75results about How to "Quick division" patented technology

The invention discloses a method for fast predicting organic pollutant n-caprylic alcohol/air distribution coefficient based on molecular structure, belonging to the technical field of quantifying structure/active relationship (QSAR) facing to the environmental risk evaluation. The method is characterized of comprising the steps of: adopting the molecular structure of atomic center fragment characterization compound; and screening the atomic center fragment combination by means of stepwise regression and partial least-squares regression, to build a group contribution model for predicting KOA.The internal authentication and the external authentication improves that the built KOA group contribution model has stability and predicting capability, and a range and distance method and a probability density method express the application domain of the group contribution model, thereby defining the application range of the model and guaranteeing the predict accuracy. The method has the effectsand benefits of being capable of fast predicting the KOA of the high flux compound, obtaining the KOA with low cost, being helpful for obtaining the high flux KOA data, and having a significant meaning for the environment supervision and the risk evaluation of chemicals.

The invention discloses a POI (Point of interest) and remote-sensing image integrated urban function zone division method. The method comprises the following steps of (1), obtaining POI data, remote-sensing images and space geographic entity data of a region; (2), selecting classes sensitive to urban function zone division from class attributes of the POI data, thereby forming evaluation classes,forming function POI data based on the POI data corresponding to the evaluation classes, matching the function POI data with the space geographic entity data, thereby obtaining an initial distributionstructure of each class of POI data in each geographic entity; (3), extracting land cover data from the remote-sensing images of the region; (4), making a decision tree classification rule based on the initial distribution structure of each class of POI data in the (2) and the land cover data in the (3); and (5), dividing urban function zones of the region according to the decision tree classification rule. According to the method, on the basis of a decision tree classification algorithm, the POI data and Landsat remote-sensing data are combined, so the urban function zones are rapidly divided.

The invention relates to an elderly cognitive function classification method based on a random forest and belongs to the technical field of biomedicine. The method disclosed by the invention comprises the following steps: dividing the elderly cognitive function into three types by adopting MMSE scale scores and education levels; extracting a key cognitive domain influencing elderly cognitive function category classification by utilizing a cognitive function score relative ratio calculation method and a Pearson linearly dependent coefficient calculation method; establishing a random forest regression model, calculating attribute significance scores of non-scale attributes, and extracting external related attributes influencing the elderly cognitive function category classification; finally, equalizing the sample set based on the extracted key cognitive domain and external related attributes by adopting an SMOTE up-sampling method, and establishing an elderly cognitive function classification model by utilizing the random forest method. Compared with a scale classification method, the method provided by the invention has the advantages that the adopted attributes are few and easy to collect, and the method has high convenience; compared with other machine learning algorithms, subdivision of the elderly cognitive function types is realized, and research of a method for performing targeted intervention on the elderly cognitive functions is facilitated.

The invention relates to an evolutionary multi-objective optimization community detection method based on affinity propagation. The method is characterized by comprising the first step of starting the evolutionary multi-objective optimization community detection method based on affinity propagation, the second step of reading in an adjacent matrix of a network, the third step of setting correlation parameters, the fourth step of selecting a similarity measuring method based on signals, the fifth step of selecting the clustering method of affinity propagation, the sixth step of selecting a multi-objective evolutionary algorithm, the seventh step of obtaining a preliminary partition result based on the fifth step and obtaining a further partition result based on the sixth step, and the eighth step of finishing the evolutionary multi-objective optimization community detection method based on affinity propagation. According to the evolutionary multi-objective optimization community detection method based on affinity propagation, a complex network can be partitioned more precisely and rapidly.

The invention discloses a fine classification method and system for power multi-element load users, and the method comprises the following steps: obtaining the historical power consumption data of thepower multi-element load users, and carrying out the preprocessing of the historical power consumption data; performing clustering analysis on the preprocessed data to obtain load characteristic curves of different types; according to the different types of load characteristic curves, using an SVM classifier to carry out training, and generating a classification prediction model; and inputting to-be-classified user historical power consumption data into the classification prediction model to obtain an output classification result. Through data preprocessing, the reliability and accuracy of the data can be improved; the clustering effect obtained by clustering analysis is more accurate; the generated classification prediction model can provide more scientific data reference for electric power decision making; and finally, the users to be classified can be classified accurately, quickly and finely.

The present invention discloses a word vector generation method based on the Gaussian distribution. The method comprises: firstly, preprocessing the corpus; secondly, using the punctuation to performtext division on the corpus; then combining the local and global information to infer the word meaning, and determining the mapping relationship between the word and the word meaning; and finally, obtaining a word vector by optimizing the objective function. The innovations and beneficial effects of the technical scheme of the present invention are as follows that: 1, words are represented based on the Gaussian distribution, point estimation characteristics of traditional word vectors are avoided, and more abundant information such as probabilistic quality, meaning connotation, an inclusion relationship, and the like can be brought to the word vectors; 2, multiple Gaussian distributions are used to represent the words, so that the linguistic characteristics of a word in the natural language can be coopered with; and 3, the similarity between the Gaussian distributions is defined based on the Hellinger distance, and by combining parameter updating and word meaning discrimination, the number of word meanings can be inferred adaptively, and the problem that the number of hypothetical word meanings of the model in the prior art is fixed is solved.

The invention provides a processing method for installation package installation failure and a cause determining method and device. The cause determining method comprises the steps that a first check value sent by a terminal and used for checking a first application installation package failing to be installed on the terminal and a download link of the first application installation package are received; the download link is used for download to obtain a second application installation package; the same checking mode as that of checking the first application installation package is adopted for checking the second application installation package to obtain a second check value; according to the comparison result of the first check value and the second check value, the cause of failure of installing the first application installation package on the terminal is determined. According to the method, as check comparison is carried out on the application installation packages downloaded through the same download link on the terminal and a server, the cause of failure of installing the installation package on the terminal is determined according to the check result, the causes for failure of the installation packages can be fast found out and classified, and then the specific problem solution can be sought.

A clustering processing method for dividing a samples into a plurality of clusters based on a feature amount of each sample, the plurality of clusters each belonging to one of a plurality of layers composed of M layers (M=2, . . . , K), the clustering processing method comprises a sample allocating step of allocating a sample targeted for processing to a cluster belonging to a first layer, based on a result of comparing the feature amount of the target sample with a representative feature amount of each of clusters belonging to the first layer; a determination step of determining whether to allocate a cluster belonging to an M−1th layer to an Mth layer; and a cluster allocating step of allocating a cluster belonging to the M−1th layer to the Mth layer, if it is determined in the determination step to allocate a cluster belonging to the M−1th layer to the Mth layer.

The invention discloses a secondary partitioning method in privacy protection record link, The invention belongs to the field of data integration and data privacy, in particular to the Bloom Filter coding of the records by each data source. Then, the following two steps are carried out: (1) LSH combined with suffix quadratic partitioning method, and block dispersion degree is introduced to adjusttwice partitioning. (2) Multi-block merging based on sliding window can improve the fault tolerance of links. The PPRL block dividing method of the invention has the characteristics of high recall rate of the LSH method and fast division of a large data set, and simultaneously effectively improves the precision rate.

The invention relates to the technical field of social networks, and in particular relates to a method for dividing a relationship circle of a lover's social network. The method includes the followingsteps: S1, generating the association attribute relationship of a user, wherein the association attribute relationship includes a user's friend relationship, interaction frequency, attention groups,the friend relationship includes one-way friend relationship, two-way friend relationship and a friend grade, and the evaluation method of the friend grade is as follows: S2, calculating the degree oftightness between users: calculating the degree of tightness between users according to the association information in the previous step; S3, performing division of a relationship circle: further dividing the relationship circle according to the tightness. The invention provides a method capable of quickly dividing a network relationship circle, accurate division of the relationship circle can berealized through a set calculation formula, and the method is high in efficiency, can carry out adaptive adjustment through changing parameters and the like, and has a wide application range.

The invention provides a logging lithology identification method and system based on a convolutional neural network. The logging lithology identification method based on the convolutional neural network comprises the steps: obtaining current well logging data; inputting the current logging data into a pre-created convolutional neural network framework optimal model to obtain each lithology probability of the current logging data; and determining the lithology corresponding to the maximum value of the lithology probability as the lithology of the current logging data. According to the logging lithology identification method, lithology can be quickly divided, and the lithology identification efficiency and precision are improved.

The invention discloses a high-efficiency internet of things identifier code division method. The method comprises the steps that 1) multiple features used for internet identifier code division are selected, and a weight value is set for each feature; 2) normalization processing is performed on the selected features; 3) the selected code identifier samples are clustered, and the weight value acts as the coefficient of a measure component in a measure function of the selected clustering algorithm; 4) weighted calculation is performed on the features of each clustering center so that the score of the corresponding cluster is obtained; and 5) multiple features which are the same with those of the step 1) are extracted from the code identifier as for the code identifier to be divided, then similarity with each clustering center in the step 4) is calculated, and the code identifier to be divided is allocated to the most similar category. Code division speed is substantially enhanced under the condition of guaranteeing certain accuracy.

The invention relates to a medical data security consensus method based on credit rating, which comprises the following steps: dividing nodes into data nodes, consensus nodes and supervision nodes, and quickly verifying node identities by adopting a node identity verification mechanism; improving an inter-node distance calculation method, a fitness value of a judgment radius and a cutting method in different optimization stages, and realizing rapid division of miner credit rating based on a self-optimization credit rating division method of an improved ocean grazing feeder; meanwhile, according to the situation that the nodes participate in the block consensus, providing a credit rating mechanism, which comprises a reward and punishment mechanism, a credit value model and a credit rating division algorithm, improving the enthusiasm of the nodes participating in the block consensus, and realizing reasonable credit rating division; providing a representative node election mechanism, andpreventing malicious nodes from becoming representative nodes as much as possible; and finally, proposing a representative node consensus mechanism to improve the block consensus efficiency. Accordingto the method, malicious nodes can be eliminated, the node participation block consensus enthusiasm is improved, the transaction delay is reduced, and the consensus efficiency is improved.

The embodiment of the invention discloses a skeleton region determination method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a sequence of original scanning image frames obtained by carrying out the continuous scanning of a target region, wherein the target region comprises a first skeleton and a second skeleton; for each original scanning image frame, determining a segmentation boundary of the first skeleton and the second skeleton according to a preset condition, wherein the preset condition comprises a first preset condition or a second preset condition, the first preset condition is a preset condition corresponding to the foreground region area change of the original scanning image frame, and the second preset condition is a preset condition corresponding to the boundary mask; and for each segmentation boundary, determining a first skeleton region according to the segmentation boundary and the original scanning image frame corresponding to the segmentation boundary. Through the technical scheme of the embodiment of the invention, the technical effect of accurately and quickly dividing different skeleton regions to construct the skeleton model is realized.

The invention provides a VCC coding unit rapid division method based on space-time depth information, which comprises the following steps: performing quadtree division on a CU of a maximum coding unit, and performing multi-type tree division after the quadtree division is ended; when the quadtree division depth of the maximum coding unit is 2 and the average depth of the space-time adjacent CUs is smaller than 2, quadtree division is terminated; when the quadtree division depth of the maximum coding unit is 3 and the average depth of the space-time adjacent CUs is less than 3, quadtree division is terminated; when the quadtree division depth of the maximum coding unit is between 2 and 4, the CU performs multi-type tree division; when the multi-type tree division depth of the maximum coding unit is 0 and the space-time adjacent average depth is less than 0.1, the CU does not need multi-type tree division, and otherwise, all division modes are traversed; and an optimal prediction mode is decided according to the rate distortion cost. According to the method, 35.72% of coding time can be saved averagely, and the average bit rate is increased less under the same coding quality.

The invention provides a software module division method based on Markov clustering, which comprises the following steps of: firstly, dispersing software into a function set, forming a function relationship matrix through data flow among functions, and representing the tightness among the functions by using the number of data items directly flowing among the functions; and then forming a self-adaptive module division method through Markov clustering by taking the functional relationship matrix as an object. By means of the operation, rapid and stable software function module division is achieved without setting the number of modules in advance.

The invention discloses a division system based on social information data. The division system comprises: a data acquisition device; a data analysis device which is used for analyzing the social data, obtaining social information and carrying out unique identification; a data deduplication device; a multimedia data conversion device which is used for extracting a link from the multimedia file ofthe social information subjected to duplicate removal; a mass object storage device which is used for storing the de-duplicated social information; a basic data storage device which is used for storing the identifier of the de-duplicated social information; a word segmentation device which is used for dividing the de-duplicated social information into a plurality of keywords; and a full-text retrieval device which is used for summarizing and duplicating the divided keywords and establishing indexes. The invention further discloses a division method based on the social information data. The invention further discloses electronic equipment and a storage medium. According to the invention, the received network information of different social media is classified and displayed according to conditions, so that a user can check the network information of different social media more intuitively.

The invention discloses a CU block division method based on AVS3 texture information, a computer device and a storage medium, and the method comprises the steps: obtaining a variance, a horizontal gradient and a vertical gradient of a to-be-divided CU block, selecting to skip some division modes according to the relation between the variance, the horizontal gradient and the vertical gradient and a first threshold value, and obtaining candidate division modes, and selecting an optimal division mode from the candidate division modes, and dividing the CU block to be divided in the optimal division mode. According to the method, texture information is extracted based on the variance and the Scharr operator to divide the CU blocks into different categories, the variance with low complexity is used for analyzing a division mode pruning strategy of flat blocks and complex blocks, rapid division of the CU blocks is achieved, the speed of an AVS3 encoder is increased, the Scharr operator is used for considering fine textures and texture directions, and the method is suitable for large-scale popularization and application. The algorithm is balanced between the coding speed and the coding quality; the method is widely applied to the technical field of video coding.