96 results about "Sequence planning" patented technology

The invention provides an automobile retired power battery disassembly sequence optimization method and a related device, and relates to the technical field of data processing. According to the method, disassembling risk priori knowledge provided by a battery manufacturer is introduced, a multi-target disassembling sequence planning mathematical model integrated with risk perception is established, and a disassembling sequence planning algorithm based on a graph attention network and a constraint perception pointer network is adopted, so that end-to-end real-time planning of disassembling sequences of different types of battery packs is realized. According to the method, the prior disassembling knowledge of an official manual can be effectively utilized, the high efficiency and safety of the disassembling process are ensured, the method can adapt to a complicated battery pack structure and a dynamically changing disassembling scene, and a brand new solution is provided for disassembling the retired power battery.

The invention belongs to the technical field of coal bed gas mining, and particularly discloses a multi-coal-bed mining optimization control method based on crossheading position stability, and the method comprises the steps: comprehensively obtaining parameters such as coal bed spacing, thickness, inclination angle, roof and floor lithology and fault distribution, and generating an accurate three-dimensional geologic model through three-dimensional modeling software; and the condition of incomplete geological analysis of a traditional method is changed. And on the aspect of crossheading arrangement, empirical operation is replaced with finite element analysis, the anchor rod distance and the support density are accurately determined, and safety and economical efficiency are improved. The mining sequence planning is changed from dependence on a fixed rule to establishment of an identification model combined with a genetic algorithm, and dynamic optimization is realized. The mining height, the mining width and the advancing speed are flexibly regulated and controlled according to the mapping relation table in the mining process, and real-time dynamic adjustment is achieved. Meanwhile, the early warning mechanism calculates scores based on crossheading stress data, is more accurate and sensitive, and has higher pertinence and effectiveness.

According to the infrared fast patrol method based on task sequence control provided by the invention, the robot is guided to pre-adjust the pan-tilt angle and the infrared focal length during advancing through task sequence planning, and the target is intelligently positioned by synchronously utilizing the equipment template library, so that continuous temperature measurement during movement is realized; performing multi-view infrared image comparative analysis on the temperature exceeding equipment through a multi-angle abnormity rechecking strategy; abnormal equipment outside a preset point location is positioned in real time in combination with a laser point cloud map and space coordinate conversion, the parking time of the robot is eliminated, the temperature measurement efficiency is improved, environmental interference is effectively eliminated, the misjudgment rate is reduced, and a high-timeliness and high-reliability global temperature monitoring defense line is constructed for safe operation of industrial equipment.

The invention discloses a hinged object robot grabbing method based on a visual language action model. A hinged object is put in a meta-universe environment for rendering, a geometric center point of the hinged object is used as a core reference point, six instance representation methods are adopted to mark the hinged object, and a sample database is established; generating six training tasks based on six instance representation methods; constructing a visual language action model based on LLaMA2 and SPHINX-X, and performing supervised training on the visual language action model according to a training task and a sample database to obtain an optimal visual language action model; constructing a joint optimizer, and selecting an optimal result from output results of the visual language action model by decomposing a hinged object manipulation sequence planning task; and S3, using the optimal visual language action model obtained in the step S3 and a joint optimizer to determine the hinged object robot grabbing method.

To provide a production sequence planning system that merges products in correct order even if a planned product merging sequence changes during an upstream process.SOLUTION: A production sequence planning system for planning an order to merge inspected products, which have been manufactured with a plurality of specifications in a production line having a plurality of manufacturing processes and sampled at an inspection process, back into a production line, is configured to: compare a target serial number nx of a product that has reached a point before an inspection product merges with a confirmed serial number na for which a production instruction to merge has been fixed; and determine whether the product with the target serial number is within a planned production sequence, based on the serial number of the product following the target serial number. If the product with the target serial number is within the planned production sequence, the system compares the target serial number with a serial number n of the inspection product to determine whether the inspected product can be merged, and updates the serial number of the merged inspection product to the fixed serial number. If the product with the target serial number is outside the planned production sequence, the system does not merge the inspection product, but merges the product with the target serial number and does not update the confirmed serial number.SELECTED DRAWING: Figure 2

Random key optimization is used to generate sequencing plans. An optimization problem is obtained to generate a sequencing plan. The optimization problem may be described according to nodes that represent decision variables of the optimization problem. A random optimization key technique may be applied to determine a vector of respective values for the nodes. The vector of respective values of the nodes may be decoded according to a sequence planning constraint to generate the sequencing plan and determine a fitness score for the sequencing plan.

The invention discloses a man-machine cooperative assembly task allocation and sequence planning method based on human factor engineering, relates to the technical field of intelligent manufacturing, and can fully consider human factors in man-machine cooperative assembly to realize efficient task allocation and sequence planning. The method comprises the following steps: acquiring a man-machine cooperation assembly task, analyzing the assembly task to obtain a decomposed sub-task set, and searching an optimal executor for assembly sub-tasks which can be executed by both people and a robot in the sub-task set; according to the method, assembling sub-task attribute division considering ergonomics is carried out, a man-machine cooperation assembling task allocation and sequence planning model is constructed, and an optimal task allocation and sequence planning scheme of a sub-task set is searched, so that the assembling efficiency is optimized, the man-machine cooperation execution difficulty is reduced, and the physiological and cognitive fatigue conditions of personnel are improved.

The invention belongs to the technical field of robot grabbing, and discloses a complex scene grabbing method and system based on multi-modal fusion and reinforcement learning, and the method comprises the steps: obtaining a language instruction of a user and grabbing a scene RGB image; obtaining a text feature, an image feature, a position feature and a capture feature based on the language instruction and the captured scene RGB image; based on a cross attention mechanism, performing feature fusion operation on the text features, the image features, the position features and the capture features to obtain cross attention features; and taking the cross attention features as a state space, taking the grabbing poses as an action space, modeling a grabbing sequence planning problem as a Markov decision process, and solving by adopting a reinforcement learning method to obtain a final complex scene grabbing model. According to the invention, through the prior fusion of the multi-modal joint modeling and the pre-training model, not only is the adaptive capacity of the robot in a complex environment improved, but also the generalization of the method and the execution effect in the real world are remarkably improved.

The invention relates to the technical field of simulation modeling, in particular to a BIM-based building embedded pipeline simulation system, which comprises a pipeline path optimization module, a space conflict detection module, a construction sequence planning module, a dynamic adjustment generation module and a resource utilization rate evaluation module. According to the method, by automatically extracting the spatial distribution information of the pipelines and analyzing the geometrical characteristics and the connection relation of the pipelines, accurate marking of potential conflict positions can be achieved, the comprehensiveness and the accuracy of spatial conflict recognition are effectively improved, and further based on extraction of the construction sequence relation and logic dependency analysis, the construction efficiency is improved. The method can achieve the intelligent judgment of the construction conflict state, achieves the dynamic adjustment of a layout path and a time range, assists in forming a targeted wiring strategy in a conflict region, optimizes the resource space matching degree, automatically recognizes a low-efficiency time period, carries out the real-time identification, improves the response capability of a construction process to the resource utilization efficiency, and improves the construction efficiency. And the precision of path adjustment and the coordination of construction organizations are improved.

The invention relates to the technical field of numerical control program adjustment, in particular to a vertical numerical control machine tool program adjustment system based on digital twinning. The system comprises an association weight distribution module and a detection sequence division module. According to the invention, the association weight distribution module is combined with the types of the to-be-detected simulation codes and historical adjustment control information to distribute the association weights of the to-be-detected simulation codes, and the detection sequence division module is matched with the association weights of the to-be-detected simulation codes to formulate corresponding detection sequences. According to the method, probability prediction is carried out on multiple associated control codes causing abnormal features, the subsequent maintenance sequence is divided through the initiation probability, regular associated control code maintenance sequence planning is achieved, associated control code detection work is carried out in a targeted mode, the influence of irrelevant control codes on the detection process is avoided, and the subsequent maintenance efficiency is improved.

Aiming at the construction of an automatic warehouse logistics system of a digital workshop production line, the invention provides a single-load automatic guided vehicle workshop path planning method based on directed acyclic graph modeling, and the method comprises the functions of task selection, task execution sequence planning and the like. The method comprises a fusion algorithm framework based on a binary discrete particle swarm optimization algorithm and a genetic algorithm component, and can solve and fully consider the diversity of task selection strategies in a short time according to information such as known task arrival time and transportation starting point, so that normal production is ensured; and the task execution sequence with the shortest total driving distance is the optimal (or close to the optimal) task execution sequence.

The invention relates to the technical field of numerical control machining, and discloses a workpiece machining method, electronic equipment, a storage medium and a program product.The method comprises the steps that cutting information of a to-be-machined workpiece is obtained, and the cutting information is obtained by conducting cutting simulation on the to-be-machined workpiece; the cutting information comprises multiple sections of cutting paths and processing sequence constraints among the cutting paths; based on the machining sequence constraint and the length of a non-cutting path between the cutting paths, machining sequence planning is conducted on the cutting paths, and a target machining sequence is obtained; and based on the cutting paths, the target machining sequence and non-cutting paths between the adjacent cutting paths in the target machining sequence, the to-be-machined workpiece is machined, and a target workpiece is obtained. The problem that the workpiece machining efficiency of the numerical control machine tool is low can be solved.

The application discloses a picking sequence planning method of a mechanical arm for picking spherical fruits cultivated by a cultivation frame, which comprises the following steps: identifying a tomato area by using a deep learning framework, identifying a fruit to be picked, modeling a traveling salesman problem and solving the problem by using a genetic algorithm to plan an optimal picking sequence for all string tomatoes to be picked, and guiding the mechanical arm to continuously pick multiple target fruits. In addition to the shortest total path, the minimum factor of each rotating joint of the mechanical arm is added in the path evaluation index for comprehensive evaluation. The planned picking sequence can not only make the total path tend to be the shortest, but also prevent the rotating angle of each joint from being too large, thereby preventing the mechanical arm from being damaged. The application can improve the picking efficiency and the picking success rate, and avoid the damage of the mechanical arm caused by the large rotating angle of the joint, and has practical significance for promoting the yield and income of the string tomato industry and the intelligentization of automatic picking equipment.

The invention discloses an unmanned aerial vehicle routing inspection sequence planning method and system for disaster situation data acquisition and return. The method comprises the steps of establishing an unmanned aerial vehicle routing inspection sequence planning task scene for unmanned aerial vehicle disaster situation data acquisition and return; in the task scene, based on a multi-path traveling salesman problem, establishing a post-disaster data acquisition and return task system model; and solving the post-disaster data acquisition and return task system model by adopting a multi-strategy improved grey wolf optimization algorithm to obtain an unmanned aerial vehicle inspection sequence scheme, and the method and the system can realize unmanned aerial vehicle inspection sequence planning.

The invention discloses an assembly sequence planning method based on improved Monte Carlo tree search, and belongs to the technical field of assembly sequence planning, the method comprises the following steps: S1, constructing an assembly basic data matrix and a tool table; s2, establishing assembly sequence mixing performance indexes; s3, iterating the improved Monte Carlo tree search algorithm; and S4, solving and outputting an optimal assembly sequence. According to the assembly sequence planning method based on improved Monte Carlo tree search, the simulation time is remarkably shortened, the optimization speed is increased, the limitation that a traditional algorithm is prone to falling into local optimum is broken through, and the understanding quality is effectively improved.

The embodiment of the application provides a dual-arm action sequence planning method for on-orbit multi-module replacement tasks, comprising: mathematically modeling the multi-module replacement task and the dual-arm system; task decomposition is performed on the multi-module replacement task, a dynamic programming algorithm and a depth-first search algorithm are used to search for a task decomposition scheme for dividing the large-scale module replacement task into multiple batches of module replacement tasks according to single-module replacement task constraints and task points in series; a dual-arm task allocation and scheduling model for multi-module replacement is constructed by considering multiple optimization objectives and constraint conditions, and an intelligent optimization algorithm is used to solve the dual-arm action sequence; the obtained solution set is evaluated by using a non-dominated solution distance method, the dual-arm action sequence scheme is sorted, and an ideal optimal planning solution is obtained. According to the technical scheme provided by the embodiment of the application, the efficiency of the dual-arm system in executing the on-orbit multi-module replacement task can be improved under the premise of avoiding dual-arm conflicts, and the continuous and healthy operation of the dual-arm system can be ensured.

The application discloses a self-task planning method for quick maneuvering rendezvous imaging of a spatial space target, and comprises the following steps: step one, multi-pulse control sequence planning: according to task requirements and considering determination of an orbit requirement, a multi-pulse control sequence is given, and the first control is implemented autonomously on the satellite; step two, closed-loop control sequence planning: after the i-th control is finished, the remaining N-i control sequences are given in combination with the orbit of the satellite after control and the orbit of the target, and the i+1-th control is implemented; wherein, i is greater than or equal to 1 and less than or equal to N-2, and the total control times are N; step three, single-pulse transfer planning: after N-1 control is finished, the last control velocity increment is calculated by using a Lambert transfer method in combination with the orbit of the satellite after control and the orbit of the target, and the N-th control is implemented; and step four, imaging task planning. The closed-loop control planning method is given by considering the influence of control errors and orbit errors, the method can ensure that the planning result of each time is basically consistent with the first planning, and the final rendezvous precision requirement is met.

The application belongs to the technical field of mobile visual inspection planning, and specifically discloses a mobile visual inspection station-point joint clustering and sequence planning method. The method is a genetic algorithm based on an alternating evolution strategy, and comprises steps of data encoding / decoding, evolution strategy updating, individual selection, individual crossover, individual mutation, population updating and the like. The length of a chromosome is equal to the number of points participating in sequencing. The integer part of a gene represents a point sequence number, and the decimal part represents a station sequence number to which the point belongs. Point sequences belonging to the same station follow the order of appearance in the chromosome; station sequences follow the order of the first appearance position. The application can integrally realize station-point clustering and sequence planning, and improve visual inspection efficiency; the proposed evolution strategy also significantly reduces the running time of the planning program. The application is suitable for large and complex component mobile visual inspection and other similar scenarios.

The present invention discloses a method for target recognition and segmentation, precise positioning, and picking sequence planning for spherical fruits obscured under dense canopies. Through fruit-branch segmentation and contour extraction, the two-dimensional features of the fruit sphere and branches are obtained. Furthermore, the method accurately positions the spherical fruits under canopy obstruction and corrects the positioning errors of the picking points. The method also determines the spatial interference relationship between the target fruit and the branches, eliminating fruits that cannot be picked due to interference between the branches and fruits. Simultaneously, the collision relationship between multiple fruits is determined and the collision coefficient is calculated. This method guides a robotic arm to complete the picking operation in the order of picking independent fruit queues and picking collision fruit queues. The present invention effectively avoids the problems of fruit misidentification, mispositioning, and excessive positioning deviations that lead to picking failures. It also prevents the robotic arm from forcibly picking fruits that are improperly positioned or severely interfered with by branches and leaves, which can cause fruit damage or damage to the robotic arm, thereby ensuring efficient harvesting.

The application discloses a truss environment-oriented two-legged robot crawling landing point sequence planning method and belongs to the technical field of robots; the method first constructs a truss undirected graph topology model based on the spatial connection relationship of truss joints and members, takes nodes to represent the truss joints, takes edges to represent the members and takes the member length as the edge weight; the A star algorithm is adopted on the topology model to search for the node sequence and the member sequence of the robot from the starting member to the target member; subsequently, the axial position of the member is described through a normalized member parameter model, and the non-connected feasible landing area set of the robot foot end is represented in combination with One-Hot coding; on this basis, the least number of steps required for the robot to reach the target position is taken as an optimization target, a mixed integer quadratic constraint programming model is established, the landing point sequence is solved, and the optimal landing point sequence satisfying the truss topology constraint and the robot motion constraint is obtained. The application can realize the landing point sequence planning of a two-legged truss crawling robot on a complex truss structure.

The invention discloses a site planning method for grinding a large structural component through a movable grinding platform. The method comprises the steps of 1, establishing an optimization model; step 2, establishing a coding data solving format; 3, solving by adopting an optimization algorithm, and solving the proposed optimization model by using an improved non-dominated sorting genetic algorithm RL-NSGA-DP integrating reinforcement learning and double-population coevolution; step 4, selecting a site sequence planning scheme from a solving result to regulate and control the mobile grinding platform; the labor and time cost is effectively reduced, and the efficiency and the automation degree of the production process are improved.

This invention discloses a 2.5D packaged die-to-die simultaneous escape routing method based on a pattern-matching binary tree. The method includes: splitting the interconnect network between multiple dies into multiple sets of simultaneous escape routing models for two-die interconnections; and dividing the signal network in each model into multiple network sets based on the separation characteristics of power and ground networks on signal networks; for each network set, performing fan-out routing planning and layer allocation based on a pattern-matching binary tree, specifically including: setting the routing mode, constructing the pattern-matching binary tree, and backtracking the pattern-matching binary tree; finally, completing the routing based on the fan-out routing planning and layer allocation results. This invention proposes a collaborative planning technology for routing sequence planning and layer allocation in 2.5D IC packaged die-to-die simultaneous escape routing, aiming to solve common routing sequence and layer allocation problems in traditional die-to-die routing. This technology, through optimized routing sequence planning and layer allocation strategies, automates the routing process and significantly improves routing quality and efficiency.

A multi-point milling path design method for the full surface feature structure of thin-walled spherical shell micro-components is disclosed, relating to the field of micro-component processing technology. This invention can meet the requirements of high-precision, stable, and controllable removal of full surface feature structures of thin-walled spherical shell micro-components under micro-scale constraints. Key technical points: The method includes spatially uniform distribution of feature structures, micro-structure processing sequence planning, and multi-point milling path generation. The thin-walled spherical shell micro-component to be processed is observed offline using an optical microscope to obtain the coordinates of feature points on the contour edge of the micro-component, and the diameter of the micro-component is obtained by fitting. Based on the secondary development platform of UG software, a micro-structure processing sequence planning method driven by the shortest processing path is established to complete the above-mentioned full surface micro-structure processing sequence planning of the micro-component. Based on the point set coordinates optimized by the spherical point set uniform distribution iterative algorithm and the processing sequence driven by the "shortest processing path," combined with the configuration characteristics of ultra-precision shape control machining equipment and the micro-structure processing requirements, the coordinates of several uniformly distributed point sets with the center of the micro-component as the origin are transformed into a machining coordinate system with the tool setting point as the origin, obtaining the original coordinates of the micro-structure point sets and the corresponding feature angles. This invention is used for full-surface milling of thin-walled spherical shell micro-components.

The invention discloses a motor assembly production management method based on data analysis and a related device, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the assembly complexity analysis through assembly production list information based on the internal uniformity information of a material; determining a response characteristic index of the motor material to generate a target function, and performing assembly sequence planning by using the assembly complexity data based on the target function; performing production time prediction based on the assembly complexity data and the assembly sequence planning data; performing resource scheduling analysis based on the production time prediction information in combination with the production line state information, and determining task allocation information based on resource scheduling information; and performing motor assembly production based on the resource scheduling information and the task allocation information, and in the motor assembly production process, analyzing the environment disturbance index of the processing equipment to perform parameter adjustment on the processing equipment. The intelligent level and the flexibility level of motor assembly production can be remarkably improved, and the production efficiency and the product quality are improved.

The application discloses a kind of tailings pond dry stoping process based on multistage belt conveyor, belong to tailings stoping technical field, mainly include tailings pond exploration, stoping area division and parameter determination, transport laying, stoping sequence planning and drainage facility arrangement;The application is based on the drainage mode of "chute+drainage culvert", including stoping I area, stoping II area, stoping area, block by using sand taking strip, sand taking path, in-pit transport road, belt transport and sand mining pit guide drainage pit, zoning, strip division is carried out in tailings pond in-pit, each area in the pit is layered and taken sand in strip by using excavator+small transport vehicle, and the dam top to dam foot is transported by belt transport, and is transported to large self-unloading truck by unloading buffer bin, effectively solve the stoping condition that tailings pond upper dam road cannot be transported by car, improve the efficiency of tailings pond stoping.

The application relates to the technical field of chip testing, and discloses an intelligent dynamic scheduling method and system for a chip testing process. The method comprises the following steps: acquiring wafer batch records of a chip testing production line, the number of parallel test heads of a machine table, and the current slot occupation state, and the wafer residual data and the number of idle slots of the current testing production line; performing queue matching, aggregation screening, time consumption alignment and sequence planning according to the wafer batch records, the number of parallel test heads of the machine table and the current slot occupation state, so as to obtain a test instruction set; and performing timing conflict detection according to the wafer residual data, the number of idle slots and the test instruction set, so as to obtain a scheduling signal sequence and output the scheduling signal sequence. The method can realize space-time dynamic cooperation of the whole chip testing process, completely eliminate the timing mismatch between material handling and testing actions, and significantly improve the production line testing efficiency and the equipment resource utilization rate.

The invention provides a self-adaptive gridding FPGA (Field Programmable Gate Array) waveform assembly line dynamic reconstruction method and system, which introduces a self-adaptive gridding algorithm, depends on modeling and safe sequence planning based on DAG (Directed Acyclic Graph), and an assembly line parallel scheduling and integrated time sequence calibration mechanism. The problems that in an existing FPGA waveform dynamic reconstruction technology, the resource fragmentation problem is caused by static partition, the system reliability risk is caused by component dependence management deficiency, and the reconstruction delay is large and the real-time performance is poor due to a serial reconstruction mode are solved. According to the method, the utilization rate of FPGA logic resources is remarkably increased, the safety and controllability of the reconstruction process are achieved, the overall reconstruction time is greatly shortened, and meanwhile the processing precision after reconstruction is guaranteed through self-adaptive time sequence calibration. And an efficient hardware dynamic reconstruction solution is provided for application scenes with extremely high requirements on real-time performance and reliability, such as software defined radio and integrated radio frequency systems.

The application discloses a micro-control rolling process time sequence planning method based on a reverse neural network, and comprises the following steps: designing a single-pass welding process; determining a temperature field distribution function heat source parameter according to experimental data of the single-pass welding process test; calculating a stable molten pool size, analyzing a heat source scanning path, and extracting a characteristic path according to the molten pool size, a stable distance and a control rolling interval; simplifying the temperature field distribution function through a control rolling point position on the characteristic path to generate a training data set; constructing a reverse neural network with temperature as input and applied time as output, training and evaluating the reverse neural network by using the data set, and obtaining an inverse function of the temperature field distribution function; substituting a control rolling temperature to obtain a control rolling time of the control rolling position; sequentially constructing a reverse neural network for other control rolling points and obtaining the control rolling time, obtaining a control rolling time difference between adjacent control rolling points in a stable phase, and forming a control rolling time sequence; and deploying the control rolling time sequence to an actual control rolling process to realize accurate control of the control rolling.

The application discloses a kind of articulated object robot grabbing methods based on visual language action model, articulated object is placed in meta-universe environment rendering, using the geometric center point of articulated object as core reference point, six instance representation method is used to mark articulated object, and sample database is established;Based on six instance representation method, six kinds of training tasks are generated;Based on LLaMA2, SPHINX-X constructs visual language action model, according to training task and sample database, the visual language action model is supervised training respectively, and the optimal visual language action model is obtained;Joint optimizer is constructed, the articulated object manipulation sequence planning task is decomposed, and the best result is selected from the output result of the visual language action model;The articulated object robot grabbing method is determined using the optimal visual language action model and joint optimizer obtained by S3 step.

The invention discloses an assembly sequence planning method based on a hierarchical genetic algorithm, and belongs to the technical field of product assembly sequence planning. The GA is improved, so that the quality of an initial solution and a filial generation solution is improved, the search speed is increased, and it is ensured that an optimal solution is found. Comprising the following steps: S1, constructing an assembly sequence planning mathematical model; s2, based on a genetic algorithm, a population is initialized, hierarchical coding is adopted, a sequence layer, a direction layer and a tool layer are mapped respectively, and the hierarchies are associated with one another and jointly act on a fitness function; s3, a fitness function is evaluated, if an index is reached, the next step is executed, otherwise, population evolution operation is improved, tournament selection is adopted in selection operation, fragment recombination crossover is adopted in crossover operation, and selective mutation is adopted in mutation operation; and S4, outputting a globally optimal solution. And the stability is higher. Compared with a traditional GA, the improved GA has the advantages that the convergence speed is higher, the optimal assembly sequence can be evolved earlier, and the global optimization capacity and the local optimization capacity are higher.