317 results about "Multiobjective optimization problem" patented technology

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 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 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 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 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 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 embodiment of the invention discloses a two-phase corundum crushing system optimization method and device based on a composite granulation process, and belongs to the technical field of control or regulation systems. The operation data of the two-phase corundum crushing system are collected in real time, the current optimal energy consumption and output ratio is calculated based on a multi-objective optimization algorithm, crushing parameters of the two-phase corundum crushing system are adjusted, and an optimization model of the two-phase corundum crushing system is constructed. And solving the multi-objective optimization problem of the optimization model by adopting a non-dominated sorting genetic algorithm in the optimization model, and adjusting the roller mill pressure of the two-phase corundum crushing system according to an optimization strategy generated by the balance scheme. According to the embodiment of the invention, the operation data of the crushing system is collected in real time, and the multi-objective optimization algorithm is combined to realize dynamic adjustment and optimization of the crushing parameters, so that the overall performance of the two-phase corundum crushing system is improved, the energy consumption is reduced, the product quality is improved, and the service life of equipment is prolonged.

The invention relates to the technical field of building energy saving and comfort level optimization, in particular to a building energy consumption and comfort level target optimization method and system based on a neural network and a genetic algorithm, by adopting the method provided by the invention, the weight and bias of a BP neural network are optimized through a grey wolf algorithm (GWO), and the prediction precision is improved; on the basis of a GWOBP model, NSGA-II is used for multi-objective optimization, a Pareto optimal solution is obtained, the optimal combination of the near-zero-energy-consumption residential building envelope structure (the types and thicknesses of wall bodies and roof thermal insulation materials, the types of windows and the horizontal sunshade overhanging length) and the number of ventilation times is selected, and the number of ventilation times is calculated. The problem of multi-objective optimization of annual primary energy consumption and annual uncomfortable degree hours of the residential building is solved, and an optimal balance point between the energy consumption and the comfort degree is found.

The invention relates to the technical field of intelligent regulation and control and energy conservation of hospital environment air quality, in particular to a big data management and intelligent evaluation system for the hospital environment air quality. The system comprises a multi-modal data acquisition module, a data processing module, a dynamic transmission modeling module, a dynamic Bayesian network risk modeling module and a prospective risk hedging and energy consumption optimization module. The system calculates a dynamic air transmission coefficient by collecting environment and people flow data, and constructs a dynamic Bayesian network model to calculate a cross-region propagation risk probability; the method is characterized in that when the risk probability exceeds a threshold value, a system actively solves a multi-objective optimization problem with minimization of energy consumption as an objective, and an optimal HVAC control instruction is generated; according to the method, the conversion from lagging evaluation to prospective risk hedging is realized, and the risk can be actively identified and regulated before the pollution exceeds the standard.

The invention relates to the field of marine engine performance monitoring and optimization, and discloses a marine engine adaptive aging prediction method and a fault diagnosis model.The prediction method comprises the following steps that a digital twin model of a marine engine is constructed, and an engine reference health state is generated; acquiring operation parameters of the marine engine in real time, and inputting the operation parameters into the digital twin model to calculate a reference performance index; identifying the degradation offset of the actuating mechanism by comparing the output of the digital twin model with the operation data of the entity engine; constructing a multi-objective optimization problem based on performance indexes of a digital twin model, and generating a Pareto optimal solution set by using an NSGA-III algorithm; and an optimal solution under a specific working condition is selected through a fuzzy comprehensive evaluation method, and degradation state evaluation and optimal control strategy generation are realized. Compared with the prior art, the method can accurately predict the degradation state of the marine engine, reduces the maintenance cost, improves the operation efficiency and reliability, and meets the dynamic optimization requirements of complex working conditions.

The present application relates to the field of power system optimization. Disclosed are a multi-objective particle swarm optimization method and system based on multi-strategy improvement. The method comprises: using a multi-strategy improved multi-objective particle swarm optimization algorithm to solve a multi-objective optimization model of a power supply of a generator state monitoring apparatus; and combining three improved strategies, i.e., adaptive adjustment of an inertia weight, coexistence of a decomposition algorithm and Pareto dominance, and introduction of a mutation factor. The present application overcomes the defects of conventional multi-objective particle swarm algorithms, and achieves a better distribution of a Pareto front, thereby obtaining the best Pareto optimal solution set. The present application solves the problems of conventional multi-objective particle swarm algorithms in solving a multi-objective optimization problem, such as premature convergence to a local non-dominated solution, and sub-optimal distribution of a Pareto front caused by an improper external archive update strategy, thereby improving the operational efficiency and reliability of a power supply system of an apparatus.

The invention discloses an unmanned aerial vehicle trajectory optimization and power distribution method, system and device based on cost evaluation reinforcement learning, and a medium, and the method comprises the steps: constructing an unmanned aerial vehicle data service scene under safety constraints, modeling a multi-objective optimization problem of unmanned aerial vehicle trajectory optimization and power distribution, and designing an unmanned aerial vehicle data service evaluation index; converting a multi-objective optimization problem into a constrained Markov decision problem; respectively designing a state space, an action space, an instant reward function and a corresponding cost function; constructing a cost evaluation critic network parallel to the reward evaluation critic network, training and updating network parameters of the unmanned aerial vehicle by using a cost evaluation SAC algorithm according to a constrained Markov decision problem, and calculating trajectory optimization and power distribution parameters of the unmanned aerial vehicle; the system, the equipment and the medium are used for implementing the method. According to the invention, ground user data service requirements of a data service scene under random and dynamic security constraints are met.

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

The invention discloses a multi-objective reinforcement learning man-machine cooperative assembly task allocation method based on neighborhood parameter migration, and the method comprises the steps: building a mathematical model which aims at minimizing the physiological fatigue accumulated value of a human operator and minimizing the maximum completion time for a multi-objective optimization problem of task allocation in a man-machine cooperative assembly system; a multi-target problem is decomposed into N standard sub-problems by adopting a weighting and decomposition strategy, and training is accelerated through a neighborhood parameter migration strategy; each sub-problem is solved based on a near-end strategy optimization algorithm of an Actor-Critic framework, Gaussian noise is added to an Actor network to simulate environment uncertainty, an action mask mechanism is introduced to process priority constraints of assembly tasks, and it is ensured that a generated task allocation scheme is always feasible. According to the method, the convergence speed and diversity of the Pareto solution set can be remarkably improved, and the assembly efficiency and operator fatigue are effectively balanced.

The invention discloses a multi-site resource and task scheduling system and scheduling method based on an improved DQN, and the system comprises a multi-dimensional model building module which is used for building a multi-dimensional model for dynamic resource scheduling and comprises a resource space formalized definition unit, a task space multi-dimensional modeling unit, a multi-objective optimization problem building unit and a dynamic scheduling process formalized description unit; the improved DQN scheduling model construction module is used for constructing a scheduling model based on an improved DQN and comprises a Markov decision modeling unit, a double-flow Q value estimation network construction unit, a spatial-temporal feature encoder construction unit, a priority experience playback mechanism construction unit and a training optimization strategy unit; according to the method, the efficiency and accuracy of resource scheduling in various fields can be improved, the model algorithm can quickly adapt to new scenes through fine adjustment, repeated parameter adjustment is reduced, and the method has wide application prospects and remarkable economic benefits.

The invention discloses a wind power plant voltage and frequency cooperative tuning control method, and relates to the technical field of tuning control, and the method comprises the steps: obtaining the real-time operation data of a wind power plant, carrying out the preprocessing of the real-time operation data of the wind power plant, carrying out the voltage fluctuation and frequency disturbance prediction through a cellular automaton model in combination with the pre-obtained power grid data, and carrying out the voltage fluctuation and frequency disturbance prediction. Obtaining a prediction state matrix; carrying out multi-objective optimization solution in combination with a pre-constructed multi-objective optimization problem and constraint conditions, and obtaining an optimal droop coefficient vector in combination with a preset rule; and the optimal droop coefficient vector is used as a control reference, and voltage and frequency cooperative tuning is carried out in combination with grid-connected point voltage and frequency obtained in real time and communication network topology data. According to the method, prediction is carried out through the cellular automaton model, the rapid and cooperative regulation capability of the voltage and the frequency of the wind power plant is improved, and therefore the urgent demand of a power system for rapid, accurate and cooperative control under the access of high-proportion new energy is better met.

The invention discloses an industrial internet resource autonomous scheduling method based on data element micro-transaction. The method comprises the following steps: acquiring industrial internet resource energy consumption; constructing a comprehensive energy consumption model; associating the model with a real-time power grid carbon factor; the method comprises the following steps: performing parallel transaction on a double-layer market of function resources and carbon quotas, quoting by a resource party based on a comprehensive energy consumption model, performing transaction according to a carbon quota difference by a task party, combining quoted price and carbon transaction cost, solving a multi-objective optimization problem, selecting a comprehensive cost optimal resource, establishing an elastic scheduling contract between the task and the resource, and executing dynamic re-negotiation. According to the actual consumption and carbon emission closed-loop settlement recorded by the contract, the carbon net balance is subjected to token excitation or purchase compensation; according to the method, the environment cost is internalized into a market economic variable, industrial internet resources meet performance requirements, meanwhile, autonomous evolution towards a low-carbon target is achieved, the resource utilization rate is increased, and economic and environmental benefits of a scheduling system are improved.

The invention provides a bistatic synthetic aperture radar track planning method and system based on structure driving, and relates to the technical field of radar track planning, and the method comprises the steps: building a BiSAR echo model based on a target scattering model; analyzing the imaging characteristics of the target under different configurations, and revealing the constraint relationship between the target structure representation and the configuration; constructing a structure-driven multi-stage trajectory planning model based on the constraint relationship between the target structure representation and the configuration; the trajectory optimization is modeled as a multi-objective optimization problem; through a non-dominated sorting genetic algorithm based on target scattering characteristic driving, a multi-target optimization problem is solved so as to plan a platform track in each stage. According to the method, the limitation of a point target model in an existing trajectory planning method can be broken through, all main lobe observation configurations capable of presenting target structure information are optimized, the retention degree of the target structure information in a BiSAR imaging result is effectively improved, and the interpretability of the image is remarkably enhanced.

The invention discloses a holographic communication system-oriented multi-dimensional interaction quality perception and resource optimization scheduling method, which comprises the following steps of: firstly, constructing a system containing objective performance and subjective experience indexes according to a scene, acquiring network, holographic and user layer data, training a subjective experience representation model, and estimating the subjective experience indexes in real time; estimating indexes at the current moment and the next moment, constructing a design vector, and fusing physical, semantic and cognitive layer multi-source knowledge to obtain an enhanced input vector; reasoning the dynamic weight of the index through a utility function and a fuzzy rule, and calculating an interaction quality comprehensive score; screening indexes to be optimized, converting the indexes to be optimized into a multi-objective optimization problem by taking a comprehensive score reaching the standard as a constraint, and searching a compromise optimal scheme; and finally, converting the scheme into a resource configuration scheme to realize self-adaptive scheduling of network and terminal resources. Accurate mapping of objective indexes and subjective experience is realized, scenes and user requirements are dynamically adapted, and immersion experience and communication performance of holographic communication are improved.