15 results about "Slack variable" patented technology

The invention discloses an industrial robot motion pose error calibration method based on constraint solution. The industrial robot motion pose error calibration method comprises the steps that S1, joint angles, tail end coordinates and temperature data of multiple measurement points are collected; s2, correcting the kinematic model by using the thermal elongation and constructing a parameter identification model; s3, constructing a mixed integer nonlinear constraint pool according to mechanical limitation; s4, introducing a semi-definite programming slack variable to convert the non-convex constraint into a linear matrix inequality; s5, combining a primal-dual interior point method and an alternating direction multiplier method to iteratively solve the optimal kinematics parameter correction; s6, introducing a square root information filtering improvement mechanism to correct a state vector square root factor, and calculating an updated nominal kinematics parameter; and S7, based on the condition number of the error Jacobian matrix, judging observability and repeatedly calibrating. According to the method, the problem of precision reduction caused by thermal errors is solved, and the identification precision, the numerical stability and the self-adaptive calibration capability of the kinematics parameters of the robot are improved.

The invention relates to the technical field of engineering material allocation, and particularly discloses a material allocation method, system and terminal, which introduces a generalized slack variable and a far-distance allocation relationship into a basic allocation model, and establishes a virtual emergency path while depicting a real allocation network, so as to relax conflicting points in the solving process, and improve the allocation efficiency. And automatically solving an allocation problem in a complex engineering scene, ensuring that the model has a solution all the time, positioning a conflict point through a non-zero value of a generalized slack variable, and dynamically iteratively optimizing to form a final material allocation scheme.

This invention proposes a priority-based hierarchical learning method. For N tasks with different priorities, the evaluation function for task i is denoted as Q. i and maintain a prediction model π i For task i with priority, and all higher priority tasks j < i, prepare a predetermined threshold ε. ij and maintain a slack variable λ ij For any i > j, if Q j (π i )+ε ij <Q j (π j ), then represents π. i Performance on task j was better than π. j If the difference is too large, then increase λ. ij Conversely, λ decreases. ij But keep λ ij >0. With λ ij j < i is used as the weight to optimize π i Repeat the previous steps until convergence, and finally obtain π. N This is the desired model. In this invention, constraints are used to describe priorities, solving the problem of traditional multi-objective optimization methods lacking a priority order. Slack variables are introduced to automatically adjust the weights of each optimization objective. The dual variable is adaptively adjusted, resulting in zero duality with the primal problem, making it a convex optimization problem that can be solved quickly by existing solvers.

The invention discloses a specific area port development evaluation method and system based on toughness-efficiency coupling. The method comprises three main steps. The method comprises the following steps: firstly, establishing a specific region port development evaluation index system containing two dimensions of toughness and efficiency according to the characteristics of specific region port development; and secondly, quantitatively evaluating the safety toughness level of the port in the specific area by using a Bayesian network method, and quantitatively evaluating the efficiency level of the port by using a slack variable-based output-oriented DEA model. And finally, quantitative evaluation of the port toughness-efficiency comprehensive level is realized by using a toughness-efficiency coupling coordination model. According to the method, various different evaluation index requirements can be more effectively synthesized, and the stability and the reliability of a comprehensive evaluation result can also be ensured; the output evaluation result comprises a toughness level and an efficiency level single dimension, and also comprises a comprehensive development dual dimension, and has clear directivity for evaluating port development and making decisions and policies.

The invention provides a common excitation amplitude sparse reconfigurable array synthesis method, and relates to an information transmission and processing technology. The method comprises the following steps: 1) constructing a mathematical optimization model through a radiation characteristic requirement and a null requirement of a to-be-synthesized wave beam, and solving the mathematical optimization model by using particularity of conjugate symmetric excitation and a continuous convex approximation algorithm; 2) utilizing a feasible point tracking algorithm, introducing a slack variable to help to solve a feasible point, and overcoming a non-feasible solution caused by non-convex constraint; the method has the advantages that a plurality of radiation directional diagrams can be formed by one sparse array, so that a plurality of different application scenes can be met; the number of array elements is reduced through the design of a sparse array, so that the system cost is further reduced; due to the characteristic of excitation weight conjugate symmetry, optimization variables are remarkably reduced, and the advantages of the method on a large-scale array are highlighted; the DRR constraint is added, so that the dynamic range and the quantization precision required by the attenuator are reduced, and the hardware complexity is simplified.

The invention provides a sum-difference beam synthesis method for sparse array co-excitation amplitude. The method comprises the following steps: constructing an array structure by adopting a configuration mode of one set of attenuator and two sets of phase shifters; a corresponding mathematical optimization model is constructed based on radiation characteristic requirements and anti-interference requirements of sum and difference beams and requirements for reducing the number of array elements, and a continuous convex approximation algorithm is used for solving; a slack variable is introduced, and a feasible point tracking algorithm is utilized to assist in obtaining a feasible solution of an optimization problem, so that the problem of a non-feasible solution possibly caused by non-convex constraint is effectively avoided. The number of attenuators is reduced by adopting the same excitation amplitude, and the number of array elements is reduced by depending on the sparse array design, so that the system cost is remarkably reduced; meanwhile, the solution space is expanded by adjusting the excitation phase, and the radiation performance is improved.

The provided is a driving priority assignment and reference velocity planning method for multi-vehicle cooperation. The driving priority assignment method includes: obtaining driving parameter data of vehicles under multi-vehicle cooperation, and identifying potential collision points between vehicles and calculating a time limit for vehicles to drive to the potential collision points, wherein the time limit includes a minimum time and a maximum time; constructing and solving a slack nonlinear programming problem based on the time limit for the vehicles to drive to the potential collision points to obtain an initial solution result, wherein during the process of constructing the slack nonlinear programming problem, a vehicle driving priority P involved in the potential collision points and a time t when the vehicles reach the potential collision points are configured as optimization variables, and introducing a slack variable; and performing iterative solution to obtain an optimal vehicle driving priority.

This invention discloses a method and apparatus for daytime scheduling planning of oil transportation. The method includes: acquiring input parameter data; inputting the input parameter data into a pre-established mixed integer programming model to determine the daytime scheduling planning data for oil transportation; the mixed integer programming model is established with the constraints of completing the monthly plan and keeping inventory data within a preset range, and with stable operation as the objective function; the objective function of the mixed integer programming model includes a penalty function; both the constraints and the objective function of the mixed integer programming model include slack variables, which can improve the scientificity, accuracy, efficiency, and flexibility of daytime scheduling planning for oil transportation, reduce the cost of daytime scheduling planning for oil transportation, and avoid problems such as uneven inventory distribution, congestion at stations, and oil shortages in some areas caused by unreasonable daytime scheduling planning for oil transportation.

The application provides a common excitation amplitude sparse reconfigurable array synthesis method and relates to information transmission and processing technology.The application comprises the following steps: 1) constructing a mathematical optimization model by using the radiation characteristic requirements and the nulling requirements of a beam to be synthesized, and solving the model by using the particularity of conjugate symmetric excitation and a continuous convex approximation algorithm; and 2) introducing a slack variable to help solve the feasible point by using a feasible point tracking algorithm, and overcoming the infeasible solution caused by the non-convex constraint.The application has the advantages that a sparse array can form multiple radiation direction patterns, and meet multiple different application scenarios; the number of array elements is reduced by designing the sparse array, so that the system cost is further reduced; the characteristic of conjugate symmetric excitation weight reduces the optimization variables, so that the application has the advantage on a large array; and the DRR constraint is added, so that the dynamic range and the quantization precision required by the attenuator are reduced, and the hardware complexity is simplified.

A computing method for obtaining slack variables in an objective function is applied to a quantum computing device. Through the use of the reinforcement learning method, a first function is obtained. Through the first function, the slack variables solution of the problem's objective function in the QUBO form are found. Consequently, the objective function in the QUBO form is optimized to be used for quantum annealers or digital annealers. Since the number of variables in the objective function is significantly reduced, the complexity of the problem is directly reduced. Furthermore, the annealer has the capability to handle more complex problems and find a high-quality solution more efficiently and accurately. Consequently, the purpose of obtaining the optimal value of the objective function can be achieved.

The provided is a driving priority assignment and reference velocity planning method for multi-vehicle cooperation. The driving priority assignment method includes: obtaining driving parameter data of vehicles under multi-vehicle cooperation, and identifying potential collision points between vehicles and calculating a time limit for vehicles to drive to the potential collision points, wherein the time limit includes a minimum time and a maximum time; constructing and solving a slack nonlinear programming problem based on the time limit for the vehicles to drive to the potential collision points to obtain an initial solution result, wherein during the process of constructing the slack nonlinear programming problem, a vehicle driving priority p involved in the potential collision points and a time t when the vehicles reach the potential collision points are configured as optimization variables, and introducing a slack variable; and performing iterative solution to obtain an optimal vehicle driving priority.

The application discloses a cloud network edge network task unloading and resource allocation method, and belongs to the technical field of calculation, estimation or counting. The method adopts a solution based on the maximum minimum criterion, proposes a strategy for solving the problem that there is a large difference between the communication calculation capabilities and the load conditions of different nodes, and realizes efficient utilization and sharing of resources. The method constructs a cloud network edge network model, including a communication model and a calculation model, describes the user unloading decision and the task processing mode, defines a user energy efficiency function and constraint conditions to jointly unload the decision and allocate resources. The generalized fraction theory and the slack variable are introduced to convert the optimization problem into an equivalent convex optimization problem, and then an iterative algorithm is proposed to obtain the optimal solution of the problem. The simulation results show that the method has good performance and effectively guarantees the fairness of the resources obtained by the user.

An autonomous vehicle uses precise control that is particularly useful with difficult scenes such as ultra narrow scenes. A plan including a planned path and a speed plan for the autonomous vehicle to traverse through a portion of a vehicle transportation network is received from a trajectory planner. An optimization operation is performed to revise the plan, where the optimization operation minimizes a lateral and heading error as compared to the planned path while applying each of a lateral offset constraint and a heading angle constraint as soft constraints. The soft constraints are represented by respective slack variables, and a penalty is applied to at least one of the slack variables that is greater than a penalty applied to the lateral and heading error to prioritize collision avoidance over maintaining the planned path. At least one control system of the autonomous vehicle is operated according to the plan as revised.

The application provides an optimal rate segmentation method for short packet communication to improve reliability, comprising the following steps: S1, establishing a communication model based on rate segmentation multiple access, containing various rate segmentation strategies, and using a limited code length domain theory to represent the error probability of user decoding a single flow; S2, constructing an optimization problem of minimizing the maximum user weighted decoding error rate based on the decoding error probability of the user end, with power allocation and code length allocation as optimization variables; S3, introducing a relaxation variable, a code length integer domain relaxation and a Taylor approximation method, converting the non-convex constraint in the optimization problem into a convex constraint, and constructing a convex constraint optimization problem; S4, solving the convex constraint optimization problem by using an iterative algorithm based on continuous convex approximation, and obtaining an optimal power allocation scheme, a code length allocation scheme and a rate segmentation strategy. The application aims to minimize the maximum user decoding error probability, thereby improving the reliability of a multi-user short packet communication scene.

The invention relates to a two-stage adjustment method for logistics parameters of an oil product transportation plan, and the method comprises the steps: obtaining parameters of an initial oil product transportation plan, and inputting a two-stage adjustment model; identifying and correcting a parameter error problem of the initial oil product transportation plan through a first-stage parameter pre-calibration optimization model, and calculating a logistics parameter output at a first stage and a slack variable under the parameter according to a preset first objective function and a first constraint condition group; based on a two-stage structure self-adjusting optimization model, the first objective function and the first constraint condition set are adjusted according to slack variables, a new second objective function and a new second constraint condition set are obtained and solved, and logistics parameters output by two stages are obtained; and outputting a complete oil product transportation plan according to the logistics parameters output in the second stage.