215 results about "Template entry" patented technology

The invention belongs to the field of artificial intelligence and finance, and relates to a text classification method, which comprises the following steps: preprocessing an original text data set, labeling classification labels to obtain a labeled text data set, inputting a training set into a large language model for keyword extraction to obtain a candidate keyword set, cleaning the candidate keyword set to obtain a classification keyword set, constructing an instruction template based on the classification keyword set, and inputting the training set and the instruction template into a large language model to obtain a prediction classification result; and based on a prediction classification result and a loss function between the classification labels, performing fine tuning on the large language model to obtain a fine tuning model, evaluating that the fine tuning model is qualified to obtain a text classification model, and inputting the to-be-classified text into the text classification model for classification. The invention further provides a text classification device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the tagged text data set can be stored in a block chain. According to the method, the text classification accuracy and stability can be improved.

The invention relates to the technical field of knowledge graph question answering, and discloses a knowledge graph retrieval reasoning method and system based on big language model enhancement, and the method comprises the steps: carrying out the entity extraction of a user question through a big language model, and generating a candidate entity list; retrieving entity nodes corresponding to the candidate entity list in a knowledge graph database through a mixed retrieval method of fuzzy matching and vector retrieval; re-screening the entity nodes by using a large language model to generate a starting node list; performing width-first traversal on each node in the starting node list in an edge-in direction and an edge-out direction, and retrieving a reasoning path in the knowledge graph; constructing the reasoning path into a prompt template, and inputting the prompt template into a large language model to generate an answer to the user question; according to the method, the big language model is utilized to analyze questions and query the knowledge graph in the knowledge graph question-answering scene, question-answering is performed based on the knowledge graph path, and the effectiveness and accuracy of user knowledge graph question-answering are improved.

The invention discloses a knowledge base question answering method and system based on intention recognition and a medium, and the method comprises the steps: receiving a user question, inputting the user question into a large language model subjected to intention recognition training, and carrying out the preliminary classification; determining a corresponding search strategy according to a classification result obtained by the preliminary classification; if the search strategy is knowledge base search questions and answers, selecting a knowledge base matched with the search strategy for knowledge retrieval, inputting the retrieved reference knowledge and cue word templates into a large language model, and answering the user questions by the large language model; and if the search strategy is large-model direct question answering, selecting a cue word template, inputting the cue word template into the large-language model, and answering the user question by the large-language model. According to the method, user questions can be preliminarily classified through user intention recognition, different search strategies are adopted for different categories, and knowledge base question answering efficiency and accuracy are effectively improved.

The invention relates to the technical field of operation ticket processing, and discloses an operation ticket generation method and device, equipment and a storage medium, which are used for improving the adaptability of the operation ticket generation method to different substation operation scenes and improving the accuracy, safety and efficiency of the generated operation ticket. The operation order generation method comprises the steps of performing semantic analysis on a to-be-processed scheduling order to obtain structured scheduling order data; performing dynamic topology analysis on the structured scheduling order data according to the real-time equipment state data to obtain a real-time topology analysis result; matching in a preset operation ticket database according to the structured scheduling order data, and inputting the processed historical topology analysis result, the ticket forming template, the structured scheduling order data, the real-time topology analysis result, the self-defined ticket forming rule and the cue word template into a large language model for ticket forming to obtain an initial operation ticket; performing safety verification on the initial operation ticket to obtain a safety verification result; and generating a target operation ticket based on the interaction information of the security verification result.

The invention discloses a CLIP-based double-prompt optimized few-sample industrial anomaly detection method. The method comprises the following steps: respectively constructing a learnable normal text prompt template and an abnormal text prompt template by utilizing learnable normal word vectors and abnormal word vectors; performing image enhancement processing and image abnormal synthesis processing on the normal sample image for model training to generate an enhanced image and an abnormal synthesis image; inputting the learnable normal text prompt template and the learnable abnormal text prompt template into a text encoder in the CLIP model to obtain text prompt embedding; respectively inputting the normal sample image, the enhanced image and the abnormal synthetic image into an image encoder in the CLIP model to obtain visual embedding; and the similarity between text prompt embedding and visual embedding is measured to carry out optimization training on learnable text prompts, so that the trained CLIP model is utilized to carry out industrial anomaly detection.

The invention provides a game strategy intelligent layout generation and dynamic optimization method and device and a storage medium. The method comprises the following steps: when a layout trigger event is detected, generating a layout generation request; in response to the layout generation request, calling a knowledge graph engine to perform correlation calculation on the relational data, and screening from a preset layout template library based on a correlation result to obtain a plurality of initial layout candidate templates; inputting the initial layout candidate template into a low-code layout editing module, and generating a corresponding layout description file according to a mapping rule of the page component and the component parameter; inputting the layout description file as a current state into a reinforcement learning decision model, and outputting target layout configuration in combination with user portrait data; and sending the target layout configuration to a front-end rendering engine, and rendering the game strategy page according to the target layout configuration. According to the method, intelligent generation and real-time dynamic optimization of the game strategy page layout can be realized, and the personalized degree of the layout and the user interaction experience are improved.

The invention discloses a multi-agent cooperative cue word engineering optimization method, system and device and a medium, and relates to the technical field of cue word engineering, the method comprises the following steps: after initially receiving a user input task, starting a cue word optimization process; an initial cue word template is created through a cue word generation agent, historical optimal templates are retrieved from a knowledge base for fusion, and candidate cue word templates are generated. And the analysis agent inputs the candidate template into the second large language model to generate a candidate answer. And the evaluation agent performs multi-dimensional evaluation on the answers and gives scores and defect analysis. And according to an analysis result, updating the cue word to generate a candidate template of the intelligent agent. And arranging the intelligent agent to cyclically execute the steps until the stop condition is met, and outputting the optimal cue word template. And finally, storing the optimal template into a knowledge base and establishing a semantic index to support subsequent retrieval reuse. According to the method, manual intervention is not needed, and a complete automatic process of generating, evaluating and optimizing the cue word is realized.

The invention relates to an intelligent bidding document compiling method, system and equipment based on a large language model. The method comprises the following steps: constructing a bid invitation knowledge graph; obtaining project demand information to determine demand fields, calling a knowledge graph association interface to match bid invitation file modules and terms for each demand field, and generating a demand field-association module mapping table; according to the mapping table and bid invitation industry specifications, a generation prompt template embedded with a dynamic nesting slot position and a knowledge graph rule is constructed, and a pre-trained bid invitation big language model is input to output a bid invitation clause draft; traversing the knowledge graph according to a demand change field to calculate a change influence radius, constructing an update prompt template in combination with a slot association rule and a constraint condition, inputting a large model to generate revised content only aiming at the demand field and an association module, replacing a corresponding position of a draft, and performing secondary verification on logic consistency through the knowledge graph, and obtaining the updated complete bid invitation file. By adopting the method, intelligence, precision and high efficiency of bid invitation file compiling can be realized.

The invention discloses a network threat knowledge automatic extraction method, electronic equipment and a storage medium, and the method comprises the following steps executed by a computer hardware system: collecting threat intelligence data related to an APT organization from a multi-source network security text, and processing the threat intelligence data to generate a standardized corpus; using the pre-training sentence vector model to generate semantic embedding for a corpus input text and a manual annotation example library text, and retrieving similar examples to construct an ICL prompt template; inputting a large language model subjected to LoRA fine tuning, and extracting structured triples of multiple types of entities and semantic relationships; generating standardized entity nodes and updated relation information by adopting semantic aggregation; and constructing an APT organization network threat intelligence knowledge graph and outputting a structured file. The method provides key technical support for APT attack tracing, threat situation awareness and automatic security policy generation.

The invention provides a method for intelligently matching bidding and tendering documents based on a large model. The method comprises the following steps: step 1, preprocessing the bidding and tendering documents; step 2, carrying out data cleaning on the pre-processed document so as to establish a pre-training data set; step 3, processing the txt text after data cleaning, and splitting the txt text into a plurality of index items; 4, questioning each index in the index text by using an instruction following function in the large model so as to generate a fine tuning data set; 5, pre-training and fine tuning are carried out based on the base large model; and step 6, constructing the cue words, the index items and the corresponding bidding file fragments into a template, inputting the template into the trained large language model, and guiding the trained large language model to perform reasoning and answering. The answering accuracy of the large model in the professional field is improved, so that the model is more excellent in the answering process.

The invention discloses a data intelligent interaction enhancement query method and system based on a large model. The method comprises the steps that S1, a user inputs a query problem through a webpage interface; s2, determining a target data table in the candidate data tables, and obtaining table structure and attribute information; s3, the query problem is rewritten; s4, acquiring a question and SQL statement pair set of which the similarity ranks top k with the query question; s5, filling a first cue word template with the rewritten query problem, the table structure and attribute of the target data table, the problem and the SQL statement, and inputting the first cue word template into a large language model to generate a target query statement; s6, executing query in the corresponding data source according to the target query statement to obtain a target query result; and S7, filling the target query result and the query question into a second cue word template, inputting the target query result and the query question into the large language model, generating a query result in a natural language form, and returning the query result to the user. According to the invention, the performance and practicability of the interactive data query system are remarkably improved.

The invention discloses a natural language database query method and system based on large-model two-channel context enhancement, and the method comprises the steps: carrying out the parallel execution of a business link and a mode link on a query statement, and respectively outputting business context information and mode link information; carrying out vectorization fusion on the two types of information to generate combined context information; generating a query intention vector based on the combined context information, and outputting a complexity label of the query statement through a complexity calculation model; and generating a prompt template according to complexity label differentiation, inputting a large model to generate an SQL statement, and executing query. Through a double-channel cooperation mechanism, the problems of semantic ambiguity, database structure complexity, poor field adaptability and the like in natural language query are solved, query accuracy and response efficiency are improved, and the method is suitable for multi-industry complex database scenes.

The invention provides a self-adaptive template reconstruction method and system based on a small amount of samples, and belongs to the field of natural language processing, and the method comprises the steps: S1, constructing an initial prompt template based on a small amount of sample data; s2, inputting the initial prompt template into a large language model to generate a question pool; s3, generating an answer pool by using a large language model; s4, performing screening and clustering analysis on the answers in the answer pool through a multi-level similarity comparison module, and dynamically adjusting weight parameters of similarity calculation in the multi-level similarity comparison module through a model self-feedback mechanism to obtain an overall score of the screened answers; s5, calculating a comprehensive similarity score of each answer group, performing expert scoring on each answer group, and reconstructing the initial prompt template based on the comprehensive similarity score and the expert score; and S6, inputting the reconstructed prompt template into a large language model for iterative updating until a final answer is obtained.

The invention discloses a high and steep slope deformation monitoring method combining an xLSTM-Attention time sequence prediction model and a large language model. The method comprises the following steps: 1) constructing a time sequence prediction model based on xLSTM and an attention mechanism; 2) setting a plurality of side slope monitoring points on the side slope to be detected, acquiring three-dimensional deformation time sequence data of the side slope monitoring points in the T time period, and performing feature extraction to obtain statistical features; inputting the three-dimensional deformation time sequence data in the T time period into a slope deformation monitoring model to obtain a deformation prediction result in a future T'time period; 3) extracting an important hysteresis feature Top-KLags of the slope deformation monitoring model to form a standardized natural language prompt template; 4) calling a pre-training language model with engineering understanding ability, and performing field fine tuning; and 5) inputting the standardized natural language prompt template into the pre-training language model after field fine tuning to obtain a slope deformation safety risk assessment report. According to the invention, efficient deformation prediction and risk analysis can be realized.

The invention relates to the technical field of research and development management, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-page application construction method and device, equipment and a medium, and the method comprises the steps: configuring a mapping relation between a template path and a script path; traversing a page module under the source file directory, and extracting a module identifier and a script path parameter; generating an entry script reference and injecting a placeholder of the template engine, and generating a page entry file; setting an entry configuration item of the construction tool and loading a module script file; executing a code compiling process, a dependency analysis process and a static resource packaging process; extracting a public dependency library of the page module and combining the public dependency library with the module script file into a public code block; and packaging the public code block and the compiled product into a deployable application package. According to the method, the mapping relation between the template entry and the script path is uniformly managed, the page entry file is automatically generated, and the construction tool entry is configured, so that the page entry configuration complexity is reduced.

The invention relates to a method and equipment for converting a natural language problem into an SQL statement and a medium, and belongs to the technical field of database management and data analysis. The method comprises the steps of receiving a query statement which is input by a user and expressed by a natural language; based on the semantic similarity between the query statement and a plurality of query templates in a database, determining a plurality of candidate query templates matched with the query statement; inputting the query statement and a plurality of candidate query templates into a large language model, and outputting a matching result which is most matched with the query statement as a matched query template; determining a corresponding analysis template and a corresponding SQL (Structured Query Language) template based on the matched query template; based on the matched analysis template, analyzing the query statement to obtain an analysis result; the SQL template is filled with the query variables in the analysis result, and an SQL query statement is generated. According to the method, the accuracy and interpretability of conversion from natural language query to SQL are improved.

The invention provides a literature extraction method, system and device based on a large language model and a storage medium, and relates to the technical field of medical research, and the method comprises the steps: constructing a literature extraction template according to a literature extraction demand; wherein the literature extraction requirements comprise extraction rules, format requirements and pre-extraction fields, and the pre-extraction fields comprise research basic information, research design types, research groups, participant characteristics, intervention measures of each research group and background medication of each research group; obtaining a to-be-extracted literature, and converting the to-be-extracted literature into a processable text format to form a first text; and inputting the first text and the literature extraction template into a large language model, performing reading analysis on the first text by applying the large language model, and extracting the first text according to the literature extraction template to obtain a literature extraction result corresponding to the to-be-extracted literature. According to the scheme, the accuracy and efficiency of document extraction are improved, and the requirements of system review can be better met.

The invention relates to a book content retrieval method and device based on intelligent word segmentation and computer equipment. The method comprises the steps of obtaining a word segmentation result of a newly added book, and performing block processing on the word segmentation result to obtain a plurality of blocks; obtaining an extraction type abstract of each block, adjusting the original prompt template according to the extraction type abstract to obtain a first semantic extraction prompt template, and performing semantic extraction on each block by utilizing the fine-tuned local semantic extraction large model and the first semantic extraction prompt template to obtain an output text of each block; obtaining a generative abstract of the newly added book, constructing a second semantic extraction prompt template according to the generative abstract and the original prompt template, inputting the output text of each block and the second semantic extraction prompt template into a global semantic extraction large model, outputting a semantic introduction of the newly added book, and storing the semantic introduction into a semantic introduction library; and performing retrieval in the semantic brief introduction library according to the user retrieval statement. By adopting the method, the efficiency and accuracy of intelligent retrieval can be improved.

The invention relates to the technical field of artificial intelligence, in particular to a method and device for processing user questions and answers and a storage medium. Comprising the steps of obtaining query questions of a user and a domain interest table corresponding to the user; determining a target field for the query problem according to the field interest table, and obtaining a cue word template corresponding to the target field; inputting the cue word template into a first large language model to construct a question rewriting large language model corresponding to the target field; inputting the query question into a question rewriting large language model to obtain a question cluster after the query question is rewritten; sequentially inputting the sub-questions in the question cluster into a second large language model according to the level from low to high so as to sequentially obtain replies corresponding to the sub-questions in the question cluster; and the final reply of the query problem is determined based on the reply of the sub-problem of the highest level in the problem cluster, so that the query problem is better understood and processed by the large language model, a more accurate reply is obtained, and the user satisfaction is improved.

The invention discloses a steel ladle hoisting safety monitoring and early warning method, system and equipment and a medium. The method comprises the following steps: acquiring double-side images in a steel ladle hoisting process; inputting the bilateral images into a multi-target detection model, and outputting bounding boxes, category labels and confidence coefficients of the plurality of key targets; obtaining a local cutting image of each key target according to the bilateral images and the bounding box, the category label and the confidence of each key target, and generating a structured text in combination with the bounding box position, the relative pixel distance and the category label of each key target; inputting the local cutting image, the structured text and the prompt word template of each key target into a multi-modal big language model, and outputting a behavior label, a risk level, a reason, a recommendation action and an instruction state of the current steel ladle hoisting scene through multi-modal reasoning; early warning is conducted according to the recommended action, and the steel ladle hoisting equipment is controlled according to the instruction state. According to the invention, intelligent monitoring and linkage early warning in the steel ladle hoisting process can be realized.

The invention discloses a public opinion home address standardization method and system, and the method comprises the following steps: S1, obtaining and processing multi-source address original data to obtain an address data block, processing the data block based on a pre-trained text embedding model to obtain a corresponding semantic vector, and storing the address data block and the corresponding semantic vector, establishing a multi-source address knowledge base; s2, processing public opinion text data through the text embedding model to obtain a query vector of the public opinion text data; s3, querying the multi-source address knowledge base based on the query vector to obtain a plurality of address data blocks related to the query vector; and S4, constructing a cue word template based on the public opinion text data and the address data blocks, and inputting the cue word template into a large language model to perform public opinion attribution address standardization analysis.

The invention provides a text generation method and system based on a large language model, demand information input by a user is responded, the demand information and a cue word template are input into the large language model, a target directory corresponding to the demand information is generated, and the cue word template comprises guide information and an initial directory; text information, corresponding to the target directory, in a knowledge base is obtained, and non-text elements are embedded in the text information; inputting the text information and the target directory into a large language model, and generating a target text consistent with the format of the target directory; and replacing the non-text element in the target text with a non-text object corresponding to the non-text element. The generated target text comprises the non-text elements, multi-modal data query and text content generation are achieved, the generated target text meets the format requirement of the target directory and has high accuracy, and the satisfaction degree of a user is improved.

The invention is suitable for the technical field of data processing, and provides an education evaluation report generation method and system, and the system comprises a data management module which is used for obtaining the data of evaluation indexes in each evaluation item in an evaluation report, and obtaining the index data; the index management module is used for adjusting the evaluation index and the weight thereof, and performing statistical processing on the index data according to the evaluation index and the weight thereof to obtain statistical data; the template management module is used for filling the index data into a Prompt template and inputting the filled Prompt template into a large language model, and the large language model analyzes facts reflected by the index data based on cue words in the Prompt template to obtain analysis content; and the report generation module is used for integrating the analysis content and the statistical data into an evaluation report template to obtain a target evaluation report. The large language model is used for deep data analysis, the evaluation report is generated, the report generation time is remarkably shortened, and manual errors and subjective deviations are reduced.

The invention provides an information recommendation method and device, electronic equipment, a storage medium and a program product, and is applied to the technical field of artificial intelligence. The method comprises the steps that first feature data of a first user and first multi-source behavior data of the first user are acquired; inputting the first feature data, the first multi-source behavior data and a pre-constructed thinking chain prompt template into a pre-trained reasoning model, and reasoning the first feature data and the first multi-source behavior data according to the thinking chain prompt template through the reasoning model, and predicting a target information type in which the first user is interested. According to the method, through the multi-source behavior data and the thinking chain prompt template, the prediction accuracy of the reasoning model on the information type interested by the user is improved.

The invention provides a knowledge editing method based on multilingual semantic retrieval, and belongs to the technical field of natural language process.The method achieves cross-lingual knowledge updating through the two stages of multilingual knowledge retrieval and context editing and comprises the steps that firstly, a multilingual retrieval model based on XLM-R is used for mapping queries and knowledge base entries to a shared semantic space, and then the shared semantic space is used for conducting context editing; semantic correlation is judged through a classifier, and target language knowledge is retrieved; and splicing the retrieval result and the query in a zero sample or small sample mode to generate a prompt template, and inputting the prompt template into a large language model to complete editing. The method is characterized in that monolingual limitation is broken through, collaborative updating of 12 languages is supported, a retrieval-editing decoupling architecture is adopted to achieve model irrelevant adaptation, and retrieval and context learning are fused to improve editing accuracy. The method can efficiently correct multi-language large model fact errors, is suitable for scenes such as search engines and intelligent customer services, avoids the cost of full-model retraining, and remarkably improves the efficiency and accuracy of cross-language knowledge updating.

The invention discloses a model robustness automatic evaluation method based on large model driving and related equipment. The method comprises the following steps: inputting a task template of a tested model into a dynamic Prompt engine module to obtain a sample generation instruction; generating an adversarial sample according to the sample generation instruction through a sample generation and verification module, and performing model evaluation on the adversarial sample; if the confrontation sample passes the model verification, extracting a high-dimensional multi-modal feedback vector of the tested model through a multi-modal feedback analysis module; a reusable semantic instruction is generated through a strategy control module according to the high-dimensional multi-modal feedback vector so as to optimize a generation strategy; and if the confrontation sample does not pass the model verification, performing effect evaluation on the confrontation sample through an evaluation and interpretation generation module and generating an interpretability evaluation report. According to the method, progressive generation of the adversarial sample and exponential-level improvement of the model robustness evaluation efficiency can be realized, a clear model improvement direction is provided for developers, and the method can be widely applied to the technical field of computers.

The invention relates to a text interaction method and device for assisting social software interaction, equipment and a medium. The method comprises the steps of obtaining a screen image of current social software in response to an obtained auxiliary interaction instruction; the auxiliary interaction instruction is used for instructing the intelligent terminal to capture a screen image according to a corresponding screenshot mode and feed back the screen image in a standard format form; preprocessing the screen image to obtain a to-be-recognized image; performing text recognition on the to-be-recognized image to obtain structured text data; the structured text data comprises text content and role affiliation information; performing semantic analysis on the structured text data to obtain a formatted prompt template; and inputting the formatted prompt template into a pre-trained auxiliary social text interaction large model to obtain an auxiliary interaction text. By adopting the method, timely and accurate interaction support can be provided for the user when the user chats on the social software, and the time for thinking, language organization and searching of the user is saved.

The invention discloses a multi-language model combined active retrieval enhancement method and system, and relates to the technical field of data retrieval, and the method comprises the following steps: obtaining a plurality of to-be-retrieved questions; building corresponding prompt templates for the plurality of black-box large language models, and inputting the to-be-retrieved question and the prompt templates into the corresponding black-box large language models to obtain analysis results of different black-box large language models on the to-be-retrieved question; when the multiple analysis results are different, the corresponding black box large language model is asked through the prompt template, a prompt of a certain analysis result error reason is obtained, the prompt and the prompt template are spliced, and a final document is obtained; and re-inputting the final document into the corresponding black box large language model, and performing answer retrieval. According to the method, an active retrieval strategy is adopted, retrieval is carried out only when the model needs knowledge, and the expenditure caused by unnecessary retrieval and the influence of low-quality retrieval results on a final generation result are reduced.

The invention discloses an OCR (Optical Character Recognition) result correction method and device, equipment and a computer storage medium. The correction method comprises the following steps: acquiring a to-be-corrected text after OCR (Optical Character Recognition), wherein the to-be-corrected text comprises one of messy code characters, character confusion errors or grammar errors caused by OCR errors; constructing a semantic correction instruction template, wherein the semantic correction instruction template comprises a task definition, a rule definition and a format constraint target; the to-be-corrected text and the semantic correction instruction template are input into a large language model, a corrected text result is output through the large language model, and the large language model is a pre-training language model. According to the method and the device, messy code characters, character confusion errors or grammar errors caused by OCR recognition errors can be accurately corrected, so that the accuracy and availability of texts are effectively improved, and high-quality basic data are provided for subsequent text processing; the method can adapt to various documents in different formats and multi-language mixed text scenes, the maintenance cost is saved, and meanwhile the correction efficiency is improved.

To solve the problem of a conventional electronic medical chart system in which it is not easy to accurately collect various clinical information included in a medical chart.SOLUTION: An information processing system or the like is provided, which has: a medical chart information storage part for storing medical chart information including an inputted text attached with a pair of first tags according to a prescribed rule through a template; a retrieval part for performing full text retrieval of a range defined by the attached pair of first tags to the stored medical chart information; and an output part for outputting a result of the full text retrieval.SELECTED DRAWING: Figure 1