120 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 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.

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.

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 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 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 method for optimizing the cutting of pipe profiles with bevel cutting in mind includes the following steps: S1. Generates map data for the row spacing of all beveled parts to facilitate rapid data acquisition for subsequent layout; S2. Generates all feasible cutting columns for the maximum-length pipe profile material to ensure a universally feasible solution; S3. Screens feasible cutting columns that compress length in the case of bevel cutting, and simultaneously determines feasibility after extending the material length for bevel cutting; S4. Uses a Cplex-based integer programming model to solve the one-dimensional stock cutting problem for the obtained feasible columns; S5. Further optimizes large-scale problems in actual production processes using a solution space search scheme based on grouped iterative optimization. Different strategies for invoking the integer programming model are used to solve problems of different scales. This solution uses a specific optimization strategy to group the pipe profiles to obtain base solutions and combines them, narrowing the solution space of a single integer programming, thereby significantly reducing the overall problem-solving time.

A physics-informed data-driven oil and gas pipeline network fault diagnosis method and system for type-imbalanced data scenarios, relating to the field of oil and gas pipeline network fault diagnosis, and aiming at solving the overfitting problem of current intelligent fault diagnosis models during processing of oil field type-imbalanced data sets, and reducing the risks of false alarms and missed alarms. Main steps are as follows: deeply understanding propagation and attenuation mechanisms of negative pressure waves when oil and gas move in a pipeline network, and establishing a negative pressure wave attenuation physical model reflecting the operating state of the pipeline network; on the basis of a long short-term memory network, constructing a deep generative adversarial model suitable for processing time series data; designing a reasonable series-parallel mechanism to fuse the physical model and a data-driven model, so as to construct a hybrid generative adversarial model; using the trained hybrid generative adversarial model to generate pipeline network fault data, and balancing an original training set; and training an intelligent fault diagnosis model to achieve pipeline fault type identification. The present invention considers both the prior knowledge from the physical model and the learning capability of the data-driven model and integrates same, and compared with simple data-driven models, uses the prior knowledge of the pipeline network contained in the physical model to reduce a parameter space search domain, thus reducing the number of estimated parameters, improving the interpretability and generalization performance of a deep generation model, and improving the physical rationality and feature distinguishability of generated fault data. Therefore, the present invention effectively overcomes the negative impact of type-imbalanced data sets on the performance of diagnosis models, further improving the accuracy of intelligent fault diagnosis of pipelines.

The present invention discloses a design method for an evolutionary UAV landing structure based on the origami principle, belonging to the technical field of UAVs. The landing structure of the UAV combines graph theory and the origami principle, calculates and conducts spatial search on the key points in the origami principle as elements in the graph, and constrains the entire design with the elements in the real physical scenario, enabling the design of the entire structure to be independent of human subjective will and allowing it to be automatically designed according to the motion requirements. The main part of the landing structure consists of a simple class capsule geometric body defined initially, and the joint part is represented by a simple cylinder for rotation and torsion. The connection method and force-bearing situation between the main part and the joint are optimized by applying combined physical parameter model predictive control to it in a physical real simulator and are obtained by screening through bistable conditions. Moreover, the finally generated physical model is easy to manufacture.

The present invention discloses a method and system for diagnosing mechanical faults of high-voltage circuit breakers based on a MIDBO-SVM model. The method first obtains a vibration signal of the circuit breaker, and then uses the obtained vibration signal in combination with a pre-built MIDBO-SVM model to diagnose the mechanical faults of the circuit breaker. During the construction of the MIDBO-SVM model, a good point set strategy and a circle chaos map are used to jointly generate an initial population method to improve the uniform ergodicity of the population. A sine-cosine search strategy and an adaptive weight factor are introduced during the individual position update stage of a rolling ball dung beetle to cause the solution to attenuate and oscillate until it approaches the global optimal solution. A Cauchy-Gauss mutation strategy is used to mutate the individual with the best current fitness in a manner away from the optimal solution, thereby expanding the spatial search range. The method also solves the problems of slow search speed and susceptibility to falling into local optimality in the later stages of traditional algorithms, thereby improving the accuracy of circuit breaker fault classification.

The application discloses a kind of optimization method and device for identifying Cu-Zn alloy cluster structure potential energy surface, comprising: randomly generating several Cu-Zn alloy clusters, constitute population structure library n pop ; develop shell script that can efficiently call Gaussian16 quantum chemistry software, carry out first local structure optimization to n pop ; innovative surface atom self-adapting reorganization strategy is proposed, iteratively move cluster surface atoms to self-adapting generated empty space sites, execute second stage structure optimization;Through crossover, variation operation, in combination with similarity detection and potential energy comparison, extract the optimal structure of binary Cu x Zn y Cluster.Using the technical scheme of the application, the local and global spatial search capability of genetic algorithm is optimized and improved, and the stability and electronic structure of Cu-Zn alloy cluster can be efficiently analyzed by calling quantum chemistry software.

A ship pipeline layout design method based on the PG-MACO algorithm, which belongs to the field of automatic ship pipeline layout optimization. By adopting an improved ant state transition calculation method, this method significantly reduces unnecessary elbows in the traditional ant colony algorithm. At the same time, an energy area is introduced and considered in the heuristic function to achieve the goal of guiding the pipeline to approach certain specific areas. It is closer to the engineering reality, avoiding excessive bending when the ant individual makes a state transition, and trying to arrange the pipeline along the bulkhead or the surface of obstacles during pipeline laying. At the same time, the PG-MACO introduces a co-evolution mechanism, enabling this algorithm to handle mixed pipelines, including single pipelines, multi-pipelines, and branch pipelines, improving the applicability of the algorithm and the ability to handle complex layout situations. By using the fact that pheromone diffuses outward and the pheromone closer to the pipeline is stronger, it guides the pipeline to more easily receive the pheromone guidance of other ants during spatial search, thereby accelerating the path-finding efficiency of the ants and accelerating the convergence of the algorithm.

The invention relates to a registration method and system for mouse brain tissue slice images. On the basis of a two-dimensional mouse brain tissue slice image to be registered and a section parameter prediction model, spatial positioning parameters are determined, and tedious three-dimensional space search and iterative matching in the prior art are effectively replaced by one-time prediction based on the section parameter prediction model, so that the matching efficiency is improved, and the robust spatial positioning parameters are given; determining deformation mapping information based on the spatial positioning parameters, the two-dimensional mouse brain tissue slice image and the three-dimensional average mouse brain template map, determining a two-dimensional labeled slice based on the spatial positioning parameters and the three-dimensional standard map, and determining a registered mouse brain tissue slice image based on the two-dimensional labeled slice and the deformation mapping information, according to the method and the system, subsequent fine alignment processing is carried out on a two-dimensional level, so that the calculation complexity and the algorithm difficulty are greatly reduced, an automatic workflow of intelligent three-dimensional positioning and efficient two-dimensional registration can be formed, and powerful support is provided for high-throughput analysis and clinical application.