2936 results about "Global optimal" patented technology

The present invention relates to a method for workforce scheduling in which workload and workload types vary during scheduling period. The method acquires agent and skill requirements for all periods and contact types; acquires the contact center information including agent skill groups, agent work groups, tour and shift scheduling rules, agent availability, objective criterion to be optimized and its parameters; develops a Mixed Integer Linear Programming (MILP) model for the scheduling environment; applied an optimization algorithm that uses the Branch and Bound algorithm with a Rounding Algorithm to improve performance; and locates a globally optimal or near optimal workforce schedule in total cost or paid time or agent satisfaction. Detailed schedules may be developed by assigning daily shifts to work patterns, and breaks scheduled to daily shifts.

An embodiment of the invention provides a method of managing a cluster of networked resources and resource groups using rule-based constraints. This method includes the step of building a globally optimal cluster configuration of the resources in accordance with the rule-based constraints and a current state of the resources, including identifying for each of the resources and resource groups an availability and quality of service, which are determined by dependencies among the resources and resource groups, resource equivalency, constraints on the resources and network policies. The method comprises the further steps of bringing the cluster of resources on-line in a systematic manner, given the current state of each of the resources and resource groups; and with the cluster of networked resources on-line, determining dynamic dependencies of and configuration infomration about the cluster of resources (i) statically at defined times and (ii) dynamically during cluster operation.

Disclosed in the invention is an improved method of a RGB-D-based simultaneously localization and mapping (SLAM) algorithm. The method comprises two parts: a front-end part and a rear-end part. The front-end part is as follows: feature detection and descriptor extraction, feature matching, motion conversion estimation, and motion conversion optimization. And the rear-end part is as follows: a 6-D motion conversion relation initialization pose graph obtained by the front-end part is used for carrying out closed-loop detection to add a closed-loop constraint condition; a non-linear error function optimization method is used for carrying out pose graph optimization to obtain a global optimal camera pose and a camera motion track; and three-dimensional environment reconstruction is carried out. According to the invention, the feature detection and descriptor extraction are carried out by using an ORB method and feature points with illegal depth information are filtered; bidirectional feature matching is carried out by using a FLANN-based KNN method and a matching result is optimized by using homography matrix conversion; a precise inliners matching point pair is obtained by using an improved RANSAC motion conversion estimation method; and the speed and precision of point cloud registration are improved by using a GICP-based motion conversion optimization method.

Dynamical methods for obtaining the global optimal solution of general optimization problems having closed form or black box objective functions, including the steps of first finding, in a deterministic manner, one local optimal solution starting from an initial point, and then finding another local optimal solution starting from the previously found one until all the local optimal solutions starting from any initial point are found, and then finding from said points the global optimal solution.

The invention discloses an intelligent scheduling system for tail-end instant logistics. The intelligent scheduling system comprises an order receiving module, an order processing module, a deliveryman searching module, an order combination module, a merchant portrait module, a deliveryman portrait module, an order dispatching decision-making module, and a deliveryman examination module. On the basis of position information of a merchant and a user as well as positioning information generated by a deliveryman terminal APP, global optimal matching is carried out on an order and a deliveryman by considering the current position of the deliveryman, the order executed by the deliveryman, portrait image of the merchant and the deliveryman, and the requirement on allocation time by the user comprehensively and then an optimal deliveryman is determined finally to complete a delivery task. On the basis of the intelligent order dispatching way, optimal order distribution is carried out at a global angle to realize optimal utilization of the whole transport capacity; the need of instant logistics and the transport capacity supply can be balanced well; the logistics delivery efficiency can be improved; the logistics delivery cost can be lowered; and the service quality of the tail-end instant logistics can be improved.

The invention discloses a method for autonomously localizing robots on the basis of laser radar. The method includes randomly generating N particles to form particle swarms around initial locations ofthe robots, and updating the particle swarms according to robot real-time movement distances and real-time rotation angles measured by sensors of the robots at current operation moments of the robots; computing the superposition quantity of point cloud of the laser radar and obstacles of maps for each particle to use the superposition quantity as a score of the particle, computing weighted position and posture average values of the particle swarms by the aid of the score, which is used as a weight, of each particle and utilizing the weighted position and posture average values as AMCL (adaptive Monte Carlo localization) estimation positions and posture; utilizing the AMCL estimation positions and posture as initial values, acquiring scanned and matched positions and posture by the aid ofscanning and matching algorithms on the basis of Gauss-Newton iterative processes and utilizing the scanned and matched positions and posture as the optimal positions and posture of the robots at thecurrent operation moments; re-sampling the particle swarms by the aid of AMCL algorithms to ultimately obtain the global optimal positions and posture of the robots during operation. The global optimal positions and posture of the robots are used as localization results. The method has the advantage that the localization convergence rate can be greatly increased, and the localization precision andthe localization stability can be greatly enhanced.

The invention discloses a network text segmenting method based on the genetic algorithm, used for segmenting short network texts. The method comprises the following steps of: evaluating a Latent Dirichlet allocation (LDA) model corresponding to a corpus by using a Gibbs sampling method, inferring latent topic information using the model, representing texts by using the latent topic information; then transforming a text-segmenting process into a multi-target optimum process by using a parallel genetic algorithm, and calculating the coherency of segmented units, the divergence among the segmented units and fitness functions by using deeper semantic information; and carrying out the genetic iteration of the text segmenting process, and determining whether the segmenting process terminates based on the similarity among multi-iteration results or the upper limit of iterations to obtain the global optimal solution for segmenting the texts. Therefore, the invention improves the accuracy for segmenting the short network texts.

The present invention discloses a method and system for collaborative scheduling of production and transportation in supply chains based on improved particle swarm optimization. The method includes the following steps: 1. setting algorithm parameters; 2. randomly generating an initial population; 3. correcting codes; 4. calculating fitness values and updating the speed and the position of particles; 5. performing tournament selection; 6. performing crossover mutation; 7. updating the population; and 8. determining whether a termination condition is satisfied; if so, outputting a globally optimal solution; if not, returning to the step 3. In the present invention, an approximately optimal solution can be obtained in view of the collaborative scheduling problem of production and transportation considering distributed storage, so that the cost is reduced for supply chains and the service level of supply chains is enhanced.

The invention discloses an AGV (Automated Guided Vehicle) route planning method and system based on an ant colony algorithm and multi-intelligent agent Q learning, improving the global optimization ability, realizing a case that an AGV learns how to avoid an obstacle in the interaction process by introducing the multi-intelligent agent Q learning into a route planning research of the AGV, and canplay independence and learning capacity of the AGV better. The AGV route planning method and system is characterized in that according to a static environment, carrying out modeling on an AGV operation environment by utilizing a grid method, and setting an initial point and a target point; according to coordinates of the initial point and the target point of the AGV, generating a global optimal route by the ant colony algorithm; enabling the AGV to move towards the target point according to the global optimal route, and when detecting that a dynamic obstacle exists in a minimum distance, carrying out selection of an obstacle avoidance strategy by an environment state corresponding to the multi-intelligent agent Q learning so as to take a corresponding obstacle avoidance action, and after ending obstacle avoidance, returning to an original route to continuously move.

A method determines a global optimal solution of a system defined by a plurality of nonlinear equations by applying a metaheuristic method to cluster a plurality of search instances into at least one group, selecting a center point and a plurality of top points from the search instances in each group and applying a local method, starting from the center point and top points for each group, to find a local optimal solution for each group in a tier-by-tier manner. Then a TRUST-TECH methodology is applied to each local optimal solution to find a set of tier-1 local optimal solutions, and the TRUST-TECH methodology is applied to each tier-1 local optimal solution to find a set of tier-2 local optimal solutions. A best solution is identified among all the local optimal solutions as the global optimal solution. The heuristic method can be a particle swarm optimization method or a genetic algorithm method.

The invention provides a multi-neural network control planning method for a robot path in an intelligent environment. The method comprises the steps that 1 a global map three-dimensional coordinate system is constructed for the carrying area of a carrier robot to acquire a walkable area coordinate in the global map three-dimensional coordinate system; 2 a training sample set is acquired; 3 the global static path planning model of the carrier robot is constructed; and 4 starting and ending coordinates in a transportation task are input into the global static path planning model based on a fuzzy neural network to acquire the corresponding optimal planning path for the carrier robot. According to the invention, the global static path planning model and a local dynamic obstacle avoidance planning model are separately established; the nonlinear fitting property of the neural network is used to find the global optimal solution quickly; and the problem of falling into a local optimum in common path planning is avoided.

The invention provides a power distribution network fault positioning method based on improvement of a binary particle swarm algorithm, the conventional binary particle swarm algorithm is improved, and the method is applied to positioning of power distribution network faults. The method comprises following steps: firstly, determining parameters including the particle swarm scale and the maximum iteration frequency etc.; then forming an expectation function of a switch according to fault information of the switch, and constructing a fitness function of power distribution network fault positioning; initializing a particle swarm, setting particle positions, and setting the speed of the particles as 0; calculating the fitness values of the particles according to the fitness function, and setting an initial global extremum; updating an individual extremum and the initial global extremum; updating the speed and position of the particle swarm; and stopping calculation when reaching the maximum iteration frequency, and outputting the global optimal position of the particle swarm, namely the practical fault state of each feed line section of a target power distribution network. According to the method, the problem of premature convergence of the conventional method can be overcome, and the convergence and the stability of the algorithm can be further improved.

The invention discloses a road detection method based on bimodal data fusion. The method comprises the following steps: transferring axes of laser radar three-dimensional points; obtaining a laser radar three-dimensional point set in a camera image range; setting a height threshold value, obtaining a subset of laser radar three-dimensional points of a barrier; obtaining a subset of laser radar three-dimensional points of a passable road area; obtaining color Gaussian mixture models of a non-road area and a passable road area; obtaining an area-of-interest of road detection on an image; structuring an energy function based on the Markov random field, calculating the minimum value of the energy function, and obtaining a global optimal image road detection result. By the adoption of the road detection method based on the bimodal data fusion, robust of complex environment is achieved, the detection result is not influenced by shadows of road surface, fine barriers in an environment sensed by a sensor can be distinguished from the passable road area; an area without laser radar data returning and the non-road can be judged, and the road detection method based on the bimodal data fusion is suitable for fields such as autonomous vehicle guidance.

The invention discloses application of deep learning based on fuzzy processing in fault diagnosis of hydraulic equipment. The application comprises the following steps of: (1) introducing a time label and fuzzy weight to pre-process operation monitoring data of the hydraulic equipment, and dividing the operation monitoring data into a training data set and a test data set; (2) taking the training data set as an input vector of a sparse self-coding network to carry out non-supervision pre-training; (3) taking label data and no-label data as an input vector training Softmax classifier of a Softmax classifier; (4) utilizing a BP algorithm to carry out fine adjustment on deep learning network parameters; and (5) carrying out intelligent diagnosis on a fault condition. According to the application disclosed by the invention, firstly, a method of introducing the time label and fuzzy weight is adopted to carry out pre-processing on data; then, sparse self-coding is used to complete high-level feature extraction of sample data, and the Softmax classifier is used to carry out classifying diagnosis on an equipment fault state to construct an ICM model; and finally, the BP algorithm is utilized to carry out fine adjustment on global optimal parameters of the whole network, so that intelligent diagnosis on the fault state is realized.

The invention discloses a PID controller parameter setting algorithm based on an improved PSO (particle swarm optimization) algorithm, and the algorithm comprises the following steps: 1, initializing the algorithm parameters; 2, switching to an iterative loop, and carrying out the updating of the position and speed of each particle; 3, randomly searching a new position in the neighborhood of a current position; 4, calculating the adaptability difference between two positions, and judging whether to accept the new position or not through a simulated annealing mechanism when the adaptability of the new position is inferior to the adaptability of an original position but is superior to the adaptability of a global optimal position; 5, updating the global optimal position of a population, carrying out the natural selection operation, carrying out the arrangement of all particles according to the adaptability values, and employing the information of a part of better particles to replace the information of the other half particles; 6, judging whether to stop the iteration or not; 7, outputting PID controller parameters or executing step 2 again. The method can achieve the automatic setting of control parameters, irons out a defect that a conventional PSO algorithm is very liable to be caught in local optimization, achieves the complementation of the simulated annealing operation and a natural selection strategy, improves the convergence precision of the algorithm under the condition that the number of convergence times of the algorithm is guaranteed, is higher in robustness and precision, and enables the PID controller to generate a more excellent control effect.

The invention relates to a method suitable for multi-view-angle automatic global registration of ground laser point cloud data. The method involves two key modules: a module for extracting semantic feature points and a module for feature matching. The method comprises: step 1, extracting the semantic feature points: obtaining the semantic feature points in a series of modes of data slicing, distance clustering, geometric element fitting and the like; step 2, matching the semantic feature points: matching the semantic feature points by constructing a triangular geometric constraint condition and a semantic constraint condition, and removing error matching in a geometric consistency clustering mode; and finally, constructing a weighted undirected graph by taking a reciprocal of a feature point number as a weight, and finally obtaining global registration parameters of all stations by taking a minimum spanning tree of the weighted undirected graph as a registration path, thereby realizing global optimal registration. According to the invention, the method suitable for multi-view-angle automatic global registration of the ground laser point cloud data is constructed; the method can effectively resist influences of noise, point density and coverage; and the method improves the laser scanning operation efficiency, thereby having very high practical values.

Disclosed is an improved technique for training a support vector machine using a distributed architecture. A training data set is divided into subsets, and the subsets are optimized in a first level of optimizations, with each optimization generating a support vector set. The support vector sets output from the first level optimizations are then combined and used as input to a second level of optimizations. This hierarchical processing continues for multiple levels, with the output of each prior level being fed into the next level of optimizations. In order to guarantee a global optimal solution, a final set of support vectors from a final level of optimization processing may be fed back into the first level of the optimization cascade so that the results may be processed along with each of the training data subsets. This feedback may continue in multiple iterations until the same final support vector set is generated during two sequential iterations through the cascade, thereby guaranteeing that the solution has converged to the global optimal solution. In various embodiments, various combinations of inputs may be used by the various optimizations. The individual optimizations may be processed in parallel.

The present invention discloses a multi-parameter multi-object chaotic particle swarm parameter optimization method. The method comprises the steps of (1) determining a target function and a parameter to be optimized, (2) initializing an algorithm, (3) calculating the target function corresponding to each individual in a population, (4) updating an individual history optimal solution, (5) updating a particle velocity and position, (6) updating a global optimal solution set, (7) updating a global optimal solution, and (8) carrying out result judgment. Compared with a common random initialization method and an existing chaotic logistic mapping particle swarm initialization method, according to the method of the invention, the performance of global optimization is improved and the stability is good, compared with a common multi-object weighted optimization method, the Pareto optimal solution technology is employed by the method, and the problem of difficult weight selection in the multi-object method is solved.