756 results about "Regular expression" patented technology

The invention relates to the technical field of data security, in particular to a data asset classification and grading and authority dynamic management system, which comprises an asset parameter acquisition module, a classification feature judgment module, a grading weight calculation module and an authority dynamic adaptation module. According to the method, metadata and path parameters are automatically collected through a distributed crawler in combination with a regular expression, naming parameters and content feature parameters are separated to generate a structured parameter set, field naming similarity is quantized through a Levenshtein distance, content character distribution features are verified through chi-square verification, and a naming similarity and distribution feature dual verification mechanism is constructed. According to the method, sensitivity, access frequency and data volume weight are dynamically distributed through an entropy weight method, grading parameters are generated through linear superposition, an RBAC model is combined with a Dijkstra algorithm to verify access path topology legality, unauthorized access is blocked, and the heterogeneous data classification grading and authority control dynamic adaptive capacity is improved.

Disclosed in the present disclosure is a low-cost and zero-shot online log parsing method based on a large language model, including: firstly, extracting content of a log in a log message using regular expressions, then, performing regular expression matching with a log template in a database; if the matching is successful, updating a log sample corresponding to the log template; if the matching fails, conducting a dialogue with the large language model to obtain a new log template; performing template correction to prevent the log template generated by the large language model from being incapable of performing regular expression matching with the log message; performing template merging when a new template is generated; performing template splitting when the log sample is updated; and for all log templates to be added to the database, firstly, normalizing the log templates by post-processing, and then storing the log templates to the database. The log template generated by the present disclosure outperforms conventional methods in terms of word parsing accuracy, with significant advantages in speed and cost compared with the direct use of the large language model to perform log parsing tasks.

The invention discloses a government affair data automatic classification and grading method, and relates to the technical field of data management and information processing. According to the method, data of different sources and formats are converted into structured formats by adopting a regular expression and a pattern matching technology, and the consistency and accuracy of the data are improved by means of a machine learning algorithm, data quality detection, anomaly correction and the like; by constructing a government domain ontology and a knowledge graph and combining a natural language processing technology, semantic classification is effectively carried out on government affair data, overlapping and uncertainty between categories are solved, robustness and accuracy of classification decision are further improved through an integrated learning algorithm, high efficiency and reliability of classification are ensured, and the method is suitable for large-scale popularization and application. By establishing a data grading dynamic adjustment and update mechanism and combining real-time data monitoring, sensitivity evaluation and automatic adjustment of access control rules, the flexibility and security of data management are ensured.

The invention discloses a judicial data file generation method and system and a computer program product. The method comprises the following steps: acquiring image data and audio data of an electronic file; preprocessing the electronic file image data; identifying the preprocessed electronic file image data by adopting an OCR (Optical Character Recognition) technology to obtain first text data; recognizing the audio data by adopting a language recognition technology to obtain second text data; fusing the first text data and the second text data to obtain fused data; analyzing the fusion data by using a pre-trained large language model, and extracting key information of the fusion data; and classifying the fusion data according to the key information, and allocating a corresponding label to each piece of fusion data to obtain a judicial data file in structured storage. According to the judicial data file generation method and system and the computer program product provided by the invention, the limitation of a conventional regular expression and NLP technology is overcome, and the accuracy and efficiency of judicial file structuring are improved.

Artificially intelligent systems and methods for financial coaching provide personalized, fiduciary-compliant financial guidance through advanced machine learning architectures with measurable performance criteria. The systems implement privacy-preserving processing pipelines that detect personally identifiable information using multi-layered pattern recognition including regular expressions for formatted data sequences, named entity recognition with confidence thresholds above 0.85, and contextual analysis algorithms. A multi-step artificial intelligence processing workflow includes automated language detection, emotional tone classification with confidence scoring, financial profile transformation using predefined templates, context-aware question rephrasing, and semantic similarity matching employing vector embeddings with financial domain vocabulary weighting applying multiplier values between 1.3-2.0. Specialized training methodologies expand datasets through mathematical transformation functions utilizing statistical standard deviations with incremental variations between 0.5-2.0. Mood-based escalation logic automatically transfers users to human advisors when emotional indicators exceed confidence thresholds above 0.8. The systems maintain response times below 5 seconds while providing regulatory compliance through curated content sources and predefined fiduciary instruction parameters.

The invention relates to the field of power question answering, in particular to a power knowledge question-answer matching method and system based on an improved retrieval enhancement generation technology, and the method comprises the steps: employing a training set to carry out the decomposition of a pre-constructed Embedding model, and carrying out the training of the Embedding model, and obtaining an Embedding fine tuning model; the method comprises the following steps: vectorizing a power field file by adopting an Embedding fine tuning model to obtain a power knowledge base, and constructing a regular expression rule base; the user consultation content is converted into a query vector, the query vector is matched with the regular expression rule base to obtain an accurate matching result, and meanwhile the query vector is matched with the power knowledge base to obtain a fuzzy matching result; semantic correlation sorting is carried out on the accurate matching result and the fuzzy matching result, and then resorting is carried out by adopting a resorter in combination with a service rule; and inputting the reordering result into a large language model to obtain a result corresponding to the user consultation content. The method can effectively solve the problem of inaccuracy during retrieval of a specific ID, and improves the retrieval precision of a retrieval enhancement generation system.

The invention discloses a financial sensitive data desensitization method and device, equipment and a storage medium, and relates to the technical field of data security, and the method comprises the steps: recognizing a sensitive data segment of to-be-desensitized financial data in an interface request, obtaining a sensitive data type and a target specific character position, and storing the sensitive data type and the target specific character position; determining to-be-encrypted data in the to-be-desensitized financial data by using a preset regular expression and based on the target specific character position; extracting a first preset number of bits of data from dynamic identification information corresponding to the to-be-desensitized financial data to obtain a first type of characters, and intercepting a second preset number of bits of data at the tail of the to-be-desensitized financial data to obtain a second type of characters; and combining and encoding the first type of characters and the second type of characters to obtain an initial character sequence, encrypting the to-be-encrypted data by using a target converted character sequence obtained by carrying out system conversion on the initial character sequence to obtain encrypted data, and desensitizing the to-be-desensitized financial data based on the encrypted data to obtain desensitized financial data. Therefore, the data desensitization efficiency can be improved.

The invention relates to the technical field of big data and data security, in particular to a cloud-edge collaboration and AI fusion data classification and grading method and system and a storage medium, cloud computing and edge computing are combined, the AI technology is used for data classification and grading, and rapid processing and efficient analysis of data are achieved; for structured data and unstructured data, different recognition and processing methods are adopted, including regular expression, keyword matching, dictionary matching, neural network, natural language processing and other algorithm models; particularly, a neural network and a natural language processing technology are combined with an unsupervised learning method and a semi-supervised learning method to realize an automatic classification and grading method of unstructured data in stages, rapid data processing and response can be carried out on an edge layer through a cloud edge collaboration mechanism, and the efficiency is improved. And deep analysis and model training are carried out by using the powerful computing power of the cloud layer.

The invention provides a port sensitive information protection method based on data desensitization, and belongs to the field of data security and privacy protection, and the method comprises the steps: 1, regularly collecting original data from each data source of a port operation system, and employing a natural language processing technology and a regular expression to recognize the sensitive information of the original data, and obtaining a sensitive information set; 2, a visual user interface is developed, a user is supported to dynamically adjust desensitization rules according to port business requirements, and the desensitization rules are stored in a rule base in a key-value pair mode; 3, generating and executing a desensitization strategy according to the type of the sensitive information and the business requirement in combination with the desensitization rule; 4, on the basis of a verification function, verifying whether the desensitized data after desensitization contains sensitive information, and confirming whether the desensitized data meets service logic requirements or not; and step 5, sharing the data to an authorized user by adopting a safe data transmission protocol. Sensitive data are effectively protected, dynamic desensitization is supported, and it is ensured that data safety is consistent with service requirements.

The invention provides a system association recommendation method and system based on semantic and business feature fusion, and belongs to the technical field of text association analysis. Text segmentation is performed on a system file based on a regular expression, and term semantic features are extracted; constructing a system associated business knowledge graph based on the association requirements of the term content of the system file and the external business information, and storing graph data by using a Neo4j graph database; creating a clause-department, clause-post and clause-professional three-layer system business knowledge network, and calculating graph structure features of each layer of network clause entity based on a Node2Vec algorithm; fusing the semantic features of the clause entities and the graph structure feature vectors, and establishing an association relationship between the clause entities in the system association business knowledge graph based on a cosine similarity method; and realizing system clause association recommendation based on a Cypher query language of a Neo4j graph database. According to the method, on the premise that the calculation complexity is reduced and the external knowledge is integrated, the system text association recommendation is more accurate.

A system and method of real-time feedback confirmation to solicit a virtual assistant response from an evolving semantic state of at least a portion of an utterance. A user accesses a virtual assistant on an electronic device having the system and / or method configured to capture a command, a question, and / or a fulfillment request from audio such as, the speech emitted from the speaking user. The speech may be intercepted by a speech engine configured to transcribe the speech into text that is matched with the fragment pattern's regular expression to generate a fragment and / or the speech may be processed with a machine learning model to identify fragments. The fragments are identified by a domain handler configured to update a data structure of the current semantic state of the utterance in real-time on an interface of an electronic device.

The invention discloses a component identifier matching method and system based on NLP semantic segmentation and a multi-level word library, and relates to the field of constructional engineering informatization, and the method comprises the steps: building a standard main word library and a preset compensation word library, carrying out the semantic segmentation of an input component identifier, and obtaining a segmented lexical element sequence, calculating semantic similarity and morphological similarity between the segmented lexical element sequence and entries in a standard main word bank, and performing standard term mapping based on the semantic similarity and the morphological similarity to obtain a standard term mapping result; carrying out structured data conversion on the segmented lexical element sequence which does not complete the standard term mapping by utilizing a regular expression mode to obtain structured data, and carrying out AI extension library matching on the segmented lexical element sequence which does not complete the structured data conversion to obtain a synonym matching result; and combining the standard term mapping result, the structured data and the synonym matching result to obtain component identification description. By implementing the method, the matching accuracy of the component identifier names in the engineering project can be improved.

The invention discloses a document content auditing method and system based on combined artificial intelligence, and relates to the technical field of content auditing. According to the method, an original document is converted and standardized into a unified format, a natural language processing technology and a regular expression are applied to extract text fragments, images and meta-information of the text fragments, the images and the meta-information of the text fragments, the images and the meta-information; the method comprises the following steps: inputting a file into a combined artificial intelligence model to respectively detect semantic violation, keyword violation and image violation, generating violation condition description and correction cases through a large language model, marking the auditing result in the file through highlighting to form an auditing report, and providing an auditing conclusion and a modification suggestion for file content auditing.

An artificial intelligence assistant analyzes network observations to generate regular expressions using an artificial intelligence model for network automation management pipelines that identify and resolve network issues. A method includes obtaining at least one instruction and information about a plurality of enterprise network assets and configuration of an enterprise network that includes the plurality of enterprise network assets and generating at least one regular expression using an artificial intelligence model based on context description of the at least one instruction and the information about the plurality of enterprise network assets and the configuration of the enterprise network. The method further includes generating at least one solution for configuring at least one network asset of the plurality of enterprise network assets based on the at least one regular expression and providing the at least one solution to cause a configuration change in the at least one network asset.

The invention provides a rule-based layout check and automatic correction system, which relates to the technical field of integrated circuits, and comprises a rule input and management module, a layout data processing module, a rule check engine, a problem analysis and visualization module, an automatic correction module and a user interaction and control interface. Through a rule template library and a complex rule analysis mechanism, an efficient rule configuration mode is provided for a user, and the user can quickly adjust parameters based on a preset template to generate a user-defined design rule, or directly upload complex rule description containing a regular expression and a mathematical formula; and the system analyzes the data into executable logic units and stores the executable logic units in a rule database. According to the mode, the complexity and the time cost of design rule configuration are remarkably reduced, so that the system can flexibly adapt to different process nodes and design requirements, the universality and the efficiency of layout inspection are improved, and convenient and diversified rule management experience is provided for designers.

The invention belongs to the technical field of dialogue generation, and relates to a digital intelligent carrier pigeon WeChat intelligent dialogue generation method based on deep learning. Dialogue data are obtained, and semantic anomaly detection is carried out through a regular expression and a BERT model in combination with a public corpus; organizing dialogue data in a mode of constructing a tree structure and introducing a three-dimensional timestamp; for emotion information of emoticons, text and visual features of expressions are combined through a hybrid encoder, a dynamic attention mechanism is adopted, weighting and memory length adjustment are carried out according to importance of historical statements, after user portrait features are introduced, user features and dialogue codes are fused, and therefore, the emotion information of the expressions is obtained. In the generation stage, through a Transform-based model and a Beam Search algorithm, a diversity penalty term is introduced in the generation process, and generation parameters are dynamically adjusted according to a user portrait, so that the diversity and security of generated dialogues are ensured, the precision and coherence of a dialogue generation model are improved, and the technical problems of context coherence and personalized generation are effectively solved.

The invention provides a vehicle-mounted voice test method and device, electronic equipment and a storage medium, and the method comprises the steps: setting a corresponding grammar rule based on the control of voice on a vehicle function, and extracting an original text corpus from the grammar rule based on a preset regular expression; inputting the original text corpora into an artificial intelligence generation content AIGC model to generalize the original text corpora, and outputting a plurality of test text corpora similar to the original text corpora in terms of semantics; converting the test text corpus into a corresponding test voice corpus, and playing the test voice corpus in a cabin of the vehicle; after the test voice corpus is played, a test result of vehicle-mounted voice is determined according to a state response and a voice interaction response of the vehicle, the state response represents a vehicle machine control response after the vehicle receives the test voice corpus, and the voice interaction response represents a vehicle machine control response after the vehicle receives the test voice corpus. The voice interaction response represents the interaction response of the vehicle machine to the test voice corpus. According to the invention, the problem that the test corpus of the vehicle-mounted voice is single in the prior art is solved.

The invention discloses a phishing document de-obfuscation and feature extraction method and an application thereof in attack detection. The de-obfuscation comprises the steps of obtaining an obfuscation macro code of a phishing document, constructing a prompt engineering template by utilizing a pre-training language model, analyzing an obfuscation logic structure and generating a de-obfuscation rule and a reduction strategy; the method comprises the following steps: structuring a confused macro code into an abstract syntax tree through an analysis tool, matching a typical confusion mode based on a regular expression, and performing simulation and cell reference analysis in combination with a function to realize structure preliminary reduction, control flow semantic reduction and operation path construction; according to the unmixing rule and the abstract syntax tree, the confusion structure is converted into a readable macro statement, a macro code instruction sequence with confusion semantics removed is generated, and a semantic sequence obtained after unmixing is output. The feature extraction comprises word feature extraction, Token feature extraction, abstract syntax tree feature extraction and relation feature extraction. According to the method, the bottleneck that traditional phishing attack detection and confusion documents are difficult to recognize is broken through, and the detection accuracy of phishing document attacks is improved.

A system and method are provided for generating a cybersecurity behavioral graph from a log files and / or other telemetry data, which can be unstructured or semi-structured data. The log files are applied to a machine learning (ML) model (e.g., a large language model (LLM)) that generates / extract from the log files entities and relationships between said entities. The entities and relationships can be constrained using a cybersecurity ontology or schema to ensure that the results are meaningful to a cybersecurity context. A graph is then generated by mapping the extracted entities to nodes in the graph and the relationships to edges connecting nodes. To more efficiently extract the entities and relationships from the data file, an LLM is used to generate regular expressions for the format of the log files. Once generated, the regular expressions can rapidly parse the log files to extract the entities and relationships.

The invention relates to the technical field of policy matching, and discloses an intelligent matching system based on big data and a matching method thereof. The system comprises a data acquisition module, a condition analysis module and an intelligent matching module. The data acquisition module is used for acquiring policy text, enterprise qualification and historical case data and constructing a matching basic database; the condition analysis module is used for extracting policy dominant conditions through a regular expression, mining implicit conditions from failure cases by utilizing an association rule algorithm, and calculating condition weights by adopting a random forest algorithm; the intelligent matching module integrates dominant and implicit conditions to calculate a matching coefficient, dynamically divides risk levels and generates an early warning, the matching comprehensiveness and the early warning sensitivity are improved by mining the implicit conditions, dynamically adjusting the matching model and reinforcing a learning optimization mechanism, upgrading from text matching to risk prejudgment is achieved, and the method has the advantages of being high in practicability and easy to popularize. The enterprise declaration success rate can be improved, and the repeated declaration rate is reduced.

The invention discloses a large model training data set construction method based on table type data. The method comprises the steps that 1, data preprocessing is carried out; step 2, constructing an automatic processing method and an extraction template based on header association; step 3, constructing a deformable template model; and step 4, binding question types and fields. According to the method, manual intervention is reduced by applying an automatic processing method based on header association and an extraction template, a question and answer template is preset, key fields are matched by using a regular expression, a table format is automatically adapted through a field identification module, and then data quality is guaranteed through a data quality evaluation and feedback mechanism; a deformable template model is provided to be combined with a dynamic adjustment mechanism, and field identification, dynamic adjustment and mapping modules cooperate to ensure accurate query; heterogeneous table data is standardized, problem types and corresponding fields are automatically bound to improve the processing efficiency, and the diversity of a data set is increased by using a data enhancement strategy based on a natural language.

The invention discloses an interaction control method and system for an intelligent agent and a front-end and back-end system, and the method achieves the automatic control of the intelligent agent on the front-end and back-end system through the integration of a large language model. The method comprises the following steps: an intelligent agent performs natural language intention recognition through a large language model, automatically selects a proper tool, extracts parameters required by the tool, converts the intention into a target task, generates a structured control instruction, maintains a complete session history and supports multiple rounds of dialogue interaction; the back end receives a user request, calls an intelligent agent to generate a structured instruction, transmits the structured instruction to the front end, and dynamically adjusts a subsequent instruction according to the received front end feedback; and the front end receives the instruction transmitted by the rear end, automatically identifies an instruction format through a regular expression, performs corresponding operation after parameter verification and feeds back a result. According to the method, unified control of the agent on the front-end UI and the back-end business logic is realized, complex multi-round dialogue and multi-step operation are supported, and the method has a wide application prospect.

The embodiment of the invention discloses a multi-modal document retrieval method and device, electronic equipment and a storage medium. The similar problems that in an existing retrieval enhancement system, multi-modal data (pictures and tables) in documents are difficult to process, the content stored in a knowledge base is incomplete, the correlation between retrieval results and problems is low, non-professionals are difficult to construct the knowledge base and a question and answer system by themselves, and the like can be solved. The method comprises the steps of obtaining a to-be-processed multi-modal document; preprocessing the to-be-processed multi-modal document according to a preset regular expression to obtain a target text list; according to the target text list, a first knowledge base and a second knowledge base are created, and an index relation exists between the first knowledge base and the second knowledge base; according to the dense vector and the sparse vector corresponding to the second knowledge base, the to-be-retrieved content is retrieved, a target retrieval result is obtained, and the target retrieval result is determined according to sub-segment results obtained through retrieval of the dense vector and the sparse vector.

The invention belongs to the technical field of log analysis, and provides a dynamic self-adaptive log analysis method and system based on a local large language model, and the method comprises the steps: 1, distributing sampling weights according to log levels, and preferentially covering key logs; 2, guiding the locally deployed LLM to generate a regular expression for log analysis through a structured prompt; 3, analyzing the logs one by one according to the generated rules by using a traditional log analysis engine, and outputting structured logs; 4, when the unanalyzed log rate exceeds a preset threshold value, asynchronous rule generation is triggered; through the rule generation and verification module based on the local large language model, automatic generation and verification of the log analysis rule are realized, the cost of manually compiling the rule is remarkably reduced, and meanwhile, the accuracy and adaptability of the rule are improved. Therefore, the problems that a traditional log analysis method highly depends on manpower and is poor in cross-domain adaptability are solved.

The invention discloses a network threat intelligence analysis attack scene graph (ASG) generation method based on deep learning and natural language processing. The method combines a deep learning model and a natural language processing technology, and improves the efficiency and accuracy of attack scene graph generation. The method comprises the steps that network threat intelligence is collected, an improved BERT-BiLSTM-CRF model is used for entity recognition, and threat attack entities are recognized in combination with a regular expression; carrying out core anaphora analysis by adopting a CR-M-SpanBERT model, and capturing a semantic context dependency relationship between entities; extracting a bidirectional context semantic dependency relationship of the text by using a dependency analysis technology, and generating an embedded representation in combination with RoBERTa-BiGRU; and finally, triple information is extracted based on a BERT-RE model, and an attack scene graph is generated. According to the method, the processing capability of the complex network threat intelligence data is improved, the robustness and adaptability are high, and the method is particularly suitable for multi-mode threat intelligence.

A method, electronic device and computer-readable storage medium for extracting entity relationships, which relates to artificial intelligence technologies such as natural language processing, knowledge graphs, deep learning, and large language models. The method for extracting entity relationships includes: inputting a target long text into a target large language model to obtain a target keyword list based on an output result of the target large language model; inputting the target keyword list into multiple target relationship agents respectively to obtain multiple target regular expressions corresponding to different entity relationships based on output results of the multiple target relationship agents; and processing texts in a preset text set using the multiple target regular expressions to obtain entity relationship extraction results.

The invention relates to the technical field of data processing, in particular to an LLM-based multi-source heterogeneous data intelligent fusion and self-adaptive processing method, which comprises the following steps of: receiving multi-source heterogeneous data, matching character sequences of mailbox fields and identity card number fields in records one by one by adopting a preset regular expression model, according to a preset key value pair mapping dictionary, items are uniformly converted, data records which cannot pass format verification, type conversion and value domain mapping are marked as doubt, and data records which cannot be processed or are marked as suspicious are separated. According to the method, a hierarchical processing flow is constructed, and deterministic format verification, type conversion and value domain mapping are arranged at the front end of a processing link, so that most simple data quality problems can be quickly processed with low calculation overhead. Then, numeric statistics and character string comparison are used to carry out fuzzy correction on the data, and spelling errors and numeric abnormities which cannot be covered by rules are further solved.

The invention discloses an intelligent question answering method and device based on the tax field, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: carrying out the validity judgment of a user question inputted by a user side through a regular expression, and carrying out the preprocessing and question rewriting of the user question based on a judgment result, so as to obtain a rewritten question; performing demand identification on the rewritten question and a corresponding current report based on a preset cue word project to obtain a user demand; executing a target data analysis task corresponding to the user demand by using a preset large model, the first knowledge base and the second knowledge base to obtain an execution result, and summarizing the execution result to obtain a target answer; the first knowledge base is a knowledge base constructed based on report structure definition information; the second knowledge base is a knowledge base constructed based on tax business index knowledge. The ability of understanding tax terms, regulation changes and business logic can be enhanced, and the accuracy and reliability of data analysis are improved.

The invention discloses an occupational information evaluation method based on rule judgment and a large language model, and belongs to the technical field of information processing, and the method comprises the following steps: S1, obtaining a multi-modal document uploaded by a user, converting the multi-modal document into a Markdown format, and completing a shunting operation and a converging operation; s2, extracting a named entity of the multi-modal document after the confluence operation, and constructing a knowledge graph; s3, obtaining query content, and matching most relevant regulation content by using the knowledge graph; s4, generating specific improvement suggestions by utilizing the rule model according to the most relevant regulation content; and S5, adjusting the specific improvement suggestions by using a large language model to obtain a final evaluation result. Through the OCR technology, regular expression analysis and context-associated picture analysis, unified structured processing of texts, tables and pictures is achieved, and the problem that unstructured data are difficult to integrate in a traditional method is effectively solved.

The invention belongs to the field of software testing, and relates to an ROS 2 software package defect detection method based on a large language model and coverage rate guidance. According to the method, function information is extracted from a to-be-tested program, and a regular expression is used for analyzing function definition, parameter characteristics and other information; the method comprises the following steps: presetting verification standards related to a to-be-tested program, and extracting physical constraints and document constraints from the verification standards; integrating the extracted function information, physical constraint and document constraint into accurate information, filling a cue word template with the accurate information, inputting the cue word template into a large language model, and generating an initial test seed; a mutation strategy is introduced, and diversified mutation operation is performed on the generated seeds, so that test cases with higher diversity are generated; optimizing the test case by adopting a coverage rate guiding mechanism, and guiding to generate a new test case by monitoring the coverage rate in real time; through a defect verification mechanism, dominant defects such as program crash and the like can be detected, and hidden defects such as violation of physical constraints or violation of document constraints and the like can be identified.