77 results about "Node splitting" patented technology

The invention relates to the technical field of artificial intelligence, in particular to a big data information analysis method and system based on artificial intelligence, and the method comprises the following steps: obtaining a jump segment direction vector, classifying label nodes with consistent directions, splitting a path lacking a mapping relation to form an interruption segment, recognizing a node with continuous directions as a bridge segment entrance, and obtaining a bridge segment direction vector; according to the method, by means of classification according to the node sequence and the channel number in the segment hopping direction, direction aggregation boundary determination and semantic field combination path position interruption recognition, the node sequence structure positioning precision and the paragraph division reasonability are improved, and the focus analysis set is generated by combining the path segments. Bridge segment connection is screened based on direction consistency, path continuation logic, behavior tracks and aggregation numbers are kept in sequence and hierarchy regularization, a behavior aggregation sequence of a semantic chain is constructed, and the continuity of path recognition in a channel aggregation area and the stability of node attribution are enhanced.

The invention belongs to the technical field of dynamic network risk prediction based on knowledge graph driving, and discloses a dynamic network risk prediction method and system based on knowledge graph driving, and the method comprises the steps: firstly obtaining a network security event and a context to construct a knowledge graph, and then detecting entity semantic drift through a preset rule and a deep semantic model, and carrying out node splitting, fusion, renaming or label updating and other structural remodeling on the knowledge graph based on a detection result, and finally carrying out risk prediction by utilizing a graph neural network model. The problem that in a traditional method, a static model cannot adapt to dynamic semantic changes is solved, the recognition capacity of a novel attack mode is improved, the risk detection false report and missing report rate is reduced, and the risk prediction efficiency is improved.

The invention discloses an online incremental learning method and system based on a self-adaptive B + tree index, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an extended self-adaptive B + tree index structure, inserting a new sample, and calculating a trajectory disturbance value; generating a disturbance score by combining the disturbance value and a node state, and adding a new sample and an associated node sample into an incremental learning scheduling queue when a condition is met; when a node splitting, merging or reconstructing event occurs, acquiring an affected node and updating a structural code of the affected node; monitoring sample label track change, adjusting a label confidence mark when a threshold value is exceeded, and limiting participation frequency according to a screening mechanism; and performing forgetting compression operation on the leaf nodes with low access frequency and small label fluctuation degree. By constructing an adaptive B + tree index and combining disturbance-driven scheduling, structure change response and forgetting compression strategies, sample management refinement, learning update high efficiency and structure maintenance controllability are realized, and the stability and resource utilization efficiency of online incremental learning are improved.

The invention discloses a storage and calculation separation key value storage delay optimization method based on prediction driving, and aims to solve the problems of performance bottleneck and high tail delay caused by passive management and high network delay in a key value storage system in a storage and calculation separation scene. The method comprises the following steps: time load prediction: deploying a time sequence prediction model at a client, and predicting a future read-write request based on a historical access sequence; active cache prefetching: according to the predicted read request, actively preloading hotspot data from a server side to a client side for caching so as to improve the cache hit rate and hide network delay; active write-in optimization: according to the predicted write request, executing maintenance at a server side through predictive pre-insertion and active node splitting, and moving high index structure adjustment overhead out of a key request path to eliminate a write delay peak; and structure sensing batch synchronization: pre-fragmenting a local write buffer by using a server index top layer model of a client cache, and combining multiple independent remote insertion operations into one efficient batch update to reduce data synchronization overhead. Compared with an existing passive management system, the characteristics of model prediction and active cooperation are fully utilized, and the average delay and the tail delay of the system are reduced.

The invention relates to the technical field of computer communication, in particular to a dynamic two-way matching tree menu resource hierarchy retrieval method and system.The method comprises the following steps that 1, when a user inputs a search keyword, whether the keyword is empty or not is judged, if not, a current node serves as a dynamic starting point, and if yes, the current node serves as a dynamic starting point; bidirectional recursion is carried out on the parent level and the child level; 2, in the bidirectional recursion process, detecting whether the current node can be matched with the keyword or not, and judging whether a matching result is stored in an array or not according to a detection result; and step 3, after bidirectional recursion is completed, traversing the array, and returning a final result according to a traversal result. According to the method, through dynamic bidirectional recursive matching of the father chain and the sub-chain, it is ensured that a complete hierarchical path of a keyword association node is reserved, efficient filtering is achieved in combination with short-circuit optimization, and the problem that father and sub-nodes are split in traditional tree data retrieval is solved.

The invention relates to the technical field of big data processing and quantum computing, in particular to a fastest path searching method, device and system in combination with traffic light delay and dynamic road conditions. According to the method, a real-time topology network is formed by combining a road network topology structure and traffic road condition data, topology solving is converted into variable solving of edges through node splitting, then constraint conditions and optimization targets are constructed by combining weights of the edges, and solving is performed by adopting a quantum algorithm. According to the method, through dynamic weight updating and quantum efficient solution, the problems of insufficient real-time performance and low calculation efficiency in traditional path planning are solved, and high-precision and low-delay technical support is provided for intelligent traffic management and vehicle-mounted navigation.

The present application discloses a warehouse operation process configuration method and apparatus, a device, and a readable storage medium. The method is applied to a process configuration engine. The method comprises: by means of an order analysis module, the process configuration engine analyzing an order to be processed, and determining a target operation process corresponding to said order; by means of a node processing module and on the basis of the target operation process and information of target goods, determining, from a plurality of process nodes, a process node where a container changes as a target service node, and splitting the target service node into a plurality of workstation tasks; calling code blocks corresponding to the workstation tasks to generate corresponding task instructions; and by means of a task link module, sending the corresponding task instructions to corresponding workstations.

The invention discloses a method for evaluating productivity alarm efficiency of a winding machine, which comprises the following steps: an alarm screening module is used for determining a main influence factor of a single coil NG, acquiring corresponding influence factor data, cleaning the acquired data, taking the influence factor as an alarm factor and the productivity as an output prediction quantity, and determining the productivity of the winding machine; establishing a data wide table of an input-output relationship; screening features with high importance for feature factors in the data wide table based on the importance of a lightgbm tree model, and giving a factor importance sequence by using MSE as the basis of node splitting; and the DEA efficiency evaluation module obtains the comprehensive efficiency through CCR calculation and obtains the technical efficiency through BCC calculation, and the scale efficiency = the comprehensive efficiency / the technical efficiency. The invention provides a winding machine capacity alarm efficiency evaluation method, which is used for positioning a top factor influencing the capacity and the yield, assisting a service output maintenance list, and assisting the service output maintenance list by utilizing a DEA efficiency evaluation method.

The invention provides a water turbine bearing bush temperature prediction model driven by feature importance, relates to the field of energy and power engineering, and provides a feature importance module to quantify the influence of each input feature on a prediction target by analyzing the reduction degree of a decision tree on a mean square error in a node splitting process so as to predict the temperature of a bearing bush of a water turbine. Therefore, effective evaluation of feature importance is realized. The extracted feature importance is converted into learnable weighted parameters, and the learnable weighted parameters are fused into input features of the trunk model to enhance expression of key features and suppress interference of redundant features, and the expression ability and prediction precision of the weighted parameters in a water turbine bearing bush temperature prediction task are enhanced. Experimental results show that the prediction precision of the FETSM in multiple time steps is obviously superior to that of the most advanced model, the FETSM has good performance in two evaluation indexes of mean square error and mean absolute error, and the effectiveness and robustness of the model in water turbine bearing bush temperature prediction are verified.

The invention discloses a valve-side dry-type bushing end screen diagnosis and evaluation method, system, medium and equipment, and the method comprises the steps: data collection and preprocessing: obtaining multi-source data of a converter transformer valve-side dry-type bushing through real-time monitoring or historical recording of a sensor; dividing the data set, and dividing the multi-source data into a training set and a test set according to a proportion; building a random forest model, generating training subsets of a plurality of decision trees from the training set by adopting replacement random sampling, randomly selecting a feature subset for each decision tree, performing node splitting based on an information gain ratio, and generating the decision trees in a depth-first mode until a preset maximum depth is reached; performing model integration and diagnosis, adopting a voting mechanism to integrate classification results of all decision trees, and outputting defect types and state evaluation grades, the state evaluation grades being grade II and grade I; and performing dynamic tuning and deployment, performing model parameter optimization by taking a test set macro average F1 score greater than or equal to 0.9 as a threshold value, and performing real-time diagnosis on the optimized model.

The application discloses a distributed storage index method and system based on multiple hash, and the method is as follows: based on a key-value storage engine, a Master / Slave architecture is adopted to build a distributed storage index system; the Master / Slave architecture comprises one Master node and multiple Slave nodes; according to an access request of a key-value pair, the Master node calls multiple linear hash functions to calculate the key of the key-value pair, and obtains the Slave nodes corresponding to all hash values; the Master node sends corresponding operation requests to the corresponding Slave nodes, including an increase operation, a deletion operation, a modification operation and a search operation; when the Master node detects that the real-time load rate of a certain Slave node exceeds a load critical point triggering node splitting, a node splitting operation is performed. The method utilizes the automatic expansion feature of linear hash, improves the scalability and resource utilization rate; based on multiple hash functions, multiple data backups are realized, data loss is avoided, and the reliability is improved; access and storage are realized by using nodes with low load rates, and load balancing is realized.

The invention discloses a data migration method and device, equipment, a storage medium and a computer program product, and relates to the technical field of data migration, and the method comprises the steps: responding to a node splitting request, carrying out the statistics of a fragmentation factor corresponding to each piece of data in a preset to-be-split node, and obtaining a fragmentation factor range statistical result; dividing data in the to-be-split node into two groups according to a fragment factor range statistical result to obtain to-be-migrated data; and migrating the to-be-migrated data from the to-be-split node to the target node through the management node. In the application, when the load of the to-be-split node reaches the load threshold, node splitting and data migration are carried out according to the data fragmentation factor in the split node, so that the system can adapt to the distribution condition of the data along with the actual use of the data. Therefore, the problem that the storage and processing pressure is unbalanced due to the fact that an existing data storage method cannot be dynamically adjusted is solved, and the method is more suitable for application scenes of mass data and high-concurrency access.

The invention provides a distributed computing method and system and related equipment. The method comprises the steps that a management node issues a fusion operator to a computing node; the fusion operator comprises two matrixes and an instruction for realizing matrix multiplication operation of the two matrixes; a plurality of matrix calculation units in the calculation node respectively execute part of the two matrix multiplication operations according to the fusion operator to obtain part of calculation results of the matrix multiplication operations; and executing a communication task to synchronize the partial calculation result. A management node only needs to send a fusion operator to a calculation node, the calculation node can complete calculation of a matrix multiplication according to the fusion operator, and the management node does not need to split the matrix multiplication operation into a plurality of subtasks for calculation and communication and then send the subtasks to the calculation node in sequence. The phenomenon that computing node resources are idle due to management node splitting and task issuing can be avoided, and the utilization rate of the resources and the efficiency of the distributed computing system are improved.

The invention relates to the technical field of medical data analysis, in particular to a heart disease key factor prediction method based on random forest, which comprises the following steps: 1, data preprocessing: preprocessing input data, separating independent variables from dependent variables, and dividing into a training set and a test set; 2, model construction and training: constructing a random forest model, generating a plurality of decision trees through a Bagging algorithm, and performing node splitting by adopting a CART algorithm; 3, performing model evaluation, and adjusting model parameters (such as the number of trees and a node splitting threshold value) to optimize prediction performance; 4, evaluating the generalization ability of the model by using an out-of-bag sample (OOB), and outputting a feature importance analysis result; and 5, model inspection: predicting a new sample based on the trained model, and generating a reliability calibration curve. By optimizing the data preprocessing process, the parameter debugging strategy and the model evaluation system, the accuracy and reliability of heart disease prediction are improved, and a scientific basis is provided for clinical diagnosis.

This solution discloses a new method for identifying RNA pseudouridine sites. This method proposes to use a variety of feature representation techniques to extract sequence features, and then uses the SVM-RFE method for feature selection to compress the feature space and optimize the feature subset. The best feature set after feature selection is input into the kernel method KeMRF based on polynomial random forest to identify pseudouridine sites in the sequence. As a newly proposed classification method, compared with the traditional random forest, KeMRF not only optimizes the discriminant criterion for node splitting, but also combines with an easy-to-interpret kernel method, making the classification performance more superior. This method reduces the training time of the model, improves the classification performance of the model, and further enhances the accuracy of identifying pseudouridine sites.

The invention relates to an interstellar molecule rotation mode prediction method, system and device and a medium wherein the interstellar molecule rotation mode prediction method comprises the following steps: obtaining core parameters of an interstellar molecule structure based on a three-dimensional structure of an interstellar molecule; the core parameters are input into a decision tree model for training, in the training process, based on the core parameters, a node splitting criterion of a decision tree is constructed, based on the node splitting criterion, nodes of the decision tree are split step by step, and the depth of the decision tree and the node splitting criterion are adjusted through a cross validation method, so that the decision tree is obtained. Selecting an optimal splitting characteristic of the node splitting criterion through information gain; the rotation mode prediction model is obtained and used for predicting the rotation mode of the interstellar molecules and corresponding physical parameters, prediction of the rotation mode of the interstellar molecules and explanation of the related physical parameters are achieved, prediction accuracy is improved through a machine learning algorithm, and data support is provided for subsequent interstellar molecule research.

The application provides a persistent learning type index system for DRAM-NVM hybrid memory, comprising: a model part: dividing data into sub-data sets, expressing each sub-data set by using a linear regression model, collecting maximum keys covered by each linear regression model, constructing a two-layer recursive model index architecture RMI, indexing local maximum keys, and retrieving a model address to which a target key belongs; a data part: comprising two types of data structures, namely a Node node class and a Buffer buffer block class; a structure type adjustment part: according to the data structure type, being divided into buffer block expansion, buffer block conversion, node conversion and node splitting, and being used for ensuring that the index always remains correct and efficient during the whole working load process. The application solves the problem that the index height is not limited, and ensures that the index can correctly and efficiently access data under any type of working load.

The invention discloses a routing method in a high-speed serial communication network, and belongs to the field of communication. The transmission process of a data packet in the entire high-speed serial communication network is split into multiple parts according to nodes, and the latter is abstracted as a data packet sent from a local port. The routing of the data packet at each node determines that the data packet is sent to a specific remaining port of the node; the address in the data packet is divided into two parts by routing: a remapping index and a routing rule index; when the data packet arrives from a protocol device or a port on the node, the node reads the routing rule index of the address, and the node queries the corresponding specific routing rule through the index according to the routing rule index table, and then decides to send the data packet to the designated port according to the indication of the routing rule. The invention can quickly establish routing rules, and realize various complex routing requirements through flexible combination, and solves the difficulty of cumbersome, complex and error-prone routing rules in complex networks.

This invention discloses a high-precision indoor personnel positioning method based on machine learning, comprising the following steps: collecting samples, labeling locations, and generating a dataset; extracting inertial features and clustering them to obtain behavioral pattern labels; constructing a signal topology map based on wireless data; constructing extreme random trees for each pattern subset, dynamically selecting splitting features and thresholds to generate an environment-adaptive tree; introducing node splitting into the topology map with topological proximity constraints, and performing local weighted regression combining topology and environmental weights to obtain each extreme random tree model; acquiring real-time data, obtaining pattern probabilities from the clustering model, selecting the corresponding tree model for traversal, and obtaining the predicted location for each pattern; fusing the predicted location output results with probabilities as weights, and updating the topology map, environmental statistics, and tree splitting thresholds. This invention achieves high-precision positioning in dynamic and complex indoor environments, improving positioning robustness and environmental adaptability.

The invention provides a test case generation method and device, equipment, a storage medium and a program product, and relates to the technical field of financial science and technology or the technical field of software testing. The method comprises the following steps: determining a splitting condition of nodes of an adaptive decision tree according to a test entropy and a failure rate gradient of a test object; performing node splitting on the adaptive decision tree represented by the test object based on the splitting condition to generate a test path, the test path being a path from a root node to a leaf node in the adaptive decision tree after node splitting; and generating a test case set according to the test path. The method provided by the invention can adapt to real-time updating of the test object and generate the test case with higher test efficiency.

This invention discloses an approximate nearest neighbor connection optimization method for encrypted databases based on PM# trees, belonging to the field of approximate nearest neighbor connection optimization technology for encrypted databases. It solves the problems of low retrieval efficiency and insecure retrieval processes in existing technologies. The method includes: projecting a high-dimensional dataset into a low-dimensional space using hash projection to obtain a low-dimensional dataset; encrypting the high-dimensional dataset to obtain an encrypted high-dimensional dataset; deleting redundant nodes generated during node splitting in the PM# tree to obtain a PM# tree, and using the PM# tree to build an index on the high-dimensional dataset to obtain an index file; encrypting the index file to obtain an encrypted index; and performing an approximate nearest neighbor connection query on the high-dimensional dataset based on the encrypted index, the low-dimensional dataset, and the encrypted high-dimensional dataset to obtain the query results. This achieves encryption of the high-dimensional dataset while reducing computational load, thus accelerating retrieval speed and improving retrieval quality.

The present application relates to the field of database technology, and proposes an operating method, server and storage medium for a distributed database. The present application sets up a main server and at least one sub-server, and divides the data of the data node into the storage area of ​​each sub-server for storage. When a key-value pair needs to be added to the database, the main server will determine the data node where the key-value pair needs to be inserted, and then send the page number and key-value pair corresponding to the data node to the sub-server where the data of the data node is located. After receiving the page number and key-value pair, the sub-server will perform key-value pair merging and data node splitting operations on the data node, and return the obtained data node splitting results to the main server. Finally, the main server performs an index node splitting operation based on the data node splitting result, thereby completing the operation of adding the key-value pair to the database. By setting it in this way, the data transmission pressure brought to each server can be reduced.

This invention provides a pipeline network segmentation method based on a node explosion algorithm, comprising: constructing an original pipeline network topology; node explosion: breaking the original pipeline network topology at designated segmentation nodes and deleting the designated segmentation nodes to obtain multiple subgraph topologies; node interpolation: in each subgraph topology, constructing edges between the designated segmentation nodes and their neighboring nodes, and re-inserting the designated segmentation nodes into each subgraph topology; and correcting the flow direction of all subgraph topologies after node interpolation. This invention solves the technical problem that the traditional DFS traversal segmentation method suffers from differences in segmentation results due to the different root node input order, and improves the efficiency of topology segmentation and the accuracy of simulation calculations.

The invention discloses a steel frame node identification method and system based on a data center, and belongs to the technical field of computer aided design, the steel frame node identification method based on the data center comprises the following steps: S1, intelligent classification and standardization processing of steel structure node members; s2, setting the connection mode and the connection mode of the steel structure node components; s3, the connection relation between the steel structure node components is calculated; s4, calculating a 3D node constructed by the steel structural member; s5, calculation of steel structure component splitting 2D nodes; and S6, calculating, matching and identifying the characteristic value of the basic node of the steel structural member. The method is used for realizing automatic and high-precision identification and classification of various nodes in the steel structure model, the technical bottlenecks of high human error rate, long modeling period and difficulty in adapting to complex node types in the traditional node identification process are solved, and the efficiency and quality of steel structure node design and construction are improved.

The invention discloses a three-dimensional node splitting method based on a graph theory, and the method comprises the following steps: S1, starting from a current checking step, detecting each node in sequence from small to large according to node serial numbers, and determining whether the current node has a surface reaching a damage condition or not; if yes, entering the step S2; if not, the detection of the current node is completed, the step S1 is repeated, and the next node is detected in sequence; s2, searching closed non-repeated separation paths connected with the current node, and when there are multiple closed non-repeated separation paths, screening out an optimal separation path according to the most flat separation surface; s3, performing node splitting, and performing attribution judgment on original new nodes, edges and surfaces by taking a separation surface with the minimum directed distance from a unit as a reference; s4, repeating the steps S1 to S3 until the detection of the last node is completed; and S5, repeating the steps S1 to S4 until the current simulation work is completed. According to the method, node splitting can be carried out more accurately, and technical support is provided for simulating rock dynamic fracture.

The application relates to the field of artificial intelligence, in particular to a big data information analysis method and system based on artificial intelligence, which comprises the following steps: acquiring a jump section direction vector and classifying label nodes with consistent directions, splitting a path lacking a mapping relationship to form an interrupted section, identifying nodes with continuous directions as bridge section entrances, rearranging path sections to generate a sliding connection sequence, and combining semantic fields to sort behavior trajectories to generate a focused analysis set; in the application, the jump section direction is classified according to node order and channel number, the direction aggregation boundary is determined, the semantic field is combined with the path position to identify an interruption, the positioning accuracy of the node sequence structure and the rationality of the paragraph division are improved, the bridge section connection is based on direction consistency screening, the path continuation logic is maintained, the behavior trajectory and the aggregation number are sorted according to the sequence and the level, the behavior aggregation sequence of the semantic chain is constructed, and the coherence of path identification in the channel aggregation area and the stability of node attribution are enhanced.

The application provides a decision tree recommendation method and system based on random sampling, which deduces the conduction path of faults among devices from the device physical topology association relationship in the device production detection scene, determines the sequential association order of each conduction node and the conduction trigger condition between nodes, groups and collects real detection data and virtual detection data according to the conduction node correspondence relationship to obtain a sampling pool, determines the decision tree branch splitting priority based on the upstream and downstream association relationship of the fault conduction path, obtains a decision tree, generates a step-by-step detection action sequence corresponding to the fault conduction path according to the reasoning path, corresponds each reasoning branch to a detection operation link to obtain a step-by-step detection action sequence, obtains an execution feedback result, updates the node splitting weight of the decision tree, adjusts the sample screening condition of the sampling pool, and obtains the sampling pool to optimize the decision tree. The application can realize the linkage adaptation of the decision tree structure and the data source, and continuously improve the accuracy of fault detection.

The invention discloses a data cleaning method and device, electronic equipment and a computer program product. Relates to the field of artificial intelligence, and comprises the following steps: collecting M data records in a target database, and extracting data information of target data corresponding to the data records from each data record, M being a positive integer; data features of the target data are generated based on the data information, the data features are input into the decision tree model, a cleaning strategy of the target data is obtained, the data features at least comprise time features and frequency features, and the decision tree model takes each data feature as a node and takes information gain as a splitting condition of the node; and processing the target data based on the cleaning strategy to obtain a target database after data cleaning. By means of the method and device, the problem that in the related technology, when data in a database is cleaned, mistaken deletion exists is solved.

The application discloses an online incremental learning method and system based on adaptive B+ tree index, relates to the technical field of artificial intelligence, and comprises the following steps: constructing an extended adaptive B+ tree index structure, inserting a new sample and calculating a track disturbance value; combining the disturbance value and a node state to generate a disturbance score, and adding the new sample and associated node samples to an incremental learning scheduling queue when a condition is met; obtaining an affected node and updating the structure code of the affected node when a node splitting, merging or reconstructing event occurs; monitoring sample label track changes, adjusting a label confidence marker when a threshold is exceeded, and limiting the participation frequency according to a screening mechanism; and performing a forgetting compression operation on a leaf node with low access frequency and small label fluctuation degree. Through the construction of the adaptive B+ tree index, the disturbance-driven scheduling, the structure change response and the forgetting compression strategy, the sample management is refined, the learning update is efficient, and the structure maintenance is controllable, so that the stability and resource utilization efficiency of the online incremental learning are improved.

The invention provides an electrical equipment classification rule self-learning method and system, and belongs to the technical field of electrical equipment management. Specifically, a C4.5 decision tree algorithm is adopted to train extracted feature data of the electrical equipment, a feature with the maximum information gain ratio is selected to perform node splitting, a classification rule in a decision tree form is formed, the classification rule is optimized based on feature importance evaluation, a data feedback mechanism is established, and a classification result is obtained. Newly generated equipment data are collected in real time and added into a training data set, the optimized classification rule is retrained and optimized through the training data set containing new data, and related nodes and rules in the decision tree model are updated. Key features are automatically screened based on information gain ratio and feature importance evaluation, low-contribution features are removed, the generalization ability of the model is improved, and overfitting is reduced. Classification rules are continuously and dynamically optimized through data feedback and incremental learning, the method adapts to dynamic factors such as equipment aging and environment change, and high accuracy is kept for a long time.