259 results about "Fitness function" patented technology

The application discloses a kind of for gyro device coil magnet and electron gun collaborative optimization design method, belong to microwave millimeter wave technical field.The application first establishes the mapping relationship between multi-coil configuration parameter and three-dimensional magnetic field distribution;Then establish the potential distribution in the two-dimensional plane of electron gun, solve electron beam trajectory under given magnetic field distribution;Establish the mapping relationship between electron gun electrode structure parameter and electron beam performance parameter;Again establish the mapping relationship between electron gun performance parameter and electron gun electrode shape and solenoid configuration parameter;Finally, establish the fitness function of evaluating electron beam quality, introduce global optimization algorithm to realize the optimization design of electron gun performance.The application has high design freedom, high universality, can realize high-performance comprehensive design, and also has the advantages of fast calculation speed, high precision, good numerical calculation stability.

The present application provides a kind of vital signs detection signal denoising method and device, it is related to physiological perception and signal processing technical field, including: based on millimeter wave radar's Doppler technique, obtains the original phase signal containing physiological signal;Original phase signal is carried out variational mode decomposition based on the search algorithm of variational mode decomposition super parameter optimization algorithm optimized by particle swarm optimization algorithm, obtain the vibration modal function information corresponding to the original phase signal;Wherein, the particle swarm optimization algorithm uses permutation entropy and fuzzy entropy as fitness function;After denoising processing is carried out to the vibration modal function, the vibration modal function after denoising processing is recombined, and high-precision denoising processing is obtained after physiological signal;From the high-precision denoising processing physiological signal, extract respiratory and heartbeat physiological signal.

This invention provides a method, system, and equipment for optimizing the mix proportions of rubber concrete, belonging to the interdisciplinary field of building materials design and artificial intelligence. The method includes: constructing a high-precision prediction model for compressive strength and relative dynamic elastic modulus using Bi-LSTM, which serves as the fitness function during the optimization process. Furthermore, three optimization objectives are introduced. NSGA-III is enhanced by introducing chaotic perturbations and an adaptive evolution mechanism, thereby improving the convergence and diversity of the solution set in the high-dimensional objective space. Based on this, a three-objective optimization framework involving compressive strength, cost, and relative dynamic elastic modulus is established, enabling a coordinated balance among these three objectives in the mix proportion design of rubber concrete. This method solves the problem of insufficient optimization of rubber concrete mix proportions.

The present application belongs to the technical field of unmanned aerial vehicle cluster control, and particularly relates to a multi-unmanned aerial vehicle cooperative target tracking path planning method based on a spider wasp optimization algorithm. The method comprises the following steps: (1) establishing a mathematical constraint model of a flight path; (2) constructing an evaluation function according to the mathematical constraint model, including a fitness function and a total distance length; (3) based on the spider peak optimization algorithm, solving the evaluation function, and constantly iterating, calculating and comparing to obtain an optimal solution or an approximate optimal solution, i.e. an optimal path; and (4) distributing the optimal path to each unmanned aerial vehicle for execution. The present application simulates the predatory behavior of spiders and the social behavior of bees, realizes efficient path planning of multi-unmanned aerial vehicles in cooperative target tracking in a complex three-dimensional environment, improves the flight efficiency and safety of unmanned aerial vehicle clusters, and enhances the adaptability of unmanned aerial vehicle clusters in complex environments.

This invention belongs to the technical field of multi-objective optimization of shield parameters and provides a method and system for multi-objective optimization of shield construction parameters. The method for multi-objective optimization of shield construction parameters includes the steps of: obtaining initial shield construction parameters; optimizing the shield construction parameters using a multi-objective optimization algorithm; predicting the excavation speed using a random forest based on the shield construction parameters in the optimization of the shield construction parameters; calculating the shield excavation energy consumption ratio based on the excavation speed and shield construction parameters; and making the maximization of the excavation speed and the minimization of the shield excavation energy consumption ratio the two fitness functions of the multi-objective optimization algorithm. This method takes into account construction efficiency and cost reduction while ensuring construction safety.

The application relates to a mode filter, a topology optimization method and an ultrasonic guided wave damage imaging artifact suppression method, relates to the field of ultrasonic detection and imaging, and the topology optimization method comprises the following steps: acquiring initial design parameters, generating an initial population, each chromosome individual in the initial population is coded by a binary logic matrix representing material distribution and meeting design constraints; a population is optimized by using a genetic algorithm, an adaptability function in the genetic algorithm is constructed based on a mode purity index and an energy transmission index, the mode purity index is the ratio of in-plane displacement integral in a transmission area in finite element simulation with the mode filter to total displacement integral, and the energy transmission index is the ratio of relative energy at a transmission end in the finite element simulation with the mode filter to relative energy of an S0 mode; and final optimized material distribution of the mode filter is output. Compared with the prior art, the application has the advantages of effectively suppressing background artifacts, giving consideration to mode regulation performance and substrate structure integrity and the like.

The application discloses a layout optimization design method of array type hanging spraying robot based on spraying track constraint, belongs to the field of spraying technology, and comprises the following steps: discretizing a three-dimensional model of an airplane to be sprayed to obtain a point cloud feature vector set; array type spraying robot units are arranged after clustering division to form an array; a spraying torch is simulated to form a circular spraying area covering the area to be sprayed on the surface to be sprayed; the divided area is traversed according to the full coverage principle; the inverse solution of the angle of each axis of the mechanical arm is obtained by using the inverse method of the Jacobian matrix, and if there is no solution, the clustering is re-divided; finally, a fitness function is calculated, and whether the set size requirement is met is judged; if the set size requirement is not met, the clustering parameters are optimized by using a particle swarm optimization algorithm until the maximum iteration number is reached, and the optimal layout and spraying track are output. According to the application, the layout of the array type hanging spraying robot is optimized according to the structural features of the airplane, the spraying efficiency and the equipment flexibility are simultaneously improved on the basis of ensuring spraying accessibility, the application is suitable for different airplane models and can be reused.

This application relates to the field of network switching technology, and in particular to a network resource scheduling method based on a binary moth optimization algorithm. The method includes the following steps: generating a service requirement model, initializing the number of moths, and defining a service fitness function; calculating an initial moth path matrix and an initial flame matrix; iteratively optimizing the initial moth path matrix and flame matrix from the initial moth path matrix and initial flame matrix until a preset iteration termination condition is met, wherein in each iteration, the service fitness matrix of the current round of moths and flames is calculated and flame attenuation is performed; updating the flame positions in the flame matrix based on the updated moth paths; and selecting the optimal path from the final flame matrix as the service forwarding path. This invention solves the problem that existing technologies cannot cope with complex and dynamically changing QoS requirements, and has the characteristics of being data-independent and having low computational resource requirements.

This invention discloses a method for constant temperature and humidity control of a cigar cabinet based on a fuzzy control algorithm, comprising the following steps: setting a target temperature and humidity range; collecting environmental data of each layer through a distributed sensor array and comparing it with the target range to obtain the temperature and humidity deviation of each layer; inputting the deviation into a temperature and humidity decoupling control module and outputting a weight adjustment signal; inputting the weight adjustment signal into a fuzzy controller to construct a multi-objective fitness function, optimizing it with the WFPSO algorithm, and outputting hierarchical fuzzy controller parameters; inputting the hierarchical fuzzy controller parameters into a hierarchical temperature and humidity model to monitor the status of each layer in real time, and outputting a drive command when the limit is exceeded; inputting the drive command and hierarchical parameters into an actuator, adjusting it, and outputting new environmental data; and using a feedforward control module to adjust in advance when a door opening / closing disturbance warning is issued, dynamically adjusting the status of the affected space layer. This invention improves the temperature and humidity control accuracy and disturbance adaptability of the cigar cabinet through hierarchical optimization and feedforward intervention control mechanisms.

This application discloses a method, apparatus, and storage medium for optimizing direct communication between a mobile phone and a satellite, relating to the field of satellite communication technology. The method includes: defining a particle vector related to direct communication between the mobile phone and the satellite, where the elements of the particle vector correspond to the uplink transmit power and uplink carrier frequency of the mobile phone, respectively; initializing a particle swarm based on the particle vector; constructing a fitness function, where the fitness value is related to transmission efficiency cost and energy consumption cost, wherein the transmission efficiency cost is used to evaluate the loss of data transmission efficiency, and the energy consumption cost is used to reflect the energy consumption cost of the mobile phone; iteratively optimizing the particle swarm using a particle swarm optimization algorithm to determine the optimal particle vector; and establishing a direct communication connection between the mobile phone and the satellite based on the optimal particle vector. Thus, this application achieves joint dynamic optimization of the mobile phone's uplink transmit power and uplink carrier frequency, reducing terminal energy consumption while ensuring the transmission efficiency of the satellite-to-ground link.

This invention discloses an automatic calibration method for airborne lidar placement deviation based on voxel sharpness. The method includes: selecting a natural calibration site containing building facades, linear markers, and point targets; collecting point cloud and POS data via a UAV flying in multiple headings; modeling the placement deviation to be calibrated using six parameters; optimizing the parameters using a genetic algorithm; using voxel sharpness as the fitness function; voxelizing the point cloud in the region of interest; counting the number of points within each voxel and calculating the sharpness score; a higher sharpness score indicates a more pronounced clustering of point clouds in the feature region; and iteratively optimizing the method through selection, crossover, and mutation operations to output the optimal placement deviation parameters. This invention uses an ordinary basketball court as a natural calibration site, eliminating the need for artificial targets. After calibration, the point cloud sharpness is improved by more than 150%, and the positioning accuracy reaches the centimeter level.

This invention discloses a method, system, medium, and product for optimizing the peak current of a dual active bridge extended phase-shift converter, belonging to the field of power electronics control. The method involves: obtaining the voltage conversion ratio and internal / external phase-shift duty cycles of the dual active bridge converter; constructing an inductor current peak function under extended phase-shift modulation, a penalized power balance equation, and a zero-voltage turn-on inequality constraint; initializing a particle swarm with the internal / external phase-shift duty cycles as particle positions; constructing a fitness function based on the minimum current peak value and constraints; iteratively solving for the optimal phase-shift parameters using a particle swarm optimization algorithm; and dynamically adjusting the constraint penalty weights based on the particle violation ratio during iteration; finally, generating a modulation signal based on the optimal phase-shift parameters to control the switching transistors, completing the optimization. By implementing this invention, the problem of poor convergence stability of existing optimization algorithms under wide operating conditions can be solved.

This invention discloses a satellite communication terminal simulation task orchestration system and device based on intelligent scheduling. It adopts a microservice architecture, with five modules working collaboratively via RESTful APIs or gRPC. The system constructs a multi-dimensional global resource view, combining historical data to drive a regression prediction model to quantify resource requirements. The intelligent scheduling decision module models complex task allocation as a multi-dimensional optimization problem, using the PSO algorithm combined with a multi-objective fitness function to find the optimal solution, supplemented by a task preprocessing mechanism to improve efficiency and effectiveness. The system manages task dependencies using a DAG and deeply integrates with Kubernetes to achieve automated task orchestration, status monitoring, and fault recovery. This effectively solves the pain points of traditional scheduling, such as resource fragmentation and static configuration waste, achieving dual-dimensional global dynamic scheduling of business and resources. This significantly improves cluster resource utilization and simulation execution efficiency, adapting to the needs of large-scale, high-concurrency simulation scenarios.

The application discloses a kind of wideband signal-oriented distributed movable antenna layout optimization method, comprising: based on swarm intelligence algorithm, antenna layout scheme is iteratively optimized until reaching iteration stop condition, the best fitness of population corresponding antenna layout scheme is regarded as the optimal scheme of antenna layout output;Wherein, the fitness of population is determined according to the wideband cumulative Cramer-Rao bound of its corresponding antenna layout scheme.The present application can directly construct the bottom mapping relationship between continuous position variable and wideband signal direction finding error lower limit by introducing wideband Cramer-Rao bound as fitness function.Because the index itself represents the theoretical minimum direction finding error of antenna layout, it can accurately quantify the spatial spectrum coupling effect of full frequency band;As fitness function, it can make the optimization directly towards the most essential optimization goal, ensure that the output physical array theoretically approximates the wideband direction finding physical limit under this degree of freedom.

This invention discloses an optimization method and system for smoke screen chaff deployment based on a view cone masking model, belonging to the field of signal jamming technology. The method includes the following steps: S1, constructing an adversarial kinematic model of the missile, target, and chaff; S2, establishing a view cone masking quantitative evaluation model with masking completeness as the core; S3, defining a fitness function with the maximum effective masking time as the objective; and S4, using an adaptive genetic algorithm to solve for the optimal deployment strategy that satisfies spatiotemporal constraints. This invention solves two major bottlenecks in existing technologies: the disconnect between effect evaluation and decision control, and the excessive reliance of advanced decision models on preset conditions. It achieves accurate, direct, and decision-driven quantitative evaluation of masking effects from the first-person perspective of the missile seeker.

The application discloses a wireless sensor network dynamic clustering routing method based on a gecko optimization, and belongs to the technical field of wireless sensor network routing optimization. The method first determines the target cluster head quantity in the current round based on the position, residual energy, neighbor relationship and survival state of a sensor node, constructs a candidate cluster head set and a cluster head selection fitness function; then represents the candidate cluster head node as a priority vector in a gecko optimization algorithm, determines the cluster head set in the current round through discretization mapping, iterative updating and fitness evaluation; then determines a data transmission path according to the communication cost between the cluster head node and a sink node, and completes clustering and data collection, fusion and forwarding; finally, performs local cluster head rotation in a cluster and global re-clustering according to the energy state of the cluster head node and the inter-cluster energy balance state. The method can improve the cluster head distribution balance, reduce network communication energy consumption and re-clustering control overhead, and prolong the network life cycle.

This application discloses a memory management method and electronic device, relating to the field of computer technology. The method includes configuring the memory of any controller in a target cluster based on its current memory management parameters, running preset test cases on the controller to obtain performance indicators under the current memory configuration, determining the fitness function value of the controller under the current memory configuration, and then determining the controller's historically optimal memory management parameters. If the current iteration count has not reached an iteration count threshold, the memory management parameters are updated based on the target update speed, and iteration continues based on the updated memory management parameters. If the current iteration count reaches the iteration count threshold, the controller is configured with appropriate memory based on its historically optimal memory management parameters. This achieves dynamic memory management of the controller, resulting in improved memory utilization.

The application relates to an SGD-PSO-based lens parameter generation method and device and a storage medium, wherein the method comprises the following steps: S1, a basic configuration of a lens in an optical system is acquired, the number of lenses, design requirements and a parameter set are obtained based on the basic configuration of the lens; S2, an optimization target is constructed based on the design requirements, a fitness function is defined according to an optimized objective function, and a particle is defined based on the parameter set, wherein the position of each particle represents a group of values of the parameter set; S3, optimization is performed in an SGD-PSO mode to obtain an optimization result; and S4, the values of the parameters in the parameter set are obtained based on the optimization result. Compared with the prior art, the global information prompt multi-particle parallel optimization algorithm proposed in the application can fully explore the solution space and improve the efficiency of the computing power, and the optimization result, efficiency, stability and diversity are greatly improved through the multi-system independent parallel local optimization algorithm, the heuristic global search algorithm and the gradient-enhanced global algorithm.

This invention discloses a path planning method for unmanned surface vessels (USVs), belonging to the field of autonomous navigation technology for USVs. The method includes: determining the navigable boundary of the USV in a multi-threat surface environment; initializing a tribe based on the navigable boundary; constructing a multi-objective fitness function, calculating the fitness value of each member in the initial tribe, and updating the fitness values ​​of the best member of each tribe, the best member of the opposing tribe, and the globally optimal member based on these fitness values; performing iterative optimization of tribe competition and member cooperation; and outputting the globally optimal path as the final planned path for the USV after satisfying the iteration termination condition. This invention improves convergence speed while considering path length, smoothness, and safety, achieving a dual balance between path optimality and navigation safety, and is suitable for complex marine environments with multiple threats.

The application discloses a lithium iron phosphate battery equivalent model parameter identification method based on an improved SCSO, which comprises the following steps: step 1, constructing a second-order equivalent circuit model to determine to-be-identified parameters; step 2, collecting current, voltage and time data of the lithium battery under a dynamic working condition, and constructing a fitness function based on the deviation of a model predicted voltage and a measured voltage; step 3, generating an initial population of the improved sand cat colony optimization algorithm by adopting a chaotic initialization strategy, and setting a search boundary in combination with parameter physical constraint settings; step 4, introducing a dynamic weight disturbance mechanism, an adaptive mutation strategy and a triangular walking strategy to iteratively update the sand cat population position, and reserving high-quality solutions by greedy selection; and step 5, repeatedly executing steps 3 and 4 until the fitness function reaches a preset precision or the number of iterations reaches a maximum value, and outputting optimal identification parameters. By dynamically adjusting the search weight to balance the global exploration and local development capability, in combination with the reinforced boundary behavior to avoid parameter out-of-bound to guarantee the physical rationality, and relying on the triangular walking strategy to improve the population diversity, the application effectively solves the inherent defects of the conventional SCSO algorithm in complex parameter optimization.

This invention relates to a method for optimizing process parameters of enameled wire stranding based on particle swarm optimization (PSO). The method includes collecting process parameters to construct a multi-objective prediction model, using a dynamic weight fitness function to adapt to different operating conditions, employing a hybrid PSO-ADE optimization approach, verifying the closed-loop and re-searching based on sensitivity, and outputting the optimal control signal. This PSO-based method for optimizing enameled wire stranding process parameters achieves nonlinear mapping by collecting process parameters to construct a multi-objective prediction model, setting dynamic weights and adaptive penalties to adjust the optimization objective according to operating conditions. It uses particle swarm optimization as the main loop, condition-triggered differential evolution to enhance diversity, balance exploration and development, introduces a verification closed loop, and re-searches based on the deviation direction and sensitivity when the target is not met. It determines variable priority based on sensitivity, granting greater search freedom. This method solves problems such as difficulty in describing multi-variable coupling, incompatibility of fixed weights with disturbances, premature convergence of single algorithms, prediction errors leading to non-compliance, and ambiguity in the direction of secondary optimization.

This invention discloses a control method and system for a variable frequency helium compressor based on genetic algorithms and neural network optimization. The method first obtains the system's target cooling capacity requirement and operating parameters; then, using two pre-trained neural network models, it predicts the system's cooling capacity and compressor power consumption based on the operating parameters; next, using system operating efficiency as the fitness function, a genetic algorithm iteratively optimizes the operating parameters, ensuring the cooling capacity requirement is maintained as a constraint during the optimization process, to obtain the optimal combination of operating parameters; finally, a safety check is performed on this combination, and control commands are output. This invention overcomes the shortcomings of traditional control methods in handling system nonlinearity and multi-parameter coupling, achieving optimal energy efficiency operation of the compressor while meeting the cooling capacity requirement, and ensuring equipment reliability through safety checks.

The application discloses a kind of millimeter wave sign detection methods based on swarm intelligence and improved wavelet threshold value, wherein the method includes: obtaining the original vital sign signal collected by millimeter wave radar;Construct the multi-objective fitness function of fusion sample entropy, pearson correlation coefficient and kurtosis;The multi-objective fitness function is optimized using the improved Harris eagle optimization algorithm, and the optimal parameter combination of CEEMDAN decomposition algorithm is adaptively solved;Based on the optimal parameter combination, the original vital sign signal is decomposed by CEEMDAN, and a plurality of intrinsic mode function components are obtained.The application constructs the cascade processing framework of "parameter optimization-signal separation-noise suppression", solves the problem that CEEMDAN parameter depends on artificial experience, respiratory and heartbeat signal band aliasing and the problem of insufficient performance of traditional wavelet threshold denoising.

The application discloses a pump station group cooperative scheduling method and system based on digital twinning, and belongs to the technical field of optimization control based on digital twinning. The method comprises the following steps: collecting multi-source heterogeneous data of the pump station group, constructing a basic framework of a digital twinning model of each pump station group, and synchronously updating; deeply fusing health degree, energy efficiency ratio, load capacity and response speed data, and calculating a pump station scheduling response willingness factor; using an improved algorithm, embedding a dynamic scheduling capability weight vector into a balance fitness function and a response weighted search mechanism for solving; using the digital twinning model of the pump station group, pre-animating and verifying a cooperative scheduling optimization scheme of each pump station, confirming hydraulic balance and operation safety, generating target rotating speed and start-stop instructions, and delivering the instructions to each pump station for execution. The application quantifies the pump station scheduling response willingness by constructing a digital twinning model, thereby guiding cooperative optimization, and realizing willingness distribution driven scheduling.

The application relates to the field of battery life prediction, in particular to a multi-feature fuel cell life prediction method based on a long short-term memory network; the method comprises the following steps: collecting voltage attenuation data and collecting running state data to construct an original input data set; analyzing the running state data, calculating a correlation coefficient according to the voltage attenuation data and each running state data, analyzing the correlation coefficient, and obtaining an input feature set; processing the input feature set and the voltage attenuation data to construct a neural network training data structure; dividing the training data structure into a training set and a test set; constructing a network architecture solution space to be searched, analyzing the network architecture solution space, constructing a fitness function, and determining an optimal network architecture parameter combination based on the fitness function; and inputting the training set into the optimal network architecture parameter combination for training to generate a target detection prediction result. The application can improve the prediction accuracy of the voltage attenuation trend.

The application discloses a kind of GaN HEMT equivalent circuit model parameter self-adapting extraction method and system, comprising: extracting parasitic parameter value by small signal equivalent circuit parasitic parameter extraction method, establishes initial population;Stripping parasitic parameter in initial population obtains each bias eigenvalue by network analysis, and simulation equivalent circuit S parameter data is obtained by calculation;Judgment is calculated according to S parameter data individual fitness;New individual is generated to replace singular individual in population until population is non-singular;According to the search range of adjustment algorithm of non-singular population;According to the individual selection, crossover, variation rule set to the individual in initial population is carried out genetic calculation, and new genetic individual is obtained;Whether the preset target is satisfied is judged, and the element parameter corresponding to optimal individual is output as the final parameter of small signal model;I-V model is optimized by genetic algorithm, and the weight matrix of fitness function is updated according to model error, and optimization is carried out again to meet the preset target, and the small signal model parameter is combined to establish large signal equivalent circuit model.The application greatly improves the modeling speed of small signal model and the accuracy of large signal model without sacrificing accuracy, which provides key technical support for reliable design of high-power, high-frequency microwave circuit.

The application provides a farm load prediction method, device and equipment based on multi-source data, and relates to the field of power load prediction.The method comprises the following steps: obtaining historical load data, breeding animal growth data, environmental data and equipment data of a farm; decomposing the historical load data according to a variational mode decomposition algorithm to obtain a plurality of load components; wherein the variational mode decomposition algorithm optimizes parameters through a sparrow search algorithm, and the sparrow search algorithm establishes a fitness function about the load components according to the breeding animal growth data; after the plurality of load components, the breeding animal growth data, the environmental data and the equipment data are spliced, the spliced data is input into a preset Transformer model to obtain a power load prediction curve of the farm.The application can improve the accuracy and availability of the power load prediction result of the farm.

The application relates to the technical field of low-altitude flight, and discloses a low-altitude flight trajectory optimization method and system, which comprises the following steps: obtaining obstacle data of a test area, determining a region complexity parameter according to the obstacle data of the test area, and determining a trajectory constraint parameter set according to the region complexity parameter; establishing a flying car trajectory simulation model according to the trajectory constraint parameter set to simulate a flying car obstacle trajectory test process, and outputting a trajectory loss parameter set corresponding to a plurality of trajectory strategies; constructing a fitness function according to the trajectory loss parameter set, optimizing the fitness function based on a particle swarm algorithm, and obtaining an optimal trajectory strategy. The safety adaptability, target diversity and solution accuracy in low-altitude flight trajectory planning of the flying car can be effectively ensured.

This invention discloses a method and system for establishing a digital twin model based on a fractional-order model, which collects the inductor current i in real time when the physical experimental platform is turned on. L1.r The inductor current i during turn-off L2.r The capacitor voltage v when it is turned on C1.r and the capacitor voltage v at turn-off C2.r ; Collect the inductor current i when the digital twin platform is turned on L1.d The inductor current i during turn-off L2.d The capacitor voltage v when it is turned on C1.d and the capacitor voltage v at turn-off C2.d The fitness function is calculated using an intelligent optimization algorithm; when the number of iterations reaches the set number, the inductance L, capacitance C, and parasitic resistance R of the capacitance are output. c Parasitic resistance R of the inductor L Parasitic resistance R of the switching transistor dson The order α of the inductor current and the order β of the capacitor voltage are used to obtain the final digital twin model. This invention can effectively improve the accuracy of the model.

The application provides a general transferable physical world optical adversarial attack method, including the following steps: step 1, constructing an agent model and selecting a small sample data set as a training basis; step 2, defining optical adversarial patch parameters and a generation space based on an adaptive graph generation algorithm; step 3, introducing a local and global disturbance repetition mechanism to enhance irrelevant high-frequency features and improve disturbance transferability; step 4, constructing a general patch search framework based on a differential evolution algorithm; step 5, using a density-aware fitness function to comprehensively attack effectiveness and disturbance secrecy, and evaluating candidate patches; and step 6, projecting the adversarial graph to the target object using a laser projection device to realize physical attack. The application is suitable for adversarial testing and security evaluation of image classification, target detection and segmentation and other visual-based deep learning systems in a physical scene, and has high engineering use value due to non-invasive deployment, fast projection and removal.