191 results about "Logic structure" patented technology

The invention relates to the field of computer vision, and particularly provides a table recognition reconstruction method and system, a terminal and a medium, and the method comprises the steps: firstly decomposing a large-size table image into a plurality of overlapped sub-images, and carrying out the table structure detection and OCR character recognition of each sub-image through parallel recognition; then, sub-graph recognition results are integrated through a coordinate mapping and confidence coefficient weighted fusion algorithm, and boundary errors are eliminated; then, automatically distinguishing common cells based on an area clustering algorithm, merging the cells and a header region, and reconstructing a complete table logic structure; further understanding header semantics through a natural language model and repairing identification errors; and finally, realizing intelligent splicing and standardized output of the cross-page table. According to the method, the memory limitation of the traditional OCR technology is broken through, an oversized table can be processed, the recognition accuracy of a complex structure is improved, and the digitization efficiency of professional documents such as financial statements and engineering drawings is improved.

The invention discloses a multi-modal index knowledge base and a construction method thereof. The construction method comprises the following steps: processing a heterogeneous document to obtain a semi-structured document; identifying a title hierarchical relationship of the document to construct a document logic structure; carrying out minimum chapter blocking on the text of the semi-structured document to obtain logic blocks; performing semantic segmentation on each logic block to obtain text blocks; the method comprises the following steps: constructing text block nodes by meta-information of text blocks, constructing non-text block nodes by meta-information of non-text elements, extracting document nodes, chapter nodes and chapter-chapter inclusion relationships according to a document logic structure, respectively extracting semantic information from the text blocks and the non-text elements, and storing the semantic information in a database; recording the corresponding relationship between the text block nodes and the semantic information and between the non-text block nodes and the semantic information; constructing a knowledge graph based on each node and relationship and storing the knowledge graph into a graph database; and constructing semantic knowledge based on the semantic information and storing the semantic knowledge into a vector database. According to the scheme, lossless retention of multi-modal information and structured organization of document logic are realized, and efficient indexing and accurate recall are facilitated.

The invention relates to the technical field of quality monitoring, in particular to a virtual simulation and real-time decision-making system for canned fish production quality risks, which is characterized in that data stability and jump accuracy are improved by adopting a filtering mode, a key path area is screened, a conduction trunk is formed, synchronous construction of a mapping and adjusting strategy is realized, and a real-time decision-making result is obtained. By introducing action time, a control time consumption difference value and a node jump amplitude trend multi-dimensional quantitative judgment standard into an execution structure, a feedback recognition mechanism of index fluctuation is enhanced, the abnormal trend response granularity is improved in cooperation with multi-dimensional nested calculation of temperature and pressure parameters, synchronous association of key control parameters and historical operation tracks in a fault chain is complemented, and the reliability of the fault chain is improved. Through starting from a beat logic structure, data cleaning, trend grouping and traceability return are performed alternately, so that a risk propagation chain obtains collaborative modeling among a pressure difference, a temperature difference and a control instruction, and risk path response consistency and intervention precision under a beat driving condition are enhanced.

The invention relates to the technical field of protection device testing, in particular to a verification method of an intelligent comprehensive protection device, which comprises the following steps of: extracting starting time and response time in a tripping state transition sequence of the protection device, calculating execution delay and action segment length, and comparing and identifying an abnormal link based on a state number sequence; the method comprises the following steps: realizing analysis and standard reconstruction of a state logic structure, forming an ordered tripping path, introducing a long short-term memory neural network to carry out time alignment on a state change sequence and a current signal segment, identifying a potential mismatch or abnormal change trend in a state chain through linkage analysis of a state-current abrupt change position, and reconstructing a state path. According to the method, path continuity and dynamic adaptability are enhanced, a random forest is adopted to take device numbers, the number of jump segments and response residual errors as multiple inputs for training modeling, device scores are output through an integrated tree voting mechanism, and recognition sorting of abnormal frequency significant devices is achieved.

The invention relates to a matrix storage operator optimization method and device, computer equipment and a readable storage medium. The method comprises the following steps: allocating memory resources for target data; determining a logic structure corresponding to the source data, establishing a first mapping relation between each logic index in the logic structure and a register address of the source data, and establishing a second mapping relation between each logic index in the logic structure and a memory address of the target data; and determining a corresponding relationship between a memory address of the target data and a register address of the source data based on the first mapping relationship and the second mapping relationship, and storing the source data stored in a register into a memory resource corresponding to the target data based on the corresponding relationship. By adopting the method, the generalization ability of the matrix storage operator can be improved.

The invention belongs to the field of natural language processing and artificial intelligence, and particularly relates to a long text writing generation method and system based on multi-agent cooperation. The method comprises the following steps: receiving a text theme input by a user, completing complex theme decoupling, and outputting an outline directory; the method comprises the following steps: collecting keywords according to a text topic and an outline catalog, obtaining a retrieval result by retrieving the Internet, a domain database and network resources, and preprocessing to obtain a reference with a uniform format; performing systematic evaluation on the reference literature from the field integrating degree and the logic relevance, and performing grading processing on the reference literature to obtain a high-quality reference literature; based on the text theme and the high-quality reference, long text content is generated according to a set logic structure and writing specifications. According to the method, the fragmented expression problem is effectively avoided, the academic value and reading experience of the text are remarkably improved, the inherent defects of a traditional long text generation technology are practically overcome, and a high-efficiency solution is provided for text creation in the professional field.

The invention discloses a power distribution network layered optimization method oriented to source-network-load interaction, and relates to the technical field of power system automation. The method comprises the following steps: constructing a dynamic adaptive collaborative architecture, and dynamically generating a three-layer logic structure of global coordination, regional autonomy and local execution according to the real-time state of the power distribution network; operating a three-layer mixed game mechanism, and determining an optimal strategy of each main body through master-slave, cooperation and bilateral interactive games; executing an evaluation-planning-operation three-level closed-loop optimization process, and taking a key performance index fed back by an operation layer as a basis for scheme correction of a planning layer; and a mixed intelligent strategy combining multi-agent deep reinforcement learning and an improved particle swarm optimization algorithm is adopted for solving. According to the method, the problems of hierarchical solidification, benefit coordination imbalance and planning operation splitting in the prior art are effectively solved, and the adaptive capacity, the economical efficiency and the flexibility of the power distribution network in the high-permeability source-network-load interaction scene are improved.

The invention discloses an agent-based test case generation method and device, equipment and a medium, and relates to the technical field of software testing, the method comprises the following steps: in response to a test case generation instruction for a target function, displaying a test case framework of the target function; in response to an interaction instruction for a preset position in the test case framework, obtaining a target test code of the target function; wherein the interaction instruction is used for triggering interaction between a first agent and a second agent, and the first agent is configured to be used for analyzing a logic structure of a function code of a target function to obtain a structure code of a test point of the target function; the second intelligent agent is configured to be used for generating a target test code based on the structure code of the test point; and displaying the target test code at a preset position to obtain a target test case of the target function. The problem that the generation accuracy of the test case is low can be solved.

The invention relates to an optimization method of a data type conversion operator, computer equipment, a readable storage medium and a computer program product. The method comprises the following steps: allocating memory resources for target data; establishing a read mapping relationship between each logic index in the logic structure of the source data and the memory address of the source data, and establishing a write mapping relationship between each logic index in the logic structure and the memory address of the target data; traversing the logic structure, aiming at any traversed logic index, determining a read address corresponding to the logic index based on the read mapping relationship, performing data reading from the memory resource corresponding to the source data based on the read address, converting the read data element from the first data type to the second data type, and storing the converted data element in the memory resource. And writing the data elements subjected to data type conversion into memory resources corresponding to the target data according to the write mapping relationship. By adopting the method, the calculation capability of the data type conversion operator can be improved.

The invention provides a query statement generation method and device, electronic equipment and a storage medium, and relates to the field of artificial intelligence, in particular to the field of computers, the field of databases and the field of deep learning. The query statement generation method comprises the steps of performing intention recognition on a demand text based on business data to obtain a demand intention; in response to the demand intention meeting a preset demand condition, processing the demand intention by using a language model to obtain an execution logic structure, the execution logic structure comprising a plurality of execution logic elements based on structured representation, and a structured relationship among the plurality of execution logic elements representing an execution process of executing the query statement; determining target mode information matched with the text semantics represented by the execution logic element from preset database mode information; performing semantic understanding on the plurality of execution logic elements and the target mode information by utilizing a language model, and outputting a plurality of pieces of intermediate structured data for describing the query statement; and generating a query statement according to the intermediate structured data.

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.

The invention relates to the technical field of large language models, in particular to an official document content generation and compliance verification system based on a large language model. Comprising an official document extraction module used for analyzing a model essay by using a large language model, extracting a writing style and a fixed sentence pattern, constructing a knowledge graph, storing format specifications, common expressions and logic structures, mining and constructing a core intention library, and storing typical intention prototype vectors; the content generation module is used for generating paragraphs through context sensing, expanding and writing keywords, analyzing behavior data, predicting writing intention vectors and performing condition control; the compliance verification module is used for warning improper words in real time, retrieving policy verification logic and verification formats, and performing pre-verification based on intention vectors; and the writing auxiliary module is used for providing real-time modification suggestions and forward-looking prompts based on writing intention vectors. According to the technical scheme, document writing efficiency and quality can be improved.

The invention discloses a task disassembling method based on a large model, and aims to automatically disassemble requirements input by a user into a series of independent and accurate minimum sub-tasks so as to support high efficiency and intelligence of task execution. Different from a traditional natural language processing method, the method adopts a large model technology to intelligently analyze and optimize input demand content, so that task description is clearer, and operability is achieved. The method comprises the following steps: firstly, deeply analyzing and adjusting a demand by utilizing the understanding ability of a large model to make the demand accord with a logic structure of task execution; then required core steps and logic levels are extracted through a multi-level task decomposition algorithm, and clear sub-task chains are generated through step-by-step decomposition; by means of the adaptability and dynamic reasoning ability of the large model technology, the decomposition efficiency and accuracy of complex tasks are remarkably improved, the burden of a user in the aspects of task planning and execution is relieved, and efficient and automatic development of intelligent task management is promoted.

The invention relates to a knowledge graph semantic enhanced embedding optimization method based on a dynamic memory network, and belongs to the technical field of knowledge graph logic query questions and answers. Explicit modeling and dynamic updating of a historical logic mode are realized by constructing a structured dynamic memory network; a nested structure and a long-term dependency relationship in complex logic query are effectively captured, the semantic understanding and generalization ability of a model to diversified structure combinations is remarkably improved, basic semantics, logic structures and memory features are dynamically fused, a gated memory updating strategy is introduced, a memory calling path is clear in the reasoning process, and the reasoning efficiency is improved. And the optimization behavior is traceable, so that the interpretability and robustness of the model are enhanced, and the embedding space optimization effect is remarkable and has interpretability. And finally, on the premise that an original upstream encoder does not need to be modified, reasoning enhancement is realized through an external dynamic memory network module, the proportion of updated parameter quantity is small, the calculation overhead is low, and the method is suitable for efficient deployment of various reasoning platforms.

The invention discloses an intelligent text calibration method and system based on a large language model, and is used for solving the technical problem that the accuracy and adaptability of text calibration are poor. According to the intelligent text calibration scheme based on the large language model, through preliminary calibration of parallel processing, the linear mode of a traditional calibration process is broken through, and the calibration efficiency is greatly improved. And the text is comprehensively and meticulously proofread from multiple dimensions such as semantic expression, logic structure and terminology by using the large language model, so that the quality of the text in the professional field can be remarkably improved, the text is ensured to meet the industrial standard and professional requirement, and the accuracy and adaptability of text calibration are improved.

The invention discloses a hydraulic machinery feasibility research report grading review method based on a large language model, comprising the following steps: collecting and analyzing an original hydraulic machinery feasibility research report to generate a logic structure tree and a structured text; based on the logic structure tree, hierarchical gating and strategy scheduling are executed, and gating scheduling data are generated; according to review strategy parameters in the gating scheduling data, executing three-constraint parallel partitioning on the structured text to obtain a sub-document set; utilizing the gating scheduling data to execute auditing adversarial compliance review on each sub-document of the sub-document set to form a preliminary review conclusion set; and aggregating the preliminary review conclusion set, executing cross-subdocument consistency judgment and result summarization, and generating a final review conclusion. According to the method, the compliance problem of the hydraulic machinery research-length document report can be completely and accurately identified.

The invention discloses an intelligent chart generation method and system suitable for multi-modal data and a storage medium, and the method comprises the steps: receiving and preprocessing the multi-modal data, and completing user intention type recognition and task routing; selecting an adaptive model according to a routing result, and constructing and generating a cue word; standardizing an initial graph code output by the model into an intermediate representation format containing a metadata field, converting the intermediate representation format into a graph code compatible with a target platform, and reserving node definition, a connection relation, layout constraint and metadata information; the method comprises the following steps: respectively completing chart type identification and layout strategy determination, node format size adjustment, overall layout alignment correction and partition adjustment, and connection line identifier optimization through four optimization components, and rendering and outputting a chart. The problem that due to the fact that an existing model lacks a special optimizing and processing mechanism for the structured chart, the effect is remarkably poor when a professional schematic chart needing an accurate logic structure and geometric constraints is generated is solved.

The invention relates to the technical field of document intelligent review, in particular to a multi-mode digital publishing intelligent review system and method based on a large model, and the system comprises a sample construction module, a multi-mode analysis module, a structure recognition module, a term verification module and an annotation output module. According to the method, a model configuration data set is generated by analyzing a subject-object combination relationship in a text and jointly screening part-of-speech, sentence pattern and semantic structure, the accuracy of semantic recognition and structure judgment is improved, and local features of semantic offset and image-text expression separation are recognized by combining a comparison relationship between an image action path and a text behavior object; a paragraph logic structure and a theme connection mode are analyzed based on semantic offset information, the problems of content dislocation and theme jump between paragraphs are effectively revealed, matching change tracks and part-of-speech continuation fluctuation in term cross-paragraph contexts are recognized, label overlapping and coverage redundancy conditions are analyzed from sentence group hierarchy, and the term cross-paragraph contexts are obtained. And forming a label combination suggestion and constructing an annotation structure record.

The invention discloses an electrical drawing security collaborative management method, system and device based on a cloud computing technology and a medium. The method comprises the steps that an initial key is generated through a quantum key distribution device, the key is divided into a plurality of sub-blocks, and a dynamic session key is generated; encrypting data blocks divided by the electrical drawing file according to the logic structure through the dynamic session key, and transmitting the encrypted data blocks to a cloud computing platform for storage; generating a temporary key to encrypt the modified content, adding a digital signature and position information, and combining the modified content after the cloud verifies the signature to generate a new electrical drawing; synchronizing the new electrical drawing full-text security operation credential update record to the block chain, and verifying the consistency of the data on the chain and the cloud drawing version; and splitting the dynamic session key into a plurality of fragments and storing the fragments in different cloud nodes. According to the invention, safe transmission, efficient collaborative management and accurate data tracing of the electrical drawings are realized, and the safety, reliability and efficiency of full-life-cycle management of the electrical drawings are remarkably improved.

The invention provides a data security encryption method and system based on high-security machine learning, and the method comprises the steps: collecting an access behavior of to-be-encrypted data, and calling context and environment meta-information to construct a structured feature sequence; establishing index mapping between the feature sequence and a historical encryption execution record, and extracting significant features by using a feature scoring and information gain evaluation method; the system executes multi-level decision path search based on hierarchical condition division according to the significant feature sequence of the current task, and judges the security level and the encryption risk situation of data; the system constructs an encryption control sequence including a parameter instruction block and an execution logic structure according to the task risk level and the strategy index; the encryption engine analyzes the instruction packet and executes a corresponding encryption task, and generates a complete record file containing metadata and a tracking tag at the same time; and the system performs statistical analysis on decryption feedback logs and encryption abnormal records, and optimizes the strategy dictionary by adopting a sample weight updating and feature re-evaluation method.

The invention discloses a function call relation-oriented code pre-training and generating method and system. The method comprises the following steps: 1) for each piece of code warehouse data in a code data set, extracting context information from the piece of code warehouse data; the context information is function call information, parameter dependence and a global logic structure; 2) training a code-related deep learning model by using the context information to obtain a code generation model; 3) inputting code demand description or a code to be edited into the code generation model to generate a corresponding target code; wherein for the input code demand description, the code generation model generates a target code meeting the demand according to the code demand description; and for an input to-be-edited code, the code generation model automatically repairs or optimizes the to-be-edited code to obtain a target code. According to the method, logic consistency and availability of generated codes are optimized, and code generation in a complex code scene is remarkably improved.

The invention discloses a logic structured data modeling-oriented germination type pre-training method and system, and belongs to the technical field of information. The method comprises the following steps: performing data logic analysis on original data to obtain a structured representation of the original data; performing data germination based on the structured representation to obtain logic structured data x, and obtaining a plurality of data sample pairs {xi-a, xi-b} according to a growth path of the logic structured data x in the data germination process; and completing training of a target model for {xi-a, xi-b} based on the data sample to obtain a germination type pre-training model. According to the method, a logic structure possibly contained in each piece of data is fully mined, and a proper germination mode is applied under the condition that different contexts are given to accurately generate target data contents, so that downstream tasks can be processed more effectively.

The invention provides a data management method and device and a readable storage medium. The method is applied to a cloud platform and comprises the steps that an operation request of a first file uploaded by a first client is received, the operation request carries an operation type and file information of the first file, the operation type comprises writing, modification and / or reading, and the file information comprises a file identifier; determining a file path according to the file identifier; determining a node mapping table corresponding to the first file in the first partition table according to the file path; the node mapping table is of a file tree structure, and nodes in the node mapping table are in one-to-one correspondence with metadata of the first file; by taking a root node in the node mapping table as a search starting point, searching layer by layer in the node mapping table according to the hierarchical relationship of the file paths to obtain a physical address; and accessing the first file according to the physical address, and executing an operation corresponding to the operation type on the first file. Efficient management of complex logic structure data in a cloud environment can be realized.

The invention relates to the technical field of semantic analysis, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a business process analysis method, device and equipment based on a natural language and a medium. Generating a target logic tree corresponding to the natural language instruction; converting the logic structure in the target logic tree into a structured code, and performing grammar verification on the structured code; correcting the structured codes which do not pass the grammar verification, and performing rendering operation on the target structured codes to obtain a visual flow chart; adjusting the visual flow chart, and updating the structured code based on the adjusted visual flow chart; and when the visual flow chart meets the flow demand of the target user, outputting a target flow chart corresponding to the natural language instruction, and performing business flow analysis on the natural language instruction by using the target flow chart. And the efficiency of business process analysis is improved.

Disclosed herein are system, method, and computer program product aspects for textual data extraction, generation, and evaluation. Text is input into a first fine-tuned large language model (LLM) to generate an atom (e.g., a textual phrase in a particular category). The atom is input into a second LLM that has been fine-tuned for structured output corresponding to the particular category of information. A logical structure is generated based on a structured output of the second LLM, wherein the logical structure represents the textual phrase of the atom and contains a contextual attribute associated with the textual phrase. The embodiment then stores the logical structure into a knowledge graph as a modifier node having a time-variant attribute (e.g., a timestamp associated with the textual phrase and / or the contextual attribute).

A logical expression acquisition unit (502) acquires a logical expression that represents a condition under which searchable encryption is permitted, by a logical structure with a plurality of attribute values and a logical operator. An encrypted tag generation unit (504) sets a secret sharing value which reflects the logical structure, to each of the plurality of attribute values, and generates an encrypted tag to be used for the searchable encryption, using a plurality of secret sharing values set to the plurality of attribute values.

The invention discloses an engineering document generation method and system based on a logic structure tree and LLM and a medium. The method comprises the steps that original data are analyzed to form an initial text segment set X; calling LLM to generate an initial technical draft based on X, extracting a logic structure, and constructing an initial logic structure tree T; maintaining a search queue, for leaf nodes in the queue, acquiring context information related to semantics from X through a retrieval enhancement mechanism, after same-level brother node deduplication and global deduplication judgment, calling LLM to expand the nodes to generate child nodes, optimizing text description, and recursively enriching a tree structure; and finally, based on the X and the optimized tree, calling LLM to generate a title, an abstract and a background, generating content, details and key point chapters through traversal, and combining into a complete engineering document. The system comprises a document analysis module, a knowledge base module, a retrieval enhancement module, a logic structure tree maintenance module and a document generation module. The method is high in automation degree, and the generated document is clear in structure, complete in content and high in specialty.

The invention discloses a report generation method based on a large model, electronic equipment and a computer program product, and relates to the technical field of artificial intelligence, the report generation method comprises the following steps: identifying a logic structure, a placeholder and a task dependency relationship in a preset report template, and initializing a context state carrier based on the logic structure, the placeholder and the task dependency relationship; obtaining a report generation requirement, converting the report generation requirement into SQL query data, and generating a cue word based on the SQL query data; and inputting the cue word into a preset generation model to obtain filling content, updating the filling content to the context state carrier, and filling placeholders of the report template based on the context state carrier to obtain a final report. According to the method, the defect of low efficiency of manual data arrangement is overcome, and the problem that a simple template filling tool cannot process a complex logical relationship is solved, so that the report generation efficiency is improved.

The invention belongs to the technical field of large language models, and provides a financial text logicality evaluation method and system based on an AI Agent. The method comprises the following steps: performing digital conversion and preprocessing on an original financial text, segmenting according to a logic structure, and extracting key information to obtain structured standard data; performing fine tuning and initialization on the pre-trained large language model, and constructing a reasoning Agent and an reflecting Agent; based on a chain thinking prompt and a logic knowledge base, a reasoning Agent is guided to analyze the structured standard data, and logic vulnerabilities are identified; the method comprises the following steps of: performing rechecking through a reflection Agent, performing classification integration on logic vulnerabilities through a topic modeling technology, outputting a logic error report, performing visual display, and performing user-defined format export. According to the method, deep context expression and complex logic errors in the financial text can be accurately recognized, and financial text analysis requirements under different business scenes are met.

The invention relates to the technical field of multi-modal document analysis, and discloses a multi-modal document analysis method and device, electronic equipment and a storage medium. The method comprises the steps that picture elements in a document are automatically recognized and extracted through a third-party library module, placeholders are generated in combination with picture positions, and the placeholders are stored in the third-party library module; and then semantic understanding and text generation are carried out by using a multi-modal large model, so that the visual content is converted into a structurable information text. The method has the advantages that by introducing the multi-modal large language model, unified analysis of multi-modal information such as texts, pictures, tables and flow charts is achieved, layout semantics and logic structures of documents are reserved, fusion analysis of image-text content is achieved, the integrity and the intelligent level of document analysis are remarkably improved, and the method is suitable for large-scale popularization and application. Compared with a traditional single-mode method, the method has the advantages that unified understanding and structured expression can be carried out on multi-mode content, and richer and more accurate structured results can be output.