257 results about "Minimum spanning tree" patented technology

The invention provides a large-scale satellite three-dimensional image data processing method, medium and system, and belongs to the technical field of satellite three-dimensional image data processing.The method comprises the steps that Gaussian filtering and wavelet noise reduction preprocessing is conducted on an image, and then cooperative computing of a GPU and a CPU is achieved based on a computing task distribution function. Through multi-scale feature extraction and matrix optimization, a stable feature matrix and a variable feature matrix are obtained. A quadtree spatial index is adopted to carry out data partitioning, and SIFT feature extraction and K-means clustering are combined to optimize feature representation. And establishing a stereoscopic image registration model, determining an optimal splicing sequence through a minimum spanning tree algorithm, realizing image splicing by adopting a progressive texture fusion method, and finally generating a high-precision three-dimensional earth surface model. According to the invention, the unification of processing efficiency and precision is realized, and the technical problem that the processing efficiency of large-scale stereoscopic image data is difficult to improve on the premise of ensuring the processing precision in the prior art is solved.

The invention provides a rail transit video intelligent analysis method, medium and system, and belongs to the technical field of rail transit, and the method comprises the steps: constructing a self-adaptive resolution image hierarchical structure, and dynamically determining an optimal resolution hierarchy based on a minimum spanning tree algorithm; constructing an abnormal target movement matrix by combining an improved frame difference method with an optical flow estimation technology; applying a Hungary algorithm to realize target tracking; constructing an abnormal target change index to evaluate a behavior abnormal degree; dividing monitoring stages according to the running state of the vehicle to realize scene self-adaption; an orbit scene attention network model is introduced to identify abnormal behaviors; quantifying a risk level through an abnormal behavior risk assessment function; implementing a hierarchical processing strategy to enable a low-resolution hierarchy to be responsible for global rapid screening and a high-resolution hierarchy to be responsible for fine analysis of key areas; the risk trend is estimated in combination with the rail transit abnormal event prediction model, and the technical problems of large calculation amount and low efficiency of high-definition video processing are effectively solved.

The invention provides a digital modeling method for enhancing connectivity of a low-porosity rock core, a medium and equipment, and relates to the field of digital modeling of rock cores, and the method comprises the following steps: reconstructing a Gaussian random field based on SAXS data to obtain an initial model of a three-dimensional digital rock core pore structure; extracting pore center points of the initial model, and constructing an initial network connecting all the pore center points; based on a minimum spanning tree algorithm, identifying mutually isolated pore clusters in the initial network, and selecting a most efficient seepage path skeleton connected with the isolated clusters; based on the seepage path skeleton, constructing a throat with fractal characteristics; and according to the total volume of the throat, performing morphological corrosion operation on an original pore area in the initial model, embedding the constructed throat into the corroded model, and then performing controllable morphological expansion operation until the volume variation of the final model is smaller than a preset error threshold. According to the method, the reconstruction precision of the digital rock core in microstructure and macroscopic connectivity is effectively improved.

The invention provides an intelligent road crack detection method, and belongs to the technical field of electrical digital data processing.The intelligent road crack detection method comprises the steps that firstly, a line structured light scanner is adopted to obtain road surface point cloud data, and attitude compensation processing is conducted in combination with an inertial navigation system; then noise is eliminated through bidirectional moving average filtering, and the depth matrix is converted into an image and segmented; gaussian background modeling is used to screen abnormal points, a connected graph is constructed through a minimum spanning tree algorithm, and pruning is carried out to obtain a crack skeleton; using a feature enhancement function to optimize the crack saliency; and finally, inputting a pre-trained multi-scale pavement damage identification network, and outputting a crack identification result and classification. According to the method, three-dimensional point cloud data, geometric feature analysis and a deep learning technology are fused, the technical problem of poor adaptability of a complex pavement environment is solved, and accurate identification of various crack types is realized.

The invention discloses an intelligent building equipment control method and system, and relates to the technical field of intelligent building equipment control, and the method comprises the steps: fusing the multi-mode association topology of two groups of data through employing a Kruskal algorithm, constructing a minimum spanning tree, setting the reciprocal of an edge weight as a pheromone value, and selecting an optimal control path through employing a maximum flow algorithm-ant colony algorithm. The method comprises the following steps: converting phase space data by using a Takens embedding method, randomly selecting a pair of adjacent phase space data from a time sequence as initial points, analyzing the state of building equipment by using a Lyapunov index, constructing a hidden Markov model, and dynamically correcting a transition probability matrix and a transmission probability matrix by using the Lyapunov index to obtain the predicted state of the building equipment. The optimal control path is selected by constructing the minimum spanning tree and combining the maximum flow algorithm-ant colony algorithm, the resource utilization rate and the control precision are improved, and the comprehensiveness and adaptability of equipment control are enhanced based on the combination of the hidden Markov model and the Lyapunov index.

The invention provides an HIVE grouping operation performance optimization and scheduling method based on key column partitioning, which relates to the technical field of data processing, and comprises the following steps: preprocessing data by constructing a key column distribution histogram and determining a key column partitioning strategy; optimizing data partitions based on resource evaluation and a dynamic programming algorithm; and calculating an optimal data transmission path by adopting a minimum spanning tree algorithm, and dynamically adjusting an allocation scheme. According to the method, HIVE grouping operation performance can be improved, dynamic balance of calculation loads is achieved, the problem of data skew is effectively solved, and large-scale data processing efficiency is improved.

The invention discloses a cross-institution financial data federated learning modeling system and a privacy compliance verification method, and relates to the technical field of financial data processing, the system comprises a federated aggregation module used for a server to align financial time sequence characteristics of each institution through a timestamp hash alignment strategy, and a privacy verification module used for verifying the privacy compliance of each institution; aggregating the aligned financial time sequence characteristics by using a federated time convolutional network, and generating time sequence model parameters for capturing cross-mechanism global time sequence dependence; the federation reasoning module is used for generating a minimum spanning tree representation of a global enterprise association relationship based on a security multi-party computing strategy by the server side on the basis of a local sub-graph of the enterprise association relationship constructed by each institution client side; the server performs cross-mechanism graph reasoning by using a federated graph attention network and an aggregation graph embedding vector and combining minimum spanning tree representation, and constructs graph model parameters for identifying enterprise associated risks; and the model joint training module is used for the server to fuse the time sequence model parameters and the graph model parameters so as to determine global model parameters.

The invention discloses a main station big data-based low-voltage transformer area topology hierarchy identification method and system, and the method comprises the steps: constructing a multi-dimensional voltage feature vector through the voltage time sequence data, collected by a main station side, of a transformer area general meter and a user ammeter, and forming an electrical feature matrix through feature extraction; and on the basis, judging the station area affiliation and the phase line affiliation of the user electric meter through similarity calculation. Then, generating a stable consensus cluster based on multi-time window clustering and the copolymerization matrix, and mapping the cluster into a virtual electric meter box node; constructing a minimum spanning tree and carrying out directionalization to obtain an initial radial topological structure; further introducing an electrical consistency criterion based on correlation of voltage drop and subtree current, performing multi-candidate structure optimization on the preliminary topology, and finally obtaining an optimal transformer area topology hierarchical structure consistent with a physical rule; high-precision automatic identification of the low-voltage transformer area topology can be realized without manual participation, and effective support is provided for digital operation and maintenance of a power distribution network.

The invention provides a driver anti-fatigue monitoring alarm method, medium and system, and belongs to the technical field of image data processing. A vehicle-mounted camera device is used for collecting a facial image sequence, and a Dlib key point screening function is used for feature point positioning; the feature extraction accuracy is improved by adopting an adaptive illumination compensation algorithm and a minimum spanning tree algorithm, eye features such as an eye closing proportion value and a blinking frequency and head motion features such as a head posture angle value and a nodding frequency are calculated, and physiological data such as blood pressure, heart rate and body temperature are collected by utilizing a smart watch. And inputting the multi-dimensional features into a pre-trained FatgueNet model through an adaptive feature selection gating mechanism, outputting a fatigue state evaluation index, and dividing alarm levels. When the severe fatigue state is recognized and the driver enters the sleepy state, the system sends a pulse stimulation instruction to the smart watch to wake up the driver, and the adaptability and accuracy of the system are improved.

The invention provides a multi-axis linkage engraving equipment control algorithm optimization method, which belongs to the technical field of engraving equipment, and comprises the following steps of: decomposing a model into a plurality of curved surface segmentation segments according to a curvature change rate, and performing NURBS curve parameterization processing on each segment to generate control point coordinates and weight coefficient data; a dynamic weight adjustment model is adopted to dynamically calculate a path planning weight coefficient according to a tool cutting force value, a surface roughness demand value and a machining efficiency index value, a minimum spanning tree path algorithm is utilized to carry out path connection sequence planning, and a layered interpolation operation framework combining preprocessing interpolation calculation and real-time interpolation compensation is established. And the optimized interpolation coordinate data is converted into an axis control instruction sequence containing a position instruction, a speed instruction and an acceleration instruction, and the technical problem that the interpolation calculation precision and the real-time performance are difficult to balance when the multi-axis linkage carving equipment is used for machining a complex curved surface is solved.

The invention discloses a road structure optimization design method and system based on multi-source data analysis, and belongs to the technical field of multi-source data analysis. The method comprises the following steps: generating a first connected graph by using a minimum spanning tree, and optimizing the first connected graph by using a Dijkstra algorithm with a weight to obtain a second connected graph; performing traffic flow simulation on the second connected graph, inserting a middle transition node into an edge with sudden gradient change between adjacent nodes, and refining a terrain grid to obtain a road structure connected graph; extracting all curve segments from the road structure connected graph, converting the curve segments into a geometric model, and determining the vehicle speed constraint of each curve segment; determining a minimum radius constraint according to the landform data; constructing a curve radius optimization model to obtain a first optimized road structure; an initial longitudinal slope is calculated, a longitudinal slope optimization model is constructed, an optimal safe longitudinal slope is solved, and a second optimized road structure is obtained; dividing road section features, and matching a material data set to obtain a road structure optimization design scheme.

The invention provides a pile-soil-structure dynamic interaction analysis method, which belongs to the technical field of civil engineering, and obtains system data through three-scale physical model experiments: the first scale pays attention to microscopic soil behaviors, the second scale and the third scale are determined based on a multiphase fluid-solid coupling equation, and the third scale is determined based on a multiphase fluid-solid coupling equation; and respectively paying attention to the mesoscopic interface action and the macroscopic overall response. A pile-soil interface control equation and a pile structure coupling equation are established based on experimental data to form a basic equation set, a domain decomposition method is adopted to divide a calculation area into a fine grid near field and a rough grid far field, and a minimum spanning tree algorithm is applied to optimize grid division. Pile body stress distribution and a soil body deformation field are obtained through numerical solution, a pile foundation transverse displacement function is obtained through analysis solution, finally the pile body stress distribution and the soil body deformation field are combined to form a complete pile-soil-structure dynamic interaction model, and the technical problem that the pile-soil-structure dynamic interaction cross-scale effect is difficult to accurately simulate is solved.

The invention relates to the technical field of intelligent scheduling, and particularly discloses an intelligent scheduling decision control method and system for a distributed car washing robot, and the method comprises the steps: constructing a weighted graph model which comprises a vehicle position, obstacle distribution and path reachability, and dynamically adjusting the weight of an edge according to the path length, energy consumption and time; optimizing the graph structure by adopting an improved minimum spanning tree algorithm, and calculating a path efficiency index in combination with fuzzy logic and a neural network technology; on the basis of the optimized subgraph, applying a shortest path algorithm to obtain a local optimal path, and introducing a dynamic adaptation coefficient to evaluate the response capability of the path to environment change; fusing the path efficiency index and the dynamic adaptation coefficient into a comprehensive scheduling feature vector, inputting the comprehensive scheduling feature vector into a trained deep learning scheduling model, and predicting an optimal scheduling strategy in combination with a task priority and a robot resource state; and dynamically adjusting the moving path and the operation density of the robot according to a scheduling result, and realizing self-adaptive evolution and cooperative control of the system.

The invention is suitable for the technical field of intelligent power grids, and relates to a G3-PLC communication characteristic-based power distribution network area physical topology identification method, which comprises the following steps of: S10, constructing a power distribution network area physical topological graph model; s20, determining an edge weight in the physical topological graph model of the distribution network area; s30, improving and constraining a minimum spanning tree (MST) based on a Prim algorithm of G3-PLC communication characteristics, and improving a Prim algorithm flow; and S40, identifying the physical topological structure of the power distribution network area. The method is simple in process and convenient to operate, on the basis of not adding extra hardware equipment and any manual operation, the weighted graph is constructed through G3-PLC communication data, the minimum spanning tree (MST) is generated through the Prim algorithm, the physical topological structure of the power distribution network area is recognized, and the recognition accuracy is high.

The invention provides a waterproof construction method for prolonging the service life of a waterproof structure of a standardized swimming pool in a stadium, and belongs to the technical field of building construction.The method comprises the steps that firstly, basic structure parameters of the swimming pool and physical and mechanical parameters of waterproof materials are obtained, a plurality of small physical models of a sequence scale are established, and stress monitoring points are set; and performing finite element analysis to obtain a stress distribution matrix. And identifying a waterproof life key area through a Kruska < l > minimum spanning tree algorithm, and establishing and fitting a waterproof structure life prediction equation set. And solving an optimal construction parameter range by adopting a multi-objective optimization algorithm, and determining an actual waterproof life key area through experiments and simulation verification. Specific construction parameters are determined according to the optimization result, the base layer of the swimming pool is processed, the waterproof layer is laid according to the parameters, and reinforced protection measures are taken for the key area. The technical problems that in the prior art, the service life of the waterproof structure of the swimming pool is not accurately predicted, construction parameter optimization is insufficient, and effective reinforced protection is difficult to conduct on the high-stress area are solved.

The invention provides a method for determining the influence of a straw returning mode on tobacco-rice rotation soil carbon emission, and belongs to the technical field of soil carbon emission. The method comprises the steps that test plots are divided, and different straw returning treatment groups are arranged; installing a static gas tank measuring system and regularly collecting a gas sample; measuring the emission of CO2 and N2O and the concentration of CH4 by using a gas chromatographic method, and calculating the carbon emission flux of the soil; analyzing a straw decomposition process by applying a soil microorganism kinetic equation; analyzing the dynamic change of a soil carbon library based on a carbon recurrent neural network model; constructing a carbon flow network topological structure by using a minimum spanning tree algorithm and identifying key nodes; according to the method, comprehensive influences of environmental factors, crop growth parameters and microbial community structures on carbon emission are integrated through a multi-head cross attention mechanism, accurate quantification and prediction of straw returning carbon emission are achieved, and a scientific basis is provided for optimizing a farmland carbon management strategy and promoting conversion from a farmland carbon source to a carbon sink.

The invention provides an offshore wind plant current collection network topology optimization method based on an improved grey wolf optimization algorithm, and the method comprises the steps: S1, obtaining basic data of an offshore wind plant, initializing the grey wolf algorithm, and generating an initial solution set; s2, adopting polar coordinates and a minimum spanning tree to optimize and expand the initial solution set; s3, optimizing a connection mode in each group; s4, executing the optimal connection of each group of fan combination; s5, performing optimal solution set exploration on the grey wolf algorithm by adopting an operator; s6, repeatedly executing until a preset maximum number of iterations is reached, and generating a topology model of the current collection system of the offshore wind plant; the method can improve the initial solution quality and the convergence efficiency, gives consideration to the global search capability and the local optimization precision, and achieves the quick and effective processing of geometric constraints.

The invention relates to the field of metal material nondestructive testing, and provides a non-ferromagnetic metal material surface profile reconstruction method based on a rotating alternating current electromagnetic field, which comprises the following steps of: determining a detection point array of a detection area, acquiring a vertical magnetic field signal z of the detection area by adopting a TMR magnetic sensing chip, and forming an initial Bz numerical matrix Bz0; performing bilateral filtering; obtaining a Bz numerical matrix Bz2 after morphological processing; obtaining a weighted Bz numerical matrix Bz3; a positive value matrix is formed, binarization processing is conducted on the positive value matrix, and a key numerical value matrix area Tkey containing crack related feature points is formed; calculating the center-of-mass coordinates of the disconnected independent areas, which are called nodes; on the basis of an improved Boruvka algorithm, new edge weight design is carried out on connecting edges between nodes, and a generated minimum spanning tree is effective representation of the surface profile of the bifurcated crack of the metal part.

The invention discloses an unmanned aerial vehicle image camera self-calibration method, system, medium and device, and relates to the technical field of camera self-calibration, and the method comprises the steps: carrying out the feature extraction and image matching of an unmanned aerial vehicle image, and constructing an undirected graph # imgabs0 #; according to the undirected graph # imgabs 1 #, generating an undirected graph # imgabs 2 # based on a minimum spanning tree; constructing an epipolar line geometric cost function based on three images of a triangular structure of an undirected graph # imgabs3 # and visible object space points in the three images; based on an undirected graph # imgabs4 #, seed three views and an optimal image of the incremental SFM are selected, an epipolar geometric cost function is used to carry out incremental unstructured bundle adjustment, and in the process, weighted iteration self-calibration is carried out on camera parameters to be calibrated; gNSS-assisted absolute orientation is utilized, GNSS constrained beam adjustment is carried out, the self-calibration precision is further improved, and optimal camera parameters after self-calibration are selected. The method can improve the precision of model reconstruction.

The invention provides a spartina alterniflora invasion risk grade evaluation method, medium and system, and belongs to the technical field of spartina alterniflora invasion risk evaluation.Intertidal zone environment monitoring equipment is established to obtain multi-dimensional environment data in a tide period, and a tide inundation influence matrix and a tide emerging growth matrix are constructed; a matrix multivariate decomposition technology is combined with a minimum spanning tree algorithm to extract key environment factors and growth factors, a multi-layer neural network is utilized to construct a risk assessment model integrated with a gating weight adjustment mechanism, network parameters are dynamically adjusted through a tidal change influence coefficient, accurate quantitative assessment of the spartina alterniflora invasion risk is realized, and the risk assessment accuracy is improved. A risk early warning mechanism of differential monitoring frequencies is established, continuous improvement of a risk assessment model is realized based on iterative optimization of monitoring frequency adjustment coefficients, and the technical problem of insufficient accuracy of spartina alterniflora intrusion risk assessment in a tidal environment is solved.

The invention belongs to the technical field of optical engineering, and discloses a tree real skeleton point extraction method and system based on a deep neural network and a minimum spanning tree algorithm, a terminal and a storage medium, and the method comprises the steps: obtaining original point cloud data; predicting the original point cloud data based on a deep neural network, and obtaining the vector, radius and category information of each point cloud; performing centralization processing on the original point cloud data according to the vector, radius and category information of each point cloud, and performing initial skeleton extraction by using a minimum spanning tree algorithm to obtain initial skeleton points; simplifying and smoothing the initial skeleton point, and updating to obtain a real skeleton point based on a real skeleton point updating method of Euclidean clustering; and outputting the real skeleton point. The tree real skeleton point extraction precision is improved, and the reconstruction precision of the tree model is improved.

The invention relates to the technical field of large models, and particularly provides a large model data enhancement method and device, and the method comprises the following steps: S1, collecting original data through a sensor cluster, grouping the data through a Pearson's correlation coefficient, and for each group, determining a head sensor through employing a minimum spanning tree algorithm, and building a generation sequence; s2, starting a generator based on a long short-term memory (LSTM) network, and training data of a head sensor by using the LSTM generator; s3, starting a ridge regression generator, and training data of other sensors by using the ridge regression generator; and S4, generating corresponding data by using the model obtained by training according to a specified demand of a user, and increasing the data volume through interpolation. Compared with the prior art, the method has the advantages that inherent complex time behaviors in the industrial production process can be learned, the relation between sensors which is crucial to accurate simulation process conditions is kept, and it is ensured that the generated data mode has high fidelity while the original process characteristics are kept.

The invention discloses a big data analysis system and method for a financial guarantee circle, and relates to the technical field of data analysis, and the method comprises the steps: collecting industrial and commercial, judicial and financial data in real time through an API gateway, and constructing a quantization guarantee network model and a quantum annealing parameter configuration file after preprocessing; a quantum annealing machine is used for executing hidden relation detection, and Bayesian confidence coefficient calibration is combined to generate a high-association guarantee ring list and a quantum calculation log; simulating a risk conduction path based on a synaptic plasticity model of the spiking neural network, and outputting a synaptic weight change table and a dynamic conduction diagram; the risk levels are mapped into a hierarchical blocking strategy matrix through a dynamic blocking decision engine, topology reinforcement is carried out by combining historical data and adopting a minimum spanning tree algorithm, and a stable subnet list and a protection path diagram are generated; and finally, performing weighted fusion on the quantum log, the conduction model and the subnet data, constructing a three-dimensional risk map, and optimizing a real-time decision by using a long and short-term memory network and an attention mechanism network.

The invention discloses a distributed dynamic task allocation method and device, equipment and a storage medium, and relates to the technical field of multi-robot control, the method is applied to a multi-robot system, and the method comprises the following steps: obtaining communication state information of each mobile robot, and determining minimum communication topological graph information by adopting a preset distributed minimum spanning tree algorithm; inputting the system parameters and the minimum communication topological graph information into a preset k-WTA network calculation formula to obtain activation signals corresponding to the mobile robots; and obtaining position information of the to-be-tracked target equipment and the mobile robots, and performing task allocation on the mobile robots according to the position information and the activation signals. According to the method, the complex communication topology in the multi-robot system is simplified through the minimum spanning tree algorithm, and calculation is performed based on the minimum communication topological graph information, so that the consumption of calculation resources is reduced, and the task allocation efficiency of each mobile robot is improved.

The invention discloses a three-dimensional graph rendering method based on a graph theory, and the method comprises the steps: taking object individual attribute elements in a three-dimensional graph scene as nodes, taking object relation attribute elements as edges, and constructing the three-dimensional graph scene into a graph G (V, E); wherein V represents a set of nodes, and E represents a set of edges; simplifying and segmenting the graph G (V, E) by adopting a minimum spanning tree or a clustering algorithm in a graph theory to form a plurality of three-dimensional graph sub-scenes which can be respectively and independently rendered; obtaining the rendering priority of each three-dimensional graphic sub-scene based on the topological structure of the three-dimensional graphic scene; and sequentially rendering the three-dimensional graphic sub-scenes based on the rendering priorities. According to the method, the three-dimensional graphic scene is represented as the graph G (V, E) based on the graph theory, and the calculation amount of single rendering is reduced by simplifying and segmenting the graph G (V, E); meanwhile, the rendering sequence is optimized in combination with the topological structure of the three-dimensional graphic scene, unnecessary calculation is remarkably reduced, and the rendering speed is increased.

The invention provides a steel structure splicing type installation method, and belongs to the technical field of steel structure construction.The method includes the steps that firstly, based on a three-dimensional design drawing, a structure map is established, and node coordinates and connection relations are identified; carrying out bearing capacity partitioning through a minimum spanning tree algorithm, and calculating a bearing capacity gravity center; carrying out stability partitioning, calculating a mass gravity center, and carrying out correlation analysis on the mass gravity center and a bearing capacity gravity center; calculating an optimal splicing sequence based on an elastic mechanical equilibrium equation, and establishing a splicing process diagram; installing a first reference node according to the process diagram, and establishing a coordinate system; connecting pieces are installed in sequence, and center-of-gravity deviation detection is carried out; when the structure reaches a critical state, carrying out overall stress measurement and adjusting node parameters; and finally, all splicing is completed, the geometric dimension and stress distribution are verified, and the problems of stability and stress balance existing in a traditional method are solved.

The invention provides a production method of a konjak-series Qingqing diet, and belongs to the technical field of konjak-series Qingqing diet, and the production method comprises the following steps: forming a primary colloid through an accurately controlled hydration process, then introducing soybean and pea proteins, and carrying out thermal gelation treatment to construct a composite gel matrix; functional plant extracts such as curcumin, tea polyphenol and a grape seed extract are added, and the release efficiency of active ingredients is improved by adopting an ultrasonic-assisted extraction technology. The method further comprises the step of adding vitamin complex and calcium and magnesium trace elements, finally, high homogenization of the product is achieved through an innovative tree-shaped multi-cavity homogenization treatment mechanism, the mechanism adopts a minimum spanning tree algorithm to optimize a fluid transmission path, and the technical problem of efficient compounding of the konjak gel and the functional plant extract is successfully solved.

The invention discloses a whole wind network wind volume deduction method based on limited actual measurement wind volume and a topological structure, and relates to the technical field of wind volume deduction. The relation between the spanning tree air volume and the residual tree string air volume is obtained; the ratio of the number of independent loops to the number of total branches in the mine ventilation network is 1 / 3; establishing a maximum air volume residual tree string model and a minimum air volume residual tree string model; a corresponding minimum air volume spanning tree and a corresponding maximum air volume spanning tree are obtained; respectively putting known air volumes into the maximum remainder tree string, the minimum remainder tree string or the maximum spanning tree and the minimum spanning tree to construct four air volume deduction models; and solving the deduction model by adopting an FLA optimization algorithm, and outputting the air volume of each branch of the whole wind network. According to the method, through air volume adjustment and global optimization deduction, the air volume of the whole mine ventilation network is deduced rapidly and accurately, the air volume monitoring defect in the prior art can be effectively overcome, and more accurate and timely mine air volume state sensing is provided.

The invention discloses a multi-robot path planning method for an electric arc additive manufacturing system. The multi-robot path planning method comprises the specific steps that a three-dimensional model is obtained, slicing and layering are conducted, environment information is obtained, and environment modeling is conducted; determining an ideal task load; performing matrix iteration, and optimizing the current task load; constructing a minimum spanning tree, and generating a two-dimensional optimal task path of each robot; and performing three-dimensional conversion and asynchronous execution. Compared with the prior art, the method has the advantages that the form requirement of an actual additive manufacturing path is fully considered, the process defects of air holes, insufficient fusion, slag inclusion and the like in the manufacturing process are effectively reduced, and the problems of region segmentation and deposition path planning in an electric arc additive manufacturing (WAAM) task cooperatively executed by multiple robots are successfully solved; and therefore, high-quality additive manufacturing of the component is realized. Meanwhile, the path design strategy proposed in the method avoids the start-stop operation of the welding gun, ensures the parallelism and independence of each robot path in the three-dimensional space, and further improves the operation efficiency and stability of the system.

The invention provides a power collection system topology optimization method and device based on a variable weight minimum spanning tree, and relates to the technical field of offshore wind power topology optimization. The method comprises the following steps: grouping a plurality of fans based on the angle difference among the plurality of fans according to the position information of the offshore booster station to obtain a plurality of fan sets; a Delaunay triangulation method is adopted to construct a reference grid connecting line of each fan set; and based on the reference grid connection line of each fan set, generating the optimal topology of the to-be-processed offshore current collection system based on a variable weight minimum spanning tree algorithm. According to the method, the grouping clustering method, the Delaunay triangulation method and the variable weight minimum spanning tree algorithm are combined, and the topology design of the offshore wind plant current collection system is efficiently, economically and rapidly achieved. According to the invention, topological connection and cable specification type selection can be optimized synchronously, and a global optimal solution is sought between the cable path length and the type cost difference caused by the change of the current-carrying capacity, so that the fine control of the overall cost of the current collection system is realized.