412 results about "Database retrieval" patented technology

The invention provides a multi-round dialogue interaction method and system based on context reconstruction and multi-library retrieval, and the method comprises the steps: firstly obtaining a multi-round dialogue data set of a target user, which comprises a plurality of dialogue round sequences composed of user input statements and system response statements; performing context reconstruction processing on the multi-round dialogue data set to generate context reconstruction characteristics of each dialogue round sequence, covering current round semantic focus offset and historical round semantic dependence intensity, and retrieving a matched multi-database retrieval result from a preset heterogeneous database based on a dynamic context window, a candidate response statement set and confidence are included. Performing multi-strategy matching on context reconstruction features and retrieval results, generating a response generation strategy to adjust candidate response semantic priorities and confidence thresholds, and finally feeding back the response generation strategy to a dialogue service system to trigger response optimization operation, and updating heterogeneous database retrieval weights and context window dynamic adjustment parameters.

The invention discloses a vertical large language model training method and system in the carbon neutralization field, and the method comprises the following steps: collecting data of the carbon neutralization field, carrying out the data preprocessing, constructing a carbon neutralization field knowledge base, and updating the carbon neutralization field knowledge base through a dynamic updating mechanism; performing dynamic semantic partitioning and vectorization coding on the text of the carbon neutralization domain knowledge base, and storing the text into a vector database; performing staged fine tuning on the pre-trained large language model based on a low-rank adaptation technology, wherein the fine tuning comprises general instruction fine tuning and carbon neutralization field professional fine tuning; a retrieval enhancement generation mechanism is adopted, knowledge fragments related to user query are retrieved through a vector database, and a large language model is input to generate answers. Compared with the prior art, the method has the advantages that the answer reliability is improved through conflict detection and source tracing, so that the large language model can more accurately adapt to knowledge requirements in the carbon neutralization field.

Embodiments of the present disclosure provide a method and system for generating a document, a computing device, and a medium. The method comprises: receiving a prompt input by a user; obtaining a first keyword from the prompt; using a vector representation of the prompt, retrieving, from a vector index database, enhancement information associated with the prompt and a second keyword associated with the enhancement information; on the basis of comparison of the first keyword and the second keyword, using the enhancement information to generate an enhanced prompt; and providing the enhanced prompt to a language model to generate a document.

The invention discloses an automatic control code generation and verification method and device, equipment and a storage medium, and relates to the technical field of automatic control, the method is applied to a large language model, and a natural language command is received; performing matching retrieval on the vector database according to the natural language command to obtain an example code snippet; obtaining API structured information corresponding to the example code snippets from a knowledge graph database; generating an initial control code based on the example code snippet and the API structured information; and performing multi-stage virtual operation verification on the initial control code in the software motion control system, and generating a target control code according to a multi-stage verification result confirmed by a user for multiple times and the initial control code. According to the method, the initial control code is automatically generated through double-database retrieval based on the large language model, then multi-stage code verification is performed through the software motion control system, the target control code is generated in combination with verification results confirmed by a user for multiple times, and the code generation speed and reliability are improved.

The invention discloses a root cause analysis method and device based on a large language model, equipment and a medium. The method comprises the steps that a target entity and a topological relation are extracted in response to a root cause analysis request; searching matched historical root cause cases in a vector database, inputting the topological relation, the historical root cause cases and cue words into a large language model to generate a doubtful point list, and extracting downstream entities in the doubtful point list; calling a detection tool to collect entity diagnosis data and identify abnormal downstream entities; returning to execute the operation of retrieving the historical root cause case until a preset iteration ending condition is met; and inputting the final doubtful point list, the abnormal diagnosis data, the topological relation and the historical root cause case into the large language model again to obtain a root cause reasoning result and generate a root cause report. According to the embodiment of the invention, through the full-chain design of natural language understanding, topological constraint, historical cases, dynamic detection and iterative reasoning, the fault positioning efficiency and the root cause accuracy are improved, and the operation and maintenance labor cost and the service fault time consumption are remarkably reduced.

The invention relates to the technical field of artificial intelligence, and particularly discloses a policy and regulation intelligent question answering method and system based on a large language model, and the method comprises the steps: segmenting a policy and regulation text into text blocks through a recursive character text segmentation method, and constructing a knowledge base in combination with expert labeling and automatic information extraction; performing semantic embedding on the text block by utilizing a pre-trained Transform model, generating a high-dimensional vector representation, and storing the high-dimensional vector representation in a Chroma database; the user question is converted into an embedded vector, relevant laws and regulations are retrieved through a Chroma database, a large language model and a knowledge fusion attention generation module are combined, and text embedding and knowledge graph information are fused to generate an answer; and the user obtains answers through the intelligent question-answer interaction interface and can view the original texts and explanations of laws and regulations. The system comprises a data acquisition and preprocessing module, a semantic embedding and storage module, a retrieval and generation module and a user interaction module. According to the method, large-size regulation texts are effectively processed, and the retrieval efficiency and the answer quality are improved.

Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

The invention belongs to the technical field of data processing. According to the multi-source heterogeneous data multi-mode mixed retrieval method and system based on large model reasoning, in the reasoning process of each step, a large language model determines sub-query questions of different retrieval modes and corresponding sub-query answers according to an initial query question; according to the sub-query question and the sub-query answer corresponding to the query mode of each inference process, adaptively determining an optimal retrieval path by taking the maximum reward function as a target, and determining a final answer corresponding to the initial query question according to the optimal retrieval path; wherein the optimal retrieval path comprises an optimal answer of each retrieval mode and a retrieval sequence of each retrieval mode, and the retrieval modes comprise database retrieval, semantic retrieval and atlas retrieval; according to the method, multi-hop reasoning of complex query is supported while the retrieval efficiency is ensured, dynamic fusion of multiple types of retrieval modes is realized, and the retrieval efficiency and the retrieval precision are greatly improved.

Computerized systems and methods are disclosed for automating Configure to Order (CTO) and Quote to Order (QTO) processes. Methods include receiving user inputs for desired product configurations, retrieving corresponding data from a bill of materials database, and calculating optimized pricing through intelligent rules based on real-time market data. Automated quotes are generated and transferred to orders in a vendor system, selected based on pre-set criteria like vendor reputation and delivery time. Validation steps reduce errors, and real-time reports are generated. The system integrates a Real-Time Data Mesh for data aggregation, a Single Pane of Glass User Interface for user interactions, and Advanced Analytics and Machine Learning Modules for implementing rule-based and learning algorithms. The system is accessible across various devices and standardizes data for uniform consumption, while also employing machine learning models to continually optimize processes. Notifications are sent to users upon successful execution of orders or completion of quotes.

The invention relates to the field of medical information processing and traditional Chinese medicine curative effect evaluation, in particular to a diabetic nephropathy traditional Chinese medicine curative effect evaluation method based on patient report outcome. The method comprises the following steps: searching related symptoms and clinical characterization data of diabetic nephropathy through a medical database, extracting target entries, performing language standardization processing, constructing an initial candidate entry set, calculating the occurrence frequency of each symptom entry, and screening entries meeting a set threshold value to form an entry set; carrying out dimension division on the entry set according to feature attributes to form a preliminary dimension structure set; forming a screening result set through preset logic screening conditions; and carrying out weighted regression fusion on the comprehensive curative effect score and clinical detection data, and outputting a patient curative effect index. In an item weight determination process, dynamic balance between subjective experience and objective statistical information is realized through a mixed weighting model, and the defects of strong subjectivity of expert scoring and large result fluctuation in a traditional method are overcome.

As disclosed herein, a computer-implemented method for travel planning is provided. The computer-implemented method may include receiving, via a conversational user interface (UI), a user input associated with a product offered by a booking application. The computer-implemented method may include retrieving, from a database associated with the booking application, at least one attribute of the product. The computer-implemented method may include determining, using a first machine learning (ML) model of a plurality of ML models, a user intent associated with the user input. The computer-implemented method may include generating, based on the at least one attribute and the user intent, a first response to the user input. The computer-implemented method may include providing, via the conversational UI, the first response to the user input. A system and a non-transitory computer-readable storage medium are also disclosed.

A method and system for verifying a structured query language (SQL) query are provided. The method includes: receiving a first request to retrieve first data that is accessible via a database; identifying an intention of the first request; generating, based on the first request, a first SQL query to retrieve the first data from the database; predicting, based on the generated first SQL query, an output of the generated first SQL query; determining whether the predicted output matches the identified intention of the first request; generating, based on the determining of whether the predicted output matches the identified intention of the first request, a second SQL query to retrieve the first data that is accessible via the database, when the predicted output of the first SQL query does not match the identified intention of the first request.

The invention provides an atmospheric pollution process analysis method, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the following steps: receiving an atmospheric pollution problem sent by a user, and respectively carrying out vector database retrieval and knowledge graph retrieval based on the atmospheric pollution problem to obtain a first retrieval result and a second retrieval result; wherein the vector database retrieval comprises the following steps: calculating cosine similarity between background information vectors in a vector database and atmospheric pollution problem vectors, and determining multiple pieces of background information of which the cosine similarity accords with a preset threshold value as a first retrieval result; the knowledge graph retrieval comprises the following steps: performing entity recognition and standardization on the atmospheric pollution problem, and generating a target knowledge triple as a second retrieval result; and fusing and inputting the atmospheric pollution problem, the first retrieval result and the second retrieval result into a large language model, and outputting an analysis result of the atmospheric pollution problem. Through the method provided by the invention, the illusion problem of a general large language model in the atmosphere field is relieved.

A disclosed method facilitates translation of natural language queries into query language statements usable to retrieve data from or write data to a particular database. The method includes obtaining a pool of shots. Each shot in the pool includes a natural language query component and a corresponding database translation component. The method further provides for vectorizing the natural language query component for each of the shots into a common vector space; receiving a natural language query from a user interface; vectorizing the natural language query within the common vector space; identifying a subset of vectorized natural language query components that satisfy a similarity metric when compared to the vectorized natural language query; and generating an LLM prompt that includes shots from the pool corresponding to the subset of the vectorized natural language query.

A machine learning based computing method for automatic managing credit risks of first users, is disclosed. The machine learning based computing method includes: receiving inputs from electronic devices associated with second users; retrieving data associated with first users from databases; preprocessing the data to remove noises, outliers, and missing values, from datasets; determining, the credit risks of the entities based on the pre-processed data by machine learning models; generating credit decisions for the entities; generating confidence scores for credit decisions to classify the credit decisions, based on correlation between the data and credit decisions; determining recommended credit values, recommended first credit limits, and recommended second credit limits, based on classification of the credit decisions; and providing an output of the credit decisions, the recommended credit values and the recommended credit limits, to the second users on user interfaces associated with electronic devices.

The embodiment of the invention discloses a medical first draft generation method and device based on a large model and electronic equipment. A specific embodiment of the method comprises the following steps: generating a multi-database retrieval formula in the medical research direction by utilizing a large language model; executing a retrieval formula to obtain a preliminary retrieval literature set and a cross-database causal network; screening and extracting the preliminary retrieval literature set to obtain a first draft data literature set and a clinical heterogeneity factor set; performing dynamic evidence chain fusion to obtain a fusion effect quantity set and a conflict mark set; meanwhile, the heterogeneity judgment script is executed, and a visual chart set is obtained; according to the cross-database causal network and the conflict mark set, executing standardized conflict analysis to obtain a conflict analysis report; and according to the medical first draft template and the conflict analysis report, integrating the literature and the chart by using a large language model and generating a medical first draft. According to the embodiment, the medical first draft is automatically generated by using the large model, the influence of manual intervention is reduced, and the integrity of the medical first draft content is improved.

In embodiments, an apparatus includes a user data interpretation circuit structured to interpret user data corresponding to a first user; a bootstrap circuit structured to: match the first user to a second user via querying one or more databases based at least in part on the user data; retrieve historical time sequence data associated with the second user via querying the one or more databases; extract a time sequence trend from the historical time sequence data; identify a portion of the historical time sequence data corresponding to the extracted time sequence trend; and generate, based at least in part on the identified portion, time sequence data corresponding to the first user; and a time sequence data provisioning circuit structured to transmit the time sequence data.

A stateful chatbot system leverages generative AI to provide an interface by which users can retrieve information from backend IoT databases of a security provider via natural language queries. Upon receiving a natural language query that corresponds to a request for information from the database, the chatbot generates a corresponding database query having a format compatible with the database. The chatbot comprises a generative model adapted to generate database queries based on natural language queries via prompt engineering using natural language and database query pairs. The chatbot queries the database with the generated database query, retrieves results comprising data / metadata that satisfy the query, and generates a summary of the results, both of which it presents as a response to the user's query. The chatbot also has access to a vulnerability database from which it can obtain information about known vulnerabilities documented therein to respond to user queries.

The invention relates to the technical field of underwater detection, and discloses a forward-looking sonar underwater detection system based on multi-beam forming. The system comprises seven modules: a sonar data acquisition module obtains a multi-beam sonar original data sequence; a historical sonar database retrieval module retrieves a matched historical detection record set according to the region identifier; the heterogeneous sonar record distinguishing module classifies the historical sets and determines a plurality of distinguishing sets; a sonar feature extraction module traverses and distinguishes the set calculation to obtain a sonar feature set value and an associated bandwidth; the multi-scale sonar analysis module processes a network layer receptive field by taking a feature association bandwidth as a beam, and analyzes original data to obtain a multi-scale detection feature set; a cross-scale sonar fusion module performs interactive fusion on the multi-scale features to generate a target interactive fusion sonar feature set; and the underwater target area evaluation module outputs a thermodynamic map containing potential target area probability distribution based on fusion feature evaluation, and adapts to complex underwater environment detection.

The invention discloses a bank intelligent operation knowledge base implementation method and system based on a large language model and RAG, and the method comprises the steps: firstly extracting business terms from structured, semi-structured and unstructured multi-source heterogeneous multi-modal data of a bank, and then constructing a business dictionary containing term similarity data and an ontology tree; quantitatively calculating the release mechanism weight, the timeliness coefficient and the punishment association degree of the supervision clauses to obtain authority values, and sorting the authority values; then using a double-constraint loss function to finely adjust the pre-trained large language model as a vector representation model, vectorizing service data, and storing the vectorized service data into a vector database; and finally, aiming at user query, retrieving in a vector database, rearranging in combination with a supervision clause sorting result, and generating a business response after compliance verification. According to the method, the bank unstructured knowledge processing capability and retrieval precision can be improved, and efficient intelligent operation is realized.

The invention relates to the technical field of artificial intelligence, in particular to a multi-agent collaborative science and technology information generation method and system, and the method comprises the steps: obtaining a user demand; performing multi-information source retrieval according to the user demand to obtain database retrieval content; generating initial science and technology information according to the user demand and the retrieval content; and auditing and optimizing the initial science and technology intelligence according to the retrieval content. According to the technical scheme of the invention, the multi-agent collaborative architecture is constructed based on the large language model, and the capabilities of information retrieval, semantic analysis, knowledge fusion and content generation are combined, so that the automatic understanding, organization and expression of the science and technology information are realized, and therefore, the efficiency, quality and intelligent level of science and technology information generation are remarkably improved. And more valuable auxiliary decision support is provided for enterprises.

The invention discloses an ecological risk assessment method for organic pollutants in a natural water body, and belongs to the field of ecological risk assessment of the natural water body, and the method comprises the steps: building a quantitative structure-activity relationship toxicity prediction model of species based on a machine learning and feature screening method; predicting and obtaining biotoxicity data in a to-be-predicted water body by using the quantitative structure-activity relationship model, establishing a species sensitivity distribution model of pollutants in the water body according to the biotoxicity data, and calculating predicted non-effect concentration; on the basis of database retrieval, acute toxicity experiments and an inter-species relationship prediction model, collecting, predicting and supplementing toxicity data of species in the water body, and evaluating the harmfulness of pollutants in the water body by using a risk entropy method and a comprehensive scoring method respectively. According to the method, ecological risk assessment is carried out by utilizing a machine learning method, the toxicity of the persistent organic pollutants in seawater and fresh water can be effectively assessed, and the ecological risk of the organic pollutants in the water body can be effectively assessed.

The invention relates to the technical field of vector database retrieval, and discloses a vector database index recommendation method, device and equipment and a medium, and the method comprises the following steps: carrying out clustering analysis on a vector data set, and extracting data distribution characteristics based on a clustering result to obtain a vector data portrait; the data distribution characteristics comprise inter-cluster center distances and whether head-tail structures exist or not; according to a preset index recommendation strategy, a recommendation index is determined according to the vector data portrait; if the inter-cluster center distance exceeds the upper limit of a set threshold interval, an IVF index is adopted as a whole; if the inter-cluster center distance is within a set threshold interval and no head-tail structure exists, an IVF index is adopted on the whole; if the head-tail structure exists, the trunk data part adopts IVF index, and the sparse data part adopts HNSW index; and if the inter-cluster center distance is smaller than the lower limit of the set threshold interval, the recommended index is HNSW. According to the method, a proper index type can be adaptively recommended according to data distribution characteristics in the vector data set.

The invention discloses a music database retrieval method and system based on feature extraction, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the preprocessing of an audio signal of a query track input by a user, dividing the audio into equal-length frame sequences, and generating three-channel spectrogram tensors, including a Mel spectrogram, a logarithmic amplitude spectrogram and a CQT spectrogram; extracting multi-modal features of the equal-length frame sequence, dividing melody motivation segments according to pitch change and stability of the audio signal, performing differential coding, generating a melody differential sequence, and modeling the melody differential sequence into a melody topological graph; and calculating the structural similarity between the melody topological graph and a database melody graph, screening a similar melody candidate set P, mapping a melody curve into a topological manifold through a topological data analysis method, extracting persistent homology features, and converting the persistent homology features into a persistent bar graph. According to the method, the retrieval precision and the matching credibility of the complex melody in the music database are remarkably improved.

The invention belongs to the technical field of artificial intelligence and big data, and relates to a database retrieval method which comprises the following steps: in response to an update request for a target field in a user data table, obtaining an initial primary key number of the user data table; acquiring a preset initial step parameter, and establishing a retrieval range according to the initial primary key number and the initial step parameter; retrieving a target field in the user data table according to the retrieval range to obtain a retrieval hit result; and extracting an updating strategy from the updating request, and performing data updating on the target field in the retrieval hit result according to the updating strategy. The invention further provides a database retrieval device, computer equipment and a storage medium. The method can be applied to business management program systems of financial science and technology, medical treatment and the like, and the problems that the retrieval process time is prolonged and the retrieval updating efficiency is low in existing database retrieval can be solved.

A system for confirming an advisory interaction with an artificial intelligence platform. The system includes a constitutional generator module configured to receive a first advisory input, retrieve an expert input, select a machine-learning process as a function of the expert input, and generate a therapeutic corrector. The system includes a constitutional advisory module configured to display a therapeutic corrector on a graphical user interface and receive a second advisory input. The system includes a best practices module the best practices module designed and configured to retrieve from an expert database a best practices training set, calculate an optimal vector output, generate an optimal vector output containing an expected therapeutic corrector implementation response, authenticate a second advisory input, and update the best practices module.

The invention discloses a sentiment analysis method and system based on a multi-modal enhanced retrieval generation technology, and belongs to the field of sentiment analysis and natural language processing. The method comprises the following steps: constructing an external multi-modal vector database oriented to an emotion analysis task; converting the user input data to obtain a query vector; aiming at a query vector corresponding to user input data, using a vector retrieval algorithm to retrieve a target feature vector from the external multi-modal vector database; constructing an emotion analysis prompt word template; according to the target feature vector, extracting other field data corresponding to the target feature vector from the external multi-modal vector database as a retrieval result; and embedding user input data and an external multi-modal vector database retrieval result into a prompt word template, calling a large language model, and generating an emotion analysis result. The invention provides an efficient and reliable multi-modal solution for the field of sentiment analysis.

The invention provides a maintenance spare part prediction system based on a big language model plug-in knowledge base, and the system comprises the steps: carrying out the information data extraction and preprocessing of a historical work order, and obtaining a preprocessing text; performing semantic vector conversion and association binding processing on the preprocessed text to obtain a vector database; extracting attribute information from the new work order, and performing semantic vector conversion on the attribute information to obtain a query vector; retrieving a work order related to the query vector from a vector database; semantic reasoning is carried out on a new work order and a related work order, a maintenance spare part prediction result is generated, natural language fault description and equipment model information in a maintenance record are fully utilized, natural language information is fully understood in the face of complexity and diversity, the spare part prediction accuracy and practicability are improved, reasonable configuration of maintenance resources is achieved, and the maintenance efficiency is improved. Spare part inventory cost and inventory waste are reduced, and maintenance efficiency is improved.

The invention discloses a smart city similar complaint event recognition method based on a graph database and semantic recognition, and relates to the technical field of smart city management, and the method comprises the steps: carrying out the vectorization calculation of a complaint event text through employing a sentence-transformers / paraphrase-multilingual-Mini LM-L12-v2 model, and carrying out the model fine tuning for the field of city governance; constructing an event-complaint-address-keyword association network model based on a graph database, defining points and edges, and realizing complaint event multi-dimensional association modeling through attribute design; preprocessing the stock data, establishing an association relationship among events, complaints, geographic positions and keywords, and importing the association relationship into a graph database; and performing preprocessing operation on the newly added complaint event, and calculating similarity based on keywords and complaint person information in combination with graph database retrieval so as to realize intelligent identification of multi-person simultaneous complaint and one-person multi-complaint. According to the invention, intelligent clustering and repeated reporting identification of city complaint events can be realized.

The invention relates to the technical field of data retrieval, in particular to a FastGPT-based intelligent question-answering system for construction project quality inspection and detection standards. According to the technical scheme, the method comprises the steps of external knowledge acquisition, document processing, vectorization, vector database, retrieval and context generation and intelligent question and answer, data can be collected by a system, a semantic segmentation technology, a QA pair generation technology and a high-dimensional vectorization technology are utilized, in combination with M3E and OpenAIEmbedding models, Top-K similarity retrieval and context dynamic recombination are achieved, and structured and hierarchical professional answers are generated. According to the method, the intelligent management and real-time application capability of the construction project quality inspection and detection standard can be remarkably improved, the manual retrieval and interpretation cost is reduced, and the digital and intelligent requirements of an engineering project are met.