68 results about "Quantum particle swarm optimization" patented technology

The invention relates to the technical field of electric acupuncture apparatuses, and discloses a method and a system for automatically adjusting stimulation parameters of an electric acupuncture apparatus. According to the method, body surface electromyographic signals, skin impedance and capillary hemodynamic parameters of a user are collected in real time through a sensor set, feature extraction and analysis are conducted through a time sequence prediction model of a Transform architecture, and physiological feature data are generated. And then, constructing a user physiological state characterization model by adopting a multi-modal neural perception fusion algorithm, and carrying out collaborative optimization on the electrical stimulation parameters through an adaptive quantum particle swarm optimization algorithm to generate a stimulation parameter combination matched with the real-time neuromuscular response characteristics of the user. The system drives electrical stimulation output according to the parameter combination, monitors physiological data changes in real time, realizes closed-loop dynamic balance of stimulation parameters and biological feedback signals through the dynamic parameter compensation module, and improves the treatment effect and safety.

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

The invention discloses a multi-degree-of-freedom mechanical arm control method based on quantum particle swarm optimization of a migration strategy. The method comprises the steps that system modeling is conducted; mPC to-be-adjusted parameter definition and constraint processing are carried out; adopting, adopting, and introducing penalty terms to construct a composite fitness function; updating particle positions by adopting a basic or enhanced quantum updating mode; triggering conditions are judged in a group diversity measurement mode, and when the conditions are met, a migration strategy and dynamic subgroup division are executed; the global optimal solution and the fitness value are loaded into a real-time MPC controller, and online adjustment is carried out; a prediction equation is constructed, tracking errors and energy consumption optimization are converted into a standard quadratic programming problem, and the solving precision is dynamically adjusted in combination with Cholesky pre-decomposition, a structured sparse solver and a warm-start and early stop strategy; and a closed-loop adaptive control system is constructed. According to the method, the precision, robustness and real-time performance of trajectory tracking control of the mechanical arm can be effectively improved.

The invention relates to the field of control, in particular to a reaction kettle operation control method and system for resin production, real-time operation parameters of a reaction kettle are obtained, a fuzzy neural network model is iteratively trained by adopting a hierarchical collaborative hybrid optimization strategy, a preceding member membership function of the fuzzy neural network model is composed of a Gaussian mixture model, and the preceding member membership function of the fuzzy neural network model is obtained. According to the optimization strategy, an improved quantum particle swarm optimization algorithm is used for carrying out global search to determine Gaussian mixture model parameters, a recursive least square algorithm is used for carrying out local search to determine consequent coefficients after each time of iteration, and in the training process, the parameters of the Gaussian mixture model are subjected to global search to determine the parameters of the Gaussian mixture model. And calculating an importance index according to the average activation degree of the fuzzy rule and the contribution of the fuzzy rule to the prediction error, removing the rule of which the importance is continuously lower than a preset threshold value, and after training is completed, generating and executing a control instruction for controlling the heating system power and the material feeding rate of the reaction kettle at the next moment according to the real-time parameters.

The invention discloses an SVM algorithm automatic driving road detection system based on quantum computing enhancement, and relates to the technical field of automatic driving road detection in which quantum computing, multi-sensor fusion and artificial intelligence technologies are combined and applied. By combining quantum calculation and an SVM algorithm, the adaptability of the system to complex road conditions and illumination changes is enhanced, and the accuracy and stability of road detection are improved; a large amount of data in a complex scene can be quickly processed by means of the powerful computing power of quantum computing, and the model robustness is good. And by utilizing the characteristic that the SVM algorithm has low requirements on annotation data, the cost and time of data annotation are reduced. Quantum calculation is introduced to efficiently optimize parameters of the SVM model, an optimal parameter combination can be found in a shorter time through quantum particle swarm optimization, and the generalization ability and performance of the model are improved. And the parallel processing capability of quantum calculation is utilized to accelerate the data processing and model reasoning process, so that the real-time performance of the system is improved, and the requirements of automatic driving on the real-time performance and high efficiency are met.

This invention relates to a traveling wave waveform morphology classification method based on wavelet transform and QPSO-BILSTM, comprising: acquiring traveling wave signals; preprocessing the traveling wave signals; extracting the time domain of each wavefront periphery; collecting a large amount of labeled traveling wave waveform data of different types as labels; extracting the time domain of the wavefront periphery as a training set and inputting it into a BiLSTM model for training and classification; optimizing the parameters of the BiLSTM model through QPSO to improve the classification accuracy; processing newly acquired traveling wave signals and inputting them into the optimized BiLSTM model to output the classification results. The classification of traveling wave waveform morphology can significantly improve the efficiency and accuracy of traveling wave front calibration; classification can quickly identify fault types, providing an important basis for subsequent fault handling; the BiLSTM model optimized by the quantum particle swarm optimization algorithm can not only better extract key features in the traveling wave waveform, but also better adapt to the complex nonlinear characteristics and diverse variation patterns of the traveling wave, thereby improving the accuracy of waveform classification.

The invention relates to the technical field of electric power system operation and control, and discloses a system-perspective networking capability evaluation index and power supply parameter mapping system, which comprises a data acquisition module, a dynamic tensor modeling module, a coupling decomposition module, a distributed optimization module, an index fusion module and an edge cloud collaboration module. The method comprises the steps of constructing a dynamic tensor by collecting power grid data in real time, decomposing and extracting coupling features and generating optimization constraints, realizing parameter cooperative adjustment based on hierarchical multi-objective optimization and quantum particle swarm optimization, and forming a closed-loop optimization process by combining entropy weight-TOPSIS weight distribution and edge federated learning feedback updating. According to the method, the time-space correlation characteristics of the power grid are extracted by improving Tucker tensor decomposition, hierarchical ADMM optimization and quantum particle swarm optimization are combined, an entropy weight-TOPSIS dynamic weighting and federated edge collaboration mechanism is adopted, a power supply parameter-network construction capability index mapping model is constructed, and the multi-target optimization precision, the real-time performance and the data privacy are improved.

The invention belongs to the technical field of control, and discloses a water surface robot high-precision trajectory tracking control method and related equipment, and the method comprises the steps: constructing a dynamic multi-modal environment map containing water surface three-dimensional geometric topology information and water flow field vector information; according to the dynamic multi-modal environment map, an improved quantum particle swarm optimization algorithm embedded into a water flow kinetic equation constraint is adopted to carry out global path planning, smooth parameterization processing is carried out on a global path obtained through planning, and a reference trajectory is generated; according to the vector information of the water flow field, flow field disturbance is predicted based on a fluid-structure interaction dynamics model, system residual disturbance is estimated by using an extended state observer, and feedforward control quantity is generated in combination; according to the real-time flow velocity of the water flow field and the trajectory tracking error, a nonlinear model prediction controller and an adaptive sliding mode controller are adaptively switched, and a control instruction is generated in combination with a feedforward control quantity to drive the water surface robot to track a reference trajectory; therefore, high-precision trajectory tracking control is realized.

The invention discloses an unmanned aerial vehicle cluster collaborative path planning method, device and system, and a storage medium. The method comprises the following steps: performing path search by using a # imgabs0 # fish optimization algorithm; quantum particle swarm optimization (QPSO-ROA) is introduced to enhance the global search capability; according to dynamic environment perception reinforcement learning (DERL), the track of an obstacle is predicted through LSTM, and parameters are optimized in real time in combination with an Actor-Critic framework; and solving optimal task allocation by applying a Hungary algorithm, dynamically adjusting task priorities, and optimizing a pitch angle to realize gliding kinetic energy recovery, thereby realizing efficient collaborative scheduling of the heterogeneous unmanned aerial vehicles. By adopting the technical scheme of the invention, the planning efficiency and safety in a complex environment are remarkably improved.

The invention relates to a micro-grid photovoltaic energy storage capacity optimization method based on double-layer multi-target collaborative decision, and the method comprises the following steps: 1, analyzing the structure and operation mode of a new energy micro-grid in a rural region, and constructing a capacity optimization configuration model of an optical storage system; step 2, optimizing an objective function by an inner layer and an outer layer; the inner-layer optimization target is to minimize the daily volatility of the complementary power generation system and minimize the daily peak-valley difference; 3, solving an optical storage capacity optimal configuration model: obtaining an optimal Pareto solution set of the target function by adopting a quantum particle swarm algorithm; 4, adopting an interactive multi-criterion decision based on compromise solution selection; 5, outputting a capacity configuration scheme with the optimal comprehensive efficiency; the method has the advantages that intermittency and peak regulation capacity are comprehensively considered, stable power supply is ensured, power supply reliability is improved, energy utilization efficiency is improved, system construction and operation cost is reduced, and economic benefits are improved.

The invention relates to the technical field of electrical safety, in particular to an electrical safety intelligent detection management and control system suitable for constructional engineering. Aligning data of different sampling frequencies in the dynamic parameter group by using a sliding window algorithm, and preprocessing and standardizing the aligned data to obtain initial electrical dynamic data; deleting noise in the initial electrical dynamic data based on an improved Kalman filtering algorithm; establishing a dual-channel deep network model according to the dual-channel architecture of the PCN parameterized convolutional network and the STAN space-time association network, and performing hyper-parameter optimization on the model by using a QPSO (Quantum Particle Swarm Optimization); and inputting the characteristic electrical dynamic data into the dual-channel deep network model for training to obtain an electrical safety score, and performing hierarchical management and control on the system based on the electrical safety score. The false alarm rate of old equipment is reduced, the service life of key equipment is prolonged, and the loss caused by electrical faults is reduced.

The invention relates to the technical field of unmanned aerial vehicle auxiliary edge computing task offloading, and particularly provides an edge computing task offloading strategy based on an improved quantum particle swarm optimization (IQP) SO (Inter Quantum Particle Swarm Optimization) algorithm, and relates to an edge computing task offloading method based on the IQP SO algorithm and an edge computing task offloading system based on the IQP SO algorithm based on the IQP SO algorithm based on the IQP SO algorithm based on the IQP SO algorithm based on the IQP SO algorithm. According to the method, an MEC unloading structure of multi-user mobile equipment (UE) supported by an unmanned aerial vehicle is established; under the structure, communication, time delay and energy consumption models are formulated to evaluate time delay and energy consumption required by the unloading task of the mobile equipment; according to the improved quantum particle swarm optimization, the unloading efficiency is improved, and the time delay and energy consumption problems of tasks are considered in the optimization process; the algorithm combines quantum characteristics, has excellent global search capability and rapid convergence characteristics, and effectively avoids the problem of global optimal solution omission caused by premature convergence when optimizing an edge unloading strategy; according to the method, the average time delay and the energy consumption of the mobile edge computing task can be remarkably reduced, and the optimization of the system is realized.

The invention relates to a multi-modal time sequence fusion Transform power load prediction method based on quantum particle swarm optimization, and belongs to the field of power load prediction. According to the method, a density clustering algorithm and a time sequence weighted interpolation method are utilized to preprocess power load and associated influence factor data, a random forest feature screening model is constructed, model parameters are optimized through quantum particle swarm optimization, the model is trained, and a power load key influence feature set is determined according to a feature importance threshold. The power load prediction method comprises the following steps: performing modal classification and standardization on key features, then establishing a multi-modal time sequence feature matrix adaptive to Transform, constructing a multi-modal time sequence fusion Transform model containing an attention modal fusion layer, and predicting the power load in the future seven days by using the optimized model. According to the method, efficient fusion of the multi-modal data and intelligent optimization of the model parameters are realized, the prediction precision is remarkably improved, and the problems of low pre-processing precision of the multi-modal data, redundancy of feature screening, insufficient optimization of the model parameters and large long-time prediction errors of a traditional prediction method are solved.

The invention provides a heterogeneous unmanned aerial vehicle forest fire rescue cooperative scheduling method based on reinforcement learning and quantum particle swarm optimization, and relates to the field of cooperative scheduling, and the method comprises the steps: constructing a cellular automaton fire spread model based on wind speed, vegetation and gradient, and generating a combustion cell priority sequence; establishing a three-state conversion model of the multi-type unmanned aerial vehicle, and combining the combustion loss and the flight time to construct a comprehensive cost; task allocation and track solving are realized through a quantum particle swarm optimization algorithm enhanced by reinforcement learning; and a scheduling scheme is corrected in real time by adopting a rolling updating mechanism, and fault-tolerant re-planning is triggered under an abnormal condition, so that the cooperative fire extinguishing efficiency of the heterogeneous unmanned aerial vehicle group is improved.

The invention discloses a GIS fault intelligent identification and visual positioning method. According to the technical scheme, the method comprises the steps that S1, a multi-source data collection platform is built, S2, an adaptive wavelet packet decomposition algorithm is adopted, and an initial point cloud is obtained; s3, constructing a GIS equipment three-dimensional point cloud topology model based on the initial point cloud; s4, setting a correlation degree threshold, screening effective candidate areas, and preliminarily determining a defect position range; and S5, identifying a fault type by adopting an adaptive quantum particle swarm optimization-graph attention network model, and outputting a fault identification result and a three-dimensional visual positioning report. The method is mainly used for fault feature extraction, defect type identification, accurate positioning and visual operation and maintenance analysis of gas insulated switchgear of various voltage classes.

The invention relates to an organization member information acquisition management system, which comprises a self-adaptive multi-mode acquisition unit, a holographic correlation modeling unit, a predictive dynamic updating unit and a heterogeneous encryption storage unit, the self-adaptive multi-mode acquisition unit is used for starting voice acquisition after verifying identity through voiceprint recognition, switching to an image interaction mode when silence occurs, synchronously docking the wearable equipment to acquire physiological parameters, and pausing acquisition when abnormity occurs; the holographic correlation modeling unit constructs a three-dimensional holographic information model, and uses a quantum particle swarm optimization algorithm to screen strong correlation information combinations; the predictive dynamic updating unit establishes an information attenuation prediction model, pushes an updating prompt in advance and generates a pre-filling template; the heterogeneous encryption storage unit adopts a space-time fragmentation encryption method to store information. The invention aims to solve the problems of single information acquisition mode, low multi-source data association degree, information updating lagging and insufficient data storage security of a traditional organization member information acquisition management system.

The present application relates to a kind of organization member information collection management systems, including adaptive multi-modal acquisition unit, holographic correlation modeling unit, forecast formula dynamic updating unit and heterogeneous encryption storage unit;Adaptive multi-modal acquisition unit starts voice collection after identity is verified by voiceprint recognition, switches to image interaction mode in silence, synchronously interfaces wearable equipment and collects physiological parameters and pauses collection when abnormal;Holographic correlation modeling unit constructs three-dimensional holographic information model, and strong correlation information combination is filtered using quantum particle swarm optimization algorithm;Forecast formula dynamic updating unit establishes information attenuation prediction model, and generates pre-populated template by pushing update reminder in advance;Heterogeneous encryption storage unit stores information using space-time slicing encryption method, the purpose of the present application solves the problems that traditional organization member information collection management system is single in information collection mode, low in multi-source data correlation degree, information update lags behind and data storage security is insufficient.

The invention discloses an intelligent distribution transformer terminal data processing method based on deep learning, and the method comprises the steps: sequentially carrying out the preprocessing and normalization processing of the sampling data of an intelligent distribution transformer terminal, and constructing a training set and a test set; constructing a deep learning combination model by means of the global optimization capability of a quantum particle swarm optimization algorithm and the time sequence modeling capability of a long-short-term memory network model; and finally, dynamically adjusting parameters of the deep learning combination model according to the evaluation precision of the real-time data. According to the method, sampling data is preprocessed through interpolation and a box graph, normalization and data set division are performed on the data according to a time sequence, a prediction precision evaluation index is established, and a deep learning combination model is constructed by combining the global optimization capability of a quantum particle swarm optimization algorithm and the time sequence modeling capability of a long-short-term memory network model. And according to the prediction result and the evaluation precision of the real-time data, model parameters are adjusted in real time.

The invention discloses a power electronic load-containing load equivalence and parameter identification method, which comprises the following steps of: preprocessing original power load data, and extracting data required by subsequent parameter identification; determining a load model structure, and performing aggregation equivalence on the power distribution network, the static load, the dynamic load and the power electronic load according to a principle that system response before and after aggregation remains unchanged; the method comprises the following steps: firstly, constructing a load model, then determining parameters, needing to be identified, of each type of load model, distinguishing dominant parameters and non-dominant parameters by adopting trajectory sensitivity, setting values of the non-dominant parameters as typical values, then identifying the dominant parameters through an improved quantum particle swarm algorithm, determining the values of the dominant parameters, and finally, performing accuracy evaluation on the established load model. According to the invention, a more accurate load model can be established for a modern power system containing a power electronic load, the simulation precision of the power system is improved, and the analysis and control of a power department on the power system are facilitated.

The application discloses a fluid antenna assisted ISAC system joint beam forming and position optimization method based on a quantum particle swarm optimization algorithm. Specifically, an ISAC multi-user system model equipped with a two-dimensional fluid antenna is established, and a beam forming optimization problem is constructed; a weighted least mean square error algorithm is used to convert a communication and rate target function, so that a complex fractional problem is converted into an integral problem, and solving is simplified; an alternating optimization algorithm is used to decompose the original joint optimization problem into multiple sub-problems; an auxiliary variable and a receiving beam former are alternately solved, an iterative solution of a beam forming vector is solved by using a successive convex approximation algorithm, and a fluid antenna position problem is solved by using a quantum particle swarm optimization algorithm. The method of the application effectively improves the communication and rate under the restrictions of the fluid antenna position, the minimum perceived beam pattern gain and the maximum base station transmission power, and is superior to a traditional ISAC system equipped with a fixed antenna.

The invention discloses a multi-source collaborative traction power supply system adaptive protection method and system, multi-source data of each end of a traction power supply system is synchronously collected, and the multi-source data comprises SVG data of a power supply side, PMU data of a traction network and TCMS data of a locomotive end; performing data cleaning and space-time alignment on the multi-source data to obtain multi-source heterogeneous data; performing nonlinear feature decoupling extraction on the multi-source heterogeneous data to obtain mixed features; constructing a cooperative control model for cooperative work of the LSTM-RF hybrid model and the quantum particle swarm optimization model, and inputting the hybrid features into the cooperative control model to obtain predicted fixed values corresponding to various faults; and cooperatively controlling compensation of the SVG at the power supply side, action of the circuit breaker of the traction network and locking of the locomotive according to the predicted fixed value.

The invention provides a low-voltage active transformer area abnormal data monitoring method and device and electronic equipment, and relates to the technical field of data monitoring. The method comprises the following steps: acquiring monitoring data of a to-be-monitored low-voltage active transformer area; feature extraction is carried out on the monitoring data; wherein the extracted features comprise a physical topology feature, a load distribution feature and an electric energy quality feature; inputting the physical topology characteristics, the load distribution characteristics and the electric energy quality characteristics into a trained theoretical line loss prediction model to obtain a theoretical line loss prediction value; according to the theoretical line loss predicted value, the actually measured line loss value and the dynamic threshold value, judging whether the monitoring data is abnormal or not; wherein the theoretical line loss prediction model comprises model parameters, the model parameters comprise kernel function parameters and coupling strength coefficients, and the theoretical line loss prediction model is obtained through training based on a quantum particle swarm optimization algorithm. According to the invention, the accuracy of theoretical line loss prediction can be improved, so that abnormal data monitoring is accurate.

This invention belongs to the field of data transmission technology and discloses a privacy-preserving network security data transmission method, including the following steps: S1. Data privacy preprocessing: Constructing a personalized differential privacy protection mechanism to perform local privatization processing on the original data to balance data privacy protection and availability; S2. Intelligent scheduling of transmission tasks: For scenarios with multiple network nodes and multiple transmission links, establishing a scheduling model with transmission latency and energy consumption as optimization objectives, and using a quantum particle swarm optimization algorithm that integrates chaotic perturbations to solve the model; S3. Trusted verification of transmitted data: Dividing the network into several sub-regions and constructing a tree-like key management structure, and using key priority allocation and cross-region node verification mechanisms. This solution, through a three-step collaborative mechanism, constructs an end-to-end network security protection system covering the entire data lifecycle, achieving the organic unity of personalized privacy protection, collaborative optimization of transmission efficiency and energy consumption, and decentralized trusted verification.

The application belongs to the technical field of control, and discloses a high-precision trajectory tracking control method for a water surface robot and related equipment, the method comprising: constructing a dynamic multi-modal environment map containing three-dimensional geometric topological information and water flow field vector information of the water surface; performing global path planning by using an improved quantum particle swarm optimization algorithm embedded with a water flow dynamics equation constraint according to the dynamic multi-modal environment map, and performing smoothing parameterization processing on the global path obtained by planning to generate a reference trajectory; predicting flow field disturbance based on a fluid-structure coupling dynamics model according to the water flow field vector information, estimating system residual disturbance by using an extended state observer, and combining a generated feedforward control amount; adaptively switching a nonlinear model predictive controller and an adaptive sliding mode controller according to real-time water flow field flow velocity and trajectory tracking error, combining the feedforward control amount, and generating a control instruction to drive the water surface robot to track the reference trajectory; and thus high-precision trajectory tracking control is achieved.

The invention discloses a power load prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical power load data and feature data related to the power load data, and preprocessing the historical power load data and the feature data; performing feature extraction and feature decoupling processing on the preprocessed historical power load data based on a variational mode decomposition technology to obtain target features; introducing a self-attention mechanism to perform multi-modal cross attention dynamic fusion on the target features and the preprocessed feature data, and constructing a power load prediction model; performing global hyper-parameter optimization on the power load prediction model by adopting a quantum particle swarm optimization algorithm to obtain a target hyper-parameter; training the power load prediction model by adopting the target hyper-parameter to obtain a target power load prediction model, and performing power load prediction by adopting the target power load prediction model; the causal reasoning capability of the model on complex business logic is enhanced, and the prediction accuracy is improved.

The invention discloses a fuel cell ship power system fault reconstruction method, which comprises the steps of building a fuel cell ship annular power system simulation model, designing an improved particle swarm algorithm, initializing a candidate solution by using a logic mapping chaos model, updating the candidate solution through a quantum particle swarm algorithm, and introducing a diversity migration strategy, particles in different ranges in the population are captured, migration individuals are selected based on the fitness and population position information, the individual with the minimum average Hamming distance in the population indicates the optimization direction of the iteration population, and the improved particle swarm algorithm is integrated into the fuel cell ship annular power system simulation model. The maximum recovery load, the minimum switching operation frequency and the maximum power supply load rate of each area serve as objective functions, optimization solution is carried out on the basis that weighted summation is converted into a single objective function, a power supply network under the fault condition is reconstructed through an improved particle swarm algorithm, a reconstruction scheme is obtained, and power system reconstruction after the fault is completed.

The present invention belongs to the technical field of high-speed monitoring point optimization and provides a method and system for optimizing the layout of high-speed monitoring points in an intelligent transportation system. The method obtains information about target monitoring roads; determines the initial area for each monitoring point based on road characteristics and traffic congestion models; uses the increase in the number of identified congestion points and the change in traffic flow after the emergency lane is activated as positive indicators, and the monitoring cost as a negative indicator, and constructs a corresponding benefit evaluation function to form an objective function; solves the problem using a quantum particle swarm optimization algorithm, or obtains several optimization schemes by adjusting the number of monitoring points and evenly distributing them; evaluates the obtained optimization schemes based on a multivariate linear regression model, and selects the final optimization scheme. The present invention can ensure the rational design of monitoring points, facilitate the effective monitoring of traffic congestion, and consider whether to temporarily activate the emergency lane.

The invention discloses a wind turbine generator health state assessment method and system. The method comprises the following steps: S1, collecting detection data of related parts of a wind turbine generator; s2, dynamically representing the operation mode of the key component based on a deformable convolution and attention mechanism combined driven feature extraction network; s3, generating health state score distribution by adopting an improved generative adversarial network, and identifying potential anomalies through a discriminator; s4, performing local health assessment on different parts of the wind turbine generator based on a multi-agent reinforcement learning collaborative decision-making mechanism, and completing overall health judgment by using centralized training, a distributed execution mechanism and an attention module; and S5, introducing quantum particle swarm optimization to carry out joint optimization on the agent cooperation strategy and the GAN parameters. The method solves the problems that feature extraction is insufficient, samples are unbalanced and fault propagation is difficult to model in existing wind turbine generator health state assessment.

The invention discloses a bevel gear power closed transmission system variable speed loading optimization method, and belongs to the field of gear transmission system loading test method optimization. The problem that according to an existing variable speed loading method, constant load loading or simple program control step loading exists, and consequently loading time is long is solved. According to the method, the dynamic characteristics of the bevel gear closed power system are analyzed, and a novel dynamic characteristic value is calculated by collecting a vibration signal and a vibration acceleration signal of the system. The optimal test loading sequence is determined by ingeniously applying a quantum particle swarm optimization method, and the method is mainly applied to the bevel gear power closed loading test.

The invention discloses an unmanned aerial vehicle task allocation method based on an anti-cosine attenuation quantum particle swarm, and relates to the technical field of intelligent optimization algorithms, and the method comprises the steps: constructing an unmanned aerial vehicle task allocation model; performing iterative solution on the unmanned aerial vehicle task allocation model by adopting a preset quantum particle swarm algorithm to obtain an optimal unmanned aerial vehicle task allocation scheme; in the iterative solution process, an anti-cosine attenuation mechanism is introduced, and real-time dynamic optimization configuration is carried out on the time-varying contraction and expansion coefficient; according to the preset quantum particle swarm algorithm, the quantum space is introduced into the particle swarm algorithm, and iteration of the quantum space particle swarm position is represented. According to the invention, the optimal unmanned aerial vehicle task allocation scheme can be quickly and efficiently obtained.