53 results about "Genetic operator" patented technology

The invention discloses an edge air defense node task allocation method and system based on a genetic algorithm and a contract net method. The method specifically comprises the following steps: establishing a target threat degree estimation and task allocation model; the method comprises the following steps: calculating a multi-platform cooperative task allocation problem by adopting an improved mixed single parent genetic algorithm, through genetic operator design of integer coding + dynamic double populations, tournament selection + reverse-order crossover + adaptive variation and combining elite pool updating of a simulated annealing criterion and a mutation strategy triggered by concentration; aiming at the problem when an air defense platform encounters an emergency situation during task execution, an improved contract net method is adopted, and dynamic air defense task redistribution is realized by expanding a contract protocol process, introducing a multi-Agent collaborative architecture, designing a consistent auction algorithm and a dynamic bidding correction rule and combining a load-sensitive contract exchange mechanism. According to the method, multi-platform cooperative air defense task allocation can be rapidly and effectively carried out, the method dynamically adapts to environmental changes, and the combat effectiveness and combat efficiency of the multi-platform cooperative air defense tasks are improved.

The invention provides an operation and maintenance management method and platform for a photovoltaic power generation system, and relates to the technical field of photovoltaic power station operation and maintenance, and the method comprises the steps: obtaining a to-be-processed operation and maintenance task set and an idle operation and maintenance personnel set; scheduling optimization is carried out on operation and maintenance tasks through an improved NSGA-II algorithm, a multi-objective optimization problem of total operation and maintenance cost minimization, total task completion time minimization and response time minimization is established, multi-objective optimization solution is carried out by adopting a clustering enhancement coding structure, a mixed population initialization strategy and a probability weighting genetic operator, and a Pareto optimal solution set is output; and selecting a final scheme from the Pareto optimal solution set, converting the final scheme into an executable format, and outputting a complete operation and maintenance scheduling scheme comprising a personnel scheduling list, a path planning instruction, a material and tool list and a total time schedule. According to the invention, multi-objective collaborative optimization of the operation and maintenance cost, the response time and the execution efficiency can be realized.

The invention provides an AI-based urban governance application system intelligent generation method and system, and belongs to the technical field of application system generation. The method comprises the following steps: acquiring urban governance multi-source data, and extracting spatio-temporal characteristics to construct a digital twinborn model; converting front and rear end components into gene sequences through gene coding, and constructing a component gene pool; inputting urban governance scene generation component requirements, and constructing a multi-target fitness function and a genetic operator by using a multi-target genetic algorithm; establishing a reinforcement learning agent in combination with an optimization target of the function, and performing collaborative search and local optimization through a genetic operator and the agent to obtain an optimal component combination; optimal component combinations are distributed through a federated learning framework, evaluation model parameters are aggregated, and a component quality evaluation standard is constructed; operating data are collected on line, the using effect is evaluated, and the component gene pool and the digital twinborn model are updated. According to the invention, the assembly selection intelligence of the urban governance application system is realized, and the assembly selection search efficiency is improved.

The present disclosure belongs to the field of flexible job shop scheduling, and relates to a dual-effect scheduling method for heterogeneous robots in a flexible job shop. In a case of strong coupling of processing and transferring, this method comprehensively considers such constraints on selection of flexible manufacturing cells (FMCs), transferring time by automatic guided vehicles (AGVs) and processing resource waste, and improves encoding schemes and genetic operators with order completion time and minimization of resource consumption as evaluation criteria. Additionally, this method can fully apply environmental characteristics of job shop to scheduling design, and automatically design more precise scheduling schemes, overcoming the deficiencies of slow response and prone to local optimal solutions existing in conventional scheduling schemes, and ensuring efficient and green operation.

A flexible job shop scheduling method based on improved HHO of co-evolution, steps include S1 data collection; S2 abstract FJSP as a mathematical model; S3 divide FJSP into machine selection sub-problem and process processing sequence sub-problem; S4 machine selection sub-problem processing: adopt DI coding method coding, map discrete machine selection sequence to continuous solution space of HHO algorithm; Each process selects the decision of the process in the machine set as a variable; After iteration solution by HHO algorithm, DI coding is mapped back to discrete machine selection sequence by decoding; S5 process processing sequence sub-problem processing: adopt OS coding method coding, and adopt genetic operator to co-evolution; S6 select one individual from DI population and OS population respectively, combine into DIOS code; Calculate the maximum completion time of DIOS code; S7 DI population and OS population co-evolution; Decode the optimal solution and output.

The invention discloses a crop early recognition method and system based on genetic programming customization characteristics and a storage medium, and the method comprises the steps: obtaining an initial image of a target region and a ground sample collected on site, generating the initial characteristics of a target crop through the spectral band of the initial image, and generating an initial population according to the initial characteristics of the target crop; calculating a feature value of the initial feature, and performing binary classification on the feature value according to a preset feature threshold value; based on the classification label and the real label of the ground sample, obtaining an accuracy rate of binary classification, and obtaining a fitness value of the initial feature according to the accuracy rate; iterating the initial population through a selection method and a genetic operator to obtain customized features of the target crop; and performing crop classification on the target crops based on the customized features, and obtaining a classification drawing result of the early crops or seasonal crops according to a classification result. According to the method, the required sample size can be reduced, and meanwhile, accurate early-stage and season crop classification and mapping results can be obtained.

This application discloses a method, apparatus, device, and medium for determining simulation scenarios, relating to the field of autonomous driving technology. The method for determining simulation scenarios includes: acquiring at least one initial scenario corresponding to a target vehicle; performing scenario testing on each initial scenario to obtain test boundary indicators for each initial scenario; using the test boundary indicators as fitness, employing genetic operators of a genetic algorithm to select, crossover, and / or mutate the scenario parameters of the initial scenarios to obtain at least one optimized scenario; updating the initial scenarios to optimized scenarios, and returning to perform scenario testing on each initial scenario to obtain test boundary indicators for each initial scenario, until a preset stopping condition is reached to obtain at least one candidate scenario; and identifying candidate scenarios with fitness greater than a preset threshold as target simulation scenarios that meet performance boundary testing requirements.

The invention discloses a vehicle path planning problem optimization method and system for landscape smoothing, and belongs to the technical field of combinatorial optimization algorithms, and the method comprises the steps: randomly generating an initial population for a vehicle path planning problem, constructing a toy problem, and carrying out the optimization of the vehicle path planning problem; the toy problem and the vehicle path planning problem are equal in scale, and the fitness landscape of the solution space has a single-peak characteristic; constructing a fitness function, and evaluating each individual in the population through the fitness function to obtain a fitness value of each individual; executing operation of a selection operator, a crossover operator and a mutation operator to generate a next generation population; and the fitness function design and evaluation and genetic operator operation are repeatedly executed until a preset termination condition is met, and an individual with the minimum original problem objective function value in the evolution process is output to serve as an optimal solution of the vehicle path planning problem. According to the method, local optimum can be jumped out, global exploration and local development are balanced, the solving quality and stability can be improved, and the method has applicability in vehicle path planning.

The invention relates to the technical field of ship navigation model tests, and discloses a giant estuary ship navigation capability prediction model test method fused with machine learning, which comprises the following steps: initializing and defining a scene vector, training an agent model, generating a population, and setting archives and parameters; executing dynamic fitness evaluation to obtain a fitness score; generating filial generations based on score selection and genetic operators and ensuring physical effectiveness; selecting a scene with the highest score to obtain a predicted risk; when the error is greater than a threshold value, storing the candidate scene; if yes, outputting the file; and if not, replacing the population and returning evaluation. According to the method, a feedback closed loop is constructed through failure judgment and updating of a failure mode file, after a failure scene verified by a high-fidelity simulator is stored in the file, the areas are avoided when the novel fitness is calculated through dynamic fitness evaluation, the search direction is automatically adjusted, and automatic and self-adaptive exploration of a high-risk scene is achieved.

The invention provides a power distribution area optimization operation method giving consideration to distributed energy characteristics and electric energy quality management requirements, and the method comprehensively considers the cooperative relationship among a photovoltaic power generation system, an energy storage system and power distribution network nodes, builds an electric energy quality evaluation index according to the voltage deviation and fluctuation of the nodes, proposes a light-storage cooperative operation mechanism, and improves the power quality management efficiency. A power distribution area double-layer optimization model including constraint conditions such as node power balance, energy storage operation, voltage and capacity limitation and power flow constraint is formed; a solving method based on an improved particle swarm algorithm is proposed, the search diversity is enhanced by introducing chaotic Tent mapping and genetic operators, the convergence speed is increased, local optimum is avoided, and the practicability of the proposed method is verified through an actual example. According to the method, the influence of distributed energy access on voltage deviation and fluctuation is fully considered, and the method has important significance in the aspects of optimized operation and electric energy quality control of a power distribution area.

The invention discloses a fixed star spectrum simulation method based on genetic algorithm and fuzzy PID compound control. Firstly, a fuzzy PID controller structure with double inputs: spectral intensity deviation # imgabs0 # and deviation rate # imgabs1 # and three outputs: proportionality coefficient increment # imgabs2 #, integral coefficient increment # imgabs3 # and differential coefficient increment # imgabs4 # is established. Then, basic work such as membership function selection, fuzzy rule making and defuzzification is completed; and finally, by means of a genetic algorithm, through coding mode formulation, initial population generation, fitness function selection and genetic operator determination, a fuzzy PID algorithm process is optimized. Practice verifies that in 3000K-9000K typical color temperature spectrum simulation, compared with a traditional fuzzy PID control algorithm, the error is remarkably reduced, and the simulation precision is greatly improved. And the performance is excellent when the full spectrum band simulation of the AM1.5 solar spectrum is carried out. The algorithm can accurately simulate a spectrum curve containing complex details, greatly enhances the accuracy and reliability of spectrum simulation, and provides key technical support for the fields of solar research, optical detection and the like.

The invention belongs to the technical field of multi-source solid waste cyclic utilization, and discloses a multi-source solid waste cyclic utilization path optimization method and system, and the method comprises the steps: solving a multi-target mathematical optimization model of a multi-source solid waste cyclic utilization system through employing a dual-stage multi-target optimization method, and obtaining a multi-source solid waste cyclic utilization optimization scheme set; selecting a preference-based solid waste optimal processing path from the optimization scheme set; in the first stage, an extreme weight vector and a center weight vector are constructed to search a heuristic solution, and in the second stage, further evolution is carried out on the basis of a heuristic solution population, so that the population has better convergence and diversity; in the second stage, deep reinforcement learning is introduced to adaptively optimize parameters of a genetic operator. Meanwhile, the invention also provides a dynamic switching method based on population fitness to avoid unnecessary waste of computing resources. According to the method, the problems of low algorithm convergence speed, difficulty in determining important parameters of genetic operators and the like in practical application are solved, and efficient optimal configuration of path optimization is realized.

The invention discloses a cancer genome variation prediction system based on multiple genetic operators, relates to the technical field of genetic variation detection, and aims to solve the problems that the traditional cancer genome variation prediction is mainly based on a static mutation rate and a single mutation type, the dynamic balance and nonlinear evolution of a tumor in mutation, selection and clone expansion processes are difficult to accurately reproduce, and the accuracy is poor. According to the cancer genome variation prediction method and the cancer genome variation prediction system, high-fidelity simulation and evolution reconstruction of the cancer genome are realized by fusing a multi-type mutation mechanism, fitness feedback and a polyclonal competition strategy. According to the method, through the synergistic effect of multiple genetic operators and dynamic parameter adjustment, the dynamic balance and nonlinear evolution of the tumor in the mutation, selection and clone expansion process can be accurately reproduced, and then the accuracy of cancer genome variation prediction is improved. The application provides a brand new technical approach for early screening of cancers, variation detection and precise medical treatment.

The invention discloses a multi-objective optimization lithium battery thermal runaway escape gas spectral wavelength selection method, which comprises the following steps: collecting near infrared spectral signals in sample gas to obtain spectral absorbance and component concentration, defining the wavelength as a decision variable, and carrying out hybrid coding based on a SparseEA double-layer coding mechanism. Selecting an optimal wavelength combination subset by taking the weight threshold as a reference standard of a forward heuristic search strategy of the sequence; then, a SparseEA genetic operator based on a weight value serving as a fitness value is used for guiding population evolution to generate filial generations, and a Pareto optimal solution is output after environment selection; and finally, determining an optimal compromise solution of gas spectral quantitative analysis in the Pareto optimal solution by using GEMONN, thereby obtaining an optimal spectral wavelength variable combination scheme. More key wavelength variables are intelligently screened out through the SFS strategy, a high-performance spectrum quantitative analysis model is constructed in a multi-objective mode, and the accuracy of multi-component spectrum feature selection is greatly improved.

This invention discloses a method, system, terminal, and medium for optimizing the production scheduling of an aluminum alloy aerospace component creep forming production line. The method includes: constructing a multi-dimensional objective function for optimizing the scheduling of the aluminum alloy aerospace component creep forming production line; generating a set of reference points; and initializing a parent population P of size N. t Using genetic operators on P t The operation yields a child population Q of size N. t P t and Q t The mixture yields a population U of size 2N. t From U t N individuals are selected. The selection process involves three stages: first, clustering-based selection; second, non-dominated sorting-based selection; and third, vector angle-based selection. This process is repeated until the iteration termination condition is met. A set of scheduling scheme solutions is obtained, and a fuzzy decision method is applied to select a high-quality production scheduling scheme. This three-stage selection method improves the ability to distinguish non-dominated solutions, strengthens the balance between population convergence and diversity, and yields high-quality production scheduling schemes.

The present invention discloses a fast non-dominated sorting genetic algorithm for the dynamic equivalent parallel machine scheduling problem, which is carried out according to the following steps: S1: Generate a population by mixing heuristic rules MODD, ATC, X-RM and a random method; S2: Calculate the objective values of the individuals in the population by a decoding method based on dynamic programming; S3: Determine whether the termination condition is satisfied; if so, end; otherwise, perform fast non-dominated sorting on the individuals in the population; S4: Generate an offspring population through genetic operators; S5: Combine the parent population and the offspring population to form a population of size 2N; S6: Perform fast non-dominated sorting on the individuals in the population, and determine the population by combining crowding degree calculation; S7: Perform neighborhood search on the individuals in the population to generate new solutions to replace the original redundant solutions. The present invention has the characteristics of improving the utilization rate of workshop machines and meeting the customer delivery date.

The present invention provides an optimization method for remote sensing data discretization based on deep Q-learning. The method comprises: extracting features from remote sensing images to obtain a feature subset, preprocessing the feature subset to obtain a candidate breakpoint set, and performing binary genetic encoding on the candidate breakpoint set to construct a discretization scheme to be optimized; designing state sets for the crossover phase and the mutation phase through the global and local search capabilities of genetic operators; constructing a fitness function with control variables and an adaptive reward function; and introducing a pair of deep Q-learnings with the same structure to perform real-time calculation of Q values ​​during the transition between the crossover state and the mutation state, thereby determining the crossover segments and mutation points of the discretization scheme to be optimized. By introducing a pair of deep Q-learnings with the same structure to the discretization scheme to be optimized, the present invention improves the search efficiency of the feature discretization method based on the evolutionary model, and can further reduce the number of breakpoints while achieving higher classification accuracy.

The application discloses a wave band selection method, belonging to the technical field of spectral analysis. It includes: based on the spectral physical continuity prior and the regression model verification set performance index, constructing the task specialization genetic algorithm, which represents the wave band subset with a binary mask vector, takes the performance index as the fitness function, and adopts the structured search operator based on the continuity prior and the traditional genetic operator to form a hybrid search mechanism; based on the population evolution state representation and the fitness improvement feedback, constructing the reinforcement learning meta-control framework for synergistically regulating the algorithm hyperparameters and the structured operator; based on the recurrent neural network, constructing the timing-enhanced reinforcement learning agent; based on the spectral physical interference mechanism and the feature variation law, generating the simulation data for the iterative training of the agent; and taking the trained agent as the meta-controller to drive the algorithm to run and complete the wave band selection. The application solves the problems of the traditional genetic algorithm, such as lack of adaptive regulation, easy premature convergence and low screening precision.

The invention relates to an energy internet system scheduling model solving method based on a niche genetic algorithm, and the method comprises the following steps: S1, constructing a multi-objective optimization mathematical model containing an economic cost objective function and an environmental protection cost objective function at the same time, and determining constraint conditions in the operation of a scheduling system; s2, representing candidate solution individuals by adopting a real number coding mode; s3, calculating fitness values of all individuals in the current population, and entering a main optimization iteration process until a termination condition is met; s4, in each round of iteration, according to the current individual fitness value, executing genetic operator operation to generate a next generation of filial generation population; s5, by calculating the normalized Euclidean distance between individuals, only the individual with the optimal local fitness is reserved; and S6, outputting the individual with the highest fitness value as the optimal solution of the final scheduling model parameters of the multi-energy complementary system. According to the method, the overall economy, the environmental protection property and the operation reliability of the scheduling scheme can be remarkably improved, and the method has good engineering applicability and popularization value.

The present invention discloses a method for detecting the facial symmetry axis of a face image based on a genetic algorithm. The steps are as follows: image preprocessing; designing chromosome encoding; establishing an initial population; designing a fitness function; defining genetic operators. Based on the genetic algorithm, the present invention transforms the problem of symmetry axis detection into the problem of population evolution, mutation, and searching for the optimal solution. The search process is determined by defining the genetic operators in the algorithm, and the optimization of the search process is completed. By defining the adaptive function of the algorithm, three factors, namely image symmetry, the perpendicularity of the symmetry axis, and the offset distance of the symmetry axis, are fully considered, thereby optimizing the selection process of the algorithm. Through the genetic evolution of the population, the optimal facial symmetry axis is obtained, thus solving the problem of facial symmetry axis detection.

The application provides a sparse antenna array optimization method and device fusing a neural network and a genetic algorithm, and the method comprises the following steps: initializing a genetic algorithm model; determining the fitness values of different individuals in an initial population based on a neural network model; judging whether a set termination condition is reached, and outputting an array element position distribution optimization result of the sparse antenna array in the case where the set termination condition is reached; otherwise, updating the population based on genetic operators and catastrophe operators, and determining the fitness values of different individuals in the updated population based on the neural network model, and repeatedly updating the population and determining the fitness values of different individuals in the updated population until the set termination condition is reached, and outputting the array element position distribution optimization result of the sparse antenna array. Therefore, the efficiency of genetic algorithm calculation can be improved, and the algorithm performance of the sparse antenna array is improved.

The application provides a feature selection method, device, equipment, medium and product. In an initialization stage, a multi-objective optimization problem is decomposed into multiple single-objective optimization sub-problems, initial solutions are randomly sampled in a search space to generate initial solutions, the initial solutions are actually evaluated, and the initial solutions are stored in an archive. In a cyclic processing stage, a multi-objective kernel ridge regression model is trained by using the archive, solutions suitable for each sub-problem are selected from the archive as initial parent populations of the sub-problems, genetic operators are used to obtain child populations, the multi-objective kernel ridge regression model is used to predict the prediction accuracy and reasoning time of the child feature solutions, the parent populations are updated according to the prediction results, and new solutions for actual evaluation are selected from the evolved populations by using a sparsity-driven filling sampling criterion to update the archive. Through sub-problem collaborative optimization assisted by a proxy model, non-dominated solutions of the original multi-objective optimization problem are obtained, and the prediction accuracy, calculation efficiency, convergence and diversity are improved.

The invention relates to the technical field of asphalt mixture modulus prediction, and discloses an asphalt mixture multi-scale modulus prediction method and system, and the method comprises the steps: obtaining a three-scale joint data set, determining the design stage weight of each scale data, and outputting a design stage dynamic modulus prediction value through a trained first prediction model. The system corresponds to the method. According to the method, the prediction precision of the design stage is remarkably improved by constructing the three-scale data system, comprehensively capturing the modulus influence factors of the asphalt mixture and combining the dynamic weight distribution mechanism adaptive to the stage; through a cross-stage constraint function driven by data quality, the difference from design to service data quality is quantified, and the problem of performance disjunction of the two is solved. A transfer learning mechanism is constructed through a genetic operator, the dynamic change of data quality distribution is adapted, and the prediction stability and adaptability in the service stage are improved; therefore, full-life-cycle modulus accurate prediction is realized, and scientific support is provided for mixture ratio optimization and pavement maintenance decision making.

The present invention discloses a method, system and storage medium for early crop recognition based on customized features of genetic programming. The method includes: obtaining an initial image of a target area and ground samples collected in the field, generating initial features of a target crop by using spectral bands of the initial image, and generating an initial population according to the initial features of the target crop; calculating eigenvalue of the initial features, and performing binary classification on the eigenvalue according to a preset feature threshold; obtaining the accuracy of the binary classification based on the classification label and the true label of the ground samples, and obtaining the fitness value of the initial features according to the accuracy; iterating the initial population through a selection method and genetic operators to obtain customized features of the target crop; classifying the target crop based on the customized features, and obtaining a classification mapping result of early crops or in-season crops according to the classification result. The present invention can obtain accurate classification and mapping results of early and in-season crops while reducing the required sample size.

The invention discloses an integrated genetic programming method for random resource-constrained project scheduling. The method specifically comprises the following steps: carrying out SRCPSP analysis on a random resource-constrained project scheduling problem and constructing a mathematical model; solving the SRCPSP on the basis of a non-fixed-length integrated genetic programming algorithm NEGP; in a PR generation stage, designing a PR updating mechanism based on complementarity; in a PR integration stage, a non-fixed-length coding structure is constructed to represent PR in a decision set, a genetic operator and a local search operator based on the non-fixed-length coding structure are designed, and the search capability of NEGP is improved. According to the production scheduling plan obtained by the method, multiple optimization objectives can be met while the SRCPSP is solved, the requirements of solving quality, solving efficiency, stability and the like are met at the same time, and the method has high practicability.

This invention relates to the field of scheduling optimization combining local tabu search strategy and genetic algorithm, specifically a workshop flexible operation scheduling method based on tabu search genetic algorithm. It aims to generate flexible operation scheduling schemes and improve production efficiency by optimizing the maximum completion time. The invention consists of two parts: a global search phase, which divides the basic elements of the genetic algorithm into chromosome encoding / decoding, population initialization, setting iterative genetic operators, and population constraint rules, and performs a fast parallel search in the population space to obtain uniformly distributed feasible solutions; and a local tabu search phase, which establishes a tabu list, sets the tabu search length, and performs iterative search for locally optimized solutions, eliminating repetitive work and avoiding premature entrapment in neighborhood optima. This method combines the advantages of genetic algorithm and tabu search, improving the search efficiency in the population space and optimizing the maximum completion time within a specified number of iterations, making it suitable for guiding flexible operation workshop production.

The invention discloses a multi-objective optimization method based on meta-learning, and the method employs a multi-objective optimization model based on meta-learning, and the model is provided with an inner and outer double-layer circulation structure. In the inner-layer circulation, genetic operators of the meta-learning multi-objective genetic algorithm are subjected to neural networking, so that the genetic operators can perform parameter adjustment in a self-adaptive manner, the genetic operators have the learnable capability and better adapt to the multi-objective optimization problem, and the flexibility of the genetic operators is enhanced; carrying out neural networking on genetic operators of the meta-learning multi-objective genetic algorithm, and selecting a potential next generation population by using a multi-layer perceptron network in a neural network; network parameters of genetic operators of neural networking serve as optimization tasks of outer layer circulation, evaluation indexes of inner layer circulation serve as objective function values of the outer layer circulation, and therefore the purpose that the network parameters in a multi-objective optimization model based on meta-learning can be trained without gradient information is achieved.

The present application provides a method, apparatus, and device for determining task scheduling information based on a genetic algorithm, relating to the fields of computers and task processing technologies. The method includes: obtaining a set of task scheduling information; processing the set of task scheduling information by genetic operators of the genetic algorithm to obtain a task processing result; processing the task processing result to obtain intermediate features of the task scheduling information and the fitness of the task scheduling information; determining genetic operators for the next round of the genetic algorithm according to the intermediate features of each task scheduling information; and processing the set of task scheduling information based on the genetic operators obtained when a preset condition is reached to obtain a set of task scheduling information with higher fitness. The method of the present application can give full play to the characteristics of the genetic algorithm that takes into account both breadth and depth search, automatically adapt the genetic operators used in each round of the genetic algorithm, and improve the accuracy of scheduling task information.

This invention belongs to the field of software refactoring and serverless computing technology, specifically relating to a fine-grained service decomposition and dynamic deployment method for object-oriented applications in edge environments. By constructing a method call graph through static analysis, and for the first time applying a hybrid heuristic algorithm combining particle swarm optimization and genetic operators to this graph partitioning problem, it can intelligently find a decomposition scheme that minimizes cross-function communication overhead while satisfying the limited resource constraints of edge nodes. This fundamentally solves the contradiction between computing resources and communication latency that traditional decomposition methods struggle to balance. Furthermore, by introducing Function-as-a-Service (FaaS) proxies and wrappers, it achieves automated refactoring of the original object-oriented code, enabling seamless migration of tightly coupled local calls to a distributed FaaS environment. This significantly reduces the technical threshold and operational complexity of application migration, achieving fine-grained resource utilization and elastic scaling.