272 results about "Constrained optimization problem" patented technology

The disclosure relates to the technical field of power system management, and in particular to a large-scale electric vehicle cluster coordinated charging optimization method based on greedy repair genetic algorithm. the method includes the following steps: Si, establishing a task model: establishing a task model of electric vehicle cluster coordinated charging in large charging stations; S2, constructing constraint conditions: constructing constraint conditions for the coordinated charging optimization problem; S3, constructing a constrained optimization problem: designing an objective function with the minimum charging cost, performing linear mathematical transformation on non-linear constraint conditions, and constructing a 0 / 1 integer linear programming problem; and S4, solving the constrained optimization problem with a custom genetic algorithm based method: carrying out genetic coding on a decision model of the charging station, and specifically designing a genetic algorithm with greedy repair operators to solve the constructed constrained optimization problem.

The invention provides an unmanned aerial vehicle (UAV)-based flux-inductance integrated beam forming and trajectory optimization method, on one hand, an FP-AROS method is proposed based on fractional programming and semi-definite relaxation technologies in combination with approximate rank-one solution construction to carry out optimization design on ISAC beams, the Cramer-Rao bound of AoD is adopted as a sensing performance evaluation index, and the FP-AROS method is adopted to carry out optimization design on the ISAC beams; target estimation performance can be reflected more directly, so that a collaborative optimization effect of communication and sensing performance is realized; on the other hand, the invention provides an improved continuous convex approximation method based on a trust domain, and the method is used for optimization design of a UAV track. The ITRSCA method obtains a local trend approximate solution of an original objective function by calculating a second derivative, and introduces a dynamic trust domain mechanism to control approximate precision, so as to ensure global convergence of an output solution; in addition, the method is combined with a penalty function method to process non-convex constraints, and the limitation of a trust domain method in a constrained optimization problem is effectively overcome.

In one embodiment, a supervisory device in a federated learning system generates an aggregated model that aggregates a plurality of machine learning models trained by trainer nodes in a federated learning system during a training round. The supervisory device computes an accuracy loss metric for the aggregated model. The supervisory device also computes a fairness loss metric for the aggregated model based on fairness-related metrics associated with the plurality of machine learning models trained by the trainer nodes. The supervisory device initiates an additional training round during which the trainer nodes retrain their machine learning models for aggregation by the apparatus, in accordance with a constrained optimization problem that seeks to optimize a tradeoff between accuracy and fairness associated with the aggregated model.

An automatic driving resource arrangement and task unloading method based on model segmentation comprises the steps that in an automatic driving system, each vehicle continuously generates a vehicle-mounted task, and a vehicle task calculation model makes a real-time decision according to collected sensing data; each base station carries out task unloading scheduling on a plurality of vehicles in the coverage range; the MEC server communicates with the base station, and carries out computing resource arrangement and unloading task calculation on the edge side; according to the model segmentation method, a lightweight shallow network is deployed for a vehicle to serve as a vehicle model, and a deep network is deployed for an MEC server to serve as an edge model; adopting a residual network Resnet as a backbone network to realize model segmentation; residual blocks in the Resnet are dynamically segmented by segmentation points; according to resource arrangement and task unloading, collaborative decision-making of resource arrangement and task unloading is modeled as a constraint optimization problem for minimizing long-term task completion time delay, then the constraint optimization problem is decoupled as a resource allocation and task unloading sub-problem, and a double-layer DDPG network is constructed to solve the sub-problem.

The invention provides a pulse passing rate improving method based on accumulation signal demodulation, and belongs to the technical field of nuclear electronics digital pulse processing. Comprising the following steps: performing baseline deduction and monopulse screening on a radiation field pulse signal; constructing a nuclear pulse signal model, fitting to obtain model parameters, and constructing a system response matrix; modeling a nuclear detection physical process into a mathematical model; solving a constraint optimization problem by using a non-negative least square method; and demodulating and calculating all the pulse signals including the stacked pulses which are discarded by stacking, so as to obtain delta pulses containing energy information. According to the invention, the accumulation signal which is abandoned by a traditional accumulation abandoning algorithm is demodulated, so that the technical problem of increasing the pulse passing rate is solved, and meanwhile, heavy peaks and high-energy trailing in an energy spectrum are inhibited. Compared with an existing accumulation discarding method, the pulse passing rate effect is remarkably improved on the premise that the energy spectrum accuracy is guaranteed.

The invention relates to the technical field of intelligent fan control, and discloses an intelligent fan control method and system for a ventilation system of a nuclear power station. The method comprises the following steps: acquiring and preprocessing differential pressure data, radioactivity monitoring data, fan operation parameters and environmental data of a nuclear power plant ventilation system to obtain a characteristic representation space; according to the feature representation space and the target monitoring parameter combination, establishing an action space including main exhaust fan control, auxiliary exhaust fan control and filter system control, and executing multi-agent analysis to obtain an initial fan control strategy; a four-layer cascade security constraint model is constructed based on the initial fan control strategy, the constraint optimization problem is solved through a projection gradient method, and the optimal fan control strategy is obtained.According to the method, the recognition and adaptability to multiple ventilation working conditions are improved, the method can adapt to long-term change factors, and it is ensured that a ventilation system operates efficiently and reliably for a long time.

The invention discloses an edge calculation method and system for new energy electric field task unloading, and the method comprises the steps: building a new energy electric field mobile edge calculation unloading model through combining the parameters of equipment and a base station and the system communication bandwidth; based on a new energy electric field mobile edge computing unloading model, dividing unloading priorities of data collected by various sensing devices; considering system unloading time delay and data task processing time delay constraints, and constructing a time delay optimization problem of load balancing; long-term energy consumption constraints of MEC in a Lyapunov decoupling time delay optimization problem are adopted, and the time delay optimization problem is simplified into an optimization problem without long-term constraints by constructing a virtual energy deficiency queue; and an AC algorithm is adopted to perform joint optimization of task unloading and resource allocation on the optimization problem without long-term constraint, and maximization of the service quality of the new energy electric field data acquisition system is realized. According to the method, data with relatively high time delay requirements in the new energy electric field can be processed in real time, and optimal acquisition and processing of intensive data are realized.

The invention discloses a high-low cycle composite fatigue life prediction method based on a particle swarm optimization physical information neural network, which aims at high-low cycle composite fatigue life prediction of turbine blades under small sample data characteristics, and comprises the following steps of: introducing a classical physical formula of a linear cumulative damage theory into a loss function as a constraint term; training fatigue test data by adopting a neural network model; deducing a constraint space of model parameters according to domain knowledge of high-low cycle composite fatigue life prediction, and converting training of a neural network into a constrained optimization problem; a particle swarm optimization algorithm is adopted to optimize the weight and the threshold value of the neural network, and the learning ability of the neural network under a small sample data set is further improved. The method provided by the invention can predict the fatigue life of the turbine component under the high-low cycle combined load, has high prediction precision under a small sample data set, and provides theoretical support for fatigue life prediction and reliability analysis of the engine turbine component.

The invention relates to the technical field of wireless communication, and discloses an unloading method in an ultra-dense millimeter wave MEC network. Aiming at the problems of insufficient terminal capability, high energy consumption of an ultra-dense base station, millimeter wave coverage limitation, communication security risk and the like under the condition of sharp increase of a calculation-intensive task, the method comprises the following steps: firstly, obtaining network basic information, constructing a network architecture containing communication, calculation unloading and a security model, and establishing a multi-constraint optimization problem; based on the optimization problem, initializing a multi-strategy black-wing plinuary optimization algorithm population through logic mapping and elite selection; updating individual positions through global and local search in algorithm iteration, and screening historical optimal individuals; and finally, configuring and unloading resources according to the configuration. According to the method, the NOMA technology, a multi-step unloading framework and a hybrid communication mode are combined, the optimization efficiency is improved through a multi-strategy black-wing optimization algorithm, the local energy consumption is reduced, the communication rate is improved, the safety is guaranteed, the optimal scheme meeting time delay and safety constraints is rapidly converged, and the user experience is improved.

The invention discloses a graph calculation-driven complex publication derivative resource content layout planning system, which belongs to the technical field of digital publication and intelligent media, and comprises a multi-modal data analysis module for analyzing heterogeneous data such as texts, images, tables and the like in publications into structured node features; a feature fusion module; a content relationship modeling module; a graph calculation driving layout optimization module; performing layout constraint modeling: converting a layout problem into a graph constraint optimization problem; a modeling optimization module; a multi-terminal adaptive rendering module; the layout generation engine is used for converting a graph calculation result into a visual layout scheme; and the interactive feedback module is used for feeding back user operation to the graph in real time to trigger layout reconstruction. According to the method, advanced technologies such as graph calculation, multi-modal fusion and constraint solution are deeply fused, the limitation that a traditional typesetting tool depends on a fixed template is broken through, and a content-driven one-stop solution is provided for scenes such as academic periodicals, electronic textbooks and digital reports.

A computer implemented method for obtaining one or more optimal solutions to a given classical constraint linear or non-linear optimization problem is provided. Each solution is specified and identified by a set of parameters and the one or more optimal solutions provide optimal values of a cost function that is a linear or non-linear function of one or more decision variables. The one or more optimal solutions fulfill one or more constraints comprising a set of equality constraints and / or a set of inequality constraints, where each equality constraint and each inequality constraint comprises a linear or non-linear complex functional form. A quantum computation component and a classical computation component of the hybrid quantum-classical computer system are used together to solve the classical constraint optimization problem by iteration. While performing the method, the quantum computation component executes a parameterized quantum circuit with surrogate phase operators and with the set of parameters.

A vehicle is controlled for travel on a road having a geometric design defined by one or a combination of a docking line, a cross-section, and a cross-section of the road such that different values of parameters of the geometric design of the road, traffic on the road, traffic rules for traffic flow on the road define different traffic scenarios. By relaxing configuration parameters of a real-world scene and tightening corresponding limiting parameters, a mixed integer non-convex constraint optimization problem for a current real-world traffic scene is converted into a mixed integer convex optimization problem for approximate representation of the real-world traffic scene, so that a carrier is controlled. A mixed integer convex optimization problem for the converted approximate representation of the real-world traffic scene is solved to generate current control commands for controlling one or more actuators of the vehicle.

The invention discloses an electric vehicle lifting torque control method and system. The method comprises the steps that driving operation, vehicle state and environment data are collected in real time and preprocessed into multi-dimensional real-time feature vectors; based on the vector, extracting and updating a driving style feature vector by using an incremental clustering and lightweight time sequence convolutional network, and further mapping to generate a personalized cost function weight coefficient in model prediction control; synchronously operating the vehicle dynamics and thermodynamics simplified model, predicting a dynamic physical constraint boundary of a driving system in a future time domain, and constructing a dynamic feasible domain curve; taking a driver instant demand as a tracking target, fusing a personalized weight and a dynamic feasible region curve, constructing and solving a finite time domain constraint optimization problem, and generating a current period target torque instruction; and filter parameters are adaptively adjusted through inverse dynamic model feedforward compensation in combination with a dynamic feasible region, a final torque control instruction is generated, and unification of safety, smoothness and individuation of torque control is achieved.

The invention provides a delay compensation control method for a double-vibrator wave energy conversion device, which comprises the following steps of: performing numerical modeling and hydrodynamic analysis on the double-vibrator wave energy conversion device, establishing a time domain motion equation of the double-vibrator wave energy conversion device, and introducing a time delay function into the time domain motion equation to simulate control signal transmission delay; simulating the execution delay of the brake by using a partial differential equation; establishing a state-space equation of the device, replacing a convolution term of the time-domain motion equation with the state-space equation, and calculating to obtain a motion state of the device; a Hamiltonian function is defined to convert a constrained optimization problem into an unconstrained optimization problem, and the Hamiltonian function is solved to obtain an optimal control criterion considering control delay so as to realize maximization of energy capture under the optimal control criterion. According to the method, the operation characteristics of a physical system are truly reflected by introducing control delay, so that the locking control method is effectively implemented in an actual physical device, and the energy capture efficiency of a wave energy conversion device and the reliability of system operation are effectively improved.

The invention discloses a stratified fuel cell temperature control method based on adaptive model-free predictive control. The method comprises the steps that firstly, an upper-layer decision maker determines the optimal stack temperature under different working conditions based on a polarization curve of a stack, and the optimal stack temperature serves as the optimal reference temperature to be output to a lower-layer controller; in the lower layer controller, based on real-time input and output data of battery operation, nonlinear prediction of the stack temperature is realized by introducing an auxiliary slack variable. And then an optimal control variable is obtained by solving an optimization problem with constraints in each execution period, and a water pump and a radiator are driven to complete optimal control of the temperature of the galvanic pile. Compared with a traditional control method, the method has the advantages that the dependency on the model is reduced; compared with a traditional model prediction control method, the method has the advantages of being low in calculation complexity, low in requirement for controller hardware and the like, and the problems that the proton exchange membrane fuel cell is high in nonlinearity and coupling performance and large in control difficulty are solved to a certain extent.

The invention relates to an unmanned aerial vehicle cluster target monitoring path planning method based on space-time constraint multi-agent reinforcement learning, and belongs to the field of unmanned aerial vehicle path planning. Comprising the following steps: acquiring an unmanned aerial vehicle cluster and environment characteristic information, establishing time and space constraint conditions, and constructing a constraint optimization problem taking minimization of total energy consumption as a target; based on the obtained feature information, mapping a constraint optimization problem into an Actor network and a Critic network corresponding to each unmanned aerial vehicle and a Critic network corresponding to an unmanned aerial vehicle cluster; carrying out reverse training on the Actor network and the Critic network based on a simulation result of the unmanned aerial vehicle cluster; and inputting the feature information at the current moment into the Actor network corresponding to each unmanned aerial vehicle for path planning. According to the method, the situation that actions of unmanned aerial vehicles at the current moment meet time / space constraint conditions is evaluated based on time / space feature information, energy consumption of an unmanned aerial vehicle cluster at the current moment is evaluated based on global state information, and therefore it is ensured that optimal path planning meeting various constraint conditions and achieving a target is found.

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.

In recent years, research on vehicle path planning mainly comprises methods based on searching, sampling and numerical optimization. A planning problem is converted into graph search based on a search method, but the efficiency is relatively low in high dimension. The sampling method is suitable for a high-dimensional space, candidate paths are generated through random or regular sampling, but the calculation complexity is high. Compared with the prior art, according to the numerical optimization method, a path is represented through a polynomial curve, a problem is converted into a constrained optimization problem, and necessary constraint conditions are added. According to the method, the path smoothness and the solving speed can be improved, so that the method is widely accepted in academic and industrial circles. According to the path planning method based on numerical optimization, a convex polygon is used for covering a sampling point feasible region, redundant regions are reduced, and the space utilization rate is improved, so that obstacle avoidance constraints and a mathematical model are simplified, and the planning efficiency of an automatic driving automobile is improved.

The invention discloses a tractor trailer robot track generation method based on a polynomial and a symbolic distance field, and the method comprises the steps: dividing grids through a point cloud map, estimating the curvature and normal direction of each grid, extracting obstacle information, and calculating a symbolic distance value corresponding to each grid; taking the initial pose of the tractor trailer robot as a root node, and based on the obstacle information, utilizing a multi-end-point heuristic graph search algorithm for spatial search in SE (2) to generate an initial path for arriving at the target area; and in combination with the symbol distance value corresponding to each grid, modeling the trajectory generation of the tractor trailer robot as a constraint optimization problem, and solving the constraint optimization problem by taking the initial path as an initial value to obtain a final trajectory. The problem that track generation is difficult due to complex kinematics, high state space dimension and deformable structure of the traction trailer robot is solved, the problems that an existing algorithm is low in efficiency and loses solution space are solved, and efficient track generation of the traction trailer robot is achieved.

A method of performing computation in a hybrid quantum-classical computing system includes computing an approximate cost function of an optimization problem with variables constrained by an inequality, wherein the inequality constraint is included using a polynomial approximation of a Heaviside step function, mapping the approximate cost function of the optimization problem to a model Hamiltonian, setting a quantum processor in an initial state, executing one or more iterations, each iteration including applying a parametrized quantum circuit to the quantum processor based on a set of variational parameters and the model Hamiltonian, measuring an expectation value of the model Hamiltonian, and replacing the set of the variational parameters with another set of variational parameters, and outputting the set of the variational parameters after executing the one or more iterations.

The invention relates to the technical field of enterprise management, in particular to an enterprise process custom configuration method and system based on a code-free technology, and the method comprises the steps: constructing a multi-layer directed hypergraph containing data, control and condition dependence, calculating an edge weight, and converting the dependence into a first-order logic constraint set; performing incremental satisfiability detection, and outputting a conflict core set containing conflict types and severity in combination with minimum unsatisfiability subset mining and strong connected component analysis; mapping the conflicts into modifiable elements, and generating parameterized candidate repair schemes based on a repair operation library and historical cases; converting the candidate scheme into a weighted constraint optimization problem, and solving an optimal scheme by adopting a branch and bound algorithm; influence propagation verification is completed in the copy, a differential patch and a rollback identifier are generated, transactional submission is carried out after passing, and the weight and the case library are updated. According to the method, automatic diagnosis and low-risk repair of implicit dependency conflicts are realized.

The application discloses a target direction finding method based on a short baseline unified model, which is realized by a computer and comprises the following steps: firstly, an improved polar coordinate representation (MPR) unified model is constructed; secondly, a closed-form initial solution of direction finding under the MPR model is obtained; then, an improved successive unconstrained minimization method based on the MPR is used to solve a quadratic constraint optimization problem to obtain the angle of the target source; thirdly, a direction finding initial solution is used to guide source deployment; fourthly, time-space reference system error calculation is performed; finally, target source TDOA measurement and time-space reference system error elimination and final solution solving are performed; the application effectively avoids the threshold effect of the short baseline positioning system based on the far-field direction finding unified model of the modified polar coordinates, so that the far-field target direction finding method of the unmanned aerial vehicle group based on the time difference of arrival measurement becomes possible; the calibration source and the target source are jointly monitored, the time-space reference system error is eliminated through the time difference measurement of the calibration source, and thus the direction finding precision is improved.

The invention relates to the technical field of regional sustainability, and discloses a regional sustainable development mode display system based on virtual simulation, and the system comprises a data collection module which is used for collecting related resource data of regional sustainability in real time; the multi-dimensional modeling module is used for constructing an agricultural production model, a social economic system model and an ecological environment model based on the resource data; the optimization decision-making module is used for generating a multi-objective and multi-constraint optimization problem based on the agricultural production model, the social economic system model and the ecological environment model and performing resource allocation optimization, and the optimization problem comprises multiple objectives of economic benefit, ecological protection and social benefit; the invention also provides a regional sustainable development mode display method based on virtual simulation. The method comprises the following steps: collecting regional sustainable resource data in real time; by adopting a real-time data acquisition and feedback control scheme based on the Internet of Things technology, precise monitoring and dynamic adjustment of regional sustainable resources are achieved.

The embodiment of the invention relates to the technical field of radars, and discloses an MIMO radar emission sequence set design optimization method, which comprises the following steps: constructing a signal model designed based on an AF emission sequence set for an MIMO radar system, selecting ISL of minimizing local AAF and CAF as an optimization criterion, selecting an energy constraint and a dynamic range constraint as constraint conditions, and selecting an energy constraint and a dynamic range constraint as constraint conditions; establishing a four-order non-convex constraint optimization problem; decomposing the fourth-order non-convex constraint optimization problem into a plurality of sub-problems which can be iteratively solved by utilizing an MBI algorithm; sequentially solving the sub-problems by means of CVX to obtain a preliminary solution; and continuously adjusting a search direction and a step length by using a BLS algorithm, and carrying out iterative optimization on the approximate solution until an Armijo condition is met, thereby obtaining a global optimal solution meeting a design requirement, and further designing a radar emission sequence set with good related characteristics, thereby improving the target detection capability, the anti-interference capability and the resolution of the MIMO radar system.

The invention discloses a multi-energy micro-grid energy regulation and control method and device and a storage medium. The method comprises the following steps of: constructing a scene prediction constraint optimization problem by utilizing a trained Warisstein generative adversarial network, and predicting a group of multi-dimensional uncertainty multi-energy micro-grid operation scenes in a future period by following known information at each moment to serve as prediction scenes; forming a pre-scheduling model and a real-time power compensation model based on the prediction scene, and forming a two-stage multi-energy micro-grid real-time energy regulation and control model; a pre-scheduling model obtains a pre-scheduling instruction of the multi-energy micro-grid based on random model prediction control by using the prediction scene information; the real-time power compensation model coordinates schedulable units in the multi-energy micro-grid according to real-time observation of uncertain variables; therefore, the energy in the multi-energy micro-grid is regulated and controlled. Through quick response and adjustment of real-time data, the system can effectively cope with fluctuation caused by uncertainty, thereby realizing accurate regulation and control of the energy of the multi-energy micro-grid.

The invention relates to an optimization design method of a multi-winding regulation and control transformer for flexible interconnection, which comprises the following steps: 1, according to a single-target optimization design mathematical model, constructing an optimization target function with the purpose of minimizing the sum of the material cost of the multi-winding transformer and the ten-year operation cost; 2, determining required constraint conditions according to basic electrical parameters of the multi-winding regulation and control transformer for flexible interconnection; under the action of the obtained optimization objective function, punishing a trend violating constraints by adopting a punishment function, so that non-constraint optimization is gradually close to a feasible region, a constraint optimization problem of the distribution transformer is adjusted into an unconstraint optimization problem, and an optimization design model of the amplitude-phase adjustment type multi-winding transformer for flexible interconnection is obtained; and 3, solving the optimal design model, obtained in the step 2, of the multi-winding regulation and control transformer for flexible interconnection by using a particle swarm optimization algorithm. According to the method, the local optimal solution and the global optimal solution of the individual can be solved, and higher solution precision can be obtained.

The invention discloses a zero-order primal dual method and device for a black-box constraint optimization problem, and the method comprises the steps: constructing a corresponding black-box constraint optimization problem based on a preset constraint optimization scene, and determining a tightly convex feasible region containing an original variable; converting the black box constraint optimization problem into an unconstrained optimization sub-problem which can be solved iteratively; on the basis of a function value query result of the black box constraint optimization problem, gradient correlation information of the unconstrained optimization sub-problem about an original variable is estimated, iterative solution is carried out on the unconstrained optimization sub-problem on the basis of the gradient correlation information, and the original variable after iterative updating is limited in the tightly convex feasible region; and when a preset convergence condition is met, obtaining a final original variable as an optimization result. A black box constraint optimization problem is converted into an unconstrained sub-problem which can be solved iteratively by introducing a near-end Lagrangian fusion optimization framework, so that efficient optimization is realized under the black box condition that only function values can be obtained.

The invention provides a double-arm robot coordinated motion planning method under multiple constraints, which belongs to the field of robots, and comprises the following steps of: establishing a task constraint mathematical model according to a double-arm robot coordinated operation task demand and a target by acquiring target information and a current state of a robot; a quadratic penalty function method is introduced to convert end effector position closed-chain equality constraint relations in two-arm coordinated operation tasks in different scenes into a target function, and a multi-constraint optimization problem of two-arm robot coordinated motion planning is constructed; solving by adopting a model prediction path integral control method based on an information theory to obtain a double-arm coordinated motion optimal track meeting a multi-constraint task requirement; and finally, applying the optimal trajectory of the two-arm coordinated motion to the robot simulation operation task. According to the method, the working efficiency and the stability of the double-arm robot during completion of a smart coordinated motion planning task can be effectively improved, and a new theoretical and method basis is provided for practical application of the double-arm robot in a complex scene.

The invention relates to the field of intelligent scheduling allocation, in particular to an edge cloud computing resource allocation optimization method based on deep learning, which adopts a space-time prediction algorithm based on multi-head attention and gating mechanism optimization to design time coding and space coding. The spatial relationship and interaction between time sequence characteristics of the computing power load and edge server nodes are captured, and meanwhile, a multi-head attention mechanism and expansion causal convolution are combined, so that instantaneous computing power load fluctuation can be captured, and the long-term trend of the computing power load can be mined; therefore, a reliable basis is provided for subsequent computing power scheduling by predicting an accurate computing power load. The invention designs an alternating direction multiplier method based on genetic algorithm optimization, which is not only suitable for a nonlinear and multi-constraint optimization problem, but also can be expanded to a larger-scale distributed edge node cloud computing system, and meanwhile, a global optimal solution is quickly approached through the genetic algorithm, so that the quality of an initial solution is improved, and model convergence is accelerated; and the distributed collaborative allocation scheduling efficiency is improved.

A vehicle includes an electric powered propulsion system. An electric energy storage system is electrically connected to the electric propulsion system and is configured to have an electrical energy storage component and a controller. A charging port is connected to the electric energy storage system and configured to connect to an external power source. The controller includes a memory and a processor. The memory stores instructions for causing the processor to optimize a charging profile based on a plurality of received parameters using a multi-objective constrained optimization problem. The received parameters include a power type of a connected external power source, and at least one of a requested ready to depart time, a targeted state of charge, and an effective range.