83 results about "Text database" patented technology

The invention discloses a multi-modal agricultural question and answer method and system for generating RAG based on retrieval enhancement, and the method comprises the steps: collecting and constructing crop disease image data containing a farmland complex background and corresponding text description, and forming an agricultural image-text knowledge database; based on the agricultural image-text knowledge database, multi-modal features of an input image and a query text are extracted, image-text joint similarity retrieval is carried out, and knowledge image-text candidates are obtained; inputting the knowledge image-text candidates into an image-text rearrangement module for rearrangement; taking the reordered image-text knowledge as condition input, accessing a large language model, and generating diagnosis description and prevention and treatment suggestions for the current crop diseases; and outputting a multi-modal question and answer result according to the image and question input by the user. According to the method, the agricultural image-text database is constructed, and multi-modal retrieval, rearrangement and large language model generation technologies are combined, so that accurate and efficient crop disease multi-modal question answering is realized, and the reliability of diagnosis and prevention suggestions is improved.

This invention discloses a method, system, and storage medium for constructing a knowledge graph in the civil aviation service field. The method includes: S1, using a BERT-BiLSTM-CRF algorithm model to extract entities and obtain interrelated entity vector sequences, feature vector sequences, and annotation sequences; S2, using a convolutional neural network model to extract sentence vectors and their contained entity vectors, and employing n filters to identify and extract a database of entity-relationship-entity triples; S3, using a conditional random field entity node integration model to integrate the annotation information and store it in the corresponding entities as entity attribute values; S4, using the triple database and the integrated entity attribute values ​​to link and fuse them to construct a civil aviation knowledge graph. This invention can obtain a comprehensive and accurate civil aviation knowledge graph of entity relationships based on a civil aviation knowledge text database. It can not only satisfy passenger knowledge question answering and querying needs but also serve as a training and educational resource, improving the overall service level.

The invention provides a blue-green algae image recognition method and system based on hierarchical self-adaption and domain driving, and the method comprises the steps: enhancing an image through employing an improved dark channel algorithm; constructing a blue-green algae biological attribute text database, and performing synonym replacement and sentence pattern recombination; multi-scale visual features are extracted through a hierarchical adaptive Swin Transform model, and key region characterization is enhanced in combination with dynamic spectrum attention; the text is input into a Bio-ALBERT model, and semantic embedding of field optimization is generated through term mask prediction and attribute relation pre-training; constructing a two-layer heterogeneous graph by using a graph attention interaction network GAIN, calculating a cross-modal association weight through a bidirectional graph attention mechanism, and outputting a cross-modal graph feature; multi-scale cross-modal association is modeled through a hierarchical graph attention fusion mechanism, and a comparison alignment loss optimization model is combined; and high-precision blue-green algae identification is realized. According to the method, through multi-scale perception, domain semantic adaptation and graph structure fusion, the accuracy of blue-green algae detection in a complex environment is improved.

The invention provides a knowledge question-answering method and device, a processor and a storage medium, and belongs to the field of information retrieval, and the method comprises the following steps: constructing a professional knowledge base for a specific field; wherein the professional knowledge base at least comprises a full-text database and a vector database; receiving a user query, and preprocessing the user query to obtain a standardized query; executing multi-path knowledge recall from the plurality of heterogeneous data sources in parallel based on the standardized query to generate a fusion recall result; wherein the plurality of heterogeneous data sources comprise but are not limited to a full-text database, a vector database, an internal retrieval API (Application Program Interface) plug-in and an internet retrieval interface; the historical interaction information, the fusion recall result and the standardized query are assembled into cue words through the instruction template; and inputting the cue word into the large language model to generate a reply text, and outputting the reply text in multiple modes. Through the method provided by the invention, the accuracy and coverage rate of knowledge recall can be improved, the answer is closer to real consultation, and the user experience is improved.

The application discloses a generalizable voice generation SQL method. The technical scheme adopted by the application is as follows: step 1), acoustic information diversification processing and speaker information standardization processing are performed on original voice input by using voice reprogramming; step 2), semantic information rich audio representation is extracted by using a self-supervised audio representation model; step 3), the audio representation obtained in step 2) is classified by using a reverse gradient and a classifier on audio of different speakers, and gradient reversal is used during reasoning to learn speaker-independent audio representation; and step 4), the audio representation obtained in step 2) is input into a relation-aware transformer together with text database features, and the output is randomly input into an SQL decoder to obtain predicted SQL, and MLE loss is calculated with real SQL. The application adopts voice reprogramming and gradient reversal classifier technology, solves direct voice to SQL parsing, reduces acoustic method and style information in audio while retaining semantic information.

This application provides a method, apparatus, storage medium, and processor for querying similar text. The method includes: when the text type of the text to be queried is a first type of vocabulary, decomposing the text to be queried into multiple short words; determining a first target word vector for each short word; determining a second target word vector for the text to be queried based on the first target word vectors of all short words, thereby determining a first similarity between each word in the text database and the text to be queried; determining multiple first words in the text database that semantically match the text to be queried based on the first similarity, and a target similarity between each word in the text database and each first word; determining words in the text database whose target similarity is greater than a preset similarity threshold as target words matching the text to be queried. This greatly reduces the situation where text vectors cannot be recognized or have large errors, improving the success rate and accuracy of text recognition.

The invention discloses a laying hen epidemic disease detection method based on DistilBERT-GATv2 and BiLSTM-TCN, and the method comprises the following steps: extracting related data from unstructured text data, and constructing a high-quality structured text database; a DistilBERT model is adopted to carry out fine tuning training on the labeled corpus, accurate recognition of a target entity is achieved, and standardized entity categories and texts are output; realizing extraction of a semantic relationship between entities, and constructing disease triple data; a cross-sentence anaphora resolution mechanism and an entity normalization algorithm are introduced, semantic references are unified, and consistency and uniqueness of node semantics in the knowledge graph are ensured; storing the constructed triple data in a graph database to complete the construction of the domain exclusive knowledge graph; visual presentation of entity nodes, relation edges and query paths is achieved by configuring a visual component. According to the method, the epidemic disease type of the laying hen can be efficiently detected and diagnosed, the accuracy of an intelligent monitoring system is improved, manual intervention is reduced, and the automation level of laying hen disease prevention and control is improved.

The application relates to an audio evaluation method, a computer device and a storage medium. By obtaining a plurality of emission probabilities corresponding to the audio to be evaluated, obtaining first transition probabilities between each template character in a template audio text corresponding to the audio to be evaluated, and second transition probabilities between each preset character in a preset audio text database, determining a first decoding path and a second decoding path based on the decoding of the emission probabilities, the first transition probabilities and the second transition probabilities, comparing a template decoding path corresponding to the template audio with the first decoding path and the second decoding path, and determining an evaluation result of the audio to be evaluated based on the first evaluation value and the second evaluation value determined thereby. Compared with the traditional multi-dimensional evaluation of audio, the scheme decodes two evaluation values based on the emission probabilities, the first transition probabilities and the second transition probabilities, and then determines the evaluation result, solves the misjudgment problem in multi-dimensional evaluation, and improves the accuracy of audio evaluation.

The application discloses a kind of civil aviation safety report theme modeling methods based on large language model and semantic enhancement, its method includes: civil aviation safety theme semantic feature model extracts the text semantic vector and event structure vector of safety report text, and obtains the fusion feature vector of safety report text by fusion;Safety report text database is carried out cluster clustering processing and obtains initial cluster set and noise sample set;Select representative front p% as the representative sample set of cluster;Construct noise sample evaluation repair mechanism module, and select the candidate cluster to which noise sample belongs using noise sample evaluation repair mechanism module;Comprehensive gain function is constructed using representative sample set in cluster, and representative sample iteration screening processing of candidate sample is carried out in cluster.The application realizes the theme modeling goal of semantic accuracy, comprehensive coverage and stable result by multi-module collaborative innovation, and provides reliable technical support for management decision.

The application relates to a text clustering method based on an improved whale optimization algorithm, and belongs to the fields of big data mining and machine learning. The method comprises the following steps: S1: obtaining scientific research text data from a scientific research text database by using Spark, and performing data cleaning; S2: performing word segmentation, stop word removal, feature selection, vectorization and dimension reduction operations on the text data after data cleaning, so that unstructured text data is converted into structured numerical data; and S3: calculating initial clustering centers by using a K-means algorithm, optimizing the clustering centers by using an improved whale optimization algorithm, and outputting clustering results. The global optimization capability of the improved whale optimization algorithm is utilized, the problem that current text clustering algorithms are prone to falling into local optimization is solved, the accuracy of clustering of scientific research text data is improved, the data dimension is reduced, and the clustering effect is enhanced.

The application discloses a multi-view clustering method and system based on self-step learning and view weighting, and belongs to the technical field of multi-view data processing. The method comprises the following steps: normalizing a multi-view data set, splicing the multi-view data, initializing each view clustering kernel and distribution matrix by using a kmeans algorithm, and calculating each view weight; each view sample weight matrix, each view clustering kernel and distribution matrix are iteratively updated in sequence through a target function; and when an iteration end condition is met, a final clustering kernel and distribution matrix are output. The clustering system comprises an acquisition module, a preprocessing module, a construction module, an optimization module and a clustering output module. The self-step learning model is used to sequentially learn clustering data and finally obtain a clustering result. Through view weighting, the model can selectively learn information of different views, thereby effectively improving clustering accuracy. The application can be applied to retrieval of an image database, a text database and the like.

The invention provides an automatic international disease classification code coding method based on a multi-synonym matching network. The method comprises the following steps: S1, preparing a medical text database; s2, a main structure layer of a multi-synonym matching network algorithm model is constructed, a main calculation structure is designed, and the main structure layer of the multi-synonym matching network algorithm model comprises an encoder, a label encoder and a decoder; s3, training by using the prepared data set for training to obtain a model weight for reasoning, and evaluating a training result; s4, setting of various parameters of the multi-modal large language model is adjusted, the multi-modal large language model is designed to be combined with a multi-synonym matching network algorithm, the function of the multi-modal large language model is matched with a method target task, and a multi-modal ICD coding model based on a multi-synonym matching network is obtained; and S5, testing the multi-mode automatic international disease classification code coding model based on the multi-synonym matching network to enable the model to meet task requirements. According to the method disclosed by the invention, the ICD coding accuracy and convenience are effectively improved.

The invention discloses a data query method and a related device, and relates to the field of computers, and the method comprises the following steps: obtaining a query text, a database mode and a description text of the database mode; based on the query text, the database mode and the description text, calling a large language model, and generating an initial structured query language corresponding to the query text; calling a large language model, extracting a mode element from the initial structured query language to obtain a first mode element having an association relationship with the query text, and establishing a mapping relationship between a target mode element and the query text; based on the last structured query language and the mapping relation, calling a large language model, and generating a current structured query language corresponding to the query text; and under the condition that the current structured query language is the same as the last structured query language, performing data query based on the current structured query language to obtain query data indicated by the query text. The data query difficulty can be reduced.

The present invention provides a college admissions consultation question-and-answer system based on an offline universal large language model. The system comprises: an input module for inputting college admissions consultation question text; a classification and retrieval model for classifying and predicting the question text to obtain a predicted classification label; screening various text databases for corresponding classifications; performing feature extraction on the question text and each document in the corresponding classification text database, performing similarity matching, and selecting the document text with the highest similarity as the output result; a combination module for organizing the question text and the output document text using prompts to form an organized text; and an offline universal large language model for taking the organized text as input and outputting college admissions consultation response data. This system utilizes the offline universal large language model to replace the answer extraction module of traditional question-and-answer systems, reducing model training costs while making the question-and-answer system more intelligent.

The invention relates to an automatic driving edge scene understanding retrieval enhancement method and device and a medium. The method comprises the following steps: carrying out fine tuning training on a cross-modal vector embedding model by adopting a difficult-to-negative and semi-difficult-to-negative sample sampling comparison learning method; the trained cross-modal vector embedding model is adopted to generate embedding of an image-text pair, the text coding model is adopted to generate embedding of text description, and the image-text pair and the text description are respectively stored in a multi-modal database and a text database which share the same index; inputting a real image of an automatic driving edge scene, generating embedding of an image-text pair, retrieving a closest scene image from a multi-modal database according to embedding information, and splicing the closest scene image with the automatic driving edge scene and the real image to obtain an automatic driving edge scene image; and inputting the spliced image, the text description corresponding to the image closest to the scene and a set prompt word into a visual language model to obtain edge scene understanding without illusion. Compared with the prior art, the illusion problem existing in edge scene understanding can be effectively relieved.

This invention discloses an entity relationship recognition method and apparatus for decision knowledge graphs. The method includes: retrieving a set of decision text information from a decision knowledge text database; preprocessing the set of decision text information to obtain a preprocessed dataset; and performing entity relationship recognition processing on the preprocessed dataset to obtain an entity relationship information set.

The present application relates to a method, system, terminal and medium for converting text clarity based on OCR technology, which includes obtaining an ancient text image; generating character parameters based on the ancient text image; determining the recognition area of ​​each character based on the character parameters; identifying each recognition area and generating shape features corresponding to each character; comparing the shape features of the characters with a preset ancient text database to determine the ancient text font style; identifying the shape features of the characters based on the ancient text font style and the ancient text database, searching for the corresponding high-definition character model and generating high-definition characters based on the high-definition character model; arranging all high-definition characters according to the original character arrangement order; performing a proofreading operation and adjusting the high-definition characters based on the proofreading results; and outputting a high-definition image showing the high-definition characters. The present application has the effect of improving the recognition of ancient text and facilitating readers to read, copy and learn ancient text.

An embodiment includes analyzing text content of a user query to identify via natural language processing (NLP) a query topic. The embodiment maps the query topic to a topic cluster at a node of a hierarchical model of a text database. The embodiment generates query demand data indicative of demand for the topic cluster based on user queries. The embodiment identifies the topic cluster as a topic-cache candidate based on the query demand data. The embodiment compares an amount of memory required for storing text associated with the first topic cluster to available cache memory. The embodiment caches the text of the topic cluster candidate upon determining that there is sufficient available cache memory space.

A text processing method includes the steps described below. Multiple texts in a preset text database are acquired, and at least one keyword is extracted from each text. Based on each keyword which is extracted, a keyword table is established. A mapping relationship between each keyword and a phrase element in a phrase set corresponding to a text in which each keyword is located is the same as a mapping relationship between the text in which each keyword is located and the phrase element in the corresponding phrase set. A keyword pair having an association relationship is determined in the keyword table. A phrase element in a phrase set corresponding to each keyword in the keyword pair are updated. According to a mapping relationship between each keyword and phrase elements in an updated phrase set, a relationship chart between a keyword and a phrase is established.

The invention relates to an information recommendation method and device, equipment, a storage medium and a program product. According to one embodiment of the invention, the method comprises the following steps: in response to an information recommendation condition meeting a user at present, obtaining a target copywriting template from a pre-constructed recommended copywriting database, the target copywriting template being presented in a natural language form; pre-determined target recommendation content is filled in the target copywriting template to obtain target recommendation information, the target recommendation content is determined based on equipment operation habits of the user in a historical time period, and the target recommendation content comprises at least one of equipment identification, a room where equipment is located and equipment operation; and pushing the target recommendation information to the user. According to the information recommendation method and device, the user can quickly and clearly understand the equipment control content recommended based on the previous operation habits of the user, so that whether recommendation is accepted or not is better determined based on own requirements, the intelligence and individuation level of information recommendation can be improved, and the user experience is improved.

The invention belongs to the technical field of model questioning and answering, and discloses a large-model rapid questioning and answering method and system based on a traffic engineering image-text database. Comprising the following steps: establishing a database, and clustering the database to construct a traffic engineering problem type set in each business field; obtaining a question language input by a user, and matching a user question type; analyzing the data in the question type of the user and a question language input by the user to generate a plurality of question answers, calculating a confidence score of each question answer, selecting the question answers based on the confidence scores, and recommending the question answers to the user; obtaining user feedback data according to the push result, analyzing the user feedback data, generating an update judgment coefficient, and executing an update instruction based on the update judgment coefficient; according to the method, whether updating is needed or not is judged according to the quantitative score of the feedback item type, the fluctuation coefficient and the updating judgment coefficient generated by the accumulation coefficient, so that the updating of the business domain module is more targeted, and resource waste caused by blind updating can be effectively avoided.

The application provides a cyanobacteria image recognition method and system based on hierarchical adaptation and field driving. In the recognition method, an improved dark channel algorithm is used to enhance the image; a cyanobacteria biological attribute text database is constructed and synonym replacement and sentence restructuring are performed; multi-scale visual features are extracted through a hierarchical adaptive Swin Transformer model, and key region representation is enhanced in combination with dynamic spectrum attention; text is input into a Bio-ALBERT model, and field-optimized semantic embedding is generated through term mask prediction and attribute relationship pre-training; a two-layer heterogeneous graph is constructed using a graph attention interaction network (GAIN), cross-modal correlation weights are calculated through a bidirectional graph attention mechanism, and cross-modal graph features are output; multi-scale cross-modal correlations are modeled through a hierarchical graph attention fusion mechanism, the model is optimized in combination with a contrast alignment loss, and high-precision cyanobacteria recognition is achieved. Through multi-scale perception, field semantic adaptation and graph structure fusion, the application improves the accuracy of cyanobacteria detection in complex environments.

The invention discloses a ship fire risk factor identification method based on text mining. The ship fire risk factor identification method comprises the following steps: 1) obtaining ship fire risk text data and establishing a text database; 2) performing word segmentation on the ship fire risk text data to obtain a ship fire risk word segmentation feature item list; 3) obtaining a ship fire risk keyword list; 4) extracting related phrases, and constructing a keyword related phrase set; 5) clustering the keyword related phrase set, and performing semantic analysis on a clustering result to obtain a ship fire risk factor list; 6) calculating the occurrence probability of the risk factors, and obtaining a ship fire risk factor probability table; according to the ship fire hazard risk factor risk assessment method, the importance of the risk factors and the fire hazard scene is quantitatively assessed through the text mining technology and the semantic analysis model.

The invention discloses a method, a device, equipment and a product for automatically transcribing an HIS (Hospital Information System) through voice of an outpatient medical record, and relates to the technical field of automatic data entry. The method comprises the following steps: firstly, converting doctor inquiry voice into a text by applying a medical ASR technology, then carrying out entity recognition, term standardization and relation extraction through a medical NLP technology, generating structured JSON data with medical record field names as keys and entity information as values, and then fusing an Accession API and computer vision template matching technology to obtain a text database; according to the method, all the control capable of being filled in the target HIS interface is automatically detected, the mapping relation between the control and the medical record field name is established, finally, the corresponding entity information is extracted from the JSON data according to the mapping relation, and the corresponding control is automatically filled through the RPA or Computer Use technology, so that the medical term recognition accuracy can be remarkably improved, and the medical term recognition efficiency is improved. And seamless integration with a multi-brand HIS system is realized, full-automatic and cross-platform input of voice medical records is achieved, and practical application and popularization are facilitated.

This invention discloses a method and system for generating auxiliary diagnostic results and reference prescriptions. First, a medical record database and a treatment strategy database are constructed and stored in a vector database and a text retrieval database, respectively. For a target medical record, vectorization and textualization are performed. Then, candidate medical records and treatment strategies are retrieved from the medical record vector database, medical record text database, treatment strategy vector database, and treatment strategy text database via four channels. Subsequently, the RRF algorithm is used to fuse and sort the vector and text channels, and the candidate results are further refined based on a pre-trained semantic ranking model to obtain a small number of reference medical records and treatment strategies highly relevant to the current condition. Finally, prompt words are constructed by combining the target medical record, reference medical records, and treatment strategies to generate candidate results such as TCM diagnosis, TCM syndrome type, Western medicine diagnosis, and TCM prescriptions, serving as auxiliary references for doctors' clinical decision-making. This significantly improves the accuracy, interpretability, and efficiency of intelligent assisted diagnosis and treatment.

The invention discloses an intelligent quantitative evaluation method and system for community fire-fighting toughness, and the method comprises the steps: collecting community fire accident cases, building a community fire accident text database in combination with a community fire-fighting toughness key stage, and recognizing community fire-fighting toughness related entities and mutual relationships; determining a community fire accident knowledge graph mode layer, establishing a knowledge graph, and concluding and extracting community fire-fighting toughness influence factors; a TOPSIS method is improved based on community fire-fighting toughness characteristics, key influence factors are screened, a community fire-fighting toughness evaluation index system is established, and a toughness network is constructed; and in combination with parameter learning and a Delphi method, establishing a Bayesian network evaluation model, carrying out causal reasoning and sensitivity analysis, quantitatively evaluating the community fire-fighting toughness level, identifying weak links and key influence indexes of the community fire-fighting toughness, and pointedly providing a promotion strategy. According to the method and the system, a practical tool is provided for a decision department to strengthen fire safety management and promote flexible sustainable community construction.

The invention relates to an electric power inspection target detection false alarm elimination method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: inputting an electric power inspection image into an image multi-modal large model for visual feature extraction to obtain visual feature information of a suspected defect target; performing analysis based on the visual feature information to obtain visual attribute parameters of the suspected defect target; searching in a power inspection business text database by using the target power transmission line and the suspected defect target to obtain business text data of power equipment to which the suspected defect target belongs; wherein the business text data comprises first feature information and second feature information; processing is carried out based on the visual attribute parameters, the first feature information and the second feature information, and a feature matching result and a similarity comparison value are obtained; and false alarm detection elimination is carried out on the suspected defect target based on the processing result. By adopting the method, false alarms can be eliminated, and the accuracy and stability of a power system are guaranteed.

The present invention relates to an AI-based risk control strategy analysis method and system, in which a risk control strategy matching vector is determined based on an optimized response vector binary, and a risk control strategy response focus vector is determined through response focus processing. Therefore, the risk control strategy matching vector and the risk control strategy response focus vector can improve the detail characterization capability of the response behavior characteristics. In this way, when matching risk control strategy label data through the risk control strategy matching vector and the risk control strategy response focus vector, the risk control strategy label data corresponding to the risk control strategy execution text to be analyzed can be accurately and reliably determined, thereby achieving accurate label classification of the risk control strategy execution text to be analyzed, providing convenience for subsequent rapid and accurate calling and retrieval of the risk control strategy execution text to be analyzed, avoiding traversal of the entire text database, and reducing unnecessary resource overhead.

This invention discloses a method for mitigating hallucinations in large-scale narratives based on multi-agent collaboration and dynamic state tracking. The method includes: generating an event graph composed of multiple atomic events using an outline agent; mapping these atomic events from an event occurrence time series to a text narrative time series using an orchestration agent; constructing a non-linear narrative outline; retrieving historical fragments related to the semantics and sentiment of the current chapter from the generated historical text; generating the text content of the current chapter using a writing agent; performing real-time logical consistency checks on the content during generation; triggering a correction mechanism when entity state changes in the generated content violate preset state transition rules; and parsing the entity state changes after the current chapter is finalized, updating the global state table and historical text database. This invention effectively mitigates hallucinations in long narratives, significantly improving the logical rigor and long-term coherence of the plot development while maintaining the literary quality of the text.

This invention discloses an event-driven incremental knowledge extraction and fusion method and apparatus. The method includes: retrieving a set of factual text information from a text database; preprocessing the set of factual text information to obtain a set of factual text information; and performing knowledge fusion processing on the set of factual text information to obtain a set of factual knowledge ontology information.