order; based on the latest version of OTTx service software, receiving a parameter change message sent by the set top box of the target user in the target migration work order after service network migration; and determining whether the set top box is migrated to the OTTx service network based on the parameter change message. The whole process does not need manual participation, automatic processing of the migration process is achieved, the migration efficiency and accuracy are accordingly improved, the process is not perceived by a user, and the user experience can be improved.

A network migration method, a network migration apparatus, a communication device, and a communication system. The method includes: implementing a migration procedure of a first IAB node according to a received first migration command in a case that a target migration command has been transmitted to a target network node, where the target network node is a downstream child IAB node of the first IAB node or UE served by the first IAB node in an IAB network, the target migration command includes migration commands corresponding to all downstream network nodes of the first IAB node in the IAB network, and the first migration command is a migration command corresponding to the first IAB node.

The application discloses a novel hybrid tractor energy migration management method, and belongs to the technical field of intelligent control of agricultural equipment. The method is aimed at the problems of low training efficiency, untimely updating and non-migration of existing energy management strategies. Firstly, a sampling mechanism enhanced soft actor-critic algorithm fusing priority experience replay is proposed to improve the offline training efficiency. Secondly, a strategy updating method based on transfer learning is proposed. Through the "pre-training-network migration-fine-tuning" mechanism, the rapid updating and adaptive optimization of the strategy in cross-working conditions are realized. The system comprises a modeling module, a training module and a migration and updating module, and is used for realizing the above method. The application can significantly improve the efficiency of strategy training and updating, and effectively improve the fuel economy of the hybrid tractor.