2406 results about "Tree structure" patented technology

The invention discloses a large-scale point cloud registration method, system and device based on a power grid power transmission line and a storage medium, and the method comprises the steps: collecting point cloud data of the power transmission line under different visual angles, and forming a source point cloud set and a target point cloud set; down-sampling point cloud data in the source point cloud set and the target point cloud set, dynamically adjusting the sampling granularity of different density regions, and obtaining representative point cloud data from the source point cloud set and the target point cloud set; extracting point cloud feature points, accelerating neighborhood search through a KD-Tree structure, and adaptively adjusting a neighborhood search radius; constructing a geometric topology model; performing coarse registration based on an improved RANSAC algorithm, selecting non-collinear feature points, and optimizing a transformation model; an improved ICP algorithm is adopted to carry out fine registration, and normal vector features are utilized to introduce weighting factors to optimize registration precision. According to the invention, large-scale power transmission line point cloud data can be efficiently and accurately processed, and the method is especially suitable for power inspection, three-dimensional modeling and maintenance scenes.

The invention relates to the technical field of visual rendering, and provides a three-dimensional data visual rendering method and a visual rendering engine system.The method comprises the steps that space segmentation processing is conducted on three-dimensional model data to be rendered through the LOD technology to generate block slice data with different geometric detail levels, organizing the block slice data through a tree structure with a spatial relationship, and outputting the tree structure as an index file; the method comprises the following steps: adaptively screening block and slice data of different levels of detail based on a user vision field range, storing the block and slice data into a rendering request queue, querying block and slice data to be rendered from the rendering request queue according to index codes of the block and slice data, and updating a layer browser database; according to the method, to-be-rendered block slice data are downloaded and analyzed in parallel in batches in a separated collaborative operation mode, and a rendering task is executed according to a data analysis result, so that the rendering scheduling frame rate is remarkably improved, and rapid visualization of urban mass three-dimensional slice data at a webpage end is realized.

The invention discloses an intelligent document analysis method and system, the method is executed by the intelligent document analysis system, and the method comprises the following steps: carrying out layout analysis on a PDF page by adopting a deep learning model; merging the block list from bottom to top by adopting a recursive algorithm; carrying out balance optimization on the binary tree structure; and outputting a result of the processed binary tree structure by adopting a preorder traversal mode. Layout analysis is carried out by adopting a deep learning model, various complex typesetting formats such as multi-column layout, image-text mixed typesetting, tables, lists and the like can be effectively identified and processed, and semantically continuous text blocks are ensured to keep continuity in a tree structure through a tree structure optimization module; a global semantic error correction module is added to carry out global document semantic representation learning and carry out adaptive adjustment and error correction on a preliminary structure, so that deep ambiguity is eliminated, logic errors are repaired, and the consistency of a final analysis result and human reading logic on the semantic level is maximized.

The invention discloses a large model reasoning method and system based on tree diagram and knowledge graph retrieval enhancement, and the method comprises the steps: segmenting a document into text blocks, recognizing the entities, relationships and factual descriptions of the text blocks, and forming a knowledge graph of the text blocks; constructing a factual summary of the documents based on the knowledge graph of the text blocks, and performing clustering recursion on all the documents by using a Gaussian mixture model to generate a similarity tree structure graph; the method comprises the following steps: obtaining a user problem, selecting a reasoning path from a similarity tree structure diagram through a large language model debate iteration method, obtaining a corresponding document cluster, pruning the cluster based on the user problem and a confidence score of a document, selecting a plurality of text blocks from the pruned document, and finally obtaining a knowledge graph based on the selected text blocks. And generating a reasoning result. The invention relates to the technical field of natural language processing, can realize retrieval enhancement based on a tree diagram and a knowledge graph, and accurately queries related information when a large model answers a complex multi-hop question, so that the reasoning accuracy is effectively ensured.

The invention provides an electronic data evidence storage verification method and system based on hierarchical hash and an intelligent contract, and relates to the technical field of block chains and data security, and the method comprises the steps: obtaining to-be-stored electronic data; to-be-stored electronic data is divided into data blocks according to a preset rule, a unique identifier and a timestamp are added to each data block, and a standardized data unit is generated; performing hierarchical hash calculation on the standardized data unit, generating a leaf node hash value by each data block, aggregating the leaf node hash values layer by layer according to a Merkle tree structure, and generating root hash; calling a block chain smart contract, submitting the tree structure hash, the timestamp and the public key encrypted signature to a block chain network, after verification of a consensus node, writing the evidence information into the block chain, analyzing a hash path submitted by a user by the smart contract to perform data verification, if the hash path is consistent with the data verification, returning verification success, otherwise, triggering an exception handling contract; through the dynamic partitioning algorithm and the layered hash structure, the calculation overhead of large file processing is reduced.

According to the knowledge text retrieval method and device, the storage medium and the computer equipment, when knowledge text retrieval is carried out, the retrieval statement and the target knowledge document corresponding to the retrieval statement are obtained firstly, the document structure tree of the target knowledge document is determined, the document structure tree is of a tree structure constructed by utilizing the directory structure of the target knowledge document, and the retrieval statement is stored in the document structure tree; according to the method, a target knowledge document is taken as a component of the target knowledge document, an index structure between each index node in the component of the target knowledge document and the target knowledge document is recorded, and leaf nodes in the index nodes are connected through pointers to form a linked list structure. The corresponding index node can be dynamically retrieved based on the association relationship between the index structure and the linked list structure of the tree structure, and the context content corresponding to the retrieval statement in the target knowledge document is intercepted by adopting different modes based on the type of the index node. And omission of unrelated contents of other chapters and key contents of the same chapter is avoided, so that the retrieval precision and quality are improved.

The invention discloses a time sequence knowledge graph reasoning method based on a large language model, which comprises the following steps of: performing vectorization expression on query entities and relationships by utilizing an embedded model, and generating vectors containing structural features and semantic information in combination with adjacent entities, relationships and time information; a candidate entity set most relevant to query is screened out from the large-scale entity set by calculating the conditional probability; constructing an event evolution tree, and performing multi-hop sampling on adjacent nodes of a query entity and a candidate entity in a time window to generate a tree structure capable of completely reflecting a historical event evolution path; the method comprises the following steps: designing a structured prompt template, and performing instruction fine tuning on a pre-trained large language model by adopting an LoRA (Low Rank fine tuning technology), so that the model better follows a task instruction to generate a reasoning result. And efficient and accurate time sequence knowledge graph reasoning is realized.

The invention discloses a 3DTiles data storage method and a map data query method.The 3DTiles data storage method comprises the following steps that (1) an LOD hierarchy pyramid type hierarchy table is established, and an LOD range index is created; 2) generating a three-dimensional object into an agent point, and segmenting a tile according to a KD-tree structure to construct a three-dimensional KD-tree index tree; and 3) storing the data of the KD-tree leaf node into a GridFS of MongoDB multi-mode storage, mapping a file path into a unique hash value, writing the unique hash value into a corresponding node according to a KD-tree node storage structure, and creating a geographic spatial index. The method creatively solves the problems that file system management is difficult due to the fact that the number of files is large, a hierarchical structure based on a file directory is difficult to support efficient spatial range retrieval, and the spatial query response time is reduced to the millisecond level in combination with a three-dimensional KD-tree index and a MongoDB geographic spatial index.

The invention belongs to the technical field of information technology and image recognition, and relates to a handwritten mathematical formula recognition method and system. The method comprises the following steps: constructing handwritten mathematical formula annotation data; constructing an initial handwritten mathematical formula recognition model, training the initial model by using the annotation data, and performing joint optimization on a sequence decoding task and a tree structure prediction task to obtain a handwritten mathematical formula recognition model; and carrying out handwritten mathematical formula identification by adopting the handwritten mathematical formula identification model obtained by training. According to the method, the advantages of a sequence decoding model and a tree decoding model are fused, the understanding and generalization ability of the model to a mathematical formula structure are effectively improved, and the accuracy of handwritten mathematical formula recognition is remarkably improved on the basis of keeping the efficient parallel training advantages of an existing encoder-decoder model-based method.

The invention relates to the technical field of data processing, in particular to an artificial intelligence data aggregation method based on big data, and the method comprises the following steps: collecting traffic monitoring original data, removing repeated data and correcting wrong data through data cleaning and preprocessing, and generating preliminary cleaning data; dynamic index adjustment is applied to the preliminarily cleaned data, the depth and branches of an index tree are dynamically adjusted according to the access frequency and types of the data items, and optimized index data are obtained. In the dynamic index adjusting process, the index tree structure is dynamically optimized according to the data access frequency and type, the retrieval efficiency of high-frequency data is improved, and the storage overhead of low-frequency data is reduced. The hierarchical data compression performs hierarchical processing on the data, the use of storage space is greatly reduced through a mode of combining lightweight compression and an advanced compression algorithm, and a foundation is laid for large-scale data processing.

The invention discloses an integrated verification and test system and method for complex rules of a substation monitoring system, and the system comprises a rule editing module which provides a visual interface to support a user to edit the complex rules containing various types of variables and time sequence constraints; the analysis and verification module is used for performing grammar, variable conflict and business compliance multi-level verification on the rules; the intelligent test case generation module is used for automatically generating test cases covering normal, boundary and abnormal conditions based on the rule tree structure; the simulation test interface module converts the use case into a standard test script, submits the standard test script to a simulation tool for execution, and outputs the standard test script to a monitoring system through protocol conversion; the result analysis module is used for comparing an execution result with an expected result and generating a structured test report; according to the invention, full-process automation of rule verification and testing is realized, the verification efficiency is remarkably improved, and the test requirements of various transformer substation business scenes are met.

The invention relates to the field of Web application, in particular to a tree structure data dynamic management method based on node hierarchical attributes and recursive updating. Comprising the following steps: S1, constructing a tree structure data system, and endowing each node with a hierarchical attribute which is used for identifying position information of the node in a tree structure; s2, receiving an operation instruction of a user for the tree structure data, wherein the operation instruction comprises node adding, node batch deleting, node moving, data exporting and importing, node querying and node quantity counting; and S3, according to different operation instructions, executing corresponding processing logic: based on node hierarchy attributes, applying a recursive update mechanism to realize dynamic management of tree structure data, and ensuring data consistency and accuracy.

The invention relates to the technical field of machine learning, and discloses a decision tree data model establishment method, which comprises the following steps of: acquiring heterogeneous data sources such as a structured data table, a time sequence data stream and graph structure data through distributed nodes, sampling the time sequence data stream by using a dynamic sliding window, and vectorizing the graph structure data through a graph embedding algorithm; a multi-stage feature selection model is constructed to screen features, and a dynamic decision tree generation framework adopting an adaptive splitting criterion is established based on the features. A tree structure is adjusted by applying a multi-objective optimization algorithm, and the performance is improved by introducing an incremental pruning mechanism. And the online model updating module monitors data distribution change, reconstructs a local sub-tree in good time, and injects noise to protect data privacy in combination with a differential privacy protection mechanism. According to the method, heterogeneous data is effectively processed, the model classification precision is improved, the complexity is reduced, the generalization ability is enhanced, the model can be updated online, and the data privacy is protected. The electronic equipment calls related instructions to execute the method, and efficient data processing and analysis can be achieved.

The invention discloses an intelligent crawler generation method and system based on a large language model and an MCP protocol, belongs to the technical field of network data collection, and solves the problem that the capability of LLM in dynamic webpage analysis and anti-crawling strategy generation links cannot be fully exerted due to the fact that LLM and browser interaction protocols cannot be effectively integrated in the prior art. The method comprises the steps of analyzing an acquisition demand based on a large language model and generating a standardized demand description document, realizing interaction between the large language model and a browser based on an MCP protocol, analyzing a page complete DOM tree structure through a crawler script generation system, and performing quality verification and intelligent repair on a generated crawler script. According to the method, the complete DOM tree and the dynamic data rendered by the browser are obtained through the MCP, and the large language model can be called to automatically analyze the element positioning strategy, so that the collection script is adaptively generated, and high efficiency, intelligence and automation of webpage data collection are ensured.

The invention discloses an intelligent element positioning method and system based on AI and a dynamic feature library, and the method comprises the steps: carrying out the anomaly detection and abnormal data capture, executing an element positioning operation through an automatic script, and recording a page DOM tree structured snapshot when the positioning is judged to be failed; multi-modal feature extraction: analyzing the DOM tree, and extracting an XPath / CSS path and semantic attributes of a target element; performing dynamic feature library retrieval and matching, calculating DOM structure similarity, and taking a high-similarity rule as a correction basis if the high-similarity rule exists; an AI intelligent rule is generated, and candidate positioning rules are generated through the classification model; performing script correction and real-time feedback, replacing the failure positioning statement and updating the dynamic feature library; and generating a structured report, and recording abnormal data. According to the method, the AI is combined with the dynamic feature library, so that the intelligent correction of element positioning in the automatic test process is realized, and the stability and efficiency of the automatic test are improved.

An electronic device is disclosed. The electronic device comprises an image acquisition unit configured to acquire medical images captured at a plurality of positions, and a processor configured to generate tree structure data of three-dimensional candidate points corresponding to points along center lines of blood vessel regions of a reference image and a sub-image of the medical images, determine a three-dimensional path based on the tree structure data, determine a three-dimensional diameter for each of the points on the three-dimensional path, and reconstruct the three-dimensional shape of the blood vessels corresponding to the three-dimensional path based on the determined three-dimensional diameter.

The embodiment of the invention provides a dynamic page rendering method and system based on a front-end framework, electronic equipment and a storage medium, and belongs to the technical field of page rendering. According to the scheme, rule expansion processing is conducted on JSON structure specifications, a metadata structure model is obtained, and the metadata structure model is used for defining page rendering rules; obtaining JSON configuration data, and performing data preprocessing on the JSON configuration data based on the metadata structure model to obtain structure data; dynamic structure analysis is conducted on the structure data through an analysis engine, a virtual document object model tree structure is obtained, and the analysis engine adopts a layered architecture; and performing difference rendering according to the virtual document object model tree structure to obtain a target page. According to the method, seamless connection between a low-code platform and professional development can be realized, the development efficiency is improved, and the page rendering dynamic capability is enhanced.

The invention discloses a structured remote sensing report generation system, method and device based on a syntax tree and a medium, the system comprises a user input layer, a core processing layer and an output and verification layer, the system receives user demand parameters and input data, automatically analyzes the demand parameters and standardizes the input data; generating a syntax tree structure conforming to industry specifications, and driving the large model to obtain a first draft of a report under the constraint of the syntax tree; and performing automatic verification and manual intervention correction on the first draft of the report to form a professional remote sensing report. According to the method, the multi-dimensional syntax tree architecture is constructed, and the hierarchical syntax analysis algorithm and the domain knowledge graph are combined, so that semantic association of the multi-modal remote sensing data and accurate mapping of terminologies are realized, automatic generation of a long document report meeting remote sensing geographic industry specifications is supported, the structured degree and logic continuity of text generation are improved, and the text generation efficiency is improved. The method can be widely applied to the fields of geological monitoring analysis, environment evaluation report generation and the like.

In one implementation, point cloud data for a point cloud is decoded. The decoder obtains features representing voxels in a tree structure, where feature for a current voxel is representative of at least a set of voxels that are still to be reconstructed. The decoder then determines an occupancy probability of the current voxel based on the feature, and decodes occupancy information of voxels in the tree structure, where whether a current voxel is occupied or not is decoded based on the occupancy probability for the current voxel. The point cloud can be reconstructed based on the occupancy information. On the encoder side, the feature for the current voxel is obtained from the voxels that are still to be encoded and encoded into a bitstream.

There is provided a method for monitoring and assessing compliance of a computing system. The method may include receiving documents containing rules. Each document may be converted to a tree structure having nodes. A mapping object may be generated to map a tree structure to another tree structure. A second mapping object may be generated to relate a tree structure to compliance controls. A third mapping object may be generated to relate compliance controls to a set of compliance control evidence. A compliance score may be determined based on the controls, the evidence, and the mappings.

According to the long-tail human movement prediction method based on adaptive hierarchical learning, coarse-grained semantic grouping is introduced through a hierarchical tree structure based on the Maslow human motivation theory, and the optimization process is rebalanced in a framework-independent mode. Firstly, thinking chain cues are designed based on the Maslow human motivation theory, a hierarchical structure for city customization is constructed by applying a large language model, and knowledge in human mobile data is fully developed through hierarchical learning. Secondly, exploring hierarchical position prediction through Gumbel interference and adaptive weight, so as to fully capture complex space-time semantics; wherein Gumbel interference is used for rebalancing learning of head and tail positions, adaptive weight performs node-level adjustment on head categories and tail categories in each layer, and these components effectively promote exploration of mobility knowledge. The invention further comprises a method adaptive to the advanced mobile prediction algorithm, and the effectiveness and applicability of other methods in long-tail mobile data are improved by embedding the adaptive hierarchical learning loss into other mobile prediction methods.

The invention relates to a signal equipment fault diagnosis rule automatic generation method, system and equipment, and the method comprises the steps: a demand cleaning processing process: carrying out the semantic analysis of an original fault diagnosis demand of signal equipment, and generating a structured output containing a demand pseudo code and a dependency graph based on a recognition result of the demand; the demand searching process is used for constructing a demand vector and a knowledge graph; performing similarity search on the vectorized query demand to obtain similar demand nodes for a user to select a root demand, and performing deep search in the knowledge graph based on the root demand to generate a fault tree structure and fault tree data; and an intelligent rule automatic generation process: generating prompt words based on the fault tree data construction rule, and generating a signal equipment fault diagnosis rule code by using a large language model. Compared with the prior art, the method has the advantages that the signal equipment fault diagnosis rule is automatically generated, and the development efficiency is improved.

The invention discloses an RPA code generation method based on an HTML and an image, and the method comprises the following steps: S1, receiving multi-modal input data provided by a user, the multi-modal input data comprising an HTML structure file of a target business system, an interface screenshot image and a natural language operation demand description; s2, performing standardized analysis on the HTML structure file, extracting DOM tree structure information, and performing visual feature enhancement processing on the interface screenshot image; and S3, inputting the processed HTML structure information, the interface screenshot image and the natural language operation requirement into a pre-trained visual language model, and outputting a target XPath path and a Python code sequence through the visual language model. According to the method, comprehensive understanding of a complex service system is realized by combining HTML structure information and an interface image, so that efficient and accurate RPA script codes capable of being executed on multiple platforms are automatically generated.

The invention particularly relates to a robot outdoor large-scale scene autonomous navigation method based on an open digital map, which comprises the following steps: extracting a road network structure, constructing a road network topological graph, and modeling a road network space by using an R tree; associating a starting point with a target point according to the road network topological graph of the R-tree structure, and performing global path planning based on a Dijkstra algorithm to obtain path points from the starting point to the target point; acquiring laser radar sensor point clouds, and generating a local voxel map by using the point clouds after the ground points are filtered out; performing local target point mapping by combining the global path line segment and a local voxel map to generate a local target point; performing local front-end path search based on a JPS algorithm to obtain a local front-end path from the current position of the robot to a local target point; a safety corridor is constructed according to the local front-end path, and trajectory optimization is carried out; and tracking the optimized trajectory, and planning the speed by considering the nonlinear model of the robot and the actual motion state. The method provided by the invention can realize outdoor large-range long-distance autonomous navigation.

The invention discloses a code vulnerability detection method based on multi-dimensional feature fusion and edge semantic enhancement, and the method comprises the steps: carrying out AST sub-tree structure compression based on a sub-tree folding rule on the basis of retaining an AST high-level grammar framework, fusing a PDG data flow and control flow dependency relationship, and constructing a lightweight program semantic graph; extracting node multi-dimensional features from the program semantic graph, extracting program-level global features in combination with an edge semantic enhancement mechanism, and performing vectorization representation; and inputting the program semantic graph features into a full connection layer, identifying potential vulnerabilities and outputting a detection result. According to the scheme, a context modeling mechanism based on a scope path is introduced, a semantic subtree, a grammar structure and scope information are combined, multi-dimensional features are extracted, multi-dimensional information fusion is realized through a dynamic attention mechanism, local and global feature splitting is prevented, and the edge semantic enhancement module is constructed to realize multi-dimensional information fusion. And the distinguishing capability and the feature expression capability of the model on heterogeneous edges are improved.

The invention discloses a power distribution network power scheduling optimization method and system, which are used for solving the technical problem of poor calculation efficiency of a power distribution network power scheduling optimization scheme caused by great calculation complexity of joint solution in a traditional power distribution network power scheduling optimization method. The method comprises the following steps: constructing an initial classification tree model according to an obtained optimal topology scheme in a plurality of power distribution network operation scenes and network operation states in the plurality of power distribution network operation scenes; performing global optimization on a tree structure of the initial classification tree model by using a mixed integer programming method based on a preset feature selection and division mechanism to generate an optimal classification tree model; and when a current power distribution network operation state of the power distribution network is received, based on a mathematical programming method, solving an optimization problem corresponding to the power distribution network by adopting the optimal classification tree model according to the current power distribution network operation state, and generating a target power distribution network power scheduling scheme.

A hardware sampling device (200) and related method for generating observations of arbitrary random variables from probabilistic bits are disclosed. The sampling device comprises a programming unit (250) for adjusting the mean value of probabilistic bits generated by each one of a plurality of probabilistic bit generators (271a-h), a binary selection tree structure (210) comprising a plurality of nodes, and a detection logic circuit (240) for detecting a tie condition for each depth stage of the binary selection tree (210). Each node comprises a selector circuit (213) connected between a pair of input terminals (211a-b) and an output terminal (212). The and configured to randomly admit one of the two input signals to the output terminal if a tie condition is detected, or else admit the input signal which represents a logical '1'. The position index bit related to the admitted input signal is supplied to the output terminal of the node and concatenated with a sequence of previously transmitted position index bits.

The invention discloses a method for designing a tree data storage mode in a relational database by using preorder traversal, which comprises the following steps: S1, basic node data of a tree structure is constructed in the relational database, each node comprises id, name, lvalue and rvalue attributes, and the lvalue and the rvalue are used for identifying the position relationship of the nodes in a tree through an interval representation method; s2, setting a query condition according to a node ID needing to be queried, and determining a descendant node range of the node through lvalue and rvalue attributes of the node; s3, the query results are subjected to ascending sort of the left value lvalue, and a sort list (sort List) is constructed; and S4, by traversing the sortedList, in combination with the temporary set level List and the cache level NodeCache, calculating and setting a father node ID (pid) of each node and whether the father node ID (pid) is a leaf node isLeaf attribute or not according to the logic of pre-order traversal, thereby completing the rapid construction of the tree structure.

In various examples, policy prediction-based motion planner systems and methods for autonomous and semi-autonomous systems and applications are provided. A scenario tree structure may be generated that represents potential behaviors of one or more peripheral agents based on perception data of a scene within which an ego vehicle operates. A joint MPC algorithm may optimize the motion of an ego vehicle within the context of the scenario tree structure to produce a policy tree structure. An MPC policy prediction model may be trained to predict the policy tree structures that a joint MPC algorithm would produce, given a set of environmental perception data. An ego vehicle may comprise a trained MPC policy prediction model that receives perception data, and based on that input predicts a policy tree structure that may be used to define a motion policy for navigating the ego vehicle through the scene.

The invention discloses a tree control rendering method, which comprises the following steps that a data list is acquired, and each piece of data in the data list comprises a hierarchy field and a node field; rendering and generating tree structure data according to the hierarchical field and the node field of each piece of data; rendering and generating a first tree control based on the first N-level nodes of the tree structure data, and displaying the first tree control; and in response to a received instruction for expanding the first tree control, loading data corresponding to a to-be-expanded node and re-rendering the first tree control to obtain a target tree control. The invention further discloses a system, computer equipment and a readable storage medium. According to the scheme provided by the invention, the data with the hierarchical structure can be effectively processed and displayed, tree-shaped hierarchical loading is reduced through a gradual loading and rendering mode, the user requirements are met, and the product performance and the user experience degree are greatly improved.