11 results about "Text filtering" patented technology

The invention discloses an intelligent customer opinion processing system based on multiple agents. A receiving module receives complaint information of a user and standardizes the complaint information. And the task distribution module realizes splitting of customer complaint tasks and intelligent agent adaptation for standardized customer complaint data through a multi-intelligent agent iterative learning framework. And the subtask execution module executes the received customer complaint task. And the feedback optimization module collects full-link indexes in real time, detects abnormal events by adopting a sliding time window, and optimizes the construction of a decision tree in the sub-task execution module. And the content filtering module is used for carrying out post-processing on output texts of the consultation agent and the risk identification agent by using a text filtering and optimizing framework based on an off-line large model. And the result storage module adopts a multi-level storage strategy to record a full-link operation log. According to the method, the problems of low efficiency, insufficient accuracy, weak dynamic adaptive capacity and the like of a traditional customer complaint processing method can be effectively solved.

The invention discloses an art image style migration method and device, equipment, a medium and a product, and relates to the field of image processing, and the method comprises the steps: obtaining a to-be-processed style picture and a to-be-processed content picture; performing style text filtering on the style picture to be processed to obtain a pure content description text; the style text filtering comprises style shift-out and picture-text distance comparison; creating a single pseudo word and expanding the single pseudo word based on a time step stage and a sub-attention layer to obtain a style pseudo word set; optimizing the style pseudo-word set by utilizing inversion of a diffusion model according to the pure content description text to obtain style representation of the style picture to be processed; and taking the style representation of the style picture to be processed as style guidance, determining content guidance according to the content picture, and determining a stylization result by utilizing denoising of the diffusion model. The stylization effect can be improved.

The invention relates to the technical field of data quality evaluation, in particular to an automatic quality evaluation method for a high-quality data set in the cultural field, which comprises the following steps: extracting semantic fragments and vector information, performing clustering analysis on a coverage structure, screening redundant samples, labeling image-text differences, and outputting a consistency evaluation result. According to the method, high-precision recognition of semantic coverage and structure density can be realized through structured extraction of semantic information and vector distribution measurement, and the discriminability of redundancy detection is enhanced through linkage screening of vector coincidence frequency and similarity threshold. The systematicness of image-text consistency marking is improved through an image-text position difference and semantic span combined marking mechanism, and it is ensured that redundant and deviated intersection samples can be accurately recognized through a synchronous contrast mechanism between text screening content and image-text difference marking results. The intensive clustering of the cultural semantic content in the vector space and the quantitative analysis capability of the structure deviation point are synchronously improved, and the comprehensive dimension and logic closure of the evaluation result are optimized.

This invention relates to the field of intelligent question answering technology, specifically to a RAG question answering system combining knowledge tracking and evidence annotation. The system includes a semantic parsing module, a sentence localization module, a knowledge path annotation module, an evidence indexing module, and an enhanced question answering module. This invention extracts explicit verb semantics, indicator entities, and time-normalized expressions through structured parsing of natural language input, ensuring structural uniformity and processing consistency of the input sentences. A tag-based text filtering strategy effectively limits the text scanning range, improving the accuracy and efficiency of indicator localization. It clearly extracts statistically significant causal links from the text, and enhances the cross-coverage of indicator fragments by imposing indicator combination constraints and labeling key sentences in path nodes, thereby improving the density and logical order consistency of indicator integration in the answer.

This disclosure belongs to the field of nuclear power technology, specifically relating to a personalized question-and-answer method and apparatus for nuclear power management regulations. This disclosure aims to address the impact of user personalization characteristics on question-and-answer results, proposing a personalized question-and-answer method for nuclear power management regulations based on the collaboration of user characteristics and a large language model. The method of this disclosure sorts the retrieved text information based on a feature association module of user characteristics and semantic units. Then, the relevant text and questions are input into a large language model, which generates personalized answers based on the user's personalized characteristics. This allows for optimized text filtering by combining user characteristics and integrating retrieval enhancement generation with a large language model to generate personalized answers, more accurately meeting the needs of different users.

This invention relates to the field of text extraction, specifically to a deep learning-based system and method for extracting key text features. The system includes a text filtering module, a graph construction module, a deep learning module, a cluster feature module, and an input fusion module. The text filtering module merges text clusters, the graph construction module generates a stereo model, the deep learning module expands the model using clustered text, the cluster feature module prunes the stereo model, and the input fusion module captures new text features to generate fused text. This invention provides a visual interface for training complex text, enhances the machine's ability to analyze the coherence of long texts, improves the generalization ability of machine learning, is suitable for processing large amounts of complex, high-dimensional text data, reduces external data requirements, adapts to changes in data distribution, reduces the computational burden on the model, and achieves stable language frameworks and precise fusion of text features.

The application discloses an LBS parallel judgment method, system, device and storage medium, comprising obtaining and performing parallel matching on user location information and service text, and performing intersection processing on a first object list and a second object list obtained through matching to obtain a target object list; matching the user location information comprises: obtaining a first data set comprising object identification and location identification from a remote cache; starting multiple processing threads and distributing the first data set to the multiple processing threads after sharding; each processing thread reads geographic range information from a near cache composed of off-heap memory and a shared disk according to the object identification and the location identification, and matches the user location information with the geographic range information to determine a matching object; and the matching objects of the multiple processing threads are summarized to obtain the first object list. The application improves the efficiency of GB-level data geographic screening and takes into account the cost, realizes decoupling of geographic screening and text filtering, and improves the LBS judgment speed.

The application discloses a scoring prediction method based on end-to-end text filtering, comprising: obtaining a to-be-predicted review text, determining a user and a commodity corresponding to the review text; obtaining at least one historical review text of the user and at least one historical review text of the commodity; obtaining a word group level convolution feature vector corresponding to the review text and a word group level convolution feature vector corresponding to the historical review text; obtaining a feature vector corresponding to the review text and a feature vector corresponding to the historical review text; determining the similarity between the review text and the historical review text according to the feature vectors; filtering the historical review text based on the similarity, and calculating a similarity text feature vector according to the filtered historical review text; fusing the feature vector of the review text and the similarity text feature vector to obtain a global text feature vector; and determining the predicted score of the review text according to the global text feature vector. The application can improve the scoring prediction performance and precision of the review text.

The invention discloses a PDF (Portable Document Format) file cross-software sequence processing method and device and a related medium, and the method comprises the following steps: carrying out structured analysis processing on a PDF file to be processed, and then carrying out non-text filtering processing to obtain a text object data set; performing logic structure reconstruction by utilizing the text object data set to obtain a paragraph structure data set; performing intermediate format serialization processing and deserialization processing on the paragraph structure data set to obtain a target paragraph data set; respectively performing consistency marking and reversibility control processing to obtain a version control data set; and performing incremental updating on the version control data set to generate a PDF file target processing result. According to the method, consistency marking and reversibility control processing are carried out on the target paragraph data set obtained through calculation to obtain the version control data set, and then incremental updating is carried out on the version control data set, so that the consistency of a logic structure and a typesetting style can be kept when the same PDF file is transmitted between different software.

The present application relates to the technical field of municipal drainage pipe network, and particularly relates to a drainage system CAD-GIS data conversion and standard marking method, which comprises the following steps: analyzing CAD data and extracting, extracting data in CAD according to types, and storing text files; processing CAD data, generating directed graph structure data from the extracted CAD data, generating KDTree search tree structure data from the X and Y positions of the text marking, filtering and finding the text near the midpoint of the preset inspection well or pipeline to obtain correct text marking content; converting the elevation text into real elevation, and obtaining the start point and end point elevation data of the preset inspection well, using the directed graph data structure and the extracted elevation data of the preset inspection well to analyze and sort the pipeline elevation and pipe diameter data; and converting into shp data. The method can greatly improve the information input efficiency of the drawing of the inspection well and the pipeline, and standardize the marking.

The invention discloses an aspect-level sentiment classification method based on context text generation and filtering. The invention provides an aspect-level sentiment classification method based on context text generation and filtering in order to solve the problems of few samples and low resources in part of fields in aspect-level sentiment classification tasks. The method comprises the following steps: firstly, carrying out context text generation by virtue of a Conditional Bert language model; secondly, a text screening and filtering algorithm is used for screening and filtering context text generation data to generate a high-quality text, and a data set is expanded; and finally, combining context text generation and filtering algorithm data to enhance the generation model. Experimental results show that the method provided by the invention effectively expands the data set, and improves the effect of aspect-level sentiment classification.