30results about How to "Guaranteed time and efficiency" patented technology

The present invention discloses a workflow scheduling method based on depth enhancement learning. The method comprises the following steps of A) collecting M task execution workflow directed acyclic graphs (DAG) in an actual execution environment as a sample pool; B) carrying out the Markov decision process (MDP) modeling on each workflow DAG to generate a task state set S; C) according to a training method DQN of a neural network, using the task state sets S generated by the M workflow DAG and the corresponding known action sets A as the input to substitute into a deep neural network formula, and obtaining the value of a neural network parameter matrix. According to the present invention, and by the above method, the defects that the execution time of the workflow scheduling method is long and the generalization performance is poor under a current distributed environment, are solved, the time efficiency of an algorithm is guaranteed acceleratedly, at the same time, the generalization performance of the algorithm itself is increased, and a scheduling machine can learn a scheduling strategy autonomously according to the actual scene characteristics.

The invention discloses a multi-view image bidirectional matching strategy with constrained object information. The multi-view image bidirectional matching strategy comprises the following steps: performing multi-view image forward matching of the constrained object information according to an imaging model of the image and the input ground object information so as to improve the reliability of matching similarity measurement and calculation by utilizing the information redundancy between the multi-view images; and performing backward matching of multiple independent stereoscopic images on the multi-view image forward matching result so as to effectively eliminate multiple matching points in the forward matching result, and improve the accuracy of the multi-view image matching result. The matching process of each point to be matched is not influenced in the matching strategy, the matching strategy is a parallel matching strategy, and high-efficiency parallel matching of multiple points to be matched is promoted. According to the method, the operating efficiency and the result reliability of multi-view image dense matching can be effectively improved, and the problems that the efficiency is low and multiple mismatching points exist in a traditional multi-view image matching method for matching single stereoscopic image and fusing multiple matching results are solved.

The invention discloses a text data stream clustering algorithm based on affinity propagation. The text data stream clustering algorithm is characterized by including the following steps: 1, carrying out dimension reduction processing on a text data set to obtain a corresponding text vector set; 2, obtaining clustering centers of all moments, and completing the clustering algorithm. By means of the text data stream clustering algorithm, the accuracy and the robustness of the algorithm can be improved without assigning the number of clusters in advance, and therefore the requirements for solving practical problems are met.

The invention provides a three-dimensional target detection method and system based on substream sparse convolution. The method comprises the following steps: obtaining initial point cloud data of a target scene; based on the initial point cloud data and the optimized point cloud voxel convolutional neural network, obtaining target point cloud features, and obtaining the optimized point cloud voxel convolutional neural network by optimizing the point cloud voxel convolutional neural network through substream sparse convolution; and performing target detection on the target scene according to the target point cloud features. According to the embodiment of the invention, the point cloud voxel convolutional neural network extraction method based on substream sparse convolution is constructed,further feature extraction is carried out by using the sparsity of the point cloud, and deep extraction can be carried out on local information more efficiently so that the efficiency and precision of three-dimensional target detection are improved.

The invention provides an edge computing time slice scheduling method based on deep reinforcement learning, which comprises the following steps: acquiring a plurality of task queues uploaded by terminal equipment, and taking the plurality of task queues as a sample pool; carrying out Markov decision process modeling on each task queue to generate a task state set and a corresponding action set; based on the task state set and the corresponding action set, obtaining a value of a neural network parameter matrix at a certain task execution moment through a neural network training method; judgingwhether the task state set is completely substituted into a neural network training method or not; if so, outputting the value of the neural network parameter matrix at the task execution moment to asample pool as a time slice scheduling result at the task execution moment; otherwise, continuing to execute Markov decision process modeling. According to the method provided by the invention, the time efficiency of the algorithm is accelerated and ensured, and the generalization performance of the algorithm is improved, so that a scheduling machine can autonomously learn a scheduling strategy according to actual scene features.

The invention discloses a virtual network mapping method for a cross-data center. Under the condition that survival capability demands and reliability of a virtual network are ensured, first of all, partition is performed on virtual nodes in the virtual network through virtual node group partition (VNGP), based on current packets, all node pair sets among the different packets are searched for, final partition is determined, and then the virtual network after the partition is mapped to a bottom-layer data center network through a GVNM sub-algorithm. According to the invention, under the condition that the survival capability demands are ensured, the virtual nodes are mapped to the same data center, such that communication resources are saved. Besides, a reliability enhancement method in the data center is also employed, such that the reliability of the virtual network is ensured, and at the same time, the efficiency is high.

The invention discloses a text data stream classification method based on word vectors and an integrated SVM (Support Vector Machine). The text data stream classification method comprises the following steps: 1, obtaining a seed text set from a text data set; 2, performing word vector expansion processing on the seed text set to obtain a corresponding feature dictionary and a noise dictionary; 3,performing feature weighted vectorization processing on the text data set to obtain a corresponding text vector set; and 4, constructing an integrated classifier to obtain classification results of all the texts. According to the method, the accuracy of the classification result can be improved by fully utilizing the data features under the condition that the calculation complexity is reduced, sothat the requirement for solving the actual problem is met.

The invention discloses a large-scale graph data set-oriented statistical significance subgraph mining method and device. The method comprises the following steps of: mining a graph data set G, correcting a statistical significance threshold value under a family error rate threshold value <alpha>, obtaining a corrected significance threshold value <delta>* and a minimum support degree threshold value <sigma> to be satisfied by mined subgraphs when the significance threshold value <delta>* is reached on the basis of an improved replacement inspection algorithm westfall-younglight; mining all the subgraphs, statistical significance threshold values p of which are smaller than or equal to the significance threshold value <delta>*, in the graph data set G; and converging support degrees of allthe mined subgraphs to the minimum support degree threshold value <sigma>. The method is capable of effectively calculating the repeated calculated amounts in the process of mining significant subgraphs.

The invention relates to a beam frame device for building construction. The beam frame device comprises a beam frame supporting table and beam frame supporting columns, wherein the four beam frame supporting columns are arranged on the lower end surface of the beam frame supporting table; two side position protection plates are symmetrically arranged on the upper end surface of the beam frame supporting table; a first rotating rod is rotationally arranged on the upper end surface of the beam frame supporting table; and a main position protection assembly is fixedly arranged outside the first rotating rod in a sleeving manner. According to the beam frame device, a combined structure of the main position protection assembly and the side position protection plates is arranged on the beam frame supporting table, and the combined structure can play a role in protection; and specifically, in the working process, constructors can play a role in safe construction under the protection of the combined structure, so that production accidents are reduced, and the beam frame device has a higher market prospect.

The invention discloses a multilayer morphological brain network construction method, an intelligent terminal and a storage medium, and the method comprises the steps: obtaining human brain image data, carrying out the preprocessing of the human brain image data, and extracting a plurality of morphological features from the human brain image data; dividing the plurality of morphological features according to brain regions based on a preset cortical map, and determining the brain region to which each morphological feature belongs; and according to the plurality of morphological features and the brain region to which each morphological feature belongs, constructing a multilayer morphological brain network. The construction method is simple, the constructed multi-layer morphological brain network integrates various morphological characteristics, the relevance and specificity between different morphological characteristics are considered, rich and comprehensive cerebral cortex morphological information is provided, and the time efficiency and economic benefits in the actual research and analysis process are guaranteed.

The invention relates to a suspender arch bridge optimization design method and system under moving load, and the method comprises the steps: S1, drawing up the size of each member of an arch bridge, and building a finite element model; s2, on the basis of linear static force calculation, a finished bridge suspender force is obtained and serves as an initial optimization value of the finished bridge suspender force; s3, on the basis of the stability and strength effect of the main arch, forming a moving load immobilization working condition, and loading the moving load according to a fixed position; s4, calculating the moving load and the initial defect based on nonlinear calculation, and iteratively optimizing the optimal suspender force; s5, completing the design of residual components according to a conventional method; the system adopts the methods of S1 to S5; according to the method, the strength and stability of the main arch are considered, the moving load and the initial defect are considered for iterative optimization, the better finished bridge suspender force is solved, the design result is closer to the actual situation of the bridge, the bridge safety performance is higher, the method is easy to achieve in a programmed mode, the optimization speed can be adaptively changed, and the method can be used for large-scale optimization design of the large-span arch bridge. And the economical efficiency is higher.

The invention provides a method for recovering selenium from Cu-In-Ga-Se waste, which comprises the following steps: adding a Cu-In-Ga-Se material into a sulfite solution to obtain a mixed solution; carrying out leaching treatment on the mixed solution in a pressurized environment, thereby obtaining the leachate obtained after leaching treatment; and adding an acid solution into the leachate for acidification decomposition, and then performing solid-liquid separation to obtain crude selenium. According to the method for recycling selenium from the copper-indium-gallium-selenium waste, sulfitewith mild properties serves as a leaching agent, leaching treatment is conducted on the copper-indium-gallium-selenium material in a pressurized environment, the requirement for corrosion prevention of equipment is low, the leaching time efficiency and the leaching rate of selenium can be guaranteed, and acid-base balance treatment is not needed subsequently; in the invention, selenium separationcan be achieved through the two steps of leaching and acidification precipitation, so that the process is short, the selenium separation efficiency is high, and the production cost is low.