755 results about "Path generation" patented technology

The invention discloses a free-form surface three-axis ball-end cutter equal approximation error finish machining tool path generation method which comprises the following steps: firstly, importing a free-form surface model to be machined, and setting data such as the radius, the line spacing, the approximation error maximum allowable value and the precision of a ball-end cutter; secondly, a group of section planes are planned according to the row spacing to intersect with the curved surface, the intersecting line serves as a cutter contact curve, and an equal-bow-height-error cutter contact iterative search method for driving cutter contact adjustment through the geometric distance is provided for the cutter contact curve on the section planes; the maximum distance between a cutter cutting envelope surface and a cutter contact track line is used as an approximation error, and an adaptive discrete method is adopted to carry out approximation error calculation; and finally, the cutter location points of the equal-bow-height-error cutter contact points serve as initial values, an equal-error cutter location point calculation method of step length self-adaptive adjustment iteration is provided, approximation errors between the cutter location points are all within an allowable range, and therefore the free-form surface three-axis ball-end cutter equal approximation error finish machining cutter path is obtained.

The invention discloses a 3D printing path planning method based on electric arc additive anisotropy and a stress field, and the method comprises the steps: constructing a CAD three-dimensional model of a part, and obtaining the stress field of the part through finite element simulation; determining a slice plane; mapping the stress field to a slice plane to form a force flow line; according to the obtained force flow line, rotation transformation regeneration is carried out according to the anisotropy of the used electric arc additive, and a reference trajectory considering the anisotropy of the material is obtained; under the principle of alternate arc starting and extinguishing, the corresponding printing sequence in the layers and between the layers is further planned, and the reference trajectory lines are connected end to end to be planned into a continuous path; and generating a code file, and printing according to the printing sequence. The comprehensive mechanical property of a printed piece is improved.

The invention discloses a software development result traceability analysis system based on version control, and relates to the technical field of intelligent software analysis. According to the method, the non-tampering property of code submission records is ensured through a block chain technology, a credible basis is provided for traceability, the code semantic analysis module generates a data set containing semantic association in combination with a clustering algorithm and weighted calculation, and the function influence positioning module constructs a function association graph by applying a graph neural network algorithm, so that the traceability is improved. An influence path of code change on system functions is automatically identified, the time cost of complex project function influence positioning is remarkably reduced, an evolution path generation module analyzes and tracks a code evolution path in combination with a time sequence, and a core module identification module clusters key nodes and positions a core function module. And the time-tracing source data storage module generates a visual relation graph and a queried data set, so that the whole-process management from code submission to result tracing is realized.

The invention provides a spinning threading action optimization method and system based on path deep learning, and the method comprises the steps: firstly obtaining a continuous three-dimensional point cloud sequence of a yarn broken end region, yarn physical attributes and mechanical arm dynamic parameters, and then constructing a tension-moment constraint related to the tensile strength of yarn according to the physical attributes and the dynamic parameters, meanwhile, on the basis of the three-dimensional point cloud sequence, a motion acceleration constraint associated with the anti-jitter capacity and an obstacle avoidance safety distance constraint associated with the obstacle distribution density are constructed, and then a loss function of a pre-constructed mechanical arm path generation neural network is optimized according to the three kinds of dynamic constraints linked with a scene; according to the invention, the first success rate and the track stability of the threading action can be improved, the threading failure caused by snapping, throwing or collision is reduced, the threading time is shortened on the premise of ensuring the safety of yarns, and the reliability of spinning operation is improved.

The invention relates to the technical field of electrical wiring. The invention discloses a method for generating a cable wiring path of a power distribution cabinet, and the method comprises the steps: firstly obtaining the multi-dimensional environment parameters of a to-be-wired power distribution cabinet, wherein the structural parameters comprise the three-dimensional layout in the cabinet, the positions of components and wire duct guide rails, the electrical parameters comprise the connection relation of the components, the cable specification and the like, and the physical constraint parameters are the allowable bending radius and the minimum electrical gap; constructing a digital twinborn model according to the parameters, and performing risk analysis on the model to obtain a safety risk level; and finally, a double-path scheme is generated and output in combination with the model and the grade, the first path is the optimal path of the prediction model, the second path is the standby path of the historical case, and the priority of the first scheme is higher. According to the method, the security risk is quantified in advance through multi-dimensional parameter acquisition, digital twin modeling and risk assessment. The current situation that traditional manual wiring depends on experience is changed through intelligent generation of an optimal path, reuse of a standard scheme of a standby path and real-time monitoring and early warning.

The invention discloses an automatic settlement path generation method fusing a knowledge graph and a tax rule, and relates to the technical field of financial science and technology and intelligent finance and tax compliance, and the method comprises the steps: S1, obtaining initial decision basis data and execution process details from a settlement path through a block chain network, employing a Hash chain structure to link each link record, s2, according to the obtained path chain, a graph database is adopted to store dynamic association attributes, and the dependency relationship of each node in the path is determined through knowledge graph reasoning, and S3, if an information fault is detected in the path chain, the matching degree of an execution result and a decision basis is compared through a timestamp verification mechanism, and if the information fault is detected in the path chain, the decision basis is determined. Judging a deviation position and marking a deviation point; according to the automatic settlement path generation method fusing the knowledge graph and the tax rules, the security and reliability of financial transactions and the tax management efficiency are effectively improved.

The invention relates to the technical field of collaborative management, in particular to a business collaboration method based on a process engine, which comprises the following steps: acquiring approval process nodes and post fields, establishing node parameter mapping, extracting customer reconciliation process field paths, generating a field coverage set, analyzing contract archiving responsibility actions, and constructing a responsibility sequence link. And matching a financial reimbursement process cooperation path, correcting an offset node to generate a cooperation task flow, calculating an internal instruction process node priority, and generating a task triggering list. According to the method, the node fields and the role identifiers in the approval process are obtained, the field consistency mapping relation is constructed, and a field continuous path screening mechanism is combined, so that the penetrating power and the tracing accuracy of parameters among cross-process nodes are improved, and the sequence of node responsibilities has a verifiable logic chain; through a node offset detection and path connectivity construction mode, accurate task execution sequence control is provided in multi-source task scheduling.

The invention relates to the technical field of education science and technology recommendation systems oriented to personalized learning path optimization, and particularly discloses an education science and technology recommendation system oriented to personalized learning path optimization. The method aims at solving the problems that an existing recommendation system is difficult to dynamically perceive a cognitive state, knowledge structure semantics and dependency relationships are ignored, and the recommendation precision is low due to data sparsity. The system comprises a multi-source data acquisition and fusion module, a dynamic cognitive state evaluation module, a knowledge graph construction and semantic enhancement module, a path generation and optimization decision module and a self-adaptive execution and feedback adjustment module. Through multi-source data fusion, real-time cognitive state quantification, knowledge graph semantic modeling, multi-target optimization path generation and closed-loop feedback adjustment, accurate recommendation of personalized learning paths is realized, and the continuity, rationality and educational effectiveness of the paths are effectively improved.

The invention relates to the technical field of machine learning, and discloses a learning path generation method based on reinforcement learning, and the method comprises the steps: obtaining the state deviation data of a target object relative to a target value, and constructing an evolution trajectory; extracting a first-order derivative and a second-order gradient of the state deviation to determine a response phase acceleration vector; when the acceleration vector exceeds a threshold value, judging a response phase sudden change and locking a time point; triggering asymmetric sampling space reconstruction of the empirical pool, and executing sampling probability attenuation on historical samples before the time point by using an exponential attenuation operator; according to the method, the logic viscosity of the strategy network to the old response phase is forcibly relieved by using an asymmetric sampling mechanism, the strategy updating lag generated by high-reward historical experience is eliminated, and the strategy remodeling efficiency of the system under the working condition of phase migration is improved.

The invention relates to the technical field of intelligent logistics and path optimization, and particularly discloses a multi-target vehicle path optimization method based on an enhanced self-attention mechanism. The method comprises the following steps: S1, constructing an enhanced self-attention encoder, and carrying out deep fusion encoding on input node features and preference vectors through a self-attention module integrating channel attention, SwiGLU activation and adaptive scaling; s2, constructing a path generation network based on a problem scale perception decoder, embedding problem scale information injection nodes, and generating a path sequence in an autoregression mode; and S3, based on a reinforcement learning framework, carrying out model training in combination with a random preference strategy, and generating a Pareto frontier approximate solution of a multi-target vehicle path problem. Through the collaborative design, the model expression ability, the generalization ability and the solving precision are improved, and an efficient and reliable solution is provided for the multi-target vehicle path problem.

The invention discloses a cable tunnel autonomous obstacle avoidance inspection method, system and equipment based on multi-sensor fusion and a medium, and relates to the technical field of data processing, and the method comprises the following specific steps: correcting motion data collected by an inspection robot, carrying out pre-integration calculation according to the corrected motion data to obtain motion change data, and carrying out data processing on the motion change data; and predicting a frame state vector of the robot at the current moment, carrying out laser vision tight coupling optimization of the inspection robot, carrying out state prediction on a dynamic obstacle, carrying out collision risk assessment in combination with the frame state vector of the inspection robot, and establishing a path planning objective function to carry out real-time path re-planning. According to the method, the problem that the convergence speed and the real-time performance of a traditional algorithm are difficult to balance in a dynamic complex scene is solved through laser and vision tight coupling optimization. Through dynamic obstacle prediction, probability risk assessment and real-time path planning, an autonomous inspection system with active obstacle avoidance and real-time path generation capabilities is established.

The invention discloses a natural language structured query generation method and system based on pattern reasoning, and relates to the technical field of natural language processing and database query. The method comprises the steps of mode knowledge graph construction in an offline stage and three-step reasoning generation in an online stage. In the offline stage, through structure analysis, semantic enrichment and data portrait processing, a database mode is converted into a knowledge graph Schema-KG rich in structure and semantic information. The online stage adopts three-step reasoning: firstly, decomposing a natural language of a user into an intention component through LLMParser, and linking the intention component to a graph entity; secondly, reasoning an optimal JOIN path on the Schema-KG based on a graph search algorithm, and generating a mode perception intermediate representation SA-IR; finally, the SA-IR is compiled into an SQL, and pre-verification is executed. According to the method, the accuracy of complex query is remarkably improved through a structured reasoning mechanism, errors during operation are effectively avoided through pre-execution verification, a clear reasoning process is provided through SA-IR intermediate representation, and the interpretability and debugging efficiency of the system are greatly improved.

The invention discloses a university education personalized learning path generation method and system based on artificial intelligence, and relates to the technical field of personalized learning path generation, and the method comprises the steps: collecting the multi-modal data of students, obtaining an input vector, and constructing a pre-constructed knowledge graph; performing knowledge state modeling through TransformerEncoder on the basis of the input vector, performing knowledge state updating through graph attention based on a pre-constructed knowledge graph to generate a knowledge state containing a knowledge point relationship, and predicting a mastering probability of each knowledge point; using CNN to extract behavior characteristics of students, generating a cognitive load state through Transform, and finally outputting a cognitive load value; on the basis of the knowledge state and the cognitive load value, constructing double-target learning path optimization, and searching and solving through a Monte Carlo tree to generate a next personalized learning task; the learning effect and the learning experience are remarkably improved.

The invention relates to the technical field of machine learning, in particular to an adaptive learning path generation method of an AI learning machine. According to the method, a subject knowledge graph containing topological logic is constructed, a knowledge point linear index sequence is combined, a continuous error mode in a user answering behavior is captured so as to lock a knowledge mastering fault interval, and a root preposition node causing an error is accurately positioned by utilizing a graph backtracking mechanism; performing time decay weighting operation on historical interaction data in combination with a memory forgetting curve, constructing a closed-loop repair path from a root weak point to a target knowledge point, and meanwhile, introducing an examination period urgency degree and a basic gating evaluation index to dynamically adjust a recommendation priority; the adaptive optimization of the learning path is realized on the premise of conforming to the cognitive law, the defects that the basic weak problem is difficult to radically treat and the time dimension evaluation is lacked in the traditional mode are effectively solved, and the examination preparation efficiency and the knowledge internalization stability are greatly improved.

The invention provides a vehicle remote control driving system and method integrating AR virtual path projection, and relates to the technical field of vehicle remote control. The system constructs a high-precision three-dimensional environment model through cooperative work of an environment sensing module and a data processing and decision module; the AR path generation unit generates a collision-free virtual path and a vehicle dynamic rehearsal picture, and the collision-free virtual path and the vehicle dynamic rehearsal picture are visually presented in an augmented reality mode through the remote control terminal outside the vehicle, so that a driver can obtain comprehensive situation awareness beyond a physical visual field outside the vehicle; and after the virtual path is confirmed or adjusted in combination with the interaction control, the vehicle control strategy unit automatically generates a cooperative control command adapting to the road condition, and an execution mechanism is driven to perform accurate execution. According to the system, the safety and operation confidence of remote control driving are improved, the blind area collision risk is effectively avoided, the trafficability of the vehicle in a complex environment is enhanced, meanwhile, the operation difficulty is greatly reduced through AR visual interaction, and intelligent and user-friendly vehicle exterior remote control driving experience is achieved.

The invention relates to a self-adaptive evaluation and precise learning path generation system based on a knowledge graph. The method comprises the following steps: constructing a standard knowledge graph and an error region knowledge graph; performing misunderstanding mode matching based on the learning interaction data of the students, updating the misunderstanding knowledge graph of the students, and generating a personal misunderstanding knowledge graph of the students; setting a graph conversion operation from the student personal misunderstanding knowledge graph to a standard knowledge graph, and determining a target graph conversion operation sequence through a search algorithm by taking the student personal misunderstanding knowledge graph as an initial state and taking a sub-graph corresponding to a learning target in the standard knowledge graph as a target state; the target image conversion operation sequence is a personalized learning path. The problems that cognitive errors are difficult to eradicate and learning paths are poor in adaptability can be effectively solved, and meanwhile by means of scientific cost evaluation and an intelligent search algorithm, it is ensured that the generated learning paths conform to student individual cognitive characteristics and have high efficiency and feasibility.

The invention discloses an airport apron operation intelligent question answering system and method based on knowledge graph reasoning, and belongs to the technical field of knowledge graphs and artificial intelligence. The method comprises the following steps: constructing an airport apron operation knowledge graph by fusing multi-modal data sources; analyzing a user query intention by using a large language model, realizing alignment of the oral entity and the map standard entity, and generating a standardized query; decomposing the standardized query into an ordered subtask sequence based on thinking chain reasoning, and generating a tool calling plan; and executing the tool calling plan, obtaining a fact basis by sequentially calling a graph query tool, and generating an interpretable answer in combination with a reasoning path. According to the method, multi-modal source data can be deeply fused, a high-precision domain knowledge graph is automatically constructed, and on the basis, an interactive, reasonable, safe and credible intelligent question and answer system is provided.

Methods and systems for generating a sound. The method includes obtaining meshes representing at least one object in a frame of an environment, the environment including a source and a receiver; determining spatial continuity information and reflection normal information of the meshes; determining, based on the spatial continuity information and the reflection normal information, reflection paths between the source and the receiver involving the at least one object, each of the reflection paths having at least one reflection point associated with the at least one object; obtaining spatially sampled results by spatially sampling a space around the at least one reflection point of reflection paths using multiple distributions of rays, the spatially sampled results correlating with geometric information of the meshes; generating reflection amplitude responses for each of multiple audible frequencies in the environment based on the spatially sampled results; and producing a sound based on the reflection amplitude responses.

The invention discloses a robot navigation and positioning method based on machine vision. The robot navigation and positioning method comprises the following steps: acquiring and preprocessing an environment image sequence; performing feature point detection and feature description by using an improved DELF model to generate a visual feature point set; obtaining a visual pose estimation result; obtaining fusion pose information; constructing a local environment map by adopting a sparse point cloud reconstruction method; generating a global environment map; generating an optimal advancing path by using a path planning method; and a robot motion control instruction is generated, path adjustment and navigation correction are performed, and high-precision autonomous positioning and navigation of the robot in a complex environment are realized.

The invention discloses a learning accompanying system based on an AI intelligent agent, and relates to the technical field of education, the learning accompanying system comprises a knowledge point learning grid module and a learning path generation module, and the knowledge point learning grid module is connected with the learning path generation module; the knowledge point learning grid module is used for generating attribute information of knowledge points based on the basic learning information, the pre-constructed knowledge information and the correlation information, and constructing a knowledge graph corresponding to the knowledge point set based on the attribute information; mapping the knowledge graph in a knowledge point learning grid corresponding to the course data information to obtain a target knowledge point learning grid; the learning path generation module is used for generating a learning plan of the student in a target time period based on the knowledge point learning grid in the knowledge point learning grid module, and generating a learning path of the student based on the learning plan; and the knowledge point learning grid module is used for displaying the learning path generated by the learning path generation module to a student end through a knowledge point learning grid. The learning efficiency of students can be improved.

The invention relates to the technical field of agricultural robot path planning, and discloses an autonomous navigation and intelligent operation path planning method for an agricultural robot. The method comprises the following steps: delimiting a target area in a digital map according to an operation intention instruction, and carrying out multi-dimensional scene deconstruction to generate an operation scene map; real-time information is obtained through multi-source sensing hierarchical scanning and is fused with a scene map, and a three-dimensional environment dynamic model containing trafficability, operation value and dynamic risk weight is constructed. And the strategy decision engine outputs a global strategy skeleton through multi-target solution space exploration and compromise solution according to the model and the operation instruction. And under skeleton constraint, performing local fine path generation to obtain an executable track sequence of the robot, and deducing the executable track sequence into a driving control instruction. In the execution process, the environment model and the strategy framework are dynamically updated through a backtracking correction thread. According to the invention, intelligent self-adaptive operation of the agricultural robot in a complex farmland environment with consideration of safety, efficiency and operation quality is realized.

The embodiment of the invention provides a robot process path data processing method and device based on geometrical characteristics, and the method comprises the steps: obtaining a process template according to the geometrical characteristics of a workpiece and a set process association rule base, and the process template comprises a process sequence and associated process parameters; extracting process sequence keywords, performing algorithm matching with a process path algorithm library to obtain a path generation algorithm, generating an initial robot motion path according to the path generation algorithm and geometric features, and performing path constraint based on process parameters; constructing a hierarchical bounding box tree structure of an obstacle, performing route interference condition detection on an initial robot motion path and the hierarchical bounding box tree according to a ray projection method, if a collision condition occurs, iteratively calculating an obstacle avoidance path offset based on a gradient descent algorithm, and recording a normal vector at a collision point to obtain a correction direction; and correcting the initial robot motion path according to the obstacle avoidance path offset and the correction direction. The robot process path planning method and device can improve the efficiency and accuracy of robot process path planning.

The invention discloses a composite shell structure topology and fiber angle optimization and equal-interval path generation method. The invention relates to the field of topological optimization and continuous fiber reinforced composite material additive manufacturing, and aims to solve the problem that continuous, equidistant and manufacturable fiber paths are difficult to obtain for a composite shell structure in the prior art. According to the method, based on isogeometric analysis and a Riisner-mindlin shell theory, with flexibility minimization as a target, topology and fiber angle collaborative optimization is carried out on a curved surface shell structure, and a macroscopic topology form and unit fiber orientation are obtained. And extracting an optimized structure boundary and mapping the optimized structure boundary to a parameter space, constructing a grey-scale map through a density field to identify a parameter space boundary, performing fairing by adopting an NURBS curve, and then mapping the optimized structure boundary back to a physical space to form a smooth space boundary. And extracting a structural skeleton in the parameter space and mapping the structural skeleton to a physical space. And based on the smooth boundary and the skeleton, generating a space fiber path meeting the equidistant requirement.

The embodiment of the invention provides an audit path generation method and device, a medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: extracting multi-source heterogeneous data from an audit system document, a historical audit report and / or business system data; identifying auditing entities in the multi-source heterogeneous data and association relationships thereof through a natural language processing model, and generating a structured entity set and an association relationship set; storing the structured entity set and the incidence relation set in a graph database to obtain an audit knowledge graph with a hierarchical relation; and generating an audit path based on the audit knowledge graph. By adopting the method, the auditing path with high coverage degree and high auditing efficiency can be generated based on the auditing knowledge graph, and the auditing coverage rate and the auditing efficiency are improved.

The invention discloses a server hardware link diagnosis method and a server hardware link diagnosis system, relates to the technical field of computer system fault diagnosis, and discloses the server hardware link diagnosis method and the server hardware link diagnosis system. Through the steps of obtaining a diagnosis rule configuration file, collecting software and hardware state data, carrying out matching analysis to generate a diagnosis path, executing diagnosis processing, carrying out correlation analysis, generating a fault report and the like, the problems of software and hardware diagnosis splitting and path stiffness in the prior art are solved, and collaborative diagnosis and dynamic path generation of software and hardware faults are realized. And the fault positioning accuracy and the system recovery timeliness are improved.

The invention relates to the technical field of computer vision and intelligent navigation, in particular to an intelligent obstacle avoidance method, system and device based on monocular pure vision. The method comprises the following steps: generating a global cost map containing all static obstacles based on a processed original image sequence; generating a global path capable of bypassing all static obstacles according to the global cost map; when the user advances according to the global path, generating a local cost map containing dynamic obstacles based on the processed current image sequence; fusing corresponding areas in the local cost map and the global cost map to obtain a final cost map; according to the final cost map and the global path, generating a target path which accords with the direction of the global path and bypasses the dynamic obstacle; and converting the target path into a navigation instruction and feeding back the navigation instruction to the user to guide the user to safely arrive at the destination position. According to the invention, efficient and safe path planning is realized while low cost is ensured, and the travel safety of a user is ensured in a complex environment.

The invention provides a ship berthing auxiliary decision-making method and device, electronic equipment and a storage medium, and belongs to the technical field of ship berthing route control, and the method comprises the steps: obtaining a multi-modal environment and ship navigation sensing data; the method comprises the following steps: extracting carbon emission factors on the basis of a multi-modal environment and ship navigation sensing data, then constructing a carbon emission scoring model according to a nonlinear collaborative amplification effect among the carbon emission factors, and generating a low-carbon semantic map; a path evaluation function fusing a ship energy efficiency model and carbon emission-berthing time window compliance constraint is utilized, path section carbon emission and berthing time window compliance serve as a search basis, a berthing path enabling the path evaluation function value to be minimum is searched in real time, and the optimal navigational speed and steering angle are generated for each path section; therefore, ship berthing aided decision-making under low-carbon compliance guidance is realized. According to the method, comprehensive optimization of low-carbon property, compliance and performability can be realized, and the port and navigation system is assisted to be upgraded to a green and intelligent direction.

The invention relates to a multi-mechanical-arm path planning method, a storage medium and detection equipment, and the multi-mechanical-arm path planning method comprises the following steps: region division: dividing an operation region into a plurality of sub-regions according to the number of mechanical arms, configuring one mechanical arm for each sub-region, and synchronously starting operation by all the mechanical arms; starting point selection: acquiring coordinate information of all target points in each sub-region, and determining an operation starting point of the mechanical arm in the sub-region based on the position relationship of the target points; and generating a path. According to the method, the collision risk of the path can be avoided, the global path optimization effect and the calculation efficiency are considered, and the precision and the efficiency of path planning can be effectively balanced.