3207 results about "Fitness function" patented technology

The invention relates to the technical field of logistics transportation scheduling, in particular to a logistics management system based on intelligent scheduling, which comprises a data acquisition module, a demand analysis module, an intelligent scheduling module, a path planning module, a monitoring and feedback module, an optimization module and the like. The data acquisition module is used for collecting information such as goods, vehicles, storage, customer demands and traffic road conditions; the demand analysis module predicts logistics demands by using a time sequence analysis or machine learning algorithm; the intelligent scheduling module adopts a greedy and dynamic programming mixed algorithm to allocate resources; the path planning module calculates an optimal path according to road conditions and starting and ending points; the monitoring and feedback module monitors logistics parameters in real time and feeds back abnormity; and the optimization module optimizes the scheduling scheme according to the fitness function based on the genetic algorithm. According to the invention, data and resources of all links of logistics are effectively integrated, accurate demand prediction, efficient resource configuration, transportation path optimization, real-time monitoring feedback and overall performance improvement of the system are realized, the logistics cost is significantly reduced, and the transportation efficiency and the service quality are improved.

The invention relates to the technical field of abalone peptide production, and discloses an abalone peptide production process optimization and intelligent decision-making method and system, and the method comprises the steps: collecting production data through a multi-source sensor, and extracting features through a convolutional neural network; the genetic algorithm optimization model is combined with an adaptive mechanism and a multi-target fitness function to generate a process parameter optimization scheme; and the multi-objective production optimization model outputs optimal production process parameters by means of an improved particle swarm algorithm. In the hierarchical intelligent decision-making model, the planning layer realizes global resource allocation, the scheduling layer adjusts a process in real time based on a Bayesian network and the like, and the operation layer controls equipment through fuzzy logic and constructs quality closed-loop control. According to the method, the problems that parameter control depends on experience, data processing is difficult, optimization is one-sided, decision making is not intelligent and the like in traditional production are solved, data can be accurately processed, the process is optimized, decision making is intelligent, production efficiency, the raw material utilization rate and product quality are effectively improved, cost is reduced, and the application prospect is wide.

A multi-objective optimization method and system for a shield construction parameter. The method comprises: acquiring an initial shield construction parameter; and optimizing the shield construction parameter by using a multi-objective optimization algorithm, during the optimization of the shield construction parameter, on the basis of the shield construction parameter, obtaining a tunneling speed by means of performing prediction on the basis of random forest, calculating a shield tunneling energy consumption ratio on the basis of the tunneling speed and the shield construction parameter, and taking the maximization of the tunneling speed and the minimization of the shield tunneling energy consumption ratio as two fitness functions of the multi-objective optimization algorithm. The multi-objective optimization method and system for a shield construction parameter take construction efficiency into consideration and reduce the construction cost while ensuring construction safety.

The present invention relates to the technical field of circuit control, and relates to a direct-current high-voltage generator regulation and control method and system based on intelligent feedback. A fuzzy control logic operation is performed on an output current flux value and an input voltage fluctuation factor of a load experimental object, so as to obtain the disturbance amplitude of a voltage fluctuation state for a current flux change trend; if a step-up transformer can execute a PID control technology by adjusting a transformation ratio, PID real-time adjustment parameters are calculated, and a crossover mutation operation of individuals is performed on the basis of a fitness function, so as to generate a first boost regulation and control scheme; and if the step-up transformer cannot execute the PID control technology by adjusting the transformation ratio, a calibrated state feedback controller is constructed on the basis of a state variable of the current step-up transformer and an adjustment signal output amplitude, so as to generate a second boost regulation and control scheme. The present invention achieves intelligent feedback regulation and control by independently analyzing components of direct-current high-voltage generators, improving the stability and accuracy of outputting high-voltage direct current.

The invention relates to the technical field of power system automation, in particular to a real-time monitoring and fault positioning system for a vehicle-mounted mobile substation. The system comprises a signal acquisition and processing unit which monitors and acquires line parameters and environmental parameters of a transformer substation and traveling wave signals generated when a fault occurs in real time; the fault type identification unit calculates the confidence coefficient of each fault type based on the traveling wave signal so as to judge the fault type generated by the traveling wave signal, and marks the arrival time of the traveling wave head; an algorithm fusion positioning unit preliminarily positions a fault point according to the fault type and the arrival time of a traveling wave head, and then constructs a fitness function through a particle swarm optimization algorithm in combination with line topology and environmental parameters to correct a preliminary positioning error; according to the system, high-precision fault positioning and rapid isolation recovery are realized, the accuracy of fault section division in the complex power distribution network is ensured by modeling switch state and branch change, and misjudgment caused by topological change or equipment overload is effectively avoided.

Disclosed is a motor fuzzy PID parameter tuning method based on an improved whale algorithm. The method comprises: building a brushless direct-current motor speed control system model, and using a fuzzy PID controller to perform motor speed control. A conventional whale algorithm is optimized by using a chaotic convergence factor, a fractional order, and Levy flights, so as to obtain an improved whale algorithm. Secondly, the overshoot of the system is used as a component of an ITAE performance index to obtain an improved ITAE performance index, and the improved ITAE performance index is used as a fitness function for the improved whale algorithm. Finally, the improved whale algorithm is used to optimize input and output membership functions of a fuzzy controller, so as to obtain optimal ΔKp, ΔKi, and ΔKd values, and Kp, Ki, and Kd parameters of a PID controller are tuned to implement motor speed control. The present invention addresses the difficulty of tuning parameters of conventional PID controllers and solves the problem of low precision of motor speed control, has the advantages of high anti-interference capability, little overshoot, and short adjustment time, and improves the dynamic characteristics and robustness of the controllers.

The invention discloses a battery energy recycling method for an underwater robot, and the method comprises the steps: integrating the equipment state and environment data of the underwater robot, screening the data through a particle filtering algorithm, capturing the depth information of an underwater environment in real time, collecting the material attribute of a battery and an energy transmission efficiency parameter, and recognizing the parameter through a k-means clustering algorithm. The method comprises the following steps: constructing constraint conditions of an energy recovery network, constructing an energy recovery network topology model according to energy loss information and node layout, obtaining an optimal path by applying an Euleran path algorithm, determining an arc curvature by adopting a non-dominated sorting genetic algorithm, dynamically updating topology model parameters, obtaining an optimized energy recovery arc, and constructing an arc optimization mechanism. A logistic regression algorithm is used for scoring arcs and identifying efficient nodes, an Apriori association rule mining algorithm is used for simplifying a network topology structure, calculating structural differences and updating a model, a fitness function algorithm is used for monitoring the network state and energy loss in real time, and the transmission efficiency is evaluated.

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention discloses an automatic test case generation method based on a large model and a particle swarm algorithm, and the method comprises the steps: designing a large language model demand analysis module, carrying out the semantic analysis of a demand document or code annotation of a software system through a large model, and generating a preliminary test case template; designing a particle swarm optimization module, and globally optimizing an input parameter combination and an execution path of the test case through a particle swarm optimization so as to improve the test coverage rate and the defect detection capability; and an automatic test execution and feedback module is designed, the particle swarm optimization evaluates the effectiveness of the test case according to the fitness function, a strategy is iteratively optimized and generated, and the test case is adjusted through a feedback mechanism. According to the method, efficient test cases can be effectively generated, various functions and boundary conditions of the system are covered, dynamic optimization of the test cases is achieved through a feedback mechanism, and comprehensiveness and reliability of system testing are ensured.

Disclosed in the present invention are a railway vehicle-track-bridge system dynamic response prediction method and system, and a medium. The method comprises: inputting vehicle speed samples and track irregularity samples into a vehicle-track-bridge system coupled random distribution physical model to obtain corresponding bridge dynamic responses and effective loads of the vehicle-track-bridge system, and extracting a global stiffness matrix of the vehicle-track-bridge system; constructing a training sample set; constructing a fitness function considering the effective loads, and then on the basis of the training sample set, using a genetic algorithm to optimize parameters of a BP neural network prediction model and training same to obtain a bridge dynamic response prediction model; and using the bridge dynamic response prediction model to carry out bridge dynamic response prediction. By introducing effective loads in a vehicle-track-bridge system into a fitness function in a genetic algorithm, a neural network model and a vehicle-track-bridge physical model are organically combined, thereby improving the prediction precision.

The invention provides an intelligent well completion multi-objective optimization method based on a multi-objective genetic algorithm, and the method comprises the steps: determining an optimization objective and a constraint condition according to a real environment of intelligent well completion; and constructing a multi-target genetic algorithm model, and executing optimization calculation of the multi-target genetic algorithm. And evaluating the influence of each ICV configuration on the optimization target by adopting a fitness function. And an initial population is generated for initialization, a current optimal individual is screened out through iterative operation, and the process is ended after data are output. The method has the beneficial effects that the calculation efficiency is improved by establishing the multi-target fitness model and optimizing the iteration strategy, so that the algorithm can quickly converge to the optimal solution under limited calculation resources. The fitness function of the scheme can accurately evaluate the influence of different parameter configurations on the optimization target, and the optimization result is more reliable and accurate. And an optimization method which is more efficient, more comprehensive and more suitable for actual production requirements is provided.

The invention discloses an emergency material intelligent scheduling system and method based on an ant colony algorithm. A demand prediction module of the emergency material intelligent scheduling system dynamically predicts material demands by using a time-space diagram neural network, and constructs a hierarchical network model integrating multi-modal transportation of unmanned aerial vehicles, ground vehicles and the like. And the optimization calculation module adopts an improved ant colony algorithm, introduces a demand urgency degree weight factor and a green weight factor to construct a multi-objective fitness function, and optimizes a transportation path in combination with a dynamic pheromone updating mechanism and a multi-ant colony collaborative strategy. The system is equipped with an edge computing driven dynamic adjustment module to realize 30-second fast path re-planning, a psychological assistance priority model is innovatively integrated, and a psychological crisis index optimization scheduling strategy is extracted through sentiment analysis. According to the method, the problems of response lag and insufficient multi-objective optimization of a traditional scheduling system are effectively solved, the transportation efficiency is remarkably improved by 25%-35%, carbon emission is reduced, and high efficiency, fairness and humanity care of emergency scheduling are guaranteed.

The invention relates to the technical field of system control data processing, in particular to a waste heat recovery heat supply system based on an injection type heat pump, which comprises a multi-source data acquisition module, a genetic algorithm optimization module, a dynamic mode switching module, a thermal stress suppression module and a self-learning updating module. The multi-source data collection module collects industrial waste heat temperature, power grid load and heat supply network backwater flow data through a temperature sensor array and a flow metering device, and feature vectors are extracted through sliding window standardization and principal component analysis. And heat transfer end difference fluctuation is restrained through closed-loop feedback. And the self-learning updating module constructs a multi-parameter incidence matrix to dynamically adjust the weight of a fitness function, and realizes parameter iterative optimization in combination with a historical optimal solution caching mechanism. The problems that the industrial waste heat recovery efficiency is low, thermoelectric coupling adjustment is delayed, and thermal stress of equipment is accumulated are solved.

The invention provides a natural gas pipeline leakage probability calculation method and system based on a genetic algorithm, and relates to the technical field of probability calculation, and the method comprises the steps: collecting pipeline operation parameters, building a pipeline structure dynamic stress model, a material fatigue model and a corrosion damage model, carrying out the coupling analysis of the three models, and obtaining a risk assessment parameter matrix; a leakage risk assessment initial model is constructed as a fitness function, and a leakage probability calculation model is obtained through genetic algorithm optimization. According to the method, the pipeline leakage risk can be accurately predicted, accurate evaluation and early warning of pipeline safety are realized, and the pipeline operation safety is improved.

The invention provides an enterprise production data micro-service granularity optimization method, a medium and a system, belongs to the technical field of production data management, and establishes a micro-service granularity evaluation equation set by obtaining a dependency relationship and business flow data of micro-services in an enterprise production data system, constructing a coupling degree and cohesion degree matrix and establishing a micro-service granularity evaluation equation set. According to the method, a hybrid optimization algorithm is adopted, the search direction of a grey wolf algorithm and group evolution of a genetic algorithm are combined, and a composite fitness function containing system response time, resource utilization rate, maintenance cost and micro-service granularity balance degree is constructed. And continuously updating the micro-service division scheme through iterative optimization until the preset number of iterations is reached and the composite fitness function meets the optimization target, and finally outputting the optimal micro-service division scheme. The technical problem that the micro-service granularity is difficult to determine in the prior art is solved according to the division scheme of multiple indexes such as the maintenance cost and the granularity balance degree.

The invention discloses a method for generating an autonomous inspection orthoimage of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle surveying and mapping and autonomous navigation. Global optimization of an air route is realized through a path optimization strategy fused by a genetic algorithm in combination with grid characteristics of an inspection area and image parameter constraints, and candidate waypoints are used as nodes; redundant waypoints are screened through leg smoothness factors, route complexity is reduced, a genetic algorithm takes flight height and waypoint spacing as constraints, a fitness function containing flight distance, turning times and overlapping rate is constructed, a global optimal path is found through population iteration, multi-target requirements are optimized and balanced, and it is ensured that the route meets the image acquisition precision requirement and also meets the requirement of image acquisition. The flight distance can be shortened, the turning frequency is reduced, the cruising ability of the unmanned aerial vehicle is adapted, efficient propelling of the inspection task is guaranteed, meanwhile, waypoint coordinates output through simulation directly adapt to a flight control system, and it is guaranteed that actual flight parameters are consistent with planning parameters.

The invention relates to the technical field of unmanned aerial vehicles. The invention discloses a task allocation and endurance optimization method for a multi-unmanned aerial vehicle system. The method comprises the steps of S1, task modeling, S2, initial population generation, S3, fitness function design, S4, genetic algorithm task optimization, S5, iterative optimization, S6, path planning and S7, data collection, prediction and adjustment. The multi-unmanned aerial vehicle system task allocation and endurance optimization method can process temporary tasks in real time, flexibly adjust a task allocation scheme, dynamically adjust flight parameters according to real-time flight conditions and task requirements, comprehensively consider various factors such as task priorities, flight paths, energy consumption and the like, and optimize the task allocation and endurance optimization of the multi-unmanned aerial vehicle system on the basis of actual flight data. And an energy consumption prediction model is established, and accurate energy consumption management and path planning are realized. According to the method, temporary tasks can be processed, task allocation and path planning are dynamically adjusted, and task execution efficiency and safety are ensured.

The invention discloses an unmanned aerial vehicle navigation positioning method and system based on big data, and the method comprises the steps: obtaining the position data, sensor data and environment data of an unmanned aerial vehicle, constructing a real-time map according to the position data and the sensor data through employing an SLAM algorithm, and carrying out the real-time positioning; carrying out sub-region division on the real-time map, and obtaining a risk score by adopting clustering analysis; obtaining path time between the sub-regions according to the environment data and the risk score, establishing a fitness function according to the risk score and the path time, and establishing a path planning model based on the fitness function; obtaining offset data according to the sensor data, establishing a path compensation strategy according to the offset data, and performing path correction; learning the path compensation strategy and the path planning model to obtain an unmanned aerial vehicle navigation model, and optimizing the unmanned aerial vehicle navigation model through a sine and cosine optimization algorithm. The method is suitable for various unmanned aerial vehicle application scenes, and wide application of the unmanned aerial vehicle technology in a complex environment is promoted.

The invention discloses a wind turbine generator anti-impact noise fault identification method based on feature embedding deep learning, and the method comprises the steps: carrying out the feature mode decomposition of an original vibration signal of a wind turbine generator, screening out an optimal mode component, and converting a time domain signal of the optimal mode component into an envelope spectrum; using the minimum envelope entropy as a fitness function, and using a sparrow search algorithm to globally optimize the filter length and the decomposition modal number of characteristic modal decomposition; and extracting time-frequency domain features, constructing a multi-dimensional time-frequency domain feature vector, inputting the multi-dimensional time-frequency domain feature vector into the combined fault recognition model, and outputting a fault classification result. According to the method, on the basis of an FMD and SSA-MEE joint optimization framework, the sensitivity limitation of a traditional envelope demodulation method on impact noise is broken through, modal aliasing is restrained, and noise robustness is enhanced. According to the method, the vibration signals are subjected to characteristic mode decomposition, the influence of early impact noise is inhibited, a CNN-GRU-Attention fault recognition model is provided, and the accuracy of fault recognition is greatly improved.

The invention discloses a digital twin-driven marine diesel engine emission prediction and optimization method, which comprises the following steps: (1) constructing a comprehensive performance mechanism model, and obtaining twin data through representation and verification of the comprehensive performance mechanism model; (2) designing a calibration module based on a deep learning algorithm, taking real data and twin data as input, and outputting calibrated data through model training; (3) constructing a combustion stage mechanism module, and optimizing a gas inlet pipeline structure and improving mixing uniformity through simulation of a combustion mechanism model on oil-gas mixing characteristics and combustion efficiency; (4) constructing a data driving model, and fitting a relationship between the turbulent energy index and emission; and (5) taking the constructed comprehensive performance mechanism model, the calibration module, the combustion stage mechanism model and the data driving model fusion technology system as a fitness function, searching an optimal oil injection control parameter based on a particle swarm optimization algorithm, and formulating an optimization control strategy adapting to different working conditions.

The invention discloses an unmanned aerial vehicle autonomous path planning method based on a visual perception module, and relates to the technical field of unmanned aerial vehicle inspection. Comprising the following steps: 1, collecting coordinates and image features of an inspection target and an obstacle in an inspection area, and building an environment model of the unmanned aerial vehicle inspection area; 2, based on the environment model, according to the maneuvering characteristics of the unmanned aerial vehicle, the collision risk level of the obstacle area is evaluated; step 3, based on the risk level, establishing a fitness function of the unmanned aerial vehicle safety planning path; and step 4, based on an improved particle swarm algorithm, optimizing the fitness function, and obtaining an optimal planning path of the current inspection area. According to the invention, the safety and reliability of the unmanned aerial vehicle during inspection task execution are improved.

The invention discloses an unmanned aerial vehicle cluster collaborative path planning method based on an improved grey wolf algorithm. The method comprises the following steps: 1, constructing an environment model of a task scene of an unmanned aerial vehicle cluster and a multi-vehicle cooperative constraint condition, and initializing unmanned aerial vehicle motion parameters; 2, generating a high-quality initial feasible solution by adopting a greedy random initialization strategy, and reducing invalid search through priori knowledge; 3, performing global optimization in combination with dynamic weight adjustment and a multi-target fitness function, and balancing multi-target conflicts; outputting an optimized path; 4, repairing the path through local path re-planning or speed adjustment, and if repairing fails, feeding back conflicting individuals to the step 3 for re-optimization to form a closed-loop cooperation mechanism; and 5, generating a 3D path planning graph and a fitness curve through a drawing function, and verifying path security and algorithm convergence. According to the invention, the global search capability is enhanced, and efficient collaborative path planning of the unmanned aerial vehicle cluster in a complex environment is realized.

The invention discloses a simulation and optimization method for a natural gas industrial system in a rough environment, and relates to the technical field of intelligent optimization, and the method comprises the steps: collecting environment data, constructing a physical constraint diffusion model, and generating an extreme environment data set through embedding a hydrodynamic equation and a thermodynamic law; in combination with real sensor data of a plurality of gas field nodes, pre-training a federated transfer learning framework, eliminating cross-environment data distribution difference through resistance feature alignment, and constructing a global environment threat prediction model; based on the simulation result and the real-time sensor data flow, a multi-target fitness function is constructed, and control variables are generated through population evolution and dynamic strategy adjustment; and inputting the control variable into a pre-trained digital twinborn verification model, comparing a simulation result with a residual error of a real-time sensor data stream, triggering an incremental learning mechanism, and updating a global environmental threat prediction model. According to the method, the high-fidelity extreme environment data set is generated through the physical constraint diffusion model, and the physical consistency of multi-field coupling simulation is improved.

The invention discloses an unmanned aerial vehicle path planning method based on global search and meta-heuristic optimization, and the method comprises the steps: firstly building an unmanned aerial vehicle path planning model under complex constraints, determining a fitness function and a constraint condition thereof, setting algorithm initialization parameters, carrying out the chaotic mapping of an initialization population position, and carrying out the optimization of the algorithm; and performing loop iteration solution by using a meta-heuristic optimization algorithm, outputting a global optimal path, finally realizing the global optimal path on the unmanned aerial vehicle, and calculating a flight distance corresponding to the optimal path to complete unmanned aerial vehicle path planning. According to the method, a meta-heuristic algorithm, namely a crown porcupine algorithm, is combined with tabu search, the global search capability is enhanced, and meanwhile, population initialization is optimized in combination with chaotic mapping, so that the randomness and unpredictability are increased, the algorithm can converge more quickly, and the robustness of the algorithm is improved. The method has obvious advantages in solving the problem of path planning with complex environment and complex constraint, and solves the problem of path planning of the unmanned aerial vehicle in the three-dimensional complex mountain environment.

The invention discloses an electromagnetic valve engineering design optimization method, and relates to the technical field of electromagnetic valve engineering design. According to the method, boundary condition parameters are clearly designed by obtaining high-pressure fluid control demand data, and an optimization constraint condition set is established, so that the design accuracy is ensured. Designing parameters are converted into optimization variables through gene coding, a fitness function is constructed through a genetic algorithm, and a solution set is generated; in a particle swarm optimization stage, design parameters are initialized to be particles, a multi-objective optimization function is defined, and a fitness function solution set is gradually optimized by utilizing an inertia weight and a group optimal guide search direction. The performance of the electromagnetic valve is verified through finite element analysis and fluid dynamics simulation, whether the control requirement is met or not is analyzed, and design parameters are finely adjusted according to the simulation result. The problems that the electromagnetic valve is insufficient in performance and unreasonable in design in the high-pressure fluid control process are effectively solved.

The invention provides a forest fire prediction method and system using a remote sensing image, and relates to the technical field of data processing, and the method comprises the steps: defining a fitness function used for evaluating the advantages and disadvantages of each individual, and each individual represents a group of weight parameters; randomly generating an initial population meeting a coding mode, wherein each individual represents a group of weight parameters; the fitness of each individual is calculated according to a fitness function, and a new generation of population is generated through selection, crossover and mutation operation; judging whether a preset termination condition is met or not, if the termination condition is met, stopping iteration, and outputting a current final weight parameter combination; according to the final weight parameter combination, combined with land surface temperature, vegetation coverage rate and vegetation water content data, calculating a forest fire danger dynamic danger index; and classifying the fire danger grades according to the forest fire danger dynamic danger index. The accuracy of the prediction result can be improved.

The invention relates to a Bluetooth connection method and system, and the method comprises the steps: obtaining Bluetooth identifiers of an earphone and a source device, and extracting a multi-connection strategy from a database; and calculating equipment positions, signal intensities and interference factors among all strategy steps, executing a three-dimensional verification matrix, comparing protocol units, and monitoring a frequency hopping sequence to realize dynamic sequence adjustment. Combining the signal intensity, the success rate and the interference risk to calculate a comprehensive weight, generating an optimized sequence, then dividing multiple subsequences for cross validation, and utilizing historical data to adjust the interference weight and reconstruct a step sequence. Collecting intensity mean values and success rate variances of multiple rounds of tests to construct a normalized fitness function, and selecting an optimal scheme after scoring each strategy. Through a multi-dimensional parameter fusion and iteration verification mechanism, traditional single-path limitation is broken through, and finally, Bluetooth connection is completed by adopting a highest score strategy, so that the problem of equipment interconnection in a complex electromagnetic environment is effectively solved.

The invention discloses a packaging tin material proportioning optimization method based on a particle swarm optimization algorithm, and the method comprises the following steps: initializing a particle swarm, dynamically adjusting the positions and speeds of particles through a fuzzy logic control system, and generating an initial material proportioning scheme; a fitness function is constructed based on a non-Euclidean geometric method, multiple indexes such as strength, toughness, corrosion resistance and cost are comprehensively considered, and fitness values of all the schemes are calculated; gradually optimizing a material proportioning scheme by using a biologically inspired particle swarm optimization algorithm; a dynamic clustering mechanism is introduced, and different material matching schemes are analyzed and optimized; dynamically switching between global search and local search through a hybrid search strategy, and further optimizing a selected scheme; and finally, outputting an optimized optimal material proportioning scheme, and evaluating the reliability of the optimal material proportioning scheme through experimental verification and simulation. According to the invention, the optimization efficiency and performance of the packaging tin material proportioning are improved, and the method is suitable for the requirements of production automation and high efficiency.

The invention discloses a satellite communication method and device and a storage medium. The satellite communication method comprises the following steps: determining the suitability between different sub-bands of a satellite and different beams of the satellite; constructing a chromosome population formed by a plurality of chromosome vectors; according to the suitability, initializing the chromosome vector; a fitness function is constructed, and the fitness function is used for indicating the severity degree of same-frequency interference occurring in the multiple coverage areas after sub-bands are allocated for all the beams according to the chromosome vectors; performing iterative optimization on the chromosome population through the fitness function according to a genetic algorithm, and determining an optimized chromosome vector; and according to the optimized chromosome vector, allocating a corresponding sub-band to each beam, and carrying out satellite communication. Therefore, the distributed sub-band with the lowest fitness can be determined for each beam, and interference between same bands is avoided.

The invention relates to the field of robot target path planning, and discloses a multi-target path planning method and device based on an improved mantis optimization algorithm, and the method comprises the steps: improving the mantis optimization algorithm, and building a grid environment map and a multi-target fitness function, an obstacle avoidance function and a smooth path function corresponding to path planning; the population position optimization corresponds to path nodes in the grid environment map, iteration is performed on all individuals of the population, and fitness values are formed; calculating comprehensive fitness by using a multi-target fitness function to obtain an updated global optimal position and a corresponding optimal fitness value; completing path planning of a multi-target fitness function; and observing the convergence curve graph and the shortest path graph, and verifying the feasibility of multi-target path planning. According to the method, the convergence speed of the algorithm is improved, the global and local search capabilities are balanced, the shortest path length is effectively reduced, path redundant nodes and path inflection points are reduced, and the path quality and efficiency are improved.