141 results about "Spatial search" patented technology

The invention provides a track board drilling parameter intelligent optimization method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: recognizing a feature region in three-dimensional scanning data through a deep learning semantic segmentation network, building a local coordinate system, calculating hole site deviation, constructing a pose correction field, and achieving the intelligent correction of drilling process parameters. And an optimal drilling track is generated by using a state space search algorithm. The method can adapt to actual geometric errors of the track plate, the drilling precision and efficiency are improved, the drilling failure rate is reduced, and the manufacturing quality of the track plate is improved.

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

The invention discloses a precipitation forecasting method based on Shearlet and L-PWC, the precipitation forecasting method is decomposed into a global space, a local space and a time angle, the time feature extraction effect is improved through the consistency evaluation of a reverse optical flow graph and a forward optical flow graph, the decomposition of a picture in a frequency domain is guided by using an optical flow with higher precision, and the time feature extraction efficiency is improved. Feature extraction is carried out on different frequency domains to obtain information to guide directional learning of multi-head attention, and more comprehensive global features are generated through assistance of other frequency band features. Extraction of local space information is guided by time information, feature enhancement of key information is completed, future weather is predicted through gradient decomposition of optical flow and time dependence before and after, and a litsea rotundifolia optimization algorithm is optimized through an ant colony algorithm, so that the algorithm can search for an optimal solution in a wider space, and the optimal solution is obtained. And the possibility that the algorithm falls into local optimum is greatly reduced, so that the ability of the model to deal with complex weather is improved.

The invention discloses a high-frequency transformer transmission extreme value frequency rapid calibration and tolerance analysis method, which comprises the following steps of collecting operation parameters of a high-frequency transformer in real time, performing standardized preprocessing, and integrating the preprocessed parameters to construct operation condition vectors in a unified format; defining a frequency response range of the high-frequency transformer, constructing a frequency search space based on the frequency response range, and establishing a target function for fitness evaluation based on the frequency search space; searching extreme value frequency points in the frequency search space by adopting a quantum evolutionary algorithm; based on the working condition vector and the extreme value frequency point, a working condition frequency deviation prediction model is constructed, and the working condition frequency deviation prediction model outputs an extreme value frequency shift offset; and calculating the dynamic tolerance bandwidth according to the predicted offset and the fluctuation range of the working condition vector. According to the invention, rapid and accurate calibration of the extreme value frequency of the high-frequency transformer and dynamic determination of the tolerance range can be realized.

The invention relates to the technical field of navigation assistance, in particular to a dynamic obstacle prediction and obstacle avoidance path planning method based on multi-sensor fusion, which realizes omnibearing perception of a dynamic obstacle through multiple sensors, provides a rich and reliable data basis for subsequent processing, and improves the accuracy of the dynamic obstacle prediction and obstacle avoidance path planning. Meanwhile, motion prediction is carried out on a dynamic obstacle through data processing fusion and model prediction, the motion inertia of the obstacle is fully considered, track prediction of the obstacle is closer to a real physical rule, a global path search problem is converted into a local but large enough space search problem through a mode of delimiting a final obstacle avoidance target area, and the search efficiency is improved. According to the method, the number of grids needing to be processed and the search calculation amount are remarkably reduced, the harsh requirement of the mobile device for the real-time performance is met, the area possibly occupied by the obstacle in the future is avoided in real time in the mode of constructing the cost function, then the obstacle avoidance planning path is generated, and the path safety is improved.

The invention provides a multi-modal data security analysis technology based on a depth fuzzy wavelet learning model. A deep fuzzy wavelet learning model is widely applied to the field of security analysis due to the self-adaptive capability of the deep fuzzy wavelet learning model in a complex data environment. According to the method, a deep fuzzy wavelet learning framework is improved and is applied to multi-modal data security analysis. According to the model, firstly, multi-modal data (texts, voices, videos and images) are subjected to feature decomposition and noise reduction processing by using a wavelet transform and fuzzy clustering combined method, key semantic, time sequence and spatial features are extracted, and high-dimensional information expression is provided for subsequent safety analysis. Secondly, constructing a deep fuzzy wavelet neural network, inputting the extracted features into an adaptive fuzzy decision layer, and capturing space-time correlation features in combination with a wavelet learning model, thereby improving the perception ability of the model to a complex attack mode; and finally, a security analysis result is enhanced by adopting a robust and spatial search optimization algorithm, so that malicious samples are effectively detected, backdoor attacks are identified, the attack defense capability is improved, and finally accurate multi-modal data security assessment is realized.

The invention discloses a tractor trailer robot track generation method based on a polynomial and a symbolic distance field, and the method comprises the steps: dividing grids through a point cloud map, estimating the curvature and normal direction of each grid, extracting obstacle information, and calculating a symbolic distance value corresponding to each grid; taking the initial pose of the tractor trailer robot as a root node, and based on the obstacle information, utilizing a multi-end-point heuristic graph search algorithm for spatial search in SE (2) to generate an initial path for arriving at the target area; and in combination with the symbol distance value corresponding to each grid, modeling the trajectory generation of the tractor trailer robot as a constraint optimization problem, and solving the constraint optimization problem by taking the initial path as an initial value to obtain a final trajectory. The problem that track generation is difficult due to complex kinematics, high state space dimension and deformable structure of the traction trailer robot is solved, the problems that an existing algorithm is low in efficiency and loses solution space are solved, and efficient track generation of the traction trailer robot is achieved.

The embodiment of the invention provides a prediction frame generation method, terminal equipment and a storage medium, and relates to the technical field of terminals. In a game scene, terminal equipment can acquire a 3D scene image and a UI image corresponding to each real frame from two game rendering instructions, and acquire a depth image and a projection matrix of a game virtual camera according to the game rendering instructions; dividing long-shot pixels and close-shot pixels in the 3D scene image by using the information; then, calculating a motion vector of each long-shot pixel based on a stereoscopic vision principle, and calculating a motion vector of each close-shot pixel based on a spatial search method; then, according to the motion vector of each long-shot pixel and the motion vector of each close-shot pixel, warping the whole 3D scene image by taking a pixel block as a unit; and finally, superposing the UI image on the distorted 3D scene image to generate a final prediction frame. According to the scheme, the image quality of the prediction frame is improved, and the method only depends on universal resources of games and is suitable for most games.

The invention discloses a front subframe lightweight design method and device based on RMNN, a medium and a program product, and the method comprises the steps: (1) constructing three simulation models of weight, stress and modal of a front subframe, and deducing a lightweight design model containing stress and modal constraints; (2) generating a population based on a uniform experimental design and a correlation criterion, performing simulation evaluation on the population, and constructing a database; (3) constructing a network architecture and training an RMNN; (4) generating a candidate frame set according to a random preferential strategy and the RMNN; and (5) establishing a radial basis function model, screening an optimal frame, performing simulation evaluation, updating the database and the population, returning to the step (3) until the optimization target reaches the standard, and outputting an optimal solution of the optimization parameter. According to the method, the feasible region is quickly searched through the RMNN, the problems that the high-dimensional design space search efficiency is low and the feasible region is difficult to recognize under multiple constraints are further solved in combination with the radial basis function model, and quick optimization can be carried out for the lightweight design of the front subframe.

A method may include storing a vector space representation of a set of concepts in a semantic ontology, wherein locations of the set of concepts in the vector space are based on weights of a neural network trained using triples from the semantic ontology; presenting a semantic search user interface including: a text input portion to receive a search query from an input device; a suggested search portion; and a search results portion; receiving, from a user computing device, the search query; converting, using at least one processor, the search query into a vector; computing, using the at least one processor, a set of possible concepts related to the search query based on a distance between the vector and other concepts in the vector space representation; and presenting the set of possible concepts related to the search query in the suggested search portion of the semantic search user interface.

The embodiment of the invention provides a track generation method, a track display method, equipment, a vehicle, a medium and a chip, and relates to the technical field of automatic driving. The trajectory generation method comprises the steps of determining a target search reference line perpendicular to a trajectory reference line according to a current position of a vehicle and the trajectory reference line; determining two search boundary points on the target search reference line according to the road boundary and the maximum steering wheel angle of the vehicle; determining a plurality of spatial search nodes on a line segment, between the two search boundary points, of the target search reference line; determining a target node for generating a target trajectory of the vehicle according to the plurality of spatial search nodes; and generating a target trajectory of the vehicle according to the target node. The distance between the two search boundary points and the road boundary is larger than a preset safety distance, and the two search boundary points are position points which the vehicle can reach when the vehicle travels from the current position at a steering angle smaller than or equal to the maximum steering angle of the steering wheel.

The invention relates to an unmanned ship track and speed intelligent control method based on a Beidou positioning system and a deep learning technology, and the method comprises the steps: building an initial environment space model for known information, dividing a planned track region into grids with the same size, simulating an obstacle through a grid method, marking an original obstacle as an infeasible region, and carrying out the calculation of the initial environment space model. The remaining grids are areas capable of track under normal conditions; a Beidou satellite positioning system (BDS) collects accurate position information, navigation speed information and the like of the unmanned ship in real time and returns the information in real time; and based on the returned information, taking the accurate position of the unmanned ship as a starting point, carrying out space search, namely emitting rays from the starting point to a target point, if the rays are blocked by an obstacle, indicating that the obstacle exists between the starting point and the target point, and then carrying out 360-degree scanning clockwise by taking the starting point as a center to obtain surrounding obstacle information, the environment space model is updated and optimized in real time; on the basis of the position of the unmanned ship and surrounding obstacle information, optimization is carried out by determining a cost function to obtain child nodes, and then the child nodes are obtained through continuous scanning and optimization in the sailing process until the unmanned ship reaches a target point; in the whole navigation process of the unmanned ship from a starting point to a target point through N child nodes, the unmanned ship updates the set navigation speed in real time according to position information and environment perception returned by a Beidou satellite positioning system (BDS); based on the real-time accurate position of the unmanned ship, the optimized track line, the updated navigation speed and the optimized environment space model, a Matlab 2018 platform is used to construct a deep learning intelligent optimization model for unmanned ship track and speed control, and model solution is carried out to realize optimal solution of control variables. The system mainly has the advantages of high real-time information precision, high sensitivity and intelligence.

The invention discloses a public institution energy consumption missing data estimation method based on an improved BPNN. The method comprises the steps that public institution energy consumption multi-feature data are acquired and preprocessed; performing feature screening; integrity check is carried out, and missing data and complete data are separated; constructing a missing data estimation model based on a BP neural network according to the complete data, performing network structure optimization by adopting a particle swarm optimization algorithm, and optimizing an initial weight threshold by adopting an enhanced chaos sparrow search algorithm; training the missing data estimation model; and inputting the missing data into a trained missing data estimation model, and estimating the missing value of the number of energy users. According to the method, the BP neural network architecture is constructed by fusing the particle swarm and the chaos sparrow collaborative optimization mechanism, reasonable estimation of the missing value of the number of energy users of the public institutions is realized, the limitation of a traditional optimization method is broken through, and the method has remarkable advantages in the aspects of parameter space search efficiency and global optimization ability.

A method includes receiving image frames of an inventory. The method includes locating a control point used to determine a spatial search in the image frames and detecting, by a machine learning model, a plurality of storage units in the spatial search. Each storage unit is associated with a unit type from a plurality of unit types. The method includes calculating a storage unit count from a plurality of storage unit counts and for each unit type from the plurality of unit types from the plurality of storage units detected based on depth analysis. Each storage unit count includes a total number of storage units associated with each unit type. The method includes determining a restock status of each unit type based on the storage unit count for each unit type, and automatically generating a replenish request based on the restock status.

The invention discloses a robust transmission method for time-sensitive network fault uncertainty, and relates to the field of industrial Internet of Things. According to the method, the fault uncertainty model and the double-layer robust optimization framework are constructed, the method adapts to complex and changeable link fault scenes in the industrial environment, deterministic transmission of key flow can be guaranteed under the worst fault condition, unpredictable interference such as temperature fluctuation and mechanical vibration can be effectively handled, and the anti-risk capacity of the network is remarkably improved. On the basis of an optimization model of mixed integer linear programming, in combination with constraint linearization and hierarchical solution architecture, the exponential complexity of full-space search is reduced to a polynomial level, and routing and scheduling optimization of a large-scale network can be completed within millisecond time. Through a space-time dimension conflict avoidance and resource reservation mechanism, the bandwidth utilization rate is maximized and the occupation of redundant resources is reduced on the premise of ensuring the real-time performance of high-priority services.

The invention discloses an optimized micro-seismic positioning method based on spatial search. The method comprises the following steps: S1, constructing a micro-seismic positioning model; s2, after spatial positioning solving is carried out on each microseismic event through the microseismic positioning model, optimization is carried out through a spatial search algorithm, and an optimal solution is obtained; and S3, introducing an inertia weight index and a learning factor to carry out multi-objective optimization. The invention provides an optimized micro-seismic positioning algorithm based on a space search algorithm, which combines the space search algorithm with a traditional positioning model, optimizes various key parameters in a micro-seismic positioning process, adopts a multi-target optimization strategy, comprehensively improves the positioning precision and the system efficiency, and overcomes the defects in a traditional method.

The present invention proposes a method for managing three-dimensional spatial scenes of a digital earth compatible with Beidou grid codes. First, based on Beidou grid codes, a Beidou grid space bounding box is defined, and its coding method is given; then, an intuitive and convenient intersection test method for the Beidou grid space bounding box is given; finally, for each object, the smallest Beidou grid space bounding box that can contain the object is found, and a hierarchical bounding box of the Beidou grid space with an R-tree structure that takes into account the polar regions is established step by step. Based on this R-tree structure, efficient spatial search can be achieved, which is particularly suitable for the spatial positions defined by Beidou grid codes.

The invention discloses a diffusion model reasoning acceleration method based on optimal time step sequence search and knowledge distillation, which constructs a unified search space covering a time step sequence and corresponding model architecture configuration on the premise of not performing overall fine adjustment on a pre-trained diffusion model, and searches an optimal time step sequence. And designing a knowledge distillation training method based on the optimal diffusion time step sequence obtained by searching, constructing a main distillation loss function in a discrete time step subspace limited by the optimal diffusion time step sequence, and introducing adjacent time step consistency loss to relieve discrete errors introduced by a non-uniform time step span. In the reasoning stage, a teacher model is frozen, a distilled student model is combined with an efficient sampler ACDMS based on UniPC and fused with flow matching dynamic correction, finite step sampling is carried out on an optimal time step sequence, and reasoning acceleration under generation quality maintenance is achieved.

Disclosed in the present invention are an HM-TRW and HAGENN structure search-based collision risk prediction method and system for an emergency rescue autonomous driving vehicle. The method comprises: abstracting the relationship between an emergency rescue vehicle and surrounding moving objects into a dynamic heterogeneous graph, using HM-TRW to capture the importance and dynamics of each surrounding moving object and various interaction relationships, and fusing same with original features of each surrounding moving object, and inputting same into a HAGENN to perform collision risk prediction. The method which embeds the dynamic heterogeneous graph is applied to a hierarchical attention-based neural network to further learn the heterogeneous characteristics and the dynamic change rules of the surrounding moving objects; and during attention calculation of different hierarchies, a positioning space is used to determine an application position of attention and a parameterized space is used for searching an attention function, and a multi-stage differential search is introduced to accelerate the search process. The present invention can more comprehensively and accurately predict the collision risks of emergency rescue autonomous driving vehicles during operation processes.

The invention provides an audio and video retrieval method and system based on a large language model and a storage medium, and relates to the technical field of computer information, and the method comprises the following steps: obtaining natural language request information related to audio and video retrieval content; performing vectorization processing on the request information according to a large language model tool, and extracting retrieval information of the request information; retrieving in a vector database by adopting a vector space search algorithm, and screening vectors matched with the retrieval information; searching in a relational database by matching index information of the vectors to obtain associated information corresponding to the vectors, the associated information including video names, text contents and starting and ending time; and sending the target video or the target audio corresponding to the vector and the associated information thereof to the client, and selecting a specified time point from the starting time to the ending time to play the target video or the target audio.

The application discloses a circuit input sensitivity analysis method based on Undef SAT, which comprises five steps of netlist analysis and input division, two groups of CNF clause construction, S set assignment and initial solving, first group of solution increment SAT processing and termination and result output. The method uses the incremental SAT processing mode to gradually determine the final assignment of the input variables of the L set, effectively avoids full space search, significantly reduces the search space, and improves the analysis and determination efficiency. The method constructs two groups of CNF clauses supporting irrelevant item propagation, directly processes the irrelevant input variables of the S set by using the Undef SAT solver, reduces the number of logic propagation, and thus reduces the operation time. The integrated process of the circuit input sensitivity analysis formed by the method is suitable for mainstream EDA tool chains, has good engineering applicability, and is suitable for different scenes such as logic synthesis stage, formal verification, functional equivalence checking and loop oscillation detection.

The invention relates to a scale-aware geographic flow clustering method and device, electronic equipment and a storage medium, and the method comprises the steps: introducing a pre-constructed scale factor to construct a relation mapping between a spatial search neighborhood of a flow and a flow length, and generating a to-be-processed flow connection cluster set based on the relation mapping and a plurality of flows; any one to-be-processed stream connection cluster is selected from the to-be-processed stream connection cluster set, and whether any one to-be-processed stream connection cluster is a strong connection stream cluster is judged; if any to-be-processed stream connection cluster is a strong connection stream cluster, adding any to-be-processed stream connection cluster into a stream clustering cluster set, otherwise, taking any to-be-processed stream connection cluster as a weak connection stream cluster, and processing the weak connection stream cluster to obtain a potential strong connection stream cluster meeting a preset condition; and obtaining all strong connection flow clusters and all potential strong connection flow clusters in the to-be-processed flow connection cluster set to generate an actual flow clustering result of the flow. Therefore, the technical problems that local spatial distribution characteristics of stream data are neglected when spatial heterogeneous stream data are clustered, accurate perception of stream analysis scales is lacked, stream clusters with different densities cannot be correctly divided and the like in related technologies are solved.

The invention relates to the technical field of data processing and engineering project process management, in particular to a full-process intelligent management method and system for bridge and tunnel maintenance engineering. The method comprises the following steps: extracting a disease geometric contour based on bridge and tunnel inner wall three-dimensional point cloud data, and calculating a centroid coordinate, a volume and a surface contour area; constructing a space retrieval sphere by using a target disease centroid, screening an adjacent disease set, and calculating a local aggregation density by combining the volume and the centroid distance; calculating the average depth of the target disease, and deducing the overlapping sharing rate of the working plane; constructing a group topology consumption equivalent through linear analysis; and inputting the extreme gradient lifting model to output an optimal repair material dosage table and a material distribution operation instruction book. According to the method, the disease space distribution and aggregation degree can be accurately evaluated, the construction loss surface overlapping probability and the material sharing potential are scientifically evaluated, the material preparation accuracy is improved, waste and cost are reduced, and the whole-process intelligent management of bridge and tunnel maintenance engineering is realized.

The invention relates to a hole tray sensing method and device and electronic equipment, and the method comprises the steps: obtaining a complete to-be-grabbed point cloud structure based on remote RGBD prior region estimation and mechanical arm RGBD camera close-range multi-view shooting; outputting an optimal grabbing posture according to feature extraction and grabbing posture simulation; and based on space search and reinforcement learning path planning, determining the optimal storage point position and path planning of the plug tray. According to the method, the plug trays completely covered by densely growing crops can be visualized, when the plug trays are closely arranged and stacked on an original culture table, the grabbing key point and pose information of the single plug tray can be conveniently and accurately judged by adopting an iterative simulation mode of combining feature extraction with grabbing postures, and meanwhile, the grabbing key point and pose information of the single plug tray can be accurately judged. The optimal storage point position of the hole tray can be quickly selected through combination of space search and convenience of reinforcement learning path planning, and the hole tray is transferred according to the planned path.

The invention discloses an image optimization method for game character and biological model design, which relates to the field of 3D game technology, including: classifying character design and defining components from the perspective of biological population; constructing different semantic tree models for different operations; performing data preparation and preprocessing of semantic tree expression for multiple types of three-dimensional characters from different sources; finding segmentation boundaries through search and verification of geometric features of end points and curvatures; performing pre-segmentation of components, and obtaining semantic tree model subtree segmentation results through boundary optimization; using semantic tree model subtrees as genetic genes to create novel character models; combining user selection with probability graph models as fitness judgment criteria in evolution to optimize solution space search paths. The problem of lack of structural innovation in game character models is solved through the guiding rules and optimization methods of batch segmentation marking and combined modeling in model innovation design.

The invention discloses an electric vehicle charging load prediction method and system based on model-data cooperative driving, and belongs to the field of electric vehicle charging load prediction. According to the method, an improved Monte Carlo inversion mechanism and a double-flow GCN-Transform combined prediction framework are used, so that the problem that prediction mechanism consistency, data adaptability and space-time heterogeneity are difficult to consider at the same time in an existing single driving mode is solved. The method comprises the following steps: firstly, constructing a charging behavior-path cost double-layer coupling physical model, inverting user behavior features through parameter space search, and generating a multi-modal feature set; secondly, capturing a spatial topological relation by adopting a graph convolutional network, and deeply fusing spatial and temporal features by utilizing a shared embedded layer and a Transform network; the method is remarkably superior to the prior art in the aspects of prediction precision, robustness and cross-scene generalization ability, and particularly, stable and reliable prediction performance can still be kept in a data incomplete or load sudden change scene.

The present disclosure discloses an emergency rescue autonomous driving vehicle collision risk prediction method and a system based on HM-TRW and HAGENN structure search, abstracts the relationship between the emergency rescue vehicle and the surrounding moving objects into a dynamic heterogeneous diagram, captures the importance and dynamic of each surrounding moving object and various interactive relations by using HM-TRW, and merges with the original characteristics of each surrounding moving object. Then input HAGENN to predict the collision risk, and apply the dynamic heterogeneous graph embedding method to the hierarchical attention neural network to further learn the heterogeneous characteristics and dynamic changes of the surrounding moving objects. When calculating the attention force of different levels, the positioning space is used to determine the application position of attention, the parameterized space is used to search the attention function, and multi-stage differential search is introduced to accelerate the above search process. The present disclosure can more comprehensively and accurately predict the collision risk in the operation process of the emergency rescue autonomous driving vehicle.

The invention belongs to the technical field of wave numerical simulation, and provides a wave system space tracking method based on parameter continuity, which comprises the following steps: S1, acquiring parameters of a wave system of all grid points in a research area through a numerical model; s2, detecting and combining the pseudo wave system and the homologous wave system of each grid point, and calculating parameters of a combined new wave system; and S3, determining a search starting point, and clustering the wave systems which are homologous with the wave system at the starting point in the computational domain by using a spatial search algorithm so as to obtain wave fields of the wave systems with different sources. According to the method, the problem of discontinuous tracking results or too many tracking results caused by interference of a pseudo-wave system or a homologous wave system in an existing method can be solved, so that a wave field which better conforms to actual conditions can be obtained.

This invention relates to the field of navigation assistance technology, and in particular to a dynamic obstacle prediction and obstacle avoidance path planning method based on multi-sensor fusion. This invention achieves comprehensive perception of dynamic obstacles through multiple sensors, providing a rich and reliable data foundation for subsequent processing. At the same time, it predicts the motion of dynamic obstacles through data processing fusion and model prediction, fully considering the motion inertia of obstacles to make their trajectory prediction closer to real physical laws. Furthermore, by "delineating the final obstacle avoidance target area", the global path search problem is transformed into a local but sufficiently large spatial search problem, significantly reducing the number of grids to be processed and the amount of search computation, meeting the stringent real-time requirements of mobile devices. Moreover, by constructing a cost function, it avoids areas that obstacles may occupy in the future in real time, thereby generating an obstacle avoidance planning path, which helps to improve path safety.

A pipe profile cutting optimization method considering beveling comprises the following steps that S1, Map data of line spacing of all beveling parts is generated, and data of subsequent layout can be rapidly obtained; s2, generating all feasible cutting columns of the pipe profile raw material with the maximum length, and ensuring a universal feasible solution; s3, screening feasible cutting columns for length compression under the condition of beveling, and performing feasibility judgment after prolonging the length of the raw material according to the beveling condition; s4, solving a one-dimensional blanking problem for the obtained ranks based on a Cplex integer programming model; and S5, further optimizing large-scale problems in the actual production process by adopting a solution space search scheme based on group iterative optimization, and solving the problems of different scales by adopting different strategies to call the integer programming model. According to the scheme, the pipe fittings are grouped through a certain optimization strategy to obtain the basic solutions for combination, the solving space of single integer programming is reduced, and therefore the whole problem solving time is greatly shortened.