211 results about "Differential evolution" patented technology

The invention discloses a deep learning-based intelligent optimization method for farmland irrigation water quantity. The method comprises the following steps of S1, obtaining a preprocessed farmland multi-mode perception data set; s2, constructing and training a multi-scale gated SIREN water content continuous prediction model by taking the preprocessed farmland multi-modal perception data set as input; s3, dividing a planned irrigation period into a plurality of time slices, defining a water volume decision vector space and constructing a differential evolution multi-target water volume optimization model; s4, outputting a Pareto optimal water quantity decision vector set meeting a multi-target constraint condition, and selecting an optimal farmland irrigation water quantity from the Pareto optimal water quantity decision vector set according to a user weight or a preset rule; s5, the optimal farmland irrigation water amount is issued to an intelligent valve control system, and an electromagnetic valve is driven to execute irrigation according to time slices. According to the invention, an intelligent irrigation prediction-decision-execution-feedback full-link closed-loop mechanism taking SIREN as a core is realized, and the method has remarkable water-saving and yield-increasing benefits and engineering deployability.

The invention discloses an unmanned aerial vehicle cluster path planning method based on an improved artificial travel mouse optimization algorithm, and the method comprises the steps: obtaining environment data, and building an environment model comprising a threat region; constructing an evaluation function of an unmanned aerial vehicle cluster path; an evaluation function of the unmanned aerial vehicle cluster path is solved based on an improved artificial travel mouse optimization algorithm, and an optimal path planning result is obtained; the improvement on the artificial travel mouse optimization algorithm comprises the following steps: improving an initial point of a random population by adopting chaotic mapping; in iteration, a differential evolution variation strategy is adopted to carry out variation on individuals, and the updating mode of the individuals is controlled by controlling variables. Firstly, a chaotic mapping method is adopted to initialize populations, population diversity and space coverage can be enhanced, and the utilization rate of the whole search space of the algorithm is improved; secondly, a differential evolution variation strategy and a hybrid updating mechanism are introduced, and the jumping ability and the global search performance of the population are enhanced;

The invention relates to a joint estimation method for SOC and SOH of a power battery, in particular to a joint estimation method for SOC and SOH of a lithium ion power battery based on FOASEKF-EKF, comprising fractional order equivalent circuit models of two parallel fractional order CPE branches, and providing a hybrid genetic algorithm HGA fusing a differential evolution strategy and an adaptive variation mechanism. Accurate estimation of SOC and terminal voltage under a fast time scale is realized by introducing a sliding-mode observer and an FOASEKF, periodic online correction is performed on model parameters and battery capacity based on an EKF under a slow time scale, and high-precision and high-robustness battery SOC and SOH joint estimation is realized. The method is suitable for complex industrial environments such as electric automobiles and rail transit, does not need to set a large number of hyper-parameters, does not excessively depend on the quality and quantity of data, has good interpretability, adaptability and engineering practicability, can still achieve high-precision cooperative estimation of SOC and SOH especially under the working conditions of frequent start and stop and unsteady operation, and has good application prospects. And misjudgment and drift estimation risks are obviously reduced.

The invention discloses an energy planning method for a low-carbon economic comprehensive energy system based on multi-agent deep reinforcement learning. The method comprises the following steps: constructing a comprehensive energy system structure including multiple energy forms such as wind power, photovoltaic, electricity-to-gas, combined heat and power generation, energy storage and the like; establishing a carbon emission intensity model of each subsystem based on an energy input and output relationship, and constructing a segmented carbon tax function to quantify the carbon cost; a double-layer two-stage Nash optimization model is designed, and the game relationship between collaboration between subsystems and external energy interaction is considered; the optimization problem is converted into a Markov decision process, and a state, an action and a reward function are defined; a multi-agent TD3 algorithm model is constructed and trained, and strategy stability and robustness are improved through a differential evolution mechanism; and deploying the trained strategy model in an actual system to realize self-adaptive energy planning and scheduling for the carbon economic target. The method has the characteristics of high adaptability, high carbon benefit and excellent intelligent cooperation capability.

The invention discloses a visual large model Token adaptive optimization method, system and device based on differential evolution and a medium, and the method comprises the steps: carrying out the data processing of an image classification data set, an instance segmentation data set and a saliency target detection data set, and obtaining all Tokens corresponding to each image through a Patch Embedding and position coding method; obtaining a plurality of groups of Tokens corresponding to each image through a random selection mode, and performing data processing to output all Tokens corresponding to each image and the plurality of groups of Tokens selected from each image; constructing a Token adaptive selection module, a self-attention optimization module and a downstream task output module; a complete Token adaptive optimization visual large model is constructed; training a reconstruction model and a complete Token self-adaptive optimized visual large model; performing model reasoning to obtain an image classification result, an instance segmentation result image and a saliency target detection result image; the system, the equipment and the medium are used for implementing the method. The method can be widely applied to various visual tasks such as image classification, instance segmentation and saliency target detection.

The invention provides a power distribution network abnormal state sensing method and system based on data processing, and relates to the technical field of data processing, and the method comprises the steps: obtaining a target function, and setting a differential evolution parameter; the differential evolution parameters comprise population size, iteration times, variation factors and crossover probability; the configuration of differential evolution parameters is iteratively optimized, various operation data are preprocessed, abnormal value and noise removal and normalization processing are included, and processed data are obtained; and extracting characteristic indexes reflecting the running state of the power distribution network from the processed data, wherein the characteristic indexes comprise statistical characteristics, frequency spectrum characteristics and time frequency characteristics of voltage and current. According to the invention, through real-time data acquisition, feature extraction, state evaluation and abnormity identification and positioning, comprehensive monitoring and timely early warning of the operation state of the power distribution network are realized, and the safety and stability of the power distribution network are effectively improved.

The invention discloses a load output prediction regulation and control method based on Transform and differential evolution, and the method comprises the steps: collecting real-time data through edge equipment, and predicting the load of a power distribution network and the output of distributed energy; the predicted power distribution network load and distributed energy output are sent to a differential evolution module arranged at the cloud end, and an optimal control instruction sequence is solved; a differential evolution module arranged at the cloud sends the optimal control instruction sequence to an edge controller, and meanwhile, the edge device monitors the local state in real time and uploads operation data and execution feedback to the cloud to obtain a cloud and edge cooperative control instruction; cloud and edge cooperative control instructions are sent to the distributed resource execution module, specific actions are executed according to the instructions issued by the cloud, and local measurement data are monitored, controlled and corrected. Compared with the prior art, the method at least has the beneficial effects that the electricity purchasing power is obviously reduced, the regulation and control smoothness is improved, the load tracking capability is enhanced, the distributed output utilization rate is improved, and the real-time performance and the robustness are enhanced.

The invention belongs to the technical field of electric digital data processing, and particularly relates to a method for optimizing errors of a logistics sorting encoder based on an improved grey wolf algorithm. According to the method, Logistic chaotic mapping and Gaussian perturbation are superposed to generate a diversity initial parameter population so as to break through the limitation of traditional random initialization, a fitness function is designed to quantify an angle compensation residual error, and parallel computing is utilized to accelerate evaluation. In the iteration process, global exploration and local development are dynamically balanced through adaptive convergence factors, a bimodal perturbation mechanism is constructed in combination with a differential evolution strategy and Levy flight variation, population effectiveness is maintained through reflection boundary processing, guiding of # imgabs0 # wolf is enhanced through dynamic weight distribution, the position of a leader wolf is updated by adopting an elitist retention strategy, and the population effectiveness is improved. And finally, outputting the optimal compensation parameter when the maximum number of iterations or the residual threshold is met, thereby improving the positioning precision and the operation efficiency of the logistics sorting system.

The invention provides a conduction oil boiler system optimization control method and system based on an adaptive algorithm. The method comprises the steps of data acquisition, data preprocessing, model training, parameter optimization and real-time control. The system comprises a sensor network, a data processing module, a model training module, an optimization calculation module and a control execution module. Boiler operation data are collected in real time through a sensor network, a combustion boiler natural gas dynamic prediction model and a circulating pump efficiency optimization model are constructed after preprocessing, parameters such as the number of boilers, the circulating pump frequency and the oil supply temperature are optimized through an adaptive differential evolution (ADE) algorithm, and finally closed-loop adjustment is achieved through a control execution module. According to the method, the data-driven modeling technology and the self-adaptive optimization capability are utilized, so that system parameters can quickly respond to process demand changes, the heat efficiency is remarkably improved, the energy consumption is reduced, the temperature fluctuation is reduced, the operation and maintenance cost is optimized, and the operation performance and the economical efficiency of the heat conduction oil boiler system are comprehensively improved.

The invention provides an unmanned aerial vehicle electric power inspection electric energy guarantee planning method and device in a multi-weather scene, and aims to solve the problem of uncertainty of unmanned aerial vehicle energy consumption and renewable energy power generation in the multi-weather scene in the prior art. Weather scenes are divided into multiple types, the influence of the weather scenes on the energy consumption of the unmanned aerial vehicle power inspection system is quantified, and a system load model is established; establishing a two-stage micro-grid planning model by using the output of the power generation equipment unit; constructing a fuzzy set based on a multi-discrete scene method, and converting the micro-grid planning model into a distributed robust optimization model by using the fuzzy set; a differential evolution-column and constraint generation algorithm is utilized to solve the distributed robust optimization model, the solving time is remarkably shortened, the solution quality is ensured, simulation experiments are utilized to verify that the method can obtain an unmanned aerial vehicle electric power inspection electric energy guarantee system planning scheme considering weather uncertainty, and economy, reliability and efficiency are balanced.

The invention belongs to the technical field of networks, and particularly relates to a data center flow scheduling method based on differential evolution fusion particle swarm optimization, which comprises the following steps that: a switch receives a data flow and judges whether a destination address of the data flow belongs to a local direct connection host, if so, the data flow is forwarded; otherwise, judging the size of the data stream, and if the data stream is a small data stream, scheduling the data stream through an ECMP algorithm, directly distributing a path and issuing a flow table; if the data stream is a big data stream, calculating an optimal path by adopting a PSODE algorithm, optimizing a link load and time delay according to the optimal path, and issuing path information to a switch to execute scheduling; according to the method, the advantages of the SDN and the heuristic algorithm are combined, link load balancing optimization and time delay control are realized in a data center network traffic high-load scene, and the overall performance of the network is further improved.

The invention designs a workshop dynamic scheduling method based on an improved genetic algorithm and multi-objective optimization. According to the method, in order to solve the problem that a scheduling scheme fails after dynamic events such as equipment failure, order insertion or material delay occur in a discrete manufacturing workshop, a greedy strategy is adopted for pre-scheduling, and rapid rescheduling is performed based on an improved genetic algorithm after the dynamic events occur. The algorithm improves search efficiency and scheduling stability through multi-population parallel evolution, differential evolution self-adaptive parameter adjustment and an elitist retention mechanism. And taking minimization of the maximum completion time, the total delay time and the equipment change frequency as multiple targets, and obtaining a comprehensive optimal solution through a weighted summation method. According to the method, the scheduling response speed and stability of the workshop in a dynamic environment can be remarkably improved, and the workshop production efficiency and the equipment utilization rate are improved.

The invention belongs to the technical field of machine learning models, particularly relates to a personnel performance evaluation method based on IWOA-SVM, and solves the problems that a traditional support vector machine (SVM) is low in precision, difficult in parameter selection and the like in performance intelligent evaluation. The method comprises the steps that Tent chaotic mapping and a pseudo-opposition learning strategy are utilized to increase the diversity and quality of an initial population, and the whale algorithm (WOA) is prevented from falling into local optimum; the global optimization capability of the WOA is improved by adopting a differential evolution mechanism; a penalty factor and kernel function parameters of the SVM are optimized through an improved whale algorithm (IWOA), and performance evaluation can be effectively carried out while optimal parameters are obtained. According to the method, the whale algorithm can be improved by using Tent chaotic mapping, pseudo-opposition learning and a differential evolution strategy, SVM parameters are searched in a global range, and better model performance is obtained.

The invention provides a cable testing method and a small handheld cable tester. The method comprises the following steps: carrying out wavelet transform noise reduction, Z-score standardization and time sequence dynamic regularization on original cable data; then constructing a geometric deep learning network by using a dispersed self-organizing structure of the rotating cube set, and extracting multi-modal depth features; fusing different modal features through a spiking neural network gating mechanism and a cross-modal attention mechanism; and finally, fault classification is carried out based on a differential evolution optimized neural network, and fault location is realized by combining a time sequence generative adversarial network and a dynamic probability neighborhood growth clustering algorithm. The system also evaluates the data contribution degree of each modal through information entropy and mutual information analysis, and optimizes the model performance by using adaptive weight distribution and lightweight neural network technologies. According to the invention, various fault types such as cable breakage, short circuit, insulation aging, poor contact and the like can be identified and positioned with high precision, and the efficiency and the accuracy of cable maintenance are remarkably improved.

The invention relates to the technical field of reinforcement learning, motor driving and the like, provides a reinforcement learning-based adaptive search self-tuning method for a motor driving algorithm, and realizes an optimal decision of an individual search behavior by performing adaptive switching among various heuristic search operators by introducing a Q learning mechanism. And meanwhile, in combination with a neighborhood search strategy based on differential evolution, the local search capability is enhanced, and the global optimization performance is improved. The method is applied to the setting problem of key parameters of the motor driving system, the I TAE performance index serves as an optimization target, the search behavior is dynamically adjusted through a self-adaptive strategy, and therefore better control performance is obtained. Compared with an existing method, the method has the remarkable advantages in the aspects of convergence speed, self-adaptive capacity and anti-interference performance.

The invention discloses a lake pollution traceability analysis method based on multi-source data assimilation and reverse diffusion, and relates to water pollution traceability analys.The lake pollution traceability analysis method comprises the steps that chemical pollution is monitored at the downstream of a lake, monitoring data are imported into a hydrodynamic force-water quality model after Kalman filtering, and spatial and temporal distribution data of pollutants in a water body are obtained; pollution source inversion is carried out based on a differential evolution Markov chain Monte Carlo method, and pollution source parameters are obtained; forward simulation is carried out, a flow velocity field and water depth are solved through a hydrodynamic force-water quality model, then a pollutant transport model is generated, pollution source parameters are used as input, a finite volume method discrete control equation is used based on the pollutant transport model to predict pollutant concentration space-time evolution, and pollution duration under natural attenuation is evaluated; and comparing real-time monitoring data with an expected effect, and evaluating a pollution traceability analysis effect. Pollution source positioning errors are reduced, pollution contribution rate calculation accuracy is improved, traceability efficiency is improved, the risk of excessive reagents is avoided, and treatment cost is reduced.

The invention relates to the technical field of equipment loading systems, and discloses an equipment loading system fault diagnosis method, which comprises the following steps: acquiring fault characteristic parameters of an equipment loading system as original data; a tuna algorithm TSO is improved, including introducing Logistic chaotic mapping in a TSO initialization stage, introducing a nonlinear adjustment strategy in a TSO spiral hunting stage to enhance the global search capability and local development capability of the algorithm, and introducing a variation method in a differential evolution method to avoid the singleness of a population. An improved tuna algorithm ITSO is adopted to optimize the weight of a good and inferior solution distance method TOPSIS algorithm, an ITSO-TOPSIS data processing model is constructed, parameter optimization is carried out on a long short-term memory network LSTM algorithm, an ITSO-LSTM fault diagnosis model is constructed, the performance of the two algorithms is improved, the method can be more efficient and accurate in the fault diagnosis process, and the fault diagnosis efficiency is improved. Technical support is provided for maintenance of the equipment filling system, and the defect of blindness of parameter selection in the training process is overcome.

The invention relates to the technical field of geological exploration, and discloses a thick loess highland coal field three-dimensional seismic exploration method, which comprises the following steps: S101, acquiring an artificial seismic source shot seismic record and superficial Rayleigh wave detector data; s102, performing grid division on the target area, and setting parameters to be inverted; s103, performing vertical ray path time estimation, multi-layer medium dispersion forward modeling and post-stack energy estimation; s104, generating a multi-target error function according to the head wave arrival time observation value, the Rayleigh wave observation dispersion curve, the theoretical ray path time, the phase velocity and the post-stack energy; s105, dividing the target region into three sub-regions, and performing differential evolution iteration until local convergence; s106, establishing a plurality of parallel annealing chains, setting chain temperatures, progressively decreasing the chain temperatures, optimizing to-be-inverted parameters, and outputting optimal to-be-inverted parameters; and S107, calculating the static correction time difference and the absorption compensation coefficient of each acquisition point. The coal seam structure imaging precision and the exploration data reliability under the complex earth surface condition are improved.

The invention relates to the technical field of satellite navigation signal processing, in particular to an integer ambiguity resolving method based on a hybrid adaptive differential evolution grey wolf algorithm, which comprises the following steps of: loading GNSS (Global Navigation Satellite System) original data to carry out decorrelation, and calculating an adaptive integer search space boundary; calculating the solution of each individual, storing, comparing the fitness of each individual, and updating and recording globally optimal, suboptimal and third optimal solutions; executing a variation-crossover-selection process of differential evolution to generate a new generation of population; parameter self-adaption and Levy flight are executed; checking whether the current number of iterations reaches a preset maximum number of iterations, if not, adding one to the counter, returning and repeating the steps of comparing the fitness of each individual, updating and recording the global optimal solution, the suboptimal solution and the third optimal solution, and if yes, starting local search to find a better global optimal wolf; and the final calculation result finally recorded in the global optimal wolf variable is output, so that the premature convergence problem in the traditional algorithm solution is solved, and the solution success rate is improved.

The invention provides a harmonic reducer performance degradation evaluation method for an industrial robot, and the method comprises the steps: setting an experiment operation condition and a data collection strategy, and obtaining a whole-life-cycle sound emission and micro-vibration synchronous data set; extracting characteristics of the acoustic emission signal and the vibration signal by using the strong sensitivity of the acoustic emission signal to the early damage of the harmonic reducer and the accurate capturing capability of the micro-vibration signal to the middle and later damage; according to monotonicity, correlation, predictability and robustness indexes of the features, performing linear weighting on each index through an entropy weight method to construct a comprehensive evaluation index, and screening an optimal feature according to a highest score; a differential evolution (DE) method is adopted to fuse the optimal acoustic emission and micro-vibration characteristics, and a multi-stage health index (HI) capable of effectively representing the health state of the harmonic reducer is constructed.

The invention provides a civil aviation fleet-oriented maintenance plan and work package integrated optimization method, which comprises the following steps of: firstly, taking minimization of total maintenance cost as a target function; constructing a mathematical planning model for integrated optimization of the fleet maintenance plan and the work package by taking a maintenance item number constraint, a maintenance item and maintenance work package corresponding relation constraint, a maintenance resource constraint, a maintenance interval constraint, a variable relation constraint, a variable value constraint and the like as constraint conditions; then, auxiliary variables are introduced to carry out linearization processing on the established mathematical programming model; and finally, solving the linearized mathematical programming model by adopting a differential evolution-adaptive large-scale neighborhood search two-stage heuristic algorithm, namely, continuously iteratively solving through a designed two-stage interaction strategy, so as to achieve the purpose of integrated optimization. According to the method, the dual goals of reducing the total maintenance cost and improving the maintenance scheme making efficiency are achieved, and a scientific solution is provided for the complex optimization problem in civil aviation maintenance management.

The invention provides an online defect detection method and system for a cable insulation layer, and relates to the technical field of cables, and the method comprises the steps: obtaining an extrusion monitoring data set based on cable production; insulating layer defect online detection is carried out according to the extrusion monitoring data set; performing trend prediction according to the insulating layer defect first map; performing feedback adjustment on the insulation extrusion scheme according to the insulation layer defect second map to obtain an extrusion adjustment space; performing defect risk optimization on the extrusion adjustment space to obtain an extrusion candidate population; and based on the multi-dimensional defect risk prediction model, performing differential evolution optimization on the extrusion adjustment space according to the extrusion candidate population to obtain an extrusion adjustment optimization result, and executing cable production online optimization according to the extrusion adjustment optimization result. According to the invention, the technical problem of low defect detection accuracy of the cable insulation layer in the prior art can be solved, and the technical effect of improving the defect detection accuracy of the cable insulation layer is achieved.

The invention discloses an optimization method and device based on a neural network model, a medium and a program product, and the method comprises the steps: (1) carrying out the modeling of an automobile rear subframe based on SFE-Concept, constructing a first-order modal maximization mathematical model according to an adaptive penalty function, generating an optimization population based on Latin hypercube, and carrying out the optimization of a first-order modal maximization mathematical model; performing first-order modal and rigidity simulation analysis on the optimized population in an Isight multidisciplinary optimization design platform; (2) generating an optimal candidate sub-population and a successful design variable vector through differential evolution based on a cubic kernel radial basis function machine learning model; (3) training the Dropout neural network model to obtain evolution parameters; (4) updating evolution parameters based on the Dropout neural network model; and (5) updating and optimizing the population based on the evolution parameters, if a convergence condition is met, outputting an optimal rear subframe, otherwise, returning to the step (2). According to the method, the evolution parameters are adaptively adjusted according to the Dropout neural network model, and the adaptability to the modal optimization problem of the rear subframe of the automobile is high.

The invention relates to the technical field of data mining, and provides a community home-based care personnel scheduling method and system based on stakeholders. According to the method, a feasible service matrix is constructed, a nursing resource scheduling model is constructed based on a community old-age care service system, a plurality of different initial scheduling schemes are evaluated respectively, and at least three initial service schemes of different advantage types are determined from the plurality of initial scheduling schemes according to evaluation results; based on differential evolution, cross recombination and a dynamic adjustment mechanism, at least three different types of initial service schemes are optimally configured according to nursing resources, in the scheduling scheme optimization process, service arrangement can be adaptively and dynamically adjusted according to actual conditions, various constraint changes can be dealt with, and the scheduling efficiency is improved. And an optimal scheduling scheme which balances the benefits of the three parties and meets various constraint conditions is generated, so that optimal scheduling of nursing resources is realized, and a theoretical basis is provided for constructing a balanced and sustainable community old-age care service system.

The invention belongs to the technical field of medical data identification, and particularly relates to a depression identification method and system based on differential evolution feature selection. Obtaining original electroencephalogram data, and preprocessing the obtained original electroencephalogram data; based on the preprocessed electroencephalogram data, selecting an optimal feature subset by adopting a feature selection method based on differential evolution; and constructing an adaptive hierarchical fusion network model based on Transform, and training based on the optimal feature subset to obtain a depression identification reference model and a reference identification result. The purpose of performing feature selection on the electroencephalogram data is to remove redundant features and irrelevant features so as to obtain an optimal feature subset, and the screened feature subset is put into training, so that not only can the training time be saved, but also the recognition rate higher than that of original data training can be obtained.

The invention relates to a cloud and mist shielding method and device based on an unmanned aerial vehicle. The cloud and mist shielding method based on the unmanned aerial vehicle comprises the following steps: performing shielding amount analysis on current environment data to obtain information of a shielding area to be generated; performing cloud distribution analysis on the to-be-generated shielding area information by adopting a cloud distribution strategy library to obtain an accurate cloud distribution position; judging whether a track corresponding to the accurate cloud distribution position exists in the current voyage decision library or not: if not, sequentially performing unified coding, cost calculation and differential evolution on the current unmanned aerial vehicle according to the accurate cloud distribution position to obtain tracks corresponding to all the unmanned aerial vehicles; if yes, the formation reinforcement learning model is adopted to optimize the flight paths corresponding to all the unmanned aerial vehicles, the optimized flight paths are obtained and distributed to all the unmanned aerial vehicles, and the unmanned aerial vehicles are made to fly to the target position according to the optimized flight paths and release the camouflage cloud and mist. According to the cloud and mist shielding method based on the unmanned aerial vehicle, the concealment and safety of our military actions are enhanced.

The invention discloses a filtering antenna design method of a naked mole algorithm based on SHAP, and relates to the crossing field of a radio frequency front end of a wireless communication system and a computer algorithm. According to the method, the SHAP value is utilized to quantify the influence of the antenna structure parameters on the antenna performance, the search strategy in the NMRA is dynamically adjusted, and the exploration capability of the algorithm in a high-dimensional space is enhanced. Firstly, the SHAP value of each antenna parameter is initialized, and then the NMRA algorithm stage is entered. In the working stage, SHAP weighted differential evolution is carried out, and in the breeding stage, all breeders update their own states according to a certain probability, and breeders with poor performance are eliminated; self-adaptive variation is carried out according to the probability pm so as to prevent from falling into local optimum; and finally, evaluating the new population, updating the current optimal solution, and outputting the optimal solution after the maximum number of iterations is reached. The method is mainly applied to the fields of short-wave satellite communication, radars and the like, the limitation that a uniform search strategy is adopted for all features in the high-dimensional optimization problem of a traditional NMRA algorithm is overcome, and it is guaranteed that the performance of a filtering antenna is good.

The invention discloses a synthetic aperture radar online trajectory planning method based on an adaptive network. The method comprises the following steps: S1, establishing a bistatic SAR system model and a task coordinate system; s2, dividing a task space; s3, establishing a multi-objective optimization model; s4, performing independent evolution in each task block by using a joint multi-objective evolutionary algorithm of a block propagation strategy, and performing joint optimization through an inter-block elite individual sharing mechanism; s5, generating a trajectory sample data set; s6, introducing a cooperative differential evolution optimization mechanism based on a regularization extreme learning machine algorithm, and constructing a large-scale adaptive network; s7, carrying out real-time track prediction and online updating; and S8, outputting an optimal trajectory result meeting the constraint. According to the method, the problems of long time consumption, high calculation complexity and insufficient network generalization performance of a multi-objective evolutionary algorithm in real-time trajectory planning are solved, and the real-time performance and precision of trajectory planning are improved.

The present application relates to geological exploration technical field, disclose a kind of thick loess plateau area coalfield 3D seismic exploration method, comprising the following steps: S101, artificial seismic source shooting record and superficial Rayleigh wave detector data are collected;S102, grid division is carried out to target area, and set to be inverted parameter;S103, vertical ray path time estimation, multilayer medium dispersion forward and poststack energy estimation are carried out;S104, according to first wave arrival time observation value, Rayleigh wave observation dispersion curve, theoretical ray path time, phase velocity and poststack energy generates multi-target error function;S105, target area is divided into three subdomains, differential evolution iteration is carried out, until local convergence;S106, establish a plurality of parallel annealing chain, set chain temperature and gradually decrease by chain, optimize to be inverted parameter, output optimal to be inverted parameter;S107, the static correction time difference and absorption compensation coefficient of each acquisition point are calculated.The present application improves coal seam structure imaging precision and exploration data reliability under complex surface conditions.

The invention relates to the technical field of power equipment state monitoring and fault diagnosis, and discloses a fusion type high-voltage switch cabinet insulation state quantitative monitoring method and system, and the method comprises the steps: firstly obtaining a partial discharge ultrahigh frequency and sound wave signal, and calculating a generalized Renyi spectrum entropy and an entropy gradient index; carrying out nonlinear correction on the medium sound wave propagation velocity by using the entropy gradient index, establishing an equivalent sound velocity model in a medium degradation state, and obtaining a standardized equivalent time difference; then, constructing a two-dimensional phase plane state space taking the standardized equivalent time difference and the generalized Renyi spectral entropy as dimensions, and analyzing a differential evolution trajectory of a state vector on a time sequence; and finally, constructing a comprehensive state loss function, mapping to generate an insulation health degree index, and carrying out life prediction. According to the invention, through medium degradation wave velocity constitutive mapping and phase plane conjoint analysis, monitoring errors caused by medium aging are solved, and dynamic accurate quantitative evaluation of the insulation state is realized.