16 results about "Distance minimization" patented technology

The invention provides a laser radar internal reference calibration method, which comprises a calibration data acquisition step of acquiring multi-frame point cloud data of different relative positions between a laser radar and a reflection plane of a target plate in a rated working range of the laser radar, and simultaneously, when each frame of point cloud data corresponds to the point cloud data, acquiring a calibration data corresponding to each frame of point cloud data; acquiring a space plane equation of the reflection plane 21 through the camera array; an internal reference resolving step: the target of internal reference calibration in the internal reference optimization model is to minimize the distance between a point on each calibration target plate and a target plate plane measured by the laser radar; and calculating the optimal solution of the system under the constraint condition by using a mathematical optimization algorithm to determine the calibration parameters of the laser radar. According to the laser radar internal reference calibration method, a global internal reference integration calibration method is provided, and the internal reference balancing the correction precision under different scanning distances and scanning inclination angles is obtained, so that the performance of the laser radar in a rated scanning range is well balanced, and the performance precision is prevented from being obviously reduced under the edge condition.

The invention relates to the technical field of automatic warehouse material, and discloses an intelligent goods allocation distribution system for automatic warehouse logistics, and the system comprises a goods allocation data collection module; a dynamic state detector; a multi-source data fusion unit; a three-dimensional storage modeling module; intelligently distributing a strategy engine; and a PLC control interface. According to the intelligent goods allocation allocation and scheduling system for automatic warehouse logistics, a goods allocation allocation scheme is generated through a multi-target Pareto optimization algorithm, the balance between carrying distance minimization min sigma alpha * Di and space utilization rate maximization Ut is achieved, and compared with a proportional static allocation rule, the warehouse space utilization rate is increased to 82%; a dynamic grid map technology is integrated, and a track cross risk area (red early warning grid) of the stacker and the AGV is marked in real time, so that the equipment collision probability is reduced by 90%; and in combination with a goods relevance knowledge graph, special goods such as medicines are clustered and stored according to temperature zones, and the energy consumption of cross-temperature-zone operation of equipment is reduced by 35%.

The invention discloses a composite propeller design method based on a regression neural network and a genetic algorithm, and belongs to the field of composite propellers. According to the method, the minimization of the distance from the pitch angle of the blade tip of the composite propeller to the optimal pitch angle of the propeller type under each working condition is taken as the optimization target of the propulsive efficiency, and the minimization of the absolute value of the difference value of the thrust coefficient KTA under the working condition of the low advance speed coefficient and the thrust coefficient KTB under the working condition of the high advance speed coefficient is taken as the optimization target of vibration and noise reduction; the constraint is that the strength of the structural performance of the composite material needs to meet a Tsai-Wu strength criterion, and constructing a ply pre-deformation optimization model; through combination of a regression neural network and a genetic algorithm, a layering angle sequence and pre-deformation geometric parameters of the composite material propeller are optimized, performance optimization of the composite material propeller in a full working condition range is realized under steady-state and non-steady-state working conditions, the balance between the minimum propulsion efficiency and vibration and noise reduction is realized, and the comprehensive performance of the composite material propeller is improved. Therefore, the working performance of the composite propeller is improved.

This invention relates to a multi-performance coordinated design method for planetary roller screws, belonging to the field of planetary roller screw technology. It includes the following steps: Step 1, establishing a calculation model for the thread engagement point slip ratio and maximum von Mises stress based on the kinematics and load distribution model of the planetary roller screw; Step 2, establishing equal and unequal structural constraints based on the correct meshing, non-interference, and contact boundary conditions of the planetary roller screw; Step 3, establishing a multi-performance optimization design model for the planetary roller screw based on the optimization objective and constraints; Step 4, using a multi-objective genetic algorithm to solve the multi-performance design model of the planetary roller screw, obtaining the Pareto front and corresponding structural design parameters; Step 5, obtaining the Pareto front range that satisfies the given limit trade-off cost. Based on the Euclidean norm and minimizing the dimensionless distance, the optimal trade-off solution and corresponding structural design parameters are obtained.

The invention discloses a scheduling method and scheduling system for tobacco harvesting and transferring collaboration.The scheduling method comprises the steps that modeling is conducted on discrete operation states and state conversion in the harvesting process of harvesters and transferring robots on the basis of a finite-state machine and a difference equation, and transferring task information of the harvesters is predicted; and scheduling the transfer robot to complete the transfer task, constructing a predictive tobacco transfer scheduling model taking minimization of the non-productive operation time of the harvesters and the movement distance of the transfer robot as objective functions, setting constraint conditions, and performing accurate optimization solution by using a multi-objective optimization algorithm. According to the scheduling method, the tobacco harvesting and transferring processes can be effectively and cooperatively scheduled, so that the tobacco harvesting efficiency is improved, and the labor intensity of workers is reduced.

The application discloses a monitoring point layout optimization method and system based on regional scale nitrogen leaching, and relates to the field of agricultural environmental protection.The method comprises the following steps: acquiring a regional area and regional data; based on the regional data, drawing a slope grade map, a groundwater level grade map and a nitrogen accumulation map of the region respectively; performing pixel-to-pixel alignment, superposition and combination calculation on the slope grade map, the groundwater level grade map and the nitrogen accumulation map of the region to generate a plurality of basic units; under a constraint condition, performing initial layout of monitoring points in each basic unit according to the regional area, an obstacle layer and a preset monitoring point spacing; based on an average distance minimization criterion, constructing a target function of the monitoring points; and based on the target function, optimizing the monitoring points in the initial layout by using a space simulated annealing algorithm to obtain an optimized layout of the monitoring points.The application can improve the monitoring accuracy of regional scale nitrogen leaching and the layout efficiency of monitoring well points.

The application discloses a data processing method applied to the field of data processing and used for high-precision positioning of a mobile robot. The method comprises the following steps: firstly, acquiring motion data of the mobile robot in a current frame, and then matching the motion data with a preset global map to determine an initial pose of the mobile robot; secondly, when the distance between the initial pose of the mobile robot and a target pose is less than a first preset threshold, taking the minimization of the distance between the motion data and a target point cloud corresponding to the target pose as a reference, the target point cloud corresponding to the motion data and the target pose is registered to determine a current pose of the mobile robot. When the mobile robot moves to the vicinity of the target pose, the implicit surface is constructed by using the point cloud registration to fit the real environment, so that the pose of the mobile robot can be accurately determined, and the mobile robot can accurately move to the target pose.

The invention discloses a reverse construction method and system for a three-dimensional non-axisymmetric parameterized nacelle model. The method comprises the following steps: extracting two-dimensional section point coordinates from a plurality of key circumferential positions of a target nacelle; an L-BFGS-B constrained optimization algorithm is adopted, the minimization of the average square distance between the generated curve and the target point is taken as the target, and iCST parameterization representation of each section is reversely solved; reconstructing a two-dimensional section by using the obtained parameters and keeping the points consistent; based on the characteristic that a fan surface is a rotating surface, moving and aligning all sections to enable radiuses of the fan to be consistent; interpolating the corresponding points among the key sections to generate a continuous transition section; arranging all sections along the circumferential direction; and finally, on the basis of all section discrete point data, a smooth and continuous three-dimensional non-axisymmetric nacelle parameterized model is constructed through a curved surface generation algorithm, and a general CAD format file can be output. According to the method, the full-parameterized three-dimensional model is automatically reconstructed from two-dimensional information, and the problems of high cost, low efficiency and poor flexibility in the prior art are solved.

The invention relates to an optimized deployment method, device and equipment of an RFID antenna, a medium and a product. The method comprises the following steps: determining an optimization target according to the receiving power of a tag antenna and the receiving power of a reader-writer antenna; the optimization target comprises a label coverage rate target and a performance target, and the performance target comprises at least one of a reader-writer interference target, a distance minimization target and a load balancing target; determining an initial deployment result of each reader-writer to be deployed according to the label coverage rate target; the initial deployment result comprises an initial position and an initial radiation power range; and according to the performance target, optimizing the initial deployment result of each to-be-deployed reader-writer to obtain a target deployment result of each to-be-deployed reader-writer. By adopting the method, iterative optimization can be carried out in a mode that top layer optimization and bottom layer optimization are matched with each other, and the optimal solution of a deployment result can be gradually approached, so that the accuracy of optimal deployment of the RFID antenna can be improved.

The invention discloses a multi-agent interaction method and agents, and belongs to the technical field of agents. The multi-agent interaction method comprises the steps that all agents collect corresponding image information, and the image information comprises relative distance information and mutual view angle information between the agents and other types of agents; the intelligent agent generates a target control instruction based on the image information; the intelligent body moves based on the target control instruction; wherein the intelligent agent is obtained based on reward function training with the purpose that the distance between the intelligent agent and other types of intelligent agents reaches the target condition, and under the condition that the intelligent agent is the first type of intelligent agent, the target condition is distance minimization; and under the condition that the intelligent agent is the second type of intelligent agent, the target condition is distance maximization. According to the multi-agent interaction method provided by the invention, chasing of multiple agents in a complex scene can be realized, so that the interaction effect among the multiple agents is improved.

Improved resource utilization based-modeling is described herein. In an example, a computer system receives layout data indicating a layout of storage areas, staging areas, and material handling equipment configured to move items to at least the storage areas. The computer system receives first association data indicating staging area-to-delivery resource associations and second association data indicating item-to-delivery resource associations corresponding to items to be delivered. The computer system generates an input to a model based on the layout data, the first association data, and the second association data. The computer system determines an output of the model based on the input. The output is generated by the model based on a storage area-to staging area travel distance minimization. The output indicates a distribution of the items across the storage areas. The computer system causes a device to present, in a user interface, the layout and the distribution based on the output.

A housing and / or installation frameworks for a modular multi-level energy system includes a set of similar cabinets configured for orthogonal (e.g., vertical and horizontal) alignment of the modules. The cabinets are configured so modules of a particular phase are oriented along an axis parallel to a reference plane. Modules of the same level of the multi-level arrangement but of different phases are mounted in each cabinet, arranged such that a module for each phase is a defined distance from the reference plane. The cabinets are arranged equidistant and orthogonal to the reference plane, minimizing distance for connections between modules of the same phase across multiple cabinets, and facilitating convenient addition or removal of levels. The framework also facilitates data and reference signal connections between local control devices of the modules, and between the local control devices and a master control device for the system.

The invention discloses a high-energy-efficiency data collection method for a space-air-ground integrated large-scale internet of things, and the method comprises the steps: firstly providing an energy consumption minimization problem model, considering the CS data reconstruction precision requirement, the flight energy consumption of an unmanned plane, and the energy consumption balance distribution of the internet of things, and decoupling an energy consumption minimization problem into two sub-problems; for the node selection sub-problem, network energy consumption balance and unmanned aerial vehicle flight distance minimization targets under CS reconstruction precision constraint are realized through a CSNS algorithm; for the energy consumption iterative optimization sub-problem, a non-convex problem is converted into a convex problem by adopting continuous convex approximation, variable relaxation, variable substitution and other technologies through a two-stage joint optimization algorithm, and the goal of energy consumption minimization of the space-air-ground integrated large-scale Internet of Things network system is achieved by joint optimization of the flight path, the flight time and node scheduling of the unmanned aerial vehicle. The effectiveness and superiority of the method are proved through the embodiment.

The invention discloses a monitoring point layout optimization method and system based on regional scale nitrogen leaching, and relates to the field of agricultural environment protection, and the method comprises the steps: obtaining a regional area and regional data; based on the regional data, respectively drawing a slope grade map, an underground water level grade map and a nitrogen accumulation map of the region; performing pixel alignment superposition and combination calculation on the gradient grade map, the underground water level grade map and the nitrogen accumulation map of the region to generate a plurality of basic units; performing initial layout on the monitoring points in each basic unit according to the area of the region, the barrier layer and a preset distance between the monitoring points under a constraint condition; constructing a target function of the monitoring points based on an average distance minimization criterion; and based on the objective function, optimizing the monitoring points in the initial layout by adopting a space simulated annealing algorithm to obtain an optimized layout of the monitoring points. According to the method, the monitoring precision of regional scale nitrogen leaching can be improved, and the monitoring well point location layout efficiency can be improved.

The present invention discloses a quadruped robot motion control method based on perceptual deep reinforcement learning, which improves the quadruped robot speed tracking optimization target into a point-to-point distance minimization optimization target and combines it with a three-dimensional environment perception network; the three-dimensional environment perception network uses multi-layer, multi-resolution environmental scanning information as privileged information to input into the teacher strategy network, calculates the loss according to the multi-objective reward mechanism based on position, and optimizes the teacher strategy network; uses the quadruped robot's onboard depth information as the input of the student network, and uses the imitation learning method to train the adaptive module and the student network; calculates the terrain completion score according to the quadruped robot's performance in nine different parameterized terrains, and increases or decreases the terrain difficulty according to the obtained score. The above method has stronger athletic ability, enables the quadruped robot to complete parkour obstacle crossing tasks in various difficult terrains, and has low computational complexity and is less affected by errors.