1113 results about "Swarm algorithms" patented technology

A system for UAV collaborative coverage path planning based on a Q-learning adaptive ant colony algorithm including a memory, an image collection device, and a plurality of UAVs loaded with a path planning module configured to: construct a 3D model in a collaborative coverage environment, by performing a cell division on the 3D model based on a scanning range of an airborne radar of each UAV, obtain one or more sub-regions; by establishing constraints of the UAV and the environment based on the determined 3D model of the region to be searched, establish a problem total cost model; perform a plurality of rounds of iterations, calculate a reward value of each ant colony and determine whether a maximum iteration count is reached, if the maximum iteration count is reached, enter a new round of iteration, otherwise, output a path corresponding to a current round of iteration as a final path.

The invention relates to the technical field of town sewage pipe network detection, and discloses a town sewage pipe network leakage detection system and method. The system comprises a data acquisition module which uses a multi-source sensor to acquire real-time operation data such as pipe network pressure, flow and the like; the feature extraction module extracts spatio-temporal features based on the multi-scale convolutional neural network, and generates a pipe network state feature matrix; the anomaly detection module inputs the feature matrix into a pre-training model and marks a potential leakage area; the optimization analysis module constructs a multi-constraint dynamic optimization model, and pipe network pressure parameters are optimized by using an adaptive particle swarm algorithm; the hierarchical execution module generates a global regulation and control sequence, dynamically matches local pressure parameters and adjusts the valve opening and the pump station power through a decision layer, a region coordination layer and an execution layer. The system and the method are accurate in detection and reasonable in regulation and control optimization, leakage risks can be effectively reduced, the operation management level of a pipe network is improved, and water resource waste and environmental pollution are reduced.

The invention discloses a modeling analysis method for optimizing an urban water supply pipe network, and belongs to the technical field of intelligent water affairs, and the method comprises the steps: building a comprehensive pipe network database, collecting hydraulic parameters through Internet of Things equipment, and fusing the hydraulic parameters with GIS topological data; constructing a dynamic hydraulic simulation model, coupling real-time data of a pressure sensor with an EPANET model, and correcting a friction resistance coefficient of a pipe section; a hybrid optimization algorithm architecture is designed, a double-target genetic algorithm is adopted for old pipe network transformation, and a constrained particle swarm algorithm is adopted for new pipe network planning; executing multi-stage optimization calculation, generating a pipe diameter feasible solution space in combination with geographical constraints, and iteratively outputting a scheme meeting a pressure threshold value; and generating a pipe network optimal configuration scheme, and outputting an engineering implementation list through spatial overlay analysis and valve regulation logic. According to the method, the problem of modeling distortion caused by a multi-source data island is solved, the defect that a static model is difficult to adapt to dynamic water demands is overcome, and the economical efficiency and the engineering implementability of a pipe network optimization scheme are remarkably improved.

The invention discloses an electromechanical equipment noise reduction method based on machine learning, and relates to the field of electromechanical equipment noise reduction, and the method comprises the steps: collecting sound pressure and vibration signals during the operation of electromechanical equipment, and collecting the operation state data of the electromechanical equipment; sparse reconstruction is carried out on the noise features; carrying out sound source localization on the reconstructed noise features by using a beam forming algorithm to obtain a noise distribution diagram; constructing a noise reduction model based on deep learning to obtain noise signals after noise reduction; extracting the frequency of the noise signal in a preset frequency range through a peak search algorithm; analyzing the acquired running state data by using a time-varying linear predictive coding algorithm TVLPC to obtain a noise frequency offset caused by the abnormal running state of the electromechanical equipment; generating an optimal solution by using a particle swarm algorithm; and according to the optimal solution of noise control, noise dominant frequency components are extracted for separation, and a plurality of phase-reversal noise reduction control signals are obtained. Aiming at low noise reduction precision of electromechanical equipment in the prior art, the noise reduction control precision is improved.

The invention relates to the technical field of 5G networks, in particular to a 5G network intelligent optimization method and system, and the method comprises the steps: obtaining 5G network signal data, recognizing an abnormal power spectral density region, carrying out the filtering extraction of the abnormal power spectral density region, and separating interference signals, interference types including narrowband interference, broadband interference and directional interference; extracting features from the obtained interference signals, and performing interference type identification according to a random forest algorithm; starting a corresponding anti-interference means according to the interference type, and generating operation state data in real time; a bee colony algorithm is adopted to simulate bee behaviors for iterative search, and a local optimal resource scheduling strategy is determined; and executing the resource scheduling strategy, feeding back an execution effect, and restarting the bee colony algorithm to determine a new resource scheduling strategy if the execution effect does not reach a set expectation. Therefore, the problems of lack of dynamic adaptive adjustment, single anti-interference means, lack of cross-base station collaboration and the like in the anti-interference aspect in the prior art are solved.

The invention provides a computer room energy consumption and computing power balance optimization method and system based on swarm intelligence, and relates to the technical field of data center management, and the method comprises the steps: collecting server node real-time operation data, constructing a multi-objective optimization function, predicting an energy consumption and computing power change curve through a recurrent neural network, and generating an initial task distribution scheme; and a global optimal allocation scheme is searched by adopting a parallel ant colony algorithm, the running state of the server is monitored, and task migration and energy consumption adjustment are executed, so that collaborative optimization of energy consumption reduction of the machine room and balanced allocation of computing power is realized, and the service quality and the resource utilization efficiency are improved.

The invention relates to the technical field of building engineering automation, and discloses a building robot collaborative operation system, a BIM modeling module is used for establishing a three-dimensional model of building engineering and dividing the three-dimensional model into a plurality of operation areas; the information acquisition module is used for acquiring state information and working environment information of the robot; the central control module is used for performing task allocation and operation planning by using an ant colony algorithm; the information sharing module is used for carrying out real-time communication between the robots through wireless communication in the operation process, and sharing operation progress, state information and environment information; the operation evaluation module is used for monitoring the operation progress and state of each robot in real time, evaluating the operation effect of the robot, adjusting task allocation and planning strategies in time according to the evaluation result, optimizing the collaborative operation process of the robot, and improving the operation efficiency of the robot. According to the invention, the problems of unreasonable task allocation, low communication efficiency, difficult operation coordination and the like in the collaborative operation of the existing building robot are solved.

The invention discloses a multi-target task and resource intelligent modeling method, particularly relates to the field of complex adversarial simulation, is used for solving the problems of dynamic constraint optimization and robustness improvement in multi-dimensional task planning, and aims at realizing multi-dimensional coupling of space-time resource parameters by constructing a three-dimensional hypergraph model, mining a parameter association rule by means of tensor decomposition, and realizing multi-dimensional optimization of the space-time resource parameters. Dynamic constraint quantization is supported, multi-dimensional index priority evaluation is fused in a constraint layered injection stage, hard constraints are recognized, a solution domain is compressed, a hybrid optimization strategy regulates and controls balance between global exploration and local optimization, and after annealing is simulated to jump out of a local extreme value, a multi-target particle swarm algorithm is used for screening a space-time resource equilibrium solution in a trimming solution domain. Digital twinborn verification promotes physical and virtual space interaction data closed loop, a parameter correlation degree matrix is corrected, scheme robustness is enhanced, efficient generation and adaptive optimization of a task planning scheme under complex constraints are realized, and system stability and multi-target cooperation capability under sudden disturbance are improved.

The invention discloses a multi-region collaborative power grid planning system and method based on an improved multi-target particle swarm optimization algorithm, relates to the technical field of power system planning, and solves the problems of multi-target coupling and cross-region coordination in traditional power grid planning by constructing an economical, environment-friendly and reliable multi-dimensional target function and introducing a game theory method to quantify a multi-target constraint relation. The system comprises a data acquisition module, a multi-objective optimization model construction module, an improved particle swarm algorithm execution module, a collaborative decision module and a result output module, the improved particle swarm algorithm adopts dynamic adaptive inertia weight, time-varying acceleration coefficient and differential mutation operation, and the convergence speed and Pareto frontier distribution quality are remarkably improved; and the collaborative decision-making module realizes cross-regional parameter interaction and scheme optimization through a hierarchical collaborative mechanism and a fuzzy entropy theory. According to the method, collaborative optimization of calculation efficiency and scheme balance is realized in multi-regional power grid collaborative planning, and technical support is provided for scientific planning of a complex power grid system.

The invention relates to the technical field of metasurface antennas, and provides a metasurface antenna parameter optimization method and system based on a convolutional neural network, and the method comprises the steps: collecting metasurface sample data; extracting a comprehensive electromagnetic feature set, and establishing a nonlinear mapping relation model; constructing an antenna performance comprehensive evaluation function, inputting the nonlinear mapping relation model and the antenna performance comprehensive evaluation function into a hybrid optimization algorithm to generate a parameter candidate set, and performing local optimization on the parameter candidate set by using a particle swarm algorithm to obtain a metasurface antenna parameter combination; and extracting electromagnetic characteristics of the metasurface antenna parameter combination, iteratively adjusting the height parameter of the resonant cavity until the height parameter meets a threshold value to obtain an electromagnetic simulation verification result, and feeding back the electromagnetic simulation verification result to the deep Q neural network model for parameter updating to obtain an optimal metasurface antenna parameter combination. According to the method, the optimization of antenna parameters is realized, the design efficiency of the metasurface antenna is improved, and the consumption of electromagnetic simulation calculation resources is reduced.

The invention relates to the technical field of PCB processing, and discloses a multi-layer circuit board drilling method and device based on machine vision, and the method comprises the steps: obtaining a multi-layer circuit board image; preprocessing the multi-layer circuit board image to obtain a to-be-identified image; inputting the to-be-recognized image into a pre-trained defect detection model to obtain defect position data; carrying out point location identification and matching on the to-be-identified image to obtain an optimal hole location coordinate, and carrying out correction according to the optimal hole location coordinate to obtain hole location information data; according to the hole site information data and the defect position data, nodes are constructed respectively, distance calculation is carried out, and a hole site node graph is generated; according to the hole site node graph, performing path planning by applying an ant colony algorithm, and performing iterative search to obtain an optimal drilling path; and drilling the multilayer circuit board according to the optimal drilling path. The method has the following effect that the drilling efficiency of the multilayer circuit board can be improved.

The invention discloses a liquid chromatogram flow velocity real-time detection and optimal control method based on multi-point sensing. The method comprises the following steps: S1, constructing a distributed acquisition network; s2, constructing a fluid transmission topological graph and generating a multi-point flow velocity time sequence; s3, inputting the fluid transmission topological graph and the multi-point flow velocity time sequence into a FlowFormer model to generate a flow velocity evolution prediction map; s4, executing local gradient scanning, and generating an optimization control objective function; s5, calling a self-adaptive fox swarm algorithm controller, inputting a flow velocity evolution prediction map and an optimization control objective function, and generating an optimal adjustment strategy; s6, the control parameters of the liquid chromatography system are adjusted in real time and fed back to the FlowFormer model; and S7, when abnormal flow velocity change is detected, triggering an adaptive fox swarm algorithm controller to execute a local escape strategy. According to the invention, multi-point sensing and an intelligent optimization algorithm are fused, and real-time detection and optimal control of the liquid chromatogram flow velocity are realized.

The invention discloses an unmanned aerial vehicle path planning and obstacle avoidance optimization method based on an improved elite colony algorithm, and the method comprises the following steps: carrying out three-dimensional grid environment modeling and obstacle generation, and obtaining a discrete model of a whole three-dimensional space according to a layering + two-dimensional grid method; elite strategy and path generation: ants select paths by using a random proportion strategy, set a transition probability function and introduce a fluctuation coefficient to prevent excessive concentration of path weights caused by pheromone volatilization; dynamic volatilization rate self-adaptive adjustment: carrying out self-adaptive adjustment according to pheromone distribution, and constructing a dynamic volatilization rate model; path cost calculation and dynamics constraint are integrated, a multi-objective planning method is applied, the path length and flight stability are optimized at the same time, and a path cost function is constructed; and performing path post-processing and smooth optimization, including processing a balanced path to improve the flyability and performing collision detection, thereby establishing an unmanned aerial vehicle dynamics constraint model, and ensuring the flight safety on the premise of path optimization.

The invention relates to the technical field of robot control, in particular to an energy consumption optimization and automatic charging control system for an intelligent pet robot. The system comprises a robot data acquisition module, an energy consumption state evaluation module, an energy efficiency optimization module, an automatic charging scheduling module and a battery health management module which are in communication connection in sequence. The system is driven by multi-dimensional sensing data, energy consumption characteristics are extracted in combination with potential energy field mapping and sparse modeling, and an energy consumption influence map is constructed; joint optimization of the path and the servo strategy is realized by using a quantum behavior particle swarm algorithm; scheduling a charging path through a graph attention network; and the battery health state is dynamically predicted based on an incremental learning algorithm, and the operation efficiency, the cruising ability and the system intelligence of the robot are improved. According to the method, a closed-loop control process is constructed from energy consumption evaluation to energy efficiency optimization, automatic charging and battery health management, good system integration and intelligent response capability are achieved, and the cruising ability and operation stability of the pet robot in a complex application scene are remarkably improved.

The invention relates to the technical field of cloud computing, in particular to a network data encryption and privacy protection system in a cloud environment, which comprises a key management unit driven by a wolf pack algorithm, an encryption algorithm optimization unit, a privacy protection strategy dynamic adjustment unit and a safety monitoring and abnormity response unit. The invention discloses a cloud environment network data encryption and privacy protection system constructed based on a wolf pack algorithm. High-security keys are dynamically generated and distributed through a key management unit, security performance and resource consumption are balanced through an encryption algorithm optimization unit, multi-target dynamic gaming and compliance guarantee are achieved through a privacy protection strategy unit, distributed attack detection and cooperative defense are completed through a security monitoring unit, and the security performance is improved through a cooperative feedback mechanism between the units. Intelligent encryption protection, dynamic strategy adjustment and efficient attack response of the full life cycle of the data in the cloud environment are realized, and the system security, the resource utilization rate and the compliance capability are remarkably improved.

The invention discloses a dynamic compensation intelligent formwork real-time regulation and control method and system, and relates to the technical field of building intelligent control. According to the method, pulse signal sensing and cooperative compensation between nodes are realized by constructing a distributed neural unit network, and a three-level compensation mechanism is triggered when displacement or stress is detected to exceed a threshold value. A generative adversarial network is innovatively adopted to construct a virtual scene library to simulate extreme working conditions, a cross-scene migratable strategy library is generated through feature alignment, and intelligent strategy matching of a new construction site environment is realized. A full-structure rigidity field is dynamically generated based on an elastic mechanical model, a supporting arm rigidity switching path is optimized in combination with an ant colony algorithm, and directional dissipation of wind vibration energy is achieved through triangular rigid network construction. The system adopts a biological heuristic self-organizing algorithm to realize redundant network reconstruction when nodes fail, a supporting arm adopts a shape memory alloy and carbon fiber composite structure, and a micro strain sensor is integrated to realize directional release of corrosion stress. According to the method, the adaptive capacity of the formwork system under the complex working condition is effectively improved, and the structural failure rate is reduced through multi-modal compensation strategy fusion.

Provided are a cluster air conditioner collaborative control method and apparatus, and a storage medium. The method comprises: under the constraint of a peak regulation market and a carbon market, obtaining the total income of an air conditioner aggregator on the basis of a peak regulation income of a peak regulation response stage, a rebound fluctuation reward and punishment income of a rebound optimization stage, an income of the carbon market and the total compensation cost of an air conditioner user; by using the maximization of the total income of the air conditioner aggregator as an objective, constructing a cluster air conditioner collaborative control model oriented to two stages of peak regulation response-rebound optimization; using a particle swarm algorithm to obtain an optimal solution of the cluster air conditioner collaborative control model to serve as an optimal temperature regulation strategy of cluster air conditioners; and performing collaborative control on all the regulatable cluster air conditioners on the basis of the optimal temperature regulation strategy of the cluster air conditioners.

The present invention relates to the technical field of power source power allocation. Disclosed are a direct current power source power allocation method and system for a generator status monitoring apparatus. The method comprises the following steps: by means of system data, constructing a variational problem model; solving the constructed variational problem model, and using a particle swarm optimization algorithm to optimize computing parameters of the variational problem model; and, on the basis of a computing result of the variational problem model, allocating the total output power of a hybrid energy storage system to an energy-type storage device and a power-type storage device according to a ratio. By means of using the convergence-guaranteed particle swarm optimization algorithm, the present invention not only excels in the solving speed but also shows significant advantages in computational accuracy; furthermore, by means of solving the variational problem model, more accurate allocation ratios are obtained; variational mode decomposition can achieve adaptive matching of the optimal center frequency and bandwidth for each mode, thus effectively separating intrinsic mode components and achieving frequency domain partitioning of signals.

The invention discloses a grid-connected inverter parameter optimization method based on an improved genetic particle swarm algorithm. The grid-connected inverter parameter optimization method comprises the following steps: constructing a target function; performing population position initialization; calculating a fitness value of each particle at an initial moment, recording an optimal solution, and storing initial global optimal particle and individual optimal particle information; random disturbance is added to the speed and the position of the particles; updating the speed and the position of the particle; performing multi-point crossover and mutation operation; according to the current optimal solution, carrying out local domain search by adopting a fine search mode based on Laplacian distribution and carrying out global neighborhood search by adopting a search mode based on Cauchy distribution; and checking convergence conditions, if convergence occurs, outputting an optimal solution, and obtaining optimal parameters of the grid-connected inverter system. The method has the remarkable effects that by optimizing three key parameters in a control system and utilizing a nonlinear dynamic strategy to enhance the global search capability and the convergence speed of the algorithm, various defects in the prior art can be overcome.

The invention provides a massage robot multi-mode fusion treatment system and method based on 3D vision, and relates to the technical field of massage robots. The region recognition module is used for establishing a human body recognition model and recognizing human body features in the processed image; the massage terminal is used for carrying out massage operation according to parts needing to be massaged and human body characteristics and monitoring skin pressure of the massaged parts in real time; the pain sense recognition module is used for human body pain sense recognition; the force adjusting module is used for adjusting skin pressure. According to the method, pertinence and safety of massage operation are remarkably improved through multi-modal image preprocessing and an initialized human body recognition model of a fusion segmentation model and a key point detection model; a global path strategy is generated and optimized through a hybrid ant colony algorithm, efficient and accurate motion control of the mechanical arm is achieved, through multi-modal data fusion analysis, the pain feeling of a user is sensed in real time, the massage strength is dynamically adjusted, and the comfort and the safety coefficient are improved.

The invention provides a pneumatic conveying system optimization method and system based on a multi-target particle swarm, and relates to the technical field of pneumatic transportation, and the method comprises the steps: collecting key transportation data affecting the conveying efficiency, the energy consumption and the system stability, employing a particle swarm algorithm, regarding each particle as a group of optimizable parameter combinations, and obtaining a particle swarm optimization parameter combination; the method comprises the following steps: calculating a pneumatic transport performance index based on an engineering formula, generating a fitness value by adopting a weighted summation method, comparing the fitness value with a preset threshold value, determining whether optimization is needed or not, carrying out iterative optimization by updating particle positions and speeds by taking minimization of pipeline wear, energy consumption and pressure fluctuation as targets, and when the fitness value meets a threshold value condition, carrying out iterative optimization on the particle positions and speeds. And selecting an optimal solution based on a minimum deviation method. The pneumatic conveying system is optimized based on the multi-target particle swarm algorithm, and the conveying efficiency and the system stability are improved under the target of minimizing pipeline abrasion, energy consumption and pressure fluctuation.

The invention provides a virtual power plant aggregation scheduling method and device based on a particle swarm optimization, and the method comprises the steps: obtaining a plurality of to-be-scheduled target resource nodes in a virtual power plant from a distributed resource node list issued by a virtual power plant scheduling platform; based on a particle swarm algorithm, determining a target scheduling strategy of the virtual power plant, the target scheduling strategy being used for indicating a target power value of each target resource node on the corresponding power parameter; and according to the target power value, obtaining an adjustable margin of each target resource node under the target scheduling strategy, and sending the adjustable margin to the virtual power plant scheduling platform. According to the method, the global search capability of the particle swarm algorithm is utilized, the approximate optimal scheduling scheme of the direct control type virtual power plant can be quickly found in a complex solution space, and the scheduling efficiency and accuracy are improved.

The invention provides an energy storage converter optimization control method based on a fusion genetic-particle swarm algorithm and a related device, and belongs to the technical field of oscillation suppression of energy storage converters. According to the method, a virtual synchronous machine simulation model is established, a second-order motion equation of the synchronous machine is analyzed to obtain a selection principle of rotational inertia and a damping coefficient, then a related control strategy function is constructed, and parameters of the control strategy function are optimized by using a fusion genetic-particle swarm optimization algorithm. Differential adjustment coefficient configuration can be implemented according to specific requirements of different power system scenes on overshoot and response time, the control strategy strain capacity is effectively improved, and the system instability risk is reduced; and meanwhile, the global search capability of the genetic algorithm and the rapid convergence characteristic of the particle swarm algorithm are fully exerted, the function parameters of the control strategy are accurately optimized, the stable regulation and control capability of the energy storage converter in the power system is enhanced, the low-frequency oscillation of the virtual synchronous machine is effectively suppressed, and the stability and dynamic regulation performance of the energy storage converter are improved.

The invention relates to the technical field of foundation treatment, in particular to a dynamic compaction construction parameter optimization method based on XGBoost-PSO, and the method comprises the steps: obtaining a plurality of groups of compaction settlement data through a compaction test; the method comprises the following steps: selecting original features from actual measurement parameters of a test, constructing interactive features by adopting the actual measurement parameters, selecting optimal interactive features according to the importance ranking of XGboost features, and analyzing the correlation between the features; based on the feature set obtained through screening, a compaction settlement prediction model is established; and performing dynamic compaction parameter optimization on the compaction settlement prediction model by using a particle swarm algorithm to finally obtain an optimal compaction parameter combination. According to the method, the tamping parameters are automatically searched and optimized on the premise that the tamping stopping standard is met by building the tamping settlement prediction model, and therefore the purposes of guaranteeing the construction quality, improving the construction efficiency and reducing the construction cost are achieved; the method is suitable for various foundation construction design stages needing dynamic compaction treatment, and has good engineering adaptability and popularization value.

The invention relates to the technical field of power system operation or management, in particular to a partial discharge monitoring strategy optimization method and system based on dynamic resource allocation, and the method comprises the steps: constructing a three-stage monitoring system comprising a sensor node, a sink node and a cloud processing center, firstly initializing monitoring parameters, and then obtaining system state information periodically or in a triggering manner, calculating the risk level of each monitoring point in combination with a dynamic risk evaluation model; constructing an efficiency-maximized resource allocation optimization model based on risk levels and resource constraints, solving an optimal scheme by adopting an improved multi-target particle swarm algorithm, and issuing the optimal scheme to each node to adjust monitoring behaviors to form closed-loop optimization; and meanwhile, model parameters are dynamically updated through an online learning mechanism. According to the method, dynamic matching of risks and resources is realized, the monitoring accuracy and the resource utilization rate are improved, the adaptability of the system to the equipment state and the environment change is enhanced, and the method is suitable for partial discharge monitoring scenes of various power equipment.

The invention provides a mechanical arm time optimal trajectory planning method based on an improved genetic particle swarm algorithm, and belongs to the field of robot control. A mechanical arm kinematic model is established, the mapping relation between a joint space and a Cartesian space of an end effector is derived, and a joint angle sequence corresponding to an end path point is calculated; under the target of time optimization, joint speed and acceleration constraints are combined, a fitness function is constructed, and an improved genetic particle swarm optimization algorithm is used for optimization; a 3-5-3 segmented interpolation method is adopted, and based on the joint angle sequence, joint position, speed and acceleration trajectories are generated; the inertia weight is dynamically adjusted in the optimization process, and the search efficiency is improved; the motion time of each section of track is further optimized, the convergence speed and precision are improved through a hybrid optimization algorithm in combination with a time optimal target and joint motion constraints, finally, the mechanical arm motion track with the optimal time is output, the time for the mechanical arm to complete a target task is effectively shortened, the working efficiency is remarkably improved, and high precision and stability are achieved.

The invention provides a visual guidance type automatic disassembly method and system for a photovoltaic module, and relates to the technical field of module disassembly, and the method comprises the steps: obtaining real-time production data, constructing a multi-objective optimization decision model, and solving through an adaptive inertia weight particle swarm algorithm; verifying and dynamically optimizing the optimization scheme by adopting a deep reinforcement learning method in combination with a digital twinning environment; production line optimization control is realized and operation data is fed back to form a closed loop. According to the invention, the disassembly efficiency is improved, the energy consumption is reduced, the production quality is optimized, and intelligent automatic disassembly is realized.

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention relates to the related technical field of distribution transformer insulation aging, in particular to a frequency domain diagnosis method, system and device for distribution transformer insulation aging and a medium, and the method comprises the steps: carrying out the frequency response test of a to-be-tested transformer, and obtaining a first frequency response curve; performing a frequency response simulation test on a transformer equivalent circuit model pre-constructed based on the to-be-tested transformer to obtain a second frequency response curve; constructing a target function based on the deviation of the first frequency response curve and the second frequency response curve; taking element parameters in the equivalent circuit model of the transformer as position vectors, taking the objective function as a fitness function, and adopting a multi-objective particle swarm algorithm to solve the element parameters to obtain optimal values of the element parameters; respectively comparing the optimal values of the element parameters with corresponding normal values to obtain insulation aging degrees; according to the method, the insulation aging degree can be obtained by comparing the element parameters obtained through optimization with the normal unaged element parameters.

The invention provides a cooperative reconnaissance and electronic countermeasure control system and method based on multiple unmanned aerial vehicles. The cooperative reconnaissance and electronic countermeasure control system comprises five modules: a communication module, a task decomposition module, a reinforcement learning decision module, a cooperative optimization module and a task evaluation module. The communication module ensures data synchronization and safe communication between the unmanned aerial vehicles through a self-organizing network and an encryption anti-interference technology. And the task decomposition module is used for refining the electronic confrontation task into reconnaissance and interference subtasks according to the task type, and adjusting the priority in real time according to the battlefield environment. The reinforcement learning decision module adopts a DQN and PPO deep reinforcement learning algorithm to optimize a reconnaissance and interference strategy, and ensures optimal task execution. And the collaborative optimization module coordinates task allocation and flight paths of the unmanned aerial vehicle through multi-agent reinforcement learning and a bee colony algorithm, and solves the problem of task conflict. And the task evaluation module monitors a task state in real time and feeds back an adjustment strategy. According to the method, battlefield changes can be flexibly coped with, resource scheduling is optimized, task execution efficiency is improved, anti-interference capability is enhanced, and stability and safety of tasks are guaranteed.