411 results about "Multiobjective optimization problem" patented technology

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 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 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 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 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 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 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.

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 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.

The invention discloses a cooperation system and method between big data and a virtual power plant, the system comprises a plurality of power generation terminals, an energy storage terminal, a power utilization terminal, an energy management cloud platform, a power grid and a power market, the power generation terminals aggregate small solar energy and wind energy dispersed in different spaces to form a unified scheduling energy supply platform; according to the method, the multi-target optimization problem in the system is effectively solved by using the multi-target particle swarm optimization algorithm, and the targets of power fluctuation minimization and income maximization are achieved. According to the method, big data and a virtual power plant are used for cooperation, scheduling optimization is carried out through an improved multi-objective particle swarm optimization algorithm and an objective function with the maximum electric-interference double-optimal metric value, and under the conditions of meeting power balance constraints, energy storage capacity constraints and the like, the optimal scheduling strategy is searched, electric power fluctuation is balanced, and multiple objectives of income are achieved; and finally, outputting a cooperative scheduling scheme satisfying multi-objective optimization, and realizing power fluctuation minimization and income maximization.

The invention provides a dynamic multi-objective optimization method and device based on CITI, and relates to the technical field of artificial intelligence. The method comprises the following steps: randomly generating an initial population for a dynamic multi-objective optimization problem, and optimizing the initial population by adopting a basis algorithm; detecting whether the environment is changed or not by adopting a population evaluation mechanism; and for the change, an initial population of a new environment is generated by adopting a dynamic multi-objective optimization algorithm CITI of a migration and interpolation strategy driven by cluster individual change, and then a control strategy is obtained. The sub-populations divided based on the individual change characteristics are more beneficial to strengthening the search task of the individuals in the cluster, so that other individuals are better guided to evolve towards a new environment. The NNI interpolation strategy can utilize adjacent point information to approximately fit geometric features of a new environment PF to improve the distributivity. Learning excellent non-dominated solutions of different historical populations helps to enhance the reliability of knowledge migration and make DMOA applicable to different types of changes.

The invention discloses a cloud side-end collaborative architecture and task unloading method for an airport apron intelligent monitoring system, and relates to the technical field of cloud side-end collaborative computing. Comprising a terminal layer, an edge server layer and a cloud server layer, the task unloading method of the system under the cloud side-end collaborative architecture is designed and comprises the steps that a task model, a time delay model, an energy consumption model and an accuracy rate model of the system are constructed, and a multi-objective optimization problem of time delay-energy consumption-accuracy rate is formed; modeling an optimization problem into a Markov decision process, and designing a state space, an action space and a reward function required by deep reinforcement learning; and designing a task unloading method based on multi-agent deep reinforcement learning, and finding an optimal task unloading strategy of the system. According to the method, the computing tasks can be dynamically, scientifically and reasonably distributed and cooperatively scheduled among the terminal, the edge and the cloud, so that the time delay, the energy consumption and the accuracy are comprehensively optimized, and the overall performance of the system is improved.

The invention belongs to the technical field of advanced manufacturing and intelligent design, and provides a topological optimization method and system for a liquid cooling plate of a lithium ion battery pack, and the method comprises the steps: building a parameterized simulation model of a battery pack liquid cooling system, constructing a multi-dimensional design variable space of the liquid cooling plate, and formally defining a multi-objective optimization problem; on the basis of the initial training data set, independently constructing a probabilistic agent model capable of predicting a target value and quantifying uncertainty for each optimization target function; performing quantum behavior enhanced multi-universe optimizer iterative optimization on the constructed agent model, selecting new sample points from a multi-dimensional design variable space of the liquid cooling plate according to a set criterion after a set number of iterations is completed, performing high-fidelity CFD simulation to obtain real data, supplementing the real data to a data set, and updating or reconstructing the agent model; and judging whether an iterative optimization condition is met or not, and if so, screening and outputting a final Pareto optimal solution set from all samples subjected to high-fidelity simulation verification. And efficient multi-objective optimal design of the liquid cooling plate is realized.

The invention provides an exercise load multi-objective optimization method based on an anti-gravity rehabilitation treadmill. The exercise load multi-objective optimization method comprises the steps of obtaining gait kinematics data, plantar pressure distribution data, electromyographic signal data and pain scores of lower limbs of a patient; based on the data, a gait asymmetry index and a myoelectricity activation sequence consistency index are screened out, the gait asymmetry index and the myoelectricity activation sequence consistency index are combined with pain scores to serve as key features, and a function grading model is constructed to obtain function grades; establishing a multi-objective optimization problem by taking minimization of a pain score, maximization of joint activity and electromyographic activation sequence consistency and minimization of muscle fatigue as objectives and taking non-overrun of articular cartilage contact stress as a constraint; an NSGA-III algorithm is adopted to solve the problem, and a Pareto optimal solution set of the weight reduction proportion and the speed of the treadmill is obtained; through a PPO reinforcement learning strategy network, taking the real-time state of a patient as input, and selecting and dynamically adjusting treadmill parameters in a Pareto solution set; and issuing the adjusted parameters to a treadmill execution mechanism in real time.

The invention discloses a trusted cooperative task unloading method in vehicle-mounted edge computing. The method comprises the following steps: 1) constructing an Internet of Vehicles environment model comprising a vehicle and a roadside unit; and 2) evaluating the cooperation reliability of the neighbor RSU according to the space environment and the historical reputation by using the space-time trust graph neural network. And 3) predicting a vehicle track and a mean value and a variance of the staying time of the vehicle in a certain RSU coverage range by using a memory enhanced movement prediction model. And 4) modeling a task unloading problem as a multi-objective optimization problem of optimizing delay, energy consumption and task success rate. 5) converting the multi-objective optimization model into a Markov decision process, defining a state space, an action space and a reward function, and actively avoiding a task timeout risk by introducing a dynamic security buffer, and 6) solving the Markov decision model by adopting a reinforcement learning algorithm, and determining an optimal task unloading scheme.

The invention discloses a vector evaluation gradient multi-objective optimization algorithm for data center cooling system decision making. The overall thought comprises the steps that multi-source sensor data related to a cooling system is obtained according to data center machine room operation logic; performing corresponding preprocessing on data acquired by the sensor to obtain an overall sample; designing a vector evaluation gradient multi-objective optimization algorithm oriented to a data center cooling system decision; the optimization algorithm comprises the definition of a multi-objective optimization problem, uniformly distributed initial populations, population division based on vector evaluation and gradient updating. The optimal decision of the air supply amount and the air supply temperature of the cooling equipment in the cooling system is mainly obtained through an optimization algorithm, and optimal decision parameters are sent to the cooling equipment so as to achieve optimal control over the cooling equipment of the data center. The algorithm provided by the invention ensures that the decision strategy of the cooling equipment can be reasonably and quickly given, so that the appropriate temperature of the machine room and the energy conservation of the cooling equipment are simultaneously realized to meet the requirement of efficient operation of the data center. The energy consumption of the data center can be reduced, and the method is of great significance in promoting green and sustainable development of the data center.

The invention relates to the technical field of bridge construction, in particular to a multi-span continuous rigid frame bridge closure jacking force optimization method and system.The method comprises the steps that firstly, construction steps corresponding to a closure sequence scheme are determined according to the closure sequence scheme; secondly, performing soil constraint degradation simulation according to a closure sequence scheme and the corresponding construction steps, and establishing a finite element model of a full bridge; then, parameters corresponding to the unit jacking force applied to each closure gap and parameters corresponding to the unit jacking force not applied to each closure gap are calculated based on the finite element model, and a to-be-solved function is constructed based on the first parameters and the second parameters; finally, a fuzzy mathematical solution is adopted, the multi-target optimization problem corresponding to the to-be-solved function is converted into a single-target linear programming problem, and the optimal solution of the jacking force is obtained.According to the scheme, the soil constraint degradation effect is comprehensively simulated, meanwhile, the multi-target problem in the deepwater environment is converted into the single-target linear programming problem, and the optimal solution of the jacking force is obtained. The solving process is simplified, and the optimal solution of the jacking force can be accurately solved.

The invention discloses a quadruped robot landing planning method based on scene decoupling and risk avoidance. The quadruped robot landing planning method aims at solving the problems that in the prior art, perception is not precise in a complex environment, and the obstacle avoidance capacity is insufficient. The method comprises the following steps: acquiring and fusing multi-source sensor data, and segmenting an original point cloud; generating a scene decoupling elevation map of a multi-layer structure by using the segmentation point cloud; constructing a multi-objective optimization problem taking risk avoidance as a core based on the elevation map, wherein the multi-objective optimization problem comprises a comprehensive cost function and a strict obstacle avoidance constraint; utilizing a reaction formula adjusting module of a capturable region theory to cope with a dynamic instability risk; and solving the multi-target and multi-constraint optimization problem in real time by adopting a hierarchical solving strategy, and finally generating an optimal foot end drop point considering safety and stability. According to the method, the terrain adaptability, the motion stability and the decision intelligence of the quadruped robot in an unstructured environment are remarkably improved through fine decoupling of a scene and quantitative avoidance of multi-source risks.

The invention provides a logistics pickup and delivery path optimization method and system, and the method comprises the steps: constructing a complete graph according to the obtained customer order data and an arc set between each pair of customer nodes, modeling a vehicle path problem VRPSPDT corresponding to the complete graph into a multi-target optimization problem, decomposing the multi-target optimization problem into a plurality of sub-problems, and carrying out the modeling of the sub-problems, and the plurality of sub-problems are iteratively solved for many times until a logistics goods taking and delivering path corresponding to the customer order data is obtained. In the nth iteration, operation of an evolutionary operator is carried out on current iteration information, a filial generation solution is obtained, the evolutionary operator comprises sequential crossover and random variation, the filial generation solution is input into a pre-trained deep reinforcement learning DRL model, the filial generation solution is optimized based on the DRL model, an improved solution is obtained, and the current population is updated based on the improved solution, and updating the current reference point according to the sub-target value of the filial generation solution. The efficiency, the accuracy and the reliability of determining the logistics goods taking and delivering path are improved.