22 results about "Binary particle swarm optimization" patented technology

The invention discloses a delivery task cooperative processing method, device and equipment based on a quadruped robot and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: analyzing a delivery task through an upper computer, combining the real-time dynamic information of an office scene, and employing an improved binary particle swarm optimization algorithm to optimize the task distribution of multiple robots, and efficient collaborative distribution in an intelligent office environment is realized. The robot receives a distribution scheme sent by the upper computer, a multi-level navigation strategy is started by utilizing environmental perception data, a topological map and a D * Lite algorithm are fused to carry out path planning, and dynamic obstacle avoidance and efficient distribution are ensured. The target path planning information is monitored in real time through the upper computer, dynamic analysis is carried out through the scene perception algorithm, the scene relevance result is obtained, the collaborative distribution strategy is executed to complete the distribution task, the task response speed and the distribution efficiency are improved, interference to the office environment is reduced, and the work efficiency is improved. And automation and intelligentization of robot distribution in an intelligent office scene are realized.

The invention discloses a load transfer method, system, equipment and medium considering new energy and load power fluctuation, and belongs to the technical field of relay protection, and the method comprises the steps: taking a switch motion moment as a first stage of load transfer, taking a set time after the switch motion as a second stage of load transfer, building a dual-stage load transfer model, and taking the set time as a second stage of load transfer; obtaining a load transfer scheme; double-stage constraint verification is executed, and operation constraint verification is carried out on the load transfer schemes of the first stage and the second stage; introducing a constraint out-of-limit penalty factor, and quantifying the degree of the load transfer scheme deviating from the constraint condition; constructing a load transfer scheme comprehensive evaluation function, and performing weighted integration; and optimizing the load transfer scheme comprehensive evaluation function by adopting an improved binary particle swarm algorithm, and determining an optimal load transfer scheme. According to the method, the limitation of a traditional static model is overcome, and the problem of operation constraint violation caused by photovoltaic output change is avoided.

The invention discloses a PCB wiring design method and system considering a pulse current fusing effect, and relates to the technical field of PCB design. The method comprises the following steps: based on a differential impedance design principle, including design rules of a differential microstrip line and a differential strip line, determining parameters such as the width, the thickness, the line spacing and the medium thickness of a wire so as to meet the impedance value requirement; based on the heat balance equation of the current-carrying conductor, a current conductor heating model and a current-carrying conductor heat dissipation model are established; on the basis of differential impedance design and in combination with the proposed binary particle swarm optimization algorithm, the electromagnetic compatibility factor can be comprehensively considered, the problems of low efficiency and electromagnetic compatibility existing in large-scale wiring design are effectively solved, rapid and accurate design of large-scale wiring can be achieved through an efficient calculation process, and the method is suitable for large-scale wiring design. The design efficiency and quality are remarkably improved, and the design requirement for the routing pulse fusing effect of a high-speed PCB signal is met.

The invention provides a flexible power distribution network interconnection device locating and sizing method and system, and belongs to the technical field of flexible power distribution networks. The method comprises the following steps: acquiring cross-court power mutual aid potential and an electrical distance between power distribution network nodes to generate a power distribution network node incidence matrix; carrying out relevance enhancement on the preliminary SOP site selection scheme based on a power distribution network node relevance matrix; a binary particle swarm optimization algorithm is adopted, an optimal SOP site selection scheme and a final power distribution network node incidence matrix are determined by enhancing the SOP site selection scheme, and the minimum mutual aid capacity which needs to be met by each SOP is calculated; and by taking the minimum mutual aid capacity as a constraint, constructing and solving a capacity optimization model taking the minimum full life cycle cost as a target, and outputting an optimal SOP constant volume scheme. According to the method, the long-term load balancing capability of the power distribution network and the cross-court flexible resource mutual aid efficiency are improved, and the intelligent level and the calculation efficiency of the optimization process are improved.

The application belongs to the field of malicious code detection, and provides a malicious code classification method based on machine learning and swarm intelligence algorithm, which comprises the following steps: S1, data preprocessing; S2, feature fusion and selection; S3, model training and classification; the application classifies malicious codes by fusing n-gram features with different n values, compared with a classification method based on a single n value, the four indexes of accuracy, precision, recall and F1-score are all improved, the improved discrete binary particle swarm optimization algorithm is used to further select the fused features, the classification precision is improved, the feature quantity is greatly reduced, and the model running speed is improved.

The invention relates to a pixel antenna multi-objective optimization method based on a BPSO-MLP algorithm, and is applied to the technical field of wireless communication antennas. Global search is carried out in a high-dimensional discrete coding space of a pixel antenna through an improved binary particle swarm optimization algorithm, a dynamic temperature parameter is introduced to adjust the shape of a Sigmoid mapping function, and a probability-driven variation mechanism is adopted to effectively balance exploration and development capabilities; a performance prediction model is established through the designed multilayer perceptron neural network, and rapid and accurate prediction of key performance indexes such as bandwidth, gain and radiation pattern of a candidate antenna structure is realized; and submitting the predicted and screened high-potential solution to full-wave electromagnetic simulation through an intelligent optimization mechanism, feeding back a simulation result to an optimization algorithm, and dynamically updating a prediction model. According to the method, the problems of high-dimensional discrete search, high simulation cost and multi-performance index coordination in pixel antenna multi-target optimization are effectively solved, and an efficient solution is provided for reconfigurable antenna design of a new-generation communication system.

The invention relates to the field of artificial intelligence, particularly discloses a high-dimensional evolutionary feature selection method fusing filtering and packaging strategies, and aims at high-dimensional, small-sample and unbalanced complex data to realize minimum discriminant feature subset screening through a multi-stage hierarchical search framework from coarse to fine and from global to local. The method comprises the steps of generating a high-quality initial population by adopting core-marginal probability sampling and preferentially entering evolution based on double filtering type integrated score sorting of symmetric uncertainty SU and ReliefF; an environment-aware unimproved counter is introduced in the global search stage, the feature adding probability and the feature removing number are dynamically adjusted to balance exploration and development, and repeated evaluation is reduced in combination with fitness cache; and when search stops, binary particle swarm optimization neighborhood refinement is triggered, local high-strength discrete search is realized by Sigmoid mapping, and global optimum is backfilled and updated. Experiments prove that the method obtains a high F1-score and a significant compression feature number on a plurality of high-dimensional data sets.

The present application belongs to the field of Android malware detection in information security, and particularly relates to a training method, device and equipment of an Android malware detection model; the method comprises the following steps: obtaining an original Android software sample dataset; using random undersampling to process the majority of Android benign samples; using boundary density weighting-based oversampling to process the minority of Android malicious samples; using evolutionary screening undersampling to process the resampled Android software sample dataset; these methods can optimize the Android software sample dataset; based on the optimized Android software sample dataset, the hyperparameters of the classifier of the Android software detection model are optimized by using a binary particle swarm optimization algorithm, so that iterative training of the Android software detection model is realized. The present application can improve the training efficiency and detection accuracy of the Android malware classification model.

The invention relates to a multi-hop routing method and device for balancing energy consumption of a power grid wireless sensor network, and belongs to the technical field of power communication networks, the method comprises the following steps: establishing a network topology model between sensor nodes and sink nodes based on the power grid wireless sensor network, the sensor nodes being randomly deployed in a rectangular area, and the sink nodes being randomly deployed in the rectangular area; the sink node is located in the center of the rectangular area; designing an opportunistic routing strategy based on an opportunistic routing protocol, and determining a forwarding area according to network topology positions and energy of all sensor nodes and overhead of each link; establishing an energy consumption balance routing model based on an opportunistic routing strategy; a routing problem of the energy consumption balance routing model is converted into a particle swarm optimization problem, and a binary particle swarm optimization algorithm is adopted to solve an optimal solution. Through multi-hop data transmission, the energy consumption of the power grid wireless sensor network is optimized, the energy consumption balance is realized, the service life of network nodes is prolonged, and the stability and the overall energy efficiency of the network are improved.

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 discloses a fault recovery method for AC / DC hybrid distribution networks based on network reconfiguration and chance constraints, belonging to the field of AC / DC distribution network fault recovery. The invention includes the following steps: Step 1: Establishing a flexible interconnected distribution network reconfiguration model; Step 2: Solving the model constructed in Step 1 using a binary particle swarm optimization algorithm; Step 3: Constructing an interconnected distribution network fault recovery model considering chance constraints; Step 4: Modeling uncertainties in wind and solar power output; Step 5: Transforming and solving the chance constraint model; Step 6: Verifying the feasibility of the model through case studies. By establishing a flexible interconnected distribution network reconfiguration model and constructing an interconnected distribution network fault recovery model considering chance constraints, this invention can significantly shorten the computation time of the optimization process and significantly improve fault recovery efficiency while effectively ensuring the load recovery effect after a fault, which is of great significance for large-scale power grid security.

The application discloses a retinal blood vessel segmentation method and system based on an automatic coding-decoding network of a discrete binary particle swarm optimization, and the segmentation method comprises the following steps: constructing a retinal blood vessel dataset; acquiring a lightweight U-shaped neural network model; acquiring an optimal U-shaped neural network model; and acquiring a segmentation result of a retinal blood vessel image through the optimal U-shaped neural network model. The discrete binary particle swarm optimization algorithm is used to automatically search and optimize the neural network structure, so that the network architecture can be automatically adjusted according to the task requirements, and meanwhile, the FPN Attention Block is introduced, the attention mechanism of the ECA-Net and the CBAM is combined, the attention mechanism is flexibly configured according to a selection factor, the encoder output is weighted, and then the decoder is fused, important local features can be effectively focused, and the segmentation precision of small blood vessels and complex structures is improved.

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 invention discloses a water, electricity and gas network management and edge application method and system based on an electric power internet of things. The method comprises the following steps: S1, collecting multi-source heterogeneous data; s2, generating a unified data stream; s3, identifying an equipment fault; s4, optimizing edge node resource allocation; s5, positioning a fault source; s6, generating and distributing a work order; according to the method, the multi-source heterogeneous data are integrated through the dynamic weight fusion model, and the data quality is improved by combining the timeliness and the integrity rate; dynamically adjusting a judgment standard based on a historical data self-adaptive threshold and an anomaly detection algorithm; edge resource allocation is optimized by adopting a binary particle swarm optimization algorithm, and response delay is reduced; a fault source is quickly positioned in combination with fault time-space correlation analysis; a work order is automatically generated according to the fault level and the influence range, and accurate distribution is carried out through a priority algorithm; the system adopts a layered architecture, integrates multifunctional edge equipment, supports isolation of an external network and an internal network, and ensures data security and real-time performance.

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 invention discloses a silicon-based reconfigurable optical logic gate structure based on a uniformly distributed phase change material, which is characterized in that a 6 [mu] m * 6 [mu] m waveguide region is designed on a silicon-based substrate, and the silicon-based reconfigurable optical logic gate structure comprises two bent input waveguides, a bias input waveguide and two output ports. The middle design area is etched into a 7 * 7 nanopore array, the pores are filled with phase change materials, and the materials can be switched between a crystalline state and an amorphous state so as to change the refractive index. The phase state of the phase change material is changed through an external electric heating control, so that the device is equivalent to different logic gate structures in different refractive index states, and the functions of an AND gate, an OR gate, an XOR gate and an XNOR gate can be reconfigured on the same chip. According to the invention, a binary particle swarm optimization algorithm is also adopted to optimize hole array arrangement so as to maximize the logic function output contrast. The device is excellent in output contrast ratio under the wavelength of 1550 nm, and expected logic function switching can be achieved. The reconfigurable optical logic gate is compact in structure, non-volatile and flexible in function, and a potential scheme is provided for integrated optical calculation.

The invention relates to the technical field of data processing, in particular to an optimization algorithm based on mixing of a binary particle swarm optimization algorithm and a simulated annealing algorithm, which is characterized by comprising the following steps of: combining binary particle swarm optimization (BPSO) and simulated annealing (SA) mechanisms to realize the balance of global exploration and local development through a staged updating strategy; the method is suitable for the Ising Model and quadratic unconstrained binary optimization (QUBO) problem. According to the method, BPSO and SA are combined, and global search and local random hopping capabilities are achieved; providing a unified optimization framework for the Isin model and the QUBO problem; the annealing temperature and the PSO inertia weight are dynamically adjusted, and self-adaptive search is achieved; and parallel realization is supported, and the method can be expanded to a hardware acceleration platform (such as an FPGA Isin machine).

The invention discloses an electric vehicle public charging facility layout capacity planning method comprising the steps that a charging station layout site selection model and a charging pile capacity optimization model are acquired, the charging station layout site selection model is constructed with user convenience and economic operation as optimization objectives, and the charging pile capacity optimization model is constructed with the user convenience and economic operation as optimization objectives; the charging pile capacity optimization model is constructed by taking a charging pile utilization rate in the charging station as an optimization target; acquiring charging demand data in the research area, and determining constraint condition boundaries of the charging station layout and location model and the charging pile capacity optimization model according to the charging demand data; and performing joint optimization solution on the charging station layout location model and the charging pile capacity optimization model by using a discrete binary particle swarm optimization algorithm and a weighted Voronoi graph algorithm to obtain a charging facility position and capacity configuration scheme in the research area. According to the invention, reasonable layout site selection and capacity configuration can be carried out on the electric vehicle public charging station, and the facility resource utilization rate and the user charging experience are improved.

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.