31 results about "Packing algorithm" patented technology

The invention discloses an AI-based localized environment application offline deployment method and device and a medium. The method comprises the steps that a localized environment is pre-detected based on probe service, a system layer configuration list is generated, heterogeneous features are extracted through a dual-channel deep learning model based on the system layer configuration list, and adaptive priority labels of the heterogeneous features are generated; the method comprises the following steps: analyzing an application program through static analysis and dynamic scanning technologies, constructing a dependency list, and constructing a version compatibility atlas based on metadata of a domestic operating system warehouse; generating a minimum-cost replacement path by using a graph attention network, performing hash check on each dependency packet, and generating an incremental update packet through a differential packaging algorithm; the resource occupation condition of the system is monitored in real time through a reinforcement learning model so as to dynamically optimize a deployment sequence and a resource allocation strategy, a deployment log is analyzed based on a natural language processing model, an error type is recognized, a repair operation is triggered, and application offline deployment is achieved.

The invention discloses a warehouse AGV path planning method based on an improved wolf pack algorithm, and the method comprises the steps: a central control system receives tasks in a unified manner, builds a task table, and carries out the priority sorting of the tasks through a task value evaluation function; according to the task information in the task table and the state of each AGV, distributing the task to the AGV which meets the task condition and is closest to the task point; a map model is initialized, a central control system plans an optimal or suboptimal path for getting goods at a designated position according to task information and path conditions of a warehouse and selecting distribution of the goods on a goods conveying belt, and if the AGV conflicts with other AGVs in the path, avoidance is carried out according to a conflict avoidance rule. According to the invention, through improvements in multiple aspects such as dynamic path planning, multi-AGV collaborative optimization, energy management, real-time path updating and effective collision avoidance strategies, the operation efficiency, the task completion rate and the safety of the warehouse AGV system are significantly improved.

Systems and methods for generating a shape-based graded lattice structure that can be used in additive manufacturing. A slicer computer system generates the lattice structure by simulating the packing of a planar region with variable-sized packing shapes, where packing shape sizes correspond to intensity values of a non-uniform physical field expected to be experienced by the article. An intermediate lattice structure is then generated using a first set of polygonal cells, followed by a second set of polygonal cells that refine the final lattice structure. Tailored sectioning and field-based smoothing can modify polygon packing algorithms to adapt lattice generation. The resultant multi-stage graded lattice structures, which may include multiple lattice patches and transition zones, from shape-based packing, tailored sectioning, field-based smoothing, and slicer-based additive manufacturing processing improve connectivity and manufacturability over traditional lattice structures.

The invention belongs to but is not limited to the technical field of collaborative region search, and discloses an air-ground unmanned cluster collaborative search path planning method based on a learning wolf pack algorithm, which combines distributed model predictive control (DMPC) with a distributed constraint optimization problem (DCOP) framework, constructs a global search objective function of a finite time domain, and performs collaborative search on the air-ground unmanned cluster collaborative search path planning based on the learning wolf pack algorithm. A DCOP model for regional search path planning is constructed; the learning type wolf pack algorithm LWPA is utilized, hierarchical learning is carried out through a reinforcement learning mechanism, algorithm parameters are dynamically adjusted, and therefore effective balance between local fine search and global exploration is achieved. The method provided by the invention has good adaptability, robustness and expandability, and effectively improves the efficiency and accuracy of target search.

The invention provides a numerical control multi-objective optimization energy-saving method and system based on an improved wolf pack algorithm, and the method comprises the steps: constructing a mixed integer linear programming model of multi-objective flexible job shop scheduling, taking machine tool distribution, a machining sequence, tool distribution and a machining speed as decision variables, and taking total weighted tardiness and total energy consumption as optimization targets; the method comprises the following steps: constructing an MOGWO-ALNS algorithm for solving a mixed integer linear programming model based on an MOGWO algorithm and an adaptive large neighborhood search mechanism, constructing a three-stage collaborative optimization architecture by the MOGWO-ALNS algorithm through a dual-threshold trigger mechanism, solving the mixed integer linear programming model based on the MOGWO-ALNS algorithm, obtaining a Pareto optimal solution set of the mixed integer linear programming model, decoding the Pareto optimal solution set, and finally obtaining the mixed integer linear programming model. And generating a multi-target balanced energy-saving scheduling scheme. According to the method, collaborative optimization of the delivery date and the energy consumption in numerical control machining is achieved, the solving efficiency and the optimization effect are improved, and the method has good engineering application value.

The invention relates to the technical field of control system data processing, in particular to a ship intelligent workshop crane hoisting path planning method, which comprises the following steps: collecting crane coordinates, obstacle distribution, load swing angle and material weight, and inputting a physical constraint model to generate a structured environment state data set; initializing wolf pack algorithm parameters based on the structured environment state data set, calculating a compressed detection wolf walking step length according to an obstacle distance, generating a global task queue by an industrial cloud platform according to a dynamic parameter set, and outputting an execution state data set by a crane end; counting emergency braking times from an execution state data set to optimize a walk step length function, comparing task time to optimize a wolf investigation proportion, analyzing historical swing data to establish a step length and swing mapping library, and optimizing a parameter set to feed back and update a physical constraint model. According to the invention, cooperation of environment perception, dynamic path planning and closed-loop verification is realized, and accident risks and production delay in a ship workshop narrow space dynamic obstacle scene are reduced.

The invention provides an energy collaborative operation method and system based on an adaptive variation grey wolf pack algorithm, and the method comprises the steps: building a collaborative operation model based on a time-of-use electricity price and a gas price, solving the cooperative operation model by using an adaptive variation grey wolf pack algorithm every other preset time period to obtain an operation strategy with the optimal economical efficiency in the time period; the adaptive variation grey wolf pack algorithm comprises the following steps: initializing an algorithm; individual evaluation and grade layering; gray wolf individual positions are updated; performing adaptive variation; and judging an algorithm termination condition. According to the technical scheme of the invention, the method achieves the cooperative operation of cold, heat and electricity multi-energy flow under the scene of time-of-use electricity price, introduces a self-adaptive variation mechanism into the gray wolf pack algorithm, obtains an optimal cooperative operation strategy, and improves the economical efficiency of the energy consumption of a user.

The invention discloses a multi-target game control method and device based on hybrid demand response, and the method comprises the steps: constructing a market architecture in which flexible resources at a power distribution network side participate in the hybrid demand response, and building a hybrid demand response scheduling model according to the information and power interaction process among all subjects in the market architecture; the method comprises the following steps: considering the uncertainty of user behaviors, and describing the potential risk of a distribution network side subject participating in an electric energy market by using conditional value-at-risk: formulating a hybrid demand response scheduling problem as a multi-target double-layer game control process, establishing an LA operation model located at the lower layer of a game model, and receiving time-of-use electricity price change electricity consumption behaviors from DSO; establishing a DSO operation model located at the upper layer of the game model; optimizing a wolf pack algorithm by using an adaptive convergence factor and an information feedback mechanism; the optimized wolf pack algorithm is used for solving an LA operation model of the lower layer and a DSO operation model of the upper layer of the game model, a multi-target Pareto optimal solution set is obtained, and a power distribution network safety scheduling strategy under the day-ahead time-of-use electricity price is formed; and reducing peak load based on a power distribution network safety scheduling strategy.

The invention belongs to but is not limited to the technical field of big data, and particularly relates to a high-dimensional function solving method and system based on a distance decision wolf pack algorithm. The improved algorithm realizes significant optimization in the aspects of migration behavior, call behavior, purse behavior, population update and the like. Experimental results show that the improved wolf pack algorithm is obviously improved in solving precision in a low-dimensional single-peak function compared with an original algorithm, and meanwhile, the solving precision of a high-dimensional multi-peak function is further improved. In addition, the algorithm also has the characteristics of good global convergence, high operation speed and the like, and is excellent in performance on a plurality of standard test functions. In the theoretical level, the convergence of the algorithm is deeply analyzed by using the non-aftereffect characteristic of the Markov chain, and the population sequence of the improved wolf pack algorithm is proved to be traversal through mathematical derivation, so that the algorithm can be converged to the global optimal solution of the problem to be solved with the probability 1.

The application provides a multi-unmanned aerial vehicle task allocation system based on a two-stage multi-population wolf swarm algorithm, comprising: an initialization module, which defines the individual wolf position in the two-stage multi-population wolf swarm algorithm as a multi-unmanned aerial vehicle task allocation scheme, divides the wolf swarm into multiple sub-populations, and comprises an ordinary group and a leader group; a wandering module: the ordinary group and the leader group explore wolves and adopt different wandering number calculation methods to reorder task numbers; a calling module: the ordinary group and the leader group use different convergence speeds to learn the scheme of the current alpha wolf; a siege module: except for the alpha wolf, the individual wolves of the ordinary group and the leader group use different threshold values to mutate or learn the scheme of the individual wolf with low fitness; an individual wolf reconstruction module: a roulette mechanism is adopted for reconstruction; and a sub-population adjustment module: after reconstruction is completed, the multi-population mechanism is started, and the leader group is reconstituted. The application improves the wolf swarm algorithm through multiple mechanisms, and improves the solving efficiency and accuracy.

This application relates to efficient convolution in tiled environments. One method includes: receiving an input tensor, the input tensor having a domain consisting of [n1, ..., n]. k The shape is defined by [t1, ...,t], where k is equal to the number of dimensions representing the input tensor; it receives tile tensor metadata, which includes: [t1, ...,t] k The tile tensor shape is defined, along with information indicating the interleaving stride to be applied relative to each dimension of the tile tensor; an output tensor comprising multiple tile tensors is constructed by applying a packing algorithm that maps each element of the input tensor to at least one slot position of one of the multiple tile tensors based on the tile tensor shape and the interleaving stride, wherein the interleaving stride results in a discontinuous mapping of the elements of the input tensor such that each of the tile tensors comprises a subset of the elements of the input tensor that are spaced apart within the input tensor according to the interleaving stride.

The application provides a sparse matrix accelerator based on a block-based packing algorithm, comprising an algorithm preprocessing module, a calculation module and a sorting module. The algorithm preprocessing module can obtain a sparse matrix comprising a flow matrix and a stationary matrix, block the flow matrix and the stationary matrix according to a preset numerical value, then perform shift and merge processing on the first non-zero element, the second non-zero element and the zero element in the matrix respectively to obtain a dense flow matrix and a dense stationary matrix, and save the address of the first non-zero element before shifting and the address of the second non-zero element before and after shifting. The calculation module can perform multiplication and addition calculation on the dense flow matrix and the dense stationary matrix to obtain a partial sum result and a first address result of the partial sum result. The sorting module can sort the partial sum result according to the first address result to obtain a result matrix. The above accelerator can improve the density of the matrix, at the same time, correct column addition can be realized, and the calculation efficiency can be improved.

The present invention relates to a method for calculating the life of consumable products based on an improved wolf pack algorithm, belonging to the technical field of life calculation methods. In order to solve the problem that the existing life calculation methods for consumable products in a new environment are vulnerable to interference and can only estimate simple parameters, and cannot be used for estimating complex parameter estimates, the life calculation method includes determining that the life of the consumable product follows a Weibull distribution; determining an objective function according to the Weibull distribution; using the improved wolf pack algorithm to solve the objective function to obtain the required parameters in the old and new environments; defining a conversion coefficient and calculating the conversion coefficient according to the required parameters; and calculating the life of the consumable product in the new environment using the conversion coefficient, which consists of five steps. The improved wolf pack algorithm has a fast convergence speed and high calculation accuracy; applying the improved wolf pack algorithm to the life conversion of consumable products in a new environment can estimate complex parameters accurately and stably.

The invention relates to an improved wolf pack algorithm-based life conversion method for a loss type product, and belongs to the technical field of life conversion methods. In order to solve the problems that an existing life calculation method of a loss type product in a new environment is easy to interfere and can only estimate simple parameters, and cannot be used for parameter estimation with complex estimation, the life conversion method comprises the following steps: determining that the life of the loss type product obeys Weibull distribution; determining a target function according to Weibull distribution; solving the objective function by adopting an improved wolf pack algorithm to obtain the solved parameters in the new and old environments; defining a conversion coefficient and calculating the conversion coefficient according to the solved parameters; and calculating the service life of the wear-type product in the new environment by using the conversion coefficient. The improved wolf pack algorithm is high in convergence speed and high in calculation precision; the improved wolf pack algorithm is used for life conversion of the wear type product in a new environment, complex parameters can be estimated, and estimation is accurate and stable.

The invention relates to the technical field of unmanned aerial vehicle route planning, in particular to an unmanned aerial vehicle three-dimensional route planning method based on a neuron chaos wolf pack algorithm. Constructing a discrete neuron hyper-chaos mapping system, improving a wolf pack algorithm based on the discrete neuron hyper-chaos mapping system in combination with a neuron attention mechanism, and determining a neuron hyper-chaos wolf pack algorithm; initializing individual positions in the wolf pack, correspondingly updating the individual positions of the wolf pack according to a neuron hyper-chaos wolf pack algorithm, and judging whether the updated individual positions of the wolf pack meet a stopping criterion of the neuron hyper-chaos wolf pack algorithm or not through a constraint cost function until the individual positions of the wolf pack are updated; determining optimal flight path points of the unmanned aerial vehicle to form an optimal solution sequence, and generating an optimal flight path of the unmanned aerial vehicle according to the optimal solution sequence; the algorithm is effectively prevented from falling into a local optimal solution, the global optimal solution search precision of the algorithm is improved, and the optimal flight path of the unmanned aerial vehicle is obtained.

The invention discloses a low-consumption and high-credibility text analysis method and device based on a large language model, and relates to the technical field of natural language processing. The method comprises the following steps: based on a preset context efficiency coefficient, performing calculation according to a context capacity maximum threshold value and a single request interception threshold value to obtain an effective load safety threshold value; on the basis of the effective load safety threshold value, according to the system instruction template, the JSON protocol template, the service text data, the current text data and the preset safety spare token length, a dynamic packing algorithm is used for carrying out request aggregation, and a JSONL request is obtained; and performing text analysis by using a large language model according to a JSONL request based on a three-level analysis mechanism of consistency verification to obtain a comprehensive judgment result and a comprehensive judgment evidence. The invention relates to a lightweight text analysis method for overcoming model illusion and resource bottleneck based on a large language model.

The invention discloses a college-bus route planning method based on a multi-constraint clustering and combined heuristic algorithm, and relates to the technical field of route planning, and the method comprises the steps: generating an information table of students needing to be picked up; according to the time difference between the school arrival time and the school departure time, the students in the student information table needing to be picked up are batched, and a batch student information table is obtained; generating initialized boarding points for students in the batch student information table, and iteratively optimizing the initialized boarding points according to constraint conditions, so as to generate a concentrated boarding point information table of a corresponding batch; generating an initial route scheme table according to the centralized boarding points in the centralized boarding point information table of each batch by using a greedy boxing algorithm; optimizing routes in the initial route scheme table by using a tabu search algorithm to obtain an optimized route scheme table; and a multi-route connection strategy of a single college bus is formulated according to the optimized route scheme table of each batch, and then a college bus connection scheduling table is obtained. According to the invention, the college scheduling line can be reasonably planned.

The invention relates to the technical field of boxing algorithms, in particular to a boxing method, device and equipment and a computer storage medium. According to the boxing method, high-quality candidate placement points are dynamically generated and maintained through a two-step point increasing method, and the comprehensiveness and tightness of space exploration are ensured; business constraints such as cargo grouping and loading sequence are converted into allowable areas in geometric space through a projection filtering point method, and deep fusion of business rules and spatial layout is realized; and through an orientation search method, intelligent preferential selection is carried out in multiple orientations, the space utilization rate is maximized, and the placement stability is ensured. The three collaboratively form a closed loop, so that the loading space utilization rate, the loading accuracy and the calculation efficiency are finally improved, and business rules are met.

The present invention discloses a method for unmanned aerial vehicle (UAV) swarm resource scheduling based on an improved wolf pack algorithm. First, a mathematical model for UAV swarm resource scheduling is established. Then, the algorithm is improved from aspects such as initializing the wolf pack with chaotic mapping, simulating the exploration wolf's wandering step length with dynamic Lévy random motion, designing a dynamic distance determination factor and an adaptive running step length, simulating the wolf pack's siege behavior with Brownian motion, and designing a wolf pack update mechanism based on the degree of difference. Finally, the improved wolf pack algorithm is used to optimize and solve the UAV resource scheduling model, and the optimal UAV swarm resource scheduling scheme is obtained. The present invention solves the problems of slow algorithm solving speed, easy obtaining of local optimal solutions, and insufficient utilization of UAV swarm resources existing in the process of UAV swarm resource scheduling.

The present invention discloses a construction method for an initial packing model of the microstructure of a composite cement paste based on a grid optimization packing algorithm, including: 1. Setting and calculating the basic parameters of the microstructure model of the composite cement paste; 2. Generating a cube of the microstructure model and dividing it into an ordered grid array in space; 3. Establishing a grid information matrix to record the information of each grid; 4. Randomly generating a grid index sequence according to the current particle size grade number, particle pore sieve size, and grid array; 5. Placing cement particles within the particle size range corresponding to the current particle size grade number; 6. Placing mineral powder particles within the particle size range corresponding to the current particle size grade number; 7. Placing fly ash particles within the particle size range corresponding to the current particle size grade number; The method of the present invention is simple and efficient, can achieve a lower water-cement ratio, helps to predict the macroscopic properties of the cement paste, and has important significance for constructing a microstructure model of a composite cement paste containing irregular particles.

The application provides a distributed index construction method based on improved iSAX coding, first, aiming at the digital feature selection problem, in order to increase the coding similarity of similar data, a similarity digital coding is designed, and through the transposition of the matrix, the problem of different weights of high bits and low bits of each digital coding in the whole digital coding is solved. Secondly, aiming at the problem of high time consumption in the traversal process caused by the too high index tree, a B+ index tree based on the similarity digital coding is designed, the number of child nodes is increased, the height of the tree is reduced, and the access speed of adjacent nodes is improved. And a leaf partition snake-shaped packing algorithm is designed, which guarantees the load balance, shortens the leaf node packing time, improves the index construction speed, and improves the overall index construction speed compared with the traditional distributed index construction algorithm, and provides a more efficient index framework for the similarity query process.

The present invention relates to a pre-packing method based on VPR, and belongs to the field of electronic design automation technology. The method performs the following steps: 1) VPR reads the primitives that come with the system in the structure description file; 2) According to the custom constraints in the structure description file, the primitives that meet the custom constraints are packaged into molecules; 3) It is judged whether each molecule is qualified; 4) When all molecules are qualified molecules, the pre-packing is completed. The pre-packing algorithm of the present invention performs pre-packing processing, which can reduce the coupling between molecules, thereby increasing the degree of freedom of molecules when packing and reducing the failure rate during packing.

The invention discloses a two-stage three-dimensional boxing method based on a double-population strategy, and provides a two-stage boxing optimization strategy, a hybrid heuristic algorithm is used as a first stage, and a splitting and replacing strategy with smaller calculated amount is used as a second stage of the algorithm. Meanwhile, an improved angle occupying method is used as a heuristic boxing algorithm, and a hybrid heuristic algorithm is improved by adopting a double-population strategy, a goods pre-combination strategy and an improved simulated annealing algorithm dynamic temperature rise strategy; the strategies of the second stage further comprise a four-corner tightening strategy, a recombination strategy and a large object sinking strategy, the calculation amount can be remarkably reduced under the condition that the quality of solutions is improved, and the diversity of populations is guaranteed. The boxing method provided by the invention has a wide prospect in the field of logistics.

The invention provides a multi-unmanned aerial vehicle cooperation method and system for electromagnetic spectrum detection, and belongs to the field of electromagnetic spectrum detection and multi-unmanned aerial vehicle cooperation. According to the method, firstly, collaborative detection is carried out on an overall task area to obtain a sparse electromagnetic spectrum map, then a global dense map is obtained through interpolation, and then clustering is carried out on the map to obtain a key task area. And finally, through a hybrid algorithm of a differential evolution algorithm and a wolf pack algorithm, completing a detection task on a key area and updating and correcting the previous electromagnetic map to obtain a final electromagnetic spectrum map. Meanwhile, on the collaborative method, the thought of the differential evolution algorithm is applied to the wolf pack algorithm, the problem that the wolf pack algorithm is prone to falling into local optimum is solved, the global search capacity is improved, and the search precision is improved.

The invention provides an online fault line selection method and system based on an improved wolf pack algorithm and a storage medium, and the method comprises the steps: 1, collecting a zero-sequence current component through a current sensor, and carrying out the variational mode decomposition to obtain four variables [delta] E1, [delta] E2, [delta] E3 and [delta] E4; 2, the four variables delta E1, delta E2, delta E3 and delta E4 after variational mode decomposition are optimized through an improved wolf pack algorithm, and optimal solutions are obtained; and step 3, the four variational mode decomposed variables delta E1, delta E2, delta E3 and delta E4 and the optimized solution of the improved wolf pack algorithm are sent to an RBF neural network for training, and a fault line selection result is obtained. According to the online fault line selection method and system based on the improved wolf pack algorithm and the storage medium, rapid positioning of a fault line can be realized, the neural network learning efficiency is higher, and conditions are created for realizing accurate, rapid and intelligent targets of fault line selection.

The invention belongs to the technical field of information, and particularly relates to a waste plastic regeneration production risk coping method based on a multivariate innovation algorithm, which is applied to waste plastic regeneration enterprises. Firstly, Monte Carlo simulation is used for analyzing the influence of uncertain factors such as raw material quality, equipment faults and market requirements on production; then, in the embodiment, a self-adaptive neural fuzzy inference system, a grey wolf pack algorithm optimized support vector machine, a variational mode decomposition and gating circulation unit and other algorithms are adopted, risks are accurately evaluated, and corresponding strategies are formulated, such as raw material detection standard establishment, equipment spare part inventory increase, flexible production plan formulation and the like; and the product quality stability and the economic benefits of enterprises are effectively improved.

This invention discloses a method for predicting the throughput capacity of a ship lock. The method involves obtaining the total cargo volume, ship type, and the proportion of cargo volume carried by each ship type for the predicted year; calculating the total number of ship trips for each ship type in the predicted year; randomly combining ship types and numbers into the same lock throughput batch; determining the platooning pattern of ships within the same throughput batch using a two-dimensional packing algorithm based on the lock's length and width and the ship's length and width; calculating the average deadweight tonnage per lock trip for each ship type in the platooning pattern that completes the predicted annual cargo volume according to the platooning pattern; and taking the maximum value among the calculated average deadweight tonnage per lock trip for several platooning patterns as the predicted annual lock throughput capacity. The advantages of this invention are its clear principle, simple calculation process, accurate results, and high efficiency, providing more precise data support for ship lock engineering design.

This invention proposes a parking space planning method based on dynamic programming and bin packing algorithm, comprising: Step 1, constructing an abstract layout of the apron, wherein the abstract layout includes setting the area of ​​the apron and defining obstacles and service lanes in the apron area; Step 2, combining the aircraft types to be parked according to their available taxiing methods to form multiple combinations of aircraft types and taxiing methods, calculating the footprint of each combination of aircraft types and taxiing methods, and calculating the width of the right taxiway based on the footprint; Step 3, treating the parking space planning problem as a complete knapsack problem; each combination of aircraft types is regarded as an item to be packed, the value of each aircraft type is regarded as the item value, the available area of ​​the apron is regarded as the knapsack capacity, determining whether each combination of aircraft types can be parked on the apron according to the parking conditions, and obtaining all possible parking space planning results in the order of each row.

The invention discloses a large fragment generator power field simulation method and a computer product, and the method comprises the steps: carrying out the discretization of the boundary of a closed space surrounding a fragment layer through employing a discrete surface element, and carrying out the discretization of the internal space of the closed space through employing a voxel method; the internal space of the closed space is iteratively filled with spherical fragments through a random confidential arrangement algorithm, and initial arrangement of the fragments is completed; the initial arrangement of the fragments is optimized through a dynamic relaxation method, fragment compaction state simulation is completed, and the spatial position and radius information of the fragments is output; generating a discrete element model of the fragments based on the spatial position and radius information of the fragments; and based on the discrete element model of the fragments, combining a finite element method and an ALE method to realize modeling of a warhead containing a large number of fragments and rapid simulation evaluation of a power field. According to the method, a more accurate and detailed simulation result is achieved, and technical support can be provided for fragment type ammunition design and rapid performance evaluation.

The application discloses a kind of 3D irregular object packing methods and systems for subtractive manufacturing, and relates to subtractive manufacturing technical field.The method includes the following steps: obtaining container raw materials and 3D objects to be packed;According to container and 3D object, collision-free space is calculated, and collision-free region is initially generated;According to the operation distance of cutting tool, the collision-free region is further filtered to form candidate placement position;According to the Euclidean distance, the candidate position is sorted to form packing layout;According to the accessible region of all 3D objects, cutting shell is formed, and cutting path is planned according to cutting shell;According to packing layout, cutting shell and cutting path, 3D object is cut.The application solves the limitations of traditional packing algorithm in subtractive manufacturing, and also provides a new solution for efficient and accurate manufacturing process.