4 results about "Session layer" patented technology

The application relates to the technical field of military simulation data engineering, and discloses a multi-dimensional semantic knowledge graph framework construction method for military simulation, which comprises the following steps: analyzing an object model extension component of a high-level architecture to construct a simulation object model incremental state sequence, and mapping a multi-dimensional coordinate of data distribution management into a discretized grid data routing domain; analyzing a federal protocol session layer log to determine a member connection reliability index, and combining the grid data routing domain to calibrate a data reachability index of each grid unit; generating an extended semantic type identifier for a semantic assertion, constructing a semantic missing tendency evaluation model associated with the extended semantic type identifier and the data reachability index, and calculating a missing risk measurement value for an incremental semantic field; and generating a global consistency compensation factor based on the missing risk measurement value and fusing the semantic triple.

Various embodiments relate to network capabilities. Devices of a network can have different capabilities. The network can provide artificial intelligence (AI) enabled, machine learning (ML) enabled, deep learning (DL) enabled networked access to these capabilities. The capabilities can share a common AI / ML / DL-enabled open layer-based net-centric logical protocol architecture. Also, different features can be achieved through different layers. As an example, AI enabled access can be achieved through the application layer, ML enabled access and DL enabled access can be achieved through the presentation layer and the session layer, and network access is achieved through the transport layer, the network layer, the link layer, and the physical layer.

The application relates to the technical field of computer simulation, and discloses a military simulation scenario description method based on a knowledge graph, which comprises the following steps: acquiring command message data and electromagnetic environment link state data, and dividing a command session set according to message header identification; constructing a multi-layer associated knowledge graph comprising a session layer, a sentence layer and a traceability layer; according to the electromagnetic environment link state data, the cumulative link connection duration is counted, and when a preset threshold is met, a scenario grounding operation is triggered, and an execution element layer is generated in the graph; the graph element scale change amplitude before and after each grounding operation is counted, and a session traceability inflation index is generated through weighted processing; finally, a complexity identification node carrying the index is established and written into the graph, and the final knowledge graph file, the replayable command message sequence and the simulation executable scene package are output. The application can quantify the structural inflation risk caused by the environment and the protocol mechanism in the description stage, and is suitable for large-scale simulation interoperation.

The application provides an AI active care intelligent interaction method based on long-term memory and multi-round dialogue, and belongs to the technical field of natural language processing.The application realizes fine-grained distinction of the importance of information in long dialogue history by constructing a weighted memory tensor based on an exponential decay mechanism, retains complete interaction tracks and highlights recent semantic contributions, and effectively solves the long dependence information loss problem caused by fixed window truncation; through deep fusion of entity embedding, weighted features of the appearance round and dialogue theme evolution path, joint encoding with timing perception ability is generated and stored in a session layer dictionary, which significantly enhances the system's ability to capture user interest migration; further, through a dynamic window detection mechanism, semantic missing areas in the dialogue are automatically identified and memory enhancement based on linear interpolation is performed, filling the information gap and improving the coherence and pertinence of active questioning.