550 results about "Multiobjective optimization problem" patented technology

The invention provides a petrochemical production process anomaly diagnosis and optimization method and system integrated with a knowledge graph, and relates to the technical field of petrochemical production, and the method comprises the steps: collecting technological parameters and equipment state data, carrying out the preprocessing and feature extraction through adaptive wavelet threshold denoising and depth variational mode decomposition, and carrying out the feature extraction; a heterogeneous graph neural network is constructed, a causal relationship graph is constructed in combination with a neural causal discovery network, the causal relationship graph is optimized through knowledge distillation, missing relationships are complemented to obtain a knowledge graph, real-time features are compared with normal feature distribution of a pre-trained contrast diffusion model, and after anomaly detection is triggered, the real-time features are extracted. A causal inference engine is combined with a knowledge graph to analyze an anomaly propagation path, an anomaly root and a prediction trend are determined based on multi-agent reinforcement learning, a multi-objective optimization problem is constructed according to the knowledge graph, an optimization strategy is generated and dynamically adjusted, and an optimization result is evaluated by combining a knowledge distillation hybrid model. And finally, progressive optimization is realized and the knowledge graph is updated.

The invention discloses a multi-user energy storage power station charging and discharging control method and system based on electricity price, and the method comprises the steps: arranging a monitoring terminal at energy storage equipment, and collecting the load data of each energy storage equipment; carrying out data preprocessing on the collected load data; constructing a cascade neural network prediction model, training the model by using the preprocessed load data, and generating a regional total load demand prediction value in a future time period; designing a deep reinforcement learning model DRL by using the regional total load demand predicted value to obtain a preliminary power adjustment amount; designing a target function considering the battery aging cost by using the initial power adjustment amount, and determining an initial value range of a coefficient in the target function of the optimization model according to a constraint condition; and a multi-objective optimization problem is considered, a Pareto optimal solution set is obtained based on the initial value range, and a final power distribution scheme is determined. The method can accurately predict the load, optimize the charging and discharging strategy, reduce the power consumption and operation cost, balance the service life of the battery and the economic benefit, and improve the intelligent and efficient level of the energy storage power station.

The invention discloses a multi-agent cooperation method based on a large language model, and relates to the field of agent cooperation control, and the method comprises the steps: constructing a multi-agent system; establishing a multi-agent coordination resource allocation model; a task allocation problem in the resource allocation model is converted into a multi-objective optimization problem, and optimization objectives comprise the minimum task completion time and the optimal resource utilization rate; and solving a multi-objective optimization problem by using an improved particle swarm optimization (PSO) to obtain an optimal cooperation scheme among the master control agent, the task creation agent, the intention arrangement agent and the at least one professional agent. The improved particle swarm optimization (PSO) evaluates the uncertainty of each candidate solution through multiple times of random sampling, and when the uncertainty exceeds a threshold value, cross validation is performed through a multi-agent voting mechanism. For uncertainty accumulation of a multi-agent system under a complex cognitive task, the efficiency and stability of collaborative decision and task scheduling of the multi-agent system are improved.

The invention relates to a mechanical arm track optimization method based on deep reinforcement learning. The mechanical arm track optimization method comprises the following steps that firstly, kinematics modeling is conducted on a mechanical arm; 2, multi-objective optimization problem modeling is carried out, trajectory planning of the mechanical arm is regarded as a multi-objective optimization problem, and the main objective of optimization comprises three aspects of trajectory precision, trajectory smoothness and energy consumption; and 3, optimizing a PPO reinforcement learning algorithm by using a near-end strategy, and designing a trajectory planning algorithm.

The invention relates to the technical field of river channel desilting treatment, in particular to a dynamic simulation and optimization decision-making-based river channel silt treatment method, which comprises the following steps of: acquiring and treating historical and real-time multi-dimensional environmental data of a to-be-treated river channel and an influence area of the to-be-treated river channel; a river basin process simulation system capable of simulating river water power, sediment transportation, pollutant migration and transformation and ecological response processes is constructed, decision variables forming a river regulation scheme are defined in a parameterized mode, a multi-target optimization problem is defined and set in a formalized mode, a multi-target optimization engine is adopted, and the river regulation scheme is optimized. The method comprises the following steps: embedding a drainage basin process simulation system as a core evaluation module into an optimization iterative loop, implementing an optimization scheme, and continuously monitoring the environmental state change in the implementation process and after the implementation; the method can accurately predict the long-term and short-term influence of different treatment schemes in multiple aspects of hydrodynamic force, silt, pollutant migration and transformation, ecological response and the like, and solves the problem of comprehensive treatment of sludge.

The invention provides a low-orbit satellite resource allocation method and device based on beam and frequency domain collaborative optimization, and the method comprises the steps: dividing the overall bandwidth of a user region into a central sub-band and an edge sub-band through employing a dynamic soft frequency reuse scheme; all ground users are subjected to spatial clustering according to geographic positions and traffic demands, coverage cells are dynamically divided according to clustering results, and balanced service is achieved; on the basis of dynamic cell division, user clusters with high service volume are selected according to priorities, a beam activation set is generated, and a hopping beam matrix is updated; establishing a channel model between a user and a beam and a switching matrix between satellites; constructing a system utility target, and constructing a multi-target optimization problem model by taking the system utility target as a main part and integrating interference, switching overhead and delay penalty; and the multi-dimensional resource collaborative optimization framework based on deep reinforcement learning reconstructs a multi-objective optimization problem model, and outputs a solution of the multi-objective optimization problem model, including an optimal bandwidth and a power allocation result. According to the invention, resource allocation can be carried out on low-orbit satellites.

The invention provides a distributed energy storage output scheduling optimization method, medium and system, and belongs to the technical field of distributed energy storage output scheduling, and the method comprises the steps: firstly, collecting the electrical parameters, load parameters, renewable energy power generation parameters and energy storage equipment parameters of a transformer area, and building an energy storage grid-connected equation set which comprehensively considers the factors; and predicting a load, renewable energy output and energy storage demand curve in a period of time in the future according to historical data. Then, a multi-time-period energy storage scheduling optimization model is constructed, and objectives comprise operation cost minimization, renewable energy consumption maximization and power grid fluctuation minimization; and solving the multi-objective optimization problem by adopting an improved particle swarm optimization algorithm to obtain a plurality of candidate schemes. And finally, comprehensively evaluating the candidate schemes by using a multi-objective decision-making method, and selecting an optimal energy storage scheduling scheme. The technical problem that in the prior art, only parameters of an energy storage system are considered, and influences of power grid input electrical parameters and load parameters are ignored is solved.

The invention provides a ground source heat pump buried pipe system design method based on building load change, comprising the following steps: S1, acquiring geological survey data, meteorological data and building load data, and presetting buried pipe heat exchange system parameters; s2, establishing a multi-physics field coupling numerical model; s3, transient simulation is carried out through a multi-physics field coupling numerical model, and system short-term thermal response characteristic data corresponding to each candidate length are acquired and stored; s4, constructing and training a long-term dynamic performance prediction model through the long and short-term memory network model; and S5, based on a multi-objective optimization algorithm, solving a multi-objective optimization problem, obtaining a group of Pareto optimal buried pipe total length solution sets, and selecting a final buried pipe optimal total length from the optimal solution sets. According to the method, the design precision of the ground source heat pump buried pipe can be remarkably improved, the long-term dynamic prediction capacity is achieved, the reliability, economical efficiency and environment friendliness of long-term operation of a ground source heat pump system can be guaranteed, and the design efficiency and reliability are improved.

The invention discloses a reservoir multi-target optimization scheduling scheme optimization method and system based on multi-party coupling, and belongs to the field of reservoir scheduling. The system comprises the steps that firstly, boundary conditions of a simulation system are determined, and then a reservoir multi-target optimization scheduling model is constructed; and then solving the reservoir multi-objective optimization scheduling model by using a non-dominated sorting genetic algorithm of the reference points, verifying the effectiveness of the solving method by using a standard multi-objective test function, and evaluating and optimizing a reservoir scheduling scheme by using a method of combining a grey target theory, a cumulative foreground theory and a fuzzy mathematical method. According to the system, the multi-objective optimization problem among flood control, power generation and ecology of the reservoir is coordinated, decision support is provided for formulating a reservoir dispatching scheme of multi-objective optimization dispatching of the reservoir, the contradiction among flood control, power generation and downstream river ecology can be effectively coordinated, and the flood control and power generation benefits are increased on the basis of considering the downstream ecological flow demand as much as possible.

The invention provides a multi-agent system initialization method and device for a question and answer large model, and relates to the technical field of question and answer large language model multi-agent systems. The method comprises the following steps: disassembling a user question and answer query task into a plurality of sub-tasks by adopting a pre-constructed question and answer planning agent, generating an agent corresponding to each sub-task, carrying out standardization processing through a formatting agent to obtain a standardized agent corresponding to each sub-task, and carrying out evaluation through observing the agents to obtain feedback information. Generating a candidate agent set based on multiple iterations; a pre-training text encoder is adopted for encoding, and semantic representation and task representation of each agent are generated; task correlation and agent diversity are calculated, and a multi-objective optimization problem is constructed; and obtaining a Pareto optimal frontier team set by adopting a non-dominated sorting algorithm, and selecting an optimal agent team set by selecting agents. According to the invention, the cooperation efficiency of the multi-agent system can be improved.

The invention discloses an intelligent traffic control system and method based on multi-agent near-end strategy optimization, and belongs to the field of intelligent traffic, Internet of Vehicles and deep reinforcement learning. The method comprises the following steps: firstly, constructing a fog-cloud collaborative three-layer architecture, and realizing real-time monitoring and dynamic regulation and control of traffic flow through cloud global decision and local sensing collaboration of a road side unit (RSU); secondly, designing indexes of'road section overlap ratio 'and'road section time overlap ratio', and solving the problem of secondary congestion caused by rerouting; then, a multi-agent near-end strategy optimization (MAPPO) algorithm is adopted, so that the traffic signal lamp is used as an autonomous agent to dynamically adjust the phase, and the limitation of single-point control is broken; and finally, through integrated optimization of rerouting and adaptive signal control, an original multi-objective optimization problem is converted into a layered multi-agent reinforcement learning problem. According to the invention, vehicle driving time and system energy consumption can be effectively reduced, road traffic efficiency is improved, and active avoidance and dynamic alleviation of urban traffic congestion are realized.

The invention discloses an optimal design method for a floating wind power-wave energy multi-energy complementary power generation platform, which comprises the following steps of: constructing an integrated and parameterized system model which is a fully-coupled and parameterized numerical model comprising all key components of the floating wind power-wave energy multi-energy complementary power generation platform; all key design parameters influencing the system performance are set as parameterized variables; establishing a multidisciplinary coupling dynamic simulation model; defining a multi-objective optimization problem including decision variables, objective functions and constraint conditions; the decision variable selects a group of core variables from the parameterized variables as optimization input; and combining the multi-objective optimization problem with a multidisciplinary coupling dynamic simulation model, executing a multi-objective optimization cycle, and generating and deciding a Pareto optimal solution set to obtain typical design schemes with different characteristics. According to the method, the global optimization design of the floating wind power-wave energy multi-energy complementary power generation platform can be realized.

The invention relates to a load regulation and control method and system based on reinforcement learning, and the method comprises the steps: 1, decomposing a multi-time scale optimization problem into a plurality of layers in a source network load storage management and control scene through employing a layered optimization frame, enabling each layer to correspond to a different time scale, and carrying out the optimization; a model prediction control algorithm is adopted in the short-term optimization layer, a mixed integer linear programming algorithm is adopted in the medium-term optimization layer, and a reinforcement learning method is adopted in the long-term optimization layer to solve a multi-objective optimization problem; and 2, long-term load regulation and control decision making: controlling the power grid to make different load regulation and control decisions through an actually adjustable reward mechanism based on reinforcement learning, so that the intelligent power grid load regulation and control system adapts to continuously changing environments and targets, and the flexibility and adaptability of the system are improved. According to the invention, the reliability and stability of power supply can be ensured, fine management and regulation of power loads are realized, and the operation efficiency, reliability and safety of a power system are improved.

The invention discloses a strategy generation method for satellite ground station resource scheduling, and relates to the technical field of spaceflight. Measurement and control tasks of multiple satellites are optimized and scheduled through the improved particle swarm optimization, and an optimal scheduling scheme is generated according to factors such as satellite states, measurement and control resources and task priorities; by monitoring satellite and ground station states and environment changes in real time, a scheduling strategy is dynamically adjusted in combination with an intelligent algorithm and historical experience data, and manual intervention and revision of the strategy are supported at the same time, so that the scheduling scheme has relatively high flexibility and adaptability; the distribution problem of measurement and control resources is converted into a multi-objective optimization problem; and an editable and extensible data reading module is designed, so that a data access mode can be flexibly selected. The automation level of satellite measurement and control scheduling can be effectively improved, and the error rate is reduced; the method has a wide application prospect in multi-satellite networking measurement and control scheduling, and has remarkable technical advantages and economic benefits.

The invention provides a coal seam hydraulic fracturing parameter dynamic optimization method based on deep learning, and belongs to the technical field of coal seam parameter optimization based on computer data processing. The method comprises the following steps: firstly, acquiring a coal seam slice CT image with spatial continuity; secondly, designing a coal seam structure-physical parameter intelligent sensing model, and performing joint prediction on coal seam image semantic recognition and physical property estimation; then, performing coal seam three-dimensional structure reconstruction and regional layering modeling by utilizing a prediction output result, and performing finite element mesh division and material attribute accurate mapping on the basis to construct a coal seam three-dimensional finite element model; and finally, a coal seam hydraulic fracturing parameter multi-objective optimization problem is constructed, a coal seam physical information embedded agent model is designed, the optimization problem is dynamically solved in combination with a multi-objective evolutionary algorithm, and optimal hydraulic fracturing parameters are obtained. On the basis of deep learning, multi-source information, structural parameters and material attributes are fused, and intelligent development of coal seam hydraulic fracturing design is promoted.

The invention relates to a collaborative optimization scheduling method and system based on low-carbon demand response, and the method comprises the following steps: constructing a source-network-load-carbon storage emission flow metering model, and obtaining the carbon emission data of each part during the operation of a power system; obtaining a dynamic carbon emission factor according to the carbon emission data of each part during the operation of the power system, issuing the dynamic carbon emission factor to a user side, guiding a user to change the power utilization habit, and achieving the dynamic transfer of the power utilization load; a stepped carbon transaction mechanism is formulated according to the power utilization load transfer condition of the user, and the user is stimulated to optimize the carbon emission cost through carbon quota distribution and carbon price grading; and constructing a multi-objective optimization problem with minimum carbon emission intensity and minimum total cost of the power system, and solving the multi-objective optimization problem through a near-end strategy optimization PPO algorithm to obtain an optimal scheduling strategy.

The invention relates to an unmanned surface vessel dynamic obstacle avoidance control method based on real-time nonlinear model predictive control, and the method comprises the steps: constructing a dynamic model, a static obstacle model and a dynamic obstacle model of an unmanned surface vessel; based on the dynamic model, the static obstacle model and the dynamic obstacle model, a multi-objective optimization model is constructed, and the multi-objective optimization model takes trajectory tracking cost, obstacle avoidance cost and control smoothing cost as objective functions and takes a motion range, speed limitation, control input limitation and a collision avoidance rule as constraint conditions; and obtaining the state of the unmanned surface vessel and the obstacle state in real time, inputting a multi-target optimization problem for solving, obtaining control input, and completing dynamic obstacle avoidance control of the unmanned surface vessel based on the control input. According to the invention, the adaptability and safety of the unmanned surface vessel in a complex navigation environment can be improved.

The invention relates to a multi-objective collaborative optimization scheduling method for an integrated energy system, and aims at a multi-energy unit optimization problem, namely a multi-parameter optimization problem, in an optimization model, an optimization model parameter initialization method based on improved chaotic mapping is adopted. An initial parameter with a high random average distribution probability is generated to improve the convergence speed of a system optimization process and reduce the probability of falling into a local optimal solution. Meanwhile, aiming at a multi-objective optimization problem, an improved grey wolf algorithm based on a Pareto optimal solution theory is adopted to realize optimal scheduling of the system for an economical efficiency optimal objective and a carbon emission minimum objective. According to the system, a comprehensive energy system architecture including water electrolysis hydrogen production using photovoltaic and wind power generation and hydrogen energy storage, power generation, heating and refrigeration is comprehensively considered, comprehensive utilization and control of various new energy power generation and utilization units are achieved, and carbon emission can be effectively reduced.

The invention discloses a self-adaptive dynamic control method and system for the machining process of a numerical control machine tool, and relates to the field of intelligent control, and the method comprises the steps: collecting machining data in real time through physical and virtual sensors, and constructing a standardized data set after layering preprocessing; a CNN-LSTM hybrid model is utilized to extract spatial-temporal characteristics to realize working condition classification, and an NSGA-II algorithm is combined to solve a multi-objective optimization problem to generate an optimal control parameter solution set; parameters are dynamically adjusted through fuzzy PID, and a GRU model is adopted to predict machining errors for feed-forward compensation, so that closed-loop control of perception-decision-execution-feedback is formed. The system continuously monitors the actual machining deviation, parameters are optimized again when the actual machining deviation exceeds a threshold value, and cooperative improvement of machining precision and efficiency is achieved. The method has the advantages that NSGA-II multi-target optimization, fuzzy PID correction and GRU error prediction compensation are recognized through CNN-LSTM working conditions, closed-loop feedback iteration is combined, the machining precision and efficiency are improved in a balanced mode, the service life of a tool is prolonged, and the method is suitable for complex working conditions.

The invention discloses a self-adaptive scheduling method for a communication unmanned aerial vehicle group, and relates to the field of unmanned aerial vehicle control, and the method comprises the steps: collecting water surface dynamic data in real time through a multi-mode sensing module, and constructing a water surface dynamic digital twinborn model; based on output data of the water surface dynamic digital twinning model, generating an electromagnetic environment simulation map by using a time-space map convolutional network; identifying the edge of a strong interference area in the electromagnetic environment simulation map, deploying an expandable intelligent reflector array, and dynamically optimizing the phase configuration of an intelligent reflector unit through a deep reinforcement learning algorithm; dividing the unmanned aerial vehicle group into a communication subgroup and a monitoring subgroup according to the spatial distribution of the strong interference area; the monitoring subgroup scans the strong interference area, obtains local fluctuation parameters, and feeds back data to the digital twinborn model in real time; and performing formation optimization on the communication subgroups by adopting a distributed model predictive control framework, solving a multi-target optimization problem, and generating an unmanned aerial vehicle position adjustment instruction.

The invention discloses an unmanned aerial vehicle resource allocation method, system and device based on multi-agent reinforcement learning and a medium, and the method comprises the steps: building a multi-target optimization problem of multi-unmanned aerial vehicle resource allocation in a data service scene for the multi-mode unmanned aerial vehicle assisted communication and sensing integrated data service scene; designing an average peak information age, an accumulated data service amount and an unmanned aerial vehicle energy consumption evaluation index; expressing a multi-objective optimization problem as a Markov decision problem, and designing a state space, an action space and a corresponding instant reward function; a multi-agent TD3 algorithm, namely an MAHTD3 algorithm, of a mixed action space is obtained by designing a discrete actor network and a continuous actor network, network parameters of an unmanned aerial vehicle are trained and updated by using the MAHTD3 algorithm according to a Markov decision model, and the unmanned aerial vehicle obtained by training is applied to a data service scene for communication resource allocation; the system, the equipment and the medium are used for implementing the method. According to the method, the data service requirements of the ground users in a random and dynamic communication and sensing integrated scene can be better met.

The invention discloses a multi-objective optimization-based multi-commodity order fulfillment method and device, equipment and a medium, and relates to the technical field of supply chain management. The multi-commodity order fulfillment method comprises the following steps: acquiring order and inventory information including a current order set, a commodity set and a warehouse inventory set; and modeling an order fulfillment problem based on an actual demand of fulfillment of a multi-commodity order in the order and inventory information. The established model is a multi-objective optimization problem containing three objectives. Generating an initial population by adopting a heuristic method based on priority and inventory feasibility; and carrying out iterative optimization on the initial population by adopting a rapid non-dominated sorting genetic algorithm with elitism based on a double-layer elitism strategy to obtain a Pareto non-dominated solution set. And selecting a final order fulfillment scheme from the Pareto non-dominated solution set according to the preference of a decision maker. According to the method, three core operation indexes are synchronously improved under the condition of meeting feasibility constraints such as inventory, processing capability and demand.

A computer implemented method for obtaining a set of Pareto-optimal solutions to a given multi-objective optimization problem with different objectives. The method generates a set of parameters on a classical computation component using the classical optimization algorithm. Based on the set of parameters, a quantum component generates a quantum state by executing a parameterized quantum circuit. A set of basis states is selected from the one quantum state The classical computation component calculates a set of objective values consisting of the objective values for each solution in the set of classical solutions, and a new estimate for the Pareto set of solutions and the corresponding Pareto front by the classical optimizer is generated. The internal state of the classical optimization algorithm is updated based on the current set of solutions and the Pareto set and their corresponding sets of objective values.

The invention discloses a semiconductor film performance cooperative control method and system based on multi-parameter coupling, and belongs to the technical field of semiconductor material manufacturing, and the method specifically comprises the steps: determining a control parameter set containing plasma power density and other parameters, and building a multi-parameter coupling response model; generating sample points by using Latin hypercube sampling, quantifying a parameter interaction effect, and forming a dynamic weight matrix in combination with a graph model; monitoring data such as plasma emission spectrum intensity ratio in real time, comparing the data with a model predicted value, and correcting the model; a multi-objective optimization problem is constructed based on the correction model and the dynamic weight matrix, and optimal parameter configuration is solved by using a conjugate gradient method; alternately applying a high-frequency pulse and a relaxation stage according to the optimal configuration, and modulating the pulse frequency according to a Fibonacci sequence; and finally, based on the substrate temperature, planning a temperature rise path by using an S-shaped curve, superimposing cosine modulation, and introducing oscillation compensation gas to compensate the reactant concentration deviation, thereby realizing accurate optimization of the material preparation process.

The invention discloses an inference task collaborative scheduling system and method oriented to multiple terminals of a swan gap. The inference task collaborative scheduling system comprises a communication module Broker, a system monitoring module SystemProfilter, a scheduling module Scheduler and an inference task execution module Worker. According to the method, a structured resource state vector is constructed based on an NDK native interface so as to comprehensively represent the real-time availability of equipment; a lightweight MQTT protocol is adopted to support low-overhead and high-robustness cross-terminal state synchronization and task distribution; a comprehensive load scoring mechanism fusing task priorities and dynamic weights is designed, a multi-objective optimization problem based on a non-dominated sorting genetic algorithm (NSGA-II) is introduced, task delay is minimized in a combined mode, terminal loads are balanced, and energy consumption is controlled; meanwhile, the execution time delay is efficiently estimated in combination with a proxy model based on linear regression, and the scheduling overhead caused by real reasoning and calling is avoided; the whole architecture is deeply adaptive to a swan-mong system, and is compatible with an Android platform through modular packaging and unified communication interface design, and efficient, self-adaptive and cross-platform collaborative scheduling oriented to a swan-mong multi-terminal reasoning task is realized for the first time.

The invention provides an unmanned aerial vehicle group coverage multi-objective optimization method based on a layered PPO architecture, and belongs to the crossing field of unmanned aerial vehicle auxiliary edge calculation and artificial intelligence optimization. The method comprises the following steps: firstly, constructing a constrained Markov decision process, and modeling a multi-objective optimization problem; secondly, the constraint is converted through a Lagrange duality method; thirdly, a layered PPO framework is designed and optimized; and finally, training a layered PPO algorithm, and gradually introducing constraints according to a progressive relationship in the training process. According to the method, spatial configuration optimization and time scheduling optimization are combined, and traffic data freshness constraint and terminal equipment mobility problems are effectively solved through a layered PPO architecture; a peak AoI index is embedded into a reinforcement learning reward function for the first time, a space-time attention mechanism and a power control strategy are fused, data acquisition frequency and cross-layer interference are dynamically adjusted, an unmanned aerial vehicle action decision process is assisted, and large-scale unmanned aerial vehicle group dynamic cooperation is supported.

The invention belongs to the technical field of radar resource scheduling, and particularly relates to a distributed radar deployment strategy optimization method for TDOA positioning, and the method comprises the specific steps: 1, carrying out the modeling of a distributed radar positioning scene for TDOA positioning; 2, constructing system performance evaluation indexes and an optimization problem, wherein the evaluation indexes comprise a key subarea KS positioning performance evaluation index and a radar reconnaissance area RSA monitoring performance evaluation index; the optimization problem comprises a positioning task-oriented single-target optimization problem and a positioning and monitoring task-considered multi-target optimization problem; and step 3, solving the optimization problem to obtain a distributed radar optimization deployment strategy.

The invention discloses a closed-loop supply chain network optimization method for improving a non-dominated sorting genetic algorithm, and relates to the technical field of calculation methods. The method comprises the following steps: establishing an optimization model; setting parameters; generating an initial solution; a fitness function value is calculated, and rapid non-dominated sorting is carried out; updating the population; judging whether the current generation is greater than the maximum number of iterations; calculating a comprehensive state value; calculating a reward function value; performing action selection and parameter adjustment; and outputting a result. Aiming at the problems that a traditional meta-heuristic algorithm is insufficient in solution set diversity, prone to falling into a local optimal solution, sensitive in parameter and complex in adjustment when solving a multi-objective optimization problem of a closed-loop supply chain network, the method is improved on the basis of an NSGA-II algorithm, an intelligent learning mechanism is introduced to enhance the adaptability and dynamic response capability of the algorithm, the optimization process is accelerated, and the optimization efficiency is improved. Therefore, the overall solution quality is improved, the local optimal risk is reduced, and the calculation efficiency is improved.

The invention discloses a self-adaptive micro-service deployment resource optimization method for cloud edge-end cooperation. The method comprises the following steps: 1) modeling a system model; 2) modeling a communication model; 3) modeling a user demand processing delay module; 4) deploying resource consumption according to a micro-service type required by user demand processing, and modeling a micro-service deployment resource consumption model and a user demand processing resource consumption model; 5) performing formulation definition on the micro-service deployment resource optimization objective function to complete heterogeneous constraint high-quality characterization; 6) aiming at the difference dimension of the multi-objective optimization function, finishing normalization processing of different dimensions; and 7) solving a multi-objective optimization problem by adopting a tabu search method to realize effective utilization of resources in a deployment process.The invention belongs to the technical field of system resource optimization of the Internet of Things, and solves the problem that the Internet of Things in the prior art has defects in the aspects of application space demand coupling, decentralized coordination and service difference.

The invention discloses a motor train unit crew traffic plan optimization compilation method and system based on an ant colony algorithm, and the method comprises the steps: obtaining crew traffic information of a motor train unit in a target region, and constructing an optimization model and constraints thereof based on the crew traffic information; the ant colony algorithm is adopted to solve the optimal solution of the optimization model, the crew in the target area is scheduled based on the crew route planning corresponding to the optimal solution, the ant colony algorithm is utilized to solve the multi-target optimization problem, the robustness and the global search capability are higher, and the optimization efficiency is higher. The method effectively improves the applicability and practicability of the optimization compilation algorithm for the crew traffic plan of the motor train unit, can help crew traffic compilation personnel to rapidly compile an implementation scheme which meets the time-varying demands of the market and customizes crew traffic rules, and reduces the compilation labor intensity of the personnel. And the compilation quality of a complex crew traffic plan with a heavy graphic operation plan can be obviously improved.