74 results about "Dependency tree" patented technology

The invention discloses a software test automation report generation optimization method which comprises the following steps: automatically registering and authenticating a multi-source test data access endpoint, uniformly collecting and normalizing test data with a multi-dimensional attribute tag, and forming a high-quality grouped data set through structured modeling and automatic clustering; further extracting event and result feature nodes, and establishing an interpretable tree rule dependency relationship; according to the method, the minimum redundancy and reusable customized rule path is generated by applying a dependency tree traversal and optimization algorithm and automatic reasoning, and the continuous adaptive evolution of the rule base among multiple projects is realized by combining conflict detection and visual interaction, so that the efficiency and expandability of data processing in a complex test scene are improved, and the test efficiency is improved. And the flexibility and automation level of report customization rule configuration are enhanced.

The invention belongs to the technical field of software management, and particularly relates to a cross-platform repair method and system based on a large language model.The method comprises the steps that a cross-platform package manager interface is used for conducting static analysis on a dependency list of a target software project, a cross-platform dependency tree is obtained, and the target software project is repaired based on the cross-platform dependency tree; respectively injecting lightweight monitoring probes into at least two operating system platforms, and collecting dynamic tracking data of each dependency component in the dependency tree; fusing the dependency tree and the dynamic tracking data, and establishing a cross-platform security map comprising a dependency calling edge, a version conflict edge and a platform vulnerability association edge; according to the method, the lightweight monitoring probe is injected into the multi-operating system platform, the loading sequence, the function call sequence and the memory layout difference of the dependent components are dynamically collected, and the cross-platform security map is constructed in combination with the static dependency tree, so that the specific potential safety hazard of the platform can be accurately identified, and the comprehensiveness and the accuracy of cross-platform risk perception are remarkably improved.

The invention belongs to the technical field of data processing, and particularly relates to a fingerprint generation method of a virtual evaluation model based on Hash operation, which comprises the following steps of: firstly, constructing a dependency tree by taking a main model file specified by a user as a root node, and initializing an access record table; establishing a static keyword protection area containing a core parameter rule base, scanning a dynamic file operated by a user, and comparing parameter definitions to generate an early warning; reading the binary data of the current node file, and generating a character string through SHA-256 after standardization; recursively processing the sub-nodes to obtain a node tree hash value, sorting the sub-nodes, splicing the sub-nodes according to a fixed format, and generating a character string through SHA-256 operation; then, a current node standardized path is spliced and generated through SHA-256 operation; and finally, by taking the root node as a starting point and the DFS as a sequential serialization tree structure, executing SHA-256 operation after adding a global collision salt value, and outputting a global unique fingerprint of the model. The problems of poor stability, low efficiency and high generation risk in a model fingerprint generation technology in the prior art can be solved.

The invention discloses an open source software supply chain security intelligent monitoring and risk assessment method, relates to the technical field of software supply chain security, and solves the problem that only existence of indirect dependence can be identified generally in the prior art, but whether a vulnerability code can be called on an actual execution path cannot be accurately judged. According to the method, through dependence locking and Hash fingerprint embedding during construction, cryptographic-level accurate traceability of a component version is realized, and version ambiguity is thoroughly eliminated; in combination with deep interception of a JVM probe for method calling and class loading events during operation, a loading source and a call stack context of each class in a real execution path can be captured, bidirectional association mapping is performed on dynamic behavior data and a static dependency tree, and whether a vulnerability component is located on an executable call chain or not is accurately identified. Compared with the defect that coarse-grained warning is carried out only through version matching in a traditional tool, the method has the advantage that by means of dynamic-static data fusion, it is avoided that a large number of unavailable indirect dependency vulnerabilities are misjudged to be high in risk.

The invention relates to the technical field of natural language processing and artificial intelligence, and discloses a large-model complex instruction-oriented fine-grained intention recognition method, which comprises the following steps of: constructing a candidate analysis forest of an input instruction, and executing entity alignment of each node and a domain knowledge graph; performing semantic compatibility verification on the candidate dependency trees by utilizing entity relationships in the atlas, and screening an optimal analytic tree in combination with syntactic probability and knowledge consistency scores; if the analytic tree meeting the threshold value does not exist, a semantic conflict edge is positioned, and candidate mounting points are searched by using a map neighborhood relation to reconstruct a dependency structure; and finally, generating standardized data containing a structured analytic path based on the optimal analytic tree, and performing fine adjustment on the large model. According to the method, logic constraint and dynamic repair are carried out on the syntactic structure by introducing the knowledge graph, analysis errors caused by multiple modification or structural ambiguity in a complex instruction are effectively solved, and the accuracy and robustness of intention recognition of a large model in the vertical field are improved.

The invention belongs to the technical field of information security, and particularly relates to a multi-security encapsulation method for a virtual evaluation simulation result, which comprises the following steps of: firstly, constructing a dependency tree by taking a main model file as a root node, and generating a unique digital fingerprint by hierarchical hash; writing the original script into the random naming new file, and starting simulation to output a binary file; then key indexes are extracted to generate a CSV file, and SHA-256 Hash is calculated; the digital fingerprints, the paths, the Hash and the like are integrated to generate a structured report; the sender signs the report Hash by using an ECDSA private key, packages and compresses the report Hash, and then encrypts and transmits the report Hash by using an RSA public key of the receiver; and finally, the receiver decrypts and decompresses, verifies and signs an ECDSA public key, and then checks report Hash in a cross manner to ensure that the result is safe and credible. According to the invention, the problems of low reliability and low security of a virtual evaluation simulation result security packaging technology in the prior art can be solved.

The embodiment of the invention provides a cross-translation-unit code vulnerability scanning method and device, equipment and a medium, and the method comprises the following steps: carrying out static inspection on a target translation unit by using an abstract syntax tree, and extracting an external dependency relationship of the target translation unit based on a source code; on the basis of the extracted external dependency relationship, generating a dependency chain of the translation unit, deleting a circular reference in the dependency chain, and generating a dependency tree; according to the dependency tree, context information related to the target translation unit is loaded, and the context information comprises declarations, method signatures, function signatures, class definitions, macro definitions and namespace information; and assembling the loaded context information into a target translation unit, generating an assembled target translation unit, and executing a vulnerability detection tool to perform static vulnerability analysis on the assembled target translation unit. The efficiency and accuracy of cross-translation unit vulnerability analysis are improved through an incremental mode of loading the dependent content as required.

The embodiment of the application provides a kind of based on syntax semantic fusion sentiment tuple extraction method and system, it is related to natural language processing technical field.The method can be after obtaining input text, using structure perception syntax encoder calculates the encoding embedding data of input text, again through bidirectional cross attention fusion, obtains the fusion representation of syntax representation and semantic representation depth interaction.Based on double similarity comparison learning framework, the unified representation of fusion representation is calculated, and candidate example is retrieved according to the unified representation.The method can be based on the structure perception graph converter of dependency tree coding to generate encoding embedding data, and through the syntax and semantic fusion realized by bidirectional cross attention mechanism, and carry out double similarity comparison learning, for the structured task of large language model Provide explicit syntax guidance, can simultaneously integrate syntax and semantic information, correctly identify complete sentiment tuple, improve the output precision of sentiment tuple extraction result.

The application provides a graphical programming code generation method, which comprises the following steps: generating a dependency tree based on a functional block instance set, performing depth-first traversal on the dependency tree, arranging the functional blocks in the order of traversal, and generating a code for calling the functional blocks according to the order of the traversal result. The application can extract necessary information from the user's programming result, determine the execution order of the functional blocks, and generate a suitable functional block calling code, which is convenient to use.

The invention discloses a code editor cross-language function positioning method based on hierarchical context driving, which comprises the following steps: by taking an interpreted language as a source language and a compiled language as a target language, analyzing a dependency tree and an abstract syntax tree of a source language project to collect source variables in a module layer, a function layer and an instance layer to construct a source tracing table; constructing a layered mapping rule base and establishing a mapping relationship and confidence coefficient between a source variable type and a target variable type in combination with the traceability table, obtaining a file preloading context and an intermediate instruction sequence of a source language, capturing a multi-dimensional calling context during cross-language calling after incremental simulation execution, determining a polymorphic analysis priority, and performing multi-dimensional analysis on the multi-dimensional calling context; according to the method, the mapped target function is matched, then the source code path and the line number of the target function are selected in the target language source code, accurate jump of the code editor is achieved, meanwhile, the source variable type traceability chain is displayed at the target function, and the positioning accuracy and the positioning efficiency of the polymorphic function in the cross-language function are improved.

The embodiment of the invention discloses a dependency management method, device and equipment for an NPM (Network Platform Management). The scheme comprises the following steps: receiving a dependency statement execution request sent by a client; executing the dependency analysis task through an analysis engine, decomposing dependency statement data corresponding to the dependency statement execution request into a plurality of dependency collection tasks, and constructing a dependency relationship graph by executing the plurality of dependency collection tasks in parallel; a dependency tree is generated based on the dependency graph, and node attributes are updated through node read-write locks corresponding to nodes in the dependency tree in the generation process of the dependency tree; and the dependency tree is fed back to the client, and server caching is carried out.

This invention discloses an intensity-aware emotion classification method based on a hierarchical knowledge-enhanced graph neural network. Its key features include: preprocessing the Chinese text to be classified by word segmentation, syntactic parsing, and dependency tree construction; extracting word-level contextual semantic vectors and global sentence representations using a pre-trained language model; identifying sentiment words and degree words in the text, calculating the dynamic sentiment intensity of lemmas, and adaptively expanding the sentiment lexicon; extracting a sentiment knowledge subgraph and aggregating knowledge embeddings, followed by seed-driven pruning to obtain a sparse dependency subgraph; fusing semantic and knowledge features to generate initial node representations, and learning these representations using a hierarchical intensity-aware graph neural network; finally, generating graph-level features through dual-path pooling, fusing global semantics, and outputting the emotion classification result through a multi-task framework. This invention solves the problem of ambiguous emotion classification caused by insufficient modeling of the combined intensity of "degree words + sentiment words" in existing technologies.

The application provides an evaluation result recognition method, device and equipment and a storage medium, which can be applied to the technical field of natural language processing. The evaluation result recognition method comprises the following steps: analyzing the dependency relationship between a plurality of words in text information comprising a plurality of evaluation subjects and description texts associated with the evaluation subjects, and constructing a syntactic dependency tree; determining the position weight of each word with respect to each evaluation subject based on the relative position relationship between each word in the text information and each evaluation subject, except the words constituting each evaluation subject; for each evaluation subject, constructing a weight matrix based on a plurality of position weights corresponding to the evaluation subject, fusing the weight matrix with the sentiment label of each opinion word recognized from the description text, and obtaining a semantic matrix; and fusing and transforming the syntactic matrix obtained by feature transformation of the syntactic dependency tree with the semantic matrix of each evaluation subject, and outputting an evaluation result.

The application discloses a kind of aspect-level sentiment analysis method based on multilevel knowledge enhancement, belong to sentiment analysis field. Including the following steps: S1: text word vector is obtained using GloVe word embedding tool, the syntax dependency tree of sentence is constructed using Stanza, and the dependency graph is constructed accordingly;S2: the context representation of sentence is extracted by inputting word vector into BiLSTM, and the dependency graph is updated using sentiment dictionary and sensitive relationship set, to realize the sentiment and syntax enhancement of sentence;S3: the enhanced dependency graph is input into GCN to model node features, to obtain specific aspect representation;S4: aspect word is enhanced using concept atlas to obtain aspect word representation, which is fused with specific aspect representation to obtain aspect representation;S5: aspect representation and context representation are coordinated and optimized using interactive attention, to obtain the final representation of sentence, so as to determine aspect sentiment tendency.The application can effectively improve the accuracy of specific aspect sentiment classification, and help merchants accurately locate problems in products or services.

The present disclosure describes a system and method for generating a dependency tree for program information collected at runtime, and enriching the dependency tree with information relating to how frequently functions are called at runtime. The present disclosure further provides for detecting vulnerabilities in functions, and generating recommendations for an optimal remediation that efficiently addresses one or more vulnerabilities. Either or both of the vulnerabilities and the remediations may be sorted and ranked, such that the most critical vulnerabilities are identified and the most effective remediations are identified.

This disclosure provides a data shuffling control method, device, and storage medium. By analyzing historical event logs of the data shuffling process in a distributed computing cluster, the interdependencies between all read / write stages in the data shuffling process are determined, generating a dependency tree. Based on the dependency tree, bottleneck stages in the data shuffling process are identified, and the parameters of the data shuffling process are adjusted according to the read / write status of the bottleneck stage. The bottleneck stage is defined as the read / write stage where the amount of file data read / written is less than a preset data volume threshold and the number of read / write operations is the highest. The data shuffling process is controlled based on the adjusted parameters. This disclosure improves the efficiency and accuracy of data shuffling parameter optimization by analyzing historical event logs and automatically adjusting the parameters based on these bottleneck stages. This ensures that the performance of data shuffling is optimized without affecting the stability and runtime of the distributed computing cluster.

The embodiment of the invention discloses a dependency analysis method, device and equipment. The scheme comprises the following steps: a server receives a dependency analysis query request sent by a client; determining whether corresponding version cache information exists or not based on a dependency configuration fingerprint corresponding to the dependency analysis query request; if not, generating a dependency tree corresponding to the dependency configuration fingerprint based on the version metadata; and returning the dependency tree to the client.

This invention discloses a method, system, device, storage medium, and product for searching fault solutions. It involves using a search engine to perform preliminary matching of fault descriptions to obtain a set of candidate fault solutions; extracting semantic vectors from both the fault descriptions and candidate fault solutions; constructing a syntactic dependency tree for the fault descriptions and candidate fault solutions to obtain a graph structure; inputting the graph structure and the corresponding semantic vectors into a graph convolutional neural network to obtain syntactic feature vectors for the fault descriptions and the candidate fault solutions; bidirectionally fusing and concatenating the syntactic feature vectors to obtain a fused feature for each candidate fault solution; and ranking the candidate fault solutions based on the fused features to obtain an ordered fault solution matching the fault description. This invention enables deep capture of semantic and syntactic structures to improve search relevance and provide more accurate results for fault solution searching.

This invention discloses a fine-grained aspect-level sentiment analysis method based on dual syntactic information, comprising: 1) modeling the contextual information of sentiment perception for each aspect and generating corresponding context-specific sentiment representations; 2) modeling sentiment feature representations enhanced by inter-contextual relationships for each aspect; 3) integrating the context-specific sentiment feature vectors and the context-enhanced sentiment feature vectors to obtain an overall sentiment feature vector for each aspect, and then mapping the overall sentiment feature vectors to a low-dimensional semantic space to obtain the final sentiment analysis prediction result. This invention improves the accuracy of fine-grained aspect-level sentiment analysis by effectively utilizing the syntactic information of phrase trees and dependency trees, and jointly modeling context-specific and inter-contextual sentiment feature representations.

This invention relates to the field of document processing technology, and more particularly to a method and system for splitting and reconstructing complex merged cells across pages. The method parses the table structure, identifies merged cells and their coordinates, and uses a dependency tree to analyze the semantic integrity boundaries of the text within each cell, dividing the text into multiple semantically independent segments. The space requirements of each segment are calculated, and an allocation matrix is ​​established to evenly distribute them into the split cells. Page-crossing conditions are determined by detecting the table position, and whether in-line splitting is supported is determined based on the row height elasticity coefficient and content density of the page-crossing row. Then, operations such as inserting soft pagination marks or migrating rows as a whole are performed to complete the page-crossing adaptation. This invention achieves intelligent splitting and page-crossing reconstruction of merged cells, ensuring the semantic coherence of table content and the visual balance of page layout.

The invention provides a hierarchical button authority management and verification method and system based on system coding, and relates to the technical field of computer authority management, and the method comprises the steps: building a local sensitive code and a button fingerprint, building a button dependence tree and a propagation link, generating an authority state bitmap, and building a verification chain; and an on-demand updating permission spreading mechanism and efficient permission verification are realized. According to the method, data redundancy can be reduced, the authority management complexity is reduced, the verification response speed is improved, and authority state consistency management in a large-scale distributed environment is supported.

A method, apparatus, and non-transitory computer readable medium for natural language processing are described. Embodiments of the present disclosure include obtaining a document comprising a first event mention and a second event mention. Some embodiments generate a dependency tree based on the document. The dependency tree is pruned by removing an irrelevant word to obtain a pruned dependency tree. Subevent relation information is generated for the first event mention and the second event mention based on the pruned dependency tree.

The application belongs to the technical field of software management, and particularly relates to a cross-platform repair method and system based on a large language model, which comprises the following steps: a cross-platform package manager interface is used to perform static analysis on a dependency list of a target software project, so as to obtain a cross-platform dependency tree; based on the cross-platform dependency tree, lightweight monitoring probes are injected into at least two operating system platforms, respectively, so as to collect dynamic tracking data of each dependent component in the dependency tree; and the dependency tree and the dynamic tracking data are fused to establish a cross-platform security graph containing dependent call edges, version conflict edges and platform vulnerability association edges; by injecting lightweight monitoring probes into multiple operating system platforms, the application dynamically collects the loading sequence, function call sequence and memory layout difference of the dependent components, and combines the static dependency tree to construct a cross-platform security graph, so that platform-specific security risks can be accurately identified, and the comprehensiveness and accuracy of cross-platform risk perception are significantly improved.

A method comprises collecting data corresponding to operation of a user interface, analyzing the data and generating a dependency tree based at least in part on the analysis. The analyzing and the generating are performed using one or more machine learning techniques. The dependency tree comprises a plurality of nodes respectively corresponding to a plurality of components of the user interface and is organized at least in part according to one or more dependent relationships between the plurality of components. Based at least in part on a structure of the dependency tree, one or more test cases for the user interface are executed.

The invention discloses a disaster recovery backup method and device, equipment and a storage medium, and relates to the technical field of data processing. The method comprises the steps of obtaining a task change event which refers to an event triggered by state change of a task, analyzing the task change event, generating a dependency tree containing a multi-level dependency relationship, obtaining standby nodes corresponding to a target task and an associated task based on the dependency tree, and sending the standby nodes to the associated task. And sending the target task and the associated task to respective corresponding standby nodes in parallel. The dependency tree takes a target task in the task change event as a root node and takes an associated task having a dependency relationship with the target task as a child node. Therefore, by generating the dependency tree containing the multi-level dependency relationship, the dependency tasks with the association relationship can be accurately identified, the target tasks and the association tasks are sent to the corresponding standby nodes in parallel, the standby nodes execute the tasks in parallel, and the problem of task omission or execution conflicts caused by disorder of the dependency relationship can be avoided.

Systems and methods for extracting information from documents and constructing corresponding knowledge maps with respect to defined knowledge models. Deep-learning based models for Natural Language Processing (NLP) are applied to tokenize words, tag, parse, and lemmatize sentences of input documents. Then an information extractor traverses the dependency tree of NLP object to recursively extract the entities of interest to the knowledge models. Finally, a knowledge map constructor traverses the dependency tree of NLP object to determine the relationships among the extracted entities and construct knowledge maps recursively following the defined knowledge models.

This invention discloses a method, apparatus, system, and medium for displaying a project dependency tree. The method includes: generating a dependency tree source file based on the obtained project source code; parsing the dependency tree source file and converting it into a corresponding dependency tree JSON file and storing it in a database; responding to a dependency tree display request sent by the front end, retrieving the corresponding target JSON file from the database and returning it to the front end, so that the front end can parse and display the corresponding target dependency tree based on the target JSON file. By pre-generating the dependency tree source file based on the project source code and converting it to JSON format for storage, a dependency tree display request can be initiated from the front end via an online browser. Based on the display request, the corresponding JSON file can be flexibly retrieved and returned to the front end, achieving the effect of quickly displaying and intuitively browsing the project dependency tree on the front-end browser. Users do not need to download additional development tools and plugins, greatly saving browsing time and improving display and development efficiency.

The invention discloses a risk control index calculation method, system and device and a storage medium. The method comprises the steps of obtaining a target risk control index corresponding to a target risk control scene and a target expression and a target rule corresponding to the target risk control index; constructing a target feature value dependency tree of the target risk control index according to the target expression; acquiring risk control event data corresponding to the target risk control scene; determining a target index value of the target risk control index according to the risk control event data, the target expression and the target feature value dependency tree; and determining a target risk control identification result according to the target index value and the target rule, and displaying the target risk control identification result through a preset visual system. Therefore, the risk control index is calculated by constructing the feature value dependence tree, and accurate calculation of the risk control index is realized.

The application discloses a port safety dynamic evaluation system and method based on double-database cooperation, and particularly relates to the field of port safety dynamic evaluation edge data processing, and is used for solving the problems of logical shock and evaluation distortion caused by high-frequency fragmented attribute changes; through receiving fragmented basic data containing equipment account and operation record attribute changes, time sequence aggregation and semantic deduplication feature extraction are completed based on a port compliance guide label dictionary, a structured incremental update instruction set is generated, and the evaluation logic dependency tree in the labeled standard database is linked, a graph walking structure decay degree and a time sequence operation frequency domain distortion wave peak number are extracted, a graph node containing a decision coefficient is formed, a damaged logic subgraph is constructed, bidirectional consistency checking and conflict resolution coding are completed, an updated state evaluation node sequence is output and atomized and synchronized to the labeled standard database, and a local recalculation trigger signal and a professional semantic prompt word are generated.

The present disclosure provides a video search method and device, electronic equipment and storage medium, relates to the technical field of artificial intelligence, in particular to the technical field of natural language processing, computer vision, intelligent search and deep learning. The specific implementation scheme is as follows: determining a search sentence to be processed and a corresponding word segmentation annotation result, wherein the word segmentation annotation result includes at least one word in the search sentence and a corresponding annotation type; determining a sentence dependency tree of the search sentence according to the at least one word and the corresponding annotation type; querying a video library according to the sentence dependency tree to obtain a target video in the video library that matches the sentence dependency tree; and generating a video search result of the search sentence according to the target video, thereby determining a video matching the search sentence through comparison of the dependency trees, avoiding manual annotation of the title of the video, reducing cost, and improving accuracy.