28 results about "Network automation" patented technology

Embodiments here in disclose a method and system for network configuration generation using a network digital twin (NDT) comprising a spatio-temporal graph neural network (STGNN) for performance-based simulation in a communication network system is disclosed. A simulation request is received from a management service (MnS) consumer, including network parameters related to live network nodes. Performance data is retrieved from a management server, and performance is assessed against a performance threshold. Multi-domain telecom data is processed using a spatio-temporal graph neural network (STGNN) by a feature fusion attention mechanism, trained via a hybrid feedback loop with real and synthetic data. Upon meeting performance thresholds, a configuration recommendation is generated by a configuration recommendation engine, including an explanation and confidence value. The configuration recommendation is transmitted to the MnS consumer, enabling network automation without altering the live network directly.

Systems and methods are described for automated anomaly detection in computer networks using a dynamic network graph. Network data describing communications among computing entities are received, and a dynamic graph is constructed and maintained whose nodes represent the entities and whose edges represent observed communications. Behavior characteristics are computed for the nodes, and the nodes are clustered using a clustering algorithm to obtain cluster assignments. Anomalies are detected by identifying nodes whose behavior characteristics deviate from those of their assigned clusters, and alerts or security actions are generated in response. The system supports incremental updates to graph structure and cluster assignments as network conditions evolve, improving detection latency and accuracy.

There is provided an apparatus arranged to: receive, from a configuration controller, a request for feedback on a network configuration in a communications network; request feedback on the network configuration from a plurality of network critic functions; aggregate feedback received from at least part of the plurality of network critic functions in response to the request; and provide the aggregated feedback to the configuration controller.

In one embodiment, a device obtains data regarding routing decisions made by a machine learning-based predictive routing engine for a network. The device determines, based on the data regarding the routing decisions, a behavior of the machine learning-based predictive routing engine. The device compares the behavior of the machine learning-based predictive routing engine to a behavioral policy for the machine learning-based predictive routing engine. The device adjusts operation of the machine learning-based predictive routing engine, when the behavior of the machine learning-based predictive routing engine violates the behavioral policy.

Systems and methods are described for automated anomaly detection in computer networks using a dynamic network graph. Network data describing communications among computing entities are received, and a dynamic graph is constructed and maintained whose nodes represent the entities and whose edges represent observed communications. Behavior characteristics are computed for the nodes, and the nodes are clustered using a clustering algorithm to obtain cluster assignments. Anomalies are detected by identifying nodes whose behavior characteristics deviate from those of their assigned clusters, and alerts or security actions are generated in response. The system supports incremental updates to graph structure and cluster assignments as network conditions evolve, improving detection latency and accuracy.

The invention discloses an automatic deployment method for a long safety chain network based on Kubernetes Operator, and the core scheme of the method is to define double-layer custom resources (CRD): ChainMakerNetwork is used for declaring chain-level global configuration, ChainMakerNode is used for declaring node-level configuration, through the cooperative work of a node controller and a network controller, the network controller is used for declaring the global configuration of the chain-level, and the network controller is used for declaring the global configuration of the chain-level and the node-level configuration of the chain-level. A node controller generates an identity for each node and reports the identity to a resource state field in advance, and a network controller aggregates information to generate a global chain configuration file after monitoring that all dependencies are ready, so that the mechanism of the scheme breaks the circular dependency that node starting depends on network configuration, and network configuration generation depends on node identity; according to the invention, one-key and declarative deployment and full-life-cycle management of the long safety chain network in the Kubernetes environment are realized, and the deployment efficiency, reliability and operation and maintenance automation level are greatly improved.

The invention provides a distribution network automatic monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: determining each automatic switch equipment on a fault line and an initial switch state of the automatic switch equipment before a fault occurs based on a real-time operation mode of a fault event; taking an automatic switch device of any power failure simulation operation in the power failure simulation operation sequence associated with the fault event as a target switch device, and based on the operation instruction type and the initial switch state, determining a final expected switch state after executing the target power failure simulation operation; determining an abnormal action judgment result based on the final expected switch state of each automatic switch device in the power failure simulation operation and the actual action information of the automatic switch device in the fault event time window; and updating a power supply path connection relation based on the final expected switch state to obtain a post-operation power supply influence range and an action abnormity judgment result, and generating a fault handling logic process. According to the invention, the traceability and logic self-consistency of the fault handling process are improved.

Automation system, apparatus and method for coupling a device network and a communication network, wherein the system verifies addressing of components in an automation system having a hyper-convergent infrastructure, where a gateway extends data telegrams to include a device-specific signature if such a signature cannot be implemented by the automation device of the automation system itself.

This application discloses a device time correction method, apparatus, device, storage medium, and program product, relating to the field of embedded technology. The method, applied to embedded devices, includes: starting a time correction service via a local configuration file when the device boots up, and setting it as a prerequisite service for business services to ensure that business services start subsequently; calling the time correction service to perform corresponding time correction operations: if the hardware detection information of the real-time clock module indicates a hardware fault or absence, then performing anomaly detection on the current system time; if the current system time is abnormal, correcting the system time using a target time generated and pre-configured based on device batch information. This time correction scheme does not rely on an external network, is automated and timing-controllable, and based on a local time source, enables embedded devices to achieve time self-correction in offline deployment environments, ensuring time consistency and the reliability of business functions even without a network.

The invention discloses an automatic training system and method for a neural network, and relates to the field of network model training, and the system comprises a parameter configuration module which is used for loading a configuration file, displaying the configuration file in a tree structure in a visual interaction interface, interactively editing the configuration file, and storing the edited configuration file; the data preparation module is used for extracting actual data based on the stored configuration file, interactively obtaining a data set and further integrating model training preparation data; the model training module is used for searching an optimal architecture and hyper-parameter combination of a neural network model by using a genetic algorithm; and the reasoning prediction module is used for loading the optimal architecture and hyper-parameter combination of the neural network model, inputting the received user data into the neural network model and further returning a prediction result. According to the invention, the training efficiency of the neural network model is effectively improved.

The application discloses a full-parameter feedback type intelligent heat room layered cooperative control system and method, relates to the technical field of centralized heating pipe network automation control, and comprises a multi-dimensional heterogeneous parameter sensing module, a layered cooperative calculation module, a dynamic decoupling execution module and a soft measurement self-recovery monitoring module. The sensing module collects hydraulic transient and thermal lag data in all directions; the cooperative calculation module constructs a dimensionless 'thermal-hydraulic coupling urgency index' based on heterogeneous data, realizes decoupling calculation of time scale and physical dimension and generates a cooperative regulation instruction; and the execution module executes anti-oscillation clamping regulation of the regulation valve according to the instruction and in combination with a dynamic penalty function. The application breaks the dimensional interference and regulation deadlock in traditional full-parameter control, completely eliminates feedback storm under the layered architecture, and innovatively converts building thermal inertia into a virtual energy storage pool of the pipe network, so that high decoupling of thermal and hydraulic forces and peak load cutting and valley filling without additional hardware are realized.

The invention relates to the technical field of network communication, and discloses a software-defined multi-path network flow real-time dynamic optimization method, which comprises the following steps of: acquiring an initial application experience intention of a user on a service flow; collecting context information of the service flow, and performing multi-mode user experience quality perception on the service flow to obtain a current user experience quality evaluation result; under specific conditions, performing dynamic intention negotiation with a user or an agent application to obtain a negotiated user intention; generating or adjusting a multi-path network strategy according to the negotiated user intention, the current user experience quality evaluation result, the context information and the state information of each available path of the current network; controlling the transmission of the service flow on a plurality of paths according to a multi-path network strategy; according to the invention, the dynamic change experience requirement of the user can be more accurately met, and the user satisfaction and the network automation level are improved.

The invention relates to the technical field of automatic operation and maintenance, and provides an electric power communication network automatic operation and maintenance device which comprises a case, an air blowing device is installed on one side of the case, a ventilation device is installed on the other side of the case, a positioning device is installed on the top in the case, and an optimization device and a dust removal device are installed on the positioning device. A temperature sensor and a humidity sensor are mounted in the case; according to the computer case, cooling and dehumidification in the computer case are achieved at the same time through the air blowing device, and compared with a heat dissipation material, the computer case is more flexible, more remarkable in effect and wider in application range; the device is optimized to carry out powerful cooling aiming at faults, equipment in the device is prevented from being further damaged or other equipment is prevented from being damaged, and the cooling effect is improved; dust removal and cooling are combined together, the occupancy rate of the space in the device is reduced, so that the device adapts to the complexity of equipment in the device, accurate cooling and dust removal can be carried out, manpower is reduced, and efficiency is improved.

The invention provides an automatic operation and maintenance method and device of network equipment, electronic equipment and a storage medium, and relates to the technical field of network operation and maintenance. The method is applied to a server of a network automation operation and maintenance system, and comprises the following steps: in response to an operation and maintenance task request of target network equipment, determining an operation and maintenance task indicated by the operation and maintenance task request, and obtaining an access parameter and a login credential of the target network equipment; and then based on the access parameter, establishing a communication connection with the target network device by using the target network protocol, and based on the login credential, automatically logging in the target network device. And further converting the operation and maintenance task into a corresponding operation and maintenance instruction, and sending the operation and maintenance instruction to the target network equipment. And obtaining an operation and maintenance result generated after the operation and maintenance instruction of the target network equipment is operated, and visually displaying the operation and maintenance result. According to the method, the operation and maintenance efficiency of the network equipment can be improved.

The application discloses a Chang'an chain network automatic deployment method based on a Kubernetes Operator, and a core scheme of the method is to define double-layer custom resources (CRD): ChainMakerNetwork is used for declaring chain-level global configuration, and ChainMakerNode is used for declaring node-level configuration; through collaborative work of a node controller and a network controller, the node controller first generates an identity for each node and reports the identity to a resource state field; after the network controller monitors that all dependent items are ready, the network controller aggregates information to generate a global chain configuration file; the scheme mechanism breaks a circular dependency of 'node startup dependent network configuration, and network configuration generation dependent on node identity', realizes one-key and declarative deployment and full life cycle management of the Chang'an chain network in a Kubernetes environment, and greatly improves deployment efficiency, reliability and operation and maintenance automation level.

The present application relates to the technical field of electric power, and discloses a kind of resident short-term electric power load automation prediction method, comprising the following steps: S1: model establishment: establish resident short-term electric power load automation prediction model;S2: regional division: the geographical position of each resident home in a large area is divided in space using K-means algorithm, and is divided into several characteristic related regions;S3: data collection: collect the total power consumption and temperature data of S2 region in the past two years;S4: data division: the data collected and counted in S3 is processed and divided by sliding window method;S5: model training: the data divided in S4 is input into the model established in S1 for data training;S6: result prediction: the future day or week dimension electric power load size of local is automatically predicted by the trained network;The present application can reduce the workload of personnel, and can make reasonable electricity price for the power department, to provide a guidance scheme for ensuring the normal work of power plant.

This disclosure describes systems, methods, and devices related to remotely testing communication network equipment. A system may include a communications network including network equipment configured to facilitate communications in the communications network; fiber connections physically connecting test equipment to a network element of the network equipment; and automated testing software remote from the network element and from the test equipment, able to: turn up the test equipment via one or more interfaces; turn up the network element via the one or more interfaces; and send, via the one or more interfaces, wireless command signals to the test equipment to perform testing of the network element via the fiber connections.

A method, apparatus, and computer program product are provided for performing federated multitask learning using a federated learning aggregation environment and federated learning distributed nodes (FLDNs). In the context of a method, the method includes determining respective sets of FLDNs from a plurality of FLDNs for training corresponding personalized global machine learning (ML) models to perform different personalized tasks, where the different personalized tasks are determined based on one or more ML cases. The method further includes generating at least one first personalized global ML model for performing the first personalized task and at least one second personalized global ML model for performing the second personalized task. The method further includes performing a respective plurality of training iterations for training the first and second global personalized ML models using the corresponding FLDN set sums and by aggregating local model parameters of the local personalized ML models independently generated by the respective FLDNs.

The collection and submission of data elements from an individual occurs at registration for a new account or updating an existing account upon cloud-based software to accept service leads and service job offers from general contractors. This method allows applying for, automatically processing, and providing management of the associations, arrangements, agreements, interactions, and transactions between general contractors, subcontractors, and customers to convert service leads to jobs on cloud-based software. The method processes the service lead data from external systems and reads the relationship with the service lead details and general contractors and subcontractors. This method creates service job terms and conditions and tracks the job status against them. It communicates job status to the users and then accepts payment for the service job. Then, the method disburses payments to general contractors and subcontractors accordingly. The method continues communications between its users regarding additional jobs.

The invention discloses an equipment time correction method and device, equipment, a storage medium and a program product, and relates to the field of embedded technologies, the method is applied to embedded equipment, and the method comprises the following steps: when the equipment is started, starting a time correction service through a local configuration file, setting the time correction service as a front dependency service of a business service, the business service is ensured to be started later; calling a time correction service to execute a corresponding time correction operation; if the hardware detection information of the real-time clock module represents that the hardware fails or does not exist, performing anomaly detection on the current system time; and if the current system time is abnormal, correcting the system time by using target time which is generated and pre-configured based on the equipment batch information. The time correction scheme does not depend on an external network, is automatic and controllable in time sequence, and realizes time self-correction of the embedded equipment in an offline deployment environment based on a local time source, so that the equipment can ensure time consistency and service function reliability under the condition of no network.

The invention relates to the technical field of mine water disaster treatment, in particular to an intelligent network automatic treatment system for mine water sump excavation from a source based on multi-equipment cooperation. Comprising one or more sets of mobile water sump sedimentation tank rapid clearing, digging and pumping drainage equipment, sludge slurry efficient removal equipment, a slurry pumping drainage pipeline, a sedimentation tank, transportation equipment and an intelligent control module. According to the invention, the movable water sump sedimentation tank rapid cleaning, digging and pumping drainage equipment is arranged, and the cleaning and digging speed is 200 m / h. The efficient sludge slurry removal equipment is high in removal capacity of 220 m / h and high in separation precision which can reach 40 microns, the moisture content of the removed sludge is smaller than 40%, the sludge slurry is automatically sucked and discharged to the efficient removal equipment, the problems that belt hair and impurities block a slurry pump and burn a motor are directly solved, intelligent network automatic remote control is achieved through the intelligent control module, and the efficiency of the sludge removal equipment is improved. Therefore, the scheme of intelligent network automatic treatment of water sump cleaning and excavation from the source is realized.

Different aspects of the invention provide a modular network component, which enables a highly scalable solution for optimum control and management of a network comprising multiple nodes. All network components together form a control and management plane which can intervene in determining what to do or how to resolve an incoming request, or up-scale or down-scale to efficiently handle the number of nodes being managed or maintaining communication with disconnected sub-groups of nodes, thereby minimizing the detrimental effects of network breakdowns, rendering a truly scalable as well as disaster tolerant network control and management network and corresponding method.

The utility model discloses distribution network automation equipment convenient to install, and relates to the technical field of distribution network automation equipment, the distribution network automation equipment convenient to install comprises a bottom plate block and an electric power distribution network automation equipment body, the side wall surface of the bottom plate block is fixedly provided with a guide block, the other side wall surface of the bottom plate block is fixedly provided with a stop block, the bottom plate block is provided with a bearing groove, and the bearing groove is provided with a plurality of through holes. The bottom of the power distribution network automation equipment body is provided with a bearing groove, the power distribution network automation equipment body is placed in the bearing groove, the bearing groove is internally provided with a limiting assembly, and the rear wall surface of the power distribution network automation equipment body is fixedly provided with a pair of box bodies. The power distribution network automation equipment body and the check block are fixed through the connecting assembly, so that the power distribution network automation equipment body is stably placed on the bottom plate block, the power distribution network automation equipment body is prevented from being impacted by the outside to generate transmission, and the installation stability of the power distribution network automation equipment body is improved.

The application discloses a semantic communication method and system based on LSTM network automatic training, constructs an LSTM network as a main body framework of a meta-optimizer, and carries out activation on the input of the meta-optimizer through two different weight matrices and a Relu function; the processed input enters forgetting gates, input gates and output gates of the LSTM network respectively, and the output of the LSTM network is obtained; the obtained output is processed through a weight matrix and a limiting factor, and a predicted learning rate value is obtained; a semantic communication main task optimizes parameters of a semantic encoder-decoder by using the predicted learning rate of the meta-optimizer, and completes semantic communication. The application can automatically train the semantic encoder-decoder and make it meet convergence requirements, solve technical problems that cannot be universally trained and actually deployed on a large scale due to heterogeneous factors such as semantic communication tasks, data types, data modalities and encoder-decoder networks, and improve the generalization ability of the application to different semantic communication tasks.

The application provides a network routing system and method based on KDN architecture and multi-agent reinforcement learning, a data plane is responsible for storing, forwarding and processing data packets, a control plane constructs original data of a network by interacting with the data plane, fills all traffic matching switch flow tables according to best path information obtained by a knowledge plane, and a management plane stores and aggregates data of a database and original data of the network constructed by the control plane, performs data processing and analysis, judges key nodes, deploys agents on the key nodes by the knowledge plane, calculates the best path between any two nodes, and transmits the best path information to the control plane; the added knowledge plane solves the problem that the prior art center does not perform network automatic exploration, the proposed multi-agent intelligent routing system can reduce network delay and packet loss rate and improve throughput, and a multi-agent selection module of the management plane can better balance performance and overhead by selecting key nodes.

The invention relates to a network automation control method and device, equipment and a medium, and relates to the technical field of computers. The method comprises the following steps: acquiring corresponding first identification data when a target object enters a target area; wherein the first identification data is multi-modal identification data representing the identity of the target object; performing feature extraction on the first identification data to obtain an identification feature vector, and comparing the identification feature vector with reference data in a preset database to determine identity data of the target object; determining a network access strategy corresponding to the target object according to the identity data; wherein the network access strategy comprises an internet protocol address allocation rule, a port allocation rule and an access permission rule; and based on the network access strategy, automatically allocating an internet protocol address and an access port to the target object so as to realize network automatic access control of the target object. By adopting the method, automatic control of the network can be realized.

The invention discloses a link channel abnormal state prediction method, which comprises the following steps: establishing an abnormal state model of a target fault, the abnormal state model being a two-dimensional curve model constructed based on a plurality of abnormal state indexes associated with the current target fault and corresponding danger values; monitoring the abnormal state index in real time, and calculating the similarity between the current state model and the abnormal state model when the danger value changes; and if the similarity reaches a preset threshold, generating a risk level and giving an alarm. According to the method, whether the abnormity occurs can be predicted, fine judgment and graded early warning are performed on the type, the severity, the possible duration and the influence range of the abnormity, a hierarchical and operable decision basis is provided for a network automatic operation and maintenance system, and thus operation and maintenance personnel are assisted to realize accurate intervention.

The invention relates to the technical field of cloud computing, in particular to an Ironic-based bare metal network automatic configuration method, system equipment and medium, and the method comprises the following steps: in the process of completing bare metal node operating system deployment through Ironic, firstly querying an MAC address of a node port and a network type and a VLAN ID of a user network; then, dynamically injecting a network configuration script and a corresponding system service into the to-be-deployed operating system based on the queried information; and finally, when the bare metal node is locally started for the first time, automatically executing the configuration script by the system service, and configuring a corresponding VLAN sub-interface for the physical network card according to the MAC address and the VLAN ID, thereby automatically finishing switching from a deployment network to a user network. According to the invention, the network automatic configuration of the bare metal service is realized without depending on an SDN controller or manually intervening a switch, so that the cost is remarkably saved, and the delivery efficiency and reliability in a large-scale deployment scene are improved.