6 results about "DEVS" patented technology

This invention belongs to the field of information security technology and discloses a method for verifying the strong opacity of time-assigned discrete event systems based on labeled temporal Petri nets. First, the time-assigned discrete event system to be analyzed is accurately modeled by constructing its labeled temporal Petri net model to formally describe the system's states, events, time constraints, and partial observability. Second, for a given time-labeled sequence (i.e., externally observed events and their timestamps), this invention proposes to efficiently enumerate all logically consistent paths with the sequence by constructing a partially modified state class graph and extracting their time constraints. Finally, based on this set of time-consistent paths, the set of all possible reachable current states (identifiers) of the system is calculated. This invention provides a method for verifying the strong opacity of time-assigned discrete event systems based on labeled temporal Petri nets, solving the problems in the background technology, reducing computational complexity, and making it more suitable for complex systems.

This invention provides a method, apparatus, and electronic device for modeling maritime rescue processes. The method includes: constructing a hierarchical model architecture driven by discrete event system specifications for the maritime rescue process, dividing the maritime rescue system model into a discrete event system layer and an object model layer; constructing a generalized rescue platform model and modular sub-models; the modular sub-models include a controller model, a maneuvering model, and a sensor model; constructing a complete rescue system simulation architecture based on the generalized rescue platform model; and performing repeated simulations and performance analysis on the complete rescue system simulation architecture to obtain a performance analysis report. This invention achieves accurate representation of the rescue system and solves the problems of poor accuracy and completeness in existing related technologies for rescue modeling.

The present invention proposes a multi-resolution model description method and system for complex product simulation, which belongs to the field of computer modeling and simulation technology. The method comprises: constructing basic models corresponding to each entity in the simulation object at each resolution, and the description of the basic model is constructed based on the DEVS atomic model and the Agent metamodel; according to the basic model, constructing a multi-resolution atomic model of the entity, and the atomic model contains the description of the basic model of different resolutions for any entity in the simulation object; according to the atomic model, constructing a multi-entity network model, and the network model is a multi-entity network formed by coupling the atomic models of each entity; according to the network model, constructing a coupling model for the simulation object in the current simulation, and the coupling model contains the network models corresponding to the simulation object under different network structures. The present invention can realize multi-level model description and model construction for complex products or systems, and meet the modeling needs of simulation scenes at different abstract levels.

The application belongs to the technical field of discrete model simulation, and provides a modeling and simulation method for supporting external event output under a DEVS framework, comprising: external event output acquisition; external event output data routing and transmission; and external event trigger chain recursive processing.The application provides the function of directly outputting data for the external event in the DEVS, thereby solving the message trigger chain error problem existing in the DEVS in the super-density time simulation, and providing more convenient modeling and simulation tasks for the modeling and simulation workers.

The present invention belongs to the field of high arch dam construction simulation modeling, and relates to a high arch dam simulation modeling optimization method based on an adverse weather dynamic response mechanism, including: 1) establishing a response mode consistent with the actual high arch dam construction for the adverse meteorological conditions that affect the high arch dam construction; 2) calculating the forecast period of the next casting bin or the forecast period of the next slab layer, analyzing the adverse weather impact during the forecast period, and obtaining the analysis results of the adverse weather impact during the forecast period; 3) according to the analysis results of the adverse weather impact during the forecast period and the established response mode, adopting a pre-response mechanism for the construction activities to be started, and adopting an in-process response mechanism for the ongoing construction activities; 4) integrating into the high arch dam construction simulation model developed based on the discrete event system principle to complete the modeling optimization. The present invention can simulate the adverse weather impact effect according to weather forecast data and adopt a response mode consistent with the actual construction, thereby optimizing the high arch dam construction simulation model and improving the simulation accuracy.

The external heterogeneity in the Discrete Event System Specification (DEVS) for simulators refers to the ability to incorporate different types of models, each with potentially different behaviors, into a single simulation environment. In the DEVS framework, a system is composed of multiple individual models, each representing a component of the system. These models can be either atomic (cannot be decomposed further) or coupled (composed of other models). This gives DEVS its hierarchical nature. The atomic or coupled models can in DEVS Markov models be fundamentally different from each other. For example, one model might represent a deterministic rule-based decision process, while another model might represent a probabilistic random variable generator. These models have different state variables, different event sets, and different transition functions, but they can still interact with each other within the same simulation.