Abstract: A network management and control method and system, and a storage medium are disclosed. The method may include: obtaining a parameter change value of a target object, wherein the parameter change value is from a Digital Twin (DT) virtual model, the DT virtual model is constructed based on a physical model, the physical model comprises entity objects of a physical network, and the parameter change value represents a change in transmission performance of the target object; inputting the parameter change value into a pre-trained perception model, to obtain a state prediction result output by the perception model; inputting the state prediction result into a pre-trained cognitive model, to obtain configuration adjustment information output by the cognitive model; and in response to the configuration adjustment information passing emulation verification, adjusting the physical model according to the configuration adjustment information.

Abstract: A private line configuration method, an optical network, a device and a readable storage medium are disclosed. The method may include, acquiring intention information of a private line, where the intention information comprises a demand for a resource allocation of the private line; determining an intention indicator and an element corresponding to the intention indicator according to the intention information, where the element is indicative of a rule to be followed in the resource allocation according to the intention indicator; determining an intention resource allocation strategy for the private line according to the intention indicator and the element; and allocating the private line with a private line network resource according to the intention resource allocation strategy.

Abstract: The present disclosure provides a service resource configuration method, including: configuring resource parameters for a service to be configured according to an action policy, calculating a timely reward in a current state, performing IV analysis according to the action policy, and ending one episode after the IV analysis is completed; calculating and updating, according to the timely reward in each state, an optimization objective policy parameter in each state; iterating a preset number of episodes to calculate and update the optimization objective policy parameter in each state; determining, according to the optimization objective policy parameter in each state in the preset number of episodes, an optimal optimization objective policy parameter in each state; and updating the action policy according to the optimal optimization objective policy parameter in each state.

Abstract: The present disclosure provides a method for optimizing OTN resources, including: determining and creating, according to an action policy, a service to be created in a current service creation state, calculating a timely reward in the current service creation state, entering a next service creation state until an episode is ended, and calculating and updating, according to the timely reward in each service creation state, an optimized objective policy parameter in each service creation state; iterating a preset number of episodes to calculate and update the optimized objective policy parameter in each service creation state; determining, according to the optimized objective policy parameter in each service creation state in the preset number of episodes, a resultant optimized objective policy parameter in each service creation state; and updating the action policy according to the resultant optimized objective policy parameter in each service creation state.

Abstract: In the present disclosure a method and a system for generating an Optical Transport Network (OTN) DT network based on vertical federated learning are disclosed. The method includes: performing homomorphic encryption on a local fault root cause identifier to obtain an encrypted fault root cause identifier; receiving all encrypted alarm sample sequences corresponding to the encrypted fault root cause identifier; generating a single-domain training set according to the encrypted fault root cause identifier and the encrypted alarm sample sequences; training the local cross-domain fault root cause identification model according to the single-domain training set to obtain a model parameter update amount of the cross-domain fault root cause identification model; and reporting the model parameter update amount to the multi-domain orchestration system.

Abstract: A network management and control method and system, a network system, and a storage medium are disclosed. The network management and control method may include: acquiring Digital Twin (DT) model data corresponding to physical objects in a physical network (S110); generating, according to the DT model data, DT cases at different levels, wherein the levels of the DT cases correspond to levels of the physical objects, and the DT cases at different levels have a functionally collaborative relationship (S120); obtaining a target analysis result according to the collaborative relationship and all the DT cases (S130); and generating network configuration information according to the target analysis result, and delivering the network configuration information to the physical network so that the physical network implements network management and control according to the network configuration information (S140).

Abstract: An information processing method, a method for generating and training a module, an electronic device, and a medium are disclosed. The method for information processing may include acquiring a transmission parameter of service information in a network, where the transmission parameter is indicative of transmission performance of the service information in the network; and predicting transmission performance of the transmission parameter by means of a pre-trained network model, where the network model comprises a Graph Convolutional Network (GCN) model that is acquired according to transmission path information of the service information in the network.

Abstract: A network management and control method and system, and a storage medium are disclosed. The method may include: obtaining a parameter change value of a target object, wherein the parameter change value is from a Digital Twin (DT) virtual model, the DT virtual model is constructed based on a physical model, the physical model comprises entity objects of a physical network, and the parameter change value represents a change in transmission performance of the target object; inputting the parameter change value into a pre-trained perception model, to obtain a state prediction result output by the perception model; inputting the state prediction result into a pre-trained cognitive model, to obtain configuration adjustment information output by the cognitive model; and in response to the configuration adjustment information passing emulation verification, adjusting the physical model according to the configuration adjustment information.

Abstract: A network management and control method and system, a network system, and a storage medium are disclosed. The network management and control method may include: acquiring Digital Twin (DT) model data corresponding to physical objects in a physical network (S110); generating, according to the DT model data, DT cases at different levels, wherein the levels of the DT cases correspond to levels of the physical objects, and the DT cases at different levels have a functionally collaborative relationship (S120); obtaining a target analysis result according to the collaborative relationship and all the DT cases (S130); and generating network configuration information according to the target analysis result, and delivering the network configuration information to the physical network so that the physical network implements network management and control according to the network configuration information (S140).

Abstract: A private line configuration method, an optical network, a device and a readable storage medium are disclosed. The method may include, acquiring intention information of a private line, where the intention information comprises a demand for a resource allocation of the private line; determining an intention indicator and an element corresponding to the intention indicator according to the intention information, where the element is indicative of a rule to be followed in the resource allocation according to the intention indicator; determining an intention resource allocation strategy for the private line according to the intention indicator and the element; and allocating the private line with a private line network resource according to the intention resource allocation strategy.

Abstract: An information processing method, a method for generating and training a module, an electronic device, and a medium are disclosed. The method for information processing may include acquiring a transmission parameter of service information in a network, where the transmission parameter is indicative of transmission performance of the service information in the network; and predicting transmission performance of the transmission parameter by means of a pre-trained network model, where the network model comprises a Graph Convolutional Network (GCN) model that is acquired according to transmission path information of the service information in the network.

Abstract: The present disclosure provides a service resource configuration method, including: configuring resource parameters for a service to be configured according to an action policy, calculating a timely reward in a current state, performing IV analysis according to the action policy, and ending one episode after the IV analysis is completed; calculating and updating, according to the timely reward in each state, an optimization objective policy parameter in each state; iterating a preset number of episodes to calculate and update the optimization objective policy parameter in each state; determining, according to the optimization objective policy parameter in each state in the preset number of episodes, an optimal optimization objective policy parameter in each state; and updating the action policy according to the optimal optimization objective policy parameter in each state.

Abstract: The present disclosure provides a method for optimizing OTN resources, including: determining and creating, according to an action policy, a service to be created in a current service creation state, calculating a timely reward in the current service creation state, entering a next service creation state until an episode is ended, and calculating and updating, according to the timely reward in each service creation state, an optimized objective policy parameter in each service creation state; iterating a preset number of episodes to calculate and update the optimized objective policy parameter in each service creation state; determining, according to the optimized objective policy parameter in each service creation state in the preset number of episodes, a resultant optimized objective policy parameter in each service creation state; and updating the action policy according to the resultant optimized objective policy parameter in each service creation state.

Abstract: The present disclosure provides a method for optimizing OTN network resources, including: determining and creating a service to be created in a current service creating state according to an action policy, calculating a timely reward, entering a next service creating state, until an Episode is finished, calculating a comprehensive optimization parameter according to the timely reward, and calculating and updating a quantization index weight vector according to the comprehensive optimization parameter, where the action policy is a probability function related to the quantization index weight vector, and the quantization index weight vector corresponds to a plurality of quantization indexes; iterating a preset number of Episodes to obtain an optimized quantization index weight vector; and updating the action policy according to the optimized quantization index weight vector.

Abstract: Provided are a method and system for implementing an SDO function, and a SDON system.

Abstract: A multi-domain controller, a single-domain controller, a software defined optical network system and a method are disclosed.

Abstract: Provided are a method and system for implementing an SDO function, and a SDON system.

Abstract: Provided are a method and apparatus for estimating a meteorology sensitive load power. The method includes: obtaining a meteorology sensitive load power estimation model; inputting a daily load curve of a date to be estimated to the meteorology sensitive load power estimation model and extracting a daily load curve dimension reduction feature of the date to be estimated; and outputting a meteorology sensitive load power based on the daily load curve dimension reduction feature of the date to be estimated and mapping relationships from daily load curve dimension reduction features onto meteorology sensitive load powers. The proposed estimation model can directly obtain the meteorology sensitive load power curve from the daily load curve, and is especially applicable to cases where meteorology data is frequently lost in practical applications.

Abstract: The invention provides a positive/negative phase shift bimetallic zone plate and production method thereof, wherein the positive/negative phase shift bimetallic zone plate comprises: a first metallic material having a positive phase shift; a second metallic material having a negative phase shift at a working energy point; wherein the first metallic material and the second metallic material are alternately arranged, so that the second metallic material replaces the blank portion in a cycle of a traditional zone plate.

Abstract: Provided are a method and system for protecting virtual network mapping, and a computer storage medium; wherein the method includes that an application layer edits and defines at least one sub-network model based on a network editing request; a network virtualization layer constructs the at least one sub-network model, and integrates the at least one sub-network model into a global network view; and a controller layer respectively maps the at least one sub-network model contained in the global network view into a working device network and a protection device network, and when the working device network has failures, specified conditions are met, and the protection device network has no failure, switches a working mapping relationship of the corresponding sub-network from the working device network to the protection device network.