8 results about "Binary particle swarm optimization" patented technology

This invention proposes a method for multi-branch fault identification and location in multi-energy systems based on intelligent algorithms. The method includes the following steps: S110, analyzing the impact of multi-energy coupled systems on traditional distribution network fault identification and location; S120, based on the results of S110, identifying the line fault types in the multi-energy coupled system, including: determining the fault area and identifying the faulty line, thereby narrowing the location range; S130, within the narrowed range, performing multi-branch fault location based on intelligent algorithms. This invention proposes a binary particle swarm optimization algorithm with adaptive characteristics, which can overcome the limitations of traditional methods and significantly improve the efficiency and accuracy of fault location.

The application provides an ICMPv6- DDoS attack detection method and system based on feature optimization, belongs to the technical field of cyberspace security, and solves the core problems of insufficient feature representation ability, missing security target constraint and high feature space redundancy of the existing detection method. The application first performs traffic aggregation and basic feature data extraction on ICMPv6 traffic, constructs an extended feature set, removes redundant features to obtain a low-redundancy candidate feature set, then completes two-stage feature selection through information gain rate filtering and an improved binary particle swarm optimization algorithm with recall rate constraint, outputs an optimal feature subset, and finally constructs a detection model based on the optimal feature subset to complete attack detection. The application significantly improves the attack detection rate, balances the detection accuracy and model lightweight, and can be widely applied to various IPv6 network security monitoring scenes.

This invention discloses a method for spatiotemporal dynamic prediction of power outage areas during typhoon disasters based on satellite remote sensing image fusion. The method includes: acquiring meteorological, geographical, and electrical equipment data of the target area and dividing the target area into several grid units; extracting the extent of water bodies in the disaster area based on satellite remote sensing images and calculating the newly added water area in each grid unit; using the coordinate information of transmission towers, distribution towers, and transformers, selecting and optimizing candidate lines through a binary particle swarm optimization algorithm to generate a grid topology and extracting its topological features; fusing meteorological data, geographical data, electrical equipment data, newly added water area, and grid topology features to construct a multi-source dataset; and constructing a typhoon power outage prediction model based on a dynamic spatiotemporal graph neural network. By performing spatiotemporal correlation analysis on the grid units, the power outage status of each grid unit at future times is dynamically predicted, resulting in a predicted power outage area. This invention enables dynamic prediction of power outage areas.

This invention proposes a collaborative service allocation method for low-Earth orbit (LEO) satellite constellations based on finite state machines, belonging to the field of LEO satellite constellation technology. First, this invention constructs an extended finite state machine spatiotemporal graph model to accurately represent on-board resources and state transitions while shielding the dynamics of LEO satellites. Then, a directed acyclic graph (DAG) model is used to represent services, establishing a graph mapping relationship between the DAG and the extended finite state machine spatiotemporal graph, and establishing a latency optimization problem. Finally, a constrained selection adaptive binary particle swarm optimization algorithm is used to solve the latency optimization problem of the graph mapping relationship, finding the mapping strategy with the lowest latency to complete the collaborative service allocation of the satellite constellation. This invention is designed for LEO satellite constellation scenarios. The service collaborative computation based on finite state machines meets the computational requirements of latency-sensitive services in these scenarios, effectively overcomes the heterogeneity of LEO satellite networks, and achieves latency optimization for LEO satellites during simultaneous transmission and computation.

The application relates to the technical field of unmanned aerial vehicle navigation, and discloses a long aircraft screening method for an unmanned aerial vehicle cluster hierarchical cooperative navigation system, which comprises the following steps: obtaining state data of each wing aircraft in the system according to a self sensor to determine the azimuth angle and the pitch angle of each wing aircraft relative to each long aircraft, and constructing an observability matrix; performing singular value decomposition on the observability matrix, and determining an observability index according to the maximum singular value and the minimum singular value after the decomposition, which is used for quantifying the observability degree of the long aircraft type of a long aircraft combination used in the unmanned aerial vehicle cluster hierarchical cooperative navigation system to the wing aircraft navigation error contribution; and based on a binary particle swarm optimization algorithm, taking the observability index of the unmanned aerial vehicle cluster as an adaptive function, iteratively searching for a target long aircraft combination, wherein in the iteration, the inertia weight of the algorithm is continuously adjusted according to the relationship between the particle fitness value and the group fitness value, and the individual learning factor and the group learning factor are continuously adjusted according to the iteration number.

This invention relates to a multi-objective optimization method for pixel antennas based on the BPSO-MLP algorithm, applied in the field of wireless communication antenna technology. An improved binary particle swarm optimization algorithm is used to perform a global search in the high-dimensional discrete coding space of the pixel antenna. A dynamic temperature parameter is introduced to adjust the shape of the sigmoid mapping function, and a probability-driven mutation mechanism is employed to effectively balance exploration and development capabilities. A performance prediction model is established through a designed multilayer perceptron neural network, enabling rapid and accurate prediction of key performance indicators such as bandwidth, gain, and radiation pattern of candidate antenna structures. An intelligent optimization mechanism submits the high-potential solutions selected through prediction to full-wave electromagnetic simulation, and the simulation results are fed back to the optimization algorithm to dynamically update the prediction model. This invention effectively solves the problems of high-dimensional discrete search, high simulation costs, and coordination of multiple performance indicators in multi-objective optimization of pixel antennas, providing an efficient solution for the design of reconfigurable antennas for next-generation communication systems.

The application discloses a far-field super-resolution bifocal generating method and system, relates to the technical field of far-field super-resolution focusing application, and comprises the following steps: acquiring an incident light field; based on an angular spectrum diffraction theory and a binary particle swarm optimization algorithm, optimizing binary phase control of a super-oscillation mask with different focal lengths; performing a Boolean logic AND operation on obtained single-focus focusing control phase distributions with different focal lengths; obtaining a far-field super-resolution bifocal focusing super-oscillation mask binary phase distribution; generating a super-oscillation mask according to a mapping relationship between the binary phase distribution and pixel point gray scale values of a picture loaded by a spatial light modulator, and obtaining a corresponding gray scale image; and using the gray scale image to modulate the phase of the incident light field, so as to obtain a focused light field and a three-dimensional intensity distribution of the focused light field. The application can convert multiple single-focus focusing control phases into bifocal focusing control phases through a Boolean logic AND operation, so that the generated bifocal can break through the diffraction limit, and the application has simple structure and is convenient to use and operate.