8 results about "Relational system" patented technology

This invention discloses a method for constructing a relationship-driven cybersecurity knowledge graph. This method, driven by relational semantics, sequentially executes the following steps: multi-source threat intelligence collection and preprocessing, relational semantic modeling, relationship-priority-driven knowledge extraction, entity alignment based on relational context, and knowledge graph construction. By constructing a relational system with entity type constraints and relational reasoning rules, it achieves complete attack chain modeling and implicit relational derivation; it employs a reverse extraction mechanism of relational identification → entity location to reduce error propagation; it introduces relational context to achieve accurate cross-source entity alignment; and finally, it establishes an indexed knowledge graph based on a graph database, supporting efficient querying and attack attribution.

Embodiments rank entity importance in monitored networks by analyzing network traffic to collect metrics representing relationships between entities. Network monitoring engines construct network graphs where nodes represent entities and edges represent relationships based on collected metrics. The system employs graph clustering algorithms to partition entities into logical clusters based on interaction patterns. Within each cluster, entities may be ranked by topological importance using metrics to identify primary entities that represent their clusters. Monitoring resources may be allocated preferentially to primary entities from each cluster. The topology-based ranking approach distributes monitoring resources throughout the network.

The application provides a display device and a switching method of a WiFi module. The method identifies a target use scenario according to an operation type corresponding to a user interaction action, and evaluates scores of each WiFi module in the scenario in real time based on performance parameters and weight coefficients recorded in a module state library. When the score of a current module is lower than a threshold value, the system automatically switches to a standby module with a higher score, and supports simultaneous operation of multiple modules. By constructing a module state library and a dynamic evaluation mechanism, the problem of poor flexibility of the WiFi module is solved. In addition, a user can customize the allocation relationship between the module and the scenario, and the system dynamically allocates resources according to priorities to prevent stuttering caused by scenario competition. The scheme considers static hardware parameters and dynamic operation indexes comprehensively, so that the system can flexibly adapt to different scenario requirements, significantly improves the use flexibility and resource utilization rate of the WiFi module, and further solves the problem of poor flexibility of the WiFi module.

The application belongs to the field of power dispatching system automation, and discloses a power grid dispatching control system reliability evaluation method and system based on a multi-dimensional state dependency graph. The application collects node states, slice group states, service call chains, system resource usage, and primary-backup consistency check results and other operation monitoring data, constructs a multi-dimensional state dependency graph (MDDG), and unifies the deployment relationship between nodes and slice groups, the cross-node data access relationship between nodes, the call relationship between services, the system resource competition relationship, and the primary-backup data consistency relationship into a dependency relationship structure, so as to reflect the dependency strength between various operation entities in the distributed dispatching control system, extend the system reliability evaluation from the traditional single-component or single-node level to the system overall structure level, and improve the expression capability for complex distributed architectures.

The systems, methods, and / or instrumentalities disclosed herein can involve adjusting an automated cooperative operation based on a situation-derived constraint. A system can be configured to receive a first data stream and / or a second data stream from a first surgical element and / or a second surgical element. The second data stream can be indicative of a second output parameter associated with a complementary task. The system can determine a relationship between the first surgical element and the second surgical element based on the first data stream and / or the second data stream. The system can determine a boundary parameter associated with the second output parameter based on the first output parameter and / or the relationship. The system can communicate an indication of the boundary parameter to the second surgical element. The system can cause the second surgical element to perform the complementary task based on the boundary parameter.

A system may display a Graphical User Interface including a source region presenting a plurality of source data-serialization elements and a destination region presenting a plurality of destination data-serialization elements. The system may receive a user input associating a first destination data-serialization element, of the plurality of destination data-serialization elements, and a first source data-serialization element of the plurality of source data-serialization elements. Responsive to receiving the user input, the system may generate and store a mapping expression that defines a mapping association between the first source data-serialization element and the first destination data-serialization element. The system may present in a mapping region of the GUI displayed concurrently with the source region and the destination region, a mapping element representing the mapping association between the first source data-serialization element and the first destination data-serialization element.

Methods and systems for managing heterogeneous cloud resources are provided. The method includes normalizing heterogeneous data for provider-specific resources into a normalized data model that maintains relationships between components. A service request specifying an application is received. An orchestration sequence for the application is determined by analyzing the normalized data model to identify a subset of provider-specific resources for an infrastructure and day-2 environment of the application, and generating a process workflow. The orchestration sequence is executed by configuring the subset of provider-specific resources according to the process workflow. The method enables efficient orchestration of diverse resources across heterogeneous provider environments using a unified data model and workflow generation approach.

Methods and systems for managing heterogeneous cloud resources are provided. The method includes normalizing heterogeneous data for provider-specific resources into a normalized data model that maintains relationships between components. A service request is received, and an orchestration sequence is determined based on the request and the normalized data model. This involves analyzing the model to identify a subset of resources called for by the request and mapping dependencies to generate a process workflow. The orchestration sequence is then executed by configuring the subset of resources according to the workflow. The normalized data model enables efficient orchestration across heterogeneous environments while maintaining relationships and dependencies between resources.