146 results about "Text database" patented technology

The invention provides a commodity information processing and querying method and system. Belongs to the technical field of information processing. The method comprises the steps of collecting text information of commodities, preprocessing the text information of the commodities through natural language processing information, and constructing a structured commodity text database; based on the high-definition image of the commodity, key features of the commodity are extracted through machine vision and are associated with information in the text database to form multi-modal commodity information representation; the method comprises the following steps: encoding a commodity text by adopting a BERT model based on Transform, and obtaining a deep semantic representation of the text; performing feature extraction on the commodity image by using a pre-trained convolutional neural network to obtain an advanced visual feature of the image; through natural language processing and machine vision technologies, the text information and the image information of the commodity are subjected to structured and multi-modal fusion, so that the processing efficiency of the commodity information is greatly improved.

A database of text associated with different domains is maintained. Large language models (LLMs) are prepared for use in the different domains by providing the associated text to an instance of an LLM. Thus, using multiple instances of the same pre-trained LLM, domain-specific LLMs are generated. The text provided to the LLM instance may be selected based on an account identifier of the user accessing the LLM, the tenant accessing the LLM, a user selection of a domain, or any suitable combination thereof. A pool of prepared LLM instances may be generated before the access request is received. If a response provided by an LLM instance in a domain to a prompt was rejected by a user and additional information was received during the session to improve the response of the LLM instance, the additional information may be to the text used to prepare future LLM instances for the domain.

The invention discloses a multilayer dynamic aggregation big language model driven letter text analysis method, which relates to the technical field of natural language processing, and comprises the following steps of: acquiring multi-format letter data and converting the multi-format letter data into a text sequence with a timestamp, performing semantic analysis and structure processing on the text sequence, generating a text block set, and storing the text block set into a text database; performing multi-task training and topic fusion on the text block set to generate a comprehensive semantic vector, constructing a dynamic bridging graph based on the comprehensive semantic vector, activating associated text blocks by the bridging graph through semantic and time sequence relevance, responding to user query, splicing the activated text block content into a context Prompt, and inputting the context Prompt into a large language model; obtaining a one-time generated answer; the method has the advantages that semantic information and time sequence information are associated through the dynamic bridging graph, accurate response of the large language model is achieved based on the context Prompt, and the method has the advantages that complex semantic association is efficiently captured, context understanding coherence is improved, and dynamic information aggregation is achieved.

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.

The invention discloses a sewage treatment question and answer method and device based on a large model, a medium and a product, and relates to the field of language model construction.The method comprises the steps that existing data related to sewage treatment are obtained; preprocessing the existing data to obtain text data; segmenting the text data to obtain text blocks; storing the text blocks in a text database, encoding the text blocks to generate semantic vectors, and storing the generated semantic vectors in a vector database; a keyword search method and a vector similarity search method are adopted, based on the sewage treatment problem, retrieval results related to the sewage treatment problem are obtained from the text database and the vector database; the retrieval results are reordered, and answer document fragments are obtained; forming prompts based on the sewage treatment questions and the answer document fragments; and inputting the prompt into the large language model to generate a sewage treatment question and answer text. According to the invention, accurate answers corresponding to sewage treatment questions can be provided, so that the risk of sewage treatment is reduced.

The invention relates to the technical field of aggregation retrieval, in particular to a distributed aggregation retrieval method for a law and regulation text database, which comprises the following steps: collecting and integrating law and regulation text data, preprocessing the law and regulation text data, removing useless punctuations and blank characters, and generating a preprocessed text set. According to the method, data integration and preprocessing are carried out on the regulation text, useless punctuations and redundant blank characters are effectively removed, the purity of text data is guaranteed, and the accuracy of subsequent word segmentation and word frequency statistics is improved; the implementation of the word segmentation and word frequency statistical process is helpful for identifying key feature vocabularies in the regulatory text, so that the accuracy of the subsequent inverted index construction stage is enhanced; by preliminarily constructing the inverted index and implementing similar item merging and low-frequency vocabulary removing operation, the index scale and redundant interference are reduced, and the index query efficiency and accuracy are improved.

The invention relates to an automatic patent document generation method and system based on retrieval enhancement, and the method comprises the steps: carrying out the feature extraction of text content inputted by a user, and obtaining keywords and key sentences of the text content; based on the keywords, performing text retrieval in a pre-constructed text database, and sorting text retrieval results; based on the key sentences, vector retrieval is carried out in a pre-constructed vector database, and vector retrieval results are sorted; reordering the text retrieval result and the vector retrieval result through a reciprocal ordering algorithm; determining an auxiliary enhanced document in the reordering result; inputting the text content input by the user and the auxiliary enhanced document into the large language model, and outputting a patent document conforming to the standard template through the large language model; wherein patent documents in a text format are stored in the text database; vectorized patent documents are stored in the vector database; the large language model is trained based on a text database and a vector database.

The invention discloses a clothing attribute identification method and system, computer equipment and a readable storage medium, and relates to the technical field of image identification. The clothing attribute identification method comprises the following steps: acquiring a to-be-identified picture; and obtaining a picture text database of the clothes, and obtaining a clothes attribute recognition model according to the picture text database of the clothes and the deep learning model. And according to the clothing attribute identification model, identifying the attribute of the clothing in the to-be-identified picture, and obtaining the attribute data of the clothing in the to-be-identified picture. In the aspect of data, massive clothing data is crawled through a crawler technology, part of open source data sets are combined, pre-labeling is performed by means of the powerful content understanding ability of a visual language large model, and a professional clothing attribute data set is constructed. In the algorithm aspect, the advantages of a Transform architecture and a CNN are combined, a lightweight network structure with a self-attention mechanism based on a local sliding frame and key point regression as an auxiliary task is designed, and the method is used for solving the problem of clothing multi-attribute recognition and giving consideration to model precision and reasoning time delay.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent paper marking content detection and identification method and system based on deep learning, and the method comprises the steps: determining a to-be-detected region through a locator; identifying a to-be-detected area based on the pixel value, and screening out an effective detection area; the uploaded characters are recognized and matched with a preset character database, and clear characters and fuzzy characters are distinguished; determining alternative preset characters corresponding to the fuzzy characters, performing screening through context semantics to obtain first preset characters, determining whether the same fuzzy characters exist or not, determining second preset characters for the same fuzzy characters, determining target characters according to the first preset characters or the second preset characters, or determining the target characters according to the target characters. Determining a writing model based on a deep learning algorithm and the plurality of clear characters, determining a third preset character based on the writing model and the fuzzy character, and determining a target character based on the first preset character and the third preset character. The accuracy of the intelligent paper marking result is improved.

The invention discloses a large model-based livestock and poultry manure treatment technology question-answering system and a use method thereof, and relates to the technical field of environmental protection and governance. In the system, firstly, a trained word segmentation model is used for performing word segmentation processing on a retrieval question input by a user; then retrieval is conducted in a text database and a vector database according to a word segmentation result, the retrieved text blocks are aggregated and deduplicated to obtain a plurality of retrieval text blocks highly related to a retrieval problem, and then large-model cue words are generated in combination with a cue word template; and finally, outputting a reply result according to the trained livestock and poultry manure treatment technology intelligent question and answer model. According to the method, the defects existing in a traditional general field large language model are overcome, the large language model can fully and accurately understand the retrieval problem of a user, a scientific and detailed reply result can be given, intelligent question answering of livestock and poultry excrement treatment knowledge is achieved, and the method has important significance in promoting development of an environment protection and treatment technology.

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.

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.

The invention relates to a data center inspection auxiliary question-answering method, device and equipment and a storage medium. Comprising the following steps: acquiring question information of an inspector, and extracting key information from the question information; matching the key information with non-text data in a preset non-text database, and determining target non-text data from the preset non-text database according to a non-text data matching result; matching the key information with question and answer data in a preset question and answer historical database, and determining target question and answer data from the preset question and answer historical database according to a question and answer data matching result; inputting the target non-text data and the target question and answer data into a preset cue word generation model to obtain a target cue word, and inputting the target cue word into a pre-trained target inspection large model to obtain reply information for the question information; and feeding back reply information to the inspection personnel. Therefore, the reply efficiency of the question information can be improved by utilizing machine learning, and inspection personnel can be helped to quickly complete inspection work.

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 discloses a civil aviation service domain knowledge graph construction method and system and a storage medium. The method comprises the steps that S1, a BERT-BiLSTM-CRF algorithm model carries out entity extraction processing, and an entity vector sequence, a feature vector sequence and a labeling sequence which are correlated with one another are obtained; s2, the convolutional neural network model extracts sentence vectors and contained entity vectors, and n filters are adopted for recognition and extraction to obtain an entity-relation-entity triple database; s3, integrating the annotation information through a conditional random field entity node integration model, correspondingly storing the annotation information in the entity and taking the annotation information as an attribute value of the entity; and S4, utilizing the triple database and the attribute value of the integrated entity to carry out link fusion construction to obtain the civil aviation knowledge graph. According to the method, the comprehensive and accurate civil aviation knowledge graph of the relation between entities can be obtained based on the civil aviation knowledge text database, knowledge questions, answers and queries of passengers can be met, the knowledge graph can be used as training and educational resources, and the overall service level 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 invention provides a construction method and device of a coal mine disaster prediction knowledge base and electronic equipment. The method comprises the steps that historical disaster cases are obtained, the disaster types of the historical disaster cases are determined, and the historical disaster cases are text descriptions performed on occurred coal mine disasters; constructing a knowledge field text database corresponding to the disaster type, and extracting terminologies in the knowledge field text database; the technical terms are classified to obtain a plurality of term categories, object attributes and data attributes of each term category are defined, a prediction knowledge base corresponding to the disaster type is constructed, the object attributes are used for representing meanings of the term categories, the data attributes are used for representing data types of the term categories, and the object attributes are used for representing meanings of the term categories; the prediction knowledge base is used for predicting coal mine disasters of disaster types. By means of the method and device, the problem that in the prior art, unstructured data is difficult to store and process is solved.

The embodiment of the invention discloses a text processing method and device based on a block chain, electronic equipment and a storage medium. The method can comprise the steps of obtaining a to-be-processed text; performing Hash calculation on the to-be-processed text to obtain a Hash value; generating a target block based on the hash value; consensus is carried out on the target block through a plurality of block chain nodes corresponding to the block chain to obtain a consensus result, and a text database is arranged in each block chain node in the plurality of block chain nodes; and when the consensus result is that the consensus is consistent, storing the to-be-processed text into the text database, and adding the target block into the block chain. According to the scheme, the text processing efficiency can be improved.

Automating conversion of drawings to indoor maps and plans. One example is a computer-implemented method comprising: preprocessing a CAD drawing to create a text database containing text from the CAD drawing and associations of the text with locations within the CAD drawing; determining a floor depicted in the CAD drawing, the determining results in a floor-level outline; identifying a plurality of room-level outlines within the floor-level outline, the plurality of room-level outlines corresponds to a respective plurality of rooms; selecting a name of a first room from the plurality of rooms, the selecting based on text within the text database; and creating an indoor map including the name of the first room, the name of the first room associated with a location of the first room within the floor-level outline.

The invention provides a knowledge question answering method and system, a storage medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: analyzing a first question, and extracting entities and entity relationships in the first question; retrieving entities and entity relationships in a knowledge graph database to obtain a plurality of key node entities and first content; based on the plurality of key node entities, performing retrieval in a text database to obtain second content; based on the plurality of key node entities, performing retrieval in a picture vector database to obtain third content; based on the plurality of key node entities, performing retrieval in a semantic vector database to obtain fourth content; the knowledge blocks in the first content, the second content, the third content and the fourth content are sorted so that content related to the first question can be output, and the knowledge graph database, the text database, the picture vector database and the semantic vector database are generated based on the knowledge files.

The invention relates to an entity extraction, storage and retrieval method based on LLM and multiple databases, entity information is extracted from a to-be-stored data set by constructing a prompt based on the LLM, an entity ID is generated based on the entity information, and entity attributes and relationships are written into a full-text library and a graph database respectively in the data storage process and are bound through the entity ID, so that the entity extraction efficiency is improved, and the data storage efficiency is improved. And the performance pressure of mass data storage of a single-class database is reduced. And after classification, synchronously writing into a full-text library and a graph database, writing entity attribute entries into the full-text library and binding the entity attribute entries with entity IDs previously written into the full-text library, and writing entity relationships into the graph database and binding the entity relationships with entity IDs previously written into the graph database. The data retrieval service is provided through the unified retrieval entry, the user does not need to increase the learning cost, the search scene is automatically judged to query the target database, the performance advantages of each database are fully played, and the mass data storage and retrieval efficiency is further 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.

The invention discloses a substation secondary equipment operation safety protection method and device and electronic equipment. The method comprises the steps that text information contained in a work ticket is recognized, and a text recognition result is obtained; based on the text recognition result, constructing a text database matched with the work ticket identifier of the work ticket; based on the data reading logic corresponding to the work ticket identifier, device control parameters of the secondary equipment operation safety protection device are determined from the text database, the protection device comprises at least one suspension assembly, the position of the suspension assembly corresponds to the position of a protection screen in a transformer substation, and the suspension assembly is used for isolating the protection screen in an unfolded state; and controlling the working state of the suspension assembly based on the device control parameters. According to the invention, the technical problem that the operation safety protection cost of the secondary equipment of the transformer substation is relatively high in the prior art is solved.

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 embodiment of the invention provides a multi-modal answer generation method and device.The multi-modal answer generation method comprises the steps that question analysis and answer generation are conducted on a question through a large language model, and at least one sub-question, an associated question, a sub-question answer and an associated question answer are obtained, performing text retrieval in a text database according to the question, each sub-question, the associated question, the sub-question answer and the associated question answer, calculating the semantic similarity of the question and each candidate initial text obtained by the text retrieval through a similarity calculation model, and screening out the initial text of which the semantic similarity is greater than a similarity threshold; the method comprises the following steps of: acquiring multimedia data of a question, performing text merging of initial texts through a large language model to obtain answer texts, and finally splicing the multimedia data and the answer texts according to a position association relationship between the multimedia data associated with the initial texts and the answer texts to obtain multi-modal answer data of the question, so as to realize the generation of the multi-modal answer data of the question.

The embodiment of the invention provides a large language model system pressure testing method and computing equipment. The large language model system pressure test method comprises the steps of performing length statistical analysis on historical input texts of a target user, and determining the proportion of the input texts with various lengths; selecting pressure test texts from the text database, and constructing a pressure test text set by using the pressure test texts; wherein the proportion of the pressure test texts with various lengths in the pressure test text set is the proportion; and generating a model calling request based on the pressure test text set, performing pressure test on the large language model system by using the model calling request, and obtaining pressure test index data of the large language model system according to test process statistical data. The pressure test text set constructed according to the method is more in line with the actual application scene condition, and the pressure test index data obtained by correspondingly adopting the pressure test text set to carry out the pressure test can also reflect the performance characteristics of a large language model system which has a large number of concurrent requests in the actual scene.

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 embodiment of the invention relates to the technical field of artificial intelligence, and discloses an intelligent interaction method and system, and the method comprises the steps: respectively employing a distance measurement algorithm and an approximate nearest neighbor search algorithm to match an index text from an index text database, and enabling the index text to serve as a current index option, after taking the index with the highest correlation in the current index options as a target index, obtaining visual chart option information corresponding to the target index; utilizing an SQL pseudo-code translation model to convert the question content into an SQL pseudo-code; converting the SQL pseudo code into a JSON data format array by utilizing a JSON data format translation model; after dimension information, data screening information and sorting information in the visual chart option information are determined according to the JSON data format array, the visual chart is output. By implementing the embodiment of the invention, more accurate and personalized data display can be provided for the user, so that the user experience and satisfaction are 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.