56 results about "Multi objective model" patented technology

Provided is a framework for fine-tuning pre-trained sequence processing models to human preferences and / or other objective(s). Instead of using reinforcement learning to fine-tune the LLM parameters towards the human preferences, example systems take a Bayesian approach which can preserve the learned prediction distributions of the pre-trained model, but adds explicit sequential preference tuned predictions in a multi-objective model fine-tuning training setup. The model can be tuned to predict posterior token probabilities conditioned on the human preferences.

The invention discloses a flexible control method for new energy grid connection, and relates to the technical field of new energy grid connection control. The method comprises the steps of obtaining grid-connected point electrical parameters through a self-measurement mode or a communication interface mode, performing harmonic feature extraction and compensation based on a deep learning network, and adopting a state estimation method to fuse multi-source data to estimate power grid parameters; and constructing a multi-objective optimization function including voltage quality, a power factor and harmonic suppression, dynamically adjusting a weight coefficient of the optimization function by using a swarm intelligence algorithm, and realizing inverter switching sequence optimization through multi-objective model prediction control. Through a dual-mode communication redundancy mechanism and local intelligent control, while a communication function of a traditional main link is reserved, a standby wireless communication link is introduced to realize real-time backup transmission of a control instruction, and through a local autonomous decision-making capability, the problem of control failure caused by a communication problem in a traditional scheme is fundamentally solved.

The invention relates to a multi-distribution-center open type vehicle path intelligent optimization method and system, and belongs to the technical field of logistics distribution optimization and intelligent transportation, and the method comprises the steps: firstly obtaining the input data of a multi-distribution-center vehicle path optimization problem, selecting a multi-distribution-center processing strategy according to the problem scale and constraint conditions, and carrying out the optimization of the multi-distribution-center vehicle path; a vehicle path optimization model is constructed, the vehicle path optimization model comprises a single-target model and a multi-target model, a multi-algorithm collaborative optimization framework is adopted for solving, and the multi-algorithm collaborative optimization framework comprises an ant colony algorithm, a variable neighborhood search optimization ant colony algorithm and a non-dominated sorting genetic algorithm; and outputting an optimal vehicle path scheme, wherein the optimal vehicle path scheme comprises a distribution route, a distribution sequence and a corresponding objective function value of each vehicle. According to the method, strategy adaptive selection and algorithm collaborative optimization are carried out, global exploration, local optimization and multi-target equalization are carried out by combining the advantages of the ant colony algorithm, the variable neighborhood search algorithm and the non-dominated sorting genetic algorithm, and the method is good in reproducibility, high in scene adaptability and high in decision support capability.

The invention discloses a county planting and breeding space collaborative optimization method, which comprises the following steps of: constructing an economic-ecological-planting and breeding coupled multi-target model, setting dynamic constraint conditions such as water resources and cultivated land, generating a Pareto non-inferior solution set by combining an NSGA-III algorithm, performing optimization such as chromosome coding and fitness calculation, and simulating a future land utilization pattern by utilizing a PLUS model. The method integrates multiple benefits, breaks through the limitation of a single target, and overcomes the defect that a traditional method is difficult to balance multiple targets; an optimization algorithm and a simulation model are deeply fused, a historical data trend is mined, and the problem of simulation and optimization disjunction is solved; through a fine constraint mechanism and optimization design, the method adapts to resource changes, improves accuracy, and provides scientific support for county planting and breeding space layout.

A method of maximizing coverage and minimizing interference in an Internet of Drones (IoD) network with multiple drones is provided. The method includes obtaining input parameters such as radio frequency parameters, drone locations, velocities, coverage areas, and the number of drones. A multi-objective model determines an initial interference and coverage based on these parameters. The iterative process continues for a predetermined time to identify the drone locations that achieve maximum coverage while minimizing interference. The approach accounts for beam angles, antenna configurations, and carrier frequencies, ensuring efficient IoD deployment. By leveraging a radio frequency-based determination model, the method dynamically adjusts drone positions to enhance network performance. The solution effectively balances coverage and interference, facilitating reliable communication in drone-based networks.

The invention belongs to the technical field of intelligent manufacturing and industrial scheduling, and provides a high-altitude platform multi-test task production scheduling method based on a clustering algorithm and multi-objective optimization, and the method comprises the steps: automatically calculating the exclusive resource demands of all working condition points in a plurality of aero-engine test tasks based on a multi-parameter coupling model; for each task, dividing a plurality of work packages by adopting a clustering algorithm based on the multi-dimensional features containing exclusive resource requirements in the task; a multi-objective model of multiple optimization objectives is constructed, all the work packages are jointly sorted globally, and the optimization objectives comprise the equipment utilization rate; and finally, under the condition that constraint of resource capacity, mutual exclusion of test cabins and task continuity is satisfied, allocating an available tester and a continuous time window to each work package, and generating an initial production scheduling plan. According to the method, the production scheduling efficiency and the resource utilization rate are effectively improved, the plan robustness is enhanced, and the method is suitable for high-complexity aero-engine test scheduling scenes.

The invention relates to an automobile thermal management system control method based on multi-target model predictive control, and belongs to the technical field of automobile thermal management systems. The method comprises the steps of establishing a discrete state space model of the thermal management system, setting a state variable, a control variable, a disturbance variable and a state space equation of the discrete state space model of the thermal management system, and then establishing a multi-target cost function and constraint conditions with stability, safety and low energy consumption of the thermal management system as targets; and then, based on fuzzy control logic, combining with an actual working condition, dynamically calculating a weight of each cost in the multi-target cost function, and finally solving an optimization problem according to the multi-target cost function under the determined weight in a discrete state space model of the thermal management system to obtain an optimal control input. And the optimal control input is transmitted to an actuator to regulate and control the automobile thermal management system. The system can adapt to various working conditions and environments, battery safety and comfort of a passenger compartment can be guaranteed, and energy efficiency and the service life of the actuator can be both considered.

The invention provides an urban environment performance prediction method and system based on multi-modal fusion and attention enhancement, and the method comprises the steps: collecting image data and numerical data, and generating a plurality of urban environment performance distribution truth value maps; preprocessing the multi-modal data to obtain an image map and a numerical value feature vector, and pairing the image map and the numerical value feature vector with the true value map to form a multi-modal data set; a multi-target model of a conditional generative adversarial network based on attention enhancement is constructed, a generator of the multi-target model comprises a two-way encoder, spatial features can be extracted based on an image map, and physical features can be extracted based on a numerical feature vector; an image coding path adopts a U-Net down-sampling structure containing a convolution block attention module, and a numerical value coding path adopts a multi-layer perceptron; and the multi-head output layer outputs a plurality of predicted urban environment performance distribution diagrams. After the model is trained, target area data are input, and three types of prediction distribution diagrams are output. According to the invention, the urban environment performance prediction effect is improved.

The application provides a multi-objective robust optimization method, system, storage medium and electronic equipment for production task planning of a multi-agent collaborative platform, and relates to the technical field of production task planning of a multi-agent collaborative platform. First, according to the production task planning resources of the multi-agent collaborative platform and a single-batch overseas order, a production task planning model with a manufacturing enterprise agent as the core is constructed under the premise of determining the sea transportation time. Then, an infinity norm ball uncertain set is designed for the sea transportation time of a large ship, a robust quadratic multi-objective model for cross-regional multi-agent production task planning is constructed, and the total delivery time deviation and total delivery cost multi-objective of the single-batch overseas order are optimized. The model considers the sea transportation plan prepared in advance by a shipping company agent and the long and uncertain sea transportation time. In addition, the model is converted into an equivalent model, a two-stage hybrid heuristic algorithm is designed for solving, and a reasonable and feasible production task planning suggestion is given for the single-batch overseas order.

The invention relates to the technical field of cotton spinning production, in particular to a cotton spinning workshop autonomous scheduling method based on an improved genetic algorithm, and the method comprises the steps: taking four performance indexes of processing waiting time, processing load time, processing time and repairing time as optimization targets, and building a corresponding target function; forming a constraint condition according to the processing moment and the processing moment as a boundary condition; realizing conversion between individuals in the population and a scheduling scheme based on encoding and decoding operations in a genetic algorithm; according to the method, a multi-target model is established by adopting an optimization target, a traditional genetic algorithm is improved by means of operations such as crossover and variation, in the algorithm solving process, as the number of iterations is increased, the change of a population mean value tends to be stable, the change of the population mean value tends to be stable, comparative analysis on the algorithm solving performance is carried out through a simulation test case, and the algorithm solving efficiency is improved. The superiority and effectiveness of the improved genetic algorithm in solving the cotton spinning workshop production scheduling problem are verified through calculation.

The application provides a robot dance automatic generation method and system based on music feature analysis, and relates to the technical field of robot control. First, a music feature spectrum covering rhythm, energy and other dimensions is constructed to convert abstract music into structured information understandable by machines, and then a dance semantic label sequence bound to a time axis is generated to provide accurate basis for action selection. By combining with a forward-looking window to predict future music features, the current and future candidate actions are filtered through a multi-objective model to realize the dual matching of action and music micro-rhythm and macro-structure. The finally generated control instruction sequence directly drives the physical robot, which breaks the dependence on video materials, realizes the automatic generation of dance with creativity and artistic expression, and improves the generation efficiency and adaptive flexibility.

The invention discloses a permanent magnet synchronous motor multi-target model prediction control method based on parallel optimization, which relates to the field of motor control, and sequentially comprises the following steps: establishing a permanent magnet synchronous motor mathematical model, performing Euler discretization-based model prediction torque control, and selecting an optimal vector based on a comprehensive optimization mechanism. Compared with the prior art, the optimal vector is selected on the basis of a comprehensive optimization mechanism, so that the control system adaptively switches the priority of multiple targets under high-speed and low-speed working conditions so as to realize multi-dimensional variable global optimization and rapid dynamic coordination, and the control is enabled to meet the performance requirements of preliminary constraint conditions, so that the control efficiency is improved. Ripples of torque and flux linkage are reduced, unstable operation of the motor and extra noise interference are overcome, and the control precision and steady-state performance of a control system are improved.

The application discloses a kind of adaptive compensation method and related device for three-phase current imbalance and harmonic, belong to power system technical field;The method is based on the instantaneous reactive power theory and the quick separation and instruction generation of fundamental wave positive sequence component and unbalance compensation component of sliding discrete fourier transform;Multi-objective model predictive current control with embedded repeated learning correction mechanism to achieve high-precision tracking of compensation current instruction and system circulation suppression;Multi-objective weight factor online dynamic adjustment mechanism based on fuzzy reasoning, according to the adaptive balance between the control emphasis of tracking accuracy, circulation suppression and switching loss according to the real-time operating state of the system;And, adaptive closed-loop management architecture based on the above module cooperation, drive active power electronic converter to generate compensation current, realize the comprehensive management of three-phase imbalance and harmonic and global optimization of system performance.

The embodiments of the present disclosure disclose a method and device for training and predicting a response duration prediction model, a medium and a product. The method comprises: obtaining a set of sample data from a sample set as a training set, wherein the sample data comprises feature information of a sample service request and a real response duration of the sample service request; training a plurality of initial multi-objective models based on the sample data in the training set to obtain a plurality of multi-objective models, wherein the learning objectives of the multi-objective models comprise a probability that a service request response duration is within a predetermined range and a predicted response duration of a service request whose response duration is within the predetermined range, and each multi-objective model corresponds to a different predetermined range. The technical solution can more accurately predict the response duration of a service request.

The application discloses an intelligent scheduling multi-objective optimization method based on a pulse propagation algorithm, relates to the field of manufacturing scheduling, and comprises the following steps: a scheduling data preparation stage, which is used for making a pre-decision for the schedulable resources in a pool and generating data of an input model; a multi-objective model establishment stage, which describes scheduling rules and optimization targets in a mathematical modeling mode; and a pulse propagation algorithm stage, which solves the above multi-objective model by designing an epsilon-constraint and a pulse propagation algorithm. The application greatly reduces the work burden of a planner, and the batch decision and specific scheduling details can be automatically completed by an embedded advanced model and intelligent algorithm. The intelligent decision mechanism not only improves work efficiency, but also ensures the accuracy and rationality of the scheduling plan.

This invention belongs to the field of power grid investment optimization technology, and relates to a regional power grid investment optimization method based on a multi-objective programming model. It includes the following steps: Step S1: Considering the decoupling requirements between regional power grid investment and electricity generation, a multi-dimensional parameter system covering investment, electricity generation, safety, and green / low-carbon parameters is constructed; Step S2: Based on the multi-dimensional parameter system, a multi-objective programming model is constructed to maximize the net present value of investment, maximize the power grid safety margin, and maximize the investment-electricity decoupling degree; Step S3: An improved multi-objective particle swarm optimization algorithm is used to solve the model, and the Pareto optimal solution set is output to obtain the optimal investment scheme. This invention quantifies the decoupling relationship between investment and electricity generation through a multi-dimensional parameter system, and optimizes the balance between economic efficiency, power supply safety, and resource allocation efficiency using a multi-objective model, solving the problems of excessive coupling between investment and electricity generation and insufficient functional value mining in existing planning.

The present application provides a multi-objective RTA model training method, system and automatic advertising bidding method, system; the training method includes constructing a heterogeneous graph based on user devices and delivered advertisements to generate embedded feature information between the user devices and the delivered advertisements, and the heterogeneous graph is constructed using a graph neural network algorithm; obtaining user-side features and advertisement-side features after real-time processing; using the user-side features, the advertisement-side features and the embedded feature information as input, and inputting them into a DNN multi-objective model for model training; verifying the DNN multi-objective model after training, and obtaining a prediction model after determining that the output indicators of the DNN multi-objective model meet the requirements: the present application can score user devices and automatically give advertisement bids through the established multi-objective model, thereby improving the efficiency and effectiveness of advertisement bidding and reducing advertising costs.

The invention relates to a continuous open caisson synchronous sinking construction method based on automatic monitoring. The method comprises the following steps that S1, open caisson attitude data and soil mass data are obtained based on an open caisson sensor network; s2, on the basis of the open caisson attitude data and the soil mass data, a multi-target model is constructed through a multi-target optimization algorithm, and the model is solved to obtain an optimal jacking force distribution scheme; s3, the open caisson is controlled to sink synchronously based on the optimal jacking force distribution scheme; and S3, constructing a deviation correction resource library, and in the process of executing S3, when deviation correction needs to be executed, adjusting the optimal jacking force distribution scheme and the earth cutting position and amount. Compared with the prior art, the method has the advantages that synchronous movement under the condition of multiple open caissons is controlled, uneven stress and position deviation due to speed difference are prevented, and the like.

The application provides a transformer area energy storage system operation regulation method and device, and belongs to the technical field of power system regulation. The method comprises the following steps: collecting transformer area historical data and constructing a historical database; training a prediction model based on the historical database, inputting real-time data of the transformer area into the prediction model for ultra-short-term prediction, and outputting future power output and load demand prediction values; taking the power output and load demand prediction values as the basis, taking the elimination of line overload, three-phase imbalance and voltage out-of-limit as the optimization target, considering the service life of the energy storage battery and the electricity economy, constructing a multi-objective model and solving the model to obtain an instruction sequence; issuing the instruction sequence to the transformer area energy storage system for execution; collecting real-time data; calculating the execution deviation according to the transformer area actual data, and adaptively updating and correcting the parameters of the prediction model and the multi-objective model. The application solves the transformer area power quality problem, prolongs the service life of the energy storage battery, and reduces the operation cost through intelligent prediction and optimization regulation.

The invention belongs to the technical field of intelligent control of a light storage system, and particularly relates to a light storage integrated intelligent operation method and system based on deep learning and multi-target model prediction control. And based on the photovoltaic output prediction value and the user load prediction value, a multi-target model prediction controller (M-MPC) is utilized to solve a multi-target optimization problem including economic cost calculation and battery attenuation cost calculation, and an optimal charging and discharging power sequence of the energy storage system is obtained. According to the technical scheme, through the synergistic effect of deep learning model prediction and multi-objective optimization control, the full life cycle operation cost of the energy storage system is reduced, the photovoltaic self-generation and self-use rate is improved, the impact on a power grid is reduced, a full-automatic closed-loop control system is realized, and the autonomy and intelligence of the optical storage system are improved.

Approximating a more complex multi-objective feed item scoring model using a less complex single objective feed item scoring model in a multistage feed ranking system of an online service. The disclosed techniques can facilitate multi-objective optimization for personalizing and ranking feeds including balancing personalizing a feed for viewer experience, downstream professional or social network effects, and upstream effects on content creators. The techniques can approximate the multi-objective model-that uses a rich set of machine learning features for scoring feed items at a second pass ranker in the ranking system-with the more lightweight, single objective model-that uses fewer machine learning features at a first pass ranker in the ranking system. The single objective model can more efficiently score a large set of feed items while maintaining much of the multi-objective model's richness and complexity and with high recall at the second pass ranking stage.

The invention discloses an intelligent scheduling multi-objective optimization method based on a pulse propagation algorithm, and relates to the field of scheduling in the manufacturing industry, and the method comprises the following steps: a scheduling data preparation stage, which is used for carrying out pre-decision making on schedulable resources in a receiving pool and generating data of an input model; in the multi-target model establishment stage, a mathematical modeling mode is adopted to describe production scheduling rules and optimization targets; in the pulse propagation algorithm stage, the multi-target model is solved by designing an epsilon-constraint and pulse propagation algorithm; according to the invention, the workload of a planner is greatly reduced; the batch decision and the specific scheduling details can be fully automatically completed by an embedded advanced model and an intelligent algorithm; the intelligent decision-making mechanism not only improves the working efficiency, but also ensures the accuracy and rationality of the production scheduling plan.

The application provides a steel coil storage location allocation method based on multi-objective optimization. It belongs to the field of intelligent storage. The steps are as follows: collecting the storage location information of the steel coil warehouse, the crane operation data and the steel coil scheduling history record, establishing a model aiming at improving the steel coil turnover rate; then, the type information of the in-stock steel coil inventory is counted, a model for improving the uniformity of the steel coil storage location allocation is established; the steel coil turnover rate and the uniformity of the steel coil are comprehensively considered, and the model constraint conditions are established according to the actual situation of the warehouse, a multi-objective optimization model is constructed, and the multi-objective model is weighted, so that the multi-objective problem is converted into a single-objective problem; the model is calculated through the hunting algorithm, and the storage location for the steel coil allocation is obtained. The steel coil storage location allocation method based on multi-objective optimization improves the scheduling speed of the high turnover rate steel coil in the warehouse, and also concentrates the same type of steel coil, improves the steel coil scheduling efficiency, and reduces the warehouse management cost.

This invention provides a method, system, and storage medium for self-optimization of process parameters in a stenter. The method includes: constructing a multi-objective model with the goal of minimizing fabric weight deviation, width deviation, and unit energy consumption, where the decision variables are temperature, machine speed, overfeed, and fan frequency; establishing a surrogate model to represent the mapping relationship between variables and objectives, and normalizing the parameter space; initializing the current solution and the Pareto front archive; generating M candidate solutions in the neighborhood of the current solution, evaluating and updating the front using the surrogate model; if the hypervolume increment of the front is lower than the threshold for N consecutive times, and the candidate solutions are too clustered, then triggering a restart; if the number of solutions in the archive is greater than or equal to 2, then selecting two parent solutions using the inverse roulette wheel based on the distribution density, generating a first type of restart point through recombination and mutation, and using a small radius; otherwise, generating a second type of restart point through global random sampling, using a large radius; setting the current solution as the restart point; looping until the termination condition is met, and outputting the Pareto front as the set of optimization parameters.

The present invention relates to a bus route planning and visual analysis method for influencing factors based on GPS data. First, city taxi order data and city point of interest data are preprocessed to obtain passenger travel origin-destination data and POI category data. Passenger boarding and alighting hotspot grids are extracted, and candidate bus stops are mined based on grid clustering. Furthermore, a multi-objective model for bus route planning is constructed, and an optimal bus route set is generated through a multi-objective optimization method. Attribute weight quantification and ranking results are obtained based on statistical route attribute information. Finally, a visual analysis system supports planners in exploring and analyzing routes in the optimal route set and supports changing route attribute weights. The system retrains based on the adjusted attribute weights or subjective ranking to update the ranking results. Through interactive exploratory analysis, an optimal bus route planning solution is obtained, providing a scientific decision-making basis and practical means for transportation departments to carry out bus route planning, thereby facilitating the development of smart cities.

The application discloses a kind of permanent magnet synchronous motor multi-objective model prediction control methods based on parallel optimization, it relates to motor control field, successively include the following steps: establishing permanent magnet synchronous motor mathematical model, model prediction torque control based on Euler discretization and optimal vector based on integrated optimization mechanism selection. Compared with prior art, the optimal vector based on integrated optimization mechanism selection introduced in the application makes the control system self-adaptive switching priority of multi-objective under high-speed and low-speed working conditions, to realize global optimization and fast dynamic coordination of multi-dimensional variable, and makes the performance requirement under the performance requirement of meeting preliminary constraint condition, reduces the ripple of torque and flux linkage, overcomes motor operation instability and additional noise interference, and improves the control precision and steady-state performance of control system.

The present invention discloses a robust multi-objective model predictive control method for an AC permanent magnet synchronous motor. This method is used in the control system of an AC permanent magnet synchronous motor and includes the following steps: collecting the operating parameters of the control system at time k; obtaining an electromagnetic torque reference value and a stator flux reference value; loading a multi-objective ranking model, and obtaining the electromagnetic torque ranking value and the stator flux ranking value based on the operating parameters at time k, after disturbance compensation using an improved full-order state observer and two-step delay compensation; the electromagnetic torque ranking value and the stator flux ranking value form a ranking value; outputting an optimal vector voltage; and achieving torque control through the vector voltage. This technical solution effectively improves system robustness, reduces computational complexity, reduces torque ripple, and improves control accuracy and the response speed of the MPTC.

The invention discloses a resource recall method, a multi-target model training method and device, electronic equipment and a computer readable storage medium. The resource recall method comprises the steps that a first user vector corresponding to a first target and a second user vector corresponding to a second target in a multi-target model are determined, the multi-target model is a recall model of a multi-tower structure, and the first target and the second target are different recall targets of the multi-target model; for each service resource in a service resource library, determining a first resource vector corresponding to the first target and a second resource vector corresponding to the second target in the multi-target model; multiplication fusion is carried out based on the first resource vector, the first user vector, the second resource vector and the second user vector to obtain a fusion score, and the fusion score is a non-negative value; and recalling the service resources according to the fusion score of each service resource. According to the scheme, multiplication fusion between any two recall targets can be achieved, and the recall effect is helped to be improved.

The application discloses a multi-target model prioritization framework and a marker combination screening method, introduces a NSGA-III multi-criteria optimization algorithm as a core innovation, and establishes a set of marker combination screening strategies with biological significance and classification performance. For a single biological feature, a basic model is constructed by using a full sRNA expression profile, and a classification performance is taken as a core index, and an optimal model is screened by using the NSGA-III. Multiple biological features and multiple groups of models are integrated and crossed to form multiple groups of evaluation models, and a processing model balanced and optimal for multiple biological features is screened by using the NSGA-III. For each biological problem, model building, redundant feature elimination and multi-dimensional screening operations are performed, and the significance of a marker in corresponding biological classification is detected, and only sRNAs with statistically significant differences are reserved as effective markers of the biological problem. Finally, the markers screened for each biological problem are combined, and a core marker combination capable of simultaneously solving all preset biological problems is formed.

The application discloses a kind of multi-objective control methods of three-level inverter based on model prediction using optimized pulse width modulation, current error, switching number, midpoint potential offset and the difference between selected switch state and optimized pulse width modulation switch state are weighted to obtain new optimization function, and the optimal vector considering multiple optimization targets is selected by rolling optimization, so as to adjust the switch state of three-level inverter in real time.The method of the patent combines the optimized pulse width modulation of three-level inverter with multi-objective model prediction algorithm, which can make the inverter output have good low-order harmonic characteristics, current fast tracking and midpoint potential balancing ability under dynamic at low switching frequency.