254 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 provides a post-earthquake emergency material distribution optimization method considering distribution fairness and demand urgency, and relates to the technical field of emergency logistics multi-target distribution optimization under the background of urban earthquake disasters, and the method comprises the following specific steps: S1, determining a basic hypothesis: considering the actual scene of disaster rescue operation, and making the basic hypothesis for the emergency material distribution problem; s2, establishing an emergency material distribution fairness model; s3, establishing a disaster point emergency material demand emergency degree evaluation index system; s4, establishing a multi-target path optimization model; and S5, solving the established multi-target model by using an improved mixed multivariate universe optimization algorithm. The mixed multivariate universe algorithm based on differential evolution is provided, higher convergence precision and better robustness and uniformity are achieved, the earthquake disaster emergency logistics distribution path optimization accuracy is improved, material demand urgency meeting and distribution fairness are guaranteed, the distribution cost is reduced, and the distribution efficiency is improved. And the disaster victim satisfaction degree and the demand emergency adaptation degree are 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 relates to an InSAR-based underground excavation space geometric parameter inversion method and system. The method comprises the steps of obtaining a ground surface deformation field of a research area where an underground excavation space is located based on an InSAR technology; underground space positioning prior information is collected according to the earth surface deformation field; geological prior information of the underground space is collected; fusing to obtain a priori information set of underground space geometric parameters; according to the prior information set, constructing a function relationship between InSAR observation deformation and ground surface three-dimensional deformation induced by underground space through a probability integral method; establishing a likelihood function based on the InSAR observation data and the earth surface deformation data; inverting the underground space geometric parameters based on the Bayesian theorem and a likelihood function to obtain underground space geometric parameter posterior distribution; and solving the underground space geometric parameter posterior distribution by using a differential evolution adaptive Metropolis algorithm to obtain an inversion result. And the accuracy and precision of the inversion result are improved.

The invention discloses a two-stage multiform differential evolution method for dispatching a mine integrated energy system, and belongs to the technical field of intelligent dispatching. The method comprises the following steps: firstly, under a multi-form optimization framework, combining domain knowledge to construct an electricity-heat alternative task and an electricity-cold alternative task, and introducing a feasibility-driven knowledge migration mechanism to realize efficient migration of knowledge between the two tasks; secondly, designing a two-stage optimization strategy, proposing a neighborhood double-variation mechanism, in the first stage, performing parallel evolution on populations corresponding to two alternative tasks through a diversity-driven neighborhood double-variation strategy, and in the second stage, performing evolution on an original task through a convergence-driven neighborhood double-variation strategy to obtain an optimal feasible solution set; and finally, analyzing the time complexity for explaining the rationality of the proposed method for optimizing the scheduling of the mine integrated energy system. The method can deal with the limitation of an existing optimization method when strong coupling constraint and multiple uncertainties are processed, and the economical efficiency, the energy utilization rate and the scheduling stability of the system are improved.

The invention relates to the technical field of hydraulic structure service behavior analysis and computational mechanics, and particularly discloses a high arch dam deformation behavior fractional order numerical analysis physical and mechanical parameter inversion method and module. A viscoplasticity parameter inversion method is provided based on a Levenberg-Marquardt method by using rheological test data. Optimizing to obtain a to-be-inverted parameter sample combination by means of central composite design; constructing an elastic and viscoelastic parameter inversion objective function in combination with deformation in-situ monitoring data; based on a multiple-input-multiple-output support vector machine, establishing an elastic and viscoelastic parameter inversion agent model; a random inertia weight, an asynchronous change learning factor, golden sine disturbance and differential evolution variation disturbance are introduced to improve a particle swarm algorithm, so that a parameter optimal inversion value search strategy is established. The method aims at improving the accuracy and objectivity of numerical analysis of the deformation state of the high arch dam, provides technical support for in-service engineering safety monitoring and dangerous case early warning, and has wide application prospects and markets.

The present invention relates to the technical field of data processing, and is a sewage treatment intelligent dosing adjustment system based on fuzzy logic, comprising: a data acquisition module, which collects water quality data of sewage and optimizes the data acquisition process, including chemical oxygen demand, ammonia nitrogen content, total phosphorus content and sewage flow; a data analysis module, which is used to receive real-time data and perform small batch gradient descent analysis on the data to obtain analysis results; an optimization module, which is used to optimize the parameters and rules of the fuzzy logic controller through differential evolution according to the analysis results to obtain optimized parameters and rules; a fuzzy logic control module, which performs fuzzy logic calculation of dosing amount through simulated annealing according to the received water quality parameters and optimized rules, and converts it into a dosing control signal. The present invention realizes comprehensive intelligent management from data acquisition to dosing adjustment by integrating advanced data acquisition, analysis, optimization and control technologies.

The invention discloses a soil pollution diffusion prediction method based on differential evolution, and relates to the technical field of soil pollution control, and the method comprises the steps of collecting soil sensing data in real time, and uploading the soil sensing data to a data center. Real-time sensing data are compared and calibrated, sensing data errors caused by environmental factors are effectively reduced, data input into a model are more accurate and reliable, meanwhile, model parameters are optimized and solved by combining differential evolution and a particle swarm optimization algorithm, the differential evolution is used for global optimization to find a proper parameter combination, and the accuracy of the model parameters is improved. Particle swarm optimization performs local fine adjustment based on a differential evolution result, and the two are alternately performed, so that the accuracy of soil pollution diffusion prediction is greatly improved, the diffusion trend of pollutants in soil can be more accurately grasped, and a more reliable basis is provided for ecological environment management and human health protection.

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 relates to the field of photovoltaic technology, in particular to a photovoltaic cell model parameter extraction method and related products, and the method comprises the steps: building a photovoltaic cell mathematical model, and determining to-be-optimized model parameters in the mathematical model; constructing a fitness function of the photovoltaic cell mathematical model; constructing a parameter optimization model based on a whale optimization algorithm and a differential evolution operator, and taking a fitness function as a target function; executing the parameter optimization model, and obtaining an optimal parameter set corresponding to model parameters to be optimized in the mathematical model; a final photovoltaic cell mathematical model is obtained through the optimal parameter set, and error verification is carried out on the photovoltaic cell mathematical model; according to the method, firstly, a mathematical model of a photovoltaic cell is constructed, to-be-optimized parameters in the mathematical model are determined, then a fitness function is defined based on multiple error indexes and physical parameter constraints, and then a parameter optimization model adaptive to multi-modal optimization is formed by combining a whale optimization algorithm and a differential evolution operator. And finally, obtaining an optimal parameter set of the photovoltaic cell model.

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 discloses a construction method of a ship underwater radiation noise online prediction model. The method comprises the following steps: collecting ship body vibration signals and far-field radiation noise signals as a sample database of a radiation noise prediction model; on the basis of a differential evolution feature optimization algorithm, two dimensions of a vibration sensor and a 1 / 3 octave frequency band of prediction model input features are optimized, and an optimal feature subset is obtained to serve as input of a prediction model; establishing an integrated learning prediction model, taking a ridge regression model as a base learning device, and determining optimal parameters of each base learning device by adopting grid search and cross validation to obtain n different base learning devices; and finally, taking a mean value of prediction results of the base learners as a final prediction result of the radiation noise prediction model, and realizing online prediction of the ship radiation noise. According to the method, the utilization rate of existing test data can be improved, the degree of dependence of a prediction model on specific distribution type data is reduced, the sensitivity of the model to small changes of data distribution is relieved, and the model robustness and prediction precision are improved.

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 discloses a method and a system for verifying a distributed power supply access unit. The method comprises the following steps: performing parameter configuration on a to-be-detected distributed power supply access unit; connecting the distributed power supply access unit to a verification device through a self-adaptive wiring terminal; performing a static performance test, a dynamic performance test and a security test on the distributed power supply access unit; and recording and analyzing data in the test process to obtain a test report. According to the invention, the inspection efficiency and accuracy of the distributed power supply access unit can be improved, the performance of the unit can be comprehensively evaluated, and faults can be accurately diagnosed and positioned; by introducing the differential evolution bat model, comprehensive, efficient and accurate inspection of the distributed power supply access unit is realized, the inspection process can be automatically completed, manual intervention is reduced, and the inspection efficiency is improved; the performance of the unit can be comprehensively evaluated, faults can be accurately diagnosed and positioned, and powerful support is provided for subsequent maintenance and replacement work.

The invention discloses a pipeline defect three-dimensional contour inversion method and device, electronic equipment and a storage medium, relates to the field of pipeline internal detection, and aims to improve a formula in an exploration stage of a secretary bird optimization algorithm through preset parameters so as to improve the precision of a pipeline defect inversion result. The preset parameter is any one of a sine and cosine parameter, a random disturbance term and a nonlinear convergence factor, the sine and cosine parameter is added in the initial stage of iteration, and the search range of the algorithm is expanded; random item disturbance is added in the middle stage of iteration, and the diversity of solutions is increased; and a nonlinear convergence factor is added in the later stage of iteration, so that the convergence capability of the algorithm is enhanced. And meanwhile, a differential evolution strategy and a local perturbation strategy are adopted to enhance the searching capability and the fitting capability of the secretary bird optimization algorithm, and the precision of the inversion profile is improved. In addition, the invention further provides a continuity correction strategy to correct an untrusted depth value and help the inversion process to be far away from local optimum, so that the precision of three-dimensional inversion is improved, and the accuracy of an inversion result is 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.