11 results about "Network variable" patented technology

A virtual machine startup control method based on a Boolean network model, which belongs to the technical field of communications. The method comprises: collecting state information of virtual machines, setting logic variables, and representing the logic variables in an algebraic form; defining network variables and parameters, and establishing a virtual machine Boolean network model on the basis of the logic variables in the algebraic form; determining an initial state and a target state, and by compiling a control algorithm, setting optimal control performance indicators and solving an optimal control sequence; and deploying the algorithm to a virtualization platform or server, determining the startup order of the virtual machines on the basis of the optimal control sequence, and monitoring operation conditions in real time. Further provided is a virtual machine startup control system based on a Boolean network model.

The present invention provides an intelligent secondary network balancing system and its network adjustment method, which includes a typical user room temperature collector, an intelligent heating balance valve, a unit control cabinet, a relay control cabinet, a heat exchange station control cabinet, a secondary network variable frequency circulating pump, and an upper platform. The typical user room temperature collector is installed in the user room of a building. The typical user room temperature collector communicates with the upper platform through the Internet of Things, and LORA communication is used between the typical user room temperature collector and the unit control cabinet. The intelligent heating balance valve is installed at the heat inlet of each household or the heat inlet of each unit or the heat inlet of each building. The intelligent heating balance valve is equipped with a supply water temperature sensor, a return water temperature sensor, a supply water pressure sensor, an inlet pressure sensor, and an outlet pressure sensor. Bus, LORA, and Bluetooth communication are used between the intelligent heating balance valve and the unit control cabinet, and Internet of Things communication is used between the intelligent heating balance valve and the upper platform.

Technology described herein can employ dynamically changing network variables and / or user entity priorities to determine admission of one or more user entities to the network. In an embodiment, a system can comprise a processor, and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise analyzing resource allocation for a quality of service flow. The operations further can comprise, based on a result of the analyzing the resource allocation, determining a threshold for quality of service for user equipment at the quality of service flow, and determining a connection decision for a user equipment based on a comparison of a quality of service prediction of a quality of service level for the user equipment to the threshold.

This invention relates to the field of industrial automation and control network technology, and discloses a real-time synchronization method system for LonWorks networks based on digital twin variables. Applied to LonWorks fieldbus networks, it is implemented based on a heterogeneous node system consisting of a general-purpose processing module, a LonTalk protocol processing module, and a high-speed serial communication bus. The general-purpose processing module carries a manager, the LonTalk protocol processing module maintains standard LonWorks network variables and has a built-in LonTalk protocol stack, and the high-speed serial communication bus transmits DTVP data frames. This invention achieves a significant leap in node computing power, freeing nodes from the limitations of dedicated communication chips and allowing them to directly utilize the high performance of industry-leading general-purpose microcontrollers to easily handle complex control algorithms, real-time data filtering, lightweight artificial intelligence inference, and other complex tasks.

The application discloses a natural gas network system model construction method and a parameter identification method. The method comprises the following steps: constructing a partial differential dynamic equation physical model of a natural gas pipeline, discretizing the model into a difference equation model, setting a topological constraint condition of the natural gas network, setting a natural gas network variable, and determining a natural gas network system model; outputting learned parameters of the model through the natural gas network system model; setting a model parameter identification loss function; updating an initial state of the natural gas network system and the learned parameters of the model in combination with the loss function; and determining parameter values of the natural gas network system model when a termination condition of the updating is met. The application does not depend on accurate model parameters of a natural gas network system, but can realize parameter identification of the system with physical interpretability only according to measurement results of the network. The established natural gas system is a linear dynamic model, has high precision, and can be widely applied to simulation calculation and scheduling decision.

The invention presents method and system for prioritizing Channel State Information (CSI) reports in 5G / 6G New Radio (NR) networks through enhanced integration with Rel-19 uplink control information (UCI) packing rules. The disclosed techniques dynamically assign priority levels to multiple CSI reports containing CSI-RS Resource Indicators (CRIs), thereby improving efficiency and reliability of data transmission under limited uplink resources. In one aspect, priority rules are based on the number of reported CRIs (M), the number of non- reported CRIs (M R ), or a combination of both, ensuring that critical information is transmitted first. The invention further conditions priority assignment on network variables such as serving cell index and CSI configuration parameters, while also enabling prioritization according to beam quality measures including SINR, RSRP, RI, and CQI. These methods provide robust omission handling, maintain backward compatibility with legacy CSI rules, and support deployment with large antenna arrays across frequency ranges FR1, FR2, FR3, and beyond.

The application provides a new energy flexible direct transmission system flexible direct current station adaptive control method, including obtaining a first reference value according to an equivalent sequence network variable, electrical quantity data and a pre-fault reference value, the first reference value representing a lower limit value of an internal electromotive force reference value of a virtual synchronous machine meeting a current limiting requirement; obtaining a second reference value according to the equivalent sequence network variable and a maximum fault current, the second reference value representing a lower limit value of an internal electromotive force reference value of a virtual synchronous machine meeting a current differential protection action criterion; obtaining a post-fault reference value according to the first reference value, the second reference value and a low voltage ride through voltage threshold value, so as to perform adaptive control of the flexible direct current station. The method does not significantly increase the complexity of the system, can adaptively adjust the post-fault reference value according to the fault severity, takes into account the current limiting requirement and the correct action demand of the current differential protection, at the same time prevents the occurrence of repeated low voltage ride through phenomenon, and effectively improves the reliability of the differential protection under the power electronic weak feedback characteristic.

The invention discloses a test case generation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a network variable information table; extracting first data used for generating a test case of a network variable from the network variable information table; performing data conversion on the first data based on a target engineering test rule to obtain second data, the target engineering test rule corresponding to the type of the first data; and based on the second data, generating a test case of the network variable, the test case being used for testing the network variable. Visibly, according to the scheme, the test case does not need to be written manually, so that batch generation of the test case can be realized for the network variable with a large scale, and the workload is remarkably reduced. And moreover, the participation of test workers is not needed, so that the test workload and the capability requirements of the test workers are reduced, and the test efficiency is integrally improved.

This disclosure relates to network resource allocation. A first device is disclosed to receive a first resource reservation message from a second device. The first reservation message is indicative of a first resource allocation requested by the second device from the first device. The first device determines a second resource allocation to be requested from a third device. After obtaining one or more network variables, the first device determines a first binding constraint based on the network variable(s), the first and the second resource allocations. In response to determining that the first binding constraint is violated, the first device determines a first external back-off amount that the first resource allocation need to reduce, and sends a first feedback message to the second device to notify the first external back-off amount. In this way, resource allocation can be optimized without the presence of a centralized controller. Communication overhead can be reduced.

Technology described herein can employ dynamically changing network variables and / or context, in realtime, to determine one or more possible contexts that can lead to network function (NF) misconfiguration, NF mismatch and / or key performance indicator (KPI) degradation. In an embodiment, an example system can comprise a processor, and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising determining a working configuration of a network function, and detecting a misconfiguration between a specified configuration and the working configuration of the network function, wherein the detecting is based on a context known to be associated with the misconfiguration. Analysis of context can comprise analyzing, based on an artificial intelligence model, the contexts of the network function as compared to known and / or stored contexts.

A method which is improved relative to the prior art for controlling electrical network variables in a feed network, in which the correlation between at least one feed variable measured at an input of the feed network and at least one network variable measured at an output of the feed network is described in a model-like manner using a model of the feed network. In the event of an incorrect description of the correlation, an adaptation of the model of the feed network is carried out, a feed variable target value for the at least one feed variable is determined from a predefined network variable target value for the at least one network variable using the model of the feed network, and the at least one feed variable is set to the determined feed variable target value by of a current converter.