90 results about "Algorithm convergence" patented technology

The invention proposes a robot path planning algorithm based on a particle swarm optimization algorithm and a dynamic window method, and relates to the field of control theories and electronic information, and the method comprises the steps: introducing an inertia weight updating strategy based on a state factor, setting adaptive parameters and crossover and mutation operators to improve the global search capability, increase the population diversity, and improve the robot path planning precision. On-line self-adaptive updating of inertia weight and learning factors is realized on line in combination with Q-learning, gene combination modes are enriched through crossover operators, convergence and exploratory performance of the algorithm are improved, an obstacle avoidance strategy of a traditional DWA algorithm is improved, weight parameters of a dynamic window evaluation function are dynamically adjusted according to real-time information of a target and an obstacle, and an obstacle avoidance algorithm is established. According to the method, the global planning is adopted, the path points generated through global planning are adopted as temporary targets, fusion of MOQLCOPSO and the improved DWA algorithm is achieved, local optimum is effectively avoided, the planning efficiency and path safety are improved, and the method is suitable for mobile robot navigation under the complex three-dimensional terrain.

The invention relates to an adaptive Layered Threshold Min-Sum (ALTMS) decoding method for an LDPC (Low Density Parity Check Code) code, which is characterized in that the adaptive Layered Threshold Min-Sum (Amplitude Layered Threshold Min-Sum) decoding method is provided; according to the method, the advantages of a threshold normalized minimum sum decoding algorithm and a threshold offset minimum sum decoding algorithm are combined, correction factors under different iterations are dynamically adjusted, and check node information updating in each iteration is adapted, so that the algorithm convergence speed and the decoding performance are effectively improved. Simulation results show that compared with three threshold minimum sum decoding algorithms under hierarchical scheduling, the ALTMS decoding algorithm provided by the invention realizes certain performance gain, and compared with a fixed parameter minimum sum decoding algorithm, the ALTMS decoding algorithm provided by the invention has better error correction performance.

The invention discloses a task allocation method and system based on an improved whale optimization algorithm framework, relates to the technical field, and is used for optimizing order type adaptive cross-domain traffic control network task allocation and improving the key task response capability of a time-sensitive traffic system. ICWOA initializes a population through Chebyshev mapping, and introduces Levy flight disturbance to enhance the optimization ability; dynamically balancing local and global search by means of adaptive parameters; relieving population diversity attenuation through randomness retention, diversity maintenance and boundary constraint; dimension type pinhole imaging reverse learning is fused to reduce high-dimensional optimization dimension interference. According to the algorithm, the convergence speed and the solving precision are better, sub-second calculation time is kept under different task scales, the task distribution efficiency is improved, and the time-sensitive scene task success rate is remarkably improved. The method solves the problems that an existing algorithm is insufficient in adaptive order type'order application-order sending 'structure and prone to falling into local optimum, population diversity attenuation and calculation speed.

The invention provides an underground cable position detection method and system based on a ground penetrating radar, relates to the technical field of ground penetrating radar imaging, and provides a fast search method of an optimal migration speed combined with F-FK, which does not need to traverse all speed values in a scanning interval, and only needs to determine a small number of key search points based on a specific sequence. The search interval is dynamically reduced by comparing the information entropy values of the search points, the number of times of calculation of the imaging entropy values is greatly reduced, and the cable data processing efficiency is greatly improved; secondly, the algorithm does not need a fixed scanning interval, the search point determined through the golden section proportion can adaptively focus the optimal speed area, even if the cable penetrates through the concrete-wet soil mixed medium section, the speed sudden change point can be accurately captured, the problem that the real speed is missed at the fixed interval is avoided, and finally, the algorithm is high in convergence speed and high in accuracy. Compared with a traditional method, the processing time of GPR data of a long measuring line and a high-density sampled cable is greatly shortened, and the method can be applied to emergency troubleshooting.

The invention discloses a federated learning method and system with a communication efficient mechanism in a heterogeneous environment, and the method comprises the steps: carrying out each round of training by a client through employing a gradient tracking algorithm with a momentum mechanism, obtaining the variable quantity of a trained local model matrix parameter, compressing the variable quantity through employing an error feedback mechanism, and uploading the compressed variable quantity to a central server; the central server calculates the average value of the compressed variable quantity uploaded by the participating clients and sends the average value to all the clients, and the clients and the central server calculate a new round of global model based on the average value; and repeating until the global model converges, and completing the training of federal learning. According to the invention, the calculation amount of the client in federal learning can be reduced, and the communication traffic between the client and the central server is reduced, so that the algorithm convergence time is reduced, and the federal learning efficiency is improved.

A gas temperature field reconstruction method using thermocouple measurement correction is disclosed. First, an Inventor 3D model and an ANSYS temperature field model are established for the gas temperature field. Then, a mapping relationship is established between the control parameters of the gas temperature field and the boundary conditions of the ANSYS temperature field model. Based on the principle of maximizing the influence of fuzzy boundary conditions on the temperature field reconstruction results, experiments are designed for precise boundary conditions and experimental data are obtained. Next, given the boundary conditions of the ANSYS temperature field model, the model is run to obtain the temperature field reconstruction results. A model quality assessment algorithm is run to calculate the fitting degree between the measured curve and the simulation curve, obtaining the algorithm fitting parameters. The algorithm fitting parameters are selected to form the loss function of the gradient descent algorithm. The learning rate, termination condition, and initial iteration parameters are determined. The gradient descent algorithm is run until convergence. Finally, the final iteration parameters are taken as the correction result of the fuzzy boundary conditions of the ANSYS temperature field model. This invention improves the spatiotemporal resolution of the temperature field reconstruction results.

The invention relates to the field of intelligent deployment of farmland equipment, in particular to an intelligent deployment system of intelligent farmland Internet of Things equipment, and the system comprises a farmland environment and crop coupling module; the mapping module is used for establishing mapping relations between crops and environments; a monitoring module; the area and equipment preprocessing module is used for quantitatively evaluating the sensitivity degree of each area of the farmland to environment change; the searching module is used for introducing an optimization searching mechanism based on an RANSAC algorithm and searching an optimal equipment layout scheme; a deployment module; and converting the optimal equipment layout scheme into an executable equipment deployment task column. According to the method, sampling and iteration logic of the RANSAC algorithm is optimized, accurate quantification of perception coverage is performed by combining an equipment vector field, the convergence speed of the algorithm is increased, and it is ensured that the output layout scheme reaches the optimal balance between the coverage effect and resource configuration.

The invention belongs to the technical field of wireless communication, and particularly relates to a post-disaster wireless communication network resource allocation method. The method comprises the following steps: arranging a corresponding strategy network for each agent, training each strategy network by adopting a multi-agent reinforcement learning model comprising each strategy network to obtain an optimal network parameter of each strategy network, and deploying each strategy network configured with the optimal network parameter to the corresponding agent. Therefore, resource allocation in the network is guided; according to the method, algorithm convergence is accelerated, single-step decision delay is reduced, the network throughput is finally improved, and real-time and reliable service quality guarantee is provided for users.

The invention discloses a comprehensive energy scheduling method and system based on genetic variation optimization, and the method comprises the steps: firstly issuing a price strategy through an energy manager based on a Stackelberg game theory, and then responding to a price signal and optimizing the own energy consumption behavior or equipment scheduling through a user aggregator, an energy supplier and an energy storage operator, therefore, a double-layer game feedback cycle taking the price as the core is formed. According to the method, a price initialization strategy inspired by improved load characteristics is designed to perform population initialization, and a more representative initial solution set is constructed, so that the convergence efficiency of the algorithm is expected to be improved from the source; meanwhile, a self-adaptive variation mechanism based on the load change rate is introduced, variation intensity is dynamically adjusted to balance exploration and utilization, and therefore the solving quality and the convergence efficiency of the algorithm are effectively improved, and the method adapts to the complex dynamic characteristics of the multi-main-body energy system.

The invention relates to a production plan Pareto optimization method considering carbon emission, punishment and balance, and belongs to the technical field of computational intelligence and production plan optimization. The method comprises the following steps: constructing a mathematical model of a heterogeneous parallel machine production plan problem considering carbon emission, rolling punishment and load balancing; and a non-dominated sorting hybrid algorithm based on Gaussian learning is proposed to optimize three conflicting targets, i.e., minimization of carbon emission, minimization of task rolling penalty and minimization of load imbalance, in the model. According to the method, a non-dominated sorting hybrid algorithm based on Gaussian learning is extracted and designed from an actual production scene, meanwhile, a Gaussian mixture model training strategy based on a disturbance solution set is designed to be combined with traditional genetic manipulation to generate new offspring individuals, and improvement of algorithm convergence and exploration ability is facilitated. The invention provides a group of high-quality non-dominated solutions for balancing carbon emission, rolling punishment and load balancing, and provides important management enlightenment for making an actual production plan of an enterprise.

The invention relates to the technical field of cloud computing and distributed computing scheduling, in particular to a server-free MapReduce multi-target scheduling optimization method. Comprising the following steps: constructing a directed acyclic graph, coding a resource allocation decision into a solution from a source node to a target node, generating an initial population by adopting a hybrid initialization strategy, evaluating fitness, calculating a congestion distance of a solution in each non-dominated leading edge, and sequentially executing tournament selection, a self-adaptive crossover strategy and a topological repair variation strategy. And periodically executing a solution injection strategy guided by heuristic search, carrying out crowding distance selection, updating the global Pareto optimal solution set, outputting the current global Pareto optimal solution set when the time is up, and otherwise, returning to iteration. The method has the positive effects that the execution cost and the execution time of the MapReduce operation are balanced, the global Pareto optimal solution set quality of the multi-target scheduling scheme is improved, and the convergence speed and the robustness of the algorithm are enhanced.

The invention discloses an improved self-adaptive single-loop method based on a hybrid dynamic mean value conjugation algorithm and application, according to a Karush-Kuhn-Tucker (KKT) optimality condition, a normalized sensitivity vector is calculated based on the hybrid dynamic mean value conjugation algorithm, and then a minimum function target point is deduced. The invention discloses an improved self-adaptive single-loop method, and relates to the field of reliability design optimization. The improved self-adaptive single-loop method comprises the following steps that basic parameters are set and initialized; deriving a normalized steepest descent direction; calculating a static conjugate scalar factor; calculating a dynamic factor value; calculating a dynamic conjugate gradient vector; deriving a normalized sensitivity vector; calculating random variables and random parameters; and solving the equivalent deterministic optimization model until the algorithm converges. Compared with a mainstream double-loop method, a single-loop method and a decoupling method, the improved self-adaptive single-loop method disclosed by the invention has the advantages of high efficiency, high accuracy and high convergence.

This invention discloses a method and system for LEO satellite communication resource allocation based on dynamic weighted graph segmentation, relating to the field of satellite communication technology. Addressing the co-channel interference problem in large-scale MIMO satellite systems, this invention models the resource allocation optimization problem to maximize system sum rate as a partitioning optimization problem on a complete user graph. The weights of the edges in the graph are configured to represent the potential interference cost when two users reuse the same time-frequency resource. The optimization objective is to maximize the sum of edge weights between different partition groups. A dynamic weighted graph segmentation algorithm is employed, utilizing physical layer channel quality index feedback to introduce auxiliary weight variables. The edge weights of the graph are dynamically updated iteratively, and graph segmentation is performed until the algorithm converges. Finally, time-frequency resource allocation is completed based on the graph segmentation results. This invention can achieve near-optimal solution performance with low complexity and fast convergence speed, significantly improving the sum rate in highly dynamic satellite communication environments.

The invention belongs to the technical field of online education, and discloses an exercise recommendation system and method based on hierarchical reinforcement learning and multi-objective optimization, and the system comprises a multi-objective optimization index definition module which is used for defining exercise diversity, exercise novelty, recommendation accuracy, learner satisfaction, learner enthusiasm, learner score and system question setting time; a comprehensive objective function is formed through weighted summation; the three-layer reinforcement learning architecture module comprises a high-level strategy, a middle-level strategy and a low-level strategy which are respectively responsible for global optimization target planning, strategy execution and personalized exercise recommendation; the cognitive diagnosis model and Transform prediction model fusion module is used for evaluating the knowledge mastering condition of the learner and optimizing a recommendation strategy; and the improved Pareto optimization method module is used for processing uncertainty in multi-target optimization, solving target conflicts and improving the convergence speed of an algorithm through dynamic parameter adjustment. According to the invention, appropriate exercises can be accurately recommended according to the learning state of each student.

The invention provides a distributed task unloading and service caching joint optimization method and device. The method comprises the steps of obtaining a computing task of a user associated with each distributed node in a current time slot; determining task processing time delay based on a processing mode of the distributed node on the computing task of the user; constructing an optimization problem which comprises a target function and a plurality of constraint conditions, wherein the target function aims at minimizing the average value of task processing time delays of all distributed nodes completing calculation tasks of all users; each distributed node is regarded as an intelligent agent with independent learning and decision-making functions; and solving the optimization problem by adopting a multi-agent-based deep reinforcement learning algorithm to obtain a task unloading strategy and a service caching strategy which are globally optimal in the current time slot. According to the method, the algorithm convergence speed can be greatly increased, the problem of huge data volume during joint decision making can be solved, and smaller task execution time delay can be obtained.

The invention discloses a large-scale disassembly line balance optimization system and method based on transfer learning. The system comprises a single-target optimization module used for correspondingly storing each single-target problem optimization solution to an independent knowledge base; the multi-objective optimization module is used for performing multi-objective problem optimization by using an evolutionary algorithm; the mapping relation building module is used for building an integer type mapping relation from each single-target problem solution set of the current generation to a multi-target problem solution set when transfer learning is triggered; and the transfer learning execution module is used for obtaining a transfer solution on the multi-target problem according to the multiple single-target problem optimization solutions in the independent knowledge base and the integer type mapping relation, and obtaining an initial population of next-generation multi-target problem optimization according to the transfer solution. Based on a transfer learning mechanism and an evolutionary algorithm, the problem of large-scale disassembly line balance optimization under the conditions of discrete decision space, high variable correlation and complex constraint is effectively solved, algorithm convergence is remarkably accelerated, and the solution set quality is improved.

This invention discloses a learning optimization method and system for solving quadratic programming problems, relating to the intersection of mathematical optimization and artificial intelligence. The method includes: inputting convex quadratic programming parameters, transforming them into a two-block separable form through auxiliary variables and indicator functions; initializing variables and LSTM states; generating iterative approximate solutions using LSTM, and determining whether sufficient descent and gradient conditions are met, triggering a gradient descent guarantee mechanism if these conditions are not satisfied; updating auxiliary variables element-wise, adaptively updating relaxation and penalty parameters, and outputting the original and dual solutions; and training the LSTM offline using identically distributed samples. This invention combines the fast inference of neural networks with the convergence guarantee of traditional algorithms, offering high real-time performance, strong robustness, and low resource consumption. It is suitable for scenarios such as high-frequency trading, autonomous driving, edge AI, and power grid scheduling, efficiently solving dynamic parameter convex quadratic programming problems.

The application provides a continuous micro-fluidic biochip high-level synthesis method considering volume management, is based on a discrete particle swarm algorithm with high solving efficiency, and comprises the following key technologies: 1) a particle coding scheme suitable for the high-level synthesis problem considering volume management is provided; 2) an automatic scheduling scheme generation method is provided, which can accurately generate a corresponding operation scheduling scheme according to the particle coding; and 3) according to the characteristics of the problem, a corresponding particle updating strategy is designed, and the convergence of the algorithm is accelerated. The application can obtain a high-level synthesis scheme satisfying volume constraints and having high quality in a shorter time.

A multi-agent path planning method based on an adaptive meme algorithm addresses the immaturity and slow convergence speed of current multi-agent path planning methods. This method employs a structure combining global optimization and local search, utilizing chromosomes as generated paths in a two-dimensional grid map and optimizing their fitness values ​​through strategies such as "selection" and "crossing." A hill-climbing algorithm is used as a sub-algorithm for local optimization to prevent generated solutions from getting trapped in local optima, thereby improving the accuracy of multi-agent motion control and enhancing the algorithm's convergence speed and optimization efficiency. This solves the problems of slow convergence speed and low path optimization accuracy that are difficult to address in current multi-agent path planning methods. Experimental results show that this method can accurately optimize multi-objective paths for multiple agents, and the average length of each path converges to the globally optimal path length with population iterations, achieving the expected results.

The application provides a kind of electromagnetic sensor resource scheduling method for spectrum monitoring area coverage optimization, comprising the following steps: selecting sensing resource scheduling optimization target, the sensing resource scheduling optimization target includes: sensing resource load and task completion effect;Analysis sensing resource scheduling optimization target, establish multi-objective optimization sensing resource scheduling model;Using the genetic algorithm based on fitness optimization designed according to the characteristics of spectrum monitoring task completion effect, find the optimal sensing resource selection scheme under the conditions of meeting sensing task requirements.In the case of meeting the requirements of spectrum monitoring area coverage, as far as possible to optimize the utilization efficiency of electromagnetic sensing resource, the multi-objective optimization model proposed can be compatible with the cooperative scheduling requirements of other sensing tasks and be easy to extend, and the time-consuming link in the scheduling process is improved to optimize the convergence efficiency of algorithm.Implementation of sensing resource scheduling automation and improve the utilization rate of sensing resource.

The invention discloses an economic dispatching method and device based on a particle swarm optimization algorithm, and the method comprises the steps: operating an improved particle swarm optimization algorithm based on an established multi-stage power system economic dispatching model, updating particle parameters to obtain optimized parameters, and substituting the optimized parameters into a preset violation degree objective function to obtain a particle feasibility judgment result; introducing a power balance equality constraint repair strategy and an inequality repair algorithm constraint repair strategy based on the obtained particle feasibility judgment result, executing an intra-group load distribution strategy, and substituting the current updated and optimized particle parameters into a preset violation degree objective function; and performing loop iteration updating until a preset iteration condition is met, updating the optimal position of the individual, and outputting an optimal solution set. The invention provides a power balance equality constraint and inequality patching algorithm constraint patching strategy which can adapt to the rapid reduction requirement of equality and inequality constraint violation in a large-scale constraint problem, and a high-quality feasible solution is obtained while algorithm convergence is accelerated.

This invention proposes a multi-frequency electromagnetic feature fusion method for abrasive particle identification, involving signal processing, sensor information fusion, and intelligent monitoring of mechanical conditions. It solves the problems of measurement distortion caused by hardware parasitic parameter coupling, the inability to directly solve the highly nonlinear time-harmonic field model analytically, and the poor convergence of conventional optimization algorithms leading to misjudgment of abrasive particle parameters in existing abrasive particle identification methods. This invention constructs a forward analytical model of the time-harmonic field and a nonlinear objective function, and utilizes the Levenberg-Marquardt (LM) optimization algorithm to jointly invert and extract the equivalent diameter, conductivity, and relative permeability of unknown metallic abrasive particles in a multi-dimensional parameter space. This invention can accurately decouple the equivalent diameter, conductivity, and permeability of abrasive particles, accurately distinguish materials, and the LM algorithm converges quickly and does not diverge, achieving millisecond-level inversion, meeting the high precision and real-time requirements of online monitoring of industrial oil.

The application discloses a method for cooperative jamming based on intelligent optimization algorithm, comprising: constructing a jamming decision model, the jamming decision model comprising a cooperative jamming decision matrix, a gain matrix, a jamming matrix, a jamming gain matrix, a jamming bandwidth ratio factor, a jam-to-signal ratio, and a jamming benefit; establishing an objective function and a constraint condition of the jamming decision model according to the jamming benefit; and using an artificial bee colony algorithm to take the jamming benefit as a fitness function and optimize the cooperative jamming decision matrix A. In different complex electromagnetic spectrum environments such as limited spectrum resources and the same frequency band shared by jamming devices and illegal users, the limited jamming resources are reasonably distributed under the condition that the jamming device of the own side can normally communicate, so that greater jamming benefit is achieved; the algorithm convergence speed and search ability are improved, and the method is helpful for making a decision with higher jamming benefit in a shorter time.

The invention relates to the technical field of automobile design, in particular to a multi-element combination-based frame lightweight optimization method, and solves the technical problems that in existing truss-type frame lightweight design, model establishment is complex, and a traditional Harris eagle algorithm is slow in convergence speed and low in precision. Analyzing strength and rigidity performance under bending and torsion working conditions and acquiring inherent frequency; then, response surface optimization is carried out by taking the I-beam as an object, and three groups of candidate solutions are obtained by taking the maximum deformation and the maximum stress as constraints and the mass as a target; optimizing a traditional Harlisia eagle algorithm to obtain an IHHO algorithm, optimizing the cross section size of the I-shaped beam by using the IHHO algorithm to obtain three groups of optimal solutions, and determining final parameters of the beam cross section after comparison; and optimizing other beams of the frame according to the steps, and finally verifying the strength, rigidity and fatigue life of the optimized frame. The method has a good application prospect in the field of design of truss type frames with lightweight requirements.

The invention provides a waveform parameter intelligent decision-making method based on virtual game Nash equilibrium solution. According to the technical scheme, the method comprises the four stages of processes of virtual game bilateral agent framework construction, Markov decision process modeling, Nash equilibrium solution algorithm design and network training optimization. The method comprises the following steps: establishing a double-agent system with a competitive relationship, defining a joint observation space and a strategy space, designing a state space, an action space and a return function, realizing dynamic description of environment interaction, adopting an NFSP network architecture and a mixed reward function, ensuring that a strategy is converged to Nash equilibrium, and implementing network training and optimization in combination with a four-stage training process. And the convergence speed and adaptability of the algorithm are improved. According to the method, reinforcement learning is innovatively used for waveform parameter solving, intelligent optimization of waveform parameters in a complex environment is realized by constructing a virtual game framework and a Nash equilibrium solving algorithm, and the robustness and efficiency of decision making are improved.

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 minimum region method flatness error evaluation algorithm based on improved teaching and learning optimization, and belongs to the technical field of geometric quantity measurement. In order to solve the problems that a traditional minimum region method is slow in convergence and prone to falling into local optimum, a teaching and learning optimization algorithm is improved through a Sine chaotic mapping population, a self-adaptive learning factor and local resampling search, and the method is applied to minimum region evaluation of flatness errors. The method comprises the specific steps of point cloud data preprocessing, Sine chaos population initialization, teaching stage adaptive updating, local resampling, convergence judgment and result output. Experiments show that compared with traditional TLBO, particle swarm optimization and other algorithms, the algorithm is higher in convergence speed and evaluation precision, higher in robustness and suitable for flatness detection scenes in high-precision manufacturing.

The invention relates to the technical field of dual active bridge converter parameter identification, in particular to a dual active bridge converter efficient parameter identification method based on sparse state acquisition and fast state deduction, which comprises the following steps: S101, defining model input; under the conventional sampling configuration of the MCU, the DAB converter collects a system state when a modulation wave and a carrier wave converge each time; s102, carrying out dynamic modeling on the DAB converter; s103, updating the time step of the system state; discretization processing is carried out to obtain a system state evolution relation; s104, performing population initialization; s105, carrying out population target fitness evaluation; s106, carrying out genetic evolution; comprising the steps of pairing selection, crossover and variation, and generating a progeny population with the scale of M; s107, judging a termination condition and outputting an optimal solution; when the fitness of the individual with the optimal target performance tends to converge, the algorithm is terminated; if not, returning to S105 to continue loop iteration; s108, extracting parameters; and after algorithm convergence, the parameter representing the optimal individual on the main target is a final parameter identification result.

This application discloses a multi-strategy adaptive particle swarm optimization method and apparatus for dynamic parking space scheduling, belonging to the field of parking space scheduling technology. The method includes: cleaning flight and parking space data to construct a flight-parking space compatibility matrix; mapping particle positions using continuous real-number encoding and initializing the population through an OBL (Optimal Boundary Learning) strategy; adaptively adjusting the inertia weight AIW based on population diversity and combining it with a random migration RI (Increase in Randomization) strategy to avoid local optima; applying boundary constraints and conflict resolution to particle positions, and using a DA (Data Determination) mechanism based on congestion distance to preserve non-dominated solutions; and outputting the optimal parking space scheduling scheme through multi-attribute decision-making after the iteration meets the termination condition. This application improves the algorithm's convergence speed and global optimization capability through multi-strategy collaborative optimization, reduces the proportion of infeasible solutions, increases parking space utilization and flight docking rate, and balances passenger travel experience with airport operational efficiency.

The present disclosure proposes a network representation learning method across medical data sources, comprising: S1, generating medical network data comprising a source network and a target network; S2, randomly sampling a set number of nodes from the source network and the target network; S3, obtaining an L-layer neural network, and calculating the structural features and expression features of the source network and the target network respectively for each layer, and calculating the distance loss between the network features of the source network and the target network; S4, obtaining the output of the source network in the L-layer neural network, and calculating the loss value according to the classification loss and the distance loss, and updating the parameters of the algorithm according to the back propagation algorithm; S5, repeating steps S2-S4 until the entire algorithm converges, so that the accuracy of the algorithm for disease classification no longer rises within multiple iterations. The present disclosure considers the problem of inconsistent data distribution between different hospital data sources, and through the extraction of network structural information and node attribute information and the minimization of feature distance, the information loss is compensated, which has a wide application space.