35 results about "Parse tree" patented technology

According to the artificial intelligence-based data analysis system and method provided by the invention, a set of novel data analysis system architecture is designed, and key technical components such as natural language processing, a large language model, database query optimization, multi-modal visualization and a semantic knowledge graph are fused; data transmission between modules is realized through unified intermediate data objects such as a structured query intention, an analytic tree and a structured query language template object, and asynchronous collaboration is realized through event driving and a message queue mechanism, so that a non-professional user can input an analysis request through a natural language; semantic analysis, structured query language query statement generation, data query and result visualization presentation are automatically completed, the method can be widely applied to data analysis scenes in the industries of government affairs, traffic, finance, education and the like, the data use efficiency is improved, the technical threshold is reduced, and the digital decision-making ability is enhanced.

The invention relates to the field of wisdom education, and particularly discloses a teaching strategy optimization model and grammar error early warning method based on data mining, and the method comprises the following steps: S1, obtaining original code data in a student code library in real time, and preprocessing the original code data to obtain a structured data matrix; s2, for the structured data matrix, generating a grammar parse tree through a grammar parser, positioning error nodes and extracting context features by using a node traversal algorithm, and constructing a single-error multi-dimensional feature vector; meanwhile, an error association rule base is constructed based on historical error data, a causal relationship across error types is identified, and dynamic knowledge graph data is formed. According to the technical scheme, association analysis and deep mining can be carried out on the multi-source learning data, dynamic solution suggestions for student individual errors or group generality errors are formed, and teachers are assisted to quickly adapt to dynamically changing learning requirements of the students.

The invention relates to the technical field of electric digital data processing, and discloses a method for detecting log abnormity of an electric power dispatching automation system, which comprises the following steps of: constructing a fixed depth analysis tree through historical log data, and extracting a log template based on word segmentation similarity; converting knowledge in the power dispatching field into vectors and storing the vectors into a knowledge base; retrieving knowledge base associated domain knowledge of the log template, inputting the large model to carry out abnormity judgment and marking a template label; after real-time log preprocessing, a template label is inherited through a parse tree matching template library and a self-adaptive threshold strategy, and online template updating is triggered for unmatched logs; and the context and high-frequency parameters of the abnormal log are aggregated, a dynamic cue word is constructed in combination with a retrieval result and a template feature weight, a large model is input for multi-dimensional root cause analysis, and template mechanism optimization is driven based on the cue word. The problems that in the prior art, manual maintenance is difficult, the generalization ability is weak, and the detection speed is low are solved, and the purposes of efficient detection, high accuracy and self-adaption are achieved.

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 intelligent question answering, and particularly relates to an experimental question answering method and system based on a knowledge graph. The method comprises the following steps of: generating a candidate combination category grammatical analysis tree for an input experimental question statement, and when vocabularies outside a preset dictionary are encountered, assigning a temporary combination category in combination with word vectors and adjacent entity types; calculating the syntactic probability of the analysis tree and the node intensity of the entity subgraph in the tree, determining a target syntactic analysis tree, and converting the target syntactic analysis tree into a logic expression; extracting an anchor point entity and a semantic relationship, determining a search depth according to sub-graph intensity, and constructing an initial graph query statement by utilizing bidirectional breadth-first search; if the query result does not exist, differential query reconstruction is carried out according to an intensity threshold value, when the intensity is high, only the semantic relation is replaced with a parent class, otherwise, the parent class and brother class relation is considered at the same time, and a sub-graph or an entity obtained through query is output as an answer. According to the method, the suitability, accuracy and fault tolerance of the experiment question-answering system can be improved, and the query efficiency and the recall rate are balanced.

The application discloses a large language model log analysis method based on relationship inference, comprising: taking a prefix parsing tree as the core, pre-processing the input log, and then traversing the prefix tree to realize template matching. If the matching is successful, the structured log is output, if the matching is unsuccessful, the relationship between the unmatched log and other logs is evaluated from the syntax and semantics. The syntax similarity between the unmatched log and the candidate log set is calculated at the syntax level, the causality strength and the number of shared entities between the unmatched log and the logs in the historical log window are evaluated through LLM at the semantic level, and the semantic correlation log is screened out by combining the semantic composite score; the screened syntax similar and semantic related log is taken as an example to construct a complete prompt containing a task instruction, a demonstration example and a log to be parsed, and a large language model is called to generate a structured template of the unmatched log; the newly generated template is adapted to the prefix parsing tree, the tree structure is perfected through dynamic updating, and more comprehensive template support is provided for subsequent log analysis.

Arrangements for optimization of database queries having multiple views are provided. A query defining multiple views may be received. A parse tree may be generated based on the query. The query may be preprocessed using the parse tree. The parse tree may be traversed to identify the multiple views. View unfolding may be executed to generate a view subtree. The view subtree may be attached to the parse tree and a view query compile tree may be generated. It may be determined whether there is another view defined by the query. In response to determining that there is not another view, the parse tree may be traversed to calculate tree depth. In response to determining that there is another view defined by the query, calculation of the tree depth may be skipped.

The invention belongs to the technical field of biomedical information extraction, and particularly relates to a recurrent neural network multi-tree fusion method in biomedical event extraction. Parsing results of different rule parsers are fused, a syntactic parse tree-based union method is provided, and sub-tree post-processing is carried out for a merging result to ensure the structure simplification and grammar rationality of a sub-tree. The invention provides an isomer branch cutting and node distillation method aiming at redundant subtrees and nodes existing in a combined tree structure. Meanwhile, branch cutting and node distillation operation are adopted, the subtree structure is further simplified, and the effect on binding and regulating complex events is good.

To improve a filtering-type example sentence-driven analysis unit forming one input sentence analysis tree with using, as one input, an OR tree obtained by expressing a plurality of analysis trees obtained through analysis of an input sentence with use of grammar rules in the form of one tree structure and also with using, as the other input, a temporarily exclusive tree obtained by the example sentence or an exclusive three pre-equipped by the apparatus.SOLUTION: In a common word sequence acquisition between an input sentence and an example sentence, a method according to the present invention includes the steps of: acquiring a plurality of common word sequences; designating the minimum number of common words; determining a common word start position in an example sentence tree; and replacing the common word with a common part of speech.SELECTED DRAWING: Figure 1

This invention relates to the field of electrical digital data processing technology and discloses a method for detecting log anomalies in power dispatch automation systems. The method includes: constructing a fixed-depth parse tree using historical log data and extracting log templates based on word segmentation similarity; converting power dispatch domain knowledge into vectors and storing them in a knowledge base; retrieving knowledge related to the log templates from the knowledge base, inputting it into a large model for anomaly detection, and labeling the templates; after real-time log preprocessing, inheriting template labels by matching the template library with the parse tree and using an adaptive threshold strategy, triggering online template updates for unmatched logs; aggregating the context and high-frequency parameters of the abnormal logs, constructing dynamic prompt words based on the retrieval results and template feature weights, inputting them into a large model for multi-dimensional root cause analysis, and optimizing the template mechanism based on the prompt words. This method solves the problems of difficult manual maintenance, weak generalization ability, and slow detection speed in existing technologies, achieving efficient detection, high accuracy, and adaptability.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an exit scenario output that characterizes an exit scenario for a business enterprise. In one aspect, a method comprises receiving, at an execution engine, data that represents ownership of assets by multiple participants and source code that defines distribution rules of distributing the assets over one or more steps among the multiple participants using a programming language; receiving, at the execution engine, an exit scenario input; automatically parsing, by the execution engine, the distribution rules defined in the source code into a parse tree; and interpreting, by the execution engine, the parse tree to produce an exit scenario output.

A method for training a machine learning model is disclosed, comprising obtaining a training set including multiple sentences and their labeled sets; generating parse trees and label trees for the sentences; determining the total loss of the sentence, which is equal to the weighted sum of the parse tree generation loss and the label tree generation loss; and training the machine learning model using the training set to minimize the total loss. This application also relates to systems, apparatus, and media for training machine learning models and for processing sentences using such machine learning models.

The application discloses a WAF rule evaluation method based on a regular analysis tree, relates to the technical field of WAF rule evaluation, and collects regular expression protection rules of an open source WAF as seeds, analyzes and constructs an expression library based on a regular analysis tree, then performs SQL syntax word recognition on a sent test expression, matches all related expressions in the rule expression library through SQL syntax words, traverses the sent expression request, judges whether the target site WAF protection rule is bypassed according to site feedback information, and further evaluates the rule protection capability of the target WAF system. The application can quickly and accurately deduce the target site WAF protection rule, and can reliably and accurately evaluate the protection capability of the target site WAF system.

One aspect of the present application provides a method for determining check results of scripts, the check results of scripts comprising error detection results in scripts and consistency matching results of scripts, the method comprising the steps of: determining a plurality of data units in each of a plurality of scripts, constructing an analytic tree structure of each script based on the plurality of data units in each script; performing layer sequence traversal detection on each layer in the analytic tree structure of each script by utilizing a preset detection rule to obtain an error detection result in each script; and obtaining at least on the basis of matching partial data subunits in the data unit corresponding to each hierarchy in the parse tree structure of one of the plurality of scripts with partial data subunits in the data unit corresponding to the corresponding hierarchy in the parse tree structure of another of the plurality of scripts, according to the matching result, the consistency matching result of one script and the other script is determined.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an exit scenario output that characterizes an exit scenario for a business enterprise. In one aspect, a method comprises receiving, at an execution engine, data that represents ownership of assets by multiple participants and source code that defines distribution rules of distributing the assets over one or more steps among the multiple participants using a programming language; receiving, at the execution engine, an exit scenario input; automatically parsing, by the execution engine, the distribution rules defined in the source code into a parse tree; and interpreting, by the execution engine, the parse tree to produce an exit scenario output.

In an embodiment, a computer stores and operates a logic encoder that is an artificial neural network that infers a fixed-size encoded logic from textual or tokenized source logic. Without machine learning, a special parser generates a parse tree that represents the source logic and a fixed-size correctly encoded tree that represents the parse tree. For finetuning the logic encoder, an encoded tree generator is an artificial neural network that accepts the fixed-size encoded logic as input and responsively infers a fixed-size incorrectly encoded tree that represents the parse tree. The neural weights of the logic encoder (and optionally of the encoded tree generator) are adjusted based on backpropagation of error (i.e. loss) as a numerically measured difference between the fixed-size incorrectly encoded tree and the fixed-size correctly encoded tree.

The application relates to an ANTLR4-based code generator design method and belongs to the field of code generation. The application generates a lexical analyzer and a syntax analyzer corresponding to an IDL syntax rule file; an IDL file is pretreated, the pretreated IDL file is generated into a syntax analysis tree through the lexical analyzer and the syntax analyzer, and the syntax analysis tree is further generated into structured data; a public interface file for generating C / C++, JAVA files is defined; the C / C++, JAVA public interface file reads a project template file, and structured data in the template file is replaced with structured data in step S2 to generate C / C++, JAVA project files. The application realizes the application of cross-platform and cross-language data distribution services.

A device may trigger a resolvable object that requires a resolution responsive to an event detected in a managed information technology environment and obtain a masked title from a title of the resolvable object by applying text processing rules to the title to obtain the masked title that includes at least one variable part that replaces a portion of the title. A device may obtain, using the masked title, a title template for the resolvable object using a machine learning model by traversing a fixed depth parse tree organized based on numbers of token positions in a masked title. A device may obtain, using the title template, a type for the resolvable object and responsive to determining that the resolvable object is of the frequent type and not of the rare type or of the novel type: identifying and automatically executing a runbook of tasks associated with the title template.

A database management system (DBMS) receives an input query and parses the received input query to generate an abstract parse tree. Next, the DBMS traverses the abstract parse tree to detect any parameterized structured query language (SQL) views. If a first parameterized SQL view is detected in the abstract parse tree, the DBMS generates a first view parse tree if a first search of a first cache for the first parameterized SQL view results in a miss. Otherwise, the DBMS retrieves, from the first cache, a previously generated view parse tree if the first search of the first cache results in a hit. Then, the DBMS generates a first query compile tree if a second search of a second cache for the first parameterized SQL view results in a miss. Finally, the DBMS generates and executes a query execution plan based on the first query compile tree.

The invention relates to computing. An information leakage prevention system comprises a server, wherein an intercept subsystem is set up for each data transfer channel, and each intercepted message transfer event or document access event is transmitted to a data extraction subsystem which constructs a parse tree of the event. The root of said parse tree is comprised of the intercepted document, and the other nodes contain information transmitted in the intercepted document, the information in which is in text or image form. The result obtained by the event data extraction subsystem is transmitted to an event data analysis subsystem which analyzes the received data using analysis technologies. The result obtained by the event data analysis subsystem is transmitted to a security policy application subsystem, where security policies apply a security policy configuration to the properties of the intercepted data by comparing policy values with the data analysis result and other event attributes. The technical result consists in reducing the time taken to identify a complete scenario of the development of events that are potentially capable of leading to the leakage of sensitive information.

The application provides a column operator blood relationship construction method, which is applied to the technical field of computers and comprises the following steps: S1) generating a parse tree ParseTree by parsing SQL through Antlr; S2) designing an abstract syntax tree AST, which is used for constructing blood relationship links from input columns to output columns and intermediate processing logic; S3) recursively traversing the parse tree ParseTree obtained in S1) to construct the abstract syntax tree AST designed in S2); S4) traversing the abstract syntax tree AST constructed in S3) and extracting a column operator blood relationship model; and S5) traversing the column operator blood relationship model and constructing point-edge relationships, and storing the column operator blood relationship into a graph database. The application provides the most fine-grained SQL parsing capability, helps users understand the operator-level blood relationship of data assets, includes column direct sources, used functions, processing caliber and indirect influences, and solves the problem that users cannot effectively enable business due to the lack of understanding of data assets.

The application discloses a training data generation method and an electronic device, relates to the technical field of model data training, and through accurate symbol tracking and cross-file reference analysis of a parsed tree model after training, structured data generated more accurately reflects the use and semantic relationship of symbols in a code library, thereby providing a solid foundation for subsequent generated training data. By associating the structured data with technical specification documents, the generated knowledge base contains official definitions and functional descriptions of code symbols, which can greatly enhance the understanding ability of a large model for underlying system code. The construction of a global symbol table takes into account conditional compilation and multi-architecture support, which enables the generated training data to cover different compilation configurations and architectures, thereby enhancing the generalization ability and practicality of the model. Therefore, the problem that existing schemes are difficult to generate effective training data in complex scenarios can be solved.

The application discloses a heuristic online log parsing method and system based on an adaptive deep parsing tree, wherein the method comprises the following steps: constructing a parsing tree, inputting the field number of each log message into different log length matching nodes, and matching the preprocessed log message to the related sub-tree according to the matching result; determining the depth of the current sub-tree according to the length of the preprocessed log message and the preset fitness; matching the several fields of the preprocessed log message to the nodes of the current sub-tree in a head-to-tail alternating manner; after the multi-layer node traversal, reaching the leaf node of the current sub-tree; finding the most matched log group of the preprocessed log message in the leaf node of the current sub-tree; updating the log template and the log ID list of the most matched log group; and obtaining the structured data of the log message set to be parsed after all the log messages are processed.

This invention provides a method and system for aspect-level sentiment quadruples extraction based on tree structure information perception, comprising: acquiring a comment statement to be extracted, and constructing the comment statement into a sentiment analysis tree according to a predefined tree structure template; wherein the sentiment analysis tree is composed of word embedding vectors constituting the comment statement and a tree structure embedding representation; for the obtained sentiment analysis tree, using a text encoder to obtain the embedding representation of the sentiment analysis tree; for nodes in the sentiment analysis tree, performing aggregation representation on the information interaction between the current node and its neighboring nodes based on a graph attention network, and injecting the representation result into the embedding representation of the sentiment analysis tree to obtain a new embedding representation; based on the new embedding representation and a preset loss function, decoding to obtain all sentiment elements in the comment statement to be extracted, wherein the sentiment elements include aspect categories, aspect words, opinion words, and aspect sentiments.

The invention relates to a data processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a first query instruction, and performing analysis processing on the first query instruction to obtain analysis tree result data; determining to-be-encrypted field data according to the analysis tree result data; determining a field encryption proportion in the to-be-encrypted field data by using a hierarchical encryption algorithm, and encrypting the to-be-encrypted field data according to the field encryption proportion to obtain an encrypted parse tree; performing serialization operation on the encrypted parse tree, and sending the serialized parse tree to a database server; and obtaining a query result corresponding to the first query instruction returned by the database server, the query result being parse tree result data obtained after the database server deserializes the serialized parse tree, and performing query according to the parse tree result data to obtain result data. By adopting the method, the data security can be provided.

The invention discloses a WEB application protection effect visualization method and device, equipment and a medium, and relates to the technical field of network security, and the method comprises the steps: carrying out the layer-by-layer analysis of a target request message through employing a plurality of preset analysis technologies after receiving the target request message, so as to obtain a layer-by-layer analysis result, and taking the target request message as a root node, and carrying out the layer-by-layer analysis of the target request message; generating a target analysis tree based on the layer-by-layer analysis result; traversing the target parse tree to perform security detection on leaf nodes in the target parse tree, judging whether the leaf nodes have security threats according to a detection result, determining a threat level, and obtaining a determination result; marking node labels on a plurality of nodes in the target parse tree according to a determination result to obtain a to-be-displayed parse tree comprising the node labels; and performing color rendering on the node labels in the to-be-displayed parse tree so as to visually display the to-be-displayed parse tree at the front end of the WEB application firewall.

The present application belongs to the technical field of intelligent question and answer, and particularly relates to an experimental question and answer method and system based on a knowledge graph. The method comprises: generating a candidate combined category syntax analysis tree for an input experimental question sentence; when a preset dictionary word is encountered, assigning a temporary combined category by combining a word vector and an adjacent entity type; calculating a syntax probability of the analysis tree and a node density of an entity subgraph in the tree, determining a target syntax analysis tree, and converting the target syntax analysis tree into a logical expression; extracting anchor entities and semantic relationships, determining a search depth according to the subgraph density, and constructing an initial graph query sentence by using bidirectional breadth-first search; if the query has no result, differentiating the query reconstruction according to the density threshold, replacing only the semantic relationship with a parent class when the density is high, or considering both the parent class and the sibling class relationship, and outputting a subgraph or an entity obtained by the query as an answer. The present application can improve the adaptability, accuracy and fault tolerance of the experimental question and answer system, and balance the query efficiency and recall rate.

This invention discloses a large-model knowledge distillation method based on counterfactual data augmentation and multi-perspective thought chains. First, this invention utilizes topic terms and parse trees to obtain key semantic features of text, and then automatically generates high-quality counterfactual data using a large language model. Next, it leverages multi-perspective thought chains to enhance the diversity of the counterfactual data, thereby improving the reasoning ability of the small language model. Compared with existing technologies, the large-model counterfactual distillation framework proposed in this invention can enhance the robustness and generalization ability of the small model's reasoning, achieving excellent performance on small language models with different architectures and datasets with different distributions.

The invention discloses an intelligent pressure measurement report automatic generation method and system. The method comprises the steps of performing multi-source fusion on pressure measurement result data, a historical case library and a domain knowledge graph; identifying a pressure measurement scene according to the multi-dimensional metadata of the pressure measurement task; designing a structured Prompt template, and filling the Prompt template according to a pressure measurement task; after converting the multi-modal data into an input format supported by a large language model, splicing the multi-modal data with a Prompt template to obtain an input text, and triggering the large language model for reasoning to obtain a reasoning result; the reasoning result is converted into executable steps, code examples are labeled, and an intelligent pressure measurement report is generated in combination with a report template embedded with a trend chart and a root cause analysis tree. The reasoning accuracy is improved, unnecessary computing resource consumption is reduced, and the suggestion generation time is shortened. And through a self-adaptive report template and a multi-format export function, the report generation time is shortened, and the labor cost is reduced.

The embodiment of the invention provides a quality evaluation method and device for SQL statements and a storage medium. The quality evaluation method and device are used for accurately evaluating the quality of the SQL statements. The method comprises the steps that a first instruction is received, the first instruction comprises an execution plan text, the execution plan text comprises a plurality of plan segments, and the plan segments comprise execution plan information of a first SQL statement on execution nodes; the first instruction is used for indicating to analyze the execution plan text and returning an index value of at least one first index based on an analysis result; in response to the first instruction, inputting the execution plan text to a first parser, generating a first parse tree, extracting an index value of at least one first index from the first parse tree, and sending the index value to an interaction device, the method and the device are used for determining the quality score corresponding to the first SQL statement by the interaction device, so that the quality of the SQL statement can be accurately evaluated.