638 results about "Differential evolution algorithm" patented technology

The invention provides an anomaly detection and maintenance decision optimization method and system for automatic test equipment, and relates to the technical field of automation, and the method comprises the steps: deploying an edge calculation unit and a multi-level sensor network to collect equipment data, carrying out the fault diagnosis through a deep network model, and constructing a maintenance decision model in combination with equipment operation data. The model is input into a quantum enhancement dual-network system to calculate a reward value, and is used for training a maintenance strategy network under a federal reinforcement learning framework. And generating a maintenance resource configuration scheme through a quantum evolutionary algorithm, and generating a maintenance scheme through optimization of the multi-criterion evaluation model and the self-adaptive multi-target differential evolutionary algorithm. According to the scheme, a high-fidelity digital twin model and a multi-task deep transfer learning network are input for verification and updating, updated data are transmitted to a field control system through an industrial Internet of Things platform, and the updated data are recorded by using a block chain technology for updating a deep network model.

The technical scheme of the invention relates to the technical field of gas detection, in particular to a hazardous chemical storage leakage positioning and tracing method based on a gas array. The method comprises the following steps: collecting gas concentration, components and environmental physical field parameters through a three-dimensional heterogeneous sensor array, and generating a multi-modal data set; and matching the noise mode feature library in the knowledge base by using a transfer learning algorithm, dynamically correcting the baseline drift of the sensor, and outputting calibrated data. A closed-loop adaptive mechanism adjusts sensor parameters and network hyper-parameters through meta reinforcement learning, neural architecture searches and optimizes a model structure, and a differential evolution algorithm updates fluid mechanics boundary conditions. Through cooperation of physical constraint and data driving, the problem of signal distortion in a complex environment is solved, the leakage source positioning precision and robustness are improved, and the method is suitable for the field of hazardous chemical substance storage safety monitoring.

The invention discloses a network security threat intelligent identification and defense method based on artificial intelligence, and the method comprises the steps: S1, collecting network flow data, user behavior data and system log data from a network security system, and outputting a finally converged network feature data set; s2, performing global optimization search on the network feature data set by applying a hyper-entropy differential evolution algorithm to generate network feature data optimized by hyper-entropy differential evolution; s3, constructing a network traffic anomaly detection probability model based on variational Bayesian reasoning by using the network feature data after hyper-entropy differential evolution optimization; s4, inputting the network feature data set into the network traffic anomaly detection probability model, and evaluating the risk probability of an abnormal behavior; and S5, generating a self-adaptive defense strategy based on a detection result, and forming defense strategy data. According to the invention, the overall stability and defense capability of the network security system are improved.

The invention relates to a multi-element microphone array sound source localization method based on multistage signal preprocessing and subspace spectrum optimization, and belongs to the technical field of acoustic detection. Aiming at the problems of poor noise immunity, weak multi-sound-source resolution capability and low calculation efficiency of the existing sound source positioning technology, a triple signal preprocessing and subspace collaborative optimization scheme is provided; firstly, incoherent noise is suppressed through phase coherent filtering, a signal is reconstructed through principal component analysis, and phase deviation is calibrated through fundamental frequency; then constructing a guiding matrix and decomposing a noise subspace, and extracting a coarse positioning result; and finally, high-precision angle optimization is realized based on a chaos initialization differential evolution algorithm, and the efficiency is improved by combining a dynamic search range and an early stop mechanism. According to the method, the anti-interference capability in a low signal-to-noise ratio environment is remarkably enhanced, the problems of missing detection and false detection during dense distribution of multiple sound sources are effectively solved, meanwhile, the positioning precision and the real-time performance are considered, and the method is suitable for acoustic fault detection of complex scenes such as power transmission line inspection.

The invention discloses an intelligent optimization decision-making method and system for an oilfield water injection system, and belongs to the technical field of oilfield development engineering. The method comprises the following steps: constructing a pipe network digital twinborn model and carrying out actual measurement verification; executing hydraulic-thermal simulation based on the model to generate a pressure-flow map; optimizing water distribution in combination with an injection allocation scheme and matching a pump set combination; generating a control instruction and executing a pressurization scheme; monitoring the state of the pipe network in real time and correcting model parameters. Through the digital twin technology and the differential evolution algorithm, global optimization decision of water distribution and pump set combination of the water injection system is achieved, and the problems that a traditional system is high in energy consumption and low in efficiency are solved.

The invention discloses a hydrogen energy system safety assessment method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing the dynamic operation parameters of a hydrogen energy system, and generating an input feature matrix; s2, inputting the feature matrix into a multi-resolution feature sensing module to generate a high-dimensional feature map; s3, inputting the high-dimensional feature map into a dynamic time bending network to generate a parameter incidence matrix; s4, optimizing feature weight distribution based on an adaptive differential evolution algorithm; s5, abnormal distribution is captured through the dynamic graph convolutional network, and a dynamic abnormal graph is generated; s6, simulating an anomaly propagation path, and generating a cascade anomaly analysis result in combination with a dynamic traceability mechanism; s7, a cascade anomaly analysis result is optimized through a grey wolf-firefly hybrid optimization algorithm; and S8, dynamic operation parameters are adjusted based on a final cascade risk prediction result, and hydrogen energy system safety assessment is completed. According to the method, deep learning, a hybrid optimization algorithm and the like are combined, and safety evaluation of abnormal detection and risk prediction of the hydrogen energy system is realized.

The invention discloses a wrist joint spasm assessment method and a wrist joint spasm assessment device based on multi-source data fusion. The rotating speed data, the rotating angular speed data and the angle change data of the wrist joint, the pressure change data of the whole palm, the acceleration change data of the palm, the torque change data of the wrist joint and the electromyographic change data of related muscles in the movement process are measured, and scoring is carried out in combination with an expert system. The evaluation of wrist spasm is realized by combining a multiple linear regression algorithm with a differential evolution algorithm; and based on the multi-source data fusion, establishing an evaluation model by using a Bi-LSTM network so as to realize spasm evaluation. The wrist serves as a spasm detection part, and compared with traditional spasm detection equipment, a more convenient data acquisition mode is achieved while the evaluation precision is guaranteed.

The invention relates to the technical field of data processing, and discloses a flue gas treatment intelligent regulation and control method and system based on artificial intelligence and a medium. The method comprises the following steps: collecting parameter data of a flue gas system through a sensor network and processing to obtain preprocessed data; using unsupervised clustering to identify working condition features and categories; constructing an emission concentration error active switching control model according to the working condition information; different working condition control parameters are optimized by adopting a differential evolution algorithm; optimized parameters are input into a controller, and modes are dynamically switched according to pollutant errors. The control strategy can be automatically switched according to the working condition characteristics of the flue gas treatment system, the complex and changeable industrial production environment can be effectively dealt with, and the system operation cost is optimized while stable and up-to-standard emission of pollutants is guaranteed.

The invention discloses a ship-based radar and AIS data fusion method and device based on the satellite internet, and relates to the technical field of ship information. The method comprises the following steps: acquiring self-motion, AIS and radar data recorded by a plurality of ships in real time through a low-orbit satellite, and performing target trajectory tracking and data filtering by adopting a first-order linear ship motion model and a self-adaptive extended Kalman filter; the method comprises the following steps: establishing an error model containing a radial error coefficient, an azimuth angle error and an effective detection distance for dynamic system errors of a shipborne radar, and realizing radar-AIS target matching and error correction based on an adaptive differential evolution algorithm and a Hungary algorithm; and finally, performing fusion processing on unknown targets sensed by multiple nodes through a multi-hypothesis tracking method to form a globally unified target observation result. According to the method, shipborne radar data and AIS data can be effectively associated, real-time monitoring of ship targets in a wide-area sea area is achieved, and reliable technical support is provided for marine supervision, safety early warning and track backtracking.

The invention provides an AGC hydropower station intelligent control method based on multi-source data fusion. Constructing a control feature vector of the multi-dimensional feature; performing spatial-temporal feature modeling on the control feature vector, and extracting a time sequence dependency relationship between power grid load change and hydraulic dynamic response and a spatial coupling effect between units; establishing a multi-objective optimization function, and dynamically adjusting the weight coefficient of each objective through fuzzy logic according to the current working condition; a self-adaptive differential evolution algorithm is adopted to carry out on-line optimization on an active power distribution coefficient of a unit and PID parameters of a speed regulator, and the requirements of guide vane opening change rate constraint and water hammer effect avoidance are met. According to the method, multi-source heterogeneous data such as power grid, hydraulic engineering and equipment states can be effectively fused, multi-target dynamic optimization control is realized through the space-time attention model and the adaptive differential evolution algorithm, and the control precision, the response speed and the equipment operation safety of the hydropower station AGC system are remarkably improved.

The invention discloses a permanent magnet synchronous motor temperature monitoring method based on digital-analog hybrid drive, and the method specifically comprises the steps: dividing a motor into a plurality of heat source nodes, including a stator winding, a stator tooth part, a rotor core and the like, connecting the nodes through a thermal resistance and thermal capacity network, building a thermal network model of the motor, and carrying out the thermal network model; calculating the initial values of thermal resistance and thermal capacity of each component of the motor, and optimizing parameters in a thermal network model by using a differential evolution algorithm based on the lumped parameter thermal network model and in combination with a data-driven strategy. Furthermore, temperature rise prediction is dynamically adjusted according to motor state data (such as stator current, rotating speed and the like) collected in real time so as to realize real-time temperature estimation. The method is high in temperature estimation precision, does not affect the performance of the motor, is simple in calculation, is good in real-time performance, and has physical interpretability. The temperature rise of the motor can be predicted in real time under different working conditions, early warning is given out, motor faults caused by too high temperature rise are effectively avoided, and the reliability and safety of the motor are improved.

The invention provides a self-adaptive volume-variable intelligent air compressor and a control system thereof, and relates to the technical field of air compressors. The control system comprises a multi-source heterogeneous sensing fusion module, a dynamic manifold optimization control module, a quantum probability fault pre-judgment module, a cross-domain energy cooperative scheduling module, a distributed self-organizing communication module and a self-adaptive meta-evolution module. And a grey Markov chain model and a wavelet decomposition-support vector machine are combined to accurately predict the real-time electricity price and the factory gas consumption load. And according to the predictions, the operation mode of the air compressor is intelligently adjusted through dynamic game equilibrium and a multi-target differential evolution algorithm. In the low ebb period of the electricity price, the operation load is automatically increased by 30%-40%, and meanwhile the gas storage capacity is increased by 25%-35%; and in the peak period of electricity price, stored gas is reasonably released, and the operation load is reduced by 20%-30%. And energy waste caused by unreasonable operation is effectively avoided.

The invention provides an ultrasonic echo signal joint noise reduction method based on improved EKF and wavelet packet cooperation, belongs to the technical field of ultrasonic detection, and solves the problems that the noise reduction effect is poor in a traditional ultrasonic signal noise reduction method and noise reduction and feature retention are difficult to balance in the wavelet packet noise reduction process. Modeling the ultrasonic echo signal and the noise signal to obtain an ultrasonic echo signal with noise; extracting a target ultrasonic echo signal by using an improved extended Kalman filter to obtain an observation equation; predicting and updating on the basis of the observation equation to obtain ultrasonic echo signal estimation; optimizing the noise covariance matrix and the observation noise covariance matrix by adopting a differential evolution algorithm to obtain an optimal parameter, executing extended Kalman filter filtering based on the optimal parameter, and generating an ultrasonic echo signal estimated value after preliminary denoising; and carrying out wavelet packet threshold denoising to obtain a final denoised ultrasonic echo signal.

The invention discloses an oil reservoir inversion method based on a differential evolution particle swarm fusion algorithm, and belongs to the technical field of oil reservoir inversion, and the method comprises the steps: carrying out the oil reservoir history fitting, and determining an objective function of the oil reservoir history fitting; establishing a particle swarm optimization algorithm; establishing a differential evolution algorithm; and based on a differential evolution particle swarm fusion algorithm, solving the oil reservoir history fitting objective function to obtain an oil reservoir inversion optimal result. By adopting the above method, through fusion of the two algorithms, the algorithms can maintain the exploration capability for the global optimal solution in the search process, and can rapidly converge to the vicinity of the optimal solution, thereby improving the precision and reliability of oil reservoir inversion.

The invention provides a poison gas leakage inversion method, device and equipment, a medium and a product. According to one example of the application, the method may include: using a stochastic simulation algorithm to determine a simulation position and simulation intensity where a poison gas concentration error is minimum, the poison gas concentration error being an error between a sampled poison gas concentration of a sampling point and a stochastic simulated poison gas concentration; based on the simulation position and the simulation intensity, constructing a position constraint condition and an intensity constraint condition of the poison gas leakage source; calculating a leakage source position of a poison gas leakage source by adopting a differential evolution algorithm and a position constraint condition; and calculating the leakage source intensity of the poison gas leakage source by adopting a quasi-Newton optimization algorithm and an intensity constraint condition.

The invention discloses a shared energy storage-producer and disagger mixed game coordinated optimization method considering value-at-risk. The method comprises the following steps: acquiring data of a shared energy storage power station and a producer and disagger group; a master-slave game model is constructed, shared energy storage in an upper layer is used as a leader, and purchase and sale electricity prices, power grid interaction electric quantity and charging and discharging strategies from a producer group, a producer group in a lower layer is used as followers, and the yield of the producer group is maximized. Determining an alliance cooperation strategy and a response to upper-layer shared energy storage by minimizing the alliance operation cost of the production and disappearing person group; the method comprises the following steps: constructing a lower-layer production and disappearing person group cooperative game model, adopting a Nash bargaining theory as a benefit distribution method, and adopting a value-at-risk theory to adjust the bargaining ability of individual production and disappearing persons: adopting a differential evolution algorithm and combining CPLEX and MOSEK commercial solvers to solve. According to the method, benefits of all parties are balanced through a mixed game mode, and a pricing scheme and an operation strategy between the shared energy storage and the producer group are determined on the basis of considering distributed photovoltaic uncertainty.

The invention belongs to the technical field of LLC resonant converters, and provides an efficiency optimization method of a wide-range LLC resonant converter. The method comprises the following steps: firstly, obtaining a full-bridge LLC resonant converter topology and a working area; secondly, establishing a full-time-domain equation of the full-bridge LLC resonant converter topology in an inductive low-gain output working area, and setting boundary conditions of the full-time-domain equation; then, on the basis of boundary conditions, switching tube turn-off current is calculated, and the switching tube turn-off current is combined with switching tube switching frequency to be represented as switching tube turn-off loss characteristic quantity related to the inductance coefficient, the quality factor and the normalized frequency; then, carrying out characteristic analysis on the turn-off current of the switching tube, and setting constraint conditions; and finally, on the basis of the set constraint conditions, the optimal values of the inductance coefficient, the quality factor and the normalized frequency are calculated by combining a differential evolution algorithm, so that the turn-off loss of the switching tube is minimum. According to the invention, the transmission efficiency in a low-gain mode and a high-gain mode can be improved, and the turn-off loss of the switching tube is reduced.

The invention provides a dynamic task optimization method based on a knowledge graph, and aims to solve the problem of dynamic task optimization. According to the method, the scene similarity is quantified by constructing the task scene knowledge graph, storing order information, geographic positions and time window features in a structured manner. In the initial stage of optimization, a differential evolution algorithm population is initialized by using a historical optimal scene based on a dynamic scene traceability mechanism, and parameters are dynamically adjusted in combination with a historical variation strategy weight. In the optimization process, task features are updated in real time, and the response capability of the algorithm to scene changes is improved. According to the method, the understanding quality and optimization efficiency are improved, a high-quality path scheme is quickly obtained, and the performance and robustness of dynamic task scheduling are remarkably enhanced.

The invention relates to an Internet of Vehicles multi-domain intelligent optimization method and system under a computing network convergence architecture, and the method comprises the steps: receiving a task request sent by an edge layer road side unit based on a pre-configured cloud edge end cooperation architecture, optimizing the user service time delay and network load balance through a differential evolution algorithm, and obtaining a network load balance optimization result. Generating an original intelligent model distribution path and distributing the pre-trained original intelligent model to an edge layer road side unit; receiving an original intelligent model and vehicle mobility data, optimizing vehicle mobility, model quality and alliance establishment cost through an alliance game algorithm, and constructing a vehicle fine-tuning alliance structure suitable for federal learning; on the basis of a vehicle fine tuning alliance structure, local model updating data and trust evaluation data of vehicles in an alliance are obtained, dynamic weighted federated learning is executed through multi-dimensional trust evaluation, security is enhanced through differential privacy and a block chain tracking mechanism, and a high-precision global intelligent model is generated and deployed to the vehicles. Reliable support is provided for automatic driving, V2X communication and the like.

The invention discloses an energy storage resource scheduling optimization method and system based on digital twinning. The method comprises the following steps: step 1, constructing an energy storage resource scheduling optimization platform based on digital twinning; establishing a digital twinborn model for describing the operation state of the energy storage equipment in combination with the physical characteristics and historical data of the energy storage system; step 2, based on the constructed scheduling optimization platform, performing joint modeling on a wind power generation system, a photovoltaic system and an energy storage system, and establishing an equivalent model of a joint power generation system; 3, based on the established equivalent model of the combined power generation system, establishing a multi-objective optimization function and physical and operation constraint conditions of a scheduling algorithm, and constructing an energy storage system resource scheduling optimization problem; and step 4, based on the established scheduling optimization problem, performing problem solving and dynamic adjustment by using a differential evolution algorithm. The running state of the energy storage system is accurately reflected, and the real-time response capability is improved; the economy and the stability of the scheduling scheme are obviously improved; and the security and feasibility of the scheduling strategy are ensured.

The multi-field coupling collaborative modeling and parameter autonomous intelligent decision-making method for the roller type quenching process comprises the steps that four key process parameters including the nozzle flow, the water ratio, the roller gap distance and the plate passing speed are collected and subjected to normalization preprocessing, and a training set and a test set are divided; the four key process parameters of the training set serve as input, the water-cooling convective heat transfer coefficient and the phase change plasticity coefficient serve as output, a BP neural network is trained, and the workpiece surface water-cooling convective heat transfer coefficient and the material internal phase change plasticity coefficient are predicted through the trained BP neural network; a multi-field coupling model composed of a temperature field, a structure phase change field and a stress-strain field is solved, and quantitative calculation of quenching core quality indexes is achieved; by taking water consumption minimization as a target, constructing a nonlinear optimization model under the condition that multiple core quality index constraints of the plate temperature, the outlet hardness and the plate shape are met; and performing global optimization on the optimization model by adopting a differential evolution algorithm, and autonomously deciding an optimal process parameter set value.

The invention discloses a wind turbine generator set state monitoring method based on multi-working-condition identification and an ESN. The method comprises the following steps: step 1, collecting operation data in a wind turbine generator set SCADA system; 2, carrying out feature engineering processing on the SCADA data; 3, performing operation condition division on the unit health data set by adopting a K-means clustering algorithm; 4, optimizing the echo state network model based on a differential evolution algorithm, establishing power prediction normal behavior models under different working conditions, and determining state monitoring thresholds of the unit under different working conditions in combination with power prediction residual analysis; and step 5, in an online monitoring stage, firstly carrying out feature engineering processing on real-time operation data of the wind turbine generator, then determining a current working condition by utilizing a working condition identification model, finally predicting active power through a corresponding echo state network model, and calculating a power residual error to judge whether the wind turbine generator runs normally or not. Practical application shows that the abnormal state of the unit can be found earlier than that of a traditional SCADA system when a fault occurs.

The invention relates to the technical field of unmanned aerial vehicle formation control, and provides a formation control method, system, device and medium for a photovoltaic panel cleaning unmanned aerial vehicle group, and the method comprises the steps: regularly obtaining the task execution information of the unmanned aerial vehicle group, combining a deep learning model to obtain a target formation, carrying out the formation switching, and carrying out the cleaning of a to-be-cleaned photovoltaic panel according to the distribution information of the to-be-cleaned photovoltaic panel. Based on a preset path planning algorithm, cluster task path information in a formation transformation period is obtained, and according to the target formation and a preset formation, an optimization target function is established, and based on an improved differential evolution algorithm, formation establishment information is obtained; and performing formation control on the unmanned aerial vehicle group based on a formation control task sequence obtained by performing formation cleaning task division according to the group task path information, a preset formation control task optimization model and formation establishment information. According to the invention, the formation of the unmanned aerial vehicle group can be adaptively replaced, the anti-interference performance and flexibility of the formation are ensured, and the photovoltaic panel cleaning efficiency and the operation safety of the unmanned aerial vehicle group are improved.

The invention discloses an EIS-based equivalent circuit model parameter identification method and device, a terminal and a storage medium, and the method comprises the steps: obtaining the EIS test data of a battery, screening the EIS data corresponding to a preset target SOC, and endowing the EIS data of different frequency points with dynamic weights; obtaining a preset equivalent circuit model, setting a parameter range of each model parameter in the equivalent circuit model, and randomly generating an initial population in the parameter range based on logarithm distribution; performing iterative optimization on the initial population through a differential evolution algorithm, and dynamically adjusting a crossover probability and a scaling factor in an iteration process through an adaptive strategy; and when a preset iteration termination condition is satisfied, outputting an optimal parameter obtained after iteration. According to the method, the equivalent circuit model parameters are identified by using the differential evolution algorithm, so that the parameter identification efficiency and accuracy are improved.

The invention discloses an underwater target electric field positioning method based on a particle swarm differential evolution hybrid algorithm, and provides the underwater target electric field positioning method based on the particle swarm differential evolution hybrid algorithm PSODE by combining a differential evolution algorithm DE and a particle swarm optimization algorithm PSO. A three-layer medium electric field radiation model closer to an actual positioning scene is utilized, multiple algorithms are fused, the risk of falling into a local optimal solution is reduced, dynamic parameters are adjusted to meet the requirements of actual application, and finally long-distance accurate positioning of the underwater constant-current electric dipole source is achieved.

The invention discloses an automobile charging pile charging allocation method based on dynamic load balancing, and the method comprises the following steps: S1, collecting the real-time operation data of a charging pile, and storing the real-time operation data in a charging pile load database; s2, constructing a charging scheduling optimization model, and setting an optimization target and a constraint condition; s3, a charging power distribution scheme is initialized, population individuals are generated by adopting a doliolaria group algorithm, and a fitness value is calculated; s4, running the dogvessel squirt group algorithm for optimization, adjusting the power by the navigator according to the optimal solution, and updating the power distribution scheme by the follower; s5, population diversity is calculated, and if the proportion of low-diversity individuals is lower than a threshold value, differential evolution algorithm optimization is triggered; s6, the fitness values are compared, and an optimal charging power distribution scheme is selected; and S7, scheduling the charging power in real time and monitoring the state by applying the optimal charging power distribution scheme. According to the invention, charging power balanced distribution is realized through an intelligent optimization algorithm, user waiting time and power grid load fluctuation are reduced, and charging station operation efficiency is improved.

The invention provides an oxidation kettle temperature prediction control method. The method comprises the following steps: S1, establishing a differential equation model; s2, converting the differential equation model into a discretized space prediction model, enabling the space prediction model to be equivalent to a first-order inertia plus lag system, determining state transition matrix parameters through system identification, and generating an oxidation kettle temperature prediction model; s3, obtaining a target function of a model prediction controller according to an error between the predicted temperature and the reference temperature; s4, improving an artificial bee colony algorithm by adopting a variation strategy of a differential evolution algorithm; s5, in the rolling optimization process of model prediction control, the improved artificial bee colony algorithm is adopted to update the optimal control input sequence in a rolling mode until the target function is minimized, and the optimal control input sequence is obtained; and S6, applying the optimal control input sequence to an oxidation kettle control system in real time, and dynamically adjusting the temperature of the oxidation kettle. The selection of the control input sequence is optimized, and the temperature control in the reaction process is more accurate and efficient.

The invention discloses an industrial park pollution source rapid identification method based on fuzzy clustering, and the method comprises the following steps: S1, collecting pollution data in an industrial park, and carrying out the preprocessing of the pollution data; s2, identifying the pollution data by adopting a fuzzy clustering algorithm combining a possibility C mean value and a fuzzy rough C mean value; s3, constructing a pollution diffusion model, calculating a diffusion path of pollutants, and performing correction in combination with an identification result; s4, dynamically adjusting parameters of the possibility C mean value and the fuzzy rough C mean value by adopting an improved differential evolution algorithm; s5, when abnormal fluctuation occurs in the pollution data, dynamically adjusting the membership degree, and correcting the pollution source identification result; and S6, outputting an optimized pollution source identification result, and generating a pollution traceability analysis report. According to the industrial park pollution source identification method, the possibility C mean value algorithm and the fuzzy rough C mean value algorithm are combined, the improved differential evolution algorithm is used for optimizing parameters, and the industrial park pollution source identification accuracy, the anti-interference performance and the self-adaptive optimization capacity are improved.

The invention discloses a naval vessel formation-oriented MADDPG super-network parameter optimization method based on differential evolution, and the method comprises the steps: constructing a naval vessel formation cooperative combat environment, setting a naval vessel as an intelligent agent, and setting task information for the intelligent agent; loading task information, and generating an initial strategy; according to the initial strategy, the intelligent agent interacts with the environment and collects data; training super-network parameters of the MADDPG model according to the data, and optimizing the super-network parameters by adopting a differential evolution algorithm to obtain an optimal parameter combination; loading the optimal parameter combination into the MADDPG model, and evaluating the performance of the intelligent agent to obtain an evaluation result; and the initial strategy is optimized according to the evaluation result, the intelligent agent interacts with the environment again, new data is collected, the super-network parameters are adjusted according to the new data, and the super-network parameters are iteratively optimized until a final training model is obtained. According to the method, coevolution optimization of the multi-agent strategy network is realized, and the convergence speed and stability of the strategy network are improved.

The invention relates to the technical field of data processing, and discloses a motor controller research and development method and system based on an intelligent algorithm. The method comprises the steps that after parameter identification is carried out on a motor, PID parameters are optimized through an adaptive differential evolution algorithm; constructing a double-loop control structure according to the optimal PID parameters; designing a virtual sensor based on the optimized double-loop structure and obtaining a health index; fusing the multi-source signal to obtain a fusion speed signal; therefore, a backstepping sliding mode controller is realized and system verification is completed. The motor control system with high fault-tolerant capability is constructed by fusing various intelligent algorithms and virtual sensor technologies, the basic operation performance of the motor can be kept even if the sensor is abnormal or fails, and the reliability and the safety of the system are improved.