222 results about "Multiobjective optimization problem" patented technology

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 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 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 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 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 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 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.

The invention discloses a rolling time domain reactive power optimization method and system containing a distributed photovoltaic power distribution network, and the method comprises the steps: firstly, employing a probabilistic prediction model to carry out the prediction of photovoltaic active power, and obtaining a future output expected value and an uncertainty interval; secondly, the prediction result is substituted into a multi-objective optimization problem with line loss, voltage deviation and reactive compensation as optimization objectives, and opportunity constraints are established by using the uncertainty interval so as to ensure the safety margin of the power grid voltage; and finally, performing rolling solution on the problem by adopting a multi-objective evolutionary algorithm guided by a power grid sensitivity index to obtain an optimal reactive compensation scheme. According to the method, through a closed-loop rolling mechanism of prediction, modeling and solving, prospective, collaborative and robust control of reactive power resources is realized, and the stability and economical efficiency of operation of the power distribution network under high-proportion photovoltaic access are improved.

The invention relates to the technical field of adaptive control, and discloses a heterogeneous energy resource multi-agent aggregation and adaptive control method, which comprises the following steps of: firstly, acquiring state information of energy equipment in real time through a sensor; based on the equipment state information, a multi-objective optimization problem is constructed, and the optimization objective is to maximize the comprehensive power generation efficiency of the system, minimize the operation cost and maintain load balance; according to the method, through a Nash equilibrium model based on the game theory, a collaboration strategy between agents is constructed, the agents are adjusted according to local information, and finally global optimal power output is achieved; and then performing adaptive control on each device, processing external disturbance by using a robust control algorithm, and enabling the system to adjust device operation parameters in real time when the environment changes. By introducing a self-adaptive dynamic optimization strategy based on an augmented Lagrangian multiplier method, real-time optimal scheduling of a multi-source system under a dynamic constraint condition is realized, and the system can still keep stable operation and global optimal control effects in a complex disturbance environment.

The invention relates to the technical field of extremely-small curved surface structures, in particular to an optimal design method, system and equipment for an extremely-small curved surface structure and a storage medium. The minimum curved surface structure optimization design method comprises the steps that a multi-objective optimization problem is determined according to design variables and optimization objectives of a three-period minimum curved surface lattice structure; constructing an approximate function relationship between the optimization target and the design variable by utilizing a self-adaptive multi-target engineering optimization method of a Kriging agent model; latin hypercube sampling and a sequential quadratic programming optimization algorithm are combined to carry out optimization design on the three-period minimal curved surface lattice structure. According to the design method, the light / bearing / wave-absorbing minimum curved surface lattice sandwich structure with good structure-performance dual characteristics can be designed, the wave-absorbing structure is filled with the minimum curved surface lattice structure in a shape follow-up mode, design and manufacturing of a wave-absorbing and bearing integrated structure are achieved, and rapid iteration and performance improvement of design of the wave-absorbing and bearing structure are supported.

The invention discloses a multi-objective optimization method and system based on building full life cycle energy consumption prediction, and belongs to the technical field of building energy saving and intelligent construction. Comprising the steps of constructing a physical-data fused building digital twin model, executing multi-time scale energy consumption prediction, solving a multi-objective optimization problem and recommending a dynamic optimal strategy. A physical information neural network is adopted to embed the building heat conduction equation into a neural network loss function, and deep fusion modeling of physical constraint and monitoring data is achieved. And a layered prediction architecture is designed, and different prediction models are adopted for a short term, a middle term and a long term and fusion output is carried out. And solving three-objective optimization of energy consumption, comfort and cost by adopting a non-dominated sorting genetic algorithm to obtain a Pareto optimal solution set. And screening an optimal scheme from the solution set for recommendation in combination with the situation information and the user preference. According to the method, the energy consumption prediction precision and the physical interpretability are effectively improved, and multi-target collaborative optimization and personalized strategy recommendation are realized.

The invention discloses an agent path planning method based on dual-archive multi-target Harris eagle optimization. The method comprises the following steps: firstly, constructing a three-dimensional environment model and defining a multi-target optimization problem; then, the paths are coded, populations are initialized, and a double-archive mechanism of convergence archives and diversity archives is introduced innovatively; in iterative optimization, dynamically switching a global exploration stage and a local development stage according to an improved nonlinear energy model, respectively guiding search by utilizing double archives, and enhancing the optimization capability in combination with Levy flight, Cauchy variation and a simulated annealing mechanism; and finally, selecting a path from the Pareto optimal solution set, smoothing and verifying the path, and outputting the path. According to the invention, through the dual-archive cooperation strategy and the improved Harris eagle optimization algorithm, the convergence speed and the diversity of solution sets are effectively balanced, the quality, efficiency and robustness of path planning in a complex constraint environment are significantly improved, and the method can be widely applied to autonomous navigation tasks of intelligent agents such as unmanned aerial vehicles, automatic guided vehicles and rescue robots.

The invention discloses a motor multi-objective optimization design method and system based on a Kriging agent model. Comprising the following steps: constructing an initial data set through an experimental design method and finite element simulation calculation, so as to train an initial Kriging agent model which takes a motor design variable combination as an input variable and takes motor performance as an output response; the proxy model serves as a target function, a multi-target optimization problem is solved, the proxy model is updated, and an updating strategy is as follows: after each round of optimization is finished, a high-error solution in a current Pareto solution set is screened based on a Kriging model prediction mean square error, a finite element response of the high-error solution is obtained and supplemented to a data set, and the proxy model is updated and trained; and repeating the process until the optimization result converges. According to the method, high-uncertainty region samples are selectively supplemented, the calculation cost is remarkably reduced while the local prediction precision of the proxy model at the Pareto leading edge is improved, and the method has good practicability and economical efficiency.

The invention discloses an intelligent water affair cooperative control method and system based on a space-time diagram neural network. The method comprises the following steps: firstly, constructing a static graph dynamic association logic diagram containing a pipe network physical topology; and then, inputting the multi-view image structure into a space-time diagram neural network, accurately predicting the liquid level and the flow of future key nodes, and particularly predicting the possible overflow risk. And finally, on the basis of the prediction result, constructing and solving a multi-objective optimization problem including multiple conflict objectives of minimizing pump station energy consumption, minimizing pipe network overflow quantity, balancing water inlet load of a sewage treatment plant and the like. Through the optimization, the system can generate a set of global cooperative control instructions for regulation and control facilities such as pumps and gates. Compared with the prior art, the method has the advantages that the prediction precision of the flood peak flow of the pipe network during rainfall can be remarkably improved, the mode conversion from passive first-aid repair to active regulation and storage is realized, urban waterlogging and sewage overflow events are effectively reduced, and stable operation of a downstream sewage treatment plant is guaranteed.

The invention relates to an aiming point dynamic stabilization and automatic calibration method fusing inertial measurement unit data, and belongs to the technical field of aiming equipment dynamic control. The method comprises the following steps: acquiring attitude data of an inertial measurement unit, and constructing a multi-source data matrix in combination with real-time position data of an aiming point and environmental interference data; after time sequence synchronization and drift suppression are carried out on multi-source data, associated feature vectors are extracted through multi-modal feature fusion; inputting a self-adaptive extended state observer to estimate total disturbance and generate an anti-interference compensation amount, constructing a kinetic model in combination with a nonlinear model prediction controller, solving a multi-objective optimization problem, and generating an attitude compensation control sequence to realize dynamic stability of an aiming point; and finally, aiming deviation is calculated in real time to trigger double-closed-loop calibration, an inner ring corrects the drift error of the inertial measurement unit, and an outer ring optimizes parameters of the observer and the controller. According to the invention, environmental interference and inertial drift are effectively suppressed, the dynamic aiming stability is improved, and the long-term aiming reliability is guaranteed through closed-loop calibration.

The invention relates to the technical field of agricultural environmental engineering and control, and discloses an agricultural non-point source pollution multistage control planning system and method based on a discussion hall architecture, and the system comprises an organization-level module, an adaptation-level module, a coordination-level module, an operation control-level module, a controlled-level module, a comprehensive integration discussion hall module, and an open external interface module. The organization level uses deep learning and causal inference to generate a full-watershed strategy; the discussion hall module fuses expert experience conversion constraints and carries out case migration; the adaptation level utilizes a recursive algorithm to dynamically correct model parameters; the coordination level adopts a hybrid optimization algorithm to solve a multi-objective optimization problem; the operation control level is combined with a feedforward feedback mechanism to generate an instruction; the controlled level executes actions and collects feedback; and the interface module is responsible for security encryption and verification of data interaction. According to the method, the expert intelligence of the discussion hall and the five-level hierarchical architecture are fused, qualitative and quantitative combination and full-basin dynamic collaborative optimization are realized, and the management and control precision and the response speed are improved.

The invention discloses a deep correlation meteorological computing power center load prediction method and system based on an artificial intelligence distillation algorithm, and belongs to the technical field of computing power center energy efficiency management. Comprising the following steps: processing multi-source data such as weather and business through a multi-modal spatial-temporal feature fusion network, and generating a unified spatial-temporal fusion feature tensor; a space-time Transform is used as a teacher model, lightweight ConvLSTM is used as a student model, training is carried out by introducing a distillation loss function of attention weight, and a high-precision and low-delay load prediction model is obtained; on the basis of a prediction result, combining clean energy and a cooling system model, constructing and solving a multi-objective optimization problem taking the minimization of the total operation cost and the maximization of the green power consumption as objectives, and generating a collaborative scheduling curve; and after deployment, the model is dynamically updated through real-time feedback and self-adaptive online distillation to form intelligent operation and maintenance. According to the method, the balance between prediction precision and speed is realized, the energy efficiency and the green power consumption rate are improved, and the method has long-term self-adaptive capability.

The invention provides a superconducting magnet optimization method based on multi-modal deep learning and reverse design, and the method comprises the steps: obtaining design parameters and corresponding performance indexes of a superconducting magnet, and constructing a multi-modal training database; based on the database, an auto-encoder network structure is constructed and is used for learning and mapping multi-modal data. A trained network structure is obtained by training an auto-encoder network, and a conditional reverse design generator is trained by using an encoder of the network. And carrying out joint optimization training on the decoder through a physical consistency rapid calibrator. After a user inputs a target performance index, the conditional reverse design generator generates a plurality of candidate potential space vectors, and the decoder converts the vectors into specific superconducting magnet design parameters. According to the method, through a reverse design mode, the problem of multi-objective optimization in traditional forward design is avoided, superconducting magnet design meeting performance requirements can be rapidly generated, and the method has high design efficiency and precision.

The invention discloses a hydroelectric generating set parameter dynamic multi-objective optimization method and system oriented to variable working conditions. The method comprises the following steps: establishing a nonlinear simulation model; establishing a multi-dimensional performance evaluation system; constructing an off-line / on-line fusion optimization system architecture; for each new working condition point, generating a high-quality initial population based on the Pareto solution set of the nearest neighbor optimized working condition point; solving a Pareto optimal solution set of the working condition points; performing incremental training on the machine learning model to form an offline knowledge base; real-time working condition data are collected in a fixed period, and future water head and power are predicted; based on a prediction result, adopting a dual warm start mechanism to solve a dynamic multi-objective optimization problem at the current moment and the future moment in parallel; and monitoring a consistency index between the online optimization solution set and the prediction solution set in real time, and triggering incremental updating of the model when the index continuously exceeds the limit. According to the method, all-working-condition continuous self-adaptive optimization and prospective regulation and control of the hydroelectric generating set can be realized.

The invention discloses a power plant wastewater treatment intelligent control method based on digital twinning, and particularly relates to the field of prediction.The power plant wastewater treatment intelligent control method comprises the steps that S1, a whole-process digital twinning body mapping a power plant wastewater treatment physical system is constructed, and multi-dimensional core parameters are collected in real time; s3, training a BP neural network model optimized by a genetic algorithm to realize prediction of water quality parameters; s4, solving a multi-objective optimization problem through an NSGA-III algorithm, and generating an optimal process parameter formula to support a decision; s6, dynamically iterating the digital twin and the prediction model based on the actual operation data, and continuously improving the prediction and optimization reliability; according to the method, through construction of the whole-process digital twinborn body and the improved LSTM time sequence feature extraction algorithm, accurate prediction of the water quality change of the power plant wastewater is realized, the problem of prediction lag in the prior art is solved, sufficient data support is provided for process adaptation, the risk that effluent exceeds the standard is effectively reduced, and stable operation of a treatment system is guaranteed.

The invention relates to the technical field of bridge engineering, and discloses a cable force optimization and linear control method in the construction process of a cable-stayed bridge, and the method comprises the steps: constructing a finite element model capable of reflecting the physical characteristics of a structure and a construction risk potential model capable of quantifying the construction constraint; on the basis of weather forecast, structure disturbance potential energy caused by environmental factors in the bridge in a future control period is predicted prospectively; constructing a multi-objective optimization problem taking energy hedging residue minimization and total construction risk potential minimization as objectives, and solving to obtain a time-sequenced cable force adjustment operation sequence; after the cable force adjustment operation is executed on site, the actual response of the structure is monitored through the sensor network. According to the method, future environment disturbance can be resisted prospectively, construction management constraints are brought into optimization decision, adaptive evolution and emerging risk identification of the model are realized through closed-loop feedback, and the accuracy, safety and intelligent level of cable-stayed bridge construction control are improved.

The invention provides an environment and equipment cluster regulation and control method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining a physiological state vector based on physiological state data; performing empirical mode decomposition on the physiological state vector, and constructing a biological rhythm digital twinborn model; based on the physiological state data, the environmental parameters and the biological rhythm digital twinborn model, using a PC algorithm of a Pearl causal inference framework to discover a causal structure between the environmental parameters and the physiological state data, and constructing an environmental physiological causal relationship model; constructing a multi-objective optimization problem according to the biological rhythm digital twinborn model and the environmental physiological causal relationship model; the multi-objective optimization problem is converted into a quadratic unconstrained binary optimization function, and a Pareto optimal solution set is obtained for the quadratic unconstrained binary optimization function according to a quantum optimization algorithm; and regulating and controlling the environment and equipment cluster based on the Pareto optimal solution set and the biological rhythm digital twinborn model.

The invention provides a multi-target path planning method and system based on distribution, and the method comprises the steps: converting a to-be-laid physical space of a cable into a geometric space, and constructing a space model; converting a multi-objective optimization problem into a general mathematical model; constructing a cable path planning model by taking the cable weight, the bundling ratio and the laying path density as optimization targets; and introducing an economic index, constructing a model in combination with an objective function, and realizing distributed planning of a cable path. The method has the advantages of being accurate in applicable scene, high in model precision, high in planning efficiency and high in multi-objective optimization capability.

This application provides a recommendation method, apparatus, and computer-readable storage medium based on multi-objective optimization. The method includes: acquiring user rating information, including ratings from multiple users for multiple items; clustering users into different clusters using K-means clustering based on the user rating information; predicting the predicted ratings of users for items in each cluster based on a probability propagation algorithm with improved resource allocation; generating an initial population in each cluster based on the predicted ratings of users for items; and solving a multi-objective optimization problem based on an accuracy objective function and a diversity objective function using an evolutionary strategy guided by accuracy-preference users, thereby obtaining the target recommendation result for each cluster. By using the above method to guide the evolutionary direction of the algorithm through user preferences, the target recommendation result can be made more biased towards the accuracy objective function while taking into account diversity.

The invention discloses a deep and far sea platform multi-target form generation and optimization method based on a geometric agent model, and belongs to the cross technical field of ocean engineering and computer aided design. According to the method, a parameterized voxel growth model based on an offshore storm environment interference field is constructed, the digital generation logic of a platform entity structure is established, and the modular assembly process is simulated; constructing a geometric agent construction process and a final general agent function based on a rapid mapping relation from the three-dimensional voxel form set G to the physical performance index set P; constructing a multi-objective optimization problem based on an NSGA-II algorithm, and aiming at searching a Pareto optimal solution set in a variable space; and finally, automatically outputting final three-dimensional form model data and a module assembling sequence according to the specific environment weight of the target sea area. According to the method, the mapping relation between the physical performance and the geometric characteristics is constructed, expensive physical field simulation is replaced with geometric calculation, and rapid and intelligent generation of the floating type platform form is achieved.