71 results about "Sequence planning" patented technology

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 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 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 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 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 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.

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 application relates to the technical field of part assembly sequence planning, in particular to an assembly body assembly sequence planning method and device based on a CAD file, wherein the method comprises the following steps: generating an occupancy grid of a functional part based on a point cloud file of the functional part in a target assembly body and a CAD model of the target assembly body; detecting interference information and contact information of the functional part based on the occupancy grid, and generating an initial part interference matrix and an initial part contact matrix; identifying interlocking relationships and static interference among the functional parts; correcting the initial part interference matrix and the initial part contact matrix; determining an assembly sequence of the functional parts and connecting parts based on a final part interference matrix and a final part contact matrix; and planning an assembly body assembly sequence of the target assembly body. Therefore, the problems in the prior art that assembly constraints cannot be automatically identified based on CAD files, assembly efficiency is low, assembly cost is high, and the requirements of modern industry for rapid response and high precision cannot be met are solved.

The invention discloses a thermal power plant steam turbine installation VR auxiliary teaching system and method based on a logic constraint map. In order to solve the problem that existing VR training lacks process logic constraints and is difficult to cultivate standardized operation habits, the method comprises the following steps: abstracting a turbine installation process into a directed acyclic graph (DAG) to construct a logic constraint graph; the system dynamically generates an executable task list through a sequence planning engine, and performs real-time precondition verification and interception on user operation by using a process rule verifier; through semantic modeling of parts, a dynamic assembly sequence verification algorithm and a virtual-real mapping interaction mechanism, operation guidance of strong logic driving is realized. Hidden construction specifications are converted into dominant and executable logic rules, the process error rate and the key component omission rate of students are effectively reduced, and teaching transformation from skill simulation to engineering thinking development is achieved.

The invention discloses a robot long-time assembly task planning method and system based on reversibility constraint. In order to solve the problems of irreversible steps and high failure cost in an assembly task, the reversibility and failure semantics of assembly subtasks are explicitly modeled in a task planning layer, a subtask irreversible risk measurement model is constructed, and risk-aware assembly task sequence planning is performed on the basis, so that the occurrence probability of irreversible failures is reduced. In the execution stage, aiming at each assembly subtask obtained through planning, a vision-language-action model is adopted to directly control the mechanical arm to complete specific operation, and effective cooperation of high-level planning and low-level operation is achieved. According to the method, the safety and success rate of the mechanical arm in a complex assembly task can be remarkably improved, and the method is suitable for long-time and multi-step industrial assembly scenes.

The embodiment of the application provides a dynamic task allocation method for a humanoid robot in an industrial scene and related equipment, and belongs to the technical field of humanoid robots. The application comprises: obtaining a task load level of a current industrial scene; the task load level comprises a low load level, a medium load level and a high load level; determining a corresponding task allocation strategy based on the task load level; the task allocation strategy comprises an event-triggered allocation strategy, an improved Hungarian algorithm allocation strategy and a hybrid heuristic algorithm allocation strategy; performing humanoid robot task allocation based on the determined task allocation strategy; the humanoid robot task allocation comprises task-robot matching, task sequence planning and cooperation scheme generation. The application can dynamically switch allocation strategies in different load scenes, takes into account real-time performance and global optimization, and improves robot utilization and industrial scene cooperation efficiency.

The invention relates to an assembly information scanning projection sequence planning method and device, electronic equipment and a storage medium. The method comprises the steps that a geometric graph, a text graph, a mark graph or a mixed graph is adopted as a graph element to define a projection graph, a projection graph is marked, and the projection graph comprises non-communicated sub-graphs; planning a scanning projection sequence between the non-communicated sub-graphs in the projection graph, and generating a scanning path between the non-communicated sub-graphs; planning a scanning path in each non-communicated sub-graph in the projection graph, and generating the scanning path in each non-communicated sub-graph; and representing a scanning projection graph which is scanned according to the scanning paths among the non-communicated sub-graphs and the scanning paths in the non-communicated sub-graphs according to a three-layer tree structure representation sequence. The target graph can be ensured to be projected orderly, the projection frequency of the target graph is improved, and the projection effect of the target graph is improved.

The invention discloses a sequence planning method for intelligent driving domain design, and relates to the technical field of vehicle automatic driving, and the method comprises the following steps: decomposing the driving behavior of a vehicle in the driving process into four types of standard driving action sequences, and expressing the motion condition of an automatic driving vehicle in the driving process through different combinations of the driving action sequences; constructing a driving action sequence library corresponding to the driving behaviors in an automatic driving vehicle controller, and obtaining current driving state information of the controlled vehicle; by constructing the mapping relation between the driving behavior and the action sequence, the system can identify the behavior stage of the vehicle in real time, and quickly adjust the planning strategy, so that the adaptability to the dynamic environment is improved; a plurality of neighborhood models are constructed for different driving behaviors, and the size and shape of the neighborhood are dynamically adjusted according to factors such as vehicle speed, acceleration and road types, so that a driving domain search space is consistent with an actual operation space, and the feasibility and safety of the driving domain are enhanced.

The invention discloses a motor sweeper operation path generation method and device, and the method comprises the steps: constructing a road connected graph based on a to-be-swept region map road boundary and a topological connection relation; coarsening the connected graph according to a preset merging strategy, and merging the fine-grained nodes into logic region nodes; constructing a final cleaning task area based on the coarsened connected graph; planning a sub-region cleaning sequence in combination with the current position of the cleaning vehicle; and constructing a global operation path. According to the invention, whole-process automation of cleaning area division and sequence planning is realized, manual intervention is not needed, and operation efficiency and reliability are improved; complex road topologies such as roundabout and the like are adapted, and sub-region division is more reasonable; a heuristic weight and an improved threshold acceptance algorithm are fused, the solving efficiency and the planning quality are balanced, and the requirements for the real-time performance and the accuracy of outdoor cleaning are met.

The application discloses a motor assembly production management method and related device based on data analysis, relates to the technical field of data analysis, and comprises the following steps: based on material internal uniformity information, assembly production list information is used for assembly complexity analysis; the response characteristic index of motor material is determined to generate a target function, and assembly sequence planning is carried out based on the target function and assembly complexity data; production time prediction is carried out based on assembly complexity data and assembly sequence planning data; resource scheduling analysis is carried out based on production time prediction information and production line state information, task allocation information is determined based on resource scheduling information; motor assembly production is carried out based on resource scheduling information and task allocation information, and in the motor assembly production process, the environmental disturbance index of processing equipment is analyzed to adjust the parameters of the processing equipment. The application can significantly improve the intelligentization and flexibility level of motor assembly production, and improve production efficiency and product quality.

This invention discloses an artificial intelligence-based intelligent dismantling sequence planning method for power batteries, belonging to the field of power battery dismantling technology. The method includes initializing a dismantling sequence simulation environment, loading a dismantling action constraint relationship network, a dismantling tool action model, and a dismantling safety rule base; employing a reinforcement learning agent to drive Monte Carlo tree search, simulating component dismantling order to generate multiple candidate sequences, and simultaneously collecting simulation data such as tool selection, time consumption, safety risk score, and resource consumption assessment for each step; constructing a multi-objective evaluation model with four dimensions: dismantling efficiency, safety, resource cost, and component integrity recovery rate, quantifying and fusing data, and calculating a comprehensive score; selecting the highest-scoring sequence as the recommended sequence, and outputting an executable process guidance document with detailed steps, a tool list, and risk warnings. This invention overcomes the limitations of human experience, covers multiple paths under complex constraints, quantifies and balances multi-objective conflicts, and improves the scientific nature of power battery dismantling.

The present application belongs to the technical field of flight simulator database management, and relates to a data synchronization method for a flight simulator configuration database, aiming to solve the problems of low synchronization efficiency and difficult guarantee of data consistency. The present application generates / calibrates synchronization parameters through graph neural operators, optimizes the optimal synchronization sequence containing execution timing through manifold, generates a semantic consistency mapping table through a parameterized quantum model in combination with type constraint Hamiltonian and semantic matching baseline; then, based on the resource allocation benchmark in the synchronization parameters, the subsystem state is modeled as a stochastic partial differential equation, and the dynamic priority strategy is obtained by solving the HJB equation; after executing the synchronization task according to the optimal synchronization sequence and the mapping table, the consistency is verified by the directional slice distance, and if it exceeds the standard, rollback is triggered. The present application constructs a closed-loop system of parameter generation, sequence planning, field mapping, dynamic control and consistency verification, improves the synchronization adaptability and accuracy, and guarantees the data consistency.

The embodiment of the invention provides a tool calling and training method and device, equipment, a storage medium and a program product. The method comprises the following steps of: aiming at a first task instruction, determining first functions required for processing the first task instruction and a dependency relationship among the first functions by utilizing a language model; acquiring a first current running state of a plurality of alternative tools with the first function; determining a first tool calling sequence by utilizing a sequence planning strategy of a language model according to the first current running state and the dependency relationship; according to the first tool calling sequence, calling corresponding tools in sequence to execute the first task instruction; wherein the sequence planning strategy is obtained through reinforcement learning training.

The application belongs to the technical field of building construction automation, and specifically discloses a collapse-resistant operation sequence planning method and device for a steel bar binding robot. The method comprises the following steps: determining a plurality of candidate binding nodes based on the current binding state and support point distribution information of a steel mesh load area currently bearing the load of the robot; performing mechanical response analysis on the steel mesh load area by using the support point distribution information to determine the optimal mechanical response data of the steel mesh load area after each candidate binding node is bound under different working conditions; determining the grid stability score corresponding to each candidate binding node based on the optimal mechanical response data, and selecting the candidate binding node with the maximum grid stability score as the next binding node; and controlling the robot to move to the next binding node for binding operation according to the planned path when the grid stability score is maximum. Through the application, a full-automatic operation mode can be realized without manual pre-binding to ensure construction stability.