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 provides an urban green land classification method based on a high-resolution remote sensing image so as to solve the problem of greater workload in the existing urban green land confirmation method. The method comprises the following steps of: extracting urban green land from the obtained urban high-resolution satellite remote sensing image; carrying out function division on urban land according to urban land characters, and dividing the urban land into residential land, industrial land, land for roads, park green land or protective green land; and determining the green land characters of the extracted urban green land according to the function division, wherein the green land can be divided into park green land, appendage green land, production green land, protective green land or other green land. The urban green land in the urban high-resolution satellite remote sensing image is firstly divided according to the urban land characters, and then the corresponding green land attributes are given to divided regions, thus the method can quickly divide the types of the urban green land and reduce the workload and has the advantage of accurate result.

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 dynamic social network community structure evolution method based on incremental clustering to solve the problems of community structure detection and communication evolution tracking in a large scale network. The method comprises a step of extracting the core node of a whole network to form a core sub graph, a step of running a hierarchical clustering algorithm on the core sub graph at a time t=0 to obtain the initial structure of a core community, and using an extended algorithm on the above basis to obtain the community structure of the whole network, and a step of using an incremental clustering algorithm to obtain the core community structure of the whole network at present time according to the dynamic evolution condition of an adjacent time network at a time t which is larger than 0 and extending the core community structure to obtain a whole community structure. Through introducing the core sub graph, the incremental calculation in the whole network is avoided, the processing speed is accelerated, and thus the method is suitable for the community discovery in the large scale network. In addition, through introducing the concept of a community structure shift, the large error of the community structure after long time evolution is avoided, and the accuracy of community evolution tracking is improved.

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 discloses a hybrid grid partitioning method of a truck torsion bar type turnover mechanism finite element model. The method comprises the steps that a geometric model of a truck torsion bar type turnover mechanism is constructed; grid partitioning software is utilized for carrying out geometric Boolean operation on the contact face between a torsion bar arm, a torsion bar shaft and a spherical hinge in the geometric model, so that the contact face between the torsion bar arm, the torsion bar shaft and the spherical hinge achieves node merging; the contact face of the torsion bar arm is expanded to form a transition area, and geometric cutting partitioning is carried out to obtain a transition area and a complex area; the torsion bar shaft and the spherical hinge are subjected to full hexahedron grid partitioning, pentahedron grid portioning is adopted for the transition area of the torsion bar arm, tetrahedral grid portioning is adopted for the complex area of the torsion bar arm, and hybrid grid portioning of the torsion bar type turnover mechanism is completed. The truck torsion bar type turnover mechanism can be subjected to fast finite element grid portioning, calculation precision is not influenced, meanwhile, pretreatment efficiency can be improved, the product development quality can be improved, time can be saved, and the human cost can be reduced.

The invention discloses a vertical lifting type stereo garage parking management system, and relates to the technical field of intelligent garages, and the system comprises an information processing module, wherein the input end of the information processing module is electrically connected with the input end of a remote control module, the input end of the information processing module is electrically connected with the input ends of an information collection module and a parking indication module, the output end of the information processing module is electrically connected with the input ends of a self-adjusting module and an information output module, the information collection module comprises an input unit, an information arrangement unit and an information output unit, and the input unit is used for collecting basic information and parking duration of a vehicle and transmitting the basic information and the parking duration to the information processing module. According to the invention, through the information collection module, basic information identification can be carried out on vehicles entering the garage, and the vehicles are divided according to the predicted parking time of the vehicles, so that the vehicles can be planned conveniently, and parking spaces can be distributed according to the parking time of the vehicles.

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.

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 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 discloses a data processing method for a database system, which comprises the following steps: mapping all the data records in a data sheet to corresponding data virtual partitions according to the mapping relationship between the data records and the data virtual partitions; mapping the data virtual partitions to corresponding database nodes on the basis of the mapping relationship between the data virtual partitions and the database nodes; and storing all the data records in the data sheet in the corresponding data virtual partitions of the corresponding database nodes. Correspondingly, the invention also discloses a database system. After the data processing method is adopted, the data repartitioning and the data distribution can be quickly carried out, so that the database clustering performance is enhanced.

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 provides a fast implementation method for diving coherent generator groups based on a hierarchical topology structure of power grids. The method utilizes the hierarchical relationship ofdifferent voltage levels of a grid, takes each 500 kV station as root node, adopts breadth-first search strategy, and delaminates global network topology, which can quickly divide all coherent generator groups of the power grid without any omissions. At the same time, there is no need to draw geography wiring diagram manually in the process of division, which greatly improves work efficiency. Inpractical applications, the method can meet the requirements of power grid coherent generator groups division under various scales and connection modes.

The invention belongs to the technical field of data mining, and discloses a method and a system for calculating a maximum information coefficient between a single variable and multiple variables in abig data set, which are used for calculating the maximum information coefficient between the single variable Y and m variables (X1, X2,..., Xm). The method: searching optimal grid division of a multi-dimensional (m + 1) space; , fixing division of m variables (X1, X2,..., Xm) by utilizing a maximum information coefficient algorithm of a single variable and m-1 variables; dividing a variable Y, and finding out more suitable division for the variable Y and m variables (X1, X2,..., Xm); and calculating a normalized maximum mutual information value to serve as a maximum information coefficient value. The problem that the existing method for calculating the maximum information coefficient cannot be applied to the calculation of the maximum information coefficient between a single variable anda multivariable can be solved; according to the approximate processing method and system for the maximum information coefficient between the single variable and the multiple variables, the maximum information coefficient value between the single variable and the multiple variables can be calculated.

The invention discloses an electrophysiological signal conjoint analysis-based human exercise ability determination method, which comprises the following steps of synchronously acquiring EMG (electromyography) and ECG (electrocardiograph) signals; performing R wave peak value identification on the ECG signal to obtain an HRV (Heart Rate Variability) signal, and performing time-domain analysis, frequency-domain analysis and nonlinear analysis on each signal; performing conjoint analysis on EMG parameters and HRV parameters, looking for a relevant parameter pair with significance by adopting correlation analysis, and making a research on the association effect of exercise ability and a heart load condition; extracting an EMG characteristic and an HRV characteristic, and calculating correlation coefficients of an integral EMG value and a root mean square of the EMG signal with the HRV parameters respectively; removing redundant information to obtain a characteristic vector by utilizing PCA (Principle Component Analysis); determining the exercise ability by taking the characteristic vector of mode identification input. According to the method, the accuracy and the convenience of a human exercise ability determination model can be effectively improved, and a basis can be provided for the selection of an astronaut.

The invention relates to an optical data broadcasting network system which comprises an optical data broadcasting node and an optical data information receiver, wherein the optical data broadcasting node is used for converting first data needing to be transmitted into illuminating light signals and transmitting the illuminating light signals; and the optical data information receiver is used for receiving the illuminating light signals and converting the received illuminating light signals into second data corresponding to the first data needing to be transmitted by the optical data broadcasting node. By the aid of the illuminating light signals used for transmitting the data, the user can judge light signal coverage conditions conveniently and rapidly in terms of light intensity without studying and knowing complex radio knowledge while illumination is obtained, so that the arranging position of the optical data broadcasting node is adjusted, and optimal coverage effects are obtained by a whole network.

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.

The present invention is a method for classifying the particle size of offshore sediments based on spectrum technology. The steps are as follows: S1. Obtain samples of offshore sediments with different particle sizes and collect spectra; S2. Divide the full spectrum of offshore sediments into m bands, Establish the particle size model of offshore sediments for each band; S3. Select n optimal models from m offshore sediment particle size models, and use the fusion algorithm for fusion processing to obtain the optimal Classification result; S4. The n optimal models and fusion algorithm used in the obtained optimal model are used as the model and method for the particle size classification of offshore sediments to predict the particle size of unknown offshore sediments. The invention breaks the original particle size analysis method, reduces the time for traditional method of marine sediment particle size measurement, realizes the prediction of offshore sediment particle size classification quickly and non-destructively, provides a theoretical basis for searching for sediment characteristic bands, and provides a theoretical basis for particle size analysis. The division provides a solution.